CRISPR Technique Incorporated with Single-Cell RNA Sequencing for Studying Hepatitis B Infection
Bibliographic record
Abstract
Single-cell RNA sequencing (scRNA-seq) provides rich transcriptomic information for studying molecular events and cell heterogeneity at the single-cell level. However, it is challenging to obtain sequence information from rare or low-abundance genes in the presence of other highly abundant genes. We report here a CRISPR-Cas9 technique for the depletion of high-abundance transcripts, resulting in preferential enrichment of rare transcripts. We demonstrate an application of this CRISPR-mediated enrichment technique to scRNA-seq of liver cells infected with hepatitis B virus (HBV). Direct sequencing without the CRISPR-mediated enrichment detected HBV RNA in only 0.6% of the cells. The CRISPR-mediated depletion of the three most abundant transcripts resulted in selective enrichment of the HBV transcript and successful sequencing of HBV RNA in more than 74% of the cells. The improvement enabled a study of HBV infection and interferon treatment of a liver cell model. Gene clusters between the control and HBV-infected Huh7.5-NTCP cells were similar, suggesting that HBV infection did not significantly alter gene expression of the host cells. The treatment with interferon alpha dramatically changed the gene expression of Huh7.5-NTCP cells. These results from the single cell RNA-seq analysis of 7370 cells are consistent with those of bulk experiments, suggesting that HBV is a "stealth virus".
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".