172 The application of single-cell resolved spatial transcriptomics to prostate adenocarcinoma reveals tumor microenvironmental signatures that correlate with distinct histological features
Notice bibliographique
Résumé
<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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».