Biology subjects

Laine, R. F.

Publications and source records attributed to Laine, R. F..

15 recordsLinked to original sources

bioRxiv2023

CLEM-Reg: An automated point cloud based registration algorithm for correlative light and volume electron microscopy

Correlative light and volume electron microscopy (vCLEM) is a powerful imaging technique that enables the visualisation of fluorescently labelled proteins within their ultrastructural context on a subcellular level. Currently, expert microscopists align vCLEM acquisitions using time-consuming and subjective manual methods. This paper presents CLEM-Reg, an algorithm that automates the 3D alignment of vCLEM datasets by leveraging probabilistic point cloud registration techniques. These point clouds are derived from segmentations of common structures in each modality, created by state-of-the-art open-source methods, with the option to leverage alternative tools from other plugins or platforms. CLEM-Reg drastically reduces the time required to register vCLEM datasets to a few minutes and achieves correlation of fluorescent signal to sub-micron target structures in EM on three newly acquired vCLEM benchmark datasets (fluorescence microscopy combined with FIB-SEM or SBF-SEM). CLEM-Reg was then used to automatically obtain vCLEM overlays to unambiguously identify TGN46-positive transport carriers involved in the trafficking of proteins between the trans-Golgi network and plasma membrane. The datasets are available in the EMPIAR and BioStudies public image archives for reuse in testing and developing multimodal registration algorithms by the wider community. A napari plugin integrating the algorithm is also provided to aid end-user adoption.

cell biology↗

bioRxiv2022

High-fidelity 3D live-cell nanoscopy through data-driven enhanced super-resolution radial fluctuation

In recent years, the development of new image analysis approaches has highlighted the possibility of recovering superresolution information from short sequences of wide-field images. Our recently developed method, SRRF (Super-Resolution Radial Fluctuations), enables long-term live-cell imaging beyond the resolution limit without specialized hardware. Here, we present eSRRF (enhanced-SRRF), a significant improvement over our initial method, enhancing image fidelity to the underlying structure and resolution. Especially, eSRRF uses automated data-driven parameter optimization, including an estimation of the number of frames necessary for optimal reconstruction. We demonstrate the improved fidelity of the images reconstructed with eSRRF and highlight its versatility and ease of use over a wide range of microscopy techniques and biological systems. We also extend eSRRF to 3D super-resolution microscopy by combining it with multi-focus microscopy (MFM), obtaining volumetric super-resolution imaging of live cells with acquisition speed of ~1 volume/second.

biophysics↗

bioRxiv2021

Content-aware frame interpolation (CAFI): Deep Learning-based temporal super-resolution for fast bioimaging

The development of high-resolution microscopes has made it possible to investigate cellular processes in 4D (3D over time). However, observing fast cellular dynamics remains challenging as a consequence of photobleaching and phototoxicity. These issues become increasingly problematic with the depth of the volume acquired and the speed of the biological events of interest. Here, we report the implementation of two content-aware frame interpolation (CAFI) deep learning networks, Zooming SlowMo (ZS) and Depth-Aware Video Frame Interpolation (DAIN), based on combinations of recurrent neural networks, that are highly suited for accurately predicting images in between image pairs, therefore improving the temporal resolution of image series as a post-acquisition analysis step. We show that CAFI predictions are capable of understanding the motion context of biological structures to perform better than standard interpolation methods. We benchmark CAFIs performance on six different datasets, obtained from three different microscopy modalities (point-scanning confocal, spinning-disk confocal and confocal brightfield microscopy). We demonstrate its capabilities for single-particle tracking methods applied to the study of lysosome trafficking. CAFI therefore allows for reduced light exposure and phototoxicity on the sample and extends the possibility of long-term live-cell imaging. Both DAIN and ZS as well as the training and testing data are made available for use by the wider community via the ZeroCostDL4Mic platform.

cell biology↗

bioRxiv2021

DeepBacs: Bacterial image analysis using open-source deep learning approaches

Deep Learning (DL) is rapidly changing the field of microscopy, allowing for efficient analysis of complex data while often out-performing classical algorithms. This revolution has led to a significant effort to create user-friendly tools allowing biomedical researchers with little background in computer sciences to use this technology effectively. Thus far, these approaches have mainly focused on analysing microscopy images from eukaryotic samples and are still underused in microbiology. In this work, we demonstrate how to use a range of state-of-the-art artificial neural-networks particularly suited for the analysis of bacterial microscopy images, using our recently developed ZeroCostDL4Mic platform. We showcase different DL approaches for segmenting bright field and fluorescence images of different bacterial species, use object detection to classify different growth stages in time-lapse imaging data, and carry out DL-assisted phenotypic profiling of antibiotic-treated cells. To also demonstrate the DL capacity to enhance low-phototoxicity live-cell microscopy, we showcase how image denoising can allow researchers to attain high-fidelity data in faster and longer imaging. Finally, artificial labelling of cell membranes and predictions of super-resolution images allow for accurate mapping of cell shape and intracellular targets. To aid in the training of novice users, we provide a purposefully-built database of training and testing data, enabling bacteriologists to quickly explore how to analyse their data through DL. We hope this lays a fertile ground for the efficient application of DL in microbiology and fosters the creation of novel tools for bacterial cell biology and antibiotic research.

microbiology↗

bioRxiv2021

Bringing TrackMate in the era of machine-learning and deep-learning.

TrackMate is an automated tracking software used to analyze bioimages and distributed as a Fiji plugin. Here we introduce a new version of TrackMate rewritten to improve performance and usability, and integrating several popular machine and deep learning algorithms to improve versatility. We illustrate how these new components can be used to efficiently track objects from brightfield and fluorescence microscopy images across a wide range of bio-imaging experiments.

bioinformatics↗

bioRxiv2021

A method for the fast and photon-efficient analysis of time-domain fluorescence lifetime image data over large dynamic ranges

Fluorescence lifetime imaging (FLIM) allows the quantification of subcellular processes in situ, in living cells. A number of approaches have been developed to extract the lifetime from time-domain FLIM data, but they are often limited in terms of dynamic range, speed, photon efficiency or precision. Here, we focus on one of the best performing methods in the field, the center-of-mass (CMM) method, that conveys advantages in terms of speed and photon efficiency over others. In this paper, however, we identify a loss of photon efficiency of CMM for short lifetimes when background noise is present. We sub-sequently present a new development and generalization of the CMM method that provides for the rapid and accurate extraction of fluorescence lifetime over a large lifetime dynamic range. We provide software tools to simulate, validate and analyze FLIM data sets and compare the performance of our approach against the standard CMM and the commonly employed leastsquare minimization (LSM) methods. Our method features a better photon efficiency than standard CMM and LSM and is robust in the presence of background noise. The algorithm is applicable to any time-domain FLIM dataset.

cell biology↗

bioRxiv2021

Single-molecule super-resolution imaging of T-cell plasma membrane CD4 redistribution upon HIV-1 binding

The first step of cellular entry for the human immunodeficiency virus type-1 (HIV-1) occurs through the binding of its envelope protein (Env) with the plasma membrane receptor CD4 and co-receptor CCR5 or CXCR4 on susceptible cells, primarily CD4+ T cells and macrophages. Although there is considerable knowledge of the molecular interactions between Env and host cell receptors that lead to successful fusion, the precise way in which HIV-1 receptors redistribute to sites of virus binding at the nanoscale remains unknown. Here, we quantitatively examine changes in the nanoscale organisation of CD4 on the surface of CD4+ T cells following HIV-1 binding. Using single-molecule super-resolution imaging, we show that CD4 molecules are distributed mostly as either individual molecules or small clusters of up to 4 molecules. Following virus binding, we observe a local 3-to-10-fold increase in cluster diameter and molecule number for virus-associated CD4 clusters. Moreover, a similar but smaller magnitude reorganisation of CD4 was also observed with recombinant gp120. For the first time, our results quantify the nanoscale CD4 reorganisation triggered by HIV-1 on host cells. Our quantitative approach provides a robust methodology for characterising the nanoscale organisation of plasma membrane receptors in general with the potential to link spatial organisation to function.

biophysics↗

bioRxiv2020

Automated cell tracking using StarDist and TrackMate

The ability of cells to migrate is a fundamental physiological process involved in embryonic development, tissue homeostasis, immune surveillance, and wound healing. Therefore, the mechanisms governing cellular locomotion have been under intense scrutiny over the last 50 years. One of the main tools of this scrutiny is live-cell quantitative imaging, where researchers image cells over time to study their migration and quantitatively analyze their dynamics by tracking them using the recorded images. Despite the availability of computational tools, manual tracking remains widely used among researchers due to the difficulty setting up robust automated cell tracking and large-scale analysis. Here we provide a detailed analysis pipeline illustrating how the deep learning network StarDist can be combined with the popular tracking software TrackMate to perform 2D automated cell tracking and provide fully quantitative readouts. Our proposed protocol is compatible with both fluorescent and widefield images. It only requires freely available and open-source software (ZeroCostDL4Mic and Fiji), and does not require any coding knowledge from the users, making it a versatile and powerful tool for the field. We demonstrate this pipelines usability by automatically tracking cancer cells and T cells using fluorescent and brightfield images. Importantly, we provide, as supplementary information, a detailed step-by-step protocol to allow researchers to implement it with their images.

cell biology↗

bioRxiv2020

ZeroCostDL4Mic: an open platform to simplify access and use of Deep-Learning in Microscopy

The resources and expertise needed to use Deep Learning (DL) in bioimaging remain significant barriers for most laboratories. We present https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki, a platform simplifying access to DL by exploiting the free, cloud-based computational resources of Google Colab. https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki allows researchers to train, evaluate, and apply key DL networks to perform tasks including segmentation, detection, denoising, restoration, resolution enhancement and image-to-image translation. We demonstrate the application of the platform to study multiple biological processes.

bioinformatics↗

bioRxiv2019

Fluctuation-based Super-Resolution Traction Force Microscopy

Cellular mechanics play a crucial role in tissue morphogenesis and homeostasis and are often misregulated in disease. Traction force microscopy (TFM) is one of the key methods that has enabled researchers to study fundamental aspects of mechanobiology; however, the power of TFM is limited by poor resolution and low throughput. Here, we propose a simplified protocol and imaging strategy, relying on super-resolution microscopy enabled by fluorophore fluctuation analysis, to enhance the output of TFM, by increasing both bead density as well as the accuracy of bead tracking in TFM gels. Our analysis pipeline can be used on either camera-based confocal or widefield microscopes and is fully compatible with available TFM analysis software. In addition, we demonstrate that our workflow can be used to gain biologically relevant information and is suitable for long-term live measurement of traction forces even in light-sensitive cells. Finally, we propose that our strategy could be used to considerably simplify the implementation of TFM screens. Our streamlined protocol can be performed with minimal hardware and software investment, and has the potential to standardize high-resolution TFM.

cell biology↗

bioRxiv2019

OptiJ: Open-source optical projection tomography of large organ samples

The three-dimensional imaging of mesoscopic samples with Optical Projection Tomography (OPT) has become a powerful tool for biomedical phenotyping studies. OPT uses visible light to visualize the 3D morphology of large transparent samples. To enable a wider application of OPT, we present OptiJ, a low-cost, fully open-source OPT system capable of imaging large transparent specimens up to 13 mm tall and 8 mm deep with 50 m resolution. OptiJ is based on off-the-shelf, easy-to-assemble optical components and an ImageJ plugin library for OPT data reconstruction. The software includes novel correction routines for uneven illumination and sample jitter in addition to CPU/GPU accelerated reconstruction for large datasets. We demonstrate the use of OptiJ to image and reconstruct cleared lung lobes from adult mice. We provide a detailed set of instructions to set up and use the OptiJ framework. Our hardware and software design are modular and easy to implement, allowing for further open microscopy developments for imaging large organ samples.

biophysics↗

bioRxiv2018

Intrinsically aggregation-prone proteins form amyloid-like aggregates and contribute to tissue aging in C. elegans

Reduced protein homeostasis and increased protein instability is a common feature of aging. Yet it remains unclear whether protein instability is a cause of aging. In neurodegenerative diseases and amyloidoses, specific proteins self-assemble into amyloid fibrils and accumulate as pathological solid aggregates in a variety of tissues. More recently, widespread protein aggregation has been described during normal aging, in the absence of disease processes. Until now, an extensive characterization of the nature of age-dependent protein aggregation and its consequences for aging has been lacking. Here, we show that age-dependent aggregates are rapidly formed by newly synthesized proteins and contain amyloid-like structures similar to disease-associated protein aggregates. Moreover, we demonstrate that age-dependent protein aggregation accelerates the functional decline of different tissues in C. elegans. Together, these finding reveal that the formation of amyloid aggregates is a generic problem of aging and likely to be an important target for strategies designed to maintain physiological functions in later stages of life.

cell biology↗

bioRxiv2018

Fast fluorescence lifetime imaging reveals the maturation process of α-synuclein aggregates in ageing Caenorhabditis elegans

The nematode worm Caenorhabditis elegans has emerged as an important model organism to study the molecular mechanisms of protein misfolding diseases associated with amyloid formation because of its small size, ease of genetic manipulation and optical transparency. Obtaining a reliable and quantitative read-out of protein aggregation in this system, however, remains a challenge. To address this problem, we here present a fast time-gated fluorescence lifetime imaging (TG-FLIM) method and show that it provides functional insights into the process of protein aggregation in living animals by enabling the rapid characterisation of different types of aggregates. More specifically, in longitudinal studies of C. elegans models of Parkinsons and Huntingtons diseases, we observed marked differences in the aggregation kinetics and the nature of the protein inclusions formed by -synuclein and polyglutamine. In particular, we found that -synuclein inclusions do not display amyloid-like features until late in the life of the worms, whereas polyglutamine forms amyloid characteristics rapidly in early adulthood. Furthermore, we show that the TG-FLIM method is capable of imaging live and non-anaesthetised worms moving in specially designed agarose micro-chambers. Taken together, our results show that the TG-FLIM method enables high-throughput functional imaging of living C. elegans that can be used to study in vivo mechanisms of aggregation and that has the potential to aid the search for therapeutic modifiers of protein aggregation and toxicity.

biophysics↗

bioRxiv2018

Automating multimodal microscopy with NanoJ-Fluidics

Fluorescence microscopy can reveal all aspects of cellular mechanisms, from molecular details to dynamics, thanks to approaches such as super-resolution and live-cell imaging. Each of its modalities requires specific sample preparation and imaging conditions to obtain high-quality, artefact-free images, ultimately providing complementary information. Combining and multiplexing microscopy approaches is crucial to understand cellular events, but requires elaborate workflows involving multiple sample preparation steps. We present a robust fluidics approach to automate complex sequences of treatment, labelling and imaging of live and fixed cells. Our open-source NanoJ-Fluidics system is based on low-cost LEGO hardware controlled by ImageJ-based software and can be directly adapted to any microscope, providing easy-to-implement high-content, multimodal imaging with high reproducibility. We demonstrate its capacity to carry out complex sequences of experiments such as super-resolved live-to-fixed imaging to study actin dynamics; highly-multiplexed STORM and DNA-PAINT acquisitions of multiple targets; and event-driven fixation microscopy to study the role of adhesion contacts in mitosis.

cell biology↗

bioRxiv2018

MiLeSIM: combining super-resolution and machine learning permits high-throughput virus structure analysis

Optical super-resolution microscopy techniques enable high molecular specificity with high spatial resolution and constitute a set of powerful tools in the investigation of the structure of supramolecular assemblies such as viruses. Here, we report on a new methodology which combines Structured Illumination Microscopy (SIM) with machine learning algorithms to image and classify the structure of large populations of biopharmaceutical viruses with high resolution. The method offers information on virus morphology that can ultimately be linked with functional performance. We demonstrate the approach on viruses produced for oncolytic viriotherapy (Newcastle Disease Virus) and vaccine development (Influenza). This unique tool enables the rapid assessment of the quality of viral production with high throughput obviating the need for traditional batch testing methods which are complex and time consuming. We show that our method also works on non-purified samples from pooled harvest fluids directly from the production line.

biophysics↗