Building And Optimization Of The 1700-nm Multiphoton Microscope And Its Application To Deep-brain Imaging In Mouse In Vivo

Multiphoton microscopy(MPM)is a commonly used imaging technique in various research fields due to its deep imaging depth,high resolution,low phototoxicity,the intrinsic ability of 3D sectioning,and function imaging.Recently,1700-nm window has been demonstrated to be a promising excitation window for deep-tissue MPM.For example,scientists succeeded in imaging subcortical structures in the mouse brain directly without damaging the overlying cerebral cortex.Following this research trend,we demonstrated research efforts in multi-photon imaging in this excitation window.In this paper,we introduce a microscopy system built based on this technique as well as the characterization of system parameters.The field of view of this microscopic system is 280?m×290?m,and the resolutions of this system are: X-0.668?m,Y-0.656?m,Z-2.83?m.Then we quantitatively compared the signal detection capability of a GaAsP and a GaAs PMT at different signal wavelengths.The results indicated that at 712 nm both the GaAsP and the GaAs PMTs yield the same signal level,below which GaAsP PMT is more efficient and above which GaAs PMT is more efficient in signal detection.Besides,we quantified the accurate signal enhancement in MPM at the 1700-nm window by using deuterium oxide(D2O)as the immersion medium.Compared with water immersion,D2 O immersion enhances signal levels in second-harmonic generation(SHG)imaging,3-photon fluorescence imaging and third-harmonic generation(THG)imaging by 8.3,24.2 and 23.9 times.These measured results are in good agreement with theoretical calculations based on our absorption measurement.In the last part of this paper,on the basis of the system construction and optimization we introduce some deep imaging results in mouse brain.Particularly,we achieved a 1700 ?m three-photon fluorescence imaging depth into the mouse brain,deeper than previous record.Finally,we summarize the problems in deep imaging experiments and the possible solutions for further system optimization.