Multi-view and multi-tile imaging offer great potential for enhancing resolution, field of view, and penetration depth in microscopy. Here we present multiview-stitcher, a versatile and modular python package for 2/3D image reconstruction that leverages registration and fusion algorithms readily available within the ecosystem for efficient use within an extensible and standardized framework. A...
Lateral inhibition mediates the adoption of alternative cell fates to produce regular cell fate patterns, with fate symmetry breaking (SB) relying on the amplification of small stochastic differences in Notch activity via an intercellular negative feedback loop. Here, we used quantitative live imaging of endogenous Scute (Sc) to study the emergence of Sensory Organ Precursor cells (SOPs) in...
The analysis of gene expression within spatial contexts has greatly advanced our understanding of cellular interactions. Spatial transcriptomics, developed through both indexing-based sequencing and microscopy-based technologies, offers unique benefits and challenges. Indexing-based methods achieve high resolution but are costly, while microscopy-based methods, such as in situ sequencing...
This project introduces a novel microscope image acquisition plugin for QuPath designed to enhance data-driven image collections by connecting a user-friendly image analysis tool with PycroManager and MicroManager for microscope control. QuPath provides quick and easy tools to generate user-defined annotations within a whole slide image to guide image collections at higher resolutions or with...
"Digital pathology and artificial intelligence (AI) applied to histopathological images are gaining interest in immuno-oncology for streamlining diagnostic and prognostic processes. This study aimed to develop a computational pipeline to analyze H&E-stained cancer tissues and identify clinically relevant tumor microenvironment features.
Our pipeline employs machine and deep learning...
"Cell division is a fundamental process in cell biology, which comprises two main phases: mitosis (nuclear division) and cytokinesis (cytoplasmic division). Despite extensive research over several decades, our understanding of cell division remains incomplete. One approach to investigate the role of specific genes in cytokinesis consists in inhibiting the genes in a cell line and to observe...
Despite advancements in oncology, triple-negative breast cancer (TNBC) remains the most aggressive subtype, characterized by poor prognosis and limited targeted treatments, with non-specific chemotherapy as the primary option. To uncover new biological mechanisms, we focus on the mechanobiology of TNBC, examining how cells and tissues perceive and integrate mechanical signals, known as...
"The handling, analysis, and storage of image data present significant challenges in a wide range of scientific disciplines. As the volume and complexity of image data continue to grow, researchers face key challenges like scalability, analysis speed, reproducibility and collaboration with peers. Containerisation technologies like Docker offer solutions to many of these challenges by providing...
Bioimage analysis is essential for advancing our understanding of cellular processes, yet traditional methods often fall short in scalability and efficiency. To address these challenges, our research focuses on developing a comprehensive infrastructure integrating data streaming and collaborative annotation for training large foundation models. Our streaming dataloader efficiently manages...
In the deep learning era, the client-server approach for bioimage analysis offers significant advantages, enhancing both extensibility and accessibility. The image analysis server can be implemented either locally or remotely, enabling efficient resource allocation while offering a wide variety of choices for the client application. Researchers can leverage powerful GPU devices suitable for...
One frequent task performed on high-resolution 3D time-lapse microscopy images is the reconstruction of cell lineage trees. The construction of such lineage trees is computationally expensive, and traditionally involves following individual cells across all time points, annotating their positions, and linking them to create complete trajectories using a 2D interface. Despite advances in...
State-of-the-art serial block face scanning electron microscopy (SBF-SEM) is used in cellular research to capture large 2D images of sliced tissue. These 2D images collectively form a 3D digital representation of the tissue. SBF-SEM was recently used to reconstruct the first 3D ultrastructural analysis of neural, glial, and vascular elements that interconnect to form the neurovascular unit...
Biological systems undergo dynamic developmental processes involving shape growth and deformation. Understanding these shape changes is key to exploring developmental mechanisms and factors influencing morphological change. One such phenomenon is the formation of the anterior-posterior (A-P) body axis of an embryo through symmetry breaking, elongation, and polarized Brachyury gene expression....
Zarrcade is a web application designed to make it easy to browse collections of OME-Zarr images. OME-Zarr is a modern file format gaining popularity in the bioimage community. Despite its cloud compatibility, it's challenging for users to make collections of these images accessible, searchable, and browsable on the web. Zarrcade addresses this by allowing users to quickly create searchable...
DaCapo is a specialized deep learning library tailored to expedite the training and application of existing machine learning approaches on large near-isotropic image data. In this correspondence, we introduce DaCapoâs unique features optimized for this specific domain, highlighting its modular structure, efficient experiment management tools, and scalable deployment capabilities. We discuss...
Recent advances in unsupervised segmentation, particularly with transformer-based models like MAESTER, have shown promise in segmenting Electron Microscopy (EM) data at the pixel level. However, despite their success, these models often struggle with capturing the full hierarchical and complex nature of EM data, where variability in texture and the intricate structure of biological components...
Digital pathology combined with AI is revolutionizing oncoimmunology by enhancing diagnostic workflows and analytical outputs. This study integrates different histopathological methods with high-throughput computational imaging to analyze the tumor microenvironment (TME).
We began by analyzing tumor tissue and structure using H&E-stained slides and computational methods. A deep learning...
High-performance computers (HPC) are essential for bioimage analysis, however the barrier to entry can be high. This project aims to simplify access to bioimage analysis tools and deep learning models on local HPC clusters, enabling frictionless access to software and large computation.
Inspired by the Bioimage ANalysis Desktop (BAND) and ZeroCostDL4Mic, we developed lightweight bash...
Determining mechanism of action (MoA) for antimicrobial compounds is key in antibiotic discovery efforts. Bacterial Cytological Profiling (BCP) is a rapid one-step assay utilising fluorescent microscopy and machine learning to discriminate between antibacterial compounds with different MoAs and help predict the MoA of novel compounds. One barrier to BCP being adopted more widely is a lack of...
While recent advancements in computer vision have greatly benefited the analysis of natural images, significant progress has also been made in volume electron microscopy (vEM). However, challenges persist in creating comprehensive frameworks that seamlessly integrate various machine learning (ML) algorithms for the automatic segmentation, detection, and classification of vEM across varied...
Instance segmentation of neurons in volumetric light microscopy images of nervous systems enables groundbreaking research in neuroscience by facilitating joint functional and morphological analyses of neural circuits at cellular resolution. Yet said multi-neuron light microscopy data exhibits extremely challenging properties for the task of instance segmentation: Individual neurons have...
Fluorescence lifetime imaging microscopy (FLIM) is a powerful technique used to probe the local environment of fluorophores. Phasor analysis is a fit-free technique based on a FFT transformation of the intensity decay that provides a visual distribution of the molecular species, clustering pixels with similar lifetimes even when they are spatially separated in the image. Phasor analysis is...
The 3D morphology of the cell nucleus is traditionally studied through high-resolution fluorescence imaging, which can be costly, time-intensive, and have phototoxic effects on cells. These constraints have spurred the development of computational ""virtual staining"" techniques that predict the fluorescence signal from transmitted-light images, offering a non-invasive and cost-effective...
Analyzing large amounts of microscopy images in a FAIR manner is an ongoing challenge, turbocharged by the large diversity of image file formats and processing approaches. Recent community work on an OME next-generation file format offers the chance to create more shareable bioimage analysis workflows. Building up on this and to address issues related to the scalability & accessibility of...
As the field of biological imaging matures from pure phenotypic observation to machine-assisted quantitative analysis, the importance of multidisciplinary collaboration has never been higher. From software engineers to network architects to deep learning experts to optics/imaging specialists, the list of professionals required to generate, store, and analyze imaging data sets of exponentially...
Quantitative analysis of bioimaging data often depends on the accurate segmentation of cells and nuclei. This is both especially important and especially difficult for the analysis of highly multiplexed imaging data, which can contain many input channels. Current deep learning-based approaches for cell segmentation in multiplexed images require simplifying the input to a small and fixed number...
Cancer, a pervasive global health concern, particularly affects the gastrointestinal (GI) tract, contributing to a significant portion of cancer cases worldwide. Successful treatment strategies necessitate an understanding of cancer heterogeneity, which spans both inter- and intra-tumor variability. Despite extensive research on genetic and cellular heterogeneity, morphological diversity in...
Scalable integration of high throughput open-source image analysis software to quantify pancreatic tissue remains elusive. Here we demonstrate an integration of Cellpose, Radial Symmetry-Fluorescent In Situ Hybridization (RS-FISH), and Fiji for a per cell assessment of mRNA copy number after RNA in-situ hybridization (RNAscope). Pipeline performance was tested against murine pancreata probed...
DNA's flexible structure and mechanics are intrinsically linked to its function. Damage to DNA disrupts essential processes, increasing cancer risk. However, it can be exploited in cancer therapeutics by targeting the DNA in cancer cells. The relationship between DNA damage and its mechanics is not well understood. New-generation metallodrugs offer a promising route for anticancer therapies -...
Deep learning has revolutionized instance segmentation, i.e. the precise localization of individual objects. In microscopy, the two most popular approaches, Stardist and Cellpose, are now used in routine to segment nuclei or cells. However, some specific applications might benefit from the segmentation of both nuclei and cells. For example, multi/hyperplexing imaging show cells associated with...
Light-sheet fluorescence microscopy (LSFM) or selective plane illumination microscopy (SPIM) is the method of choice for studying organ morphogenesis and function as it permits gentle and rapid volumetric imaging of biological specimens over days. In such inhomogeneous samples, however, sample-induced aberrations, including absorption, scattering, and refraction, degrade the image,...
Advancements in microscopy technologies, such as light sheet microscopy, allow life scientists to acquire data with better spatial and temporal resolution, enhancing potential insights into cellular, developmental, and stem cell biology. This has generated a need for robust computational tools to analyze large-scale image data.
We present Mastodon, a plugin for the ImageJ software, designed...
In-situ cryo electron tomography (cryo-ET) is a powerful technique to visualize cellular components in native context and near-atomic resolution. The workflow often involves the use of a focused ion beam (FIB) to thin down the vitrified cell into a ~200 nm thick âcryo lamellaâ. Considering the low abundance of certain subcellular structures and the extremely limited volume of the cryo lamella,...
Chromosomal instability (CIN) is a hallmark of cancer that drives metastasis, immune evasion and treatment resistance. CIN results from chromosome mis-segregation events during anaphase, as excessive chromatin is packaged in micronuclei (MN). CIN can therefore be effectively quantified by enumerating micronuclei frequency using high-content microscopy. Despite recent advancements in automation...
Microscopy image data is abundant today, but evaluating it manually is nearly impossible. For instance, to study drug-induced cell morphology changes in prenatal cardiomyocytes, researchers acquire high-resolution images of stained tissue slides. These images can contain over 10,000 cells and nuclei. However, manual evaluation is time-consuming and often lacks sufficient capacity.
To...
Hyperspectral imaging (HSI) and fluorescence lifetime imaging microscopy (FLIM) have revolutionized bioimaging by introducing new dimensions of data analysis. HSI combines imaging and spectroscopy to capture detailed fluorescence spectra, allowing simultaneous measurement of many fluorophores in a single field-of-view. FLIM provides an extra temporal resolution by measuring photon arrival...
Cell mechanics impact cell shape and drive crucial morphological changes during development and disease. However, quantifying mechanical parameters in living cells in a tissue context is challenging. Here, we introduce OptiCell3D, a computational pipeline to infer mechanical properties of cells from 3D microscopy images. OptiCell3D leverages a deformable cell model and gradient-based...
Creation of machine learning networks for biological imaging tasks often suffers from a crucial gap: the subject matter experts who understand the images well are not typically computationally comfortable training neural networks, and lack simple ways getting started to do so. We present here Piximi (piximi.app), a free, open-source "Images To Discovery" web app designed to make it easy to...
Mosquito-borne diseases pose a major global health threat, especially in tropical and subtropical regions, worsened by global warming. Mosquito species identification is vital for research, but traditional methods are costly and require expertise. Deep learning offers a promising solution, yet Convolutional Neural Network (CNN) models often perform well only in controlled environments....
Cell tracking is a key computational task in live single-cell microscopy. With the advent of automated high-throughput microscopy platforms, the amount of data quickly exceeds what humans are able to overlook. Thus, reliable and uncertainty-aware data analysis pipelines to process the collected amounts of data become crucial. In this work, we investigate the problem of quantifying uncertainty...
With live cell microscopy and image analysis, we can follow dividing cells or bacteria to obtain cell lineages. From these lineages, we can study cellular dynamics at different scales: during one cell cycle, over multiple cell cycles, or comparatively between sister and cousin cells. However, while a few frameworks have emerged to analyze tracking data, they focus on cell migration and not on...
Cell tracking and lineage provide unique insights to study bacterial growth and dynamics. Tracking strongly relies on segmentation quality, and integrating accurate and robust segmentation algorithms is a key challenge when developing end-to-end tracking tools. Omnipose, a state of the art deep learning algorithm developed for bacteria segmentation, proved to outperform more traditional...
Although we have sequenced the entire genome, we still do not understand how many diseases evolve and progress. This is partly due to the challenge in observing the nanometre scale interactions of flexible DNA molecules, and the large conformational landscape that can encourage or inhibit some protein binding mechanisms. Our atomic force microscopy (AFM) techniques can probe biomolecular...
Recent advancements in image-based spatial RNA profiling enable to resolve tens to hundreds of distinct RNA species with high spatial resolution. In this context, the ability to assign detected RNA transcripts to individual cells is crucial for downstream analysis, such as in-situ cell type calling. To this end, a DAPI channel is acquired, from which nuclei can be segmented by state-of-the art...
Advanced cell cultures based on human stem cells are of great interest to improve human safety and reduce, refine, and replace animal tests when evaluating substances. Human induced pluripotent stem cells (hiPSCs) can be turned into beating heart muscle cells known as cardiomyocytes, which model the early developmental stages of the embryonic heart. It is essential to reliably identify these...
Phase-contrast microscopy has become the gold standard to determine the shape and track the growth of individual bacterial cells, contributing to the identification of different cellular morphologies and to the development of models for cell size homeostasis. Although phase-contrast microscopy is well suited to the characterization of well separated, individual cellsâsuch as those formed by...
Supervised learning algorithms for image segmentation provide exceptional results in situations where they can be applied. However, their performance diminishes when the training data is limited, or the image conditions vary considerably. Such is the case of localizing suitable acquisition points for cryo-electron tomography (CryoET): the image conditions change during screening sessions, and...
Image-based cell profiling has become a fundamental approach for understanding biological function across diverse conditions. However, as bio-imaging datasets scale, existing tooling for image-based profiling has largely been co-opted from software that initially catered to biologists running smaller, and often interactive, experimental workflows. Here, we introduce pollen, a fast and...
Single Molecule Localization Microscopy (SMLM) surpasses diffraction limit by separating signal from individual proteins in time. Although the usual resolution limit is around 20 nm for most techniques, DNA-PAINT [1] and RESI [2] achieve the resolution at the level of individual proteins. Inferring protein positions from sets of localizations is key to examining oligomeric configurations of...
Based on the NEUBIAS (bio image analysts) and COMULIS (Correlated Multimodal Imaging in Life science) community effort, we have build a data base of image analysis software, aiming to be feed and used by the community. In this workshop, we will show how to search efficiently for an existing tool in bio image analysis, how to add a new tool or ressource and how to curate an existing entry, that...
The Human Protein Atlas (HPA) is a comprehensive resource that maps the human proteome by documenting the expression and localization of proteins in human tissues, cells, and organs. It integrates various high-throughput technologies and techniques to provide detailed information about where and how proteins are expressed within the human body. Open access is one of the key features of this...
Cryo-electron tomography (cryo-ET) and subtomogram averaging (STA) have become the go-to methods for examining cellular structures in their near-native states.
However, cryo-ET, adaptable to various biological questions, often requires specific data processing adjustments, rendering a one-size-fits-all approach ineffective.
Moreover, cryo-ET is becoming increasingly complex, with more...
Measuring the quality of fluorescence microscopy images is critical for applications including automated image acquisition and image processing. Most image quality metrics used are full-reference metrics, where a test image is quantitatively compared to a reference or âground truthâ image. Commonly used full-reference metrics in fluorescence microscopy include NRMSE, PSNR, SSIM and Pearsonâs...
Non-gallstone pancreatitis is a painful and common inflammation of the pancreas without specific, causal treatment. Here, we study the effect of a novel treatment candidate on mouse pancreatitis. To determine treatment efficacy, we ask how inflamed and treated pancreases resemble their untreated and healthy counterparts in histopathology slices.
For this, we take two approaches: For one,...
Accurate analysis of microscopy images is hindered by the presence of noise. This noise is usually signal-dependent and often additionally correlated along rows or columns of pixels. Current self- and unsupervised denoisers can address signal-dependent noise, but none can reliably remove noise that is also row- or column-correlated. Here, we present the first fully unsupervised deep...
We present the Virtual Embryo Zoo, an innovative web app for visualizing and sharing embryo cell tracking data. This platform empowers researchers to investigate single-cell embryogenesis of six commonly studied model organisms: Drosophila, zebrafish, C. elegans, Ascidian, mouse, and Tribolium, through an intuitive and accessible web-based interface. The Virtual Embryo Zoo viewer allows users...
