Project description:In this work, we compared different protocols to prepare single-cell suspensions used for scRNAseq and suggest an optimized dissociation protocol for mouse retina, which preserves cell morphology to a higher level leading to an overall increase of gene number per cell. We compared scRNAseq libraries generated with our optimized protocol to publicly available scRNAseq data of mouse retina. We further demonstrate a pipeline to reduce noise in scRNAseq caused by multiplets and ambient RNA.
Project description:In this work, we compared different protocols to prepare single-cell suspensions used for scRNAseq and suggest an optimized dissociation protocol for mouse retina, which preserves cell morphology to a higher level leading to an overall increase of gene number per cell. We compared scRNAseq libraries generated with our optimized protocol to publicly available scRNAseq data of mouse retina. We further demonstrate a pipeline to reduce noise in scRNAseq caused by multiplets and ambient RNA.
Project description:Single cell combinatorial indexing RNA sequencing (sci-RNA-seq) is a powerful method for recovering gene expression data from an exponentially scalable number of individual cells or nuclei. However, sci-RNA-seq is a complex protocol that has historically exhibited variable performance on different tissues, as well as lower sensitivity than alternative methods. Here we report a simplified, optimized version of the three-level sci-RNA-seq protocol that is faster, higher yield, more robust, and more sensitive, than the original sci-RNA-seq3 protocol, with reagent costs on the order of 1 cent per cell or less. We showcase the optimized protocol via whole organism analysis of an E16.5 mouse embryo, profiling ~380,000 nuclei in a single experiment. Finally, we introduce a “tiny sci-*” protocol for experiments where input is extremely limited.
Project description:Purpose: The goals of this study are to study the expression of Sfxn3 in C57B/L6 mouse retina and retinal cell types Methods: Library prepared followed by 10X Genomics standard protocol. Transcriptome were generated by high throughput sequencing