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. 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 referencemore »
- Authors:
-
[2];
[3];
- Univ. of Chicago, IL (United States). Dept. of Medicine; Univ. of Chicago, IL (United States). Biophysical Sciences Graduate Program
- Univ. of Chicago, IL (United States). Dept. of Medicine
- Univ. of Chicago, IL (United States). Dept. Human Genetics
- Univ. of Chicago, IL (United States). Dept. of Medicine; Univ. of Chicago, IL (United States). Dept. Human Genetics
- Univ. of Chicago, IL (United States). Dept. of Medicine; Argonne National Lab. (ANL), Argonne, IL (United States). Center for Nanoscale Materials
- Publication Date:
- Research Org.:
- Argonne National Laboratory (ANL), Argonne, IL (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER). Biological Systems Science Division
- OSTI Identifier:
- 1629109
- Grant/Contract Number:
- AC02-06CH11357
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Scientific Reports
- Additional Journal Information:
- Journal Volume: 10; Journal Issue: 1; Journal ID: ISSN 2045-2322
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES
Citation Formats
Selewa, Alan, Dohn, Ryan, Eckart, Heather, Lozano, Stephanie, Xie, Bingqing, Gauchat, Eric, Elorbany, Reem, Rhodes, Katherine, Burnett, Jonathan, Gilad, Yoav, Pott, Sebastian, and Basu, Anindita. Systematic Comparison of High-throughput Single-Cell and Single-Nucleus Transcriptomes during Cardiomyocyte Differentiation. United States: N. p., 2020.
Web. doi:10.1038/s41598-020-58327-6.
Selewa, Alan, Dohn, Ryan, Eckart, Heather, Lozano, Stephanie, Xie, Bingqing, Gauchat, Eric, Elorbany, Reem, Rhodes, Katherine, Burnett, Jonathan, Gilad, Yoav, Pott, Sebastian, & Basu, Anindita. Systematic Comparison of High-throughput Single-Cell and Single-Nucleus Transcriptomes during Cardiomyocyte Differentiation. United States. https://doi.org/10.1038/s41598-020-58327-6
Selewa, Alan, Dohn, Ryan, Eckart, Heather, Lozano, Stephanie, Xie, Bingqing, Gauchat, Eric, Elorbany, Reem, Rhodes, Katherine, Burnett, Jonathan, Gilad, Yoav, Pott, Sebastian, and Basu, Anindita. Thu .
"Systematic Comparison of High-throughput Single-Cell and Single-Nucleus Transcriptomes during Cardiomyocyte Differentiation". United States. https://doi.org/10.1038/s41598-020-58327-6. https://www.osti.gov/servlets/purl/1629109.
@article{osti_1629109,
title = {Systematic Comparison of High-throughput Single-Cell and Single-Nucleus Transcriptomes during Cardiomyocyte Differentiation},
author = {Selewa, Alan and Dohn, Ryan and Eckart, Heather and Lozano, Stephanie and Xie, Bingqing and Gauchat, Eric and Elorbany, Reem and Rhodes, Katherine and Burnett, Jonathan and Gilad, Yoav and Pott, Sebastian and Basu, Anindita},
abstractNote = {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.},
doi = {10.1038/s41598-020-58327-6},
journal = {Scientific Reports},
number = 1,
volume = 10,
place = {United States},
year = {Thu Jan 30 00:00:00 EST 2020},
month = {Thu Jan 30 00:00:00 EST 2020}
}
Free Publicly Available Full Text
Publisher's Version of Record
Other availability
Search WorldCat to find libraries that may hold this journal
Save to My Library
You must Sign In or Create an Account in order to save documents to your library.
Works referenced in this record:
The Human Cell Atlas: from vision to reality
journal, October 2017
- Rozenblatt-Rosen, Orit; Stubbington, Michael J. T.; Regev, Aviv
- Nature, Vol. 550, Issue 7677
Massively Parallel Single-Cell RNA-Seq for Marker-Free Decomposition of Tissues into Cell Types
journal, February 2014
- Jaitin, D. A.; Kenigsberg, E.; Keren-Shaul, H.
- Science, Vol. 343, Issue 6172
Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells
journal, May 2013
- Shalek, Alex K.; Satija, Rahul; Adiconis, Xian
- Nature, Vol. 498, Issue 7453
Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq
journal, April 2014
- Treutlein, Barbara; Brownfield, Doug G.; Wu, Angela R.
- Nature, Vol. 509, Issue 7500
Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets
journal, May 2015
- Macosko, Evan Z.; Basu, Anindita; Satija, Rahul
- Cell, Vol. 161, Issue 5
Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells
journal, May 2015
- Klein, Allon M.; Mazutis, Linas; Akartuna, Ilke
- Cell, Vol. 161, Issue 5
Single-cell RNA sequencing reveals a signature of sexual commitment in malaria parasites
journal, September 2017
- Poran, Asaf; Nötzel, Christopher; Aly, Omar
- Nature, Vol. 551, Issue 7678
The Drosophila embryo at single-cell transcriptome resolution
journal, August 2017
- Karaiskos, Nikos; Wahle, Philipp; Alles, Jonathan
- Science, Vol. 358, Issue 6360
Massively parallel single-nucleus RNA-seq with DroNc-seq
journal, August 2017
- Habib, Naomi; Avraham-Davidi, Inbal; Basu, Anindita
- Nature Methods, Vol. 14, Issue 10
A comparative strategy for single-nucleus and single-cell transcriptomes confirms accuracy in predicted cell-type expression from nuclear RNA
journal, July 2017
- Lake, Blue B.; Codeluppi, Simone; Yung, Yun C.
- Scientific Reports, Vol. 7, Issue 1
Single-nucleus and single-cell transcriptomes compared in matched cortical cell types
journal, December 2018
- Bakken, Trygve E.; Hodge, Rebecca D.; Miller, Jeremy A.
- PLOS ONE, Vol. 13, Issue 12
Advantages of Single-Nucleus over Single-Cell RNA Sequencing of Adult Kidney: Rare Cell Types and Novel Cell States Revealed in Fibrosis
journal, December 2018
- Wu, Haojia; Kirita, Yuhei; Donnelly, Erinn L.
- Journal of the American Society of Nephrology, Vol. 30, Issue 1
RNA velocity of single cells
journal, August 2018
- La Manno, Gioele; Soldatov, Ruslan; Zeisel, Amit
- Nature, Vol. 560, Issue 7719
Integrating single-cell transcriptomic data across different conditions, technologies, and species
journal, April 2018
- Butler, Andrew; Hoffman, Paul; Smibert, Peter
- Nature Biotechnology, Vol. 36, Issue 5
Single-Cell Transcriptomic Analysis of Cardiac Differentiation from Human PSCs Reveals HOPX-Dependent Cardiomyocyte Maturation
journal, October 2018
- Friedman, Clayton E.; Nguyen, Quan; Lukowski, Samuel W.
- Cell Stem Cell, Vol. 23, Issue 4
A Comparative Assessment of Human and Chimpanzee iPSC-derived Cardiomyocytes with Primary Heart Tissues
journal, October 2018
- Pavlovic, Bryan J.; Blake, Lauren E.; Roux, Julien
- Scientific Reports, Vol. 8, Issue 1
Wishbone identifies bifurcating developmental trajectories from single-cell data
journal, May 2016
- Setty, Manu; Tadmor, Michelle D.; Reich-Zeliger, Shlomit
- Nature Biotechnology, Vol. 34, Issue 6
The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells
journal, March 2014
- Trapnell, Cole; Cacchiarelli, Davide; Grimsby, Jonna
- Nature Biotechnology, Vol. 32, Issue 4
Snakemake--a scalable bioinformatics workflow engine
journal, August 2012
- Koster, J.; Rahmann, S.
- Bioinformatics, Vol. 28, Issue 19
UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy
journal, January 2017
- Smith, Tom; Heger, Andreas; Sudbery, Ian
- Genome Research, Vol. 27, Issue 3
Cutadapt removes adapter sequences from high-throughput sequencing reads
journal, May 2011
- Martin, Marcel
- EMBnet.journal, Vol. 17, Issue 1
STAR: ultrafast universal RNA-seq aligner
journal, October 2012
- Dobin, Alexander; Davis, Carrie A.; Schlesinger, Felix
- Bioinformatics, Vol. 29, Issue 1
featureCounts: an efficient general purpose program for assigning sequence reads to genomic features
journal, November 2013
- Liao, Y.; Smyth, G. K.; Shi, W.
- Bioinformatics, Vol. 30, Issue 7
MEME SUITE: tools for motif discovery and searching
journal, May 2009
- Bailey, T. L.; Boden, M.; Buske, F. A.
- Nucleic Acids Research, Vol. 37, Issue Web Server
UMAP: Uniform Manifold Approximation and Projection
journal, September 2018
- McInnes, Leland; Healy, John; Saul, Nathaniel
- Journal of Open Source Software, Vol. 3, Issue 29
SC3: consensus clustering of single-cell RNA-seq data
journal, March 2017
- Kiselev, Vladimir Yu; Kirschner, Kristina; Schaub, Michael T.
- Nature Methods, Vol. 14, Issue 5
Bias, robustness and scalability in single-cell differential expression analysis
journal, February 2018
- Soneson, Charlotte; Robinson, Mark D.
- Nature Methods, Vol. 15, Issue 4
IL-33 expression in response to SARS-CoV-2 correlates with seropositivity in COVID-19 convalescent individuals
journal, April 2021
- Stanczak, Michal A.; Sanin, David E.; Apostolova, Petya
- Nature Communications, Vol. 12, Issue 1
Fully phased human genome assembly without parental data using single-cell strand sequencing and long reads
journal, December 2020
- Porubsky, David; Ebert, Peter; Audano, Peter A.
- Nature Biotechnology, Vol. 39, Issue 3
Synovial cell cross-talk with cartilage plays a major role in the pathogenesis of osteoarthritis
journal, July 2020
- Chou, Ching-Heng; Jain, Vaibhav; Gibson, Jason
- Scientific Reports, Vol. 10, Issue 1
Transcriptional signatures in prefrontal cortex confer vulnerability versus resilience to food and cocaine addiction-like behavior
journal, April 2021
- Navandar, Mohit; Martín-García, Elena; Maldonado, Rafael
- Scientific Reports, Vol. 11, Issue 1
SC3: consensus clustering of single-cell RNA-seq data
journalarticle, January 2017
- Kiselev, Vy; Kirschner, K.; Schaub, Mt
- Nature Publishing Group
Bias, robustness and scalability in single-cell differential expression analysis
text, January 2018
- Soneson, Charlotte; Robinson, Mark D.
- Nature Publishing Group
Snakemake—a scalable bioinformatics workflow engine
journal, May 2018
- Köster, Johannes; Rahmann, Sven
- Bioinformatics, Vol. 34, Issue 20
Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
journal, April 2020
- Mereu, Elisabetta; Lafzi, Atefeh; Moutinho, Catia
- Nature Biotechnology, Vol. 38, Issue 6
featureCounts: An efficient general-purpose program for assigning sequence reads to genomic features
text, January 2013
- Liao, Yang; Smyth, Gordon K.; Shi, Wei
- arXiv
Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets
journal, May 2015
- Macosko, Evan Z.; Basu, Anindita; Satija, Rahul
- Cell, Vol. 161, Issue 5
Wishbone identifies bifurcating developmental trajectories from single-cell data
journal, May 2016
- Setty, Manu; Tadmor, Michelle D.; Reich-Zeliger, Shlomit
- Nature Biotechnology, Vol. 34, Issue 6
Resistance to autosomal dominant Alzheimer’s disease in an APOE3 Christchurch homozygote: a case report
journal, November 2019
- Arboleda-Velasquez, Joseph F.; Lopera, Francisco; O’Hare, Michael
- Nature Medicine, Vol. 25, Issue 11
Works referencing / citing this record:
Transcriptional profiling of microglia; current state of the art and future perspectives
journal, December 2019
- Gerrits, Emma; Heng, Yang; Boddeke, Erik W. G. M.
- Glia, Vol. 68, Issue 4