Similar Datasets
Project description: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.
2022-11-30 | GSE174293 | GEO
Project description: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.
2022-11-30 | GSE199344 | GEO
Project description:High-Throughput Transcriptome Profiling of Single Nuclei and Single Synapses Using Single-Cell Total-RNA-Seq
| PRJNA819430 | ENA
Project description:High-Throughput Transcriptome Profiling of Single Nuclei and Single Synapses Using Single-Cell Total-RNA-Seq [Hs]
| PRJNA729162 | ENA
Project description:High-Throughput Transcriptome Profiling of Single Nuclei and Single Synapses Using Single-Cell Total-RNA-Seq [Mm]
| PRJNA819431 | ENA
Project description:Single-cell transcriptome profiling of heterogeneous tissues can provide high-resolution windows into developmental dynamics and environmental responses, but its application to plants has been limited. Here, we used the high-throughput Drop-seq approach to profile >12,000 cells from Arabidopsis roots. This identified numerous distinct cell types, covering all major root tissues and developmental stages, and illuminated specific marker genes for these populations. In addition, we demonstrate the utility of this approach to study the impact of environmental conditions on developmental processes. Analysis of roots grown with or without sucrose supplementation uncovers changes in the relative frequencies of cell types in response to sucrose. Finally, we characterize the transcriptome changes that occur across endodermis development and identify nearly 800 genes with dynamic expression as this tissue differentiates. Collectively, we demonstrate that single-cell RNA-seq can be used to profile developmental processes in plants and show how they can be altered by external stimuli.
| S-EPMC6758921 | biostudies-literature
Project description:Most methods for single-cell transcriptome sequencing amplify the termini of polyadenylated transcripts, capturing only a small fraction of the total cellular transcriptome. This precludes the detection of many long non-coding, short non-coding and non-polyadenylated protein-coding transcripts and hinders alternative splicing analysis. We, therefore, developed VASA-seq to detect the total transcriptome in single cells, which is enabled by fragmenting and tailing all RNA molecules subsequent to cell lysis. The method is compatible with both plate-based formats and droplet microfluidics. We applied VASA-seq to more than 30,000 single cells in the developing mouse embryo during gastrulation and early organogenesis. Analyzing the dynamics of the total single-cell transcriptome, we discovered cell type markers, many based on non-coding RNA, and performed in vivo cell cycle analysis via detection of non-polyadenylated histone genes. RNA velocity characterization was improved, accurately retracing blood maturation trajectories. Moreover, our VASA-seq data provide a comprehensive analysis of alternative splicing during mammalian development, which highlighted substantial rearrangements during blood development and heart morphogenesis.
| S-EPMC9750877 | biostudies-literature
Project description:Single cell chromatin accessibility profiling and transcriptome sequencing are the most widely used technologies for single-cell genomics. Here, we present Microwell-seq3, a high-throughput and facile platform for high-sensitivity single-nucleus chromatin accessibility or full-length transcriptome profiling. The method combines a preindexing strategy and a penetrable chip-in-a-tube for single nucleus loading and DNA amplification and therefore does not require specialized equipment. We used Microwell-seq3 to profile chromatin accessibility in more than 200,000 single nuclei and the full-length transcriptome in ~50,000 nuclei from multiple adult mouse tissues. Compared with the existing polyadenylated transcript capture methods, integrative analysis of cell type-specific regulatory elements and total RNA expression uncovered comprehensive cell type heterogeneity in the brain. Gene regulatory networks based on chromatin accessibility profiling provided an improved cell type communication model. Finally, we demonstrated that Microwell-seq3 can identify malignant cells and their specific regulons in spontaneous lung tumors of aged mice. We envision a broad application of Microwell-seq3 in many areas of research.
| S-EPMC10966074 | biostudies-literature
Project description:Single-cell RNA-seq is a powerful tool in decoding the heterogeneity in complex tissues by generating transcriptomic profiles of the individual cell. Here, we report a single-nuclei RNA-seq (snRNA-seq) transcriptomic study on human retinal tissue, which is composed of multiple cell types with distinct functions. Six samples from three healthy donors are profiled and high-quality RNA-seq data is obtained for 5873 single nuclei. All major retinal cell types are observed and marker genes for each cell type are identified. The gene expression of the macular and peripheral retina is compared to each other at cell-type level. Furthermore, our dataset shows an improved power for prioritizing genes associated with human retinal diseases compared to both mouse single-cell RNA-seq and human bulk RNA-seq results. In conclusion, we demonstrate that obtaining single cell transcriptomes from human frozen tissues can provide insight missed by either human bulk RNA-seq or animal models.
| S-EPMC6917696 | biostudies-literature
Project description:The development of new drugs is multidisciplinary and systematic work. High-throughput techniques based on "-omics" have driven the discovery of biomarkers in diseases and therapeutic targets of drugs. A transcriptome is the complete set of all RNAs transcribed by certain tissues or cells at a specific stage of development or physiological condition. Transcriptome research can demonstrate gene functions and structures from the whole level and reveal the molecular mechanism of specific biological processes in diseases. Currently, gene expression microarray and high-throughput RNA-sequencing have been widely used in biological, medical, clinical, and drug research. The former has been applied in drug screening and biomarker detection of drugs due to its high throughput, fast detection speed, simple analysis, and relatively low price. With the further development of detection technology and the improvement of analytical methods, the detection flux of RNA-seq is much higher but the price is lower, hence it has powerful advantages in detecting biomarkers and drug discovery. Compared with the traditional RNA-seq, scRNA-seq has higher accuracy and efficiency, especially the single-cell level of gene expression pattern analysis can provide more information for drug and biomarker discovery. Therefore, (sc)RNA-seq has broader application prospects, especially in the field of drug discovery. In this overview, we will review the application of these technologies in drug, especially in natural drug and biomarker discovery and development. Emerging applications of scRNA-seq and the third generation RNA-sequencing tools are also discussed.
| S-EPMC7013098 | biostudies-literature