Biology subjects

Passmore, J. B.

Publications and source records attributed to Passmore, J. B..

6 recordsLinked to original sources

bioRxiv2026

System Identification and Control for Optogenetics in Mammalian Nucleocytoplasmic Transport

Optogenetics enables experiments in out-of-equilibrium conditions to clarify biological mechanisms and quantify biophysical parameters. However, modelling and control techniques to study mammalian cell biology under optogenetic perturbation remain underutilised. Here, we benchmark these methods within mammalian cells by steering nucleocytoplasmic transport via the optogenetic LEXY protein in outcome-driven microscopy. First, we employ system identification to obtain models that predict transport dynamics by minimising the prediction error. We quantify this prediction accuracy for one biophysical model and two black-box models. Second, we evaluate closed-loop control efficacy by steering transport along a predefined trajectory using model-free Proportional Integral (PI) control, model-based Linear Quadratic Regulation (LQR) and Model Predictive Control (MPC). Both the predictive models and the applied control techniques demonstrate robust performance against cell-to-cell variation. This biological variation is quantified by the parameter distributions obtained from model identification with single-cell trajectories. While we show that model-free techniques such as PI and gain-scheduled PI achieve steering without explict model knowledge, predictive architectures offer better performance under this cell-to-cell variation and time-varying setpoints. Moreover, black-box predictive accuracy suggests that this model-based control is possible, even when explicit mechanistic understanding is missing. Ultimately, we demonstrate that predictive modelling and optogenetics enable quantitative characterisation and precise manipulation of mammalian cells, while offering practical guidelines for the implementation of these techniques. SIGNIFICANCEOptogenetics provides the unique ability to steer cellular processes with light. Variable and localised light inputs allow us to extract more data and guide living cells to specific states. Despite this potential, application of predictive modelling and control techniques with optogenetics remain underutilised in mammalian cell biology. Here, we benchmark these techniques within mammalian cells to steer nucleocytoplasmic transport. Benchmarking these techniques demonstrates their direct applicability to mammalian systems, enabling the extraction of new biological insight in outcome-driven microscopy.

bioengineering↗

bioRxiv2025

Patterned illumination enables denser deep-learning based single-molecule localization microscopy

Single-molecule localization microscopy (SMLM) improves resolution beyond diffraction limits by imaging cellular structures at the nanometer scale. Recent advances include using modulation patterns to improve localization precision, and deep learning to accurately process high-density samples with overlapping fluorophore emissions, thus improving imaging speed. A method combining these two advances, SIMCODE, is presented here, allowing high-density modulation-enhanced SMLM. SIMCODE achieved resolution improvements in high-density areas compared to SMLM, deep learning-based SMLM (DECODE), and modulation-enhanced SMLM alone (SIMFLUX). In DNA-PAINT imaging of COS-7 cells, SIMCODE showed improvements in the Fourier Ring Correlation and resolution-scaled Pearson coefficient, with overall improvement increasing as imaging buffer concentration increased five-fold. Modulation-enhanced localization microscopy combined with deep learning thus produced higher quality reconstructions at higher emitter densities (i.e., [~]3x the number of detected spots). This will enable faster imaging, higher labeling densities, and more flexibility in fluorophore choice, which are important for studying dynamic processes and densely labeled structures.

biophysics↗

bioRxiv2024

Spherical harmonics texture extraction for versatile analysis of biological objects

The characterization of phenotypes in cells or organisms from microscopy data largely depends on differences in the spatial distribution of image intensity. Multiple methods exist for quantifying the intensity distribution - or image texture - across objects in natural images. However, many of these texture extraction methods do not directly adapt to 3D microscopy data. Here, we present Spherical Texture extraction, which measures the variance in intensity per angular wavelength by calculating the Spherical Harmonics or Fourier power spectrum of a spherical or circular projection of the angular mean intensity of the object. This method provides a 20-value characterization that quantifies the scale of features in the spherical projection of the intensity distribution, giving a different signal if the intensity is, for example, clustered in parts of the volume or spread across the entire volume. We apply this method to different systems and demonstrate its ability to describe various biological problems through feature extraction. The Spherical Texture extraction characterizes biologically defined gene expression patterns in Drosophila melanogaster embryos, giving a quantitative read-out for pattern formation. Our method can also quantify morphological differences in Caenorhabditis elegans germline nuclei, which lack a predefined pattern. We show that the classification of germline nuclei using their Spherical Texture outperforms a convolutional neural net when training data is limited. Additionally, we use a similar pipeline on 2D cell migration data to extract polarization direction, quantifying the alignment of fluorescent markers to the migration direction. We implemented the Spherical Texture method as a plugin in ilastik, making it easy to install and apply to any segmented 3D or 2D dataset. Additionally, this technique can also easily be applied through a Python package to provide extra feature extraction for any object classification pipeline or downstream analysis. Author summaryWe introduce a novel method to extract quantitative data from microscopy images by precisely measuring the distribution of intensities within objects in both 3D or 2D. This method is easily accessible through the object classification workflow of ilastik, provided the original image is segmented into separate objects. The method is specifically designed to analyze mostly convex objects, focusing on the variation in fluorescence intensity caused by differences in their shapes or patterns. We demonstrate the versatility of our method by applying it to very different biological samples. Specifically, we showcase its effectiveness in quantifying the patterning in D. melanogaster embryos, in classifying the nuclei in C. elegans germlines, and in extracting polarization information from individual migratory cells. Through these examples, we illustrate that our technique can be employed across different biological scales. Furthermore, we highlight the multiple ways in which the data generated by our method can be used, including quantifying the strength of a specific pattern, employing machine learning to classify diverse morphologies, or extracting directionality or polarization from fluorescence intensity.

bioinformatics↗

bioRxiv2022

GelMap: Intrinsic calibration and deformation mapping for expansion microscopy

Expansion microscopy (ExM) is a powerful technique to overcome the diffraction limit of light microscopy by physically expanding biological specimen in three dimensions. Nonetheless, using ExM for quantitative or diagnostic applications requires robust quality control methods to precisely determine expansion factors and to map deformations due to anisotropic expansion. Here we present GelMap, a flexible workflow to introduce a fluorescent grid into pre-expanded hydrogels that scales with expansion and reports deformations. We demonstrate that GelMap can be used to precisely determine the local expansion factor and to correct for deformations without the use of cellular reference structures or pre-expansion ground truth images. Moreover, we show that GelMap aids sample navigation for correlative uses of expansion microscopy. Finally, we show that GelMap is compatible with expansion of tissue and can be readily implemented as a quality control step into existing ExM workflows.

cell biology↗

bioRxiv2020

Mitochondrial fission factor (MFF) is a critical regulator of peroxisome maturation

Peroxisomes are highly dynamic subcellular compartments with important functions in lipid and ROS metabolism. Impaired peroxisomal function can lead to severe metabolic disorders with developmental defects and neurological abnormalities. Recently, a new group of disorders has been identified, characterised by defects in the membrane dynamics and division of peroxisomes rather than by loss of metabolic functions. However, the contribution of impaired peroxisome plasticity to the pathophysiology of those disorders is not well understood. Mitochondrial fission factor (MFF) is a key component of both the peroxisomal and mitochondrial division machinery. Patients with MFF deficiency present with developmental and neurological abnormalities. Peroxisomes (and mitochondria) in patient fibroblasts are highly elongated as a result of impaired organelle division. The majority of studies into MFF-deficiency have focused on mitochondrial dysfunction, but the contribution of peroxisomal alterations to the pathophysiology is largely unknown. Here, we show that MFF deficiency does not cause alterations to overall peroxisomal biochemical function. However, loss of MFF results in reduced import-competency of the peroxisomal compartment and leads to the accumulation of pre-peroxisomal membrane structures. We show that peroxisomes in MFF-deficient cells display alterations in peroxisomal redox state and intra-peroxisomal pH. Removal of elongated peroxisomes through induction of autophagic processes is not impaired. A mathematical model describing key processes involved in peroxisome dynamics sheds further light into the physical processes disturbed in MFF-deficient cells. The consequences of our findings for the pathophysiology of MFF-deficiency and related disorders with impaired peroxisome plasticity are discussed.

cell biology↗

bioRxiv2019

The human proteome co-regulation map reveals functional relationships between proteins

The annotation of protein function is a longstanding challenge of cell biology that suffers from the sheer magnitude of the task. Here we present ProteomeHD, which documents the response of 10,323 human proteins to 294 biological perturbations, measured by isotope-labelling mass spectrometry. Using this data matrix and robust machine learning we create a co-regulation map of the cell that reflects functional associations between human proteins. The map identifies a functional context for many uncharacterized proteins, including microproteins that are difficult to study with traditional methods. Co-regulation also captures relationships between proteins which do not physically interact or co-localize. For example, co-regulation of the peroxisomal membrane protein PEX11{beta} with mitochondrial respiration factors led us to discover a novel organelle interface between peroxisomes and mitochondria in mammalian cells. The co-regulation map can be explored at www.proteomeHD.net.

systems biology↗