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High-Throughput Transcriptome Profiling of Single Nuclei and Single Synapses Using Single-Cell Total-RNA-Seq [Mm]

ABSTRACT: We developed the first droplet-based single-cell total-RNA-seq method. We refer to this platform as Multiple Annealing and Tailing-based Quantitative scRNA-seq in Droplet (MATQ-Drop). With the detection of nascent RNA species, we showed that the cell atlas of human brain samples could be effectively constructed based on nascent RNA species. Furthermore, we observed that only lncRNA species are sufficient to construct the cell atlas, suggesting that MATQ-Drop allows a large-scale identification of the cell-type-specific lncRNA species. Beyond total-RNA profiling for single nuclei, we also showed that MATQ-Drop could be used to profile the transcripts in different neuronal sub-compartments. Based on transcriptome profiling, we were able to determine different types of sub-compartments—in particular, synapses. Here we have referred to the transcriptome of individual synapses as the synaptome, and we identified different subtypes of pre-synapses and post-synapses. Furthermore, between pre-synapses and post-synapses, as well as between pre-synapses and the nuclei, we were able to identify different functional enrichments. Our result unveils unprecedented new insights about gene expression in individual synapses. It also demonstrates the feasibility of using MATQ-drop to profile the transcriptome of sub-cellular compartments.

ORGANISM(S): Mus musculus

PROVIDER: GSE199344 | GEO | 2022/11/30

REPOSITORIES: GEO

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Project description:The ability to measure the number of gene-specific mRNA molecules in individual mammalian cells has transformed the transcriptomics field. Among the key technologies enabling single-cell mRNA sequencing has been Droplet Sequencing (Drop-Seq). While this method works efficiently for mammalian cells, its direct application to yeast cells has been problematic due to cell-type specific differences such as size, doublet formation rate, and cell wall. Here we introduce YeastDropSeq, a single-cell RNA sequencing method for the study of transcriptomics in yeast. We modified and optimized the original Drop-Seq method to address the issues that emerged from smaller cell sizes and the presence of a cell wall in yeast. We also quantified the rate of doublet formation through a species-mixing experiment. As proof-of-principle application of the YeastDropSeq, we investigated the transcriptomic effects of mycophenolic acid (MPA), a lifespan-extending compound that decreases de novo GMP synthesis. We compared transcript levels between cells treated with MPA and cells treated with DMSO and/or guanine, MPA’s epistatic agent. We discovered that isogenic populations of yeast cells contain transcriptionally distinct subpopulations and that the subpopulation structures were maintained despite the different treatment conditions. We found that cells treated with MPA experience an upregulation of genes coding for proteins involved in DNA replication stress-response, antioxidation, pre-RNA processing, and translation initiation. Conversely, a downregulation of mRNA expression was observed for genes encoding translation initiation and elongation factors, the 40S and 60S ribosomal subunits, and for genes involved in metal transport and mitochondrial function. Additionally, we elucidated that expression levels of several genes of unknown function were affected by the MPA treatment. YeastDropSeq will accelerate biological discovery by facilitating droplet-based transcriptomics of yeast cells.

Dataset Information

High-Throughput Transcriptome Profiling of Single Nuclei and Single Synapses Using Single-Cell Total-RNA-Seq [Mm]

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