Droplet-Based Microfluidics with Mass Spectrometry for Microproteomics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 it