172 The application of single-cell resolved spatial transcriptomics to prostate adenocarcinoma reveals tumor microenvironmental signatures that correlate with distinct histological features
Bibliographic record
Abstract
<h3>Background</h3> Prostate cancer is the most common cancer among men worldwide, and second leading cause of cancer deaths in American men. New spatial methods to resolve distinct tumor microenvironmental features are needed due to significant intratumor heterogeneity. High resolution untargeted spatial transcriptomics provides a unique window into this disease, by permitting the simultaneous detection of known and novel prostate cancer biomarkers. Here we apply STOmics stereo-seq spatial transcriptomics workflow to a prostate cancer specimen, to identify ties between gene expression and disease pathology. <h3>Methods</h3> A stage IV Prostate adenocarcinoma frozen tissue OCT block was selected from BioChain’s repository, with a RIN score of 8.3. This sample was processed through the STOMics workflow, with cryosectioning performed on the OCT section, which was mounted directly on the chip after block acclimation in the cryostat. Standard protocols were followed per Complete Genomics’ guidelines for fixing, staining and imaging the tissue section. A cDNA library was prepared post permeabilization and reverse transcription. Post-sequencing, standard single-cell QC metrics were applied, and a threshold set to distinguish highly variable genes, followed by normalization, dimensionality reduction, and leiden clustering, to produce distinct gene expression clusters. Spatial alignment was performed to map the distinct clusters spatially to a digitized hematoxylin and eosin stained slide, to map the gene expression back to tissue morphology. <h3>Results</h3> The unique leiden clusters, defined purely based on differential gene expression within the transcriptomic sequencing, clustered into distinct biological regions within the prostate cancer tissue. Most apparent, the distinct boundaries of differentially expressed gene clusters appeared in many cases to map to distinct transitional regions of morphological change. Functionally, clusters high in epithelial cell content included significant log fold increase in several genes associated with more aggressive prostate cancer over benign disease such as KLK3, MMP26, and MALAT1, also containing markers associated with cell motility. Other distinct clusters included genes associated with protein folding and maturation, epithelial to mesenchymal transition, and ion transport. Several immunoglobulin heavy constant gamma genes were noted in a region enriched in stromal content. <h3>Conclusions</h3> Development of an analytical pipeline to interrogate single-cell resolved spatial transcriptomics data permitted a more comprehensive understanding of differential gene expression at the cell and tissue levels, which allowed us to tease out distinct gene expression pathways mapped to particular tissue regions. Our work provides a framework to resolve untargeted transcriptomics at high resolution, permitting a more complete understanding of the tumor microenvironment.
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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".