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. 2022 Jun 24;13:3606. doi: 10.1038/s41467-022-31340-1

Fig. 9. Intravital two-photon imaging reveals the collective patterns of glioma oncostream dynamics in vivo.

Fig. 9

A Schematic representation of cell implantation for intravital two-photon imaging. The inset shows glioma cells implantation at 0.8 mm depth. CW: glass cranial window (created in BioRender.com). B Representative photograph of the head of an animal implanted with a cranial window showing the metallic head-bar (HB) positioned on the skull posterior to the cranial window. a: anterior; p: posterior. C A high-resolution 3D z-stack spanning up to 300 µm depth (starting at the brain’s surface) acquired on a multiphoton microscope, imported into the Imaris. 300 x–y frames from the brain’s surface were taken at a depth increment of 1 µm (voxel size = 1) at a resolution of 1024 × 1024 pixels. XYZ axes of the 3D image are shown in white (596 × 596 × 300 µm), and the yellow line shows the exact imaging plane for time-lapse data acquisition in vivo (at 120 µm depth). Red fluorescent protein: normal brain parenchyma. GFP+: tumor cells. D This panel represents the X–Y and the Y–Z plane of the reconstructed 3D image (shown in C) using the Orthoslicer 3D function of the Imaris viewer to illustrate the depth of the imaging plane. The X–Y plane shown at 120 µm depth illustrates the actual imaging position for movie #14 shown in (E). E Single representative time-lapse two-photon image of glioma cells within the tumor core (Movie #14) imaged at a depth of 120 µm, showing Zones A, B, and C. F Individual cell trajectories of the intravital time-lapse experiment. G Speed distribution and mean speed (µm/h) for Zones A, B, and C, as indicated in (E). H Angle Velocity distribution for each zone’s in movie #14. The plot shows the proportion of cells moving in the angle direction θ for each zone. I Likelihood analysis of the dynamic patterns determined for each zone of Movie #14. The frequency distribution ρ flock (red), ρ stream (yellow), and ρ swarm (blue) are shown. The estimation of the black line (data) uses a non-parametric assessment (kernel density estimator) to determine the structure of each zone. AW:0 or AW:1.