Automatic monitoring of neural activity with single-cell resolution in behaving Hydra.

The ability to record every spike from every neuron in a behaving animal is one of the holy grails of neuroscience. Here, we report coming one step closer towards this goal with the development of an end-to-end pipeline that automatically tracks and extracts calcium signals from individual neurons in the cnidarian Hydra vulgaris. We imaged dually labeled (nuclear tdTomato and cytoplasmic GCaMP7s) transgenic Hydra and developed an open-source Python platform (TraSE-IN) for the Tracking and Spike Estimation of Individual Neurons in the animal during behavior. The TraSE-IN platform comprises a series of modules that segments and tracks each nucleus over time and extracts the corresponding calcium activity in the GCaMP channel. Another series of signal processing modules allows robust prediction of individual spikes from each neuron's calcium signal. This complete pipeline will facilitate the automatic generation and analysis of large-scale datasets of single-cell resolution neural activity in Hydra, and potentially other model organisms, paving the way towards deciphering the neural code of an entire animal.

Encystation and Stress Responses under the Control of Ubiquitin-like Proteins in Pathogenic Amoebae.

Amoebae found in aquatic and terrestrial environments encompass various pathogenic species, including the parasite Entamoeba histolytica and the free-living Acanthamoeba castellanii. Both microorganisms pose significant threats to public health, capable of inducing life-threatening effects on humans. These amoebae exist in two cellular forms: trophozoites and cysts. The trophozoite stage is the form used for growth and reproduction while the cyst stage is the resistant and disseminating form. Cysts occur after cellular metabolism slowdown due to nutritional deprivation or the appearance of environmental conditions unfavourable to the amoebae's growth and division. The initiation of encystation is accompanied by the activation of stress responses, and scarce data indicate that encystation shares factors and mechanisms identified in stress responses occurring in trophozoites exposed to toxic compounds derived from human immune defence. Although some "omics" analyses have explored how amoebae respond to diverse stresses, these studies remain limited and rarely report post-translational modifications that would provide knowledge on the molecular mechanisms underlying amoebae-specific stress responses. In this review, we discuss ubiquitin-like proteins associated with encystation and cell survival during oxidative damage. We aim to shed light on the signalling pathways involved in amoebic defence mechanisms, with a focus on their potential clinical implications against pathogenic amoebae, addressing the pressing need for effective therapies.

Automatic monitoring of whole-body neural activity in behaving Hydra.

The ability to record every spike from every neuron in a behaving animal is one of the holy grails of neuroscience. Here, we report coming one step closer towards this goal with the development of an end-to-end pipeline that automatically tracks and extracts calcium signals from individual neurons in the cnidarian Hydra vulgaris. We imaged dually labeled (nuclear tdTomato and cytoplasmic GCaMP7s) transgenic Hydra and developed an open-source Python platform (TraSE-IN) for the Tracking and Spike Estimation of Individual Neurons in the animal during behavior. The TraSE-IN platform comprises a series of modules that segments and tracks each nucleus over time and extracts the corresponding calcium activity in the GCaMP channel. Another series of signal processing modules allows robust prediction of individual spikes from each neuron's calcium signal. This complete pipeline will facilitate the automatic generation and analysis of large-scale datasets of single-cell resolution neural activity in Hydra, and potentially other model organisms, paving the way towards deciphering the neural code of an entire animal.

Automatic diagnosis and classification of breast surgical samples with dynamic full-field OCT and machine learning.

Purpose: The adoption of emerging imaging technologies in the medical community is often hampered when they provide a new unfamiliar contrast that requires experience to be interpreted. Dynamic full-field optical coherence tomography (D-FF-OCT) microscopy is such an emerging technique. It provides fast, high-resolution images of excised tissues with a contrast comparable to H&E histology but without any tissue preparation and alteration. Approach: We designed and compared two machine learning approaches to support interpretation of D-FF-OCT images of breast surgical specimens and thus provide tools to facilitate medical adoption. We conducted a pilot study on 51 breast lumpectomy and mastectomy surgical specimens and more than 1000 individual 1.3×1.3 mm2 images and compared with standard H&E histology diagnosis. Results: Using our automatic diagnosis algorithms, we obtained an accuracy above 88% at the image level (1.3×1.3 mm2) and above 96% at the specimen level (above cm2). Conclusions: Altogether, these results demonstrate the high potential of D-FF-OCT coupled to machine learning to provide a rapid, automatic, and accurate histopathology diagnosis with minimal sample alteration.

Live Cells Imaging and Comparative Phosphoproteomics Uncover Proteins from the Mechanobiome in Entamoeba histolytica.

Entamoeba histolytica is a protozoan parasite and the causative agent of amoebiasis in humans. This amoeba invades human tissues by taking advantage of its actin-rich cytoskeleton to move, enter the tissue matrix, kill and phagocyte the human cells. During tissue invasion, E. histolytica moves from the intestinal lumen across the mucus layer and enters the epithelial parenchyma. Faced with the chemical and physical constraints of these diverse environments, E. histolytica has developed sophisticated systems to integrate internal and external signals and to coordinate cell shape changes and motility. Cell signalling circuits are driven by interactions between the parasite and extracellular matrix, combined with rapid responses from the mechanobiome in which protein phosphorylation plays an important role. To understand the role of phosphorylation events and related signalling mechanisms, we targeted phosphatidylinositol 3-kinases followed by live cell imaging and phosphoproteomics. The results highlight 1150 proteins, out of the 7966 proteins within the amoebic proteome, as members of the phosphoproteome, including signalling and structural molecules involved in cytoskeletal activities. Inhibition of phosphatidylinositol 3-kinases alters phosphorylation in important members of these categories; a finding that correlates with changes in amoeba motility and morphology, as well as a decrease in actin-rich adhesive structures.

Reformulating Optical Flow to Solve Image-Based Inverse Problems and Quantify Uncertainty.

From meteorology to medical imaging and cell mechanics, many scientific domains use inverse problems (IPs) to extract physical measurements from image movement. To this end, motion estimation methods such as optical flow (OF) pre-process images into motion data to feed the IP, which then inverts for the measurements through a physical model. However, this combined OFIP pipeline exacerbates the ill-posedness inherent to each technique, propagating errors and preventing uncertainty quantification. We introduce a Bayesian PDE-constrained framework that transforms visual information directly into physical measurements in the context of probability distributions. The posterior mean is a constrained IP that tracks brightness while satisfying the physical model, thereby translating the aperture problem from the motion to the underlying physics; whereas the posterior covariance derives measurement error out of image noise. As we illustrate with traction force microscopy, our approach offers several advantages: more accurate reconstructions; unprecedented flexibility in experiment design (e.g., arbitrary boundary conditions); and the exclusivity of measurement error, central to empirical science, yet still unavailable under the OFIP strategy.

Domain Adapted Multitask Learning for Segmenting Amoeboid Cells in Microscopy.

The method proposed in this paper is a robust combination of multi-task learning and unsupervised domain adaptation for segmenting amoeboid cells in microscopy. A highlight of this work is the manner in which the model's hyperparameters are estimated. The detriments of ad-hoc parameter estimation are well known, but this issue remains largely unaddressed in the context of CNN-based segmentation. Using a novel min-max formulation of the segmentation cost function our proposed method analytically estimates the model's hyperparameters, while simultaneously learning the CNN weights during training. This end-to-end framework provides a consolidated mechanism to harness the potential of multi-task learning to isolate and segment clustered cells from low contrast brightfield images, and it simultaneously leverages deep domain adaptation to segment fluorescent cells without explicit pixel-level re- annotation of the data. Experimental validations on multi-cellular images strongly suggest the effectiveness of the proposed technique, and our quantitative results show at least 15% and 10% improvement in cell segmentation on brightfield and fluorescence images respectively compared to contemporary supervised segmentation methods.

4D live imaging and computational modeling of a functional gut-on-a-chip evaluate how peristalsis facilitates enteric pathogen invasion.

Physical forces are essential to biological function, but their impact at the tissue level is not fully understood. The gut is under continuous mechanical stress because of peristalsis. To assess the influence of mechanical cues on enteropathogen invasion, we combine computational imaging with a mechanically active gut-on-a-chip. After infecting the device with either of two microbes, we image their behavior in real time while mapping the mechanical stress within the tissue. This is achieved by reconstructing three-dimensional videos of the ongoing invasion and leveraging on-manifold inverse problems together with viscoelastic rheology. Our results show that peristalsis accelerates the destruction and invasion of intestinal tissue by Entamoeba histolytica and colonization by Shigella flexneri. Local tension facilitates parasite penetration and activates virulence genes in the bacteria. Overall, our work highlights the fundamental role of physical cues during host-pathogen interactions and introduces a framework that opens the door to study mechanobiology on deformable tissues.

Tracking receptor motions at the plasma membrane reveals distinct effects of ligands on CCR5 dynamics depending on its dimerization status.

G-protein-coupled receptors (GPCR) are present at the cell surface in different conformational and oligomeric states. However, how these states impact GPCRs biological function and therapeutic targeting remains incompletely known. Here, we investigated this issue in living cells for the CC chemokine receptor 5 (CCR5), a major receptor in inflammation and the principal entry co-receptor for Human Immunodeficiency Viruses type 1 (HIV-1). We used TIRF microscopy and a statistical method to track and classify the motion of different receptor subpopulations. We showed a diversity of ligand-free forms of CCR5 at the cell surface constituted of various oligomeric states and exhibiting transient Brownian and restricted motions. These forms were stabilized differently by distinct ligands. In particular, agonist stimulation restricted the mobility of CCR5 and led to its clustering, a feature depending on ß-arrestin, while inverse agonist stimulation exhibited the opposite effect. These results suggest a link between receptor activation and immobilization. Applied to HIV-1 envelope glycoproteins gp120, our quantitative analysis revealed agonist-like properties of gp120s. Distinct gp120s influenced CCR5 dynamics differently, suggesting that they stabilize different CCR5 conformations. Then, using a dimerization-compromized mutant, we showed that dimerization (i) impacts CCR5 precoupling to G proteins, (ii) is a pre-requisite for the immobilization and clustering of receptors upon activation, and (iii) regulates receptor endocytosis, thereby impacting the fate of activated receptors. This study demonstrates that tracking the dynamic behavior of a GPCR is an efficient way to link GPCR conformations to their functions, therefore improving the development of drugs targeting specific receptor conformations.

Automated staining analysis in digital cytopathology and applications.

The progress of digital pathology in recent years has been an opportunity for the development of automated image analysis algorithms for quantitative measurements and computer aided diagnosis. With those new methods comes the need for high staining quality and reproducibility, as image analysis tools are typically more sensible to slight stain variations than trained pathologists. This article presents a method for the automated analysis of cytology slides stains specifically adapted to the challenges encountered in digital cytopathology. In particular, the variety of cell types in cytology slides, the 3D distribution of the cellular material, the presence of superposed cells and the need for independent analysis of sub-cellular compartments are addressed. The proposed method is applied to the quantification of staining variations for quality control, resulting from changes in the staining protocol such as reagent immersion time or a reagent change. Another demonstrated application is the selection of staining protocol parameters that maximize the visible details in nucleus. Finally the analysis pipeline is also used to compare different stain normalization algorithms on digital cytology slides. Code available at: https://gitlab.com/vitadx/articles/automated_staining_analysis.

DHA-containing phospholipids control membrane fusion and transcellular tunnel dynamics.

Metabolic studies and animal knockout models point to the critical role of polyunsaturated docosahexaenoic acid (22:6, DHA)-containing phospholipids (DHA-PLs) in physiology. Here, we investigated the impact of DHA-PLs on the dynamics of transendothelial cell macroapertures (TEMs) triggered by RhoA inhibition-associated cell spreading. Lipidomic analyses showed that human umbilical vein endothelial cells (HUVECs) subjected to a DHA diet undergo a 6-fold enrichment in DHA-PLs at the plasma membrane (PM) at the expense of monounsaturated oleic acid-containing PLs (OA-PLs). Consequently, DHA-PL enrichment at the PM induces a reduction in cell thickness and shifts cellular membranes towards a permissive mode of membrane fusion for transcellular tunnel initiation. We provide evidence that a global homeostatic control of membrane tension and cell cortex rigidity minimizes overall changes of TEM area through a decrease of TEM size and lifetime. Conversely, low DHA-PL levels at the PM lead to the opening of unstable and wider TEMs. Together, this provides evidence that variations of DHA-PL levels in membranes affect cell biomechanical properties.

3D cell morphology detection by association for embryo heart morphogenesis.

Advances in tissue engineering for cardiac regenerative medicine require cellular-level understanding of the mechanism of cardiac muscle growth during embryonic developmental stage. Computational methods to automatize cell segmentation in 3D and deliver accurate, quantitative morphology of cardiomyocytes, are imperative to provide insight into cell behavior underlying cardiac tissue growth. Detecting individual cells from volumetric images of dense tissue, poised with low signal-to-noise ratio and severe intensity in homogeneity, is a challenging task. In this article, we develop a robust segmentation tool capable of extracting cellular morphological parameters from 3D multifluorescence images of murine heart, captured via light-sheet microscopy. The proposed pipeline incorporates a neural network for 2D detection of nuclei and cell membranes. A graph-based global association employs the 2D nuclei detections to reconstruct 3D nuclei. A novel optimization embedding the network flow algorithm in an alternating direction method of multipliers is proposed to solve the global object association problem. The associated 3D nuclei serve as the initialization of an active mesh model to obtain the 3D segmentation of individual myocardial cells. The efficiency of our method over the state-of-the-art methods is observed via various qualitative and quantitative evaluation.

Cytokine receptor cluster size impacts its endocytosis and signaling.

The interleukin-2 receptor (IL-2R) is a cytokine receptor essential for immunity that transduces proliferative signals regulated by its uptake and degradation. IL-2R is a well-known marker of clathrin-independent endocytosis (CIE), a process devoid of any coat protein, raising the question of how the CIE vesicle is generated. Here, we investigated the impact of IL-2Rγ clustering in its endocytosis. Combining total internal reflection fluorescence (TIRF) live imaging of a CRISPR-edited T cell line endogenously expressing IL-2Rγ tagged with green fluorescent protein (GFP), with multichannel imaging, single-molecule tracking, and quantitative analysis, we were able to decipher IL-2Rγ stoichiometry at the plasma membrane in real time. We identified three distinct IL-2Rγ cluster populations. IL-2Rγ is secreted to the cell surface as a preassembled small cluster of three molecules maximum, rapidly diffusing at the plasma membrane. A medium-sized cluster composed of four to six molecules is key for IL-2R internalization and is promoted by interleukin 2 (IL-2) binding, while larger clusters (more than six molecules) are static and inefficiently internalized. Moreover, we identified membrane cholesterol and the branched actin cytoskeleton as key regulators of IL-2Rγ clustering and IL-2-induced signaling. Both cholesterol depletion and Arp2/3 inhibition lead to the assembly of large IL-2Rγ clusters, arising from the stochastic interaction of receptor molecules in close correlation with their enhanced lateral diffusion at the membrane, thus resulting in a default in IL-2R endocytosis. Despite similar clustering outcomes, while cholesterol depletion leads to a sustained IL-2-dependent signaling, Arp2/3 inhibition prevents signal initiation. Taken together, our results reveal the importance of cytokine receptor clustering for CIE initiation and signal transduction.

Bioimage Analysis and Cell Motility.

Bioimage analysis (BIA) has historically helped study how and why cells move; biological experiments evolved in intimate feedback with the most classical image processing techniques because they contribute objectivity and reproducibility to an eminently qualitative science. Cell segmentation, tracking, and morphology descriptors are all discussed here. Using ameboid motility as a case study, these methods help us illustrate how proper quantification can augment biological data, for example, by choosing mathematical representations that amplify initially subtle differences, by statistically uncovering general laws or by integrating physical insight. More recently, the non-invasive nature of quantitative imaging is fertilizing two blooming fields: mechanobiology, where many biophysical measurements remain inaccessible, and microenvironments, where the quest for physiological relevance has exploded data size. From relief to remedy, this trend indicates that BIA is to become a main vector of biological discovery as human visual analysis struggles against ever more complex data.

Aspergillus fumigatus, One Uninucleate Species with Disparate Offspring.

Establishment of a fungal infection due to Aspergillus fumigatus relies on the efficient germination of the airborne conidia once they penetrate the respiratory tract. However, the features of conidial germination have been poorly explored and understood in this fungal species as well as in other species of filamentous fungi. We show here that the germination of A. fumigatus is asynchronous. If the nutritional environment and extensive gene deletions can modify the germination parameters for A. fumigatus, the asynchrony is maintained in all germinative conditions tested. Even though the causes for this asynchrony of conidial germination remain unknown, asynchrony is essential for the completion of the biological cycle of this filamentous fungus.

Performing point-of-care molecular testing for SARS-CoV-2 with RNA extraction and isothermal amplification.

To respond to the urgent need for COVID-19 testing, countries perform nucleic acid amplification tests (NAAT) for the detection of SARS-CoV-2 in centralized laboratories. Real-time RT-PCR (Reverse transcription-Polymerase Chain Reaction), used to amplify and detect the viral RNA., is considered, as the current gold standard for diagnostics. It is an efficient process, but the complex engineering required for automated RNA extraction and temperature cycling makes it incompatible for use in point of care settings [1]. In the present work, by harnessing progress made in the past two decades in isothermal amplification and paper microfluidics, we created a portable test, in which SARS-CoV-2 RNA is extracted, amplified isothermally by RT-LAMP (Loop-mediated Isothermal Amplification), and detected using intercalating dyes or fluorescent probes. Depending on the viral load in the tested samples, the detection takes between twenty minutes and one hour. Using a set of 16 pools of naso-pharyngal swab eluates, we estimated a limit of detection comparable to real-time RT-PCR (i.e. 1 genome copies per microliter of clinical sample) and no cross-reaction with eight major respiratory viruses currently circulating in Europe. We designed and fabricated an easy-to-use portable device called "COVIDISC" to carry out the test at the point of care. The low cost of the materials along with the absence of complex equipment will expedite the widespread dissemination of this device. What is proposed here is a new efficient tool to help managing the pandemics.

Interrogating RNA and protein spatial subcellular distribution in smFISH data with DypFISH.

Advances in single-cell RNA sequencing have allowed for the identification of cellular subtypes on the basis of quantification of the number of transcripts in each cell. However, cells might also differ in the spatial distribution of molecules, including RNAs. Here, we present DypFISH, an approach to quantitatively investigate the subcellular localization of RNA and protein. We introduce a range of analytical techniques to interrogate single-molecule RNA fluorescence in situ hybridization (smFISH) data in combination with protein immunolabeling. DypFISH is suited to study patterns of clustering of molecules, the association of mRNA-protein subcellular localization with microtubule organizing center orientation, and interdependence of mRNA-protein spatial distributions. We showcase how our analytical tools can achieve biological insights by utilizing cell micropatterning to constrain cellular architecture, which leads to reduction in subcellular mRNA distribution variation, allowing for the characterization of their localization patterns. Furthermore, we show that our method can be applied to physiological systems such as skeletal muscle fibers.

Missing Self-Induced Activation of NK Cells Combines with Non-Complement-Fixing Donor-Specific Antibodies to Accelerate Kidney Transplant Loss in Chronic Antibody-Mediated Rejection.

BACKGROUND: Binding of donor-specific antibodies (DSAs) to kidney allograft endothelial cells that does not activate the classic complement cascade can trigger the recruitment of innate immune effectors, including NK cells. Activated NK cells contribute to microvascular inflammation leading to chronic antibody-mediated rejection (AMR). Recipient NK cells can also trigger antibody-independent microvascular inflammation by sensing the absence of self HLA class I molecules ("missing self") on allograft endothelial cells. This translational study investigated whether the condition of missing self amplifies DSA-dependent NK cell activation to worsen chronic AMR. METHODS AND RESULTS: Among 1682 kidney transplant recipients who underwent an allograft biopsy at Lyon University Hospital between 2004 and 2017, 135 fulfilled the diagnostic criteria for AMR and were enrolled in the study. Patients with complement-fixing DSAs identified by a positive C3d binding assay (n=73, 54%) had a higher risk of transplant failure (P=0.002). Among the remaining patients with complement-independent chronic AMR (n=62, 46%), those in whom missing self was identified through donor and recipient genotyping exhibited worse allograft survival (P=0.02). In multivariable analysis, only proteinuria (HR: 7.24; P=0.01) and the presence of missing self (HR: 3.57; P=0.04) were independent predictors for transplant failure following diagnosis of chronic AMR. Cocultures of human NK cells and endothelial cells confirmed that addition of missing self to DSA-induced NK cell activation increased endothelial damage. CONCLUSIONS: The assessment of missing self at the time of diagnosis of chronic AMR identifies patients at higher risk for kidney transplant failure.

Evaluating the Stability of Spatial Keypoints via Cluster Core Correspondence Index.

Detection and analysis of informative keypoints is a fundamental problem in image analysis and computer vision. Keypoint detectors are omnipresent in visual automation tasks, and recent years have witnessed a significant surge in the number of such techniques. Evaluating the quality of keypoint detectors remains a challenging task owing to the inherent ambiguity over what constitutes a good keypoint. In this context, we introduce a reference based keypoint quality index which is based on the theory of spatial pattern analysis. Unlike traditional correspondence-based quality evaluation which counts the number of feature matches within a specified neighborhood, we present a rigorous mathematical framework to compute the statistical correspondence of the detections inside a set of salient zones (cluster cores) defined by the spatial distribution of a reference set of keypoints. We leverage the versatility of the level sets to handle hypersurfaces of arbitrary geometry, and develop a mathematical framework to estimate the model parameters analytically to reflect the robustness of a feature detection algorithm. Extensive experimental studies involving several keypoint detectors tested under different imaging scenarios demonstrate efficacy of our method to evaluate keypoint quality for generic applications in computer vision and image analysis.