Streamlining single-cell spatial transcriptomics for human kidney tissue
Bibliographic record
Abstract
Single-cell spatial technologies have emerged in recent years, enabling characterization of tissue architecture and organization at unprecedented resolution. However, computational analysis of spatial transcriptomics data remains a bottleneck for scientific discoveries in the absence of dedicated bioinformatics expertise. Here, we describe a novel cell area normalization method and workflow to annotate 15 kidney cell types from a dataset generated using NanoString’s CosMx single-cell-resolution spatial transcriptomic platform. This approach enabled a comparison between two healthy kidney biopsies and two diseased samples. We validated our pipeline’s accuracy using gene expression analysis, demonstrating increased sensitivity compared with other normalization methods and consistency with pathological changes observed in biopsy-proven diabetic kidney disease (DKD). Using precise cell type annotation, we observed significant changes in the proportions of podocytes and immune cells in DKD, with regional enrichment of immune cells and differential gene expression. Injured proximal tubules showed the expected upregulation of HAVCR1 and VCAM1, as well as other genes associated with diabetes, including IL18, ITGA3, and ITGB1. The workflow, now fully integrated into the BioTuring SpatialX (Lens V2.0), is available as a platform designed for users with no formal bioinformatics training, providing accessible web-based tools for spatial data analysis.
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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) | 0.000 | 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) | 0.000 | 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".