Sohani Das Sharma for assistance with cell culture and preparation, Erin C. in gene expression of patient-derived glioma neurospheres and uncover subpopulations similar to those observed in human glioma tissue. Single cell RNA-Seq is a powerful approach to quantifying cellular heterogeneity with both basic and clinical research applications1,2,3,4. As a result, considerable effort has been devoted to increasing the throughput and accuracy of these methods including the introduction of unique molecular identifiers (UMIs)5 and barcoding techniques that facilitate pooled library construction6. Recent advances in single cell RNA-Seq have resulted in dramatically increased scalability with a concomitant reduction in library preparation costs7,8,9,10,11. Microfluidic technology has played a crucial role in the advancement of single cell expression analysis by reducing reagent volumes, allowing Pranoprofen high-fidelity single cell isolation, and enabling robust and automated workflows for RNA extraction and amplification12,13,14,15. New tools for single cell RNA-Seq exploit highly scalable microfluidic platforms, including aqueous droplets7,8,10 and microwell arrays9,11, and have facilitated miniaturization of split-pool barcoding methods for labeling cDNA libraries from hundreds or thousands of individual cells in parallel. These techniques are leading to new applications of single cell RNA-Seq including large-scale, unbiased analysis of tissues and tumors without the need for cell sorting7. We recently reported single cell RNA-Seq in a solid-state microwell array platform9. Microwell arrays have several important advantages over droplet-based devices for single cell analysis including low sample and reagent dead volume, short cell loading time, and enhanced compatibility with short-term cell culture, cell perturbation assays, and optical imaging16,17,18. The last two features are particularly useful in minimizing sample degradation prior to cell lysis and allow the experimenter to examine and tune cell loading, identify multiplets or cell debris, and use fluorescence microscopy to determine marker composition and cell viability. In addition, high-efficiency capture of individual cells from a small sample is relatively straightforward with microwells, because cells and beads can be loaded into microwells by repeatedly flowing them over the array until all of them are captured by gravity. While our original system was capable of profiling a few hundred cells per experiment with library preparation costs of $0.10C$0.20 per cell, it suffered from several key drawbacks including low cell and molecular capture efficiency and a lack of automation9. Here, we report significant improvements of microwell-based single cell RNA-Seq in these three areas with no effect on overall cost. In addition, we demonstrate the compatibility of this system with the simple, 3-end library preparation scheme SCRB-Seq19 and the commercially available barcoded Drop-Seq capture beads reported by Macosko is the number of subtype-specific genes detected in cell is the number of subtype-specific genes detected in the entire dataset, and is the number of genes detected in cell i. Analysis of Single Cell RNA-Seq Data Generated by Pranoprofen the Fluidigm C1 System As described above, we sequenced UKp68 NIH-3T3 murine Pranoprofen fibroblasts as part of a performance Pranoprofen test for our system. This same cell line was sequenced using the Fluidigm C1 system by Macoscko et al.7. We downloaded the raw SRA data for these experiments from GEO accession GSE701151 and converted these data to 192 fastq files, corresponding to 192 single cell profiles using fastq-dump in the SRA Toolkit package. We then aligned each fastq file to a concatenated human-mouse pre-assembled transcriptome Pranoprofen using bwa-mem and identified uniquely aligned reads just as described above. Because the Fluidigm C1 data set originated from a mixed species experiment in which human HEK cells were mixed with murine 3T3 cells, we identified cells with >90% of the reads aligned to the murine transcriptome and quantified the number of genes detected per cell at two different read depths (Supplementary Fig. S4). Additional Information Accession codes: The RNA-Seq data generated in this study has been deposited in the Gene Expression Omnibus hosted by the National Center for Biotechnology Information under accession “type”:”entrez-geo”,”attrs”:”text”:”GSE85575″,”term_id”:”85575″,”extlink”:”1″GSE85575. http://www.nature.com/srep How to cite this article: Yuan, J. and Sims, P. A. An Automated Microwell Platform for Large-Scale Single Cell RNA-Seq. Sci. Rep. 6, 33883; doi: 10.1038/srep33883 (2016). Supplementary Material Supplementary Information:Click here to view.(396K, pdf) Supplementary Video S1:Click here to view.(3.2M, avi) Acknowledgments The authors thank Dr. Sohani Das Sharma for assistance with cell culture and preparation, Erin C. Bush for assistance with library preparation and sequencing, and Dr. Harris Wang for the loan of a syringe pump. P.A.S. is supported by K01EB016071 from NIH/NIBIB, R33CA202827 from NIH/NCI, and U54CA193313 from NIH/NCI. Footnotes Columbia University has filed a patent application based on this work. Author Contributions J.Y. and P.A.S. conceived and designed the automated microwell array system. J.Y. fabricated the microwell array devices,.