No abstract

Sample multiplexing facilitates single‐cell sequencing by reducing costs, revealing subtle difference between similar samples, and identifying artifacts such as cell doublets. However, universal and cost‐effective strategies are rather limited. Here, we reported a concanavalin A‐based sample barcoding strategy (CASB), which could be followed by both single‐cell mRNA and ATAC (assay for transposase‐accessible chromatin) sequencing techniques. The method involves minimal sample processing, thereby preserving intact transcriptomic or epigenomic patterns. We demonstrated its high labeling efficiency, high accuracy in assigning cells/nuclei to samples regardless of cell type and genetic background, and high sensitivity in detecting doublets by three applications: 1) CASB followed by scRNA‐seq to track the transcriptomic dynamics of a cancer cell line perturbed by multiple drugs, which revealed compound‐specific heterogeneous response; 2) CASB together with both snATAC‐seq and scRNA‐seq to illustrate the IFN‐γ‐mediated dynamic changes on epigenome and transcriptome profile, which identified the transcription factor underlying heterogeneous IFN‐γ response; and 3) combinatorial indexing by CASB, which demonstrated its high scalability.

Autophagy is an evolutionary conserved degradative process contributing to cytoplasm quality control, metabolic recycling and cell defense. Aging is a universal phenomenon characterized by the progressive accumulation of impaired molecular and reduced turnover of cellular components. Recent evidence suggests a unique role for autophagy in aging and age-related disease. Indeed, autophagic activity declines with age and enhanced autophagy may prevent the progression of many age-related diseases and prolong life span. All tissues experience changes during aging, while the role of autophagy in different tissues varies. This review summarizes the links between autophagy and aging in the whole organism and discusses the physiological and pathological roles of autophagy in the aging process in tissues such as skeletal muscle, eye, brain, and liver.

Ovarian cancer (OC) is the second leading cause of death in gynecological cancer. Multiple study have shown that the efficacy of tumor immunotherapy is related to tumor immune cell infiltration (ICI). However, so far, the Immune infiltration landscape of tumor microenvironment (TME) in OC has not been elucidated. In this study, We organized the transcriptome data of OC in the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, evaluated the patient’s TME information, and constructed the ICI scores to predict the clinical benefits of patients undergoing immunotherapy. Immune-related genes were further used to construct the prognostic model. After clustering analysis of ICI genes, we found that patients in ICI gene cluster C had the best prognosis, and their tumor microenvironment had the highest proportion of macrophage M1 and T cell follicular helper cells. This result was consistent with that of multivariate cox (multi-cox) analysis. The prognostic model constructed by immune-related genes had good predictive performance. By estimating Tumor mutation burden (TMB), we also found that there were multiple genes with statistically different mutation frequencies in the high and low ICI score groups. The model based on the ICI score may help to screen out patients who would benefit from immunotherapy. The immune-related genes screened may be used as biomarkers and therapeutic targets.