Rapid, accurate particle tracking by calculation of radial symmetry centers

Tracing the evolution of single-cell cancer 3D genomes: an atlas for cancer gene discovery

Although three-dimensional (3D) genome structures are altered in cancer cells, little is known about how these changes evolve and diversify during cancer progression. Leveraging genome-wide chromatin tracing to visualize 3D genome folding directly in tissues, we generated 3D genome cancer atlases of murine lung and pancreatic adenocarcinoma. Our data reveal stereotypical, non-monotonic, and stage-specific alterations in 3D genome folding heterogeneity, compaction, and compartmentalization as cancers progress from normal to preinvasive and ultimately to invasive tumors, discovering a potential structural bottleneck in early tumor progression. Remarkably, 3D genome architectures distinguish histologic cancer states in single cells, despite considerable cell-to-cell heterogeneity. Gene-level analyses of evolutionary changes in 3D genome compartmentalization not only showed compartment-associated genes are more homogeneously regulated, but also elucidated prognostic and dependency genes in lung adenocarcinoma and a previously unappreciated role for polycomb-group protein Rnf2 in 3D genome regulation. Our results demonstrate the utility of mapping the single-cell cancer 3D genome in tissues and illuminate its potential to identify new diagnostic, prognostic, and therapeutic biomarkers in cancer.

Single-molecule imaging of SWI/SNF chromatin remodelers reveals bromodomain-mediated and cancer-mutants-specific landscape of multi-modal DNA-binding dynamics

Despite their prevalent cancer implications, the in vivo dynamics of SWI/SNF chromatin remodelers and how misregulation of such dynamics underpins cancer remain poorly understood. Using live-cell single-molecule tracking, we quantify the intranuclear diffusion and chromatin-binding of three key subunits common to all major human SWI/SNF remodeler complexes (BAF57, BAF155 and BRG1), and resolve two temporally distinct stable binding modes for the fully assembled complex. Super-resolved density mapping reveals heterogeneous, nanoscale remodeler binding "hotspots" across the nucleoplasm where multiple binding events (especially longer-lived ones) preferentially cluster. Importantly, we uncover distinct roles of the bromodomain in modulating chromatin binding/targeting in a DNA-accessibility-dependent manner, pointing to a model where successive longer-lived binding within "hotspots" leads to sustained productive remodeling. Finally, systematic comparison of six common BRG1 mutants implicated in various cancers unveils alterations in chromatin-binding dynamics unique to each mutant, shedding insight into a multi-modal landscape regulating the spatio-temporal organizational dynamics of SWI/SNF remodelers.

Structured Stochastic Curve Fitting without Gradient Calculation

Optimization of parameters and hyperparameters is a general process for any data analysis. Because not all models are mathematically well-behaved, stochastic optimization can be useful in many analyses by randomly choosing parameters in each optimization iteration. Many such algorithms have been reported and applied in chemistry data analysis, but the one reported here is interesting to check out, where a naïve algorithm searches each parameter sequentially and randomly in its bounds. Then it picks the best for the next iteration. Thus, one can ignore irrational solution of the model itself or its gradient in parameter space.

Time-Resolved Inspection of Ionizable Lipid-Facilitated Lipid Nanoparticle Disintegration and Cargo Release at an Early Endosomal Membrane Mimic

Advances in lipid nanoparticle (LNP) design have contributed notably to the emergence of the current clinically approved mRNA-based vaccines and are of high relevance for delivering mRNA to combat diseases where therapeutic alternatives are sparse. LNP-assisted mRNA delivery utilizes ionizable lipid-mediated cargo translocation across the endosomal membrane driven by the acidification of the endosomal environment. However, this process occurs at a low efficiency, a few percent at the best. Utilizing surface-sensitive fluorescence microscopy with a single LNP and mRNA resolution, we have investigated pH-controlled interactions between individual LNPs and a planar anionic supported lipid bilayer (SLB) formed on nanoporous silica, mimicking the electrostatic conditions of the early endosomal membrane. For LNPs with an average diameter of 140 nm, fusion with the anionic SLB preferentially occurred when the pH was reduced from 6.6 to 6.0. Furthermore, there was a delay in the onset of LNP fusion after the pH drop, and upon fusion, a significant fraction (>70%) of mRNA was released into the acidic solution representing the endosomal lumen, while a fraction of mRNA remained bound to the SLB even after reversing the pH to neutral cytosolic conditions. Finally, a comparison of the fusion efficiency of two LNP formulations with different surface concentrations of gel-forming lipids correlated with differences in the protein translation efficiency previously observed in human primary cell transfection studies. Together, these findings emphasize the relevance of biophysical investigations of ionizable lipid-containing LNP-assisted mRNA delivery mechanisms while potentially also offering means to optimize the design of LNPs with enhanced endosomal escape capabilities.

Meta Shack-Hartmann wavefront sensor with large sampling density and large angular field of view: phase imaging of complex objects

Shack-Hartmann wavefront sensors measure the local slopes of an incoming wavefront based on the displacement of focal spots created by a lenslet array, serving as key components for adaptive optics for astronomical and biomedical imaging. Traditionally, the challenges in increasing the density and the curvature of the lenslet have limited the use of such wavefront sensors in characterizing slowly varying wavefront structures. Here, we develop a metasurface-enhanced Shack-Hartmann wavefront sensor (meta SHWFS) to break this limit, considering the interplay between the lenslet parameters and the performance of SHWFS. We experimentally validate the meta SHWFS with a sampling density of 5963 per mm2 and a maximum acceptance angle of 8° which outperforms the traditional SFWFS by an order of magnitude. Furthermore, to the best of our knowledge, we demonstrate the first use of a wavefront sensing scheme in single-shot phase imaging of highly complex patterns, including biological tissue patterns. The proposed approach opens up new opportunities in incorporating exceptional light manipulation capabilities of the metasurface platform in complex wavefront characterization.

Cholesterol-Dependent Membrane Deformation by Metastable Viral Capsids Facilitates Entry

Nonenveloped viruses employ unique entry mechanisms to breach and infect host cells. Understanding these mechanisms is crucial for developing antiviral strategies. Prevailing perspective suggests that nonenveloped viruses release membrane pore-forming peptides to breach host membranes. However, the precise involvement of the viral capsid in this entry remains elusive. Our study presents direct observations elucidating the dynamically distinctive steps through which metastable reovirus capsids disrupt host lipid membranes as they uncoat into partially hydrophobic intermediate particles. Using both live cells and model membrane systems, our key finding is that reovirus capsids actively deform and permeabilize lipid membranes in a cholesterol-dependent process. Unlike membrane pore-forming peptides, these metastable viral capsids induce more extensive membrane perturbations, including budding, bridging between adjacent membranes, and complete rupture. Notably, cholesterol enhances subviral particle adsorption, resulting in the formation of pores equivalent to the capsid size. This cholesterol dependence is attributed to the lipid condensing effect, particularly prominent at an intermediate cholesterol level. Furthermore, our results reveal a positive correlation between membrane disruption extent and efficiency of viral variants in establishing infection. This study unveils the crucial role of capsid-lipid interaction in nonenveloped virus entry, providing new insights into how cholesterol homeostasis influences virus infection dynamics.

Nascent flagellar basal bodies are immobilized by rod assembly in Bacillus subtilis

Flagella are complex, trans-envelope nanomachines that localize to species- specific cellular addresses. Here we study the localization dynamics of the earliest stage of basal body formation in Bacillus subtilis using a fluorescent fusion to the C-ring protein FliM. We find that B. subtilis basal bodies do not exhibit dynamic subunit exchange and are largely stationary at steady state, consistent with flagellar assembly through the peptidoglycan. Rare basal bodies were observed to be mobile however, and the frequency of basal body mobility is elevated both early in basal body assembly and when the rod is mutated. Thus, basal body mobility is a precursor to patterning and we propose that rod polymerization probes the peptidoglycan superstructure for pores of sufficient diameter that permit rod completion. Furthermore, mutation of the rod also disrupts basal body patterning in a way that phenocopies mutation of the cytoplasmic flagellar patterning protein FlhF. We infer that conformational changes in the basal body exchange information between rod synthesis and the cytoplasmic patterning proteins to restrict assembly at certain pores established by a grid-like pattern pre-existent in the peptidoglycan itself.

IMPORTANCE

Bacteria insert flagella in a species-specific pattern on the cell body, but how patterns are achieved is poorly understood. In bacteria with a single polar flagellum, a marker protein localizes to the cell pole and nucleates the assembly of the flagellum at that site. Bacillus subtilis assembles ∼15 flagella over the length of the cell body in a grid-like pattern and lacks all proteins associated with targeted assembly in polarly flagellated bacteria. Here we show that B. subtilis basal bodies are mobile soon after assembly and become immobilized when the flagellar rod transits the peptidoglycan wall. Moreover, defects in the flagellar rod lead to an asymmetric distribution of flagella with respect to the midcell. We conclude that the patterning of flagella is different in B. subtilis , and we infer that the B. subtilis rod probes the peptidoglycan for holes that can accommodate the machine.

WRN inhibition leads to its chromatin-associated degradation via the PIAS4-RNF4-p97/VCP axis

Synthetic lethality provides an attractive strategy for developing targeted cancer therapies. For example, cancer cells with high levels of microsatellite instability (MSI-H) are dependent on the Werner (WRN) helicase for survival. However, the mechanisms that regulate WRN spatiotemporal dynamics remain poorly understood. Here, we used single-molecule tracking (SMT) in combination with a WRN inhibitor to examine WRN dynamics within the nuclei of living cancer cells. WRN inhibition traps the helicase on chromatin, requiring p97/VCP for extraction and proteasomal degradation in a MSI-H dependent manner. Using a phenotypic screen, we identify the PIAS4-RNF4 axis as the pathway responsible for WRN degradation. Finally, we show that co-inhibition of WRN and SUMOylation has an additive toxic effect in MSI-H cells and confirm the in vivo activity of WRN inhibition using an MSI-H mouse xenograft model. This work elucidates a regulatory mechanism for WRN that may facilitate identification of new therapeutic modalities, and highlights the use of SMT as a tool for drug discovery and mechanism-of-action studies.

Machine Learning for Single-Molecule Localization Microscopy: From Data Analysis to Quantification

Single-molecule localization microscopy (SMLM) is a versatile tool for realizing nanoscale imaging with visible light and providing unprecedented opportunities to observe bioprocesses. The integration of machine learning with SMLM enhances data analysis by improving efficiency and accuracy. This tutorial aims to provide a comprehensive overview of the data analysis process and theoretical aspects of SMLM, while also highlighting the typical applications of machine learning in this field. By leveraging advanced analytical techniques, SMLM is becoming a powerful quantitative analysis tool for biological research.

Cross-correlation image analysis for real-time single particle tracking

Accurately measuring the translations of objects between images is essential in many fields, including biology, medicine, chemistry, and physics. One important application is tracking one or more particles by measuring their apparent displacements in a series of images. Popular methods, such as the center of mass, often require idealized scenarios to reach the shot noise limit of particle tracking and, therefore, are not generally applicable to multiple image types. More general methods, such as maximum likelihood estimation, reliably approach the shot noise limit, but are too computationally intense for use in real-time applications. These limitations are significant, as real-time, shot-noise-limited particle tracking is of paramount importance for feedback control systems. To fill this gap, we introduce a new cross-correlation-based algorithm that approaches shot-noise-limited displacement detection and a graphics processing unit-based implementation for real-time image analysis of a single particle.

Molecular fingerprinting of biological nanoparticles with a label-free optofluidic platform

Label-free detection of multiple analytes in a high-throughput fashion has been one of the long-sought goals in biosensing applications. Yet, for all-optical approaches, interfacing state-of-the-art label-free techniques with microfluidics tools that can process small volumes of sample with high throughput, and with surface chemistry that grants analyte specificity, poses a critical challenge to date. Here, we introduce an optofluidic platform that brings together state-of-the-art digital holography with PDMS microfluidics by using supported lipid bilayers as a surface chemistry building block to integrate both technologies. Specifically, this platform fingerprints heterogeneous biological nanoparticle populations via a multiplexed label-free immunoaffinity assay with single particle sensitivity. First, we characterise the robustness and performance of the platform, and then apply it to profile four distinct ovarian cell-derived extracellular vesicle populations over a panel of surface protein biomarkers, thus developing a unique biomarker fingerprint for each cell line. We foresee that our approach will find many applications where routine and multiplexed characterisation of biological nanoparticles are required.

A microrheological examination of insulin-secreting β-cells in healthy and diabetic-like conditions

Pancreatic β-cells regulate glucose homeostasis through glucose-stimulated insulin secretion, which is hindered in type-2 diabetes. Transport of the insulin vesicles is expected to be affected by changes in the viscoelastic and transport properties of the cytoplasm. These are evaluated in situ through particle-tracking measurements using a rat insulinoma β-cell line. The use of inert probes assists in decoupling the material properties of the cytoplasm from the active transport through cellular processes. The effect of glucose-stimulated insulin secretion is examined, and the subsequent remodeling of the cytoskeleton, at constant effects of cell activity, is shown to result in reduced mobility of the tracer particles. Induction of diabetic-like conditions is identified to alter the mean-squared displacement of the passive particles in the cytoplasm and diminish its reaction to glucose stimulation.

RASP: Optimal Single Puncta Detection in Complex Cellular Backgrounds

Super-resolution and single-molecule microscopies have been increasingly applied to complex biological systems. A major challenge of these approaches is that fluorescent puncta must be detected in the low signal, high noise, heterogeneous background environments of cells and tissue. We present RASP, Radiality Analysis of Single Puncta, a bioimaging-segmentation method that solves this problem. RASP removes false-positive puncta that other analysis methods detect and detects features over a broad range of spatial scales: from single proteins to complex cell phenotypes. RASP outperforms the state-of-the-art methods in precision and speed using image gradients to separate Gaussian-shaped objects from the background. We demonstrate RASP's power by showing that it can extract spatial correlations between microglia, neurons, and α-synuclein oligomers in the human brain. This sensitive, computationally efficient approach enables fluorescent puncta and cellular features to be distinguished in cellular and tissue environments, with sensitivity down to the level of the single protein. Python and MATLAB codes, enabling users to perform this RASP analysis on their own data, are provided as Supporting Information and links to third-party repositories.

Real-Time 3D Tracking of Multi-Particle in the Wide-Field Illumination Based on Deep Learning

In diverse realms of research, such as holographic optical tweezer mechanical measurements, colloidal particle motion state examinations, cell tracking, and drug delivery, the localization and analysis of particle motion command paramount significance. Algorithms ranging from conventional numerical methods to advanced deep-learning networks mark substantial strides in the sphere of particle orientation analysis. However, the need for datasets has hindered the application of deep learning in particle tracking. In this work, we elucidated an efficacious methodology pivoted toward generating synthetic datasets conducive to this domain that resonates with robustness and precision when applied to real-world data of tracking 3D particles. We developed a 3D real-time particle positioning network based on the CenterNet network. After conducting experiments, our network has achieved a horizontal positioning error of 0.0478 μm and a z-axis positioning error of 0.1990 μm. It shows the capability to handle real-time tracking of particles, diverse in dimensions, near the focal plane with high precision. In addition, we have rendered all datasets cultivated during this investigation accessible.

A charge-dependent long-ranged force drives tailored assembly of matter in solution

The interaction between charged objects in solution is generally expected to recapitulate two central principles of electromagnetics: (1) like-charged objects repel, and (2) they do so regardless of the sign of their electrical charge. Here we demonstrate experimentally that the solvent plays a hitherto unforeseen but crucial role in interparticle interactions, and importantly, that interactions in the fluid phase can break charge-reversal symmetry. We show that in aqueous solution, negatively charged particles can attract at long range while positively charged particles repel. In solvents that exhibit an inversion of the net molecular dipole at an interface, such as alcohols, we find that the converse can be true: positively charged particles may attract whereas negatives repel. The observations hold across a wide variety of surface chemistries: from inorganic silica and polymeric particles to polyelectrolyte- and polypeptide-coated surfaces in aqueous solution. A theory of interparticle interactions that invokes solvent structuring at an interface captures the observations. Our study establishes a nanoscopic interfacial mechanism by which solvent molecules may give rise to a strong and long-ranged force in solution, with immediate ramifications for a range of particulate and molecular processes across length scales such as self-assembly, gelation and crystallization, biomolecular condensation, coacervation, and phase segregation.

Precise localization of corneal reflections in eye images using deep learning trained on synthetic data

We present a deep learning method for accurately localizing the center of a single corneal reflection (CR) in an eye image. Unlike previous approaches, we use a convolutional neural network (CNN) that was trained solely using synthetic data. Using only synthetic data has the benefit of completely sidestepping the time-consuming process of manual annotation that is required for supervised training on real eye images. To systematically evaluate the accuracy of our method, we first tested it on images with synthetic CRs placed on different backgrounds and embedded in varying levels of noise. Second, we tested the method on two datasets consisting of high-quality videos captured from real eyes. Our method outperformed state-of-the-art algorithmic methods on real eye images with a 3-41.5% reduction in terms of spatial precision across data sets, and performed on par with state-of-the-art on synthetic images in terms of spatial accuracy. We conclude that our method provides a precise method for CR center localization and provides a solution to the data availability problem, which is one of the important common roadblocks in the development of deep learning models for gaze estimation. Due to the superior CR center localization and ease of application, our method has the potential to improve the accuracy and precision of CR-based eye trackers.

Integrating magnetic tweezers and single-molecule FRET: A comprehensive approach to studying local and global conformational changes simultaneously

This chapter presents the integration of magnetic tweezers with single-molecule FRET technology, a significant advancement in the study of nucleic acids and other biological systems. We detail the technical aspects, challenges, and current status of this hybrid technique, which combines the global manipulation and observation capabilities of magnetic tweezers with the local conformational detection of smFRET. This innovative approach enhances our ability to analyze and understand the molecular mechanics of biological systems. The chapter serves as our first formal documentation of this method, offering insights and methodologies developed in our laboratory over the past decade.

Determining intrinsic potentials and validating optical binding forces between colloidal particles using optical tweezers

Understanding the interactions between small, submicrometer-sized colloidal particles is crucial for numerous scientific disciplines and technological applications. In this study, we employ optical tweezers as a powerful tool to investigate these interactions. We utilize a full image reconstruction technique to achieve high precision in characterizing particle pairs that enable nanometer-scale measurement of their positions. This approach captures intricate details and provides a comprehensive understanding of the spatial arrangement between particles, overcoming previous limitations in resolution. Moreover, our research demonstrates that properly accounting for optical binding forces to determine the intrinsic interaction potential is vital. We employ a discrete dipole approximation approach to calculate optical binding potentials and achieve a good agreement between the calculated and observed binding forces. We incorporate the findings from these simulations into the assessment of the intrinsic interaction potentials and validate our methodology by using short-range depletion attraction induced by micelles as an example.

Cholesterol-Dependent Membrane Deformation by Metastable Viral Capsids Facilitates Entry

Non-enveloped viruses employ unique entry mechanisms to breach and infect host cells. Understanding these mechanisms is crucial for developing antiviral strategies. Prevailing perspective suggests that non-enveloped viruses release membrane lytic peptides to breach host membranes. However, the precise involvement of the viral capsid in this entry remains elusive. Our study presents direct observations elucidating the dynamically distinctive steps through which metastable reovirus capsids disrupt host lipid membranes as they uncoat into partially hydrophobic intermediate particles. Using both live cells and model membrane systems, our key finding is that reovirus capsids actively deform and permeabilize lipid membranes in a cholesterol-dependent process. Unlike membrane lytic peptides, these metastable viral capsids induce more extensive membrane perturbations, including budding, bridging between adjacent membranes, and complete rupture. Notably, cholesterol enhances subviral particle adsorption, resulting in the formation of pores equivalent to the capsid size. This cholesterol dependence is attributed to the lipid condensing effect, particularly prominent at intermediate cholesterol level. Furthermore, our results reveal a positive correlation between membrane disruption extent and efficiency of viral variants in establishing infection. This study unveils the crucial role of capsid-lipid interaction in non-enveloped virus entry, providing new insights into how cholesterol homeostasis influences virus infection dynamics.

A genetic circuit on a single DNA molecule as an autonomous dissipative nanodevice

Realizing genetic circuits on single DNA molecules as self-encoded dissipative nanodevices is a major step toward miniaturization of autonomous biological systems. A circuit operating on a single DNA implies that genetically encoded proteins localize during coupled transcription-translation to DNA, but a single-molecule measurement demonstrating this has remained a challenge. Here, we use a genetically encoded fluorescent reporter system with improved temporal resolution and observe the synthesis of individual proteins tethered to a DNA molecule by transient complexes of RNA polymerase, messenger RNA, and ribosome. Against expectations in dilute cell-free conditions where equilibrium considerations favor dispersion, these nascent proteins linger long enough to regulate cascaded reactions on the same DNA. We rationally design a pulsatile genetic circuit by encoding an activator and repressor in feedback on the same DNA molecule. Driven by the local synthesis of only several proteins per hour and gene, the circuit dynamics exhibit enhanced variability between individual DNA molecules, and fluctuations with a broad power spectrum. Our results demonstrate that co-expressional localization, as a nonequilibrium process, facilitates single-DNA genetic circuits as dissipative nanodevices, with implications for nanobiotechnology applications and artificial cell design.

Single-cell transcriptional dynamics in a living vertebrate

The ability to quantify transcriptional dynamics in individual cells via live imaging has revolutionized our understanding of gene regulation. However, such measurements are lacking in the context of vertebrate embryos. We addressed this deficit by applying MS2-MCP mRNA labeling to the quantification of transcription in zebrafish, a model vertebrate. We developed a platform of transgenic organisms, light sheet fluorescence microscopy, and optimized image analysis that enables visualization and quantification of MS2 reporters. We used these tools to obtain the first single-cell, real-time measurements of transcriptional dynamics of the segmentation clock. Our measurements challenge the traditional view of smooth clock oscillations and instead suggest a model of discrete transcriptional bursts that are organized in space and time. Together, these results highlight how measuring single-cell transcriptional activity can reveal unexpected features of gene regulation and how this data can fuel the dialogue between theory and experiment.

Detecting DNA Loops Using Tethered Particle Motion

The range of motion of a micron-sized bead tethered by a single polymer provides a dynamic readout of the effective length of the polymer. The excursions of the bead may reflect the intrinsic flexibility and/or topology of the polymer as well as changes due to the action activity of ligands that bind the polymer. This is a simple yet powerful experimental approach to investigate such interactions between DNA and proteins as demonstrated by experiments with the lac repressor. This protein forms a stable, tetrameric oligomer with two binding sites and can produce a loop of DNA between recognition sites separated along the length of a DNA molecule.

Harnessing Viscoelasticity to Suppress Irreversibility Buildup in a Colloidal Stirling Engine

Typically, the rate at which a heat engine can produce useful work is constrained by the buildup of irreversibility with increasing operating speed. Here, using a recently developed reservoir engineering technique, we designed and quantified the performance of a colloidal Stirling engine operating in a viscoelastic bath. While the bath acts like a viscous fluid in the quasistatic limit, and the engine's performance agrees with equilibrium predictions, on reducing the cycle time to the bath's structural relaxation time, the increasingly elastic response of the bath aids suppress the buildup of irreversibility. We show that the elastic energy stored during the isothermal compression step of the Stirling cycle facilitates quick equilibration in the isothermal expansion step. This results in equilibriumlike efficiencies even for cycle times shorter than the equilibration time of the colloidal particle.

Flagellar motor remodeling during swarming requires FliL

FliL is an essential component of the flagellar machinery in some bacteria, but a conditional one in others. The conditional role is for optimal swarming in some bacteria. During swarming, physical forces associated with movement on a surface are expected to exert a higher load on the flagellum, requiring more motor torque to move. FliL was reported to enhance motor output in several bacteria and observed to assemble as a ring around ion-conducting stators that power the motor. In this study we identify a common new function for FliL in diverse bacteria-Escherichia coli, Bacillus subtilis, and Proteus mirabilis. During swarming, all these bacteria show increased cell speed and a skewed motor bias that suppresses cell tumbling. We demonstrate that these altered motor parameters, or "motor remodeling," require FliL. Both swarming and motor remodeling can be restored in an E. coli fliL mutant by complementation with fliL genes from P. mirabilis and B. subtilis, showing conservation of a swarming-associated FliL function across phyla. In addition, we demonstrate that the strong interaction we reported earlier between FliL and the flagellar MS-ring protein FliF is confined to the RBM-3 domain of FliF that links the periplasmic rod to the cytoplasmic C-ring. This interaction may explain several phenotypes associated with the absence of FliL.

Deep learning-driven adaptive optics for single-molecule localization microscopy

The inhomogeneous refractive indices of biological tissues blur and distort single-molecule emission patterns generating image artifacts and decreasing the achievable resolution of single-molecule localization microscopy (SMLM). Conventional sensorless adaptive optics methods rely on iterative mirror changes and image-quality metrics. However, these metrics result in inconsistent metric responses and thus fundamentally limit their efficacy for aberration correction in tissues. To bypass iterative trial-then-evaluate processes, we developed deep learning-driven adaptive optics for SMLM to allow direct inference of wavefront distortion and near real-time compensation. Our trained deep neural network monitors the individual emission patterns from single-molecule experiments, infers their shared wavefront distortion, feeds the estimates through a dynamic filter and drives a deformable mirror to compensate sample-induced aberrations. We demonstrated that our method simultaneously estimates and compensates 28 wavefront deformation shapes and improves the resolution and fidelity of three-dimensional SMLM through >130-µm-thick brain tissue specimens.

The Emergence of Functional Ultrasound for Noninvasive Brain-Computer Interface

A noninvasive brain-computer interface is a central task in the comprehensive analysis and understanding of the brain and is an important challenge in international brain-science research. Current implanted brain-computer interfaces are cranial and invasive, which considerably limits their applications. The development of new noninvasive reading and writing technologies will advance substantial innovations and breakthroughs in the field of brain-computer interfaces. Here, we review the theory and development of the ultrasound brain functional imaging and its applications. Furthermore, we introduce latest advancements in ultrasound brain modulation and its applications in rodents, primates, and human; its mechanism and closed-loop ultrasound neuromodulation based on electroencephalograph are also presented. Finally, high-frequency acoustic noninvasive brain-computer interface is prospected based on ultrasound super-resolution imaging and acoustic tweezers.

Single-molecule localization microscopy based on denoising, interpolation and local maxima

A single fluorescent molecule is highly likely to be located at the center pixel position of a raw image diffused spot in an ideal situation. Even if the molecule and the center pixel position do not completely overlap, they are very close. A single-molecule localization method based on denoising, interpolation and local maxima (DIL) is proposed. The low-resolution raw image is denoised and interpolated, and a new image with a pixel size equal to that of the super-resolution image is attained. The local maxima of the new image are extracted. With this method, it is found that the local maxima positions can be regarded as the fluorescent molecule positions. Simulation results demonstrate that the DIL single-molecule localization accuracy reaches ∼18 nm when the Gaussian noise variance is equal to 0.01. Experimental results demonstrate that the DIL localization methodology is comparable to the Gaussian fitting algorithm and is faster.

High-Volume-Rate 3-D Ultrasound Imaging Using Fast-Tilting and Redirecting Reflectors

Three-dimensional ultrasound imaging has many advantages over 2-D imaging such as more comprehensive tissue evaluation and less operator dependence. However, developing a low-cost and accessible 3-D ultrasound solution with high volume rate and imaging quality remains a challenging task. Recently, we proposed a 3-D ultrasound imaging technique: fast acoustic steering via tilting electromechanical reflectors (FASTER), which uses a fast-tilting acoustic reflector to steer ultrafast plane waves elevationally to achieve high-volume-rate 3-D imaging with conventional 1-D transducers. However, the initial FASTER implementation requires a water tank for acoustic wave conduction and cannot be conveniently used for regular handheld scanning. To address these limitations, here, we developed a novel ultrasound probe clip-on device that encloses a fast-tilting reflector, a redirecting reflector, and an acoustic wave conduction medium. The new FASTER 3-D imaging device can be easily attached to or removed from clinical ultrasound transducers, allowing rapid transformation from 2-D to 3-D imaging. In vitro B-mode studies demonstrated that the proposed method provided comparable imaging quality to conventional, mechanical-translation-based 3-D imaging while offering a much faster volume rate (e.g., 300 versus  ∼  10 Hz). We also demonstrated 3-D power Doppler (PD) and 3-D super-resolution ultrasound localization microscopy (ULM) with the FASTER device. An in vivo imaging study showed that the FASTER device could clearly visualize the 3-D anatomy of the basilic vein. These results suggest that the newly developed redirecting reflector and the clip-on device could overcome key hurdles for future clinical translation of the FASTER 3-D imaging technology.

Assessment of coronary microcirculation alterations in a porcine model of no-reflow using ultrasound localization microscopy: a proof of concept study

BACKGROUND

Coronary microvascular obstruction also known as no-reflow phenomenon is a major issue during myocardial infarction that bears important prognostic implications. Alterations of the microvascular network remains however challenging to assess as there is no imaging modality in the clinics that can image directly the coronary microvascular vessels. Ultrasound Localization Microscopy (ULM) imaging was recently introduced to map microvascular flows at high spatial resolution (∼10 μm). In this study, we developed an approach to image alterations of the microvascular coronary flow in ex vivo perfused swine hearts.

METHODS

A porcine model of myocardial ischemia-reperfusion was used to obtain microvascular coronary alterations and no-reflow. Four female hearts with myocardial infarction in addition to 6 controls were explanted and placed immediately in a dedicated preservation and perfusion box manufactured for ultrasound imaging. Microbubbles (MB) were injected into the vasculature to perform Ultrasound Localization Microscopy (ULM) imaging and a linear ultrasound probe mounted on a motorized device was used to scan the heart on multiple slices. The coronary microvascular anatomy and flow velocity was reconstructed using dedicated ULM algorithms and analyzed quantitatively.

FINDINGS

We were able to image the coronary microcirculation of ex vivo swine hearts at a resolution of tens of microns and measure flow velocities ranging from 10 mm/s in arterioles up to more than 200 mm/s in epicardial arteries. Under different aortic perfusion pressures, we measured in large arteries of a subset of control hearts an increase of flow velocity from 31 ± 11 mm/s at 87 mmHg to 47 ± 17 mm/s at 132 mmHg (N = 3 hearts, P < 0.05). This increase was compared with a control measurement with a flowmeter in the aorta. We also compared 6 control hearts to 4 hearts in which no-reflow was induced by the occlusion and reperfusion of a coronary artery. Using average MB velocity and average density of MB per unit of surface as two ULM quantitative markers of perfusion, we were able to detect areas of coronary no-reflow in good agreement with a control anatomical pathology analysis of the cardiac tissue. In the no-reflow zone, we measured an average perfusion of 204 ± 305 MB/mm2 compared to 3182 ± 1302 MB/mm2 in the surrounding re-perfused area.

INTERPRETATION

We demonstrated this approach can directly image and quantify coronary microvascular obstruction and no-reflow on large mammal perfused hearts. This is a first step for noninvasive, quantitative and affordable assessment of the coronary microcirculation function and particularly coronary microvascular anatomy in the infarcted heart. This approach has the potential to be extended to other clinical situations characterized by microvascular dysfunction.

FUNDING

This study was supported by the French National Research Agency (ANR) under ANR-21-CE19-0002 grant agreement.

Flagellar motor remodeling during swarming requires FliL

FliL is an essential component of the flagellar machinery in some bacteria, but a conditional one in others. The conditional role is for optimal swarming in some bacteria. During swarming, physical forces associated with movement on a surface are expected to exert a higher load on the flagellum, requiring more motor torque to move. Bacterial physiology and morphology are also altered during swarming to cope with the challenges of surface navigation. FliL was reported to enhance motor output in several bacteria and observed to assemble as a ring around ion-conducting stators that power the motor. In this study we identify a common new function for FliL in diverse bacteria - Escherichia coli, Bacillus subtilis and Proteus mirabilis . During swarming, all these bacteria show increased cell speed and a skewed motor bias that suppresses cell tumbling. We demonstrate that these altered motor parameters, or 'motor remodeling', require FliL. Both swarming and motor remodeling can be restored in an E. coli fliL mutant by complementation with fliL genes from P. mirabilis and B. subtilis , showing conservation of swarming-associated FliL function across phyla. In addition, we demonstrate that the strong interaction we reported earlier between FliL and the flagellar MS-ring protein FliF is confined to the RBM-3 domain of FliF that links the periplasmic rod to the cytoplasmic C-ring. This interaction may explain several phenotypes associated with the absence of FliL.

High-Throughput Single-Molecule Sensors: How Can the Signals Be Analyzed in Real Time for Achieving Real-Time Continuous Biosensing?

Single-molecule sensors collect statistics of single-molecule interactions, and the resulting data can be used to determine concentrations of analyte molecules. The assays are generally end-point assays and are not designed for continuous biosensing. For continuous biosensing, a single-molecule sensor needs to be reversible, and the signals should be analyzed in real time in order to continuously report output signals, with a well-controlled time delay and measurement precision. Here, we describe a signal processing architecture for real-time continuous biosensing based on high-throughput single-molecule sensors. The key aspect of the architecture is the parallel computation of multiple measurement blocks that enables continuous measurements over an endless time span. Continuous biosensing is demonstrated for a single-molecule sensor with 10,000 individual particles that are tracked as a function of time. The continuous analysis includes particle identification, particle tracking, drift correction, and detection of the discrete timepoints where individual particles switch between bound and unbound states, yielding state transition statistics that relate to the analyte concentration in solution. The continuous real-time sensing and computation were studied for a reversible cortisol competitive immunosensor, showing how the precision and time delay of cortisol monitoring are controlled by the number of analyzed particles and the size of the measurement blocks. Finally, we discuss how the presented signal processing architecture can be applied to various single-molecule measurement methods, allowing these to be developed into continuous biosensors.

Single-frame deep-learning super-resolution microscopy for intracellular dynamics imaging

Single-molecule localization microscopy (SMLM) can be used to resolve subcellular structures and achieve a tenfold improvement in spatial resolution compared to that obtained by conventional fluorescence microscopy. However, the separation of single-molecule fluorescence events that requires thousands of frames dramatically increases the image acquisition time and phototoxicity, impeding the observation of instantaneous intracellular dynamics. Here we develop a deep-learning based single-frame super-resolution microscopy (SFSRM) method which utilizes a subpixel edge map and a multicomponent optimization strategy to guide the neural network to reconstruct a super-resolution image from a single frame of a diffraction-limited image. Under a tolerable signal density and an affordable signal-to-noise ratio, SFSRM enables high-fidelity live-cell imaging with spatiotemporal resolutions of 30 nm and 10 ms, allowing for prolonged monitoring of subcellular dynamics such as interplays between mitochondria and endoplasmic reticulum, the vesicle transport along microtubules, and the endosome fusion and fission. Moreover, its adaptability to different microscopes and spectra makes it a useful tool for various imaging systems.

High-resolution spatial multi-omics reveals cell-type specific nuclear compartments

The mammalian nucleus is compartmentalized by diverse subnuclear structures. These subnuclear structures, marked by nuclear bodies and histone modifications, are often cell-type specific and affect gene regulation and 3D genome organization1-3. Understanding nuclear organization requires identifying the molecular constituents of subnuclear structures and mapping their associations with specific genomic loci in individual cells, within complex tissues. Here, we introduce two-layer DNA seqFISH+, which allows simultaneous mapping of 100,049 genomic loci, together with nascent transcriptome for 17,856 genes and a diverse set of immunofluorescently labeled subnuclear structures all in single cells in cell lines and adult mouse cerebellum. Using these multi-omics datasets, we showed that repressive chromatin compartments are more variable by cell type than active compartments. We also discovered a single exception to this rule: an RNA polymerase II (RNAPII)-enriched compartment was associated with long, cell-type specific genes (> 200kb), in a manner distinct from nuclear speckles. Further, our analysis revealed that cell-type specific facultative and constitutive heterochromatin compartments marked by H3K27me3 and H4K20me3 are enriched at specific genes and gene clusters, respectively, and shape radial chromosomal positioning and inter-chromosomal interactions in neurons and glial cells. Together, our results provide a single-cell high-resolution multi-omics view of subnuclear compartments, associated genomic loci, and their impacts on gene regulation, directly within complex tissues.

High-resolution eye-tracking via digital imaging of Purkinje reflections

Reliably measuring eye movements and determining where the observer looks are fundamental needs in vision science. A classical approach to achieve high-resolution oculomotor measurements is the so-called dual Purkinje image (DPI) method, a technique that relies on the relative motion of the reflections generated by two distinct surfaces in the eye, the cornea and the back of the lens. This technique has been traditionally implemented in fragile and difficult to operate analog devices, which have remained exclusive use of specialized oculomotor laboratories. Here we describe progress on the development of a digital DPI, a system that builds on recent advances in digital imaging to enable fast, highly precise eye-tracking without the complications of previous analog devices. This system integrates an optical setup with no moving components with a digital imaging module and dedicated software on a fast processing unit. Data from both artificial and human eyes demonstrate subarcminute resolution at 1 kHz. Furthermore, when coupled with previously developed gaze-contingent calibration methods, this system enables localization of the line of sight within a few arcminutes.

Three-dimensional deep regression-based light scattering imaging system for nanoscale exosome analysis

Exosomes are extracellular vesicles that serve as promising intrinsic nanoscale biomarkers for disease diagnosis and treatment. Nanoparticle analysis technology is widely used in the field of exosome study. However, the common particle analysis methods are usually complex, subjective, and not robust. Here, we develop a three-dimensional (3D) deep regression-based light scattering imaging system for nanoscale particle analysis. Our system solves the problem of object focusing in common methods and acquires light scattering images of label-free nanoparticles as small as 41 nm in diameter. We develop a new method for nanoparticle sizing with 3D deep regression, where the 3D time series Brownian motion data of single nanoparticles are input as a whole, and sizes are output automatically for both entangled and untangled nanoparticles. Exosomes from the normal and cancer liver cell lineage cells are observed and automatically differentiated by our system. The 3D deep regression-based light scattering imaging system is expected to be widely used in the field of nanoparticle analysis and nanomedicine.

Machine learning enables precise holographic characterization of colloidal materials in real time

Holographic particle characterization uses in-line holographic video microscopy to track and characterize individual colloidal particles dispersed in their native fluid media. Applications range from fundamental research in statistical physics to product development in biopharmaceuticals and medical diagnostic testing. The information encoded in a hologram can be extracted by fitting to a generative model based on the Lorenz-Mie theory of light scattering. Treating hologram analysis as a high-dimensional inverse problem has been exceptionally successful, with conventional optimization algorithms yielding nanometer precision for a typical particle's position and part-per-thousand precision for its size and index of refraction. Machine learning previously has been used to automate holographic particle characterization by detecting features of interest in multi-particle holograms and estimating the particles' positions and properties for subsequent refinement. This study presents an updated end-to-end neural-network solution called CATCH (Characterizing and Tracking Colloids Holographically) whose predictions are fast, precise, and accurate enough for many real-world high-throughput applications and can reliably bootstrap conventional optimization algorithms for the most demanding applications. The ability of CATCH to learn a representation of Lorenz-Mie theory that fits within a diminutive 200 kB hints at the possibility of developing a greatly simplified formulation of light scattering by small objects.

Diffusion Analysis of NAnoscopic Ensembles: A Tracking-Free Diffusivity Analysis for NAnoscopic Ensembles in Biological Samples and Nanotechnology

The rapid development of microscopic techniques over the past decades enables the establishment of single molecule fluorescence imaging as a powerful tool in biological and biomedical sciences. Single molecule fluorescence imaging allows to study the chemical, physicochemical, and biological properties of target molecules or particles by tracking their molecular position in the biological environment and determining their dynamic behavior. However, the precise determination of particle distribution and diffusivities is often challenging due to high molecule/particle densities, fast diffusion, and photobleaching/blinking of the fluorophore. A novel, accurate, and fast statistical analysis tool, Diffusion Analysis of NAnoscopic Ensembles (DANAE), that solves all these obstacles is introduced. DANAE requires no approximations or any a priori input regarding unknown system-inherent parameters, such as background distributions; a requirement that is vitally important when studying the behavior of molecules/particles in living cells. The superiority of DANAE with various data from simulations is demonstrated. As experimental applications of DANAE, membrane receptor diffusion in its natural membrane environment, and cargo mobility/distribution within nanostructured lipid nanoparticles are presented. Finally, the method is extended to two-color channel fluorescence microscopy.

High Volume Rate 3-D Ultrasound Imaging Using Fast-Tilting and Redirecting Reflectors

3-D ultrasound imaging has many advantages over 2-D imaging such as more comprehensive tissue evaluation and less operator dependence. Although many 3-D ultrasound imaging techniques have been developed in the last several decades, a low-cost and accessible solution with high imaging volume rate and imaging quality remains elusive. Recently we proposed a new, high volume rate 3-D ultrasound imaging technique: Fast Acoustic Steering via Tilting Electromechanical Reflectors (FASTER), which uses a water-immersible and fast-tilting acoustic reflector to steer ultrafast plane waves in the elevational direction to achieve high volume rate 3-D ultrasound imaging with conventional 1-D array transducers. However, the initial implementation of FASTER imaging only involves a single fast-tilting acoustic reflector, which is inconvenient to use because the probe cannot be held in the regular upright position. Also, conventional FASTER imaging can only be performed inside a water tank because of the necessity of using water for acoustic conduction. To address these limitations of conventional FASTER, here we developed a novel ultrasound probe clip-on device that encloses a fast-tilting reflector, a redirecting reflector, and an acoustic wave conduction medium. The new FASTER 3-D imaging device can be easily attached to or removed from clinical ultrasound transducers, allowing rapid transformation from 2-D to 3-D ultrasound imaging. In vitro B-mode imaging studies demonstrated that the proposed method provided comparable imaging quality (e.g., spatial resolution and contrast-to-noise ratio) to conventional, mechanical-translation-based 3-D imaging while providing a much faster 3-D volume rate (e.g., 300 Hz vs ∼10 Hz). In addition to B-mode imaging, we also demonstrated 3-D power Doppler imaging and 3-D super-resolution ultrasound localization microscopy with the newly developed FASTER device. An in vivo imaging study showed that the FASTER device could clearly visualize the 3-D anatomy of the basilic vein of a healthy volunteer, and customized beamforming was implemented to accommodate the speed of sound difference between the acoustic medium and the imaging object (e.g., soft tissue). These results suggest that the newly developed redirecting reflector and the clip-on device could overcome key hurdles for future clinical translation of the FASTER 3-D imaging technology.

The spatial resolution limit of phagocytosis

Antibody-opsonized bacteria interact with Fc receptors in macrophages and trigger signaling cascades, which induce phagocytosis. The signaling pathways ultimately lead to actin polymerization that induces the protrusion of the membrane around the bacterium until it is completely engulfed. Although many proteins involved in the phagocytic cup formation have already been identified, it is still unclear how far the initial stimulus created by the bacterium-cell contact propagates in the cell. We hypothesize that this spreading distance is closely related to the spatial resolution limit of phagocytosis, the smallest distance in which two stimuli can be differentiated. Here, we probe this resolution limit by using holographic optical tweezers to attach pairs of immunoglobulin G-coated polystyrene microparticles (as models for opsonized bacteria) to murine macrophages in distances ranging from zero to several micrometers. By using 2-μm-sized particles, we found that the particles can be internalized jointly into one phagosome if they are attached to the cell very close together, but that they are taken up separately if they are attached far from each other. To explain this, we developed a model of the signaling process, which predicts the probabilities for separate uptake for different particle sizes and distances using cellular parameters including the average receptor distance. We tested the model by measuring the separate uptake probabilities for particles with a diameter of 1 to 3 μm and for cells with reduced numbers of Fcγ receptors and found very good agreement. Our model shows that the phagocytic uptake behavior can be explained by assuming an effective phagocytic signaling range of about 500 nm. Interestingly, this value corresponds to the lower size limit of phagocytosis. Our work provides quantitative access to spatial parameters of cellular signaling during phagocytosis and thereby contributes to a more quantitative understanding of cellular information processing.

Simulation-based inference for non-parametric statistical comparison of biomolecule dynamics

Numerous models have been developed to account for the complex properties of the random walks of biomolecules. However, when analysing experimental data, conditions are rarely met to ensure model identification. The dynamics may simultaneously be influenced by spatial and temporal heterogeneities of the environment, out-of-equilibrium fluxes and conformal changes of the tracked molecules. Recorded trajectories are often too short to reliably discern such multi-scale dynamics, which precludes unambiguous assessment of the type of random walk and its parameters. Furthermore, the motion of biomolecules may not be well described by a single, canonical random walk model. Here, we develop a two-step statistical testing scheme for comparing biomolecule dynamics observed in different experimental conditions without having to identify or make strong prior assumptions about the model generating the recorded random walks. We first train a graph neural network to perform simulation-based inference and thus learn a rich summary statistics vector describing individual trajectories. We then compare trajectories obtained in different biological conditions using a non-parametric maximum mean discrepancy (MMD) statistical test on their so-obtained summary statistics. This procedure allows us to characterise sets of random walks regardless of their generating models, without resorting to model-specific physical quantities or estimators. We first validate the relevance of our approach on numerically simulated trajectories. This demonstrates both the statistical power of the MMD test and the descriptive power of the learnt summary statistics compared to estimates of physical quantities. We then illustrate the ability of our framework to detect changes in α-synuclein dynamics at synapses in cultured cortical neurons, in response to membrane depolarisation, and show that detected differences are largely driven by increased protein mobility in the depolarised state, in agreement with previous findings. The method provides a means of interpreting the differences it detects in terms of single trajectory characteristics. Finally, we emphasise the interest of performing various comparisons to probe the heterogeneity of experimentally acquired datasets at different levels of granularity (e.g., biological replicates, fields of view, and organelles).

Simultaneous Sizing and Refractive Index Analysis of Heterogeneous Nanoparticle Suspensions

Rapid and reliable characterization of heterogeneous nanoparticle suspensions is a key technology across the nanosciences. Although approaches exist for homogeneous samples, they are often unsuitable for polydisperse suspensions, as particles of different sizes and compositions can lead to indistinguishable signals at the detector. Here, we introduce holographic nanoparticle tracking analysis, holoNTA, as a straightforward methodology that decouples size and material refractive index contributions. HoloNTA is applicable to any heterogeneous nanoparticle sample and has the sensitivity to measure the intrinsic heterogeneity of the sample. Specifically, we combined high dynamic range k-space imaging with holographic 3D single-particle tracking. This strategy enables long-term tracking by extending the imaging volume and delivers precise and accurate estimates of both scattering amplitude and diffusion coefficient of individual nanoparticles, from which particle refractive index and hydrodynamic size are determined. We specifically demonstrate, by simulations and experiments, that irrespective of localization uncertainty and size, the sizing sensitivity is improved as our extended detection volume yields considerably longer particle trajectories than previously reported by comparable technologies. As validation, we measured both homogeneous and heterogeneous suspensions of nanoparticles in the 40-250 nm size range and further monitored protein corona formation, where we identified subtle differences between the nanoparticle-protein complexes derived from avidin, bovine serum albumin, and streptavidin. We foresee that our approach will find many applications of both fundamental and applied nature where routine quantification and sizing of nanoparticles are required.

Micromechanical remodeling of the extracellular matrix by invading tumors: anisotropy and heterogeneity

Altered tissue mechanics is an important signature of invasive solid tumors. While the phenomena have been extensively studied by measuring the bulk rheology of the extracellular matrix (ECM) surrounding tumors, micromechanical remodeling at the cellular scale remains poorly understood. By combining holographic optical tweezers and confocal microscopy on in vitro tumor models, we show that the micromechanics of collagen ECM surrounding an invading tumor demonstrate directional anisotropy, spatial heterogeneity and significant variations in time as tumors invade. To test the cellular mechanisms of ECM micromechanical remodeling, we construct a simple computational model and verify its predictions with experiments. We find that collective force generation of a tumor stiffens the ECM and leads to anisotropic local mechanics such that the extension direction is more rigid than the compression direction. ECM degradation by cell-secreted matrix metalloproteinase softens the ECM, and active traction forces from individual disseminated cells re-stiffen the matrix. Together, these results identify plausible biophysical mechanisms responsible for the remodeled ECM micromechanics surrounding an invading tumor.

Single-shot self-supervised object detection in microscopy

Object detection is a fundamental task in digital microscopy, where machine learning has made great strides in overcoming the limitations of classical approaches. The training of state-of-the-art machine-learning methods almost universally relies on vast amounts of labeled experimental data or the ability to numerically simulate realistic datasets. However, experimental data are often challenging to label and cannot be easily reproduced numerically. Here, we propose a deep-learning method, named LodeSTAR (Localization and detection from Symmetries, Translations And Rotations), that learns to detect microscopic objects with sub-pixel accuracy from a single unlabeled experimental image by exploiting the inherent roto-translational symmetries of this task. We demonstrate that LodeSTAR outperforms traditional methods in terms of accuracy, also when analyzing challenging experimental data containing densely packed cells or noisy backgrounds. Furthermore, by exploiting additional symmetries we show that LodeSTAR can measure other properties, e.g., vertical position and polarizability in holographic microscopy.

RS-FISH: precise, interactive, fast, and scalable FISH spot detection

Fluorescent in-situ hybridization (FISH)-based methods extract spatially resolved genetic and epigenetic information from biological samples by detecting fluorescent spots in microscopy images, an often challenging task. We present Radial Symmetry-FISH (RS-FISH), an accurate, fast, and user-friendly software for spot detection in two- and three-dimensional images. RS-FISH offers interactive parameter tuning and readily scales to large datasets and image volumes of cleared or expanded samples using distributed processing on workstations, clusters, or the cloud. RS-FISH maintains high detection accuracy and low localization error across a wide range of signal-to-noise ratios, a key feature for single-molecule FISH, spatial transcriptomics, or spatial genomics applications.

Imp2p forms actin-dependent clusters and imparts stiffness to the contractile ring

The contractile ring must anchor to the plasma membrane and cell wall to transmit its tension. F-BAR domain containing proteins including Imp2p and Cdc15p in fission yeast are likely candidate anchoring proteins based on their mutant phenotypes. Cdc15p is a node component, links the actin bundle to the plasma membrane, recruits Bgs1p to the division plane, prevents contractile ring sliding, and contributes to the stiffness of the contractile ring. Less is known about Imp2p. We found that similarly to Cdc15p, Imp2p contributes to the stiffness of the contractile ring and assembles into protein clusters. Imp2p clusters contain approximately eight Imp2p dimers and depend on the actin network for their stability at the division plane. Importantly, Imp2p and Cdc15p reciprocally affect the amount of each other in the contractile ring, indicating that the two proteins influence each other during cytokinesis, which may partially explain their similar phenotypes.

Particle Localization Using Local Gradients and Its Application to Nanometer Stabilization of a Microscope

Particle localization plays a fundamental role in advanced biological techniques such as single-molecule tracking, superresolution microscopy, and manipulation by optical and magnetic tweezers. Such techniques require fast and accurate particle localization algorithms as well as nanometer-scale stability of the microscope. Here, we present a universal method for three-dimensional localization of single labeled and unlabeled particles based on local gradient calculation of particle images. The method outperforms state-of-the-art localization techniques in high-noise conditions, and it is capable of 3D nanometer accuracy localization of nano- and microparticles with sub-millisecond calculation time. By localizing a fixed particle as fiducial mark and running a feedback loop, we demonstrate its applicability for active drift correction in sensitive nanomechanical measurements such as optical trapping and superresolution imaging. A multiplatform open software package comprising a set of tools for local gradient calculation in brightfield, darkfield, and fluorescence microscopy is shared for ready use by the scientific community.

3D super-resolution live-cell imaging with radial symmetry and Fourier light-field microscopy

Live-cell imaging reveals the phenotypes and mechanisms of cellular function and their dysfunction that underscore cell physiology, development, and pathology. Here, we report a 3D super-resolution live-cell microscopy method by integrating radiality analysis and Fourier light-field microscopy (rad-FLFM). We demonstrated the method using various live-cell specimens, including actins in Hela cells, microtubules in mammary organoid cells, and peroxisomes in COS-7 cells. Compared with conventional wide-field microscopy, rad-FLFM realizes scanning-free, volumetric 3D live-cell imaging with sub-diffraction-limited resolution of ∼150 nm (x-y) and 300 nm (z), milliseconds volume acquisition time, six-fold extended depth of focus of ∼6 µm, and low photodamage. The method provides a promising avenue to explore spatiotemporal-challenging subcellular processes in a wide range of cell biological research.

A prediction model for nanoparticle diffusion behavior in fibrous materials considering steric and hydrodynamic resistances

Precise prediction of the hindered diffusion behavior of electroneutral particles in fibrous media plays a critical role in the development of drugs, polymer membranes, and porous electrodes. However, the random microstructure and unknown coupling relationship of multiple resistance mechanisms lead to the lack of a universal prediction model. In this work, a dual-resistance model is proposed by reconstructed pore-scale simulations, which presents the coexistence of steric and hydrodynamic resistances in the multiplication form. The simulation results show that the relationship between steric resistance and structural parameters (porosity, fiber radius, and particle radius) is exponential, while that for hydrodynamic resistance is polynomial. The dominant diffusion resistance is found to change from hydrodynamic to steric resistance with a decrease in porosity. The fluorescent polystyrene microsphere diffusivity in a series of SiO₂ fibrous media is determined by single-particle tracking experiments, quantitatively confirming the dual-resistance model. The present model can be used for rapid diffusivity prediction and fibrous membrane and drug design.

Kinetics of phagosome maturation is coupled to their intracellular motility

Immune cells degrade internalized pathogens in phagosomes through sequential biochemical changes. The degradation must be fast enough for effective infection control. The presumption is that each phagosome degrades cargos autonomously with a distinct but stochastic kinetic rate. However, here we show that the degradation kinetics of individual phagosomes is not stochastic but coupled to their intracellular motility. By engineering RotSensors that are optically anisotropic, magnetic responsive, and fluorogenic in response to degradation activities in phagosomes, we monitored cargo degradation kinetics in single phagosomes simultaneously with their translational and rotational dynamics. We show that phagosomes that move faster centripetally are more likely to encounter and fuse with lysosomes, thereby acidifying faster and degrading cargos more efficiently. The degradation rates increase nearly linearly with the translational and rotational velocities of phagosomes. Our results indicate that the centripetal motion of phagosomes functions as a clock for controlling the progression of cargo degradation.

Recovering mixtures of fast-diffusing states from short single-particle trajectories

Single-particle tracking (SPT) directly measures the dynamics of proteins in living cells and is a powerful tool to dissect molecular mechanisms of cellular regulation. Interpretation of SPT with fast-diffusing proteins in mammalian cells, however, is complicated by technical limitations imposed by fast image acquisition. These limitations include short trajectory length due to photobleaching and shallow depth of field, high localization error due to the low photon budget imposed by short integration times, and cell-to-cell variability. To address these issues, we investigated methods inspired by Bayesian nonparametrics to infer distributions of state parameters from SPT data with short trajectories, variable localization precision, and absence of prior knowledge about the number of underlying states. We discuss the advantages and disadvantages of these approaches relative to other frameworks for SPT analysis.