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Massively parallel nanowell-based single-cell gene expression profiling

2017; BioMed Central; Volume: 18; Issue: 1 Linguagem: Inglês

10.1186/s12864-017-3893-1

1471-2164

Leonard D. Goldstein, Ying-Jiun Jasmine Chen, Jude Dunne, Alain Mir, Hermann Hubschle, Joseph Guillory, Wenlin Yuan, Jingli Zhang, Jeremy Stinson, Bijay S. Jaiswal, Kanika Bajaj Pahuja, Ishminder K. Mann, Thomas Schaal, Leo Li‐Ying Chan, Sangeetha Anandakrishnan, Chun-wah Lin, Patricio Espinoza, Syed Akhtar Husain, Harris Shapiro, Karthikeyan Swaminathan, Sherry H.-Y. Wei, Maithreyan Srinivasan, Somasekar Seshagiri, Zora Modrušan,

Nanopore and Nanochannel Transport Studies

Technological advances have enabled transcriptome characterization of cell types at the single-cell level providing new biological insights. New methods that enable simple yet high-throughput single-cell expression profiling are highly desirable. Here we report a novel nanowell-based single-cell RNA sequencing system, ICELL8, which enables processing of thousands of cells per sample. The system employs a 5,184-nanowell-containing microchip to capture ~1,300 single cells and process them. Each nanowell contains preprinted oligonucleotides encoding poly-d(T), a unique well barcode, and a unique molecular identifier. The ICELL8 system uses imaging software to identify nanowells containing viable single cells and only wells with single cells are processed into sequencing libraries. Here, we report the performance and utility of ICELL8 using samples of increasing complexity from cultured cells to mouse solid tissue samples. Our assessment of the system to discriminate between mixed human and mouse cells showed that ICELL8 has a low cell multiplet rate (< 3%) and low cross-cell contamination. We characterized single-cell transcriptomes of more than a thousand cultured human and mouse cells as well as 468 mouse pancreatic islets cells. We were able to identify distinct cell types in pancreatic islets, including alpha, beta, delta and gamma cells. Overall, ICELL8 provides efficient and cost-effective single-cell expression profiling of thousands of cells, allowing researchers to decipher single-cell transcriptomes within complex biological samples.