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| Status |
Public on Dec 29, 2020 |
| Title |
BJ MSC; b4cd8875-d973-44d5-869c-73b7f8c4da93 |
| Sample type |
SRA |
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| Source name |
Differentiated in vitro mesenchymal stem cell (MSC) lines
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| Organism |
Homo sapiens |
| Characteristics |
cell type: Differentiated in vitro mesenchymal stem cell (MSC) lines
condition: MSC
transfer status: BJ
clonality: BJ MSC
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| Treatment protocol |
For mtDNA depleted BJ fibroblast generation: A 1000x stock of 2’,3’-dideoxycytidine (ddC, Sigma, Cat. # D5782) was prepared in water. BJ cells cultured in complete media with 50 µg/ml uridine were added ddC to an appropriate final concentration. Cells were passaged every 3-4 d and fresh ddC added over the course of 3 weeks. After ddC treatment, total DNA was extracted (Qiagen, Cat. # 69504) and mtDNA quantified using SYBR Select Master Mix for CFX (Life Technologies, Cat. # 4472942). For mitochondria transfer generation: Mitochondria were harvested from HEK293T DsRed cells, LP351 PBMC, or LP298 PBMC using a Qproteome Mitochondria Isolation Kit (Qiagen, Cat. # 37612) following manufacturer’s protocol. Mitochondrial pellets were resuspended in PBS at a concentration ~1 mg total protein/mL. Mitochondrial suspensions were delivered into ρ0 fibroblast cells using a mitochondrial transfer tool developed by NanoCav, LLC. Transferred fibroblasts were cultured in complete media with 50 μg/mL uridine for 4 d following mitochondria delivery. On day 5, cells were shifted to uridine-free complete media prepared with 10% dialyzed FBS (Life Technologies, Cat. #26400-044). On day 8, cells were shifted to glucose-free, galactose-containing medium (DMEM without glucose (Gibco, Cat. # 11966025) supplemented with 10% dialyzed FBS and 4.5 g/l galactose). Colonies emerged ~10 d post-delivery and cells were shifted back to uridine-free medium before colonies were counted by microscopy or isolated using cloning rings. For iPSC reprogramming: Fibroblast lines were reprogrammed to iPSCs using StemRNA-NM Reprogramming kit (Stemgent, Cat. # 00-0076) following manufacturer’s protocol. Briefly, fibroblasts were plated on a matrigel (Corning, Cat. # 356234) coated 6-well plate at 2 x 105 cells/well on Day 0. Daily transfections of non-modified (NM)-RNA reprogramming cocktail were carried out from days 1-4 using Lipofectamine RNAiMAX (ThermoFisher, Cat. # 13778100. On days 10-12, iPSC colonies were identified by staining with Tra 1-60 antibody (Stemgent, Cat. # 09-0068). Tra 1-60+ iPSC colonies were picked and re-plated on matrigel coated 12-well plates and maintained in mTeSR 1 (Stemcell Technologies, 85850). For MSC differentiation: MSC lines were generated from iPSCs using STEMdiff Mesenchymal Progenitor Kit (Stemcell Technologies, Cat. #05240) following manufacturer’s protocol. The provided substrate was used for the initial differentiation but the cells were plated on plastic for further passaging.
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| Growth protocol |
HEK293T expressing mitochondrial-targeted DsRed protein (Human Embryonic Kidney, pMitoDsRed, Clontech Laboratories) were generated as previously described (Miyata et al., 2014). BJ (Human Foreskin Fibroblast, CRL-2522), ADF (Adult Dermal Fibroblast, PCS-201-012), NDF (Neonatal Dermal Fibroblast, PCS-201-010). BJ, NDF, ADF, and HEK293T DsRed cells were cultured in “complete media” containing DMEM (Corning, Cat. # 10013CV) supplemented with 10% Fetal Bovine Serum (FBS, Hyclone, Cat. # SH30088.03HI0), penicillin-streptomycin (Corning, Cat. # 30-002-CI), GlutaMax (ThermoFisher, Cat. # 35050-061), and non-essential amino acids (MEM NEAA, ThermoFisher, Cat. # 11-140-050). BJ ρ0, NDF ρ0, and ADF ρ0 fibroblasts were cultured in complete media supplemented with 50 µg/ml uridine (Sigma, Cat. # U3003). IPSCs were cultured on matrigel (Corning, Cat. # 356234) coated plates in mTeSR1 media (StemCell Technologies, Cat. # 85850) according to manufacturer’s protocol. MSCs were cultured in defined, MesenCult-ACF media (StemCell Technologies, Cat. # 05449) following manufacturer’s protocol. Cells were tested frequently for mycoplasma using a universal mycoplasma detection kit (ATCC, Cat. # 30-1012K).
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| Extracted molecule |
total RNA |
| Extraction protocol |
Fibroblasts, iPSCs, and MSCs were grown in biological triplicates and technical duplicates to 70-80% confluence and purified using the RNeasy Mini Kit (Qiagen, Cat. # 74104) and RNase-free DNase (Qiagen, Cat. # 79254) following manufacturer’s protocols. All samples showed a A260/280 ratio > 1.99 (Nanodrop; Thermo Scientific). Prior to library preparation, quality control of the RNA was performed using the Advanced Analytical Technologies Fragment Analyzer (Advanced Analytical, Inc.) and analyzed using PROSize 2.0.0.51 software. RNA Quality Numbers (RQNs) were computed per sample with final scores between 8.1-10, indicating intact total RNA per sample prior to library preparation.
Strand-specific ribosomal RNA (rRNA) depleted RNA-Seq libraries were prepared from 1 µg of total RNA using the KAPA Stranded RNA-Seq Kit with Ribo-Erase (Kapa Biosystems, Roche). Briefly, rRNA was depleted from total RNA samples, the remaining RNA was heat fragmented, and strand-specific cDNA was synthesized using a first strand random priming and second strand dUTP incorporation approach. Fragments were then A-tailed, adapters were ligated, and libraries were amplified using high-fidelity PCR. All libraries were prepared in technical duplicates per sample (n = 60 samples, 120 libraries total), and resulting raw sequencing reads merged for downstream alignment and analysis. Libraries were paired-end sequenced at 2x150 bp on an Illumina NovaSeq 6000.
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| Library strategy |
RNA-Seq |
| Library source |
transcriptomic |
| Library selection |
cDNA |
| Instrument model |
Illumina NovaSeq 6000 |
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| Description |
b4cd8875-d973-44d5-869c-73b7f8c4da93.rsem.txt.gz
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| Data processing |
Pre-Processing: Fibroblasts, iPSCs, and MSCs were each sequenced in biological triplicates and technical duplicates (n = 60 total samples) to account for variation in extraction and culturing. Raw sequencing reads were converted into fastq files and filtered for low quality reads and Illumina sequencing adapter contamination using bcl2fastq (Illumina). Reads were then quasi-mapped and quantified to the Homo sapiens GENCODE 28 (GRCh38.p12, Ensembl 92, April 2018) transcriptome using the alignment-free transcript level quantifier Salmon v0.9.1 (Harrow et al., 2012; Mudge and Harrow, 2015; Patro et al., 2017). A quasi-mapping index was prepared using parameters ?salmon index -k 31 ?type quasi?, and comprehensive transcript level estimates were calculated using parameters ?salmon quant -l A ?seqBias ?gcBias --discardOrphansQuasi?. Transcript level counts were collapsed to gene level (HGNC) counts, transcripts per million abundances (TPM) and estimated lengths using R Bioconductor package tximport v1.6.0 (Soneson et al., 2015).
Differential Gene Expression Analysis: The resulting sample gene count matrix was size factor normalized and analyzed for pairwise differential gene expression using R Bioconductor package DESeq2 v1.18.1. Expression changes were estimated using an empirical Bayes procedure to generate moderated fold change values with design ?~ Batch + Sample?, modeling batch effect variation due to day of RNA extraction (Huber et al., 2015; Love et al., 2014). Significance testing was performed using the Wald test, and resulting P values were adjusted for multiple testing using the Benjamini-Hochberg procedure (Benjamini and Hochberg, 1995). DEGs were filtered using an adjusted false discovery rate (FDR) q value < 0.05 and an absolute log2 transformed fold-change > 1.
Gene Expression PCA: Variance stabilized transform (VST) values in the gene count matrix were calculated and plotted for principal component analysis (PCA) using R Bioconductor packages DESeq2, FactoMineR, and factoextra, as described in the metabolomics methods (Huber et al., 2015; Love et al., 2014). PCA of nuclear-encoded mitochondrial protein and mtDNA transcripts were extracted using localization evidence derived from MitoMiner v4.0, subsetting VST matrices using genes listed in MitoCarta 2.0 (Calvo et al., 2016; Smith and Robinson, 2016). Scatterplots and MA plots of gene expression fold-changes between fibroblasts, iPSCs, and MSCs were performed, and Pearson/Spearman correlation coefficients calculated, using R package ggpubr v0.1.6 (https://cran.r-project.org/web/packages/ggpubr/index.html). Genes of interest were extracted and averaged clonal heatmaps were prepared using R Bioconductor packages pheatmap v1.0.8 and gplots v3.0.1 (Gregory Warnes, 2016; Kolde, 2015). Venn diagram intersections of DEG lists were generated using Venny 2.1.0 (http://bioinfogp.cnb.csic.es/tools/venny/index.html)
Metabolic Transcript GSVA: GSVA on metabolic transcripts were performed similarly to metabolomics data as noted above. Pathway-level metabolic gene set enrichment analysis was performed using R Bioconductor package GSVA v1.26.0 function gsva() with parameters ?method = gsva, rnaseq = FALSE, abs.ranking = FALSE, min.sz = 5, max.sz = 500? using a log2(TPM + 1) transformed gene expression matrix (Hanzelmann et al., 2013). GSVA pathway enrichment scores per sample were extracted and assessed for significance using R Bioconductor package limma v3.34.9, as described above except with a Benjamini-Hochberg adjusted P value threshold = 0.01. Pathway metabolite sets were constructed using the KEGG PATHWAY Database, utilizing gene sets annotated to the metabolic pathways overview map HSA01100 (Kanehisa et al., 2012). Significance testing across clones and conditions for each gene set were calculated using Kruskal-Wallis ANOVA.
Gene Set Overrepresentation Analysis: DEGs were extracted and analyzed for pathway/gene ontology (GO) term overrepresentation using the R Bioconductor package clusterProfiler v3.6.0 and ReactomePA v1.22.0, using a background gene set of all genes expressed with at least one read count in the sample gene count matrix (Yu and He, 2016; Yu et al., 2012). Overrepresented Reactome/KEGG pathways and GO terms were identified across DEG lists and conditions using clusterProfiler function compareCluster() with significance testing cutoffs of P < 0.05, and an adjusted FDR < 0.25.
CellNet Classification Analysis: We performed the CellNet algorithm to assess the similarity of cell-and-tissue (C/T) type specific gene regulatory networks (GRNs) between established C/T cohorts and our transfer samples (Cahan et al., 2014; Morris et al., 2014; Radley et al., 2017). All raw RNA-seq sample FASTQs were re-quantified and quasi-mapped to a modified Homo sapiens Ensembl 80 transcriptome pre-packaged with the CellNet R package using Salmon v0.8.2 (Patro et al., 2017). Transcript counts were then collapsed to gene level counts using tximport v1.6.0 and downsampled per 100,000 reads for CellNet input (Soneson et al., 2015). Samples were then benchmarked and classified against provided C/T type samples from the CellNet Processor repository (June 20th, 2017), using a random forest classifier to assess classification probability of a sample to a given C/T type.
HLA Class I Genotyping: MHC Class I HLA genotypes were identified using OptiType v1.3.1(Szolek et al., 2014). All raw RNA-seq sample FASTQs were aligned to the HLA Class I reference transcriptome packaged in OptiType using BWA MEM v0.7.17 with standard parameters (Li and Durbin, 2010). HLA subset reads were then analyzed for Class I genotype using OptiType in paired-end RNA mode with standard parameters.
Genome_build: Homo sapiens GENCODE 28 (GRCh38.p12, Ensembl 92, April 2018)
Supplementary_files_format_and_content: Salmon v0.9.1 quantification output files for each respective sample after quasi-mapping to the GENCODE 28 reference transcriptome. Columns are (in order): (1) Name of transcript from GENCODE 28 reference, (2) Length of the transcript (nt), (3) Effective length of the transcript, correcting for sequencing and GC fragment bias, (4) transcripts per million of the transcript (TPM), and (5) number of estimated reads mapping to each quantified transcript.
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| Submission date |
Jun 15, 2018 |
| Last update date |
Dec 30, 2020 |
| Contact name |
Fasih Mubtasim Ahsan |
| Organization name |
UCLA
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| Department |
Pathology and Laboratory Medicine
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| Lab |
Teitell Lab, MRL 4-567
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| Street address |
675 Charles E. Young Drive South
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| City |
Los Angeles |
| State/province |
CA |
| ZIP/Postal code |
90095-1732 |
| Country |
USA |
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| Platform ID |
GPL24676 |
| Series (1) |
| GSE115871 |
Pressure-Driven Mitochondrial Transfer Pipeline Generates Mammalian Cells of Desired Genetic Combinations and Fates |
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| Relations |
| BioSample |
SAMN09430990 |
| SRA |
SRX4222372 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSM3192250_b4cd8875-d973-44d5-869c-73b7f8c4da93.quant.sf.txt.gz |
3.3 Mb |
(ftp)(http) |
TXT |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data provided as supplementary file |
| Processed data are available on Series record |
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