DOI: 10.5061/dryad.4mw6m90hm
title: Pre-ciliated tubal epithelial cells are prone to initiation of high-grade serous ovarian carcinoma

description: The distal region of the uterine (Fallopian) tube is commonly associated
 with high-grade serous carcinoma (HGSC), the predominant and most
 aggressive form of ovarian or extra-uterine cancer. Specific cell states
 and lineage dynamics of the adult tubal epithelium (TE) remain
 insufficiently understood, hindering efforts to determine the cell of
 origin for HGSC. Here, we report a comprehensive census of cell types and
 states of the mouse uterine tube. We show that distal TE cells expressing
 the stem/progenitor cell marker Slc1a3 can differentiate
 into both secretory (Ovgp1+) and ciliated (Fam183b+) cells. Inactivation
 of Trp53 and Rb1, whose pathways are commonly
 altered in HGSC, leads to elimination of targeted Slc1a3+ cells
 by apoptosis, thereby preventing their malignant transformation. In
 contrast, pre-ciliated cells (Krt5+, Prom1+, Trp73+)
 remain cancer-prone and give rise to serous tubal intraepithelial
 carcinomas and overt HGSC. These findings identify transitional
 pre-ciliated cells as a previously unrecognized cancer-prone cell state
 and point to pre-ciliation mechanisms as novel diagnostic and therapeutic
 targets.
description: <strong>Single-cell RNA-sequencing library
 preparation</strong> For TE single cell
 expression and transcriptome analysis we isolated TE from C57BL6 adult
 estrous female mice. In 3 independent experiments a total of 62 uterine
 tubes were collected. Each uterine tube was placed in sterile PBS
 containing 100 IU ml<sup>-1</sup> of penicillin and 100 µg
 ml<sup>-1</sup> streptomycin (Corning, 30-002-Cl), and
 separated in distal and proximal regions. Tissues from the same region
 were combined in a 40 µl drop of the same PBS solution, cut open
 lengthwise, and minced into 1.5-2.5 mm pieces with 25G needles. Minced
 tissues were transferred with help of a sterile wide bore 200 µl pipette
 tip into a 1.8 ml cryo vial containing 1.2 ml A-mTE-D1 (300 IU
 ml<sup>-1</sup> collagenase IV mixed with 100 IU
 ml<sup>-1</sup> hyaluronidase; Stem Cell Technologies, 07912,
 in DMEM Ham’s F12, Hyclone, SH30023.FS). Tissues were incubated with loose
 cap for 1 h at 37°C in a 5% CO<sub>2</sub> incubator. During
 the incubation tubes were taken out 4 times and tissues suspended with a
 wide bore 200 µl pipette tip. At the end of incubation, the tissue-cell
 suspension from each tube was transferred into 1 ml TrypLE (Invitrogen,
 12604013) pre-warmed to 37°C, suspended 70 times with a 1000 µl pipette
 tip, 5 ml A-SM [DMEM Ham’s F12 containing 2% fetal bovine serum (FBS)]
 were added to the mix, and TE cells were pelleted by centrifugation 300x g
 for 10 minutes at 25°C. Pellets were then suspended with 1 ml pre-warmed
 to 37°C A-mTE-D2 (7 mg ml<sup>-1</sup> Dispase II, Worthington
 NPRO2, and 10 µg ml<sup>-1</sup> Deoxyribonuclease I, Stem
 Cell Technologies, 07900), and mixed 70 times with a 1000 µl pipette tip.
 5 ml A-mTE-D2 was added and samples were passed through a 40 µm cell
 strainer, and pelleted by centrifugation at 300x g for 7 minutes at +4°C.
 Pellets were suspended in 100 µl microbeads per
 10<sup>7</sup> total cells or fewer, and dead cells were
 removed with the Dead Cell Removal Kit (Miltenyi Biotec, 130-090-101)
 according to the manufacturer’s protocol. Pelleted live cell fractions
 were collected in 1.5 ml low binding centrifuge tubes, kept on ice, and
 suspended in ice cold 50 µl A-Ri-Buffer (5% FBS, 1% GlutaMAX-I,
 Invitrogen, 35050-079, 9 µM Y-27632, Millipore, 688000, and 100 IU
 ml<sup>-1</sup> penicillin 100 μg
 ml<sup>-1</sup> streptomycin in DMEM Ham’s F12). Cell aliquots
 were stained with trypan blue for live and dead cell calculation. Live
 cell preparations with a target cell recovery of 5,000-6,000 were loaded
 on Chromium controller (10X Genomics, Single Cell 3’ v2 chemistry) to
 perform single cell partitioning and barcoding using the microfluidic
 platform device. After preparation of barcoded, next-generation sequencing
 cDNA libraries samples were sequenced on Illumina NextSeq500
 System. <strong>Download
 and alignment of single-cell RNA sequencing data</strong>
 For sequence alignment, a custom reference for mm39 was built
 using the cellranger (v6.1.2, 10x
 Genomics) <em>mkref</em> function. The mm39.fa soft-masked
 assembly sequence and the mm39.ncbiRefSeq.gtf (release 109) genome
 annotation last updated 2020-10-27 were used to form the custom reference.
 The raw sequencing reads were aligned to the custom reference and
 quantified using the
 cellranger <em>count</em> function. 
 <strong>Preprocessing and batch
 correction</strong> All preprocessing and data
 analysis was conducted in R (v.4.1.1 (2021-08-10)). The cellranger count
 outs were first modified with
 the <em>autoEstCont</em> and <em>adjustCounts</em> functions from SoupX (v.1.6.1) to output a corrected matrix with the ambient RNA signal (soup) removed (https://github.com/constantAmateur/SoupX). To preprocess the corrected matrices, the Seurat (v.4.1.1) <em>NormalizeData</em>, <em>FindVariableFeatures</em>, <em>ScaleData</em>, <em>RunPCA</em>, <em>FindNeighbors</em>, and <em>RunUMAP</em> functions were used to create a Seurat object for each sample (https://github.com/satijalab/seurat). The number of principal components used to construct a shared nearest-neighbor graph were chosen to account for 95% of the total variance. To detect possible doublets, we used the package DoubletFinder (v.2.0.3) with inputs specific to each Seurat object. DoubletFinder creates artificial doublets and calculates the proportion of artificial k nearest neighbors (pANN) for each cell from a merged dataset of the artificial and actual data. To maximize DoubletFinder’s predictive power, mean-variance normalized bimodality coefficient (BC<sub>MVN</sub>) was used to determine the optimal pK value for each dataset. To establish a threshold for pANN values to distinguish between singlets and doublets, the estimated multiplet rates for each sample were calculated by interpolating between the target cell recovery values according to the 10x Chromium user manual. Homotypic doublets were identified using unannotated Seurat clusters in each dataset with the <em>modelHomotypic</em> function. After doublets were identified, all distal and proximal samples were merged separately. Cells with greater than 30% mitochondrial genes, cells with fewer than 750 nCount RNA, and cells with fewer than 200 nFeature RNA were removed from the merged datasets. To correct for any batch defects between sample runs, we used the harmony (v.0.1.0) integration method (github.com/immunogenomics/harmony). <strong>Clustering parameters and annotations</strong> After merging the datasets and batch-correction, the dimensions reflecting 95% of the total variance were input into Seurat’s <em>FindNeighbors</em> function with a k.param of 70. Louvain clustering was then conducted using Seurat’s <em>FindClusters</em> with a resolution of 0.7. The resulting 19 clusters were annotated based on the expression of canonical genes and the results of differential gene expression (Wilcoxon Rank Sum test) analysis. One cluster expressing lymphatic and epithelial markers was omitted from later analysis as it only contained 2 cells suspected to be doublets. To better understand the epithelial populations, we reclustered 6 epithelial populations and reapplied harmony batch correction. The clustering parameters from <em>FindNeighbors</em> was a k.param of 50, and a resolution of 0.7 was used for <em>FindClusters</em>. The resulting 9 clusters within the epithelial subset were further annotated using differential expression analysis and canonical markers. <strong>Pseudotime analysis</strong> Potential of heat diffusion for affinity-based transition embedding (PHATE) is dimensional reduction method to more accurately visualize continual progressions found in biological data <sup>35</sup>. A modified version of Seurat (v4.1.1) was developed to include the ‘RunPHATE’ function for converting a Seurat Object to a PHATE embedding. This was built on the phateR package (v.1.0.7) (https://github.com/scottgigante/seurat/tree/patch/add-PHATE-again). In addition to PHATE, pseudotime values were calculated with Monocle3 (v.1.2.7), which computes trajectories with an origin set by the user <sup>36,55–57</sup>. The origin was set to be a progenitor cell state confirmed with lineage tracing experiments. 35. Moon, K. R. et al. Visualizing structure and transitions in high-dimensional biological data. Nat Biotechnol 37, 1482–1492 (2019). doi:10.1038/s41587-019-0336-3 36. Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019). doi:10.1038/s41586-019-0969-x 55. Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nature Biotechnology 32, 381–386 (2014). doi:10.1038/nbt.2859 56. Qiu, X. et al. Single-cell mRNA quantification and differential analysis with Census. Nature Methods 14, 309–315 (2017). doi:10.1038/nmeth.4150 57. Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat Methods 14, 979–982 (2017). doi:10.1038/nmeth.4402
description: # Pre-ciliated tubal epithelial cells are prone to initiation of
 high-grade serous ovarian carcinoma
 [https://doi.org/10.5061/dryad.4mw6m90hm](https://doi.org/10.5061/dryad.4mw6m90hm) ## Description of the data and file structure These are prepared Seurat Objects for quick analysis of cell states present within the distal and proximal mouse uterine tubes (oviducts). These files are intended to complement code deposited in Zenodo ([https://doi.org/10.5281/zenodo.13830750](https://doi.org/10.5281/zenodo.13830750)) and GitHub ([https://github.com/coulterr24/MouseTE_scRNA](https://github.com/coulterr24/MouseTE_scRNA)) for further exploration of the cell types/states present within the mouse uterine tube. File Contents: * **Distal_Epi_Cells.rds** (Filtered Seurat object containing all distal epithelial cells) * **Distal_Epi_PHATE_Monocle3.rds** (Filtered Seurat object containing all distal epithelial cells with PHATE and Monocle analysis) * **Distal_Epi_PHATE.rds** (Filtered Seurat object containing all distal epithelial cells with PHATE analysis) * **Proximal_Epi_Cells.rds** (Filtered Seurat object containing all proximal epithelial cells) * **Proximal_Filtered_Cells.rds** (Filtered Seurat object containing all proximal cells) * **Distal_Filtered_Cells.rds** (Filtered Seurat object containing all distal cells) * **Unfiltered_Mouse_Proximal.rds** (Seurat object containing all proximal cells prior to quality control) * **Unfiltered_Mouse_Distal.rds** (Seurat object containing all distal cells prior to quality control)