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. High-throughput 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 time-series 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 DroNc-seq yields similar results to Drop-seq on matched samples and can be successfully used to generate reference maps for the human cell atlas.
Highlights
The identification and characterization of cell types from solid tissues and organs in the human body is the necessary basis for a comprehensive reference map of all human cells[1]
We performed a systematic comparison of Drop-seq and DroNc-seq using time-course data from human induced pluripotent stem cells (iPSCs) differentiating into cardiomyocytes (CMs)
To approximate how many cell barcodes were accidentally associated with 2 cells in our www.nature.com/scientificreports experiment, we mixed iPSCs from chimp into the Drop-seq run from cell line 1 on day 7
Summary
The identification and characterization of cell types from solid tissues and organs in the human body is the necessary basis for a comprehensive reference map of all human cells[1]. DroNc-seq obtains gene expression profiles from isolated nuclei which are more amenable for direct dissociation from tissues while maintaining membrane integrity Both approaches can be used to characterize cellular composition of complex tissues. We performed a systematic comparison of Drop-seq and DroNc-seq using time-course data from human iPSCs differentiating into cardiomyocytes (CMs) This allowed us to compare Drop-seq and DroNc-seq with respect to read depth, transcriptome composition, cell types detected, and cellular differentiation trajectories. These assessments are important for integrative analyses and interpretation of data produced using high-throughput single-cell and single-nucleus RNA-seq in general, and with Drop-seq and DroNc-seq in particular. By identifying differences and similarities between Drop-seq and DroNc-seq, this study will aid efforts such as the HCA that require the integration of single-cell and single-nucleus RNA-seq data from various tissues and laboratories into a common platform
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