Journal of Mammary Gland Biology and Neoplasia最新文献

Patient-Derived Xenografts (PDXs) are the preclinical models which best recapitulate inter- and intra-patient complexity of human breast malignancies, and are also emerging as useful tools to study the normal breast epithelium. However, data analysis generated with such models is often confounded by the presence of host cells and can give rise to data misinterpretation. For instance, it is important to discriminate between xenografted and host cells in histological sections prior to performing immunostainings. We developed Single Cell Classifier (SCC), a data-driven deep learning-based computational tool that provides an innovative approach for automated cell species discrimination based on a multi-step process entailing nuclei segmentation and single cell classification. We show that human and murine cell contextual features, more than cell-intrinsic ones, can be exploited to discriminate between cell species in both normal and malignant tissues, yielding up to 96% classification accuracy. SCC will facilitate the interpretation of H&E- and DAPI-stained histological sections of xenografted human-in-mouse tissues and it is open to new in-house built models for further applications. SCC is released as an open-source plugin in ImageJ/Fiji available at the following link: https://github.com/Biomedical-Imaging-Group/SingleCellClassifier .

Despite rapid advances in our knowledge of the cellular heterogeneity and molecular regulation of the mammary gland, how these relate to 3D cellular organization remains unclear. In addition to hormonal regulation, mammary gland development and function is directed by para- and juxtacrine signaling among diverse cell-types, particularly the immune and mesenchymal populations. Precise mapping of the cellular landscape of the breast will help to decipher this complex coordination. Imaging of thin tissue sections has provided foundational information about cell positioning in the mammary gland and now technological advances in tissue clearing and subcellular-resolution 3D imaging are painting a more complete picture. In particular, confocal, light-sheet and multiphoton microscopy applied to intact tissue can fully capture cell morphology, position and interactions, and have the power to identify spatially rare events. This review will summarize our current understanding of mammary gland cellular organization as revealed by microscopy. We focus on the mouse mammary gland and cover a broad range of immune and stromal cell types at major developmental stages and give insights into important tissue niches and cellular interactions.

The mammary gland is a highly dynamic organ which undergoes periods of expansion, differentiation and cell death in each reproductive cycle. Partly because of the dynamic nature of the gland, mammary epithelial cells (MECs) are extraordinarily heterogeneous. Single cell RNA-seq (scRNA-seq) analyses have contributed to understand the cellular and transcriptional heterogeneity of this complex tissue. Here, we integrate scRNA-seq data from three foundational reports that have explored the mammary gland cell populations throughout development at single-cell level using 10× Chromium Drop-Seq. We center our analysis on post-natal development of the mammary gland, from puberty to post-involution. The new integrated study corresponds to RNA sequences from 53,686 individual cells, which greatly outnumbers the three initial data sets. The large volume of information provides new insights, as a better resolution of the previously detected Procr⁺ stem-like cell subpopulation or the identification of a novel group of MECs expressing immune-like markers. Moreover, here we present new pseudo-temporal trajectories of MEC populations at two resolution levels, that is either considering all mammary cell subtypes or focusing specifically on the luminal lineages. Interestingly, the luminal-restricted analysis reveals distinct expression patterns of various genes that encode milk proteins, suggesting specific and non-redundant roles for each of them. In summary, our data show that the application of bioinformatic tools to integrate multiple scRNA-seq data-sets helps to describe and interpret the high level of plasticity involved in gene expression regulation throughout mammary gland post-natal development.

The developing mammary gland depends on several transcription-dependent networks to define cellular identities and differentiation trajectories. Recent technological advancements that allow for single-cell profiling of gene expression have provided an initial picture into the epithelial cellular heterogeneity across the diverse stages of gland maturation. Still, a deeper dive into expanded molecular signatures would improve our understanding of the diversity of mammary epithelial and non-epithelial cellular populations across different tissue developmental stages, mouse strains and mammalian species. Here, we combined differential mammary gland fractionation approaches and transcriptional profiles obtained from FACS-isolated mammary cells to improve our definitions of mammary-resident, cellular identities at the single-cell level. Our approach yielded a series of expression signatures that illustrate the heterogeneity of mammary epithelial cells, specifically those of the luminal fate, and uncovered transcriptional changes to their lineage-defined, cellular states that are induced during gland development. Our analysis also provided molecular signatures that identified non-epithelial mammary cells, including adipocytes, fibroblasts and rare immune cells. Lastly, we extended our study to elucidate expression signatures of human, breast-resident cells, a strategy that allowed for the cross-species comparison of mammary epithelial identities. Collectively, our approach improved the existing signatures of normal mammary epithelial cells, as well as elucidated the diversity of non-epithelial cells in murine and human breast tissue. Our study provides a useful resource for future studies that use single-cell molecular profiling strategies to understand normal and malignant breast development.

The architecture of the mouse mammary gland is highly dynamic and constantly remodeled during pubertal development and estrous cycle-driven sprouting and regression of alveolar side branches. During each of these developmental stages, turnover is driven by distinct subsets of mammary epithelial cells. Extensive previous research has shed light on the unique morphological and cell biological characteristics of each stage. However, technological shortcomings failed to capture the dynamics and single-cell contributions to mammary remodeling. Here, we developed in vivo imaging strategies to follow the same mammary ducts over time and quantify the dynamics of mammary gland growth and remodeling from single-cell level to organ scale. Using a combination of intravital microscopy and genetic reporter systems we show how proliferative heterogeneity drives ductal morphogenesis during different developmental stages. To visualize pubertal growth at the cellular level, we performed long-term time-lapse imaging of extending terminal end buds through a mammary imaging window. We show that single-cells within the terminal end buds are extremely motile and continuously exchange position whilst the duct is elongating. To visualize short-term remodeling in the adult mammary gland at the single cell level, we performed multi-day intravital imaging in photoconvertible Kikume Green-Red mice and fluorescent ubiquitination-based cell cycle indicator mice. We demonstrate that the contribution of single-cells to estrous-driven remodeling is highly variable between cells in the same micro-environment. To assess the effects of this dynamic proliferative contribution on the long-term stability of tissue architecture, we developed a repeated skin flap method to assess mammary gland morphology by intravital microscopy over extended time spans for up to six months. Interestingly, in contrast to the short-term dynamic remodeling, the long-term morphology of the mammary gland remains remarkably stable. Together, our tool box of imaging strategies allows to identify and map transient and continuing dynamics of single cells to the architecture of the mammary gland.

Single cell RNA sequencing (scRNAseq) of human milk-derived cells (HMDCs) makes highly detailed analyses of the biology of human lactation possible. We explore this powerful application as an exciting tool to inspect the cellular composition of human milk. We point out some important challenges unique to this approach and highlight the importance of collaborations between biologists and well-trained bioinformaticians to utilize these data to their maximum potential. We extend this focus by discussing the first two such studies that describe HMDCs via scRNAseq and a variety of important questions in the field that warrant attention through further research. The stage is set to apply scRNAseq in human lactation biology, potentially leading to new insights regarding the molecular and cellular diversity of human secretory mammary epithelial cells.

An increasing number of '-omics' datasets, generated by labs all across the world, are becoming available. They contain a wealth of data that are largely unexplored. Not every scientist, however, will have access to the required resources and expertise to analyze such data from scratch. Fortunately, a growing number of investigators is dedicating their time and effort to the development of user friendly, online applications that allow researchers to use and investigate these datasets. Here, we will illustrate the usefulness of such an approach. Using regulation of Wnt7b expression as an example, we will highlight a selection of accessible tools and resources that are available to researchers in the area of mammary gland biology. We show how they can be used for in silico analyses of gene regulatory mechanisms, resulting in new hypotheses and providing leads for experimental follow up. We also call out to the mammary gland community to join forces in a coordinated effort to generate and share additional tissue-specific '-omics' datasets and thereby expand the in silico toolbox.