Enrichment of Low-Abundance and Low-Molecular-Weight Proteins in Human Milk Using Mass Spectrometry-Based Proteomics
Bibliographic record
Abstract
Human milk is a uniquely complex biological fluid, rich in bioactive components such as proteins, antibodies, enzymes, and hormones that are critical for immunity, development, and disease prevention. However, studying these trace proteins requires specialized depletion methods to counteract the dominance of abundant proteins. In this study, we systematically evaluated five different protein depletion methods: centrifugation, organic solvent-based approaches, acid precipitation, and a commercial kit (CK) to assess their efficacy in enriching low-abundance proteins (LAPs) for LC-MS/MS analysis. The comparative analysis of different depletion methods selectively enriched the sequencing depth of LAPs, highlighting the necessity of method selection in milk proteomics research. Our results further revealed that perchloric acid (PerCA) precipitation was the most effective method for identifying unique low-molecular-weight proteins (LMWPs), and optimization of the abundant protein depletion strategy could greatly increase (>10%) the extraction of LMWPs from milk samples. Additionally, several new proteins were found in our investigation when compared to publicly available milk proteome data. Thus, expanding the list of human milk proteomes enriched the data set and offered valuable insights into the complex biological functions of human milk and its impact on neonatal research.
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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 | 0.001 | 0.001 |
| 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".