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      Applying Single-Cell Analysis to Gonadogenesis and DSDs (Disorders/Differences of Sex Development)

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          Abstract

          The gonads are unique among the body’s organs in having a developmental choice: testis or ovary formation. Gonadal sex differentiation involves common progenitor cells that form either Sertoli and Leydig cells in the testis or granulosa and thecal cells in the ovary. Single-cell analysis is now shedding new light on how these cell lineages are specified and how they interact with the germline. Such studies are also providing new information on gonadal maturation, ageing and the somatic-germ cell niche. Furthermore, they have the potential to improve our understanding and diagnosis of Disorders/Differences of Sex Development (DSDs). DSDs occur when chromosomal, gonadal or anatomical sex are atypical. Despite major advances in recent years, most cases of DSD still cannot be explained at the molecular level. This presents a major pediatric concern. The emergence of single-cell genomics and transcriptomics now presents a novel avenue for DSD analysis, for both diagnosis and for understanding the molecular genetic etiology. Such -omics datasets have the potential to enhance our understanding of the cellular origins and pathogenesis of DSDs, as well as infertility and gonadal diseases such as cancer.

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          Dimensionality reduction for visualizing single-cell data using UMAP

          Advances in single-cell technologies have enabled high-resolution dissection of tissue composition. Several tools for dimensionality reduction are available to analyze the large number of parameters generated in single-cell studies. Recently, a nonlinear dimensionality-reduction technique, uniform manifold approximation and projection (UMAP), was developed for the analysis of any type of high-dimensional data. Here we apply it to biological data, using three well-characterized mass cytometry and single-cell RNA sequencing datasets. Comparing the performance of UMAP with five other tools, we find that UMAP provides the fastest run times, highest reproducibility and the most meaningful organization of cell clusters. The work highlights the use of UMAP for improved visualization and interpretation of single-cell data.
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            Large-scale simultaneous measurement of epitopes and transcriptomes in single cells

            Recent high-throughput single-cell sequencing approaches have been transformative for understanding complex cell populations, but are unable to provide additional phenotypic information, such as protein levels of cell-surface markers. Using oligonucleotide-labeled antibodies, we integrate measurements of cellular proteins and transcriptomes into an efficient, sequencing-based readout of single cells. This method is compatible with existing single-cell sequencing approaches and will readily scale as the throughput of these methods increase.
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              Visualization and analysis of gene expression in tissue sections by spatial transcriptomics.

              Analysis of the pattern of proteins or messengerRNAs (mRNAs) in histological tissue sections is a cornerstone in biomedical research and diagnostics. This typically involves the visualization of a few proteins or expressed genes at a time. We have devised a strategy, which we call "spatial transcriptomics," that allows visualization and quantitative analysis of the transcriptome with spatial resolution in individual tissue sections. By positioning histological sections on arrayed reverse transcription primers with unique positional barcodes, we demonstrate high-quality RNA-sequencing data with maintained two-dimensional positional information from the mouse brain and human breast cancer. Spatial transcriptomics provides quantitative gene expression data and visualization of the distribution of mRNAs within tissue sections and enables novel types of bioinformatics analyses, valuable in research and diagnostics.
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                Author and article information

                Journal
                Int J Mol Sci
                Int J Mol Sci
                ijms
                International Journal of Molecular Sciences
                MDPI
                1422-0067
                10 September 2020
                September 2020
                : 21
                : 18
                : 6614
                Affiliations
                Department of Anatomy and Developmental Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton 3800, Victoria, Australia; martin.estermann@ 123456monash.edu
                Author notes
                Author information
                https://orcid.org/0000-0002-8623-2720
                https://orcid.org/0000-0002-1670-5545
                Article
                ijms-21-06614
                10.3390/ijms21186614
                7555471
                32927658
                87d19eb3-1b5f-44af-a681-a18a58b481b7
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 18 August 2020
                : 08 September 2020
                Categories
                Review

                Molecular biology
                dsd,sex differentiation,gonad,ovary,testis,single-cell,cancer,infertility,single-cell rna sequencing,sex determination

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