Characterization of transcript enrichment and detection bias in single-nucleus RNA-seq for mapping of distinct human adipocyte lineages
- Anushka Gupta1,
- Farnaz Shamsi2,
- Nicolas Altemose1,
- Gabriel F. Dorlhiac3,
- Aaron M. Cypess4,
- Andrew P. White5,
- Nir Yosef6,7,8,9,
- Mary Elizabeth Patti10,
- Yu-Hua Tseng2,11 and
- Aaron Streets1,3,8
- 1University of California at Berkeley–University of California at San Francisco Graduate Program in Bioengineering, Berkeley, California 94720, USA;
- 2Section on Integrative Physiology and Metabolism, Joslin Diabetes Center, Harvard Medical School, Boston, Massachusetts 02115, USA;
- 3Biophysics Graduate Group, University of California at Berkeley, Berkeley, California 94720, USA;
- 4Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institutes of Health, Bethesda, Maryland 20892, USA;
- 5Department of Orthopedic Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts 02115, USA;
- 6Center for Computational Biology, University of California, Berkeley, Berkeley, California 94720, USA;
- 7Department of Electrical Engineering and Computer Sciences, University of California at Berkeley, Berkeley, California 94720, USA;
- 8Chan Zuckerberg Biohub, San Francisco, California 94158, USA;
- 9Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard University, Cambridge, Massachusetts 02139, USA;
- 10Research Division, Joslin Diabetes Center, Boston, Massachusetts 02115, USA;
- 11Harvard Stem Cell Institute, Harvard University, Cambridge, Massachusetts 02138, USA
Abstract
Single-cell RNA sequencing (scRNA-seq) enables molecular characterization of complex biological tissues at high resolution. The requirement of single-cell extraction, however, makes it challenging for profiling tissues such as adipose tissue, for which collection of intact single adipocytes is complicated by their fragile nature. For such tissues, single-nucleus extraction is often much more efficient and therefore single-nucleus RNA sequencing (snRNA-seq) presents an alternative to scRNA-seq. However, nuclear transcripts represent only a fraction of the transcriptome in a single cell, with snRNA-seq marked with inherent transcript enrichment and detection biases. Therefore, snRNA-seq may be inadequate for mapping important transcriptional signatures in adipose tissue. In this study, we compare the transcriptomic landscape of single nuclei isolated from preadipocytes and mature adipocytes across human white and brown adipocyte lineages, with whole-cell transcriptome. We show that snRNA-seq is capable of identifying the broad cell types present in scRNA-seq at all states of adipogenesis. However, we also explore how and why the nuclear transcriptome is biased and limited, as well as how it can be advantageous. We robustly characterize the enrichment of nuclear-localized transcripts and adipogenic regulatory lncRNAs in snRNA-seq, while also providing a detailed understanding for the preferential detection of long genes upon using this technique. To remove such technical detection biases, we propose a normalization strategy for a more accurate comparison of nuclear and cellular data. Finally, we show successful integration of scRNA-seq and snRNA-seq data sets with existing bioinformatic tools. Overall, our results illustrate the applicability of snRNA-seq for the characterization of cellular diversity in the adipose tissue.
Footnotes
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at https://www.genome.org/cgi/doi/10.1101/gr.275509.121.
- Received March 24, 2021.
- Accepted December 10, 2021.
This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date (see https://genome.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.
