An unbiased genomewide screen uncovers 7 genes that drive hematopoietic stem cell fate from mouse embryonic stem cells
Bibliographic record
Abstract
ABSTRACT: Hematopoietic stem cells (HSCs) possess the ability to long term reconstitute all the blood lineages and generate all blood cell types. As such, the in vitro generation of HSCs remains a central goal in regenerative medicine. Despite many efforts and recent advancements in the field, there is still no robust, reproducible, and efficient protocol for generating bona fide HSCs in vitro. This suggests that certain regulatory elements have yet to be uncovered. Here, we present a novel and unbiased approach to identifying endogenous components to specify HSCs from pluripotent stem cells. We performed a genomewide CRISPR activator screening during mesodermal differentiation from mouse embryonic stem cells. After in vitro differentiation, mesodermal KDR+ precursors were transplanted into primary and secondary immunodeficient NSG mice. This approach led to the identification of 7 genes (Spata2, Aass, Dctd, Eif4enif1, Guca1a, Eya2, and Net1) that, when activated during mesoderm specification, induce the generation of hematopoietic stem and progenitor cells. These cells are capable of serial engraftment and multilineage output (erythroid, myeloid, and T and B lymphoid) in vivo. Single-cell RNA sequencing further revealed that activating these 7 genes biases the embryoid bodies toward intraembryonic development, instead of extraembryonic, increasing the number of mesodermal progenitors that can generate HSCs. Our findings underscore the importance of differentiation during the first germ layer specification to generate definitive blood 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".