Single-cell RNA-sequencing library preparation

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^-1 of penicillin and 100 µg ml^-1 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^-1 collagenase IV mixed with 100 IU ml^-1 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% CO₂ 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^-1 Dispase II, Worthington NPRO2, and 10 µg ml^-1 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 10⁷ 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^-1 penicillin 100 μg ml^-1 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.

Download and alignment of single-cell RNA sequencing data

For sequence alignment, a custom reference for mm39 was built using the cellranger (v6.1.2, 10x Genomics) mkref 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 count function.

Preprocessing and batch correction

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 autoEstCont and adjustCounts 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) NormalizeData, FindVariableFeatures, ScaleData, RunPCA, FindNeighbors, and RunUMAP 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_MVN) 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 modelHomotypic 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).

Clustering parameters and annotations

After merging the datasets and batch-correction, the dimensions reflecting 95% of the total variance were input into Seurat’s FindNeighbors function with a k.param of 70. Louvain clustering was then conducted using Seurat’s FindClusters 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 FindNeighbors was a k.param of 50, and a resolution of 0.7 was used for FindClusters. The resulting 9 clusters within the epithelial subset were further annotated using differential expression analysis and canonical markers.

Pseudotime analysis

Potential of heat diffusion for affinity-based transition embedding (PHATE) is dimensional reduction method to more accurately visualize continual progressions found in biological data ³⁵. 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 ^36,55–57. 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