Spatial transcriptome of developmental mouse brain reveals temporal dynamics of gene expressions and heterogeneity of the claustrum
Bibliographic record
Abstract
ABSTRACT During the development of the mammalian cerebral cortex, numerous neurons are arranged in a six-layer structure with an inside-out fashion to form the neocortex and wire neural circuits. This process includes cell proliferation, differentiation, migration, and maturation, supported by precise genetic regulation. To understand this sequence of processes at the cellular and molecular levels, it is necessary to characterize the fundamental anatomical structures by gene expression. However, markers established in the adult brain sometimes behave differently in the fetal brain, actively changing during development. Spatial transcriptomes yield genome-wide gene expression profiles from each spot patterned on tissue sections, capturing RNA molecules from fresh-frozen sections and enabling sequencing analysis while preserving spatial information. However, a deeper understanding of this data requires computational estimation, including integration with single-cell transcriptome data and aggregation of spots on the single-cell cluster level. The application of such analysis to biomarker discovery has only begun recently, and its application to the developing fetal brain is largely unexplored. In this study, we performed a spatial transcriptome analysis of the developing mouse brain to investigate the spatiotemporal regulation of gene expression during development. Using these data, we conducted an integrated study with publicly available mouse data sets, the adult brain’s spatial transcriptome, and the fetal brain’s single-cell transcriptome. Our data-driven analysis identified novel molecular markers of the choroid plexus, piriform cortex, thalamus, and claustrum. In addition, we revealed that the internal structure of the embryonic claustrum is composed of heterogeneous cell populations.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".