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SUMMARYDevelopmentally programmed polyploidy (whole-genome-duplication) of cardiomyocytes is common across evolution. Functions of such polyploidy are essentially unknown. Here, we reveal roles for precise polyploidy levels in cardiac tissue. We highlight a conserved asymmetry in polyploidy level between cardiac chambers inDrosophilalarvae and humans. InDrosophila, differential Insulin Receptor (InR) sensitivity leads the heart chamber to reach a higher ploidy/cell size relative to the aorta chamber. Cardiac ploidy-reduced animals exhibit reduced heart chamber size, stroke volume, cardiac output, and acceleration of circulating hemocytes. TheseDrosophilaphenotypes mimic systemic human heart failure. Using human donor hearts, we reveal asymmetry in nuclear volume (ploidy) and insulin signaling between the left ventricle and atrium. Our results identify productive and likely conserved roles for polyploidy in cardiac chambers and suggest precise ploidy levels sculpt many developing tissues. These findings of productive cardiomyocyte polyploidy impact efforts to block developmental polyploidy to improve heart injury recovery.

To study the behavior of freely moving model organisms such as zebrafish (Danio rerio) and fruit flies (Drosophila) across multiple spatial scales, it would be ideal to use a light microscope that can resolve 3D information over a wide field of view (FOV) at high speed and high spatial resolution. However, it is challenging to design an optical instrument to achieve all of these properties simultaneously. Existing techniques for large-FOV microscopic imaging and for 3D image measurement typically require many sequential image snapshots, thus compromising speed and throughput. Here, we present 3D-RAPID, a computational microscope based on a synchronized array of 54 cameras that can capture high-speed 3D topographic videos over a 135-cm^2 area, achieving up to 230 frames per second at throughputs exceeding 5 gigapixels (GPs) per second. 3D-RAPID features a 3D reconstruction algorithm that, for each synchronized temporal snapshot, simultaneously fuses all 54 images seamlessly into a globally-consistent composite that includes a coregistered 3D height map. The self-supervised 3D reconstruction algorithm itself trains a spatiotemporally-compressed convolutional neural network (CNN) that maps raw photometric images to 3D topography, using stereo overlap redundancy and ray-propagation physics as the only supervision mechanism. As a result, our end-to-end 3D reconstruction algorithm is robust to generalization errors and scales to arbitrarily long videos from arbitrarily sized camera arrays. The scalable hardware and software design of 3D-RAPID addresses a longstanding problem in the field of behavioral imaging, enabling parallelized 3D observation of large collections of freely moving organisms at high spatiotemporal throughputs, which we demonstrate in ants (Pogonomyrmex barbatus), fruit flies, and zebrafish larvae.

Ophthalmic microsurgery is technically difficult because the scale of required surgical tool manipulations challenge the limits of the surgeon's visual acuity, sensory perception, and physical dexterity. Intraoperative optical coherence tomography (OCT) imaging with micrometer-scale resolution is increasingly being used to monitor and provide enhanced real-time visualization of ophthalmic surgical maneuvers, but surgeons still face physical limitations when manipulating instruments inside the eye. Autonomously controlled robots are one avenue for overcoming these physical limitations. In this article, we demonstrate the feasibility of using learning from demonstration and reinforcement learning with an industrial robot to perform OCT-guided corneal needle insertions in an ex vivo model of deep anterior lamellar keratoplasty (DALK) surgery. Our reinforcement learning agent trained on ex vivo human corneas, then outperformed surgical fellows in reaching a target needle insertion depth in mock corneal surgery trials. This article shows the combination of learning from demonstration and reinforcement learning is a viable option for performing OCT-guided robotic ophthalmic surgery.

This paper presents a microscopic imaging technique that uses variable-angle illumination to recover the complex polarimetric properties of a specimen at high resolution and over a large field-of-view. The approach extends Fourier ptychography, which is a synthetic aperture-based imaging approach to improve resolution with phaseless measurements, to additionally account for the vectorial nature of light. After images are acquired using a standard microscope outfitted with an LED illumination array and two polarizers, our vectorial Fourier ptychography (vFP) algorithm solves for the complex 2x2 Jones matrix of the anisotropic specimen of interest at each resolved spatial location. We introduce a new sequential Gauss-Newton-based solver that additionally jointly estimates and removes polarization-dependent imaging system aberrations. We demonstrate effective vFP performance by generating large-area (29 mm2), high-resolution (1.24 μm full-pitch) reconstructions of sample absorption, phase, orientation, diattenuation, and retardance for a variety of calibration samples and biological specimens.

This work demonstrates a multi-lens microscopic imaging system that overlaps multiple independent fields of view on a single sensor for high-efficiency automated specimen analysis. Automatic detection, classification and counting of various morphological features of interest is now a crucial component of both biomedical research and disease diagnosis. While convolutional neural networks (CNNs) have dramatically improved the accuracy of counting cells and sub-cellular features from acquired digital image data, the overall throughput is still typically hindered by the limited space-bandwidth product (SBP) of conventional microscopes. Here, we show both in simulation and experiment that overlapped imaging and co-designed analysis software can achieve accurate detection of diagnostically-relevant features for several applications, including counting of white blood cells and the malaria parasite, leading to multi-fold increase in detection and processing throughput with minimal reduction in accuracy.

Anterior uveitis is the most common form of intraocular inflammation, and one of its main signs is the presence of white blood cells (WBCs) in the anterior chamber (AC). Clinically, the true composition of cells can currently only be obtained using AC paracentesis, an invasive procedure to obtain AC fluid requiring needle insertion into the AC. We previously developed a spectroscopic optical coherence tomography (SOCT) analysis method to differentiate between populations of RBCs and subtypes of WBCs, including granulocytes, lymphocytes and monocytes, both in vitro and in ACs of excised porcine eyes. We have shown that different types of WBCs have distinct characteristic size distributions, extracted from the backscattered reflectance spectrum of individual cells using Mie theory. Here, we further develop our method to estimate the composition of blood cell mixtures, both in vitro and in vivo. To do so, we estimate the size distribution of unknown cell mixtures by fitting the distribution observed using SOCT with a weighted combination of reference size distributions of each WBC type calculated using kernel density estimation. We validate the accuracy of our estimation in an in vitro study, by comparing our results for a given WBC sample mixture with the cellular concentrations measured by a hemocytometer and SOCT images before mixing. We also conducted a small in vivo quantitative cell mixture validation pilot study which demonstrates congruence between our method and AC paracentesis in two patients with uveitis. The SOCT based method appears promising to provide quantitative diagnostic information of cellular responses in the ACs of patients with uveitis.

We present a tomographic imaging technique, termed Deep Prior Diffraction Tomography (DP-DT), to reconstruct the 3D refractive index (RI) of thick biological samples at high resolution from a sequence of low-resolution images collected under angularly varying illumination. DP-DT processes the multi-angle data using a phase retrieval algorithm that is extended by a deep image prior (DIP), which reparameterizes the 3D sample reconstruction with an untrained, deep generative 3D convolutional neural network (CNN). We show that DP-DT effectively addresses the missing cone problem, which otherwise degrades the resolution and quality of standard 3D reconstruction algorithms. As DP-DT does not require pre-captured data or pre-training, it is not biased towards any particular dataset. Hence, it is a general technique that can be applied to a wide variety of 3D samples, including scenarios in which large datasets for supervised training would be infeasible or expensive. We applied DP-DT to obtain 3D RI maps of bead phantoms and complex biological specimens, both in simulation and experiment, and show that DP-DT produces higher-quality results than standard regularization techniques. We further demonstrate the generality of DP-DT, using two different scattering models, the first Born and multi-slice models. Our results point to the potential benefits of DP-DT for other 3D imaging modalities, including X-ray computed tomography, magnetic resonance imaging, and electron microscopy.

There is potential clinical significance in identifying cellular responses in the anterior chamber (AC) of the eye, which can indicate hyphema (an accumulation of red blood cells [RBCs]) or aberrant intraocular inflammation (an accumulation of white blood cells [WBCs]). In this work, we developed a spectroscopic OCT analysis method to differentiate between populations of RBCs and subtypes of WBCs, including granulocytes, lymphocytes and monocytes, both in vitro and in ACs of porcine eyes. We developed an algorithm to track single cells within OCT data sets, and extracted the backscatter reflectance spectrum of each single cell from the detected interferograms using the short-time Fourier transform (STFT). A look-up table of Mie back-scattering spectra was generated and used to correlate the backscatter spectral features of single cells to their characteristic sizes. The extracted size distributions based on the best Mie spectra fit were significantly different between each cell type. We also studied theoretical backscattering models of single RBCs to further validate our experimental results. The described work is a promising step towards clinically differentiating and quantifying AC blood cell types.

Since its invention, the microscope has been optimized for interpretation by a human observer. With the recent development of deep learning algorithms for automated image analysis, there is now a clear need to re-design the microscope's hardware for specific interpretation tasks. To increase the speed and accuracy of automated image classification, this work presents a method to co-optimize how a sample is illuminated in a microscope, along with a pipeline to automatically classify the resulting image, using a deep neural network. By adding a ""physical layer"" to a deep classification network, we are able to jointly optimize for specific illumination patterns that highlight the most important sample features for the particular learning task at hand, which may not be obvious under standard illumination. We demonstrate how our learned sensing approach for illumination design can automatically identify malaria-infected cells with up to 5-10% greater accuracy than standard and alternative microscope lighting designs. We show that this joint hardware-software design procedure generalizes to offer accurate diagnoses for two different blood smear types, and experimentally show how our new procedure can translate across different experimental setups while maintaining high accuracy.

We integrate spectroscopic optical coherence tomography (SOCT) with stimulated Raman scattering (SRS) to enable simultaneously multiplexed spatial and spectral imaging with sensitivity to many endogenous biochemical species that play an important role in biology and medicine. The combined approach, termed SRS-SOCT, overcomes the limitations of each individual method. Ultimately, SRS-SOCT has the potential to achieve fast, volumetric, and highly sensitive label-free molecular imaging. We demonstrate the approach by imaging excised human adipose tissue and detecting the lipids' Raman signatures in the high-wavenumber region.

Recent advances in three-dimensional (3D) tissue engineering have concomitantly generated a need for new methods to visualize and assess the tissue. In particular, methods for imaging intact volumes of whole tissue, rather than a single plane, are required. Herein, we describe the use of multiphoton microscopy, combined with optical clearing, to noninvasively probe decellularized lung extracellular matrix scaffolds and decellularized, tissue-engineered blood vessels. We also evaluate recellularized lung tissue scaffolds. In addition to nondestructive imaging of tissue volumes greater than 4 mm(3), the lung tissue can be visualized using three distinct signals, combined or singly, that allow for simple separation of cells and different components of the extracellular matrix. Because the 3D volumes are not reconstructions, they do not require registration algorithms to generate digital volumes, and maintenance of isotropic resolution is not required when acquiring stacks of images. Once a virtual volume of tissue is generated, structures that have innate 3D features, such as the lumens of vessels and airways, are easily animated and explored in all dimensions. In blood vessels, individual collagen fibers can be visualized at the micron scale and their alignment assessed at various depths through the tissue, potentially providing some nondestructive measure of vessel integrity and mechanics. Finally, both the lungs and vessels assayed here were optically cleared, imaged, and visualized in a matter of hours, such that the added benefits of these techniques can be achieved with little more hassle or processing time than that associated with traditional histological methods.

Tissue-engineered blood vessels (TEVs) are typically produced using the pulsatile, uniaxial circumferential stretch to mechanically condition and strengthen the arterial grafts. Despite improvements in the mechanical integrity of TEVs after uniaxial conditioning, these tissues fail to achieve critical properties of native arteries such as matrix content, collagen fiber orientation, and mechanical strength. As a result, uniaxially loaded TEVs can result in mechanical failure, thrombus, or stenosis on implantation. In planar tissue equivalents such as artificial skin, biaxial loading has been shown to improve matrix production and mechanical properties. To date however, multiaxial loading has not been examined as a means to improve mechanical and biochemical properties of TEVs during culture. Therefore, we developed a novel bioreactor that utilizes both circumferential and axial stretch that more closely simulates loading conditions in native arteries, and we examined the suture strength, matrix production, fiber orientation, and cell proliferation. After 3 months of biaxial loading, TEVs developed a formation of mature elastic fibers that consisted of elastin cores and microfibril sheaths. Furthermore, the distinctive features of collagen undulation and crimp in the biaxial TEVs were absent in both uniaxial and static TEVs. Relative to the uniaxially loaded TEVs, tissues that underwent biaxial loading remodeled and realigned collagen fibers toward a more physiologic, native-like organization. The biaxial TEVs also showed increased mechanical strength (suture retention load of 303 ± 14.53 g, with a wall thickness of 0.76 ± 0.028 mm) and increased compliance. The increase in compliance was due to combinatorial effects of mature elastic fibers, undulated collagen fibers, and collagen matrix orientation. In conclusion, biaxial stretching is a potential means to regenerate TEVs with improved matrix production, collagen organization, and mechanical properties.

We present a new OCT method for flow speed quantification and directional velocimetry: particle streak velocimetry-OCT (PSV-OCT). PSV-OCT generates two-dimensional, 2.5-vector component (vx ,|vy |,vz ) maps of microscale flow velocity fields. Knowledge of 2.5-vector components also enables the estimation of total flow speed. The enabling insight behind PSV-OCT is that tracer particles in sparsely-seeded fluid flow trace out streaks in (x,z,t)-space. The streak orientations in x-t and z-t yield vx and vz , respectively. The in-plane (x-z plane) residence time yields the out-of-plane speed |vy |. Vector component values are generated by fitting streaks to a model of image formation that incorporates equations of motion in 3D space. We demonstrate cross-sectional estimation of (vx ,|vy |,vz ) in two important animal models in ciliary biology: Xenopus embryos (tadpoles) and mouse trachea.

OCT is a popular cross-sectional microscale imaging modality in medicine and biology. While structural imaging using OCT is a mature technology in many respects, flow and motion estimation using OCT remains an intense area of research. In particular, there is keen interest in maximizing information extraction from the complex-valued OCT signal. Here, we introduce a Bayesian framework into the data workflow in OCT-based velocimetry. We demonstrate that using prior information in this Bayesian framework can significantly improve velocity estimate precision in a correlation-based, model-based framework for Doppler and transverse velocimetry. We show results in calibrated flow phantoms as well as in vivo in a Drosophila melanogaster (fruit fly) heart. Thus, our work improves upon the current approaches in terms of improved information extraction from the complex-valued OCT signal.