A simple microcomputer-based system for real-time analysis of cell
behaviour
JULIAN A. T. DOW*, JOHN M. LACKJE and KENNETH V. CROCKET
Department of Cell Biology, University of Glasgow, Glasgow G12 8QQ, Scotland
•Author for correspondence
Summary
An image analysis package based on a BBC
microcomputer has been developed, which can
simultaneously track many moving cells in vitro.
Cells (rabbit neutrophil leucocytes, BHK C13
fibroblasts, or PC12 phaeochromocytoma cells)
are viewed under phase optics with a monochrome TV camera, and the signal digitized.
Successive frames are acquired by the computer
as a 640x256 pixel array. Under controlled lighting conditions, cells can readily be isolated from
the background by binary filtering. In real-time
tracking, the positions of a given cell in successive frames are obtained by searching the area
around the cell's centroid in the previous frame.
A simple box-search algorithm is described,
which proves highly successful at low cell densities. The resilience of different search algorithms to various exceptional conditions (such as
collisions) is discussed. The success of this system in real-time tracking is largely dependent
upon the leisurely speed of movement of cells,
and on obtaining a clean, high quality optical
image to analyse. The limitations of this technique for different cell types, and the possible
configurations of more sophisticated hardware,
are outlined. This system provides a versatile and
automated solution to the problem of studying
the movement of tissue cells.
Introduction
necessary to measure the cells' positions over a period
of time.
From this, it can be seen that there are two classes of
data commonly required in studies of cell behaviour:
'static measures', for example of spread areas, number
or length of processes, or cell orientation; and
'dynamic measures', for example of speed and direction of cell locomotion, rate of adhesion, or of response
to chemoattractants. Static and dynamic measurements are very laborious to make, and both are highly
amenable to computer techniques (Noble & Levine,
1986). In this paper, we will concentrate on the
problem of dynamic cell tracking in real time.
Traditionally, studies of cell locomotion were performed manually, using a 'post-analysis' strategy:
rather than tracing the movement of a single cell in
real-time, the movements of several cells were first
recorded on cine film or video tape. The paths of
several cells could then be traced by hand, and
displacements, speeds and angles calculated manually
(Fig. 1A). That this procedure is extremely time-
Many tissue cells, in vitro, move over their substratum, be it a collagen gel or a plastic Petri dish. The
phenomenon of cell locomotion has been studied
intensively for many years, both as a topic of purely
academic interest, and because of the role of cell
motility and guidance in embryonic development,
in wound healing and inflammation, and in cancer
metastasis (Bellairs et al. 1982).
Time-lapse recordings of moving cells contain a
plethora of information, most of which must be
rejected in order to focus on the topic of particular
interest. For example, an experimenter would wish to
quantify the extent of cell spreading on a particular
surface, or the degree of alignment of cells on some
oriented substratum, like a stressed collagen gel.
Alternatively, it is frequently desirable to measure the
speed and locomotory parameters of cells moving
toward a source of chemoattractant, in which case it is
Journal of Cell Science 87, 171-182 (1987)
Printed in Great Britain © The Company of Biologists Limited 1987
Key words: BBC microcomputer, image analysis, cell
behaviour, neutrophils, growth cones, BHK fibroblasts.
171
Fig. 1. Comparison of three strategies for the analysis of cell locomotion. A. Manual: cells are filmed under time-lapse, the
film processed, the cell tracks transcribed individually to paper by hand, then their displacements and angles of turn
measured by hand, and locomotion parameters worked out on a calculator. B. Computer-assisted: here, the cell tracks are
traced with a stylus on a digitizing pad, and the coordinate measurements and subsequent calculations performed by a
computer. C. Computer-centred: here, the cells are tracked by video; successive video frames are grabbed, digitized, and
the cell positions calculated, all under computer control.
intensive is witnessed by the fact that Weiss, one of the
pioneers of cell behaviour studies, left 10 km of cine
film unanalysed at the time of his retirement (A. S. G.
Curtis, personal communication).
Clearly, any reduction of labour is desirable. We
have defined two possible levels of computer intervention in this manual procedure; computer-assisted
and computer-centred. In a computer-assisted system
(Fig. IB), the subjective judgement of cell position
and manual tracking of the cell still rests with the
experimenter; however, by using a digitizing pad, all
subsequent measurement and calculation can be performed by a microcomputer (Lackie & Burns, 1983).
This greatly simplifies the task, though a time lapse
cine film of a 30 min experiment requires several hours
of post-analysis, even after the film has returned from
the processing laboratory.
A computer system that can automatically locate
objectively and track cells would free the experimenter
for more rewarding tasks. If such a computer-centred
system (Fig. 1C) could perform quickly enough, it
would be possible to avoid even having to record
pictures and post-analysing them; the record of the
experiment need only be a series of x, y coordinates for
each of a series of cells. Systems that could perform
such a task are available now, based on commercial
framestores, but at formidable cost (£5OOO-£5OOOO).
This paper describes a simple microcomputer-centred
system, based on an inexpensive (£100) framegrabber,
172
J. A. T. Dow et al.
which can track a large number of moving cells in real
time, and thus offers many experimental opportunities.
Materials and methods
Considerations in system design
For a computer-centred system to be useful, it must
perform at least as well as the system it is intended to
replace. A popular subject of cell motility studies is the
polymorphonuclear leucocyte (PMN), or neutrophil
(Wilkinson, 1982; Lackie & Wilkinson, 1984). In our
department, a standard film sequence for analysis
would be of 300 frames, recorded at 6-s intervals, and
thus corresponding to 30 min real-time. From each
run, the tracks of up to 20 cells would be recorded at
1-min intervals. A real-time computer-centred system,
therefore, needs to record the positions of not one, but
at least 20 cells, at less than 1-min intervals, to afford a
significant improvement.
To be able to track cells whilst they are moving, it
must be possible to grab the frame, analyse it, store the
coordinate pairs, and grab the next frame, before the
cells have moved too far to be relocated. The speed, or
'cycle time', in which these tasks can be accomplished
is thus critical. Neutrophils move unusually fast (at up
to 20 /zm min~ 1 , compared with their diameter of only
8^im). A sample rate of at least 2 min" 1 was found
necessary in pilot experiments. For most other cells,
which display a spread morphology, far longer cycle
times are acceptable. BHK cells, for example, move at
less than O-S/immin" 1 , and so an image capture rate
of one frame in 5 min is adequate.
A post-analysis strategy was considered, in which
digitized images would be stored on disc, and then
analysed later; however, the storage requirements
for a single run (10 frame min" 1 X 15 min X 20 kbyte
frame" 1 = 3 Mbyte per experiment) were impracticable. If x, y coordinates were stored, the disc storage
required per experiment would fall considerably (15
readings X 50 coordinate pairs X 10 bytes per coordinate pair = 7-5 kbyte per experiment), so that a single
floppy disc of 200 kbyte capacity, could hold data from
perhaps 30 experiments. This was the approach
adopted.
System configuration
Cells were viewed through a Leitz Diavert or Leitz
Ortholux microscope, equipped with phase-contrast,
bright-field, dark-field or Nomarski optics. A Panasonic Newvicon-tube video camera was attached to a
beam-splitting eyepiece, so that the image could be
simultaneously viewed directly, and on a Panasonic
12-inch monochrome monitor. Cells were maintained
at 37 °C by an air curtain device (a modified domestic
fan-heater) (Wilkinson et al. 1982). The TV camera
was connected to a Video digitizer unit (£100: Watford
Electronics), which was connected in turn to the 'User
Port' of a BBC Master microcomputer, equipped with
twin floppy disc drives, a plotter, a printer and a
monochrome monitor. The combined system was
capable of digitizing a TV image to a binary, 640x256
pixel (or image point), image within 2s. Using a 10X
lens, as in most of these experiments, a digitized field
of 720/urn X 460 fim was obtained, corresponding to a
pixel size of around 1-5/im.
The cell tracking algorithm
The central problem in computer tracking, be it of
cells, limbs or missiles, is to identify, and measure the
position of, the same object in successive frames. For
some objects, or for noisy images, this problem can
demand the very highest level of computer power.
However, applications in cell biology need not be so
demanding; the simple 'box-search' algorithm described below can be implemented successfully on a
small microcomputer, providing certain experimental
conditions are met.
The algorithm for the box search is simple
(Fig. 2A); given that the x, y coordinates of the cell
in the previous frame are known, and that the cell
stands out clearly from its background (i.e. its pixels
are distinctive), all the distinctive pixels within a
specified radius of the last known position are counted,
and the x and 3' coordinates of each such pixel
summed. (Mathematically, this corresponds to taking
the zero- and first-order moments of the object.) If no
distinctive pixels are found, then the cell is known to
be lost; otherwise the (visual) centroid of the cell is
found by dividing the sums of the x and 3' coordinates
by the number of pixels found in the box. Such an
approach, using moments, is extremely widespread in
image analysis: it is, though, rather surprising that so
simple a search algorithm can perform adequately in
cell tracking studies. The implementation of the box
search in BASIC is described in Appendix 1; note
that, strictly, a circular search area should be used, but
it is far faster, and just as effective, to search a square
'box'. The cell tracks will not be biased, provided that
the diameter of the box is great enough to encompass
even the fastest-moving cells.
There are certain obvious limitations on a box
search. The first (Fig. 2B) is that fast-moving targets
may move partially or completely out of the box
between successive frames, and their tracks may thus
end abruptly. To correct this, it is necessary either to
increase the size of the box, or to decrease the
sampling interval. In either case, the ideal box 'radius'
(in /im) is clearly equal to the maximum expected
distance moved by a cell between frames (i.e. cell
speed (in izmmin" 1 ) multiplied by the sampling
interval (in min)). There are drawbacks either to very
large boxes (the search time rises as the number of
pixels within the box, or the square of the radius of the
Q
Fig. 2. The box search algorithm. A. To find the new positions of a given cell in successive frames, all white pixels within
a fixed radius of the previously determined position (marked with a cross) are identified, and their moments calculated.
This measure thus provides the visual centroid of the cell (marked with a circle). B. Problem: fast-moving cells. A fastmoving cell may move partially, or even completely, out of the box between frames. C. Problem: close encounters. If
another cell (shaded) intrudes into the box around a target cell, the resulting centroid will be displaced.
Automatic cell tracking
173
box); or to very short sampling intervals (few targets
could be tracked in the spare time between frames).
The second limitation of the box search concerns
the possibility that a second cell might intrude into
another cell's box (Fig. 2C). In this case, there are
four possibilities; (1) the computer's impression of the
cell's centroid will be temporarily distorted; (2) the
box algorithm will switch allegiance to the other cell;
Fig. 3. Digitized computer images of BHK C13 cells viewed under various types of light microscopy. Phase-contrast:
A, photomicrograph; B, binary digitized image. Dark field: C, photomicrograph; D, binary digitized image. Nomarski:
E, photomicrograph; F, binary digitized image. Bar, 50jUm.
174
jf. A. T. Dow et al.
(3) both cells move out of the search area in opposite
directions, resulting in termination of the track; or (4)
if both cells were initially tracked, then one cell might
become tracked twice, while the other stops being
tracked altogether. These problems can be overcome
by keeping the cell density low, so that the chances
that two cells move within one box radius of each other
are slight; or by reducing the size of the box (but see
the arguments above). In any case, it is possible to
check, on each cycle, that no two sets of x, y
coordinates are identical; if they are, one track is
terminated. From the above, it is clear that a working
compromise must be obtained between cell density,
sampling interval and box size. In our hands, it was
possible to track neutrophils at the density previously
used routinely, of 0-5Xl0 6 ml~ 1 , corresponding to
50-100 cells per field of 720 fim X 460 ^m. Taking the
maximum speed of neutrophils as 20 jum min~', it was
possible to track 40 cells every 30 s, using a box radius
of 10 (Um. Using a faster, machine-code box routine, 50
cells could be scanned every 20 s, using a box radius of
8/xm.
It can be seen that there are two classes of error in
tracking: premature loss of a track; and mistracking,
or switching allegiance between cells. Neither of these
errors will lead to a serious error in estimating
population speeds or persistences (persistence, a concept derived from the random-walk theory, is roughly
equivalent to the mean time between changes of
direction); provided that tracking errors are not systematic (for example, unusually fast cells must not
be lost preferentially), and that errors are rare. For
example, if a tracking allegiance switched between two
cells once during a run of 30 readings, only one data
point (that at the transition) would be an inaccurate
reflection of the cell population.
There are other, more sophisticated algorithms, for
example 'flood fill' searches, in which only contiguous
Fig. 4. Digitized computer images. Rabbit neutrophils: A, photomicrograph; B, a binary digitized image. PC1Z cell:
C, photomicrograph; D, image digitized to 160x256 pixels and 8 grey levels. Bars,
Automatic cell tracking
175
pixels contribute to the calculation of cell centroids,
which perform better at high cell densities; or those in
which higher moments for cell shapes are calculated
(Dunn & Brown, 1986). However, these are rather
more complicated and time-intensive to implement,
and are more suited to 'static', rather than 'dynamic',
measures.
Cells
Rabbit neutrophils were prepared by peritoneal lavage
(Vickere* al. 1986), and stored at 5°C for up to 3 days
before use. A significant decrease in mean speed, and
in the fraction of moving cells, was noted in this
interval. They were observed in filming chambers of
acid-cleaned glass in 50 % Hanks'-Hepes salt solution,
Scale= 100 JJ
Fig. 5. Typical cell tracks obtained using the techniques described in this paper, for rabbit PMN leucocytes. Cells were
tracked for up to 25min. Bar, 100 ^m.
Table 1. Comparison of typical locomotion parameters for different cell types
Speed
Augmented
diffusion constant
Persistence
OtmV)
Cell type
176
Mean
S.D.
n
Mean
S.D.
n
27
3
26
0-9
0-2
26
18
206
208
18
0-06
0-01
18
6
229
27
6
0-03
001
6
Mean
S.D.
Rabbit PMNs
(fresh)
7-8
0-5
26
BHK C13
fibroblasts
0-9
0-7
PC 12 neuroblastoma
growth cones
0-5
0-07
J. A. T. Dow et al.
50 % peritoneal exudate. Cells were allowed 15 min to
warm up before the start of the experiment, which ran
for 25 min.
BHK fibroblasts (clone C13) were harvested from
routine cultures in the department (Edwards &
Campbell, 1971), and plated at 20000ml" 1 onto
acid-cleaned glass coverslips, in HECT medium
(Hepes-buffered Glagow-modified Eagle's medium,
supplemented with calf serum and tryptose phosphate
broth). They were kept at 37°C at all times, and
allowed to settle for at least 6 h before filming.
PC12 phaeochromocytoma cells (gift from Drs A.
McCruden & P.Schutz) were grown at 37°C on
tissue culture plastic, in bicarbonate-buffered Eagle's
Medium, supplemented with 10% foetal calf serum
and 10% horse serum. Neuronal differentiation was
triggered by adding nerve growth factor (mouse 7 S ;
Sigma Chemicals) at lOOngml" 1 , one day before
study. Multiple neurites are observed under such
conditions (O'Lague et al. 1985).
Tests of the computer system
The static and dynamic properties of the box search
algorithm were tested by computer simulation. Repeated measurements were made of stationary discs
generated by computer, and the mean and standard
errors of the centroids thus determined were calculated. In another set of experiments, box and disc radii
were set to values simulating a neutrophil-tracking
experiment, and tracks obtained for different (known)
speeds. The accuracy of speed measurement by the
computer could thus be assessed.
The accuracy of the computer-centred cell tracking
system was tested by comparison with the computerassisted technique previously in use in the department. Time-lapse films, which had previously been
analysed by computer-assisted methods, were projected onto the wall, and viewed with a TV camera.
The microcomputer acquired and analysed each cine
frame, advancing the projector automatically. As in
the computer-assisted system, data from every tenth
frame were stored for later analysis. After tracking was
complete, data were processed by the same program
that had been used for the computer-assisted system,
and the population displacements, speeds and persistences obtained by the two systems compared. Speed,
persistence, and augmented diffusion coefficient are
parameters based on a pseudo-random walk model for
cell locomotion, proposed by Dunn (1983), and applied to neutrophils by Wilkinson et al. (1984).
Repeatability of the computer-centred system was
assessed by analysing the same sequence of cine film
four times, and comparing the locomotion parameters
obtained on each run. Similarly, the stability of the
location algorithm was obtained by tracking the cells
on a single frame as for a normal experiment, but
without advancing the film between tracking cycles.
In this way, a population of 'stationary' cells was
obtained.
The accuracy of hand tracking was also assessed; in
these experiments, 26 individual tracks from a typical
neutrophil-tracking run were plotted by computer.
The experimenter then selected 20 'representative'
tracks and entered them, via a digitizing tablet, into a
computer file. The population locomotion parameters
obtained by this method were compared, using Student's /-test, with the 'true' values obtained by the
computer. This experiment thus assessed directly the
reliability of a human experimenter in reproducing
the intricacies of cell tracks.
Results
Digitized images from various cell types under
various conditions
Photomicrographs and digitized images of BHK cells
are shown in Fig. 3. Bright-field images were of low
contrast, and unsuitable for digitization. In phase
contrast, cells are visible as dark grey objects, with
white halos, against a mid-grey background (Fig. 3A).
These images digitize rather well, and if a binary
50
Time (min)
100
Fig. 6. Locomotion parameters as a function of time
during a single experiment. Tracking started immediately
the cells were placed on the microscope stage. Cells were
tracked every 20 s over successive 5-min periods, and their
locomotion parameters calculated for each individual
period. Abbreviations: 5, speed in Jim min" 1 ;
P, persistence in s; R, augmented diffusion constant in
Jim s~
s~ . Data are shown as mean ± S.D. for, typically,
n = 20.
Automatic cell tracking
177
filtering level is chosen carefully it is possible to obtain
a good picture of the cell, suitable for tracking
(Fig. 3B). Although cells are clearly visible in the
dark-field image (Fig. 3C), this illumination also
shows up particulate debris in the medium, normally
invisible in the light microscope. The resulting image
(Fig. 3D) is thus far too noisy, despite the large
'signal' size. Finally, Nomarski optics, while providing
plenty of information for the human eye (Fig. 3E), do
not provide an easy image for computer analysis
(Fig. 3F). The cells are shown only by a dark strip
down one side, and a light one down the other. This
provides a powerful pseudo-relief effect to the human
eye, but is difficult for a computer to interpret, as the
centre of the cell is the same brightness as the
background.
Neutrophils provide extremely good phase objects
(Fig. 4A), which digitize readily (Fig. 4B). Similarly,
PC12 cells are clearly visible under phase conditions
(Fig. 4C), and the club-like growth cones are particularly prominent in digitized images (Fig. 4D). For all
three cell types, therefore, conventional phase contrast
seems the best optical system for computer analysis.
75—,
Available
to track
Cell tracks
Neutrophil tracks from a typical experiment are shown
in Fig. 5. As can be seen, neutrophils move in zigzags, frequently doubling back on themselves. These
tracks were plotted within 5 min of the end of the
experiment, a considerable saving in time compared
with the conventional methodology.
It proved possible to track, not only neutrophils,
but also BHK fibroblasts and PC12 growth cones. The
locomotion parameters of these cell types are shown in
Table 1. It can be seen that neutrophils move an order
of magnitude faster, and with an order of magnitude
lower persistence, than either BHK or PC12 cells
(which show a strong similarity in their locomotion
parameters).
Cell locomotion parameters are plotted as a function
of time since the start of the experiment in Fig. 6.
Whereas the speed of cells is relatively constant, both
the persistence and diffusion coefficient rise gradually
to a peak, then decline. It can be seen that the normal
warm-up period allowed (15 min) ensures that the
cells are performing at maximum persistence; however, it is also clear that the cells locomote usefully for
longer than the 30 min for which they are normally
Selection
program
Tracking
program
Analysis
program
50 —
25 —
0—
Experimental sequence
Fig. 7. Survival curve for neutrophil tracks during a typical experiment. 'Off, denotes cells moving offscreen: 'Lost',
denotes those lost through near misses or other causes.
178
J. A. T. Dow et al.
60-80 cells on the screen at the start of the experiment, the computer normally systematically scans the
central screen area, selecting the first 50 cells encountered. Assuming that the distribution of the cells
on-screen is initially random, this corresponds to a
random cell selection. (It is also possible to select
individual cells deliberately for tracking, although
considerations of objectivity preclude this for routine
use.)
Some of the cells initially selected will be lost as they
move off-screen; some by collisions with other cells
(the computer automatically terminates tracks with
the same coordinates); and a few for ill-defined
reasons. After the experiment finishes, the experimenter is presented with each track in turn, providing
the opportunity to select only moving cells with at
least five coordinate pairs, for further analysis. The
maximum number of cells accepted by the computerassisted analysis program was 20, so a fairly steep rate
of attrition, from the 50 cells originally selected, was
acceptable. Fig. 7 illustrates the drop-out rates as a
typical experiment proceeded. Clearly, then, this
system is capable of producing an adequate number of
acceptable tracks.
i
elfra
1
u
/
a
a.
•a
u
8.
(2) (2) (2)
J/
(24)
/
/
(24)
/
13 10S3
(24)/
/
/
/
/(24)
1
30
10
20
True speed (pixel frame" 1 )
Fig. 8. Graph of measured versus true speed for a
computer-simulated moving cell. Data are shown with
standard errors (except where error bars would be
obscured by the symbols): numbers in parenthesis denote
the number of speed determinations. Cells are tracked
accurately until their displacement between frames
approximates the box radius; above this, speeds are
underestimated, and cells rapidly lost.
Testing the computer systems
The determination of the centroids of stationary,
computer-generated discs revealed no inaccuracy or
random fluctutation in the computer search algorithm.
When moving discs were tracked, the speeds determined by the computer were accurate until the distance moved by the disc between frames approached
filmed. Why these changes occur is not clear; previous
analyses have not revealed these trends so clearly and
the observation raises interesting questions.
It is important to establish the reliability of the
tracking system, in not losing cells too fast. From
Table 2. Repeatability of the computer-centred system
Computer-analysed data
Run 2
Run 3
Run
Mean
Parameter
Number of tracked
cells
Speed (/immin" 1 )
Persistence (s)
Augmented diffusion
coefficient (/jm 2 9~')
Mean
S.D.
13
7-0
37
1-0
S.D.
15
Mean
S.D.
0-7
6
8-0
38
0-3
1-3
Mean
S.D.
16
17
7-5
34
10
0-5
9
0-3
Mean
Hand-analysed
data
Run 4
0-7
7
0-4
0-7
8
0-3
8-0
30
11
S.D.
20
—
7-4
60
1-8
0-3
S
0-2
Population parameters derived from several analyses of the same cine sequence.
Table 3. Accuracy of hand-digitizing of computer-plotted tracks
True value
Hand-derived value
P (2-tailed)
Parameter
Speed (/immin" 1 )
Persistence (s)
Augmented diffusion
coefficient (^m 2 s - 1 )
Mean
S.D.
7-8
0-5
26
9-2
0-5
20
<0-l
27
0-9
3
0-2
26
26
32
1-S
4
0-23
20
20
<0-l
<0-l
Mean
S.D.
Automatic cell tracking
179
Fig. 9. Comparison of computer-generated and hand-drawn cell tracks from the same cine film sequence, for two typical
cells. Top row: computer-tracked, every second frame used, giving an effective sampling rate of one point every 12s.
Middle row: computer-tracked, every tenth frame used, giving an effective sampling rate of one point every 60s. Bottom
row: hand-tracked cells, at the routine sampling rate of one point every 60s. Bar, 100^m.
the box radius. Under such circumstances, speeds
were underestimated, and cells were lost prematurely
(Fig. 8). This is unlikely to prove a serious problem in
practice; the box radius used (24 pixels) would correspond to a radius of 50/im when using a 10 X
objective. In a neutrophil-tracking experiment, at
3 frames min~', this corresponds to a maximum trackable cell speed of 150jUmmin~', at least five times
greater than the maximum speed observed.
The quality of image produced by digitizing cine
films projected onto the wall was surprisingly good,
and the computer had little difficulty in tracking the
cells. The population locomotion parameters for a
typical experiment, obtained by computer-assisted
and computer-centred systems, are compared in
Table 2. It is clear that the repeatability of the
computer-centred system is very high; that is, when
presented with the same cells, it produces effectively
the same results. However, these data differ slightly
from those previously produced by computer-assisted
hand-tracking. In particular, hand-tracked cells have
higher persistences and diffusion coefficients than
computer-tracked cells, although the differences in
speeds measured by the two systems are small.
The differences between computer-assisted and
computer-centred determinations of cell locomotion
might be due to shortcomings in either system. When
computer generated tracks are transcribed by hand,
cell speeds, persistences and diffusion coefficients rise
artefactually (Table 3). This is clearly due to a
smoothing effect on the track data. Fig. 9 compares
180
Jf. A. T. Dow et al.
the tracks derived for the same cells in computer
analyses, with those plotted by hand. Again, the
repeatability of the computer-centred system is excellent, and the tracks it generates are noticeably more
jagged than those plotted by hand.
Discussion
The system described here is extremely simple, and
yet highly effective at tracking neutrophils in real time.
It also has potential in tracking spread cells. As cells in
culture round up to divide, it would also be possible to
estimate the number of divisions occurring, or even to
adapt the tracking program to detect a division in a cell
being tracked, and to continue to follow each daughter
cell individually. On a finer scale, it may be possible to
track neuronal growth cones, allowing the system to be
applied to many problems in developmental neurobiology.
Results obtained by the computer-centred system
differ slightly (but significantly) from those previously
obtained. Speeds, persistences and population diffusion constants are all lower when determined by the
computer-centred system. It seems that this reflects
the superior ability of the computer to follow higherfrequency fluctuations in cell position; human participation in the analysis process leads to a significant
'smoothing' of the data. In this sense, the computercentred system offers improvements, not just in speed
of operation, but also in objectivity. A possible problem, however, is that cells that 'jiggle' in one position
without moving can produce anomalously high speeds
and low persistences, if samples are taken at the high
rates possible in a computer centred system. Nonlocomotory noise can also be produced by rapid shape
changes in otherwise slow-moving cells. Care must be
taken therefore in applying such powerful computer
techniques to cell locomotion; and computer data
recording should be seen as an adjunct to, not a
substitute for, manual experimentation on any given
system.
It is important to note that the 'secret' of the system
is that all the potentially difficult, or slow, computer
tasks are obviated by careful experimental protocol. In
particular, there is no need to introduce sophisticated
collision-detection software if cells are plated at
reasonably low densities. Nor is there any need for
image enhancement or filtering if care is taken to
produce a clean, evenly illuminated light-microscopic
image.
The microcomputer used in these experiments (the
BBC) is almost ubiquitous in British universities.
However, the algorithms discussed are applicable to
the wide range of microcomputers for which imagegrabbing hardware can be purchased. In particular,
users of IBM PC and Apple computers should have
little difficulty in emulating the system described here.
As to cost, if it is assumed that all interested parties
already have access to a microscope, video camera,
monitor and a suitable, inexpensive microcomputer,
then the only additional costs are for the digitizer
(£100 at the time of writing), and for software
development. It is hoped that this paper will assist in
the latter.
Thanks are due to Miss S. Kitson for technical assistance,
and to Professor A. S. G. Curtis and Dr F. Lyall for their
constructive comments. This work was supported in part by
BP Venture Research Group, and by the General Funds of
the University of Glasgow.
Appendix 1
The box search algorithm
Implementation in BASIC
.v = 500 : y=500
6=20
rx=0 : ry=0 : rn=0
: REM previous cell position
: REM radius of search box in pixels
: REM initialize running totals of
: REM x and y moments, and of
: REM number of white pixels
FOR;n=.v-fc TO.v+6
FOR n=y-b TOy+b
IF POINT(m,n)>0 THEN rx=rc+m :ry=ry + n :rn=m + \
: REM in BBC BASIC, POINT(x,y)
: REM returns the colour of the
: REM pixel at x,y
NEXT m
NEXT n
IF rn>0 THEN x=rx/ni : y=r\>/rv
Appendix 2
The structure of the tracking program
Rather than provide a machine-specific BASIC listing, the
major tasks required of a tracking program are described in
flowchart form, from which it should be easy to derive a
specific application. Comments are in parenthesis.
INITIALIZE VARIABLES
Array AT (max. number of cells)
Array v (max. number of cells)
c=0 (this counts the number of cells being tracked)
Open an output disc file
SET UP THE MICROSCOPE IMAGE
Loop: get an image: until keypress (this allows the user to
adjust the picture quality)
FIND THE CELLS
For n = 100 to 1000
For m = 100 to 1000 (Scan the central area of the screen with two
DO-loops)
If POINT(n,m)>0 then PROCbox (If pixel is white, call box
routine to find the centroid)
Store .v and y coordinates in x(c) and y(c)
Increment c
Blank off the pixels (so they aren't 'found' twice)
If c = max. number of cells then jump to main loop
TRACK THE CELLS
Loop:
Get an image
For n = l to c (i.e. for each track)
If x(n)>0 call box (x(n)=0 implies the cell has been lost)
If cell found
write new coordinates to disc
store new coordinates in x(n) and y(n)
remove any duplicated tracks
Or if no cell found
set .v(n) and y(n) to 0
write 0,0 to disc
Finish loop if max. number of cycles reached, or user presses key
FINISH
Close disc file
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{Received 29 September 1986-Accepted
27 October 1986)