Abstract: FR-OR58
Multiplexed Single-Nucleus RNA and ATAC Sequencing in the Renal Biopsy Specimen
Session Information
- Kidney Pathology: From Classic Clinicopathologic Studies to Computational Pathology
November 03, 2023 | Location: Room 109, Pennsylvania Convention Center
Abstract Time: 05:33 PM - 05:42 PM
Category: Pathology and Lab Medicine
- 1800 Pathology and Lab Medicine
Authors
- Xu, Lubin, Peking Union Medical College Hospital, Dongcheng-qu, Beijing, China
- Li, Yun, Beijing Institute of Genomics Chinese Academy of Sciences, Beijing, Beijing, China
- Huang, Zheng, Beijing Institute of Genomics Chinese Academy of Sciences, Beijing, Beijing, China
- Wang, Minxian, Beijing Institute of Genomics Chinese Academy of Sciences, Beijing, Beijing, China
- Jiang, Lan, Beijing Institute of Genomics Chinese Academy of Sciences, Beijing, Beijing, China
- Chen, Limeng, Peking Union Medical College Hospital, Dongcheng-qu, Beijing, China
Background
Multi-omic technology allows simultaneous transcriptomic and epigenomic profiling in various kidney cell types, bringing about opportunities to identify crucial pathways, novel biomarkers, and physiology-relevant disease subtypes, while limited by cost and the amount of tissue required.
Methods
We developed a novel multiplexed droplet-based single-cell/nucleus sequencing technique to achieve higher throughput and lower cost with less tissue. We applied this method to simultaneously profile transcriptomic (RNAseq) and chromosomic access (ATACseq) data in 27 patients who underwent renal biopsy due to various kidney diseases, ten time-zero transplant biopsy samples, and seven paracancerous kidney samples.
Results
After quality control, we yielded about 120,000 nuclei (mean nuclei number 2727 per sample) with both transcriptomic and epigenomic profiling data. For snRNAseq, the median gene number per cell is more than 1100. For snATAC-seq, the median fragments number per cell is about 6000, and the TSS enrichment score is 8.83. Major renal cells types including proximal tubule cells, descending thin limb cells, thick ascending limb cells, distal convoluted tubule cells, connecting tubule cells, principal cells, intercalated cells, podocytes, endothelial cells, vascular smooth muscle cells, as well as infiltrating B cells, T cells, and fibroblasts can be robustly identified from the snRNAseq dataset.
Conclusion
We developed a novel single-nucleus multi-omic approach that offers rich transcriptomic and epigenomic data, showing promise as a molecular renal pathology technique.
Funding
- Government Support – Non-U.S.