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Frank A. Voelker, DVM, DACVP
www.flagshipbio.com
NIEHS Invited Presentation 2009
Image Analysis in Toxicology and Discovery
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Topics…….
Introduction
Aperio Analysis Tools
Concepts and Approaches
Guidelines and Pitfalls
Analytical Strategies
Applications and using Genie™
Summary
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Image Analysis Tools at Hand……
1. Positive Pixel Count
2. Color Deconvolution
3. IHC Nuclear
4. IHC Membrane
5. Co-localization
6. Microvessel Analysis
7. Micromet Analysis
Genie™: Histology Pattern Recognition
Analysis Algorithms
Preprocessing Utility
From Aperio --- www.aperio.com
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General Analytical Approaches…….
Area Based Analysis
Rare Event AnalysisCell Based Analysis
Pixel CountIHC DeconvolutionCo-localization
Rare EventIHC NuclearMembraneAngiogenesis
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Two Different Approaches for Analysis
Cellular Hypertrophy/Atrophy
Cell Numbers
Tissue Infiltrates (eg. Fibrosis)
Other Structural Alterations
Cellular Hypertrophy/Atrophy
Cell Numbers
Tissue Infiltrates (eg. Fibrosis)
Other Structural Alterations
Histochemistry
IHC
ISH
Histochemistry
IHC
ISH
Quantify Substances using Special Stains
Usually measuring area or number
Usually measuring area and/or intensity
Quantify Histomorphologic Change
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Morphologic Approach……
Liver: Hepatocellular hypertrophy, bile duct hyperplasia, necrosis, acute and chronic inflammation, extramedullary hematopoiesis, periportal fibrosis, fatty change, glycogen accumulation.
Kidney: Tubular basophilia, hyaline droplet degeneration, casts, tubular necrosis.
Spleen: Lymphoid hyperplasia/atrophy, extramedullary hematopoiesis
Lung: Alveolar edema, pneumonia, congestion.
Heart: myocardial fibrosis.
Adrenal gland: cortical hypertrophy, cortical vacuolation.
Skin: Acute and chronic inflammation, acanthosis
Liver: Hepatocellular hypertrophy, bile duct hyperplasia, necrosis, acute and chronic inflammation, extramedullary hematopoiesis, periportal fibrosis, fatty change, glycogen accumulation.
Kidney: Tubular basophilia, hyaline droplet degeneration, casts, tubular necrosis.
Spleen: Lymphoid hyperplasia/atrophy, extramedullary hematopoiesis
Lung: Alveolar edema, pneumonia, congestion.
Heart: myocardial fibrosis.
Adrenal gland: cortical hypertrophy, cortical vacuolation.
Skin: Acute and chronic inflammation, acanthosis
Biggest Problem: Distinguishing target from nontarget tissue
Quantifying Common Microscopic ToxPath Changes using H&E or Special Stains
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pS6 Ser235 Immunostain of Breast Carcinoma
Analysis of average cytoplasmic stain intensity using the pixel count tool may be useful in evaluating a neoplasm if there is little background or nonspecific staining.
Introducing the Concept of “Targeted Cell” Analysis
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Fibrosis in Livers of Zucker Rats
Control Rat No. 12 Fenofibrate Rat No. 5
Pioglitazone Rat No. 3
Variations in fibrosis (blue) about small portal triad veins (T) as depicted using Masson’s Trichrome stain
C
Compound X Rat No 2
T
T
T
T
0
0.5
1
1.5
2
2.5
3
C F P X
Use of the Positive Pixel Count Tool enables “visually apparent” analysis of a change
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Quantitation of PAS Stain for Glycogen in Livers of DIO Mice Administered XXX Using the Aperio Color Deconvolution Tool
PAS-stained Section Aperio Markup Image
0
5
10
15
20
1 2 3
Using the Color Deconvolution Tool enables quantitation of things visually obscured by counterstaining
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Cyclin D1 Immunostain of Human Breast Carcinoma
Use of the IHC Nuclear Analysis Tool to Determine Percent and Degree of Positivity of Neoplastic Cell Nuclei. Stromal Nuclei are Excluded
from Evaluation.
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Quantifying Inflammation in Tissue using the Nuclear Analysis Tool…
Different cell types often can be distinguished from each other in the same tissue based on nuclear diameter. Here lymphocyte nuclei are smaller than mammary carcinoma nuclei.
This makes it possible to count lymphocyte numbers per unit area of tissue cross section to determine degree of infiltration.Algorithm: IHC Nuclear (cell-based)
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Mouse Liver - Hepatocellular Hypertrophy
Total Hepatocyte Nuclei = 167 Average Nuclear Size = 160 µm² 508 nuclei/mm²
Total Hepatocyte Nuclei = 199 Average Nuclear Size =140 µm² 706 nuclei/mm²
Algorithm: IHC Nuclear (cell-based)
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Some Guidelines for Analysis of Slides from Experimental Studies
Take care to assure immediate optimal fixation for all tissue samples. Uniformity of handling as well as fixation time is important.
Staining procedures for all slides in a study need to be performed simultaneously in a single batch to assure uniformity of stain.
Sampling must be strictly representational as well as consistent. Care must be taken to assure exact uniformity of analysis with respect to anatomical location (eg. Tissue trimming, sectioning)
A preliminary evaluation of image analysis tools between some slides of varying stain intensities will help assure that analysis values are established optimally for all slides in the study.
Take care to assure immediate optimal fixation for all tissue samples. Uniformity of handling as well as fixation time is important.
Staining procedures for all slides in a study need to be performed simultaneously in a single batch to assure uniformity of stain.
Sampling must be strictly representational as well as consistent. Care must be taken to assure exact uniformity of analysis with respect to anatomical location (eg. Tissue trimming, sectioning)
A preliminary evaluation of image analysis tools between some slides of varying stain intensities will help assure that analysis values are established optimally for all slides in the study.
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Anatomic Consistency in Sampling…..
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Consistency of Sample Area Selection for Morphometric Analysis within the Median Lobe of the Mouse Liver
1 2 3
Select samples within approximately the same region of the same lobe of the liver for consistency of analysis. As an assurance of sampling homogeneity, areas should have roughly similar pixel count values.
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Sirius Red Stain Depicting Myocardial Fibrosis in a Mouse
Precision in level of section is required for accurately comparing amounts of fibrosis between treatment groups
Analysis Tool: Color Deconvolution (area-based)
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Consistency of Study Conditions can Affect Morphometric Analysis
Variations in duration of fasting prior to necropsy can result in large differences in hepatocellular glycogen thus leading to
inaccurate analysis
Mouse Livers
263 nuclei/mm²
212 nuclei/mm²
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Three Possible Strategies for Measuring Brown Stains using the Positive Pixel Count Analysis Tool
1. Quantitate the percentage area of all brown pixels in the section or in selected areas of the section.
2. If the chromagen staining is very extensive in the target cell population, measure only the brownest (darker) pixels in selected areas of the section.
3. If the chromagen staining is uniform in character and very extensive in the target cell population, measure stain intensity as an index of concentration.
1. Quantitate the percentage area of all brown pixels in the section or in selected areas of the section.
2. If the chromagen staining is very extensive in the target cell population, measure only the brownest (darker) pixels in selected areas of the section.
3. If the chromagen staining is uniform in character and very extensive in the target cell population, measure stain intensity as an index of concentration.
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Percent of Liver Tissue Staining for Transferrin Receptor(CD71) in Female Mice by Immunohistochemistry
* p .01 **p .001
0
5
10
15
20
25
1 2 3 4
*
**
Control 100 mg/kg
250 mg/kg1000 mg/kg
%
Measuring all of the brown pixels in the sample area
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Cytochrome p450 Reductase Immunostaining of Centrilobular Hepatocytes
Widespread staining with centrilobular distribution of more intense staining
21 Aperio in TBD / Voelker / 08/24/06
Quantitation of Cytochrome p450 Reductase Immunostaining of Centrilobular Hepatocytes by Aperio
Original Image Markup Image
Measuring only the area of more intense stainColor deconvolution (area-based)
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Quantitation of VEGF Immunostaining in Livers of Mice administered XXX for 52 Weeks
44.00
45.00
46.00
47.00
48.00
49.00
50.00
51.00
Control Males
Control Females
1000 mg/kg Males
1000 mg/kg FemalesComparing stain intensity
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Fibrosis in Livers of Zucker Rats
Control Rat No. 12 Fenofibrate Rat No. 5
Pioglitazone Rat No. 3
Variations in fibrosis (blue) about small portal triad veins (T) as depicted using Masson’s Trichrome stain
C
Compound X Rat No 2
T
T
T
T
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Percent of Cross-sectional Liver Area of Zucker Rats Occupied by Hepatocellular Fibrosis
0
0.5
1
1.5
2
2.5
3
Percent
Staining
Percent Area Positive Pixels (ie. Fibrosis)
Vehicle Control
Fenofibrate
Pioglitazone
Compound X
*P ≤ 0.05
* * *
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bFGF Immunostaining in Livers of Mice Administered Compound xy for 52 Weeks
1000 mg/kg Male 2068
Positive staining in a minority cell type (Kupffer cells in this case) may lead to low percentage values that are highly variable.
Percent Positive Pixels
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
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PTEN Immunostain of Squamous Cell Carcinoma in Human Lung
Nonspecific staining of surrounding stroma can make analysis of marker in neoplastic target tissue difficult.
The Problem of Nontarget Tissue Staining
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pS6 Ser235 Immunostain of Squamous Cell Carcinoma in Human Lung
Estimation of Average stain intensity should take into account negative-staining regions of target tissue as well as positive-staining regions
Analysis of Average Stain Intensity in Target Tissue
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“H” Scoring is a Convention for Determining Average Stain Intensity of Target Tissue
Now “H” Score evaluation is automatically calculated in Aperio’s IHC Deconvolution Algorithm using attribute outputs in the following similar formula:
(Nwp/Ntotal)x(100) + Np/Ntotal)x(200) + Nsp/Ntotal)x(300) = “H” Score Where:Nwp = Number of weakly positive pixelsNp = Number of moderately positive pixelsNsp = Number of strongly positive pixelsNtotal = Total number negative + positive pixels
With the old subjective scoring method, the pathologist visually scored staining features of cells (eg. cytoplasmic, nuclear, or membranous staining) by intensity of stain according to grades 0, 1+ , 2+ or 3+ using the following formula:
(1)x(%1+)x(%Area) + (2)x(%2+) x (%Area) + (3)x(%3+)x(%Area) = “H” Score
Not available with IHC Nuclear and Membrane Algorithms
(For a maximum of 300)
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Genie™……..
Recognizing the importance of Object Recognition in the future of image analysis, Aperio has recently obtained an exclusive worldwide license from Los Alamos National Laboratory (LANL) for the use of LANL’s Genetic Imagery Exploration (Genie™) image pattern recognition technology in the digital pathology market.
Object recognition software components of Genie™ have been incorporated into specific ScanScope image analysis algorithms for the 10.0 release (GLP compliance and object recognition)
The structure of this incorporation has been designed to meet the needs of the pathology and image analysis community, but it will continue to evolve based on developing needs.
Recognizing the importance of Object Recognition in the future of image analysis, Aperio has recently obtained an exclusive worldwide license from Los Alamos National Laboratory (LANL) for the use of LANL’s Genetic Imagery Exploration (Genie™) image pattern recognition technology in the digital pathology market.
Object recognition software components of Genie™ have been incorporated into specific ScanScope image analysis algorithms for the 10.0 release (GLP compliance and object recognition)
The structure of this incorporation has been designed to meet the needs of the pathology and image analysis community, but it will continue to evolve based on developing needs.
Introducing the concept of using histology pattern recognition software as a preprocessing machine to segregate target from nontarget tissue during analysis
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New Strategy Diagrams for Image Analysis
1 2
3
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Tumor Cell-Specific Biomarker Analysis using Genie Histology Pattern Recognition Software
Pulmonary adenocarcinoma stained for pS6-Ser240 Genie mark-up image. Tumor cells = blue
Positive pixel count analysis of tumor cells IHC nuclear analysis of tumor cells
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Immunostain Analysis of Human Breast Tumor Tissue Micro Arrays
Multiple Genie™ Training Classifiers may be needed in analysis of a TMA slide because of tumor heterogeneity.
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Tumor Cell-Specific Biomarker Analysis of TMA Breast Tumor Samples using Genie Histology Pattern Recognition Software
IHC
Genie Mark-up
Positive Pixel Mark-up
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Uniform Analysis of Study Samples is Obtained Despite Using Multiple Genie™ Training Classifiers
Targeted Tissue Selection and Isolation by Genie™
Subsequent Uniform Analysis of Isolated Target Tissue for area/intensity
Morphologically Variable Samples Trained Individually for Genie Target Tissue Selection
Separate target tissue training of each sample does not affect final target tissue analysis.
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Quantitation of Splenic Extramedullary Hematopoiesis in a Mouse using Genie™ and the Aperio Positive Pixel Count Tool
H&E Stain
Genie™ Markup Image
Positive Pixel Markup Image
Results: EMH comprises 50.2% positive pixels in evaluation area
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Quantitation of Periarteriolar Lymphoid Tissue in a Mouse Spleen using Genie and the Aperio Positive Pixel Count Tool
Aperio Positive Pixel Markup Image
H&E Stain
Genie Markup Image
Result: Lymphoid tissue comprises 30.1% of positive pixels in splenic cross-sectional area
Extrapolating to an entire tissue section demands more robust training than for a simple image.
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Bile Duct Hyperplasia in Rat Liver
Hyperplastic Bile Ducts = GreenHepatic Parenchyma = RedPeriportal Inflammatory Cells = BluePeriductal Collagen = BrownBile Duct Lumena + Sinusoids = Yellow
First pass Genie histology pattern identification with minimal training. Genie™ can simultaneously analyze three or more tissue areas
Then analyze up to three tissue areas using colocalization tool
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Quantitation of Hepatocellular Necrosis
Use of Genie™ as a preprocessing utility to identify regions of hepatic necrosis (red) and areas of normal liver (green)
Subsequent quantitation of necrotic areas using a pixel count tool to allow precise grading
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Using Genie™ to Discriminate Between Nuclear and Cytoplasmic Markers
Human Breast Carcinoma Stained for Estrogen Receptor
The ability of Geni to discriminate between nuclear and cytoplasmic regions of a neoplasm allows separate biomarker intensity measurement for both nuclear and cytoplasmic markers.
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Cynomolgus Monkey Lung
Use of Genie™ as a preprocessing utility to identify regions of smooth muscle (green)
Subsequent quantitation of pulmonary smooth muscle using a pixel count tool
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Cynomolgus Monkey Lung
Use of Genie™ as a preprocessing utility to identify regions of bronchiolar epithelium (green)
Subsequent isolation and analysis of only bronchiolar epithelium using the positive pixel count or other analysis tool
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Islet Cell Mass of Mouse Pancreas
Measurement of Pancreatic Islet Cell Mass using Genie™ Followed by the Colocalization Algorithm
(A/B)C=Islet Cell MassA=Total Islet Area in Section
B=Total Pancreas Area in SectionC=Pancreatic Weight
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Quantitating Dog Thyroid Gland Tissue Components
Use of Genie™ as a preprocessing utility to identify thyroid gland follicular epithelium (green), colloid (red) and C-cells (blue)
Then quantitate each separate tissue component area using the colocalization tool.
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Measuring Cellular Hypertrophy of two cell types in a Dog Thyroid Gland
Then apply IHC nuclear algorithm on same image to get numbers of artificially colored brown and blue nuclei.
Apply colocalization algorithm to calculate respective areas of brown follicular epithelium and blue c-cells. each.
Total Brown Area/Total Brown Nuclei = Mean Follicular Cell Area. Do same calculation for blue nuclei.
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Summary
The ability to digitize entire slides and perform morphometric analysis on images has been valuable in allowing the rapid and practical measurement of tissue biomarkers for pharmaceutical research and development.
A number of strategies and examples have been presented for using various image analysis algorithms in the measurement of tissue changes and tissue biomarkers. Image analysis of specific target tissues can be particularly challenging in cases with large and morphologically intricate areas of tissue, or when tissue staining is nonspecific.
Genie™, a histology pattern recognition tool, has been introduced as a preprocessing utility capable of identifying and categorizing specific histologic tissue types, thus allowing subsequent analysis of target regions by standard image analysis tools.
Significant challenges remain in developing practical procedures and methods appropriate for the analysis of oncology and toxicology specimens. Recent object recognition advancements may assist in this effort.
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Acknowledgements
Ms. Kimberly Merriam, TBG, BMD Novartis
Ms. Jeanette Rheinhardt, TBG, BMD Novartis
Dr. Allen Olson, Aperio Technologies, Inc.
Dr. Kate Lillard-Wetherell, Aperio Technologies, Inc.
Mr. James Deeds, Oncology Research Novartis
Dr. Rudi Bao, Oncology Research Novartis
Dr. Humphrey Gardner, TBG, BMD Novartis
Dr. Alokesh Duttaroy, DMDA Novartis
Dr. Steve Potts, Aperio Technologies, Inc
Dr. Reginald Valdez, Novartis
Dr Oliver Turner, Novartis
Others
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Frank VoelkerDVM MS Diplomate ACVP
Flagship Biosciences LLC was created by industry leading molecular pathologists to fill the growing need for advanced digital technology in drug development and medical devices. Our pathologists deliver quantitative results so our customers can make efficacy and toxicology assessments faster.
Contact me via email at: frank {at} flagshipbio.comBoulder, Colorado
