Towards community-driven metadata standards for light microscopy: tiered specifications extending the OME model

Evaluation of image analysis tools for the measurement of cellular morphology

Morphological cell analysis offers a means of identification and classification of key morphological measurement parameters linked to cell bioactivity and cell health and, as such, it is of great interest to academic and industrial research sectors. Widespread adoption of this approach has yet to occur, partially due to the lack of alignment in analysis methodologies and output metrics, limiting data comparability. Work within the cell metrology and wider multidisciplinary community aims to reduce data variability through the improved alignment of image acquisition and analysis methodologies. Furthermore, to improve data comparability, research has also focused on the identification of a minimal set of morphological measurands, often termed critical quality attributes (CQAs), which are traceable to standardised (SI) units of measurement. Whilst efforts in defining CQAs have progressed significantly for healthcare applications, there are still numerous measurement challenges associated with image analysis of cultured cells due, in part, to their complex heterogenous nature. This review evaluates the various automated image analysis tools developed for morphological analysis of four commonly considered cell morphological features: the nucleus, actin cytoskeleton, mitochondria, and the cell membrane. The measurement methodologies and outputs from each tool have been evaluated and coinciding outputs have been highlighted as potential CQAs.

Bridging imaging-based in vitro methods from biomedical research to regulatory toxicology

Imaging technologies are being increasingly used in biomedical research and experimental toxicology to gather morphological and functional information from cellular models. There is a concrete opportunity of incorporating imaging-based in vitro methods in international guidelines to respond to regulatory requirements with human relevant data. To translate these methods from R&D to international regulatory acceptance, the community needs to implement test methods under quality management systems, assess inter-laboratory transferability, and demonstrate data reliability and robustness. This article summarises current challenges associated with image acquisition, image analysis, including artificial intelligence, and data management of imaging-based methods, with examples from the developmental neurotoxicity in vitro battery and phenotypic profiling assays. The article includes considerations on specific needs and potential solutions to design and implement future validation and transferability studies.

Practical considerations for data exploration in quantitative cell biology

Data exploration is an essential step in quantitative cell biology, bridging raw data and scientific insights. Unlike polished, published figures, effective data exploration requires a flexible, hands-on approach that reveals trends, identifies outliers and refines hypotheses. This Opinion offers simple, practical advice for building a structured data exploration workflow, drawing on the authors' personal experience in analyzing bioimage datasets. In addition, the increasing availability of generative artificial intelligence and large language models makes coding and improving data workflows easier than ever before. By embracing these practices, researchers can streamline their workflows, produce more reliable conclusions and foster a collaborative, transparent approach to data analysis in cell biology.

The Spheroid Light Microscopy Image Atlas for morphometrical analysis of three-dimensional cell cultures

The application of three-dimensional (3D) cell cultures such as spheroids and organoids is growing in popularity both in academia and industry. However, morphology of the 3D architecture remains remarkably understudied. Here, we introduce an open-access Spheroid Light Microscopy Image Atlas (SLiMIA) that can serve as a training set for morphology studies of 3D cell cultures. We provide images with a variety of metadata: 9 microscopes, 47 cell lines, 8 culture media, 4 spheroid formation methods and multiple cell seeding densities; totalling approximately 8,000 images of spheroids. This comprehensive dataset can guide spheroid researchers and promote economizing of resources by advancing 3D cell culture optimization, standardization and implementation by the community at large. Considering the exponentially growing interest in spheroid morphometrical analyses and the emerging technological possibilities to do so, this atlas can be applied to train and develop image segmentation models to deepen our understanding of 3D spheroid morphometry in biomedical research.

Harmonizing the Generation and Pre-publication Stewardship of FAIR bioimage data

Together with the molecular knowledge of genes and proteins, biological images promise to significantly enhance the scientific understanding of complex cellular systems and to advance predictive and personalized therapeutic products for human health. For this potential to be realized, quality-assured bioimage data must be shared among labs at a global scale to be compared, pooled, and reanalyzed, thus unleashing untold potential beyond the original purpose for which the data was generated. There are two broad sets of requirements to enable bioimage data sharing in the life sciences. One set of requirements is articulated in the companion White Paper entitled "Enabling Global Image Data Sharing in the Life Sciences," which is published in parallel and addresses the need to build the cyberinfrastructure for sharing bioimage data (arXiv:2401.13023 [q-bio.OT], https://doi.org/10.48550/arXiv.2401.13023). Here, we detail a broad set of requirements, which involves collecting, managing, presenting, and propagating contextual information essential to assess the quality, understand the content, interpret the scientific implications, and reuse bioimage data in the context of the experimental details. We start by providing an overview of the main lessons learned to date through international community activities, which have recently made generating community standard practices for imaging Quality Control (QC) and metadata (Faklaris et al., 2022; Hammer et al., 2021; Huisman et al., 2021; Microscopy Australia, 2016; Montero Llopis et al., 2021; Rigano et al., 2021; Sarkans et al., 2021). We then provide a clear set of recommendations for amplifying this work. The driving goal is to address remaining challenges and democratize access to common practices and tools for a spectrum of biomedical researchers, regardless of their expertise, access to resources, and geographical location.

High-throughput image processing software for the study of nuclear architecture and gene expression

High-throughput imaging (HTI) generates complex imaging datasets from a large number of experimental perturbations. Commercial HTI software programs for image analysis workflows typically do not allow full customization and adoption of new image processing algorithms in the analysis modules. While open-source HTI analysis platforms provide individual modules in the workflow, like nuclei segmentation, spot detection, or cell tracking, they are often limited in integrating novel analysis modules or algorithms. Here, we introduce the High-Throughput Image Processing Software (HiTIPS) to expand the range and customization of existing HTI analysis capabilities. HiTIPS incorporates advanced image processing and machine learning algorithms for automated cell and nuclei segmentation, spot signal detection, nucleus tracking, nucleus registration, spot tracking, and quantification of spot signal intensity. Furthermore, HiTIPS features a graphical user interface that is open to integration of new analysis modules for existing analysis pipelines and to adding new analysis modules. To demonstrate the utility of HiTIPS, we present three examples of image analysis workflows for high-throughput DNA FISH, immunofluorescence (IF), and live-cell imaging of transcription in single cells. Altogether, we demonstrate that HiTIPS is a user-friendly, flexible, and open-source HTI software platform for a variety of cell biology applications.

Establishing a conceptual framework for holistic cell states and state transitions

Cell states were traditionally defined by how they looked, where they were located, and what functions they performed. In this post-genomic era, the field is largely focused on a molecular view of cell state. Moving forward, we anticipate that the observables used to define cell states will evolve again as single-cell imaging and analytics are advancing at a breakneck pace via the collection of large-scale, systematic cell image datasets and the application of quantitative image-based data science methods. This is, therefore, a key moment in the arc of cell biological research to develop approaches that integrate the spatiotemporal observables of the physical structure and organization of the cell with molecular observables toward the concept of a holistic cell state. In this perspective, we propose a conceptual framework for holistic cell states and state transitions that is data-driven, practical, and useful to enable integrative analyses and modeling across many data types.

Morphomics via next-generation electron microscopy

The living body is composed of innumerable fine and complex structures. Although these structures have been studied in the past, a vast amount of information pertaining to them still remains unknown. When attempting to observe these ultra-structures, the use of electron microscopy (EM) has become indispensable. However, conventional EM settings are limited to a narrow tissue area, which can bias observations. Recently, new trends in EM research have emerged, enabling coverage of far broader, nano-scale fields of view for two-dimensional wide areas and three-dimensional large volumes. Moreover, cutting-edge bioimage informatics conducted via deep learning has accelerated the quantification of complex morphological bioimages. Taken together, these technological and analytical advances have led to the comprehensive acquisition and quantification of cellular morphology, which now arises as a new omics science termed 'morphomics'.

Community-developed checklists for publishing images and image analyses

Images document scientific discoveries and are prevalent in modern biomedical research. Microscopy imaging in particular is currently undergoing rapid technological advancements. However, for scientists wishing to publish obtained images and image-analysis results, there are currently no unified guidelines for best practices. Consequently, microscopy images and image data in publications may be unclear or difficult to interpret. Here, we present community-developed checklists for preparing light microscopy images and describing image analyses for publications. These checklists offer authors, readers and publishers key recommendations for image formatting and annotation, color selection, data availability and reporting image-analysis workflows. The goal of our guidelines is to increase the clarity and reproducibility of image figures and thereby to heighten the quality and explanatory power of microscopy data.

A community-driven approach to enhancing the quality and interpretability of microscopy images

Scientific publications in the life sciences regularly include image data to display and communicate revelations about cellular structure and function. In 2016, a set of guiding principles known as the 'FAIR Data Principles' were put forward to ensure that research data are findable, accessible, interoperable and reproducible. However, challenges still persist regarding the quality, accessibility and interpretability of image data, and how to effectively communicate microscopy data in figures. This Perspective article details a community-driven initiative that aims to promote the accurate and understandable depiction of light microscopy data in publications. The initiative underscores the crucial role of global and diverse scientific communities in advancing the standards in the field of biological images. Additionally, the perspective delves into the historical context of scientific images, in the hope that this look into our past can help ongoing community efforts move forward.

Fully automated point spread function analysis using PyCalibrate

Reproducibility is severely limited if instrument performance is assumed rather than measured. Within optical microscopy, instrument performance is typically measured using sub-resolution fluorescent beads. However, the process is performed infrequently as it is requires time and suitably trained staff to acquire and then process the bead images. Analysis software still requires the manual entry of imaging parameters. Human error from repeatedly typing these parameters can significantly affect the outcome of the analysis, rendering the results less reproducible. To avoid this issue, PyCalibrate has been developed to fully automate the analysis of bead images. PyCalibrate can be accessed either by executing the Python code locally or via a user-friendly web portal to further improve accessibility when moving between locations and machines. PyCalibrate interfaces with the BioFormats library to make it compatible with a wide range of proprietary image formats. In this study, PyCalibrate analysis performance is directly compared with alternative free-access analysis software PSFj, MetroloJ QC and DayBook 3 and is demonstrated to have equivalent performance but without the need for user supervision.

Community-developed checklists for publishing images and image analyses

Images document scientific discoveries and are prevalent in modern biomedical research. Microscopy imaging in particular is currently undergoing rapid technological advancements. However for scientists wishing to publish the obtained images and image analyses results, there are to date no unified guidelines. Consequently, microscopy images and image data in publications may be unclear or difficult to interpret. Here we present community-developed checklists for preparing light microscopy images and image analysis for publications. These checklists offer authors, readers, and publishers key recommendations for image formatting and annotation, color selection, data availability, and for reporting image analysis workflows. The goal of our guidelines is to increase the clarity and reproducibility of image figures and thereby heighten the quality and explanatory power of microscopy data is in publications.

Building a FAIR image data ecosystem for microscopy communities

Bioimaging has now entered the era of big data with faster-than-ever development of complex microscopy technologies leading to increasingly complex datasets. This enormous increase in data size and informational complexity within those datasets has brought with it several difficulties in terms of common and harmonized data handling, analysis, and management practices, which are currently hampering the full potential of image data being realized. Here, we outline a wide range of efforts and solutions currently being developed by the microscopy community to address these challenges on the path towards FAIR bioimaging data. We also highlight how different actors in the microscopy ecosystem are working together, creating synergies that develop new approaches, and how research infrastructures, such as Euro-BioImaging, are fostering these interactions to shape the field.

Providing open imaging data at scale: An EMBL-EBI perspective

Biological imaging is one of the primary tools by which we understand living systems across scales from atoms to organisms. Rapid advances in imaging technology have increased both the spatial and temporal resolutions at which we examine those systems, as well as enabling visualisation of larger tissue volumes. These advances have huge potential but also generate ever increasing amounts of imaging data that must be stored and analysed. Public image repositories provide a critical scientific service through open data provision, supporting reproducibility of scientific results, access to reference imaging datasets and reuse of data for new scientific discovery and acceleration of image analysis methods development. The scale and scope of imaging data provides both challenges and opportunities for open sharing of image data. In this article, we provide a perspective influenced by decades of provision of open data resources for biological information, suggesting areas to focus on and a path towards global interoperability.

Big data in contemporary electron microscopy: challenges and opportunities in data transfer, compute and management

The second decade of the twenty-first century witnessed a new challenge in the handling of microscopy data. Big data, data deluge, large data, data compliance, data analytics, data integrity, data interoperability, data retention and data lifecycle are terms that have introduced themselves to the electron microscopy sciences. This is largely attributed to the booming development of new microscopy hardware tools. As a result, large digital image files with an average size of one terabyte within one single acquisition session is not uncommon nowadays, especially in the field of cryogenic electron microscopy. This brings along numerous challenges in data transfer, compute and management. In this review, we will discuss in detail the current state of international knowledge on big data in contemporary electron microscopy and how big data can be transferred, computed and managed efficiently and sustainably. Workflows, solutions, approaches and suggestions will be provided, with the example of the latest experiences in Australia. Finally, important principles such as data integrity, data lifetime and the FAIR and CARE principles will be considered.

Partnering with health sciences libraries to address challenges in bioimaging data management and sharing

Federal mandates, publishing requirements, and an interest in open science have all generated renewed attention on research data management and, in particular, data sharing practices. Due to the size and types of data they produce, bioimaging researchers confront specific challenges in aligning their data with FAIR principles, ensuring that it is findable, accessible, interoperable, and reusable. Although not always recognized by researchers, libraries can, and have been, offering support for data throughout its lifecycle by assisting with data management planning, acquisition, processing and analysis, and sharing and reuse of data. Libraries can educate researchers on best practices for research data management and sharing, facilitate connections to experts by coordinating sessions using peer educators and appropriate vendors, help assess the needs of different researcher groups to identify challenges or gaps, recommend appropriate repositories to make data as accessible as possible, and comply with funder and publisher requirements. As a centralized service within an institution, health sciences libraries have the capability to bridge silos and connect bioimaging researchers with specialized data support across campus and beyond.

Spatial and temporal organization of the genome: Current state and future aims of the 4D nucleome project

The four-dimensional nucleome (4DN) consortium studies the architecture of the genome and the nucleus in space and time. We summarize progress by the consortium and highlight the development of technologies for (1) mapping genome folding and identifying roles of nuclear components and bodies, proteins, and RNA, (2) characterizing nuclear organization with time or single-cell resolution, and (3) imaging of nuclear organization. With these tools, the consortium has provided over 2,000 public datasets. Integrative computational models based on these data are starting to reveal connections between genome structure and function. We then present a forward-looking perspective and outline current aims to (1) delineate dynamics of nuclear architecture at different timescales, from minutes to weeks as cells differentiate, in populations and in single cells, (2) characterize cis-determinants and trans-modulators of genome organization, (3) test functional consequences of changes in cis- and trans-regulators, and (4) develop predictive models of genome structure and function.

Prevalence and practices of immunofluorescent cell image processing: a systematic review

Background

We performed a systematic review that identified at least 9,000 scientific papers on PubMed that include immunofluorescent images of cells from the central nervous system (CNS). These CNS papers contain tens of thousands of immunofluorescent neural images supporting the findings of over 50,000 associated researchers. While many existing reviews discuss different aspects of immunofluorescent microscopy, such as image acquisition and staining protocols, few papers discuss immunofluorescent imaging from an image-processing perspective. We analyzed the literature to determine the image processing methods that were commonly published alongside the associated CNS cell, microscopy technique, and animal model, and highlight gaps in image processing documentation and reporting in the CNS research field.

Methods

We completed a comprehensive search of PubMed publications using Medical Subject Headings (MeSH) terms and other general search terms for CNS cells and common fluorescent microscopy techniques. Publications were found on PubMed using a combination of column description terms and row description terms. We manually tagged the comma-separated values file (CSV) metadata of each publication with the following categories: animal or cell model, quantified features, threshold techniques, segmentation techniques, and image processing software.

Results

Of the almost 9,000 immunofluorescent imaging papers identified in our search, only 856 explicitly include image processing information. Moreover, hundreds of the 856 papers are missing thresholding, segmentation, and morphological feature details necessary for explainable, unbiased, and reproducible results. In our assessment of the literature, we visualized current image processing practices, compiled the image processing options from the top twelve software programs, and designed a road map to enhance image processing. We determined that thresholding and segmentation methods were often left out of publications and underreported or underutilized for quantifying CNS cell research.

Discussion

Less than 10% of papers with immunofluorescent images include image processing in their methods. A few authors are implementing advanced methods in image analysis to quantify over 40 different CNS cell features, which can provide quantitative insights in CNS cell features that will advance CNS research. However, our review puts forward that image analysis methods will remain limited in rigor and reproducibility without more rigorous and detailed reporting of image processing methods.

Conclusion

Image processing is a critical part of CNS research that must be improved to increase scientific insight, explainability, reproducibility, and rigor.

The Twenty Questions of bioimage object analysis

The language used by microscopists who wish to find and measure objects in an image often differs in critical ways from that used by computer scientists who create tools to help them do this, making communication hard across disciplines. This work proposes a set of standardized questions that can guide analyses and shows how it can improve the future of bioimage analysis as a whole by making image analysis workflows and tools more FAIR (findable, accessible, interoperable and reusable).

Tissue-Like 3D Standard and Protocols for Microscope Quality Management

This article outlines a global study conducted by the Association of Biomedical Resource Facilities (ABRF) Light Microscopy Research Group (LMRG). The results present a novel 3D tissue-like biologically relevant standard sample that is affordable and straightforward to prepare. Detailed sample preparation, instrument-specific image acquisition protocols and image analysis methods are presented and made available to the community. The standard consists of sub-resolution and large well characterized relative intensity fluorescence microspheres embedded in a 120 µm thick 3D gel with a refractive index of 1.365. The standard allows the evaluation of several properties as a function of depth. These include the following: 1) microscope resolution with automated analysis of the point-spread function (PSF), 2) automated signal-to-noise ratio analysis, 3) calibration and correction of fluorescence intensity loss, and 4) quantitative relative intensity. Results demonstrate expected refractive index mismatch dependent losses in intensity and resolution with depth, but the relative intensities of different objects at similar depths are maintained. This is a robust standard showing reproducible results across laboratories, microscope manufacturers and objective lens types (e.g., magnification, immersion medium). Thus, these tools will be valuable for the global community to benchmark fluorescence microscopes and will contribute to improved scientific rigor and reproducibility.

Reporting reproducible imaging protocols

A reproducible imaging protocol should include four main detailed sections. The first should describe the sample preparation and include details about the tissue and/or cell culture preparation, the staining procedure, the optical grade of the coverslip, and the type of mounting media used to mount the sample. The second section should describe the configuration and components of the microscope and include the type of stand, stage, illumination, and detector, as well as the emission (EM) and excitation (EX) filters, objective, and immersion medium specifications. Specialized microscopes may have other important components in the optical path to include. The third section should describe the settings used to acquire an image like the exposure and/or dwell time, final magnification and optical resolution, the pixel and field of view (FOV) sizes, time intervals for any time lapse, total power at the objective (i.e., directed at your sample) and number of planes and step size used to collect a 3-dimensional image, and order of operations used in multi-dimensional image acquisitions. The final section should include details about the image analysis workflow such as the image processing steps, segmentation and measurement methods used to extract information from the image, data size, and necessary computing hardware and networking requirements if data sets are >1 GB, as well as citations and versions for the software and code used to perform any of these steps. Every effort should be made to make an example dataset with accurate metadata available online. Finally, specifics about the type of replicates included in the experiment and details about the statistical analysis conducted are also necessary.

Setting up a data management infrastructure for bioimaging

While the FAIR (Findable, Accessible, Interoperable, and Re-usable) principles are well accepted in the scientific community, there are still many challenges in implementing them in the day-to-day scientific process. Data management of microscopy images poses special challenges due to the volume, variety, and many proprietary formats. In particular, appropriate metadata collection, a basic requirement for FAIR data, is a real challenge for scientists due to the technical and content-related aspects. Researchers benefit here from interdisciplinary research network with centralized data management. The typically multimodal structure requires generalized data management and the corresponding acquisition of metadata. Here we report on the establishment of an appropriate infrastructure for the research network by a Core Facility and the development and integration of a software tool MDEmic that allows easy and convenient processing of metadata of microscopy images while providing high flexibility in terms of customization of metadata sets. Since it is also in the interest of the core facility to apply standards regarding the scope and serialization formats to realize successful and sustainable data management for bioimaging, we report on our efforts within the community to define standards in metadata, interfaces, and to reduce the barriers of daily data management.

Elucidating the structure and function of the nucleus-The NIH Common Fund 4D Nucleome program

Genomic architecture appears to play crucial roles in health and a variety of diseases. How nuclear structures reorganize over different timescales is elusive, partly because the tools needed to probe and perturb them are not as advanced as needed by the field. To fill this gap, the National Institutes of Health Common Fund started a program in 2015, called the 4D Nucleome (4DN), with the goal of developing and ultimately applying technologies to interrogate the structure and function of nuclear organization in space and time.

Quality assessment in light microscopy for routine use through simple tools and robust metrics

Although there is a need to demonstrate reproducibility in light microscopy acquisitions, the lack of standardized guidelines monitoring microscope health status over time has so far impaired the widespread use of quality control (QC) measurements. As scientists from 10 imaging core facilities who encounter various types of projects, we provide affordable hardware and open source software tools, rigorous protocols, and define reference values to assess QC metrics for the most common fluorescence light microscopy modalities. Seven protocols specify metrics on the microscope resolution, field illumination flatness, chromatic aberrations, illumination power stability, stage drift, positioning repeatability, and spatial-temporal noise of camera sensors. We designed the MetroloJ_QC ImageJ/Fiji Java plugin to incorporate the metrics and automate analysis. Measurements allow us to propose an extensive characterization of the QC procedures that can be used by any seasoned microscope user, from research biologists with a specialized interest in fluorescence light microscopy through to core facility staff, to ensure reproducible and quantifiable microscopy results.

Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey

Background:  Knowing the needs of the bioimaging community with respect to research data management (RDM) is essential for identifying measures that enable adoption of the FAIR (findable, accessible, interoperable, reusable) principles for microscopy and bioimage analysis data across disciplines. As an initiative within Germany's National Research Data Infrastructure, we conducted this community survey in summer 2021 to assess the state of the art of bioimaging RDM and the community needs. Methods: An online survey was conducted with a mixed question-type design. We created a questionnaire tailored to relevant topics of the bioimaging community, including specific questions on bioimaging methods and bioimage analysis, as well as more general questions on RDM principles and tools. 203 survey entries were included in the analysis covering the perspectives from various life and biomedical science disciplines and from participants at different career levels. Results: The results highlight the importance and value of bioimaging RDM and data sharing. However, the practical implementation of FAIR practices is impeded by technical hurdles, lack of knowledge, and insecurity about the legal aspects of data sharing. The survey participants request metadata guidelines and annotation tools and endorse the usage of image data management platforms. At present, OMERO (Open Microscopy Environment Remote Objects) is the best known and most widely used platform. Most respondents rely on image processing and analysis, which they regard as the most time-consuming step of the bioimage data workflow. While knowledge about and implementation of electronic lab notebooks and data management plans is limited, respondents acknowledge their potential value for data handling and publication. Conclusion: The bioimaging community acknowledges and endorses the value of RDM and data sharing. Still, there is a need for information, guidance, and standardization to foster the adoption of FAIR data handling. This survey may help inspiring targeted measures to close this gap.

Standard metadata for 3D microscopy

Recent advances in fluorescence microscopy techniques and tissue clearing, labeling, and staining provide unprecedented opportunities to investigate brain structure and function. These experiments' images make it possible to catalog brain cell types and define their location, morphology, and connectivity in a native context, leading to a better understanding of normal development and disease etiology. Consistent annotation of metadata is needed to provide the context necessary to understand, reuse, and integrate these data. This report describes an effort to establish metadata standards for three-dimensional (3D) microscopy datasets for use by the Brain Research through Advancing Innovative Neurotechnologies® (BRAIN) Initiative and the neuroscience research community. These standards were built on existing efforts and developed with input from the brain microscopy community to promote adoption. The resulting 3D Microscopy Metadata Standards (3D-MMS) includes 91 fields organized into seven categories: Contributors, Funders, Publication, Instrument, Dataset, Specimen, and Image. Adoption of these metadata standards will ensure that investigators receive credit for their work, promote data reuse, facilitate downstream analysis of shared data, and encourage collaboration.

Microscopy-BIDS: An Extension to the Brain Imaging Data Structure for Microscopy Data

The Brain Imaging Data Structure (BIDS) is a specification for organizing, sharing, and archiving neuroimaging data and metadata in a reusable way. First developed for magnetic resonance imaging (MRI) datasets, the community-led specification evolved rapidly to include other modalities such as magnetoencephalography, positron emission tomography, and quantitative MRI (qMRI). In this work, we present an extension to BIDS for microscopy imaging data, along with example datasets. Microscopy-BIDS supports common imaging methods, including 2D/3D, ex/in vivo, micro-CT, and optical and electron microscopy. Microscopy-BIDS also includes comprehensible metadata definitions for hardware, image acquisition, and sample properties. This extension will facilitate future harmonization efforts in the context of multi-modal, multi-scale imaging such as the characterization of tissue microstructure with qMRI.

The European Bioinformatics Institute (EMBL-EBI) in 2021

The European Bioinformatics Institute (EMBL-EBI) maintains a comprehensive range of freely available and up-to-date molecular data resources, which includes over 40 resources covering every major data type in the life sciences. This year's service update for EMBL-EBI includes new resources, PGS Catalog and AlphaFold DB, and updates on existing resources, including the COVID-19 Data Platform, trRosetta and RoseTTAfold models introduced in Pfam and InterPro, and the launch of Genome Integrations with Function and Sequence by UniProt and Ensembl. Furthermore, we highlight projects through which EMBL-EBI has contributed to the development of community-driven data standards and guidelines, including the Recommended Metadata for Biological Images (REMBI), and the BioModels Reproducibility Scorecard. Training is one of EMBL-EBI's core missions and a key component of the provision of bioinformatics services to users: this year's update includes many of the improvements that have been developed to EMBL-EBI's online training offering.

Micro-Meta App: an interactive tool for collecting microscopy metadata based on community specifications

For quality, interpretation, reproducibility and sharing value, microscopy images should be accompanied by detailed descriptions of the conditions that were used to produce them. Micro-Meta App is an intuitive, highly interoperable, open-source software tool that was developed in the context of the 4D Nucleome (4DN) consortium and is designed to facilitate the extraction and collection of relevant microscopy metadata as specified by the recent 4DN-BINA-OME tiered-system of Microscopy Metadata specifications. In addition to substantially lowering the burden of quality assurance, the visual nature of Micro-Meta App makes it particularly suited for training purposes.