Evidence map›Paper›PMID 33815362›Full record

ArticleFrontiers in immunology2021

Single-Cell Transcriptomic Analyses Define Distinct Peripheral B Cell Subsets and Discrete Development Pathways.

Alexander Stewart, Joseph Chi-Fung Ng, Gillian Wallis, Vasiliki Tsioligka, Franca Fraternali, Deborah K Dunn-Walters

Abstract read
In one paragraph

Article in Frontiers in immunology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 108 papers.

0numbers the graph read from it
0cells of the map it votes in
108citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

108 citing papers in PubMed.

  1. IgA2Cell reports. Medicine · 2025
    Trial
  2. B-Cell Aware Analysis of Single-Cell Transcriptomics Data.Methods in molecular biology (Clifton, N.J.) · 2027
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48 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Alexander StewartSchool of Biosciences and Medicine, University of Surrey, Guildford, United Kingdom.
Joseph Chi-Fung NgRandall Centre for Cell and Molecular Biophysics, School of Basic and Medical Biosciences, King's College London, London, United Kingdom.
Gillian WallisSchool of Biosciences and Medicine, University of Surrey, Guildford, United Kingdom.
Vasiliki TsioligkaSchool of Biosciences and Medicine, University of Surrey, Guildford, United Kingdom.
Franca FraternaliRandall Centre for Cell and Molecular Biophysics, School of Basic and Medical Biosciences, King's College London, London, United Kingdom.
Deborah K Dunn-WaltersSchool of Biosciences and Medicine, University of Surrey, Guildford, United Kingdom.

Funding

Biotechnology and Biological Sciences Research Council BB/T002212/1Medical Research Council MR/L01257X/2
6 · The paper itself

Abstract

Separation of B cells into different subsets has been useful to understand their different functions in various immune scenarios. In some instances, the subsets defined by phenotypic FACS separation are relatively homogeneous and so establishing the functions associated with them is straightforward. Other subsets, such as the "Double negative" (DN, CD19+CD27-IgD-) population, are more complex with reports of differing functionality which could indicate a heterogeneous population. Recent advances in single-cell techniques enable an alternative route to characterize cells based on their transcriptome. To maximize immunological insight, we need to match prior data from phenotype-based studies with the finer granularity of the single-cell transcriptomic signatures. We also need to be able to define meaningful B cell subsets from single cell analyses performed on PBMCs, where the relative paucity of a B cell signature means that defining B cell subsets within the whole is challenging. Here we provide a reference single-cell dataset based on phenotypically sorted B cells and an unbiased procedure to better classify functional B cell subsets in the peripheral blood, particularly useful in establishing a baseline cellular landscape and in extracting significant changes with respect to this baseline from single-cell datasets. We find 10 different clusters of B cells and applied a novel, geometry-inspired, method to RNA velocity estimates in order to evaluate the dynamic transitions between B cell clusters. This indicated the presence of two main developmental branches of memory B cells. A T-independent branch that involves IgM memory cells and two DN subpopulations, culminating in a population thought to be associated with Age related B cells and the extrafollicular response. The other, T-dependent, branch involves a third DN cluster which appears to be a precursor of classical memory cells. In addition, we identify a novel DN4 population, which is IgE rich and closely linked to the classical/precursor memory branch suggesting an IgE specific T-dependent cell population.

Indexed as

Gene Expression ProfilingImmunologic MemorySingle-Cell AnalysisAdultB-LymphocytesHumansMaleSignal TransductionB cell developmentB cellsB cell subsetscell atlasmemory B cellssingle-cellRNAseq

Identifiers

PMID33815362
PMCPMC8012727

What Socratic holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.