Index sorting resolves heterogeneous murine hematopoietic stem cell populations
Bibliographic record
Abstract
Recent advances in the cellular and molecular biology of single stem cells have uncovered significant heterogeneity in the functional properties of stem cell populations. This has prompted the development of approaches to study single cells in isolation, often performed using multiparameter flow cytometry. However, many stem cell populations are too rare to test all possible cell surface marker combinations, and virtually nothing is known about functional differences associated with varying intensities of such markers. Here we describe the use of index sorting for further resolution of the flow cytometric isolation of single murine hematopoietic stem cells (HSCs). Specifically, we associate single-cell functional assay outcomes with distinct cell surface marker expression intensities. High levels of both CD150 and EPCR associate with delayed kinetics of cell division and low levels of differentiation. Moreover, cells that do not form single HSC-derived clones appear in the 7AAD(dim) fraction, suggesting that even low levels of 7AAD staining are indicative of less healthy cell populations. These data indicate that when used in combination with single-cell functional assays, index sorting is a powerful tool for refining cell isolation strategies. This approach can be broadly applied to other single-cell systems, both to improve isolation and to acquire additional cell surface marker information.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".