Rare Cell Proteomic Reactor Applied to Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC)-based Quantitative Proteomics Study of Human Embryonic Stem Cell Differentiation
Bibliographic record
Abstract
The molecular basis governing the differentiation of human embryonic stem cells (hESCs) remains largely unknown. Systems-level analysis by proteomics provides a unique approach to tackle this question. However, the requirement of a large number of cells for proteomics analysis (i.e. 10(6)-10(7) cells) makes this assay challenging, especially for the study of rare events during hESCs lineage specification. Here, a fully integrated proteomics sample processing and analysis platform, termed rare cell proteomic reactor (RCPR), was developed for large scale quantitative proteomics analysis of hESCs with ∼50,000 cells. hESCs were completely extracted by a defined lysis buffer, and all of the proteomics sample processing procedures, including protein preconcentration, reduction, alkylation, and digestion, were integrated into one single capillary column with a strong cation exchange monolith matrix. Furthermore, on-line two-dimensional LC-MS/MS analysis was performed directly using RCPR as the first dimension strong cation exchange column. 2,281 unique proteins were identified on this system using only 50,000 hESCs. For stable isotope labeling by amino acids in cell culture (SILAC)-based quantitative study, a ready-to-use and chemically defined medium and an in situ differentiation procedure were developed for complete SILAC labeling of hESCs with well characterized self-renewal and differentiation properties. Mesoderm-enriched differentiation was studied by RCPR using 50,000 hESCs, and 1,086 proteins were quantified with a minimum of two peptides per protein. Of these, 56 proteins exhibited significant changes during mesoderm-enriched differentiation, and eight proteins were demonstrated for the first time to be overexpressed during early mesoderm development. This work provides a new platform for the study of rare cells and in particular for further elucidating proteins that govern the mesoderm lineage specification of human pluripotent stem cells.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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 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".