Browsing by Author "Huang, Fang"

Now showing 1 - 3 of 3
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    Active PSF shaping and adaptive optics enable volumetric localization microscopy through brain sections
    (Springer Nature, 2018-08) Mlodzianoski, Michael J.; Cheng-Hathaway, Paul J.; Bemiller, Shane M.; McCray, Tyler J.; Liu, Sheng; Miller, David A.; Lamb, Bruce T.; Landreth, Gary E.; Huang, Fang; Anatomy and Cell Biology, IU School of Medicine
    Application of single-molecule switching nanoscopy (SMSN) beyond the coverslip surface poses substantial challenges due to sample-induced aberrations that distort and blur single-molecule emission patterns. We combined active shaping of point spread functions and efficient adaptive optics to enable robust 3D-SMSN imaging within tissues. This development allowed us to image through 30-μm-thick brain sections to visualize and reconstruct the morphology and the nanoscale details of amyloid-β filaments in a mouse model of Alzheimer's disease.
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    Deep learning-driven adaptive optics for single-molecule localization microscopy
    (Springer Nature, 2023) Zhang, Peiyi; Ma, Donghan; Cheng, Xi; Tsai, Andy P.; Tang, Yu; Gao, Hao-Cheng; Fang, Li; Bi, Cheng; Landreth, Gary E.; Chubykin, Alexander A.; Huang, Fang; Anatomy, Cell Biology and Physiology, School of Medicine
    The inhomogeneous refractive indices of biological tissues blur and distort single-molecule emission patterns generating image artifacts and decreasing the achievable resolution of single-molecule localization microscopy (SMLM). Conventional sensorless adaptive optics methods rely on iterative mirror changes and image-quality metrics. However, these metrics result in inconsistent metric responses and thus fundamentally limit their efficacy for aberration correction in tissues. To bypass iterative trial-then-evaluate processes, we developed deep learning-driven adaptive optics for SMLM to allow direct inference of wavefront distortion and near real-time compensation. Our trained deep neural network monitors the individual emission patterns from single-molecule experiments, infers their shared wavefront distortion, feeds the estimates through a dynamic filter and drives a deformable mirror to compensate sample-induced aberrations. We demonstrated that our method simultaneously estimates and compensates 28 wavefront deformation shapes and improves the resolution and fidelity of three-dimensional SMLM through >130-µm-thick brain tissue specimens.
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    Three-dimensional nanoscopy of whole cells and tissues with in situ point spread function retrieval
    (Nature, 2020-05) Xu, Fan; Ma, Donghan; MacPherson, Kathryn P.; Liu, Sheng; Bu, Ye; Wang, Yu; Tang, Yu; Bi, Cheng; Kwok, Tim; Chubykin, Alexander A.; Yin, Peng; Calve, Sarah; Landreth, Gary E.; Huang, Fang; Anatomy and Cell Biology, School of Medicine
    Single-molecule localization microscopy is a powerful tool for visualizing subcellular structures, interactions and protein functions in biological research. However, inhomogeneous refractive indices inside cells and tissues distort the fluorescent signal emitted from single-molecule probes, which rapidly degrades resolution with increasing depth. We propose a method that enables the construction of an in situ 3D response of single emitters directly from single-molecule blinking datasets, and therefore allows their locations to be pinpointed with precision that achieves the Cramér-Rao lower bound and uncompromised fidelity. We demonstrate this method, named in situ PSF retrieval (INSPR), across a range of cellular and tissue architectures, from mitochondrial networks and nuclear pores in mammalian cells to amyloid-β plaques and dendrites in brain tissues and elastic fibers in developing cartilage of mice. This advancement expands the routine applicability of super-resolution microscopy from selected cellular targets near coverslips to intra- and extracellular targets deep inside tissues.