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      Testicular germ cell tumors arise in the absence of sex-specific differentiation

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          ABSTRACT

          In response to signals from the embryonic testis, the germ cell intrinsic factor NANOS2 coordinates a transcriptional program necessary for the differentiation of pluripotent-like primordial germ cells toward a unipotent spermatogonial stem cell fate. Emerging evidence indicates that genetic risk factors contribute to testicular germ cell tumor initiation by disrupting sex-specific differentiation. Here, using the 129.MOLF-Chr19 mouse model of testicular teratomas and a NANOS2 reporter allele, we report that the developmental phenotypes required for tumorigenesis, including failure to enter mitotic arrest, retention of pluripotency and delayed sex-specific differentiation, were exclusive to a subpopulation of germ cells failing to express NANOS2. Single-cell RNA sequencing revealed that embryonic day 15.5 NANOS2-deficient germ cells and embryonal carcinoma cells developed a transcriptional profile enriched for MYC signaling, NODAL signaling and primed pluripotency. Moreover, lineage-tracing experiments demonstrated that embryonal carcinoma cells arose exclusively from germ cells failing to express NANOS2. Our results indicate that NANOS2 is the nexus through which several genetic risk factors influence tumor susceptibility. We propose that, in the absence of sex specification, signals native to the developing testis drive germ cell transformation.

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          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            Comprehensive Integration of Single-Cell Data

            Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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              Integrating single-cell transcriptomic data across different conditions, technologies, and species

              Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.
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                Author and article information

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                Journal
                Development
                The Company of Biologists
                0950-1991
                1477-9129
                May 01 2021
                May 01 2021
                April 28 2021
                : 148
                : 9
                Affiliations
                [1 ]Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
                [2 ]Department of Cell Biology, New York University, New York, NY 10003, USA
                Article
                10.1242/dev.197111
                © 2021

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