Performance comparison of high throughput single-cell RNA-Seq platforms in complex tissues
Yolanda Colino-Sanguino,
Laura Rodriguez de la Fuente,
Brian Gloss,
Andrew M.K. Law,
Kristina Handler,
Marina Pajic,
Robert Salomon,
David Gallego-Ortega,
Fatima Valdes-Mora
Affiliations
Yolanda Colino-Sanguino
Cancer Epigenetic Biology and Therapeutics Laboratory, Children's Cancer Institute, Lowy Cancer Centre, Kensington, NSW, Australia; School of Clinical Medicine, Faculty of Medicine & Health, University of New South Wales Sydney, NSW, Australia
Laura Rodriguez de la Fuente
Cancer Epigenetic Biology and Therapeutics Laboratory, Children's Cancer Institute, Lowy Cancer Centre, Kensington, NSW, Australia; School of Clinical Medicine, Faculty of Medicine & Health, University of New South Wales Sydney, NSW, Australia; The Kinghorn Cancer Centre, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia; School of Biomedical Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney, NSW, Australia
Brian Gloss
Westmead Research Hub, Westmead Institute for Medical Research, Sydney, NSW, Australia
Andrew M.K. Law
School of Clinical Medicine, Faculty of Medicine & Health, University of New South Wales Sydney, NSW, Australia; The Kinghorn Cancer Centre, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia
Kristina Handler
Institute of Experimental Immunology, University of Zürich, Zürich, Switzerland
Marina Pajic
School of Clinical Medicine, Faculty of Medicine & Health, University of New South Wales Sydney, NSW, Australia; The Kinghorn Cancer Centre, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia
Robert Salomon
Institute for Biomedical Materials & Devices (IBMD), Faculty of Science, University of Technology Sydney, Sydney, NSW, Australia; ACRF Liquid Biopsy Program, Children's Cancer Institute, Lowy Cancer Centre, Kensington, NSW, Australia
David Gallego-Ortega
School of Clinical Medicine, Faculty of Medicine & Health, University of New South Wales Sydney, NSW, Australia; The Kinghorn Cancer Centre, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia; School of Biomedical Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney, NSW, Australia; Corresponding author. School of Clinical Medicine, Faculty of Medicine & Health, University of New South Wales Sydney, NSW, Australia.
Fatima Valdes-Mora
Cancer Epigenetic Biology and Therapeutics Laboratory, Children's Cancer Institute, Lowy Cancer Centre, Kensington, NSW, Australia; School of Clinical Medicine, Faculty of Medicine & Health, University of New South Wales Sydney, NSW, Australia; Corresponding author. Cancer Epigenetic Biology and Therapeutics Laboratory, Children's Cancer Institute, Lowy Cancer Centre, Kensington, NSW, Australia.
Single-cell transcriptomics has emerged as the preferred tool to define cell identity through the analysis of gene expression signatures. However, there are limited studies that have comprehensively compared the performance of different scRNAseq systems in complex tissues. Here, we present a systematic comparison of two well-established high throughput 3′-scRNAseq platforms: 10× Chromium and BD Rhapsody, using tumours that present high cell diversity. Our experimental design includes both fresh and artificially damaged samples from the same tumours, which also provides a comparable dataset to examine their performance under challenging conditions. The performance metrics used in this study consist of gene sensitivity, mitochondrial content, reproducibility, clustering capabilities, cell type representation and ambient RNA contamination. These analyses showed that BD Rhapsody and 10× Chromium have similar gene sensitivity, while BD Rhapsody has the highest mitochondrial content. Interestingly, we found cell type detection biases between platforms, including a lower proportion of endothelial and myofibroblast cells in BD Rhapsody and lower gene sensitivity in granulocytes for 10× Chromium. Moreover, the source of the ambient noise was different between plate-based and droplet-based platforms. In conclusion, our reported platform differential performance should be considered for the selection of the scRNAseq method during the study experimental designs.
