3D reconstruction of skin and spatial mapping of immune cell density, vascular distance and effects of sun exposure and aging

Mapping the human body at single cell resolution in three dimensions (3D) is important for understanding cellular interactions in context of tissue and organ organization. 2D spatial cell analysis in a single tissue section may be limited by cell numbers and histology. Here we show a workflow for 3D reconstruction of multiplexed sequential tissue sections: MATRICS-A (Multiplexed Image Three-D Reconstruction and Integrated Cell Spatial - Analysis). We demonstrate MATRICS-A in 26 serial sections of fixed skin (stained with 18 biomarkers) from 12 donors aged between 32-72 years. Comparing the 3D reconstructed cellular data with the 2D data, we show significantly shorter distances between immune cells and vascular endothelial cells (56 µm in 3D vs 108 µm in 2D). We also show 10-70% more T cells (total) within 30 µm of a neighboring T helper cell in 3D vs 2D. Distances of p53, DDB2 and Ki67 positive cells to the skin surface were consistent across all ages/sun exposure and largely localized to the lower stratum basale layer of the epidermis. MATRICS-A provides a framework for analysis of 3D spatial cell relationships in healthy and aging organs and could be further extended to diseased organs.

Comparison of Multiplexed Immunofluorescence Imaging to Chromogenic Immunohistochemistry of Skin Biomarkers in Response to Monkeypox Virus Infection

Over the last 15 years, advances in immunofluorescence-imaging based cycling methods, antibody conjugation methods, and automated image processing have facilitated the development of a high-resolution, multiplexed tissue immunofluorescence (MxIF) method with single cell-level quantitation termed Cell DIVETM. Originally developed for fixed oncology samples, here it was evaluated in highly fixed (up to 30 days), archived monkeypox virus-induced inflammatory skin lesions from a retrospective study in 11 rhesus monkeys to determine whether MxIF was comparable to manual H-scoring of chromogenic stains. Six protein markers related to immune and cellular response (CD68, CD3, Hsp70, Hsp90, ERK1/2, ERK1/2 pT202_pY204) were manually quantified (H-scores) by a pathologist from chromogenic IHC double stains on serial sections and compared to MxIF automated single cell quantification of the same markers that were multiplexed on a single tissue section. Overall, there was directional consistency between the H-score and the MxIF results for all markers except phosphorylated ERK1/2 (ERK1/2 pT202_pY204), which showed a decrease in the lesion compared to the adjacent non-lesioned skin by MxIF vs an increase via H-score. Improvements to automated segmentation using machine learning and adding additional cell markers for cell viability are future options for improvement. This method could be useful in infectious disease research as it conserves tissue, provides marker colocalization data on thousands of cells, allowing further cell level data mining as well as a reduction in user bias.

Multiscale, multimodal analysis of tumor heterogeneity in IDH1 mutant vs wild-type diffuse gliomas

Glioma is recognized to be a highly heterogeneous CNS malignancy, whose diverse cellular composition and cellular interactions have not been well characterized. To gain new clinical- and biological-insights into the genetically-bifurcated IDH1 mutant (mt) vs wildtype (wt) forms of glioma, we integrated data from protein, genomic and MR imaging from 20 treatment-naïve glioma cases and 16 recurrent GBM cases. Multiplexed immunofluorescence (MxIF) was used to generate single cell data for 43 protein markers representing all cancer hallmarks, Genomic sequencing (exome and RNA (normal and tumor) and magnetic resonance imaging (MRI) quantitative features (protocols were T1-post, FLAIR and ADC) from whole tumor, peritumoral edema and enhancing core vs equivalent normal region were also collected from patients. Based on MxIF analysis, 85,767 cells (glioma cases) and 56,304 cells (GBM cases) were used to generate cell-level data for 24 biomarkers. K-means clustering was used to generate 7 distinct groups of cells with divergent biomarker profiles and deconvolution was used to assign RNA data into three classes. Spatial and molecular heterogeneity metrics were generated for the cell data. All features were compared between IDH mt and IDHwt patients and were finally combined to provide a holistic/integrated comparison. Protein expression by hallmark was generally lower in the IDHmt vs wt patients. Molecular and spatial heterogeneity scores for angiogenesis and cell invasion also differed between IDHmt and wt gliomas irrespective of prior treatment and tumor grade; these differences also persisted in the MR imaging features of peritumoral edema and contrast enhancement volumes. A coherent picture of enhanced angiogenesis in IDHwt tumors was derived from multiple platforms (genomic, proteomic and imaging) and scales from individual proteins to cell clusters and heterogeneity, as well as bulk tumor RNA and imaging features. Longer overall survival for IDH1mt glioma patients may reflect mutation-driven alterations in cellular, molecular, and spatial heterogeneity which manifest in discernable radiological manifestations.

Platform for Quantitative Evaluation of Spatial Intratumoral Heterogeneity in Multiplexed Fluorescence Images

We introduce THRIVE (Tumor Heterogeneity Research Interactive Visualization Environment), an open-source tool developed to assist cancer researchers in interactive hypothesis testing. The focus of this tool is to quantify spatial intratumoral heterogeneity (ITH), and the interactions between different cell phenotypes and noncellular constituents. Specifically, we foresee applications in phenotyping cells within tumor microenvironments, recognizing tumor boundaries, identifying degrees of immune infiltration and epithelial/stromal separation, and identification of heterotypic signaling networks underlying microdomains. The THRIVE platform provides an integrated workflow for analyzing whole-slide immunofluorescence images and tissue microarrays, including algorithms for segmentation, quantification, and heterogeneity analysis. THRIVE promotes flexible deployment, a maintainable code base using open-source libraries, and an extensible framework for customizing algorithms with ease. THRIVE was designed with highly multiplexed immunofluorescence images in mind, and, by providing a platform to efficiently analyze high-dimensional immunofluorescence signals, we hope to advance these data toward mainstream adoption in cancer research. Cancer Res; 77(21); e71-74. ©2017 AACR.

Stromal-Based Signatures for the Classification of Gastric Cancer

Treatment of metastatic gastric cancer typically involves chemotherapy and monoclonal antibodies targeting HER2 (ERBB2) and VEGFR2 (KDR). However, reliable methods to identify patients who would benefit most from a combination of treatment modalities targeting the tumor stroma, including new immunotherapy approaches, are still lacking. Therefore, we integrated a mouse model of stromal activation and gastric cancer genomic information to identify gene expression signatures that may inform treatment strategies. We generated a mouse model in which VEGF-A is expressed via adenovirus, enabling a stromal response marked by immune infiltration and angiogenesis at the injection site, and identified distinct stromal gene expression signatures. With these data, we designed multiplexed IHC assays that were applied to human primary gastric tumors and classified each tumor to a dominant stromal phenotype representative of the vascular and immune diversity found in gastric cancer. We also refined the stromal gene signatures and explored their relation to the dominant patient phenotypes identified by recent large-scale studies of gastric cancer genomics (The Cancer Genome Atlas and Asian Cancer Research Group), revealing four distinct stromal phenotypes. Collectively, these findings suggest that a genomics-based systems approach focused on the tumor stroma can be used to discover putative predictive biomarkers of treatment response, especially to antiangiogenesis agents and immunotherapy, thus offering an opportunity to improve patient stratification. Cancer Res; 76(9); 2573-86. ©2016 AACR.

Optimized multiplex immunofluorescence single-cell analysis reveals tuft cell heterogeneity

Intestinal tuft cells are a rare, poorly understood cell type recently shown to be a critical mediator of type 2 immune response to helminth infection. Here, we present advances in segmentation algorithms and analytical tools for multiplex immunofluorescence (MxIF), a platform that enables iterative staining of over 60 antibodies on a single tissue section. These refinements have enabled a comprehensive analysis of tuft cell number, distribution, and protein expression profiles as a function of anatomical location and physiological perturbations. Based solely on DCLK1 immunoreactivity, tuft cell numbers were similar throughout the mouse small intestine and colon. However, multiple subsets of tuft cells were uncovered when protein coexpression signatures were examined, including two new intestinal tuft cell markers, Hopx and EGFR phosphotyrosine 1068. Furthermore, we identified dynamic changes in tuft cell number, composition, and protein expression associated with fasting and refeeding and after introduction of microbiota to germ-free mice. These studies provide a foundational framework for future studies of intestinal tuft cell regulation and demonstrate the utility of our improved MxIF computational methods and workflow for understanding cellular heterogeneity in complex tissues in normal and disease states.

Pointwise mutual information quantifies intratumor heterogeneity in tissue sections labeled with multiple fluorescent biomarkers

BACKGROUND

Measures of spatial intratumor heterogeneity are potentially important diagnostic biomarkers for cancer progression, proliferation, and response to therapy. Spatial relationships among cells including cancer and stromal cells in the tumor microenvironment (TME) are key contributors to heterogeneity.

METHODS

We demonstrate how to quantify spatial heterogeneity from immunofluorescence pathology samples, using a set of 3 basic breast cancer biomarkers as a test case. We learn a set of dominant biomarker intensity patterns and map the spatial distribution of the biomarker patterns with a network. We then describe the pairwise association statistics for each pattern within the network using pointwise mutual information (PMI) and visually represent heterogeneity with a two-dimensional map.

RESULTS

We found a salient set of 8 biomarker patterns to describe cellular phenotypes from a tissue microarray cohort containing 4 different breast cancer subtypes. After computing PMI for each pair of biomarker patterns in each patient and tumor replicate, we visualize the interactions that contribute to the resulting association statistics. Then, we demonstrate the potential for using PMI as a diagnostic biomarker, by comparing PMI maps and heterogeneity scores from patients across the 4 different cancer subtypes. Estrogen receptor positive invasive lobular carcinoma patient, AL13-6, exhibited the highest heterogeneity score among those tested, while estrogen receptor negative invasive ductal carcinoma patient, AL13-14, exhibited the lowest heterogeneity score.

CONCLUSIONS

This paper presents an approach for describing intratumor heterogeneity, in a quantitative fashion (via PMI), which departs from the purely qualitative approaches currently used in the clinic. PMI is generalizable to highly multiplexed/hyperplexed immunofluorescence images, as well as spatial data from complementary in situ methods including FISSEQ and CyTOF, sampling many different components within the TME. We hypothesize that PMI will uncover key spatial interactions in the TME that contribute to disease proliferation and progression.

Abstract P3-05-06: Automated analysis of Her2 FISH using combined Immunofluorescence and FISH signals

Introduction: Qualifying patients for Her2 targeted therapy is currently done by detecting Her2 protein overexpression or gene amplification using immunohistochemistry and/or FISH. We have recently developed a method for detecting both signals on the same tissue section allowing direct correlation of protein expression and gene copy number on a cell by cell basis. Accurate assessment of Her2 gene copy number is critical and can pose a challenge due to tumor heterogeneity. This paper reports the accuracy of a proprietary FISH dot counting algorithm on a cell-by-cell basis, potentially allowing analysis of thousands instead of dozens of tumor cells.

Method: Automatic FISH signal counts were compared to manual counts of 888 cells selected from 19 invasive ductal breast carcinoma samples exhibiting varying degrees of Her2 expression collected between June 2011 and March 2012. Tissue sections (4 µm) were mounted on positively charged slides, baked and processed through deparaffinization, rehydration and antigen retrieval, then stained for immunofluorescence (IF) using Cy5 labeled Her2 and Cy3 labeled cytokeratin antibodies, counterstained with DAPI, and imaged using InCell 2000 analyzer with GE-proprietary acquisition and processing software. Images were collected at 10x magnification and digitally stitched to span the entire tissue section. A pathologist then selected separate tumor and adjacent normal epithelium regions for subsequent imaging at 40x magnification. Slides were subsequently processed for FISH by pepsin digestion and then subjected to FISH by using PathVysion kit (Abbott Molecular, Des Plaines, IL). After hybridization and subsequent high stringency washes, samples were DAPI stained and mounted for microscopy. Samples were imaged at 40x at the same regions recorded for 40x IF acquisition, using filtersets appropriate for FISH fluorophores and DAPI.

A proprietary automated processing algorithm was used to analyze combined IF and FISH signals and derive case specific Her2 score from the tumor and/or adjacent normal epithelium. Cell-level dot counting accuracy was assessed using two metrics comparing automated counts to manual counts: cell classification agreement, where a normal cell was defined as having 3 or less Her2 and Cep17 dots; and dot-counting match, where a difference of more than 20% in absolute counts was considered an error.

Result: Our automatic results gave an overall cell-by-cell classification agreement of 88% (range 71% to 98% by case). Combining classification agreement and counting match, our algorithm gave an overall accuracy of 81% (range 63% to 97% by case). Restricting to tumor tissues (as judged by pathologist review of IF) classification agreement and accuracy were 84% and 72%, respectively.

Conclusion: The observed variability in algorithm performance between the different cases was due to the fact that error root causes were case dependent. For instance, the main cause of over-counting errors was image noise and artifacts. On the other hand, the main cause of under-counting was low image contrast, especially in highly amplified cases. These results are an early indication of the promise of automatic dot counting applied to breast cancer slides multiplexed for Her2 IF and FISH.

Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P3-05-06.

Abstract P3-05-05: HER2 Expression and Gene copy analysis by Immunofluorescence and Fluorescence in situ Hybridization, on a Single formalin-fixed paraffin-embedded tissue section

Introduction: Breast cancer is the most common cancer for women worldwide. HER2 expression and gene copy number are important when determining eligibility for adjuvant therapy and/or chemotherapy medications. One challenging issue for breast cancer testing is intratumoral heterogeneity of HER2 gene amplification. Intratumoral heterogeneity can make it difficult to localize target cells of interest. Serial tissue sections used for independent H&E, IHC and FISH stains also increase the difficulty to localize targets due to cellular truncation. We have developed a system to assess both HER2 expression and gene copy number on the same cell.

Method: Immunofluorescence (IF) and Fluorescence in situ Hybridization (FISH) were performed on tissue sections from 19 patients with invasive ductal breast carcinoma. Cases were selected based on prior HER2 FISH results (HER2:Chromosome 17 = ratio) representing unamplified (<2.0), amplified (≥2.0) and equivocal (1.8–2.2). Samples were collected from June 2011 – February 2012. Tissue sections were cut at 4uM from formalin-fixed paraffin-embedded tissue blocks. Slides were stained with antibodies for HER2 (Clone #D8F12, Cell Signaling, Danvers, MA), cytokeratin (Clone #AE1, eBioscience, San Diego, CA) and Pan cytokeratin (Clone #PCK-26, Sigma-Aldrich, St. Louis, MO). The whole tissue imaging was performed on the In-Cell (GE Healthcare, Chalfont St. Giles, UK) at 10X. Proprietary software developed by GRC (GE Global Research, Niskayuna, NY) controlled the hardware and performed numerous algorithmic functions. Regions of Interest (ROI) were selected by a pathologist on a whole tissue image and coordinates were recorded by the software. The slides were then imaged at 40x using the previously recorded ROI's. The same slides were stained with the PathVysion HER2/CEP17 FISH kit (Abbott Molecular, Des Plaines, IL). Slides were registered to the previous IF scan using recorded coordinates and tissue morphology recognition algorithms. The sections were imaged for FISH at 40X using the previous ROI selections. Cases were assessed for successful protein and genetic expression using proprietary visualization tools for combined analysis.

Results: We evaluated a total of 22 breast cancer cases with 19 cases detecting both protein and gene expression. Of the three cases that could not be evaluated the rationale is as follows: tissue damage incurred during imaging, insufficient focus during the FISH imaging portion, and poor signal to noise of the FISH dots.

Conclusion: The reported incidence of intratumoral HER2 amplification heterogeneity is as high as 30%. The challenges associated with tumor heterogeneity may benefit from a standardize analysis method. Using integrated images generated by this system, pathologist is able to select the appropriate cells for HER2 copy number enumeration based on the expression level of HER2 protein, in the same cell, allowing rapid identification of intratumoral heterogeneity.

Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P3-05-05.

Cell-based quantification of molecular biomarkers in histopathology specimens

AIMS

To investigate the use of a computer-assisted technology for objective, cell-based quantification of molecular biomarkers in specified cell types in histopathology specimens, with the aim of advancing current visual estimation and pixel-level (rather than cell-based) quantification methods.

METHODS AND RESULTS

Tissue specimens were multiplex-immunostained to reveal cell structures, cell type markers, and analytes, and imaged with multispectral microscopy. The image data were processed with novel software that automatically delineates and types each cell in the field, measures morphological features, and quantifies analytes in different subcellular compartments of specified cells.The methodology was validated with the use of cell blocks composed of differentially labelled cultured cells mixed in known proportions, and evaluated on human breast carcinoma specimens for quantifying human epidermal growth factor receptor 2, estrogen receptor, progesterone receptor, Ki67, phospho-extracellular signal-related kinase, and phospho-S6. Automated cell-level analyses closely matched human assessments, but, predictably, differed from pixel-level analyses of the same images.

CONCLUSIONS

Our method reveals the type, distribution, morphology and biomarker state of each cell in the field, and allows multiple biomarkers to be quantified over specified cell types, regardless of their abundance. It is ideal for studying specimens from patients in clinical trials of targeted therapeutic agents, for investigating minority stromal cell subpopulations, and for phenotypic characterization to personalize therapy and prognosis.

Improved automatic detection and segmentation of cell nuclei in histopathology images

Automatic segmentation of cell nuclei is an essential step in image cytometry and histometry. Despite substantial progress, there is a need to improve accuracy, speed, level of automation, and adaptability to new applications. This paper presents a robust and accurate novel method for segmenting cell nuclei using a combination of ideas. The image foreground is extracted automatically using a graph-cuts-based binarization. Next, nuclear seed points are detected by a novel method combining multiscale Laplacian-of-Gaussian filtering constrained by distance-map-based adaptive scale selection. These points are used to perform an initial segmentation that is refined using a second graph-cuts-based algorithm incorporating the method of alpha expansions and graph coloring to reduce computational complexity. Nuclear segmentation results were manually validated over 25 representative images (15 in vitro images and 10 in vivo images, containing more than 7400 nuclei) drawn from diverse cancer histopathology studies, and four types of segmentation errors were investigated. The overall accuracy of the proposed segmentation algorithm exceeded 86%. The accuracy was found to exceed 94% when only over- and undersegmentation errors were considered. The confounding image characteristics that led to most detection/segmentation errors were high cell density, high degree of clustering, poor image contrast and noisy background, damaged/irregular nuclei, and poor edge information. We present an efficient semiautomated approach to editing automated segmentation results that requires two mouse clicks per operation.

Modulation of cultured neural networks using neurotrophin release from hydrogel-coated microelectrode arrays

Polyacrylamide and poly(ethylene glycol) diacrylate hydrogels were synthesized and characterized for use as drug release and substrates for neuron cell culture. Protein release kinetics was determined by incorporating bovine serum albumin (BSA) into hydrogels during polymerization. To determine if hydrogel incorporation and release affect bioactivity, alkaline phosphatase was incorporated into hydrogels and a released enzyme activity determined using the fluorescence-based ELF-97 assay. Hydrogels were then used to deliver a brain-derived neurotrophic factor (BDNF) from hydrogels polymerized over planar microelectrode arrays (MEAs). Primary hippocampal neurons were cultured on both control and neurotrophin-containing hydrogel-coated MEAs. The effect of released BDNF on neurite length and process arborization was investigated using automated image analysis. An increased spontaneous activity as a response to the released BDNF was recorded from the neurons cultured on the top of hydrogel layers. These results demonstrate that proteins of biological interest can be incorporated into hydrogels to modulate development and function of cultured neural networks. These results also set the stage for development of hydrogel-coated neural prosthetic devices for local delivery of various biologically active molecules.

Associative image analysis: a method for automated quantification of 3D multi-parameter images of brain tissue

Brain structural complexity has confounded prior efforts to extract quantitative image-based measurements. We present a systematic 'divide and conquer' methodology for analyzing three-dimensional (3D) multi-parameter images of brain tissue to delineate and classify key structures, and compute quantitative associations among them. To demonstrate the method, thick ( approximately 100 microm) slices of rat brain tissue were labeled using three to five fluorescent signals, and imaged using spectral confocal microscopy and unmixing algorithms. Automated 3D segmentation and tracing algorithms were used to delineate cell nuclei, vasculature, and cell processes. From these segmentations, a set of 23 intrinsic and 8 associative image-based measurements was computed for each cell. These features were used to classify astrocytes, microglia, neurons, and endothelial cells. Associations among cells and between cells and vasculature were computed and represented as graphical networks to enable further analysis. The automated results were validated using a graphical interface that permits investigator inspection and corrective editing of each cell in 3D. Nuclear counting accuracy was >89%, and cell classification accuracy ranged from 81 to 92% depending on cell type. We present a software system named FARSIGHT implementing our methodology. Its output is a detailed XML file containing measurements that may be used for diverse quantitative hypothesis-driven and exploratory studies of the central nervous system.