F.2 Single cell CRISPR/Cas-9 lineage tracing reveals fitness axis in glioblastoma
Bibliographic record
Abstract
Background: We’ve adopted a novel approach that combines cellular barcoding with CRISPR/Cas-9 technology and single-cell RNA sequencing known as continuous lineage tracing to track the development, treatment and inevitable recurrence of glioblastoma. Methods: Patient derived glioma initiating cell lines were engineered with expressed DNA barcodes with CRISPR/Cas-9 targets and engrafted into NOD scid-mice. Clonal and relationships are surmised through identification of expressed barcodes, and cells were characterized by their transcriptional profiles. Phylogenetic lineage trees are created using lineage reconstructive algorithms to define cell fitness and expansion. Results: Our work has revealed a significant amount of intra-clonal cell state heterogeneity, suggesting that tumour cells engage in phenotype switching prior to therapeutic intervention. Phylogenetic lineage trees allowed us to define a gene signature of cell fitness. GBMs exist along a transcriptional gradient between undifferentiated but “high-fit” cells and terminally differentiated, “low-fit” cells, lending further evidence that these tumours consist of pools of cells that are capable of recapitulating the tumour microenvironment after treatment. Conclusions: We have successfully engineered a set of glioma initiating tumours with a novel lineage tracing technique, creating a powerful tool for real-time tracing of tumour growth through the analysis of highly detailed singe-cell RNA sequencing data with associated clonal and phylogenetic relationships.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".