Iso-Seq enables discovery of novel isoform variants in human retina at single cell resolution
Bibliographic record
Abstract
Abstract Recent single cell transcriptomic profiling of the human retina provided important insights into the genetic signals in heterogeneous retinal cell populations that enable vision. However, conventional single cell RNAseq with 3’ short-read sequencing is not suitable to identify isoform variants. Here we utilized Iso-Seq with full-length sequencing to profile the human retina at single cell resolution for isoform discovery. We generated a retina transcriptome dataset consisting of 25,302 nuclei from three donor retina, and detected 49,710 known transcripts and 241,949 novel transcripts across major retinal cell types. We surveyed the use of alternative promoters to drive transcript variant expression, and showed that 1-8% of genes utilized multiple promoters across major retinal cell types. Also, our results enabled gene expression profiling of novel transcript variants for inherited retinal disease (IRD) genes, and identified differential usage of exon splicing in major retinal cell types. Altogether, we generated a human retina transcriptome dataset at single cell resolution with full-length sequencing. Our study highlighted the potential of Iso-Seq to map the isoform diversity in the human retina, providing an expanded view of the complex transcriptomic landscape in the retina.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".