Conserved microglial programmes, non-transferable classifiers: boundaries of the single-cell TBI molecular clock
- 1Department of Neurosurgery, Affiliated Zhangjiagang Hospital of Soochow University, Zhangjiagang, China
Abstract
Background: Traumatic brain injury (TBI) pathology evolves over hours to months, and molecular staging could complement clinical and imaging-based assessment. Single-cell transcriptomics captures cell-type-resolved temporal programs, but whether a transcriptional "molecular clock" trained on one injury model can be transferred to others remains unknown. Methods: We compiled four public mouse TBI single-cell/single-nucleus datasets spanning two injury models - controlled cortical impact (GSE277487; CEREBRI) and fluid percussion injury (GSE160763) - plus an independent mild-FPI cohort reserved for validation (GSE247339), totalling 307,991 cells across four pathological stages (control, acute, subacute, chronic). Classifiers were evaluated at cell and sample levels (held-out libraries) against a permutation null, with per-cell-type, strict leave-one-dataset-out (LODO) generalization, glial-only transfer and gene-level SHAP analyses. Results: Within GSE277487, sample-level balanced accuracy reached 0.67 (permutation null 0.32 +/- 0.09; p = 0.001), and the signal was reproduced in the independent mild-FPI cohort (0.40 vs null 0.19 +/- 0.05; p = 0.0010). Cell-level cross-validation overestimated performance by ~0.16, quantifying within-sample leakage. Cross-dataset generalization was poor (strict LODO balanced accuracy 0.20-0.31); the acute immediate-early response was untransferable (recall 0.002) and 80% of chronic (6-month) cells were assigned to the subacute class. Glial-only models raised subacute recall across models (0.91 and 0.75), but precision was indistinguishable from a subacute-only classifier (0.51 vs 0.50; 0.58 vs 0.58) and one-vs-rest AUROC was at or below chance (0.38-0.54) across four classifier families - classifier collapse, not transferable discrimination. The reproducible cross-model component was instead a gene-expression programme: a microglial complement/lysosomal damage programme (Trem2-Apoe axis) was up-regulated across the severe CCI and FPI models (13/13 concordant genes), whereas the immediate-early module was model-specific. Conclusions: A single-cell molecular clock for TBI staging is feasible within datasets once within-sample leakage is controlled, but it does not transfer across datasets as a classifier: apparent cross-model transfer reflected classifier collapse onto the majority class, and the reproducible cross-model component was a microglial complement/lysosomal gene-expression programme. These findings define the boundaries of cross-dataset time inference in TBI and provide evaluation criteria for claims of transferable single-cell classifiers.
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BibTeX
@misc{gao2026conserved,
title = {{Conserved microglial programmes, non-transferable classifiers: boundaries of the single-cell TBI molecular clock}},
author = {R. Gao and Q. Yan and L. Zhang and X. Xu and X. Qian and M. Li and Y. Zhang},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.07.749982},
url = {https://www.biorxiv.org/content/10.64898/2026.09.07.749982v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Conserved microglial programmes, non-transferable classifiers: boundaries of the single-cell TBI molecular clock
AU - R. Gao
AU - Q. Yan
AU - L. Zhang
AU - X. Xu
AU - X. Qian
AU - M. Li
AU - Y. Zhang
PY - 2026
DA - 2026/09/07
PB - bioRxiv
DO - 10.64898/2026.09.07.749982
UR - https://www.biorxiv.org/content/10.64898/2026.09.07.749982v1
AB - Background: Traumatic brain injury (TBI) pathology evolves over hours to months, and molecular staging could complement clinical and imaging-based assessment. Single-cell transcriptomics captures cell-type-resolved temporal programs, but whether a transcriptional "molecular clock" trained on one injury model can be transferred to others remains unknown. Methods: We compiled four public mouse TBI single-cell/single-nucleus datasets spanning two injury models - controlled cortical impact (GSE277487; CEREBRI) and fluid percussion injury (GSE160763) - plus an independent mild-FPI cohort reserved for validation (GSE247339), totalling 307,991 cells across four pathological stages (control, acute, subacute, chronic). Classifiers were evaluated at cell and sample levels (held-out libraries) against a permutation null, with per-cell-type, strict leave-one-dataset-out (LODO) generalization, glial-only transfer and gene-level SHAP analyses. Results: Within GSE277487, sample-level balanced accuracy reached 0.67 (permutation null 0.32 +/- 0.09; p = 0.001), and the signal was reproduced in the independent mild-FPI cohort (0.40 vs null 0.19 +/- 0.05; p = 0.0010). Cell-level cross-validation overestimated performance by ~0.16, quantifying within-sample leakage. Cross-dataset generalization was poor (strict LODO balanced accuracy 0.20-0.31); the acute immediate-early response was untransferable (recall 0.002) and 80% of chronic (6-month) cells were assigned to the subacute class. Glial-only models raised subacute recall across models (0.91 and 0.75), but precision was indistinguishable from a subacute-only classifier (0.51 vs 0.50; 0.58 vs 0.58) and one-vs-rest AUROC was at or below chance (0.38-0.54) across four classifier families - classifier collapse, not transferable discrimination. The reproducible cross-model component was instead a gene-expression programme: a microglial complement/lysosomal damage programme (Trem2-Apoe axis) was up-regulated across the severe CCI and FPI models (13/13 concordant genes), whereas the immediate-early module was model-specific. Conclusions: A single-cell molecular clock for TBI staging is feasible within datasets once within-sample leakage is controlled, but it does not transfer across datasets as a classifier: apparent cross-model transfer reflected classifier collapse onto the majority class, and the reproducible cross-model component was a microglial complement/lysosomal gene-expression programme. These findings define the boundaries of cross-dataset time inference in TBI and provide evaluation criteria for claims of transferable single-cell classifiers.
ER -