Abstract

Single-cell RNA-sequencing (scRNA-seq) provides high-resolution insights into complex tissues. Cardiac tissue, however, poses a major challenge due to the delicate isolation process and the large size of mature cardiomyocytes. Regardless of the experimental technique, captured cells are often impaired and some capture sites may contain multiple or no cells at all. All this refers to “low quality” potentially leading to data misinterpretation. Common standard quality control parameters involve the number of detected genes, transcripts per cell, and the fraction of transcripts from mitochondrial genes. While cutoffs for transcripts and genes per cell are usually user-defined for each experiment or individually calculated, a fixed threshold of 5% mitochondrial transcripts is standard and often set as default in scRNA-seq software. However, this parameter is highly dependent on the tissue type. In the heart, mitochondrial transcripts comprise almost 30% of total mRNA due to high energy demands. Here, we demonstrate that a 5%-threshold not only causes an unacceptable exclusion of cardiomyocytes but also introduces a bias that particularly discriminates pacemaker cells. This effect is apparent for our in vitro generated induced-sinoatrial-bodies (iSABs; highly enriched physiologically functional pacemaker cells), and also evident in a public data set of cells isolated from embryonal murine sinoatrial node tissue (Goodyer William et al. in Circ Res 125:379–397, 2019). Taken together, we recommend omitting this filtering parameter for scRNA-seq in cardiovascular applications whenever possible.

Highlights

  • The advent of single-cell RNA-sequencing has permitted a deeper understanding of the complexity and heterogeneity of tissues

  • Characterizing cardiac tissue, is problematic due to the delicate isolation process and the large size of mature cardiomyocytes that exceeds the diameter of the applied microfluidic systems

  • We implemented the more widely preferred uniform manifold approximation and projection (UMAP) method for dimensionality reduction and visualization instead of a t-distributed stochastic neighbor embedding (t-SNE) based visualization as used by Goodyer et al, we obtained a comparable plot for the dataset (Online Resource 2)

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Summary

Introduction

The advent of single-cell RNA-sequencing (scRNA-Seq) has permitted a deeper understanding of the complexity and heterogeneity of tissues. Characterizing cardiac tissue, is problematic due to the delicate isolation process and the large size of mature cardiomyocytes that exceeds the diameter of the applied microfluidic systems. Facing these technical limitations, single-nucleus sequencing (snRNASeq) was established as commonly accepted alternative to study postnatal hearts despite omitting cytoplasmic and mitochondrial mRNA species [4,5,6]. In the meantime, another promising platform was introduced in the field, namely, the ICELL8 system (Takara Bio, USA), which uses a large-bore nozzle dispenser to distribute single cells of up to 150 μM in diameter, thereby

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