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

Journal Article · · Scientific Reports
 [1];  [2]; ORCiD logo [2];  [2];  [2];  [3];  [4];  [4];  [4];  [5]; ORCiD logo [4];  [6]
  1. Univ. of Chicago, IL (United States). Dept. of Medicine; Univ. of Chicago, IL (United States). Biophysical Sciences Graduate Program; DOE/OSTI
  2. Univ. of Chicago, IL (United States). Dept. of Medicine
  3. Univ. of Chicago, IL (United States). Dept. of Medicine; Univ. of Chicago, IL (United States). Biophysical Sciences Graduate Program
  4. Univ. of Chicago, IL (United States). Dept. Human Genetics
  5. Univ. of Chicago, IL (United States). Dept. of Medicine; Univ. of Chicago, IL (United States). Dept. Human Genetics
  6. Univ. of Chicago, IL (United States). Dept. of Medicine; Argonne National Lab. (ANL), Argonne, IL (United States). Center for Nanoscale Materials
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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 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.

Research Organization:
Argonne National Laboratory (ANL), Argonne, IL (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Biological and Environmental Research (BER). Biological Systems Science Division
Grant/Contract Number:
AC02-06CH11357
OSTI ID:
1629109
Journal Information:
Scientific Reports, Journal Name: Scientific Reports Journal Issue: 1 Vol. 10; ISSN 2045-2322
Publisher:
Nature Publishing GroupCopyright Statement
Country of Publication:
United States
Language:
English

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