Single Cell RNA Sequencing of Human Milk-Derived Cells Reveals Sub-Populations of Mammary Epithelial Cells with Molecula

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Single Cell RNA Sequencing of Human Milk-Derived Cells Reveals Sub-Populations of Mammary Epithelial Cells with Molecular Signatures of Progenitor and Mature States: a Novel, Non-invasive Framework for Investigating Human Lactation Physiology Jayne F. Martin Carli 1 & G. Devon Trahan 2 & Kenneth L. Jones 2,3 & Nicole Hirsch 4 & Kristy P. Rolloff 4 & Emily Z. Dunn 4 & Jacob E. Friedman 5,6 & Linda A. Barbour 4,7 & Teri L. Hernandez 4,8 & Paul S. MacLean 4 Jenifer Monks 9 & James L. McManaman 9 & Michael C. Rudolph 4,6

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Received: 31 July 2020 / Accepted: 27 October 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020

Abstract Cells in human milk are an untapped source, as potential “liquid breast biopsies”, of material for investigating lactation physiology in a non-invasive manner. We used single cell RNA sequencing (scRNA-seq) to identify milk-derived mammary epithelial cells (MECs) and their transcriptional signatures in women with diet-controlled gestational diabetes (GDM) with normal lactation. Methodology is described for coordinating milk collections with single cell capture and library preparation via cryopreservation, in addition to scRNA-seq data processing and analyses of MEC transcriptional signatures. We comprehensively characterized 3740 cells from milk samples from two mothers at two weeks postpartum. Most cells (>90%) were luminal MECs (luMECs) expressing lactalbumin alpha and casein beta and positive for keratin 8 and keratin 18. Few cells were keratin 14+ basal MECs and a small immune cell population was present (50% of the cells in either comparison group, and a ln(fold change) >0.22 or < −0.22, were tested. A cluster marker was considered unique if it was identified in a single cluster, although this does not preclude expression of that gene by cells in other clusters. UMAP coordinates were exported for use in the scRNA-seq browser, Loupe (10x Genomics). Expression values (UMI counts) are not normalized when viewed in Loupe but are useful for exploration of cell signatures by biologists. See the Sup. Methods (markdown) file for detailed descriptions and complete R scripts and Sup. Tab. 1– 4 for other analytical resources.

Pseudotime Analysis Single Cell RNA Sequencing Viable cells, as determined above, were adjusted to 700 cells/uL and used as input to the 10x Genomics GEM capture and 3-prime V3 single cell sequencing

Cell data were formatted for use with Monocle3 v0.2.1 from Seurat within R. A new UMAP was generated using the

J Mammary Gland Biol Neoplasia

default protocol shown on the Monocle3 website (https://coletrapnell-lab.github.io/monocle3/docs/introduction/), using 9 principal components again as the input. Pseudotime was calculated using all partitions to facilitate the identification of progenitor cells. The resulting pseudotime was stored in the Seurat object to view in the original UMAP space. Pearson correlation was used to identify genes with a linear relationship to pseudotime.

Pathway Analysis Genes correlated with pseudotime, based on Pearson’s corr