sciCSR infers B cell state transition and predicts class-switch recombination dynamics using single-cell transcriptomic data
Bibliographic record
Abstract
Abstract Class-switch recombination (CSR) is an integral part of B cell maturation. Steady-state analyses of isotype distribution (e.g. B cell receptor [BCR] repertoire analysis of snapshots during an immune response) do not directly measure CSR dynamics, which is crucial in understanding how B cell maturation is regulated across time. We present sciCSR (pronounced ‘scissor’, single-cell inference of class switch recombination), a computational pipeline which analyses CSR events and dynamics of B cells from single-cell RNA-sequencing (scRNA-seq) experiments. sciCSR re-analyses transcriptomic sequence alignments to differentiate productive heavy-chain immunoglobulin transcripts from germline “sterile” transcripts. From a snapshot of B cell scRNA-seq data, a Markov state model is built by the pipeline to infer the dynamics and direction of CSR. Applying sciCSR on SARS-CoV-2 vaccination time-course scRNA-seq data, we observe that sciCSR predicts, using data from an earlier timepoint in the collected time-course, the isotype distribution of BCR repertoires of subsequent timepoints with high accuracy (cosine similarity ∼ 0.9). sciCSR also recapitulates CSR patterns in mouse models where B cell maturation was perturbed using gene knockouts. sciCSR infers cell state transitions using processes specific to B cells, identifies transitions which are often missed by conventional RNA velocity analyses, and can reveal insights into the regulation of CSR and the dynamics of B cell maturation during an immune response.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".