Single-cell transcriptomics is a powerful technique employed to characterize cellular identity. In recent years, advances in the technology have since enabled its application to the generation of global human and mouse single-cell transcriptome atlases. However, despite these advances, single-cell approaches remain technologically challenging, given that the minuscule amount of RNA present is entirely used up in the experiments. Therefore, it is essential to ensure the quality and purity of the resulting single-cell transcriptomes. Now, a team of investigators at the Research Center for Molecular Medicine (CeMM), in Austria, have developed a new method for RNA sequencing (RNA-seq) that could help quantitate and normalize complex cell-specific effects of pharmacological changes within cells—pancreatic islet cells in the current analysis.
“Single-cell RNA-seq (scRNA-seq) is emerging as a powerful tool to dissect cell-specific effects of drug treatment in complex tissues,” the new study authors wrote. “This application requires high levels of precision, robustness, and quantitative accuracy—beyond those achievable with existing methods for mainly qualitative single-cell analysis.”
Findings from the new study were published recently in Genome Biology through an article entitled “Single-cell RNA-seq with spike-in cells enables accurate quantification of cell-specific drug effects in pancreatic islets.”
Type 1 diabetes is a chronic disease in which the body’s immune system mistakenly attacks and destroys the pancreas’ insulin-producing beta cells. Regenerative medicine aims to replenish beta-cell mass, and thus support and ultimately substitute the current insulin replacement therapies. Alterations to islet composition, including insufficient beta-cell function and beta-cell dedifferentiation, also contribute to type 2 diabetes. Therefore, a deeper understanding of the identity and crosstalk of the different islet cell types leads to a better characterization of both forms of diabetes and may contribute to the development of novel therapeutic concepts.
In the current study, CeMM researchers from two contributing laboratories identified unexpectedly high hormone expression in non-endocrine cell types, both in their dataset as well as other published single-cell studies. They set out to elucidate whether this would be the result of contamination by RNA molecules, for example, from dying cells and how it could be removed to obtain a more reliable dataset. Such contamination seems present in single-cell RNA-seq data from most tissues but was most visible in pancreatic islets.
Islet endocrine cells are exclusively devoted to the production of single hormones, and insulin in beta cells and glucagon in alpha cells are expressed to higher levels than typical “housekeeping” genes. Thus, redistribution of these transcripts to other cell types was highly pronounced. Based on this observation, their goal was to develop, validate, and apply a method to determine and computationally remove such contamination experimentally.
In their investigation, the research team used spiked-in cells from different cell types, both mouse and human samples, that they added to their pancreatic islet samples. Importantly, the transcriptomes of these spike-in cells were fully characterized. This allowed them to control internally and accurately the level of RNA contamination in single-cell RNA-seq, giving that the human transcripts detected in the mouse spike-in cells constitute contaminating RNA.
In this way, they found that the samples had a contamination level of up to 20% and were able to define the contamination in each sample. They then developed a novel bioinformatics approach to remove contaminating reads from single cell transcriptomes computationally.
“We found that contamination by cell-free RNA can constitute up to 20% of reads in human primary tissue samples, and we show that the ensuing biases can be removed effectively using a novel bioinformatics algorithm,” the authors noted. “Applying our method to both human and mouse pancreatic islets treated ex vivo, we obtain an accurate and quantitative assessment of cell-specific drug effects on the transcriptome. We observed that FOXO inhibition induces dedifferentiation of both alpha and beta cells, while artemether treatment upregulates insulin and other beta cell marker genes in a subset of alpha cells. In beta cells, dedifferentiation and insulin repression upon artemether treatment occurs predominantly in mouse but not in human samples.”
Having obtained a “decontaminated” transcriptome, from which the spurious signal has been removed, they proceeded to characterize how the cellular identity in the different cell types responded to the treatment with three different drugs. They found that a small molecule inhibitor of the transcription factor, FOXO1 induces dedifferentiation of both alpha and beta cells.
Furthermore, they studied the antimalaria drug artemether, which had been found to diminish the function of alpha cells and could induce insulin production in both in vivo and in vitro studies. The effects of the drug artemether were species-specific and cell-type-specific. In alpha cells, a fraction of cells increases insulin expression and gain aspects of beta-cell identity, both in mouse and human samples. Importantly, researchers found that in human beta cells, there is no significant change in insulin expression, whereas, in mouse islets, beta cells reduce their insulin expression and overall beta cell identity.
“This new method for quantitative, error-correcting, scRNA-seq data normalization using spike-in reference cells helps clarify complex cell-specific effects of pharmacological perturbations with single-cell resolution and high quantitative accuracy,” the authors concluded.