Breaking down barriers in nanoscale imaging through an innovative platform for sharing, visualizing, and analyzing localization microscopy data
Explore the PlatformFor centuries, scientists peered through a "frosted glass window" at the intricate details of cellular life, limited by the diffraction barrier of light microscopy.
Super-resolution microscopy shattered the 200-nanometer barrier, revealing cellular structures with unprecedented clarity at the molecular level.
Single-Molecule Localization Microscopy works like pointillist painting, precisely locating individual molecules across thousands of frames to construct nanoscale images.
SMLM earned its developers the 2014 Nobel Prize in Chemistry, revolutionizing our ability to visualize cellular machinery.
However, this breakthrough created a data challenge. Each super-resolution dataset can total many gigabytes, making sharing and collaboration difficult 1 . Most SMLM data remained inaccessible, violating FAIR principles (Findability, Accessibility, Interoperability, and Reusability) essential for scientific progress 7 .
ShareLoc addresses this bottleneck, transforming how the scientific community shares, visualizes, and builds upon localization microscopy data.
ShareLoc's elegantly designed two-component system serves both data storage and analysis needs, creating a seamless experience for researchers 1 .
Leveraging Zenodo, CERN's established open-access repository, researchers can upload SMLM data up to 50GB per dataset.
Built on ImJoy for interactive data science tools directly in web browsers.
ShareLoc's losslessly compressed binary file format (*.smlm) substantially reduces file sizes and loading times while maintaining data integrity 1 . This format is:
| Feature | Description | Benefit to Researchers |
|---|---|---|
| Data Storage | Zenodo-based, up to 50GB/dataset | Automatic DOI generation, permanent archiving |
| File Format | Custom *.smlm lossless compression | Faster transfers, smaller storage footprint |
| Visualization | WebGL-based browser viewer | No installation required, handles billions of localizations |
| Accessibility | Web-based, cross-platform | Works on desktop and mobile devices |
| Data Types | Supports localizations and raw images | Comprehensive SMLM data management |
ShareLoc's WebGL-powered viewer enables instant visualization of massive datasets with billions of localizations, functioning similarly to Google Maps with efficient data streaming 1 .
Understanding ShareLoc's transformative potential through a research scenario from data submission to visualization and reuse.
Researchers collect thousands of raw fluorescence images using SMLM setups like PALM or STORM, generating gigabytes of data as fluorophores blink across frames.
Specialized software (ThunderSTORM, Picasso) processes raw images to generate localization files with precise molecular coordinates and parameters.
Localization data is converted to ShareLoc's compressed *.smlm format, reducing file size by up to 80% while preserving all data and metadata 1 .
Researchers log in with Zenodo credentials, upload *.smlm files, and automatically receive DOIs for their datasets.
The ShareLoc team conducts quality review before making datasets publicly available.
Approved datasets become immediately accessible through ShareLoc's web-based viewer, requiring no specialized software.
ShareLoc's implementation has created measurable impacts on the SMLM research community:
| Parameter | Before ShareLoc | With ShareLoc | Improvement |
|---|---|---|---|
| Data Accessibility | Isolated in individual labs | Centralized platform with DOIs | 100% increase in findability |
| Visualization Requirements | Specialized software needed | Standard web browser sufficient | Eliminates installation barriers |
| File Transfer | Difficult for large datasets | Compressed format + streaming | ~80% reduction in transfer times |
| Data Reusability | Limited to original research team | Available to entire community | Enables new analytical methods |
ShareLoc's visualization engine handles datasets containing billions of individual localizations, rendering them smoothly through efficient level-of-detail display techniques 1 .
Most significantly, ShareLoc enables research pathways previously impractical, such as computational biologists accessing diverse SMLM datasets from different laboratories without generating their own experimental data.
Conducting single-molecule localization microscopy requires specialized reagents and materials, each playing a critical role in generating high-quality data suitable for sharing through platforms like ShareLoc.
| Reagent/Material | Function in SMLM | Application Examples |
|---|---|---|
| Photoswitchable Fluorophores | Emit light when activated, enabling single-molecule detection | PA-GFP, Dronpa, rsCherry for PALM |
| Photoswitchable Fluorescent Proteins | Genetically encodable tags for specific protein labeling | mEos, Dendra2 for tagging proteins in live cells |
| Oxygen Scavenging Systems | Reduce photobleaching, extend fluorophore longevity | Glucose oxidase/catalase mixtures in STORM buffers |
| Thiol Compounds | Enhance fluorophore photoswitching in STORM | β-mercaptoethanol, MEA in imaging buffers |
| Specific Labeling Probes | Target structures not amenable to genetic tagging | Alexa Fluor647, Cy3B conjugated to antibodies for DNA, tubulin |
| High-Precision Slides/Coverslips | Provide minimal background for sensitive detection | #1.5 thickness coverslips for optimal resolution |
| Immobilization Media | Secure samples during extended acquisition | Mowiol, ProLong Gold for fixed specimens |
The choice of fluorophores, imaging buffers, and sample preparation methods directly impacts data quality and is crucial information to document when sharing datasets through platforms like ShareLoc.
ShareLoc demonstrates how open science platforms can accelerate research progress by making SMLM data findable, accessible, interoperable, and reusable (FAIR).
The implications for machine learning in microscopy are profound. Deep learning approaches require large, diverse training datasets—precisely what ShareLoc provides in a structured, accessible format 7 .
"The development of further analytical methods could greatly benefit from easy access to SMLM data generated worldwide. This is especially true for machine learning approaches and notably deep learning, whose performance hinges strongly on the amount of training data" 7 .
ShareLoc promotes reproducibility and validation in super-resolution microscopy. With more datasets available for comparison, researchers can:
This is particularly valuable in a field where techniques continue to evolve rapidly.
Enhanced support for temporal dimensions
Combining with electron microscopy data
Sophisticated in-browser tools via ImJoy
ShareLoc represents more than just a technical solution to data sharing—it embodies a shift toward more collaborative, open, and efficient scientific discovery.
By removing barriers to accessing the fascinating nanoscale world revealed by super-resolution microscopy, the platform empowers researchers across the globe to build upon each other's work, develop new analytical methods, and accelerate our understanding of life's most fundamental processes.
To explore ShareLoc datasets or contribute your own, visit the platform at https://shareloc.xyz/