Understanding Erythropoiesis Using Quantitative Proteomics and Single Cell Mass Cytometry
Bibliographic record
Abstract
The process of erythropoiesis whereby hematopoietic stem cells (HSC) differentiate into cells with increasingly restricted potential is regulated by a network of lineage-specifying transcription factors (LS-TFs) that promote erythropoiesis while simultaneously repressing other hematopoietic lineages. While TFs that stimulate differentiation towards the erythroid lineage (such as GATA1 and KLF1/EKLF) are more abundant in erythroid progenitors and precursors, these proteins are also expressed in hematopoietic stem/progenitor cells (HSPCs) although at very low levels. This suggests that the dosage of TFs plays an important role in lineage determination. Consistent with this, previous lineage reprogramming experiments have shown that ectopic expression of the same LS-TF can give rise to distinct cell fates depending on the TF's abundance. Furthermore, a controversial model proposed that TFs belonging to competing hematopoietic lineages are co-expressed in bipotential progenitors, and that changes in their relative levels drive differentiation towards one fate or another. Taken together, this suggests that changes in LS-TFs stoichiometry may be central to cell fate choice and lineage commitment. While gene regulatory networks have been established to model the process of cell fate decision in bipotential progenitors, these network models are based on mRNA measurements and have not integrated protein levels of TFs. This is a problem as protein levels do not always correlate with mRNA levels, and as such the gene regulatory network underlying erythroid lineage determination is currently unclear. While standard proteomic approaches such as Western blot or data-dependent mass spectrometry (i.e. shotgun mass spectrometry) are useful to measure changes in the relative level of a single protein over time, these approaches do not provide information on the relative levels between several proteins in the same sample, and as such, changes in protein stoichiometry for master regulators of erythropoiesis remain mostly unknown. To address these questions and to provide a better understanding of the role and importance of quantitative changes in LS-TFs for the process of erythroid lineage commitment, we have used a combination of single cell proteomic (i.e. mass cytometry or CyTOF) and targeted mass spectrometry (i.e. SRM for selected reaction monitoring coupled with the spiking of known amounts of internal standard peptides) approaches to measure changes in protein levels of master regulators of hematopoiesis and erythropoiesis. As a model system for human erythropoiesis, we used cord-blood derived CD34+ HSPCs that are driven to differentiate along the erythroid lineage using a combination of growth factors and cytokines 1. Cells were harvested every second day from HSPCs to differentiated erythroid cells. For CyTOF analysis, cells were barcoded at each time-point, combined and stained with a cocktail of antibodies against 11 cell surface markers and 16 TFs. Clustering analysis was then used to establish a temporal trajectory of erythropoiesis. The data revealed that competing LS-TFs proteins (e.g. KLF1 a pro-erythroid factor and FLI1 a pro-megakaryocyte factor) are co-expressed in bipotential progenitors at equimolar levels. Furthermore, relative levels of KLF1 vs FLI1 change gradually as the cells progress along the erythroid trajectory. Finally, ectopic expression of FLI1 in early progenitors actively deviates cells from their preferred erythroid trajectory towards a megakaryocytic lineage 2. Thus, our results support the concept that temporally-regulated quantitative changes in TFs protein levels in individual hematopoietic progenitors are key determinants of cell fate decision in human erythropoiesis. Current studies are ongoing to identify additional pairs of LS-TFs that regulate other hematopoietic transitions. Furthermore, a dynamic gene regulatory network of erythroid lineage commitment that integrates protein and mRNA data for master regulators of hematopoiesis has been established. Giarratana MC, Kobari L, Lapillonne H, et al. Ex vivo generation of fully mature human red blood cells from hematopoietic stem cells. Nat Biotechnol. 2005;23(1):69-74. Palii CG, Cheng Q, Gillespie MA, et al. Single-Cell Proteomics Reveal that Quantitative Changes in Co-expressed Lineage-Specific Transcription Factors Determine Cell Fate. Cell Stem Cell. 2019;24(5):812-820 e815. Disclosures No relevant conflicts of interest to declare.
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 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".