Biology subjects

Vasdekis, A. E.

Publications and source records attributed to Vasdekis, A. E..

8 recordsLinked to original sources

bioRxiv2026

Space-Time Light-Sheet Microscopy

Light-sheet microscopy (LSM) has revolutionized bioimaging by delivering high-contrast volumetric resolution with minimal photodamage. Spatial wavefront shaping, used to gen{-}erate lattice and Airy light-sheets, has been particularly effective in advancing LSM be{-}yond the Rayleigh limit. Despite its broad adoption, most LSM implementations rely on rigid dual-objective geometries that complicate sample handling and impose a trade-off between imaging field of view (FoV) and axial resolution. Here, we introduce space-time light-sheet microscopy (ST-LSM), a single-objective strategy that exploits space-time (ST) correlations for the first time. ST-LSM goes beyond separate spatial or temporal modulation to jointly modulate the spatiotemporal spectral structure of a pulse. This uniquely enabled light-sheets with wavelength-scale thickness over millimeter-scale dis{-}tances. When compared to state-of-the-art approaches, ST-LSM eliminates the dual-objective constraint, expands the sample-accessible volume by 25x, and increases the FoV by 10x without sacrificing sectioning resolution. We demonstrate the versatility of ST-LSM by using a single setup to image specimens across four orders of magnitude in size, from whole roots and developing embryos, down to mammalian cells with sub-cellular axial resolution. These results position ST-LSM as an accessible and high-performance optical microscopy platform at a variety of biological scales, by translating space-time wave-packet physics into a practical imaging modality.

biophysics↗

bioRxiv2025

Mitigating the Field-of-View - Resolution Tradeoff by Photon Superlocalization

Optical imaging systems are fundamentally constrained by a tradeoff between field of view (FoV) and spatial resolution. Long working-distance objectives, routinely used in biological imaging and especially in light-sheet microscopy, provide large FoV but reduced numerical aperture (NA) and magnification, broadening the point spread function (PSF) while coarsening detector sampling. As a result, even PSF-limited resolution is often undersampled. Here we demonstrate photon superlocalization as a strategy to mitigate this tradeoff. On intensified detectors, individual photons form multipixel detection clouds that can be centroided and reassigned to a finer virtual grid, thereby increasing the effective sampling frequency without optical modifications or FoV penalty. Proof-of-principle epifluorescence and light-sheet experiments show that photon superlocalization restores near-PSF-limited resolution and reveals subcellular structure otherwise obscured by undersampling. This approach provides a generalizable, photon-efficient pathway for improving spatial resolution across imaging modalities constrained by the FoV-resolution tradeoff.

biophysics↗

bioRxiv2025

Microbial Cell-Adaptive Segmentation in Quantitative Phase Imaging

Quantitative phase imaging (QPI) enables label-free microscopy with exceptional signal-to-background contrast and provides direct measurements of key biophysical parameters such as cell mass. Despite these advantages, QPI lacks a generalized, accurate, and computationally efficient segmentation method, limiting its use in high-throughput single-cell biology. Here, we introduce microbial Cell-Adaptive Segmentation (mCAS), a purpose-built framework for QPI. mCAS is an object-based, modular pipeline that leverages the robust phase-edge signal to achieve accurate segmentation without Fourier filtering, deconvolution, or deep learning. It runs efficiently on standard hardware, is readily implemented in common software platforms, and requires no curated training data. mCAS consistently outperformed both intensity-based and deep learning approaches, achieving over 98% accuracy with up to tenfold faster processing. By eliminating the segmentation bottleneck, mCAS enables scalable, high-fidelity analysis of QPI datasets and provides a broadly applicable foundation for label-free, high-throughput single-cell biology. We validate its generality across diverse bacterial species, morphologies, and intracellular complexities. Significance StatementQuantitative phase imaging (QPI) enables label-free, noninvasive measurement of cellular biophysics, yet its broader adoption has been limited by the absence of a generalized and computationally efficient segmentation strategy. We present microbial Cell-Adaptive Segmentation (mCAS), a framework purpose-built for QPI that achieves high accuracy without training, specialized hardware, or intensive preprocessing. mCAS generalizes across microbial species and morphologies, outperforming state-of-the-art deep learning approaches while processing thousands of single-cell images in minutes. By eliminating the segmentation bottleneck, mCAS makes QPI broadly accessible for high-throughput, label-free single-cell biology and expands the computational toolkit for quantitative imaging in microbiology.

systems biology↗

bioRxiv2024

Noisy hemoglobin consumption by erythrocytic Plasmodium falciparum parasites: origins and possible implications

Non-genetic cell-to-cell phenotypic differences can significantly impact pathogen physiol-ogy and virulence, leading to unexpected phenomena such as antibiotic persistence. Here, we introduce the role of such non-genetic phenotypic differences in the host, with a focus on hemoglobin consumption by Plasmodium falciparum during the erythrocytic stage of parasite development. Through imaging, we quantified the substantial variability in hemoglobin (Hb) concentration among uninfected red blood cells (RBCs), and subse-quently measured the rate of Hb consumption by parasites at different stages of their life cycle. This revealed a similarly significant variability among different infected RBCs. By employing a mathematical model, we demonstrated that this variability in Hb consumption can be attributed to non-genetic differences in host RBCs, marking the first evidence of this phenomenon in malaria parasite physiology. These findings underscore the im-portance of incorporating non-genetic host variability into models of disease progression and treatment strategies for malaria and potentially other pathogen-related diseases.

biophysics↗

bioRxiv2024

Near-Zero Photon Bioimaging

Enhancing the reliability and reproducibility of optical microscopy by reducing specimen irradiance continues to be an important biotechnology target. As irradiance levels are reduced, however, the particle nature of light is heightened, giving rise to Poisson noise, or photon sparsity that restricts only a few (0.5%) image pixels to comprise a photon. Photon-sparsity can be addressed by collecting more than 200 photons per pixel; this, however, requires extended acquisition durations and, thus, suboptimal imaging rates. Here, we introduce near-zero photon imaging, a method that operates at kHz rates and 10,000-fold lower irradiance than modern microscopy. To achieve this performance, we deployed a judiciously designed epi-fluorescence microscope enabling ultralow background and artificial intelligence that learns to reconstruct biological images from as low as 0.01 photons per pixel. We demonstrate that near-zero photon imaging captures the structure of both multicellular and subcellular targets with high fidelity, including features represented by nearly zero photons. Beyond optical microscopy, the near-zero photon imaging paradigm can be applied in remote sensing, covert applications, and biological or biomedical imaging that utilize damaging or quantum light.

biophysics↗

bioRxiv2021

Density Fluctuations Yield Distinct Growth and Fitness Effects in Single Bacteria

Single-cells grow by increasing their biomass and size. Here, we report that while mass and size accumulation rates of single Escherichia coli cells are exponential, their density fluctuates during growth. As such, the rates of mass and size accumulation of a single-cell are generally not the same, but rather cells differentiate into increasing one rate with respect to the other. This differentiation yields a previously unknown density homeostasis mechanism, which we support mathematically. Further, growth differentiation challenges ongoing efforts to predict single-cell reproduction rates (or fitness-levels), through the accumulation rates of size or mass. In contrast, we observe that density fluctuations can predict fitness, with only high fitness individuals existing in the high density fluctuation regime. We detail our imaging approach and the invisible microfluidic arrays that critically enabled increased precision and throughput. Biochemical production, infections, and natural communities start from few, growing, cells, thus, underscoring the significance of density-fluctuations when considering non-genetic variability.

biophysics↗

bioRxiv2020

Deep Learning Classification of Lipid Droplets in Quantitative Phase Images

We report the application of supervised machine learning to the automated classification of lipid droplets in label-free, quantitative-phase images. By comparing various machine learning methods commonly used in biomedical imaging and remote sensing, we found convolutional neural networks to outperform others, both quantitatively and qualitatively. We describe our imaging approach, all implemented machine learning methods, and their performance with respect to computational efficiency, required training resources, and relative method performance measured across multiple metrics. Overall, our results indicate that quantitative-phase imaging coupled to machine learning enables accurate lipid droplet classification in single living cells. As such, the present paradigm presents an excellent alternative of the more common fluorescent and Raman imaging modalities by enabling label-free, ultra-low phototoxicity, and deeper insight into the thermodynamics of metabolism of single cells. Author SummaryRecently, quantitative-phase imaging (QPI) has demonstrated the ability to elucidate novel parameters of cellular physiology and metabolism without the need for fluorescent staining. Here, we apply label-free, low photo-toxicity QPI to yeast cells in order to identify lipid droplets (LDs), an important organelle with key implications in human health and biofuel development. Because QPI yields low specificity, we explore the use of modern machine learning methods to rapidly identify intracellular LDs with high discriminatory power and accuracy. In recent years, machine learning has demonstrated exceptional abilities to recognize and segment objects in biomedical imaging, remote sensing, and other areas. Trained machine learning classifiers can be combined with QPI within high-throughput analysis pipelines, allowing for efficient and accurate identification and quantification of cellular components. Non-invasive, accurate and high-throughput classification of these organelles will accelerate research and improve our understanding of cellular functions with beneficial applications in biofuels, biomedicine, and more.

microbiology↗

bioRxiv2019

Microbial phenotypic heterogeneity in response to a metabolic toxin: continuous, dynamically shifting distribution of formaldehyde tolerance in Methylobacterium extorquens populations

While microbiologists often make the simplifying assumption that genotype determines phenotype in a given environment, it is becoming increasingly apparent that phenotypic heterogeneity (in which one genotype generates multiple phenotypes simultaneously even in a uniform environment) is common in many microbial populations. The importance of phenotypic heterogeneity has been demonstrated in a number of model systems involving binary phenotypic states (e.g., growth/non-growth); however, less is known about systems involving phenotype distributions that are continuous across an environmental gradient, and how those distributions change when the environment changes. Here, we describe a novel instance of phenotypic diversity in tolerance to a metabolic toxin within wild-type populations of Methylobacterium extorquens, a ubiquitous phyllosphere methylotroph capable of growing on the methanol periodically released from plant leaves. The first intermediate in methanol metabolism is formaldehyde, a potent cellular toxin that is lethal in high concentrations. We have found that at moderate concentrations, formaldehyde tolerance in M. extorquens is heterogeneous, with individual cells minimum tolerance levels ranging between 0 mM and 8 mM. This form of heterogeneity is continuous in terms of threshold (in the range of maximum tolerances possible), yet binary in outcome (at a given formaldehyde concentration, cells either grow normally or die, with no intermediate phenotype), and it is not associated with any detectable genetic mutations. Moreover, tolerance distributions within the population are dynamic, changing over time in response to growth conditions. We characterized this phenomenon using bulk liquid culture experiments, colony growth tracking, flow cytometry, time-lapse microscopy, and genome resequencing. Finally, we used mathematical modeling to better understand the processes by which cells change phenotype, and found evidence for both stochastic, bidirectional phenotype diversification and responsive, directed phenotype shifts, depending on the growth substrate and the presence of toxin.

microbiology↗