Biological Imaging

Cell painting assays generate high-dimensional, multi-channel imaging data that enable systematic characterization of cellular phenotypes. Increasingly, such assays are performed in longitudinal settings and under chronic perturbations, introducing additional challenges related to imaging variability, focus-field heterogeneity, and consistency across time points. Existing analysis workflows often require substantial manual adaptation to handle these complexities, limiting scalability and reproducibility. In this paper, we propose SCALE (Stable Cell painting Analysis for Longitudinal Experiments), an integrated, end-to-end analysis pipeline designed for robust longitudinal analysis of cell painting data. The pipeline combines nucleus-centered segmentation, automated quality control, feature extraction, and signal aggregation within a modular and configurable framework. Once assay-specific configurations are specified, the pipeline executes in a fully automated manner from raw images to downstream summary statistics and analysis-ready outputs. We demonstrate the utility of the pipeline using a chronic radiation exposure cell painting dataset, illustrating its ability to support consistent longitudinal comparisons across conditions and time points.

Microscope systems have traditionally separated data acquisition and analysis into distinct phases, limiting real-time insights and experimental adaptability. While open-source tools exist for device control, there remains a need for integrated software frameworks that combine acquisition and analysis. Here we present deepthought, a framework built on domain-driven design principles that unifies microscope control and data analysis through a common domain language. This framework enables automated high-throughput imaging with real-time analysis capabilities, demonstrated through studies of DNA damage responses in live cells. By implementing deepthought on a standard wide-field microscope, we achieved sample sizes exceeding 10,000 cells per condition and identified rare cellular subpopulations, capabilities typically limited to specialized high-content systems. Our approach provides a foundation for developing context-aware microscopy software that can dynamically adjust experimental parameters based on real-time analysis of biological phenomena.

Recent developments in large format electron microscopy have enabled generation of images that provide detailed ultrastructural information on normal and diseased cells and tissues. Analyses of these images increase our understanding of cellular organization and interactions and disease-related changes therein. In this manuscript, we describe a workflow for two-dimensional (2D) and three-dimensional (3D) imaging, including both optical and scanning electron microscopy (SEM) methods, that allow pathologists and cancer biology researchers to identify areas of interest from human cancer biopsies. The protocols and mounting strategies described in this workflow are compatible with 2D large format EM mapping, 3D focused ion beam-SEM and serial block face-SEM. The flexibility to use diverse imaging technologies available at most academic institutions makes this workflow useful and applicable for most life science samples. Volumetric analysis of the biopsies studied here revealed morphological, organizational and ultrastructural aspects of the tumor cells and surrounding environment that cannot be revealed by conventional 2D EM imaging. Our results indicate that although 2D EM is still an important tool in many areas of diagnostic pathology, 3D images of ultrastructural relationships between both normal and cancerous cells, in combination with their extracellular matrix, enables cancer researchers and pathologists to better understand the progression of the disease and identify potential therapeutic targets.

We are launching a machine learning (ML) competition focused on particle picking in cryo-electron tomography (cryoET) data, a crucial task in structural biology. To support this, we have created a comprehensive suite of open-source tools to develop resources for our competition, including copick for dataset management, napari plugins for interactive visualization, utilities for converting particle picks to segmentation masks, and PyTorch tools for custom dataset sampling. These resources streamline the processes of data handling, labeling, and visualization, allowing participants to focus on model development. By leveraging these tools, competitors will be better equipped to tackle the unique challenges of cryoET data and push forward advancements in particle picking techniques.

Single Particle Tracking (SPT) is one of the most widespread techniques to evaluate particle mobility in a variety of situations, such as in cellular and model membrane dynamics. The proposed TRAIT2D Python library is developed to provide object tracking, trajectory analysis and produce simulated datasets with graphical user interface. The tool allows advanced users to customise the analysis to their requirements. Availability and implementation: the software has been coded in Python, and can be accessed from: https://github.com/Eggeling-Lab-Microscope-Software/TRAIT2D, or the pypi and condaforge repositories. A comprehensive user guide is provided at https://eggeling-lab-microscope-software.github.io/TRAIT2D/.

Modern high-throughput microscopy methods such as light-sheet imaging and electron microscopy are capable of producing petabytes of data inside of a single experiment. Storage of these large images, however, is challenging because of the difficulty of moving, storing, and analyzing such vast amounts of data, which is often collected at very high data rates (>1GBps). In this report, we provide a comparison of the performance of several compression algorithms using a collection of published and unpublished datasets including confocal, fMOST, and pathology images. We also use simulated data to demonstrate the efficiency of each algorithm as image content or entropy increases. As a result of this work, we recommend the use of the BLOSC algorithm combined with ZSTD for various microscopy applications, as it produces the best compression ratio over a collection of conditions. CCS CONCEPTS* Applied computing [->] Bioinformatics; Imaging.

In situ sequencing (ISS) offers a powerful approach for spatially resolved gene expression profiling within tissue samples, but the complexity of analyzing the resulting data has limited its broader use. Here, we present iss-nf, a Nextflow-based, end-to-end workflow designed to streamline the decoding of large ISS datasets. The workflow automates critical steps, including image registration, fluorescent spot detection, transcript decoding, and quality control (QC), offering a scalable, reproducible, and user-friendly solution for ISS data analysis. We successfully applied iss-nf on multiple large datasets, including publicly available mouse brain and breast cancer tissue datasets, as well as an in-house non-small cell lung cancer (NSCLC) ISS dataset. The workflow is designed to be accessible for both experienced researchers, as well as newcomers to spatial transcriptomics, providing a robust tool for analyzing large-scale ISS data. Our results suggest that iss-nf is a valuable contribution to the growing field of spatial transcriptomics, enabling precise, modular, reproducible, and, by means of automated tiling and parallelization, scalable analysis of tissue-specific gene expression.

Segmentation of biological images identifies regions of an image which correspond to specific features of interest, which can be analysed quantitatively to answer biological questions. This task has long been a barrier to conducting large-scale biological imaging studies as it is time- and labour-intensive. Modern artificial intelligence segmentation tools can automate this process, but require high quality segmentation data for training, which is challenging to acquire. Biological segmentation data has been produced for many years, but this data is not often reused to develop new tools as it is hard to find, access, and use. Recent disparate efforts (Iudin, et al., 2023; Xu, et al., 2021; Vogelstein, et al., 2018; Ermel, et al., 2024) have been made to facilitate deposition and re-use of these valuable datasets, but more work is needed to increase re-usability. In this work, we review the current state of publicly available annotation and segmentation datasets and make specific recommendations to increase re-usability following FAIR (findable, accessible, interoperable, re-usable) principles (Wilkinson, et al., 2016) for the future.

Spatial analyses can reveal important interactions between and among cells and their microenvironment. However, most existing staining methods are limited to a handful of markers per slice, thereby limiting the number of interactions that can be studied. This limitation is frequently overcome by registering multiple images to create a single composite image containing many markers. While there are several existing image registration methods for whole slide images (WSI), most have specific use cases. Here, we present the Virtual Alignment of pathoLogy Image Series (VALIS), a fully automated pipeline that opens, registers (rigid and/or non-rigid), and saves aligned slides in the ome.tiff format. VALIS has been tested with 273 immunohistochemistry (IHC) samples and 340 immunofluorescence (IF) samples, each of which contained between 2-69 images per sample. The registered WSI tend to have low error and are completed within a matter of minutes. In addition to registering slides, VALIS can also using the registration parameters to warp point data, such as cell centroids previously determined via cell segmentation and phenotyping. VALIS is written in Python and requires only few lines of code for execution. VALIS therefore provides a free, opensource, flexible, and simple pipeline for rigid and non-rigid registration of IF and/or IHC that can facilitate spatial analyses of WSI from novel and existing datasets.

Powerful, workflow-agnostic and interactive visualisation is essential for the ad-hoc, human-in-the-loop workflows typical of cryo-electron tomography (cryo-ET). While several tools exist for visualisation and annotation of cryo-ET data, they are often integrated as part of monolithic processing pipelines, or focused on a specific task and offering limited reusability and extensibility. With each software suite presenting its own pros and cons and often tools tailored to address specific challenges, seamless integration between available pipelines is often a difficult task. As part of the effort to enable such flexibility and move the software ecosystem towards a more collaborative and modular approach, we developed blik, an open-source napari plugin for visualisation and annotation of cryo-ET data (source code: https://github.com/brisvag/blik). blik offers fast, interactive, and user-friendly 3D visualisation thanks to napari, and is built with extensibility and modularity at the core. Data is handled and exposed through well-established scientific Python libraries such as numpy arrays and pandas dataframes. Reusable components (such as data structures, file read/write, and annotation tools) are developed as independent Python libraries to encourage reuse and community contribution. By easily integrating with established image analysis tools - even outside of the cryo-ET world - blik provides a versatile platform for interacting with cryo-ET data. On top of core visualisation features - interactive and simultaneous visualisation of tomograms, particle picks and segmentations - blik provides an interface for interactive tools such as manual particle picking, surface-based and filament-based particle picking and image segmentation, as well as simple filtering tools. Additional self-contained napari plugins developed as part of this work also implement interactive plotting and selection based on particle features, and label interpolation for easier segmentation. Finally, we highlight the differences with existing software and showcase bliks applicability in biological research.

Recent advances in spatial proteomics have enabled high-dimensional protein analysis within tissue samples, yet few methods accurately detect low-abundance functional proteins. Spatial MIST (Multiplex In Situ Tagging) is one such technique, capable of profiling over 100 protein markers spatially at single-cell resolution on tissue sections and cultured cells. However, despite the availability of various open-source tools for image registration and visualization, no dedicated software exists to align the images and analyze spatial MIST data effectively. To address this gap, we present MIST-Explorer, a comprehensive, user-friendly toolkit for the visualization and analysis of single-cell spatial MIST array data. Developed in Python with a PyQt6-based graphical interface, MIST-Explorer streamlines the spatial omics workflow--from image preprocessing and registration to cell segmentation and protein quantification. The software supports two workflows: one for preprocessed datasets and another for raw image inputs, ensuring broad compatibility across experimental designs. Key features include tile-based image registration using Astroalign and PyStackReg, deep learning-based segmentation with StarDist, multi-channel visualization with layer controls, and an interactive analysis module offering ROI selection along with histograms, heatmaps, and UMAP plots. MIST-Explorer generates spatially resolved expression tables readily compatible with downstream single-cell analysis pipelines. By integrating all major steps into a single platform, MIST-Explorer empowers researchers to derive biological insights from complex spatial omics datasets without requiring extensive computational expertise. Availability and implementationFreely available at https://github.com/MIST-Explorer/MIST-Explorer.

Electron Microscopy (EM) is widely used in many scientific fields, particularly in life sciences, offering high-resolution information on the ultrastructure of biological organisms. Accurate characterization of EM image quality is important for assessing the EM tool performance, in addition to sample preparation protocol, imaging conditions, etc. This paper provides an overview of tools we developed as plugins for the popular image processing package Fiji (ImageJ) (1). These tools include signal-to-noise ratio analysis, contrast evaluation, and resolution analysis, as well as the capability to import images acquired on custom FIB-SEM instruments (2). We have also made these tools available in Python, with both versions available on GitHub.

Cryo-electron tomography (cryo-ET) datasets are rich sources of information capable of describing the localizations, structures, and interactions of macromolecules. However, most current methods for localizing particles in cryo-electron tomograms are limited to macromolecules with known structure, require extensive manual annotations, and/or are computationally expensive. Here, we present PickET, a method for localizing macromolecules in tomograms that does not rely on expert annotations and prior structures. Its performance is demonstrated on a diverse dataset comprising over a hundred tomograms from publicly available datasets, varying in sample types, sample preparation conditions, microscope hardware, and image processing workflows. We demonstrate that PickET can simultaneously localize macromolecules of various shapes, sizes, and abundance. The predicted particle localizations can be used for 3D classification and de novo structural characterization. Our fully unsupervised approach is efficient and scalable, and enables high-throughput analysis of cryo-ET data.

Tissue microarrays (TMAs) enable researchers to analyse hundreds of tissue samples simultaneously by embedding multiple samples into single arrays, enabling conservation of valuable tissue samples and experimental reagents. Moreover, profiling TMAs allows efficient screening of tissue samples for translational and clinical applications. Multiplexed imaging technologies allow for spatial profiling of proteins at single cell resolution, providing insights into tumour microenvironments (TMEs) and disease mechanisms. High-plex spatial single cell protein profiling is a powerful tool for biomarker discovery and translational cancer research, however, there remain limited options for end-to-end computational analysis of this type of data. Here, we introduce PRISM, a Python package for interactive, end-to-end analyses of TMAs with a focus on translational and clinical research using multiplexed proteomic data from the CODEX, Phenocycler Fusion (Akoya Biosciences), Comet (Lunaphore), MACSima (Miltenyi Biotec), CosMx and Cellscape (Bruker Spatial Biology) platforms. PRISM leverages the SpatialData framework to standardise data storage and ensure interoperability with single cell and spatial analysis tools. It consists of two main components: TMA Image Analysis for marker-based tissue masking, TMA dearraying, cell segmentation, and single cell feature extraction; and AnnData Analysis for quality control, clustering, iterative cell-type annotation, and spatial analysis. Integrated as a plugin within napari, PRISM provides an intuitive and purely interactive graphical interface for real-time and human-in-the-loop analyses. PRISM supports efficient multi-resolution image processing and accelerates bioinformatics workflows using efficient scalable data structures, parallelisation and GPU acceleration. By combining modular flexibility, computational efficiency, and a completely interactive interface, PRISM simplifies the translation of raw multiplexed images to actionable clinical insights, empowering researchers to explore and interact effectively with spatial omics data.

Technological development has led to ever-increasing amounts of data in high-content screening. For utilizing such data in an efficient, thorough, and user-friendly manner we developed the R package H5CellProfiler. H5CellProfiler is based on R packages data.table, ggvis, ggplot2 and shiny. H5CellProfiler launches a browser that allows scientists to analyze large single-cell datasets and make statistical summaries and graphs on their local desktop in a fast and memory-efficient manner. In addition, single-cell track labels are calculated and broken tracks are re-connected based on user-defined thresholds resulting in unique sets of annotated tracks.

Spatial proteomics provides a spatially resolved view of protein expression and localization within cells and tissues by mapping the location and abundance of proteins. There is a need for fully-integrated end-to-end imaging workflows for spatial proteomic analysis that are flexible, high-throughput, and support graphical and interactive visualizations. We present a modular and interactive spatial proteomic image analysis workflow with individual steps containerized that empowers biomedical researchers to reproducibly execute and customize complex analyses. Our workflow consists of cell segmentation, unsupervised clustering, validation of clusters on the image, and cell type clustering results visualization. Users can utilize a form-based graphical interface to execute and customize multi-step workflows with a single click or interactively adjust image processing steps within the workflow, apply workflows to various datasets, and modify input parameters as needed. We illustrated the functionality of our workflow using a cancer imaging dataset consisting of a tissue microarray (TMA) stained by high-plex immunohistochemistry. This TMA contained a variety of cancer and tissue cell types to assess the broad applicability of this workflow to different biopsy and tissue types.

Multiplexed tissue imaging can illuminate complex spatial protein expression patterns in healthy and diseased specimens. Large-scale atlas programs such as Human Tumor Atlas Network and are relying heavily on highly-multiplexed approaches including CyCIF and CODEX to image up to 100 antigens. Such high dimensionality allows a deep understanding of cellular diversity and spatial structure, but can provide a challenge for image visualization and exploration. One challenge for data portals and visualization tools is the generation of an informative and pleasing image preview that captures the full heterogeneity of the image, rather than relying on a multi-channel overlay that may be restricted to 4-6 channels. We describe Miniature, a tool to automatically generate informative image thumbnails from multiplexed tissue images in an unsupervised and scalable manner. Miniature aims to aid researchers in understanding tissue heterogeneity and identifying potential pathological features without extensive manual intervention. Miniature uses a choice of unsupervised dimensionality reduction methods including uniform manifold embedding and projection (UMAP), t-distributed stochastic neighbor Embedding (t-SNE), and principal Component analysis (PCA) to reduce on-tissue pixels from a low-resolution, high dimensional image to two or three dimensions. Pixels are then color encoded by their coordinate in low dimensional space using a choice of color maps. We show that perceptually distinct regions in Miniature thumbnails reflect known pathological features seen in both the source multiplexed tissue image and H&E imaging of the same sample. We evaluate Miniature parameters for dimensionality reduction and pixel color encoding to recommend default configurations that maximize perceptual trustworthiness to both the low-dimensional embedding and high-dimensional image and provide high Mantel correlation between the perceived color difference (delta E 2000) and distance in high- and low-dimensional space. By simulating color vision deficiency, we show that Miniature thumbnails are accessible to all. We demonstrate that Miniature thumbnails are suitable for a wide range of multiplexed tissue imaging modalities and show their application in the Human Tumor Atlas Network Data Portal.

Bioimaging experiments in plasma medicine generate complex datasets that go beyond conventional imaging studies by combining microscopy data with heterogeneous metadata from biological experiments and highly parameterized gas plasma treatments. Plasma exposure conditions, such as device configuration, gas composition, and treatment conditions, are critical determinants of biological outcome, yet they are rarely captured in a standardized, machine-readable, and reusable manner. To address this gap, we present a research data management (RDM) workflow that operationalizes the Findable, Accessible, Interoperable, and Reusable (FAIR) principles across the bioimaging data lifecycle in plasma medicine. The workflow is implemented as a structured pipeline integrating open-source tools, including OMERO for image data management, eLabFTW as an electronic laboratory notebook, Adamant for schema-driven metadata collection, and Micro-Meta App for standardized documentation of microscopy acquisition settings that are connected via programming interfaces to enable persistent linkage of metadata to image datasets using standardized annotations. The workflow is documented in a reproducible tutorial with an open-source Python Jupyter notebook hosted on GitHub. By integrating plasma treatment metadata with imaging data, this approach improves reproducibility, cross-study comparability, and data reuse in plasma medicine research.

MotivationHigh-resolution biological imaging in spatial biology produces data in many proprietary formats. The lack of compatibility between these formats restricts reproducibility and analysis, creates access issues, and makes early data analysis a challenge. ResultsHere, we introduce a graphical user interface (GUI) application designed to convert proprietary image formats into standardized formats like OME-TIFF and TIFF which we have termed Rawtunda. This tool addresses the need for easy and efficient handling of large, complex imaging data generated in spatial biology and microscopy, facilitating data sharing, analysis, and long-term storage. Featuring an intuitive interface the application supports users in converting.mcd and.ndpi formats, generated from Image Mass Cytometry (IMC) and Digital Pathology scanners, respectively. This resource aims to improve interoperability of spatial biology datasets, streamline data management workflows, and promote reproducibility in imaging research and analysis, preserving crucial image metadata. The app ensures compatibility with downstream tools and is designed for both bioinformaticians and bench biologists without experience in coding. Availability and implementationThe software, the documentation, and examples are available as open-source a https://med.virginia.edu/spatial-biology-core/rawtunda/ under the Copywrite of University of Virginia.

PSSR2 improves and expands on the previously established PSSR (Point-Scanning Super-Resolution) workflow for simultaneous super-resolution and denoising of undersampled microscopy data. PSSR2 is designed to put state-of-the-art technology into the hands of the general microscopy and biology research community, enabling user-friendly implementation of PSSR workflows with little to no programming experience required, especially through its integrated CLI and Napari plugin.