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.
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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".