Spatial heterogeneity of the cytosol revealed by machine learning-based 3D particle tracking

Development and Characterization of 50 nanometer diameter Genetically Encoded Multimeric Nanoparticles

The mechanisms that regulate the physical properties of the cell interior remain poorly understood, especially at the mesoscale (10nm-100nm). Changes in these properties have been suggested to be crucial for both normal physiology and disease. Many crucial macromolecules and molecular assemblies such as ribosomes, RNA polymerase, and biomolecular condensates span the mesoscale size range. Therefore, we need better tools to study the cellular environment at this scale. A recent approach has been to use genetically encoded multimeric nanoparticles (GEMs), which consist of self-assembling scaffold proteins fused to fluorescent tags. After translation of the fusion protein, the monomers self-assemble into bright and stable nanoparticles of defined geometry that can be visualized by fluorescence microscopy. Physical properties of the cell can then be inferred through analysis of the motion of these particles, an approach called nanorheology. Previously, 40nm-GEMs elucidated TORC1 kinase as a regulator of cytoplasmic crowding. However, extremely sensitive microscopes were required. Here, we describe the development and characterization of a 50 nm diameter GEM that is brighter and probes a larger length scale. 50nm-GEMs will make high-throughput nanorheology accessible to a broader range of researchers and reveal new insights into the biophysical properties of cells.

Diffusive lensing as a mechanism of intracellular transport and compartmentalization

While inhomogeneous diffusivity has been identified as a ubiquitous feature of the cellular interior, its implications for particle mobility and concentration at different length scales remain largely unexplored. In this work, we use agent-based simulations of diffusion to investigate how heterogeneous diffusivity affects the movement and concentration of diffusing particles. We propose that a nonequilibrium mode of membrane-less compartmentalization arising from the convergence of diffusive trajectories into low-diffusive sinks, which we call 'diffusive lensing,' is relevant for living systems. Our work highlights the phenomenon of diffusive lensing as a potentially key driver of mesoscale dynamics in the cytoplasm, with possible far-reaching implications for biochemical processes.

Single molecule imaging unveils cellular architecture, dynamics and mechanobiology

The biomechanical regulation of the cytoskeleton and cell adhesions underlies various essential cellular functions. Studying them requires visualizing their nanostructure and molecular dynamics with evermore precise spatio-temporal resolution. In this review we will focus on the recent advances in single molecule fluorescence imaging techniques and discuss how they improve our understanding of mechanically sensitive cellular structures such as adhesions and the cytoskeleton. We will also discuss future directions for research, emphasizing on the 3D nature of cellular structures and tissues, their mechanical regulation at the molecule level, as well as how super-resolution microscopy will enhance our knowledge on protein structure and conformational changes in the cellular context.

Inferring Stochastic Rates from Heterogeneous Snapshots of Particle Positions

Many imaging techniques for biological systems-like fixation of cells coupled with fluorescence microscopy-provide sharp spatial resolution in reporting locations of individuals at a single moment in time but also destroy the dynamics they intend to capture. These snapshot observations contain no information about individual trajectories, but still encode information about movement and demographic dynamics, especially when combined with a well-motivated biophysical model. The relationship between spatially evolving populations and single-moment representations of their collective locations is well-established with partial differential equations (PDEs) and their inverse problems. However, experimental data is commonly a set of locations whose number is insufficient to approximate a continuous-in-space PDE solution. Here, motivated by popular subcellular imaging data of gene expression, we embrace the stochastic nature of the data and investigate the mathematical foundations of parametrically inferring demographic rates from snapshots of particles undergoing birth, diffusion, and death in a nuclear or cellular domain. Toward inference, we rigorously derive a connection between individual particle paths and their presentation as a Poisson spatial process. Using this framework, we investigate the properties of the resulting inverse problem and study factors that affect quality of inference. One pervasive feature of this experimental regime is the presence of cell-to-cell heterogeneity. Rather than being a hindrance, we show that cell-to-cell geometric heterogeneity can increase the quality of inference on dynamics for certain parameter regimes. Altogether, the results serve as a basis for more detailed investigations of subcellular spatial patterns of RNA molecules and other stochastically evolving populations that can only be observed for single instants in their time evolution.

Mitigating transcription noise via protein sharing in syncytial cells

Bursty transcription allows nuclei to concentrate the work of transcribing mRNA into short, intermittent intervals, potentially reducing transcriptional interference. However, bursts of mRNA production can increase noise in protein abundances. Here, we formulate models for gene expression in syncytia, or multinucleate cells, showing that protein abundance noise may be mitigated locally via spatial averaging of diffuse proteins. Our modeling shows a universal reduction in protein noise, which increases with the average number of nuclei per cell and persists even when the number of nuclei is itself a random variable. Experimental data comparing distributions of a cyclin mRNA that is conserved between brewer's yeast and a closely related filamentous fungus Ashbya gossypii confirm that syncytism is permissive of greater levels of transcriptional noise. Our findings suggest that division of transcriptional labor between nuclei allows syncytia to sidestep tradeoffs between efficiency and precision of gene expression.

Syncytial Assembly Lines: Consequences of Multinucleate Cellular Compartments for Fungal Protein Synthesis

Fast growth and prodigious cellular outputs make fungi powerful tools in biotechnology. Recent modeling work has exposed efficiency gains associated with dividing the labor of transcription over multiple nuclei, and experimental innovations are opening new windows on the capacities and adaptations that allow nuclei to behave autonomously or in coordination while sharing a single, common cytoplasm. Although the motivation of our review is to motivate and connect recent work toward a greater understanding of fungal factories, we use the analogy of the assembly line as an organizing idea for studying coordinated gene expression, generally.

The power of weak, transient interactions across biology: A paradigm of emergent behavior

A growing list of diverse biological systems and their equally diverse functionalities provides realizations of a paradigm of emergent behavior. In each of these biological systems, pervasive ensembles of weak, short-lived, spatially local interactions act autonomously to convey functionalities at larger spatial and temporal scales. In this article, a range of diverse systems and functionalities are presented in a cursory manner with literature citations for further details. Then two systems and their properties are discussed in more detail: yeast chromosome biology and human respiratory mucus.

Inferring stochastic rates from heterogeneous snapshots of particle positions

Many imaging techniques for biological systems - like fixation of cells coupled with fluorescence microscopy - provide sharp spatial resolution in reporting locations of individuals at a single moment in time but also destroy the dynamics they intend to capture. These snapshot observations contain no information about individual trajectories, but still encode information about movement and demographic dynamics, especially when combined with a well-motivated biophysical model. The relationship between spatially evolving populations and single-moment representations of their collective locations is well-established with partial differential equations (PDEs) and their inverse problems. However, experimental data is commonly a set of locations whose number is insufficient to approximate a continuous-in-space PDE solution. Here, motivated by popular subcellular imaging data of gene expression, we embrace the stochastic nature of the data and investigate the mathematical foundations of parametrically inferring demographic rates from snapshots of particles undergoing birth, diffusion, and death in a nuclear or cellular domain. Toward inference, we rigorously derive a connection between individual particle paths and their presentation as a Poisson spatial process. Using this framework, we investigate the properties of the resulting inverse problem and study factors that affect quality of inference. One pervasive feature of this experimental regime is the presence of cell-to-cell heterogeneity. Rather than being a hindrance, we show that cell-to-cell geometric heterogeneity can increase the quality of inference on dynamics for certain parameter regimes. Altogether, the results serve as a basis for more detailed investigations of subcellular spatial patterns of RNA molecules and other stochastically evolving populations that can only be observed for single instants in their time evolution.

How it feels in a cell

Life emerges from thousands of biochemical processes occurring within a shared intracellular environment. We have gained deep insights from in vitro reconstitution of isolated biochemical reactions. However, the reaction medium in test tubes is typically simple and diluted. The cell interior is far more complex: macromolecules occupy more than a third of the space, and energy-consuming processes agitate the cell interior. Here, we review how this crowded, active environment impacts the motion and assembly of macromolecules, with an emphasis on mesoscale particles (10-1000 nm diameter). We describe methods to probe and analyze the biophysical properties of cells and highlight how changes in these properties can impact physiology and signaling, and potentially contribute to aging, and diseases, including cancer and neurodegeneration.

Vast heterogeneity in cytoplasmic diffusion rates revealed by nanorheology and Doppelgänger simulations

The cytoplasm is a complex, crowded, actively driven environment whose biophysical characteristics modulate critical cellular processes such as cytoskeletal dynamics, phase separation, and stem cell fate. Little is known about the variance in these cytoplasmic properties. Here, we employed particle-tracking nanorheology on genetically encoded multimeric 40 nm nanoparticles (GEMs) to measure diffusion within the cytoplasm of individual fission yeast (Schizosaccharomyces pombe) cellscells. We found that the apparent diffusion coefficients of individual GEM particles varied over a 400-fold range, while the differences in average particle diffusivity among individual cells spanned a 10-fold range. To determine the origin of this heterogeneity, we developed a Doppelgänger simulation approach that uses stochastic simulations of GEM diffusion that replicate the experimental statistics on a particle-by-particle basis, such that each experimental track and cell had a one-to-one correspondence with their simulated counterpart. These simulations showed that the large intra- and inter-cellular variations in diffusivity could not be explained by experimental variability but could only be reproduced with stochastic models that assume a wide intra- and inter-cellular variation in cytoplasmic viscosity. The simulation combining intra- and inter-cellular variation in viscosity also predicted weak nonergodicity in GEM diffusion, consistent with the experimental data. To probe the origin of this variation, we found that the variance in GEM diffusivity was largely independent of factors such as temperature, the actin and microtubule cytoskeletons, cell-cyle stage, and spatial locations, but was magnified by hyperosmotic shocks. Taken together, our results provide a striking demonstration that the cytoplasm is not "well-mixed" but represents a highly heterogeneous environment in which subcellular components at the 40 nm size scale experience dramatically different effective viscosities within an individual cell, as well as in different cells in a genetically identical population. These findings carry significant implications for the origins and regulation of biological noise at cellular and subcellular levels.

PRD-2 mediates clock-regulated perinuclear localization of clock gene RNAs within the circadian cycle of Neurospora

The transcription-translation negative feedback loops underlying animal and fungal circadian clocks are remarkably similar in their molecular regulatory architecture and, although much is understood about their central mechanism, little is known about the spatiotemporal dynamics of the gene products involved. A common feature of these circadian oscillators is a significant temporal delay between rhythmic accumulation of clock messenger RNAs (mRNAs) encoding negative arm proteins, for example, frq in Neurospora and Per1-3 in mammals, and the appearance of the clock protein complexes assembled from the proteins they encode. Here, we report use of single-molecule RNA fluorescence in situ hybridization (smFISH) to show that the fraction of nuclei actively transcribing the clock gene frq changes in a circadian manner, and that these mRNAs cycle in abundance with fewer than five transcripts per nucleus at any time. Spatial point patterning statistics reveal that frq is spatially clustered near nuclei in a time of day-dependent manner and that clustering requires an RNA-binding protein, PRD-2 (PERIOD-2), recently shown also to bind to mRNA encoding another core clock component, casein kinase 1. An intrinsically disordered protein, PRD-2 displays behavior in vivo and in vitro consistent with participation in biomolecular condensates. These data are consistent with a role for phase-separating RNA-binding proteins in spatiotemporally organizing clock mRNAs to facilitate local translation and assembly of clock protein complexes.

Machine Learning of Spatial Data

Properties of spatially explicit data are often ignored or inadequately handled in machine learning for spatial domains of application. At the same time, resources that would identify these properties and investigate their influence and methods to handle them in machine learning applications are lagging behind. In this survey of the literature, we seek to identify and discuss spatial properties of data that influence the performance of machine learning. We review some of the best practices in handling such properties in spatial domains and discuss their advantages and disadvantages. We recognize two broad strands in this literature. In the first, the properties of spatial data are developed in the spatial observation matrix without amending the substance of the learning algorithm; in the other, spatial data properties are handled in the learning algorithm itself. While the latter have been far less explored, we argue that they offer the most promising prospects for the future of spatial machine learning.

Polarisome assembly mediates actin remodeling during polarized yeast and fungal growth

Dynamic assembly and remodeling of actin is critical for many cellular processes during development and stress adaptation. In filamentous fungi and budding yeast, actin cables align in a polarized manner along the mother-to-daughter cell axis, and are essential for the establishment and maintenance of polarity; moreover, they rapidly remodel in response to environmental cues to achieve an optimal system response. A formin at the tip region within a macromolecular complex, called the polarisome, is responsible for driving actin cable polymerization during polarity establishment. This polarisome undergoes dynamic assembly through spatial and temporally regulated interactions between its components. Understanding this process is important to comprehend the tuneable activities of the formin-centered nucleation core, which are regulated through divergent molecular interactions and assembly modes within the polarisome. In this Review, we focus on how intrinsically disordered regions (IDRs) orchestrate the condensation of the polarisome components and the dynamic assembly of the complex. In addition, we address how these components are dynamically distributed in and out of the assembly zone, thereby regulating polarized growth. We also discuss the potential mechanical feedback mechanisms by which the force-induced actin polymerization at the tip of the budding yeast regulates the assembly and function of the polarisome.