Biology subjects

Fazel, M.

Publications and source records attributed to Fazel, M..

15 recordsLinked to original sources

bioRxiv2026

Heparan Sulfate Controls Nanoscale Assembly of GPC3-Wnt Receptor Complexes

Glypican-3 (GPC3) is a heparan sulfate proteoglycan that is highly expressed in hepatocellular carcinoma and promotes tumor progression through Wnt3a/{beta}-catenin signaling. However, how the nanoscale organization of GPC3 at the cell surface controls signaling remains unclear. Here, we combined nano-resolution MINFLUX imaging, single-molecule tracking, and functional assays to define the spatial architecture and dynamics of GPC3 on hepatoma cells. We found that GPC3 exists as both single molecules and nanoscale clusters and switches between confined and free diffusions on the plasma membrane. Heparan sulfate (HS) chains create nanoscale corrals that limit GPC3 movement, whereas removal of HS increases diffusive heterogeneity and disrupts confinement. Wnt3a stimulation induces the formation of higher-order GPC3 assemblies and enhances {beta}-catenin signaling, while loss of HS markedly reduces this response. MINFLUX DNA-PAINT further revealed that HS chains orchestrate the spatial distribution of Wnt3a and promote its association with the Wnt receptor, Frizzled-1, an essential step for pathway activation. Collectively, these findings reveal that HS controls the nanoscale organization and dynamics of GPC3 to promote Wnt receptor assembly and efficient {beta}-catenin signaling in hepatoma cells.

cell biology↗

bioRxiv2026

Re-establishment of TAD boundary organization during DNA replication

A ubiquitous feature of higher order genome organization is the presence of topologically associating domains (TADs). The chromatin architectural proteins cohesin and CTCF are known critical organizers of TADs, but the mechanisms of TAD establishment and maintenance, including their accurate duplication during genome replication, are not well characterized. To address this gap, we used high-throughput imaging-based CRISPR/Cas9 knock-out screening to discover chromatin factors involved in maintenance and establishment of TADs. Among the cellular factors that affect TAD organization, we found enrichment for cell cycle proteins, especially components of the DNA replication machinery. Accordingly, we demonstrate that TADs undergo temporary unfolding during S-phase DNA replication and that interference with progression through replication impedes restoration of normal TAD structure. Mechanistically, inhibition of the RPA complex prevents cohesin and CTCF binding, delays post-replication TAD re-folding and affects TAD folding in non-cycling cells. These results provide novel insights into how TAD structures are re-established during genome duplication.

cell biology↗

bioRxiv2025

Highly multiplexed spectral FLIM via physics informed data analysis

Spectral fluorescence lifetime imaging (S-FLIM) allows for the simultaneous deconvolution of signal from multiple fluorophore species by leveraging both spectral and lifetime information. However, existing analyses still face multiple difficulties in decoding information collected from typical S-FLIM experiments. These include: using information from pre-calibrated spectra in environments that may differ from the cellular context in which S-FLIM experiments are performed; limitations in the ability to deconvolute species due to overlapping spectra; high photon budget requirements, typically about a hundred photons per pixel per species. Yet information on the spectra themselves are already encoded in the data and do not require pre-calibration. What is more, efficient photon-by-photon analyses are possible reducing both the required photon budget and making it possible to use larger budgets in order to discriminate small differences in spectra to resolve spatially co-localized fluorophore species. To achieve this, we propose a Bayesian S-FLIM framework capable of simultaneously learning spectra and lifetimes photon-by-photon ultimately using limited photon counts and being highly data efficient. We demonstrate the proposed framework using a range of synthetic and experimental data and show that it can deconvolve up to 9 species with heavily overlapped spectra.

biophysics↗

bioRxiv2025

Simultaneous particle tracking, phase retrieval and point spread function reconstruction

3D tracking and localization of particles, typically fluorescently labeled biomolecules, provides a direct means of monitoring cellular transport and communication. However, sample-induced wavefront distortions of emitted fluorescent light as it passes through the sample and onto the detector often yield point spread function (PSF) aberrations, presenting an important challenge to 3D particle tracking using pre-calibrated PSFs. PSF calibration is typically performed outside cellular samples, ignoring sample-induced aberrations, which can result in localization errors on the order of tens to hundreds of nanometers, ultimately compromising sub-diffraction limited tracking. In practice, correcting sample-induced aberrations currently requires sample-specific hardware adjustments, such as adaptive optics. Yet, information on sample-induced aberrations and PSF shape can be directly decoded from data collected using a 3D imaging setup (e.g., bi-focal). To this end, we propose a framework for simultaneous particle tracking, phase retrieval, and PSF reconstruction (SPT-PR) directly from the input data themselves. We apply it to sub-diffraction tracking of lytic granules released at the immunological synapse of T cells revealing slower motions in proximity of the plasma cell membrane, consistent with assembly of the fusion machinery and, ultimately, degranulation and release of toxic payloads. To accomplish this, we operate within a Bayesian paradigm, placing continuous priors on all possible pupil phase and amplitudes warranted by the data without limiting ourselves to a finite Zernike set-thereby allowing capture of intricate pupil phase details. We benchmark our framework using a wide range of synthetic and experimental data from static to diffusing particles, and generalize to multiple diffusing particles with overlapping PSFs. Further, as a result of simultaneous particle tracking, phase retrieval, and PSF reconstruction, we retrieve the pupil phase with errors smaller than 10% under a range of realistic scenarios while demonstrating that for tracking lytic granules under an idealized Gaussian PSF assumption, we recover discrepancies as large as hundreds of nanometers.

biophysics↗

bioRxiv2025

Bayesian Inference of Binding Kinetics from Fluorescence Time Series

The study of binding kinetics via the analysis of fluorescence time traces is often con-founded by measurement noise and photophysics. Although photoblinking can be mitigated by using labels less likely to photoswitch, photobleaching generally cannot be eliminated. Current methods for measuring binding and unbinding rates are therefore limited by concurrent photobleaching events. Here, we propose a method to infer binding and unbinding rates alongside photobleaching rates using fluorescence intensity traces. Our approach is a two-stage process involving analyzing individual regions of interest (ROIs) with a Hidden Markov Model to infer the fluorescence intensity levels of each trace. We then use the inferred intensity level state trajectory from all ROIs to infer kinetic rates. Our method has several advantages, including the ability to analyze noisy traces, account for the presence of photobleaching events, and provide uncertainties associated with the inferred binding kinetics. We demonstrate the effectiveness and reliability of our method through simulations and data from DNA origami binding experiments.

biochemistry↗

bioRxiv2024

Bayesian nonparametric analysis of residence times for protein-lipid interactions in Molecular Dynamics simulations

Molecular Dynamics (MD) simulations are a versatile tool to investigate the interactions of proteins within their environments, in particular of membrane proteins with the surrounding lipids. However, quantitative analysis of lipid-protein binding kinetics has remained challenging due to considerable noise and low frequency of long binding events, even in hundreds of microseconds of simulation data. Here we apply Bayesian nonparametrics to compute residue-resolved residence time distributions from MD trajectories. Such an analysis characterizes binding processes at different timescales (quantified by their kinetic off-rate) and assigns to each trajectory frame a probability of belonging to a specific process. In this way, we classify trajectory frames in an unsupervised manner and obtain, for example, different binding poses or molecular densities based on the timescale of the process. We demonstrate our approach by characterizing interactions of cholesterol with six different G-protein coupled receptors (A2AAR, {beta}2AR, CB1R, CB2R, CCK1R, CCK2R) simulated with coarse-grained MD simulations with the MARTINI model. The nonparametric Bayesian analysis allows us to connect the coarse binding time series data to the underlying molecular picture and, thus, not only infers accurate binding kinetics with error distributions from MD simulations but also describes molecular events responsible for the broad range of kinetic rates.

biophysics↗

bioRxiv2022

Building Fluorescence Lifetime Maps Photon-by-photon by Leveraging Spatial Correlations

Fluorescence lifetime imaging microscopy (FLIM) has become a standard tool in the quantitative analysis of sub-cellular environments. However, quantitative FLIM analyses face several challenges. First, spatial correlations between pixels are often ignored as signal from individual pixels is analyzed independently thereby limiting spatial resolution. Second, existing methods deduce photon ratios instead of absolute lifetime maps. Next, the number of lifetime components contributing to the signal is unknown, while excited state lifetimes with <1 ns difference are difficult to discriminate. Finally, existing analyses require high photon budgets, and often cannot rigorously propagate experimental uncertainty into values over lifetime maps and number of components involved. To overcome all of these challenges simultaneously and self-consistently at once, we propose the first doubly nonparametric framework. That is, we learn the number of fluorescent species (through beta-Bernoulli process priors) and absolute lifetime maps of these species (through Gaussian process priors) by leveraging information from pulses not leading to observed photon. We benchmark our algorithm using a broad range of synthetic and experimental data and demonstrate its robustness across a number of scenarios including cases where we recover lifetime differences between components as small as 0.3 ns with merely 1000 photons.

biophysics↗

bioRxiv2022

Fluorescence Lifetime: Beating the IRF and interpulse window

Fluorescence lifetime imaging (FLIM) has been essential in capturing spatial distributions of chemical species across cellular environments employing pulsed illumination confocal setups. However, quantitative interpretation of lifetime data continues to face critical challenges. For instance, fluorescent species with known in vitro excited state lifetimes may split into multiple species with unique lifetimes when introduced into complex living environments. What is more, mixtures of species, that may be both endogenous and introduced into the sample, may exhibit; 1) very similar lifetimes; as well as 2) wide ranges of lifetimes including lifetimes shorter than the instrumental response function (IRF) or whose duration may be long enough to be comparable to the interpulse window. By contrast, existing methods of analysis are optimized for well-separated and intermediate lifetimes. Here we broaden the applicability of fluorescence lifetime analysis by simultaneously treating unknown mixtures of arbitrary lifetimes- outside the intermediate, goldilocks, zone-for data drawn from a single confocal spot leveraging the tools of Bayesian nonparametrics (BNP). We benchmark our algorithm, termed BNP-lifetime analysis of BNP-LA, using a range of synthetic and experimental data. Moreover, we show that the BNP-LA method can distinguish and deduce lifetimes using photon counts as small as 500.

biophysics↗

bioRxiv2022

Single Photon smFRET. I. Theory and Conceptual Basis

We present a unified conceptual framework and the associated software package for single molecule Forster Resonance Energy Transfer (smFRET) analysis from single photon arrivals leveraging Bayesian nonparametrics, BNP-FRET. This unified framework addresses the following key physical complexities of a single photon smFRET experiment, including: 1) fluorophore photophysics; 2) continuous time kinetics of the labeled system with large timescale separations between photophysical phenomena such as excited photophysical state lifetimes and events such as transition between system states; 3) unavoidable detector artefacts; 4) background emissions; 5) unknown number of system states; and 6) both continuous and pulsed illumination. These physical features necessarily demand a novel framework that extends beyond existing tools. In particular, the theory naturally brings us to a hidden Markov model (HMM) with a second order structure and Bayesian nonparametrics (BNP) on account of items 1, 2 and 5 on the list. In the second and third companion manuscripts, we discuss the direct effects of these key complexities on the inference of parameters for continuous and pulsed illumination, respectively. Why It MatterssmFRET is a widely used technique for studying kinetics of molecular complexes. However, until now, smFRET data analysis methods required specifying a priori the dimensionality of the underlying physical model (the exact number of kinetic parameters). Such approaches are inherently limiting given the typically unknown number of physical configurations a molecular complex may assume. The methods presented here eliminate this requirement and allow estimating the physical model itself along with kinetic parameters, while incorporating all sources of noise in the data.

biophysics↗

bioRxiv2022

Single Photon smFRET. II. Application to Continuous Illumination

Here we adapt the Bayesian nonparametrics (BNP) framework presented in the first companion manuscript to analyze kinetics from single photon, single molecule Forster Resonance Energy Transfer (smFRET) traces generated under continuous illumination. Using our sampler, BNP-FRET, we learn the escape rates and the number of system states given a photon trace. We benchmark our method by analyzing a range of synthetic and experimental data. Particularly, we apply our method to simultaneously learn the number of system states and the corresponding kinetics for intrinsically disordered proteins (IDPs) using two-color FRET under varying chemical conditions. Moreover, using synthetic data, we show that our method can deduce the number of system states even when kinetics occur at timescales of interphoton intervals. Why It MattersIn the first companion manuscript of this series, we developed new methods to analyze noisy smFRET data. These methods eliminate the requirement of a priori specifying the dimensionality of the physical model describing a molecular complexs kinetics. Here, we apply these methods to experimentally obtained datasets with samples illuminated by time-invariant laser intensities. In particular, we study interactions of IDPs.

biophysics↗

bioRxiv2022

Single Photon smFRET. III. Application to Pulsed Illumination

Forster resonance energy transfer (FRET) using pulsed illumination has been pivotal in leveraging lifetime information in FRET analysis. However, there remain major challenges in quantitative single photon, single molecule FRET (smFRET) data analysis under pulsed illumination including: 1) simultaneously deducing kinetics and number of system states; 2) providing uncertainties over estimates, particularly uncertainty over the number of system states; 3) taking into account detector noise sources such as crosstalk, and the instrument response function contributing to uncertainty; in addition to 4) other experimental noise sources such as background. Here, we implement the Bayesian nonparametric framework described in the first companion manuscript that addresses all aforementioned issues in smFRET data analysis specialized for the case of pulsed illumination. Furthermore, we apply our method to both synthetic as well as experimental data acquired using Holliday junctions. Why It MattersIn the first companion manuscript of this series, we developed new methods to analyze noisy smFRET data. These methods eliminate the requirement of a priori specifying the dimensionality of the physical model describing a molecular complexs kinetics. Here, we apply these methods to experimentally obtained datasets with samples illuminated by laser pulses at regular time intervals. In particular, we study conformational dynamics of Holliday junctions.

biophysics↗

bioRxiv2021

Robust, Fiducial-Free Drift Correction for Super-resolution Imaging

We describe a robust, fiducial-free method of drift correction for use in single molecule localization-based super-resolution methods. The method combines periodic 3D registration of the sample using brightfield images with a fast post-processing algorithm that corrects residual registration errors and drift between registration events. The method is robust to low numbers of collected localizations, requires no specialized hardware, and provides stability and drift correction for an indefinite time period.

biophysics↗

bioRxiv2020

Comparing Lifeact and Phalloidin for Super-resolution Imaging of Actin in Fixed Cells

Visualizing actin filaments in fixed cells is of great interest for a variety of topics in cell biology such as cell division, cell movement, and cell signaling. We investigated the possibility of replacing phalloidin, the standard reagent for super-resolution imaging of F-actin in fixed cells, with the actin binding peptide lifeact. We compared the labels for use in single molecule based super-resolution microscopy, where AlexaFluor 647 labeled phalloidin was used in a (d)STORM modality and Atto 655 labeled lifeact was used in a single molecule imaging, reversible binding modality. We found that imaging with lifeact had a comparable resolution in reconstructed images and provided several advantages over phalloidin including lower costs, the ability to image multiple regions of interest on a coverslip without degradation, simplified sequential super-resolution imaging, and more continuous labeling of thin filaments.

cell biology↗

bioRxiv2019

Sub-Nanometer Precision using Bayesian Grouping of Localizations

Single-molecule localization microscopy super-resolution methods such as DNA-PAINT and (d)STORM can generate multiple observed localizations over the time course of data acquisition from each dye or binding site that are not a priori assigned to those specific dyes or binding sites. We describe a Bayesian method of grouping and combining localizations from multiple blinking/binding events that can improve localization precision to better than one nanometer. The known statistical distribution of the number of binding/blinking events per dye/docking strand along with the precision of each localization event are used to estimate the true number and location of emitters in closely-spaced clusters.

biophysics↗

bioRxiv2019

Bayesian Multiple Emitter Fitting using Reversible Jump Markov Chain Monte Carlo

In single molecule localization-based super-resolution imaging, high labeling density or the desire for greater data collection speed can lead to clusters of overlapping emitter images in the raw super-resolution image data. We describe a Bayesian inference approach to multiple-emitter fitting that uses Reversible Jump Markov Chain Monte Carlo to identify and localize the emitters in dense regions of data. This formalism can take advantage of any prior information, such as emitter intensity and density. The output is both a posterior probability distribution of emitter locations that includes uncertainty in the number of emitters and the background structure, and a set of coordinates and uncertainties from the most probable model.

biophysics↗