See-N-Seq: RNA sequencing of target single cells identified by microscopy via micropatterning of hydrogel porosity
Bibliographic record
Abstract
Single cell RNA sequencing has the potential to elucidate transcriptional programs underlying key cellular phenotypes and behaviors. However, many cell phenotypes are incompatible with indiscriminate single cell sequencing because they are rare, transient, or can only be identified by imaging. Existing methods for isolating cells based on imaging for single cell sequencing are technically challenging, time-consuming, and prone to loss because of the need to physically transport single cells. Here, we developed See-N-Seq, a method to rapidly screen cells in microwell plates in order to isolate RNA from specific single cells without needing to physically extract each cell. Our approach involves encapsulating the cell sample in a micropatterned hydrogel with spatially varying porosity to selectively expose specific cells for targeted RNA extraction. Extracted RNA can then be captured, barcoded, reverse transcribed, amplified, and sequenced at high-depth. We used See-N-Seq to isolate and sequence RNA from cell-cell conjugates forming an immunological synapse between T-cells and antigen presenting cells. In the hours after synapsing, we found time-dependent bifurcation of single cell transcriptomic profiles towards Type 1 and Type 2 helper T-cells lineages. Our results demonstrate how See-N-Seq can be used to associate transcriptomic data with specific functions and behaviors in single cells.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".