Massively parallel single cell RNA-Seq for marker-free decomposition of tissues into cell types

By Diego Jaitin, E. Kenigsberg, Hadas Keren-Shaul, Naama Elefant, F. Paul, I. Zaretsky, A. Mildner, Nadav Cohen, Steffen Jung, Amos Tanay and 1 more. Science, 2014

1,700 citations recorded by the source corpus.

Sequencing of RNA from thousands of individual immune cells allows unbiased identification of cellular subtypes. In multicellular organisms, biological function emerges when heterogeneous cell types form complex organs. Nevertheless, dissection of tissues into mixtures of cellular subpopulations is currently challenging. We introduce an automated massively parallel single-cell RNA sequencing (RNA-seq) approach for analyzing in vivo transcriptional states in thousands of single cells. Combined with unsupervised classification algorithms, this facilitates ab initio cell-type characterization of splenic tissues. Modeling single-cell transcriptional states in dendritic cells and additional hematopoietic cell types uncovers rich cell-type heterogeneity and gene-modules activity in steady state and after pathogen activation. Cellular diversity is thereby approached through inference of variable and dynamic pathway activity rather than a fixed preprogrammed cell-type hierarchy. These data demonstrate single-cell RNA-seq as an effective tool for comprehensive cellular decomposition of complex tissues. Introducing MARS-Seq Immune cells are typically differentiated by surface markers; however, this designation is somewhat crude and does not allow for fine distinctions that might be characterized by their RNA transcripts. Jaitin et al. (p. 776) used massively parallel single-cell RNA-sequencing (MARS-Seq) analysis to explore cellular heterogeneity within the immune system by assembling an automated experimental platform that enables RNA profiling of cells sorted from tissues using flow cytometry. More than 1000 cells could be sequenced, and unsupervised clustering analysis of the RNA profiles revealed distinct cellular groupings that corresponded to B cells, macrophages, and dendritic cells. This approach provides the ability to perform a bottom-up characterization of in vivo cell-type landscapes independent of cell markers or prior knowledge.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/627e2f096abfe71bbdcbb6c8fbf10334b9ac8601

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Citations in the corpus

A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response (2016, background): “Along with previous successes (Jaitin et al., 2014; Klein et al., 2015; Macosko et al., 2015), and other novel analytical approaches (Dixit et al., co-submitted manuscript), large-scale analyses of single cell behavior should enable systematic understanding of the complex regulatory programs at…”
Design and Analysis of Single-Cell Sequencing Experiments. (2015, methodology): “These methods have been used to discriminate cell types in healthy tissues (Jaitin et al., 2014; Zeisel et al., 2015), to study differentiation dynamics (Treutlein et al., 2014), to discover rare cell types (Gru¨n et al., 2015), to investigate the transcriptome response upon external signals…”
Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data (2017, background): “12 Enumeration of cancer and immune cell types Recent technical developments in single-cell RNA-Seq technology enable researchers to directly access information about both the proportion of all cell types in a tumor and their gene expression characteristics (Efroni, Ip, Nawy, Mello, & Birnbaum, 2015”
Comparison and evaluation of statistical error models for scRNA-seq (2021, background): “Although the use of unique molecular identifiers (UMIs), random sequences that label individual molecules, has been a promising approach to limit amplification bias [6, 7], variation due to sequencing depth still arises in such datasets and can be a major source of technical variance.”
Massively parallel digital transcriptional profiling of single cells (2016, positive): “Indexed sequencing libraries were constructed using the reagents in the GemCode Single-Cell 30 Library Kit, following these steps: (1) end repair and A-tailing; (2) adapter ligation; (3) postligation cleanup with SPRIselect; (4) sample index PCR and cleanup.”
Removal of batch effects using distribution‐matching residual networks (2016, background): “, 2013), (Jaitin et al., 2014), Drop-seq enables researchers to analyze many thousands of cells in parallel, thus offers a better understanding of complex cell types of cellular states.”
Single-Cell Analysis of Crohn’s Disease Lesions Identifies a Pathogenic Cellular Module Associated with Resistance to Anti-TNF Therapy (2019, methodology): “Based on our previous work, we used an expectation maximization (EM)like clustering algorithm, which iteratively learns the gene expression profiles of the different cell populations while estimating batch-specific background noise rates (Figures S1E–S1I; STAR Methods) (Jaitin et al., 2014; Paul et ”
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets (2015, background): “, 2013) or (with automation) a few thousand cells (Jaitin et al., 2014), typically after first separating the cells by flow sorting (Shalek et al.”
CellMarker: a manually curated resource of cell markers in human and mouse (2018, methodology): “used unsupervised classification to accurately identify 580 cell markers for five immune cells (B cell, Natural killer cell, Macrophage, Monocyte and Plasmacytoid dendritic cell) by parallel single-cell RNA sequencing (13).”
Tissue-Resident Macrophage Enhancer Landscapes Are Shaped by the Local Microenvironment (2014, methodology): “We used a derivation of MARS-seq as described (Jaitin et al., 2014), developed for single-cell RNA-seq to produce expression libraries with a minimum of two replicates per population.”