Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Placental gene expression-based cell type deconvolution: Cell proportions drive preeclampsia gene expression differences

ABSTRACT: The placenta mediates adverse pregnancy outcomes, including preeclampsia, characterized by gestational hypertension and proteinuria. Placental cell type heterogeneity in preeclampsia is not well-understood and limits mechanistic interpretation of bulk gene expression measures. We generated single-cell RNA-sequencing samples for integration with existing data to create the largest deconvolution reference of 19 fetal and 8 maternal cell types from placental villous tissue at term. We deconvoluted eight published microarray case-control studies of preeclampsia. Our findings indicate substantial placental cellular heterogeneity in preeclampsia that predict previously observed bulk gene expression differences. Our deconvolution reference lays the groundwork for cellular heterogeneity-aware investigation into placental dysfunction and adverse birth outcomes.

ORGANISM(S): Homo sapiens

PROVIDER: GSE182381 | GEO | 2022/08/22

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Similar Datasets

Cell-free DNA Methylation and Transcriptomic Signature Prediction of Pregnancies with Adverse Outcomes [WGBS]

Project description:Although analysis of maternal plasma cell-free content has been employed for screening of genetic abnormalities within a pregnancy, limited attention has been paid to its use for the detection of adverse pregnancy outcomes (APOs) based on placental function. Here we investigated cell-free DNA and RNA content of 102 maternal and 25 cord plasma samples. Employing a novel deconvolution methodology, we found that during the first trimester, placenta-specific DNA increased prior to the subsequent development of gestational diabetes with no change in patients with preeclampsia while decreasing with maternal obesity. Moreover, using cell-free RNA sequencing, APOs revealed seventy-one differentially expressed genes early in pregnancy. We noticed the upregulation of S100A8, MS4A3 and MMP8 that have been already associated with APOs but also the upregulation of BCL2L15 and the downregulation of ALPL that have never been associated with APOs. We constructed a classifier with a positive predictive ability (AUC) of 0.91 for APOs, 0.86 for preeclampsia alone and 0.64 for GDM. We conclude that placenta-specific cell-free nucleic acids during early gestation provide the possibility of predicting APOs prior to the emergence of characteristic clinical features.

2020-10-19 | GSE154348 | GEO

Cell composition analysis of bulk genomics using single cell data

Project description:Single-cell expression profiling is a rich resource of cellular heterogeneity. While profiling every sample under study is advantageous, such workflow is time consuming and costly. We devised CPM - a deconvolution algorithm in which cellular heterogeneity is inferred from bulk expression data based on pre-existing collection of single-cell RNA-seq profiles. We applied CPM to investigate individual variation in heterogeneity of murine lung cells during in vivo influenza virus infection, revealing that the relations between cell quantities and clinical outcomes varies in a gradual manner along the cellular activation process. Validation experiments confirmed these gradual changes along the cellular activation trajectory. Additional analysis suggests that clinical outcomes relate to the rate of cell activation at the early stages of this process. These findings demonstrate the utility of CPM as a mapping deconvolution tool at single-cell resolution, and highlight the importance of such fine cell landscape for understanding diversity of clinical outcomes.

2019-03-15 | GSE113530 | GEO

SCDC: Deconvolution of Bulk Gene Expression by Single-Cell RNA Sequencing Data

Project description:This paper describes SCDC, a deconvolution method for bulk RNA-seq that leverages cell-type specific gene expression profiles from multiple scRNA-seq reference datasets.

2019-08-30 | GSE136148 | GEO

A Hidden Proteome Encoded by circRNAs in Human Placentas: Implications for Uncovering Preeclampsia Pathogenesis

Project description:Circular RNAs (circRNAs) are consistently overlooked as translational templates due to computational and experimental limitations. Here, we used multi-omics approaches to identify new translational events of circRNAs in human placentas, and assessed the roles of circRNA-encoded proteins in placentation and associated disorders based on the recently proposed protein bait hypothesis. The combination of placental circRNA sequencing and bioinformatics analysis revealed 219607 circRNAs, of which 35% were identified by the Coding Potential Assessment Tool (CPAT) as having >50% coding probability. Ribosomal profiling further identified 528 novel coding circRNAs, including 217 that coded for microproteins verified by LC-MS/MS. Based on these data, we established the Placental Microprotein Bank (PMB) for coding circRNAs and their coded proteins. These proteins have lower molecular weights, higher stable indices and hydrophobicity compared to the canonical proteins, and potentially link to placental development and adverse pregnancy outcomes such as pre-eclampsia. Furthermore, we identified circPRKCB119aa as a novel preeclampsia-related microprotein that enhanced trophoblast autophagy by inhibiting phosphorylation of its cognate linear-spliced isoform protein kinase C (PKC) β. In summary, we discovered a hidden circRNA-encoded proteome in the human placenta, which offers new insights into the mechanisms underlying placentation and its disorders such as, but not limited to, preeclampsia.

2022-02-06 | GSE196032 | GEO

Comprehensive Analysis of Preeclampsia-Associated DNA Methylation in the Placenta

Project description:Background: A small number of recent reports have suggested that altered placental DNA methylation may be associated with early onset preeclampsia. It is important that further studies be undertaken to confirm and develop these findings. We therefore undertook a systematic analysis of DNA methylation patterns in placental tissue from 24 women with preeclampsia and 24 with uncomplicated pregnancy outcome. Methods: We analyzed the DNA methylation status of approximately 27,000 CpG sites in placental tissues in a massively parallel fashion using an oligonucleotide microarray. Follow up analysis of DNA methylation at specific CpG loci was performed using the Epityper MassArray approach and high-throughput bisulfite sequencing. Results: Preeclampsia-specific DNA methylation changes were identified in placental tissue samples irrespective of gestational age of delivery. In addition, we identified a group of CpG sites within specific gene sequences that were only altered in early onset-preeclampsia (EOPET) although these DNA methylation changes did not correlate with altered mRNA transcription. We found evidence that fetal gender influences DNA methylation at autosomal loci but could find no clear association between DNA methylation and gestational age. Conclusion: Preeclampsia is associated with altered placental DNA methylation. Fetal gender should be carefully considered during the design of future studies in which placental DNA is analyzed at the level of DNA methylation. Further large-scale analyses of preeclampsia-associated DNA methylation are necessary. Bisulphite converted DNA from the 48 samples were hybridized to the Illumina Infinium 27k Human Methylation Beadchip v1.2

2014-07-12 | E-GEOD-59274 | biostudies-arrayexpress

Placental gene expression profiling for the identification of clinically relevant subclasses of human preeclampsia

Project description:Preeclampsia (PE) is a complex, heterogeneous disorder of pregnancy, demonstrating considerable variability in observed maternal symptoms and fetal outcomes. We hypothesized that this heterogeneity is due to the existence of multiple molecular forms of PE. To address our hypothesis, we created a large (N=330) human placental microarray data set consisting of seven previously published studies (GSE30186, GSE10588, GSE24129, GSE25906, GSE43942, GSE4707, and GSE44711) and 157 highly annotated samples from a BioBank (below). Applying unsupervised clustering to this combined data set revealed multiple distinct molecular groups of placentas, including three clinically relevant potential origins of PE based on gene expression and correlating patient records.

2016-05-10 | GSE75010 | GEO

Genome-wide analysis of placental gene expression in severe preterm preeclampsia

Project description:Analysis of genome-wide gene expression in placentas from women with preterm severe preeclampsia, with or without HELLP syndrome, compared to gestational age-matched controls. The hypothesis tested in the present study was that placental transcriptomic changes in preeclampsia are considerably different from controls. The results provide important information on placental transcriptomic changes in preeclampsia.

2018-12-30 | GSE66273 | GEO

Placental transcriptome analysis of hypertensive pregnancies identifies distinct gene expression profiles of preeclampsia superimposed on chronic hypertension

Project description:The pathogenesis of preeclampsia superimposed on chronic hypertension is poorly understood relative to preeclampsia occurring in pregnant people without chronic hypertension. Placental transcriptomes in pregnancies complicated by de novo preeclampsia and superimposed preeclampsia have not been previously compared.

2022-05-30 | GSE204835 | GEO

DNA methylation profiles in preeclampsia and healthy control placentas

Project description:Genome wide DNA methylation profiling of normal and preeclampsia placental samples. Illumina Infinium HumanMethylation450 BeadChip (450K array) was used to obtain DNA methylation profiles in placental samples. Samples included 16 samples from healthy uncomplicated pregnancies and 8 samples from pregnancies affected by preeclampsia.

2016-04-12 | GSE75196 | GEO

Single cell RNA-sequencing reveals placenta cellular heterogeneity in adverse pregnancy

Project description:The placenta is a highly heterogeneous organ and is closely related to adverse pregnancy. The previous bulk sequencing of whole tissue could not show the characteristics of individual cells and the interactions between cells. Here, we select the placental tissues of the gestational diabetes group(GDM), preeclampsia group(PE), advanced age group(GL) and normal control group for single-cell sequencing in order to explain the mechanism of related diseases in more depth.nated spatial and temporal regulation of gene expression in the murine hindlimb determines the identity of mesenchymal progenitors and the development of diversity of musculoskeletal tissues they form. Hindlimb development has historically been studied with lineage tracing of individual genes selected a priori, or at the bulk tissue level, which does not allow for the determination of single cell transcriptional programs yielding mature cell types and tissues. To identify the cellular trajectories of lineage specification during limb bud development, we used single cell mRNA sequencing (scRNA-seq) to profile the developing murine hindlimb.

2021-05-04 | GSE173193 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data