Breast pericytes: a newly identified driver of tumor cell proliferation.

Introduction: Effective treatment of breast cancer remains a formidable challenge, partly due to our limited understanding of the complex microenvironmental factors that contribute to disease pathology. Among these factors are tissue-resident perivascular cells, which play crucial roles in shaping vascular basement membranes, maintaining vessel integrity, and communicating with adjacent endothelial cells. Despite their essential functions, perivascular cells have been relatively overlooked.

Transcriptomic analysis of the 12 major human breast cell types reveals mechanisms of cell and tissue function.

A fundamental question in biology, central to our understanding of cancer and other pathologies, is determining how different cell types coordinate to form and maintain tissues. Recognizing the distinct features and capabilities of the cells that compose these tissues is critical. Unfortunately, the complexity of tissues often hinders our ability to distinguish between neighboring cell types and, in turn, scrutinize their transcriptomes and generate reliable and tractable cell models for studying their inherently different biologies.

Comprehensive identification, isolation, and culture of human breast cell types.

Tissues are formed and shaped by cells of many different types and are orchestrated through countless interactions. Deciphering a tissue's biological complexity thus requires studying it at cell-level resolution, where molecular and biochemical features of different cell types can be explored and thoroughly dissected. Unfortunately, the lack of comprehensive methods to identify, isolate, and culture each cell type from many tissues has impeded progress.

Stochastic gradient descent for optimization for nuclear systems.

The use of gradient descent methods for optimizing k-eigenvalue nuclear systems has been shown to be useful in the past, but the use of k-eigenvalue gradients have proved computationally challenging due to their stochastic nature. ADAM is a gradient descent method that accounts for gradients with a stochastic nature. This analysis uses challenge problems constructed to verify if ADAM is a suitable tool to optimize k-eigenvalue nuclear systems.

Lost in transduction: Critical considerations when using viral vectors.

The application of retroviral vectors in the laboratory requires considerations that often go overlooked but are often easy to circumvent. Here, we discuss the relationship between the observed transduction efficiency of a cell population and per-cell viral insertions-and describe how differential cell-type susceptibilities can confound results. We consider the math underlying this problem and review an alternative approach to the commonly used "multiplicity of infection" (MOI) method of titering and using viral vectors in the biomedical research laboratory.