Comment in 2024

Spatial proteomics holds the potential to transform the study of proteins in situ in complex tissues, but it needs to be integrated with other layers of omics data to gain a holistic view of cellular function, heterogeneity and interactions, and the underlying mechanisms of these processes. I highlight current challenges and emerging opportunities for multi-omic spatial protein profiling to advance basic research and translational applications.

Spatial proteomics is advancing rapidly, transforming physiological and biomedical research by enabling the study of how multicellular structures and intercellular communication shape tissue function in health and disease. Through the analysis of large human tissue collections, spatial proteomics will reveal the complexities of human tissues and uncover multicellular modules that can serve as drug targets and diagnostics, paving the way for precision medicine and revolutionizing histopathology.

Multiplexed tissue imaging has transformed tissue biology by revealing cellular diversity and interactions, but the analysis of its massive datasets remains a bottleneck. Here, we provide an overview of computational advancements, discuss current challenges and envision an AI-driven future in which integrated tools streamline analysis and visualization, unlocking the full potential of multiplexed imaging for breakthroughs in spatial biology.

Spatial mass spectrometry (MS)-proteomics is a rapidly evolving technology, particularly in the form of Deep Visual Proteomics (DVP), which allows the study of single cells directly in their native environment. We believe that this approach will reshape our understanding of tissue biology and redefine fundamental concepts in cell biology, tissue physiology and ultimately human health and disease.

Spatial proteomics has transformed cancer research by providing unparalleled insights into the microenvironmental landscape of tumors. Here we discuss how these technologies have significantly advanced our understanding of cell–cell interactions, tissue organization and spatially coordinated mechanisms underlying antitumor immune responses, and will pave the way for emerging breakthroughs in cancer research.

Biomaterials are revolutionizing organoid development by offering tunable platforms that provide instructive cues, which enhance cell fate transitions, tissue-level functions and reproducibility. These advances are crucial for harnessing the translational potential of organoids.

Risks from AI in basic biology research can be addressed with a dual mitigation strategy that comprises basic education in AI ethics and community governance measures that are tailored to the needs of individual research communities.

Artificial intelligence-enabled computational tools not only help us to elucidate biological processes but also facilitate the programming of biology through molecular and cellular engineering.

New approaches in artificial intelligence (AI), such as foundation models and synthetic data, are having a substantial impact on many areas of applied computer science. Here we discuss the potential to apply these developments to the computational challenges associated with producing synapse-resolution maps of nervous systems, an area in which major ambitions are currently bottlenecked by AI performance.

Mass spectrometry-based proteomics provides broad and quantitative detection of the proteome, but its results are mostly presented as protein lists. Artificial intelligence approaches will exploit prior knowledge from literature and harmonize fragmented datasets to enable mechanistic and functional interpretation of proteomics experiments.

Methods for predicting bimolecular interactions are seeing tremendous growth, but challenges remain in capturing the full physical complexity of these interactions.

The success of deep learning in analyzing bioimages comes at the expense of biologically meaningful interpretations. We review the state of the art of explainable artificial intelligence (XAI) in bioimaging and discuss its potential in hypothesis generation and data-driven discovery.

Advancements in artificial intelligence (AI) have led to unprecedented success in modeling technically challenging domains including language, audio, image and video understanding. Here we discuss the opportunities represented by recent AI methods to advance immunology research.

Spatial omics technologies have transformed biomedical research by offering detailed, spatially resolved molecular profiles that elucidate tissue structure and function at unprecedented levels. AI can potentially unlock the full power of spatial omics, facilitating the integration of complex datasets and discovery of novel biomedical insights.

Breakthroughs in AI and multimodal genomics are unlocking the ability to study the tumor microenvironment. We explore promising machine learning techniques to integrate and interpret high-dimensional data, examine cellular dynamics and unravel gene regulatory mechanisms, ultimately enhancing our understanding of tumor progression and resistance.

By exploiting recent advances in modern artificial intelligence and large-scale functional genomic datasets, sequence-to-function models learn the relationship between genomic DNA and its multilayer gene regulatory functions. These models are poised to uncover mechanistic relationships across layers of cellular biology, which will transform our understanding of cis gene regulation and open new avenues for discovering disease mechanisms.

Multimodal large language models have been recognized as a historical milestone in the field of artificial intelligence and have demonstrated revolutionary potentials not only in commercial applications, but also for many scientific fields. Here we give a brief overview of multimodal large language models through the lens of bioimage analysis and discuss how we could build these models as a community to facilitate biology research.

Imaging the 4D choreography of subcellular events in living multicellular organisms at high spatiotemporal resolution could reveal life’s fundamental principles. Yet extracting these principles from petabyte-scale image data requires fusing advanced light microscopy and cutting-edge machine learning models with biological insight and expertise.

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.

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.