dc.contributor.advisor | You, Sixian | |
dc.contributor.author | Liu, Kunzan | |
dc.date.accessioned | 2024-08-21T18:58:08Z | |
dc.date.available | 2024-08-21T18:58:08Z | |
dc.date.issued | 2024-05 | |
dc.date.submitted | 2024-07-10T12:59:45.091Z | |
dc.identifier.uri | https://hdl.handle.net/1721.1/156342 | |
dc.description.abstract | Label-free imaging through two-photon autofluorescence (2PAF) of NAD(P)H allows for non-destructive and high-resolution visualization of cellular activities in living systems. However, its application to thick tissues and organoids has been restricted by its limited penetration depth within 300µm, largely due to tissue scattering at the typical excitation wavelength (∼750nm) required for NAD(P)H. Here, we demonstrate that the imaging depth for NAD(P)H can be extended to over 700µm in living engineered human multicellular microtissues by adopting multimode fiber (MMF)-based low-repetition-rate high-peak-power three-photon (3P) excitation of NAD(P)H at 1100nm. This is achieved by having over 0.5MW peak power at the band of 1100±25nm through adaptively modulating multimodal nonlinear pulse propagation with a compact fiber shaper. Moreover, the 8-fold increase in pulse energy at 1100nm enables faster imaging of monocyte behaviors in the living multicellular models. These results represent a significant advance for deep and dynamic metabolic and structural imaging of intact living biosystems. The modular design (MMF with a slip-on fiber shaper) is anticipated to allow wide adoption of this methodology for demanding in vivo and in vitro imaging applications, including cancer research, autoimmune diseases, and tissue engineering. | |
dc.publisher | Massachusetts Institute of Technology | |
dc.rights | In Copyright - Educational Use Permitted | |
dc.rights | Copyright retained by author(s) | |
dc.rights.uri | https://rightsstatements.org/page/InC-EDU/1.0/ | |
dc.title | Deep and Dynamic Metabolic and Structural Imaging in Living Tissues | |
dc.type | Thesis | |
dc.description.degree | S.M. | |
dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | |
mit.thesis.degree | Master | |
thesis.degree.name | Master of Science in Electrical Engineering and Computer Science | |