Transcript Image Analysis in Toxicology and Discovery
Image Analysis in Toxicology and Discovery
Frank A. Voelker, DVM, DACVP www.flagshipbio.com
NIEHS Invited Presentation 2009
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……
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Analysis Algorithms
Positive Pixel Count Color Deconvolution IHC Nuclear IHC Membrane Co-localization Microvessel Analysis Micromet Analysis
Preprocessing Utility
Genie
™
: Histology Pattern Recognition
From Aperio --- www.aperio.com
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General Analytical Approaches…….
Area Based Analysis Pixel Count IHC Deconvolution Co-localization Cell Based Analysis www.flagshipbio.com
IHC Nuclear Membrane Angiogenesis Rare Event Rare Event Analysis
Two Different Approaches for Analysis
Quantify Histomorphologic Change
Cellular Hypertrophy/Atrophy Cell Numbers Tissue Infiltrates (eg. Fibrosis) Other Structural Alterations Usually measuring area or number
Quantify Substances using Special Stains
Histochemistry IHC ISH Usually measuring area and/or intensity www.flagshipbio.com
Morphologic Approach……
Quantifying Common Microscopic ToxPath Changes using H&E or Special Stains
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 www.flagshipbio.com
Introducing the Concept of “Targeted Cell” Analysis 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.
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Fibrosis in Livers of Zucker Rats
Use of the Positive Pixel Count Tool enables “visually apparent” analysis of a change T Control Rat No. 12 T Fenofibrate Rat No. 5 T T C Pioglitazone Rat No. 3 Compound X Rat No 2
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C F P Variations in fibrosis (blue) about small portal triad veins (T) as depicted using Masson’s Trichrome stain X www.flagshipbio.com
Quantitation of PAS Stain for Glycogen in Livers of DIO Mice Administered XXX Using the Aperio Color Deconvolution Tool
Using the Color Deconvolution Tool enables quantitation of things visually obscured by counterstaining PAS-stained Section www.flagshipbio.com
Aperio Markup Image
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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) www.flagshipbio.com
Mouse Liver - Hepatocellular Hypertrophy
Total Hepatocyte Nuclei = 199 Average Nuclear Size =140 µm² 706 nuclei/mm² Total Hepatocyte Nuclei = 167 Average Nuclear Size = 160 µm² 508 nuclei/mm² Algorithm: IHC Nuclear (cell-based) www.flagshipbio.com
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.
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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
Analysis Tool: Color Deconvolution (area-based) Precision in level of section is required for accurately comparing amounts of fibrosis between treatment groups www.flagshipbio.com
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 263 nuclei/mm² www.flagshipbio.com
Mouse Livers 212 nuclei/mm²
Three Possible Strategies for Measuring Brown Stains using the Positive Pixel Count Analysis Tool
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Quantitate the percentage area of all brown pixels in the section or in selected areas of the section.
If the chromagen staining is very extensive in the target cell population, measure only the brownest (darker) pixels in selected areas of the section.
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
Measuring all of the brown pixels in the sample area
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.01 **p
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Cytochrome p450 Reductase Immunostaining of Centrilobular Hepatocytes
Widespread staining with centrilobular distribution of more intense staining www.flagshipbio.com
Quantitation of Cytochrome p450 Reductase Immunostaining of Centrilobular Hepatocytes by Aperio
Original Image Markup Image Measuring only the area of more intense stain Color deconvolution (area-based)
21 Aperio in TBD / Voelker / 08/24/06
Quantitation of VEGF Immunostaining in Livers of Mice administered XXX for 52 Weeks
Comparing stain intensity www.flagshipbio.com
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Control Males 1000 mg/kg Males Control Females 1000 mg/kg Females
Fibrosis in Livers of Zucker Rats
T Control Rat No. 12 T Fenofibrate Rat No. 5 T C T Pioglitazone Rat No. 3 Compound X Rat No 2 Variations in fibrosis (blue) about small portal triad veins (T) as depicted using Masson’s Trichrome stain www.flagshipbio.com
Percent of Cross-sectional Liver Area of Zucker Rats Occupied by Hepatocellular Fibrosis
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Percent Area Positive Pixels (ie. Fibrosis)
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Percent Staining
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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.
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Percent Positive Pixels
The Problem of Nontarget Tissue Staining PTEN Immunostain of Squamous Cell Carcinoma in Human Lung Nonspecific staining of surrounding stroma can make analysis of marker in neoplastic target tissue difficult.
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Analysis of Average Stain Intensity in Target Tissue 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
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“H” Scoring is a Convention for Determining Average Stain Intensity of Target Tissue 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
(For a maximum of 300)
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 pixels Np = Number of moderately positive pixels Nsp = Number of strongly positive pixels Ntotal = Total number negative + positive pixels
Not available with IHC Nuclear and Membrane Algorithms www.flagshipbio.com
Genie™……..
Introducing the concept of using histology pattern recognition software as a preprocessing machine to segregate target from nontarget tissue during analysis
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
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) image pattern recognition technology in the digital pathology market. Object recognition software components of Genie
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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.
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New Strategy Diagrams for Image Analysis
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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 www.flagshipbio.com
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 www.flagshipbio.com
Uniform Analysis of Study Samples is Obtained Despite Using Multiple Genie™ Training Classifiers
Morphologically Variable Samples Trained Individually for Genie Target Tissue Selection Targeted Tissue Selection and Isolation by Genie™ Subsequent Uniform Analysis of Isolated Target Tissue for area/intensity 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 Results: EMH comprises 50.2% positive pixels in evaluation area www.flagshipbio.com
Positive Pixel Markup Image
Quantitation of Periarteriolar Lymphoid Tissue in a Mouse Spleen using Genie and the Aperio Positive Pixel Count Tool
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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Aperio Positive Pixel Markup Image
Bile Duct Hyperplasia in Rat Liver
First pass Genie histology pattern identification with minimal training
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Genie ™ can simultaneously analyze three or more tissue areas Hyperplastic Bile Ducts = Green Hepatic Parenchyma = Red Periportal Inflammatory Cells = Blue Periductal Collagen = Brown Bile Duct Lumena + Sinusoids = Yellow Then analyze up to three tissue areas using colocalization tool www.flagshipbio.com
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 www.flagshipbio.com
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 www.flagshipbio.com
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 www.flagshipbio.com
Islet Cell Mass of Mouse Pancreas
Measurement of Pancreatic Islet Cell Mass using Genie ™ Followed by the Colocalization Algorithm (A/B)C=Islet Cell Mass A=Total Islet Area in Section B=Total Pancreas Area in Section C=Pancreatic Weight www.flagshipbio.com
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. www.flagshipbio.com
Measuring Cellular Hypertrophy of two cell types in a Dog Thyroid Gland
Apply colocalization algorithm to calculate respective areas of brown follicular epithelium and blue c-cells.
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Then apply IHC nuclear algorithm on same image to get numbers of artificially colored brown and blue nuclei. 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 Voelker DVM 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.com
Boulder, Colorado
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