Multiview deconvolution approximation multiphoton microscopy of tissues and zebrafish larvae

Fluorescence radial fluctuation enables two-photon super-resolution microscopy

Despite recent improvements in microscopy, it is still difficult to apply super-resolution microscopy for deep imaging due to the deterioration of light convergence properties in thick specimens. As a strategy to avoid such optical limitations for deep super-resolution imaging, we focused on super-resolution radial fluctuation (SRRF), a super-resolution technique based on image analysis. In this study, we applied SRRF to two-photon microscopy (2P-SRRF) and characterized its spatial resolution, suitability for deep observation, and morphological reproducibility in real brain tissue. By the comparison with structured illumination microscopy (SIM), it was confirmed that 2P-SRRF exhibited two-point resolution and morphological reproducibility comparable to that of SIM. The improvement in spatial resolution was also demonstrated at depths of more than several hundred micrometers in a brain-mimetic environment. After optimizing SRRF processing parameters, we successfully demonstrated in vivo high-resolution imaging of the fifth layer of the cerebral cortex using 2P-SRRF. This is the first report on the application of SRRF to in vivo two-photon imaging. This method can be easily applied to existing two-photon microscopes and can expand the visualization range of super-resolution imaging studies.

Multimodal imaging of the dynamic brain tumor microenvironment during glioblastoma progression and in response to treatment

Tumors evolve in a dynamic communication with their native tissue environment and recruited immune cells. The diverse components of the tumor microenvironment (TME) can critically regulate tumor progression and therapeutic response. In turn, anticancer treatments may alter the composition and functions of the TME. To investigate this continuous dialog in the context of primary brain cancers, we developed a multimodal longitudinal imaging strategy. We combined macroscopical magnetic resonance imaging with subcellular resolution two-photon intravital microscopy, and leveraged the power of single-cell analysis tools to gain insights into the ongoing interactions between different components of the TME and cancer cells. Our experiments revealed that the migratory behavior of tumor-associated macrophages is different in genetically distinct glioblastomas, and in response to macrophage-targeted therapy. These results underscore the importance of studying cancer longitudinally in an in vivo setting, to reveal complex and dynamic alterations in the TME during disease progression and therapeutic intervention.

Two plus one is almost three: a fast approximation for multi-view deconvolution

Multi-view deconvolution is a powerful image-processing tool for light sheet fluorescence microscopy, providing isotropic resolution and enhancing the image content. However, performing these calculations on large datasets is computationally demanding and time-consuming even on high-end workstations. Especially in long-time measurements on developing animals, huge amounts of image data are acquired. To keep them manageable, redundancies should be removed right after image acquisition. To this end, we report a fast approximation to three-dimensional multi-view deconvolution, denoted 2D+1D multi-view deconvolution, which is able to keep up with the data flow. It first operates on the two dimensions perpendicular and subsequently on the one parallel to the rotation axis, exploiting the rotational symmetry of the point spread function along the rotation axis. We validated our algorithm and evaluated it quantitatively against two-dimensional and three-dimensional multi-view deconvolution using simulated and real image data. 2D+1D multi-view deconvolution takes similar computation time but performs markedly better than the two-dimensional approximation only. Therefore, it will be most useful for image processing in time-critical applications, where the full 3D multi-view deconvolution cannot keep up with the data flow.