During the COVID-19 pandemic, the interplay between the processes of immunity and senescence is drawing more and more intensive attention. SARS-CoV-2 infection induces senescence in lung cells, failure to clear infected cells and increased presence of inflammatory factors could lead to a cytokine storm and acute respiratory disease syndrome (ARDS), which together with aging and age-associated disease lead to 70% of COVID-19-related deaths. Studies on how senescence initiates upon viral infection and how to restrict excessive accumulation of senescent cells to avoid harmful inflammation are crucially important. Senescence can induce innate immune signaling, and innate immunity can engage cell senescence. Here, we mainly review the innate immune pathways, such as cGAS-STING, TLRs, NF-κB, and NLRP3 inflammasome, participating in the senescence process. In these pathways, IFN-I and inflammatory factors play key roles. At the end of the review, we propose the strategies by which we can improve the immune function and reduce inflammation based on these findings.
The liver consists predominantly of hepatocytes and biliary epithelial cells (BECs), which serve distinct physiological functions. Although hepatocytes primarily replenish their own population during homeostasis and injury repair, recent findings have suggested that BECs can transdifferentiate into hepatocytes when hepatocyte-mediated liver regeneration is impaired. However, the cellular and molecular mechanisms governing this BEC-to-hepatocyte conversion remain poorly understood largely because of the inefficiency of existing methods for inducing lineage conversion. Therefore, this study introduces a novel mouse model engineered by the Zhou's lab, where hepatocyte senescence is induced by the deletion of the fumarylacetoacetate (Fah) gene. This model facilitates the efficient conversion of BECs to hepatocytes and allows for the simultaneous lineage tracing of BECs; consequently, a transitional liver progenitor cell population can be identified during lineage conversion. This study also outlines the technical procedures for utilizing this model to determine the underlying cellular and molecular mechanisms of BEC-to-hepatocyte conversion and provides new insights into liver regeneration and its underlying molecular mechanism.
Embryo quality is a critical determinant of clinical outcomes in assisted reproductive technology (ART). A recent clinical trial investigating preimplantation DNA methylation screening (PIMS) revealed that whole genome DNA methylation level is a novel biomarker for assessing ART embryo quality. Here, we reinforced and estimated the clinical efficacy of PIMS. We introduce PIMS-AI, an innovative artificial intelligence (AI) based model, to predict the probability of an embryo producing live birth and subsequently assist ART embryo selection. Our model demonstrated robust performance, achieving an area under the curve (AUC) of 0.90 in cross-validation and 0.80 in independent testing. In simulated embryo selection, PIMS-AI attained an accuracy of 81% in identifying viable embryos for patients. Notably, PIMS-AI offers significant advantages over conventional preimplantation genetic testing for aneuploidy (PGT-A), including enhanced embryo discriminability and the potential to benefit a broader patient population. In conclusion, our approach holds substantial promise for clinical application and has the potential to significantly improve the ART success rate.
Lysosomes are the degradation centers and signaling hubs in the cell. Lysosomes undergo adaptation to maintain cell homeostasis in response to a wide variety of cues. Dysfunction of lysosomes leads to aging and severe diseases including lysosomal storage diseases (LSDs), neurodegenerative disorders, and cancer. To understand the complexity of lysosome biology, many research approaches and tools have been developed to investigate lysosomal functions and regulatory mechanisms in diverse experimental systems. This review summarizes the current approaches and tools adopted for studying lysosomes, and aims to provide a methodological overview of lysosomal research and related fields.
The lipid droplet (LD) is a conserved organelle that exists in almost all organisms, ranging from bacteria to mammals. Dysfunctions in LDs are linked to a range of human metabolic syndromes. The formation of protein complexes on LDs is crucial for maintaining their function. Investigating how proteins interact on LDs is essential for understanding the role of LDs. We have developed an effective method to uncover protein–protein interactions and protein complexes specifically on LDs. In this method, we conduct co-immunoprecipitation (co-IP) experiments using LD proteins extracted directly from isolated LDs, rather than utilizing proteins from cell lysates. To elaborate, we begin by purifying LDs with high-quality and extracting LD-associated proteins. Subsequently, the co-IP experiment is performed on these LD-associated proteins directly, which would enhance the co-IP experiment specificity of LD-associated proteins. This method enables researchers to directly unveil protein complexes on LDs and gain deeper insights into the functional roles of proteins associated with LDs.
In animal cells, the Golgi apparatus serves as the central hub of the endomembrane secretory pathway. It is responsible for the processing, modification, and sorting of proteins and lipids. The unique stacking and ribbon-like architecture of the Golgi apparatus forms the foundation for its precise functionality. Under cellular stress or pathological conditions, the structure of the Golgi and its important glycosylation modification function may change. It is crucial to employ suitable methodologies to study the structure and function of the Golgi apparatus, particularly when assessing the involvement of a target protein in Golgi regulation. This article provides a comprehensive overview of the diverse microscopy techniques used to determine the specific location of the target protein within the Golgi apparatus. Additionally, it outlines methods for assessing changes in the Golgi structure and its glycosylation modification function following the knockout of the target gene.
Migrasomes are a novel type of cell organelle that form on the retraction fibers at the rear of migrating cells. In recent years, numerous studies have unveiled the mechanisms of migrasome formation and have highlighted significant roles of migrasomes in both physiological and pathological processes. Building upon the strategies outlined in published works and our own research experiences, we have compiled a comprehensive set of protocols for observing migrasomes. These step-by-step instructions encompass various aspects such as cell culture, labeling, imaging, in vitro reconstitution, and statistical analysis. We believe that these protocols serve as a valuable resource for researchers exploring migrasome biology.
Ribophagy, the cellular process wherein ribosomes are selectively self-digested through autophagy, plays a pivotal role in maintaining ribosome turnover. Understanding the molecular regulatory mechanisms governing ribophagy is pivotal to uncover its significance. Consequently, the establishment of methods for detecting ribophagy becomes important. In this protocol, we have optimized, enriched, and advanced existing ribophagy detection techniques, including immunoblotting, fluorescence microscopy, and transmission electron microscopy (TEM), to precisely monitor and quantify ribophagic events. Particularly noteworthy is the introduction of TEM technology for yeast ribophagy detection. In summary, the delineated methods are applicable for detecting ribophagy in both yeast and mammals, laying a solid foundation for further exploring the physiological importance of ribophagy and its potential implications in diverse cellular environments.
The endoplasmic reticulum (ER) is an essential component of the endomembrane system in eukaryotes and plays a crucial role in protein and lipid synthesis, as well as the maintenance of calcium homeostasis. Morphologically, the ER is composed primarily of sheets and tubules. The tubular ER is composed of a network of tubular membrane structures, each with diameters ranging from 30 to 50 nanometers. In recent years, there has been in-depth research on the molecular mechanisms of membrane shaping and membrane fusion of the tubular ER. However, there is still limited understanding of the specific physiological functions of the tubular ER. Here, we report a protocol that combines differential centrifugation and immunoprecipitation to specifically enrich microsomes originating from the tubular ER in yeast. The ER tubule-derived microsomes can be further used for proteomic and lipidomic studies or other biochemical analyses.
The development of nucleoside deaminase-containing base editors realized targeted single base change with high efficiency and precision. Such nucleoside deaminases include adenosine and cytidine deaminases, which can catalyze adenosine-to-inosine (A-to-I) and cytidine-to-uridine (C-to-U) conversion respectively. These nucleoside deaminases are under the spotlight because of their vast application potential in gene editing. Recent advances in the engineering of current nucleoside deaminases and the discovery of new nucleoside deaminases greatly broaden the application scope and improve the editing specificity of base editors. In this review, we cover current knowledge about the deaminases used in base editors, including their key structural features, working mechanisms, optimization, and evolution.
Efficient quantification of fatty-acid (FA) composition (fatty-acidome) in biological samples is crucial for understanding physiology and pathophysiology in large population cohorts. Here, we report a rapid GC-FID/MS method for simultaneous quantification of all FAs in numerous biological matrices. Within eight minutes, this method enabled simultaneous quantification of 50 FAs as fatty-acid methyl esters (FAMEs) in femtomole levels following the efficient transformation of FAs in all lipids including FFAs, cholesterol-esters, glycerides, phospholipids and sphingolipids. The method showed satisfactory inter-day and intra-day precision, stability and linearity (R2 > 0.994) within a concentration range of 2–3 orders of magnitude. FAs were then quantified in typical multiple biological matrices including human biofluids (urine, plasma) and cells, animal intestinal content and tissue samples. We also established a quantitative structure-retention relationship (QSRR) for analytes to accurately predict their retention time and aid their reliable identification. We further developed a novel no-additive retention index (NARI) with endogenous FAMEs reducing inter-batch variations to 15 seconds; such NARI performed better than the alkanes-based classical RI, making meta-analysis possible for data obtained from different batches and platforms. Collectively, this provides an inexpensive high-throughput analytical system for quantitative phenotyping of all FAs in 8-minutes multiple biological matrices in large cohort studies of pathophysiological effects.
G protein-coupled receptors (GPCRs) are a large family of cell membrane proteins that are important targets for drug discovery. Nanobodies, also known as VHH (variable domains of heavy chain-only antibodies, HcAbs) antibodies, are small antibody fragments derived from camelids that have gained significant attention as potential therapeutics for targeting GPCRs due to their advantages over conventional antibodies. However, there are challenges in developing nanobodies targeting GPCRs, among which epitope accessibility is the most significant because the cell membrane partially shields the GPCR surface. We developed a universal protocol for making nanobodies targeting GPCRs using the cell membrane extract of GPCR-overexpressing HEK293 cells as the llama/alpaca immunization antigen. We constructed an immune VHH library and identified nanobodies by phage display bio-panning. The monoclonal nanobodies were recombinantly expressed in Escherichia coli (E. coli) and purified to characterize their binding potency.
Tumor metastasis, responsible for approximately 90% of cancer-associated mortality, remains poorly understood. Here in this study, we employed a melanoma lung metastasis model to screen for metastasis-related genes. By sequential tail vein injection of mouse melanoma B16F10 cells and the subsequently derived cells from lung metastasis into BALB/c mice, we successfully obtained highly metastatic B16F15 cells after five rounds of in vivo screening. RNA-sequencing analysis of B16F15 and B16F10 cells revealed a number of differentially expressed genes, some of these genes have previously been associated with tumor metastasis while others are novel discoveries. The identification of these metastasis-related genes not only improves our understanding of the metastasis mechanisms, but also provides potential diagnostic biomarkers and therapeutic targets for metastatic melanoma.
Optical tweezers have elucidated numerous biological processes, particularly by enabling the precise manipulation and measurement of tension. One question concerns the biological relevance of these experiments and the generalizability of these experiments to wider biological systems. Here, we categorize the applicability of the information garnered from optical tweezers in two distinct categories: the direct relevance of tension in biological systems, and what experiments under tension can tell us about biological systems, while these systems do not reach the same tension as the experiment, still, these artificial experimental systems reveal insights into the operations of biological machines and life processes.
The prediction of affinity between TCRs and peptides is crucial for the further development of TIL (Tumor-Infiltrating Lymphocytes) immunotherapy. Inspired by the broader research of drug-protein interaction (DPI), we propose an atom-level peptide-TCR interaction (PTI) affinity prediction model APTAnet using natural language processing methods. APTAnet model achieved an average ROC-AUC and PR-AUC of 0.893 and 0.877, respectively, in ten-fold cross-validation on 25,675 pairs of PTI data. Furthermore, experimental results on an independent test set from the McPAS database showed that APTAnet outperformed the current mainstream models. Finally, through the validation on 11 cases of real tumor patient data, we found that the APTAnet model can effectively identify tumor peptides and screen tumor-specific TCRs.
Substrate stiffness is a microenvironment with a certain stiffness constructed by the extracellular matrix and adjacent cells, which plays an important role in the growth and development of cells and tissue formation. Studies have indicated that the stiffness of the brain is about 0.1–1 kPa. The physiological and pathological processes of the nervous system are mediated by the substrate stiffness that the neurons suffer. However, how substrate stiffness regulates these processes remains to be studied. Culturing neurons on substrates with different stiffness in vitro is one of the best methods to study the role of stiffness in regulating neuronal development and activity. In this study, by changing the preparation time and the activation time of polyacrylamide gel, we provide an improved method that achieves a low toxic substrate environment for better primary neuron adhesion and development. Hope that this method is convenient for those studying the role of substrate stiffness in neurons.
Determining correlations between molecules at various levels is an important topic in molecular biology. Large language models have demonstrated a remarkable ability to capture correlations from large amounts of data in the field of natural language processing as well as image generation, and correlations captured from data using large language models can also be applicable to solving a wide range of specific tasks, hence large language models are also referred to as foundation models. The massive amount of data that exists in the field of molecular biology provides an excellent basis for the development of foundation models, and the recent emergence of foundation models in the field of molecular biology has really pushed the entire field forward. We summarize the foundation models developed based on RNA sequence data, DNA sequence data, protein sequence data, single-cell transcriptome data, and spatial transcriptome data respectively, and further discuss the research directions for the development of foundation models in molecular biology.
ChatGPT explores a strategic blueprint of question answering (QA) to deliver medical diagnoses, treatment recommendations, and other healthcare support. This is achieved through the increasing incorporation of medical domain data via natural language processing (NLP) and multimodal paradigms. By transitioning the distribution of text, images, videos, and other modalities from the general domain to the medical domain, these techniques have accelerated the progress of medical domain question answering (MDQA). They bridge the gap between human natural language and sophisticated medical domain knowledge or expert-provided manual annotations, handling large-scale, diverse, unbalanced, or even unlabeled data analysis scenarios in medical contexts. Central to our focus is the utilization of language models and multimodal paradigms for medical question answering, aiming to guide the research community in selecting appropriate mechanisms for their specific medical research requirements. Specialized tasks such as unimodal-related question answering, reading comprehension, reasoning, diagnosis, relation extraction, probability modeling, and others, as well as multimodal-related tasks like vision question answering, image captioning, cross-modal retrieval, report summarization, and generation, are discussed in detail. Each section delves into the intricate specifics of the respective method under consideration. This paper highlights the structures and advancements of medical domain explorations against general domain methods, emphasizing their applications across different tasks and datasets. It also outlines current challenges and opportunities for future medical domain research, paving the way for continued innovation and application in this rapidly evolving field. This comprehensive review serves not only as an academic resource but also delineates the course for future probes and utilization in the field of medical question answering.
Mitochondrial base editing tools hold great promise for the investigation and treatment of mitochondrial diseases. Mitochondrial DNA base editors (mitoBEs) integrate a programmable transcription-activator-like effector (TALE) protein with single-stranded DNA deaminase (TadA8e-V106W, APOBEC1, etc.) and nickase (MutH, Nt.BspD6I(C), etc.) to achieve heightened precision and efficiency in mitochondrial base editing. This innovative mitochondrial base editing tool exhibits a number of advantages, including strand-selectivity for editing, high efficiency, and the capacity to perform diverse types of base editing on the mitochondrial genome by employing various deaminases. In this context, we provide a detailed experimental protocol for mitoBEs to assist others in achieving proficient mitochondrial base editing.
