Mutation Patterns Predict Drug Sensitivity in Acute Myeloid Leukemia.

Purpose: The inherent genetic heterogeneity of acute myeloid leukemia (AML) has challenged the development of precise and effective therapies. The objective of this study was to elucidate the genomic basis of drug resistance or sensitivity, identify signatures for drug response prediction, and provide resources to the research community.

Experimental Design: We performed targeted sequencing, high-throughput drug screening, and single-cell genomic profiling on leukemia cell samples derived from patients with AML.

Microglia promote anti-tumour immunity and suppress breast cancer brain metastasis.

Breast cancer brain metastasis (BCBM) is a lethal disease with no effective treatments. Prior work has shown that brain cancers and metastases are densely infiltrated with anti-inflammatory, protumourigenic tumour-associated macrophages, but the role of brain-resident microglia remains controversial because they are challenging to discriminate from other tumour-associated macrophages. Using single-cell RNA sequencing, genetic and humanized mouse models, we specifically identify microglia and find that they play a distinct pro-inflammatory and tumour-suppressive role in BCBM.

A spatially resolved single-cell genomic atlas of the adult human breast.

The adult human breast is comprised of an intricate network of epithelial ducts and lobules that are embedded in connective and adipose tissue. Although most previous studies have focused on the breast epithelial system, many of the non-epithelial cell types remain understudied. Here we constructed the comprehensive Human Breast Cell Atlas (HBCA) at single-cell and spatial resolution.

A spatially resolved single cell genomic atlas of the adult human breast.

The adult human breast comprises an intricate network of epithelial ducts and lobules that are embedded in connective and adipose tissue. While previous studies have mainly focused on the breast epithelial system, many of the non-epithelial cell types remain understudied. Here, we constructed a comprehensive Human Breast Cell Atlas (HBCA) at single-cell and spatial resolution.

Preneoplastic stromal cells promote BRCA1-mediated breast tumorigenesis.

Women with germline BRCA1 mutations (BRCA1) have increased risk for hereditary breast cancer. Cancer initiation in BRCA1 is associated with premalignant changes in breast epithelium; however, the role of the epithelium-associated stromal niche during BRCA1-driven tumor initiation remains unclear. Here we show that the premalignant stromal niche promotes epithelial proliferation and mutant BRCA1-driven tumorigenesis in trans.

Wound healing in aged skin exhibits systems-level alterations in cellular composition and cell-cell communication.

Delayed and often impaired wound healing in the elderly presents major medical and socioeconomic challenges. A comprehensive understanding of the cellular/molecular changes that shape complex cell-cell communications in aged skin wounds is lacking. Here, we use single-cell RNA sequencing to define the epithelial, fibroblast, immune cell types, and encompassing heterogeneities in young and aged skin during homeostasis and identify major changes in cell compositions, kinetics, and molecular profiles during wound healing.

Microfluidic platform accelerates tissue processing into single cells for molecular analysis and primary culture models.

Tissues are complex mixtures of different cell subtypes, and this diversity is increasingly characterized using high-throughput single cell analysis methods. However, these efforts are hindered, as tissues must first be dissociated into single cell suspensions using methods that are often inefficient, labor-intensive, highly variable, and potentially biased towards certain cell subtypes. Here, we present a microfluidic platform consisting of three tissue processing technologies that combine tissue digestion, disaggregation, and filtration.

An in vitro vascularized micro-tumor model of human colorectal cancer recapitulates in vivo responses to standard-of-care therapy.

Around 95% of anti-cancer drugs that show promise during preclinical study fail to gain FDA-approval for clinical use. This failure of the preclinical pipeline highlights the need for improved, physiologically-relevant in vitro models that can better serve as reliable drug-screening and disease modeling tools. The vascularized micro-tumor (VMT) is a novel three-dimensional model system (tumor-on-a-chip) that recapitulates the complex human tumor microenvironment, including perfused vasculature, within a transparent microfluidic device, allowing real-time study of drug responses and tumor-stromal interactions.

Single-Cell Transcriptome Analysis Workflow for Splenic Myeloid-Derived Suppressor Cells from Murine Breast Cancer Models.

Single-cell transcriptomics is a powerful tool to study previously unrealized cellular heterogeneity at the resolution of individual cells. Most of the previous knowledge in cell biology is based on data generated by bulk analysis methods, which provide averaged readouts that usually mask cellular heterogeneity. This approach is challenging when the biological effect of interest is limited to a subpopulation within a cell type.

Integrated Single-Cell Transcriptomics and Chromatin Accessibility Analysis Reveals Regulators of Mammary Epithelial Cell Identity.

The mammary epithelial cell (MEC) system is a bilayered ductal epithelium of luminal and basal cells, maintained by a lineage of stem and progenitor populations. Here, we used integrated single-cell transcriptomics and chromatin accessibility analysis to reconstruct the cell types of the mouse MEC system and their underlying gene regulatory features in an unbiased manner. We define differentiation states within the secretory type of luminal cells, which forms a continuous spectrum of general luminal progenitor and lactation-committed progenitor cells.

Experimental Considerations for Single-Cell RNA Sequencing Approaches.

Single-cell transcriptomic technologies have emerged as powerful tools to explore cellular heterogeneity at the resolution of individual cells. Previous scientific knowledge in cell biology is largely limited to data generated by bulk profiling methods, which only provide averaged read-outs that generally mask cellular heterogeneity. This averaged approach is particularly problematic when the biological effect of interest is limited to only a subpopulation of cells such as stem/progenitor cells within a given tissue, or immune cell subsets infiltrating a tumor.

Profiling human breast epithelial cells using single cell RNA sequencing identifies cell diversity.

Breast cancer arises from breast epithelial cells that acquire genetic alterations leading to subsequent loss of tissue homeostasis. Several distinct epithelial subpopulations have been proposed, but complete understanding of the spectrum of heterogeneity and differentiation hierarchy in the human breast remains elusive. Here, we use single-cell mRNA sequencing (scRNAseq) to profile the transcriptomes of 25,790 primary human breast epithelial cells isolated from reduction mammoplasties of seven individuals.

Targeting the Mevalonate Pathway Suppresses VHL-Deficient CC-RCC through an HIF-Dependent Mechanism.

Clear cell renal cell carcinoma (CC-RCC) is a devastating disease with limited therapeutic options available for advanced stages. The objective of this study was to investigate HMG-CoA reductase inhibitors, also known as statins, as potential therapeutics for CC-RCC. Importantly, treatment with statins was found to be synthetically lethal with the loss of the von Hippel-Lindau () tumor suppressor gene, which occurs in 90% of CC-RCC driving the disease.

Approaches to identifying synthetic lethal interactions in cancer.

Targeting synthetic lethal interactions is a promising new therapeutic approach to exploit specific changes that occur within cancer cells. Multiple approaches to investigate these interactions have been developed and successfully implemented, including chemical, siRNA, shRNA, and CRISPR library screens. Genome-wide computational approaches, such as DAISY, also have been successful in predicting synthetic lethal interactions from both cancer cell lines and patient samples.