Distinct Layer 6b transcriptomic subtypes parcellate the cortical mantle
Bibliographic record
Abstract
Layer 6b (L6b) neurons are a sparse population of deep neocortical neurons that govern both healthy and disordered brain states. L6b neurons have qualitatively been characterized as a thin lamina within the deepest layer of the cerebral cortex, yet the precise cell-type-specific properties and spatial organization of these neurons across the cortical mantle remain unresolved. Here, we combine single-cell RNA sequencing, highly multiplexed fluorescent in situ hybridization, and single-cell spatial transcriptomics to comprehensively characterize L6b cell-type identity, molecular heterogeneity, and spatial organization. In doing so, we identify and spatially resolve multiple distinct L6b subtypes with unique molecular signatures. To investigate the spatial organization of these subtypes across the brain, we generated a single-cell spatial transcriptomics dataset comprising 450,496 cells, offering the most extensive spatial mapping of L6b subtypes to date. Using a data-driven approach to analyze this dataset, we identify that the spatial patterning of L6b varies across the cortical mantle according to a patchwork-like composition of subtypes, which can notably extend beyond the classically defined deep location of L6b for some subtypes. We also find that L6b neurons can be transcriptionally separable but spatially intermingled with Layer 6a neurons, illustrating that a deep location within the cortex is neither sufficient nor necessary for assessing L6b identity. Our work provides the most comprehensive cellular phenotyping of L6b to date, reveals a cell-type-specific spatial-molecular framework for interpreting L6b properties and function, and will guide future investigations on the role of L6b cell subtypes and molecules in brain health and disorder.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".