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
TL;DR: Drop-seq will accelerate biological discovery by enabling routine transcriptional profiling at single-cell resolution by separating them into nanoliter-sized aqueous droplets, associating a different barcode with each cell's RNAs, and sequencing them all together.
read more
About: This article is published in Cell. The article was published on 21 May 2015. and is currently open access. The article focuses on the topics: Single-cell analysis & Single cell sequencing.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Synthetic Analyses of Single-Cell Transcriptomes from Multiple Brain Organoids and Fetal Brain
Yoshiaki Tanaka,Bilal Cakir,Yangfei Xiang,Gareth J. Sullivan,Gareth J. Sullivan,In-Hyun Park +5 more
TL;DR: A systematic approach is taken to compare the single-cell transcriptomes of various human cortical brain organoids together with fetal brain to define the identity of specific cell types and differentiation routes in each method to identify unique developmental programs in each protocol compared to fetal brain.
189
Metabolic heterogeneity underlies reciprocal fates of T H 17 cell stemness and plasticity
Peer W. F. Karmaus,Xiang Chen,Seon Ah Lim,Andrés A. Herrada,Thanh-Long M. Nguyen,Beisi Xu,Yogesh Dhungana,Sherri Rankin,Wenan Chen,Celeste Rosencrance,Kai Yang,Yiping Fan,Yong Cheng,John Easton,Geoffrey Neale,Peter Vogel,Hongbo Chi +16 more
TL;DR: It is demonstrated that TH17 cells in a mouse model of autoimmune disease are functionally and metabolically heterogeneous; they contain a subset with stemness-associated features but lower anabolic metabolism, and a reciprocal subset with higher metabolic activity that supports transdifferentiation into TH1-like cells.
Generation of vascularized brain organoids to study neurovascular interactions
TL;DR: In this article , the authors developed brain organoids and blood vessel organoids independently, and then fused them together to obtain vascularized brain organoid, which contained functional blood-brain barrier-like structures, as well as microglial cells.
189
Single-cell RNA sequencing in breast cancer: Understanding tumor heterogeneity and paving roads to individualized therapy.
TL;DR: A brief introduction on the currently available scRNA‐seq approaches along with their corresponding strengths and weaknesses is provided and may act as a reference for the selection of suitable methods for research.
189
Single-Cell Transcriptomics Uncovers Glial Progenitor Diversity and Cell Fate Determinants during Development and Gliomagenesis.
Qinjie Weng,Qinjie Weng,Jincheng Wang,Jincheng Wang,Jiajia Wang,Jiajia Wang,Danyang He,Zuolin Cheng,Feng Zhang,Feng Zhang,Ravinder Verma,Lingli Xu,Lingli Xu,Xinran Dong,Yunfei Liao,Xuelian He,Andrew S. Potter,Liguo Zhang,Chuntao Zhao,Mei Xin,Qian Zhou,Bruce J. Aronow,Perry J. Blackshear,Jeremy N. Rich,Qiaojun He,Wenhao Zhou,Mario L. Suvà,Ronald R. Waclaw,S. Steven Potter,Guoqiang Yu,Q. Richard Lu,Q. Richard Lu +31 more
TL;DR: The results resolve the dynamic repertoire of common and divergent glial progenitors during development and tumorigenesis and highlight Zfp36l1 as a molecular nexus for balancing glial cell-fate decision and controlling gliomagenesis.
188
References
•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.
•Proceedings Article
A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise
Martin Ester,Hans-Peter Kriegel,Jörg Sander,Xiaowei Xu +3 more
- 02 Aug 1996
TL;DR: In this paper, a density-based notion of clusters is proposed to discover clusters of arbitrary shape, which can be used for class identification in large spatial databases and is shown to be more efficient than the well-known algorithm CLAR-ANS.
20.3K
•Proceedings Article
A density-based algorithm for discovering clusters in large spatial Databases with Noise
Martin Ester,Hans-Peter Kriegel,Jörg Sander,Xiaowei Xu +3 more
- 01 Jan 1996
TL;DR: DBSCAN, a new clustering algorithm relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape, is presented which requires only one input parameter and supports the user in determining an appropriate value for it.
Spatial reconstruction of single-cell gene expression data
TL;DR: Seurat is a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns, and correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups.
mRNA-Seq whole-transcriptome analysis of a single cell.
Fuchou Tang,Catalin Barbacioru,Yangzhou Wang,Ellen Nordman,Clarence Lee,Nanlan Xu,Xiaohui Wang,John Bodeau,Brian B. Tuch,Asim Siddiqui,Kaiqin Lao,M. Azim Surani +11 more
TL;DR: A single-cell digital gene expression profiling assay with only a single mouse blastomere is described, which detected the expression of 75% more genes than microarray techniques and identified 1,753 previously unknown splice junctions called by at least 5 reads.
Related Papers (5)
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