Characterization of AP-MALDI and ESI for a Differential Mobility Spectrometer
by
Priya Agrawal
Submitted to the Department of Electrical Engineering and Computer Science
in Partial Fulfillment of the Requirements for the Degree of
Master of Engineering in Electrical Engineering and Computer Science
MASSACHUSETTS INS
OF TECHNOLOGY
at the Massachusetts Institute of Technology
May 19, 2005.
Copyright
rl
JUL 18 2005
2005 Priya Agrawal. All rights reserved.
I
LIBRARIES
The author hereby grants to M.I.T. permission to reproduce and
distribute publicly paper and electronic copies of this thesis
and to grant others the right to do so.
Author
r
PriyA Agrawal
Department of Electrical Engineering and Computer Science
May 19, 2005
Certified by
Dr. Cristina E. Davis
Charles Stark Draper Laboratory
Thesis Supervisor
Certified by
Professor Angela M. Belcher
Professo r--"ologicgl and Materials Science and Engineering
Thesis Advisor
Accepted by
Arthur C. Smith
Chairman, Department Committee on Graduate Theses
BARKER
E
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2
Characterization of AP-MALDI and ESI for a Differential Mobility Spectrometer
by
Priya Agrawal
Submitted to the
Department of Electrical Engineering and Computer Science
May 19, 2005
In Partial Fulfillment of the Requirements for the Degree of
Master of Engineering in Electrical Engineering and Computer Science
ABSTRACT
The following thesis entails the construction, testing, modification, and analysis of two
systems that couple sample ion introduction methods with a Differential Mobility
Spectrometer (DMS).
The sample ionization methods used with a custom designed
interface for the DMS were Electrospray Ionization (ESI) and Atmospheric Pressure
Matrix Assisted Laser Desorption Ionization (AP-MALDI).
In addition to system
development, Fourier transform and decision tree analyses were explored as alternatives
to lead-cluster mapping and genetic algorithms for analyzing and classifying data
produced by the systems for large biomolecules. Findings from testing and experiments
using the prototype system have led to a second generation design of the interface.
Results from data analysis have also provided new insights into different methods for
classifying data whose form changes drastically for different sample introduction
methods.
Technical Supervisor: Dr. Cristina Davis
Title: Group Leader Bioengineering, Principal Member of the Technical Staff
Thesis Advisor: Professor Angela M. Belcher
Title: Assoc Professor, Biological Engineering and Materials Science and Engineering
3
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4
14
ACKNOWLEDGEMENT
May 19, 2005
The following thesis is the last leg of a great journey in research and academics. My
experience at Draper and MIT has truly been a great one, and I have many people to
thank for making this possible.
First and foremost, I would like to thank my mother, my father, and my brother. They
have given me the confidence and ability to achieve my goals and to strive for excellence
in everything I do. I would never have been able to come this far without their constant
love, encouragement, and wisdom.
I am especially grateful to have had such an extraordinary group of colleagues to work
with at Draper Labs. Many thanks are due Dr. Cristina Davis for the opportunity to work
on such an interesting engineering problem, and for the guidance, optimism, and
motivation to tackle tough problems. I would especially like to thank Dr. Angela Zapata
and Ernie Kim for teaching me so much, for their technical expertise, and for their
patience in fielding all of my questions and ideas. I thank Melissa Krebs for her patience
in explaining analysis tools with me, and listening to me when I had exciting ideas. I
thank Sarah Cohen, Mariana Shnayderman, and Dr. Malinda Tupper for their advice
patience, and sense of humor. Thanks to Dr. George Schmidt, Joseph Sarcia, and Loretta
Mitrano in the Education Office for coordinating the Draper Fellow program, and making
my experience here possible.
At MIT, I would like to thank Professor Angela Belcher for advising me on my thesis,
and Professor Mildred Dresselhaus for supporting and advising my academic career.
Finally, I would like to extend a special thanks to my friends Ruth, Chaitra, Neha, Kiran,
Tara, and Shruti for all the great times we've had together during the few free moments
I've had from school these past 5 years.
This thesis was prepared at The Charles Stark Draper Laboratory, Inc. under Department
of the Army, Cooperative Agreement DAMD-17-02-2-0006.
Publication of this thesis does not constitute approval by Draper of the sponsoring agency
of the findings of conclusions contained therein. It is published for the exchange and
stimulation of ideas.
f0
5
Priya Agriwal
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6
Table of Contents
I
12
In tro du ction ...............................................................................................................
14
B ack ground ...............................................................................................................
14
2.1
Differential Mobility Spectrometer................................................................
2.1.1
Operation Principle of Differential Mobility Spectrometry................... 14
Traditional Uses of Differential Mobility Spectrometry ....................... 18
2.1.2
18
2.2
Electrospray Ionization ..................................................................................
19
Operation principle of Electrospray Ionization......................................
2.2.1
20
Prior Uses of the ESI Technique...........................................................
2.2.2
21
2.3
A P -M A LD I....................................................................................................
21
Operation Principle of AP-MALDI......................................................
2.3.1
22
Prior Uses of the AP-MALDI technique ...............................................
2.3.2
Advantages and Potential of the ESI-DMS and AP-MALDI-DMS systems ... 23
2.4
23
D ata Analysis ...............................................................................................
2.5
24
Fourier Methods....................................................................................
2.5.1
26
2.5.2
Decision Trees ......................................................................................
27
Lead Cluster Mapping and Genetic Algorithms ....................................
2.5.3
28
Thesis Summary............................................................................................
2.6
29
ESI-DMS Instrumentation Development and Analysis.........................................
3
29
The ESI-DMS System..................................................................................
3.1
31
3.2
T he E SI U nit ..................................................................................................
31
1
Interface.......................................................................................
3.3
Prototype
34
Spray Pattern Characterization ......................................................................
3.4
34
Experimental Design..............................................................................
3.4.1
. . 35
R esults..................................................................................................
3.4 .2
36
3.5
Current Measurement Experiments ...............................................................
37
3.5.1
E xperim ent 1.........................................................................................
38
Experiment 2.........................................................................................
3.5.2
41
........................................................................
Probe
Experiments
Langmuir
3.6
42
3.6.1
Initial Testing with the Langmuir Probe................................................
43
Results of Initial Test Using Langmuir Probe ..............................
3.6.1.1
45
3.6.2
Air Pump and Breakup Gas effect on Current .......................................
49
Modified T-junction and Capillary Tube Interface.......................................
3.7
50
Design of the Capillary T-junction ......................................................
3.7.1
51
3.7.2
Experim ents ...........................................................................................
. . 52
3.7 .3
Results..................................................................................................
53
3.8
Prototype 1 Interface.....................................................................................
53
Design of the Interface...........................................................................
3.8.1
3.8.2
Experimental Method for Testing of the Prototype I Interface.............. 54
2
Resu lts.......................................................................................................
3 .8 .3
3.9
Modified Ion-Optics Interface ......................................................................
3.9.1
3.9.2
3.9.3
3.9.4
Hardware/Instrumentation Setup ...........................................................
Initial Test of the Modified Ion-Optics Interface..................................
Results of Initial Experiment with Modified Ion-Optics Interface........
Experimental Method for the Modified Ion-Optics Interface................
7
55
56
57
60
61
63
3.9.5
Results....................................................................................................
3.10 Data Analysis M ethods ..................................................................................
Fourier Transform s. ..............................................................................
3.10.1
3.10.2
Decision Tree Analysis.........................................................................
3.10.3
Correlogic Analysis ...............................................................................
Com parison of Analysis M ethods.........................................................
3.10.4
AP-MALDI-DMS Instrumentation Development and Analysis ...........................
4.1
Background ....................................................................................................
4.2
Hardw are Description - m aterials and methods.............................................
4.2.1
The AP-M ALDI-DM S system ..............................................................
4.2.2
The M ALDI unit ....................................................................................
4.2.3
The interface ...........................................................................................
Sam ple Preparation technique................................................................
4.2.4
Preliminary Testing.......................................................................................
4.3
4.3.1
Experimental Conditions ......................................................................
4.3.2
Results of Preliminary Testing..............................................................
:.................
4.4
Optim ization ...................................................................................
4.4.1
Sam ple Depletion Tim e ........................................................................
4.4.2
Effect of Break up Gas on Signal Quality .............................................
Effect of Pump Flow Rate on Signal Quality ........................................
4.4.3
Effect of Extraction Voltage on Signal Quality.....................................
4.4.4
4.5
Data Analysis for Finding Trends in the Data ...............................................
4.5.1
Scans from the AP-M ALDI-DM S System ...............................................
Determining that Signal is Present Using Fourier Methods ...................
4.5.2
Finding Trends from Optimization Experiments................
4.5.3
Peak Analysis..........................................................................................
4.5.4
5
Sum m ary and Conclusions .....................................................................................
ESI...................................................................................................................
5.1
4
5.2
AP-M ALDI.....................................................................................................
Data analysis ...................................................................................................
5.3
6
Future W ork ............................................................................................................
6.1
ESI...................................................................................................................
Shorter Ion Path Length ..........................................................................
6.1.1
Better insulation for electronics/HV .......................................................
6.1.2
Interaction between sam ple and carrier gas ............................................
6.1.3
6.2
AP-M ALD I.....................................................................................................
6.3
Data Analysis ..................................................................................................
6.3.1
Fourier Analyses .....................................................................................
Statistical analyses ..................................................................................
6.3.2
7
Appendix.................................................................................................................
8
References...............................................................................................................
8
65
65
66
72
80
81
83
83
84
84
84
85
86
87
87
88
90
91
92
94
96
98
99
100
102
103
105
105
107
109
109
110
110
110
111
111
112
112
112
113
114
List of Figures
Figure 1 Graph of ion mobility as a function of electric field for three hypothetical ion
sp ecie s.......................................................................................................................
15
Figure 2 Picture of the MEMS-based Differential Mobility Spectrometer. ................ 16
17
Figure 3 Schematic of ion trajectory through the DMS.................................................
Figure 4 (a) Varying electric field due to RF voltage waveform (b) Constant electric field
due to compensation voltage.................................................................................
17
19
Figure 5 Electrospray needle and plum e[18].................................................................
20
Figure 6 Depiction of ion dispersion from aerosol droplets. .........................................
24
Figure 7 Example of positive ion spectral data scan....................................................
30
Figure 8 Block diagram of ESI/AP-MALDI-DMS system ..........................................
Figure 9 AP-MALDI or ESI unit in open position on the left and closed position on the
32
right on the sample detector interface....................................................................
interface.
of
ESI/AP-MALDI-DMS
cross
section
through
Figure 10 Ion and gas flows
Figure courtesy of Ernest S. Kim, Draper Laboratory, Cambridge, MA.............. 33
Figure 11 SDI with Agilent ESI capillary interface components: 1. External capillary
34
shield 2. Glass capillary shield and inlet...............................................................
Figure 12 Plume diameter for various sample flow rates and nebulizing gas flow rates. 36
37
Figure 13 Schematic for current measurement experiment A. .....................................
Figure 14 Plot of current measured at plate versus sample flow rate ............................ 38
39
Figure 15 Current measurement setup with perpendicularly oriented ESI needle .....
40
Figure 16 Infusion rate versus current for perpendicular current measurements ......
Figure 17 Current measured versus extraction voltage for vertical needle geometry. ..... 41
42
Figure 18 Setup of Langmuir probe for initial test ........................................................
Figure 19 ESI current as a function of sample flow rate for different extraction voltage
43
lev els .........................................................................................................................
Figure 20 ESI current as a function of sample flow rate for two different concentrations
44
o f samp le ...................................................................................................................
45
Figure 21 ESI current as a function of Langmuir probe location. ................................
46
Figure 22 Langmuir probe setup in second set of experiments ....................................
47
Figure 23 Langmuir probe setup with ESI needle ........................................................
50
Figure 24 Cross-sectional view of capillary T-junction interface ................................
51
......................................
DMS
system
configuration
Modified
T-junction
-Figure 25
Figure 26 Inner component of prototype 1 interface where arcing was observed........ 54
56
Figure 27 Data replicate from prototype 1 Interface Data set .....................................
Figure 28 Schematic of the modified ESI-DMS interface with modified ion-optics....... 58
Figure 29 Schematic of voltage divider for ESI-DMS interface ...................................
59
Figure 30 (a) DMS positive ion spectra for the protein BSA with extraction voltage
turned off (b) DMS spectra with extraction voltage turned on and off in synch with
sample injection (c) DMS spectra with extraction voltages on for entire duration of
62
experim ent.................................................................................................................
Figure 31 Plot of single replicate from modified ion-optics interface data set.............. 65
Figure 32 Raw DMS spectra for data collected using modified T-junction capillary
67
in terface .....................................................................................................................
Figure 33 1-Dimensional Fourier transform magnitude (left) and phase (right).......... 68
9
Figure 34 Fourier transform magnitude (top) and phase (bottom) plots for data taken
69
using the prototype 1 interface...............................................................................
taken
using
plots
for
data
and
phase
(right)
magnitude
(left)
Figure 35 Fourier transform
70
the modified ion-optics interface. .........................................................................
Figure 36 Fourier transform magnitude and phase plots for data collected with the
71
modified T-junction interface ..............................................................................
Figure 37 Classification error for varying testing and training set sizes, for three different
74
datasets ......................................................................................................................
Figure 38 Error and minimum cost for optimally pruned tree for data collected using
76
m odified T-junction interface. ..............................................................................
Figure 39 Cost versus number of terminal nodes in tree built with 2400 data scans from
prototype 1 interface data set (top), and modified ion-optics interface data set
78
(b otto m).....................................................................................................................
Figure 40 Best pruned classification tree for modified ion-optics interface................. 79
Figure 41 Cutaway view of major substance flows in the prototype 1 interface.......... 85
Figure 42 (a) SDI with AP-MALDI capillary interface components: 1. Teflon high
voltage insulator, 2. break-up gas cylinder, 3. capillary extension. (b) SDI with ESI
capillary interface components: 1. break-up gas outlet, 2. glass capillary inlet (figure
86
courtesy of Ernest K im ) ........................................................................................
88
Figure 43 Timing diagram of sample introduction........................................................
Figure 44 Ion spectra from AP-MALDI-DMS system. (a) Shows data taken when laser is
off (b) Data recorded when laser fired on blank plate (c) Data collected when laser
89
fired on sample spot ...............................................................................................
90
Figure 45 AP-MALDI target plate sample spot layout..................................................
Figure 46 DMS signal intensity as a function of break up gas flow rate...................... 94
96
Figure 47 Trend from experiment to vary pump flow rate. ..............................................
98
Figure 48 Trends from experiment to vary extraction voltage ...................
99
Figure 49 Typical scan taken during blank region ablation...........................................
100
...................................
laser
ablation.
sample
Figure 50 Typical data scan taken during
Figure 51 Magnitude of Fourier transform of sample ablation spectra against magnitude
101
of Fourier transform of blank ablation spectra. ......................................................
10
List of Tables
Table 1 System components for ESI-DMS system...........................................................
31
Table 2 Conditions varied for electrospray plume pattern characterization.................. 35
Table 3 Experimental conditions for Langmuir probe test ...........................................
48
Table 4 Operating Conditions for Experiment using the prototype 1 interface............ 54
Table 5 Sampling sequence for modified ion-optics interface data collection.............. 55
Table 6 Voltage at outer capillary shield when voltage on inner capillary shield is -3200
V ................................................................................................................................
59
Table 7 Summary of experimental conditions for initial test with modified ion-optics
60
in terface .....................................................................................................................
Table 8 Summary of operating conditions for modified ion-optics interface data set...... 64
Table 9 Sampling sequence for modified ion-optics interface data collection............. 65
Table 10 Experimental conditions for initial experiments with AP-MALDI-DMS system.
87
...................................................................................................................................
Table 11 AP-MALDI-DMS experimental conditions for sample depletion time
91
ex perimen ts...............................................................................................................
92
for
Break
up
Gas......................
Table 12 AP-MALDI-DMS experimental conditions
Table 13 AP-MALDI-DMS experimental conditions for pump flow rate experiments... 95
Table 14 AP-MALDI-DMS experimental conditions for extraction voltage experiments.
97
...................................................................................................................................
11
1 Introduction
In an ideal world, one would be able to rapidly detect and classify any and all types of
chemical and biological substances in minute concentrations and volumes with small,
easy-to-use instruments. The number of applications for which such sensors are required
is numerous, from disease diagnosis to airborne toxin detection.
However, sensors
currently available have only some, but not all of these attributes. In particular, there are
few, if any, sensors that are small and easy to use for detecting and identifying large biomolecules.
Draper Laboratory has recently developed a Micro-Electro Mechanical
Sensor (MEMS)-based Differential Mobility Spectrometer (DMS) that is small, userfriendly, and has been demonstrated to quickly detect chemicals at low concentrations.
The aim of the research is to develop a small, easy-to-use system that employs this sensor
to identify large bio-molecules of high molecular weight at low concentrations.
The DMS does not require additional reagents to detect ions and it detects substances
very quickly using only small volumes of analyte. It operates by detecting the ions of an
analyte, and using the properties of those ions as identifiers for that particular
substance[1].
One of the major challenges in creating this detection and classification
system is to generate and keep intact ionized versions of these bio-molecules and to
transport these ions to the DMS. Most methods for ionizing substances tend to destroy
the intra-molecular bonds of large bio-molecules and preclude the detection of the
original bio-molecule[2, 3]. The system designed and tested in this project involves two
different sample ionization and introduction methods, known for their ability to ionize
substances while maintaining macro-molecular structure, coupled to the DMS through a
common interface. The resulting system will provide us with the ability to sense and
12
analyze large proteins and bio-molecules with a system that has several use and cost
advantages over current methods for such analyses.
The DMS generally works with gases, presenting another challenge in converting biomolecules to the gas phase.
In both ionization techniques, the sample of interest is
converted from the solid or liquid phases to the gas phase. The two methods of chemical
ionization investigated were Electrospray Ionization (ESI) and Atmospheric Pressure Matrix Assisted Laser Desorption Ionization (AP-MALDI).
Electrospray ionization
converts samples in solution to ions in the gas phase by creating a fine aerosol spray of
the sample ions.
Atmospheric pressure - matrix assisted laser desorption ionization
involves the laser ablation of a crystallized sample to ionize the sample and convert it to
the gas phase.
The motivation behind the interfacing of these sample ionization assemblies with a
differential mobility spectrometer is that these methods present several advantages over
traditional forms of biological analyses[4]. AP-MALDI and ESI both preserve molecular
properties that are destroyed by other forms of ionization[2, 5]. The ESI and AP-MALDI
methods are considered to be 'soft' ionization methods where the softness of an ion
transfer method can be defined as "the degree to which fragmentation of the ions is
avoided."[6] The properties that are preserved by these ionization processes are essential
in classifying large molecules.
This thesis describes the work done to initially construct, test, and optimize the ESI-DMS
and AP-MALDI-DMS systems and the first stages of work in arriving at a robust
detection and classification system. In the following sections, the fundamental principles
13
of this sensor and these sample introduction methods will first be described, followed by
descriptions of the experimentation and testing done to create the systems, and the
methods of data analysis used to determine system function and substance classification.
From the initial design stage, we have come to the point where we have been able to
successfully introduce substances into the DMS from both the AP-MALDI and the ESI
sample introduction methods, and preliminarily analyze data from the system. The two
different systems have been tested and optimized to the point where it is necessary to
implement a second generation system design to further improve ion sensing and
classification abilities.
2 Background
2.1 Differential MobilitySpectrometer
2.1.1 Operation Principle of Differential Mobility Spectrometry
The Differential
Mobility Spectrometer
(DMS)
detects and identifies chemical
compounds by measuring the differential mobility of their corresponding ions[l]. Ion
mobility is a property that relates to the ability of ions to be deflected and guided by
electric fields.
An ion's mobility in an electric field in a given gas environment is
dependent upon the charge, size, and mass of the ion[7]. As the strength of the electric
field increases, the mobility of ions changes non-linearly. Mason and McDaniel [7]
found that ion mobility is highly dependent on field strength, and changes non-linearly as
the field strength increases.
Figure 1 shows that the change in ion mobilities as a
function of the electric field applied for three hypothetical substances is non-linear. The
14
non-linearity of ion mobility and the greater separation between mobilities at higher
electric fields, are essential properties for classifying substances with the DMS.
..
I
**
Species A
I
*
Species B
K(E)
Species C
E= 2
E <1000
E
Figure 1 Graph of ion mobility as a function of electric field for three hypothetical ion species.
Draper Laboratories has recently developed a DMS (Figure 2) with detection limits in the
parts-per-billion and parts-per-trillion range that operates under atmospheric pressure and
temperature conditions [8].
DMS is loosely based on the principles of Ion Mobility Spectrometry (IMS). IMS works
by processing pulses of ions. Sample ions are pulsed into a constant DC voltage electric
field, which as show in Figure 1, causes the ions to have certain mobilities. These
mobilities are measured by the amount of time if takes for the ions from a particular pulse
In contrast to IMS, the DMS is a Radio
to travel through a drift tube region[8].
Frequency (RF) filter and produces an added dispersive force which further enhances
mobility differences. The DMS is different from the IMS in that the mobility of ions are
measured according to the compensation voltage necessary to correct for the oscillatory
path of the ion caused by the RF field. One of the main advantages that DMS has over
IMS is that ions can be introduced into the sensor continuously whereas in IMS, ions are
15
introduced in pulses.
This allows for more continuous monitoring and sensing of
biologicals when time plays a critical factor in a particular application. The DMS is less
complex than the IMS, in part because it does not require the precise timing of ion travel.
Additionally, the simpler DMS instrument is also more conducive to miniaturization than
the IMS. The continuous sensing property also simplifies the sample injection process so
that samples can be injected in either a continuous or pulsed manner.
1cm
Figure 2 Picture of the MEMS-based Differential Mobility Spectrometer.
Just before injection into the DMS, analyte is combined with a carrier gas, passed through
an ionizing source (an ultraviolet light or a radioactive beta-particle source), then
introduced into the DMS. The inner ionization source ionizes the carrier gas, which then
transfers charge to the analyte. In some cases, if the ionization potential is higher than
that of the analyte, then the analyte may be ionized directly. In the DMS detector (Figure
3) ions are carried in a stream of nitrogen gas between two charged plates. The plates in
the DMS have a radio frequency electric field applied to them (Figure 4a). The changing
field causes ions to have different mobilities that in turn cause the ions to travel in an
oscillatory fashion, with a bias towards one of the plates[9].
16
The bias in travel is
--
--
dependent upon how the ion's mobility changes in that particular field and is caused by
the 30% duty cycle of the RF voltage waveform.
RF electric
Ionization
Source
Sample
in
Fow
Ion Trajectories
-~Detector
Compensation
electric field
Figure 3 Schematic of ion trajectory through the DMS.
In order to control which ions reach the detector and when, an additional DC voltage,
called the compensation voltage (V,), is applied to the plates. This DC voltage creates a
constant electric field that corrects for the bias in the ion's path, and is applied to reverse
the effect of the RF field. Some ions will be deflected more than others by the varying
RF field, so different ions can be guided through the region between the plates to the
detector by applying different compensation voltages to the metal plates.
EA(
ERF (o)
(b
(a)
Figure 4 (a) Varying electric field due to RF voltage waveform (b) Constant electric field due to
compensation voltage.
17
-~
2.1.2 Traditional Uses of Differential Mobility Spectrometry
In other applications, DMS is often used as a gas phase ion separation pre-filtering
technique for mass spectrometry [10]. Prior separation of ions increases the performance
of the mass spectrometer.
For instance, when a DMS is connected to a Gas
Chromatography-Mass sjpectrometer (GC-MS), the resulting analysis can often be more
accurate[ 11, 12]. The operation of gas chromatography is analogous to a race; substances
which travel faster in the gas phase reach the end of the GC column faster. If ions are
filtered prior to introduction into the column, then separation among substances is even
more pronounced. The analogy is that in the GC race, adding a DMS to the front/back
staggers the racers so that they are guaranteed to cross the finish-line - or reach the mass
spectrometer - individually.
The DMS has also been used with other sample introduction techniques. A pyrolysisDMS system has been shown to successfully detect trace quantities of Bacillus
spores[13]. Another system using pyrolysis-gas chromatography has also been used to
chemically characterize bacteria[14]
A system consisting of a headspace sampler and
gas chromatography column has been used with the DMS to detect bacterial headspace
[15].
2.2 ElectrosprayIonization
There are several characteristics of ESI which make it a desirable method of sample
introduction to the DMS sensor. One of these characteristics is the "softness" of the ESI
method which allows both the preservation of non-covalent interactions between
molecules in the gas phase that existed in solution and the study of three-dimensional
18
conformations[16].
The "softness" of the ESI method aids differentiation between
specific proteins of interest from other closely related interferent proteins or other organic
material because macromolecular structure is preserved, making ESI an attractive
technology to couple to the DMS sensor[2, 6].
2.2.1 Operation principle of Electrospray Ionization
Electrospray Ionization (ESI) is a method of ionized sample introduction for mass
spectrometry which facilitates the analysis of high molecular weight compounds such as
proteins and nucleotides by mass spectrometry[17]. ESI converts liquid samples to ions
in the gas phase by aerosolizing the sample in the presence of an electric field using a
nebulizing needle which creates a "nebula" of ions.
Figure 5 Electrospray needle and plume[18]
The ESI nebulizer needle assembly consists of two concentric needles. Sample solution
travels through the innermost needle, while a nitrogen (N2 ) nebulizing gas travels
between the outer and inner needles. When the sample solution comes out of the tip of
the inner needle it forms a dipolar layer at the meniscus of the solution. The presence of
the strong electric field, where the needle is the positive electrode, causes positive ions to
19
come to the outer surface of the meniscus during the redistribution of charge within the
solution at the capillary tip to create field-free conditions within the liquid[6].
The
repulsive forces between the positive ions at the surface overcome the surface tension of
the solution and cause the liquid to move further in the direction of the electric field,
forming a Taylor cone (Figure 5). A jet of positive ions extends out from the cone, and
then disperses into a plume of positive ion droplets. These droplets disperse further when
the solvent is evaporated by a heated drying gas, such as nitrogen.
As the solvent
evaporates, and ions come closer together, the repulsive force between the like charges
causes the ion droplets to further split apart into smaller clusters and the ions to disperse
(Figure 6).
Figure 6 Depiction of ion dispersion from aerosol droplets.
2.2.2 Prior Uses of the ESI Technique
The ESI method of converting samples to ions in the gas phase has been in existence for
many years and has been used for many different applications. The most widely used
application for ESI is in mass spectrometry. ESI-MS has been used to analyze peptides,
proteins[19], nucleic acids[16, 20], lipids, carbohydrates [21], and inorganic and
organometallic complexes[22].
Outside the context of Mass Spectrometry, ESI is an
20
important method for the creation of aerosols, and for the electrostatic dispersion of
liquids[6].
2.3 AP-MALDI
2.3.1 Operation Principle of AP-MALDI
AP-MALDI is a technique that is most often used to generate ionized gases from solid
and dried liquid samples of non-volatile materials[23, 24]. When a sample is ionized
using AP-MALDI, the sample is dispersed into a matrix that absorbs and transfers energy
to the sample when the mixture is ablated with an ultraviolet laser source. The matrix is a
compound mixture chosen for certain properties which allow for successful ionization of
the experimental sample. These properties include the ability to dissolve the analyte and
recrystallize with it, and the ability to absorb energy from the laser. The matrix must also
not react with the analyte, but should still be able to transfer some positive charge to it.
The wavelength of the laser is selected to excite the matrix without altering the
experimental sample; the experimental sample is only ionized. The abrupt transfer of
energy from matrix to sample causes the sample to desorb and form an ionized gas[2].
These ions are then transported into the analytical sensor of choice via a carrier gas.
One of the unique characteristics of the AP-MALDI ionization process is that it is gentle
enough to desorb and ionize large molecules while keeping the entire molecule intact[5].
This trait of AP-MALDI makes it a particularly useful technique for analyzing proteins,
peptides, and other biological macromolecules for mass spectrometry analysis [3, 25, 26].
Additionally, AP-MALDI allows for the analysis of analytes in a large range of
molecular weights at small concentrations[23]. The ionization technique has several
21
advantages in chemical analysis over other methods of chemical ionization[23].
AP-
MALDI preserves chemical structure and intramolecular interactions that are often
destroyed by other methods[3].
The ability to preserve structure is important for the
detection and sensing of molecules with high molecular weights, such as proteins. Other
forms of ionization tend to destroy or fragment these kinds of larger molecules. For solid
and liquid samples for example, pyrolysis is a highly destructive process, and can fracture
and damage the samples, resulting in detection of pieces and fragments of the compound
or molecule of interest [27].
2.3.2 Prior Uses of the AP-MALDI technique
Initially, the AP-MALDI was coupled with a time-of-flight mass spectrometer (TOFMS), which has a large mass range and high sensitivity [2]. In 1995, the AP-MALDI
sample introduction technique was used with a quadrupole mass spectrometer to
demonstrate the direct sequencing of proteins and peptides of unknown structure[2]. The
AP-MALDI technique was then subsequently demonstrated to work successfully with
Fourier Transform Mass Spectrometry (FTMS), allowing for the high resolution analysis
of complex peptide mixtures.
High kinetic energy and extensive meta-stable decay
characteristic were thought to be obstacles that prohibited, or made insensitive, the use of
AP-MALDI with FTMS, but were overcome by decoupling the AP-MALDI source from
the superconducting magnet [28].
The unique ionization characteristics of the AP-
MALDI process makes it extremely attractive to interface with novel analytical sensors,
especially those that also operate at atmospheric pressure.
22
2.4 Advantages and Potential of the ESI-DMS and AP-MALDIDMS systems
As discussed above, the ESI and AP-MALDI techniques are soft ionization techniques
that require few reagents to ionize analytes of interest. Hence, ESI and AP-MALDI are
good techniques for ionizing large bio-molecules such as proteins because they will not
fragment the ions, and will allow for detection based on properties of the entire ionized
molecule. These ionization methods coupled to the DMS have the potential to be an
extremely powerful new system to detect and classify biological substances with the use
of sophisticated classification algorithms.
The DMS is small and portable, and also requires no extra reagents other than carrier
gasses to detect ions. ESI-DMS and AP-MALDI-DMS systems would provide the ability
to detect and analyze large bio-molecules at low concentrations in small volumes quickly,
and efficiently, at very low cost. Additionally, instruments that can analyze ions at
atmospheric pressure can be coupled to both ESI and AP-MALDI sources with only
minor changes[24].
The applications for such systems are numerous, providing great
motivation behind the research described in the following thesis.
2.5 Data Analysis
The data collected by the DMS varies in three dimensions: time, ion abundance, and
compensation voltage. A typical positive background scan showing reactive ion peaks at
-17.9 V, -14 V, and -7.5 V (Figure 7). Each scan represents a slice in time for which ion
abundances versus compensation voltages are recorded.
23
Cuso 1961I-.15--
\-
Figure 7 Example of positive ion spectral data scan
Data collected using the ESI-DMS and AP-MALDI-DMS systems are different from
previous types of DMS data collected because the sample introduction methods are
different. Current methods for analyzing DMS data are computationally expensive, and
can not be used for every set of data generated. It is therefore necessary to develop other
methods for analyzing data which can provide valuable information without resorting to
lead-cluster mapping and genetic algorithms.
2.5.1 Fourier Methods
The Fourier transform is a mathematical transformation of signals that forms the basis for
modem signal processing, and is used in the context of this research to help extract
information from signals for which methods of analysis are not yet well formed.
The Fourier transform breaks signals up into a sum of periodic waveforms, and
transforms them from time varying signals to frequency varying signals. Inspection of
signals in the frequency domain is often the first step in developing methods for further
processing and extracting information from unknown signals.
discrete Fourier transform is:
24
The equation for the
X(k)=
x(j)Nijl)(kl-)
for k =0,1,2,...,(N -1)
and
WN =ej(2,T/N)
j=1
Where X(k) is the transformed signal, x(j) is the original data signal, and N is the
number of points in the original signal. Each point in X(k)is computed by multiplying
every point in x(j) by a power of
which is called the "frequency", and then
(~),
summing those all together. The transformed signal has the same number of points as the
original signal does. For all otherk, the transform X(k) is equal to 0.
Each point in the transform X(k) is the sum of all the points in the original signal, where
each point has been multiplied by a different root of 1.
The transformed signal is a
complex signal, because it involves multiplications by roots of 1 - complex numbers.
The transform, because it is complex, can then be broken up into a magnitude and phase.
From the magnitude and phase of the Fourier transform, one can fully and uniquely
reconstruct the original signal using the following equation:
x(j) = -
X(kojl(l
Nk=1
for k=O0,1,2,...,(-)adONe(t)
Each scan of DMS data has compensation voltage V, on the horizontal axis, and
abundance V, on the vertical axis, so the integer index j, is analogous to the index of V'.
When we make the transformation from x(j) to X(k), the only thing left in common
between k and jis that they both range from 0 to (N - 1). By convention, k is usually
deemed frequency because most signals vary over time. In the case of DMS data, k
refers to the voltage analog of frequency, 1
AV,
25
The Fourier transform is helpful in extracting information about periodicities in the data
which may not be obvious in the raw data. For a further explanation of the Fourier
transform and its properties, see Oppenheim and Willsky [29].
As shown in the
following chapters, the Fourier transform can be used to detect differences in data taken
under different conditions. The results of these analyses show differences in data much
more clearly than the raw data does itself.
2.5.2 Decision Trees
Decision trees are non-parametric classification models that can be used to classify and
recognize non-linear patterns in data. Decision trees have simple tree structures where
each node of the tree is associated with some classification test, and each leaf of the tree
is associated with a specific classification outcome. The input, also called the predictor,
is tested at each node by the classification test. Whether or not the predictor satisfies the
classification test will determine what the next test or classification applied to the
predictor is. The classification tests can be expressed as if-then statements. The decision
trees are constructed by partitioning data in increasingly homogeneous subsets according
to algorithms described by Breiman, et al[30].
Decision trees have previously been used to classify proteins that were analyzed by mass
spectrometry to distinguish between diseased and non-diseased specimens [31-33].
In
both cases, proteins from humans with and without cancer were successfully
differentiated using decision tree analysis on MS data of those proteins. Decision trees
have also been used in the context of medical diagnosis and chemical analysis [34].
26
2.5.3 Lead Cluster Mapping and Genetic Algorithms
The use of genetic algorithms, developed by Correlogic Systems, Inc. (Bethesda, MD), in
analyzing DMS data is currently being explored. These algorithms have been used to
detect prostate cancer [35] and ovarian cancer [36] using proteomic patterns in serum.
This section will provide a brief overview of the algorithms used by Correlogic in their
software, ProteomeQuest@, to find biomarkers in DMS data collected at Draper
Laboratory. At this time, we are exploring their use for several types of DMS data.
ProteomeQuest@ employs lead cluster mapping and genetic algorithms to construct
models for classifying biological substances. The data sets are divided into three portions
which are used separately in the three stages of the algorithm - training, testing, and
validation.
Lead cluster mapping first randomly selects a user-defined number of
features, usually between 5 and 12, on which to build the cluster maps. Each feature
corresponds to one pixel from a DMS data file which contains 250 scans in the V,
dimension and 200 scans in the time dimension, for 50,000 total pixels. Each data file is
then mapped to the feature space using the intensities of the selected features. After the
first data file is mapped on the feature coordinate system, a user defined radius specifying
the degree to which the model will fit the data, is drawn around that point to define a
cluster. If after the next file is mapped to the feature coordinate system it falls within that
first radius, the centroid of the cluster is redefined around the two data files. If not, a new
cluster is formed around the second data file. After all the data files have been mapped,
each cluster is labeled according to what types of data files define that particular cluster,
thereby creating cluster map. The testing set of data is used to determine how accurately
the cluster map classifies data it was not constructed with. If the accuracy is low, then
27
another cluster map is constructed using a different random set of features. This process
of training and testing is repeated until a user defined number of maps, called the
population, is achieved.
Once the cluster map population has been built, the genetic algorithms come into play.
Each map has associated with it an accuracy value calculated by the performance of the
map on the testing data.
Using the maps that achieved higher accuracies, new maps are
made using some combination of the features from the higher achieving maps. If a high
accuracy is achieved, then this new map replaces a lower performing map in the
population. This process of attempting combinations of features from maps made while
creating the map population is continued for a user-specified number of generations, at
which point the best map available will be selected, or until a map is able to achieve a
high target accuracy. Once this final map is determined, it is used to classify the data in
the independent, blinded validation data set to come up with a final accuracy for that
cluster map model.
The following thesis presents work done to develop alternative methods for classifying
data, and compares the results of those analyses
with those obtained
using
ProteomeQuest@ lead cluster mapping and genetic algorithm tools.
2.6 Thesis Summary
Chapter 2 will describe the experiments and hardware optimization tasks completed in
order to optimize the ESI-DMS system and to determine how to classify different
substances. Chapter 3 will provide a description of the AP-MALDI-DMS system and the
work done to characterize and optimize that system. Both chapters contain discussions of
28
the data analysis methods used for the different forms of data extracted from each system.
These chapters will be followed by a summary of conclusions from each system, plus
another chapter detailing ideas for future work to continue the optimization and
improvement in functionality of both systems.
3 ESI-DMS Instrumentation Development and Analysis
This chapter describes the function, design, and optimization of the Sample-to-Detector
Interface (SDI) which couples the ESI and AP-MALDI units to the DMS sensor. This
chapter is divided into characterization of the interface, experimental testing of three
different versions of the interface and system setup, and data analysis techniques for
DMS signal output. The results and conclusions of the testing and optimization described
in this chapter are currently being used to design the second generation of the interface
and system. Additionally, data analysis tools investigated here will be further developed
to determine whether more robust methods for identifying and quantifying biological
substances can be created.
3.1
The ESI-DMS System
A block diagram of the entire ESI/AP-MALDI-DMS system is shown in Figure 8, and
corresponding components are listed in Table 1.
The ESI or AP-MALDI sample
introduction unit is connected to the SDI, which is in turn connected to the DMS unit. A
small pressure drop of approximately 0.013 atm is applied to the outlet of the DMS to
facilitate the transport of ions through the sensor. The DMS unit is connected to a
standard notebook PC which controls the sensor and collects data.
29
mass flow
conlrollers
gas heaters
control / data acquisition
co mput er
nit roge n
gas
rer gas
breakup gas
to
u, E
+
-- sample
-------
Pump
exast
exus
sample
flow
carne r gas
flow
Figure 8 Block diagram of ESI/AP-MALDI-DMS system
A list of parts used in the system is summarized in Table 1. Nitrogen (UHP grade 5,
Middlesex Gases and Technologies, Everett, MA) flows through the mass flow
controllers, is heated by the low flow heaters, and then fed into the inlet of the carrier gas
and breakup gas lines. Gas flows in the system are controlled by mass flow controllers
(M100B, MKS Instruments, Wilmington, MA), and are heated by low flow gas process
heaters. The temperature of the heaters is regulated by variable AC voltage waveform
generators that control the amplitude of the AC voltage waveform applied to the metal
heater block, subsequently heating the block to a corresponding temperature. A high
voltage power supply, (Stanford Research Systems) provides the voltages necessary to
produce the high electric fields for extracting ions from the AP-MALDI sample plume.
30
Table 1 System components for ESI-DMS system.
Model and Manufacturer
G1607A, Agilent Technologies, Palo
Alto, CA
G1972A, Agilent Technologies, Palo
Alto, CA
MDP-1, Sionex Corporation, Waltham,
Part
ESI Unit
AP-MALDI Unit
Differential Mobility Spectrometer
Custom Made, Draper Laboratory,
Cambridge, MA.
Air Cadet 5730-40, Cole-Parmer,
Vernon Hills, IL
M100B, MKS Instruments,
Wilmington, MA
PS350/5000V-25W, Stanford
research Systems, Sunnyvale, CA
AHPF-062, Omega Engineering
Prototype I Interface
Air pump
Mass flow controllers
High voltage power supply
Low flow gas process heaters
3.2 The ESI Unit
The ESI unit consists of a nebulizing needle and an ionization chamber. Analyte solution
is drawn into a syringe and then delivered to the nebulizing needle through 1/16 inch
outer diameter Teflon PTFE tubing. The output flow rate of the syringe is controlled by a
syringe pump (HA2000P, Harvard Apparatus, Holliston, MA). Nitrogen gas is supplied
to the nebulizing needle via 1/8 inch outer diameter Teflon PTFE tubing. The unit is
connected electrically to earth ground by a spring-loaded pin that makes contact with
grounded portions of the interface when the interface is in the closed position; these
features are described in further detail below.
3.3 Prototype 1 Interface
A custom designed interface was used to couple the ESI and AP-MALDI units to the
DMS sensor. The interface is designed such that commercially available ESI and APMALDI units available from Agilent Technologies can be easily swapped on and off the
interface. The mating mechanism is a simple hinge and latch assembly that also allows
31
the user to swing the ESI or AP-MALDI units from open position to closed position to
allow adjustment of parts in the ionization chamber without dismantling the interface.
Figure 9 shows a drawing of the interface with the AP-MALDI unit attached.
The
drawing on the left shows the unit in the open position. The drawing on the right shows
the unit in the closed position with the latch engaged.
Figure 9 AP-MALDI or ESI unit in open position on the left and closed position on the right on the
sample detector interface
A cross-section of the SDI is shown in Figure 10 with labeled gas and ion flows. Analyte
ions are attracted to the inlet of the interface by an electric field and slight suction exerted
by a pump applied to the outlet of the DMS. The break up gas is directed against the
flow of analyte ions, breaking up any ion clusters and drying any remaining liquid to
prevent liquid from entering and damaging the DMS.
Heated carrier gas enters the
interface and flows in the same direction as the analyte ion path. The carrier gas is
ionized when it passes through the radiation source within the DMS, subsequently
transferring charge to analyte molecules through collisions.
32
Heated
Heated
Break-up gas carrier gas
gas
DMS
Analyte
ions
2.4 i n
Figure 10 Ion and gas flows through cross section of ESI/AP-MALDI-DMS interface. Figure
courtesy of Ernest S. Kim, Draper Laboratory, Cambridge, MA.
The ESI and AP-MALDI units each have easily interchangeable components that must be
attached to the interface (Figure 11). The ESI components include a capillary shield and
inlet to which the high extraction voltage is applied (Figure 11, item 2). An external
shield which focuses the break up gas flow, and also includes an additional break up gas
outlet, is placed over the capillary inlet, (Figure 11, item 1). This shield both focuses the
break up gas flow against the flow of ions from the ESI needle, and also focuses the
electric field by reducing the surface area of the high voltage inlet which is exposed to the
ESI needle. Components for the AP-MALDI unit are discussed further in Chapter 3.
33
Figure 11 SDI with Agilent ESI capillary interface components: 1. External capillary shield 2. Glass
capillary shield and inlet.
Several experiments were designed to evaluate the performance of the ESI-DMS
interface. These experiments investigated the following: properties of ions generated by
the ESI method, characteristics of sample introduction, shape and form of the sample
aerosol plume emitted form the ESI needle, and ways of manipulating the plume.
Conclusions from these experiments then provided the impetus for further investigation
to determine system function, leading eventually to design changes in the ESI/APMALDI-DMS interface.
3.4 Spray Pattern Characterization
The goal of the first set of experiments was to characterize the shape and quality of the
aerosol created by the ESI needle. The results would provide information that would
guide further experiments to vary the position of the ESI needle relative to the SDI.
3.4.1
Experimental Design
Measurements of electrospray plumes were collected for several different parameters to
characterize the spray pattern of the electrospray needle. Nebulizing gas was flowed into
the nebulizing needle, and a colored aqueous solution was injected into the electrospray
needle. The resulting spray pattern was collected on filter paper some distance away
34
from the needle tip. The nebulizing gas flow rate, the distance between needle and filter
paper, and the sample injection rate were all varied to examine their effect on the
electrospray plume. Table 2 summarizes the different conditions for which a plume
pattern was collected. Plumes for all combinations of conditions were collected.
Table 2 Conditions varied for electrospray plume pattern characterization
Condition set points
3.4.2
Sample Flow Rate
(pl/min)
5
100
Nebulizing Gas
Flow Rate (L/min)
400
800
1.0
1.2
1.6
2.0
Distance to Filter
paper (cm)
2
3.5
5
Results
Each pattern was analyzed qualitatively to gain a general sense of what variations in
parameters would cause changes in the spray, and what types of changes would occur.
As shown in Figure 12, as the distance between the needle and the filter paper was
increased, the diameter of the plume became larger and less dense, as expected for a low
sample flow rate of 5 pl/min. The trend is not as clear for the 100 pl/min sample flow
rate measurements.
These measurements were taken for a spray accumulation lasting
only 5 seconds. The edges of the collected pattern were difficult to determine, leading to
variability in the measurement of the pattern diameter.
Across the different
measurements, as the nebulizing gas flow rate increases, the plume diameter decreases,
indicating that the high gas flow rate causes the jet to be more spatially focused. For all
subsequent experiments, the flow rate for the nebulizer gas was set to 1 L/min to provide
an ionized aerosol that was not too narrow.
35
14
12
Sample Flow Rates
-+-5 ul/min, 2 cm
-u-5 ul/min, 3.5 cm
10
E
E
cc
is5
6
-
-r-
8
K-
-&-5 ul/min, 5 cm
ul/min, 2 cm
x-100
-1
*-100 ul/min, 3.5 cm
-.-
4
100 ul/min, 5 cm
2
0
0
0.5
1
2
1.5
2 .5
Nebulizing Gas Flow Rate (L/min)
Figure 12 Plume diameter for various sample flow rates and nebulizing gas flow rates.
3.5 Current Measurement Experiments
Several different experiments were conducted to confirm the operating principle of the
ESI method, and to confirm that the ions created by the needle were traveling properly
along the pathway to the DMS. One goal for these tests was to maximize the number of
ions generated by the ESI method by varying operating conditions while measuring the
current between the ESI needle and a detection point some distance away from the
needle. An increase in measured current is caused by an increase in the number of ions
produced, translating to a stronger ion signal in the DMS, and resulting in increased
sensitivity for the entire system.
Observations from these experiments led to design
changes in the configuration of ion-optics in the interface, and changes to the path
traveled by ions from the ESI needle into the DMS.
36
Several different geometries of the ESI needle were tested to determine the effect on the
current generated at different distances away from the needle.
Additionally, several
different methods of measuring current generated by the ESI spray were tested in
attempts to improve upon the accuracy of the current readings. One of the challenges in
conducting these experiments was that the current being measured was extremely small
and that it was to be measured across a large distance which would cause further
attenuation of the measured ion signal. Current data collected at non-vacuum conditions
would also be highly susceptible to noise and other disturbances.
3.5.1
Experiment 1
In the first experiment, current was measured as a function of the sample injection rate.
The ESI needle was aligned on the axis of desired ion path travel (Figure 13). The spray
was manipulated by an electric field between the electrospray needle which was
connected to earth ground, and the capillary orifice which was connected to an extraction
voltage of -3000 V.
ESI Needle
-3000V
Ion Collection
Plate
Capillary
-
-
orifice
Figure 13 Schematic for current measurement experiment A.
In the configuration shown in Figure 13, the ions would be sprayed from the needle,
attracted into the orifice by the electric field between the needle and the orifice, and
37
directed towards a metal plate which is connected to an ammeter. The distance from the
needle to the capillary orifice entrance was 6.5 mm, and the distance from the capillary
orifice fitting was 15 mm. The flow rate of the nebulizing gas was 1 L/min. The sample
flow rate was varied across the values 0, 100, 250, 500, 750, and 1000 ptl/min.
Measurements were taken when the current reading had stabilized.
0.05
0.04
<0.03
0.02
0.01
0
-0.01
0
200
400
600
800
1000
1200
Sample Injection Rate (pl/m in)
Figure 14 Plot of current measured at plate versus sample flow rate
As shown in Figure 14, as the sample flow rate increased, the current measured also
increased.
This result confirms the intuitive hypothesis that as more sample is
aerosolized per unit time, more ions will travel to the detector plate per unit time, and
measured current - charge per time - will therefore increase.
3.5.2
Experiment 2
The previous experiment confirmed the operation principle behind the electrospray
ionization method.
However, it did not confirm that the electric field between the
capillary orifice and the needle was the driving force in attracting ions through the orifice
opening to the collector plate because the needle was spraying aerosolized ions directly at
the detector plate.
38
To test the efficacy of the electric field in attracting ions into the capillary orifice, another
experiment was conducted with the same configuration for the capillary interface and the
detector plate, but with the ESI needle now oriented perpendicularly to the direction of
the desired final ion path. The horizontal distance from the needle tip to the orifice, a,
was set to lcm, the vertical distance from the needle tip to the center of the capillary, b,
orifice was 7.1 mm, and the distance from the capillary orifice to the metal detector plate
was 13.5 mm.
4-
ESI Needle
Ion Collection
-3000V
b
a
t
Plate
c
Capillary
orifice
A
Figure 15 Current measurement setup with perpendicularly oriented ESI needle
The flow rate of the nebulizing gas was set to 1 L/min based on spray characterization
tests described earlier, and two experiments to determine the effect of separately varying
the sample flow rate and extraction voltage were conducted.
The goal of these
experiments was to determine that amount of ions that could be generated and how that
amount could be varied using an ESI needle geometry configuration more similar to that
39
of the commercially available ESI unit. The sample flow was varied across the values 0,
50, 100, 175, 250, 500, and 750 p.l/min. The extraction voltage was set to -3000 V.
0.005
0.004
0.003
0.002
0.001
-1
-0.001 0
2
400
600
800
-0.002
-0.003
-0.004
Infusion Rate (pl/m in)
Figure 16 Infusion rate versus current for perpendicular current measurements
As the sample infusion rate increased, the measured current increased as shown in Figure
16. This indicates that ions were attracted into the capillary orifice and to the metal
detection plate. Current measurements for flow rates less than about 200 pl/min were
negative, which means ions might have been traveling in a direction opposite to the
electric field. However, the negative measurements are likely to be due to noise which
caused the picoammeter to fluctuate from positive to negative readings. This experiment
confirmed the expectation that current would increase for higher sample flow rates, even
when the ESI needle was positioned vertically.
In the next experiment which would further prove the principle of the ESI method, for a
sample infusion rate of 500 pl/min, the extraction voltage was varied across the values 0,
-1.0, -1.5, -2.0, -2.5, -3.0, -3.5, -4, and -4.5 kV. Single measurements were taken because
the current reading stabilized over time. As the potential difference between the ESI
needle and the capillary orifice increased in Figure 17, the measured ion current also
increased. This result confirms the idea that as the electric field increases in strength,
40
proportional to the increase in the potential difference between the needle and orifice, the
number of ions directed to the detector plate will increase.
0.005
0.004
=L
0.003
0.0021
0.001
0
-0.001
---
-
-4
-3
-2
,
0
-0.002
-0.003
Extraction Voltage (kV)
Figure 17 Current measured versus extraction voltage for vertical needle geometry.
3.6 Langmuir Probe Experiments
Langmuir probes are typically used to measure currents due to ion travel in plasmas[37].
The ESI needle can be treated as an ion source, so a Langmuir probe would improve the
method for measuring ion currents and lend greater accuracy to the measurements.
Additionally, the Langmuir probe would allow for current measurement experiments in a
setup that was far more similar to the actual ESI-DMS interface so conclusions from
these tests could more easily be applied to the actual ESI-DMS system. The goal of these
experiments was to determine the conditions at which ion generation and transport
occurred most optimally.
The following section describes the different experiments
conducted using the Langmuir probe in a setup similar to the modified capillary Tjunction interface.
The outcomes of these experiments contributed to the decision to
modify the ion-optics setup in the prototype 1 interface, and led to a deeper
understanding of the effect of the ion-optics on ion travel.
41
3.6.1
Initial Testing with the Langmuir Probe
The first Langmuir probe configuration is shown below in Figure 18.
A T-junction
fitting with a copper electrode, and a fused silica capillary tube were attached to the
carrier gas heater outlet. The nebulizing needle was positioned at an angle to the Tjunction with 0 = 75', x = 0.5 cm, y = 1.5 cm, where 6 is the angle from the horizontal,
and x and y are the horizontal distance and vertical distance, respectively, of the needle
from the center of the glass capillary. The probe, a thin wire threaded through a GC
column until slightly protruding from the other end of the column, was positioned in
between the charged T-junction and the nebulizing needle. As shown in the figure below,
the probe is placed directly in the stream of ions.
Liquid
Carrier
sample
,
Nebulizergas
-3500 V
Glass capillary
Nebulizer
needle
Capillary entrance
Nebulized
analyte and
solvent
Langmuir probe
amreter
-5 V
Figure 18 Setup of Langmuir probe for initial test
Current measurements were taken for various sample flow rates with QPUMP = 500
ml/min, and
QNEB =
1 L/min. The probe was biased relative to the ammeter to allow for a
more accurate reading of current. Measurements were taken at 1 minute intervals, as the
42
sample flow rate was varied. The experiments were repeated with different extraction
voltages and a sample solution of 1mM acetic acid in water, and with different sample
concentrations at an extraction voltage of -3.5 kV. As the extraction voltage was turned
on, and as the sample concentration increased, the measured current was expected to
increase, as the increase in these two parameters should increase the total number of ions
that reach the detector. Another experiment was conducted to determine the effects that
different positions of the probe within the path of the ion spray would have on the total
ion current measured. The results of these tests are shown below.
3.6.1.1 Results of Initial Test Using Langmuir Probe
-+-HV=OV --
HV=-3500V
34
33
32
____
______
-
31
-
30
29
28
27
-
26
25
24
----
0
10
20
40
30
50
60
70
Sample Row Rate(pl/min)
Figure 19 ESI current as a function of sample flow rate for different extraction voltage levels
As shown in Figure 19, when the extraction voltage was set at -3.5 kV, the measured
current increased as the sample flow rate increased, in a somewhat linear fashion. When
the extraction voltage source was turned off, current decreased as the sample flow rate
increased. As the sample flow rate changes, the absolute value of the change in both
lines is nearly the same. One way to interpret this graph is that when the power supply is
turned off, the polarity of the current changes, so the sign of the trends are opposites.
43
36 ................................................................................ .........
.
!...............................
35
34
-
-
33
E 32
31
30
-
--
29
1 mM acetic acid
-
28
.5 mM acetic acid-
27
0
20
80
60
40
Sample Flow Rate [pl/min]
100
120
Figure 20 ESI current as a function of sample flow rate for two different concentrations of sample
In another test using the Langmuir probe setup of Figure 18, the sample flow rate was
varied and current measured for two different sample concentrations.
Contrary to
expectation, the lower concentration sample of acetic acid in Figure 20 produced a larger
current than the higher concentration sample did.
The plot of Figure 21 shows the change in current as the x-position of the probe was
varied for two different y-positions.
As the distance from the probe to the orifice
increased, current also decreased, for both y-positions. We suspect the trend is nonlinear
because the probe may have been positions of higher ion density in some positions than
others. Because ions were traveling in the direction of the electric field, and because the
field is non-uniformly distributed given the geometry of charged components, then the
plot below yields information about where the field might be strongest as evidence by
higher current which means that more ions were traveling in that location.
44
38
- 0.5 mm
-ay=2mm
37
36
a,
C.)
35 -I
___________
34
33
32
4
4.5
5
5 .5
6
6.5
7
x-distance from orifice (mm)
Figure 21 ESI current as a function of Langmuir probe location.
3.6.2
Air Pump and Breakup Gas effect on Current
Following the initial test of the Langmuir probe setup where the probe was positioned
between the nebulizing needle and the t-junction electrode, further testing was conducted
with the Langmuir probe setup as shown in Figure 22 in place of the T-junction from the
initial round of testing. These experiments were used to determine ion density at various
locations on the ion path for different operating conditions and geometries. This involved
placing the Langmuir probe between the nebulizer needle and the high voltage orifice, as
well as at various positions inside the glass capillary. The purpose of these experiments
was to determine the optimal conditions for which the maximum number of ions are
generated and transported through the interface to the DMS. The physical setup of the
experiment is shown in Figure 23.
45
heated
breakup
gas
To pump
I
Septum, allowin passage
of Langmuir probe while
maintaining a seal
glass capillary
inside metal tube
Langmuir probe
-e
Connects to bias
voltage and
picoammeter
Tube surrounding
glass capillary at HV
at HV = -3.2 kV
Figure 22 Langmuir probe setup in second set of experiments
The probe was threaded through a setup very similar to that used in the modified
capillary T-junction interface. One of the T-junctions allowed for breakup gas to flow in
a direction opposite to the desired flow of ions. The second T junction allowed for the
pump to pull on the gasses that were traveling inside the glass capillary tube. The pump
does not actually create vacuum conditions; the system only sustains a 0.015 atm pressure
drop from the ionization chamber to the outlet of the DMS.
construction is shown in Figure 22.
46
A cross-section of this
heated
breaku
Nebulizer needle
To pump
Rear end of
Langmuir probe
X
heated
breakup
gas
+spicoa
plastic
tubing
Connects to bias
voltage and
meter
at HV = -3.2 kV
insulator
Breakup gas
manifold/nozzle
Figure 23 Langmuir probe setup with ESI needle
Experiments were conducted to determine the effect of different gas flow rates, and
needle geometries on the strength of the ion current generated by the ESI needle. High
breakup gas flow rates would cause a decrease in measured current because the gas
would flow against the desired ion path, deflecting ions and preventing them from
reaching the detection point. Low breakup gas flow rate would increase the current
measured because the force against ion flow would have lessened, allowing more ions to
reach the detection point thereby increasing measured current.
Changes in the pump flow rate would cause the opposite effect on ion measurements that
changes in break up gas would cause. Increasing the pump flow rate should cause the
current to increase as more ions are propelled to the detector on a faster gas flow.
Decreasing the pump flow rate would decrease the current measurement because fewer
ions would be transported to the detector in a given amount of time.
47
The third parameter varied was the needle geometry. By changing the position and angle
of the ESI nebulizing needle with respect to the T-junction and probe, different parts of
the ion plume would be positioned in different regions of the electric field between the
electrode and the needle. In addition, the nature of the field itself would change as the
geometry of the needle and electrode changed. These experiments were designed to find
the optimum combination of break up gas, pump flow rate, and needle geometry patterns.
A summary of testing conditions is shown in Table 3.
Table 3 Experimental conditions for Langmuir probe test
o
Range
0 - 90 0
x, y
0.5 - 2 cm
Qs
QBU
Qpump
0 - 50 pUmin
1 - 4 Umin
500
150
mumin
~ 1 L/min
Variable
QNEB
Notes
Angle of nebulizer needle measured from
horizontal (i.e. 900 is vertical)
Position of nebulizer needle tip from orifice axis and
face of breakup gas shield
Sample flow rate
Breakup gas flowrate
Pump flowrate
Nebulizer gas flowrate, set to 1 Umin
This experiment failed on multiple occasions because of electrical arcing, which required
the experiment to be stopped and restarted. After many trials, and greater difficulty in
discerning the conditions under which arcing occurred, this line of experimenting was
abandoned. There were two main reasons for steering research efforts away from current
measurement using this particular setup.
The first is that during these experiments,
success was achieved in classification of proteins using the prototype 1 interface, as
described further below, using Correlogic's ProteomeQuest@ Software. Secondly, drift
in the baseline of the current measurements was noticed, and is believed to have been
obscuring any trends in the actual current measurement. This drift may have been caused
by problems with the equipment being used to make measurements.
48
In addition, the leftmost T-junction was not actually connected to ground through the
carrier gas heater as was assumed. There was no specific electric potential applied to the
metal T-junction, so when the inner copper electrode was charged to -3.5kV, and the ESI
needle was grounded, the strong electric fields caused charge polarization of the Tjunction which then led to arcing. When the electric field across a certain distance is
large enough, the dielectric strength of the insulating material, in this case air, can be
overcome so that charge can be transferred.
The arcing itself occurred in a periodic
fashion because after the initial breakdown, the charge distribution in the T-junction
would be balanced or relaxed, but the continued presence of the electric field caused repolarization, and more discharge. This phenomenon is called a relaxation oscillator.
The problems with conducting this experiment corroborated the finding that the external
capillary shield of the interface needed to be floated at a different voltage in order to
prevent arcing. By making sure that all conducting components were connected to a
specific potential, the creation of a relaxation oscillator could be avoided.
Also, the
smaller the potential difference between the metal components, the less arcing there
would be.
3.7 Modified T-junctionand Capillary Tube Interface.
The modified T-junction/capillary
tube interface was designed to simplify the
components of the prototype I Interface to learn more about the different conditions
under which ions would optimally be transported to the DMS. This interface would also
help to identify any malfunction in the prototype 1 interface which could interfere with
the flow of ions, from the ESI source to the DMS sensor. One of the major goals of this
49
round of experimentation was to determine whether or not sample was reaching the DMS
based on the principles which guided the design of the prototype 1 Interface. If sample
was successfully introduced into the DMS, then the data collected could also be used to
determine whether different biological substances could be differentiated from one
another.
3.7.1
Design of the Capillary T-junction
One of the first data sets collected with the ESI-DMS system used an interface which was
comprised of only the very basic components needed to transport ions from the ESI
needle to the DMS.
These components include an inlet for carrier gas, a fused silica
capillary tube through which ions are transported to the DMS, and an electrode to which
the high voltage power supply is connected. A cross sectional view of the interface is
shown in Figure 24.
Carrier Gas
Ion
Ion
path
Electrode Capillary tube
Figure 24 Cross-sectional view of capillary T-junction interface
50
3.7.2
Experiments
Data was collected for sample solutions of glucose, mannitol, urea, and desmosine, and a
50:50 mixture of urea and desmosine in de-ionized (DI) water. Sample solutions were
made in concentrations of 10 mM and 100 pM in DI water. The extraction voltage
applied to the capillary inlet was -4kV. The nebulizing gas flow was set to 1 L/min, the
carrier gas was set to 300 ml/min, and the break up gas flow was set to 4 L/min. Gas
heaters were turned on until temperature stabilized.
See Appendix for temperature
stabilization data. Sample was injected into the ESI needle at a flow rate of 50 pl/min. A
drawing of the system setup for these experiments is shown in Figure 25.
The
electrospray needle was oriented vertically with respect to the axis of the ion path through
the DMS.
From syringe pump
Nebulizing
Gas
Nebulizer
.
Needle
Electrospray
Voltage
T
.......
DMS
-G
Ground Plate
Sa np e - Uetec r
Interface
To exhaust vent
Figure 25 Modified T-junction
51
--
DMS system configuration
Data was collected in the following manner:
data was recorded continuously for 10
minutes of background data without any sample injection, then sample was injected in
twenty one-minute pulses with one minute breaks in between. This sampling sequence
would create many replicates of data to be used to inspect the changes in the ion spectra
due to sample introduction. Additionally, this number of replicates was needed to create
a dataset large enough to successfully
process using classification
algorithms
implemented by Correlogic.
3.7.3
Results
After analysis with Correlogic's lead cluster mapping and genetic algorithms, it was
concluded that data taken for different proteins could not be meaningfully differentiated.
Possible reasons for this may have been that the path length along which ions must travel
was too long, and was causing the ions to lose charge by the time they reached the DMS.
Additionally, it was assumed that by shielding the inner Ni-63 radioactive ionizing source
within the DMS, the efficiency of the ESI process could be better observed. However,
shielding of the ionization source may have reduced the number of ions entering the
DMS and adversely affected the sensitivity of the sensor.
These results also indicated that the amount of ionized material reaching the DMS was
insufficient for detection. In order to transport more ions created by ESI to the sensor, a
slight pressure drop was applied to the outlet of the DMS in subsequent experiments
using an air pump. The pressure difference from the inlet of the SDI to the outlet of the
DMS was at most 0.015 atm, so the system would essentially still be operating at
atmospheric pressure.
52
3.8 Prototype 1 Interface
The ESI unit is attached to the interface by a hinge so that the unit can swing open and
closed to allow for adjustment of components in the ionization chamber. The interface
was designed to allow the ESI and AP-MALDI units to be easily interchanged from the
interface, and so employs the mechanical mating scheme used by the Agilent TOF-MS.
Further detail of interface design is located in 3.3.
3.8.1
Design of the Interface
Conclusions from characterization experiments of flow rates, pressure differentials, and
ion path lengths gathered allowed for the successful test of and data collection with the
prototype 1 interface. Several changes were made to the initial design. First, the DMS
sensor was attached to the interface at a much shorter distance than in the initial design.
Additionally, a great deal of electrical breakdown was observed in the interface for
voltages as small as -2500 V. As shown in the below, arcing was observed at the areas
circled.
The arcing occurred because the distance between elements connected to
different electrical potentials was too small. The electric field generated was strong
enough to overcome the breakdown strength of air, and create arcing. Additionally, these
parts contained sharp edges, at which the electric field became particularly strong. After
trimming some of the metal away on the parts where evidence of arcing - bum marks were discovered, the extraction voltage could be increased to -4.5 kV with no additional
arcing.
53
Figure 26 Inner component of prototype 1 interface where arcing was observed
3.8.2
Experimental Method for Testing of the Prototype I Interface
Based on observations and conclusions from the ESI characterization experiments and
others, a dataset using the prototype I interface was collected for the proteins BSA and
ubiquitin. Operating conditions for the system are summarized in Table 4.
Table 4 Operating Conditions for Experiment using the prototype 1 interface
Experimental Value
System Parameter
Carrier Gas
Carrier Gas Temperature
Break-up Gas
Break-Up Gas Temperature
Nebulization Gas
Pump Flow Rate
Extraction Voltage
DMS RF voltage
Sample (n 50:50 methanol: water, 1 mM
Acetic acid in a 5 ml disposable syringe at
room temperature)
Sample Infusion Rate
50 ml/min
hour until stabilized
Heaters on for
3 L/min
Heaters on for 1/2 hour until stabilized
1 L/min
500 ml/min
-3200V
1400 V
50 pg/mI BSA
50 pg/ml ubiquitin
20 pl/min
Samples of Bovine Serum Albumin (BSA) and ubiquitin were prepared by diluting the
proteins to a concentration of 50 ug/ml in a solvent of 50:50:0.1 %water: methanol: acetic
acid.
These samples were loaded into 3 ml syringes from Becton Dickinson. Each
syringe needle tip was connected to the ESI sample inlet via 1/16 inch inner diameter
Teflon tubing. Each syringe was loaded onto the Harvard Apparatus PHD 2000 syringe
54
pump which injected sample into the needle at a rate of 20 [1l/min. The sample sequence
for collecting data consisted of the collection of 4 minutes of background data collection,
and 1 minute of data collection when the analyte solution is injected into the ESI needle.
This sequence is summarized in Table 5.
Table 5 Sampling sequence for modified ion-optics interface data collection
Time (min)
DMS action
Sample
off
0
Start recording
4
on
off
End recording
5
3.8.3
Results
Figure 27 shows a plot of one data replicate representing 5 minutes of data.
The
horizontal axis is in units of scans which are equivalent to time: I scan is recorded every
1.5 seconds. The vertical axis represents the compensation voltage, which ranges from
-40 V to +10 V. The pixel intensity represents the ion abundance detected. The dark line
running across the center of the plot represents a high abundance peak in the ion spectra.
Sample is actively injected into the system between scans 80 and 120. There is a small
time lag between when the syringe pump begins injection, and when the sample reaches
the sensor, so the spectrum is still perturbed after scan 120.
55
250
200
CD
0)150
0
0
100
2
E
0
0
20
40
60
80
100
120
140
160
180
200
Scan Number
Figure 27 Data replicate from prototype
1 Interface Data set
Correlogic algorithms were used to develop models for classification which could
distinguish between ubiquitin and BSA with 100% accuracy. The results of their analyses
revealed that the most successful classification models were built from data obtained
when the radioactive ionization source inside the DMS sensor was left unshielded, and in
the 2 minutes of data collected right after sample was no longer actively injected into the
ESI needle.
3.9 Modified Ion-Optics Interface
The prototype 1 ESI-DMS interface was modified to allow a higher voltage to be applied
to the outer capillary tube shield, and will be referred to as the modified ion-optics
interface.
The motivation behind this design change came from conclusions drawn
56
during current measurement experiments with the Langmuir probe that the metal
surrounding the entrance to the high voltage capillary shield also needed to be connected
to a relatively high potential.
Previously, this metal fixture was connected to earth
ground. The modified application of high voltages is expected to create a stronger, more
directed electric field that will extract more ions from the ESI plume, and accelerate them
into the inlet of the DMS sensor.
3.9.1
Hardware/instrumentation Setup
In the new design, a layer of Teflon insulation was added in between the capillary shield
and the rest of the interface to electrically isolate the capillary shield, see Figure 28.
Because the Teflon insulation would be positioned between two elements of different
electric potentials, it was necessary to ensure that the thickness of the insulation was
sufficient to prevent breakdown of the material occurring.
The dielectric strength of
Teflon is 1500 kV/inch. Based on restrictions of part sizes in the interface, a Teflon
insulating shield 43 mm in thickness was selected to isolate the external capillary shield
from the rest of the interface. A drawing of the interface after modification is shown in
Figure 28.
57
Sample -Detector Interface
ESI Unit-
High Voltage
extraction capillary
Figure 28 Schematic of the modified ESI-DMS interface with modified ion-optics.
A voltage divider was built to apply different voltages to the capillary shield and the
inner capillary tube using a single power supply. A schematic of the voltage divider is
show in Figure 29. When the inner capillary is connected to a voltage of -3.2 kV, and the
outer capillary shield is connected to the node 'E', shown in Figure 29, the voltage
applied to the outer capillary shield is approximately -2.7 kV.
58
-HV
0
c
B
A
E
F
G
H
K
0
N
M
Q
P
GND
Figure 29 Schematic of voltage divider for ESI-DMS interface
The voltage divider offers flexibility in generating a wide range of voltages thus enabling
optimization. Table 6 lists the voltages measured at the outer capillary shield when it is
connected to the various nodes shown in Figure 29 and an inner capillary voltage of 3200 V is applied.
Node as labeled on
Figure 29
A
B
C
o
E
F
G
H
I
J
K
L
M
N
o
P
Q
Outer Capillary Shield
Volt~ge (kV)
-3.133
-3.029
-2.926
-2.822
-2.722
-2.62
-2.517
-2.415
-2.313
-2.211
-2.11
-2.01
-1.908
-1.808
-1.703
-1.6
-1.502
Table 6 Voltage at outer capillary shield when voltage on inner capillary shield is -3200 V.
59
With the ESI needle grounded, these voltages create a strong electric field directing ions
into the capillary entrance.
The modification was made to improve on the previous
design which did not appear to effectively extract ions from the ESI sample plume. The
new design is expected to create a stronger field effect thus improving ion extraction.
Preliminary results indicate an improvement with the modified interface.
3.9.2
Initial Test of the Modified Ion-Optics Interface
In these experiments, the carrier gas was run at a flow rate of 300 ml/min, the breakup
gas at 3 L/min, nebulizing gas at 1 L/min, and pump at a flow rate of 500 ml/min.
Additionally, the rate of sample infusion was 10 ml/min, the RF voltage was set to 1.4
kV, and the DMS radioactive ionization source was left unshielded. The carrier gas,
break up gas, and pump and their respective heaters were on through all experiments.
The DMS was allowed to flush with N2 in between experiments to remove any possible
saturation due to water. A summary of experimental conditions is located in Table 7.
Table 7 Summary of experimental conditions for initial test with modified ion-optics interface.
System Parameter
Carrier Gas
Carrier Gas Temperature
Break-up Gas
Break-Up Gas Temperature
Nebulization Gas
Pump Flow Rate
Extraction Voltage
Voltage applied to External Cap
DMS RF voltage
Sample (50:50 methanol: water, 1 mM Acetic
acid in a 5 ml disposable syringe at room
-temperature)
Sample Infusion Rate
Experimental Value
300 ml/min
Heaters on for 1/2 hour until stabilized
3 L/min
Heaters on for
hour until stabilized
1 L/min
500 ml/min
-3200 V
-2700 V
1400 V
50 pg/ml BSA
20 pl/min
In the first test, the extraction voltage was turned off, and sample was pulsed on and off
for 1 minute, with 3 minutes in between on-pulses.
60
In the second test, the extraction
voltage was kept on for the duration of the experiment. In the third experiment, the
Extraction voltage was turned on for 1 minute before the sample was infused. Both
sample and extraction voltage were turned on for one minute, and then simultaneously
shut off.
The purpose of conducting tests in this manner was to generate data to
compare the effects of the different factors on the ion spectra. From data taken in the first
test, the effect of sample introduction alone could be observed. From the second test, the
effect of the extraction voltage on the entire duration of data collection could be
observed. The data from the third experiment would provide information on the effect
that the absence of an electric field would have on the spectra after sample was no longer
being ejected.
It would provide data to compare the effects of the electric field on
attracting residual ions from the ionization chamber into the DMS.
These experiments
were also repeated when the ionization source inside the DMS was shielded so that the
efficacy of the ESI needle as the sole source of ions could be seen.
3.9.3 Results of Initial Experiment with Modified Ion-Optics Interface
The changes in data for experiments where the ionization source was shielded were too
subtle to reveal any obvious trends to the observer.
The results from experiments
conducted with an unshielded DMS ionization source showed much clearer evidence of
changes that the modified ion-optics interface might be having on the ion spectra, and are
discussed below.
The spectrum in (a) was collected with no extraction voltage applied (Vext = 0 V).
During collection of the spectrum in Figure 30(b), the inner capillary and outer capillary
shield voltages were set to -3.2 kV and -2.7 kV, respectively. The voltages were turned
61
on at the time of injection and immediately turned off at the end of the injection. For the
spectrum shown in Figure 30 (c), the inner and outer capillary shield voltages were again
set to -3.2 kV and -2.7kV, however, they were left on during the entire experiment. As
can be seen, the recovery time for the reactive ion peaks is longer in frame (c) of than it is
in frame (b) and shows greater perturbations than in frame (a).
10
10
Cz
CO
0
0
>-1
>-10
C -20
0)
C(S-20
C-3
C -30
0
E
E
0
0
-40
100
300
200
400
Scan Number
-40
500
100
(a)
300
200
400
Scan Number
500
(b)
10
CD>-10o
C
0
0
40
100
200
3
4
Scan Number
500
(c)
Figure 30 (a) DMS positive ion spectra for the protein BSA with extraction voltage turned off (b)
DMS spectra with extraction voltage turned on and off in synch with sample injection (c) DMS
spectra with extraction voltages on for entire duration of experiment.
These perturbations are evidence that the modified ion-optics interface has made some
difference in the ion spectra. Additionally, the long recovery time for the reactive ion
peak indicated that the modified interface caused a change in the typical behavior of the
ion spectra after sample injection.
As a result, a larger more rigorous data set was
62
collected to verify the observations of the initial test data, and to determine whether the
proteins BSA and ubiquitin could be better differentiated from one another using this
modified system.
3.9.4
Experimental Method for the Modified Ion-Optics Interface
The modified interface was further evaluated by generating an additional dataset to test
with the classification algorithms. Ubiquitin and BSA were employed to generate 120
data files of each protein collected with the same experimental conditions as those of the
original prototype 1 interface as described in the previous section, 3.8.2. Improvements
in classification accuracy observed in these experiments will serve as evidence of
improved performance for this interface.
A summary of experimental conditions is shown in Table 8. All conditions were the
same as those in the experiments run with the prototype I Interface so that only changes
due to the modification of the ion-optics would be seen between the two datasets. As a
result of Correlogic's finding that classification was possible with greater accuracy far
data taken when the internal Ni63 source inside the DMS was unshielded, only data with
the unshielded ionization source was collected in this experiment.
63
Table 8 Summary of operating conditions for modified ion-optics interface data set
System Parameter
Carrier Gas
Carrier Gas Temperature
Break-up Gas
Break-Up Gas Temperature
Nebulization Gas
QDump
Experimental Value
50 ml/min
Heaters on for hour until stabilized
3 L/min
Heaters on for 1/2 hour until stabilized
1 L/min
500 ml/min
nominal
Extraction Voltage
Voltage applied to External Cap
DMS RF voltage
Sample (in 50:50 methanol: water, 1 mM
Acetic acid in a 5 ml disposable syringe at
room temperature)
Sample Infusion Rate
-3200 V
-2700 V
1400 V
50 pg/ml BSA
50 pg/ml Ubiquitin
20 pl/min
Before the start of data collection, heaters in the system were both turned on for an hour
to allow the temperature of the gasses to stabilize. Additionally the DMS sensor was
powered on 20 minutes prior to data collection to allow the sensor to warm up. It was
observed that such a warm-up period drastically reduced the amount of electric noise that
arose later on in data collection. The sampling sequence, as summarized in Table 9, first
collects 4 minutes of background data, and then 1 minute of data in which the syringe
pump injects sample into the ESI needle at a flow rate of 20 pl/min. Each 5 minute cycle
was collected in sets of 40, on 3 different days, at different times of day, for a total of 120
replicates for each protein. By splitting the data collection over different days and times,
any day-to-day variations in the instrumentation that would affect the data could be
accounted for in later analyses. The syringe pump software and DMS software were both
set to automatically collect these 40 replicate sets. In between each set collection, the
DMS was allowed to purge at 400 ml/min of carrier gas for 15 minutes to remove any
residual analyte from the ion pathway. The gas temperatures were then allowed to restabilize over the course of an hour after returning all flow rate set points to experimental
conditions.
64
Table 9 Sampling sequence for modified ion-optics interface data collection
Time (min)
DMS action
Sample
0
Start recording
off
4
on
off
End recording
5
Results
3.9.5
One replicate of data collected using the modified ion-optics interface is shown in Figure
31. Given the slightly different sampling sequence, the portion of the replicate in which
sample is actively injected into the system occurs in between scans 1 and 50.
10
5
0
-5
(D
0)
0
-10
-15
-20
U)
0.
E
0
-25
0
-30
-35
-40
20
40
60
80
100
120
140
160
180
Scan Number
Figure 31 Plot of single replicate from modified ion-optics interface data set
3.10 Data Analysis Methods
One of the greatest challenges in designing the ESI-DMS classification system is the
development of data analysis methods. The different methods for introducing ions into
65
the DMS greatly change the resulting spectra, so methods for extracting information
about biomarkers must change according to the form of the data.
In developing the ESI-DMS system, it was necessary to discern whether ions were being
introduced into the DMS before the development of classification tools could even be
considered. Fourier transform calculations were used to determine whether or not there
were changes in the DMS spectra due to ions which could not be easily discerned in the
raw data. In the following sections, Fourier transforms of data taken using the different
ESI-DMS interfaces will be presented.
Classification by construction of decision trees is also introduced as an alternative to lead
cluster mapping and genetic algorithms as a method for developing models for
classifying biological substances.
3.10.1
Fourier Transforms.
Fourier transforms, introduced in section 2.5.1, have traditionally been used to show the
frequency content of time varying electrical signals. The data in the case of the DMS are
also electrical signals, but vary based on different compensation voltages instead of time.
The Fourier transform, however, can still be used to inspect the data from this sensor
because it preserves all information from the original signal and can be used to uniquely
reconstruct the original untransformed signal. Additionally, there are many techniques in
existence for other applications which extract information from seemingly indecipherable
signals using Fourier transforms. For instance, ion-encephalogram signals are processed
using Fourier methods to reveal information about irregularities in the heartbeat[38].
66
For data obtained from the ESI-DMS system, the Fourier transform was employed to
determine whether or not changes in the spectra indiscernible in the raw data had
occurred due to the introduction of ions via the ESI-DMS interface. Figure 31 shows an
The units on the horizontal axis
example of raw spectra obtained from the DMS.
The vertical axis
correspond to time - one scan is recorded every 1.6 seconds.
corresponds to compensation voltage axis and pixel intensity corresponds to the ion
abundance detected, both in units of Volts. In regions 1 and 2 of Figure 32, sample is not
injected into the nebulizing needle.
In regions 3 and 4, sample is injected into the
nebulizing needle, and a slight change can be seen in the DMS spectra.
10
5
0
-5
-10
0
-15
C
-25
E
0
-30
-35
-40
A~
1
150
100
50
250
200
scar number
2
3
4
Figure 32 Raw DMS spectra for data collected using modified T-junction capillary interface.
The plots shown in Figure 33 show the Fourier transform of the raw data from Figure 32.
The transform plot was created by computing the one-dimensional discrete Fourier
67
transforms of each scan from the raw data. The plot on the left hand side of Figure 33
shows the magnitude of each of the scan transforms, and the plot on the right hand side of
Figure 33 shows the phase of each scan transform. At scan 120, when sample is first
introduced into the system, a slight change can be seen visually in the spectra of Figure
32. When inspecting both the magnitude and phase of the Fourier transforms of these
scans, however, a much more striking change is apparent between the last two regions
and the first two regions than is apparent in the raw data.
1
450
450
400
400
350
350
300
300
250
250
4
3
2
1
4
3
2
Cr
LL 200
200
150
150
50
100
150
200
50
2M0
100
-
150
:200
2M0
Scan Number
Scan Number
Figure 33 1-Dimensional Fourier transform magnitude (left) and phase (right)
Fourier transform calculations for data taken using the prototype 1 interface and
modified-ion optics interface also show clear differences between data taken when
sample was introduced into the sensor and when it was not.
In Figure 34, a subtle
difference can be seen in the transformed scans collected when sample was introduced
into the DMS sensor, scans 80 through 120, using the prototype 1 interface. All other
scans were collected when sample solution was not actively injected into the system. The
change in the scans can also be seen in the plot of the phase of the Fourier transforms of
this data.
68
120
100
80
CD,
= 60
CU
C.
40
20
20
40
60
80
100
120
140
160
180
200
Scan Number
120'
100,
80
60
40
L. . 4 0
Scan Number
Figure 34 Fourier transform magnitude (top) and phase (bottom) plots for data taken using the
prototype 1 interface.
Fourier transforms computed using data collected with the modified ion-optics interface
are shown in Figure 35. In this data, sample was injected into the system during scans 1
through 50. The plot of the magnitudes of the transforms show much more variation at
69
higher frequencies for scans collected when sample was injected into the system than do
the scans collected when sample was not injected.
120
100
80
0
LL
20
20
40
60
80
100
120
140
160
180
200
Scan Number
1 80
se
a,
6C
a,
Scan Number
Figure 35 Fourier transform magnitude (left) and phase (right) plots for data taken using the
modified ion-optics interface.
In the case where signal is very faint, such as in the data collected using the modified Tjunction interface, even Fourier transformation can not amplify differences enough to
70
indicate changes in spectra. The Fourier transform magnitude and phase plots for such
data are shown in Figure 36.
450
400
350
r
300
C
250
200
L
150
100
50
200
400
800
600
1000
1400
1200
1UU
10U
Scan Number
450
>3"
wr.
400
~.,
1,
-C
350
'I
S
300
1
P
>4
250
it
~.
t
U-
100
50
200
400
600
1000
800
1200
1400
1600
1800
Scan Number
Figure 36 Fourier transform magnitude and phase plots for data collected with the modified Tjunction interface
The striking changes seen in the Fourier transforms of DMS data indicate that the Fourier
representation of DMS data may be used to more readily detect the presence of
71
biomarkers because differences are more obvious in these transformations than in the
unaltered representation of the data. Fourier Transform computations help to amplify
changes in the DMS spectra, and have been useful in determining when ions have
successfully been transported from the ESI unit to the DMS using different interfaces.
3.10.2
Decision Tree Analysis
As described in the introduction, decision tree analysis is a method for building anonparametric classification model.
The creation of a decision tree, also known as a
classification tree, yields a tree structure that contains classification tests at the nodes, and
classes at the leaves.
To construct a decision tree to classify the proteins BSA and ubiquitin, an equal number
of replicates were randomly chosen from the dataset for each protein.
The Matlab
function treefitO, which uses the algorithm for creating decision trees as described by
Breiman [30] was given the training scans from each protein, and labels for each of the
those scans indicating the classification of that scan. Another random sampling of the
dataset, again an equal number of scans from each protein, was taken to create a testing
dataset. The testing data set was classified using the resulting decision tree to determine
how accurately that tree could classify data. Random sampling of the entire data set was
used to make sure that neither the testing set nor the training set was biased.
Additionally, random sampling would allow for the anomalies in data that occurred from
replicate to replicate to be incorporated, or rather ignored, in the creation of the decision
trees. By randomly sampling the entire dataset to create the training data set, anomalies
72
would be more likely to be ignored as classification features because fewer samples
would be likely to have the same anomalies if they were randomly chosen.
To determine the optimal number of scans to use in creating decision trees and to
characterize the performance of decision trees on ESI-DMS data, trees were built with
multiples of 10 from 10 to 200 scans from each of the protein data sets. The data set for
each protein had about 40 scans during which sample was injected. Because there was
evidence of ions entering the sensor for some time after sample was no longer inject, the
10 scans after the 40 scan 'on' period were also considered suitable for building the
classification trees.
Since there were 120 data replicates per protein, and 50 scans per
replicate that could be used for training, there were a total of 6000 scans from which to
randomly select the training and testing sets. Twenty trees were built with each different
set size.
Each of these 20 trees was then tested with an equally sized testing set to determine the
true accuracy. The percentage of incorrectly classified scans was computed and averaged
over the 20 trees for each of the training set sizes. A similar method was used to compute
the classification error for trees constructed using data collected with the modified Tjunction/capillary interface for urea and desmosine. Because this dataset was smaller, a
maximum of only 1200 scans could be used to build the training and testing sets. A
smaller step size in training and testing set sizes was used in order to observe a trend.
A plot of the resulting average accuracies is shown in Figure 37.
In this plot, the
accuracy trends for the data set from the prototype I interface, the modified ion-optics
interface, and the modified T-junction interface are shown. For the datasets from these
73
three interfaces, as the number of scans in the training and testing sets increases, the
amount of error in classifying the testing set decays nearly exponentially.
Error in
classification of the prototype I interface data is higher than error in classification for the
modified ion-optics interface for each of the training and testing set sizes, and
classification error for the T-junction interface is higher than errors for both of those
interfaces.
The change in classification error for increasing tree size became almost
negligible, with the error in classification using 1600 scans nearly the same as that for
trees built with 2000 and 2400 scans.
For both prototype 1 and modified ion-optics
datasets, however, classification error was the smallest for trees created with 2400
randomly chosen data scans. The tree with lowest classification error for the modified Tjunction interface was constructed using 1200 scans.
These large trees were used for
subsequent analyses.
*
-+-
Error in Modified Interface Data
-u-- Error in Prototype
I Interface Data
Error in T-junction Data
60
55
10
45 05
S400-
30
M2520
1510-
0
500
1500
1000
2000
2500
Training and Testing Set Size
Figure 37 Classification error for varying testing and training set sizes, for three different datasets
74
When decision trees are built with many data-points, the chances of over-fitting the
classification model to the data and worsening classification accuracy rises greatly. One
way to prevent over-fitting is to prune the decision tree after training. Pruning involves
the removal of some classification test nodes, and replacing them with class assignments.
The removal of test nodes effectively removes branches from the decision tree, hence
pruning. As branches are removed, the tree will actually be more robust in classification
because the chances of misclassification will be lower in the long run.
One method for determining the optimal pruning level of a tree is called cross-validation.
The first step in cross-validation is determining the resubstitution error. Resubstitution
error is the proportion of the original training data that is misclassified by different
subsets of the original, unpruned tree. In the cross validation method, the original data
set is divided into a set that is 10% of the original data, and another set that is 90% of the
original data. A tree is built using 90% of the data, and then tested by classifying the
separated 10% of data with that tree. This is repeated until all the different 10% portions
of data have been classified. For trees of different sizes that are pruned version of the
original tree, the resubstitution error is first calculated, and then cross-validation is used
to compute the true-error for the different trees.
75
0.5
I
-
~
I
0.45
I
I
--
Cross-validation
-
o
Resubstitution
Min + I std. err.
Best choice
80
90
0.4
0.35
0.3
0.25
0
L,
0.2
-
0.15
0.1
0.05
0
0
10
20
30
50
60
40
Number of terminal nodes
70
Figure 38 Error and minimum cost for optimally pruned tree for data collected using modified Tjunction interface.
Figure 38 shows a plot of the cross-validation error and minimum cost for different tree
sizes of data collected using the modified T-junction capillary interface. It is evident that
the optimal classification tree contains many terminal nodes. This indicates that the data
is not easily classifiable because many different test nodes are necessary to make a
decision.
In comparison to the number of nodes in the optimally pruned trees for
prototype 1 and modified ion-optics, this dataset requires an extremely large number of
classification tests.
These results imply two different conclusions.
First, urea and desmosine may be
extremely similar in the way they alter the DMS spectra and so classification using
76
decision trees may be difficult as evidenced by the large number of terminal nodes in the
optimally pruned tree. Second, the interface may not have properly transported ions to
the sensor, so ion signal may have been extremely weak. The weak ion signal may have
made the spectra of the two substances look very similar, making classification difficult.
In both cases, the resulting decision tree shows that classification can not be easily
performed on this data. The large number of terminal nodes also means that the tree may
be over-fitted to the data, and will perform poorly on data not used for training.
For the plots in Figure 39, the resubstitution error decreases with increasing tree size.
However, after a certain point, the cross-validation error no longer decreases, and in fact
seems to increase. The optimal tree-pruning level is indicated by the circled part of the
cross-validation trend line. The optimal pruning level corresponds to the cross-validation
error which is within one standard error of the minimum. This assures that although the
optimum tree is not chosen, a smaller tree, less computationally expensive tree that
performs roughly as well as the optimal tree is chosen. For the dataset collected using the
prototype 1 interface, the optimal number of terminal nodes for a tree built with 2400
scans is about 19. For the dataset collected using the modified ion-optics interface, the
optimal number of terminal nodes for a tree built with 2400 scans is about 21.
The
difference in the optimal tree sizes for the different data sets is very small. This result
also shows that a large number of data scans can be correctly classified by a relatively
small tree, because trees with up to 80 terminal nodes were tested as well, but did not
show the smallest error.
77
0.5
0.45
o
Cross-validation
Resubstitution
Min+ 1 std.err.
Best choice
0.4 0.35
0.3
*0.25-
..
0.2
-
\
0.150.15
0 -
0
30
20
10
60
50
40
Number of terminal nodes
80
70
05
90
100
SCross-validation
0.45
-- Resubstitution
+ 1 std. err.
SBest choice
-Mmn
0.4
0.35 e tc oc
0.5
&0.42
0.25
0.15
0.1
0.05
0 -
0
10
20
50
40
30
Number of terminal nodes
60
70
80
Figure 39 Cost versus number of terminal nodes in tree built with 2400 data scans from prototype 1
interface data set (top), and modified ion-optics interface data set (bottom).
78
An example of an optimally pruned classification tree for the modified ion-optics
interface is shown below in Figure 40.
Figure 40 Best pruned classification tree for modified ion-optics interface
Each of the nodes in the decision trees can be interpreted as bio-markers for the two
different proteins. The nodes in the tree are labeled with the vector indices for which the
ion abundances of the protein scans differed most.
The topmost node in each tree
provides the broadest division between the two proteins, and can therefore be considered
the most prominent biomarker. Both trees seem to have the indices 159 and 123-126 in
common at the top of the trees. These correspond to compensation voltages of -8.237V,
and -15.5V to -14.9. These compensation voltage markers will be compared in the next
section to biomarkers found using lead cluster mapping and genetic algorithms
implemented by Correlogic Systems, Inc.
79
3.10.3
Correlogic Analysis
Correlogic analyses have previously been used by researchers in this group to find
biomarkers. The analyses and classification models provided by Correlogic have up until
this point been the standard method for analyzing and classifying data collected using the
DMS sensor for different sample introduction methods including pyrolysis, and SPME
headspace sampling.
Analyses via lead cluster mapping were unable to satisfactorily classify data collected
with the modified T-junction capillary interface. Classification accuracy was about 60%
which is equivalent to randomly classifying data. In the data taken using the modified Tjunction capillary interface, Correlogic analyses were unable to successfully determine
the presence of biomarkers that would allow us to distinguish between proteins that were
very similar in composition. One of the reasons that this may have occurred is that the
data was taken while the Ni63 radiation source inside of the DMS sensor was shielded.
For data collected using the prototype I interface, Correlogic was able to build three
models that correctly classified BSA and ubiquitin with 100% accuracy. Classification
accuracy for data collected using the modified ion-optics interface was not as high, with
some models achieving high accuracies, but not 100%. Many of the features found in the
successful models as classifiers were located in regions of data collected just after active
sample injection had stopped.
The implications of these results are twofold. First, the ESI-DMS system has been shown
to be successful in analyzing bio-molecules in a way that allows for their classification.
Second, most information about bio-markers is obtained after sample is no longer
80
injected through the ESI needle. This could be because the spectra at that point was no
longer perturbed by the solvent ions, and that solvent ions are no longer obscuring the
detection of bio-molecule ions.
3.10.4
Comparison of Analysis Methods
These results of Correlogic analyses on data collected using the modified T-junction
capillary interface were corroborated by the large errors in classification using decision
trees. Both methods of analysis were unable to classify that dataset with great accuracy
which indicates that either the urea and desmosine ions alter DMS spectra very similarly,
or that the signal from both ions was so weak that they essentially looked the same.
The results of Correlogic's analyses for the prototype 1 and modified ion-optics
interfaces were corroborated by the analyses using decision trees. While lead cluster
mapping yielded high rates of classification accuracy, decision tree analysis provided
relatively lower accuracy rates. However, these rates were based on preliminary analyses
with decision trees, and may be lowered through further refinement of the application of
that technique to this data.
One of the advantages of the method of decision tree classification is that it can create
non-parametric classification functions. It seems that much of the data obtained from the
DMS is nonlinear data in that the data is very different based on the mixture of
substances present in the sample.
It is unclear whether the biomarkers for different
substances are simply added together when mixtures of those substances are introduced
into the DMS.
The features specific to certain bio-molecules might not be easily
deconvolved from the signals and biomarkers created by other substances.
81
One of the problems with analyses using lead cluster mapping and genetic algorithms
may be that biomarkers chosen may actually be artifacts introduced by instrumentation
effects. To combat these problems, data must be collected over a longer period of time so
that artifacts due to normal instrument variation are not picked out as common features
for many different data replicates. Decision tree classification may also fall prone to
classification based on artifacts present in many replicates of training data. However,
decision tree partitions are based on the entire data scan, rather than on randomly chosen
features in the data, which make it more robust against artifacts that may be randomly
chosen as biomarkers. Lead cluster mapping is also a computationally expensive, and
may not perform well on data replicates which contain large numbers of features, because
the chances for randomly choosing the correct biomarkers will decrease as the number of
features across which the model is being built grows.
Decision trees provide a relatively simple way of classifying large numbers of chemicals,
and may prove to be less computationally expensive than the lead-cluster mapping
algorithms implemented in ProteomeQuest@. In preliminary testing, decision trees have
been shown to provide relatively low classification error rates for large numbers of
sample scans. With further work to explore different ways of building training and tests
of data sets, these errors rates may potentially go to zero. In the work presented, trees
were built on the basis of single data scans, rather than on entire replicates. If the biomarkers for chemicals are dependent on time as well as on compensation voltage, then it
will be necessary to develop a method for creating decision trees based on 2-dimensional
data.
82
4 AP-MALDI-DMS Instrumentation Development and
Analysis
This chapter describes the function and optimization of the SDI when used to couple the
AP-MALDI unit to the DMS. This chapter is divided into sections on the intial test of the
interface, optimization experiments for the system, and data analysis techniques for DMS
signal output.
Results from these experiments are aiding the design of a second
generation interface and AP-MALDI-DMS system.
4.1
Background
One of the main purposes for using the AP-MALDI technique with the DMS is that it is a
'soft'
ionization process.
Whereas other methods of ionization tend to destroy
macromolecules, the degree to which ions are fragmented in the AP-MALDI process is
relatively low [2].
If large molecules are fragmented during ionization, then the
individual components will be detected by the DMS, and the macromolecule as a whole
will be harder to identify. Preservation of the macromolecule as a whole in an ionized
state will simplify the resultant DMS spectra, and improve its ability to identify the
specific macromolecule.
A detailed description of the AP-MALDI process is discussed in Chapter 1.
The
following sections describe in detail the instrumental setup of the AP-MALDI-DMS
system, the experimental
procedures and results of the hardware optimization
experiments, and data analysis methods used for the DMS data generated. As in the ESIDMS system, ions are created by the AP-MALDI unit, transported from the point of
ionization to the DMS sensor by the interface, and detected by the DMS sensor. From
83
the following experimentation and analysis, it was found that the AP-MALDI-DMS
system did successfully create ions and transport them to the DMS sensor. Additionally,
findings from the optimization experiments validated the function of the interface as it
was designed to operate.
4.2 Hardware Description - materials and methods
4.2.1 The AP-MALDI-DMS system
A block diagram of the entire AP-MALDI-DMS system is shown in Figure 8. The APMALDI sample introduction unit is connected to the Sample-to-Detector Interface (SDI)
which is in turn connected to the DMS unit. The outlet of the DMS is connected to a
pump which only creates a very small pressure drop of at most .013 atm from the
ionization chamber to the outlet of the DMS.
DMS control and data acquisition is
achieved using a computer. A complete description of the system is located in section
3.1.
4.2.2 The MALDI unit
The AP-MALDI unit used in these studies is an accessory for the Agilent, Inc. Time of
Flight mass spectrometer (TOF-MS, G1969A, Agilent Technologies, Palo Alto, CA).
The unit consists of a translational sample plate, a laser, and a control unit operated via
computer. A camera, mirrors, and an LCD screen allow the user to view the position of
the laser relative to the target plate prior to and during sample ablation. The unit's target
software allows the specification of the laser firing pattern, repetition rate, plate
geometry, and desorption time. Users also have the capability of changing the power
output of the laser, which can be optimized for different matrix compounds. Samples are
84
dispensed on the surface of a 96 spot gold-coated stainless steel target plate. For all
studies conducted, the laser firing rate was set to 10 Hz and the laser ablation pattern to a
spiral geometry. Each spot was ablated for 60 seconds.
4.2.3 The interface
A small schematic of the gas flows through the AP-MALDI-DMS interface is shown in
Figure 41.
As discussed in Section 3.3, heated break up gas exits the front of the
interface to break apart sample ion clusters before they enter the interface, and the carrier
gas flow provides the nitrogen environment for DMS operation. After ion extraction by
the high voltage electric field, a small pressure drop created by a pump at the outlet of the
DMS propels ions into the sensor. The ions travel from the AP-MALDI unit to the DMS
via a glass capillary located in the center of the interface.
Gas
Break-upCarrier
Gas
----.
....
.----.
..> o DM S
Sample
ions
Glass
Capillary
Figure 41 Cutaway view of major substance flows in the prototype 1 interface
A metal extraction capillary tube and Teflon PTFE shield for use with the AP-MALDI
unit is attached to the interface in place of the capillary shield that was present in the ESIDMS interface.
Figure 42 shows the different attachments to the interface for the
different ionization methods. The additional extraction capillary for the AP-MALDI unit
85
is necessary because the location of ionization is further away in the AP-MALDI unit
than it is in the ESI unit. The extraction capillary acts as an extension to overcome the
change in distance so that the strength of the electric field between the target plate and the
extraction capillary is maintained.
Figure 42 (a) SDI with AP-MALDI capillary interface components: 1. Teflon high voltage insulator,
2. break-up gas cylinder, 3. capillary extension. (b) SDI with ESI capillary interface components: 1.
break-up gas outlet, 2. glass capillary inlet (figure courtesy of Ernest Kim).
A macro view of the interface, with the AP-MALDI sample introduction unit attached to
the interface in the open position is shown in Figure 9. The interface was designed for
operation with both ESI and AP-MALDI introduction units, and so it employs the
mechanical mating scheme used by the Agilent TOF-MS. The Agilent TOF-MS and the
Prototype I interface both use a hinge and latch mechanism to allow the ESI and APMALDI sample introduction units to be easily interchanged. This mechanism also allows
easy access into the ionization chamber for component adjustment, sample loading, and
clean-up.
4.2.4 Sample Preparation technique
Samples for AP-MALDI ionization must first be mixed with a matrix that will aid in the
ionization of the sample. When the laser fires on the sample-matrix mixture, matrix
molecules absorb enough energy to break the crystal bonds causing matrix-sample
86
clusters to desorb from the plate, and charge transfer from matrix ions to the sample.
The compounds used as matrix were a-cyano-4-hydroxycinnamic acid (a-CHC) and
dihydroxybenzoic acid. The matrix is first diluted in a solvent mixture which is 75%
water, 24% isopropyl alcohol, and 1% acetic acid to a concentration of 2 mg/ml. The
analyte is then dissolved in the matrix solution to a concentration of 100 nM. Volumes of
0.5 pl and 1 tl were micro-pipetted onto a target plate spot, and allowed to dry at room
temperature for 5 minutes.
4.3 Preliminary Testing
4.3.1 Experimental Conditions
Initial experimental conditions for the AP-MALDI-DMS system were based on required
operating conditions for the DMS, Agilent TOF operation, and tests on the AP-MALDI
to determine the optimal pump flow rate. The carrier gas was operated at 350 ml/min,
and the pump flow rate at 500 ml/min. Typical experimental conditions are summarized
in Table 10.
The RF voltage of the DMS sensor was set to +1400 V to allow for
maximum ion separation and filtration, and compensation voltage was set to scan across a
voltage range of -40 V to +10 V DC in 250 steps of 0.2 V.
Table 10 Experimental conditions for initial experiments with AP-MALDI-DMS system.
System Parameters
Carrier Gas Flow Rate
Carrier Gas Heater
Break Up gas Flow Rate
Break Up Gas Heater
Pump Flow Rate
Extraction Voltage
RF Voltage
Experiment condition
350 ml/min
On
1.5 L/min
Off
500 ml/min
-3000V
1400V
87
Data was collected in the following sequence: collection of background DMS signal for
1 minute, laser ablation on clean plate surface for 1 minute, background collection for 1
minute, and then laser ablation on plate with sample for 1 minute, as shown in Figure 43.
Each sequence was replicated 5 times per sample type. Data was collected when the laser
was off and when firing on a clean surface of the target plate so that the effects of laser
fire could be isolated from the data obtained when a sample was ablated. By collecting
data for different conditions of the laser and the plate, the ion signal could be discerned
from possible background noise rising from the laser or target plate surface contaminants.
CI)
CU
1 mn
1 min
1 min
Blank
Spot
Sample
spot
1 mn
Plate position
Figure 43 Timing diagram of sample introduction
4.3.2 Results of Preliminary Testing
Results from this preliminary testing indicated there was a clear change in the DMS
spectra from the background signal during the time when the laser ablated plate areas that
contained sample-matrix mixtures. AP-MALDI-DMS data can be visualized as a three
dimensional plot where the horizontal axis is time, measured in scan numbers (1.5
sec/scan), and the vertical axis is the compensation voltage axis. The intensity of the
pixel coloe for any given time and compensation voltage pair refers to the ion abundance
detected.
A typical 3-dimensional plot of data is shown in Figure 44. The left most
frame shows a plot of data corresponding to DMS signal recorded when the laser is
88
turned off. The middle frame was recorded when a blank or unspotted area of the target
plate was ablated. As can be seen, there is no noticeable change from the background
data in the first frame. The rightmost frame of data in Figure 44 was recorded during
laser ablation of a sample spot on the target plate. There is a clear difference in the data roughly 12-14 peaks per scan - from data taken when sample was not ablated. The
presence of clear changes in signal when sample was ablated is evidence that AP-MALDI
ionized substances are successfully being introduced into the DMS via the custom
interface.
10
10
5
5
5
0
0
0
-5
-5
-5
-10
-10
-10
C.,
10
-15
C.,
C.,
-15
-15
-20
-20
-20
-25
-25
-25
-30
-30
-30
-35
-35
-35
-40
30
20
10
scan number
(a)
-40
30
10
20
scan number
(b)
-40
30
20
10
scan number
(c)
Figure 44 Ion spectra from AP-MALDI-DMS system. (a) Shows data taken when laser is off (b) Data
recorded when laser fired on blank plate (c) Data collected when laser fired on sample spot.
89
These data are further analyzed in the data analysis section below. Subsequent to this
test, further experiments were conducted to optimize the operating conditions of the APMALDI-DMS system.
4.4 Optimization
Upon the initial proof-of-principle demonstration discussed in the previous section,
studies were conducted to determine the experimental conditions corresponding to
optimum instrument performance. Instrument parameters that could easily be adjusted,
and believed to have an impact on ion signal were optimized to produce strong, lownoise, reproducible DMS spectra.
The studies included determination of sample
depletion time, optimal ion extraction voltage, and optimal flow rates for the break up
gas, and the air pump. The spotting pattern for the sample plates is shown in Figure 45
below. Blank spots were left between sample spots to prevent samples from adjacent
spots from mixing, and to provide clean areas for ablation when it was necessary to
collect baseline data of spectra where sample was not ablated.
1
A
B
-
2
3
4
5
6
7
8
9
----
-----
C
D
E
F
G
H
Figure 45 AP-MALDI target plate sample spot
90
layout
10
11
12
4.4.1 Sample Depletion Time
To accurately design system optimization experiments, it was necessary to first determine
how long a spot could be ablated before sample depletion led to reduced ion signal in the
DMS.
The sample depletion time dictated how long sample spots were ablated in all
subsequent experiments.
A summary of conditions under which this study was
conducted is shown in Table 11. In 3 replicates of this study, 1 minute of background
data was collected followed by the collection of 3 minutes of data during which laser
ablated a target plate spot containing desmosine crystallized with the a-CHC matrix. The
laser was set to remain in the same position relative to the sample spot for each replicate.
Table 11 AP-MALDI-DMS experimental conditions for sample depletion time experiments.
Experiment condition
350 ml/min
On
1.5 L/min
Off
500 ml/min
-3000 V
1400 V
System Parameters
Carrier Gas Flow Rate
Carrier Gas Heater
Break Up gas Flow Rate
Break Up Gas Heater
Cumulative Pump Flow Rate
Extraction Voltage
RF Voltage
The effects of laser ablation on the sample spot were examined qualitatively to determine
the extent of sample depletion that occurred over time. The results of this study showed
that sample could be ablated for 180 seconds without any noticeable change in the levels
of the most intense (measured as peak height) ion peaks so samples could safely be
ablated for at least 40 seconds.
For each subsequent optimization study, data was
collected in the following manner: 40 seconds of background collection followed by 40
seconds of collection during laser ablation of a sample spotted area of the target plate.
91
4.4.2 Effect of Break up Gas on Signal Quality
The break up gas flows in a direction opposite to the flow of ions from the AP-MALDI
target plate in order to break apart the ion clusters. De-clustering sample-matrix ions
leads to "cleaner" analyte ions, enhancing the detection specificity of the particular
analyte. Different break up gas flow rates are thus expected to change the quality of ions
that reach the sensor. As the break up gas flow rate is increased, the force breaking up
the ion clusters also increases.
However, the amount of ion reaching the sensor is
expected to decrease as the total force against the ion flow increases. The goal of this
experiment is to determine the optimal flow rate which will provide the best declusterization, but will also allow a sufficient number of them to reach the DMS.
Testing conditions for this experiment are listed in Table 12. The break up gas flow was
varied between 2 and 4 L/min in 1 L/min intervals; a flow rate of 0.2 L/min was tested
instead of zero flow in order to maintain a nitrogen environment in the ionization region.
Table 12 AP-MALDI-DMS experimental conditions for Break up Gas.
Experiment condition
350 ml/min
On
4L/min, 3L/min, 2 L/min, 0.2 L/min
Off
500 ml/min
-3000 V
1400 V
System Parameters
Carrier Gas Flow Rate
Carrier Gas Heater
Break Up gas Flow Rate
Break Up Gas Heater
Cumulative Pump Flow Rate
Extraction Voltage
RF Voltage
A plot of average DMS signal intensity as a function of break up gas flow rate is shown
in Figure 46. The plot represents the average peak intensities averaged over about 900
peaks. As the breakup gas flow rate increased from 0.2 L/min to 2 L/min, the average ion
peak intensity decreased. The trend is not as clear for high break up gas levels however.
From flow rates of 2 L/min to 3 L/min, the average ion peak intensity seems to have
92
increased for two of the experiments, but decreased for one of the experiments. When
changing the flow rate from 3 L/min to 4 L/min, the average ion intensity increased for
all three replicates of the experiment.
This result loosely confirms the expectation that the DMS signal intensity is
decreased by an increase in the break up gas flow rate. However, the increase in average
intensity for low break up gas flow rate may not actually be desirable. As discussed in
greater depth in 4.5.3, the peaks seen in the AP-MALDI-DMS data seem to be highly
correlated to the laser fire of the ionization unit and are due to the detection of large ion
clusters. Although a low break up gas flow rate produces large ion peaks, these peaks
may be due to large ion clusters desorbed during ablation, which would not reveal data
about individually ionized bio-molecules. It is more desirable to detect individual ions of
bio-molecules in order to better identify the analytes using subtler features in the DMS
data.
The subtle features in ion mobility of certain ionized bio-molecules may be
obscured by the large peaks caused by detection of ion clusters.
93
1.3
I
I
I
I
I
III
1.2-
1.1
C
CM
0.9
0.8-
0.7
0.7
--
0
0.5
1
1.5
2.5
2
3
3.5
4
4.5
Gas Flow (L/min)
Figure 46 DMS signal intensity as a function of break up gas flow rate.
4.4.3 Effect of Pump Flow Rate on Signal Quality
The amount of pump pull exerted on the DMS is expected to have a directly proportional
effect on the strength of the ion signal. The pressure difference created between the
ionization region and the DMS acts as a directional driving force transporting the ions
into the sensor. This driving force is a small pressure difference of at most about 0.013
atm. The pump pull rates discussed below are cumulative flow rates out of the DMS
comprised of gas flows due to the carrier gas and the interface inlet flow. Experimental
conditions for the pump pull optimization experiments are listed in Table 13.
The
cumulative pump flow rate was varied between 0 and 500 ml/min in 100 m/min
intervals. When the pump pull rate was 500 ml/min and the carrier gas flow rate was
94
constant at 350 m/min, the flow due to sample from the ionization chamber was 150
ml/min.
Table 13 AP-MALDI-DMS experimental conditions for pump flow rate experiments.
Experiment condition
350 mI/min
On
1.5 L/min
Off
Oml/min - 500 ml/min
-3000 V
1400 V
System Parameters
Carrier Gas Flow Rate
Carrier Gas Heater
Break Up gas Flow Rate
Break Up Gas Heater
Cumulative Pump Flow Rate
Extraction Voltage
RF Voltage
Figure 47 shows a plot of DMS peak intensity as a function of pump pull flow rate. Each
point is an average peak intensity averaged over approximately 900 peaks. Among the
different optimization experiments, pump flow rate results showed the clearest trend. For
cumulative flow rates between -350 and -150 ml/min, the average peak intensity is less
than 0.4 V.
Between flow rates of -150 and 50 ml/min, there is a clear jump to an
average peak intensity of about 0.8 V.
These results show that when the carrier gas flow rate is set to 350 ml/min, average signal
intensity increases significantly when the cumulative flow rate out of the system is set to
400 ml/min. This result makes sense given the direction of gas flows in the interface. If
the cumulative pump flow rate is 400 ml/min or more, about 350 ml/min is contributed
by the carrier gas. The remaining 50 ml/min of gas flow comes from the ionization
chamber where the analyte ions are created.
The pump pull creates a weak force in
addition to that of the high electric field to draw ions into the inlet of the interface, and
ultimately to the DMS.
95
I
1
0.9-
0.8-
0.7
>,
~
% 0.6d-
03
CM
4)~
~i0.5
/
0.4-
0.3-
0.2'I
-400
-300
-200
-100
Pump Flow Rate, (ml/min)
0
100
200
Figure 47 Trend from experiment to vary pump flow rate.
4.4.4 Effect of Extraction Voltage on Signal Quality
As the voltage applied to the extraction capillary increases, the electric field between the
target plate and the capillary increases in strength.
Equation I describes the Electric
Field between the sample plate and the extraction capillary, with the simplifying
assumption that the sample plate and extraction capillary are points. In Equation 1, V is
the potential difference between sample plate and extraction capillary, d is the distance
between the two points, and E is the strength of the electric field between them.
E = V/d
Equation 1
Equation 1 shows that as the voltage applied to the extraction capillary increases, the
electric field between the fused silica capillary entrance and the sample plate becomes
96
larger. The stronger electric field causes a larger number of ions to be attracted into the
extraction capillary. Thus, increasing the extraction voltage is expected to increase the
magnitude of the ion signal. A table of the operating parameters for this experiment is
listed in Table 14. The different extraction voltages for which data were collected in this
study were -3, -2.5, -2, -1.5, -1, and -0.5 kV. Three replicates, following the sample
pattern described at the end of 4.4.1 were collected for each voltage.
Table 14 AP-MALDI-DMS experimental conditions for extraction voltage experiments.
System Parameters
Carrier Gas Flow Rate
Carrier Gas Heater
Break Up gas Flow Rate
Break Up Gas Heater
Cumulative Pump Flow Rate
Extraction Voltage
RF Voltage
RF Voltage
Experiment condition
350 ml/min
On
1.5 L/min
Off
500 ml/min
-3, -2.5, -2, -1.5, -1, -0.5 kV
1400 V
1400 V
Figure 48 shows average ion signal intensity as a function of the potential difference
between the AP-MALDI target plate and the SDI capillary inlet. Each point is an average
peak intensity averaged over approximately 900 peaks.
As the extraction voltage was
increased from -500 V to -1000 V, the ion peak signal increased in intensity.
From
extraction voltages between -1000 V and -3000 V, the average ion peak intensity appears
to fluctuate around a maximum.
These results show that when the extraction voltage is set to -500 V and the electric field
is very weak, ion signal is lower than for any other extraction voltage. This confirms the
expectation that the electric field is a guiding force in directing ions to the DMS through
the SDI. For higher potential differences between the capillary inlet and the target plate,
the effect on the average ion signal intensity is unclear, as averages from the three
97
experiments seem to both increase and decrease for certain changes in extraction voltage.
What is clear is that a high electric field is necessary to achieve greater ion signal
intensity.
1.15
-
I
I
I
-2500
-2000
-1500
I
1.1
1.05
C
7
0.95-
G)
C
0.9G)
0.
G)
0)
0.85-
4:
0.80.75-
V
0.70.65
-3500
~
-
-3000
-___
- ____
___________
-1000
-500
0
Extraction Voltage (V)
Figure 48 Trends from experiment to vary extraction voltage
4.5 Data Analysis for Finding Trends in the Data
This section describes the analysis performed on the DMS data for the experiments
described above. Because the AP-MALDI-DMS system is a novel one-of-a kind system,
this form of data has never before been encountered. Analysis of such data is therefore a
non-trivial problem because the methods for analyzing this data are non-existent. Several
different methods for analyzing the data were used, and the results of these analyses are
98
described below.
It should be noted that there is still much work to be done in
developing methods for analyzing AP-MALDI-DMS data.
4.5.1 Scans from the AP-MALDI-DMS System
When data was collected during times the laser was not turned on, the ion spectra
appeared to be relatively flat with some additive sensor noise.
A representative of
background data is shown in Figure 49.
1
0.8
0.6
U)
0.4
C
Cu
C
S
0.2
-3-3
-5
-0
1-10
-
0
-0.2
-0.4
-4 0
II
-35
I
-30
-25
I
I
I
-20
-15
-10
Compensation Voltage (V)
I
-5
ii
0
5
1
10
Figure 49 Typical scan taken during blank region ablation
When areas of the target plate that were spotted with sample solution were ablated the
data obtained seemed to have several peaks across each scan. One typical scan is shown
in Figure 50. Peaks in the data appear about every 19-20 data points. Each scan typically
has about 12-14 peaks when sample-matrix crystals are ablated.
99
1
I
I
I
-35
-30
-25
I
I
I
I
I
I
-5
0
5
0.8-
0.6-
0.4CO
C
<
0.2-
0-
-0.2
-0.4
-40
-20
-15
-10
Compensation Voltage (V)
10
Figure 50 Typical data scan taken during sample laser ablation.
4.5.2 Determining that Signal is Present Using Fourier Methods
To verify that ion spectra were indeed different when sample was ablated, Fourier
transform methods, employed during analysis of the data obtained from the ESI-DMS
system, were also used. As described in the introduction, Fourier transforms preserve all
information of the original signal, so that the original signal can be fully reconstructed
given the phase and magnitude of the Fourier transform. The Fourier transform separates
data into its frequency components, where the frequency is based on the the change in
compensation voltage rather than the typical change in time. The analog to the traditional
Fourier transform unit of Hertz in these calculations is
100
1
-
A VC
200 r
150 -
1
100 -
C0
50
.
.150
-10oi
-1 SE
0
50
100
150
200
250
30 0
Frequency (k)
Figure 51 Magnitude of Fourier transform of sample ablation spectra against magnitude of Fourier
transform of blank ablation spectra.
Vector Fourier transforms were computed to determine whether there were changes
between different sets of scans. The data vectors for which the transforms were computed
each consisted of 40 data scans, concatenated to form I data vector. Figure 51 shows the
resulting Fourier transforms of the data. The upper plot shows the transform of a vector
of data taken when sample was ablated, referred to for the rest of this section as the
sample transform, overlaid on the transform of data taken when a blank region of the
plate was ablated, referred to as the background transform from here onwards. The lower
plot, which has been offset by -100 for comparison, shows the background transform
overlaid on the sample transform.
101
It is clear that the sample transform has large peaks whereas the background transform is
relatively flat. The presence of peaks in the sample transform is a clear indicator that the
signal did change quite drastically when sample on the AP-MALDI target plate was
ablated. This method of analysis can be used to detect any changes in data that seem
periodic or that are difficult to discern visually.
4.5.3 Finding Trends from Optimization Experiments
The goal of conducting experiments in which the operating conditions of the APMALDI-DMS system were varied was to quantify the effect that each of the varying
parameters had on the total ion signal. The results will be used to determine the optimal
operating conditions for the system, and to further conduct experiments to determine
whether biological substances can be usefully identified and quantified with this system.
One of the features of the data which presented challenges to analysis was the presence
of a great deal of electrical noise due to the fluctuations in the ion sensor. It was unclear
which data were due to actual ion detection, and which were due to noise. Additionally,
the detector sporadically recorded extremely high ion abundances which were clearly
artifacts as they were much larger than typical abundances seen in the rest of the data.
Several different outlier tests were experimented with to eliminate artifacts which
contaminated the data.
Based on the form of the data, it was concluded that ions created by the AP-MALDI
source were in fact reaching the ion detector. However, the locations of these peaks
along the compensation voltage axis were due more in part to the frequency of the laser
fire than on filtration by the varied compensation voltage. Although each data scan is
102
recorded relative to compensation voltage, there is a time component to each scan
because the sensor takes some time to scan through the entire compensation voltage
range. The formula for calculating the length of the scan in time is
#scas .00555sec +.isec
scan
step)
Since there are 250 steps, or data points, per scan, the length of 1 scan is 1.4875 seconds.
Because the laser fire occurs at a frequency of 10 Hz, or once every 0.1 sec, about 14 APMALDI laser pulses occur in the time it takes for the DMS to record one data scan, and
about 18 data points are collected per laser pulse. The peaks in the data scans collected
occurred at roughly the same rate, so it was concluded that ions were introduced into the
sensor every time the laser fired, but that they were introduced as large ion clusters that
traveled too fast to be properly filtered and detected by the DMS.
4.5.4 Peak Analysis
It was necessary to develop a standard measure of signal strength to determine how
different variables in the experimental setup affected the strength of the ion signal. The
data obtained from each of the experiments generally had evenly spaced ion abundance
peaks of varying heights with a common baseline in between these peaks as previously
shown in Figure 50.
From conclusions reached about the nature of the peaks in the data, it was determined
that information from just the peaks in the data scans would be sufficient to draw
conclusions about the effects of variation of operating conditions.
extracting peak information from each scan then arose.
103
The problem of
The computational software
package Matlab has several predefined functions for image processing which can be
applied to one-dimensional vectors such as the data recorded by the DMS sensor. These
functions select peaks based on different criteria. It was found that a combination of
these algorithms provided the best method for creating an automated peak extraction
function. The Matlab code used is included in Appendix A.
In addition to selecting peaks from the data, it was necessary to correct for a baseline
offset that varied between experiments so that peak intensities could be compared.
Because the number of peaks in a data scan is small relative to the number of total data
points in the scan, a line of best fit, applied using Matlab's bestfito function fell
along that baseline fairly well. By subtracting the midpoint of the line of best fit from the
peaks in that scan, bias should have been removed from each of the peaks. Problems
with this method arose because each scan was then corrected by a different value. The
alternative method chosen finds the minimum recorded abundance in an entire
experiment, and corrects all peaks in that experiment by that value.
To compute the signal strength for a particular experiment, the average intensity, and the
standard deviation of the peaks was calculated. The justification for averaging all the
peaks together, regardless of the compensation voltage at which that peak occurred, was
due to the fact that the peaks were not being greatly affected by the compensation
voltage.
104
5 Summary and Conclusions
5.1 ES!
The results of testing on three versions of the interface between ESI and the DMS have
yielded important conclusions about the interface, and resulting data analyses. First, the
dynamic interactions of the different flow rates through the system have an important
impact on the strength of the ion signal detected by the DMS. Because the modified Tjunction/capillary interface did not have a pump attached to the outlet of the DMS, the
ion signal may have been very weak. Additionally, the internal ionization source of the
DMS was shielded which further weakened the ion signal by preventing the ionization of
additional molecules.
This weak signal did not provide sufficient information from
which analyses by lead cluster mapping and analyses by decision tree could successfully
build classification models.
The prototype 1 interface performed much better than the T-junction capillary interface in
that ions were successfully introduced into the DMS such that classification models with
high accuracy could be built from the resultant data. A slight pressure drop was also used
to counter the gas flows that may have deflected ions away from the extraction capillary
inlet.
This interface was then further optimized by modification to the ion-optics
configuration of the interface capillary shields. An intermediate voltage between ground
and the high extraction capillary voltage was applied to the outer capillary shield. This
intermediate voltage would serve to further focus the electric field to deflect ions into the
extraction capillary tube.
105
The interface using modified ion-optics also performed well in detecting ions of different
proteins. The resultant data did not achieve high rates of accuracy for models built using
lead-cluster mapping, however. These low accuracy rates may have been due to the fact
that lead cluster mapping relies on random selection of features, and may not have chosen
the correct features on which to classify the data. Decision tree analyses performed better
on this dataset than on the two previous data sets. This indicates that the modified ionoptics interface may indeed have enhanced the transport of ions from the ionization
chamber to the DMS, but the lead-cluster mapping analysis may have missed key
biomarkers.
After several experiments with various system conditions and variations of the basic
prototype I interface, several conclusions about the interface and corresponding
parameters can be drawn. First, after ions are generated by the ESI unit, they should be
transported over as short a distance as possible to the DMS in order to keep them in a
charged state long enough to be detected. It is necessary to select a low flow rate that
will allow the ions generated to be properly filtered and detected by the DMS, and to
avoid saturating and damaging the sensor.
Second, the configuration of the electric field to attract ions into the interface is
important. Whereas the entire interface sans capillary inlet had been connected to earth
ground, it is necessary to connect the external capillary shield to an intermediate voltage
in order to better focus the electric field, and the subsequent path along which ions will
be propelled into the DMS.
By holding the external capillary shield to some voltage
between the high extraction voltage and earth ground, the potential difference across
106
small gaps is reduced, decreasing the chances that arcing will occur in the interface. In
other areas of the interface, it is also necessary to make sure that insulation gaps between
areas of high electric potential differences are small to prevent breakdown and current
conduction across insulators - air and Teflon.
Third, ensuring that solvent is evaporated prior to ions entering the interface capillary is
important to prevent blockage of the pathway and damage to the sensor. However, there
is a tradeoff between increasing the flow of break up gas to dry the solvent, and
decreasing that flow to allow more ions to enter the sensor. A break up gas flow rate of 3
L/min caused sufficient evaporation of solvent without a great reduction in the ion signals
detected by the DMS.
Fourth, the ionization source inside the DMS is necessary to create a spectrum that can be
successfully analyzed using current methods of analysis. By shielding this source, the
effects of ionization due solely to the ESI method would be better detected. However, in
the first generation design of the prototype 1 interface, ions created via ESI may not have
stayed in an ionized state long enough to be detected by the DMS so the ESI unit
functions as more of an aerosol spray mechanism.
The efficacy of ESI as an ionization
method will be better tested in the second generation interface design which will
comprise a shorter path length from point of ionization to the DMS sensor.
5.2 AP-MALDI
Experiments to vary operating conditions in the AP-MALDI-DMS showed that the
interface operated as believed. As the break up gas flow rate was increased, the ion
signal seemed to decrease.
As the pump flow rate and the extraction voltage were
107
increased, the ion signal also increased. Prior to testing of the interface with the ESI unit,
these tests with the AP-MALDI method showed that the interface was successful in
introducing ions to the DMS sensor, and that the potential for creating a system with
greater sensitivity and specificity is great.
Plans to conduct the optimization studies described are under way. It is important to
verify the results obtained from this round of studies while closely monitoring sample
build-up and other experimental conditions leading to DMS sensor malfunction. One of
the potential problems encountered with the studies performed for this thesis is that the
sensor may have become saturated with sample, leading to loss of sensitivity. It was
noticed that as experimentation progressed, problems with electrical noise in the DMS
data increased. This is also probably the reason why large statistical spreads in the data
were obtained further confounding trends in the ion signal.
These problems can be
resolved by further refining the flow of what seem to be large ion clusters through the
DMS. The large peaks in the data which correspond in time to the fire of the laser leave
little confidence that the ion clusters are filtered enough to separate and register as
different species with different ion mobilities. Results may change due to changes in the
ion-optics configuration of the SDI. Once optimum experimental parameters have been
determined, the ability for the DMS to identify and quantify biological molecules should
be further investigated.
Because peaks in the AP-MALDI-DMS system data are indistinguishable, it will be
worthwhile to ensure that ion clusters created by laser ablations are properly fragmented
to prevent saturation of the DMS by large clusters of analyte.
108
Additionally, further
development is necessary in methods of analyses for AP-MALDI-DMS data. Many
different methods of analyses depend on the form of data being analyzed. In this case,
the form of the AP-MALDI data seems to be very different from other types of DMS data
previously collected, so current methods of analysis may not extract information that is
encoded differently in AP-MALDI-DMS data. Once further work is done to resolve the
large ion cluster problems and to develop tools of data analysis, the AP-MALDI-DMS
may be further evaluated as a tool for identifying and quantifying bio-molecules.
5.3 Data analysis
Fourier and Decision tree analyses have both been shown to be very promising
techniques for further developing analysis tools for ESI and AP-MALDI DMS data.
Fourier Transforms are valuable in enhancing differences among data collected at
different conditions for both the ESI-DMS and AP-MALDI-DMS systems. Decision tree
analysis has preliminarily been shown to yield only an 11% classification error rate for
data collected using the modified Prototype I interface for ESI. Decision trees may fail in
classification if data is encountered for certain unknown analytes that were not used to
initially create the tree. However, decision trees provide a fast way to classify data with
relatively little complexity. Further work is necessary to refine the way these techniques
are used for analyzing data collected with the DMS and to reduce classification error
rates.
6 Future Work
This chapter describes the work that should be done to carry the research described
forward, and to improve and expand upon these results.
109
6.1 ESI
6.1.1 Shorter Ion Path Length
In the second generation design, the length that the ions must travel must be shortened. A
shorter ion path will decrease the amount of time that the ions have to collide with one
another and lose their ionized state. As ions have more time to become de-ionized, fewer
ions that will reach the drift region in time to be detected. Deionization could greatly
worsen the sensitivity of the detector if for small concentrations of sample, the sample
quickly loses it added charge, and goes undetected.
6.1.2 Better insulation for electronics/HV
One of the lesser understood characteristics of the interface are the shape and strength of
the electric fields that are created by the high voltage power source.
In the current
interface, the ESI needle is grounded, the outer capillary shield is set to -2700 volts, and
the inner shield is set at -3200 volts. This creates some unconventionally shaped electric
fields, which are distorted even further by the sharp edges and discontinuities in the
shapes of the components to which these potentials are applied.
Some different types of insulation that may be used in future generations of the interface
are humiseal, which has a dielectric strength between 2000 and 3500 V/mm, or Paralene
which will uniformly coat corners and prevent interactions due to corner discontinuities
in the electric field.
110
6.1.3 Interaction between sample and carrier gas
One of the parameters in the interface which can strongly impact the quality of the ion
signal is the flow rate of the carrier gas. Sample ions are first created by the ESI needle,
but must reach DMS intact. The radiation source inside of the DMS sensor ionizes any
remaining un-ionized nitrogen gas, which upon collision with the sample ions, transfers
charge. If the carrier gas flow rate were increased, the amount of nitrogen that is ionized
that will transfer charge to the sample ions is also increased. However, if relying upon
the radiation source in the DMS to create the ions, the point of using the ESI needle as an
ionization source is lost; it is reduced simply to an aerosolizer. Additional work can be
conducted to understand how large macromolecules are ionized and how they travel
through the drift region of the DMS.
6.2 AP-MALDI
As with the ESI-DMS system, the problem of maintaining sample in ionization states is
also present in the AP-MALDI-DMS interface. The problem here is further complicated
by the fact that many of the sample ions are traveling through the sensor in large clusters,
and are traveling too fast to be properly filtered and scattered by the RF fields in the
DMS drift region. Further work must be done in optimizing the system to reduce the
amount of ion clusters that reach the DMS.
If the problem can not be solved by
modifications to hardware, then additional work must be done in developing methods of
data analysis that will extract bio-markers from AP-MALDI-DMS data for large ion
clusters.
111
6.3 Data Analysis
Based on the outcomes of data analysis and the types of features that are necessary for
substance classification, hardware must be accordingly modified so as to produce or
enhance these features.
6.3.1 Fourier Analyses
Cepstrum analysis, a technique used in speech processing, may be of value in processing
AP-MALDI-DMS data. Cepstrum analysis essentially separates the periodic content of a
signal from the aperiodic content in the frequency domain. This type of analysis may be
useful in separating periodic content due to the laser firing frequency of the AP-MALDI
unit from aperiodic information in the spectra which may be due to biomarker
information.
6.3.2 Statistical analyses
Fourier analyses typically assume that signals resulting from combinations of other
signals and perhaps noise can are independent. Other types of analyses, such as Principal
Component Analysis (PCA) by Single Value Decomposition (SVD) and Independent
Component Analysis (ICA) do not assume statistical independence of component signals.
PCA and ICA have the potential to be used in the future as methods for analyzing DMS
data resulting from samples of mixed analytes, and may be of use in classifying biomolecules analyzed by DMS.
112
7 Appendix
voltage = 500;
for FileCounter =
for spot =
[28:43]
[1 2 3)
scanpeaks =
zeros(200,200);
scanindices = EventScanNumbers{FileCounter)(spot*2)
EventScanNumbers{FileCounter)(spot*2+1)];
correctionvalue(FileCounter,spot) = min(min(Data{FileCounter}
(scanindices,:)));
for ScanNum = scanindices
scan = Data{FileCounter)(ScanNum,
:);
% findpeak positions , 1 for peak, 0 for nonpeak
peakpositions = imextendedmax(scan,.3);
%multiplication to extract peaks, and zero non-peaks
peakvector = scan .* peakpositions;
peakvector = PositivePeaks(peakvector);
% find the peak positions , 1 for peak, 0 for nonpeak
peakpositions =
imregionalmax(peakvector,4);
% multiplication to extract peaks, and zero non-peaks
peakvector = peakvector .* peakpositions;
c2 = 1; c3 = 1;
goodpeaks(c3) = 0;
for c2 = 1:length(peakvector)
if (peakvector(c2) > 0)
goodpeaks(c3) = peakvector(c2) correctionvalue(FileCounter, spot);
c3 = c3+1;
end
end
peak(ScanNum)= mean(goodpeaks);
scanpeaks(ScanNum, 1:length(goodpeaks)) = goodpeaks
clear goodpeaks peakvector peakpositions scan;
end
SEMs(FileCounter, spot) = SEM(scanpeaks, length(scanindices));
FilePeakaverages{FileCounter, spot) = nonzeros(peak);
FileSpotAverages(FileCounter,spot) =
mean(FilePeakaverages{FileCounter,spot)(:,:));
clear peak, scanindices;
end
clear scanpeaks;
end
113
8 References
[1]
[2]
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