High-throughput targeted long-read single cell sequencing reveals the clonal and transcriptional landscape of lymphocytes
Bibliographic record
Abstract
Abstract High-throughput single-cell RNA-Sequencing is a powerful technique for gene expression profiling of complex and heterogeneous cellular populations such as the immune system. However, these methods only provide short-read sequence from one end of a cDNA template, making them poorly suited to the investigation of gene-regulatory events such as mRNA splicing, adaptive immune responses or somatic genome evolution. To address this challenge, we have developed a method that combines targeted long-read sequencing with short-read based transcriptome profiling of barcoded single cell libraries generated by droplet-based partitioning. We use Repertoire And Gene Expression sequencing (RAGE-seq) to accurately characterize full-length T cell (TCR) and B cell (BCR) receptor sequences and transcriptional profiles of more than 7,138 lymphocytes sampled from the primary tumour and draining lymph node of a breast cancer patient. With this method we show that somatic mutation, alternate splicing and clonal evolution of T and B lymphocytes can be tracked across these tissue compartments. Our results demonstrate that RAGE-Seq is an accessible and cost-effective method for high-throughput deep single cell profiling, applicable to a wide range of biological challenges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".