NR4A1 and NR4A2 orphan nuclear receptors regulate endothelial-to-hematopoietic transition in mouse hematopoietic stem cell specification.

Hematopoietic stem cells (HSCs) sustain life-long hematopoiesis and emerge during mid-gestation from hemogenic endothelial progenitors via an endothelial-to-hematopoietic transition (EHT). The full scope of molecular mechanisms governing this process remains unclear. The NR4A subfamily of orphan nuclear receptors act as tumor suppressors in myeloid leukemogenesis and have never been implicated in HSC specification.

Machine learning determines stemness associated with simple and basal-like canine mammary carcinomas.

Simple and complex carcinomas are the most common type of malignant Canine Mammary Tumors (CMTs), with simple carcinomas exhibiting aggressive behavior and poorer prognostic. Stemness is an ability associated with cancer initiation, malignancy, and therapeutic resistance, but is still few elucidated in canine mammary tumor subtypes. Here, we first validated, using CMT samples, a previously published canine one-class logistic regression machine learning algorithm (OCLR) to predict stemness (mRNAsi) in canine cancer cells.

A machine learning one-class logistic regression model to predict stemness for single cell transcriptomics and spatial omics.

Cell annotation is a crucial methodological component to interpreting single cell and spatial omics data. These approaches were developed for single cell analysis but are often biased, manually curated and yet unproven in spatial omics. Here we apply a stemness model for assessing oncogenic states to single cell and spatial omic cancer datasets.