Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding
Alexander B. Rosenberg,1*† Charles M. Roco,2* Richard A. Muscat,1 Anna Kuchina,1 Paul Sample,1 Zizhen Yao,3 Lucas T. Graybuck,3 David J. Peeler,2 Sumit Mukherjee,1 Wei Chen,4 Suzie H. Pun,2 Drew L. Sellers,2,5 Bosiljka Tasic,3 Georg Seelig1,4,6†
1Department of Electrical Engineering, University of Washington,Seattle, WA, USA. 2Department of Bioengineering, University of Washington, Seattle, WA, USA. 3Allen Institute for Brain Science, Seattle, WA, USA. 4Molecular Engineering and Sciences Institute, University of Washington, Seattle, WA, USA. 5Institute for Stem Cell and Regenerative Medicine, Seattle, WA, USA. 6Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
*These authors contributed equally to this work.
To facilitate scalable profiling of single cells, we developed split-pool ligation-based transcriptome sequencing (SPLiT-seq), a single-cell RNA-seq (scRNA-seq) method that labels the cellular origin of RNA through combinatorial barcoding. SPLiT-seq is compatible with fixed cells or nuclei, allows efficient sample multiplexing, and requires no customized equipment. We used SPLiT-seq to analyze 156,049 single-nucleus transcriptomes from postnatal day 2 and 11 mouse brains and spinal cords. More than 100 cell types were identified, with gene expression patterns corresponding to cellularfunction, regional specificity, and stage of differentiation. Pseudotime analysis revealed transcriptional programs driving four developmental lineages, providing a snapshot ofearly postnatal development in the murine central nervous system. SPLiT-seq providesa path toward comprehensive single-cell transcriptomic analysis of other similarly complex multicellular systems.