Heterogeneous combinatorial expression of Hoxd genes in single cells during limb development
Bibliographic record
Abstract
BACKGROUND: Global analyses of gene expression during development reveal specific transcription patterns associated with the emergence of various cell types, tissues, and organs. These heterogeneous patterns are instrumental to ensure the proper formation of the different parts of our body, as shown by the phenotypic effects generated by functional genetic approaches. However, variations at the cellular level can be observed within each structure or organ. In the developing mammalian limbs, expression of Hox genes from the HoxD cluster is differentially controlled in space and time, in cells that will pattern the digits and the forearms. While the Hoxd genes broadly share a common regulatory landscape and large-scale analyses have suggested a homogenous Hox gene transcriptional program, it has not previously been clear whether Hoxd genes are expressed together at the same levels in the same cells. RESULTS: We report a high degree of heterogeneity in the expression of the Hoxd11 and Hoxd13 genes. We analyzed single-limb bud cell transcriptomes and show that Hox genes are expressed in specific combinations that appear to match particular cell types. In cells giving rise to digits, we find that the expression of the five relevant Hoxd genes (Hoxd9 to Hoxd13) is unbalanced, despite their control by known global enhancers. We also report that specific combinatorial expression follows a pseudo-time sequence, which is established based on the transcriptional diversity of limb progenitors. CONCLUSIONS: Our observations reveal the existence of distinct combinations of Hoxd genes at the single-cell level during limb development. In addition, we document that the increasing combinatorial expression of Hoxd genes in this developing structure is associated with specific transcriptional signatures and that these signatures illustrate a temporal progression in the differentiation of these 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 | 0.000 | 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) | 0.001 | 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".