Systematic Comparison of High-throughput Single-Cell and Single-Nucleus Transcriptomes during Cardiomyocyte Differentiation

Abstract

A comprehensive reference map of all cell types in the human body is necessary for improving our understanding of fundamental biological processes and in diagnosing and treating disease. Highthroughput single-cell RNA sequencing techniques have emerged as powerful tools to identify and characterize cell types in complex and heterogeneous tissues. However, extracting intact cells from tissues and organs is often technically challenging or impossible, for example in heart or brain tissue. Single-nucleus RNA sequencing provides an alternative way to obtain transcriptome profiles of such tissues. To systematically assess the differences between high-throughput single-cell and single-nuclei RNA-seq approaches, we compared Drop-seq and DroNc-seq, two microfluidic-based 3′ RnA capture technologies that profile total cellular and nuclear RNA, respectively, during a time course experiment of human induced pluripotent stem cells (iPSCs) differentiating into cardiomyocytes. Clustering of timeseries transcriptomes from Drop-seq and DroNc-seq revealed six distinct cell types, five of which were found in both techniques. Furthermore, single-cell trajectories reconstructed from both techniques reproduced expected differentiation dynamics. We then applied DroNc-seq to postmortem heart tissue to test its performance on heterogeneous human tissue samples. Our data confirm that DroNcseq yields similar results to Drop-seq on matched samples and can be successfully used to generate reference maps for the human cell atlas.

Gene expression variability in human and chimpanzee populations share common determinants

“…Cell type heterogeneity has likely affected previous comparative studies of gene regulation that used primary tissue samples, including studies from our own lab. We and others have commented on this property of primary tissue comparisons in the past ( Avila Cobos et al, 2018 ; Blekhman et al, 2008 ; Newman et al, 2015 ; Selewa et al, 2020 ), but without single cell data it was impossible to effectively assess the magnitude of this effect. Our findings further underscore the need for single cell measurements to disentangle sources of variation in bulk RNA-seq data, especially from primary tissues.…”

“Single-nucleus RNA-seq2 reveals a functional crosstalk between liver zonation and ploidy”

“…In this context, single-nucleus RNA-seq has emerged as a complementary approach that relies on the unbiased assessment of nuclei from all cells present in a tissue [20,21,36]. The analysis of the nuclear transcriptome has been proven to be very powerful to study cell type diversity in the mouse and human tissues, including brain [21,[23][24][25][26][27]111]; spinal cord [50]; breast cancer [51]; kidney [29][30][31][32]; lung [28]; heart [33,34]; and a variety of human tumor samples [36].…”

“…Single-nucleus RNA-seq (snRNA-seq) has emerged as a complementary approach to study complex tissues at a single-cell level [20,21], including brain [21][22][23][24][25][26][27], lung [28], kidney [29][30][31][32] and heart [33,34] in mouse and human frozen samples [35,36]. However, there are no snRNA-seq methods tailored for frozen liver tissues.…”

Big Data Approaches in Heart Failure Research

“…Since cardiomyocytes (CM) are too large for many cell sorting approaches, single nucleus RNAseq can be applied, which involves isolating the nucleus rather than the whole cell prior to sequencing. The transcriptional profile of single cell and single nucleus RNAseq has been reported to be comparable during CM differentiation [74]. As the transcriptional profiles of mono-and polynucleated CM were reported to be similar [75], application of single nucleus RNAseq on cardiac tissue is encouraged.…”