Biology subjects

Weiss, L. E.

Publications and source records attributed to Weiss, L. E..

15 recordsLinked to original sources

bioRxiv2026

Local Confinement within Plasma Membrane Nanodomains Drives Constitutive Activity of GPCRs

Many G protein coupled receptors (GPCRs) exhibit constitutive (basal) activity, where they can signal in the absence of ligand binding through spontaneous conformational transitions that facilitate G protein coupling and downstream signaling. This intrinsic baseline activity is critical for cellular homeostasis and can be modulated by the receptors conformational ensemble, membrane organization, and interactions with intracellular effectors. In this study, we use live-cell signaling assays, fluorescence cross-correlation spectroscopy (FCCS), and single-particle tracking (SPT) to investigate how membrane organization influences the basal activity of two class A GPCRs: the M1 muscarinic receptor (M1R) and the adenosine A2A receptor (A2AR). In live-cell signalling assays, M1R showed minimal agonist-independent Ca{superscript 2} responses, while A2AR exhibited significant basal cAMP production that was eliminated by an inverse agonist. FCCS showed that, without ligand, only a small portion of M1R co-diffuses with its cognate G11 protein, whereas a much larger fraction of A2AR co-diffuses with the GS protein. SPT revealed that A2AR, but not M1R, is enriched in slowly diffusing, confined states with spatial scales around 150-200 nm and sensitivity to cholesterol- and raft-modulating agents, consistent with localization in lipid-raft nanodomains. Dual-color tracking and diffusion mapping demonstrated that a significant portion of A2AR and GS share confinement domains under basal conditions, while M1R and G11 only show such co-confinement in the active state. These findings support a model where the co-confinement of GPCRs and G proteins within plasma membrane nanodomains--rather than stable pre-coupled RG complexes-- determines the level of constitutive GPCR activity.

biophysics↗

bioRxiv2026

Analyzing single-molecule dynamics with both complex types of motion and complex transition kinetics: Benchmarking of ExaTrack

Single-particle tracking (SPT) is a tool of growing importance which enables biologists to better understand the dynamics of protein interactions at the single-molecule level and in vivo. However, the stochastic nature of the motion of single molecules, the wide variety of types of motion that they can experience and complex transition kinetics between the different states of motion are challenging factors that complicate the interpretation of SPT data. This article presents and benchmarks the tool ExaTrack. Like previous tools, it can handle particles moving in one or multiple states of motion with transitions between states. Its unique feature is that it can simultaneously handle a wide range of complex types of motion such as diffusive motion, directed motion and confined motion while also managing a variety of transition kinetics such as memoryless first-order transitions or more complex time-dependent state transitions. This manuscript focuses on the benchmarking of ExaTrack on simulated data.

biophysics↗

bioRxiv2025

α-Synuclein driven cell susceptibility in Parkinson's disease

Early cellular events in Parkinsons disease (PD) remain elusive. While aggregation of -synuclein (Syn) into Lewy bodies marks advanced pathology, smaller Syn oligomers have been implicated in prodromal stages. Here we map Syn oligomers at single-particle resolution in post-mortem brain tissue from Braak stage 3/4 PD cases and matched controls. Quantitative imaging of 9,882 neurons across four regions captured over 112 million Syn oligomers. Mean intracellular -Syn burden was unchanged between groups, but PD samples contained a higher fraction of neurons whose oligomer load exceeded a specific aggregation threshold. We term these aggregation-susceptible cells (ASCs). ASC enrichment in vulnerable regions supports a population-level model in which early pathology arises from a stochastic shift in cellular composition rather than altered Syn aggregation kinetics. This human-tissue, large-scale dataset provides a quantitative framework for detecting ASCs and for testing population-level interventions in PD and related proteinopathies.

neuroscience↗

bioRxiv2025

Condition-dependent, amorphous protein agglomerates control cytoplasmic rheology

Molecular crowding in the bacterial cytoplasm restricts the diffusion of large molecules, impacting cellular processes. However, how nutrient availability influences cytoplasmic rheology is not well understood. With single-particle tracking in Escherichia coli, we observed a threefold variation in the diffusion of a 40-nm particle across exponential growth conditions. Previously suggested determinants of rheology did not account for this variation; instead, we found a strong anticorrelation between the diffusion coefficient and the abundance of amino acid metabolism proteins, persisting upon genetic perturbations and showing that lower diffusion is associated with increased viscoelasticity. Photoactivated light microscopy revealed that some amino acid metabolism proteins form clusters. Electron microscopy showed that these proteins could form amorphous agglomerates at physiological concentrations in vitro, likely driven by their low intrinsic disorder, high compactness and hydropathy score. These findings show that protein agglomerates regulate cytoplasmic rheology in a condition-dependent manner, suggesting an underappreciated level of cytoplasmic organization. HighlightsO_LIDiffusion of 40-nm particles varies threefold across growth conditions in E. coli C_LIO_LICytoplasmic diffusion inversely correlates with COG-E protein abundance COG-E proteins form agglomerates that increase cytoplasmic viscoelasticity C_LIO_LIProtein compactness and hydrophobicity predict condition-dependent crowding effects C_LI

biophysics↗

bioRxiv2025

Patch deconvolution for Fourier light-field microscopy

Imaging flow cytometry using Fourier light-field microscopy enables high-throughput three-dimensional cellular imaging, capable of capturing thousands of events per second. However, volumetric reconstruction speed remains orders of magnitude slower than the acquisition speed. The current state of art uses Richardson-Lucy algorithm, restricted to just 5-10 reconstructed per second with GPU acceleration. This limitation hinders real-time applications such as cell sorting and thus has bottlenecked the widespread adoption of 3D imaging flow cytometry. We introduce patch deconvolution, an optimisation compatible with the Richardson-Lucy framework that significantly accelerates convergence, achieving over 100-200 reconstructions per second on standard GPUs, a 20-40 fold improvement over Richardson-Lucy. Validated on both simulated and experimental datasets, patch deconvolution achieves reconstruction quality comparable to Richardson-Lucy in both static and flow data. This supports rapid cell sorting based on spatial features and enables advanced applications, such as detecting rare spatial events in large cell populations, which would otherwise be indistinguishable in traditional flow cytometry.

biophysics↗

bioRxiv2024

Vortex light field microscopy: 3D spectral single-molecule imaging with a twist

We introduce vortex light field microscopy (VLFM), a novel method for snapshot 3D spectral single-molecule localization microscopy. Inspired by the azimuthal phase profile of optical vortices, we place an azimuthally oriented prism array immediately after the microlens array in a Fourier light field microscope (FLFM). This innovative arrangement causes the axial position and spectral peak for a point emitter to be encoded in the radial and azimuthal displacement of point-spread-function (PSF) respectively. This enables simultaneous detection of 3D position and emission peak of individual fluorophores with 25 nm spatial precision and 3 nm spectral precision over a 4 m depth of field (DOF). We illustrate the spectral scalability of our method by performing four-color 3D single particle tracking of freely diffusing fluorescent beads, and two-color 3D dSTORM imaging of microtubules and mitochondria in fixed COS-7 cells, without the need for spectrally distinct fluorophores.

biophysics↗

bioRxiv2024

Detecting directed motion and confinement in single-particle trajectories using hidden variables

Single-particle tracking is a powerful tool for understanding protein dynamics and characterizing microenvironments. As the motion of unconstrained nanoscale particles is governed by Brownian diffusion, deviations from this behavior are biophysically insightful. However, the stochastic nature of particle movement and the presence of localization error pose a challenge for the robust classification of non-Brownian motion. Here, we present aTrack, a versatile tool for classifying track behaviors and extracting key parameters for particles undergoing Brownian, confined, or directed motion. Our tool quickly and accurately estimates motion parameters from individual tracks. Further, our tool can analyze populations of tracks and determine the most likely number of motion states. We show the working range of our approach on simulated tracks and demonstrate its application for characterizing particle motion in cells and for biosensing applications. aTrack is implemented as a stand-alone software, making it simple to analyze track data.

biophysics↗

bioRxiv2024

Large-scale visualisation of α-synuclein oligomers in Parkinson's disease brain tissue

Parkinsons disease (PD) is a common neurodegenerative condition characterised by the presence in the brain of large intraneuronal aggregates, known as Lewy bodies and Lewy neurites, containing fibrillar -synuclein. According to the amyloid hypothesis, these large end-stage species form from smaller soluble protein assemblies, often termed oligomers, which are proposed as early drivers of pathogenesis. To date, however, this hypothesis has remained controversial, at least in part because it has not been possible to directly visualise oligomeric aggregates in human brain tissue. Therefore, their presence, abundance and distributions have remained elusive. Here, we present ASA-PD (Advanced Sensing of Aggregates - Parkinsons Disease), an imaging method to generate large-scale -synuclein oligomer maps in post-mortem human brain tissue. We combined autofluorescence suppression with single-molecule fluorescence methods, which together, enable the detection of nanoscale -synuclein aggregates. To demonstrate the utility of this platform, we captured [~]1.2 million oligomers from the anterior cingulate cortex in human post-mortem brain samples from PD and healthy control patients. Our data revealed a specific subpopulation of nanoscale oligomers that represent an early hallmark of the proteinopathy that underlies PD. We anticipate that quantitative information about oligomer distributions provided by ASA-PD will enable mechanistic studies to reveal the pathological processes caused by -synuclein aggregation.

neuroscience↗

bioRxiv2023

RASP: Optimal single fluorescent puncta detection in complex cellular backgrounds

Super-resolution and single-molecule microscopy are increasingly applied to complex biological systems. A major challenge of this approach 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 in precision and speed, using image gradients to separate Gaussian-shaped objects from background. We demonstrate RASPs power by showing it can extract spatial correlations between microglia, neurons, and -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 a sensitivity down to the level of the single protein.

biophysics↗

bioRxiv2023

The nuclear basket regulates distribution and mobility of nuclear pore complexes in budding yeast

Nuclear pore complexes (NPCs) mediate all traffic between the nucleus and the cytoplasm and are among the most stable protein assemblies in cells. Budding yeast cells carry two variants of NPCs which differ in the presence or absence of the nuclear basket proteins Mlp1, Mlp2 and Pml39. The binding of these basket proteins occurs very late in NPC assembly and Mlp-positive NPCs are excluded from the region of the nuclear envelope that borders the nucleolus. Here, we use recombination-induced tag exchange (RITE) to investigate the stability of all the NPC subcomplexes within individual NPCs. We show that the nuclear basket proteins Mlp1, Mlp2 and Pml39 remain stably associated with NPCs through multiple cell-division cycles, and that Mlp1/2 are responsible for the exclusion of NPCs from the nucleolar territory. In addition, we demonstrate that binding of the FG-nucleoporins Nup1 and Nup2 depletes also Mlp-negative NPCs from this region by an independent pathway. We develop a method for single NPC tracking in budding yeast and observe that NPCs exhibit increased mobility in the absence of nuclear basket components. Our data suggest that the distribution of NPCs on the nucleus is governed by multiple interaction of nuclear basket proteins with the nuclear interior. Significance StatementO_LIA subset of yeast nuclear pore complexes have a basket structure at their nuclear face. The stoichiometry, architecture and molecular functions of the basket are not clear. C_LIO_LIThe authors use a tag exchange method to show stable binding of nuclear basket proteins through multiple generations. They also find that multiple nuclear basket proteins disfavour localization of NPCs proximal to the nucleolus. C_LIO_LIThese findings demonstrate that having a nuclear basket is a final and stable state for yeast NPCs. The methods developed can be applied to gain more insight into the properties of NPCs and other stable complexes as they age. C_LI

cell biology↗

bioRxiv2021

Bioluminescent Synthetic Cells Communicate with Natural Cells and Self-Activate Light-Responsive Proteins

Development of regulated cellular processes and signaling methods in synthetic cells is essential for their integration with living materials. Light is an attractive tool to achieve this, but the limited penetration depth into tissue of visible light restricts its usability for in-vivo applications. Here, we describe the synthesis and application of blue-light-generating synthetic cells using bioluminescence, dismissing the need for an external light source. First, the lipid membrane and internal composition of light-producing synthetic cells were optimized to enable high-intensity emission. Next, we show these cells capacity for triggering bioprocesses in natural cells by initiating asexual sporulation of dark-grown mycelial cells of the fungus Trichoderma atroviride in a quorum-sensing like mechanism. Finally, we demonstrate regulated transcription and membrane recruitment in synthetic cells using bioluminescent self-activating fusion proteins. These functionalities pave the way for deploying synthetic cells as embeddable microscale light sources that are capable of activating engineered processes inside tissues.

bioengineering↗

bioRxiv2020

VIPR: Vectorial Implementation of Phase Retrieval for fast and accurate microscopic pixel-wise pupil estimation

In microscopy, proper modeling of the image formation has a substantial effect on the precision and accuracy in localization experiments and facilitates the correction of aberrations in adaptive optics experiments. The observed images are subject to polarization effects, refractive index variations and system specific constraints. Previously reported techniques have addressed these challenges by using complicated calibration samples, computationally heavy numerical algorithms, and various mathematical simplifications. In this work, we present a phase retrieval approach based on an analytical derivation of the vectorial diffraction model. Our method produces an accurate estimate of the system phase information (without any prior knowledge) in under a minute.

biophysics↗

bioRxiv2019

High-throughput multicolor 3D localization in live cells by depth-encoding imaging flow cytometry

Imaging flow cytometry replaces the canonical point-source detector of flow cytometry with a camera, unveiling subsample details in 2D images while maintaining high-throughput. Here we show that the technique is inherently compatible with 3D localization microscopy by point-spread-function engineering, namely the encoding of emitter depth in the emission pattern captured by a camera. By exploiting the laminar-flow profile in microfluidics, 3D positions can be extracted from cells or other objects of interest by calibrating the depth-dependent response of the imaging system using fluorescent microspheres mixed with the sample buffer. We demonstrate this approach for measuring fluorescently-labeled DNA in vitro and the chromosomal compaction state in large populations of live cells, collecting thousands of samples each minute. Furthermore, our approach is fully compatible with existing commercial apparatus, and can extend the imaging volume of the device, enabling faster flowrates thereby increasing throughput.

biophysics↗

bioRxiv2019

Single particle diffusion characterization by deep learning

Diffusion plays a crucial role in many biological processes including signaling, cellular organization, transport mechanisms, and more. Direct observation of molecular movement by single-particle tracking experiments has contributed to a growing body of evidence that many cellular systems do not exhibit classical Brownian motion, but rather anomalous diffusion. Despite this evidence, characterization of the physical process underlying anomalous diffusion remains a challenging problem for several reasons. First, different physical processes can exist simultaneously in a system. Second, commonly used tools to distinguish between these processes are based on asymptotic behavior, which is inaccessible experimentally in most cases. Finally, an accurate analysis of the diffusion model requires the calculation of many observables, since different transport modes can result in the same diffusion power-law , that is obtained from the commonly used mean-squared-displacement (MSD) in its various forms. The outstanding challenge in the field is to develop a method to extract an accurate assessment of the diffusion process using many short trajectories with a simple scheme that is applicable at the non-expert level.\n\nHere, we use deep learning to infer the underlying process resulting in anomalous diffusion. We implement a neural network to classify single particle trajectories according to diffusion type - Brownian motion, fractional Brownian motion (FBM) and Continuous Time Random Walk (CTRW). We further use the net to estimate the Hurst exponent for FBM, and the diffusion coefficient for Brownian motion, demonstrating its applicability on simulated and experimental data. The networks outperform time averaged MSD analysis on simulated trajectories while requiring as few as 25 time-steps. Furthermore, when tested on experimental data, both network and ensemble MSD analysis converge to similar values, with the network requiring half the trajectories required for ensemble MSD. Finally, we use the nets to extract diffusion parameters from multiple extremely short trajectories (10 steps).

biophysics↗

bioRxiv2018

Revealing the nanoscale morphology of the primary cilium using super-resolution fluorescence microscopy

Super-resolution (SR) microscopy has been used to observe structural details beyond the diffraction limit of ~250 nm in a variety of biological and materials systems. By combining this imaging technique with both computer-vision algorithms and topological methods, we reveal and quantify the nanoscale morphology of the primary cilium, a tiny tubular cellular structure (~2-6 m long and 200-300 nm diameter). The cilium in mammalian cells protrudes out of the plasma membrane and is important in many signaling processes related to cellular differentiation and disease. After tagging individual ciliary transmembrane proteins, specifically Smoothened (SMO), with single fluorescent labels in fixed cells, we use three-dimensional (3D) single-molecule SR microscopy to determine their positions with a precision of 10-25 nm. We gain a dense, pointillistic reconstruction of the surfaces of many cilia, revealing large heterogeneity in membrane shape. A Poisson surface reconstruction (PSR) algorithm generates a fine surface mesh, allowing us to characterize the presence of deformations by quantifying the surface curvature. Upon impairment of intracellular cargo transport machinery by genetic knockout or small-molecule treatment of cells, our quantitative curvature analysis shows significant morphological differences not visible by conventional fluorescence microscopy techniques. Furthermore, using a complementary SR technique, 2-color, 2D STimulated Emission Depletion (STED) microscopy, we find that the cytoskeleton in the cilium, the axoneme, also exhibits abnormal morphology in the mutant cells, similar to our 3D results on the SMO-measured ciliary surface. Our work combines 3D SR microscopy and computational tools to quantitatively characterize morphological changes of the primary cilium under different treatments and uses STED to discover correlated changes in the underlying structure. This approach can be useful for studying other biological or nanoscale structures of interest.

biophysics↗