Abstract
The underlying cellular events regulating kidney disease progression and metabolic maintenance are incompletely understood. Current droplet microfluidics-based single-cell profiling methods are limited in throughput, sample multiplexing ability and experimental costs. In the first project, we optimized single-cell combinatorial indexing (sci) RNA sequencing to analyze >300,000 cells from mouse kidney fibrosis models. We presented diverse injury states of the proximal tubule, including one population with transiently activated lipid metabolism and accumulation of PLIN2-coated lipid droplets. In the second project, we profiled human kidney samples from different anatomical regions with sci-based simultaneous RNA and open chromatin accessibility sequencing and spatially resolved metabolomics, and analyzed over a million single-cell transcriptomes, epigenomes and metabolomes. We identified the same tubular cell types have distinct molecular signatures depending on regional location. Overall, these studies promote our understanding of kidney metabolism and heterogeneity and present new therapeutic targets.
Committee Chair
Benjamin Humphreys
Degree
Doctor of Philosophy (PhD)
Author's Department
Biology & Biomedical Sciences (Molecular Genetics & Genomics)
Document Type
Dissertation
Date of Award
12-11-2023
Language
English (en)
DOI
https://doi.org/10.7936/qsqe-p140
Author's ORCID
https://orcid.org/0000-0003-3697-5662
Recommended Citation
Li, Haikuo, "Investigating Kidney Cellular Heterogeneity and Metabolism with Single-Cell Combinatorial Indexing and Spatial Multiomics" (2023). Arts & Sciences Theses and Dissertations. 3198.
The definitive version is available at https://doi.org/10.7936/qsqe-p140