研究目的
To provide a high-performance, user-friendly, and open-source image analysis framework for super-resolution microscopy to process large volumes of data and extract quantitative information, addressing the need for specialized tools in nanoscale biological studies.
研究成果
NanoJ provides a comprehensive, open-source toolbox that enhances the accessibility and reliability of super-resolution microscopy analysis. It enables high-performance processing of large datasets, supports live-cell imaging with low phototoxicity, and offers tools for quality control and structural modeling. The modular design allows for future expansions, and integration with ImageJ ensures broad usability. This framework sets a standard for quantitative SRM and facilitates advancements in biological research by improving data accuracy and reproducibility.
研究不足
The framework relies on the availability of compatible hardware (e.g., GPUs for acceleration) and may require specific sample preparations. Some methods, like drift correction, assume linear shifts and may not handle non-linear distortions perfectly. The quality assessment depends on having a diffraction-limited reference image, and FRC resolution mapping can be biased for certain fluorophore distributions. The fluidics system is custom-built and may not be universally applicable without modifications.
1：Experimental Design and Method Selection:
The NanoJ framework is designed as a modular set of ImageJ-based plugins for various super-resolution microscopy (SRM) analysis tasks, including drift correction, channel registration, image reconstruction, quality assessment, structural modeling, and fluidics control. It utilizes algorithms like cross-correlation for drift estimation, radial symmetry for SRRF, and single-particle analysis for VirusMapper, with GPU acceleration for high performance.
2：Sample Selection and Data Sources:
Biological samples include Cos7 cells expressing UtrCH-GFP for actin imaging, fixed microtubules labeled with Alexa Fluor-647, vaccinia virus particles labeled for core, lateral bodies, and membrane, Sulfolobus acidocaldarius cells labeled for S-layer and CdvB, and multi-color beads (e.g., TetraSpeck?) for channel registration. Data are acquired using fluorescence microscopes, with specific modalities like SIM, STED, SMLM, and DNA-PAINT.
3：List of Experimental Equipment and Materials:
Fluorescence microscopes (unspecified models), GPUs for computation, Arduino? for fluidics control, LEGO? syringe pumps, peristaltic pumps, custom fluidics hardware, coverslips, fluorescent dyes (e.g., Alexa Fluor-647, TetraSpeck? beads), and biological reagents (antibodies, viruses, cells).
4：Experimental Procedures and Operational Workflow:
For each module: NanoJ-Core performs drift correction and channel registration on raw image sequences; NanoJ-SRRF reconstructs super-resolution images from short bursts of diffraction-limited frames; NanoJ-SQUIRREL assesses image quality by comparing SRM images to diffraction-limited references; NanoJ-VirusMapper models structures from multiple aligned images; NanoJ-Fluidics automates liquid exchange on microscope stages. Procedures involve image acquisition, processing via GUI or macros, and analysis integration.
5：Data Analysis Methods:
Methods include cross-correlation analysis for drift and registration, radial symmetry calculations for SRRF, error mapping and FRC for quality assessment, single-particle averaging for structural modeling, and automated scripting for fluidics. Statistical techniques involve signal-to-noise ratio estimation, TRE for registration accuracy, and interpolation methods. Software tools include ImageJ, Fiji, Java, OpenCL, and custom algorithms implemented in NanoJ modules.
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