Brion Daryl Sarachan

Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue

Limitations on the number of unique protein and DNA molecules that can be characterized microscopically in a single tissue specimen impede advances in understanding the biological basis of health and disease. Here we present a multiplexed fluorescence microscopy method (MxIF) for quantitative, single-cell, and subcellular characterization of multiple analytes in formalin-fixed paraffin-embedded tissue. Chemical inactivation of fluorescent dyes after each image acquisition round allows reuse of common dyes in iterative staining and imaging cycles. The mild inactivation chemistry is compatible with total and phosphoprotein detection, as well as DNA FISH. Accurate computational registration of sequential images is achieved by aligning nuclear counterstain-derived fiducial points. Individual cells, plasma membrane, cytoplasm, nucleus, tumor, and stromal regions are segmented to achieve cellular and subcellular quantification of multiplexed targets. In a comparison of pathologist scoring of diaminobenzidine staining of serial sections and automated MxIF scoring of a single section, human epidermal growth factor receptor 2, estrogen receptor, p53, and androgen receptor staining by diaminobenzidine and MxIF methods yielded similar results. Single-cell staining patterns of 61 protein antigens by MxIF in 747 colorectal cancer subjects reveals extensive tumor heterogeneity, and cluster analysis of divergent signaling through ERK1/2, S6 kinase 1, and 4E binding protein 1 provides insights into the spatial organization of mechanistic target of rapamycin and MAPK signal transduction. Our results suggest MxIF should be broadly applicable to problems in the fields of basic biological research, drug discovery and development, and clinical diagnostics.

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.

Predicting continuous epitopes in proteins

The ability to predict antigenic sites on proteins is crucial for the production of synthetic peptide vaccines and synthetic peptide probes of antibody structure. Large number of amino acid propensity scales based on various properties of the antigenic sites like hydrophilicity, flexibility/mobility, turns and bends have been proposed and tested previously. However these methods are not very accurate in predicting epitopes and non-epitope regions. We propose algorithms that combine 14 best performing individual propensity scales and give better prediction accuracy as compared to individual scales.

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.

Implementing a user interface management system for existing applications using an object-oriented database tool

Numerous engineering application systems have been developed over the past twenty years, and many of these applications will continue to be used for many years to come. Examples of such applications include CAD Systems, finite‐element analysis packages and inspection systems. Because many of these applications were developed before graphical workstations became available, they often have simple command‐line user interfaces. Thus, there is a need for a graphical user interface management system (UIMS) that can be used to build point‐and‐click style interfaces for these existing engineering applications.

Evaluation of different multi-pinhole imaging geometries for SPECT imaging of Parkinsonian disorders

Focusing multi-pinhole (MP) collimators are increasingly being used for small animal as well as targeted VOI imaging. We use focused MP collimators to improve the resolution and sensitivity of photons detected from the striatal region of the brain. Simulations were based on activity distributions derived from clinical SPECT images of normal and Parkinson’s Disease patients injected with 99mTc-Trodat. Radioactive counts extracted from the clinical images were mapped onto regions of the Zubal brain phantom for input into the SPECT simulator. Simulated images were then generated modeling single pinhole (SP), nine pinhole (9PH) and 21 pinhole (21PH) collimators attached to one of the heads of a clinical SPECT scanner. The images were reconstructed using OSEM and evaluated after every iteration. The resulting image quality was evaluated using the ideal VOIs from the Zubal phantom for metrics such as contrast to noise ratio (CNR), bias, mean Uptake Ratio and standard error of the mean. In addition, for cross validation an automated feature detection and analysis tool was used for the detection and stratification of the simulated PD images. The CNR for the 9PH and 21PH was observed to increase by 66% and 81% while the corresponding noise levels dropped by 71% and 84%. Similarly the absolute bias was 64%, 28% and 22% for the SP, 9PH and 21PH respectively. Our results showed an improved performance of the MP collimators over the SP collimator configuration. The 21PH case performed well in terms of CNR and absolute bias, while the 9PH case resulted in the most accurate estimate of the true Uptake Ratio. The MP configurations were consistently observed to be superior to the single pinhole. In conclusion, focusing MP collimators were found to give improved quantification and better resolution compared to traditional SPECT acquisitions. The improved CNR enables more refined 3D visualization of the striatum, which could translate to better stratification of Parkinsonain disorders.