生物物理学报:英文版最新文献

The size and growth of a cell can be described by three related physical parameters: volume, density and mass. All the three are coupled to numerous biochemical reactions and biophysical properties of a cell. It is therefore not surprising that cell size and growth pattern are tightly regulated across all kingdoms of life. Indeed, deregulation of cell size and growth has been found to be associated with diseases. Yet, how cells regulate their size and how cell size connects to cell function remain poorly understood, partly due to the difficulties to precisely measure the size and growth of single cells. In this review, we summarize methods of measuring cell volume, density, and mass, and discuss how the new technologies may advance our understanding of cell size control.

Cells and tissues are exquisitely organized in a complex but ordered manner to form organs and bodies so that individuals can function properly. The spatial organization and tissue architecture represent a keynote property underneath all living organisms. Molecular architecture and cellular composition within intact tissues play a vital role in a variety of biological processes, such as forming the complicated tissue functionality, precise regulation of cell transition in all living activities, consolidation of central nervous system, cellular responses to immunological and pathological cues. To explore these biological events at a large scale and fine resolution, a genome-wide understanding of spatial cellular changes is essential. However, previous bulk RNA sequencing and single-cell RNA sequencing technologies could not obtain the important spatial information of tissues and cells, despite their ability to detect high content transcriptional changes. These limitations have prompted the development of numerous spatially resolved technologies which provide a new dimension to interrogate the regional gene expression, cellular microenvironment, anatomical heterogeneity and cell-cell interactions. Since the advent of spatial transcriptomics, related works that use these technologies have increased rapidly, and new methods with higher throughput and resolution have grown quickly, all of which hold great promise to accelerate new discoveries in understanding the biological complexity. In this review, we briefly discussed the historical evolution of spatially resolved transcriptome. We broadly surveyed the representative methods. Furthermore, we summarized the general computational analysis pipeline for the spatial gene expression data. Finally, we proposed perspectives for technological development of spatial multi-omics.

Single-cell RNA sequencing (scRNA-seq) is a revolutionary tool to explore cells. With an increasing number of scRNA-seq data analysis tools that have been developed, it is challenging for users to choose and compare their performance. Here, we present an overview of the workflow for computational analysis of scRNA-seq data. We detail the steps of a typical scRNA-seq analysis, including experimental design, pre-processing and quality control, feature selection, dimensionality reduction, cell clustering and annotation, and downstream analysis including batch correction, trajectory inference and cell-cell communication. We provide guidelines according to our best practice. This review will be helpful for the experimentalists interested in analyzing their data, and will aid the users seeking to update their analysis pipelines.

Single-cell sequencing has become one of the most powerful and popular techniques in dissecting molecular heterogeneity and modeling the cellular architecture of a biological system. During the past twenty years, the throughput of single-cell sequencing has increased from hundreds of cells to over tens of thousands of cells in parallel. Moreover, this technology has been developed from sequencing transcriptome to measure different omics such as DNA methylome, chromatin accessibility, and so on. Currently, multi-omics which can analyze different omics in the same cell is rapidly advancing. This work advances the study of many biosystems, including the nervous system. Here, we review current single-cell multi-omics sequencing techniques and describe how they improve our understanding of the nervous system. Finally, we discuss the open scientific questions in neural research that may be answered through further improvement of single-cell multi-omics sequencing technology.

Many recent references show that living cells can form some membrane-less organelles by liquid-liquid phase separation (LLPS) of biomolecules, like proteins and nucleic acids. LLPS has been confirmed to link with many important biological functions in living cells, and one of the most important functions of biomolecular condensates is in the field of RNA transcription. Many studies confirm that mammalian RNA polymerase II (Pol II) molecules containing the CTD with different phosphorylation level are purposed to shuttle between initiation condensates and elongation condensates of RNA transcription. Traditional ensemble assays often experience difficulties in quantitively and directly recording the transient recruitment of Pol II CTD. Novel single-molecule approach - DNA curtains can be used to directly visualize biomolecular condensates formation and also recruitment of RNA polymerase II (Pol II) carboxyl-terminal domain (CTD) at the target sites in solution and in real time. This method can offer the potential for new insights into the mechanism of gene transcription. Here, we highlight the detailed protocol of DNA curtains method for studying LLPS.

The assembly of biomolecular condensates is driven by liquid-liquid phase separation. To understand the structure and functions of these condensates, it is essential to characterize the underlying driving forces, e.g., protein-protein and protein-RNA interactions. As both structured and low-complexity domains are involved in the phase separation process, NMR is probably the only technique that can be used to depict the binding topology and interaction modes for the structured and nonstructured domains simultaneously. Atomic-resolution analysis for the intramolecular and intermolecular interactions between any pair of components sheds light on the mechanism for phase separation and biomolecular condensate assembly and disassembly. Herein, we describe the procedures used for the most extensively employed NMR techniques to characterize key interactions for biomolecular phase separation.

Fluorescence correlation spectroscopy (FCS) investigates the temporal relationship of fluctuating fluorescence signals reflecting underlying molecular processes occurring in a solution sample or a single live cell. This review article introduces the principles of two basic and most used FCS techniques: fluorescence auto-correlation spectroscopy (FACS) and fluorescence cross-correlation spectroscopy (FCCS). Combined, FACS and FCCS techniques can quantitatively analyze multiple properties of molecule or nanoparticle samples, including molar concentration, diffusion coefficient and hydrodynamic radius, homo- or hetero-interaction, fluorescence brightness, etc. Not surprisingly, FCS techniques have long been used to investigate molecular mechanisms of biomolecular phase separation, first in the lipid bilayer and more recently in cell cytosol and nucleoplasm. The latter applications are especially exciting since a whole new class of membraneless cellular organelles have been discovered, which are proposed to be results of biomolecule liquid-liquid phase separation (LLPS). LLPS research can benefit significantly from the multifunctionality and single-molecule sensitivity of a variety of FCS techniques, particularly for live-cell studies. This review illustrates how FACS and FCCS techniques can be used to investigate multiple aspects of the molecular mechanisms of LLPS, and summarizes FCS applications to LLPS research in vivo and in vitro.

Liquid-liquid phase separation (LLPS) has proved to be ubiquitous in living cells, forming membraneless organelles (MLOs) and dynamic condensations essential in physiological processes. However, some underlying mechanisms remain challenging to unravel experimentally, making theoretical modeling an indispensable aspect. Here we present a protocol for understanding LLPS from fundamental physics to detailed modeling procedures. The protocol involves a comprehensive physical picture on selecting suitable theoretical approaches, as well as how and what to interpret and resolve from the results. On the particle-based level, we elaborate on coarse-grained simulation procedures from building up models, identifying crucial interactions to running simulations to obtain phase diagrams and other concerned properties. We also outline field-based theories which give the system's density profile to determine phase diagrams and provide dynamic properties by studying the time evolution of density field, enabling us to characterize LLPS systems with larger time and length scales and to further include other nonequilibrium factors such as chemical reactions.

Numerous biomacromolecules undergo liquid-liquid phase separation (LLPS) inside living cells and LLPS plays important roles in their functions. The droplets formed by LLPS molecules are complex fluids and their behavior follows fluid mechanics, thus studies on rheological and material properties are required to gain full insight into the biophysical mechanism of these droplets. Biophysical force spectroscopy techniques are particularly useful in this aspect. Indeed, atomic force microscopy and optical tweezers have been used to quantify the elasticity and the viscoelasticity of LLPS droplets. The Biomembrane Force Probe (BFP) is a single-molecule technique designed to investigate liquid-like objects and is more suitable to quantify the material properties of LLPS droplets, but its usage on LLPS droplets is not yet described. Here we present an experimental protocol to measure the Young's modulus of LLPS droplets using BFP, we believe that the application of BFP on phase separation studies can be expanded and will be very helpful in deciphering the underlying principles of LLPS.

Abnormal aggregation of amyloid proteins, e.g. amyloid β (Aβ), Tau and α-synuclein (α-syn), is closely associated with a variety of neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD). Cellular and animal models are useful to explore the neuropathology of amyloid aggregates in disease initiation and progression. In this protocol, we describe detailed procedures for how to establish neuronal and PD mouse models to evaluate amyloid pathologies including self-propagation, cell-to-cell transmission, neurotoxicity, and impact on mouse motor and cognitive functions. We use α-syn, a key pathogenic protein in PD, as an example to demonstrate the application of the protocol, while it can be used to investigate the pathologies of other amyloid proteins as well. The established disease models are also useful to assess the activities of drug candidates for therapeutics of neurodegenerative diseases.