DOI: 10.5061/dryad.6t1g1jx8q
title: Progesterone signaling in oviductal epithelial cells modulates the immune response to support preimplantation embryonic development

description: More than 60% of pregnancy losses occur during the first trimester,
 highlighting the need to understand the role of the oviduct in early
 pregnancy. In this study, we conditionally ablated the classical
 progesterone receptor (Pgr) in oviductal epithelial cells called Pgrd/d
 mouse model. We found that 40% of embryos collected from Pgrd/d females
 were non-viable or developmentally delayed, indicating that epithelial PGR
 expression is crucial for embryonic development. Single-cell
 RNA-sequencing revealed upregulation of proinflammatory genes, including
 interleukin 22 (IL-22), in the epithelial cells of Pgrd/d females.
 Pharmacological inhibition of inflammation using non-steroidal
 anti-inflammatory drugs significantly reduced IL-22 levels in the oviducts
 and rescued embryonic developmental rates in Pgrd/d females. Co-culture of
 wild-type zygotes with IL-22 significantly decreased the number of
 expanded blastocysts. Our findings suggest that progesterone signaling is
 vital for immunoregulation and normal preimplantation development,
 potentially providing insights for developing diagnostic tools and
 therapeutic strategies to address pregnancy failures. 
description: Oviducts were collected at 0.5 dpc from
 <em>Pgr</em><sup>f/f</sup> and
 <em>Pgr</em><sup>d/d</sup> (<em>n</em>
 = 8 mice/group). In another group of exogenous P<sub>4</sub>
 treatment, oviducts were collected from OVX females treated with Veh
 (<em>n</em> = 4 mice), P<sub>4</sub> for 2 h
 (<em>n</em> = 4 mice), or 24 h (<em>n</em> = 3
 mice). Oviducts were pooled and collected in L15+1%FBS and placed on ice.
 The oviduct was dissected into distal (infundibulum with ampulla, referred
 to as InfAmp) or proximal (isthmus with UTJ, referred to as IsthUTJ)
 regions. Oviductal cell isolation was performed using 0.25% trypsin-EDTA
 (T4049, MilliporeSigma) enzymatic digestion as previously described. Cell
 clumps and tissue debris were strained twice through 40-μm cell strainers.
 Cells were then spun down and resuspended in 0.04% BSA (11020–021,
 AlbuMax, ThermoFisher Scientific) in PBS. The final cell concentration was
 targeted for 8,000 cells/run. scRNA-seq libraries were performed using the
 manufacturer’s protocol (10X Genomics Inc). Single Cell 3’ v2 chemistry
 was used. Briefly, individual cells (~8,000 cells/run) were separated into
 droplets by Gel bead in EMulsion (GEM) technology using 10X Chromium
 Controller. Emulsion beads were broken, and barcoded cDNAs were pooled for
 amplification. Libraries generated were sequenced using an Illumina
 NovoSeq 6000, targeting 1 billion reads for the pool in 1 lane,
 paired-end, and 100-bp read length.  The computer was
 equipped with AMD Ryzen 9 5900X 12-Core Processor, 24 Threads, and used as
 a server using the Linux operating system. Raw data were processed using
 CellRanger-6.0.1 with mouse reference genome mm10-r-102 for sequence
 alignment. Web output for each dataset was included in Table S1. Generated
 loom files were then read in scanpy v1.9.1 using JupyterLab33 for analyses
 and saved in an h5ad format. For subsequent data analysis and
 visualization, a virtual machine (Docker) was used to create the analysis
 environment. Analysis packages including scanpy 1.9.1, anndata 0.7.5, umap
 0.4.6, numpy 1.19.4, scipy 1.5.3, pandas 1.1.4, scikit-learn 0.23.2,
 statsmodels 0.12.1, python-igraph 0.8.3, Louvain 0.7.0, and leidenalg
 0.8.2 were installed on the Docker. scRNA-seq data were then processed
 using similar pipeline, analysis packages, and cutoffs as our previous
 work, except “highly variable genes” were set to 4000 genes. Analysis was
 carried out using scanpy which was designed specifically for scRNA-seq
 data QC and analysis inspired by Seurat’s clustering tutorial. Principal
 component analysis (PCA) and Uniform Manifold Approximation and Projection
 (UMAP) clustering analyses were performed using 10 neighbors and PCs 1–37
 based on variance ratio. Batch balanced K-Nearest Neighbor (BBKNN) was
 used to correct technical artifacts from multiple sample collections. The
 top 1000 differentially expressed genes were used for the determination of
 biological processes (BPs) enriched in each sample.
description: # Progesterone signaling in oviductal epithelial cells modulates the
 immune response to support preimplantation embryonic development
 [https://doi.org/10.5061/dryad.6t1g1jx8q](https://doi.org/10.5061/dryad.6t1g1jx8q) ## Description of the data and file structure Codes for single-cell RNA-sequencing analysis of data from mouse oviducts. WT and cKO data were collected from Pgrf/f (WT) or Wnt7aCre/+;Pgrf/f (cKO) at 0.5 days post coitus (dpc) another dataset was collected from C57BL6/J adult mice that were ovariectomized and treated with progesterone (P4) at 1 mg/mouse for 2 or 24 hours compared to vehicle control. The oviducts were dissected into infundibulum+ampulla (InfAmp or IA) and isthmus+UTJ (IsthUTJ or IU) regions. ### Files and variables | File name | Descriptions | Conditions | | :-------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------ | :-------------------------------------- | | VP2h24hIAIU\_060724-GitHub.ipynb | Oviducts from WT mice after OVX and treated with vehicle or progesterone at 1 mg/mouse | 2 or 24 hours of treatment | | WTcKO0\_5dpc\_060424-GitHub.ipynb | Oviducts from females mice with specific deletion of progesterone receptor in epithelial cells of the reproductive tract and control littermates | collected at 0.5 days post coitus (dpc) | | Data\_File-S1 | Complete HOMER peak annotations and GREAT Gene Ontology (GO) Biological Process (BP) analysis results for peaks in PGR ChIP-seq dataset from the wild-type oviducts | collected at 2.5 days post coitus (dpc) | ## Code/software JupyterLab is used to run and view the code file. Single cell RNA-seq raw sequencing data were processed using CellRanger-6.0.1 with mouse reference genome mm10-r-102 for sequence alignment. Generated loom files were then read in scanpy v1.9.1 using JupyterLab for analyses and saved in an h5ad format. For subsequent data analysis and visualization, a virtual machine (Docker) was used to create the analysis environment. Analysis packages including scanpy 1.9.1, anndata 0.7.5, umap 0.4.6, numpy 1.19.4, scipy 1.5.3, pandas 1.1.4, scikit-learn 0.23.2, statsmodels 0.12.1, python-igraph 0.8.3, Louvain 0.7.0, and leidenalg 0.8.2 were installed on the Docker. ## Access information Other publicly accessible locations of the data: * NCBI GSE277515