Muscle Specific Tissue Image Analysis Quantitative Assessment Article Analysis from the 3 articles attached complete the article analysis paper ( attached)

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2
Validation of a Muscle-Specific Tissue Image Analysis
Tool for Quantitative Assessment of Dystrophin Staining
in Frozen Muscle Biopsies
Famke Aeffner, DVM, PhD; Crystal Faelan, PhD; Steven A. Moore, MD, PhD; Alexander Moody, BS; Joshua C. Black, PhD; Jay S.
Charleston, PhD; Diane E. Frank, PhD; Johannes Dworzak, BSc; J. Kris Piper, MS; Manish Ranjitkar, MS; Kristin Wilson, DVM, PhD;
Suzanne Kanaly, DVM, PhD; Daniel G. Rudmann, DVM, PhD; Holger Lange, PhD; G. David Young, DVM; Anthony J. Milici, PhD
Context.—Duchenne muscular dystrophy is a rare,
progressive, and fatal neuromuscular disease caused by
dystrophin protein loss. Common investigational treatment
approaches aim at increasing dystrophin expression in
diseased muscle. Some clinical trials include assessments
of novel dystrophin production as a surrogate biomarker of
efficacy, which may predict a clinical benefit from
treatment.
Objectives.—To establish an immunofluorescent scanning and digital image analysis workflow that provides an
objective approach for staining intensity assessment of the
Accepted for publication April 17, 2018.
Published online August 31, 2018.
Supplemental digital content is available for this article at
archivesofpathology.org in the February 2019 table of contents.
From Flagship Biosciences Inc, Westminster, Colorado (Drs
Aeffner, Faelan, Black, Wilson, Kanaly, Rudmann, Lange, Young,
and Milici and Mr Moody); the Department of Pathology, Carver
College of Medicine, University of Iowa, Iowa City (Dr Moore); and
Sarepta Therapeutics Inc, Cambridge, Massachusetts (Drs Charleston
and Frank and Messrs Dworzak, Piper, and Ranjitkar). Dr Moore is
now with Oregon Health and Science University, Portland, and Dr
Rudmann is now with Charles River Laboratories, Ashland, Ohio.
This study was supported by Sarepta Therapeutics. Dr Moore was
supported in part by grant U54-NS053672 from the National
Institutes of Health (Bethesda, Maryland), the Iowa Wellstone
Muscular Dystrophy Cooperative Research Center (Iowa City). Dr
Moore had a fee-for-services contract with Sarepta Therapeutics and
Flagship Biosciences; Drs Aeffner, Faelan, Black, Wilson, Kanaly,
Rudmann, Lange, Young, and Milici and Mr Moody were full-time or
part-time employees at Flagship Biosciences; Drs Charleston and
Frank and Messrs Dworzak, Piper, and Ranjitkar were full-time
employees and shareholders of Sarepta Therapeutics; and Drs
Faelan, Lange, Young, and Milici had stock options with Flagship
Biosciences during production of this work. Dr Milici is the author of
the patent that pertains to this work: US 9,784,665—Methods for
Quantitative Assessment of Muscle Fibers in Muscular Dystrophy
(MuscleMap). Drs Aeffner, Black, and Rudmann and Mr Moody were
employees at the time of the study but are no longer with Flagship
Biosciences; Dr Aeffner maintains stock in Flagship Biosciences. Drs
Charleston and Frank and Messrs Piper and Dworzak are full-time
employees of Sarepta Therapeutics and have stock options in the
company. Mr Ranjitkar was with Sarepta Therapeutics at the time of
the study; he is no longer, but maintains stock in Sarepta
Therapeutics.
Corresponding author: Anthony J. Milici, PhD, Flagship Biosciences Inc, 7575 W 103rd Ave, Ste 102, Westminster, CO 80021
(email: amilici@flagshipbio.com).
Arch Pathol Lab Med—Vol 143, February 2019
immunofluorescence dystrophin labeling and determination of the percentage of biomarker-positive fibers in
muscle cryosections.
Design.—Optimal and repeatable digital image capture
was achieved by a rigorously qualified fluorescent scanning process. After scanning qualification, the MuscleMap
(Flagship Biosciences, Westminster, Colorado) algorithm
was validated by comparing high-power microscopic field
total and dystrophin-positive fiber counts obtained by
trained pathologists to data derived by MuscleMap. Next,
the algorithm was tested on whole-slide images of
immunofluorescent-labeled muscle sections from Duchenne muscular dystrophy, Becker muscular dystrophy,
and control patients.
Results.—When used under the guidance of a trained
pathologist, the digital image analysis tool met predefined
validation criteria and demonstrated functional and
statistical equivalence with manual assessment. This work
is the first, to our knowledge, to qualify and validate
immunofluorescent scanning and digital tissue imageanalysis workflow, respectively, with the rigor required
to support the clinical trial environments.
Conclusions.—MuscleMap enables analysis of all fibers
within an entire muscle biopsy section and provides data
on a fiber-by-fiber basis. This will allow future clinical
trials to objectively investigate myofibers’ dystrophin
expression at a greater level of consistency and detail.
(Arch Pathol Lab Med. 2019;143:197–205; doi: 10.5858/
arpa.2017-0536-OA)
M
uscular dystrophies are a genetically and phenotypically heterogeneous group of muscle diseases characterized by myonecrosis, regeneration, and endomysial
fibrosis leading to progressive muscle wasting.1 Duchenne
muscular dystrophy (DMD) is a severe, X-linked, recessive
disorder that affects approximately 1 in 3500 to 5000 live
male births worldwide, making it one of the most common
recessive disorders in humans.2–5 Becker muscular dystrophy (BMD) is milder and less frequent than DMD, affecting
1 in 11 500 males.4,6,7
The skeletal muscle histopathologic hallmarks of DMD
are muscle fiber atrophy and hypertrophy, myonecrosis, and
regeneration with progressively more severe endomysial
fibrosis and fatty replacement leading to a gradual decline in
MuscleMap Validation—Aeffner et al 197
muscle function.8 In the clinical trial setting, therapeutic
interventions designed to restore dystrophin production and
functional localization can only be directly confirmed by
evaluation of appropriately sampled muscle biopsy specimens.9–11
Even though reliable dystrophin quantification is urgently needed to support efficacy findings in multiple
ongoing clinical trials, assessment of the protein’s expression remains challenging.12 In recent years, the need to
detect subtle changes in biomarkers of disease progression
within the tissue context has spurred the development of
image analysis techniques. With computational means to
assess biomarkers, researchers can rapidly analyze hundreds to thousands of myofibers across entire muscle
sections, aiming for an objective assessment of the tissue.
Paramount for reproducible image analysis of fluorescent
signals is a standardized scanning approach on properly
qualified equipment by trained personnel. In addition, the
importance of exposure times for optimal scanning
conditions and analysis is critical. Specifically, within the
context of dystrophin quantification in DMD research,
many publications (when scanning conditions are disclosed) document that an exposure time was sought that
put dystrophin staining intensity of control samples in the
middle of the dynamic range.13 However, when the
described method is used to quantify dystrophin changes
in BMD and DMD samples, that approach limits the overall
dynamic range for those samples, making the detection
and quantification of small changes in dystrophin levels
tremendously challenging.
No image analysis studies conducted before this validation study, to our knowledge, included the appropriate
scanning qualification and algorithm validation that has the
potential to accurately quantify small, yet meaningful,
increases in dystrophin expression at the rigor required for
use as outcome measures in clinical trials.13–16 Additionally,
many publications do not specify whether the analysis
process was overseen or guided by a pathologist experienced in evaluating immunofluorescent-labeled muscle
tissues.
With this current work, we are documenting a workflow
and scanning qualification process in combination with an
algorithm validation that recapitulates a manual scoring
paradigm. These results display functional and statistical
equivalence to manual assessment of dystrophin expression
at the sarcolemmal membrane. Workflow, personnel
training, and documentation were performed with a rigor
that mimics the intended use setting of a clinical trial. This
semiautomated methodology enables quantification of
muscle biomarkers for clinical trials and research studies
for potential use as early surrogate endpoints in the
evaluation of treatment effects and disease progression.
MATERIALS AND METHODS
Human Sample Collection
All muscle biopsies were initially obtained for routine diagnostic
testing at the University of Iowa (Iowa City). Residual frozen tissue
from those biopsies was subsequently accrued by the Tissue and
Cell Culture Repository component of the University of Iowa’s Paul
D. Wellstone Muscular Dystrophy Cooperative Research Center
(Iowa City). Biopsies in the repository are approved for research by
the University of Iowa institutional review board (protocol
200510769).
198 Arch Pathol Lab Med—Vol 143, February 2019
Patient and Sample Information
Sample ID
CTRL-A
CTRL-B
DMD-A
DMD-B
DMD-C
CTRL-1
CTRL-2
CTRL-3
CTRL-4
CTRL-5
BMD-1
BMD-2
BMD-3
BMD-4
BMD-5c
DMD-1
DMD-2
DMD-3
DMD-4
DMD-5
DMD-6
DMD-7
DMD-8
DMD-9
Age, y/S
at Time
of Biopsy
DMD Mutation Status
1/M
3/M
6/M
6/M
7/M
20/M
Young child/M
3/M
10/F
7/F
53/M
14/M
8/M
4/M
10/F
4/M
7/M
6/M
6/M
4/M
2/M
8/M
1/M
8/M
NA
NA
Unknown
Unknown
Unknown
NA
NA
NA
NA
NA
Unknowna
Duplication of exons 21–29
Duplication of exons 3–4
c.2380G.A, p.E794K
Unknown
Deletion of exons 45–57
Unknown
c.251dupT, p.L84Ffs*5
Unknown
Stop mutation in exon 7
c.1615C.T, p.R539*
Unknown
c.1615C.T, p.R539*
Duplication of exons 50–55
Abbreviations: BMD, Becker muscular dystrophy; BMD-5c, manifesting
DMD mutation carrier; CTRL, control; DMD, Duchenne muscular
dystrophy; NA, not applicable.
a
Diagnostic immunostaining was interpreted by the reviewing
pathologist as strongly suggestive of the presence of an in-frame
deletion.
Muscle Sample Sectioning
Immunofluorescent labeling was carried out on unfixed cryosections from DMD and BMD patient samples. Muscle samples
that were considered to have no diagnostic abnormalities indicative
of BMD, DMD, or other neuromuscular disorders were used as
controls (CTRLs). The samples for this study (Table) were selected
by a pathologist to represent a wide range of histopathologic
disease severities across the spectrum of BMD and DMD based
upon routine evaluation of hematoxylin-eosin–stained sections.
Cryosections were cut at 10-lm thickness and mounted on
Superfrost glass slides (Fisher Scientific, Hampton, New Hampshire). Sections were briefly air dried at room temperature, then
stored 1 to 5 days at 808C until immunostaining was performed.
Immunofluorescent Labeling
Slides were brought to room temperature and dual-labeled with
anti-dystrophin at 1:25 (MANDYS106, clone 2C6, obtained directly
from Glenn Morris, Wolfson Centre for Inherited Neuromuscular
Disease, Oswestry, United Kingdom) and anti-merosin at 1:400
(ab11576, clone 4H8, lot GR95776-14; Abcam, Cambridge, United
Kingdom) antibodies. Briefly, sections were incubated for 60
minutes at ambient temperature with 100 lL of a primary antibody
cocktail diluted in phosphate-buffered saline. After incubation, the
slides were washed twice for 5 minutes with phosphate-buffered
saline then incubated with 100 lL of a detection cocktail that
consisted of Alexa Fluor 594 goat anti-mouse immunoglobulin
(Ig)G2a (A21135, lot 1366503; Molecular Probes, Eugene, Oregon)
and Alexa Fluor 488 rabbit anti-rat IgG (H þ L) (A21210, lot
53122A; Molecular Probes), each at 1:400 in phosphate-buffered
saline for 30 min. After incubation, the slides were washed twice for
MuscleMap Validation—Aeffner et al
Figure 1. Schematic examples of the manual-scoring paradigm. Schematic ring structures
each represent a muscle fiber with its
membrane. Different shades of gray represent
different staining intensities (white, no staining). The examples cover a range of fiber
staining with their corresponding scores. All
scores are independent of fiber size or shape.
5 minutes with phosphate-buffered saline. Coverslips were
mounted with ProLong Gold (ThermoFisher Scientific, Waltham,
Massachusetts).
Slide Scanning and Manual Image Annotation
Slides were scanned at 320 in the fluorescein isothiocyanate
(FITC) and tetramethylrhodamine (TRITC) detection channels on a
3DHISTECH Pannoramic MIDI fluorescent scanner (PANMIDI;
PerkinElmer, Waltham, Massachusetts) at optimal exposure times
and following established protocols (see supplemental digital
content containing Figures 1 and 2 at www.archivesofpathology.
org in the February 2019 table of contents). Scanned images were
manually annotated within ImageScope (Leica Biosystems, Buffalo
Grove, Illinois) before analysis. One large inclusion annotation was
drawn around the muscle tissue intended for analysis. Smaller
exclusion annotations were placed around areas omitted from
analysis, eg, tissue folds, staining artifacts, regions of fibrofatty
tissue, areas of necrosis, among others. All annotations were
performed by a trained technician and reviewed and approved by a
pathologist.
Selection of High-Power Fields for Analysis
To enable manual pathology review, high-power field (HPF)
images were generated from the whole slide scans of samples
CTRL-1 through 5, BMD-1 through 5, and DMD-1 through 9. The
GridMap (Flagship Biosciences, Westminster, Colorado) algorithm
plugin for ImageScope was used to generate 15 HPF annotations
(size, 750 3 750 lm) selected at random locations throughout the
annotated tissue of each slide resulting in a total of 285 HPF
images. These grid annotations were transferred to the singlechannel TRITC or FITC images, and images were exported that
captured the content of each HPF.
Manual Scoring
Four board-certified pathologists performed the manual scoring
of samples. All pathologists participated in a training session to
gain proficiency in (1) applying the scoring paradigm, and (2) the
use of ImageJ software (National Institutes of Health, Bethesda,
Maryland) for manual scoring.
After training and before scoring samples, all pathologists were
required to demonstrate linear correlation for both total fiber count
and positive fiber count (minimum requirement, Pearson r 0.75)
to a predefined gold standard per a fifth experienced pathologist by
scoring a training set of images under standardized conditions
described below. All pathologists obtained a linear correlation
Arch Pathol Lab Med—Vol 143, February 2019
greater than 0.98 for the total number of fibers and greater than
0.94 for the dystrophin-positive fibers.
Manual Scoring Paradigm
For merosin labeling, any labeled structure that was identified by
the pathologist as a muscle fiber was counted in ImageJ as a muscle
fiber. Fibers cut off by the edge of the HPF were counted as muscle
fibers if both a piece of the membrane and a region of the
sarcoplasm that the membrane encompasses were visible and
unambiguously identifiable as muscle fibers. This procedure yielded
total fiber counts for each HPF evaluated.
For dystrophin labeling, myofibers at or above a predefined
labeling intensity threshold were counted as positive if that labeling
was 30% or more of the individual fiber’s total circumference
(Figure 1). Visually identifying membrane staining at or above the
intensity threshold was included in pathologist training and testing.
Manual Scoring Process
The 285 HPFs generated (described above) were separated into
their individual FITC and TRITC channels, resulting in 570 HPFs
total. Those HPFs were split evenly among the 4 pathologists, with
matching FITC and TRITC images assigned to the same pathologist
for manual evaluation of total fiber numbers (by FITC) or
dystrophin-positive fibers (by TRITC). To ensure the greatest
amount of comparability between the individual pathologist’s
assessments, all pathologists evaluated HPFs individually in an
enclosed and darkened room under standardized conditions. All
pathologists used the same computer, software, and monitors to
evaluate HPFs, within the same room, and with the same
controlled lighting conditions. Room access and usage was
restricted and documented. Pathologists only had access to the
HPFs they were assigned. They evaluated those images per the
scoring paradigm described above and saved the counts generated
in ImageJ to the computer. Those data were considered the raw
data of the pathologist’s scores, including both a total fiber count
and a positive fiber count.
Tissue Image Analysis: MuscleMap
The MuscleMap (MM) algorithm was specifically developed for
the analysis of muscle fiber biomarkers and used for dystrophin
quantification in the current application. For this study, the
algorithm defined fibers based on both morphometrics and the
FITC labeling for merosin. Based on the FITC signal, a membrane
mask was generated allowing quantification of dystrophin per
TRITC labeling in membrane areas only. Primary data outputs
MuscleMap Validation—Aeffner et al 199
detection parameters were adjusted on a per-HPF basis. The
manual scoring results were available for final algorithm tuning to
ensure that detection of positive fibers closely mirrored manual
evaluation results. Algorithms were reviewed, and algorithm results
were collected for statistical analysis to ensure that the developed
algorithm solutions would meet basic validation criteria. Finalized
parameters of positive-fiber classification (including stainingintensity thresholds) were uniform across samples and carried
forward without further changes.
Algorithm Testing Phase A
For this phase of the algorithm validation, 95 dual-labeled HPFs
(CTRLs [30 of 95; 32%], BMDs [23 of 95; 24%], and DMDs [42 of
95; 44%]) were selected via stratified random sampling to be
analyzed via MM without prior knowledge of the manual scoring
results. Those HPFs were different from those used during
algorithm development and tuning, although HPFs from individual
patients contributed to both tuning and testing sets. Algorithms
were developed and adjusted on a per-HPF basis to correctly
identify individual myofibers based on merosin labeling. For
classification of dystrophin-positive fibers, parameters and thresholds were carried forward from the previous tuning phase without
any modifications. Algorithms were reviewed by a pathologist, and
algorithm results were collected for statistical analysis to test
whether algorithm solutions would meet the basic validation
criteria.
Algorithm Testing Phase B
Figure 2. Schematic overview of the algorithm validation process.
Abbreviations: HPF, high-power field; WSI, whole-slide image.
included, but are not limited to, mean membrane intensity for each
fiber in a biopsy, cumulative percentage membrane-intensity
curves for all fibers in the biopsy, and classification of individual
fibers as either positive or negative based on pathologist-chosen
dystrophin intensity and membrane completeness thresholds (ie,
the percentage of the membrane circumference that displayed
dystrophin labeling above the intensity threshold). Those pathologist-chosen settings were set at the beginning of the study and
maintained on all samples.
After MM passed phase A, the algorithms were tested on whole
slide images (WSIs) of the same sample set. In this scenario, no
manual scoring data were available for direct data comparison.
Instead, pathology evaluation …
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