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

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Abstract

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 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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Most cited references 10

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Comparative Analysis of Single-Cell RNA Sequencing Methods.

Christoph Ziegenhain, Beate Vieth, Swati Parekh … (2017)

Single-cell RNA sequencing (scRNA-seq) offers new possibilities to address biological and medical questions. However, systematic comparisons of the performance of diverse scRNA-seq protocols are lacking. We generated data from 583 mouse embryonic stem cells to evaluate six prominent scRNA-seq methods: CEL-seq2, Drop-seq, MARS-seq, SCRB-seq, Smart-seq, and Smart-seq2. While Smart-seq2 detected the most genes per cell and across cells, CEL-seq2, Drop-seq, MARS-seq, and SCRB-seq quantified mRNA levels with less amplification noise due to the use of unique molecular identifiers (UMIs). Power simulations at different sequencing depths showed that Drop-seq is more cost-efficient for transcriptome quantification of large numbers of cells, while MARS-seq, SCRB-seq, and Smart-seq2 are more efficient when analyzing fewer cells. Our quantitative comparison offers the basis for an informed choice among six prominent scRNA-seq methods, and it provides a framework for benchmarking further improvements of scRNA-seq protocols.

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Maps of random walks on complex networks reveal community structure

M Rosvall, C. Bergstrom (2007)

To comprehend the multipartite organization of large-scale biological and social systems, we introduce a new information theoretic approach that reveals community structure in weighted and directed networks. The method decomposes a network into modules by optimally compressing a description of information flows on the network. The result is a map that both simplifies and highlights the regularities in the structure and their relationships. We illustrate the method by making a map of scientific communication as captured in the citation patterns of more than 6000 journals. We discover a multicentric organization with fields that vary dramatically in size and degree of integration into the network of science. Along the backbone of the network -- including physics, chemistry, molecular biology, and medicine -- information flows bidirectionally, but the map reveals a directional pattern of citation from the applied fields to the basic sciences.

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Accounting for technical noise in single-cell RNA-seq experiments.

Philip Brennecke, Simon Anders, Jong Kim … (2013)

Single-cell RNA-seq can yield valuable insights about the variability within a population of seemingly homogeneous cells. We developed a quantitative statistical method to distinguish true biological variability from the high levels of technical noise in single-cell experiments. Our approach quantifies the statistical significance of observed cell-to-cell variability in expression strength on a gene-by-gene basis. We validate our approach using two independent data sets from Arabidopsis thaliana and Mus musculus.

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Author and article information

Journal

Journal ID (nlm-journal-id): 101215604

Journal ID (pubmed-jr-id): 32338

Journal ID (nlm-ta): Nat Methods

Journal ID (iso-abbrev): Nat. Methods

Title: Nature methods

ISSN (Print): 1548-7091

ISSN (Electronic): 1548-7105

Publication date Nihms-submitted: 15 August 2017

Publication date (Electronic): 28 August 2017

Publication date (Print): October 2017

Publication date PMC-release: 28 February 2018

Volume: 14

Issue: 10

Pages: 955-958

Affiliations

[1 ]Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge MA

[2 ]Broad Institute of MIT and Harvard, Cambridge MA

[3 ]McGovern Institute, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge MA

[4 ]John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA

[5 ]Department of Physics, Harvard University, Cambridge, MA

[6 ]Howard Hughes Medical Institute, Department of Biology, Koch Institute of Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge MA

Author notes

[# ]Correspondence to: zhang@ 123456broadinstitute.org (FZ) and aregev@ 123456broadinstitute.org (AR)

[&]

Current address: Department of Medicine, University of Chicago, Chicago IL; Center for Nanoscale Materials, Argonne National Laboratory, Lemont IL

[*]

These authors contributed equally to this work

Article

Manuscript ID: NIHMS898077

DOI: 10.1038/nmeth.4407

PMC ID: 5623139

PubMed ID: 28846088

SO-VID: 3e5eb67c-8b54-4277-9323-b96f6a8eeaa3

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

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Abstract

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Most cited references 10

Comparative Analysis of Single-Cell RNA Sequencing Methods.

Maps of random walks on complex networks reveal community structure

Accounting for technical noise in single-cell RNA-seq experiments.

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