Cells Keep Diverse Company in Diseased Tissues
Bibliographic record
Abstract
Emerging spatial profiling technologies have revolutionized our understanding of how tissue architecture shapes disease progression, yet the contribution of cellular diversity remains underexplored. In this issue, Ding and colleagues introduce multiomics and ecological spatial analysis (MESA), an ecology-inspired framework that integrates spatial and single-cell expression data to quantify tissue diversity across multiple scales. MESA both identifies distinct cellular neighborhoods and computes a variety of diversity metrics alongside the identification of diversity "hotspots." Applied to human tonsil tissue, MESA revealed previously undetected germinal center organization, whereas in spleen tissue of a murine lupus model, MESA highlights increasing cellular diversity with disease progression. Importantly, diversity hotspots do not correspond to conventional compartments identified by existing methods, presenting an orthogonal metric of spatial organization. In colorectal cancer, MESA's diversity metrics outperformed established subtypes at predicting patient survival, whereas in hepatocellular carcinoma, multiomic integration identified significantly more ligand-receptor interactions between immune cells compared with single-modality analysis. This work establishes cellular diversity within tissues as a critical correlate of disease progression and underscores the value of multiomic integration in spatial biology. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".