Biology subjects

Bernstein, M. N.

Publications and source records attributed to Bernstein, M. N..

5 recordsLinked to original sources

bioRxiv2023

Monkeybread: A Python toolkit for the analysis of cellular niches in single-cell resolution spatial transcriptomics data

Spatial transcriptomics technologies enable the spatially resolved measurement of gene expression within a tissue specimen. With these technologies, researchers can investigate how cells organize into cellular niches which are defined as distinct regions in the tissue comprising a specific composition of cell types or phenotypes. While general-purpose software tools for the exploratory analysis of spatial transcriptomics data exist, there is a need for tools that specialize in the analysis of cellular organization into niches. This can further enhance the downstream application of these data towards drug target discovery, target validation, and biomarker development. We present Monkeybread: A Python toolkit for analyzing cellular organization and intercellular communication in single-cell resolution spatial transcriptomics data. We applied Monkeybread to a human melanoma sample to demonstrate its utility in identifying cellular niches with diverse immunogenic compositions in the tumor microenvironment. We found that these niches were differentially enriched for immunogenic and tolerogenic macrophage populations that could be correlated to T cell abundance. These findings highlight how Monkeybread can be used for revealing underlying biology of the tumor microenvironment, and in the future, for understanding the influence of these niches on response to available treatments and discovery of novel drug targets.

bioinformatics↗

bioRxiv2022

SpatialCorr: Identifying Gene Sets with Spatially Varying Correlation Structure

Recent advances in spatially resolved transcriptomics technologies enable both the measurement of genome-wide gene expression profiles and their mapping to spatial locations within a tissue. A first step in spatial transcriptomics data analysis is identifying genes with expression that varies spatially, and robust statistical methods exist to address this challenge. While useful, these methods do not detect spatial changes in the coordinated expression within a group of genes. To this end, we present SpatialCorr, a method for identifying sets of genes with spatially varying correlation structure. Given a collection of gene sets pre-defined by a user, SpatialCorr tests for spatially induced differences in the correlation of each gene set within tissue regions, as well as between and among regions. An application to cutaneous squamous cell carcinoma demonstrates the power of the approach for revealing biological insights not identified using existing methods.

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bioRxiv2020

CHARTS: A web application for characterizing and comparing tumor subpopulations in publicly available single-cell RNA-seq datasets

BackgroundSingle-cell RNA-seq (scRNA-seq) enables the profiling of genome-wide gene expression at the single-cell level and in so doing facilitates insight into and information about cellular heterogeneity within a tissue. Perhaps nowhere is this more important than in cancer, where tumor and tumor microenvironment heterogeneity directly impact development, maintenance, and progression of disease. While publicly available scRNA-seq cancer datasets offer unprecedented opportunity to better understand the mechanisms underlying tumor progression, metastasis, drug resistance, and immune evasion, much of the available information has been underutilized, in part, due to the lack of tools available for aggregating and analysing these data. ResultsWe present CHARacterizing Tumor Subpopulations (CHARTS), a computational pipeline and web application for analyzing, characterizing, and integrating publicly available scRNA-seq cancer datasets. CHARTS enables the exploration of individual gene expression, cell type, malignancy-status, differentially expressed genes, and gene set enrichment results in subpopulations of cells across multiple tumors and datasets. ConclusionCHARTS is an easy to use, comprehensive platform for exploring single-cell subpopulations within tumors across the ever-growing collection of public scRNA-seq cancer datasets. CHARTS is freely available at charts.morgridge.org.

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bioRxiv2019

Hierarchical cell type classification using mass, heterogeneous RNA-seq data from human primary cells

Cell type annotation is a fundamental task in the analysis of single-cell RNA-sequencing data. In this work, we present CellO, a machine learning-based tool for annotating human RNA-seq data with the Cell Ontology. CellO enables accurate and standardized cell type classification by considering the rich hierarchical structure of known cell types, a source of prior knowledge that is not utilized by existing methods. Furthemore, CellO comes pre-trained on a novel, comprehensive dataset of human, healthy, untreated primary samples in the Sequence Read Archive, which to the best of our knowledge, is the most diverse curated collection of primary cell data to date. CellOs comprehensive training set enables it to run out-of-the-box on diverse cell types and achieves superior or competitive performance when compared to existing state-of-the-art methods. Lastly, CellOs linear models are easily interpreted, thereby enabling exploration of cell type-specific expression signatures across the ontology. To this end, we also present the CellO Viewer: a web application for exploring CellOs models across the ontology. HighlightO_LIWe present CellO, a tool for hierarchically classifying cell type from single-cell RNA-seq data against the graph-structured Cell Ontology C_LIO_LICellO is pre-trained on a comprehensive dataset comprising nearly all bulk RNA-seq primary cell samples in the Sequence Read Archive C_LIO_LICellO achieves superior or comparable performance with existing methods while featuring a more comprehensive pre-packaged training set C_LIO_LICellO is built with easily interpretable models which we expose through a novel web application, the CellO Viewer, for exploring cell type-specific signatures across the Cell Ontology C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=199 SRC="FIGDIR/small/634097v2_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@208aeeorg.highwire.dtl.DTLVardef@814bd8org.highwire.dtl.DTLVardef@9a7c39org.highwire.dtl.DTLVardef@5195fb_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2016

MetaSRA: normalized sample-specific metadata for the Sequence Read Archive

MotivationThe NCBIs Sequence Read Archive (SRA) promises great biological insight if one could analyze the data in the aggregate; however, the data remain largely underutilized, in part, due to the poor structure of the metadata associated with each sample. The rules governing submissions to the SRA do not dictate a standardized set of terms that should be used to describe the biological samples from which the sequencing data are derived. As a result, the metadata include many synonyms, spelling variants, and references to outside sources of information. Furthermore, manual annotation of the data remains intractable due to the large number of samples in the archive. For these reasons, it has been difficult to perform large-scale analyses that study the relationships between biomolecular processes and phenotype across diverse diseases, tissues, and cell types present in the SRA.\n\nResultsWe present MetaSRA, a database of normalized SRA sample-specific metadata following a schema inspired by the metadata organization of the ENCODE project. This schema involves mapping samples to terms in biomedical ontologies, labeling each sample with a sample-type category, and extracting real-valued properties. We automated these tasks via a novel computational pipeline.\n\nAvailabilityThe MetaSRA database is available at http://deweylab.biostat.wisc.edu/metasra. Software implementing our computational pipeline is available at https://github.com/deweylab/metasra-pipeline.\n\nContactcdewey@biostat.wisc.edu

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