We propose a new method of molecular counting, or inferring the number of fluorescent emitters when only their combined intensity contributions can be observed. This problem occurs regularly in quantitative microscopy of biological complexes at the nano-scale, below the resolution limit of super-resolution microscopy, where individual objects can no longer be visually separated. Our proposed...
Bioimage analysis research workflows often require the use of various software tools and demand significant computational power and high interactivity. These workflows can result in inconsistent results due to dependencies on particular software and operating systems, an issue that becomes especially evident as computationally intensive methods like deep learning become more common.
To tackle...
Understanding the underlying mechanisms of Autism Spectrum Disorder (ASD) requires detailed analysis of behaviour in model organisms. Here, we present a complete image analysis pipeline designed to analyze ASD-like behaviours in Drosophila, providing insights into the associated mechanisms.
Our pipeline begins with the acquisition of high-resolution, large-format videos capturing...
Immunofluorescence is a powerful technique for the detection of multiple specific markers in cells; however, the fixation process prevents the study of the cells’ motility. We therefore propose a tool that helps find back tracked cells after immunofluorescence (IF). A slice of tissue is imaged during 48h at different positions with a 20x objective, creating movies on which cells are manually...
Dense, stroma-rich tumors with high extracellular matrix (ECM) content are highly resistant to chemotherapy, the standard treatment. Determining the spatial distribution of cell markers is crucial for characterizing the mechanisms of potential targets. However, an end-to-end computational pipeline has been lacking. Therefore, we developed a robust image analysis pipeline for quantifying the...
Joint analysis of different data modalities is a promising approach in developmental biology which allows to study the connection between cell-type specific gene expression and cell phenotype. Normally, using analysis methods in correlative manner poses a lot of limitations; however, studying a stereotypic model organism gives a unique opportunity to jointly analyze data obtained from...
Plasma membrane processes like clathrin-mediated endocytosis and constitutive exocytosis are often studied using diffraction-limited imaging methods. Structured Illumination Microscopy (SIM) with Total Internal Reflection Fluorescence (TIRF) offers a super-resolution technique for these studies in living cells. However, no automated, publicly available tool has existed for processing TIRF-SIM...
Stem cells are unique in their self-renewal and differentiation capacity into many cell types. They are an attractive platform for modelling human tissue in vitro, but producing homogenous cultures is challenging due to limited control of the microenvironment. Automating differentiation protocols can reduce human error and increase efficiency and yield.
Here we present an open-source...
Sub-cellular structures that are visualized as spots with fluorescence microscopy are ubiquitous in microscopy data. However, automated and accurate detection of such spots is often a challenging task. Additionally, many microscopy datasets contain multiple channels, where in addition to the spots and the cells also a second structure is visualized, such as the nucleus in single-molecule FISH...
The past 10 years have seen an explosion of algorithms, especially deep learning algorithms, which can make bioimage analysis for specific problems vastly more straightforward. Unfortunately, the median computational comfort for biologists is not always high enough to enable them to install deep learning tools successfully, and the vast majority are not comfortable working at the terminal or...
The landscape of computational bioimaging is rapidly evolving, featuring a vast array of tools, workflows, and documentation. This growth underscores the need for more accessible analytical tools to deliver comprehensive capabilities to the scientific community. The BioImage.IO Chatbot, developed in response to this need, leverages Large Language Models (LLMs) and Retrieval Augmented...
BrainGlobe is a community-driven initiative built around a set of interoperable, user-friendly, open-source software tools for neuroanatomy. It provides atlases for human, mouse, rat and zebrafish brains through a consistent interface. Each atlas includes a “standard” reference image for sample registration and corresponding brain region segmentations. This allows data from several samples to...
A longstanding quest in biology is to understand how the fertilized egg gives rise to multiple cell types organized in tissues and organs of specialized shape, size and function to suit the lifestyle of multicellular organisms. The integration of molecular, microscopy and image analysis techniques increasingly enables scientists to quantify developmental processes in live developing embryos...
Recent advances in super-resolution fluorescence microscopy have enabled the study of nanoscale sub-cellular structures. However, the two main techniques used—Single Molecule Localization Microscopy (SMLM) and STimulated Emission Depletion (STED)—are fundamentally different and difficult to reconcile. We recently published a protocol enabling to perform both techniques on the same neuron...
We will learn how to use conv-paint, a fast and interactive pixel classification tool for multi dimensional images. A graphical user interface is integrated into the image viewer napari, but we will also learn how to script the software from the python ecosystem. As a napari-plugin, conv-paint can easily be integrated with other plugins into complex image processing pipelines, even by users...
Fluorescence microscopy is an indispensable tool to visualize spatio-temporal biological mechanisms at sub-micron resolution across all areas of life sciences. However, microscopists still face a difficult trade-off between imaging resolution, throughput, light sensitivity, and scale. In practice, balancing these factors often comes down to limiting the scope of investigation: for example,...
Microscopic imaging enables us to investigate cells and how they change, but since subtle changes are hard to see by eye, we need tools such as deep learning to help us see.
Here, we are combining label-free microscopy with deep learning to predict stem cell differentiation outcomes. This is highly relevant, as the differentiation process is labor intensive, costly and subject to high...
Immunological synapses, important in cancer immunotherapy, are transient structures formed at the interface between lymphocytes and antigen-presenting cells. To assess the efficacy of immune therapy responses, we present a bioimage analysis workflow for quantifying cell interactions and identifying immune synapses in cancer patient samples using Imaging Flow Cytometry (IFC).
IFC acquires...
Image restoration methods often suffer from the "Regression to the Mean" (R2M) effect, leading to blurry results due to their inability to restore high-frequency details. This is problematic in microscopy, where the loss of such fine details can deter subsequent analysis and downstream processing.
In this work, we propose to tackle this challenge through a data-driven approach, by...
Advances in imaging and computation have enabled the detailed recording and quantification of spatial biological processes. However, there remains a gap in the statistical analysis and modeling of spatial information. A key challenge is to discover co-observed processes that statistically explain spatial localization patterns. We address this problem in the framework of spatial point...
Cross-correlation is a versatile mathematical technique for analyzing image data. It provides insights into spatial distributions, temporal dynamics, and geometric colocalization by quantifying relationships between image components. With this project, we explore a specific application of cross-correlation, namely the autocorrelation, to boost image resolution in post-processing. We...
FBIAS is a network of image analysts that provides remote support to users lacking access to such expertises services, usually beginning with a 1-hour open-desk session followed by project-based assistance. A feasibility study to compare different multiple tracking methods applied to videos of rainbow trouts was conducted by FBIAS, inside Institut Curie in collaboration with IERP, an INRAE...
With the Cell Painting assay we quantify cell morphology using six dyes to stain eight cellular components: Nucleus, mitochondria, endoplasmic reticulum, nucleoli, cytoplasmic RNA, actin, golgi aparatus, and plasma membrane. After high-throughput fluorescence microscopy, image analysis algorithms then extract thousands of morphological features from each single cell’s image. By comparing of...
In our increasingly data-centric world, converting images into knowledge is a challenge across various scientific fields. Acknowledging the immense possibilities of this drive to visualise and understand data, the FAIR Image Analysis Workflow working group is developing FAIR image analysis workflows within the Galaxy platform. Although the Galaxy platform is well-established in scientific...
The Fiji platform, a cornerstone in scientific imaging, has undergone several major developments to meet evolving community needs, focusing on cross-language integration and modernization to enhance its utility and accessibility.
Recent key initiatives include: upgrading Fiji to OpenJDK 21 with the new Jaunch launcher, which supports both JVM and Python runtimes; developing SciJava Ops for...
Quantitatively characterizing cellular morphology is a pivotal step in comprehending cellular structure and, by extension, cellular function. Electron microscopy (EM) and expansion microscopy (ExM) are complementary techniques that grant access to the intricate world of cellular ultrastructure. Nevertheless, the absence of automated, universally applicable frameworks for extracting...
For our high-content-screening service at TEFOR-Paris-Saclay, we developed a data-management and bioimage-analysis framework, which we share here with the bioimage-analysis-community.
The filesystem-based-image-database (fsdb) is designed to help labs and core facilities manage, analyze, and archive big data while being invisible to the user of the data within.
Using Linux’s authentication...
DNA double-strand breaks (DSBs) can seriously threaten the integrity of the affected cell. To maintain its genetic information, the cell needs to repair DSBs as faithfully as possible.
In Escherichia coli, the RecBCD complex recognises and processes DSBs to generate a single-stranded DNA coated with the RecA protein, which is used to search for a homologous repair template. However, little is...
Cell cycle regulation and cell size control in yeast is governed by complex protein interactions and can vary greatly depending on genetic disposition. A powerful tool to study cell cycle progression is high-throughput live-cell imaging where dynamic cellular processes can be observed for single cells over time. However, studies based on this technique are limited in scope due to the lack of...
Machine learning (ML) is becoming pivotal in life science research, offering powerful tools for interpreting complex biological data. In particular, explainable ML provides insights into the reasoning behind model predictions, highlighting the data features that drove the model outcome. Our work focuses on building explainable ML models for microscopy images. These models not only classify...
Recent developments investigating DNA topology have led to a wealth of knowledge regarding genome stability and cellular functions. The use of Atomic Force Microscopy (AFM) has had great success in imaging high-resolution DNA topology at the nanoscale, revealing insights into fundamental biological processes, which was not previously possible using traditional structural biology techniques....
We have established long-term light sheet imaging of human brain organoid development for the spatiotemporal profiling of developmental morphodynamics underlying human brain patterning and morphogenesis. Here, we present and analyze a novel dual-channel, multi-mosaic and multi-protein labeling strategy combined with a demultiplexing approach. To achieve this, we have developed Light-Insight, a...
The translation of raw data into quantitative information, which is the ultimate goal of microscopy-based research studies, requires the implementation of standardized data pipelines to process and analyze the measured images. Image quality assessment (IQA) is an essential ingredient for the validation of each intermediate result, but it frequently relies on ground-truth images, visual...
The development of single-molecule imaging approaches to study cellular machineries reconstituted from purified components is generating more diverse and complex datasets. These datasets can reveal the dynamic motion of protein assemblies, exchange kinetics of individual components and landscape of pathways supporting essential cellular reactions. However, few solutions exist for processing...
Fluorescence microscopy, a key driver for progress in the life sciences, faces limitations due to the microscope’s optics, fluorophore chemistry, and photon exposure limits, necessitating trade-offs in imaging speed, resolution, and depth. Here, we introduce MicroSplit, a deep learning-based computational multiplexing technique that enhances the imaging of multiple cellular structures within a...
Brain organoids enable mechanistic study of human brain development and provide opportunities to explore self-organization in unconstrained developmental systems. We have established long-term (~3 weeks) lightsheet microscopy on multi-mosaic neural organoids (MMOs) generated from sparse mixing of ulabelled iPSCs with fluorescently labeled human iPSCs (Actin-GFP, Histone-GFP, Tubulin-RFP,...
Quantitative analysis of biological phenomena is practically a requirement in contemporary research, particularly when dealing with image data. AI-assisted tools simplify complex tasks like image segmentation, even for those without computational expertise. Supervised machine learning excels in classifying data from minimal manual annotations. While several software solutions exist for...
The analysis of dynamic organelles remains a formidable challenge, though key to understanding biological processes. We introduce Nellie, an automated and unbiased pipeline for segmentation, tracking, and feature extraction of diverse intracellular structures. Nellie adapts to image metadata, eliminating user input. Nellie's preprocessing pipeline enhances structural contrast on multiple...
nlScript is a novel toolbox for creating unified scripting interfaces based on natural language in applications where a large number of configuration options renders traditional Graphical User Interfaces unintuitive and intricate. nlScript's concept is based on 3Dscript, where users describe in natural English sentences how 3Dscript’s rendering engine should animate volumetric microscope data...
By integrating multi-omics data into logical graph representations, systems biology aims to model biological interactions to be studied in health and disease. In this project, a multi-layered heterogeneous network was built from data made available in public databases, relating diseases and compounds to the human interactome. Morphological profiles of compound-treated cells were also included...
The increasing frequency of heatwaves we are experiencing at present strongly affects plant fertility and crops yields. While the whole plant suffers during hot periods, pollen development is especially sensitive. Our project aims to understand how some plants cope better than others with elevated temperatures. For this, we screened plants from populations with a diverse genetic background...
Image exploration and quality control (QC) are essential first steps in any bioimage analysis task. Traditionally, researchers manually inspect randomly sampled images, examine metadata, and extract image features to investigate the data. This process ensures a deeper understanding of the image data and allows for informed algorithm development. However, it often requires multiple open-source...
In this work, we present a comprehensive Python tool designed for automated large-scale cytotoxicity analysis, focusing on immune-target cell interactions. With the capacity to handle microscopic imaging with a 24-hours imaging duration of up to 100,000 cells in interaction per frame, pyCyto offers a robust and scalable solution for high-throughput image analysis. Its architecture comprises...
We are excited to introduce the community to pymmcore-plus, a new package for controlling microscopes through the open-source software Micro-Manager within a pure Python environment. pymmcore-plus is an extension of pymmcore, the original Python 3.x bindings for the C++ Micro-Manager core and, as such, it operates independently of Java, eliminating the need for Java dependencies. A key feature...
The migration of molecules and cellular organelles is essential for cellular functions. However, analysing such dynamics is challenging due to the high spatial and temporal resolution required and the accurate analysis of the diffusional tracks. Here, we investigate the migration modes of peroxisome organelles in the cytosol of living cells. Peroxisomes predominantly migrate randomly, but...
An intriguing question in cancer biology is the complex relationship between molecular profiles, as provided by transcriptomics, and their phenotypic manifestations at the cellular and the tissue scale. Understanding this relationship will enable us to comprehend the functional impact of transcriptomic deregulations and identify potential biomarkers in the context of precision medicine.
One...
"Biological imaging often generates three-dimensional images with a very large number of elements. In particular, microtomography results in images of several Giga-Bytes. The analysis of such data requires fast and efficient image processing software solutions, especially when user interaction is necessary. In some cases, algorithmic methods have been proposed but are seldom implemented within...
Budding yeasts growing in micro-fluidic devices constitute a convenient model to study how eukaryotic cells respond and adapt to sudden environmental change. Many of the intracellular changes needed for this adaptation are spatial and transient — they are observed in the dynamic localization of proteins from one sub-cellular compartment to another. These dynamics can be captured experimentally...
Reconstructing the connectome is a complex task, due to the diversity of neuronal forms and the density of the environment.
At LOB, in collaboration with IDV, we have developed a microscope combining multicolor two-photon excitation by wavelength mixing and serial block acquisition. This imaging method (Chroms), applied to a Brainbow retrovirus-labeled mouse brain, provides several...
Methods in biomedical image processing are changing very fast due to the high attention to computer vision and machine learning. Keeping up with trends and testing interesting routes in biomedicine is therefore very challenging. With this report, I would like to showcase a project-based teaching scheme that focuses on a single biomedical topic and aims to create progress in this field. Here,...
High-resolution atomic force microscopy (AFM) provides unparalleled visualisation of molecular structures and interactions in liquid, achieving sub-molecular resolution without the need for labelling or averaging. This capability enables detailed imaging of dynamic and flexible molecules like DNA and proteins, revealing their own conformational changes as well as interactions with one another....
As a biologist with a new dataset the cost of labelling and training a new ML model is prohibitive to downstream analysis. One solution is to utilise pre-trained networks in a transfer learning approach. Community efforts such as the BioImage-Model-Zoo have increased the availability of pre-trained models. However, how should the best pre-trained model for a particular task and dataset be...
Recent new developments in microscopy image analysis are heavily biased towards blob-like structures (cells, nuclei, vesicles), while tools for the exploration of filaments or curvilinear objects (spines, biopolymers, neurites, vessels, cilia) are often underrepresented. To address this gap, we developed the BigTrace plugin for Fiji, powered by the imglib2 library and using BigVolumeViewer for...
Epithelial tissue dynamics is tightly controlled spatio-temporally in numerous developmental processes and this precision is essential for the correct formation and homeostasis of organs. Quantifying epithelia dynamics is an important step in understanding this regulation but usually involves huge movies spanning thousands of cells and several hours. Thus, efficient and user-friendly tools are...
The lack of screenable phenotypes in scalable cell models has limited progress in drug discovery and early diagnostics for complex diseases. Here we present a novel unbiased phenotypic profiling platform that combines high-throughput cell culture automation, Cell Painting, and deep learning. We built various models to extract meaningful features at single cell level, including deep learning...
Deriving scientifically sound conclusions from microscopy experiments typically requires batch analysis of large image data sets. It is critical that these analysis workflows are reproducible. In addition, it is advantageous of the workflows are scalable and can be readily deployed on high performance compute infrastructures.
Here, we will present how the established Nextflow workflow...
