Loss of PPARγ activity characterizes early protumorigenic stromal reprogramming and dictates the therapeutic window of opportunity.

Although robustly expressed in the disease-free (DF) breast stroma, CD36 is consistently absent from the stroma surrounding invasive breast cancers (IBCs). In this study, we primarily observed CD36 expression in adipocytes and intralobular capillaries within the DF breast. Larger vessels concentrated in interlobular regions lacked CD36 and were instead marked by the expression of CD31. When evaluated in perilesional capillaries surrounding ductal carcinoma in situ, a nonobligate IBC precursor, CD36 loss was more commonly observed in lesions associated with subsequent IBC. Peroxisome proliferator-activated receptor γ (PPARγ) governs the expression of CD36 and genes involved in differentiation, metabolism, angiogenesis, and inflammation. Coincident with CD36 loss, we observed a dramatic suppression of PPARγ and its target genes in capillary endothelial cells (ECs) and pericytes, which typically surround and support the stability of the capillary endothelium. Factors present in conditioned media from malignant cells repressed PPARγ and its target genes not only in cultured ECs and pericytes but also in adipocytes, which require PPARγ for proper differentiation. In addition, we identified a role for PPARγ in opposing the transition of pericytes toward a tumor-supportive myofibroblast phenotype. In mouse xenograft models, early intervention with rosiglitazone, a PPARγ agonist, demonstrated significant antitumor effects; however, following the development of a palpable tumor, the antitumor effects of rosiglitazone were negated by the repression of PPARγ in the mouse stroma. In summary, PPARγ activity in healthy tissues places several stromal cell types in an antitumorigenic state, directly inhibiting EC proliferation, maintaining adipocyte differentiation, and suppressing the transition of pericytes into tumor-supportive myofibroblasts.

MULTI-seq: sample multiplexing for single-cell RNA sequencing using lipid-tagged indices.

Sample multiplexing facilitates scRNA-seq by reducing costs and identifying artifacts such as cell doublets. However, universal and scalable sample barcoding strategies have not been described. We therefore developed MULTI-seq: multiplexing using lipid-tagged indices for single-cell and single-nucleus RNA sequencing. MULTI-seq reagents can barcode any cell type or nucleus from any species with an accessible plasma membrane. The method involves minimal sample processing, thereby preserving cell viability and endogenous gene expression patterns. When cells are classified into sample groups using MULTI-seq barcode abundances, data quality is improved through doublet identification and recovery of cells with low RNA content that would otherwise be discarded by standard quality-control workflows. We use MULTI-seq to track the dynamics of T-cell activation, perform a 96-plex perturbation experiment with primary human mammary epithelial cells and multiplex cryopreserved tumors and metastatic sites isolated from a patient-derived xenograft mouse model of triple-negative breast cancer.

Dissecting the stem cell niche with organoid models: an engineering-based approach.

For many tissues, single resident stem cells grown in vitro under appropriate three-dimensional conditions can produce outgrowths known as organoids. These tissues recapitulate much of the cell composition and architecture of the in vivo organ from which they derive, including the formation of a stem cell niche. This has facilitated the systematic experimental manipulation and single-cell, high-throughput imaging of stem cells within their respective niches. Furthermore, emerging technologies now make it possible to engineer organoids from purified cellular and extracellular components to directly model and test stem cell-niche interactions. In this Review, we discuss how organoids have been used to identify and characterize stem cell-niche interactions and uncover new niche components, focusing on three adult-derived organoid systems. We also describe new approaches to reconstitute organoids from purified cellular components, and discuss how this technology can help to address fundamental questions about the adult stem cell niche.

Mapping hormone-regulated cell-cell interaction networks in the human breast at single-cell resolution.

The rise and fall of estrogen and progesterone across menstrual cycles and during pregnancy regulates breast development and modifies cancer risk. How these hormones impact each cell type in the breast remains poorly understood because they act indirectly through paracrine networks. Using single-cell analysis of premenopausal breast tissue, we reveal a network of coordinated transcriptional programs representing the tissue-level response to changing hormone levels. Our computational approach, DECIPHER-seq, leverages person-to-person variability in breast composition and cell state to uncover programs that co-vary across individuals. We use differences in cell-type proportions to infer a subset of programs that arise from direct cell-cell interactions regulated by hormones. Further, we demonstrate that prior pregnancy and obesity modify hormone responsiveness through distinct mechanisms: obesity reduces the proportion of hormone-responsive cells, whereas pregnancy dampens the direct response of these cells to hormones. Together, these results provide a comprehensive map of the cycling human breast.

Identification of WEE1 as a potential molecular target in cancer cells by RNAi screening of the human tyrosine kinome.

Breast cancers can be classified into those that express the estrogen (ER) and progesterone (PR) receptors, those with ERBB2 (HER-2/Neu) amplification, and those without expression of ER, PR, or amplification of ERBB2 (referred to as triple-negative or basal-like breast cancer). In order to identify potential molecular targets in breast cancer, we performed a synthetic siRNA-mediated RNAi screen of the human tyrosine kinome. A primary RNAi screen conducted in the triple-negative/basal-like breast cancer cell line MDA-MB231 followed by secondary RNAi screens and further studies in this cell line and two additional triple-negative/basal-like breast cancer cell lines, BT20 and HCC1937, identified the G2/M checkpoint protein, WEE1, as a potential therapeutic target. Similar sensitivity to WEE1 inhibition was observed in cell lines from all subtypes of breast cancer. RNAi-mediated silencing or small compound inhibition of WEE1 in breast cancer cell lines resulted in an increase in gammaH2AX levels, arrest in the S-phase of the cell cycle, and a significant decrease in cell proliferation. WEE1-inhibited cells underwent apoptosis as demonstrated by positive Annexin V staining, increased sub-G1 DNA content, apoptotic morphology, caspase activation, and rescue by the pan-caspase inhibitor, Z-VAD-FMK. In contrast, the non-transformed mammary epithelial cell line, MCF10A, did not exhibit any of these downstream effects following WEE1 silencing or inhibition. These results identify WEE1 as a potential molecular target in breast cancer.

Balancing Act: Cell Polarity and Shape Compete to Ensure Robust Development.

In this issue of Developmental Cell, Niwayama et al. (2019) describe a model in which cell polarity and cell shape compete to determine the orientation of cell division in the pre-implantation mouse embryo. This model explains how simple cell-intrinsic rules lead to robust tissue-level morphogenesis and lineage segregation.

DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors.

Single-cell RNA sequencing (scRNA-seq) data are commonly affected by technical artifacts known as "doublets," which limit cell throughput and lead to spurious biological conclusions. Here, we present a computational doublet detection tool-DoubletFinder-that identifies doublets using only gene expression data. DoubletFinder predicts doublets according to each real cell's proximity in gene expression space to artificial doublets created by averaging the transcriptional profile of randomly chosen cell pairs. We first use scRNA-seq datasets where the identity of doublets is known to show that DoubletFinder identifies doublets formed from transcriptionally distinct cells. When these doublets are removed, the identification of differentially expressed genes is enhanced. Second, we provide a method for estimating DoubletFinder input parameters, allowing its application across scRNA-seq datasets with diverse distributions of cell types. Lastly, we present "best practices" for DoubletFinder applications and illustrate that DoubletFinder is insensitive to an experimentally validated kidney cell type with "hybrid" expression features.