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Updated: Jun 27, 2025
Flow Cytometric Characterization of Murine B Cell Development
Published on: January 22, 2021
Unravelling B cell heterogeneity: insights into flow cytometry-gated B cells from single-cell multi-omics data
Jane I Pernes1,2, Atheer Alsayah1,3, Felicia Tucci1,4
1Department of Biochemistry, University of Oxford, Oxford, United Kingdom.
Insights
Single-cell multi-omics reveals B cell subset heterogeneity and contamination in flow cytometry gating. A new tool, AlliGateR, improves B cell purity for accurate immune cell characterization.
Area of Science:
- Immunology
- Single-cell multi-omics
- Flow cytometry
Background:
- B cells are crucial for adaptive immunity, traditionally identified using flow cytometry.
- Discrepancies exist between flow cytometry-defined B cell subsets and their molecular signatures.
- Standard B cell gating strategies may lead to cellular contamination and ambiguity in subset identification.
Purpose of the Study:
- To address discrepancies between flow cytometry-defined B cell subsets and their molecular identities.
- To characterize cellular contamination within standard flow cytometry gating using single-cell multi-omics.
- To resolve ambiguities surrounding unconventional B cell subsets and assess disease-specific heterogeneity.
Main Methods:
- Analysis of multi-omics single-cell data from healthy individuals and patients across various diseases.
- Characterization of cellular contamination in flow cytometry-based gating.
- Development and application of the AlliGateR tool for identifying additional gating markers.
Main Results:
- Flow cytometry-defined B cell populations exhibit significant heterogeneity, varying across disease states.
- Standard gating strategies can lead to cellular contamination, impacting functional study implications.
- The AlliGateR tool, using non-linear gating (CD20, CD21, CD24), enhances the purity of naïve and memory B cell populations.
Conclusions:
- Reconsideration of B cell subset definitions is necessary, leveraging single-cell multi-omics for refined characterization.
- Single-cell multi-omics bridges the gap between surface marker-based annotations and molecular B cell characteristics.
- The AlliGateR tool offers a solution to improve the accuracy of B cell subset identification in immunological studies.
Introduction:
B cells play a pivotal role in adaptive immunity which has been extensively characterised primarily via flow cytometry-based gating strategies. This study addresses the discrepancies between flow cytometry-defined B cell subsets and their high-confidence molecular signatures using single-cell multi-omics approaches.
Methods:
By analysing multi-omics single-cell data from healthy individuals and patients across diseases, we characterised the level and nature of cellular contamination within standard flow cytometric-based gating, resolved some of the ambiguities in the literature surrounding unconventional B cell subsets, and demonstrated the variable effects of flow cytometric-based gating cellular heterogeneity across diseases.
Results:
We showed that flow cytometric-defined B cell populations are heterogenous, and the composition varies significantly between disease states thus affecting the implications of functional studies performed on these populations. Importantly, this paper draws caution on findings about B cell selection and function of flow cytometric-sorted populations, and their roles in disease. As a solution, we developed a simple tool to identify additional markers that can be used to increase the purity of flow-cytometric gated immune cell populations based on multi-omics data (AlliGateR). Here, we demonstrate that additional non-linear CD20, CD21 and CD24 gating can increase the purity of both naïve and memory populations.
Discussion:
These findings underscore the need to reconsider B cell subset definitions within the literature and propose leveraging single-cell multi-omics data for refined characterisation. We show that single-cell multi-omics technologies represent a powerful tool to bridge the gap between surface marker-based annotations and the intricate molecular characteristics of B cell subsets.
