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Super-resolution fluorescence microscopy revolutionizes cell biology research and provides novel insights on how proteins are organized at the nanoscale and in the cellular context. In order to extract a maximum of information, specialized tools for image analysis are necessary. Here, we introduce the LocAlization Microscopy Analyzer (LAMA), a comprehensive software tool that extracts quantitative information from single-molecule super-resolution imaging data. LAMA allows characterizing cellular structures by their size, shape, intensity, distribution, as well as the degree of colocalization with other structures. LAMA is freely available, platform-independent and designed to provide direct access to individual analysis of super-resolution data.
Background: Novel microscopic techniques which bypass the resolution limit in light microscopy are becoming routinely established today. The higher spatial resolution of super-resolution microscopy techniques demands for precise correction of drift, spectral and spatial offset of images recorded at different axial planes.
Methods: We employ a hydrophilic gel matrix for super-resolution microscopy of cellular structures. The matrix allows distributing fiducial markers in 3D, and using these for drift correction and multi-channel registration. We demonstrate single-molecule super-resolution microscopy with photoswitchable fluorophores at different axial planes. We calculate a correction matrix for each spectral channel, correct for drift, spectral and spatial offset in 3D.
Results and discussion: We demonstrate single-molecule super-resolution microscopy with photoswitchable fluorophores in a hydrophilic gel matrix. We distribute multi-color fiducial markers in the gel matrix and correct for drift and register multiple imaging channels. We perform two-color super-resolution imaging of click-labeled DNA and histone H2B in different axial planes, and demonstrate the quality of drift correction and channel registration quantitatively. This approach delivers robust microscopic data which is a prerequisite for data interpretation.