Cell-Type-Specific Surfaceome Profiling of 100-500 Isolated Cells using a Droplet-Based Magnetic Affinity Purification System
Bibliographic record
Abstract
ABSTRACT Cell surface proteins (CSPs) represent an important source of biomarkers and therapeutic targets. However, due to the inherent sensitivity limitations of existing technologies, tissue and cell-type-specific surfaceomes remain poorly characterized, especially in the context of human diseases. Herein, we develop nanoMAPS (nanoscale Magnetic Affinity Purification System), a miniaturized proteomic sample preparation method for surfaceome profiling of as few as 100-500 cells (1000× to 100,000× lower than existing technologies). We demonstrate that the miniaturization of magnetic bead-based affinity purification inside a single droplet can efficiently improve the recovery of surface proteins and reduce non-specific absorption of intracellular proteins. By applying nanoMAPS to human immune cells isolated from PBMCs, we demonstrate robust identification of both well-known cell-type-specific surface markers and candidate proteins. We establish nanoMAPS as a promising platform to expand surface proteomics from cultured cells to primary cells isolated from patients or mouse models.
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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) | 0.000 | 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) | 0.001 | 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 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".