Single-Cell Biology Shared Resource

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SINGLE-CELL BIOLOGY SHARED RESOURCE – PROJECT SUMMARY/ABSTRACT The Single-Cell Biology Shared Resource was founded in 2017 with the mission of pioneering and deploying cutting-edge single-cell genomics technologies to members of the CSHL Cancer Center and surrounding research community. The Shared Resource is led by Faculty Head Jesse Gilis, Ph.D., and Director Jonathan Preall, Ph.D. The Single-Cell Biology Shared Resource is designed to be a comprehensive, collaborative Shared Resource facility that supports every stage of a single-cell genomics project, including experimental design, protocol development, sample preparation, library construction, sequencing logistics, primary data processing, and in-depth embedded analysis. Shared Resource personnel frequently work side-by-side with researchers, customizing the workflow to meet the unique needs of the project. Examples of the most common single-cell workflows supported by the Shared Resource include: 3’-digital transcriptome profiling, immune receptor profiling, ATAC-sequencing, copy-number variation, proteogenomics, and single-nucleus RNAseq. In addition to standard, established protocols, the Shared Resource assists users in designing and executing custom single- cell workflows with downstream tailored data analysis. The Single-Cell Biology Shared Resource houses and operates capital equipment for handling most stages of common single-cell workflows. A critical component of this Shared Resource’s mission is to provide education and training on the analysis of single-cell genomics data. Semi-annual workshops and regular computational office hours give researchers the opportunity to learn common computational pipelines and become familiar with emerging techniques in data analysis. Since its establishment, the Shared Resource has been used by 12 CSHL Cancer Center laboratories (32% of members), resulting in 5 co-authored publications. In summary, the Single-Cell Biology Shared Resource provides comprehensive bench-to-publication support of both off-the-shelf and customized genomics workflows, dramatically streamlining the process of collecting these challenging and high-impact datasets.