Dataset Information

Single-cell RNA-seq reveals cell type-specific transcriptional signatures at the maternal-foetal interface during pregnancy.

ABSTRACT: Growth and survival of the mammalian embryo within the uterine environment depends on the placenta, a highly complex vascularized organ comprised of both maternal and foetal tissues. Recent experiments demonstrate that the zinc finger transcriptional repressor Prdm1/Blimp1 is essential for specification of spiral artery trophoblast giant cells (SpA-TGCs) that invade and remodel maternal blood vessels. To learn more about functional contributions made by Blimp1+ cell lineages here we perform the first single-cell RNA-seq analysis of the placenta. Cell types of both foetal and maternal origin are profiled. Comparisons with microarray datasets from mutant placenta and in vitro differentiated trophoblast stem cells allow us to identify Blimp1-dependent transcripts enriched in SpA-TGCs. Our experiments provide new insights into the functionally distinct cell types present at the maternal-foetal interface and advance our knowledge of dynamic gene expression patterns controlling placental morphogenesis and vascular mimicry.

SUBMITTER: Nelson AC

PROVIDER: S-EPMC4848515 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

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Single-cell RNA-seq reveals cell type-specific transcriptional signatures at the maternal-foetal interface during pregnancy.

Nelson Andrew C AC Mould Arne W AW Bikoff Elizabeth K EK Robertson Elizabeth J EJ

Nature communications 20160425

Growth and survival of the mammalian embryo within the uterine environment depends on the placenta, a highly complex vascularized organ comprised of both maternal and foetal tissues. Recent experiments demonstrate that the zinc finger transcriptional repressor Prdm1/Blimp1 is essential for specification of spiral artery trophoblast giant cells (SpA-TGCs) that invade and remodel maternal blood vessels. To learn more about functional contributions made by Blimp1+ cell lineages here we perform the ...[more]

PMID: 27108815

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Project description:BackgroundCanine prostate adenocarcinoma (PAC) and transitional cell carcinoma (TCC) are typically characterized by metastasis and chemoresistance. Cell lines are important model systems for developing new therapeutic strategies. However, as they adapt to culturing conditions and undergo clonal selection, they can diverge from the tissue from which they were originally derived. Therefore, a comprehensive characterization of cell lines and their original tissues is paramount.MethodsThis study compared the transcriptomes of nine canine cell lines derived from PAC, PAC metastasis and TCC to their respective original primary tumor or metastasis tissues. Special interests were laid on cell culture-related differences, epithelial to mesenchymal transition (EMT), the prostate and bladder cancer pathways, therapeutic targets in the PI3K-AKT signaling pathway and genes correlated with chemoresistance towards doxorubicin and carboplatin.ResultsIndependent analyses for PAC, PAC metastasis and TCC revealed 1743, 3941 and 463 genes, respectively, differentially expressed in the cell lines relative to their original tissues (DEGs). While genes associated with tumor microenvironment were mostly downregulated in the cell lines, patient-specific EMT features were conserved. Furthermore, examination of the prostate and bladder cancer pathways revealed extensive concordance between cell lines and tissues. Interestingly, all cell lines preserved downstream PI3K-AKT signaling, but each featured a unique therapeutic target signature. Additionally, resistance towards doxorubicin was associated with G2/M cell cycle transition and cell membrane biosynthesis, while carboplatin resistance correlated with histone, m- and tRNA processing.ConclusionComparative whole-transcriptome profiling of cell lines and their original tissues identifies models with conserved therapeutic target expression. Moreover, it is useful for selecting suitable negative controls, i.e., cell lines lacking therapeutic target expression, increasing the transfer efficiency from in vitro to primary neoplasias for new therapeutic protocols. In summary, the dataset presented here constitutes a rich resource for canine prostate and bladder cancer research.

Project description:Single-cell RNA sequencing (scRNA-seq) technologies have precipitated the development of bioinformatic tools to reconstruct cell lineage specification and differentiation processes with single-cell precision. However, current start-up costs and recommended data volumes for statistical analysis remain prohibitively expensive, preventing scRNA-seq technologies from becoming mainstream. Here, we introduce single-cell amalgamation by latent semantic analysis (SALSA), a versatile workflow that combines measurement reliability metrics with latent variable extraction to infer robust expression profiles from ultra-sparse sc-RNAseq data. SALSA uses a matrix focusing approach that starts by identifying facultative genes with expression levels greater than experimental measurement precision and ends with cell clustering based on a minimal set of Profiler genes, each one a putative biomarker of cluster-specific expression profiles. To benchmark how SALSA performs in experimental settings, we used the publicly available 10X Genomics PBMC 3K dataset, a pre-curated silver standard from human frozen peripheral blood comprising 2,700 single-cell barcodes, and identified 7 major cell groups matching transcriptional profiles of peripheral blood cell types and driven agnostically by < 500 Profiler genes. Finally, we demonstrate successful implementation of SALSA in a replicative scRNA-seq scenario by using previously published DropSeq data from a multi-batch mouse retina experimental design, thereby identifying 10 transcriptionally distinct cell types from > 64,000 single cells across 7 independent biological replicates based on < 630 Profiler genes. With these results, SALSA demonstrates that robust pattern detection from scRNA-seq expression matrices only requires a fraction of the accrued data, suggesting that single-cell sequencing technologies can become affordable and widespread if meant as hypothesis-generation tools to extract large-scale differential expression effects.

Dataset Information

Single-cell RNA-seq reveals cell type-specific transcriptional signatures at the maternal-foetal interface during pregnancy.

Publications

Single-cell RNA-seq reveals cell type-specific transcriptional signatures at the maternal-foetal interface during pregnancy.

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