Label-free metabolic and structural profiling of dynamic biological samples using multimodal optical microscopy with sensorless adaptive optics

Abstract

Label-free optical microscopy has matured as a noninvasive tool for biological imaging; yet, it is criticized for its lack of specificity, slow acquisition and processing times, and weak and noisy optical signals that lead to inaccuracies in quantification. We introduce FOCALS (Fast Optical Coherence, Autofluorescence Lifetime imaging, and Second harmonic generation) microscopy capable of generating NAD(P)H fluorescence lifetime, second harmonic generation (SHG), and polarization-sensitive optical coherence microscopy (OCM) images simultaneously. Multimodal imaging generates quantitative metabolic and morphological profiles of biological samples in vitro, ex vivo, and in vivo. Fast analog detection of fluorescence lifetime and real-time processing on a graphical processing unit enables longitudinal imaging of biological dynamics. We detail the effect of optical aberrations on the accuracy of FLIM beyond the context of undistorting image features. To compensate for the sample-induced aberrations, we implemented a closed-loop single-shot sensorless adaptive optics solution, which uses computational adaptive optics of OCM for wavefront estimation within 2 s and improves the quality of quantitative fluorescence imaging in thick tissues. Multimodal imaging with complementary contrasts improves the specificity and enables multidimensional quantification of the optical signatures in vitro, ex vivo, and in vivo, fast acquisition and real-time processing improve imaging speed by 4–40 × while maintaining enough signal for quantitative nonlinear microscopy, and adaptive optics improves the overall versatility, which enable FOCALS microscopy to overcome the limits of traditional label-free imaging techniques.

Computational Photon Counting Using Multithreshold Peak Detection for Fast Fluorescence Lifetime Imaging Microscopy

“…In response to the arrival of one or more photons, HPDs and PMTs produce waveforms that have peaks of various heights. To examine the differences between HPD (R10467U-40, Hamamatsu) and PMT (H10721-210, Hamamatsu) photon peak height distribution, we examined two-photon fluorescence of NADH in concentrations from 1 to 5 mM on our custom FLIM system (similar to our previously published system; , see Figure S1) using both detectors and digitizing the amplified output at 5 GS/s. The peak height distributions (Figure ) match well with previously experimental , and theoretical comparisons of HPD and PMT performance.…”

“…To acquire time-resolved photon counts using computational photon counting, a standard optical setup for a two-photon fluorescence microscope was used and the fluorescence was collected in the epi-direction by an HPD (Figure S1). The detector output is amplified and directly sampled at 5 GS/s (Figure a), computationally converted into photon counts using GPU-accelerated processing ,, (Figure b), compressed into one 25 ns time block for each pixel composing of the average photon counts over two laser periods (Figure c), and then interleaved and shifted based on the time bin within the line of data that has the maximum value (Figure d). Digitization at 5 GS/s leads to interleaved sampling since the laser period (12.5 ns) is not a multiple of the sampling period (0.2 ns), so data can be averaged into a time period of twice the laser period (25 ns) and then interleaved to have 0.1 ns time bins over a 12.5 ns period.…”

Upconversion nanoparticles for super-resolution quantification of single small extracellular vesicles

“…Super-resolution microscopy techniques have emerged by pushing the resolution beyond the diffraction limit towards nanometre scales [9][10][11][12][13]. The most commonly used techniques include structured illumination microscopy (SIM), stimulated emission depletion (STED) microscopy, and single-molecule localization microscopy (SMLM) of stochastic optical reconstruction microscopy (STORM) and photoactivated localization microscopy (PALM) [14,15].…”

Advances in Optical Coherence Tomography Imaging Technology and Techniques for Choroidal and Retinal Disorders

“…SAO optimization methods and algorithms include Zernike Mode Hill Climbing [ 57 ], stochastic parallel gradient descent [ 58 , 59 ], deep reinforcement learning [ 60 ], and others. SAO features have been tested to some extent in CAO models as well [ 58 , 61 ]. Since its description 15 years ago, AO-OCT is still not commonly used in ophthalmology clinics due to certain limitations.…”