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      Single-cell connectomic analysis of adult mammalian lungs

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          Abstract

          Single-cell network analysis demonstrates species-conserved functional roles for pulmonary alveolar cell types.

          Abstract

          Efforts to decipher chronic lung disease and to reconstitute functional lung tissue through regenerative medicine have been hampered by an incomplete understanding of cell-cell interactions governing tissue homeostasis. Because the structure of mammalian lungs is highly conserved at the histologic level, we hypothesized that there are evolutionarily conserved homeostatic mechanisms that keep the fine architecture of the lung in balance. We have leveraged single-cell RNA sequencing techniques to identify conserved patterns of cell-cell cross-talk in adult mammalian lungs, analyzing mouse, rat, pig, and human pulmonary tissues. Specific stereotyped functional roles for each cell type in the distal lung are observed, with alveolar type I cells having a major role in the regulation of tissue homeostasis. This paper provides a systems-level portrait of signaling between alveolar cell populations. These methods may be applicable to other organs, providing a roadmap for identifying key pathways governing pathophysiology and informing regenerative efforts.

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          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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            Proteomics. Tissue-based map of the human proteome.

            Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. Copyright © 2015, American Association for the Advancement of Science.
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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

                Journal
                Sci Adv
                Sci Adv
                SciAdv
                advances
                Science Advances
                American Association for the Advancement of Science
                2375-2548
                December 2019
                04 December 2019
                : 5
                : 12
                : eaaw3851
                Affiliations
                [1 ]Department of Biomedical Engineering, Yale University, New Haven, CT 06511, USA.
                [2 ]Vascular Biology and Therapeutics, Yale University, New Haven, CT 06520, USA.
                [3 ]Medical Scientist Training Program, Yale School of Medicine, New Haven, CT 06510, USA.
                [4 ]Section of Pulmonary, Critical Care and Sleep Medicine, Yale University, New Haven, CT 06520, USA.
                [5 ]Yale Systems Biology Institute, Yale University, West Haven, CT 06516, USA.
                [6 ]Pulmonary and Critical Care Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA.
                [7 ]Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.
                [8 ]Department of Anesthesiology, Yale University, New Haven, CT 06510, USA.
                [9 ]Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06510, USA.
                [10 ]Applied Mathematics Program, Yale University, New Haven, CT 06511, USA.
                [11 ]Thoracic Surgery, Yale School of Medicine, New Haven, CT 06510, USA.
                [12 ]Department of Pathology, Yale University, New Haven, CT 06520, USA.
                Author notes
                [* ]Corresponding author. Email: naftali.kaminski@ 123456yale.edu (N.K.); laura.niklason@ 123456yale.edu (L.E.N.)
                Author information
                http://orcid.org/0000-0003-1441-6122
                http://orcid.org/0000-0003-4280-9070
                http://orcid.org/0000-0002-1296-9738
                http://orcid.org/0000-0002-7714-8076
                http://orcid.org/0000-0001-5442-3189
                http://orcid.org/0000-0002-5560-6136
                http://orcid.org/0000-0003-3409-9371
                http://orcid.org/0000-0001-6738-1029
                http://orcid.org/0000-0002-0074-0346
                http://orcid.org/0000-0001-8691-2614
                http://orcid.org/0000-0001-5471-8833
                http://orcid.org/0000-0001-5917-4601
                http://orcid.org/0000-0002-5156-504X
                Article
                aaw3851
                10.1126/sciadv.aaw3851
                6892628
                31840053
                41103450-7b4e-4bf8-bd65-e72be26b63fd
                Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

                This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

                History
                : 14 December 2018
                : 18 September 2019
                Funding
                Funded by: doi http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R01HL127349
                Funded by: doi http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: U01HL122626
                Funded by: doi http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R21 EB024889
                Funded by: doi http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R01 HL138540
                Funded by: doi http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: T32GM007205
                Funded by: doi http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: F30HL143880
                Funded by: doi http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R01HG008383
                Funded by: doi http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R01GM131642
                Funded by: doi http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: T32GM007205
                Funded by: doi http://dx.doi.org/10.13039/100000054, National Cancer Institute;
                Award ID: U54CA209992
                Funded by: Humacyte Inc;
                Funded by: German Research Foundation;
                Award ID: SCHU 3147/1-1
                Funded by: Three Lakes Partners;
                Funded by: doi http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: UO1 HL1455670
                Funded by: doi http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: RO1 HL127349
                Funded by: doi http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: T32GM007205
                Funded by: doi http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: T32GM007205
                Categories
                Research Article
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                Systems Biology
                Systems Biology
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                Nielsen Marquez

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