Asthma is a chronic, heterogeneous disease driven by interactions between the immune and stromal compartments. These interactions are governed by distinct spectrum of clinically distinct adaptive immune compartments and their effects on innate immune cells and structural stromal cells. Previous studies have identified key immune cells and their effector functions contributing to asthma pathology and disease manifestations, but this knowledge lacks resolution at the tissue level. This project aims to resolve the spatial biology of asthma in patients through spectral flow cytometry, spatial proteomics, single-cell and spatial transcriptomics, and spatial lipidomics. We will use the previously established U-BIOPRED asthma cohort and the ongoing 3TR-ABC and TESSLA cohorts to construct a multimodal tissue atlas of cellular neighborhoods in the asthmatic airway wall. In the first stage of the project, we will characterize immune and stromal cells across matched bronchial biopsies, nasal brushes, bronchoalveolar lavage fluid, and blood from healthy controls and patients with asthma using spectral flow cytometry and spatial proteomics. Integrating spatial proteomics and spectral flow cytometry will elucidate pathogenic cells and tissue neighborhoods at the protein level. At the same time, matched samples will be used for single-cell transcriptomics, and suitable samples will be used for spatial transcriptomics. Lastly, samples of sufficient quality will be used for spatial lipidomics. With these methodologies, we will explore the immune composition of patients with asthma to identify common cell phenotypes, interactions, and spatial localization contributing to disease pathology. Analyzing a large number of samples with these methodologies requires substantial computing power to integrate the samples and extract biologically significant findings in a meaningful way. Specific analyses require high computing power, including cell segmentation of spatial proteomics, spatial transcriptomics, and spatial lipidomics data. Segmentation is crucial to biological interpretation and analysis outputs and must therefore be performed carefully and repeatedly across samples. Single-cell and spatial transcriptomics require extended computing resources and hours for batch correction, sample integration, integration of reference datasets for annotation, dimensionality reduction and clustering, and spatial deconvolution. Our previous pipeline used roughly 400 core-hours per spatial transcriptomics sample at a basic analysis level. Other pipelines, which are still under development, are expected to require similar computing power, especially for bulk processing such as single-cell transcriptomics and spectral flow cytometry. The large number of samples (>100) and sample types will also require extended storage: raw data are currently securely stored on institutional storage at Karolinska Institutet, while de-identified, processed and active data required for analysis will be stored on Arrhenius and serve as the basis for downstream analyses. As such, we request NAISS resources for working storage and a scalable analysis pipeline matched to the needs of this expansive project. Access to these computational resources will be crucial to the success of this project