Advantages of Incomplete Digestion in Human Hair Shaft Proteomics, a Focus on Cuticular Keratins
Bibliographic record
Abstract
ABSTRACT Rationale The proteome of the hair shaft has been increasingly studied by mass spectrometry for sensitive, accurate, and comprehensive characterization of major hair proteins such as cuticular keratins for biomedical and forensic applications. As an external appendage of human skin, the shaft of scalp hair is formed by dead keratinized cells that are biologically and chemically stable. The extraction and digestion of hair shaft proteins have been bottlenecks in hair proteomics. Methods We present a straightforward and reliable sample preparation procedure using a commercial Precellys homogenizer in mild basic conditions. We further shortened the sample preparation procedure by implementing an overnight tryptic digestion for partial proteolysis instead of a 3‐day complete digestion. Results Using this method, we achieved over 75% protein extraction efficiency from the shaft of human scalp hair, and the limited proteolysis improved keratin sequence coverage. The robustness of our method was confirmed by high reproducibility, with R 2 values exceeding 0.95 in pairwise quantitative comparisons via spectra counting across different operators, processes, and laboratories. Conclusions We developed a facile and robust sample preparation strategy for human hair shafts. The improved sequence coverage in cuticular keratins by shortened and incomplete proteolysis is critical for the identification of genetically variant peptides in keratins.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".