Scaling single cell transcriptomics through split pool barcoding
Alexander B. Rosenberg1*, Charles M. Roco2*, Richard Muscat1, Anna Kuchina1, Sumit Mukherjee1, Will Chen3, David J. Peeler2, Zizhen Yao4, Bosiljka Tasic4, Drew L. Sanders2,
Suzie H. Pun2, Georg Seelig1,5
1Department of Electrical Engineering, University of Washington, Seattle, WA; 2Department of Bioengineering, University of Washington, Seattle, WA; 3Molecular Engineering and Sciences Institute, University of Washington, Seattle, WA; 4Allen Institute for Brain Science, Seattle, WA; 5Department of Computer Science and Engineering, University of Washington, Seattle, WA
*Authors contributed equally to work
Constructing an atlas of cell types in complex organisms will require a collective effort to characterize billions of individual cells. Single cell RNA sequencing (scRNA-seq) has emerged as the main tool for characterizing cellular diversity, but current methods use custom microfluidics or microwells to compartmentalize single cells, limiting scalability and widespread adoption. Here we present Split-Pool Ligation Transcriptome sequencing (SPLiT-seq), a scRNA-seq method that labels the cellular origin of RNA through combinatorial indexing. SPLiT-seq scales exponentially, uses only basic laboratory equipment, and costs 1 cent per cell. We used this approach to sequence 107,000 single cell transcriptomes from a postnatal day 5 whole mouse brain, providing the first global snapshot at this stage of development. As sequencing capacity increases, SPLiT-seq will enable profiling of billions of cells in a single experiment.