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 Gelfand; Kristin Ardlie; David A. Weitz; Orit Rozenblatt-Rosen; Feng Zhang; Aviv Regev

doi:10.1038/nmeth.4407

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 Gelfand, Kristin Ardlie, David A. Weitz, Orit Rozenblatt-Rosen, Feng Zhang, Aviv Regev

Research output: Contribution to journal › Article › peer-review

Abstract

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.

Original language	English
Pages (from-to)	955-958
Number of pages	4
Journal	Nature Methods
Volume	14
Issue number	10
DOIs	https://doi.org/10.1038/nmeth.4407
State	Published - 1 Oct 2017
Externally published	Yes

All Science Journal Classification (ASJC) codes

Biotechnology
Biochemistry
Molecular Biology
Cell Biology

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1038/nmeth.4407

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title = "Massively parallel single-nucleus RNA-seq with DroNc-seq",

abstract = "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.",

author = "Naomi Habib and Inbal Avraham-Davidi and Anindita Basu and Tyler Burks and Karthik Shekhar and Matan Hofree and Choudhury, {Sourav R.} and Fran{\c c}ois Aguet and Ellen Gelfand and Kristin Ardlie and Weitz, {David A.} and Orit Rozenblatt-Rosen and Feng Zhang and Aviv Regev",

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doi = "10.1038/nmeth.4407",

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T1 - Massively parallel single-nucleus RNA-seq with DroNc-seq

AU - Habib, Naomi

AU - Avraham-Davidi, Inbal

AU - Basu, Anindita

AU - Burks, Tyler

AU - Shekhar, Karthik

AU - Hofree, Matan

AU - Choudhury, Sourav R.

AU - Aguet, François

AU - Gelfand, Ellen

AU - Ardlie, Kristin

AU - Weitz, David A.

AU - Rozenblatt-Rosen, Orit

AU - Zhang, Feng

AU - Regev, Aviv

PY - 2017/10/1

Y1 - 2017/10/1

N2 - 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.

AB - 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.

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U2 - 10.1038/nmeth.4407

DO - 10.1038/nmeth.4407

M3 - مقالة

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SN - 1548-7091

VL - 14

SP - 955

EP - 958

JO - Nature Methods

JF - Nature Methods

IS - 10

ER -

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

Abstract

All Science Journal Classification (ASJC) codes

UN SDGs

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