Researchers have developed nano-org, an open-access database for standardized storage, comparison, and analysis of single-molecule localization microscopy (SMLM) data to study protein organization at the nanoscale.
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Super-resolution microscopy, such as SMLM, has changed the way scientists visualize cellular structures by breaking the diffraction limits of traditional light microscopy. By pinpointing the exact positions of fluorescently labeled molecules, this type of microscopy can achieve nanometer-scale resolution and generate highly detailed coordinate datasets.
However, these datasets are often convoluted and lack standardization, making large-scale comparisons difficult. Existing systems also miss key features like metadata on imaging conditions, or drift and blinking corrections, limiting the potential for broader analyses. The study, published in Nature Communications, sets out to tackle this gap.
Building Nano-Org
The authors have created nano-org to address these challenges, a web-based platform built on the Django framework with an SQLite backend. Users can register, verify their email, and upload datasets in formats such as CSV or HDF5, containing localization data from techniques like PALM, dSTORM, or PAINT.
Before uploading their findings, researchers can curate raw data, after which nano-org automatically performs a quality assessment. Key metrics such as localization density and average localization precision give an immediate snapshot of data quality. Each dataset also includes curated metadata, covering the SMLM modality, target protein, cell type, fluorophore details, and applied corrections like drift or blinking suppression to ensure reproducibility and context.
Comparing Protein Distributions
A defining feature of nano-org is its ability to statistically compare protein distributions across datasets. The platform divides micrographs into 3 × 3 μm regions of interest, further broken into 30 nm2 bins that correspond to typical localization precisions, allowing data to be standardized for comparison.
When users submit datasets for analysis, nano-org converts coordinate data into comparable metrics and calculates dissimilarity scores using a dedicated statistical algorithm. Researchers can then search for datasets with similar nanoscale patterns, helping identify conditions that produce comparable protein architectures.
To manage computationally intensive comparisons, the system integrates with high-performance computing (HPC) resources via the University of Birmingham’s BlueBEAR supercomputer, ensuring efficiency and scalability.
Tested Capabilities
The researchers tested nano-org’s capabilities in various applications. They compared nanoscale organization patterns of immune receptor proteins TIGIT and NKp30, demonstrating the platform’s ability to distinguish between cell types and signaling states based on protein clustering.
Another test examined the effects of the drug nocodazole, which disrupts microtubules. The results showed clear, dose-dependent changes in spatial organization; higher doses corresponded with greater dissimilarity scores, consistent with microtubule disassembly.
The team also compared data generated from different SMLM systems, such as ONI and N-STORM. While minor platform-related differences emerged, biological patterns remained dominant. This finding highlights the importance of metadata for normalization and reinforces nano-org’s robustness in identifying true biological variation.
By combining rich metadata, automated quality control, and scalable computation, the platform enables reproducible, cross-experimental analysis of nanoscale structures. As super-resolution microscopy evolves, resources like nano-org are vital for standardizing data sharing and accelerating discoveries in spatial nano-omics, from cell signaling to disease mechanisms.
Journal Reference
Shirgill S. et al. (2025). Nano-org, a functional resource for single-molecule localisation microscopy data. Nature Communications 16, 8674. DOI: 10.1038/s41467-025-63674-x, https://www.nature.com/articles/s41467-025-63674-x