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Dive into the research topics where Ellen T. Gelfand is active.

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Featured researches published by Ellen T. Gelfand.


Nature Methods | 2017

Massively parallel single-nucleus RNA-seq with DroNc-seq

Naomi Habib; Inbal Avraham-Davidi; Anindita Basu; Tyler Burks; Karthik Shekhar; Matan Hofree; Sourav R Choudhury; François Aguet; Ellen T. Gelfand; Kristin Ardlie; David A. Weitz; Orit Rozenblatt-Rosen; Feng Zhang; Aviv Regev

Single-nucleus RNA sequencing (sNuc-seq) profiles RNA from tissues that are preserved or cannot be dissociated, but it does not provide high throughput. Here, we develop DroNc-seq: massively parallel sNuc-seq with droplet technology. We profile 39,111 nuclei from mouse and human archived brain samples to demonstrate sensitive, efficient, and unbiased classification of cell types, paving the way for systematic charting of cell atlases.


bioRxiv | 2016

Local genetic effects on gene expression across 44 human tissues

François Aguet; Andrew Anand Brown; Stephane E. Castel; Joe R. Davis; Pejman Mohammadi; Ayellet V. Segrè; Zachary Zappala; Nathan S. Abell; Laure Frésard; Eric R. Gamazon; Ellen T. Gelfand; Machael J Gloudemans; Yuan He; Farhad Hormozdiari; Xiao Li; Xin Li; Boxiang Liu; Diego Garrido-Martín; Halit Ongen; John Palowitch; YoSon Park; Christine B. Peterson; Gerald Quon; Stephan Ripke; Andrey A. Shabalin; Tyler C. Shimko; Benjamin J. Strober; Timothy J. Sullivan; Nicole A. Teran; Emily K. Tsang

Expression quantitative trait locus (eQTL) mapping provides a powerful means to identify functional variants influencing gene expression and disease pathogenesis. We report the identification of cis-eQTLs from 7,051 post-mortem samples representing 44 tissues and 449 individuals as part of the Genotype-Tissue Expression (GTEx) project. We find a cis-eQTL for 88% of all annotated protein-coding genes, with one-third having multiple independent effects. We identify numerous tissue-specific cis-eQTLs, highlighting the unique functional impact of regulatory variation in diverse tissues. By integrating large-scale functional genomics data and state-of-the-art fine-mapping algorithms, we identify multiple features predictive of tissue-specific and shared regulatory effects. We improve estimates of cis-eQTL sharing and effect sizes using allele specific expression across tissues. Finally, we demonstrate the utility of this large compendium of cis-eQTLs for understanding the tissue-specific etiology of complex traits, including coronary artery disease. The GTEx project provides an exceptional resource that has improved our understanding of gene regulation across tissues and the role of regulatory variation in human genetic diseases.


Nature Genetics | 2017

Enhancing GTEx by bridging the gaps between genotype, gene expression, and disease

Barbara E. Stranger; Lori E. Brigham; Richard Hasz; Marcus Hunter; Christopher Johns; Mark C. Johnson; Gene Kopen; William F. Leinweber; John T. Lonsdale; Alisa McDonald; Bernadette Mestichelli; Kevin Myer; Brian Roe; Michael Salvatore; Saboor Shad; Jeffrey A. Thomas; Gary Walters; Michael Washington; Joseph Wheeler; Jason Bridge; Barbara A. Foster; Bryan M. Gillard; Ellen Karasik; Rachna Kumar; Mark Miklos; Michael T. Moser; Scott Jewell; Robert G. Montroy; Daniel C. Rohrer; Dana R. Valley

Genetic variants have been associated with myriad molecular phenotypes that provide new insight into the range of mechanisms underlying genetic traits and diseases. Identifying any particular genetic variants cascade of effects, from molecule to individual, requires assaying multiple layers of molecular complexity. We introduce the Enhancing GTEx (eGTEx) project that extends the GTEx project to combine gene expression with additional intermediate molecular measurements on the same tissues to provide a resource for studying how genetic differences cascade through molecular phenotypes to impact human health.


bioRxiv | 2017

DroNc-Seq: Deciphering cell types in human archived brain tissues by massively-parallel single nucleus RNA-seq

Naomi Habib; Anindita Basu; Inbal Avraham-Davidi; Tyler Burks; Sourav R Choudhury; François Aguet; Ellen T. Gelfand; Kristin Ardlie; David A. Weitz; Orit Rozenblatt-Rosen; Feng Zhang; Aviv Regev

Single nucleus RNA-Seq (sNuc-Seq) profiles RNA from tissues that are preserved or cannot be dissociated, but does not provide the throughput required to analyse many cells from complex tissues. Here, we develop DroNc-Seq, massively parallel sNuc-Seq with droplet technology. We profile 29,543 nuclei from mouse and human archived brain samples to demonstrate sensitive, efficient and unbiased classification of cell types, paving the way for charting systematic cell atlases.


Protocol exchange | 2017

DroNc-seq step-by-step

Anindita Basu; Inbal Avraham-Davidi; Naomi Habib; Aviv Regev; Feng Zhang; Karthik Shekhar; Matan Hofree; David A. Weitz; Orit Rozenblatt-Rosen; Tyler Burks; Sourav R Choudhury; François Aguet; Ellen T. Gelfand; Kristin Ardlie

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Anindita Basu

University of Pennsylvania

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Aviv Regev

Massachusetts Institute of Technology

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Feng Zhang

Massachusetts Institute of Technology

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