Biology subjects

Schapiro, D.

Publications and source records attributed to Schapiro, D..

3 recordsLinked to original sources

bioRxiv2018

The landscape of S100B+ and HLA-DR+ dendritic cell subsets in tonsils at the single cell level via high-parameter mapping

Dendritic cells (DC) (classic, plasmacytoid, inflammatory) are an intense focus of interest because of their role in inflammation, autoimmunity, vaccination and cancer. We present a tissue-based classification of human DC subsets in tonsils with a high-parameter (>40 markers) immunofluorescent approach, cell type-specific image segmentation and the use of bioinformatics platforms. Through this deep phenotypic and spatial examination, classic cDC1, cDC2, pDC subsets have been further refined and a novel subset of DC co-expressing IRF4 and IRF8 identified. Based on unique tissue locations within the tonsil, and close interactions with T cells (cDC1) or B cells (cDC2), DC subsets can be further subdivided by correlative phenotypic changes associated with these interactions. In addition, monocytes and macrophages expressing HLA-DR or S100AB are identified and localized in the tissue. This study thus provides a whole tissue in situ catalog of human DC subsets and their cellular interactions within spatially defined niches.

immunology↗

bioRxiv2018

Modelling cell-cell interactions from spatial molecular data with spatial variance component analysis

Technological advances allow for assaying multiplexed spatially resolved RNA and protein expression profiling of individual cells, thereby capturing physiological tissue contexts of single cell variation. While methods for the high-throughput generation of spatial expression profiles are increasingly accessible, computational methods for studying the relevance of the spatial organization of tissues on cell-cell heterogeneity are only beginning to emerge. Here, we present spatial variance component analysis (SVCA), a computational framework for the analysis of spatial molecular data. SVCA enables quantifying the effect of cell-cell interactions, as well as environmental and intrinsic cell features on the expression levels of individual genes or proteins. In application to a breast cancer Imaging Mass Cytometry dataset, our model allows for robustly estimating spatial variance signatures, identifying cell-cell interactions as a major driver of expression heterogeneity. Finally, we apply SVCA to high-dimensional imaging-derived RNA data, where we identify molecular pathways that are linked to cell-cell interactions.

bioinformatics↗

bioRxiv2017

Systematic analysis of cell phenotypes and cellular social networks in tissues using the multiplexed image cytometry analysis toolbox (miCAT)

Single-cell, spatially resolved omics analysis of tissues is poised to transform biomedical research and clinical practice. We have developed an open-source computational histology topography analysis toolbox (histoCAT) to enable the interactive, quantitative, and comprehensive exploration of phenotypes of individual cells, cell-to-cell interactions, microenvironments, and morphological structures within intact tissues. histoCAT will be useful in all areas of tissue-based research. We highlight the unique abilities of histoCAT by analysis of highly multiplexed mass cytometry images of human breast cancer tissues.\n\nTechnological advances in the multi-parametric analysis of single cells have revealed an unprecedented heterogeneity of cellular phenotypes and functional states that are concealed in population-based studies1-3. Each cellular phenotype is defined by the interplay of its internal state and the environment in which ...

systems biology↗