Biology subjects

Shepherd, D. P.

Publications and source records attributed to Shepherd, D. P..

16 recordsLinked to original sources

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

Curved Axially Scanned Light-Sheet Microscopy

Light-sheet fluorescence microscopy enables high-throughput multidimensional imaging. However, attempts to further improve throughput by incorporating commercially available or custom high space-bandwidth product objectives have been limited by objective field curvature, which violates the co-planar overlap required for light-sheet imaging and ultimately reduces the usable field of view. Inspired by machine-vision strategies that curve the image plane to match the field curvature, we introduce curved axially scanned light-sheet microscopy, which adapts the light-sheet excitation to the detection objectives field curvature via synchronized control of the remote refocus scan and motorized mirror. Using our technique, we increase the usable field of view along the light-sheet propagation axis from [~]2.5 mm to [~]6.3 mm for a commercial high-SBP detection objective with significant field curvature, while maintaining sub-cellular resolution.

biophysics↗

bioRxiv2025

Massively parallel evolution reveals a biophysical scaling law between cell size and internal cell density

Using a massively parallel evolution platform, we selected Saccharomyces cerevisiae for increased cell size to test how cellular architecture adapts to biophysical constraints. As cells evolved larger size, they became less spherical and showed reduced carrying capacity without changes in maximum growth rate. Optical diffraction tomography revealed that individual cells with greater volume consistently exhibited lower internal density, a relationship that persisted across replicate populations and evolved isolates. The largest cells often contained enlarged vacuoles, suggesting that vacuole expansion may sometimes contribute to reduced density. Extending this analysis beyond yeast, 68 species spanning major phylogenetic clades also demonstrate a negative scaling between cell density and cell size. Together, these results suggest that decreased internal density is a conserved consequence of increasing cell size, revealing a fundamental cellular trade-off between volume expansion and material concentration.

biophysics↗

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

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↗

bioRxiv2024

Fully-Automated Multicolour Structured Illumination Module for Super-resolution Microscopy

In the rapidly advancing field of biological imaging, there is a great need for high-resolution imaging techniques that are both cost-effective and accessible, for example to better observe and understand dynamics in intracellular processes. Structured illumination microscopy (SIM) is the method of choice to achieve high axial and lateral resolution in living samples due to its optical sectioning and minimal phototoxicity. However, the high cost and complexity of conventional SIM systems limit their wide application. In our work, we present an open-source, fully-automated, two-color structured illumination module that is compatible with commercially available microscope stands. The compact design, consisting of low-cost single-mode fiber-coupled lasers and a digital micromirror device (DMD), is integrated into the open-source acquisition and control software (ImSwitch) in order to realize real-time super-resolution imaging. This developed system achieves up to a 1.55-fold improvement in lateral resolution compared to conventional wide-field microscopy. To rationally design this module, we developed a model to ensure optimal DMD diffraction per-formance using tilt and roll pixels, thus covering a wide range of low-cost video projectors for use in coherent SIM setups. Our goal is to democratize SIM-based super-resolution microscopy by providing both comprehensive open-source documentation and a modular software framework that works with various hardware components (e.g. cameras, stages) and reconstruction algorithms. In this way, we try to upgrade as many devices as possible to the super-resolution realm.

cell biology↗

bioRxiv2023

A Structured Illumination Microscopy Framework with Spatial-Domain Noise Propagation.

Biological images captured by microscopes are characterized by heterogeneous signal-to-noise ratios (SNRs) due to spatially varying photon emission across the field of view convoluted with camera noise. State-of-the-art unsupervised structured illumination microscopy (SIM) reconstruction algorithms, commonly implemented in the Fourier domain, do not accurately model this noise and suffer from high-frequency artifacts, user-dependent choices of smoothness constraints making assumptions on biological features, and unphysical negative values in the recovered fluorescence intensity map. On the other hand, supervised methods rely on large datasets for training, and often require retraining for new sample structures. Consequently, achieving high contrast near the maximum theoretical resolution in an unsupervised, physically principled, manner remains an open problem. Here, we propose Bayesian-SIM (B-SIM), an unsupervised Bayesian framework to quantitatively reconstruct SIM data, rectifying these shortcomings by accurately incorporating known noise sources in the spatial domain. To accelerate the reconstruction process, we use the finite extent of the point-spread-function to devise a parallelized Monte Carlo strategy involving chunking and restitching of the inferred fluorescence intensity. We benchmark our framework on both simulated and experimental images, and demonstrate improved contrast permitting feature recovery at up to 25% shorter length scales over state-of-the-art methods at both high- and low-SNR. B-SIM enables unsupervised, quantitative, physically accurate reconstruction without the need for labeled training data, democratizing high-quality SIM reconstruction and expands the capabilities of live-cell SIM to lower SNR, potentially revealing biological features in previously inaccessible regimes.

biophysics↗

bioRxiv2023

Projective oblique plane structured illumination microscopy

Structured illumination microscopy (SIM) can double the spatial resolution of a fluorescence microscope and video rate live cell imaging in a two-dimensional format has been demonstrated. However, rapid implementations of 2D SIM typically only cover a narrow slice of the sample immediately at the coverslip, with most of the cellular volume out of reach. Here we implement oblique plane structured illumination microscopy (OPSIM) in a projection format to rapidly image an entire cell in a 2D SIM framework. As no mechanical scanning of the sample or objective is involved, this technique has the potential for rapid projection imaging with doubled resolution. We characterize the spatial resolution with fluorescent nanospheres, compare projection and 3D imaging using OPSIM and image mitochondria and ER dynamics across an entire cell at up to 2.7 Hz. To our knowledge, this represents the fastest whole cell SIM imaging to date.

cell biology↗

bioRxiv2022

Inferring gene expression models from snapshot RNA data

1Gene networks, key toward understanding a cells regulatory response, underlie experimental observations of single cell transcriptional dynamics. While information on the gene network is encoded in RNA expression data, existing computational frameworks cannot currently infer gene networks from such data. Rather, gene networks--composed of gene states, their connectivities, and associated parameters--are currently deduced by pre-specifying gene state numbers and connectivity prior to learning associated rate parameters. As such, the correctness of gene networks cannot be independently assessed which can lead to strong biases. By contrast, here we propose a method to learn full distributions over gene states, state connectivities, and associated rate parameters, simultaneously and self-consistently from single molecule level RNA counts. Notably, our method propagates noise originating from fluctuating RNA counts over networks warranted by the data by treating networks themselves as random variables. We achieve this by operating within a Bayesian nonparametric paradigm. We demonstrate our method on the lacZ pathway in Escherichia coli cells, the STL1 pathway in Saccharomyces cerevisiae yeast cells, and verify its robustness on synthetic data.

biophysics↗

bioRxiv2022

Resolution doubling in light-sheet microscopy via oblique plane structured illumination

Structured illumination microscopy (SIM) doubles the spatial resolution of a fluorescence microscope without requiring high laser powers or specialized fluorophores. However, the excitation of out-of-focus fluorescence can accelerate photobleaching and phototoxicity. In contrast, light-sheet fluorescence microscopy (LSFM) largely avoids exciting out-of-focus fluorescence, thereby enabling volumetric imaging with low photo-bleaching and intrinsic optical sectioning. Combining SIM with LSFM would enable gentle 3D imaging at doubled resolution. However, multiple orientations of the illumination pattern, which are needed for isotropic resolution doubling in SIM, are challenging to implement in a light-sheet format. Here we show that multidirectional structured illumination can be implemented in oblique plane microscopy, a LSFM technique that uses a single objective for excitation and detection, in a straightforward manner. We demonstrate isotropic lateral resolution below 150nm, combined with lower photo-toxicity compared to traditional SIM systems and volumetric acquisition speed exceeding 1Hz.

biophysics↗

bioRxiv2022

A computational proposal for tracking multiple molecules in a multi-focus confocal setup

Tracking single molecules continues to provide new insights into the fundamental rules governing biological function. Despite continued technical advances in fluorescent and non-fluorescent labeling as well as data analysis, direct observations of trajectories and interactions of multiple molecules in dense environments remain aspirational goals. While confocal methods provide a means to deduce dynamical parameters with high temporal resolution, such as diffusion coefficients, they do so at the expense of spatial resolution. Indeed, on account of a confocal volumes symmetry, typically only distances from the center of the confocal spot can be deduced. Motivated by the need for true three dimensional high speed tracking in densely labeled environments, we propose a computational tool for tracking many fluorescent molecules traversing multiple, closely spaced, confocal measurement volumes providing independent observations. Various realizations of this multiple confocal volumes strategy have previously been used for long term, large area, tracking of one fluorescent molecule in three dimensions. What is more, we achieve tracking by directly using single photon arrival times to inform our likelihood and exploit Hamiltonian Monte Carlo to efficiently sample trajectories from our posterior within a Bayesian nonparametric paradigm. A nonparametric paradigm here is warranted as the number of molecules present are, themselves, a priori unknown. Taken together, we provide a computational framework to infer trajectories of multiple molecules at once, below the diffraction limit (the width of a confocal spot), in three dimensions at sub-millisecond or faster time scales.

biophysics↗

bioRxiv2021

Miniature Structured Illumination Microscope for in vivo 3D Imaging of Brain Structures with Optical Sectioning

We present a high-resolution miniature, light-weight fluorescence microscope with electrowetting lens and onboard CMOS for high resolution volumetric imaging and structured illumination for rejection of out-of-focus and scattered light. The miniature microscope (SIMscope3D) delivers structured light using a coherent fiber bundle to obtain optical sectioning with an axial resolution of 18 m. Volumetric imaging of eGFP labeled cells in fixed mouse brain tissue at depths up to 220 m is demonstrated. The functionality of SIMscope3D to provide background free 3D imaging is shown by recording time series of microglia dynamics in awake mice at depths up to 120 m in the brain.

bioengineering↗

bioRxiv2020

Multicolor structured illumination microscopy and quantitative control of coherent light with a digital mirror device

Structured illumination microscopy (SIM) is a broadly applicable super-resolution microscopy technique which does not impose photophysics requirements on fluorescent samples. Multicolor SIM implementations typically rely on liquid crystal on silicon (LCoS) spatial light modulators (SLMs) for precise patterning of the excitation light, but digital micromirror devices (DMDs) are a promising alternative, owing to their lower cost, increased imaging rate, and simplified experimental timings. Given these advantages, why do existing DMD SIM implementations either rely on incoherent projection, resulting in an order of magnitude lower signal-to-noise, or utilize coherent light at only a single wavelength? The primary obstacle to realizing a multicolor coherent DMD SIM microscope is the lack of an efficient approach for dealing with the blazed grating effect. To address this challenge, we developed quantitative tools applicable to a single DMD acting as a polychromatic diffractive optic. These include a closed form solution of the blaze and diffraction conditions, a forward model of DMD diffraction, and a forward model of coherent pattern projection. We applied these to identify experimentally feasible configurations using a single DMD as a polychromatic diffractive optic for combinations of three and four common fluorophore wavelengths. Based on these advances, we constructed a DMD SIM microscope for coherent light which we used to validate these models, develop a high-resolution optical transfer function measurement technique, and demonstrate SIM resolution enhancement for calibration samples, fixed cells, and live cells. This low-cost setup opens the door to applying DMDs in polychromatic applications which were previously restricted to LCoS SLMs.

biophysics↗

bioRxiv2019

High throughput imaging identifies a spatially localized response of primary fetal pulmonary artery endothelial cells to insulin-like growth factor 1 treatment

A common strategy to measure the efficacy of drug treatment is the in vitro comparison of ensemble readouts with and without treatment, such as proliferation and cell death. A fundamental assumption underlying this approach is there is minimal cell to cell variability in the response to drug. Here, we demonstrate that ensemble and non-spatial single cell readouts applied to primary cells lead to incomplete conclusions due to cell to cell variability. We exposed primary fetal pulmonary artery endothelial cells (PAEC) isolated from healthy newborn healthy and persistent pulmonary hypertension of the newborn (PPHN) sheep to the growth hormone insulin-like growth factor 1 (IGF-1). We found that IGF-1 increased proliferation and branch points in tube formation assays but not angiogenic signaling proteins at the population level for both cell types. We hypothesized that this molecular ambiguity was due to the presence of cellular subpopulations with variable responses to IGF-1. Using high throughput single cell imaging, we discovered a spatially localized response to IGF-1. This suggests localized signaling or heritable cell response to external stimuli may ultimately be responsible for our observations. Discovering and further exploring these rare cells is critical to finding new molecular targets to restore cellular function.

biophysics↗

bioRxiv2017

Predicting single-cell transcription dynamics even when the central limit theorem fails

Despite substantial experimental and computational efforts, mechanistic modeling remains more predictive in engineering than in systems biology. The reason for this discrepancy is not fully understood. Although randomness and complexity of biological systems play roles in this concern, we hypothesize that significant and overlooked challenges arise due to specific features of single-molecule events that control crucial biological responses. Here we show that modern statistical tools to disentangle complexity and stochasticity, which assume normally distributed fluctuations or enormous datasets, don't apply to the discrete, positive, and non-symmetric distributions that characterize spatiotemporal mRNA fluctuations in single-cells. We demonstrate an alternate approach that fully captures discrete, non-normal effects within finite datasets. As an example, we integrate single-molecule measurements and these advanced computational analyses to explore Mitogen Activated Protein Kinase induction of multiple stress response genes. We discover and validate quantitatively precise, reproducible, and predictive understanding of diverse transcription regulation mechanisms, including gene activation, polymerase initiation, elongation, mRNA accumulation, spatial transport, and degradation. Our model-data integration approach extends to any discrete dynamic process with rare events and realistically limited data.\n\nSignificance StatementSystems biology seeks to combine experiments with computation to predict complex biological behaviors. However, despite tremendous data and knowledge, most biological models make terrible predictions. By analyzing single-cell-single-molecule measurements of mRNA in yeast during stress response, we explore how prediction accuracy is controlled by experimental distributions shapes. We find that asymmetric data distributions, which arise in measurements of positive quantities, can cause standard modeling approaches to yield excellent fits but make meaningless predictions. We demonstrate advanced computational tools that solve this dilemma and achieve predictive understanding of many spatiotemporal mechanisms of transcription control including RNA polymerase initiation and elongation and mRNA accumulation, transport and decay. Our approach extends to any discrete dynamic process with rare events and realistically limited data.

systems biology↗