Journal of Histotechnology
ISSN: 0147-8885 (Print) 2046-0236 (Online) Journal homepage: https://www.tandfonline.com/loi/yhis20
Optical density-based image analysis method for
the evaluation of hematoxylin and eosin staining
precision
Elizabeth Chlipala, Christine M. Bendzinski, Kevin Chu, Joshua I. Johnson,
Miles Brous, Karen Copeland & Brad Bolon
To cite this article: Elizabeth Chlipala, Christine M. Bendzinski, Kevin Chu, Joshua I. Johnson,
Miles Brous, Karen Copeland & Brad Bolon (2020) Optical density-based image analysis method
for the evaluation of hematoxylin and eosin staining precision, Journal of Histotechnology, 43:1,
29-37, DOI: 10.1080/01478885.2019.1708611
To link to this article: https://doi.org/10.1080/01478885.2019.1708611
© 2020 The Author(s). Published by Informa
UK Limited, trading as Taylor & Francis
Group.
Published online: 23 Jan 2020.
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JOURNAL OF HISTOTECHNOLOGY
2020, VOL. 43, NO. 1, 29–37
https://doi.org/10.1080/01478885.2019.1708611
Optical density-based image analysis method for the evaluation of hematoxylin
and eosin staining precision
Elizabeth Chlipala a, Christine M. Bendzinskia, Kevin Chua, Joshua I. Johnsona, Miles Brousa, Karen Copelandb
and Brad Bolon c
a
Premier Laboratory, LLC, Longmont, CO, USA; bBoulder Statistics, LLC, Steamboat Springs, CO, USA; cGEMpath, Inc, Longmont, CO, USA
ABSTRACT
KEYWORDS
Staining quality and reproducibility are essential factors to monitor laboratory quality assurance. In
the last decade, there has been an increase in the use of digital pathology and image analysis.
While the adoption of these tools provides a potential means to track staining precision by optical
density (OD), it also presents challenges. Results from image analysis are more sensitive to
variations in staining than microscopic evaluation by a pathologist. There are two goals with this
study. The first was to track the precision of hematoxylin and eosin (H&E) staining, in both nuclear
and cytoplasmic components by OD. The second was to determine the impact of different preanalytical and analytical variables on the OD results. Specifically, the endpoints investigated were
quality parameters including impacts of section thickness, protocol manipulation, expired hematoxylin on staining precision and reproducibility of staining over time. Our results show that image
analysis of H&E-stained tissue sections is a viable tool for assessing and verifying staining quality.
We also show that OD analysis results for H&E-stained sections are affected by changing preanalytical and/or reagent variables. These authors chose a graphical rather than fully statistical
analysis of the results to highlight the utility of visual aids in demonstrating H&E staining
reproducibility.
Reproducibility; hematoxylin
and eosin; image analysis;
optical density; staining
precision; quality control;
pre-analytics; staining
reproducibility
Introduction
The hematoxylin and eosin (H&E) stain is the most frequently used histochemical stain in clinical and research
laboratories [1]. This stain has been used for over a century
to highlight the structures of cytoplasmic and nuclear
components in cells and tissues [1], where hematoxylin
stains nuclear and eosin stains cytoplasmic structures.
The H&E section can provide a tremendous amount of
information [2], and thus is used routinely in many applications, leading to a large volume of sections processed
using this stain by virtually all histology laboratories.
Therefore, H&E staining quality and reproducibility are
critical considerations in the interpretation of results [3]
and must play a central role in defining necessary procedures needed to build an effective laboratory quality assurance (QA) program. The College of American Pathologists
(CAP) and the National Society for Histotechnology
(NSH) define good-quality H&E staining as nuclei exhibiting ‘blue to blue-black hematoxylin’ with ‘crisp’ chromatin
patterns and cytoplasm displaying ‘tritonal eosin’, i.e., three
tinctorial shades, to permit the differentiation of distinct
cell types [4]. While some H&E staining variation has been
reduced through the use of automated batch-staining
instruments, the precision of the stain is limited by several
CONTACT Elizabeth Chlipala
liz@premierlab.com
pre-analytic factors, i.e., tissue collection, fixation, grossing,
processing, paraffin embedding, and microtomy. There
exists a great deal of published guidance for three of these
pre-analytic factors: tissue collection, fixation, processing
and paraffin embedding regarding troubleshooting H&E
staining quality [5–7]. These three pre-analytic factors will
not be addressed in this publication. However, relatively
little has been reported regarding reliable attainment of
H&E color characteristics, a standardization which will be
required for reliable digital analysis of staining quality and
reproducibility. As automated image analysis solutions are
increasingly being utilized not only for immunohistochemical staining and H&E-stained samples, modern histology
practices require a detailed understanding of what factors
can impact H&E staining precision and reproducibility.
Consistency in these factors is required to optimize the
acquisition and analysis of digital imaging data.
This study explores how pre-analytic and analytic
decisions may impact the staining quality of each dye
component producing conventional H&E-stained tissue
sections. The first purpose of this study was to quantify
staining precision over an extended time (4 months)
using an optical density (OD)-based image analysis algorithm that is available as a turn-key test (i.e., ready for use
Premier Laboratory, LLC, Longmont, CO, USA
© 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/),
which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
30
E. CHLIPALA ET AL.
without the need for extensive coding) in commercially
available digital imaging software. OD is the measure of
the absorbance of light through a sample. Optical density
is proportional to stain intensity. The greater the amount
of stain present, the greater the optical density.
Additionally, using the same metrics, the second objective was to assess the effects of factors such as section
thickness, staining protocol, and expired dye reagents on
the quality of H&E staining as assessed using an image
analysis algorithm. Both parts of the study evaluated the
impact on each stained tissue component (nuclear with
hematoxylin and cytoplasmic with eosin) separately to
see whether or not one color was more likely to be
problematic in performing the digital assessments of
staining precision and reproducibility. The advantage of
digital analysis is that the resulting quantitative data
comes in the form of average OD values calculated separately but simultaneously for each stained tissue component. OD is an intensity metric that correlates at a scale
that is analogous to what the human eye interprets when
qualitatively evaluating an H&E-stained section [8]. The
hypothesis was that one could devise a reliable method to
provide an unbiased quantitative evaluation of H&E
staining quality that is better and faster at detecting subtle
changes in staining quality than the human eye alone.
The impact of color differences in H&E-stained sections depends on the nature of the evaluation to be
conducted. Pathologists and other experienced morphologists are capable of adjusting their qualitative interpretation of staining to ignore variation induced by preanalytic factors such as incomplete fixation, section thickness, and staining variation. In contrast, the quantitative
results produced by automated image analysis algorithms
are more sensitive to any subtle change that may impact
algorithm accuracy. This heightened sensitivity is helpful
in tracking the precision and detecting abnormalities in
staining, but the introduction of automated interpretation as a step in the QA process requires that the limitations imposed by this sensitivity be well understood
when the parameters are set for judging stain quality.
The increasing use of digital pathology and image analysis solutions to guide clinical decision-making requires
that H&E staining quality should be tested and tracked
with the same unbiased, quantitative approach that will
be relied upon for the screening of patient samples.
fixed, paraffin-embedded (FFPE) human tissues and
embedded in a single block (Figure 1). The tissue microarrays (TMA) were assembled manually. Tissue samples
came from control blocks purchased from a human tissue
bank (ProteoGenex, USA). Specimens were obtained by
the tissue bank using their standard informed consent
procedures as stated in the informational materials on the
ProteoGenex corporate website (https://www.proteo
genex.com). All fixation was performed at room temperature (RT); fixation time were unavailable. The length of
time for archiving the blocks at RT prior to their acquisition and sectioning also was not available.
Selected tissues were chosen to represent a range of
anticipated staining intensities for both hematoxylin and
eosin as imparted using a standard automated H&E staining protocol (Table 1, Row 4) [5–7]. Tissues were identified as follows: placenta, uterus, colon adenocarcinoma
(COAD), thyroid, prostate adenocarcinoma (PRAD), kidney, tonsil, and liver (Figure 1). Serial sections were cut at
4 µm to test all parameters except for section thickness. To
measure the impact of section thickness, tissues were cut
with thicknesses ranging from 2 µm to 10 µm in 1 µm
increments. Sections were mounted on positively charged,
coated slides (Tanner Scientific, USA), air-dried overnight
at RT, and baked at 60⁰C for 30 min prior to staining.
Hematoxylin and eosin staining
Instruments
All slides were stained on a Sakura Tissue-Tek® Prisma™
Automated Slide Stainer (Sakura Finetek, USA). The parameters for various staining runs are given in Table 1.
Staining runs were performed at RT. After staining, slides
were cover-slipped with a Sakura Tissue-Tek® Glas™ coverslipper (Sakura).
Materials and methods
Tissues and slides
Tissue cores were taken with an 8 mm-diameter TruPunch disposable biopsy punch (Sklar Instruments, USA)
from previously prepared neutral buffered 10% formalin-
Figure 1. Tissue microarray (TMA) with 8 mm-diameter punch
biopsies of H&E-stained human placenta, uterus, colon adenocarcinoma (COAD), thyroid, prostate adnenocarcinoma (PRAD),
kidney, tonsil, and liver. Scale bar = 5 mm.
JOURNAL OF HISTOTECHNOLOGY
Table 1. Hematoxylin and eosin staining protocol variations –
time manipulations.
Protocol
No.
0
1
2
3
4*
5
6
7
8
Hematoxylin
Normal
30 sec
30 sec
1 min
1 min
4 min
6 min
8 min
15 min
20 min
Differentiation
5% glacial
acetic acid
10 min
5 min
4 min
3 min
1 min
30 sec
30 sec
30 sec
0 sec
Eosin Y
Alcoholic
10 sec
30 sec
30 sec
30 sec
1 min
3 min
5 min
15 min
20 min
1st Alcohol after
Eosin Y
50% – 5 min
70% – 5 min
70% – 3 min
95% – 2 min
95% – 2 min
95% – 30 sec
95% – 30 sec
95% – 30 sec
100% – 1 min
*Protocol No. 4 represents the standard H&E staining procedure practiced in
the authors’ laboratory.
Staining reagents
Staining runs incorporated a mixture of ‘off the shelf’
commercial reagents and purpose-concocted solutions
made in-house. All staining runs utilized Hematoxylin–
Normal Strength (812) and Eosin Y, Alcoholic (832)
(Anatech, USA), while the 5% aq. glacial acetic acid
(8817–46) (Macron Fine Chemicals, USA) and 0.5%
aq. ammonium hydroxide (NH4OH) (BDH3014)
(BDH VWR Analytical, USA) solutions were prepared
in-house from commercial reagents. Hematoxylin and
eosin staining reagents from the same lots were used
throughout the course of the study except when two
different lots of expired hematoxylin were used (lot
numbers noted in Figure 5). The glacial acetic acid and
NH4OH solutions were prepared once in five-gallon
batches. All reagents were stored at RT.
31
upon the volume of slides stained, the type of staining
solutions utilized, and the mode of staining [7].
To investigate the effects of section thickness as a preanalytic variable on H&E stain quality, nine slides each
with different section thicknesses (as described above)
were stained simultaneously in the same staining run.
This portion of the project was performed once. For
evaluation of the effect of expired hematoxylin, slides
were stained over time with two different lots of expired
hematoxylin. Every Thursday for 8 weeks, one slide was
stained with expired hematoxylin Lot 5261 (exp. 08/31/
2015, 30 weeks past the effective use date), while another
slide was stained with expired hematoxylin Lot 5390
(exp. 02/28/2016, 6 weeks past the effective use date).
Image analysis
Stained TMA slides were scanned at 20X using the
Aperio ScanScope® XT imaging system and
ImageScope® software (v12.1.0.5029; Aperio, USA).
Each tissue core (8 per slide) was analyzed separately.
Average optical density (OD) of the hematoxylin and
eosin stains was determined by a customized area quantification algorithm generated in HALO™ Image
Analysis software (Indica Labs, USA).
The customization was performed by a vendortrained histology technician who generated the algorithm according to the manufacturer’s instructions.
A total of 888 human tissue cores were analyzed.
Statistical analysis
Staining variables
Staining protocols were tested by independently staining nine slides with moderate to extreme variations in
staining protocols (Table 1). The times that the slides
spent immersed in hematoxylin, glacial acetic acid,
eosin Y, and alcohols were all manipulated. Each protocol was numbered from 0 to 8 for a total of 9 different
protocol variations with Protocol #4 (Table 1) representing the standard automated method used to routinely stain human and animal tissue sections in our
laboratory. Protocol #4 conforms to conventional practice for H&E staining for vertebrate tissue sections [5,6].
To measure reproducibility in evaluating H&E staining precision, one slide was stained daily (Monday–
Friday) for a period of 4 months (from 4/13/2016 to 7/
27/2016), for a total of 73 slides. All staining solutions
were changed bi-weekly in accordance with the internal
laboratory quality control (QC) policy. This schedule
had been previously determined for our laboratory by
a regular review of the H&E-stained control slides. This
review conforms to industry guidelines that are based
Where warranted, variability in staining intensity over
time, precision, the relationship between section thickness, reagent immersion times, and use of expired
reagents on the OD of H&E staining components was
evaluated using JMP statistical software (version_13,
SAS Institute, USA) according to the vendor’s instructions. The Tukey–Kramer all pairwise multiple comparison test was used to analyze these endpoints, with p ≤
0.05 set as the limit for considering a result to be significant. However, this paper presents graphical analyses of the results as such depictions are likely to be as
effective and much faster relative to formal statistical
analysis when performing QC evaluations of H&E staining precision and reproducibility.
Results
Staining precision over time
The changes in staining over time are subtle visually
when assessed by the human eye (Figure 2(a)) but are
32
E. CHLIPALA ET AL.
Figure 2. Evaluation of visual and optical density (OD) based staining precision over time. (a) Panel shows a 2-week subset dated 5/31/
16 to 6/10/16 from middle of the 4-month study using H&E stained sections of kidney, tonsil, and liver. Scale bar = 100 µm. (b) Graph
for these representative images shows a range of OD values for hematoxylin and eosin (y-axis). The variations in OD for hematoxylin
and eosin are shown separately for each staining run, indicated by a number of days elapsed (x-axis). Tissues identified by colored lines
are placenta (purple), uterus (blue), colon adenocarcinoma (COAD, aqua), thyroid (green), prostate adenocarcinoma (PRAD, lime
green), kidney (gold), tonsil (orange) and liver (red).
graphically much more noticeable when analyzed quantitatively by digital image analysis (Figure 2b). The effect
depended on which staining component and tissue type
were being evaluated.
Overall, hematoxylin staining OD among tissues with
different cell populations and distinct patterns of H&E
staining was consistent over time. The exception was
tonsil which showed modestly higher hematoxylin
variability (Figure 2b). The coefficients of variation
(CV) across all samples were less than 4.4% for all tissue
except tonsil, which had a CV of 5.4%.
Variation in eosin staining OD was cyclical
(Figure 2b), with peaks occurring every 2 weeks.
This periodicity correlates to regular changes made
to incorporate fresh reagents. With the exception of
liver and uterus, the CV across all samples were
below 10%, even with cyclic variation over reagent
lifetimes. The CV for the liver was 13.8% and for
the uterus was 12.2% (Figure 2b).
Impact of section thickness
The staining OD increased with section thickness,
with the rate of increase dependent on the tissue
type and stained tissue component. The intensity of
staining visibly increased with greater section thickness (Figure 3a). This effect was mostly due to
obvious enhancement in eosin OD, while hematoxylin OD was largely stable across most tissues
except for tonsil (Figure 3b). Formal regression
JOURNAL OF HISTOTECHNOLOGY
33
Figure 3. Effect of section thickness on H&E staining. (a) Panel shows representative images of even section thicknesses ranging from 2
to 10 µm with odd-numbered thicknesses excluded for simplicity. Note that 2 µm sections are stained lighter than 10 µm sections.
Tissues are colon adenocarcinoma (COAD, prostate adenocarcinoma (PRAD), and liver. These tissues represented a large optical
density (OD) range for eosin staining, which was greatly affected by the thickness variability. Scale bar = 100 µm. (b) Graph shows OD
comparison (y-axis) for both hematoxylin and eosin staining across section thicknesses ranging from 2 to 10 µm in increasing 1 µm
increments (x-axis). Tissues identified by colored lines are placenta (purple), uterus (blue), colon adenocarcinoma (COAD, aqua),
thyroid (green), prostate adenocarcinoma (PRAD, lime green), kidney (gold), tonsil (orange) and liver (red).
analysis indicated a second-order (curved) relationship between OD and section thickness.
Impact of varying staining protocol parameters
As expected, altering incubation times for the various reagent baths on the automated tissue stainer
substantially impacted H&E staining quality visually
(Figure 4a).The extent of this increased or decreased
shift in staining intensity depended on the combination of adjusted incubation times applied across
the series of solutions used throughout the protocol.
The staining intensity increased as the immersion
time in hematoxylin and eosin increased and the
immersion time in the differentiating solutions
decreased (Figure 4a). For Protocol 0, e.g., slides were
in hematoxylin for 30 sec and eosin for 10 sec, with
differentiation of 10 min and 50% alcohol for 5 min.
Therefore, all tissues stained with Protocol 0 showed
a much lighter staining intensity, detectable both
visually (Figure 4a) and by measurement of OD
(Figure 4b). In contrast, Protocol 8 stipulated 20 min
for both hematoxylin and eosin reagents and 0 s for both
differentiation and 95% alcohol. These sections were
stained much more intensely. The hematoxylin OD
showed the greatest increase between Protocol 3 and
Protocol 4 (the standard protocol), with more consistent
results as compared to the other protocols.
34
E. CHLIPALA ET AL.
Figure 4. Effect of staining protocol variations (reagent incubation times) on H&E staining intensities. Staining protocol conditions are
detailed in Table 1. (a) Panel shows a representative set of tissues affected by changing the times in staining solutions, the
differentiation solution and alcohol reagents as stated in Table 1. Tissues exhibiting a range of optical densities (OD) for both
hematoxylin and eosin are placenta, thyroid, and tonsil. Scale bar = 100 µm. (b) Graph compares OD (y-axis) of both hematoxylin and
eosin across the eight different protocols from Table 1 (x-axis). Tissues are identified by colored lines: placenta (purple), uterus (blue),
colon adenocarcinoma (COAD, aqua), thyroid (green), prostate adenocarcinoma (PRAD, lime green), kidney (gold), tonsil (orange) and
liver (red).
Impact of expired hematoxylin
The intensity of hematoxylin staining is affected by
the age of the stain solution. This variation was
either minimally visible or indistinguishable with
the naked eye (Figure 5a), In contrast, hematoxylin
OD was reduced considerably when using an
expired reagent when a quantitative digital analysis
was performed (Figure 5b). Older reagents showed
a greater reduction in stain OD. The oldest reagent,
Lot 5261 (expired 08/31/2015, 30 weeks prior to
starting this study) yielded the lowest OD, while
Lot 5390 (expired 2/28/2016, 6 weeks prior to starting the study) stained slightly more intensely on
average. Each expired lot exhibited a significantly
decreased intensity relative to the unexpired lot in
mean OD staining across all tissues. Unsurprisingly,
eosin staining, which used an unexpired reagent lot,
was not affected by this round of experiments.
Taken together, these data highlight the importance
of image analysis for tracking staining quality.
Discussion
The H&E stain is the workhorse of tissue analysis in
basic research and diagnostic laboratories around the
world. Qualitative evaluation of stained sections often is
sufficient for routine purposes, but the advent of digital
image analysis techniques has raised the possibility that
objective quantification of staining properties will gain
importance in providing data for clinical diagnostic
purposes. The current study was undertaken to investigate several pre-analytic and analytic variables that
JOURNAL OF HISTOTECHNOLOGY
35
Figure 5. Use of expired hematoxylin impacts staining intensity, i.e., hematoxylin staining intensity decreased as the age of the
expired reagents increased. (a) Panel shows representative placenta, uterus, and tonsil sections with a variable affinity for expired
hematoxylin. The tissues do not exhibit qualitative differences in hematoxylin intensity when stained with this expired reagent. An
unexpired lot for eosin was used throughout this experiment. Scale bar = 100 µm (b) Graph shows the trend in hematoxylin OD
(y-axis) with two expired reagent lots and an unexpired lot. The x-axis shows from left to right the oldest hematoxylin (Lot 5261 Ex,
expired for 30 weeks at initiation of study initiation), the next oldest (Lot 5390 Ex, expired for 6 weeks at initiation of the study) and the
unexpired Lot 5822 nEx. Tissues identified by colored lines are placenta (purple), uterus (blue), colon adenocarcinoma (COAD, aqua),
thyroid (green), prostate adenocarcinoma (PRAD, lime green), kidney (gold), tonsil (orange) and liver (red).
impact the color characteristics of H&E-stained tissue
sections. Our data shows that automated OD analysis of
blue and red tints in H&E-stained sections provides
a precise and reproducible quantitative assessment of
staining quality. This data also demonstrated that H&E
staining protocols will need to be designed deliberately
to optimize the capacity for automated analytical systems to acquire unbiased data.
Our first discovery was that changes in OD could be
detected in every experiment conducted during the study
36
E. CHLIPALA ET AL.
for precision, section thickness, protocol manipulation,
and reagent expiration. Precision of staining over time
was reasonably consistent in terms of hematoxylin, but
eosin staining was cyclical, with peaks and troughs
directly correlated with the reagent change schedule.
With eosin OD values, the highest were noted after
reagent changes and the lowest just prior to the change
(Figure 2b). Increasing section thickness resulted in
greater heightened staining intensity and OD for the
eosin component across the range of thicknesses, while
hematoxylin staining was consistent for thicknesses more
than 4 µm. Overall, boosting staining times enhanced OD
for both staining components, though notably the hematoxylin OD leveled out for times over 4 min in this stain.
Lastly, expired hematoxylin reagents showed a decrease in
staining intensity with a greater loss of intensity evident
for the oldest solutions. Taken together, these findings
demonstrate the importance of standardizing pre-analytic
variables as much as possible in order to limit variations in
H&E staining intensity.
The OD of both hematoxylin and eosin varied based on
the tissue type, specifically those with greater nuclear density. Tissues containing more tightly packed nuclei, such as
lymphatic tissue, had higher hematoxylin OD. Normal OD
ranges observed in our study are approximately 0.25 to 0.45
for hematoxylin and 0.15 to 0.30 for eosin when using
routine 4 μm section thicknesses and the standard staining
protocol (Protocol 4, Table 1). Not all tissues were affected
equally by the various changes introduced in this study. For
example, tonsil with its densely populated nuclei was more
susceptible to section thickness-related variation in hematoxylin staining than other tissues. This pattern suggests
that the choice of tissue type on control slides for QA
monitoring should be carefully considered. Further work
will be required to ascertain whether or not a single tissue
will suffice, or if multiple tissues exhibiting a range of
structural attributes (varying nuclear and cytoplasmic
staining characteristics) should be employed for QA.
The H&E stain is undoubtedly robust and will
remain a staple of biological research. In general,
extreme variation in staining protocols, section thickness, and use of expired reagents still produces, with
most parameters, a qualitatively ‘readable’ slide when
assessed by experienced morphologists. However, divergence among these factors across staining runs and
laboratories will necessitate a tightened range of QC
and QA procedures in order to ensure that sections
are equally ‘readable’ for automated analytical platforms. This is especially important if artificial intelligence and algorithm training is to play a useful part in
data acquisition and interpretation. Therefore, troubleshooting H&E stain quality should not only include the
staining procedure and reagents, but other pre-analytic
factors such as fixation and processing. Furthermore,
the results suggest that the robustness of hematoxylin
exceeded that of eosin, as eosin OD tended to fluctuate
more in each case. These previously indicated trends
could not be observed visually in some tissues, suggesting the use of OD-based image analysis could be
a helpful quality assurance tool.
Quality assurance (QA) procedures similar to ours
have previously been described for H&E staining, but it
has been customary to use a more qualitative approach,
e.g., visually examining positive control stained slides
side-by-side and over time [9]. The quantitative
approach reported here offers the steady accrual of
easily understandable data that allows staining precision
to be determined and demonstrated objectively rather
than using a ‘pass-fail’ strategy. In addition, slight differences in H&E staining quality that may be introduced
during histology processing can give rise to staining
variations among sections within the same staining
run. Depending on the study objective, a single control
slide for QA purposes may not be able to confirm the
suitability of staining over the entire spectrum of color
differences that may be visible after a given staining run.
Thus, image analysis provides a sensible solution for an
unbiased, high-throughput, rapid method to evaluate
staining quality for all sections.
The accessibility and necessity for laboratories to
conduct image analysis for QA purposes has increased
in recent years. Due to the use of average OD for all
tracking, this study does not explicitly address the
guidelines set forth by CAP and NSH for high-quality
H&E staining [4]. For example, using the current
curated image analysis solution, neither their definition
of chromatin patterns within the nuclei nor the tritonal
feature of eosin could be assessed. Issues with these very
important characteristics of the stain are typically due to
incomplete fixation or processing [7], rather than an
analytic issue with the reagents themselves. Fixative
type, time in fixative, and temperature are often outside
the direct control of histology laboratories that must
acquire specimens from tissue banks, and in the authors’
experience this source of pre-analytic variation cannot
be reduced readily. A practical means of mitigating such
factors is to request and record these variables for all
purchased tissues, and (where feasible within the laboratory) to develop standard operating procedures (SOP)
that help standardize and record these variables.
However, the analytical strategy this study reported
may be a useful means for laboratories to either establish
or re-evaluate their QA procedures for tracking the
precision and reproducibility of H&E staining. For
example, a laboratory must determine how often to
change out old reagents for new, and this interval is
JOURNAL OF HISTOTECHNOLOGY
unique to the volume and type of samples submitted. In
addition, while the image analysis solution presented
here cannot check for crisp nuclear staining, welldefined chromatin patterns or tritonal eosin, the process
of obtaining digital images that was done for this study
will facilitate an easy side-by-side comparison of whole
slide images. A simple QC step using an image viewer
would determine a ‘pass or fail’ score for chromatin
definition, and the image analysis algorithm could handle staining precision over time.
Conclusions
Hematoxylin and eosin will always be the gold standard of
histological stains. The H&E slide has a profound impact
on steering clinical decision-making in a particular direction, whether it results in a primary diagnosis or ordering
additional stains. In the coming age of automated analysis
and artificial intelligence, the ability to produce a readable
H&E section despite major pre-analytic and analytic
changes begs the question: ‘Is the mere readability of
a section enough or should we strive for a more precise
standard for histological staining?’ There are undoubtedly
arguments for both cases, but the use of OD-based digital
image analysis provides an efficient, quick way to strive for
an increased standardization of staining results.
Accordingly, the authors encourage other researchers to
adopt this automated analytical method as a QA procedure
in their own laboratories.
Acknowledgments
The authors would like to thank Adam Smith of Indica Labs
(Albuquerque, NM) for image analysis guidance and the use of
HALO Image Analysis software. Ada Feldman of Anatech, Ltd.
(Battle Creek, MI) generously supplied the expired hematoxylin–normal strength reagents.
Declaration of conflicting interests
The authors declared no potential conflicts of interest with
respect to the research, authorship, and publication of this article.
37
Declaration of ethical research practices
The authors confirm that human tissue samples acquired
from ProteoGenex, Inc. (a tissue bank) were collected using
appropriate informed consent practices according to
ProteoGenex, Inc. standard operating procedures.
ORCID
Elizabeth Chlipala
http://orcid.org/0000-0002-2194-7645
http://orcid.org/0000-0002-6065-1492
Brad Bolon
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