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An optimized protocol for single cell transcriptional profiling by combinatorial indexing

ABSTRACT: 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.

ORGANISM(S): Mus musculus

PROVIDER: GSE186824 | GEO | 2021/11/05

REPOSITORIES: GEO

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Project description:Mammalian embryogenesis is characterized by rapid cellular proliferation and diversification. Within a few weeks, a single cell zygote gives rise to millions of cells expressing a panoply of molecular programs, including much of the diversity that will subsequently be present in adult tissues. Although intensively studied, a comprehensive delineation of the major cellular trajectories that comprise mammalian development in vivo remains elusive. For mouse embryogenesis in particular, we and others have performed single cell or single nucleus RNA-seq data (scRNA-seq) during implantation, gastrulation and organogenesis. Here we set out to integrate several single cell RNA-seq datasets (scRNA-seq) that collectively span mouse gastrulation and organogenesis. However, a technical challenge that we faced is that the datasets that we sought to integrate were generated by different groups at different times using different scRNA-seq technologies. In particular, probably because there was no overlapping timepoint, the integration of scRNA-seq data generated at E8.5 (cells, 10X Genomics) and E9.5 (nuclei, sci-RNA-seq3) was challenging (Cao et al. 2019; Pijuan-Sala et al. 2019). To address this, we set out to generate new data at E8.5 that might serve to “bridge” these two datasets. Because of how quickly changes are occurring during this window of development, we focused on individual, somite-resolved E8.5 embryos using a simplified, optimized version of sci-RNA-seq3. We selected 12 embryos from 2 separate litters harvested at E8.5, including a single primitive streak stage embryo (prior to somitogenesis) and 11 embryos staged in 1-somite increments from 2 to 12 somites. The optimized sci-RNA-seq3 method markedly improved data quality, with 9-fold higher UMIs and 6-fold higher gene detection per nucleus, relative to (Cao et al. 2019). Overall, we collected published data (Cheng et al. 2019; Mohammed et al. 2017; Pijuan-Sala et al. 2019), the new E8.5 data, and published data from one study spanning E9.5 to E13.5 but with deeper sequencing of those libraries (Cao et al. 2019). Altogether, we define cell states at each of 19 successive stages spanning E3.5 to E13.5, heuristically connect them to their pseudo-ancestors and pseudo-descendants. Despite being constructed through automated procedures, the resulting trajectories of mammalian embryogenesis (TOME) are largely consistent with our contemporary understanding of mammalian development. In addition, the new E8.5 data itself comprises a foundational resource for mammalian developmental biology (especially for the early somitogenesis), and are made available in a way that will facilitate their ongoing annotation by the research community.

Project description:Mammalian embryogenesis is characterized by rapid cellular proliferation and diversification. Within a few weeks, a single cell zygote gives rise to millions of cells expressing a panoply of molecular programs, including much of the diversity that will subsequently be present in adult tissues. Although intensively studied, a comprehensive delineation of the major cellular trajectories that comprise mammalian development in vivo remains elusive. For mouse embryogenesis in particular, we and others have performed single cell or single nucleus RNA-seq data (scRNA-seq) during implantation, gastrulation and organogenesis. Here we set out to integrate several single cell RNA-seq datasets (scRNA-seq) that collectively span mouse gastrulation and organogenesis. However, a technical challenge that we faced is that the datasets that we sought to integrate were generated by different groups at different times using different scRNA-seq technologies. In particular, probably because there was no overlapping timepoint, the integration of scRNA-seq data generated at E8.5 (cells, 10X Genomics) and E9.5 (nuclei, sci-RNA-seq3) was challenging (Cao et al. 2019; Pijuan-Sala et al. 2019). To address this, we set out to generate new data at E8.5 that might serve to “bridge” these two datasets. Because of how quickly changes are occurring during this window of development, we focused on individual, somite-resolved E8.5 embryos using a simplified, optimized version of sci-RNA-seq3. At the same time, to obtain higher quality data across E9.5-E13.5, we performed a deeper sequencing (specifically, three additional Novaseq runs) of previously reported libraries (Cao et al. 2019). Compared to the previous data (Cao et al. 2019), the median UMI count per cell improved from 671 to 1,434, while the median genes detected per cell improved from 518 to 735. Overall, we collected published data (Cheng et al. 2019; Mohammed et al. 2017; Pijuan-Sala et al. 2019), the new E8.5 data, and the deeper sequencing of previous libraries (Cao et al. 2019). Altogether, we define cell states at each of 19 successive stages spanning E3.5 to E13.5, heuristically connect them to their pseudo-ancestors and pseudo-descendants. Despite being constructed through automated procedures, the resulting trajectories of mammalian embryogenesis (TOME) are largely consistent with our contemporary understanding of mammalian development. In addition, the data of deeper sequencing of previously reported libraries itself comprises a foundational resource for mammalian developmental biology, and are made available in a way that will facilitate their ongoing annotation by the research community.

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An optimized protocol for single cell transcriptional profiling by combinatorial indexing

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