A microfluidic platform enables comprehensive gene expression profiling of mouse retinal stem cells
Bibliographic record
Abstract
Loss of photoreceptors due to retinal degeneration is a major cause of untreatable visual impairment and blindness. Cell replacement therapy, using retinal stem cell (RSC)-derived photoreceptors, holds promise for reconstituting damaged cell populations in the retina. One major obstacle preventing translation to the clinic is the lack of validated markers or strategies to prospectively identify these rare cells in the retina and subsequently enrich them. Here, we introduce a microfluidic platform that combines nickel micromagnets, herringbone structures, and a design enabling varying flow velocities among three compartments to facilitate a highly efficient enrichment of RSCs. In addition, we developed an affinity enrichment strategy based on cell-surface markers that was utilized to isolate RSCs from the adult ciliary epithelium. We showed that targeting a panel of three cell surface markers simultaneously facilitates the enrichment of RSCs to 1 : 3 relative to unsorted cells. Combining the microfluidic platform with single-cell whole-transcriptome profiling, we successfully identified four differentially expressed cell surface markers that can be targeted simultaneously to yield an unprecedented 1 : 2 enrichment of RSCs relative to unsorted cells. We also identified transcription factors (TFs) that play functional roles in maintenance, quiescence, and proliferation of RSCs. This level of analysis for the first time identified a spectrum of molecular and functional properties of RSCs.
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 | 0.000 | 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 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".