Massive and parallel expression profiling using microarrayed single-cell sequencing

Sanja Vickovic, Patrik L. Ståhl, Fredrik Salmén, Sarantis Giatrellis, Jakub Orzechowski Westholm, Annelie Mollbrink, José Fernández Navarro, Joaquin Custodio, Magda Bienko, Lesley-Ann Sutton, Richard Rosenquist, Jonas Frisén and Joakim Lundeberg ()
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Sanja Vickovic: Science for Life Laboratory, School of Biotechnology, KTH Royal Institute of Technology
Patrik L. Ståhl: Karolinska Institute
Fredrik Salmén: Science for Life Laboratory, School of Biotechnology, KTH Royal Institute of Technology
Sarantis Giatrellis: Karolinska Institute
Jakub Orzechowski Westholm: Science for Life Laboratory, Stockholm University
Annelie Mollbrink: Science for Life Laboratory, Karolinska Institute
José Fernández Navarro: Karolinska Institute
Joaquin Custodio: Science for Life Laboratory, Karolinska Institute
Magda Bienko: Science for Life Laboratory, Karolinska Institute
Lesley-Ann Sutton: Science for Life Laboratory, Genetics and Pathology, Uppsala University
Richard Rosenquist: Science for Life Laboratory, Genetics and Pathology, Uppsala University
Jonas Frisén: Karolinska Institute
Joakim Lundeberg: Science for Life Laboratory, School of Biotechnology, KTH Royal Institute of Technology

Nature Communications, 2016, vol. 7, issue 1, 1-9

Abstract: Abstract Single-cell transcriptome analysis overcomes problems inherently associated with averaging gene expression measurements in bulk analysis. However, single-cell analysis is currently challenging in terms of cost, throughput and robustness. Here, we present a method enabling massive microarray-based barcoding of expression patterns in single cells, termed MASC-seq. This technology enables both imaging and high-throughput single-cell analysis, characterizing thousands of single-cell transcriptomes per day at a low cost (0.13 USD/cell), which is two orders of magnitude less than commercially available systems. Our novel approach provides data in a rapid and simple way. Therefore, MASC-seq has the potential to accelerate the study of subtle clonal dynamics and help provide critical insights into disease development and other biological processes.

Date: 2016
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DOI: 10.1038/ncomms13182

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