Microscopy-Based High-Content Screening.

Image-based screening is used to measure a variety of phenotypes in cells and whole organisms. Combined with perturbations such as RNA interference, small molecules, and mutations, such screens are a powerful method for gaining systematic insights into biological processes. Screens have been applied to study diverse processes, such as protein-localization changes, cancer cell vulnerabilities, and complex organismal phenotypes. Recently, advances in imaging and image-analysis methodologies have accelerated large-scale perturbation screens. Here, we describe the state of the art for image-based screening experiments and delineate experimental approaches and image-analysis approaches as well as discussing challenges and future directions, including leveraging CRISPR/Cas9-mediated genome engineering.

Bridging structural and cell biology with cryo-electron microscopy.

Most life scientists would agree that understanding how cellular processes work requires structural knowledge about the macromolecules involved. For example, deciphering the double-helical nature of DNA revealed essential aspects of how genetic information is stored, copied and repaired. Yet, being reductionist in nature, structural biology requires the purification of large amounts of macromolecules, often trimmed off larger functional units. The advent of cryogenic electron microscopy (cryo-EM) greatly facilitated the study of large, functional complexes and generally of samples that are hard to express, purify and/or crystallize. Nevertheless, cryo-EM still requires purification and thus visualization outside of the natural context in which macromolecules operate and coexist. Conversely, cell biologists have been imaging cells using a number of fast-evolving techniques that keep expanding their spatial and temporal reach, but always far from the resolution at which chemistry can be understood. Thus, structural and cell biology provide complementary, yet unconnected visions of the inner workings of cells. Here we discuss how the interplay between cryo-EM and cryo-electron tomography, as a connecting bridge to visualize macromolecules in situ, holds great promise to create comprehensive structural depictions of macromolecules as they interact in complex mixtures or, ultimately, inside the cell itself.

Lipid droplets and cellular lipid metabolism.

Among organelles, lipid droplets (LDs) uniquely constitute a hydrophobic phase in the aqueous environment of the cytosol. Their hydrophobic core of neutral lipids stores metabolic energy and membrane components, making LDs hubs for lipid metabolism. In addition, LDs are implicated in a number of other cellular functions, ranging from protein storage and degradation to viral replication. These processes are functionally linked to many physiological and pathological conditions, including obesity and related metabolic diseases. Despite their important functions and nearly ubiquitous presence in cells, many aspects of LD biology are unknown. In the past few years, the pace of LD investigation has increased, providing new insights. Here, we review the current knowledge of LD cell biology and its translation to physiology.

Mass spectrometry in the postgenomic era.

Mass spectrometry (MS) is rapidly becoming an essential tool for biologists and biochemists in their efforts to throw light on molecular mechanisms within cellular systems. Used in unison with genome sequence data, MS has developed into the method of choice for identifying proteins, elucidating their posttranslational modifications, and reading out their functional interactions. Variations of the method have even begun to enable accurate mass determination of intact protein complexes, allowing for direct determination of subunit stoichiometry and the interactions between the subunits. Advances in mass spectrometric technologies have also led to great improvements in our ability to probe and define many of the other key molecular players in cells, including the all important lipid components. We provide here some perspectives on the explosion of applications of MS to protein science, systems biology, proteomics, lipidomics, and cell biology in general.

Cellular heterogeneity: do differences make a difference?

A central challenge of biology is to understand how individual cells process information and respond to perturbations. Much of our knowledge is based on ensemble measurements. However, cell-to-cell differences are always present to some degree in any cell population, and the ensemble behaviors of a population may not represent the behaviors of any individual cell. Here, we discuss examples of when heterogeneity cannot be ignored and describe practical strategies for analyzing and interpreting cellular heterogeneity.

PhaSepDB in 2022: annotating phase separation-related proteins with droplet states, co-phase separation partners and other experimental information.

Phase separation (PS) proteins form droplets to regulate myriad membraneless organelles (MLOs) and cellular pathways such as transcription, signaling transduction and protein degeneration. PS droplets are usually liquid-like and can convert to hydrogel/solid-like under certain conditions. The PS behavior of proteins is regulated by co-PS partners and mutations, modifications, oligomerizations, repeat regions and alternative splicing of the proteins. With growing interest in PS condensates and associated proteins, we established PhaSepDB 1.0, which provided experimentally verified PS proteins and MLO-related proteins. The past few years witnessed a surge in PS-related research works; thus, we kept updating PhaSepDB. The current PhaSepDB contains 1419 PS entries, 770 low-throughput MLO-related entries and 7303 high-throughput MLO-related entries. We provided more detailed annotations of PS proteins, including PS verification experiments, regions used in experiments, phase diagrams of different experimental conditions, droplet states, co-PS partners and PS regulatory information. We believe that researchers can go further in studying PS proteins with the updated PhaSepDB (http://db.phasep.pro/).

Gravity-Oriented Microfluidic Device for Biocompatible End-to-End Fabrication of Cell-Laden Microgels.

Droplet microfluidics are extensively utilized to generate monodisperse cell-laden microgels in biomedical applications. However, maintaining cell viability is still challenging due to overexposure to harsh conditions in subsequent procedures that recover the microgels from the oil phase. Here, a gravity-oriented microfluidic device for end-to-end fabrication of cell-laden microgels is reported, which integrates dispersion, gelation, and extraction into a continuous workflow. This innovative on-chip extraction, driven by native buoyancy and kinetically facilitated by pseudosurfactant, exhibits 100% retrieval efficiency for microgels with a wide range of sizes and stiffnesses. The viability of encapsulated cells is perfectly maintained at ≈98% with minimal variations within and between batches. The end-to-end fabrication remarkably enhances the biocompatibility and practicality of microfluidics-based cell encapsulation and is promising to be compatible with various applications ranging from single-cell analysis to clinical therapy.

Computing the Riemannian curvature of image patch and single-cell RNA sequencing data manifolds using extrinsic differential geometry.

Most high-dimensional datasets are thought to be inherently low-dimensional-that is, data points are constrained to lie on a low-dimensional manifold embedded in a high-dimensional ambient space. Here, we study the viability of two approaches from differential geometry to estimate the Riemannian curvature of these low-dimensional manifolds. The intrinsic approach relates curvature to the Laplace-Beltrami operator using the heat-trace expansion and is agnostic to how a manifold is embedded in a high-dimensional space. The extrinsic approach relates the ambient coordinates of a manifold's embedding to its curvature using the Second Fundamental Form and the Gauss-Codazzi equation. We found that the intrinsic approach fails to accurately estimate the curvature of even a two-dimensional constant-curvature manifold, whereas the extrinsic approach was able to handle more complex toy models, even when confounded by practical constraints like small sample sizes and measurement noise. To test the applicability of the extrinsic approach to real-world data, we computed the curvature of a well-studied manifold of image patches and recapitulated its topological classification as a Klein bottle. Lastly, we applied the extrinsic approach to study single-cell transcriptomic sequencing (scRNAseq) datasets of blood, gastrulation, and brain cells to quantify the Riemannian curvature of scRNAseq manifolds.

Visualizing the Multistep Process of Protein Aggregation in Live Cells.

Protein aggregation is a biological phenomenon in which aberrantly processed or mutant proteins misfold and assemble into a variety of insoluble aggregates. Decades of studies have delineated the structure, interaction, and activity of proteins in either their natively folded structures or insoluble aggregates such as amyloid fibrils. However, a variety of intermediate species exist between these two extreme states in the protein folding landscape. Herein, we collectively term these intermediate species as misfolded protein oligomers, including soluble oligomers and preamyloid oligomers that are formed by unfolded or misfolded proteins. While extensive tools have been developed to study folded proteins or amyloid fibrils, research to understand the properties and activities of misfolded protein oligomers has been limited by the lack of methods to detect and interrogate these species in live cells.In this Account, we describe our efforts in the development of chemical methods that allow for the characterization of the multistep protein aggregation process, in particular the misfolded protein oligomers, in living cells. As the start of this journey, we attempted to develop a fluorogenic method wherein the misfolded oligomers could turn on the fluorescence of chemical probes that are conjugated to the protein-of-interest (POI). To this end, we produced a series of destabilized HaloTag variants, formulating the primary component of the AgHalo sensor, which misfolds and aggregates when cells are subjected to stress. When AgHalo is covalently conjugated with a solvatochromic fluorophore, misfolding of the AgHalo conjugate would activate fluorescence, resulting in the observation of misfolded oligomers. Following this work, we extended the scope of detection from AgHalo to any protein-of-interest via the AggTag method, wherein the POIs are genetically fused to self-labeling protein tags (HaloTag or SNAP-tag). Focusing on the molecular rotor-based fluorophores, we applied the modulated fluorescent protein (FP) chromophore core as a prototype for the AggTag probes, to enable the fluorogenic detection of misfolded soluble oligomers of multiple proteins in live cells. Next, we further developed the AggTag method to distinguish insoluble aggregates from misfolded oligomers, using two classes of probes that activate different fluorescence emission toward these two conformations. To enable this goal, we applied physical organic chemistry and computational chemistry to discover a new category of triode-like fluorophores, wherein the π orbitals of either an electron density regulator or the donor-acceptor linkages are used to control the rotational barriers of fluorophores in the excited states. This mechanism allows us to rationally design molecular rotor-based fluorophores that have desired responses to viscosity, thus extending the application of the AggTag method.In summary, our work allows the direct monitoring of the misfolded protein oligomers and differentiation of insoluble aggregates from other conformations in live cells, thus enabling studies of many currently unanswered questions in protein aggregation. Future directions are to develop methods that enable quantitative analyses of the protein aggregation process. Further, new methods are needed to detect and to quantify the formation and maturation of protein or RNA condensates that form membraneless organelles.

Molecular movies... coming to a lecture near you.

Scientific visualizations are powerful tools for communicating the intricacies of cellular and molecular structures and dynamics. There is a disconnect, however, between the research data sets we seek to represent and the kinds of animation that allow us to visualize and communicate them. Scientists are increasingly turning to 3D animation software borrowed from the entertainment industry to import, animate, and even create simulations of their data.

Stochastic reaction networks in dynamic compartment populations.

Compartmentalization of biochemical processes underlies all biological systems, from the organelle to the tissue scale. Theoretical models to study the interplay between noisy reaction dynamics and compartmentalization are sparse, and typically very challenging to analyze computationally. Recent studies have made progress toward addressing this problem in the context of specific biological systems, but a general and sufficiently effective approach remains lacking. In this work, we propose a mathematical framework based on counting processes that allows us to study dynamic compartment populations with arbitrary interactions and internal biochemistry. We derive an efficient description of the dynamics in terms of differential equations which capture the statistics of the population. We demonstrate the relevance of our approach by analyzing models inspired by different biological processes, including subcellular compartmentalization and tissue homeostasis.

Stick-slip model for actin-driven cell protrusions, cell polarization, and crawling.

Cell crawling requires the generation of intracellular forces by the cytoskeleton and their transmission to an extracellular substrate through specific adhesion molecules. Crawling cells show many features of excitable systems, such as spontaneous symmetry breaking and crawling in the absence of external cues, and periodic and propagating waves of activity. Mechanical instabilities in the active cytoskeleton network and feedback loops in the biochemical network of activators and repressors of cytoskeleton dynamics have been invoked to explain these dynamical features. Here, I show that the interplay between the dynamics of cell-substrate adhesion and linear cellular mechanics is sufficient to reproduce many nonlinear dynamical patterns observed in spreading and crawling cells. Using an analytical formalism of the molecular clutch model of cell adhesion, regulated by local mechanical forces, I show that cellular traction forces exhibit stick-slip dynamics resulting in periodic waves of protrusion/retraction and propagating waves along the cell edge. This can explain spontaneous symmetry breaking and polarization of spreading cells, leading to steady crawling or bipedal motion, and bistability, where persistent cell motion requires a sufficiently strong transient external stimulus. The model also highlights the role of membrane tension in providing the long-range mechanical communication across the cell required for symmetry breaking.

Practices, Potential, and Perspectives for Detecting Predisease Using Raman Spectroscopy.

Raman spectroscopy shows great potential for practical clinical applications. By analyzing the structure and composition of molecules through real-time, non-destructive measurements of the scattered light from living cells and tissues, it offers valuable insights. The Raman spectral data directly link to the molecular composition of the cells and tissues and provides a "molecular fingerprint" for various disease states. This review focuses on the practical and clinical applications of Raman spectroscopy, especially in the early detection of human diseases. Identifying predisease, which marks the transition from a healthy to a disease state, is crucial for effective interventions to prevent disease onset. Raman spectroscopy can reveal biological processes occurring during the transition states and may eventually detect the molecular dynamics in predisease conditions.

Protein phase separation and determinants of in cell crystallization.

Liquid-liquid phase separation (LLPS) in cells is known as a complex physicochemical process causing the formation of membrane-less organelles (MLOs). Cells have well-defined different membrane-surrounded organelles like mitochondria, endoplasmic reticulum, lysosomes, peroxisomes, etc., however, on demand they can create MLOs as stress granules, nucleoli and P bodies to cover vital functions and regulatory activities. However, the mechanism of intracellular molecule assembly into functional compartments within a living cell remains till now not fully understood. in vitro and in vivo investigations unveiled that MLOs emerge after preceding liquid-liquid, liquid-gel, liquid-semi-crystalline, or liquid-crystalline phase separations. Liquid-liquid and liquid-gel MLOs form the majority of cellular phase separation events, while the occurrence of micro-sized crystals in cells was only rarely observed, however can be considered as a result of a preceding protein phase separation event. In vivo, also known and termed as in cellulo crystals, are reported since 1853. In some cases, they have been linked to vital cellular functions, such as storage and detoxification. However, the occurrence of in cellulo crystals is also associated to diseases like cataract, hemoglobin C diseases, etc. Therefore, better knowledge about the involved molecular processes will support drug discovery investigations to cure diseases related to in cellulo crystallization. We summarize physical and chemical determinants known today required for phase separation initiation and formation and in cellulo crystal growth. In recent years it has been demonstrated that LLPS plays a crucial role in cell compartmentalization and formation of MLOs. Here we discuss potential mechanisms and potential crowding agents involved in protein phase separation and in cellulo crystallization.

Virtual cell model for osmotic pressure calculation of charged biomolecules.

The osmotic pressure of dilute electrolyte solutions containing charged macro-ions as well as counterions can be computed directly from the particle distribution via the well-known cell model. Originally derived within the Poisson-Boltzmann mean-field approximation, the cell model considers a single macro-ion centered into a cell, together with counterions needed to neutralize the total cell charge, while it neglects the phenomena due to macro-ion correlations. While extensively applied in coarse-grained Monte Carlo (MC) simulations of continuum solvent systems, the cell model, in its original formulation, neglects the macro-ion shape anisotropy and details of the surface charge distribution. In this paper, by comparing one-body and two-body coarse-grained MC simulations, we first establish an upper limit for the assumption of neglecting correlations between macro-ions, and second, we validate the approximation of using a non-spherical macro-ion. Next, we extend the cell model to all-atom molecular dynamics simulations and show that protein concentration-dependent osmotic pressures can be obtained by confining counterions in a virtual, spherical subspace defining the protein number density. Finally, we show the possibility of using specific interaction parameters for the protein-ion and ion-ion interactions, enabling studies of protein concentration-dependent ion-specific effects using merely a single protein molecule.

Endocytosis as a stabilizing mechanism for tissue homeostasis.

Cells in tissues communicate by secreted growth factors (GF) and other signals. An important function of cell circuits is tissue homeostasis: maintaining proper balance between the amounts of different cell types. Homeostasis requires negative feedback on the GFs, to avoid a runaway situation in which cells stimulate each other and grow without control. Feedback can be obtained in at least two ways: endocytosis in which a cell removes its cognate GF by internalization and cross-inhibition in which a GF down-regulates the production of another GF. Here we ask whether there are design principles for cell circuits to achieve tissue homeostasis. We develop an analytically solvable framework for circuits with multiple cell types and find that feedback by endocytosis is far more robust to parameter variation and has faster responses than cross-inhibition. Endocytosis, which is found ubiquitously across tissues, can even provide homeostasis to three and four communicating cell types. These design principles form a conceptual basis for how tissues maintain a healthy balance of cell types and how balance may be disrupted in diseases such as degeneration and fibrosis.

Fluorescent Probes for Live Cell Thiol Detection.

Thiols play vital and irreplaceable roles in the biological system. Abnormality of thiol levels has been linked with various diseases and biological disorders. Thiols are known to distribute unevenly and change dynamically in the biological system. Methods that can determine thiols' concentration and distribution in live cells are in high demand. In the last two decades, fluorescent probes have emerged as a powerful tool for achieving that goal for the simplicity, high sensitivity, and capability of visualizing the analytes in live cells in a non-invasive way. They also enable the determination of intracellular distribution and dynamitic movement of thiols in the intact native environments. This review focuses on some of the major strategies/mechanisms being used for detecting GSH, Cys/Hcy, and other thiols in live cells via fluorescent probes, and how they are applied at the cellular and subcellular levels. The sensing mechanisms (for GSH and Cys/Hcy) and bio-applications of the probes are illustrated followed by a summary of probes for selectively detecting cellular and subcellular thiols.

Microtubules as platforms for probing liquid-liquid phase separation in cells - application to RNA-binding proteins.

Liquid-liquid phase separation enables compartmentalization of biomolecules in cells, notably RNA and associated proteins in the nucleus. Besides having critical functions in RNA processing, there is a major interest in deciphering the molecular mechanisms of compartmentalization orchestrated by RNA-binding proteins such as TDP-43 (also known as TARDBP) and FUS because of their link to neuron diseases. However, tools for probing compartmentalization in cells are lacking. Here, we developed a method to analyze the mixing and demixing of two different phases in a cellular context. The principle is the following: RNA-binding proteins are confined on microtubules and quantitative parameters defining their spatial segregation are measured along the microtubule network. Through this approach, we found that four mRNA-binding proteins, HuR (also known as ELAVL1), G3BP1, TDP-43 and FUS form mRNA-rich liquid-like compartments on microtubules. TDP-43 is partly miscible with FUS but immiscible with either HuR or G3BP1. We also demonstrate that mRNA is essential to capture the mixing and demixing behavior of mRNA-binding proteins in cells. Taken together, we show that microtubules can be used as platforms to understand the mechanisms underlying liquid-liquid phase separation and their deregulation in human diseases.

Optimum Cell Pathlength or Volume for Absorbance Detection in Liquid Chromatography: Transforming Longer Cell Results to Virtual Shorter Cells.

The cell volume permissible for a specified degree of loss of efficiency can be computed from response volume considerations. For an open tubular column, the permissible illuminated length can be computed for the unretained peak. Similar estimations can be made for the maximum permissible cell volume with a packed column for a given column efficiency and flow rate. The packed column case does require an assumption on the degree to which the cell behaves as a mixer. An altogether different question is if the data from a long cell, with its considerable advantage in S/N, can be used and the associated dispersion mathematically removed. Experimental data for a variable path length (0-60 mm) HPLC detection cell indicate that an exponential model fits the observed dispersion. Once fit parameters are determined, the same can be applied to a peak, not part of the original training set, obtained in a longer path cell and the effects of dispersion mathematically reversed without major loss of S/N. Results with shorter path cell dispersion characteristics are then obtained with much higher S/N. A comparison is made with Fourier transform-inverse Fourier transform based deconvolution that can be used to achieve the same ends.