No abstract

Glioblastoma multiforme (GBM) is the most common and aggressive adult primary central nervous system tumor. Unfortunately, GBM is resistant to the classic chemotherapy drug, temozolomide (TMZ). As well as its classic DNA-targeting effects, the off-target effects of TMZ can have pro-survival or pro-death roles and regulate GBM chemoradiation sensitivity. Endoplasmic reticulum (ER) stress is one of the most common off-target effects. ER stress and its downstream induction of autophagy, apoptosis, and other events have important roles in regulating TMZ sensitivity. Autophagy is an evolutionarily conserved cellular homeostasis mechanism that is closely associated with ER stress-induced apoptosis. Under ER stress, autophagy cannot only remove misfolded/unfolded proteins and damaged organelles and degrade and inhibit apoptosis-related caspase activation to reduce cell damage, but may also promote apoptosis dependent on ER stress intensity. Although some protein interactions between autophagy and apoptosis and common upstream signaling pathways have been found, the underlying regulatory mechanisms are still not fully understood. This review summarizes the possible mechanisms underlying the current known off-target roles of ER stress and downstream autophagy in the regulation of cell fate and evaluates their role in TMZ treatment and their potential as therapeutic targets.

Quantifying RNAs in their spatial context is crucial to understanding gene expression and regulation in complex tissues. In situ transcriptomic methods generate spatially resolved RNA profiles in intact tissues. However, there is a lack of a unified computational framework for integrative analysis of in situ transcriptomic data. Here, we introduce an unsupervised and annotation-free framework, termed ClusterMap, which incorporates the physical location and gene identity of RNAs, formulates the task as a point pattern analysis problem, and identifies biologically meaningful structures by density peak clustering (DPC). Specifically, ClusterMap precisely clusters RNAs into subcellular structures, cell bodies, and tissue regions in both two- and three-dimensional space, and performs consistently on diverse tissue types, including mouse brain, placenta, gut, and human cardiac organoids. We demonstrate ClusterMap to be broadly applicable to various in situ transcriptomic measurements to uncover gene expression patterns, cell niche, and tissue organization principles from images with high-dimensional transcriptomic profiles.