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  • Scientific breakthroughs can change how we understand and live in the world, disrupting long-held assumptions and concepts and raising new questions for philosophy and science. To address these challenges, we describe a model for collaboration of scientists with philosophers and ethicists, and its benefits to the research process and outcomes.

    • Jeantine E. Lunshof
    • Julia Rijssenbeek
  • Tissues, organs and organ systems are composed of interacting cells (the cellome). We discuss the emergence of an omics approach that we refer to as cellomics. It enables cellome-wide analysis in whole-organ or whole-body specimens, based on advanced three-dimensional imaging and image analysis technology. We think that cellomics will pave the way for the incorporation of cellular, intercellular and spatial information across millions of cells in our body.

    • Tomoki T. Mitani
    • Etsuo A. Susaki
    • Hiroki R. Ueda
  • In vivo developmental atlases provide a crucial reference for the new class of stem-cell-derived human embryo models, helping accelerate insights into the mechanisms of human development.

    • Muzlifah Haniffa
    • Aidan Maartens
    • Sarah A. Teichmann
  • Advancements in methods that enable in vitro culture of mammalian embryos have become an essential way of investigating mammalian early embryonic development and modeling developmental and pregnancy-related disorders. Here, we discuss the recent method development in this space and analyze current challenges and future directions.

    • Xulun Wu
    • Jinglei Zhai
    • Hongmei Wang
  • The creation of multiple stem-cell-derived models of mammalian embryogenesis is opening many new doors to study human development and brings a need for scientists to demonstrate responsible dialog over the associated ethical issues.

    • Magdalena Zernicka-Goetz
  • Recent methodological advances in measurements of geometry and forces in the early embryo and its models are enabling a deeper understanding of the complex interplay of genetics, mechanics and geometry during development.

    • Zong-Yuan Liu
    • Vikas Trivedi
    • Idse Heemskerk
  • I discuss how recent advancements in lineage tracing methods have enabled rare insight into the cell fate and trajectory during development.

    • Bushra Raj
  • The field of bioimage analysis is poised for a major transformation, owing to advancements in imaging technologies and artificial intelligence. The emergence of multimodal foundation models — which are akin to large language models (such as ChatGPT) but are capable of comprehending and processing biological images — holds great potential for ushering in a revolutionary era in bioimage analysis.

    • Loïc A. Royer
  • Here we discuss the prospects of bioimage analysis in the context of the African research landscape as well as challenges faced in the development of bioimage analysis in countries on the continent. We also speculate about potential approaches and areas of focus to overcome these challenges and thus build the communities, infrastructure and initiatives that are required to grow image analysis in African research.

    • Mai Atef Rahmoon
    • Gizeaddis Lamesgin Simegn
    • Michael A. Reiche
  • 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).

    • Beth A. Cimini
    • Kevin W. Eliceiri
  • We dream of a future where light microscopes have new capabilities: language-guided image acquisition, automatic image analysis based on extensive prior training from biologist experts, and language-guided image analysis for custom analyses. Most capabilities have reached the proof-of-principle stage, but implementation would be accelerated by efforts to gather appropriate training sets and make user-friendly interfaces.

    • Anne E. Carpenter
    • Beth A. Cimini
    • Kevin W. Eliceiri
  • The future of bioimage analysis is increasingly defined by the development and use of tools that rely on deep learning and artificial intelligence (AI). For this trend to continue in a way most useful for stimulating scientific progress, it will require our multidisciplinary community to work together, establish FAIR (findable, accessible, interoperable and reusable) data sharing and deliver usable and reproducible analytical tools.

    • Damian Dalle Nogare
    • Matthew Hartley
    • Florian Jug
  • Concurrent advances in imaging technologies and deep learning have transformed the nature and scale of data that can now be collected with imaging. Here we discuss the progress that has been made and outline potential research directions at the intersection of deep learning and imaging-based measurements of living systems.

    • Morgan Schwartz
    • Uriah Israel
    • David Van Valen
  • The bridging of domains such as deep learning-driven image analysis and biology brings exciting promises of previously impossible discoveries as well as perils of misinterpretation and misapplication. We encourage continual communication between method developers and application scientists that emphases likely pitfalls and provides validation tools in conjunction with new techniques.

    • Talley Lambert
    • Jennifer Waters
  • In the ever-evolving landscape of biological imaging technology, it is crucial to develop foundation models capable of adapting to various imaging modalities and tackling complex segmentation tasks.

    • Jun Ma
    • Bo Wang
  • I share my opinions on the benefits of and bottlenecks for hyperspectral and time-resolved imaging. I also discuss current and future perspectives for analyzing these types of data using the phasor approach.

    • Leonel Malacrida
  • A key step toward biologically interpretable analysis of microscopy image-based assays is rigorous quantitative validation with metrics appropriate for the particular application in use. Here we describe this challenge for both classical and modern deep learning-based image analysis approaches and discuss possible solutions for automating and streamlining the validation process in the next five to ten years.

    • Jianxu Chen
    • Matheus P. Viana
    • Susanne M. Rafelski