Enhancer regulatory networks globally connect non-coding breast cancer loci to cancer genes.

Background: Genetic studies have associated thousands of enhancers with breast cancer (BC). However, the vast majority have not been functionally characterized. Thus, it remains unclear how BC-associated enhancers contribute to cancer.

Caveolin-1 regulates context-dependent signaling and survival in Ewing Sarcoma.

Cellular plasticity is a hallmark function of cancer, but many of the underlying mechanisms are not well understood. In this study, we identify Caveolin-1, a scaffolding protein that organizes plasma membrane domains, as a context-dependent regulator of survival signaling in Ewing sarcoma (EwS). Single cell analyses reveal a distinct subpopulation of EwS cells, which highly express the surface marker CD99 as well as Caveolin-1.

Single-cell and spatial transcriptomics identify COL6A3 as a prognostic biomarker in undifferentiated pleomorphic sarcoma.

Undifferentiated pleomorphic sarcoma (UPS) and related tumors are the most common type of soft tissue sarcoma. However, this spectrum of tumors has different etiologies with varying rates of metastasis and survival. Two dermal-based neoplasms in this class of pleomorphic sarcomas, atypical fibroxanthoma (AFX) and pleomorphic dermal sarcoma (PDS), are challenging to differentiate at initial biopsy but vary significantly in prognosis.

Towards functional maps of non-coding variants in cancer.

Large scale cancer genomic studies in patients have unveiled millions of non-coding variants. While a handful have been shown to drive cancer development, the vast majority have unknown function. This review describes the challenges of functionally annotating non-coding cancer variants and understanding how they contribute to cancer.

Enhancer regulatory networks globally connect non-coding breast cancer loci to cancer genes.

Genetic studies have associated thousands of enhancers with breast cancer. However, the vast majority have not been functionally characterized. Thus, it remains unclear how variant-associated enhancers contribute to cancer.

Protocol to dissociate epithelia from non-pregnant and pregnant mouse cervical tissue for single-cell RNA-sequencing.

A challenge in studying cervical epithelial cell biology at the single-cell level is that differentiated subtypes, in particular mucus-secreting goblet cells, are sensitive to disassociating enzymes making isolation of all epithelial subpopulations difficult. Here we present a protocol to dissociate epithelia from non-pregnant and pregnant mouse cervical tissue for single-cell RNA-sequencing. We describe steps for harvesting cervices, preparing cervical tissue, dissociation of cervical cells, and viability checks.

Breaking enhancers to gain insights into developmental defects.

Despite ground-breaking genetic studies that have identified thousands of risk variants for developmental diseases, how these variants lead to molecular and cellular phenotypes remains a gap in knowledge. Many of these variants are non-coding and occur at enhancers, which orchestrate key regulatory programs during development. The prevailing paradigm is that non-coding variants alter the activity of enhancers, impacting gene expression programs, and ultimately contributing to disease risk.

CHD-associated enhancers shape human cardiomyocyte lineage commitment.

Enhancers orchestrate gene expression programs that drive multicellular development and lineage commitment. Thus, genetic variants at enhancers are thought to contribute to developmental diseases by altering cell fate commitment. However, while many variant-containing enhancers have been identified, studies to endogenously test the impact of these enhancers on lineage commitment have been lacking.

Dynamic states of cervical epithelia during pregnancy and epithelial barrier disruption.

The cervical epithelium undergoes changes in proliferation, differentiation, and function that are critical to ensure fertility and maintain pregnancy. Here, we identify cervical epithelial subtypes in non-pregnant, pregnant, and in labor mice using single-cell transcriptome and spatial analysis. We identify heterogeneous subpopulations of epithelia displaying spatial and temporal specificity.

Global chromatin landscapes identify candidate noncoding modifiers of cardiac rhythm.

Comprehensive cis-regulatory landscapes are essential for accurate enhancer prediction and disease variant mapping. Although cis-regulatory element (CRE) resources exist for most tissues and organs, many rare - yet functionally important - cell types remain overlooked. Despite representing only a small fraction of the heart's cellular biomass, the cardiac conduction system (CCS) unfailingly coordinates every life-sustaining heartbeat.

A single factor elicits multilineage reprogramming of astrocytes in the adult mouse striatum.

SignificanceOutside the neurogenic niches, the adult brain lacks multipotent progenitor cells. In this study, we performed a series of in vivo screens and reveal that a single factor can induce resident brain astrocytes to become induced neural progenitor cells (iNPCs), which then generate neurons, astrocytes, and oligodendrocytes. Such a conclusion is supported by single-cell RNA sequencing and multiple lineage-tracing experiments.

Computational identification of clonal cells in single-cell CRISPR screens.

Background: Single-cell CRISPR screens are powerful tools to understand genome function by linking genetic perturbations to transcriptome-wide phenotypes. However, since few cells can be affordably sequenced in these screens, biased sampling of cells could affect data interpretation. One potential source of biased sampling is clonal cell expansion.

FBA: feature barcoding analysis for single cell RNA-Seq.

Motivation: Single cell RNA-Seq (scRNA-Seq) has broadened our understanding of cellular heterogeneity and provided valuable insights into cellular functions. Recent experimental strategies extend scRNA-Seq readouts to include additional features, including cell surface proteins and genomic perturbations. These 'feature barcoding' strategies rely on converting molecular and cellular features to unique sequence barcodes, which are then detected with the transcriptome.

Blastocyst-like structures generated from human pluripotent stem cells.

Limited access to embryos has hampered the study of human embryogenesis and disorders that occur during early pregnancy. Human pluripotent stem cells provide an alternative means to study human development in a dish. Recent advances in partial embryo models derived from human pluripotent stem cells have enabled human development to be examined at early post-implantation stages.

Identification and characterization of cellular heterogeneity within the developing renal interstitium.

Kidney formation requires the coordinated growth of multiple cell types including the collecting ducts, nephrons, vasculature and interstitium. There is a long-held belief that interactions between progenitors of the collecting ducts and nephrons are primarily responsible for kidney development. However, over the last several years, it has become increasingly clear that multiple aspects of kidney development require signaling from the interstitium.

Author Correction: Using Gjd3-CreEGFP mice to examine atrioventricular node morphology and composition.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Global Analysis of Enhancer Targets Reveals Convergent Enhancer-Driven Regulatory Modules.

Single-cell screens enable high-throughput functional assessment of enhancers in their endogenous genomic context. However, the design of current studies limits their application to identifying the primary gene targets of enhancers. Here, we improve the experimental and computational parameters of single-cell enhancer screens to identify the secondary gene targets of enhancers.

SCINA: A Semi-Supervised Subtyping Algorithm of Single Cells and Bulk Samples.

Advances in single-cell RNA sequencing (scRNA-Seq) have allowed for comprehensive analyses of single cell data. However, current analyses of scRNA-Seq data usually start from unsupervised clustering or visualization. These methods ignore prior knowledge of transcriptomes and the probable structures of the data.