RT Journal Article
SR Electronic
T1 Learned adaptive multiphoton illumination microscopy
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.08.14.251314
DO 10.1101/2020.08.14.251314
A1 Henry Pinkard
A1 Hratch Baghdassarian
A1 Adriana Mujal
A1 Ed Roberts
A1 Kenneth H. Hu
A1 Daniel Haim Friedman
A1 Ivana Malenica
A1 Taylor Shagam
A1 Adam Fries
A1 Kaitlin Corbin
A1 Matthew F. Krummel
A1 Laura Waller
YR 2020
UL http://biorxiv.org/content/early/2020/08/15/2020.08.14.251314.abstract
AB Multiphoton microscopy is a powerful technique for deep in vivo imaging in scattering samples. However, it requires precise, sample-dependent increases in excitation power with depth in order to maintain signal while minimizing photodamage. We show that cells with identical fluorescent labels imaged in situ can be used to train a physics-based machine learning model that solves this problem. After this training has been performed, the correct illumination power can be predicted and adaptively adjusted at each point in a 3D volume on subsequent samples as a function of the sample’s shape, without the need for specialized fluorescent labelling. We use this technique for in vivo imaging of immune responses in mouse lymph nodes following vaccination, with imaging volumes 2-3 orders of magnitude larger than previously reported. We achieve visualization of physiologically realistic numbers of antigen-specific T cells for the first time, and demonstrate changes in the global organization and motility of dendritic cell networks during the early stages of the immune response.Competing Interest StatementThe authors have declared no competing interest.