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Updated: Jul 16, 2025
09:09
Isolation of Nuclei from Flash-Frozen Liver Tissue for Single-Cell Multiomics
Published on: December 9, 2022
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Technical optimization of spatially resolved single-cell transcriptomic datasets to study clinical liver disease
Brittany Rocque1, Kate Guion1, Pranay Singh1
1University of Southern California.
Research Square
|September 18, 2023
Summary
Tissue handling impacts RNA quality in liver disease studies. This research integrates spatial transcriptomics and single nucleus RNA sequencing to create a single-cell resolution map of liver fibrosis, revealing cell interactions and gene expression patterns.
Area of Science:
- Biotechnology
- Genomics
- Pathology
Background:
- Single cell and spatial omics offer deep insights into disease mechanisms.
- Transcriptomic platforms face limitations in sample size and pre-analytical variable impact.
- Spatial transcriptomics lacks single-cell resolution, necessitating deconvolution methods.
Conclusions:
- Established a framework for analyzing biobanked liver samples using integrated omics.
- Highlighted the importance of tissue handling for reliable transcriptomic analysis in liver disease.
- Demonstrated the utility of combining spatial and single-cell data for understanding liver cell phenotypes and interactions.
