P–555 Recurrent pregnancy loss is associated with changes in the pre-pregnant endometrial gland transcriptome

Abstract Study question Do endometrial gland factors influence recurrent pregnancy loss? Summary answer The endometrial gland transcriptome during the window of implantation is altered in women with recurrent pregnancy loss compared to controls. What is known already Secretions from endometrial glands contribute to the uterine environment that supports the attachment and implantation of the embryo in early pregnancy. Studies have attempted to identify an endometrial gene expression pattern associated with recurrent pregnancy loss however, the cellular heterogeneity within the endometrium may obscure important differences in specific cell populations. Study design, size, duration An observational study comparing controls and women with recurrent pregnancy loss. Participants/materials, setting, methods Endometrial samples were collected during the implantation period of the menstrual cycle from five matched participant egg donor controls and women with recurrent pregnancy loss. Endometrial glands were isolated from fresh endometrial biopsies and RNA sequencing was performed. A differential gene expression analysis and a gene ontology enrichment analysis was performed between egg donor controls and women with recurrent pregnancy loss. Main results and the role of chance This study reports a glandular epithelium specific gene expression profile and demonstrates differential gene expression of endometrial glands from women with recurrent pregnancy loss compared to controls. 18 genes were upregulated and 1 gene was downregulated in the endometrial glands from women with recurrent pregnancy loss compared to controls (5% false discovery rate). Biological processes which contain genes that were differentially expressed in women with recurrent pregnancy loss compared to controls include epithelial cell migration and regulation of secretion by the cell. Limitations, reasons for caution This is an observational study with a relatively small sample size. Wider implications of the findings: This study identified differences in gene expression in women with recurrent pregnancy loss that are specifically associated with endometrial glands rather than endometrium as a whole. These differences could be used to identify a perturbed endometrium, isolate causes of recurrent pregnancy loss and develop targeted therapies. Trial registration number Not applicable

Download Full-text

Assessing Differential Gene Expression with Small Sample Sizes in Oligonucleotide Arrays Using a Mean-Variance Model

Biometrics ◽

10.1111/j.1541-0420.2006.00675.x ◽

2007 ◽

Vol 63 (1) ◽

pp. 41-49 ◽

Cited By ~ 17

Author(s):

Jianhua Hu ◽

Fred A. Wright

Keyword(s):

Gene Expression ◽

Differential Gene Expression ◽

Small Sample ◽

Sample Sizes ◽

Variance Model ◽

Oligonucleotide Arrays ◽

Differential Gene ◽

Small Sample Sizes ◽

Mean Variance

Download Full-text

The NBP Negative Binomial Model for Assessing Differential Gene Expression from RNA-Seq

Statistical Applications in Genetics and Molecular Biology ◽

10.2202/1544-6115.1637 ◽

2011 ◽

Vol 10 (1) ◽

Cited By ~ 59

Author(s):

Yanming Di ◽

Daniel W Schafer ◽

Jason S Cumbie ◽

Jeff H Chang

Keyword(s):

Gene Expression ◽

Differential Gene Expression ◽

Negative Binomial ◽

Small Sample Size ◽

Small Sample ◽

Dispersion Parameter ◽

Additional Parameter ◽

Rna Seq ◽

Data Set ◽

Differential Gene

We propose a new statistical test for assessing differential gene expression using RNA sequencing (RNA-Seq) data. Commonly used probability distributions, such as binomial or Poisson, cannot appropriately model the count variability in RNA-Seq data due to overdispersion. The small sample size that is typical in this type of data also prevents the uncritical use of tools derived from large-sample asymptotic theory. The test we propose is based on the NBP parameterization of the negative binomial distribution. It extends an exact test proposed by Robinson and Smyth (2007, 2008). In one version of Robinson and Smyth’s test, a constant dispersion parameter is used to model the count variability between biological replicates. We introduce an additional parameter to allow the dispersion parameter to depend on the mean. Our parametric method complements nonparametric regression approaches for modeling the dispersion parameter. We apply the test we propose to an Arabidopsis data set and a range of simulated data sets. The results show that the test is simple, powerful and reasonably robust against departures from model assumptions.

Download Full-text

Select Gene Expression Across Variable PrPSc Permissive Ovine Microglia

10.21203/rs.2.13583/v1 ◽

2019 ◽

Author(s):

Valerie McElliott ◽

Kelcey Dinkel ◽

Zachary Nesbit ◽

James B. Stanton

Keyword(s):

Gene Expression ◽

Cell Culture ◽

Prion Protein ◽

Differential Gene Expression ◽

Cellular Prion Protein ◽

Transmissible Spongiform Encephalopathies ◽

Specific Cell ◽

Culture Models ◽

Differential Gene ◽

Cell Culture Models

Abstract Abstract Abstract Background: Transmissible spongiform encephalopathies (TSEs) are a group of fatal, neurodegenerative diseases that affect multiple species, including sheep, cattle, and humans. A misfolded, pathogenic isoform (PrPD) of the normal, host-encoded, cellular prion protein (PrPC) is the causative agent for TSEs. While there have been advances in understanding TSEs, antemortem diagnostic tests are limited in many species, and there are no effective treatment protocols. Filling these reagent gaps will require knowledge of the molecular pathophysiology of PrPD accumulation. Previous work has suggested that the extracellular matrix (i.e., fibronectin 1) and physiological functions (i.e., cell division) maybe key factors for cellular prion permissibility, at least in specific cell culture models. Using a natural scrapie isolate, six immortalized, ovine microglial clones, of varying permissiveness to classical scrapie were evaluated for differential gene expression in seven genes based on previous RNASeq studies (fibronectin 1 [FN1], follistatin-like 1 [FSTL1], osteonectin [SPARC], survivin [BIRC5], syndecan 4 [SDC4], AXL receptor tyrosine kinase [AXL], and prion protein [PRNP]), and to determine correlations with prion permissibility. Results: Significant differential gene expression was frequently observed for survivin, follistatin-like 1 and osteonectin between clones, and when evaluated relative to PRNP expression. However, only fibronectin 1 and survivin were significantly correlated with prion permissibility, and only when evaluated relative to PRNP expression. Inoculation had a significant effect on follistatin-like 1, syndecan 4, and osteonectin. Conclusions: Similar to previous studies in other systems, fibronectin and mitotic rate show promise as potential determinants of prion permissibility in ovine microglia. As determinants of prion permissibility, the expression of fibronectin 1 and survivin coupled with PRNP could be utilized as biomarkers for detection of prion permissibility phenotype in ovine microglia, and perhaps other cell culture models of prion disease.

Download Full-text

Cell type-specific differential expression for spatial transcriptomics

10.1101/2021.12.26.474183 ◽

2021 ◽

Author(s):

Dylan M Cable ◽

Evan Murray ◽

Vignesh Shanmugam ◽

Simon Zhang ◽

Michael Z Diao ◽

...

Keyword(s):

Gene Expression ◽

Differential Expression ◽

Differential Gene Expression ◽

Cell Types ◽

Specific Cell ◽

Cell Type ◽

Unified Framework ◽

Wide Range ◽

Cell Type Specific ◽

Differential Gene

Spatial transcriptomics enables spatially resolved gene expression measurements at near single-cell resolution. There is a pressing need for computational tools to enable the detection of genes that are differentially expressed across tissue context for cell types of interest. However, changes in cell type composition across space and the fact that measurement units often detect transcripts from more than one cell type introduce complex statistical challenges. Here, we introduce a statistical method, Robust Cell Type Differential Expression (RCTDE), that estimates cell type-specific patterns of differential gene expression while accounting for localization of other cell types. By using general log-linear models, we provide a unified framework for defining and identifying gene expression changes for a wide-range of relevant contexts: changes due to pathology, anatomical regions, physical proximity to specific cell types, and cellular microenvironment. Furthermore, our approach enables statistical inference across multiple samples and replicates when such data is available. We demonstrate, through simulations and validation experiments on Slide-seq and MERFISH datasets, that our approach accurately identifies cell type-specific differential gene expression and provides valid uncertainty quantification. Lastly, we apply our method to characterize spatially-localized tissue changes in the context of disease. In an Alzheimer's mouse model Slide-seq dataset, we identify plaque-dependent patterns of cellular immune activity. We also find a putative interaction between tumor cells and myeloid immune cells in a Slide-seq tumor dataset. We make our RCTDE method publicly available as part of the open source R package https://github.com/dmcable/spacexr.

Download Full-text