Comment in 2021

Rigorous record-keeping and quality control are required to ensure the quality, reproducibility and value of imaging data. The 4DN Initiative and BINA here propose light Microscopy Metadata Specifications that extend the OME Data Model, scale with experimental intent and complexity, and make it possible for scientists to create comprehensive records of imaging experiments.

Deep learning algorithms are powerful tools for analyzing, restoring and transforming bioimaging data. One promise of deep learning is parameter-free one-click image analysis with expert-level performance in a fraction of the time previously required. However, as with most emerging technologies, the potential for inappropriate use is raising concerns among the research community. In this Comment, we discuss key concepts that we believe are important for researchers to consider when using deep learning for their microscopy studies. We describe how results obtained using deep learning can be validated and propose what should, in our view, be considered when choosing a suitable tool. We also suggest what aspects of a deep learning analysis should be reported in publications to ensure reproducibility. We hope this perspective will foster further discussion among developers, image analysis specialists, users and journal editors to define adequate guidelines and ensure the appropriate use of this transformative technology.

To make machine-learning analyses in the life sciences more computationally reproducible, we propose standards based on data, model and code publication, programming best practices and workflow automation. By meeting these standards, the community of researchers applying machine-learning methods in the life sciences can ensure that their analyses are worthy of trust.

We present the AIMe registry, a community-driven reporting platform for AI in biomedicine. It aims to enhance the accessibility, reproducibility and usability of biomedical AI models, and allows future revisions by the community.

Life scientists in Africa have had limited opportunity to participate in international advanced scientific training programs and workshops, which largely benefit researchers in North America, Europe and the Asia–Pacific region. Here, we chronicle the strategies adopted and challenges encountered in organizing Imaging Africa, an all-expenses-paid, continent-wide practical workshop in optical microscopy hosted in South Africa from 13 to 17 January 2020. Our experience can help steer other groups who similarly seek to organize impactful and sustainable training initiatives in Africa.

DOME is a set of community-wide recommendations for reporting supervised machine learning–based analyses applied to biological studies. Broad adoption of these recommendations will help improve machine learning assessment and reproducibility.

The most commonly used omics databases are a compilation of results from primarily male-only and sex-agnostic studies. The pervasive use of these databases critically hinders progress toward fully accounting for the biology of sex differences.

Bioimaging data have significant potential for reuse, but unlocking this potential requires systematic archiving of data and metadata in public databases. We propose draft metadata guidelines to begin addressing the needs of diverse communities within light and electron microscopy. We hope this publication and the proposed Recommended Metadata for Biological Images (REMBI) will stimulate discussions about their implementation and future extension.

The community-driven initiative Quality Assessment and Reproducibility for Instruments & Images in Light Microscopy (QUAREP-LiMi) wants to improve reproducibility for light microscopy image data through quality control (QC) management of instruments and images. It aims for a common set of QC guidelines for hardware calibration and image acquisition, management and analysis.

Immunogenomics studies have been largely limited to individuals of European ancestry, restricting the ability to identify variation in human adaptive immune responses across populations. Inclusion of a greater diversity of individuals in immunogenomics studies will substantially enhance our understanding of human immunology.

Interest in cryo-ET is rapidly growing, but many technical challenges still require solutions.

Integrative structural biology, the culmination of experimental and theoretical methods, will provide a holistic view of molecular processes.

It is time for structural biologists to embrace the challenge of quantitatively describing functional energy landscapes.

Cryo-ET, which yields both structural and spatial information, will help unlock the molecular architecture of cells.

Whole-cell models, still in the early stages of development, are poised to open the next frontier in integrative modeling.

Many challenges and considerations must be evaluated when expanding and supporting new cryo-electron microscopy facilities.

The future of macromolecular crystallography includes new X-ray sources, enhanced remote-accessible capabilities and time-resolved methods to capture intermediate structures along reaction pathways.

Computational protein modeling rapidly advances structural knowledge of viral proteins, but methods for modeling protein complexes still need improvement.

Advances in cryo-EM technology will open a new era of RNA-only 3D structure determination.

Biocurators, the backbone of the wwPDB, manage structural biology data deposition, quality, and integrity, and provide integral support to the research community worldwide.