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 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".