Optimizing scalable full-transcript coverage single cell RNA sequencing using Smart-seq3xpress
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PROVIDER: PRJEB51155 | ENA |
REPOSITORIES: ENA
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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).
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Project description:Generally, when LCM is used in diverse transcriptomic analyses, several hundred, if not thousands, of cells are needed to obtain high quality of RNA-seq data. As some cellular populations are very small and tissue often in scarcity, we aimed to carefully document the lowest number of cells needed to retrieve sequencable libraries. We started with capturing 120 cells and subsequently scaled down to 50 cells, 30 cells, 10 cells, 5 cells, 2 cells and finally 1 cell. By optimizing multiple steps in the procedure, including direct lysis of cells without performing RNA isolation, we developed LCM-seq that couples LCM with Smart-seq2 for robust and efficient polyA-based RNA sequencing. We applied LCM-seq to mouse and human neuron samples, and demonstrated that LCM-seq can allow us to acquire high quality RNA-seq data from mouse and human tissues to conduct various transcriptomic studies. Developing new sequencing technology LCM-seq to efficiently sequence mouse and human tissues
2016-07-05 | E-GEOD-76514 | biostudies-arrayexpress
Project description:We report dynamics of X-chromosome upregulation (XCU) along X-chromosome inactivation (XCI) in mESCs as they differentiate into EpiSCs. F1 hybrid C57BL6/J × CAST/EiJ male and female mESCs were adapted to 2i/LIF and female cells grown in serum/LIF conditions were differentiated using Fgf2 and Activin A for 1, 2, 4 and 7 days to induce random XCI. scRNA-seq was performed using the Smart-seq3 protocol, providing full-length coverage together with molecular counting using UMIs. Allelic resolution is achieved using strain-specific SNPs in the data. We reveal dynamic balancing of X alleles as cells undergo XCI to compensate dosage imbalances between sexes as well as between X and autosomes. Furthermore, we reveal that female naïve mESCs with two active X chromosomes lack XCU on both alleles which has major implications for reprogramming studies. Finally, we estimate allelic transcriptional burst kinetics from the data and find that progressively increased burst frequencies underlies the XCU process.
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