Label-free visualization and characterization of extracellular vesicles in breast cancer.

Despite extensive interest, extracellular vesicle (EV) research remains technically challenging. One of the unexplored gaps in EV research has been the inability to characterize the spatially and functionally heterogeneous populations of EVs based on their metabolic profile. In this paper, we utilize the intrinsic optical metabolic and structural contrast of EVs and demonstrate in vivo/in situ characterization of EVs in a variety of unprocessed (pre)clinical samples. With a pixel-level segmentation mask provided by the deep neural network, individual EVs can be analyzed in terms of their optical signature in the context of their spatial distribution. Quantitative analysis of living tumor-bearing animals and fresh excised human breast tissue revealed abundance of NAD(P)H-rich EVs within the tumor, near the tumor boundary, and around vessel structures. Furthermore, the percentage of NAD(P)H-rich EVs is highly correlated with human breast cancer diagnosis, which emphasizes the important role of metabolic imaging for EV characterization as well as its potential for clinical applications. In addition to the characterization of EV properties, we also demonstrate label-free monitoring of EV dynamics (uptake, release, and movement) in live cells and animals. The in situ metabolic profiling capacity of the proposed method together with the finding of increasing NAD(P)H-rich EV subpopulations in breast cancer have the potential for empowering applications in basic science and enhancing our understanding of the active metabolic roles that EVs play in cancer progression.

Single-shot two-dimensional spectroscopic magnetomotive optical coherence elastography with graphics processing unit acceleration.

Biomechanical contrast within tissues can be assessed based on the resonant frequency probed by spectroscopic magnetomotive optical coherence elastography (MM-OCE). However, to date, in vivo MM-OCE imaging has not been achieved, mainly due to the constraints on imaging speed. Previously, spatially-resolved spectroscopic contrast was achieved in a "multiple-excitation, multiple-acquisition" manner, where seconds of coil cooling time set between consecutive imaging frames lead to total acquisition times of tens of minutes. Here, we demonstrate an improved data acquisition speed by providing a single chirped force excitation prior to magnetomotion imaging with a BM-scan configuration. In addition, elastogram reconstruction was accelerated by exploiting the parallel computing capability of a graphics processing unit (GPU). The accelerated MM-OCE platform achieved data acquisition in 2.9 s and post-processing in 0.6 s for a 2048-frame BM-mode stack. In addition, the elasticity sensing functionality was validated on tissue-mimicking phantoms with high spatial resolution. For the first time, to the best of our knowledge, MM-OCE images were acquired from the skin of a living mouse, demonstrating its feasibility for in vivo imaging.

Automated sensorless single-shot closed-loop adaptive optics microscopy with feedback from computational adaptive optics.

Traditional wavefront-sensor-based adaptive optics (AO) techniques face numerous challenges that cause poor performance in scattering samples. Sensorless closed-loop AO techniques overcome these challenges by optimizing an image metric at different states of a deformable mirror (DM). This requires acquisition of a series of images continuously for optimization - an arduous task in dynamic in vivo samples. We present a technique where the different states of the DM are instead simulated using computational adaptive optics (CAO). The optimal wavefront is estimated by performing CAO on an initial volume to minimize an image metric, and then the pattern is translated to the DM. In this paper, we have demonstrated this technique on a spectral-domain optical coherence microscope for three applications: real-time depth-wise aberration correction, single-shot volumetric aberration correction, and extension of depth-of-focus. Our technique overcomes the disadvantages of sensor-based AO, reduces the number of image acquisitions compared to traditional sensorless AO, and retains the advantages of both computational and hardware-based AO.

Detection of weak near-infrared optical imaging signals under ambient light by optical parametric amplification.

We present a detection method based on optical parametric amplification to amplify and detect near-infrared (NIR) optical imaging signals. A periodically poled lithium niobate crystal is employed as an optical parametric amplifier (OPA), which provides excellent quasi-phase-matching conditions for the optical parametric amplification process. A weak reflectance imaging signal at 1465 nm is amplified by the OPA with a high gain of up to 92 dB, and the amplified optical signal is detected with a low-cost photodetector under ambient light conditions. Such a high gain leads to a detection limit of 23 pW under a 5 MHz detection bandwidth, which is remarkably lower than the theoretical value of a NIR photomultiplier tube (PMT). By exploiting the advantages of the OPA, the incident power needed for microscopy or imaging is reduced by 40-60 dB. The high imaging gain of the OPA also significantly enhances the imaging penetration depth by selectively detecting the weak signal reflected from deep tissue structures. The successful implementation of the OPA enables a robust and sensitive detection method that offers the potential to replace PMTs in imaging applications within the NIR spectral range.

Local wavefront mapping in tissue using computational adaptive optics OCT.

The identification and correction of wavefront aberrations is often necessary to achieve high-resolution optical images of biological tissues, as imperfections in the optical system and the tissue itself distort the imaging beam. Measuring the localized wavefront aberration provides information on where the beam is distorted and how severely. We have recently developed a method to estimate the single-pass wavefront aberrations from complex optical coherence tomography (OCT) data. Using this method, localized wavefront measurement and correction using computational OCT was performed in ex vivo tissues. The computationally measured wavefront varied throughout the imaged OCT volumes and, therefore, a local wavefront correction outperformed a global wavefront correction. The local wavefront measurement was also used to generate tissue aberration maps. Such aberration maps could potentially be used as a new form of tissue contrast.

Wavefront measurement using computational adaptive optics.

In many optical imaging applications, it is necessary to correct for aberrations to obtain high quality images. Optical coherence tomography (OCT) provides access to the amplitude and phase of the backscattered optical field for three-dimensional (3D) imaging samples. Computational adaptive optics (CAO) modifies the phase of the OCT data in the spatial frequency domain to correct optical aberrations without using a deformable mirror, as is commonly done in hardware-based adaptive optics (AO). This provides improvement of image quality throughout the 3D volume, enabling imaging across greater depth ranges and in highly aberrated samples. However, the CAO aberration correction has a complicated relation to the imaging pupil and is not a direct measurement of the pupil aberrations. Here we present new methods for recovering the wavefront aberrations directly from the OCT data without the use of hardware adaptive optics. This enables both computational measurement and correction of optical aberrations.

Automated computational aberration correction method for broadband interferometric imaging techniques.

Numerical correction of optical aberrations provides an inexpensive and simpler alternative to the traditionally used hardware-based adaptive optics techniques. In this Letter, we present an automated computational aberration correction method for broadband interferometric imaging techniques. In the proposed method, the process of aberration correction is modeled as a filtering operation on the aberrant image using a phase filter in the Fourier domain. The phase filter is expressed as a linear combination of Zernike polynomials with unknown coefficients, which are estimated through an iterative optimization scheme based on maximizing an image sharpness metric. The method is validated on both simulated data and experimental data obtained from a tissue phantom, an ex vivo tissue sample, and an in vivo photoreceptor layer of the human retina.

Computed Optical Interferometric Imaging: Methods, Achievements, and Challenges.

Three-dimensional high-resolution optical imaging systems are generally restricted by the trade-off between resolution and depth-of-field as well as imperfections in the imaging system or sample. Computed optical interferometric imaging is able to overcome these longstanding limitations using methods such as interferometric synthetic aperture microscopy (ISAM) and computational adaptive optics (CAO) which manipulate the complex interferometric data. These techniques correct for limited depth-of-field and optical aberrations without the need for additional hardware. This paper aims to outline these computational methods, making them readily available to the research community. Achievements of the techniques will be highlighted, along with past and present challenges in implementing the techniques. Challenges such as phase instability and determination of the appropriate aberration correction have been largely overcome so that imaging of living tissues using ISAM and CAO is now possible. Computed imaging in optics is becoming a mature technology poised to make a significant impact in medicine and biology.

Automated interferometric synthetic aperture microscopy and computational adaptive optics for improved optical coherence tomography.

In this paper, we introduce an algorithm framework for the automation of interferometric synthetic aperture microscopy (ISAM). Under this framework, common processing steps such as dispersion correction, Fourier domain resampling, and computational adaptive optics aberration correction are carried out as metrics-assisted parameter search problems. We further present the results of this algorithm applied to phantom and biological tissue samples and compare with manually adjusted results. With the automated algorithm, near-optimal ISAM reconstruction can be achieved without manual adjustment. At the same time, the technical barrier for the nonexpert using ISAM imaging is also significantly lowered.

Noise characterization of broadband fiber Cherenkov radiation as a visible-wavelength source for optical coherence tomography and two-photon fluorescence microscopy.

Optical sources in the visible region immediately adjacent to the near-infrared biological optical window are preferred in imaging techniques such as spectroscopic optical coherence tomography of endogenous absorptive molecules and two-photon fluorescence microscopy of intrinsic fluorophores. However, existing sources based on fiber supercontinuum generation are known to have high relative intensity noise and low spectral coherence, which may degrade imaging performance. Here we compare the optical noise and pulse compressibility of three high-power fiber Cherenkov radiation sources developed recently, and evaluate their potential to replace the existing supercontinuum sources in these imaging techniques.

Stability in computed optical interferometric tomography (part I): stability requirements.

As imaging systems become more advanced and acquire data at faster rates, increasingly dynamic samples can be imaged without concern of motion artifacts. For optical interferometric techniques such as optical coherence tomography, it often follows that initially, only amplitude-based data are utilized due to unstable or unreliable phase measurements. As systems progress, stable phase maps can also be acquired, enabling more advanced, phase-dependent post-processing techniques. Here we report an investigation of the stability requirements for a class of phase-dependent post-processing techniques - numerical defocus and aberration correction with further extensions to techniques such as Doppler, phase-variance, and optical coherence elastography. Mathematical analyses and numerical simulations over a variety of instabilities are supported by experimental investigations.

Stability in computed optical interferometric tomography (Part II): in vivo stability assessment.

Stability is of utmost importance to a wide range of phase-sensitive processing techniques. In Doppler optical coherence tomography and optical coherence elastography, in addition to defocus and aberration correction techniques such as interferometric synthetic aperture microscopy and computational/digital adaptive optics, a precise understanding of the system and sample stability helps to guide the system design and choice of imaging parameters. This article focuses on methods to accurately and quantitatively measure the stability of an imaging configuration in vivo. These methods are capable of partially decoupling axial from transverse motion and are compared against the stability requirements for computed optical interferometric tomography laid out in the first part of this article.

Viewing-angle enlargement in holographic augmented reality using time division and spatial tiling.

Viewing angle enlargement is essential for SLM-based 3D holographic display. An idea of constructing equivalent-curved-SLM-array (ECSA) is proposed by linear phase factor superimposition. Employing the time division and spatial tiling (TDST) techniques, an ECSA-based horizontal 4f optical system is designed and built. The horizontal viewing angle of a single SLM is increased to 3.6 times when retaining the same hologram area. An interlaced holographic display technique is developed to remove the flicker effect. Holographic augmented reality is performed using the TDST system. Floating holographic 3D image with parallax and accommodation effects is achieved. Both TDST and interlaced technique may extend to multiple SLMs system to achieve larger viewing angle.

Ultra-parallel label-free optophysiology of neural activity.

The electrical activity of neurons has a spatiotemporal footprint that spans three orders of magnitude. Traditional electrophysiology lacks the spatial throughput to image the activity of an entire neural network; besides, labeled optical imaging using voltage-sensitive dyes and tracking Ca2+ ion dynamics lack the versatility and speed to capture fast-spiking activity, respectively. We present a label-free optical imaging technique to image the changes to the optical path length and the local birefringence caused by neural activity, at 4,000 Hz, across a 200 × 200 µm2 region, and with micron-scale spatial resolution and 300-pm displacement sensitivity using Superfast Polarization-sensitive Off-axis Full-field Optical Coherence Microscopy (SPoOF OCM). The undulations in the optical responses from mammalian neuronal activity were matched with field-potential electrophysiology measurements and validated with channel blockers. By directly tracking the widefield neural activity at millisecond timescales and micrometer resolution, SPoOF OCM provides a framework to progress from low-throughput electrophysiology to high-throughput ultra-parallel label-free optophysiology.

Dynamics and all-optical control of solitons at the interface of optical superlattices with spatially modulated nonlinearity.

We find the existence of two kinds of solitons at the interface of optical superlattices with both spatially modulated nonlinearity and linear refraction index. The first kind of solitons can either drift across the lattice, or deflect to the uniform nonlinear medium. The dynamics of such solitons mainly depends on their powers. The other kind of solitons can stably propagate along the surface, and can be controlled by additional Gaussian beams. In addition, we demonstrate the input-angle-dependent reflection, trapping, and refraction with nearly no losses by launching sech-shaped solitons.

Fraunhofer computer-generated hologram for diffused 3D scene in Fresnel region.

A Fraunhofer computer-generated hologram (CGH) is proved to be valid in display for three-dimensional (3D) objects from the Fresnel to the far-field region without a Fourier lens for reconstruction. To quickly compute large and complicated 3D objects that consist of slanted diffused surfaces in the Fresnel region, a Fraunhofer-based analytical approach using a basic-triangle tiling diffuser is developed. Both theoretical and experimental results reveal that Fraunhofer CGH can perform the same effects as Fresnel CGH but require less calculation time. Impressive 3D solid effects are achieved in the Fresnel region.

High-speed full analytical holographic computations for true-life scenes.

We develop a novel method to generate hologram of three-dimensional (3D) textured triangle-mesh-model that is reconstructed from ordinary digital photos. This method allows analytically encoding the 3D model consisting of triangles. In contrast to other polygon based holographic computations, our full analytical method will free oneself from the numerical error that is in the angular spectrum due to the Whittaker-Shannon sampling. In order to saving the computation time, we employ the GPU platform that is remarkably superior to the CPU's. We have rendered a true-life scene with colored textures as the first demo by our homemade software. The holographic reconstructed scene possesses high performances in many aspects such as depth cues, surface textures, shadings, and occlusions, etc. The GPU's algorithm performs hundreds of times faster than those of CPU.

Three-dimensional imaging with monocular cues using holographic stereography.

Two quantitative criteria are derived to evaluate monocular cues in holographic stereograms. We find that the reconstruction has correct monocular cues when the whole scene is located in a so-called "monocular cues area" with compatible monocular and binocular cues. In contrast, incorrect monocular cues appear when the scene is in the other two areas, namely, the "visible multi-imaging area" and the "lacking information area." A pupil-function integral imaging algorithm is developed to simulate monocular observation, and a holographic printing system is set up to fabricate full-parallax holographic stereograms. Both simulations and experiments agree with the criteria.

Dynamic Tracking Algorithm for Time-Varying Neuronal Network Connectivity using Wide-Field Optical Image Video Sequences.

Propagation of signals between neurons and brain regions provides information about the functional properties of neural networks, and thus information transfer. Advances in optical imaging and statistical analyses of acquired optical signals have yielded various metrics for inferring neural connectivity, and hence for mapping signal intercorrelation. However, a single coefficient is traditionally derived to classify the connection strength between two cells, ignoring the fact that neural systems are inherently time-variant systems. To overcome these limitations, we utilized a time-varying Pearson's correlation coefficient, spike-sorting, wavelet transform, and wavelet coherence of calcium transients from DIV 12-15 hippocampal neurons from GCaMP6s mice after applying various concentrations of glutamate. Results provide a comprehensive overview of resulting firing patterns, network connectivity, signal directionality, and network properties. Together, these metrics provide a more comprehensive and robust method of analyzing transient neural signals, and enable future investigations for tracking the effects of different stimuli on network properties.

Simultaneous two-photon activation and imaging of neural activity based on spectral-temporal modulation of supercontinuum light.

SIGNIFICANCE: Recent advances in nonlinear optics in neuroscience have focused on using two ultrafast lasers for activity imaging and optogenetic stimulation. Broadband femtosecond light sources can obviate the need for multiple lasers by spectral separation for chromatically targeted excitation. AIM: We present a photonic crystal fiber (PCF)-based supercontinuum source for spectrally resolved two-photon (2P) imaging and excitation of GCaMP6s and C1V1-mCherry, respectively. APPROACH: A PCF is pumped using a 20-MHz repetition rate femtosecond laser to generate a supercontinuum of light, which is spectrally separated, compressed, and recombined to image GCaMP6s (930 nm excitation) and stimulate the optogenetic protein, C1V1-mCherry (1060 nm excitation). Galvanometric spiral scanning is employed on a single-cell level for multiphoton excitation and high-speed resonant scanning is employed for imaging of calcium activity. RESULTS: Continuous wave lasers were used to verify functionality of optogenetic activation followed by directed 2P excitation. Results from these experiments demonstrate the utility of a supercontinuum light source for simultaneous, single-cell excitation and calcium imaging. CONCLUSIONS: A PCF-based supercontinuum light source was employed for simultaneous imaging and excitation of calcium dynamics in brain tissue. Pumped PCFs can serve as powerful light sources for imaging and activation of neural activity, and overcome the limited spectra and space associated with multilaser approaches.