Understanding the kidney one cell at a time

Abstract

A revolution in cellular measurement technology is underway. Whereas prior studies have been able to analyze only the averaged outputs from renal tissue, we now can accurately monitor genome-wide gene expression, regulation, function, cellular history, and cellular interactions in thousands of individual cells in a single experiment. These methods are key drivers in changing our previous morphotype-based organ and disease descriptions to unbiased genomic definitions and therefore improving our understanding of kidney development, homeostasis, and disease.

Keywords: RNA-seq; epigenome; kidney disease; single cell.

Figure 1:. Various single cell RNAseq methods and technologies

Many methods have been developed to separate cells systematically. a. Microfluidic flow technology encapsulates single cells into aqueous droplets in oil. b. Fluorescent Activated Cell Sorting (FACS) can be used for single cell plating in a highly specific manner. FACS allows for concurrent quantitative and qualitative multi-parametric analyses of single cells c. Commercially available microfluidic chips can provide programmed single-cell lysis, RNA extraction, and cDNA synthesis for numerous cells on a single chip at the same time. RNA amplification could be done either in a pooled PCR reaction or individually, and sequencing can be performed for length transcripts or only for the 3’ end of the transcripts. The green indicates the use of individual amplification methods, whereas the red indicates the use of pooled amplification methods.

Figure 2:. Single cells omics analysis Various methods exist to analyze single cell data to provide transcriptomic, epigenomic, spatial, and proteomic information.

Borrowed/Adapted from Goltsev Y, Samusik N, Kennedy-Darling J et al. Deep Profiling of Mouse Splenic Architecture with CODEX Multiplexed Imaging Cell vol 174, Issue 4, Pages 968-981.e15; https://doi.org/10.1016/j.cell.2018.07.010 [29] and Borrowed/Adapted from Ståhl P, Salmén F, Vickovic S et al Visualization and analysis of gene expression in tissue sections by spatial transcriptomics Science 01 Jul 2016: Vol. 353, Issue 6294, pp. 78-82 DOI: 10.1126/science.aaf2403 [59] with permission from AAAS .

Figure 3:. Application of Single Cell RNA Seq information to understand kidney health and disease

a. Single-cell transcriptomic clustering profiles can reveal and identify cell clusters that represent specific cell populations changes in cell population in control and diseased patients’ populations. b. Using pseudotime analysis, single-cell RNA sequencing data can help elucidate cell developmental trajectories during differentiation. c. Single-cell RNA sequencing data can be used to determine expression of genes in specific cells. These genes can be clustered by expression to determine gene regulatory pathways.