Scalable full-transcript coverage single cell RNA sequencing of PBMCs using Smart-seq3xpress
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PROVIDER: PRJEB50980 | ENA |
REPOSITORIES: ENA
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Project description:Plate-based single-cell RNA-sequencing methods with full-transcript coverage typically excel at sensitivity but are more resource and time-consuming. Using miniaturized and streamlined Smart-seq3xpress protocol, we sequence >26,000 human peripheral blood mononuclear cells to generate a highly granular gene- and isoform-level atlas.
2022-05-16 | E-MTAB-11452 | biostudies-arrayexpress
Project description:Optimizing scalable full-transcript coverage single cell RNA sequencing using Smart-seq3xpress
| PRJEB51155 | ENA
Project description:Smart-seq3xpress was carefully optimized and >1,000 conditions were evaluated. This data submission is organized in 15 datasets that each contain fastq files, unmapped bam files, read count tables, UMI count tables and a barcode annotation file. The barcode_annotation.txt files contain the exact factors/variables tested. Below a short description of each set of experiments: K562_lowvolume: Evaluation of scaling volumes of Smart-seq3 (indicated volume refers to total volume in PCR), whether overlay was used and if cDNA was bead-cleaned or diluted prior to tagmentation. Cell input was K562 cells. The columns \\"treatment\\", \\"volume\\" and \\"VL\\" indicate the experimental parameters. HEK_lowvolume: Evaluation of scaling volumes of Smart-seq3 (indicated volume refers to total volume in PCR), whether overlay was used and if cDNA was bead-cleaned or diluted prior to tagmentation. Cell input was HEK293FT cells. The columns \\"treatment\\", \\"volume\\" and \\"VL\\" indicate the experimental parameters. overlays: Evaluation of the effect of various overlays when generating HEK293FT libraries in 1 uL total volume. The column \\"condition\\" indicates the applied overlay. tagmentations: Evaluation of input cDNA vs Tn5 amount during tagmentation. Purified cDNA from one 384-well plate was used as input into various conditions of tagmentations. The experiments contain evaluation of cDNA amount with fixed Tn5 amount or varying Tn5 amount while keeping the default volume (2 uL) of the tagmentation reaction or scaling the reaction volume. The column \\"condition\\" contains a string indicating reaction volume, cDNA input and Tn5 ATM enzyme amount. If no volume is indicated, reaction was performed in 2 uL. HomeTn5: Evaluation of tagmentation using in-house produced Tn5 enzyme (Picelli et al., 2015) when tagmenting cDNA generated from HEK293FT cells in 1 uL total volume. The column \\"Tn5concentration\\" indicates the Tn5 reaction concentration at 2 uL reaction volume. cycles_cleanups: Optimization of Smart-seq3xpress (column \\"experiment\\" shows \\"direct_tag\\") in regards to clean-ups after cDNA synthesis (column \\"condition\\": noclean, Exo+FastAP, ExoSAP) and dilution volume (9 or 19 uL); PCR cycle numbers (column \\"pcr_input\\") and ATM Tn5 enzyme amount (column \\"ATM\\"). Cell input was HEK293FT cells. PreAmp_Polymerase: Evaluation of various PCR polymerases during initial cDNA amplification. The polymerases are indicated in column \\"polymerase\\". We also evaluated several TSO concentrations (concentration in RT is given) and fwd/rev PCR primers (concentration given in PCR reaction). Cell input was HEK293FT cells. TDE1: Large optimization of tagmentation conditions using the TDE1 Tn5 enzyme. We varied reactions by changing PCR polymerase (KAPA / SeqAmp), PCR extension time and the number of PCR cycles during cDNA amplification. During tagmentation, we varied the amount of TDE1 enzyme, the amount of DMF in the tagmentation reaction buffer and the presence of Tween-20 in the final post-tagmentation PCR. Cell input was HEK293FT cells. TSOs_RT_v1-7: Large scale evaluation of conditions relating to RT and PCR, with a focus on new template-switching oligo (TSO) designs. In total, >20,000 cells and >500 conditions are contained in these datasets. The barcode annotation file contains precise information on the reaction conditions of Lysis, RT, PCR as well as utilized TSO designs. Data was generated from HEK293FT cells and hPBMC (Lonza).
2022-05-16 | E-MTAB-11467 | biostudies-arrayexpress
Project description:We developed an automated high-throughput Smart-seq3 (HT Smart-seq3) workflow via robotic implementation and established best practices to consistently achieve high cell capture efficiency and data quality. In comparison with the 10X platform, HT Smart-seq3 analysis of primary CD4+ T-cells demonstrated superior sensitivity in gene detection and similar capability to capture major cellular heterogeneity upon sufficient scaling up. Notably, through T-cell receptor (TCR) reconstruction, HT Smart-seq3 identified more productive pairs of alpha and beta chains without additional primer design, enabling more comprehensive profiling of TCRs. Collectively, HT Smart-seq3 provides a cost-effective and scalable method for characterization of single-cell transcriptomes and immune repertoires.
2024-07-02 | GSE270917 | GEO
Project description:We developed an automated high-throughput Smart-seq3 (HT Smart-seq3) workflow via robotic implementation and established best practices to consistently achieve high cell capture efficiency and data quality. In comparison with the 10X platform, HT Smart-seq3 analysis of primary CD4+ T-cells demonstrated superior sensitivity in gene detection and similar capability to capture major cellular heterogeneity upon sufficient scaling up. Notably, through T-cell receptor (TCR) reconstruction, HT Smart-seq3 identified more productive pairs of alpha and beta chains without additional primer design, enabling more comprehensive profiling of TCRs. Collectively, HT Smart-seq3 provides a cost-effective and scalable method for characterization of single-cell transcriptomes and immune repertoires.
2024-06-24 | GSE270511 | GEO
Project description:We developed an automated high-throughput Smart-seq3 (HT Smart-seq3) workflow via robotic implementation and established best practices to consistently achieve high cell capture efficiency and data quality. In comparison with the 10X platform, HT Smart-seq3 analysis of primary CD4+ T-cells demonstrated superior sensitivity in gene detection and similar capability to capture major cellular heterogeneity upon sufficient scaling up. Notably, through T-cell receptor (TCR) reconstruction, HT Smart-seq3 identified more productive pairs of alpha and beta chains without additional primer design, enabling more comprehensive profiling of TCRs. Collectively, HT Smart-seq3 provides a cost-effective and scalable method for characterization of single-cell transcriptomes and immune repertoires.
2024-07-02 | GSE270928 | GEO
Project description:Improved Smart-Seq for sensitive full-length transcriptome profiling in single cells. Cells of four different origins were profiled using commercial SMARTer and compared to five variants of an improved protocol (Smart-Seq2).
2013-09-01 | E-GEOD-49321 | biostudies-arrayexpress
Project description:Improved Smart-Seq for sensitive full-length transcriptome profiling in single cells.
2013-09-01 | GSE49321 | GEO
Project description:Current single-cell RNA sequencing (scRNA-seq) methods with high cellular throughputs sacrifice full-transcript coverage and often sensitivity. Here we describe Smart-seq3xpress, which miniaturizes and streamlines the Smart-seq3 protocol to substantially reduce reagent use and increase cellular throughput. Smart-seq3xpress analysis of peripheral blood mononuclear cells resulted in a granular atlas complete with common and rare cell types. Compared with droplet-based single-cell RNA sequencing that sequences RNA ends, the additional full-transcript coverage revealed cell-type-associated isoform variation.
| S-EPMC9546772 | biostudies-literature
Project description:Large-scale sequencing of RNAs from individual cells can reveal patterns of gene, isoform and allelic expression across cell types and states. However, current single-cell RNA-sequencing (scRNA-seq) methods have limited ability to count RNAs at allele- and isoform resolution, and long-read sequencing techniques lack the depth required for large-scale applications across cells. Here, we introduce Smart-seq3 that combines full-length transcriptome coverage with a 5’ unique molecular identifier (UMI) RNA counting strategy that enabled in silico reconstruction of thousands of RNA molecules per cell. Importantly, a large portion of counted and reconstructed RNA molecules could be directly assigned to specific isoforms and allelic origin, and we identified significant transcript isoform regulation in mouse strains and human cell types. Moreover, Smart-seq3 showed a dramatic increase in sensitivity and typically detected thousands more genes per cell than Smart-seq2. Altogether, we developed a short-read sequencing strategy for single-cell RNA counting at isoform and allele-resolution applicable to large-scale characterization of cell types and states across tissues and organisms.
2020-04-14 | E-MTAB-8735 | biostudies-arrayexpress