Sedimentation solid-state NMR is a novel method for sample preparation in solid-state NMR (ssNMR) studies. It involves the sedimentation of soluble macromolecules such as large protein complexes. By utilizing ultra-high centrifugal forces, the molecules in solution are driven into a high-concentrated hydrogel, resulting in a sample suitable for solid-state NMR. This technique has the advantage of avoiding the need for chemical treatment, thus minimizing the loss of sample biological activity. Sediment ssNMR has been successfully applied to a variety of non-crystalline protein solids, significantly expanding the scope of solid-state NMR research. In theory, using this method, any biological macromolecule in solution can be transferred into a sedimented solute appropriate for solid-state NMR analysis. However, specialized equipment and careful handling are essential for effectively collecting and loading the sedimented solids to solid-state NMR rotors. To improve efficiency, we have designed a series of loading tools to achieve the loading process from the solution to the rotor in one step. In this paper, we illustrate the sample preparation process of sediment NMR using the H1.4-NCP¹⁶⁷ complex, which consists of linker histone H1.4 and nucleosome core particle, as an example.

The whole heart decellularized extracellular matrix (ECM) has become a promising scaffold material for cardiac tissue engineering. Our previous research has shown that the whole heart acellular matrix possesses the memory function regulating neural stem cells (NSCs) trans-differentiating to cardiac lineage cells. However, the cell subpopulations and phenotypes in the trans-differentiation of NSCs have not been clearly identified. Here, we performed single-cell RNA sequencing and identified 2,765 cells in the recellularized heart with NSCs revealing the cellular diversity of cardiac and neural lineage, confirming NSCs were capable of trans-differentiating into the cardiac lineage while maintaining the original ability to differentiate into the neural lineage. Notably, the trans-differentiated heart-like cells have dual signatures of neuroectoderm and cardiac mesoderm. This study unveils an in-depth mechanism underlying the trans-differentiation of NSCs and provides a new opportunity and theoretical basis for cardiac regeneration.

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.

Met1-linked ubiquitination (Met1-Ub), also known as linear ubiquitination, is a newly identified atypical type of polyubiquitination that is assembled via the N-terminal methionine (Met1) rather than an internal lysine (Lys) residue of ubiquitin. The linear ubiquitin chain assembly complex (LUBAC) composed of HOIP, HOIL-1L and SHARPIN is the sole E3 ubiquitin ligase that specifically generates Met1-linked ubiquitin chains. The physiological role of LUBAC-mediated Met1-Ub has been first described as activating NF-κB signaling through the Met1-Ub modification of NEMO. However, accumulating evidence shows that Met1-Ub is broadly involved in other cellular pathways including MAPK, Wnt/β-Catenin, PI3K/AKT and interferon signaling, and participates in various cellular processes including angiogenesis, protein quality control and autophagy, suggesting that Met1-Ub harbors a potent signaling capacity. Here, we review the formation and cellular functions of Met1-linked ubiquitin chains, with an emphasis on the recent advances in the cellular mechanisms by which Met1-Ub controls signaling transduction.

Alzheimer’s disease (AD) has been conceptualized as a syndrome of brain network dysfunction. Recent imaging connectomics studies have provided unprecedented opportunities to map structural and functional brain networks in AD. By reviewing molecular, imaging, and computational modeling studies, we have shown that highly connected brain hubs are primarily distributed in the medial and lateral prefrontal, parietal, and temporal regions in healthy individuals and that the hubs are selectively and severely affected in AD as manifested by increased amyloid-beta deposition and regional atrophy, hypo-metabolism, and connectivity dysfunction. Furthermore, AD-related hub degeneration depends on the imaging modality with the most notable degeneration in the medial temporal hubs for morphological covariance networks, the prefrontal hubs for structural white matter networks, and in the medial parietal hubs for functional networks. Finally, the AD-related hub degeneration shows metabolic, molecular, and genetic correlates. Collectively, we conclude that the brain-network-hub-degeneration framework is promising to elucidate the biological mechanisms of network dysfunction in AD, which provides valuable information on potential diagnostic biomarkers and promising therapeutic targets for the disease.

CX-5461, also known as pidnarulex, is a strong G4 stabilizer and has received FDA fast-track designation for BRCA1- and BRCA2- mutated cancers. However, quantitative measurements of the unfolding rates of CX-5461-G4 complexes which are important for the regulation function of G4s, remain lacking. Here, we employ single-molecule magnetic tweezers to measure the unfolding force distributions of c-MYC G4s in the presence of different concentrations of CX-5461. The unfolding force distributions exhibit three discrete levels of unfolding force peaks, corresponding to three binding modes. In combination with a fluorescent quenching assay and molecular docking to previously reported ligand-c-MYC G4 structure, we assigned the ~69 pN peak corresponding to the 1:1 (ligand:G4) complex where CX-5461 binds at the G4’s 5'-end. The ~84 pN peak is attributed to the 2:1 complex where CX-5461 occupies both the 5' and 3'. Furthermore, using the Bell-Arrhenius model to fit the unfolding force distributions, we determined the zero-force unfolding rates of 1:1, and 2:1 complexes to be (2.4 ± 0.9) × 10⁻⁸ s⁻¹ and (1.4 ± 1.0) × 10⁻⁹ s⁻¹ respectively. These findings provide valuable insights for the development of G4-targeted ligands to combat c-MYC-driven cancers.

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.

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.

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.