Rapid Single‐Cell Proteomics Using Nanoconfined Enzyme Reactors on a Microscale Digital Microfluidics Platform
Bibliographic record
Abstract
Abstract Multicellular organisms exhibit cellular heterogeneity, crucial for understanding physiological and pathological processes. Single‐cell proteomics (SCP) enables exploration of this diversity but faces challenges such as sample loss due to nonspecific adsorption and relies on free protease solutions for enzymatic digestion. Here, a microfluidic platform is reported that enhances proteomic analysis of single cells by integrating nanoconfined enzyme reactors with digital microfluidics (DMF). Trypsin immobilized on NHS‐activated magnetic beads via click chemistry (Try@Fe 3 O 4 ) shows improved stability and enzyme loading, reducing autolysis risks. Using DMF‐Try@Fe 3 O 4 , it achieves over twice the sequence coverage and four times the peptide matches for standard proteins in 10 min compared to conventional 10‐h methods. The densely packed enzymes in the nanoscale microenvironment enhance reaction rates. This system identifies 3,916 and 1,849 protein groups from 50 HeLa cells and single cells, respectively, showing 27% and 201% increases over tube digestion. The platform also classifies leukocyte subtypes (HL‐60, Jurkat, and Raji, with N = 20 for each) with SCP and identifies key upregulated proteins. Proteomic analysis of gemcitabine‐treated PANC‐1 cells reveal alterations consistent with known drug mechanisms. This approach enhances protein digestion efficiency and identification rates, offering a rapid, automated SCP solution for high‐throughput applications and broader biological investigations.
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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".