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Sample GSM7796205

Query DataSets for GSM7796205

Status

Public on Sep 23, 2023

Title

H1031

Sample type

SRA

Source name

amnion

Organism

Homo sapiens

Characteristics

tissue: amnion
cell type: amnion cells
treatment: no inflammation

Treatment protocol

intra-amniontic injection of LPS or saline. Intra-amniotic + subcutaneous injection Adalimumab

Extracted molecule

total RNA

Extraction protocol

For scRNA seq experiements of chorioamniondecidua samples were collected right after delivery, digested, and cell suspension was sorted for live cells. For both human and rhesus bulk RNA seq experiments, amnion was physically separated from chorion and decidua, snap-frozen, and RNA was harvested using Trizol reagent.
Bulk RNAseq: Fifteen amnion samples were used (ctrl n=3; LPS n=6; and Adal+LPS n=6). The integrity of purified total RNA from amnion was assessed using the RNA High-Sensitivity Assay on the TapeStation 2200 (Agilent Technologies). 200-300 ng of starting material were used as input material for the NEBNext rRNA Depletion kit (cat# E6350). RNA libraries were then prepared using the NEBNext Ultra II RNA kit (cat# E7765). Two hundred ng of each final library was subject to capture hybridization using Illumina TruSeq Exome, (cat #20020490) using Illumina Exome Panel (cat #20020183) according to the manufacturer’s instructions. Quality control for the final libraries was performed using the DNA D1000 Assay (TapeStation 2200 - Agilent Technologies) and quantified using a Qubit dsDNA BR Assay (Life Technologies). Diluted libraries were pooled and sequenced 50 single-end on a HiSeq3000 (Illumina).
Single-cell (sc)RNA-seq: For scRNA-seq analysis, eight unseparated chorioamnion-decidua (CAD) samples were used (Ctrl n=2; LPS n=3; and Adal+LPS n=3. Note that 6/8 tissues were also used for bulk RNA-seq analysis (Supplementary Table I). Live-sorted CAD cells were microfluidically partitioned with single cell capture, barcoding, and library construction (10X genomics chromium platform, Pleasanton, California). Single-cell RNA-seq libraries were prepared using the 10X Single Cell 3′ v2 Reagent Kits. Specifically, single cell suspensions were loaded on a Chromium Controller instrument to generate single-cell Gel Bead-In- EMulsions (GEMs). GEM-RT were performed in a Veriti 96-well thermal cycler (Thermo Fisher Scientific, Waltham, MA), following which, GEMs were harvested and the cDNAs were amplified and cleaned up with SPRIselect Reagent Kit. Indexed sequencing libraries were constructed using Chromium Single-Cell 3′ Library Kit for enzymatic fragmentation, end-repair, A-tailing, adapter ligation, ligation cleanup, sample index PCR, and PCR cleanup. The barcoded sequencing libraries were quantified using the KAPA Library Quantification Kit (KAPA Biosystems, Wilmington, MA). Sequencing libraries were loaded on a NovaSeq2 (Illumina, San Diego, CA) with a custom sequencing setting (26bp for Read 1 and 91bp for Read 2).

Library strategy

RNA-Seq

Library source

transcriptomic

Library selection

cDNA

Instrument model

Illumina NovaSeq 6000

Description

bulk RNAseq
H1031

Data processing

Bulk RNA-seq The reads were mapped with STAR 2.5.3a to the Macaca mulatta genome (Mmul 8.0.1) or to the human genome (GRCh38). The counts for each gene were obtained by using the options --quantMode GeneCounts. Differential expression analyses were carried out using DESeq2. The normalized counts were obtained from the DESeq2 analysis. Principal Component Analysis (PCA) was performed with the plotPCA function in DESeq2 after regularized log transformation. Heatmaps were plotted on the log2 value of the normalized counts. Inference of GO terms, Wiki pathways, and KEGG pathways were generated using Enrichr 63.
Single-cell (sc)RNA-seq: Data processing including quality control, read alignment (to the reference genome Mmul 8.0.1), and gene quantification was conducted using the 10X Cell Ranger software (version 2.1.1). The samples were then merged into a single expression matrix using the cellranger aggr pipeline. The R package Seurat (v3.1.2) was used to cluster the cells in the merged matrix. Cells with less than 500 transcripts or 100 genes, or more than 5% of mitochondrial expression were first filtered out as low-quality cells. The Seurat function NormalizeData was used to normalize the raw counts. Variable genes were identified using the FindVariableGenes function. The ScaleData function was used to scale and center expression values in the dataset, the number of unique molecular identifiers (UMI) was regressed against each gene. Principal component analysis (PCA), and uniform manifold approximation and projection (UMAP) were used to reduce the dimensions of the data, and the first two dimensions were used in the plots. The FindClusters function was used to cluster the cells. Marker genes were found using the FindAllMarkers function for each cluster, which employs the Wilcoxon Rank Sum Test to determine the significance and the Benjamini-Hochberg Procedure to correct for multiple comparisons. Cell types were annotated based on the marker genes and their match to canonical markers. The module scores were calculated using the AddModuleScore function. Sub-clustering on the amnion cells was performed. The same functions described above were used to obtain the sub-clusters. We obtained eight high-quality scRNA-seq profiles from CAD samples. Analysis of cellular trajectory by RNA velocity was performed using the Python package scVelo 64. In one Ctrl sample we did not detect any amnion cells and therefore we excluded this sample from the analysis. Thus, final analyses were conducted on 7 samples (ctrl n=1; LPS n=3; and Adalimumab+LPS n=3). In order to illustrate the variations of cell types in amnion after intrauterine infection we used hdWGCNA package 28 to perform co-expression network analysis on genes expressed in at least 5% of the cells. MetacellsByGroups function was used to construct metacells in each treatment group. The resulting metacells expression matrix was normalized by calling NormalizeMetacells function and then used for network analysis. In order to ensure scale-free topology of the co-expression network TestSoftPowers function was used to perform parameter sweep. The sweep identified 10 as the optimum soft power threshold to use when constructing the co-expression network by calling ConstructNetwork function with otherwise default parameters. WGCNA dendrogram was visualized with PlotDendrogram function whereas ModuleFeaturePlot was used to map harmonized module eigengenes, computed with ModuleEigengene function, onto the dimensionality reduction plot. The modules identified by hdWGCNA analysis were functionally characterized by performing enrichment tests with enrichR package. For each of the modules, a set of 100 most connected genes (ModuleConnectivity function) was tested for enrichment. Top 10 GO Biological Process terms for each module with adjusted p-value <0.05 were shown along with the enrichment score.
Assembly: Macaca mulatta genome (Mmul 8.0.1)
Assembly: Homo sapiens GRCh38
Supplementary files format and content: Bulk RNA-seq_Raw counts_Rhesus_Amnion_Presicce.txt
Supplementary files format and content: Bulk RNA-seq_Raw counts_Human_Amnion_Presicce.txt

Submission date

Sep 22, 2023

Last update date

Sep 23, 2023

Contact name

Suhas G Kallapur

Organization name

UCLA

Street address

10833 Le Conte Avenue

City

Los Angeles