Cell division machinery drives cell-specific gene activation during differentiation in Bacillus subtilis.

When faced with starvation, the bacterium Bacillus subtilis transforms itself into a dormant cell type called a "spore". Sporulation initiates with an asymmetric division event, which requires the relocation of the core divisome components FtsA and FtsZ, after which the sigma factor σF is exclusively activated in the smaller daughter cell. Compartment-specific activation of σF requires the SpoIIE phosphatase, which displays a biased localization on one side of the asymmetric division septum and associates with the structural protein DivIVA, but the mechanism by which this preferential localization is achieved is unclear. Here, we isolated a variant of DivIVA that indiscriminately activates σF in both daughter cells due to promiscuous localization of SpoIIE, which was corrected by overproduction of FtsA and FtsZ. We propose that the core components of the redeployed cell division machinery drive the asymmetric localization of DivIVA and SpoIIE to trigger the initiation of the sporulation program.

Live-cell imaging powered by computation.

The proliferation of microscopy methods for live-cell imaging offers many new possibilities for users but can also be challenging to navigate. The prevailing challenge in live-cell fluorescence microscopy is capturing intra-cellular dynamics while preserving cell viability. Computational methods can help to address this challenge and are now shifting the boundaries of what is possible to capture in living systems. In this Review, we discuss these computational methods focusing on artificial intelligence-based approaches that can be layered on top of commonly used existing microscopies as well as hybrid methods that integrate computation and microscope hardware. We specifically discuss how computational approaches can improve the signal-to-noise ratio, spatial resolution, temporal resolution and multi-colour capacity of live-cell imaging.

Phase diversity-based wavefront sensing for fluorescence microscopy.

Fluorescence microscopy is an invaluable tool in biology, yet its performance is compromised when the wavefront of light is distorted due to optical imperfections or the refractile nature of the sample. Such optical aberrations can dramatically lower the information content of images by degrading image contrast, resolution, and signal. Adaptive optics (AO) methods can sense and subsequently cancel the aberrated wavefront, but are too complex, inefficient, slow, or expensive for routine adoption by most labs. Here we introduce a rapid, sensitive, and robust wavefront sensing scheme based on phase diversity, a method successfully deployed in astronomy but underused in microscopy. Our method enables accurate wavefront sensing to less than λ/35 root mean square (RMS) error with few measurements, and AO with no additional hardware besides a corrective element. After validating the method with simulations, we demonstrate calibration of a deformable mirror > 100-fold faster than comparable methods (corresponding to wavefront sensing on the ~100 ms scale), and sensing and subsequent correction of severe aberrations (RMS wavefront distortion exceeding λ/2), restoring diffraction-limited imaging on extended biological samples.

Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy.

Optical aberrations hinder fluorescence microscopy of thick samples, reducing image signal, contrast, and resolution. Here we introduce a deep learning-based strategy for aberration compensation, improving image quality without slowing image acquisition, applying additional dose, or introducing more optics into the imaging path. Our method (i) introduces synthetic aberrations to images acquired on the shallow side of image stacks, making them resemble those acquired deeper into the volume and (ii) trains neural networks to reverse the effect of these aberrations. We use simulations to show that applying the trained 'de-aberration' networks outperforms alternative methods, and subsequently apply the networks to diverse datasets captured with confocal, light-sheet, multi-photon, and super-resolution microscopy. In all cases, the improved quality of the restored data facilitates qualitative image inspection and improves downstream image quantitation, including orientational analysis of blood vessels in mouse tissue and improved membrane and nuclear segmentation in C. elegans embryos.

Cell division machinery drives cell-specific gene activation during bacterial differentiation.

When faced with starvation, the bacterium Bacillus subtilis transforms itself into a dormant cell type called a "spore". Sporulation initiates with an asymmetric division event, which requires the relocation of the core divisome components FtsA and FtsZ, after which the sigma factor σF is exclusively activated in the smaller daughter cell. Compartment specific activation of σF requires the SpoIIE phosphatase, which displays a biased localization on one side of the asymmetric division septum and associates with the structural protein DivIVA, but the mechanism by which this preferential localization is achieved is unclear. Here, we isolated a variant of DivIVA that indiscriminately activates σF in both daughter cells due to promiscuous localization of SpoIIE, which was corrected by overproduction of FtsA and FtsZ. We propose that a unique feature of the sporulation septum, defined by the cell division machinery, drives the asymmetric localization of DivIVA and SpoIIE to trigger the initiation of the sporulation program.

Phase Diverse Phase Retrieval for Microscopy: Comparison of Gaussian and Poisson Approaches.

Phase diversity is a widefield aberration correction method that uses multiple images to estimate the phase aberration at the pupil plane of an imaging system by solving an optimization problem. This estimated aberration can then be used to deconvolve the aberrated image or to reacquire it with aberration corrections applied to a deformable mirror. The optimization problem for aberration estimation has been formulated for both Gaussian and Poisson noise models but the Poisson model has never been studied in microscopy nor compared with the Gaussian model. Here, the Gaussian- and Poisson-based estimation algorithms are implemented and compared for widefield microscopy in simulation. The Poisson algorithm is found to match or outperform the Gaussian algorithm in a variety of situations, and converges in a similar or decreased amount of time. The Gaussian algorithm does perform better in low-light regimes when image noise is dominated by additive Gaussian noise. The Poisson algorithm is also found to be more robust to the effects of spatially variant aberration and phase noise. Finally, the relative advantages of re-acquisition with aberration correction and deconvolution with aberrated point spread functions are compared.

Experimental and theoretical model for the origin of coiling of cellular protrusions around fibers.

Protrusions at the leading-edge of a cell play an important role in sensing the extracellular cues during cellular spreading and motility. Recent studies provided indications that these protrusions wrap (coil) around the extracellular fibers. However, the physics of this coiling process, and the mechanisms that drive it, are not well understood. We present a combined theoretical and experimental study of the coiling of cellular protrusions on fibers of different geometry. Our theoretical model describes membrane protrusions that are produced by curved membrane proteins that recruit the protrusive forces of actin polymerization, and identifies the role of bending and adhesion energies in orienting the leading-edges of the protrusions along the azimuthal (coiling) direction. Our model predicts that the cell's leading-edge coils on fibers with circular cross-section (above some critical radius), but the coiling ceases for flattened fibers of highly elliptical cross-section. These predictions are verified by 3D visualization and quantitation of coiling on suspended fibers using Dual-View light-sheet microscopy (diSPIM). Overall, we provide a theoretical framework, supported by experiments, which explains the physical origin of the coiling phenomenon.

Label-free cleared tissue microscopy and machine learning for 3D histopathology of biomaterial implants.

Tissue clearing of whole intact organs has enhanced imaging by enabling the exploration of tissue structure at a subcellular level in three-dimensional space. Although clearing and imaging of the whole organ have been used to study tissue biology, the microenvironment in which cells evolve to adapt to biomaterial implants or allografts in the body is poorly understood. Obtaining high-resolution information from complex cell-biomaterial interactions with volumetric landscapes represents a key challenge in the fields of biomaterials and regenerative medicine. To provide a new approach to examine how tissue responds to biomaterial implants, we apply cleared tissue light-sheet microscopy and three-dimensional reconstruction to utilize the wealth of autofluorescence information for visualizing and contrasting anatomical structures. This study demonstrates the adaptability of the clearing and imaging technique to provide sub-cellular resolution (0.6 µm isotropic) 3D maps of various tissue types, using samples from fully intact peritoneal organs to volumetric muscle loss injury specimens. Specifically, in the volumetric muscle loss injury model, we provide 3D visualization of the implanted extracellular matrix biomaterial in the wound bed of the quadricep muscle groups and further apply computational-driven image classification to analyze the autofluorescence spectrum at multiple emission wavelengths to categorize tissue types at the injured site interacting with the biomaterial scaffolds.

Three-dimensional structured illumination microscopy with enhanced axial resolution.

The axial resolution of three-dimensional structured illumination microscopy (3D SIM) is limited to ∼300 nm. Here we present two distinct, complementary methods to improve axial resolution in 3D SIM with minimal or no modification to the optical system. We show that placing a mirror directly opposite the sample enables four-beam interference with higher spatial frequency content than 3D SIM illumination, offering near-isotropic imaging with ∼120-nm lateral and 160-nm axial resolution. We also developed a deep learning method achieving ∼120-nm isotropic resolution. This method can be combined with denoising to facilitate volumetric imaging spanning dozens of timepoints. We demonstrate the potential of these advances by imaging a variety of cellular samples, delineating the nanoscale distribution of vimentin and microtubule filaments, observing the relative positions of caveolar coat proteins and lysosomal markers and visualizing cytoskeletal dynamics within T cells in the early stages of immune synapse formation.

An Exact Hypergraph Matching algorithm for posture identification in embryonic C. elegans.

The nematode Caenorhabditis elegans (C. elegans) is a model organism used frequently in developmental biology and neurobiology [White, (1986), Sulston, (1983), Chisholm, (2016) and Rapti, (2020)]. The C. elegans embryo can be used for cell tracking studies to understand how cell movement drives the development of specific embryonic tissues. Analyses in late-stage development are complicated by bouts of rapid twitching motions which invalidate traditional cell tracking approaches. However, the embryo possesses a small set of cells which may be identified, thereby defining the coiled embryo's posture [Christensen, 2015]. The posture serves as a frame of reference, facilitating cell tracking even in the presence of twitching. Posture identification is nevertheless challenging due to the complete repositioning of the embryo between sampled images. Current approaches to posture identification rely on time-consuming manual efforts by trained users which limits the efficiency of subsequent cell tracking. Here, we cast posture identification as a point-set matching task in which coordinates of seam cell nuclei are identified to jointly recover the posture. Most point-set matching methods comprise coherent point transformations that use low order objective functions [Zhou, (2016) and Zhang, (2019)]. Hypergraphs, an extension of traditional graphs, allow more intricate modeling of relationships between objects, yet existing hypergraphical point-set matching methods are limited to heuristic algorithms which do not easily scale to handle higher degree hypergraphs [Duchenne, (2010), Chertok, (2010) and Lee, (2011)]. Our algorithm, Exact Hypergraph Matching (EHGM), adapts the classical branch-and-bound paradigm to dynamically identify a globally optimal correspondence between point-sets under an arbitrarily intricate hypergraphical model. EHGM with hypergraphical models inspired by C. elegans embryo shape identified posture more accurately (56%) than established point-set matching methods (27%), correctly identifying twice as many sampled postures as a leading graphical approach. Posterior region seeding empowered EHGM to correctly identify 78% of postures while reducing runtime, demonstrating the efficacy of the method on a cutting-edge problem in developmental biology.

Incorporating the image formation process into deep learning improves network performance.

We present Richardson-Lucy network (RLN), a fast and lightweight deep learning method for three-dimensional fluorescence microscopy deconvolution. RLN combines the traditional Richardson-Lucy iteration with a fully convolutional network structure, establishing a connection to the image formation process and thereby improving network performance. Containing only roughly 16,000 parameters, RLN enables four- to 50-fold faster processing than purely data-driven networks with many more parameters. By visual and quantitative analysis, we show that RLN provides better deconvolution, better generalizability and fewer artifacts than other networks, especially along the axial dimension. RLN outperforms classic Richardson-Lucy deconvolution on volumes contaminated with severe out of focus fluorescence or noise and provides four- to sixfold faster reconstructions of large, cleared-tissue datasets than classic multi-view pipelines. We demonstrate RLN's performance on cells, tissues and embryos imaged with widefield-, light-sheet-, confocal- and super-resolution microscopy.

Practical considerations for quantitative light sheet fluorescence microscopy.

Fluorescence microscopy has evolved from a purely observational tool to a platform for quantitative, hypothesis-driven research. As such, the demand for faster and less phototoxic imaging modalities has spurred a rapid growth in light sheet fluorescence microscopy (LSFM). By restricting the excitation to a thin plane, LSFM reduces the overall light dose to a specimen while simultaneously improving image contrast. However, the defining characteristics of light sheet microscopes subsequently warrant unique considerations in their use for quantitative experiments. In this Perspective, we outline many of the pitfalls in LSFM that can compromise analysis and confound interpretation. Moreover, we offer guidance in addressing these caveats when possible. In doing so, we hope to provide a useful resource for life scientists seeking to adopt LSFM to quantitatively address complex biological hypotheses.

Stereotyped behavioral maturation and rhythmic quiescence in C. elegans embryos.

Systematic analysis of rich behavioral recordings is being used to uncover how circuits encode complex behaviors. Here, we apply this approach to embryos. What are the first embryonic behaviors and how do they evolve as early neurodevelopment ensues? To address these questions, we present a systematic description of behavioral maturation for Caenorhabditis elegans embryos. Posture libraries were built using a genetically encoded motion capture suit imaged with light-sheet microscopy and annotated using custom tracking software. Analysis of cell trajectories, postures, and behavioral motifs revealed a stereotyped developmental progression. Early movement is dominated by flipping between dorsal and ventral coiling, which gradually slows into a period of reduced motility. Late-stage embryos exhibit sinusoidal waves of dorsoventral bends, prolonged bouts of directed motion, and a rhythmic pattern of pausing, which we designate slow wave twitch (SWT). Synaptic transmission is required for late-stage motion but not for early flipping nor the intervening inactive phase. A high-throughput behavioral assay and calcium imaging revealed that SWT is elicited by the rhythmic activity of a quiescence-promoting neuron (RIS). Similar periodic quiescent states are seen prenatally in diverse animals and may play an important role in promoting normal developmental outcomes.

Multiscale fluorescence imaging of living samples.

Fluorescence microscopy is a highly effective tool for interrogating biological structure and function, particularly when imaging across multiple spatiotemporal scales. Here we survey recent innovations and applications in the relatively understudied area of multiscale fluorescence imaging of living samples. We discuss fundamental challenges in live multiscale imaging and describe successful examples that highlight the power of this approach. We attempt to synthesize general strategies from these test cases, aiming to help accelerate progress in this exciting area.

Nanotopography modulates cytoskeletal organization and dynamics during T cell activation.

Exposure to MHC-antigen complexes on the surface of antigen-presenting cells (APCs) activates T cells, inducing the formation of the immune synapse (IS). Antigen detection at the APC surface is thus a critical step in the adaptive immune response. The physical properties of antigen-presenting surfaces encountered by T cells in vivo are believed to modulate T cell activation and proliferation. Although stiffness and ligand mobility influence IS formation, the effect of the complex topography of the APC surface on this process is not well understood. Here we investigate how nanotopography modulates cytoskeletal dynamics and signaling during the early stages of T cell activation using high-resolution fluorescence microscopy on nanofabricated surfaces with parallel nanoridges of different spacings. We find that although nanoridges reduce the maximum spread area as compared with cells on flat surfaces, the ridges enhance the accumulation of actin and the signaling kinase ZAP-70 at the IS. Actin polymerization is more dynamic in the presence of ridges, which influence the directionality of both actin flows and microtubule (MT) growth. Our results demonstrate that the topography of the activating surface exerts both global effects on T cell morphology and local changes in actin and MT dynamics, collectively influencing T cell signaling.

Plasticity in structure and assembly of SARS-CoV-2 nucleocapsid protein.

Worldwide SARS-CoV-2 sequencing efforts track emerging mutations in its spike protein, as well as characteristic mutations in other viral proteins. Besides their epidemiological importance, the observed SARS-CoV-2 sequences present an ensemble of viable protein variants, and thereby a source of information on viral protein structure and function. Charting the mutational landscape of the nucleocapsid (N) protein that facilitates viral assembly, we observe variability exceeding that of the spike protein, with more than 86% of residues that can be substituted, on average by three to four different amino acids. However, mutations exhibit an uneven distribution that tracks known structural features but also reveals highly protected stretches of unknown function. One of these conserved regions is in the central disordered linker proximal to the N-G215C mutation that has become dominant in the Delta variant, outcompeting G215 variants without further spike or N-protein substitutions. Structural models suggest that the G215C mutation stabilizes conserved transient helices in the disordered linker serving as protein-protein interaction interfaces. Comparing Delta variant N-protein to its ancestral version in biophysical experiments, we find a significantly more compact and less disordered structure. N-G215C exhibits substantially stronger self-association, shifting the unliganded protein from a dimeric to a tetrameric oligomeric state, which leads to enhanced coassembly with nucleic acids. This suggests that the sequence variability of N-protein is mirrored by high plasticity of N-protein biophysical properties, which we hypothesize can be exploited by SARS-CoV-2 to achieve greater efficiency of viral assembly, and thereby enhanced infectivity.

The HIV-1 Viral Protease Is Activated during Assembly and Budding Prior to Particle Release.

HIV-1 encodes a viral protease that is essential for the maturation of infectious viral particles. While protease inhibitors are effective antiretroviral agents, recent studies have shown that prematurely activating, rather than inhibiting, protease function leads to the pyroptotic death of infected cells, with exciting implications for efforts to eradicate viral reservoirs. Despite 40 years of research into the kinetics of protease activation, it remains unclear exactly when protease becomes activated. Recent reports have estimated that protease activation occurs minutes to hours after viral release, suggesting that premature protease activation is challenging to induce efficiently. Here, monitoring viral protease activity with sensitive techniques, including nanoscale flow cytometry and instant structured illumination microscopy, we demonstrate that the viral protease is activated within cells prior to the release of free virions. Using genetic mutants that lock protease into a precursor conformation, we further show that both the precursor and mature protease have rapid activation kinetics and that the activity of the precursor protease is sufficient for viral fusion with target cells. Our finding that HIV-1 protease is activated within producer cells prior to release of free virions helps resolve a long-standing question of when protease is activated and suggests that only a modest acceleration of protease activation kinetics is required to induce potent and specific elimination of HIV-infected cells. IMPORTANCE HIV-1 protease inhibitors have been a mainstay of antiretroviral therapy for more than 2 decades. Although antiretroviral therapy is effective at controlling HIV-1 replication, persistent reservoirs of latently infected cells quickly reestablish replication if therapy is halted. A promising new strategy to eradicate the latent reservoir involves prematurely activating the viral protease, which leads to the pyroptotic killing of infected cells. Here, we use highly sensitive techniques to examine the kinetics of protease activation during and shortly after particle formation. We found that protease is fully activated before virus is released from the cell membrane, which is hours earlier than recent estimates. Our findings help resolve a long-standing debate as to when the viral protease is initially activated during viral assembly and confirm that prematurely activating HIV-1 protease is a viable strategy to eradicate infected cells following latency reversal.

Cytonemes coordinate asymmetric signaling and organization in the Drosophila muscle progenitor niche.

Asymmetric signaling and organization in the stem-cell niche determine stem-cell fates. Here, we investigate the basis of asymmetric signaling and stem-cell organization using the Drosophila wing-disc that creates an adult muscle progenitor (AMP) niche. We show that AMPs extend polarized cytonemes to contact the disc epithelial junctions and adhere themselves to the disc/niche. Niche-adhering cytonemes localize FGF-receptor to selectively adhere to the FGF-producing disc and receive FGFs in a contact-dependent manner. Activation of FGF signaling in AMPs, in turn, reinforces disc-specific cytoneme polarity/adhesion, which maintains their disc-proximal positions. Loss of cytoneme-mediated adhesion promotes AMPs to lose niche occupancy and FGF signaling, occupy a disc-distal position, and acquire morphological hallmarks of differentiation. Niche-specific AMP organization and diversification patterns are determined by localized expression and presentation patterns of two different FGFs in the wing-disc and their polarized target-specific distribution through niche-adhering cytonemes. Thus, cytonemes are essential for asymmetric signaling and niche-specific AMP organization.

Plasticity in structure and assembly of SARS-CoV-2 nucleocapsid protein.

Worldwide SARS-CoV-2 sequencing efforts track emerging mutations in its spike protein, as well as characteristic mutations in other viral proteins. Besides their epidemiological importance, the observed SARS-CoV-2 sequences present an ensemble of viable protein variants, and thereby a source of information on viral protein structure and function. Charting the mutational landscape of the nucleocapsid (N) protein that facilitates viral assembly, we observe variability exceeding that of the spike protein, with more than 86% of residues that can be substituted, on average by 3-4 different amino acids. However, mutations exhibit an uneven distribution that tracks known structural features but also reveals highly protected stretches of unknown function. One of these conserved regions is in the central disordered linker proximal to the N-G215C mutation that has become dominant in the Delta variant, outcompeting G215 variants without further spike or N-protein substitutions. Structural models suggest that the G215C mutation stabilizes conserved transient helices in the disordered linker serving as protein-protein interaction interfaces. Comparing Delta variant N-protein to its ancestral version in biophysical experiments, we find a significantly more compact and less disordered structure. N-G215C exhibits substantially stronger self-association, shifting the unliganded protein from a dimeric to a tetrameric oligomeric state, which leads to enhanced co-assembly with nucleic acids. This suggests that the sequence variability of N-protein is mirrored by high plasticity of N-protein biophysical properties, which we hypothesize can be exploited by SARS-CoV-2 to achieve greater efficiency of viral assembly, and thereby enhanced infectivity.