Description

Scaling single cell transcriptomics through split pool barcoding

Alexander B. Rosenberg^1*, Charles M. Roco^2*, Richard Muscat¹, Anna Kuchina¹, Sumit Mukherjee¹, Will Chen³, David J. Peeler², Zizhen Yao⁴, Bosiljka Tasic⁴, Drew L. Sanders²,

Suzie H. Pun², Georg Seelig^1,5

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.