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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
1Division of Abdominal Organ Transplantation and Hepatobiliary Surgery, Department of Surgery, Keck School of Medicine, University of Southern California, 1510 San Pablo Street, Suite 412, Los Angeles, CA, 90033, USA.
Scientific Reports
|February 13, 2024
Summary
Tissue handling impacts transcriptomic data quality in liver fibrosis studies. This research developed a framework for single-cell spatial transcriptomics in biobanked samples, revealing cell phenotypes and interactions.
Area of Science:
- Molecular biology
- Bioinformatics
- Pathology
Background:
- Single-cell and spatial omics offer deep insights into disease mechanisms but face challenges with cost and pre-analytical variability.
- Spatial transcriptomics lacks single-cell resolution, necessitating deconvolution methods for cell type prediction.
- Biobanked clinical samples are valuable but require careful handling for reliable omics analysis.
Purpose of the Study:
- To establish best practices for tissue handling, data processing, and downstream analysis of biobanked liver samples using spatial transcriptomics and single nucleus RNA sequencing (snRNAseq).
- To generate a spatially resolved, single-cell dataset from advanced fibrosis liver samples.
- To investigate cell-cell interactions using both snRNAseq and spatial transcriptomics data.
Main Methods:
- Performed spatial transcriptomics and snRNAseq on matched normal and advanced fibrosis liver specimens.
- Applied single-cell mapping of spatial transcriptomes using paired snRNAseq data.
- Utilized ligand-receptor analysis for predicting cell-cell interactions from snRNAseq data.
Main Results:
- Tissue preservation techniques significantly impact transcriptomic data quality, particularly in fibrotic liver tissue.
- Generated a high-resolution dataset with 24 distinct liver cell phenotypes mapped in space.
- Observed poor correlation between cell-cell interactions predicted by snRNAseq ligand-receptor analysis and spatial transcriptomics.
Conclusions:
- Tissue handling protocols are critical for the integrity of transcriptomic data in liver disease research.
- The study provides a validated framework for creating spatially resolved, single-cell datasets from biobanked liver samples.
- Highlights discrepancies in predicting cell-cell interactions between different omics approaches, suggesting further methodological refinement is needed.
