Autophagy is a homeostatic mechanism involved in the disposal of damaged organelles, denatured proteins as well as invaded pathogens through a lysosomal degradation pathway. Recently, increasing evidences have demonstrated its role in both innate and adaptive immunity, and thereby influence the pathogenesis of inflammatory diseases. The detection of autophagy machinery facilitated the measurement of autophagy during physiological and pathophysiological processes. Autophagy plays critical roles in inflammation through influencing the development, homeostasis and survival of inflammatory cells, including macrophages, neutrophils and lymphocytes; effecting the transcription, processing and secretion of a number of cytokines, as well as being regulated by cytokines. Recently, autophagy‐dependent mechanisms have been studied in the pathogenesis of several inflammatory diseases, including infectious diseases, Crohn's disease, cystic fibrosis, pulmonary hypertension, chronic obstructive pulmonary diseases and so on. These studies suggested that modulation of autophagy might lead to therapeutic interventions for diseases associated with inflammation. Here we highlight recent advances in investigating the roles of autophagy in inflammation as well as inflammatory diseases.

The combination of spatial transcriptomics (ST) and single cell RNA sequencing (scRNA-seq) acts as a pivotal component to bridge the pathological phenomes of human tissues with molecular alterations, defining in situ intercellular molecular communications and knowledge on spatiotemporal molecular medicine. The present article overviews the development of ST and aims to evaluate clinical and translational values for understanding molecular pathogenesis and uncovering disease-specific biomarkers. We compare the advantages and disadvantages of sequencing- and imaging-based technologies and highlight opportunities and challenges of ST. We also describe the bioinformatics tools necessary on dissecting spatial patterns of gene expression and cellular interactions and the potential applications of ST in human diseases for clinical practice as one of important issues in clinical and translational medicine, including neurology, embryo development, oncology, and inflammation. Thus, clear clinical objectives, designs, optimizations of sampling procedure and protocol, repeatability of ST, as well as simplifications of analysis and interpretation are the key to translate ST from bench to clinic.

The idea that tumour microenvironment (TME) is organised in a spatial manner will not surprise many cancer biologists; however, systematically capturing spatial architecture of TME is still not possible until recent decade. The past five years have witnessed a boom in the research of high‐throughput spatial techniques and algorithms to delineate TME at an unprecedented level. Here, we review the technological progress of spatial omics and how advanced computation methods boost multi‐modal spatial data analysis. Then, we discussed the potential clinical translations of spatial omics research in precision oncology, and proposed a transfer of spatial ecological principles to cancer biology in spatial data interpretation. So far, spatial omics is placing us in the golden age of spatial cancer research. Further development and application of spatial omics may lead to a comprehensive decoding of the TME ecosystem and bring the current spatiotemporal molecular medical research into an entirely new paradigm.