Proteomic/transcriptomic analysis of erythropoiesis
Bibliographic record
Abstract
PURPOSE OF REVIEW: Erythropoiesis is a hierarchical process by which hematopoietic stem cells give rise to red blood cells through gradual cell fate restriction and maturation. Deciphering this process requires the establishment of dynamic gene regulatory networks (GRNs) that predict the response of hematopoietic cells to signals from the environment. Although GRNs have historically been derived from transcriptomic data, recent proteomic studies have revealed a major role for posttranscriptional mechanisms in regulating gene expression during erythropoiesis. These new findings highlight the need to integrate proteomic data into GRNs for a refined understanding of erythropoiesis. RECENT FINDINGS: Here, we review recent proteomic studies that have furthered our understanding of erythropoiesis with a focus on quantitative mass spectrometry approaches to measure the abundance of transcription factors and cofactors during differentiation. Furthermore, we highlight challenges that remain in integrating transcriptomic, proteomic, and other omics data into a predictive model of erythropoiesis, and discuss the future prospect of single-cell proteomics. SUMMARY: Recent proteomic studies have considerably expanded our knowledge of erythropoiesis beyond the traditional transcriptomic-centric perspective. These findings have both opened up new avenues of research to increase our understanding of erythroid differentiation, while at the same time presenting new challenges in integrating multiple layers of information into a comprehensive gene regulatory model.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".