Streamlining Spatial Transcriptomics for Human Kidney Tissue
Bibliographic record
Abstract
Abstract Single-cell spatial technologies have emerged in recent years, enabling tissue architecture and organization characterization at unprecedented resolution. However, computational analysis of spatial transcriptomics data is often a bottleneck for scientific discoveries in the absence of a dedicated bioinformatician. Here, we describes a workflow to annotate cell types from a dataset generated using NanoString’s CosMx single-cell resolution spatial transcriptomic technology, enabling a comparison between healthy kidney biopsies and diseased tissue. We validated our pipeline’s accuracy with both gene expression analysis and pathological changes associated with biopsy-proven diabetic kidney disease (DKD). Through precise cell type annotation, we observed significant changes in the proportions of podocytes and immune cells in DKD, with DKD tissue showing regional enrichment of immune cells and differential gene expression. Notably, injured proximal tubules had the expected increased expression of HAVCR1 and VCAM1 and genes associated with diabetes, including IL18 , ITGA3 and ITGB1 . The entire workflow, now fully integrated into the BioTuring SpatialX (Lens V2.0), is available as a platform designed for users with no formal bioinformatics training.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".