Comparison of different digestion methods for proteomic analysis of isolated cells and FFPE tissue samples
Bibliographic record
Abstract
Proteomics of human tissues and isolated cellular subpopulations create new opportunities for therapy and monitoring of a patients' treatment in the clinic. Important considerations in such analysis include recovery of adequate amounts of protein for analysis and reproducibility in sample collection. In this study we compared several protocols for proteomic sample preparation: i) filter-aided sample preparation (FASP), ii) in-solution digestion (ISD) and iii) a pressure-assisted digestion (PCT) method. PCT method is known for already a decade [1], however it is not widely used in proteomic research. We assessed protocols for proteome profiling of isolated immune cell subsets and formalin-fixed paraffin embedded (FFPE) tissue samples. Our results show that the ISD method has very good efficiency of protein and peptide identification from the whole proteome, while the FASP method is particularly effective in identification of membrane proteins. Pressure-assisted digestion methods generally provide lower numbers of protein/peptide identifications, but have gained in popularity due to their shorter digestion time making them considerably faster than for ISD or FASP. Furthermore, PCT does not result in substantial sample loss when applied to samples of 50 000 cells. Analysis of FFPE tissues shows comparable results. ISD method similarly yields the highest number of identifications. Furthermore, proteins isolated from FFPE samples show a significant reduction of cleavages at lysine sites due to chemical modifications with formaldehyde-such as methylation (+14 Da) being among the most common. The data we present will be helpful for making decisions about the robust preparation of clinical samples for biomarker discovery and studies on pathomechanisms of various diseases.
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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".