The imaging datasets from "SEC mediates phase transition of SPT5 during transcriptional pause release" are publicly available via the BioImage Archive database

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.

This dataset contains segmentation masks generated with Cellpose for a subset of the images included in the BioImage Archive submission S-BIAD144

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

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.

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). In order 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 the segmentation.zip archive into analysis/segmentation.

Filenames contain a hint on 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

Samples of R. glauca, R. sorocarpa, R. warnstorfii were collected by Uwe Schwarz from an arable stubble field near Aichtal Grötzingen in Baden-Württemberg, Germany on 09/13/2021 (geographic coordinates: 48.638275 N, 9.2534083 E, elevation: 376 m, precision: 10 m). To evaluate phylogenetic distances, Lunularia cruciata (L.) Dumort. ex Lindb. was additionally sampled near the lab site on 12/08/2021 at 51.494848 N, 11.942323 E. The specimens were brought to the lab at IPB in sterile petri dishes, where plant material was isolated, washed under a light microscope to remove dirt and other residues, filled into Eppendorf tubes and shock-frozen. Voucher specimens were stored in the herbarium Haussknecht Jena (voucher id’s: Riccia glauca: JE04010991, Riccia sorocarpa: JE04010990, Riccia warnstorfii: JE04010989, Lunularia cruciata: JE04010993). For image acquisition, a Zeiss Axio Scope.A1 HAL 100/HBO, 6x HD/DIC, M27, 10x/23 microscope with an achromatic-aplanatic 0.9 H D Ph DIC condenser was used for microscopy utilizing the objectives EC Plan Neofluar 2.5x/0.075 M27 (a=8.8mm), Plan-Apochromat 5x/0.16 M27 (a=12.1mm), Plan-Apochromat 10x/0.45 M27 (a=2.1mm), Plan-Apochromat 20x/0.8 M27 (a=0.55mm), and Plan-Apochromat 40x/0.95 Korr M27 (a=0.25mm) using the EC PN and the Fluar 40x/1.30 III and PA 40x/0.95 III filters for DIC. The conversion filter CB3 and the interference filter wideband green were used to improve digital reproduction of colors. The color balance was adjusted in the camera and in software accordingly. For macroscopy and for preparing microscopy slides, a binocular microscope Zeiss Stemi 2000c was used. For macroscopic images, the Venus Optics Laowa 25mm 2.5-5.0x ultra-macro for Canon EF and the Canon EF-RF adapter were used. To acquire digital images, a full-frame, high-resolution camera (Canon EOS RP, 26 megapixel) was used and adapted to the photographic objectives or to the microscopes using binocular phototubes with sliding prism 30°/23 (Axio Scope.A1) and 100:0/0:100 reversed image (Stemi 2000c) using 60-T2 camera adapter for Canon EOS and a Canon EF-RF adapter. To construct images with extended depth-of-field, images were recorded at different focal planes. This “focus stacking” approach was automatized for macroscopy by attaching the camera to a Cognisys StackShot macro rail fixed on a Novoflex macro stand, and for microscopy by adapting a Cognisys StackShot motor to the fine adjustment of the microscope using two cogged wheels, one small wheel (1 cm diameter) adapted on the motor and one large wheel (8.5 cm diameter) on the fine adjustment of the microscope. The two cogged wheels were coupled with a toothed belt to obtain fine step increments of the stepping motor for high magnifications. A Cognisys StackShot controller was used to control the amount and distance of the stepping motor with the following controller settings: Dist/Rev: 3200 stp, Backlash: 0 steps, # pics: 1, Tsettle: 100.0 ms, Toff: 450.0 ms, Auto Return: yes, Speed: 3000 st/sec, Tlapse: off, Tpulse: 800.0 ms, Tramp: 100 ms, Units: steps, Torque: 6, Hi Precision: Off, LCD Backlight: 10, Mode: Auto-Step using between 25 steps (magnification 1x) and 50 steps (magnification 25x) and 100 steps (magnification 400x) (number of steps depending on aperture settings and effective magnification). Raw images were recorded in CR3-format and pre-processed with Adobe Camera RAW. Non-destructive image processing such as corrections of the field curvature, removal of chromatic aberration, color balance, increase of contrast and brightness were performed in Adobe Camera RAW. Images were then exported to TIFF-format and any image processing steps were recorded in individual Adobe XMP-files. Multi-focus image fusion was performed on the individual images in the z-stacks using the software Helicon Focus 8.1.1 and by choosing the algorithms depth map and pyramid with different settings of radius (4, 8, 16, 24) and smoothing (2, 4). The best composite image was chosen manually and retained. When composite images contained specimen that were larger than the frame, several images were stitched together using the panorama stitching function in the software Affinity Photo 1.10.5. Images were manually segmented and interfering background removed using the flood select, brush selection and freehand selection tools in the software Affinity Photo. In order to determine the scale, a stage micrometer was photographed separately with any of the objectives and microscope combinations. The scale was calculated per pixel for each combination and scale bars were put post-hoc onto the segmented images. Meta-data including species name, taxonomic rank information (NCBI-Taxon and GBIF taxonomy identifiers), voucher specimen id, image acquisition date, an object description including the name of the captured phenotypic character(s), the used objective, microscope, and magnification were associated with any raw image based on unique respective file names. Individual file names, name within an image focus stack and name within an image stitching stack were recorded additionally to facilitate subsequent automized image processing in workflows. Python scripts were created to automatize image fusion and image stitching tasks. Image features were estimated using the R package EBImage (Pau et al., 2010) by extracting the histograms of the red, green, and blue channels of the bioimages representing the visible spectra of the thalli of the different species. To investigate relationships of the image properties and the molecular traits, distance-based ReDundancy Analyses (dbRDA) were performed using the dbrda function of the package vegan (Peters et al., 2018a). Spectral values other than pure black (all RGB channels zero) and pure white (all RGB channels one) were extracted from the histogram models and used as traits in a dbRDA model. A Euclidean distance measure was used for the ordination. The dbRDA-model with the largest explained variance was chosen using forward variable selection and the ordistep function. The goodness of fit statistic (squared correlation coefficient) was determined for the remaining variables by applying the envfit function on the dbRDA ordination model. Raw camera and pre-processed imaging data in CR3 and TIFF format, respectively, were deposited to the BioImage Archive (BioStudies) using the command line IBM Aspera software tool ascp version 3.8.1.161274 to ensure that data has been transmitted without errors. The raw bioimaging data is available under the BioStudies identifier S-BIAD443 (https://www.ebi.ac.uk/biostudies/studies/S-BIAD443). Processed images were converted to the Bio-Formats OME-TIFF format by creating intermediate ZARR-pyramid tiles using the bioformats2raw converter version 0.4.0 and then using the raw2ometiff version 0.3.0 software tool to create the final pyramid images. Processed images and the metadata were first aggregated in a TSV table and then deposited to the Image Data Resource (https://idr.openmicroscopy.org/search/?query=Name:137) under accession number idr0137 using the software Globus Connect Personal 3.1.6.

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

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.

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 ...

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.

Samples of were collected in Southern Sweden in September 2022 and Germany in October 2022. The specimens were brought to the lab at IPB in sterile petri dishes and stored for five days in a sample incubator to let plants acclimatize. Plant material was isolated, washed under a light microscope to remove dirt and other residues, filled into Eppendorf tubes and shock-frozen. Voucher specimens were stored in the herbarium Haussknecht Jena (voucher barcodes: JE04010739, JE04010740, JE04010741, JE04010742, JE04010743, JE04010744, JE04010745, JE04010746, JE04010747, JE04010748, JE04010749, JE04010750, JE04010751, JE04010752, JE04010753, JE04010754). For image acquisition, a Zeiss Axio Scope.A1 HAL 50, 6x HD/DIC, M27, 10x/23 microscope with an achromatic-aplanatic 0.9 H D Ph DIC condenser was used for microscopy utilizing the objectives EC Plan Neofluar 2.5x/0.075 M27 (a=8.8mm), Plan-Apochromat 5x/0.16 M27 (a=12.1mm), Plan-Apochromat 10x/0.45 M27 (a=2.1mm), Plan-Apochromat 20x/0.8 M27 (a=0.55mm), and Plan-Apochromat 40x/0.95 Korr M27 (a=0.25mm) using the EC PN and the Fluar 40x/1.30 III and PA 40x/0.95 III filters for DIC. The conversion filter CB3 and the interference filter wideband green were used to improve digital reproduction of colors. The color balance was adjusted in the camera and in software accordingly. For macroscopy and for preparing microscopy slides, a binocular stereo microscope Zeiss Stemi 2000c was used. For macroscopic images, the Venus Optics Laowa 25mm 2.5-5.0x ultra-macro for Canon EF and the Canon EF-RF adapter were used. To acquire digital images, a full-frame, high-resolution camera (Canon EOS RP, 26 megapixel) was used and adapted to the photographic objectives or to the microscopes using binocular phototubes with sliding prism 30°/23 (Axio Scope.A1) and 100:0/0:100 reversed image (Stemi 2000c) using 60-T2 camera adapter for Canon EOS and a Canon EF-RF adapter. To construct images with extended depth-of-field, images were recorded at different focal planes and by attaching the camera to a Cognisys StackShot macro rail fixed on a Novoflex macro stand, and for microscopy by adapting a Cognisys StackShot motor to the fine adjustment of the microscope using two cogged wheels, one small wheel (1 cm diameter) adapted on the motor and one large wheel (8.5 cm diameter) on the fine adjustment of the microscope. The two cogged wheels were coupled with a toothed belt to obtain fine step increments of the stepping motor for high magnifications. A Cognisys StackShot controller was used to control the amount and distance of the stepping motor with the following controller settings: Dist/Rev: 3200 stp, Backlash: 0 steps, # pics: 1, Tsettle: 100.0 ms, Toff: 450.0 ms, Auto Return: yes, Speed: 3000 st/sec, Tlapse: off, Tpulse: 800.0 ms, Tramp: 100 ms, Units: steps, Torque: 6, Hi Precision: Off, LCD Backlight: 10, Mode: Auto-Step using between 25 steps (magnification 1x) and 50 steps (magnification 25x) and 100 steps (magnification 400x) (number of steps depending on aperture settings and effective magnification). Raw images were recorded in CR3-format and pre-processed with Adobe Lightroom Classic (2022 version) where non-destructive image processing such as corrections of the field curvature, removal of chromatic aberration, color balance, increase of contrast and brightness were performed (NELSON 2012). Images were then exported to TIFF-format and any image processing steps were recorded in individual Adobe XMP-files. Multi-focus image fusion was performed on the individual images in the z-stacks using the software Helicon Focus 8.2.9 and by choosing the algorithms depth map and pyramid with different settings of radius (4, 8, 16, 24) and smoothing (2, 4). The best composite image was chosen manually and retained. When composite images contained specimen that were larger than the frame, several images were stitched together using the Photomerge-Reposition function in the software Adobe Photoshop 2023. Images were manually segmented and interfering background removed using the object selection tools. In order to determine the scale, a stage micrometer was photographed separately with any of the objectives and microscope combinations. The scale was calculated per pixel for each combination and scale bars were put post-hoc onto the segmented images. Measurements of morphometric characters were performed manually with ImageJ / Fiji (SCHINDELIN 2012). After setting up the scales for the individual images in the Image-Properties menu, the following morphometric characters were measured: thallus width [µm], thallus length [µm], thallus with violet pigments [0/1], ventral scales [0/1], ventral scales with slime cells [0/1], ventral scales with violet pigments [0/1], ventral scales with hairs [0/1], air pores [0/1], width of ring cells of air pores in adaxial view [µm], height of ring cells p fair pores in cross section [µm], number of ring cells of air pores in cross section [#], width of ring cells of air pores in cross section [µm], height of ring cells of air pores in cross section [µm], width of epidermis cells in cross section [µm], height of epidermis cells in cross section [µm], width of subepidermal cells in cross section [µm], height of subepidermal cells in cross section [µm], width of thallus in cross section [µm], height of thallus in cross section [µm], height of thallus wing in cross section [µm], angle of thallus wing in cross section [°], width of thallus wing in cross section [µm], area of thallus in cross section [µm2]. Lengths and widths were measured using the Measure function from the Analyze menu and saved in CSV files. To automate the measurement of areas, a pixel classification model was first trained using the plugin LabKit (Arzt et al. 2022) by selecting representative background and foreground areas, training and saving the classifier, which was then imported in the plugin StarDist (WEIGERT et al. 2020), which was used to automatically segment the images. Segmented areas were then measured using the Measure function from the Analyze menu and results were saved. CSV files with all individual morphometric measurements of all specimens were joined into one single table and used for subsequent data analyses. Metadata including species name, taxonomic rank information (NCBI-Taxon and GBIF taxonomy identifiers), voucher specimen id, image acquisition date, an object description including the name of the captured phenotypic character(s), the used objective, microscope, and magnification were associated with any raw image based on unique respective file names. Individual file names, name within an image focus stack and name within an image stitching stack were recorded additionally to facilitate subsequent automized image processing in workflows. Python scripts were created to automatize image fusion and image stitching tasks. Raw camera and pre-processed imaging data in CR3 and TIFF format, respectively, were deposited to the BioImage Archive (BioStudies) using the command line IBM Aspera software tool ascp version 3.8.1.161274 to ensure that data has been transmitted without errors. The raw bioimaging data is available under the BioStudies identifier S-BIAD824 (https://www.ebi.ac.uk/biostudies/studies/S-BIAD824). Processed images were converted to the Bio-Formats OME-TIFF format by creating intermediate ZARR-pyramid tiles using the bioformats2raw converter version 0.7.0 and then using the raw2ometiff version 0.5.0 software tool to create the final pyramid images. Processed images and the metadata were first aggregated in a TSV table and then deposited to the Image Data Resource using the software Globus Connect Personal 3.1.6.

Version History In March 2025, sequencing results revealed one species to have a different identification in this study. The metadata for "Riccia gougetiana" has been updated to "Riccia ciliifera".

This is a benchmark dataset used to develop and test a new metric for quantitatively describing vacuome organization in developing root tissues.

This Zenodo record is a copy of BioImage Archive study S-BIAD2226, deposited here solely in order to meet funders requirements but probably with some loss of functionality. If you intend to work with these data, use of the BioImage Archive original is highly recommended.

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