Comparative Analysis of Droplet- vs. Microwell-based Whole Transcriptome Single-Cell Sequencing Technologies in Complex Human Tissues
Creators
- 1. Department of Internal Medicine V, Haematology & Oncology, Comprehensive Cancer Center Innsbruck (CCCI) and Tyrolean Cancer Research Institute (TKFI), Medical University of Innsbruck (MUI), Innsbruck, Austria
- 2. Department of Urology, Medical University of Innsbruck, Innsbruck, Austria
- 3. Biocenter, Institute of Bioinformatics, Medical University of Innsbruck, Austria
- 4. Department of Pathology, Medical University Innsbruck, Innsbruck, Austria
Description
In the past decade, high-dimensional single-cell omics tools have enabled scientists to study the tumor microenvironment (TME) in unprecedented detail. However, recent investigations suggest that each technique has its unique strengths but also technology-inherent limitations. Here we directly compared two commercially available high-throughput single-cell RNA sequencing (scRNA-seq) technologies - droplet-based 10X Chromium vs. microwell-based BD Rhapsody - using paired samples from patients with localized prostate cancer (PCa) undergoing a radical prostatectomy.
Although high technical consistency was observed in unraveling the whole transcriptome, the relative abundance of detectable cell populations differed. This could in part be ascribed to differences in the performance to recover cells with low-mRNA content. Hence, immune cells such as neutrophils are underrepresented in data generated with the widely used droplet-based scRNA-seq protocol, highlighting the importance of considering platform limitations in low mRNA content cell recovery. In contrast, droplet-based scRNA-seq demonstrated superiority in terms of recovering cells of epithelial origin. Moreover, we discovered platform-dependent variabilities in mRNA quantification and cell-type marker annotation, affecting the composition of identified tissue profiles and the exploratory value of the generated datasets. Overall, our study emphasizes the importance of carefully selecting the appropriate scRNA-seq platform to improve cell type representation and obtain a more comprehensive and accurate understanding of the TME.
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