EFB Dengue Literature Review

Single-Cell RNA Sequencing

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Single-Cell RNA Sequencing

Overview

Single-cell RNA sequencing (scRNA-seq) enables transcriptomic profiling at single-cell resolution, allowing identification of cell states, subtypes, and developmental trajectories within heterogeneous populations. When paired with single-cell TCR or BCR sequencing (e.g., 10x Genomics 5’ V(D)J), it enables simultaneous characterisation of gene expression and antigen receptor clonality.

Key Points from Literature

  • scRNA-seq + scTCR-seq of activated CD4⁺ T cells in dengue: Ansari2025 performed 10x Genomics Chromium 5’ scRNA-seq + scTCR-seq on FACS-sorted CD38⁺HLA-DR⁺ CD4⁺ T cells from 4 acute dengue patients (4,361 cells total). This identified: (1) CXCR5⁺ Tfh-like cluster, (2) IL-21⁺ helper Tph, (3) GZMB⁺ cytotoxic Tph, and (4) regulatory/exhausted populations. Paired TCR data showed largely distinct clonotype usage between helper and cytotoxic Tph (only 13 shared clonotypes) (see Ansari2025 - Peripheral T Helper Subset Drives B Cell Response in Dengue, n=4 patients).

  • Distinction from bulk RNA-seq: Prior studies in this wiki used bulk RNA-seq of sorted B cell populations (e.g., Jenks2018 - DN2 B Cells and EF Pathway in SLE — 2,154 DEGs across B cell subsets; Scharer2019 - Epigenetic Programming in SLE B Cells — 5,090 DEGs). scRNA-seq complements these by resolving heterogeneity within sorted populations but has lower per-cell gene detection sensitivity.

  • 10x Genomics platform: The 10x Chromium system used in Ansari2025 is the same platform used for scV(D)J BCR sequencing in Woodruff2020 (ASC repertoire in COVID-19). The 5’ chemistry enables simultaneous gene expression + TCR/BCR sequencing from the same cell (see BCR Sequencing for the BCR application).

  • LANDMARK B CELL scRNA-seq — defining the alternative lineage (Sutton2021): Two complementary scRNA-seq platforms applied to B cells from malaria-exposed and non-exposed donors: (1) Smart-seq2 (plate-based, full-length mRNA): 163 Pf-tetramer⁺ B cells from 11 donors (3 malaria-exposed, 8 non-exposed), providing full-length BCR sequences for SHM quantification and V gene usage analysis. (2) 10x Chromium 3’ (droplet-based): >12,000 total B cells from 4 donors (2 malaria-exposed Kenyan adults + 2 non-exposed Australian donors), providing high-throughput unbiased clustering. Combined with CITE-seq surface protein measurement (CD11c, CXCR3, CD21, CD27), this identified 9 transcriptomic clusters separated into two developmental branches — an “alternative lineage” (atBC1, atBC2, atBC3, MBC1) defined by TBX21, ITGAX, FCRL5, and a “classical lineage” (MBC2, MBC3, actBC). Pseudotime analysis (Slingshot) placed these on separate trajectories from naive B cells. This is the most comprehensive scRNA-seq characterisation of the T-bet⁺/CD11c⁺ B cell population to date (see Sutton2021 - Alternative Lineage B Cells in Vaccination and Infection, 4 cohorts).

  • Cross-disease transcriptomics argues atypical B cells share one differentiation programme. Atypical B cells from individuals with malaria, HIV, and autoimmune disease exhibit similar transcriptional profiles, suggesting a shared differentiation trajectory across immune contexts rather than disease-specific phenocopies (see Glaros2025 - Multilayered Identity of B Cell Memory, review, no original data, citing Holla 2021 Sci Adv, human). See Atypical B Cell Contradictions, where this bears on the “identical or merely similar?” question.

  • ⚠ Transcriptomics at steady state does not resolve memory fate — the differences may be epigenetic. Recent transcriptomic analyses find GC-derived and GC-independent memory B cells “closely related at the transcriptional level, with only relatively subtle differences,” despite clearly distinct behaviour on reactivation. The review’s proposed resolution is that the fate difference is epigenetically rather than transcriptionally encoded — chromatin accessibility at PC-associated loci — which is a limit on what scRNA-seq alone can settle (review). Relevant to interpreting Sutton2021 - Alternative Lineage B Cells in Vaccination and Infection’s steady-state finding of absent PC-programme genes in atBCs; see DN2 B Cell Contradictions.

  • ★ RNA velocity has been reported flowing in opposite directions between the same two populations. Velocity analyses of DN and switched-memory compartments are cited supporting a high flow from DN1 → SM (DN1 as an SM precursor) and a high flow from SM → DN1 (DN1 as SM progeny), plus a flow from unswitched memory → DN2/DN3. A 2023 review presents both without resolving them: “Some DN1 cells could be precursors of SM B cells, while others could be progeny of SM B cells” (see Beckers2023 - Origins and Functions of DN B Cells, review, citing Stewart 2021 — all three velocity claims trace to the same study).

  • ⚠ Methodological caution the wiki should carry: velocity direction is an inference, not a measurement. RNA velocity infers differentiation direction from the ratio of unspliced to spliced transcripts at a single timepoint. It is sensitive to gene selection, kinetic-model assumptions, and the composition of the cells included in the embedding — all of which differ between the studies above. Where two velocity analyses of the same axis disagree, neither should be cited as establishing precedence; the wiki treats DN1↔SM ordering as open pending clonal or longitudinal evidence (see Beckers2023 - Origins and Functions of DN B Cells, review; see Switched Memory B Cell, Double-Negative B Cell).

  • scRNA-seq is what defined the DN1-3 scheme as transcriptionally, not just phenotypically, distinct. Single-cell RNA sequencing was used to confirm DN1-3 in COVID-19 patients and healthy donors, and to assign functional roles — DN1 as switched-memory precursors, DN2/DN3 as precursors of extrafollicular ASCs (see Beckers2023 - Origins and Functions of DN B Cells, review, Table 1, citing Woodruff 2020 / Stewart 2021).

Contradictions & Debates

None documented in current wiki sources.

RNA Sequencing, BCR Sequencing, FACS Sorting, Peripheral Helper T Cell, CITE-seq, Early Memory B Cell, Atypical B Cell, Age-Associated B Cell

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