News & Comment

As a vast and diverse country, Brazil faces significant regional disparities that highlight the urgent need to democratize access to scientific resources and opportunities. To help develop solutions to address this challenge, we launched the Bioimaging Brasil initiative, aimed at expanding access to advanced bioimaging techniques such as intravital microscopy. By leveraging a broad spectrum of technologies — from standard microscopes paired with cell phone cameras to cutting-edge laser scanning confocal microscopes — the initiative fosters collaboration, strengthens research capacity and serves as a global model for equitable technology dissemination.

Advances in cellular physiology research have created the need to visualize metabolic processes in both time and space. Vibrational microscopy has emerged as a promising method at the forefront of this field. Here, we summarize recent progress and offer perspectives on the application of vibrational microscopy for uncovering metabolic actions in vivo.

Raman microscopy enables label-free, high-resolution molecular imaging, treating the entire Raman spectrum as a phenotype for profiling cell heterogeneity. While challenges remain in speed, sensitivity and resolution, advancements in coherent Raman scattering, spatial multiplexing and Raman probe techniques continue to expand its potential for characterizing complex biological systems and fostering exploratory and data-driven biology.

Vibrational microscopy opens a new window onto understanding life at the molecular level. Yet the vibrational signals from chemical bonds are weaker than the fluorescence signal from a dye by many orders of magnitude. Detecting such weak signal from a tight focus under a microscope is extremely challenging. I have devoted my career to overcoming such a daunting barrier through the development of advanced chemical microscopes over the past 25 years. In this historical Comment, I am honored to share my journey of serendipity-driven innovation and entrepreneurship in the growing field of chemical imaging, with a focus on the invention of vibrational photothermal microscopy.

It has been 25 years since the first 3D coherent Raman microscope was reported. Owing to the contributions of many researchers worldwide, coherent Raman microscopy has blossomed as a field of its own and found wide applications in chemical, material, environmental, biological and medical applications. Here I highlight the emergence of nonlinear optical spectroscopy and microscopy and their key technical milestones that led to the rapid expansion and wide use of this imaging modality for biomedicine.

Discussions at a recent conference on microscopy technology dissemination spotlighted the importance of setting technology adoption capable of producing scientific outcome as the end goal. This Comment examines current global efforts in microscopy dissemination and summarizes the challenges and paths forward.

In the Big Data era, a change of paradigm in the use of molecular dynamics is required. Trajectories should be stored under FAIR (findable, accessible, interoperable and reusable) requirements to favor its reuse by the community under an open science paradigm.

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.