Bibliographic record
Abstract
Abstract From genomics to transcriptomics to proteomics, microfluidic tools underpin recent advances in single‐cell biology. Detection of specific proteoforms—with single‐cell resolution—presents challenges in detection specificity and sensitivity. Miniaturization of protein immunoblots to single‐cell resolution mitigates these challenges. For example, in microfluidic western blotting, protein targets are separated by electrophoresis and subsequently detected using fluorescently labeled antibody probes. To quantify the expression level of each protein target, the fluorescent protein bands are fit to Gaussians; yet, this method is difficult to use with noisy, low‐abundance, or low‐SNR protein bands, and with significant band skew or dispersion. In this study, we investigate segmentation‐based approaches to robustly quantify protein bands from single‐cell protein immunoblots. As compared to a Gaussian fitting pipeline, the segmentation pipeline detects >1.5× more protein bands for downstream quantification as well as more of the low‐abundance protein bands (i.e., with SNR ∼3). Utilizing deep learning‐based segmentation approaches increases the recovery of low‐SNR protein bands by an additional 50%. However, we find that segmentation‐based approaches are less robust at quantifying poorly resolved protein bands (separation resolution, R s < 0.6). With burgeoning needs for more single‐cell protein analysis tools, we see microfluidic separations as benefitting substantially from segmentation‐based analysis approaches.
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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 | 0.001 |
| 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".