Characterization of transcript enrichment and detection bias in single-nucleus RNA-seq for mapping of distinct human adipocyte lineages

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.

Single-nuclei Transcriptome of Human AT Reveals Metabolically Distinct Depot-Specific Adipose Progenitor Subpopulations

“…Other studies have shown differences in sn and sc RNAseq data that may contribute differences in our dataset and others. 6, 50 Indeed the higher degree of concordance of our data with that of Emont et al, who performed snRNASeq, compared with Vijay et al, who used scRNASeq, support such differences. In addition, while some studies such as Vijay et al suggest a larger number of human ASC subtypes than our current data, few of their computationally defined types have been experimentally or functionally validated.…”

“…Single-cell RNA sequencing (scRNAseq) data in mice and humans confirm significant heterogeneity of the AT cellular landscape and identify multiple functionally distinct populations of adipocyte progenitor cells (preadipocytes), immune cells, and other cell types. [1][2][3][4][5][6] This cellular diversity has important implications for an understanding of obesity and metabolic disease pathophysiology and development of translational therapy. scRNAseq methodology has numerous limitations for analysis of AT, including the requirement for collagenase digestion of tissue, which may alter the transcriptome and eliminate cell subpopulations, the inability to capture large, fragile mature adipocytes which are disrupted by microfluidic sorting, and the inability to study banked, frozen samples.…”

“…We now know that this approach lacks rigor, as scRNAseq data from mice and humans demonstrate substantial heterogeneity in ASC and adipocytes. 5,6,16,17 Our goal was to apply snRNAseq analysis to banked frozen AT and describe the complete transcriptome of human VAT and SAT. We studied VAT and SAT samples from a cohort of lean human subjects and subjects with obesity.…”

“…We now know that this approach lacks rigor, as scRNAseq data from mice and humans demonstrate substantial heterogeneity in ASC and adipocytes. 5, 6, 16, 17…”

Differences in molecular sampling and data processing explain variation among single-cell and single-nucleus RNA-seq experiments

“…Recently, Gupta et al 2022 introduced a normalization scheme designed to account for discrepant gene length bias in cell-vs-nucleus comparisons 21 . Specifically, they propose separating total transcript counts (i.e., observed UMIs per gene per cell) into exon - and intron -derived components and scaling solely the intron UMI counts by gene length multiplied by the transcriptome-wide rate of internal priming site occurrence ( Figure 1C ).…”