We present JDLL, an agile Java library that offers a comprehensive toolset/API to unify the development of high-end applications of DL for bioimage analysis and to streamline their installation and maintenance. JDLL provides all the functions required to consume DL models seamlessly, without being burdened by the configuration of the Python-based DL frameworks, within Java bioimage informatics platforms. Moreover, it allows the deployment of pre-trained models in the Bioimage Model Zoo (BMZ) by shipping the logic to connect to the BMZ website, download and run a selected model inference.

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.

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.

The last decade has been characterized by breakthroughs in fluorescence microscopy techniques illustrated by spatial resolution improvement but also in live-cell imaging and high-throughput microscopy techniques. This led to a constant increase in the amount and complexity of the microscopy data for a single experiment. Because manual analysis of microscopy data is very time consuming, subjective, and prohibits quantitative analyses, automation of bioimage analysis is becoming almost unavoidable. We built an informatics workflow called Substructure Analyzer to fully automate signal analysis in bioimages from fluorescent microscopy. This workflow is developed on the user-friendly open-source platform Icy and is completed by functionalities from ImageJ. It includes the pre-processing of images to improve the signal to noise ratio, the individual segmentation of cells (detection of cell boundaries) and the detection/quantification of cell bodies enriched in specific cell compartments. The main advantage of this workflow is to propose complex bio-imaging functionalities to users without image analysis expertise through a user-friendly interface. Moreover, it is highly modular and adapted to several issues from the characterization of nuclear/cytoplasmic translocation to the comparative analysis of different cell bodies in different cellular sub-structures. The functionality of this workflow is illustrated through the study of the Cajal (coiled) Bodies under oxidative stress (OS) conditions. Data from fluorescence microscopy show that their integrity in human cells is impacted a few hours after the induction of OS. This effect is characterized by a decrease of coilin nucleation into characteristic Cajal Bodies, associated with a nucleoplasmic redistribution of coilin into an increased number of smaller foci. The central role of coilin in the exchange between CB components and the surrounding nucleoplasm suggests that OS induced redistribution of coilin could affect the composition and the functionality of Cajal Bodies.

Metabolic studies and animal knockout models point to the critical role of polyunsaturated docosahexaenoic acid (22:6, DHA)-containing phospholipids (PLs) in physiology. Here, we study the impact of DHA-PLs on the dynamics of transendothelial cell macroapertures (TEMs) tunnels triggered by the RhoA GTPase inhibitory exotoxin C3 from Clostridium botulinum. Through lipidomic analyses, we show that primary human umbilical vein endothelial cells (HUVECs) subjected to DHA-diet undergo a 6-fold DHA-PLs enrichment in plasma membrane at the expense of monounsaturated OA-PLs. In contrast, OA-diet had almost no effect on PLs composition. Consequently, DHA treatment increases the nucleation rate of TEMs by 2-fold that we ascribe to a reduction of cell thickness. We reveal that the global transcellular area of cells remains conserved through a reduction of the width and lifetime of TEMs. Altogether, we reveal a homeostasis between plasma membrane DHA-PLs content and large-scale membrane dynamics.

Accurate measurements of cell morphology and behaviour are fundamentally important for understanding how disease, molecules and drugs affect cell function in vivo. Here, by using muscle stem cell (muSC) responses to injury in zebrafish as our biological paradigm, we established a 'ground truth' for muSC behaviour. This revealed that segmentation and tracking algorithms from commonly used programs are error-prone, leading us to develop a fast semi-automated image analysis pipeline that allows user-defined parameters for segmentation and correction of cell tracking. Cell Tracking Profiler (CTP) is a package that runs two existing programs, HK Means and Phagosight within the Icy image analysis suite, to enable user-managed cell tracking from 3D time-lapse datasets to provide measures of cell shape and movement. We demonstrate how CTP can be used to reveal changes to cell behaviour of muSCs in response to manipulation of the cell cytoskeleton by small-molecule inhibitors. CTP and the associated tools we have developed for analysis of outputs thus provide a powerful framework for analysing complex cell behaviour in vivo from 4D datasets that are not amenable to straightforward analysis.

Cellular morphodynamics can be used as markers for many physiological and pathological processes. This protocol provides a step-by-step guide to identify variations in motility and morphology within (or across) cell populations using non-invasive live imaging and reproducible image analysis techniques such as segmentation and tracking. Detailed instructions cover all the way from cell culturing and labelling to automatic image and statistical analyses, including the definition of multiple descriptors that characterise the shape and movement of cells in a quantitative manner. All methods are available as free open-source software and illustrated by video tutorials.

Preclinical studies of psychiatric disorders use animal models to investigate the impact of environmental factors or genetic mutations on complex traits such as decision-making and social interactions. Here, we introduce a method for the real-time analysis of the behaviour of mice housed in groups of up to four over several days and in enriched environments. The method combines computer vision through a depth-sensing infrared camera, machine learning for animal and posture identification, and radio-frequency identification to monitor the quality of mouse tracking. It tracks multiple mice accurately, extracts a list of behavioural traits of both individuals and the groups of mice, and provides a phenotypic profile for each animal. We used the method to study the impact of Shank2 and Shank3 gene mutations-mutations that are associated with autism-on mouse behaviour. Characterization and integration of data from the behavioural profiles of Shank2 and Shank3 mutant female mice revealed their distinctive activity levels and involvement in complex social interactions.

Advances in single cell RNA sequencing have allowed for the identification and characterization of cellular subtypes based on quantification of the number of transcripts in each cell. However, cells may differ not only in the number of mRNA transcripts that they exhibit, but also in their spatial and temporal distribution, intrinsic to the definition of their cellular state. Here we describe DypFISH, an approach to quantitatively investigate the spatial and temporal subcellular localization of RNA and protein, by combining micropatterning of cells with fluorescence microscopy at high resolution. We introduce a range of analytical techniques for quantitatively interrogating single molecule RNA FISH data in combination with protein immunolabeling over time. Strikingly, our results show that constraining cellular architecture reduces variation in subcellular mRNA and protein distributions, allowing the characterization of their localization and dynamics with high reproducibility. Many tissues contain cells that exist in similar constrained architectures. Thus DypFISH reveals reproducible patterns of clustering, strong correlative influences of mRNA-protein localization on MTOC orientation when they are present and interdependent dynamics globally and at specific subcellular locations which can be extended to physiological systems.

Elucidating protein functions and molecular organisation requires to localise precisely single or aggregated molecules and analyse their spatial distributions. We develop a statistical method SODA (Statistical Object Distance Analysis) that uses either micro- or nanoscopy to significantly improve on standard co-localisation techniques. Our method considers cellular geometry and densities of molecules to provide statistical maps of isolated and associated (coupled) molecules. We use SODA with three-colour structured-illumination microscopy (SIM) images of hippocampal neurons, and statistically characterise spatial organisation of thousands of synapses. We show that presynaptic synapsin is arranged in asymmetric triangle with the 2 postsynaptic markers homer and PSD95, indicating a deeper localisation of homer. We then determine stoichiometry and distance between localisations of two synaptic vesicle proteins with 3D-STORM. These findings give insights into the protein organisation at the synapse, and prove the efficiency of SODA to quantitatively assess the geometry of molecular assemblies.

Preclinical studies of psychiatric disorders require the use of animal models to investigate the impact of environmental factors or genetic mutations on complex traits such as decision-making and social interactions. Here, we present a real-time method for behavior analysis of mice housed in groups that couples computer vision, machine learning and Triggered-RFID identification to track and monitor animals over several days in enriched environments. The system extracts a thorough list of individual and collective behavioral traits and provides a unique phenotypic profile for each animal. On mouse models, we study the impact of mutations of genes Shank2 and Shank3 involved in autism. Characterization and integration of data from behavioral profiles of mutated female mice reveals distinctive activity levels and involvement in complex social configuration.

Introduction/ Background For complex diseases, commercially available anti- bodies directed again specific protein sometimes lack the specificity required for clinical purpose. Therefore, novel techniques have been proposed recently to better assess the expression status of molecules at the mRNA level, e.g. by in situ hybridization. With such a technique, single-molecule signals can then be quantified on a cell-by-cell basis in whole tissue slides. However, highly heterogeneous expression of mRNA is often observed at the tissue level, requiring the development of accurate and reproducible quantification methods. Aims In this work, our aim is to propose and evaluate a methodology for the quantification of mRNA expression in digital slides to help biomedical researchers to analyze their large-scale imaging data. In particular, we will work on a dataset of breast cancer tissue samples and evaluate the accuracy of such a methodology to quantify Breast cancer susceptibility gene 1 (BRCA1) mRNA expression levels. BRCA1 is a tumor suppressor gene associated with the triple negative breast cancer (TNBC) subtype. An accurate technique to determine BRCA1 protein tumoral expression status in TNBC would allow for informed decision and choosing adapted treatments. Methods Data: To design and evaluate our methodology, we use the data from [1]. In that study, the BRCA1 mRNA expression was assessed by in situ hybridization using RNAscope technology [2] (ACD-Bioke) for formalin-fixed, paraffin-embedded tissue samples. 88 glass slides were scanned (Hamamatsu, 40X, 0.23µm/pixel) and transferred on a Cytomine server (http://www.cytomine.be/) [3]. These slides correspond to 62 tumours (TNBC subtype). Using Cytomine web manual annotation tools, a total of more than 200 regions of interest were manually drawn by an expert in these whole-slide images. Then, independent assessment was performed by two observers in these ROI: they manually draw point annotations corresponding to tens of thousands of BRCA1 mRNA expression signals. Algorithms: We have developed the Icytomine plugin to communicate between Icy and Cytomine. It allows the importation of the subtumor images at a desired resolution and the expert annotations from Cytomine and the exportation of detection results from Icy software (http://icy.bioimageanalysis.org) [4]. In order to quantify mRNA signals, we combined several image processing routines in the Icy. Our workflow is composed of three steps: 1) color deconvolution [5] to extract the dark brown staining, 2) detection of spots based on undecimated wavelet transform [6], 3) post-processing to remove artifacts based on size, shape and color features. This transform is fast and ideal for representing isotropic objects. We will use a colocalisation coefficient to quantify the concordance between the experts. For a purpose of automation, we use scripts and protocols of Icy.Results An illustration of our results is given in Figure Our ongoing large-scale empirical study stresses the need for more confident ground-truths hence more robust algorithms. Interestingly, our current strategy detects accurately spot locations. We believe it will help us to create a larger ground-truth dataset using expert proofreading. We will report at the conference our best quantitative results. Overall, we believe that our approach will speedup in a confident way the quantification of mRNA signals in whole-slide tissue samples.

We present a novel bioimage informatics workflow that combines Icy and Cytomine software and their algorithms to enable large-scale analysis of digital slides from multiple sites. In particular, we apply this workflow on renal biopsies and evaluate empirically our approach for the automatic detection of glomeruli in hundreds of tissue sections.

Bioimage informatics has emerged as a new interdisciplinary research endeavor for bringing the power of computational and mathematical sciences into the biological imaging arena. We describe an open-source software platform, Icy, that proposes a comprehensive framework for easy algorithm development and deployment fostering community-oriented efforts. Icy offers a platform to share and publish collaborative algorithm developments, while promoting re-usability and code sharing to ease the development of new algorithms, and simplifying user's feedback and support through a community web site.

Current research in biology uses evermore complex computational and imaging tools. Here we describe Icy, a collaborative bioimage informatics platform that combines a community website for contributing and sharing tools and material, and software with a high-end visual programming framework for seamless development of sophisticated imaging workflows. Icy extends the reproducible research principles, by encouraging and facilitating the reusability, modularity, standardization and management of algorithms and protocols. Icy is free, open-source and available at http://icy.bioimageanalysis.org/.

This paper introduces ICY: a new GPL open source software providing an integrated and innovative multi platform development environment for image analysis applications. The aim of this framework is to create a common flexible platform for both applied mathematicians developing new algorithms and biologists seeking for a powerful and intuitive tool for image analysis. In this paper we detail the collaborative website and the actual Java desktop application. http://icy.bioimageanalysis.org(1).

This paper presents a new method to accelerate the automatic tracking of multiple articulated objects in video sequences. The tracking method exploits the physics modeling of the articulated objects. The physics approach has numerous advantages such as the capability to let multiple models interact when tracking simultaneously multiple objects. Solving the tracking problem is generally done with an iterative scheme involving the computation of numerous forces, which usually turns out to be time consuming. This paper tackles the computational cost bottleneck by introducing a multi scale decomposition of the force maps. At each step of the tracking optimization scheme the multiple force maps are efficiently combined using a quadtree structure to recover the resulting forces. We demonstrate through multiple experiments that our results are both exact and drastically faster than the standard computation scheme.