A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure.
Maayan Baron,Adrian Veres,Samuel L. Wolock,Aubrey L. Faust,Renaud Gaujoux,Amedeo Vetere,Jennifer Hyoje Ryu,Bridget K. Wagner,Shai S. Shen-Orr,Allon M. Klein,Douglas A. Melton,Itai Yanai +11 more
Reads0
Chats0
TLDR
A droplet-based, single-cell RNA-seq method is implemented to determine the transcriptomes of over 12,000 individual pancreatic cells from four human donors and two mouse strains and provides a resource for the discovery of novel cell type-specific transcription factors, signaling receptors, and medically relevant genes.Abstract:
Although the function of the mammalian pancreas hinges on complex interactions of distinct cell types, gene expression profiles have primarily been described with bulk mixtures. Here we implemented a droplet-based, single-cell RNA-seq method to determine the transcriptomes of over 12,000 individual pancreatic cells from four human donors and two mouse strains. Cells could be divided into 15 clusters that matched previously characterized cell types: all endocrine cell types, including rare epsilon-cells; exocrine cell types; vascular cells; Schwann cells; quiescent and activated stellate cells; and four types of immune cells. We detected subpopulations of ductal cells with distinct expression profiles and validated their existence with immuno-histochemistry stains. Moreover, among human beta- cells, we detected heterogeneity in the regulation of genes relating to functional maturation and levels of ER stress. Finally, we deconvolved bulk gene expression samples using the single-cell data to detect disease-associated differential expression. Our dataset provides a resource for the discovery of novel cell type-specific transcription factors, signaling receptors, and medically relevant genes.read more
Citations
More filters
Journal ArticleDOI
Comprehensive Integration of Single-Cell Data.
Tim Stuart,Andrew Butler,Paul J. Hoffman,Christoph Hafemeister,Efthymia Papalexi,William M. Mauck,Yuhan Hao,Marlon Stoeckius,Peter Smibert,Rahul Satija +9 more
TL;DR: 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.
Journal ArticleDOI
Integrating single-cell transcriptomic data across different conditions, technologies, and species.
TL;DR: An analytical strategy for integrating scRNA-seq data sets based on common sources of variation is introduced, enabling the identification of shared populations across data sets and downstream comparative analysis.
Journal ArticleDOI
Fast, sensitive and accurate integration of single-cell data with Harmony.
Ilya Korsunsky,Nghia Millard,Jean Fan,Kamil Slowikowski,Fan Zhang,Kevin Wei,Yuriy Baglaenko,Michael B. Brenner,Po-Ru Loh,Po-Ru Loh,Po-Ru Loh,Soumya Raychaudhuri +11 more
TL;DR: Harmony, for the integration of single-cell transcriptomic data, identifies broad and fine-grained populations, scales to large datasets, and can integrate sequencing- and imaging-based data.
Journal ArticleDOI
Extrapulmonary manifestations of COVID-19.
Aakriti Gupta,Aakriti Gupta,Mahesh V. Madhavan,Kartik Sehgal,Kartik Sehgal,Nandini Nair,Shiwani Mahajan,Tejasav S. Sehrawat,Behnood Bikdeli,Behnood Bikdeli,Neha Ahluwalia,John C. Ausiello,Elaine Wan,Daniel E. Freedberg,Ajay J. Kirtane,Sahil A. Parikh,Mathew S. Maurer,Anna S. Nordvig,Domenico Accili,Joan M. Bathon,Sumit Mohan,Kenneth A. Bauer,Kenneth A. Bauer,Martin B. Leon,Harlan M. Krumholz,Nir Uriel,Mandeep R. Mehra,Mitchell S.V. Elkind,Mitchell S.V. Elkind,Gregg W. Stone,Allan Schwartz,David D. Ho,John P. Bilezikian,Donald W. Landry +33 more
TL;DR: The extrapulmonary organ-specific pathophysiology, presentations and management considerations for patients with COVID-19 are reviewed to aid clinicians and scientists in recognizing and monitoring the spectrum of manifestations, and in developing research priorities and therapeutic strategies for all organ systems involved.
Posted ContentDOI
Comprehensive integration of single cell data
Tim Stuart,Andrew Butler,Paul J. Hoffman,Christoph Hafemeister,Efthymia Papalexi,William M. Mauck,Marlon Stoeckius,Peter Smibert,Rahul Satija +8 more
TL;DR: This work presents a strategy for comprehensive integration of single cell data, including the assembly of harmonized references, and the transfer of information across datasets, and demonstrates how anchoring can harmonize in-situ gene expression and scRNA-seq datasets.
References
More filters
Journal ArticleDOI
Trimmomatic: a flexible trimmer for Illumina sequence data
TL;DR: Timmomatic is developed as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data and is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested.
Journal ArticleDOI
Fast gapped-read alignment with Bowtie 2
TL;DR: Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
Journal Article
Visualizing Data using t-SNE
TL;DR: A new technique called t-SNE that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map, a variation of Stochastic Neighbor Embedding that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map.
Journal ArticleDOI
edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.
TL;DR: EdgeR as mentioned in this paper is a Bioconductor software package for examining differential expression of replicated count data, which uses an overdispersed Poisson model to account for both biological and technical variability and empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference.
Journal ArticleDOI
Bioconductor: open software development for computational biology and bioinformatics
Robert Gentleman,Vincent J. Carey,Douglas M. Bates,Benjamin M. Bolstad,Marcel Dettling,Sandrine Dudoit,Byron Ellis,Laurent Gautier,Yongchao Ge,Jeff Gentry,Kurt Hornik,Torsten Hothorn,Wolfgang Huber,Stefano Maria Iacus,Rafael A. Irizarry,Friedrich Leisch,Cheng Li,Martin Maechler,A. J. Rossini,Günther Sawitzki,Colin A. Smith,Gordon K. Smyth,Luke Tierney,Jean Yang,Jianhua Zhang +24 more
TL;DR: Details of the aims and methods of Bioconductor, the collaborative creation of extensible software for computational biology and bioinformatics, and current challenges are described.
Related Papers (5)
Massively parallel digital transcriptional profiling of single cells
Grace X.Y. Zheng,Jessica M. Terry,Phillip Belgrader,Paul Ryvkin,Zachary Bent,Ryan Wilson,Solongo B. Ziraldo,Tobias Daniel Wheeler,Geoffrey P. McDermott,Junjie Zhu,Mark T. Gregory,Joe Shuga,Luz Montesclaros,Jason G. Underwood,Donald A. Masquelier,Stefanie Y. Nishimura,Michael Schnall-Levin,Paul Wyatt,Christopher Hindson,Rajiv Bharadwaj,Alexander Wong,Kevin D. Ness,Lan Beppu,H. Joachim Deeg,Christopher McFarland,Keith R. Loeb,Keith R. Loeb,William J. Valente,William J. Valente,Nolan G. Ericson,Emily A. Stevens,Jerald P. Radich,Tarjei S. Mikkelsen,Benjamin J. Hindson,Jason H. Bielas +34 more
Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets
Evan Z. Macosko,Evan Z. Macosko,Anindita Basu,Anindita Basu,Rahul Satija,Rahul Satija,James Nemesh,James Nemesh,Karthik Shekhar,Melissa Goldman,Melissa Goldman,Itay Tirosh,Allison R. Bialas,Nolan Kamitaki,Nolan Kamitaki,Emily M. Martersteck,John J. Trombetta,David A. Weitz,Joshua R. Sanes,Alex K. Shalek,Alex K. Shalek,Alex K. Shalek,Aviv Regev,Aviv Regev,Aviv Regev,Steven A. McCarroll,Steven A. McCarroll +26 more