Protocol for generating high-fidelity proteomic profiles using DROPPS
Bibliographic record
Abstract
Deep mass spectrometry-based proteomic profiling of rare cell populations has been constrained by sample input requirements. Here, we present a protocol for droplet-based one-pot preparation for proteomic samples (DROPPS), an accessible low-input platform that generates high-fidelity proteomic profiles of 100–2,500 cells. We describe steps for depositing cellular material, cell lysis, and digesting proteins in the same microliter-droplet well. We anticipate DROPPS will accelerate biology-driven proteomic research for a multitude of rare cell populations. For complete details on the use and execution of this protocol, please refer to Waas et al. 1 • DROPPS is an accessible low-input proteomic method for 100–2,500 cells • Cells can be deposited with standard pipette or cell sorter • LC-MS/MS-compatible reagents are used for cell lysis and protein digestion Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Deep mass spectrometry-based proteomic profiling of rare cell populations has been constrained by sample input requirements. Here, we present a protocol for droplet-based one-pot preparation for proteomic samples (DROPPS), an accessible low-input platform that generates high-fidelity proteomic profiles of 100–2,500 cells. We describe steps for depositing cellular material, cell lysis, and digesting proteins in the same microliter-droplet well. We anticipate DROPPS will accelerate biology-driven proteomic research for a multitude of rare cell populations.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.034 | 0.036 |
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".