Biology subjects

Xu, L. W. Q.

Publications and source records attributed to Xu, L. W. Q..

6 recordsLinked to original sources

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

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

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↗

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↗