Neurodegenerative diseases affect 1 in 12 people globally and remain incurable. Central to their pathogenesis is a loss of neuronal protein maintenance and the accumulation of protein aggregates with ageing^1,2. Here we engineered bioorthogonal tools³ that enabled us to tag the nascent neuronal proteome and study its turnover with ageing, its propensity to aggregate and its interaction with microglia. We show that neuronal protein half-life approximately doubles on average between 4-month-old and 24-month-old mice, with the stability of individual proteins differing among brain regions. Furthermore, we describe the aged neuronal ‘aggregome’, which encompasses 1,726 proteins, nearly half of which show reduced degradation with age. The aggregome includes well-known proteins linked to diseases and numerous proteins previously not associated with neurodegeneration. Notably, we demonstrate that neuronal proteins accumulate in aged microglia, with 54% also displaying reduced degradation and/or aggregation with age. Among these proteins, synaptic proteins are highly enriched, which suggests that there is a cascade of events that emerge from impaired synaptic protein turnover and aggregation to the disposal of these proteins, possibly through microglial engulfment of synapses. These findings reveal the substantial loss of neuronal proteome maintenance with ageing, which could be causal for age-related synapse loss and cognitive decline.

Cellular and tissue structures arise from a few cell shapes, which undergo transformations based on biophysical constraints. Despite links between signaling pathways and cellular geometry, whole-proteome orchestration in association with cell shape is underexplored. In this study, over 1 million single cells stained for 11,998 proteins across 11 cell lines in the Human Protein Atlas were analyzed for organelle, pathway, and single-protein levels in association with cellular shapespace. We found that cell and nuclear shapes across cell lines exist in a shared continuum. The subcellular organelle topology varies across cell lines but remains consistent within each cell line's shapespace. At the single-protein level, cells of different shapes in the same cell-cycle phase might be preparing for different fates, and many non-cell-cycle proteins expressed shape-based abundance variation. Using a shape-based coordinate framework, we analyzed the distribution shift of protein spatial localization under drug perturbation.

Cells integrate exogenous and endogenous signals to grow, repair, or die. This is likely achieved through dynamic functional associations between genes, but measuring these relationships at scale is non-trivial. Here, we evaluate genetic associations in response to cell-cycle interruption, genotoxic perturbation, and nutrient deprivation using conditional genetic interaction (GI) mapping in human cells. In five maps measuring ∼250,000 GIs or higher-order environmental interactions, we discover widespread rewiring of relationships between genes, complexes, and ontologies across conditions. Specific bioprocesses drive the rewiring signal in each environmental state, as highlighted in our findings that the TIP60 and PP2A complexes radically alter their interaction profiles after inhibition of ATR. This resource reveals numerous genetic relationships for the fields of DNA damage signaling, DNA repair, and cell-cycle control and explores their context specificity. Our work advances a framework for using GI maps to explore environmental rewiring.

Biological functions depend on the spatiotemporal distribution of proteins within cells. Key cellular activities such as signal transduction, metabolism, cell cycle and cell death are driven by the interactions of proteins that are localized in multiple cellular compartments. Such multilocalization can even allow protein with identical sequences to display multifunctionality, a phenomenon known as moonlighting. Despite its biological importance, the relationship between protein localization and function remains underexplored. In this Review, we discuss the known mechanisms of protein localization (including RNA transport, role of proteoforms and molecular interactions) and how subcellular localization controls protein function. Proper regulation of protein localization is crucial for specialized cell and tissue functions, including cell differentiation, polarization and the epithelial–mesenchymal transition. Protein mislocalization can also have important roles in pathological processes, such as in cancer, neurodegeneration and autoimmunity. We end with a discussion of current technological and conceptual challenges in the field of subcellular proteomics and spatial biology. Addressing these challenges will allow us to link the dynamic nature of protein localization and function across biological scales and contexts, with great impact on fundamental cell biology and clinical applications.

How proteins localize to specific compartments, function in coordination with other biomolecules and, ultimately, contribute to diverse cellular activities are crucial questions in cell biology. Complicating the answers to these questions are multilocalizing and multifunctional proteins, whose impact on the cell depends on both spatial and temporal contexts. Therefore, contextualizing protein functions based on their subcellular localization is necessary to fully understand cell behaviours. Recent advances in instrumentation and protein labelling techniques are rapidly increasing the availability of tools, technologies and applications that measure and control protein localization and compartment-specific function. In this Review, we first discuss microscopy, mass spectrometry-based correlation profiling and proximity labelling methods that assign localizations to proteins, ranging from cellular compartments to protein–protein interactions. We next examine the available tools for manipulating protein localization and measuring the effects of these manipulations, including localization tags and bifunctional molecules. For each technology, we assess the strengths and weaknesses that ultimately determine their usefulness. We conclude with an outlook on future technological advances in the field of spatial subcellular proteomics and their potential implications for cell biology and clinical applications.

Despite the importance of imaging in biological and medical research, a large body of informative and precious image data never sees the light of day. To ensure scientific rigor as well as the reuse of data for scientific discovery, image data need to be made FAIR (findable, accessible, interoperable and reusable). Image data experts are working together globally to agree on common data formats, metadata, ontologies and supporting tools toward image data FAIRification. With this Perspective, we call on public funders to join these efforts to support their national scientists. What researchers most urgently need are openly accessible resources for image data storage that are operated under long-term commitments by their funders. Although existing resources in Australia, Japan and Europe are already collaborating to enable global image data sharing, these efforts will fall short unless more countries invest in operating and federating their own open data resources. This will allow us to harvest the enormous potential of existing image data, preventing substantial loss of unrealized value from past investments in imaging acquisition infrastructure.

Analysis of biological images relies heavily on segmenting the biological objects of interest in the image before performing quantitative analysis. Deep learning (DL) is ubiquitous in such segmentation tasks, but can be cumbersome to apply, as it often requires a large amount of manual labeling to produce ground-truth data, and expert knowledge to train the models. More recently, large foundation models, such as SAM, have shown promising results on scientific images. They, however, require manual prompting for each object or tedious post-processing to selectively segment these objects. Here, we present FeatureForest, a method that leverages the feature embeddings of large foundation models to train a random forest classifier, thereby providing users with a rapid way of semantically segmenting complex images using only a few labeling strokes. We demonstrate the improvement in performance over a variety of datasets and provide an open-source implementation in napari that can be extended to new models.

Identifying cell types in highly multiplexed images is essential for understanding tissue spatial organization. Current cell-type annotation methods often rely on extensive reference images and manual adjustments. In this work, we present a tool, the Robust Image-Based Cell Annotator (RIBCA), that enables accurate, automated, unbiased, and fine-grained cell-type annotation for images with a wide range of antibody panels without requiring additional model training or human intervention. Our tool has successfully annotated over 3 million cells, revealing the spatial organization of various cell types across more than 40 different human tissues. It is open source and features a modular design, allowing for easy extension to additional cell types.

The COVID-19 pandemic has resulted in millions of deaths and affected socioeconomic structure worldwide and the search for new antivirals and treatments are still ongoing. In the search for new drug targets and to increase our understanding of the disease, we applied large-scale immunofluorescence profiling to explore host cell response to SARS-CoV-2 infection. Among the 602 host proteins studied in this host response profiling, changes in abundance and subcellular localization were observed for 97 proteins, with 45 proteins showing increased abundance and 10 reduced abundance. 20 proteins displayed changed localization upon infection and an additional 22 proteins displayed altered abundance and localization, together contributing to diverse reshuffling of the host cell protein landscape during infection. We then selected existing and approved small-molecule drugs (n = 123) against our identified host response proteins and identified one compound - elesclomol, that significantly reduced antiviral activity. Our study introduces a novel, targeted and systematic approach based on host protein profiling, to identify new targets for drug repurposing. The dataset of > 100,000 immunofluorescence images from this study are published as a resource available for further studies. Author summary: In this study we have evaluated a new approach for identifying drugs that could be used as antiviral drugs, in this case demonstrated for SARS CoV-2. By mining the literature for reported interactions between SARS CoV-2 viral components and host cell proteins, we identified a few hundred host proteins suggested to interact with the virus upon infection. To explore these viral-host interaction proteins further, we developed an image based assay using immunofluorescence and confocal microscopy to visualize the host proteins within infected and non infected cells. This was possible due to the proteome wide collection of antibodies generated within the Human Protein Atlas project, with the aim to systematically map the human proteome in cells and across tissues. The host proteins that altered their location or abundance level upon infection were regarded as putative targets for drug repurposing and we subsequently tested 123 drugs that were targeting a subset of these host proteins. Applying these drugs on two different cell types infected with SARS-CoV-2, revealed a non toxic antiviral effect for one compound that can be explored further as a treatment regimen for SARS-CoV-2 infection. The approach is novel since it combines a targeted approach for drug repurposing screening, giving insight into mechanism of action from start. As such it has the potential to accelerate drug repurposing or identification of targets for new drugs.

Primary cilia are critical organelles found on most human cells. Their dysfunction is linked to hereditary ciliopathies with a wide phenotypic spectrum. Despite their significance, the specific roles of cilia in different cell types remain poorly understood due to limitations in analyzing ciliary protein composition. We employed antibody-based spatial proteomics to expand the Human Protein Atlas to primary cilia. Our analysis identified the subciliary locations of 715 proteins across three cell lines, examining 128,156 individual cilia. We found that 69% of the ciliary proteome is cell-type specific, and 78% exhibited single-cilia heterogeneity. Our findings portray cilia as sensors tuning their proteome to effectively sense the environment and compute cellular responses. We reveal 91 cilia proteins and found a genetic candidate variant in CREB3 in one clinical case with features overlapping ciliopathy phenotypes. This open, spatial cilia atlas advances research on cilia and ciliopathies.