Abstract A20: Single-cell immune TCR repertoire profiling in the context of immunotherapy by using three 10x Genomics libraries
Bibliographic record
Abstract
Abstract 10x Genomics single-cell sequencing provides a comprehensive and scalable solution for cell characterization and gene expression profiling of hundreds to tens of thousands of cells. For performance comparison of single-cell immune TCR repertoire profiling, we tested three libraries: (1) 5-prime gene expression, (2) direct enrichment for TCR, and (3) post-cDNA-amplification enrichment for TCR, on two invariant natural killer T (iNKT) cells, on B240 and B241, with iNKT purity at 90% and 45%, respectively. We also performed correlation analysis between gene expression data and clonotype data by library 3. About 1,100 cells of B240 and 580 cells of B241 were sequenced. The numbers of productive clonotypes of B240 were identified as 165, 284, and 332 for the three libraries, respectively, while 47 clonotypes overlapped among the libraries. The numbers of productive clonotypes of B241 were 88, 152, and 148, respectively, while 35 clonotypes overlapped. Each sample is a mixture of iNK T cells and non-invariant NK T cells. We were able to identify the clusters of iNKT cells using the 5’ gene expression data by library 1 in tSNE plots for both B240 and B241. More importantly, the clusters correlated strongly with the TCR clonotypes identifying iNKT cells by library 3. Based upon our data, we noticed that the number of clonotypes identified is proportional to the number of cells sequenced regardless of libraries. Library 1 has low sensitivity in terms of clonotype detection, due to un-enrichment of TCR genes, and mainly detected single chain in each cell (beta chain most of the time). The other two libraries are able to identify high and similar number of clonotypes. However, library 3 is able to associate gene expression pattern with TCR clonotype usage, which is a big advantage over the other two libraries for 10x Genomics single-cell immune TCR repertoire profiling. Note: This abstract was not presented at the conference. Citation Format: Yuan Qi, Jin Seon Im, Xiaoping Su. Single-cell immune TCR repertoire profiling in the context of immunotherapy by using three 10x Genomics libraries [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr A20.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".