Single-cell analysis of germinal-center B cells informs on lymphoma cell of origin and outcome

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Abstract

Single-cell transcriptomic analysis of germinal center B cells identifies multiple linked populations, most of which represent cell of origin of lymphomas. The newly identified cell of origin of diffuse large B cell lymphoma informs on novel prognostic subgroups.

Abstract

In response to T cell–dependent antigens, mature B cells are stimulated to form germinal centers (GCs), the sites of B cell affinity maturation and the cell of origin (COO) of most B cell lymphomas. To explore the dynamics of GC B cell development beyond the known dark zone and light zone compartments, we performed single-cell (sc) transcriptomic analysis on human GC B cells and identified multiple functionally linked subpopulations, including the distinct precursors of memory B cells and plasma cells. The gene expression signatures associated with these GC subpopulations were effective in providing a sc-COO for ∼80% of diffuse large B cell lymphomas (DLBCLs) and identified novel prognostic subgroups of DLBCL.

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Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Michael Love, Wolfgang Huber, Simon Anders (2014)

In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.

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Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

A. Subramanian, P Tamayo, V K Mootha … (2005)

Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

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featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

Y. Liao, G K Smyth, W Shi (2014)

Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.

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Author and article information

Contributors

Antony B. Holmes:

ORCID: https://orcid.org/0000-0001-7559-2678

Role: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing

Clarissa Corinaldesi: Role: Formal analysisRole: InvestigationRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing

Qiong Shen:

ORCID: https://orcid.org/0000-0001-6313-3642

Role: InvestigationRole: MethodologyRole: Resources

Rahul Kumar: Role: Formal analysis

Nicolo Compagno: Role: Investigation

Zhong Wang:

ORCID: https://orcid.org/0000-0002-9980-8864

Role: InvestigationRole: Resources

Mor Nitzan: Role: Formal analysis

Eli Grunstein: Role: Resources

Laura Pasqualucci:

ORCID: https://orcid.org/0000-0001-6819-7370

Role: Data curationRole: InvestigationRole: Writing - review & editing

Riccardo Dalla-Favera: Role: ConceptualizationRole: Funding acquisitionRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing - review & editing

Katia Basso:

ORCID: https://orcid.org/0000-0002-9316-6039

Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: SupervisionRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing

Journal

Journal ID (nlm-ta): J Exp Med

Journal ID (iso-abbrev): J Exp Med

Journal ID (publisher-id): jem

Title: The Journal of Experimental Medicine

Publisher: Rockefeller University Press

ISSN (Print): 0022-1007

ISSN (Electronic): 1540-9538

Publication date Collection: 05 October 2020

Publication date (Electronic): 30 June 2020

Volume: 217

Issue: 10

Electronic Location Identifier: e20200483

Affiliations

[1 ]Institute for Cancer Genetics, Columbia University, New York, NY

[2 ]Department of Pathology and Cell Biology, Columbia University, New York, NY

[3 ]Department of Otolaryngology Head and Neck Surgery, Columbia University, New York, NY

[4 ]Department of Microbiology and Immunology, Columbia University, New York, NY

[5 ]Department of Genetics and Development, Columbia University, New York, NY

[6 ]The Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY

[7 ]Harvard University, Cambridge, MA

Author notes

Correspondence to Katia Basso: kb451@ 123456cumc.columbia.edu

Riccardo Dalla-Favera: rd10@ 123456cumc.columbia.edu

Disclosures: N. Compagno is currently employed at Novartis. No other disclosures were reported.

[*]

A.B. Holmes and C. Corinaldesi contributed equally to this paper.

Author information

Antony B. Holmes https://orcid.org/0000-0001-7559-2678

Qiong Shen https://orcid.org/0000-0001-6313-3642

Zhong Wang https://orcid.org/0000-0002-9980-8864

Laura Pasqualucci https://orcid.org/0000-0001-6819-7370

Katia Basso https://orcid.org/0000-0002-9316-6039

Article

Publisher ID: jem.20200483

DOI: 10.1084/jem.20200483

PMC ID: 7537389

PubMed ID: 32603407

SO-VID: 2a73b60e-4c3d-4250-9216-3e8f9dab2ce6

License:

This article is distributed under the terms of an Attribution–Noncommercial–Share Alike–No Mirror Sites license for the first six months after the publication date (see http://www.rupress.org/terms/). After six months it is available under a Creative Commons License (Attribution–Noncommercial–Share Alike 4.0 International license, as described at https://creativecommons.org/licenses/by-nc-sa/4.0/).

History

Date received : 12 March 2020

Date revision received : 12 May 2020

Date accepted : 15 May 2020

Page count

Pages: 20

Funding

Funded by: National Institutes of Health, DOI http://dx.doi.org/10.13039/100000002;

Award ID: R35CA-210105

Funded by: NCI, DOI http://dx.doi.org/10.13039/100000054;

Award ID: P30CA013696

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Single-cell analysis of germinal-center B cells informs on lymphoma cell of origin and outcome

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Most cited references 71

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

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