Biology subjects

Presse, S.

Publications and source records attributed to Presse, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Biased stochastic motor dynamics drive bidirectional, processive translocation by the AAA+ disaggregase Hsp104.

Protein aggregation disrupts proteostasis and drives neurodegeneration. Hsp104 is a hexameric, ring-shaped AAA+ ATPase that dissolves protein aggregates, yet how hexamers translocate and extract polypeptides trapped in mechanically resistant aggregates remains unclear. Using substrates that recapitulate the physical constraints of aggregates, we establish that Hsp104 is a processive, bidirectional translocase that can dynamically switch direction while threading a single polypeptide. On mechanically restrained substrates and prions, Hsp104 hexamers execute biased stochastic transitions among three conformational states at individual interprotomer interfaces: closed, extended, and a previously unobserved hyperextended form. These transitions follow kinetically favored paths rather than a rigid rotary sequence. The resulting biased stochastic stepping, enabled by the conformational plasticity of Hsp104 hexamers, underpins operational adaptability and redefines the functional logic of AAA+ motors.

biochemistry↗

bioRxiv2026

Stochastic colonization and host-to-host transmission shape gut bacterial variability

Understanding the kinetic processes that govern bacterial population dynamics within hosts is critical in developing effective strategies to control microbiota. However, inferring population dynamics is challenging due to large host-to-host bacterial population variability stemming from stochastic colonization events, as well as the inability to continuously monitor the bacterial population without disturbing the host. Using C. elegans fed E. coli under different diets, we show that early colonization acts as a stochastic bottleneck that drives substantial divergence in host-level bacterial loads, and that the spreading of bacteria from colonized worms to sterile ones regulates this variability by altering effective colonization pressure. These conclusions are reinforced using a simulation-based inference framework that quantifies stochastic within-host population dynamics from discrete snapshot data, enabling inference of effective colonization and growth rates across heterogeneous hosts with variable carrying capacities. Applying this framework, we further demonstrate that the bacterial predator B. bacteriovorus reduces average gut bacterial loads by two orders of magnitude, primarily by suppressing environmental recolonization and subsequent host-to-host transmission rather than eliminating established intra-host populations. Together, these results reveal that host-associated microbial population dynamics are strongly impacted by environmental colonization processes that modulate stochastic entry events.

microbiology↗

bioRxiv2025

GPU-accelerated, self-optimizing processing for 3D multiplexed iterative RNA-FISH experiments

Imaging-based spatial transcriptomic approaches rely on iterative labeling and imaging of carefully prepared samples, followed by solving a computational inverse problem to determine the location and identity of the targeted RNA. Because these approaches require high-resolution optics, the Nyquist-Shannon determined voxel size is small relative to typical tissue sample footprints. A common solution to speed up both experiments and computation is to increase the distance in between focal planes, trading off local information content to sample a larger imaging area in a reasonable time. In this work we introduce a GPU-accelerated computational framework, merfish3d-analysis, designed to speed up the computational processing of barcoded, in situ imaging-based spatial transcriptomics. Using this framework, we quantify the information lost due to axial sampling changes in simulated imaging-based spatial transcriptomic experiments, robustly reprocess publicly available multiplexed error-robust fluorescence in situ hybridization (MERFISH) datasets, and analyze new MERFISH experiments performed on a post-mortem human olfactory bulb sample. To improve the quality of experimental data in the post-mortem human sample, we designed a multi-step autofluorescence quenching protocol specific for in situ imaging-based spatial transcriptomic strategies. Taken together, we hope that the sample preparation protocols and single workstation, GPU-accelerated processing will further democratize imaging-based spatial transcriptomic experiments.

bioinformatics↗

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

Bayesian Nonparametrics for FRET using Realistic Integrative Detectors

Biomolecular dynamics are often strikingly heterogeneous, with individual molecules sampling different states and kinetics--violating the "average molecule" assumption. Yet FRET analyses cannot resolve such variability or distinguish states differing mainly in kinetics, rather than FRET efficiency, as molecular configurations are projected onto 1D FRET signals. Here we introduce BNP-FRET-Bin, inferring state numbers and their kinetics directly from FRET data. In doing so, we eliminate user-specified parameters and expose molecule-to-molecule heterogeneity revealing new biologically relevant Holliday junction states with near identical FRET efficiencies.

biophysics↗

bioRxiv2025

DNA origami-based platform for multi-axial single-molecule force spectroscopy reveals hidden dynamics in Holliday junctions

Biomolecules in living cells experience complex multi-directional mechanical forces that regulate their structure, dynamics, and function. However, most single-molecule techniques primarily exert force along a single axis, thereby failing to emulate the mechanical environment of cells. Here, we demonstrate that single-axis force application fundamentally restricts conformational dynamics by kinetically trapping molecules within distinct dynamic classes, preventing interconversion and exploration of the full conformational landscape. We developed MAESTRO (Multi-Axial Entropic Spring Tweezer along a Rigid Origami), a molecular platform that applies up to 9 pN forces from up to 4 directions simultaneously using programmable ssDNA entropic springs anchored to a DNA origami scaffold. We applied MAESTRO to Holliday junctions (HJs), 4-way DNA intermediates that experience multi-directional tension during homologous recombination. Counterintuitively, we discovered >5x slower kinetics of the HJ conformations under multi-axial tension than under tension-free conditions, enabling direct observations of previously hidden HJ conformational intermediates. Most remarkably, we discovered that multi-axial forces unlock conformational dynamics, enabling interconversion between 5 distinct kinetic classes that remain kinetically inaccessible under zero force or single-axial tension. Furthermore, we demonstrated that tension regulates T7 endonuclease I cleavage site selection, directly linking mechanical environments and molecular mechanics to enzymatic function. By overcoming single-axis limitations, MAESTRO opens new frontiers in molecular mechanobiology, revealing how multi-directional cellular force environments are essential for unlocking the full conformational landscape of biomolecules, and that these complex force patterns serve as master regulators of biological function through mechanisms hidden from conventional approaches.

biophysics↗

bioRxiv2025

Physics-Inspired Single-Particle Tracking Accelerated with Parallelism

Traditional particle tracking methods approximate each pixel as reporting a continuous grayscale intensity, an assumption valid for long exposures with many detected photons. At short exposures, however, pixels record only a few discrete photon counts, and this quantization introduces both statistical and computational challenges. We present QTrack, a likelihood-based and parallelized framework that directly models discrete photon detections while exploiting correlations across all frames. By doing so, QTrack surpasses the Cramer - Rao lower bound (CRLB) prediction assuming continuous intensity and localization-and-linking. To make this possible in practice, QTrack exploits the parallelism inherent in both likelihood evaluation and posterior sampling through vectorization, multi-threading, and GPU acceleration, with lightweight interthread communication. On a single mid-range GPU (Nvidia GTX 1060, 6 GB), QTrack achieves up to a 50-fold speedup compared to a serial CPU implementation, while maintaining full accuracy and data efficiency. Together, these advances establish that short exposures with quantized photon detections are not a limitation but an opportunity: when modeled rigorously, they enable localization and diffusion coefficient estimates beyond the CRLB prediction for continuous-intensity data, setting a new standard for single-particle tracking.

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

REPOP: bacterial population quantification from platecounts

Bacterial counts from native environments, such as soil or the animal gut, often show substantial variability across replicate samples. This heterogeneity is typically attributed to genetic or environmental factors. A common approach to estimating bacterial populations involves successive dilution and plating, followed by multiplying colony counts by dilution factors. This method, however, overestimates the heterogeneity in bacterial population because it conflates the inherent uncertainty in drawing a subsample from the total population with the uncertainty in the sample arising from biological origins. In other words, this approach may obscure features that may otherwise be present in the data hinting at the presence of genuine subpopulations. For example, in plate counting applied to C. elegans gut microbiota, observed multimodality is often interpreted as large host-to-host variance, while the randomness introduced by measurement is frequently ignored. To explicitly account for the uncertainty introduced by dilution and plating randomness, we introduce REPOP, a PyTorch-based library to REconstruct POpulations from Plates within a Bayesian framework. Beyond simple cases, REPOP addresses more complex scenarios, including multimodal populations and correcting the mathematically subtle, but experimentally relevant, bias introduced by excluding plates deemed too crowded to distinguish individual colonies. We demonstrate REPOPs ability to resolve distinct population peaks otherwise obscured by standard multiplication methods. Applications to both simulated and experimental datasets, including bacterial samples of different concentrations and ones from the gut microbiota of C. elegans, show that REPOP accurately recovers the underlying multimodality by properly accounting for error propagation, where naive multiplication fails. REPOP is available on GitHub: https://github.com/LabPresse/REPOP.

microbiology↗

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↗

bioRxiv2025

Single-Photon Single-Particle Tracking

Mobile biological particles, ranging from biomolecules to viral capsids, often diffuse faster than 1 {micro}m2/s, resulting in severe motion blur in conventional millisecond-scale imaging. While shorter exposures may help provide the data needed to capture faster dynamics, quantization of signal intensity per pixel at such exposures eventually interferes with our ability to track. In the extreme case of binary (1-bit-per-pixel) output-- where going from 8-bit conventional grayscale imaging to 1-bit directly corresponds to a 255-fold faster acquisition rate--no existing tracking methods can be used, as these methods fundamentally rely on intensity-based localization, which does not leverage the binary output. For this reason, we introduce single-photon single-particle tracking (SP2T), a framework that bypasses localization and linking by estimating particle numbers and trajectories directly by jointly considering 1-bit image stacks. While cameras capable of microsecond-scale exposures, typically based on scientific CMOS (sCMOS) sensors or single-photon detectors (SPDs), are increasingly central to this effort, in this work, we focus on single-photon avalanche diode (SPAD). Single-photon detector (SPD) arrays offer microsecond exposures over large fields of view (512x512 pixels). SP2T accounts for detector-specific artifacts such as hot and cold pixels and is validated with programmed fluorescent bead trajectories and biological systems (aerolysin and ganglioside). These experiments, in addition to simulations, reveal that analysis performed with longer exposures can bias diffusion coefficient estimates (up to 70% for particles with diffusion coefficients of 5 {micro}m2/s) and distort jump-distance distributions, underscoring the need for photon-by-photon tracking in fast-diffusion regimes. Moreover, SP2T delivers substantial computational gains--achieving more than a 50-fold GPU speedup over CPU-based likelihood tracking methods that assume continuous intensity, when compared on datasets with the same frame size and number of frames. Together, these advances establish SP2T as a robust, data-efficient solution for unbiased particle tracking with millisecond-to-microsecond temporal resolution.

biophysics↗

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↗

bioRxiv2024

Learning Continuous 2D Diffusion Maps from Particle Trajectories without Data Binning

Diffusion coefficients often vary across regions, such as cellular membranes, and quantifying their variation can provide valuable insight into local membrane properties such as composition and stiffness. Toward quantifying diffusion coefficient spatial maps and uncertainties from particle tracks, we use a Bayesian method and place Gaussian Process (GP) Priors on the maps. For the sake of computational efficiency, we leverage inducing point methods on GPs arising from the mathematical structure of the data giving rise to non-conjugate likelihood-prior pairs. We analyze both synthetic data, where ground truth is known, as well as data drawn from live-cell singlemolecule imaging of membrane proteins. The resulting tool provides an unsupervised method to rigorously map diffusion coefficients continuously across membranes without data binning.

biophysics↗

bioRxiv2023

Substrate-interacting pore loops of two ATPase subunits determine the degradation efficiency of the 26S proteasome

The 26S proteasome is the major eukaryotic protease responsible for protein quality control, proteostasis, and the modulation of numerous vital processes through the degradation of regulatory proteins. Commitment to degradation occurs when conserved pore loops in the proteasomal heterohexameric ATPase motor engage the flexible initiation region of a polyubiquitinated protein substrate for subsequent mechanical unfolding and translocation into a proteolytic chamber. Here, we used in vitro biochemical and single-molecule FRET-based assays with mutant reconstituted 26S proteasomes from yeast to characterize how the pore-1 loops of individual ATPase subunits in the AAA+ motor contribute to the different steps of substrate degradation and affect the proteasome conformational dynamics. We found that the pore-1 loop of the Rpt6 ATPase subunit plays particularly important roles in substrate capture, engagement, and unfolding, while the pore-1 loop of the Rpt4 ATPase is critical for providing sufficient grip for substrate unraveling and maintaining a processing-competent state of the proteasome. Interestingly, these pore-1-loop contributions correlate with their positions in the spiral-staircase arrangements of ATPase subunits in the substrate-free and substrate-degrading proteasome, providing new insights into the mechanisms of substrate processing by the 26S proteasome and related hexameric ATPase motors.

biochemistry↗

bioRxiv2023

An accurate probabilistic step finder for time-series analysis

Noisy time-series data is commonly collected from sources including Forster Resonance Energy Transfer experiments, patch clamp and force spectroscopy setups, among many others. Two of the most common paradigms for the detection of discrete transitions in such time-series data include: hidden Markov models (HMMs) and step-finding algorithms. HMMs, including their extensions to infinite state-spaces, inherently assume in analysis that holding times in discrete states visited are geometrically-or, loosely speaking in common language, exponentially-distributed. Thus the determination of step locations, especially in sparse and noisy data, is biased by HMMs toward identifying steps resulting in geometric holding times. In contrast, existing step-finding algorithms, while free of this restraint, often rely on ad hoc metrics to penalize steps recovered in time traces (by using various information criteria) and otherwise rely on approximate greedy algorithms to identify putative global optima. Here, instead, we devise a robust and general probabilistic (Bayesian) step-finding tool that neither relies on ad hoc metrics to penalize step numbers nor assumes geometric holding times in each state. As the number of steps themselves in a time-series are, a priori unknown, we treat these within a Bayesian nonparametric (BNP) paradigm. We find that the method developed, Bayesian Nonparametric Step (BNP-Step), accurately determines the number and location of transitions between discrete states without any assumed kinetic model and learns the emission distribution characteristic of each state. In doing so, we verify that BNP-Step can analyze sparser data sets containing higher noise and more closely-spaced states than otherwise resolved by current state-of-the-art methods. What is more, BNP-Step rigorously propagates measurement uncertainty into uncertainty over state transition locations, numbers, and emission levels as characterized by the posterior. We demonstrate the performance of BNP-Step on both synthetic data as well as data drawn from force spectroscopy experiments. SIGNIFICANCEMany time-series data sets exist which are challenging to analyze with current state-of-the-art methods, either because they contain excessive noise or because they violate one or more assumptions inherent to the chosen analysis method. To our knowledge, BNP-Step is the first time-series analysis algorithm which leverages Bayesian nonparametrics to learn the number and location of transitions between states and the emission levels associated to each state, while providing rigorous estimates of uncertainty for the learned quantities. We anticipate our algorithm will allow analysis of data sets at levels of noise or sparsity beyond what current state-of-the-art methods allow, and could potentially reveal previously unknown features in data sets analyzed using existing methods.

biophysics↗

bioRxiv2023

Single-Molecule Reaction-Diffusion

We propose to capture reaction-diffusion on a molecule-by-molecule basis from the fastest acquirable timescale, namely individual photon arrivals. We illustrate our method on intrinsically disordered human proteins, the linker histone H1.0 as well as its chaperone prothymosin , as these diffuse through an illuminated confocal spot and interact forming larger ternary complexes on millisecond timescales. Most importantly, single-molecule reaction-diffusion, smRD, reveals single molecule properties without trapping or otherwise confining molecules to surfaces. We achieve smRD within a Bayesian paradigm and term our method Bayes-smRD. Bayes-smRD is further free of the average, bulk, results inherent to the analysis of long photon arrival traces by fluorescence correlation spectroscopy. In learning from thousands of photon arrivals continuous spatial positions and discrete conformational and photophysical state changes, Bayes-smRD estimates kinetic parameters on a molecule-by-molecule basis with two to three orders of magnitude less data than tools such as fluorescence correlation spectroscopy thereby also dramatically reducing sample photodamage.

biophysics↗

bioRxiv2023

BNP-Track: A framework for multi-particle superresolved tracking

When tracking fluorescently labeled molecules (termed "emitters") under widefield microscopes, point spread function overlap of neighboring molecules is inevitable in both dilute and especially crowded environments. In such cases, superresolution methods leveraging rare photophysical events to distinguish static targets nearby in space introduce temporal delays that compromise tracking. As we have shown in a companion manuscript, for dynamic targets, information on neighboring fluorescent molecules is encoded as spatial intensity correlations across pixels and temporal correlations in intensity patterns across time frames. We then demonstrated how we used all spatiotemporal correlations encoded in the data to achieve superresolved tracking. That is, we showed the results of full posterior inference over both the number of emitters and their associated tracks simultaneously and self-consistently through Bayesian nonparametrics. In this companion manuscript we focus on testing the robustness of our tracking tool, BNP-Track, across sets of parameter regimes and compare BNP-Track to competing tracking methods in the spirit of a prior Nature Methods tracking competition. We explore additional features of BNP-Track including how a stochastic treatment of background yields greater accuracy in emitter number determination and how BNP-Track corrects for point spread function blur (or "aliasing") introduced by intraframe motion in addition to propagating error originating from myriad sources (such as criss-crossing tracks, out-of-focus particles, pixelation, shot and camera artefact, stochastic background) in posterior inference over emitter numbers and their associated tracks. While head-to-head comparison with other tracking methods is not possible (as competitors cannot simultaneously learn molecule numbers and associated tracks), we can give competing methods some advantages in order to perform approximate head-to-head comparison. We show that even under such optimistic scenarios, BNP-Track is capable of tracking multiple diffraction-limited point emitters conventional tracking methods cannot resolve thereby extending the superresolution paradigm to dynamical targets.

biophysics↗

bioRxiv2023

Dynamic superresolution by Bayesian nonparametric image processing

Assessing dynamic processes at single molecule scales is key toward capturing life at the level of its molecular actors. Widefield superresolution methods, such as STORM, PALM, and PAINT, provide nanoscale localization accuracy, even when distances between fluorescently labeled single molecules ("emitters") fall below lights diffraction limit. However, as these superresolution methods rely on rare photophysical events to distinguish emitters from both each other and background, they are largely limited to static samples. In contrast, here we leverage spatiotemporal correlations of dynamic widefield imaging data to extend superresolution to simultaneous multiple emitter tracking without relying on photodynamics even as emitter distances from one another fall below the diffraction limit. We simultaneously determine emitter numbers and their tracks (localization and linking) with the same localization accuracy per frame as widefield superresolution does for immobilized emitters under similar imaging conditions ({approx}50 nm). We demonstrate our results for both in cellulo data and, for benchmarking purposes, on synthetic data. To this end, we avoid the existing tracking paradigm relying on completely or partially separating the tasks of emitter number determination, localization of each emitter, and linking emitter positions across frames. Instead, we develop a fully joint posterior distribution over the quantities of interest, including emitter tracks and their total, otherwise unknown, number within the Bayesian nonparametric paradigm. Our posterior quantifies the full uncertainty over emitter numbers and their associated tracks propagated from origins including shot noise and camera artefacts, pixelation, stochastic background, and out-of-focus motion. Finally, it remains accurate in more crowded regimes where alternative tracking tools cannot be applied.

biophysics↗