Single-Cell Profiling Reveals Sex-Based Transcriptional Programs in Healthy Human Kidney
Bibliographic record
Abstract
Background: Single-cell transcriptomics provide unprecedented insight into disease states in the kidney, yet our understanding of the transcriptional programs of human kidney cells at homeostasis is limited by difficulty accessing healthy, fresh tissue. Sex-based dichotomy in human kidney cells remains unaddressed, but may underpin acute and chronic kidney diseases e.g. progressive diabetic kidney disease and IRI which exhibit a male preponderance. Methods: We sequenced single-cell suspensions of 19 pre-implantation living donor biopsies (9 male, 10 female)(10X Genomics). Analyses were performed with Cellranger and Seurat in R. Sex-based transcriptomic differences were examined using varimax-rotated principal component analysis, machine learning approaches and differential expression analysis. Results: 27677 high-quality cells forming 23 clusters were identified with several immune populations and all anticipated parenchymal populations. Individual kidney populations were examined for separation due to donor sex, with clear separation observed for the PT population alone using varimax-rotated principal component analysis (Fig1a). Machine learning identified the most discriminant subset of genes (Model1: 80 genes) that could correctly classify cell sex (AUC 0.98). 75 genes were differentially expressed between males and females (p-value <0.05, LogFC>0.25). Anti-oxidant metallothionein genes were increased in females. Pathway analysis revealed metabolism-related processes (oxidative phosphorylation, and the TCA cycle) as increased in males (Fig 1B).Figure 1:: Identifying sex-biased gene expression in proximal tubule cells. (A) Varimax rotation of PCA components shows clear separation between PT cells from males and females (B) Depiction of top-ranking terms identified by GSEA analysis as being enriched in males and females respectively. Abbreviations: GSEA: Gene Set Enrichment Analysis; PT, proximal tubular NES, normalized enrichment score.Conclusions: We report striking sex-based transcriptional differences in PT cells, suggesting higher baseline metabolic activity in males, and increased anti-oxidant metallothionein genes in females. These sex-based differences in PT gene expression may provide insights into the well-recognized, but previously unexplained sexual dimorphism observed in kidney diseases.
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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) | 0.002 | 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".