Biology subjects

Henriques, R.

Publications and source records attributed to Henriques, R..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

Processive movement of Staphylococcus aureus essential septal peptidoglycan synthases is independent of FtsZ treadmilling and drives cell constriction

Bacterial cell division is mediated by the tubulin-homolog FtsZ, which recruits peptidoglycan (PG) synthesis enzymes to the division site. Septal PG synthases promote inward growth of the division septum, but the mechanisms governing the spatiotemporal regulation of these enzymes are poorly understood. Recent studies on various organisms have proposed different models for the relationship between the movement and activity of septum-specific PG synthases and FtsZ treadmilling. Here, we studied the movement dynamics of conserved cell division proteins relative to the rates of septum constriction and FtsZ treadmilling in the Gram-positive pathogen Staphylococcus aureus. The septal PG synthesis enzyme complex FtsW/PBP1 and its putative activator protein, DivIB, moved processively, around the division site, with the same velocity. Impairing FtsZ treadmilling did not affect FtsW and DivIB velocities or septum constriction rates. Contrarily, inhibition of PG synthesis slowed down or completely stopped both septum constriction and the directional movement of FtsW/PBP1 and DivIB. Our findings support a model for S. aureus in which a single population of processively moving FtsW/PBP1 remains associated with DivIB to drive cell constriction independently of treadmilling FtsZ filaments.

microbiology↗

bioRxiv2023

Pattern-centric transformation of omics-data sources grounded on multi-wise gene associations aids predictive tasks in TCGA while ensuring interpretability.

MotivationThe increasing prevalence of omics data sources is pushing the study of regulatory mechanisms underlying complex diseases such as cancer. However, the vast quantities of features produced and the inherent interplay between them lead to a level of complexity that hampers both descriptive and predictive tasks, requiring custom-built algorithms that can extract relevant information from these sources of data. ResultsWe propose a transformation that moves data centered on molecules (e.g. transcripts and proteins) to a new data space focused on putative regulatory modules given by statistically relevant patterns of coexpression. The proposed transformation extracts patterns from the data through biclustering and uses them to create new variables with guarantees of interpretability and discriminative power. The transformation is shown to achieve dimensionality reductions of up to 99% and to increase the predictive performance of various classifiers across multiple omics layers. Our results suggest that a transformation of omics data from gene-centric to pattern-centric data provides benefits to both prediction tasks and human interpretation. The proposed approach is expected to greatly support further bioinformatic analyses for precision medicine applications. AvailabilitySoftware code and the raw results generated are available at github.com/Andrempp/Pattern-Centric-Transformation. Contactandremppatricio@tecnico.ulisboa.pt Supplementary informationSupplementary data are available at Journal Name online.

genomics↗

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

Fast4DReg: Fast registration of 4D microscopy datasets

Unwanted sample drift is a common issue that plagues microscopy experiments, preventing accurate temporal quantification of biological processes. While multiple methods and tools exist to correct images post-acquisition, performing drift correction of large 3D videos using open-source solutions remains challenging and time-consuming. Here we present a new tool developed for ImageJ/Fiji called Fast4DReg that can quickly correct axial and lateral drift in 3D video microscopy datasets. Fast4DReg works by creating intensity projections along multiple axes and estimating the drift between frames using 2D cross-correlations. Using synthetic and acquired datasets, we demonstrate that Fast4DReg performs better than other state-of-the-art open-source drift correction tools and significantly outperforms them in speed (5x to 60x). We also demonstrate that Fast4DReg can be used to register misaligned channels in 3D using either calibration slides or misaligned images directly. Altogether Fast4DReg provides a quick and easy-to-use method to correct 3D imaging data before further visualization and analysis. Fast4DReg is available on GitHub.

bioinformatics↗

bioRxiv2022

BioImage Model Zoo: A Community-Driven Resource for Accessible Deep Learning in BioImage Analysis

Deep learning-based approaches are revolutionizing imaging-driven scientific research. However, the accessibility and reproducibility of deep learning-based workflows for imaging scientists remain far from sufficient. Several tools have recently risen to the challenge of democratizing deep learning by providing user-friendly interfaces to analyze new data with pre-trained or fine-tuned models. Still, few of the existing pre-trained models are interoperable between these tools, critically restricting a models overall utility and the possibility of validating and reproducing scientific analyses. Here, we present the BioImage Model Zoo (https://bioimage.io): a community-driven, fully open resource where standardized pre-trained models can be shared, explored, tested, and downloaded for further adaptation or direct deployment in multiple end user-facing tools (e.g., ilastik, deepImageJ, QuPath, StarDist, ImJoy, ZeroCostDL4Mic, CSBDeep). To enable everyone to contribute and consume the Zoo resources, we provide a model standard to enable cross-compatibility, a rich list of example models and practical use-cases, developer tools, documentation, and the accompanying infrastructure for model upload, download and testing. Our contribution aims to lay the groundwork to make deep learning methods for microscopy imaging findable, accessible, interoperable, and reusable (FAIR) across software tools and platforms.

bioinformatics↗

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↗

bioRxiv2022

TOR acts as metabolic gatekeeper for auxin-dependent lateral root initiation in Arabidopsis thaliana

Plants post-embryonic organogenesis requires matching the available metabolic resources to the developmental programs. The root system is determined by the formation of lateral roots (LR), which in Arabidopsis thaliana entails the auxin-induced activation of founder cells located in the pericycle. While the allocation of sugars to roots influences root branching, how sugar availability is sensed for auxin-triggered formation of LRs remains unknown. Here, we combine metabolic profiling with cell-specific genetic interference to show that LR formation is an important sink for carbohydrate accompanied by a switch to glycolysis. We show that the target-of-rapamycin (TOR) kinase is locally activated in the pericycle and the founder cells and that both chemical and genetic inhibition of TOR kinase lead to a block of LR initiation. TOR marginally affects the auxin-induced transcriptional response of the pericycle but modulates the translation of ARF19, ARF7 and LBD16, three key targets of auxin signalling. These data place TOR as a gatekeeper for post-embryonic LR formation that integrates local auxin-dependent pathways with systemic metabolic signals, modulating the translation of auxin induced gene expression.

plant biology↗

bioRxiv2021

Enrichment analysis on regulatory subspaces: a novel direction for the superior description of cellular responses to SARS-CoV-2

StatementThe enrichment analysis of discriminative cell transcriptional responses to SARS-CoV-2 infection using biclustering produces a broader set of superiorly enriched GO terms and KEGG pathways against alternative state-of-the-art machine learning approaches, unraveling novel knowledge. Motivation and methodsThe comprehensive understanding of the impacts of the SARS-CoV-2 virus on infected cells is still incomplete. This work identifies and analyses the main cell regulatory processes affected and induced by SARS-CoV-2, using transcriptomic data from several infectable cell lines available in public databases and in vivo samples. We propose a new class of statistical models to handle three major challenges, namely the scarcity of observations, the high dimensionality of the data, and the complexity of the interactions between genes. Additionally, we analyse the function of these genes and their interactions within cells to compare them to ones affected by IAV (H1N1), RSV and HPIV3 in the target cell lines. ResultsGathered results show that, although clustering and predictive algorithms aid classic functional enrichment analysis, recent pattern-based biclustering algorithms significantly improve the number and quality of the detected biological processes. Additionally, a comparative analysis of these processes is performed to identify potential pathophysiological characteristics of COVID-19. These are further compared to those identified by other authors for the same virus as well as related ones such as SARS-CoV-1. This approach is particularly relevant due to a lack of other works utilizing more complex machine learning tools within this context.

bioinformatics↗

bioRxiv2021

DISA tool: discriminative and informative subspace assessment with categorical and numerical outcomes

MotivationPattern discovery and subspace clustering play a central role in the biological domain, supporting for instance putative regulatory module discovery from omic data for both descriptive and predictive ends. In the presence of target variables (e.g. phenotypes), regulatory patterns should further satisfy delineate discriminative power properties, well-established in the presence of categorical outcomes, yet largely disregarded for numerical outcomes, such as risk profiles and quantitative phenotypes. ResultsDISA (Discriminative and Informative Subspace Assessment), a Python software package, is proposed to assess patterns in the presence of numerical outcomes using well-established measures together with a novel principle able to statistically assess the correlation gain of the subspace against the overall space. Results confirm the possibility to soundly extend discriminative criteria towards numerical outcomes without the drawbacks well-associated with discretization procedures. A case study is provided to show the properties of the proposed method. AvailabilityDISA is freely available at https://github.com/JupitersMight/DISA under the MIT license. Contact{leonardoalexandre@tecnico.ulisboa.pt,rmch@tecnico.ulisboa.pt} and {rs.costa@fct.unl.pt}

bioinformatics↗

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

Physical mechanisms of ESCRT-III-driven cell division in archaea

Living systems propagate by undergoing rounds of cell growth and division. Cell division is at heart a physical process that requires mechanical forces, usually exerted by protein assemblies. Here we developed the first physical model for the division of archaeal cells, which despite their structural simplicity share machinery and evolutionary origins with eukaryotes. We show how active geometry changes of elastic ESCRT-III filaments, coupled to filament disassembly, are sufficient to efficiently split the cell. We explore how the non-equilibrium processes that govern the filament behaviour impact the resulting cell division. We show how a quantitative comparison between our simulations and dynamic data for ESCRTIII-mediated division in Sulfolobus acidocaldarius, the closest archaeal relative to eukaryotic cells that can currently be cultured in the lab, and reveal the most likely physical mechanism behind its division.

biophysics↗

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

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↗

bioRxiv2020

Live cell imaging of the hyperthermophilic archaeon Sulfolobus acidocaldarius identifies complementary roles for two ESCRTIII homologues in ensuring a robust and symmetric cell division

Live-cell imaging has revolutionized our understanding of dynamic cellular processes in bacteria and eukaryotes. While similar techniques have recently been applied to the study of halophilic archaea, our ability to explore the cell biology of thermophilic archaea is limited, due to the technical challenges of imaging at high temperatures. Here, we report the construction of the Sulfoscope, a heated chamber that enables live-cell imaging on an inverted fluorescent microscope. Using this system combined with thermostable fluorescent probes, we were able to image Sulfolobus cells as they divide, revealing a tight coupling between changes in DNA compaction, segregation and cytokinesis. By imaging deletion mutants, we observe important differences in the function of the two ESCRTIII proteins recently implicated in cytokinesis. The loss of CdvB1 compromises cell division, causing occasional division failures and fusion of the two daughter cells, whereas the deletion of cdvB2 leads to a profound loss of division symmetry, generating daughter cells that vary widely in size and eventually generating ghost cells. These data indicate that DNA separation and cytokinesis are coordinated in Sulfolobus, as is the case in eukaryotes, and that two contractile ESCRTIII polymers perform distinct roles to ensure that Sulfolobus cells undergo a robust and symmetrical division. Taken together, the Sulfoscope has shown to provide a controlled high temperature environment, in which cell biology of Sulfolobus can be studied in unprecedent details.

microbiology↗

bioRxiv2020

vLUME: 3D Virtual Reality for Single-molecule Localization Microscopy

Super-Resolution (SR) Microscopy based on 3D Single-Molecule Localization Microscopy (SMLM) is now well established1,2 and its wide-spread adoption has led to the development of more than 36 software packages, dedicated to quantitative evaluation of the spatial and temporal detection of fluorophore photoswitching3. While the initial emphasis in the 3D SMLM field has clearly been on improving resolution and data quality, there is now a marked absence of 3D visualization approaches that enable the straightforward, high-fidelity exploration of this type of data. Inspired by the horological phosphorescence points that illuminate watch-faces in the dark, we present vLUME (Visualization of the Universe in a Micro Environment, pronounced volume) a free-for-academic-use immersive virtual reality-based (VR) visualization software package purposefully designed to render large 3D-SMLM data sets. vLUME enables robust visualization, segmentation and quantification of millions of fluorescence puncta from any 3D SMLM technique. vLUME has an intuitive user-interface and is compatible with all commercial VR hardware (Oculus Rift/Quest and HTC Vive, Supplementary Video 1). vLUME accelerates the analysis of highly complex 3D point-cloud data and the rapid identification of defects that are otherwise neglected in global quality metrics.

biophysics↗

bioRxiv2020

Super-Beacons: open-source probes with spontaneous tunable blinking compatible with live-cell super-resolution microscopy

Localization based super-resolution microscopy relies on the detection of individual molecules cycling between fluorescent and non-fluorescent states. These transitions are commonly regulated by high-intensity illumination, imposing constrains to imaging hardware and producing sample photodamage. Here, we propose single-molecule self-quenching as a mechanism to generate spontaneous photoswitching independent of illumination. To demonstrate this principle, we developed a new class of DNA-based open-source Super-Resolution probes named Super-Beacons, with photoswitching kinetics that can be tuned structurally, thermally and chemically. The potential of these probes for live-cell friendly Super-Resolution Microscopy without high-illumination or toxic imaging buffers is revealed by imaging Interferon Inducible Transmembrane proteins (IFITMs) at sub-100nm resolutions.

cell biology↗

bioRxiv2019

Closed mitosis requires local disassembly of the nuclear envelope

At the end of mitosis, eukaryotic cells must segregate both copies of their replicated genome into two new nuclear compartments (1). They do this either by first dismantling and later reassembling the nuclear envelope in a so called \"open mitosis\", or by reshaping an intact nucleus and then dividing into two in a \"closed mitosis\" (2, 3). However, while mitosis has been studied in a wide variety of eukaryotes for over a century (4), it is not known how the double membrane of the nuclear envelope is split into two at the end of a closed mitosis without compromising the impermeability of the nuclear compartment (5). In studying this problem in the fission yeast Schizosaccharomyces pombe, a classical model for closed mitosis (5), we use genetics, live cell imaging and electron tomography to show that nuclear fission is achieved via local disassembly of the nuclear envelope (NE) within the narrow bridge that links segregating daughter nuclei. In doing so, we identify a novel inner NE-localised protein Les1 that restricts the process of local NE breakdown (local NEB) to the bridge midzone and prevents the leakage of material from daughter nuclei. The mechanics of local NEB in a closed mitosis closely mirror those of NEB in open mitosis (3), revealing an unexpectedly deep conservation of nuclear remodelling mechanisms across diverse eukaryotes.

cell biology↗

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↗