This dataset is identical in content with the ISS (in-situ-sequencing) data published at the BioImage Archive: https://www.ebi.ac.uk/biostudies/bioimages/studies/S-BSST700

Here, the file names have been changed and a decoding code codebook and experimental metadata file have been added.

The purpose of this upload is to make the data readily useable with the ISS analysis code at ...

These slides were used in a workshop at the 2025 E-Science Tage in Heidelberg. Workshop Abstract: Effective Research Data Management (RDM) requires collaboration between infrastructure providers, support units, and domain-specific experts across scientific disciplines. Microscopy, or bioimaging, is a widely used technology at universities and research institutions, generating large, multi-dimensional datasets. Scientists now routinely produce microscopy data using advanced imaging modalities, often through centrally-provided instruments maintained by core facilities. Bioimaging data management presents unique challenges: files are often large (e.g., 15+ GB for whole slide images), come in various proprietary formats, and are accessed frequently for viewing as well as for complex image processing and analysis workflows. Collaboration between experimenters, clinicians, group leaders, core facility staff, and image analysts adds to the complexity, increasing the risk of data fragmentation and metadata loss. The DFG-funded project I3D:bio and the consortium NFDI4BIOIMAGE, part of Germany’s National Research Data Infrastructure (NFDI), are addressing these challenges by developing solutions and best practices for managing large, complex microscopy datasets. This workshop introduces the challenges of bioimaging RDM to institutional support personnel, including, for example, library staff, IT departments, and data stewards. Participants will explore the bioimaging RDM system OMERO, and apply structured metadata annotation and object-oriented data organization to a simple training dataset. OMERO offers centralized, secure access to data, allowing collaboration and reducing the data fragementation risk. Moreover, participants will experience the benefits of OME-Zarr, a chunked open file format designed for FAIR data sharing and remote access. OME-Zarr enables streaming of large, N-dimensional array-typed data over the Internet without the need to download whole files. An expanding toolbox for leveraging OME-Zarr for bioimaging data renders this file type a promising candidate for a standard file format suitable for use in FAIR Digital Object (FDO) implementations for microscopy data. OME-Zarr has become a pillar for imaging data sharing in two bioimaging-specific data repositories, i.e., the Image Data Resource (IDR) and the BioImage Archive (BIA). The team of Data Stewards from both abovenmentioned projects help researchers and research support staff to manage und publish bioimaging data. By the end of the workshop, participants will have gained hands-on experience with bioimaging data and will be aware of support resources like the NFDI4BIOIMAGE Help Desk for addressing specific local use cases. Our goal is to promote collaboration across disciplines to effectively manage complex bioimaging data in compliance with the FAIR principles.

Mass spectrometry imaging (MSI) has revolutionized the study of tissue metabolism by enabling the visualization of small molecule metabolites (SMMs) with high spatial resolution. However, comprehensive SMM imaging databases for different organ tissues are lacking, hindering our understanding of spatial organ metabolism. To address this resource gap, we present a large-scale SMM imaging gallery for mouse brain, kidney, and liver, capturing SMMs spanning eight chemical super classes and encompassing over 40 metabolic pathways. Manual curation and display of these imaging data sets unveil spatial patterns of metabolites that are less documented in the reported organs. Specifically, we identify 65 SMMs in brain coronal sections and 71 in sagittal tissue sections, including spatial patterns for neurotransmitters. Furthermore, we map 98 SMMs in kidneys and 66 SMMs in liver, providing insights into their amino acid and glutathione metabolism. Our insightful SMM imaging gallery serves as a critical resource for the spatial metabolism research community, filling a significant resource gap. This resource is freely available for download and can be accessed through the BioImage Archive and METASPACE repositories, providing high-quality annotated images for potential future computational models and advancing our understanding of tissue metabolism at the spatial level.

Sample volume electron microscopy dataset cropped from EMPIAR-10819 and fluorescence dataset from BioImage Archive S-BSST707 for the CLEM-Reg paper.

This data repository contains the source code and source data of our study. Raw bioimages represent cFOS labeling in different brain areas of mice after behavioral analyses (Pavlovian fear conditioning paradigms).We provide the code and training datasets that we used to generate expert and consensus models and ensembles, a model library that contains our validated consensus ensembles, the source data and our code used for the analyses, and the complete bioimage datasets of two laboratories (Lab-Wue1 [283 images] and Lab-Mue [24 images]). Official repository of our study "On the objectivity, reliability, and validity of deep learning enabled bioimage analyses." You can find our paper at eLife. In addition, we also provide all code in our GitHub repository. File organization: bioimage_data.zip: This folder contains the raw image data of all laboratories and an Excel sheet ("image_mapping.xlsx") that contains all metadata to associate the images with experimental data, like genotype, treatment condition (see code below) or whether the image was used for model training. Treatment condition code: - lab-wue1: homecage (H), context control (-), context conditioned (+) - lab-mue: early retrieval (Ext), late retrieval (Ret) - lab-inns1: control (Ctrl), extinction (Ext) - lab-inns2: Saline, L-DOPA responder, L-DOPA non-responder - lab-wue2: wildtype (WT), gad1b knock-down (KO) For each laboratory, we provide all labels predicted by the different models or ensembles as indicated with the path names: "*/labels/initialization_variant/model_type/model_or_ensemble/identifier/", and all regions in which bioimage analysis was performed. For two laboratories (lab-wue1 and lab-mue), we also provide all microscopy images. model_library.zip: This folder contains a selection of one validated consenus ensembles for each of the five bioimage datasets. source_data.zip: This folder contains the source data of our study and is organized according to the individual figures in which the data is presented. In each figure folder, you find a readme file that provides more detailed information about the respective files and which notebook was used to perform the analysis. test_data.zip: This folder contains the test dataset of lab-wue1. train_data.zip: This folder contains all training datasets that were generated in the course of this study. This includes all microscopy images, the labels of the individual experts, and the computed consensus labels. requirements.txt: This file contains a list of all packages and their versions that are required for local installation and execution of our codes. Bioimage analysis of fluorescent labels is widely used in the life sciences. Recent advances in deep learning (DL) allow automating time-consuming manual image analysis processes based on annotated training data. However, manual annotation of fluorescent features with a low signal-to-noise ratio is somewhat subjective. Training DL models on subjective annotations may be instable or yield biased models. In turn, these models may be unable to reliably detect biological effects. An analysis pipeline integrating data annotation, ground truth estimation, and model training can mitigate this risk. To evaluate this integrated process, we compared different DL-based analysis approaches. With data from two model organisms (mice, zebrafish) and five laboratories, we show that ground truth estimation from multiple human annotators helps to establish objectivity in fluorescent feature annotations. Furthermore, ensembles of multiple models trained on the estimated ground truth establish reliability and validity. Our research provides guidelines for reproducible DL-based bioimage analyses. A detailed description of specimen, microscopy methods, reagents and image processing can be found in the method section of our paper. Information about images, training datasets, codes, deep learning models and model ensembles is provided in the ReadMe files, here in this repository.

The data is presented in an Excel file with multiple tabs that contain the numerical values corresponding to flow cytometry data, Kaplan-Meir analysis, bioluminescent recording from live animals, CFUs, PFUs from murine lungs, qPCR for phage DNA, fluorescent signal quantifications from microscopy images of murine alveolar macrophages. All microscopy images associated to the manuscript are available in the Bioimage Archive repository (see “Related Dataset”).

Segmentation masks for images published with the paper describing

"Multiple direct RNA padlock probing in combination with in-situ sequencing (mudRapp-seq)":

Ahmad S, Gribling-Burrer AS, Schaust J, Fischer SC, Ambil UB, Ankenbrand MJ, Smyth RP. Visualizing the transcription and replication of influenza A viral RNAs in cells by multiple direct RNA padlock probing and in-situ sequencing (mudRapp-seq) (in review)

Raw images are published in the Bioimage Archive (identifier pending). To use these masks, run the data formatting code in the accompanying code repository to get the raw data in the correct structure and extract this zip archive into the repository root (the folder structure in the archive matches the folder structure of the repository).

Filenames in analysis/segmentation contain a hint about how they were created:

cp: direct segmentation with a cellpose model (nuclei, cells)

cpws: cell segmentation through watershed with nucleus masks as seeds

cpmc: manually corrected cellpose segmentations

Besides the final segmentation masks, the training data are included in data/training and the models in models/cellpose.

Changes:

v1.1 training data and models added

This is the dataset for the BioRxiv pre-print: "In vivo single-molecule imaging of RecB reveals efficient repair of DNA damage in Escherichia coli".

This article has now been published, and the accompanying data can be found on the BioImage Archive repository.

ABSTRACT: Efficient DNA repair is crucial for maintaining genome integrity and ensuring cell survival. In Escherichia coli, RecBCD plays a crucial role in processing DNA ends following a DNA double-strand break (DSB) to initiate repair by homologous recombination. While RecBCD has been extensively studied in vitro, less is known about how it contributes to rapid and efficient repair in living bacteria. Here, we perform single-molecule microscopy to investigate DNA repair in real-time in E. coli. We quantify RecB single-molecule mobility and monitor the induction of the DNA damage response (SOS response) in individual cells. We show that RecB binding to broken DNA ends leads to efficient repair without SOS induction. In contrast, in a RecB mutant with modified activities leading to the activation of an alternative repair pathway, repair is less efficient and leads to high SOS induction. Our findings reveal how subtle alterations in RecB activity profoundly impact the efficiency of DNA repair in E. coli.

DATASET DESCRIPTION:

See README.txt file for a throughout description of the dataset.

In summary, this dataset contains:

• Fluorescence microscopy images (OME-TIFF format) - in "Images_raw" subfolders;
• Manually curated cell segmentation outputs from BACMMAN* (TIFF and .db formats) -in "BACMMAN_outputs" subfolders;
• Configuration files used in BACMMAN* to perform the segmentation and the measurements (JSON format) - in in "BACMMAN_outputs" subfolders;
• Cell segmentation files to perform tracking in MATLAB (HDF5 format) - in "Segmentation_files" subfolders;
• Derived numerical data (CSV and MATLAB formats): measurements of SOS mean intensity for each segmented bacteria, nucleoid area for each segmented bacteria, outputs from tracking with computed Dapp values - in "Quantification_results" subfolders;
• Analysis scripts for single-particle tracking in E .coli (MATLAB format) - in "Analysis_scripts-Single-particle_tracking.zip" and also available at https://gitlab.com/MEKlab/tracking-in-e.coli/

*BACteria in Mother Maching Analyzer (BACMMAN; https://github.com/jeanollion/bacmman)

The main dataset folders are organized by genotype name and a short description (e.g. MEK707-RecB-HaloTag_SOS) . Within the main genotype folders, there are 4 data type subfolders: "Images_raw"; "BACMMAN_outputs"; "Segmentation_files"; "Quantification_results". Within these subfolders the data is categorized using a combination of the strain name, antibiotic treatment when applicable, and the dataset number (e.g.MEK707-cipro-04-Dataset-2).

The relationship between data and publication figures can be found in the "Dataset -publication_figures_relations.txt" file and in the main README.txt.

Licences:
The license for reuse of the dataset is CC-BY 4.0
The license for reuse of single-particle tracking in E. coli analysis scripts is MIT

This data set contains code and data needed to generate the figures and tables presented in "Pooled optical screening in bacteria using chromosomally expressed barcodes". See link to the publication in Related Material.Optical pooled screening is an important tool to study dynamic phenotypes for libraries of genetically engineered cells. However, the desired engineering often requires that the barcodes used for in situ genotyping are expressed from the chromosome. This has not previously been achieved in bacteria. Here we describe a method for in situ genotyping of libraries with genomic barcodes in Escherichia coli. The method is applied to measure the intracellular maturation time of 84 red fluorescent proteins.All microscopy images and the corresponding image analysis code and outputs are found on the BioImage Archive. See link in Related Material.

Using a high-throughput microscopy pipeline, we found that inhibition of dual leucine zipper kinase (DLK) increased neuronal connectivity in primary cortical cultures.

Original data for Figure 1-7 & Figures EV 1-4. The original images associated with Figure EV4B can be found on BioImage Archive (https://doi.org/10.6019/S-BIAD2092).

Additional file 2: Table S5. Predicted subcellular locations and P values of proteins in the literature biomarker dataset and the human protein atlas biomarker dataset.

We conducted a longitudinal experiment in which we monitored a K18-seeded hTau.P301L mouse model using correlative whole-brain microscopy and resting-state functional MRI. This repository contains the raw data of the resting-state functional (rsf)MRI and 3D anatomical scans of this study. Annotated processed data containing functional connectivity (FC) matrices extracted from four-voxel-sized regions-of-interest (ROIs) are also attached.

Five different samples of Drosophila melanogaster Kc167 cells were stained with Hoechst 33342, a DNA stain. The last sample (labeled nodsRNA, CIL 21789) is of wild-type cells. Each of the other four samples (labeled 48, 340, Anillin, and mad2, CIL 21749, 21739, 21759, and 21773, respectively) has a different gene knocked down by RNAi. This is the set that were treated with mad2 RNAi. There are 10 images of each sample, for a total of 50 images. The images were acquired on a Zeiss Axiovert 200M microscope. A recommended use for these five image sets is to algorithmically compute the number of cells in each image. A tab-delimited text file containing this information can be found at the URL indicated in the attribution box. The recommended citation when using these images is: 'We used the Drosophila Kc167 1 image set (Carpenter, et al., Genome Biology, 2006) available from the Broad Bioimage Benchmark Collection (www.broad.mit.edu/bbbc).'

Most neurological disorders share a phenotype of synaptic dysfunction. High-content microscopy is indispensable in the drug discovery process but the sensitivity relies on the accuracy of mature synapse detection. We present a workflow whereby synchronous calcium bursting is measured in GCaMP6f-transduced neurons. The same cultures are then subjected to Proximity Ligation Assay (PLA) to label mature synapses, the neurite network and nuclei. The integration of functional and morphological information provides a rich fingerprint of neuronal connectivity, deployable in different experimental conditions.

This image set is of Transfluor assay where an orphan GPCR (G-protein coupled receptor) is stably integrated into a beta-arrestin GFP (green) expressing U2OS cell line also showing nucleus (red). After one hour incubation with a compound, the cells were quenched with fixative (formaldehyde) and the plate was read on Cellomics ArrayScan HCS Reader using the GPCR Bioapplication. Negative results are a more uniform distribution of GFP throughout the cytoplasm and positive results are a peri-nuclear localization. Recommended attribution: 'We used the SBS Roche Transfluor image set provided by Ilya Ravkin and available from the Broad Bioimage Benchmark Collection (www.broad.mit.edu/bbbc).' Analysis of the data set can be downloaded from http://www.broadinstitute.org/bbbc/sbs_roche_transfluor.html

Describes Oregon Health and Science University Library's efforts to address researcher needs related to sharing and publishing bioimaging data sets. We are proposing a solution that focused that faciliates utility, not just preservation.