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Published on: January 9, 2019
Single Nucleus Total RNA Sequencing of Formalin-Fixed Paraffin-Embedded Gliomas
Ziye Xu1, Lingchao Chen2, Xin Lin1
1Department of Laboratory Medicine of The First Affiliated Hospital & Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou, 310003, China.
Abstract:
Gliomas, the predominant form of brain cancer, comprise diverse malignant subtypes with limited curative therapies available. The insufficient understanding of their molecular diversity and evolutionary processes hinders the advancement of new treatments. Technical complexities associated with formalin-fixed paraffin-embedded (FFPE) clinical samples hinder molecular-level analyses of gliomas. Current single-cell RNA sequencing (scRNA-seq) platforms are inadequate for large-scale clinical applications. In this study, automated snRandom-seq is developed, a high-throughput single-nucleus total RNA sequencing platform optimized for archival FFPE samples. This platform integrates automated single-nucleus isolation and droplet barcoding systems with the random primer-based scRNA-seq chemistry, accommodating a broad spectrum of sample types. The automated snRandom-seq is applied to analyze 116 492 single nuclei from 17 FFPE samples of various glioma subtypes, including rare clinical samples and matched primary-recurrent glioblastomas (GBMs). The study provides comprehensive insights into the molecular characteristics of gliomas at the single-cell level. Abundant non-coding RNAs (ncRNAs) with distinct expression profiles across different glioma clusters and uncovered promising recurrence-related targets and pathways in primary-recurrent GBMs are identified. These findings establish automated snRandom-seq as a robust tool for scRNA-seq of FFPE samples, enabling exploration of molecular diversities and tumor evolution. This platform holds significant implications for large-scale integrative and retrospective clinical research.
Insights
A new automated single-nucleus RNA sequencing method (snRandom-seq) analyzes formalin-fixed paraffin-embedded (FFPE) glioma samples. This advances understanding of brain cancer molecular diversity and evolution.
Area of Science:
- Oncology
- Genomics
- Bioinformatics
Background:
- Gliomas are diverse brain cancers with limited treatment options.
- Understanding glioma molecular diversity and evolution is crucial for new therapies.
- Analyzing formalin-fixed paraffin-embedded (FFPE) clinical samples is challenging for molecular studies.
Purpose of the Study:
- To develop a high-throughput single-nucleus RNA sequencing platform for FFPE samples.
- To analyze the molecular characteristics of various glioma subtypes, including primary-recurrent glioblastomas (GBMs).
- To identify non-coding RNAs and recurrence-related targets in gliomas.
Main Methods:
- Development of automated snRandom-seq, a platform integrating automated single-nucleus isolation and droplet barcoding.
- Application of snRandom-seq to 116,492 single nuclei from 17 FFPE glioma samples.
- Analysis of non-coding RNA expression and identification of recurrence-related pathways.
Main Results:
- Automated snRandom-seq effectively analyzes FFPE samples, accommodating diverse glioma subtypes.
- Distinct non-coding RNA expression profiles were identified across glioma clusters.
- Promising recurrence-related targets and pathways were uncovered in primary-recurrent GBMs.
Conclusions:
- Automated snRandom-seq is a robust tool for single-cell RNA sequencing of FFPE samples.
- The platform enables exploration of glioma molecular diversity and tumor evolution.
- This technology has significant implications for large-scale integrative and retrospective clinical research in brain cancer.
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