Biology subjects

Unser, M.

Publications and source records attributed to Unser, M..

10 recordsLinked to original sources

bioRxiv2023

Surpassing Challenges in Structured-Illumination Microscopy with FlexSIM

Super-resolution structured-illumination microscopy (SIM) is a powerful technique that allows one to surpass the diffraction limit by up to a factor two. Yet, its practical use is hampered by its sensitivity to imaging conditions which makes it prone to reconstruction artifacts. In this work, we present FlexSIM, a flexible SIM reconstruction method capable to handle highly challenging data. Specifically, we demonstrate the ability of FlexSIM to deal with the distortion of patterns, the high level of noise encountered in live imaging, as well as out-of-focus fluorescence. Moreover, we show that FlexSIM achieves state-of-the-art performance over a variety of open SIM datasets.

biophysics↗

bioRxiv2020

GRAPHIC: GRAPH-BASED HIERARCHICAL CLUSTERING FOR SINGLE-MOLECULE LOCALIZATION MICROSCOPY

We propose a novel method for the clustering of point-cloud data that originate from single-molecule localization microscopy (SMLM). Our scheme has the ability to infer a hierarchical structure from the data. It takes a particular relevance when quantitatively analyzing the biological particles of interest at different scales. It assumes a prior neither on the shape of particles nor on the background noise. Our multiscale clustering pipeline is built upon graph theory. At each scale, we first construct a weighted graph that represents the SMLM data. Next, we find clusters using spectral clustering. We then use the output of this clustering algorithm to build the graph in the next scale; in this way, we ensure consistency over different scales. We illustrate our method with examples that highlight some of its important properties.

biophysics↗

bioRxiv2020

Deep Learning Enables Individual Xenograft Cell Classification in Histological Images by Analysis of Contextual Features

Patient-Derived Xenografts (PDXs) are the preclinical models which best recapitulate inter- and intra-patient complexity of human breast malignancies, and are also emerging as useful tools to study the normal breast epithelium. However, data analysis generated with such models is often confounded by the presence of host cells and can give rise to data misinterpretation. For instance, it is important to discriminate between xenografted and host cells in histological sections prior to performing immunostainings. We developed Single Cell Classifier (SCC), a data-driven deep learning-based computational tool that provides an innovative approach for automated cell species discrimination based on a multi-step process entailing nuclei segmentation and single cell classification. We show that human and murine cells contextual features, more than cell-intrinsic ones, can be exploited to discriminate between cell species in both normal and malignant tissues, yielding up to 96% classification accuracy. SCC will facilitate the interpretation of H&E stained histological sections of xenografted human-in-mouse tissues and it is open to new in-house built models for further applications. SCC is released as an open-source plugin in ImageJ/Fiji available at the following link: https://github.com/Biomedical-Imaging-Group/SingleCellClassifier. Author summaryBreast cancer is the most commonly diagnosed tumor in women worldwide and its incidence in the population is increasing over time. Because our understanding of such disease has been hampered by the lack of adequate human preclinical model, efforts have been made in order to develop better approaches to model the human complexity. Recent advances in this regard were achieved with Patient-Derived Xenografts (PDXs), which entail the implantation of human-derived specimens to recipient immunosuppressed mice and are, thus far, the preclinical system best recapitulating the heterogeneity of both normal and malignant human tissues. However, histological analyses of the resulting tissues are usually confounded by the presence of cells of different species. To circumvent this hurdle and to facilitate the discrimination of human and murine cells in xenografted samples, we developed Single Cell Classifier (SCC), a deep learning-based open-source software, available as a plugin in ImageJ/Fiji, performing automated species classification of individual cells in H&E stained sections. We show that SCC can reach up to 96% classification accuracy to classify cells of different species mainly leveraging on their contextual features in both normal and tumor PDXs. SCC will improve and automate histological analyses of human-in-mouse xenografts and is open to new in-house built models for further classification tasks and applications in image analysis.

systems biology↗

bioRxiv2020

CryoGAN: A New Reconstruction Paradigm for Single-particle Cryo-EM Via Deep Adversarial Learning

We present CryoGAN, a new paradigm for single-particle cryo-EM reconstruction based on unsupervised deep adversarial learning. The major challenge in single-particle cryo-EM is that the imaged particles have unknown poses. Current reconstruction techniques are based on a marginalized maximum-likelihood formulation that requires calculations over the set of all possible poses for each projection image, a computationally demanding procedure. CryoGAN sidesteps this problem by using a generative adversarial network (GAN) to learn the 3D structure that has simulated projections that most closely match the real data in a distributional sense. The architecture of CryoGAN resembles that of standard GAN, with the twist that the generator network is replaced by a model of the cryo-EM image acquisition process. CryoGAN is an unsupervised algorithm that only demands projection images and an estimate of the contrast transfer function parameters. No initial volume estimate or prior training is needed. Moreover, CryoGAN requires minimal user interaction and can provide reconstructions in a matter of hours on a high-end GPU. In addition, we provide sound mathematical guarantees on the recovery of the correct structure. CryoGAN currently achieves a 8.6 [A] resolution on a realistic synthetic dataset. Preliminary results on real {beta}-galactosidase data demonstrate CryoGANs ability to exploit data statistics under standard experimental imaging conditions. We believe that this paradigm opens the door to a family of novel likelihood-free algorithms for cryo-EM reconstruction.

biochemistry↗

bioRxiv2020

Virtual-SMLM, a virtual environment for real-time interactive SMLM acquisition

Although single molecule localisation microscopy enables for the visualisation of cells nanoscale organisation, its dissemination remains limited mainly due to the complexity of the associated imaging acquisition, impacting on outputs reliability and reproducibility. We propose here the first all-in-one fully virtual environment for SMLM acquisition: Virtual-SMLM, including on-the-fly interactivity and real time display. It relies on a novel realistic approach to simulate fluorophores photo-physics based on independent pseudo-continuous emission traces. It also facilitates for user-specific experimental and optical environment design. As such, it constitutes a unique tool for the training of both users and machine learning approaches to automated SMLM, as well as for experimental validation, whilst providing realistic data sets for the development of image reconstruction algorithms and data analysis software.

biophysics↗

bioRxiv2019

DeepImageJ: A user-friendly plugin to run deep learning models in ImageJ

DeepImageJ is a user-friendly solution that enables the generic use of pre-trained deep learn ing (DL) models for biomedical image analysis in ImageJ. The deepImageJ environment gives access to the largest bioimage repository of pre-trained DL models (BioImage Model Zoo). Hence, non-experts can easily perform common image processing tasks in life-science research with DL-based tools including pixel and object classification, instance segmentation, denoising or virtual staining. DeepImageJ is compatible with existing state-of-the-art solutions and it is equipped with utility tools for developers to include new models. Very recently, several train ing frameworks have adopted the deepImageJ format to deploy their work in one of the most used software in the field (ImageJ). Beyond its direct use, we expect deepImageJ to contribute to the broader dissemination and reuse of DL models in life-sciences applications and bioimage informatics.

bioengineering↗

bioRxiv2018

Super-resolution fight club: A broad assessment of 2D & 3D single-molecule localization microscopy software

With the widespread uptake of 2D and 3D single molecule localization microscopy, a large set of different data analysis packages have been developed to generate super-resolution images. To guide researchers on the optimal analytical software for their experiments, we have designed, in a large community effort, a competition to extensively characterise and rank these options. We generated realistic simulated datasets for popular imaging modalities - 2D, astigmatic 3D, biplane 3D, and double helix 3D - and evaluated 36 participant packages against these data. This provides the first broad assessment of 3D single molecule localization microscopy software, provides a holistic view of how the latest 2D and 3D single molecule localization software perform in realistic conditions, and ultimately provides insight into the current limits of the field.

biophysics↗

bioRxiv2018

Imaging beyond the super-resolution limits using ultrastructure expansion microscopy (UltraExM)

For decades, electron microscopy (EM) was the only method able to reveal the ultrastructure of cellular organelles and molecular complexes because of the diffraction limit of optical microscopy. In recent past, the emergence of superresolution fluorescence microscopy enabled the visualization of cellular structures with so far unmatched spatial resolution approaching virtually molecular dimensions. Despite these technological advances, currently super-resolution microscopy does not permit the same resolution level as provided by electron microscopy, impeding the attribution of a protein to an ultrastructural element. Here, we report a novel method of near-native expansion microscopy (UltraExM), enabling the visualization of preserved ultrastructures of macromolecular assemblies with subdiffraction-resolution by standard optical microscopy. UltraExM revealed for the first time the ultrastructural localization of tubulin glutamylation in centrioles. Combined with super-resolution microscopy, UltraExM unveiled the centriolar chirality, an ultrastructural signature, which was only visualizable by electron microscopy.

cell biology↗

bioRxiv2018

Imaging neural activity in the ventral nerve cord of behaving adult Drosophila

To understand neural circuits that control limbs, one must measure their activity during behavior. Until now this goal has been challenging, because the portion of the nervous system that contains limb premotor and motor circuits is largely inaccessible to large-scale recording techniques in intact, moving animals - a constraint that is true for both vertebrate and invertebrate models. Here, we introduce a method for 2-photon functional imaging from the ventral nerve cord of behaving adult Drosophila melanogaster. We use this method to reveal patterns of activity across nerve cord populations during grooming and walking and to uncover the functional encoding of moonwalker ascending neurons (MANs), moonwalker descending neurons (MDNs), and a novel class of locomotion-associated descending neurons. This new approach enables the direct investigation of circuits associated with complex limb movements.

neuroscience↗

bioRxiv2016

FlyLimbTracker: an active contour based approach for leg segment tracking in unmarked, freely behaving Drosophila

Understanding the biological underpinnings of movement and action requires the development of tools for precise, quantitative, and high-throughput measurements of animal behavior. Drosophila melanogaster provides an ideal model for developing such tools: the fly has unparalleled genetic accessibility and depends on a relatively compact nervous system to generate sophisticated limbed behaviors including walking, reaching, grooming, courtship, and boxing. Here we describe a method that uses active contours to semi-automatically track body and leg segments from video image sequences of unmarked, freely behaving Drosophila. We show that this approach is robust to wide variations in video spatial and temporal resolution and that it can be used to measure leg segment motions during a variety of locomotor and grooming behaviors. FlyLimbTracker, the software implementation of this method, is open-source and our approach is generalizable. This opens up the possibility of tracking leg movements in other species by modifications of underlying active contour models.\n\nAuthor SummaryIn terrestrial animals, including humans, fundamental actions like locomotion and grooming emerge from the displacement of multiple limbs through space. Therefore, precise measurements of limb movements are critical for investigating and, ultimately, understanding the neural basis for behavior. The vinegar fly, Drosophila melanogaster, is an attractive animal model for uncovering general principles about limb control since its genome and nervous system are easy to manipulate. However, existing methods for measuring leg movements in freely behaving Drosophila have significant drawbacks: they require complicated experimental setups and provide limited information about each leg. Here we report a new method - and provide its open-source software implementation, FlyLimbTracker - for tracking the body and leg segments of freely behaving flies using only computational image processing approaches. We illustrate the power of this method by tracking fly limbs during five distinct walking and grooming behaviors and from videos across a wide range of spatial and temporal resolutions. Our approach is generalizable, allowing researchers to use and customize our software for limb tracking in Drosophila and in other species.

bioinformatics↗