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Published on: April 29, 2014
Single-cell spatial transcriptomics of fixed, paraffin-embedded biopsies reveals colitis-associated cell networks
Elvira Mennillo1, Madison L Lotstein2,3, Gyehyun Lee1
1Division of Gastroenterology, Department of Medicine, University of California, San Francisco, San Francisco, CA 94143, USA.
Imaging-based, single-cell spatial transcriptomics (iSCST) on archived FFPE biopsies identifies inflammation-associated fibroblasts and monocytes as key in ulcerative colitis. This framework aids biomarker discovery for patient risk stratification.
Area of Science:
- Biotechnology
- Genomics
- Immunology
Background:
- Archived formalin-fixed, paraffin-embedded (FFPE) tissues offer vast potential for translational research.
- Developing robust imaging-based, single-cell spatial transcriptomics (iSCST) for FFPE samples is crucial for analyzing clinical specimens.
Purpose of the Study:
- To establish a reliable iSCST framework for analyzing archived FFPE mucosal biopsies from inflammatory bowel disease (IBD) patients.
- To benchmark different iSCST platforms for FFPE tissue analysis.
Main Methods:
- Benchmarking three iSCST platforms using 57 FFPE mucosal biopsies (9 healthy controls, 11 ulcerative colitis patients).
- Applying a uniform data processing pipeline including cell segmentation, transcript detection, annotation, differential gene expression, and neighborhood enrichment.
- Validating iSCST findings with external bulk transcriptomic datasets.
Main Results:
- A custom 290-plex Xenium gene panel demonstrated high sensitivity and specificity for transcript detection in FFPE tissues.
- Identified inflammation-associated fibroblasts (IAFs) and monocytes as a colitis-associated cellular neighborhood.
- Discovered distinct transcriptomic signatures associated with Vedolizumab (VDZ) responsiveness, differentiating responders from non-responders.
Conclusions:
- The optimized iSCST framework enables the study of colitis-associated cellular networks in routine clinical FFPE samples.
- FFPE-based biomarkers derived from iSCST can be integrated into clinical workflows for potential patient risk stratification.
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