Comment[GEAAccession]	E-GEAD-617
MAGE-TAB Version	1.1
Investigation Title	Comparison of iPSC-derived megakaryocytes with distinct let-7 activity by scRNA-seq
Experiment Description	Comparison of iPSC-derived megakaryocytes with distinct let-7 activity by scRNA-seq. let-7 low/high subpopulations have been sorted by FACS and subjected to 10x genomics single cell-RNA seq analysis.
Experimental Design	innate behavior design	population based design
Experimental Factor Name	treatment
Experimental Factor Type	treatment
Person Last Name	Chen	Takayama
Person First Name	Si Jing	Naoya
Person Affiliation	Innovation regenerative medicine, Chiba University	
Person Roles	submitter	
Public Release Date	2024-02-23
Protocol Name	P-GEAD-1833	P-GEAD-1834	P-GEAD-1835	P-GEAD-1836	P-GEAD-1837
Protocol Type	sample collection protocol	nucleic acid extraction protocol	nucleic acid library construction protocol	nucleic acid sequencing protocol	normalization data transformation protocol
Protocol Description	The imMKCLs were cultured as described before. The reverse transfection of 150 ng mRNAs (75 ng each) was done with StemFect mRNA (Stemgent) in 50 uL of the cell suspension for 30 min. After 24 hours, the imMKCLs were sorted by flow cytometry Arial II based on their ley-7 activity. Collected cells were subjected to further investigation.	Total RNA was extracted using the microRNeasy Micro Kit according to the manufacturer guidelines. Briefly, cell lysates were prepared by adding the buffer RLT. The cell lysates were centrifuged to collect the supernatant. Ethanol was added and mixed properly. The lysates were collected into the spin column and centrifuged at 8,000 g, and the flow through was discarded. The column was washed with washing buffer (RW1) followed by a DNase treatment.	Cells were resuspended in PBS with BSA and loaded onto a Chromium Next GEM Chip G Single Cell Kit (10x Genomics, USA). The Gel Beads in emulsion (GEMs) generation and barcoding, reverse transcription, and cDNA generation and library construction were performed following the manufacturer s protocol.	Dual indexed, single cell libraries were pooled and sequenced in paired end reads on Novaseq6000 (Illumina).	10x Genomics derived datasets were collected, and quality control was performed to filter out low-quality and contaminated cells. Generally, reads were pre-processed with the Cell Ranger pipeline v.3.0.2 (10x Genomics). Downstream analysis and visualization were done using Seurat (version 4.0.5) implemented in R (version 4.1.1). After inspection of the quality control metrics, cells with > 15% of mitochondrial content or < 2500 detected genes were excluded from the downstream analysis. We normalized and scaled the unique molecular identifier (UMI) counts using the regularized negative binomial regression. Afterward, we performed linear dimensionality reduction (principal component analysis) and used the top 20 principal components to perform the unsupervised Uniform Manifold Approximation and Projection (UMAP) and clustering, which were computed at a range of resolutions from 1.2 to 0.05. The let-7a-5p high and low imMKCL populations were combined for all subsequent analyses. Cell clusters were identified using the FindClusters function from Seurat. Five clusters for Dox ON samples were identified (resolution 0.2). The FindAllMarkers function implemented in Seurat was used to identify DEGs between different clusters. The Wilcoxon test was performed on each gene, and the P value and adjusted P value for statistical significance were computed. Genes with adjusted P values less than 0.01 were considered significant.
SDRF File	E-GEAD-617.sdrf.txt
Comment[AEExperimentType]	RNA-seq of coding RNA from single cells
Comment[BioProject]	PRJDB15883
Comment[Last Update Date]	2024-02-23