Droplet-Based Microfluidics with Mass Spectrometry for Microproteomics
Bibliographic record
Abstract
Microproteomics, the profiling of protein expressions in small cell populations or individual cells, is essential for understanding complex biological systems. However, sample loss and insufficient sensitivity of analytical techniques pose severe challenges to this field. Microfluidics, particularly droplet-based microfluidics, provides an ideal approach by enabling miniaturized and integrated workflows to process samples and offers several advantages, including reduced sample loss, low reagent consumption, faster reaction times, and improved throughput. Droplet-based microfluidics manipulates droplets of fluids to function as discrete reaction units, enabling complex chemical reactions and biological workflows in a miniaturized setting. This article discusses a variety of on-chip functions of droplet-based microfluidics, including cell sorting, cell culture, and sample processing. We then highlight recent advances in the mass spectrometry (MS)-based analysis of single cells using droplet-based microfluidic platforms, including digital microfluidics (DMF). Finally, we review the integrated DMF–MS systems that enable automated and parallel proteomic profiling of single cells with high sensitivity and discuss the applications of the technology and its future perspectives.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".