Snapshot hyperspectral imaging of intracellular lasers.

Intracellular lasers are emerging as powerful biosensors for multiplexed tracking and precision sensing of cells and their microenvironment. This sensing capacity is enabled by quantifying their narrow-linewidth emission spectra, which is presently challenging to do at high speeds. In this work, we demonstrate rapid snapshot hyperspectral imaging of intracellular lasers. Using integral field mapping with a microlens array and a diffraction grating, we obtain images of the spatial and spectral intensity distribution from a single camera acquisition. We demonstrate widefield hyperspectral imaging over a 3 × 3 mm2 field of view and volumetric imaging over 250 × 250 × 800 µm3 (XYZ) volumes with a lateral (XY) resolution of 5 µm, axial (Z) resolution of 10 µm, and a spectral resolution of less than 0.8 nm. We evaluate the performance and outline the challenges and strengths of snapshot methods in the context of characterizing the emission from intracellular lasers. This method offers new opportunities for a diverse range of applications, including high-throughput and long-term biosensing with intracellular lasers.

Speckle-dependent accuracy in phase-sensitive optical coherence tomography.

Phase-sensitive optical coherence tomography (OCT) is used to measure motion in a range of techniques, such as Doppler OCT and optical coherence elastography (OCE). In phase-sensitive OCT, motion is typically estimated using a model of the OCT signal derived from a single reflector. However, this approach is not representative of turbid samples, such as tissue, which exhibit speckle. In this study, for the first time, we demonstrate, through theory and experiment that speckle significantly lowers the accuracy of phase-sensitive OCT in a manner not accounted for by the OCT signal-to-noise ratio (SNR). We describe how the inaccuracy in speckle reduces phase difference sensitivity and introduce a new metric, speckle brightness, to quantify the amount of constructive interference at a given location in an OCT image. Experimental measurements show an almost three-fold degradation in sensitivity between regions of high and low speckle brightness at a constant OCT SNR. Finally, we apply these new results in compression OCE to demonstrate a ten-fold improvement in strain sensitivity, and a five-fold improvement in contrast-to-noise by incorporating independent speckle realizations. Our results show that speckle introduces a limit to the accuracy of phase-sensitive OCT and that speckle brightness should be considered to avoid erroneous interpretation of experimental data.

Coherence function-encoded optical palpation.

Optical palpation maps stress at the surface of biological tissue into 2D images. It relies on measuring surface deformation of a compliant layer, which to date has been performed with optical coherence tomography (OCT). OCT-based optical palpation holds promise for improved clinical diagnostics; however, the complexity and cost hinder broad adoption. In this Letter, we introduce coherence function-encoded optical palpation (CFE-OP) using a novel optical profilometry technique that exploits the envelope of the coherence function rather than its peak position, which is typically used to retrieve depth information. CFE-OP utilizes a Fabry-Perot laser diode (bandwidth, 2.2 nm) and a single photodiode in a Michelson interferometer to detect the position along the coherence envelope as a function of path length. This technique greatly reduces complexity and cost in comparison to the OCT-based approach. We perform CFE-OP on phantom and excised human breast tissue, demonstrating comparable mechanical contrast to OCT-based optical palpation and the capability to distinguish stiff tumor from soft benign tissue.

Stem cell migration and mechanotransduction on linear stiffness gradient hydrogels.

The spatial presentation of mechanical information is a key parameter for cell behavior. We have developed a method of polymerization control in which the differential diffusion distance of unreacted cross-linker and monomer into a prepolymerized hydrogel sink results in a tunable stiffness gradient at the cell-matrix interface. This simple, low-cost, robust method was used to produce polyacrylamide hydrogels with stiffness gradients of 0.5, 1.7, 2.9, 4.5, 6.8, and 8.2 kPa/mm, spanning the in vivo physiological and pathological mechanical landscape. Importantly, three of these gradients were found to be nondurotactic for human adipose-derived stem cells (hASCs), allowing the presentation of a continuous range of stiffnesses in a single well without the confounding effect of differential cell migration. Using these nondurotactic gradient gels, stiffness-dependent hASC morphology, migration, and differentiation were studied. Finally, the mechanosensitive proteins YAP, Lamin A/C, Lamin B, MRTF-A, and MRTF-B were analyzed on these gradients, providing higher-resolution data on stiffness-dependent expression and localization.

Optimal compressive multiphoton imaging at depth using single-pixel detection.

Compressive sensing can overcome the Nyquist criterion and record images with a fraction of the usual number of measurements required. However, conventional measurement bases are susceptible to diffraction and scattering, prevalent in high-resolution microscopy. In this Letter, we explore the random Morlet basis as an optimal set for compressive multiphoton imaging, based on its ability to minimize the space-frequency uncertainty. We implement this approach for wide-field multiphoton microscopy with single-pixel detection, which allows imaging through turbid media without correction. The Morlet basis promises a route for rapid acquisition with lower photodamage.

Ultrahigh-Resolution Optical Coherence Elastography Images Cellular-Scale Stiffness of Mouse Aorta.

Cellular-scale imaging of the mechanical properties of tissue has helped to reveal the origins of disease; however, cellular-scale resolution is not readily achievable in intact tissue volumes. Here, we demonstrate volumetric imaging of Young's modulus using ultrahigh-resolution optical coherence elastography, and apply it to characterizing the stiffness of mouse aortas. We achieve isotropic resolution of better than 15 µm over a 1-mm lateral field of view through the entire depth of an intact aortic wall. We employ a method of quasi-static compression elastography that measures volumetric axial strain and uses a compliant, transparent layer to measure surface axial stress. This combination is used to estimate Young's modulus throughout the volume. We demonstrate differentiation by stiffness of individual elastic lamellae and vascular smooth muscle. We observe stiffening of the aorta in regulator of G protein signaling 5-deficient mice, a model that is linked to vascular remodeling and fibrosis. We observe increased stiffness with proximity to the heart, as well as regions with micro-structural and micro-mechanical signatures characteristic of fibrous and lipid-rich tissue. High-resolution imaging of Young's modulus with optical coherence elastography may become an important tool in vascular biology and in other fields concerned with understanding the role of mechanics within the complex three-dimensional architecture of tissue.

Depth-encoded optical coherence elastography for simultaneous volumetric imaging of two tissue faces.

Depth-encoded optical coherence elastography (OCE) enables simultaneous acquisition of two three-dimensional (3D) elastograms from opposite sides of a sample. By the choice of suitable path-length differences in each of two interferometers, the detected carrier frequencies are separated, allowing depth-ranging from each interferometer to be performed simultaneously using a single spectrometer. We demonstrate depth-encoded OCE on a silicone phantom and a freshly excised sample of mouse liver. This technique minimizes the required spectral detection hardware and halves the total scan time. Depth-encoded OCE may expedite clinical translation in time-sensitive applications requiring rapid 3D imaging of multiple tissue surfaces, such as tumor margin assessment in breast-conserving surgery.

Ultrahigh-resolution optical coherence elastography.

Visualizing stiffness within the local tissue environment at the cellular and subcellular level promises to provide insight into the genesis and progression of disease. In this Letter, we propose ultrahigh-resolution optical coherence elastography (UHROCE), and demonstrate 3D imaging of local axial strain of tissues undergoing compressive loading. We combine optical coherence microscopy (OCM) and phase-sensitive detection of local tissue displacement to produce strain elastograms with resolution (x,y,z) of 2×2×15 µm. We demonstrate this performance on a freshly excised mouse aorta and reveal the mechanical heterogeneity of vascular smooth muscle cells and elastin sheets, otherwise unresolved in a typical, lower resolution optical coherence elastography (OCE) system.

Investigation of optical coherence micro-elastography as a method to visualize micro-architecture in human axillary lymph nodes.

BACKGROUND: Evaluation of lymph node involvement is an important factor in detecting metastasis and deciding whether to perform axillary lymph node dissection (ALND) in breast cancer surgery. As ALND is associated with potentially severe long term morbidity, the accuracy of lymph node assessment is imperative in avoiding unnecessary ALND. The mechanical properties of malignant lymph nodes are often distinct from those of normal nodes. A method to image the micro-scale mechanical properties of lymph nodes could, thus, provide diagnostic information to aid in the assessment of lymph node involvement in metastatic cancer. In this study, we scan axillary lymph nodes, freshly excised from breast cancer patients, with optical coherence micro-elastography (OCME), a method of imaging micro-scale mechanical strain, to assess its potential for the intraoperative assessment of lymph node involvement. METHODS: Twenty-six fresh, unstained lymph nodes were imaged from 15 patients undergoing mastectomy or breast-conserving surgery with axillary clearance. Lymph node specimens were bisected to allow imaging of the internal face of each node. Co-located OCME and optical coherence tomography (OCT) scans were taken of each sample, and the results compared to standard post-operative hematoxylin-and-eosin-stained histology. RESULTS: The optical backscattering signal provided by OCT alone may not provide reliable differentiation by inspection between benign and malignant lymphoid tissue. Alternatively, OCME highlights local changes in tissue strain that correspond to malignancy and are distinct from strain patterns in benign lymphoid tissue. The mechanical contrast provided by OCME complements the optical contrast provided by OCT and aids in the differentiation of malignant tumor from uninvolved lymphoid tissue. CONCLUSION: The combination of OCME and OCT images represents a promising method for the identification of malignant lymphoid tissue. This method shows potential to provide intraoperative assessment of lymph node involvement, thus, preventing unnecessary removal of uninvolved tissues and improving patient outcomes.

Light-deformable microrobots shape up for the biological obstacle course.

Euglena gracilis microalga has been transformed into a soft bio-microrobot with light-controlled motion and deformation that can address diverse bio-challenges, such as drug delivery, diseased cell removal, and photodynamic therapy.

Multimodal mechano-microscopy reveals mechanical phenotypes of breast cancer spheroids in three dimensions.

Cancer cell invasion relies on an equilibrium between cell deformability and the biophysical constraints imposed by the extracellular matrix (ECM). However, there is little consensus on the nature of the local biomechanical alterations in cancer cell dissemination in the context of three-dimensional (3D) tumor microenvironments (TMEs). While the shortcomings of two-dimensional (2D) models in replicating in situ cell behavior are well known, 3D TME models remain underutilized because contemporary mechanical quantification tools are limited to surface measurements. Here, we overcome this major challenge by quantifying local mechanics of cancer cell spheroids in 3D TMEs. We achieve this using multimodal mechano-microscopy, integrating optical coherence microscopy-based elasticity imaging with confocal fluorescence microscopy. We observe that non-metastatic cancer spheroids show no invasion while showing increased peripheral cell elasticity in both stiff and soft environments. Metastatic cancer spheroids, however, show ECM-mediated softening in a stiff microenvironment and, in a soft environment, initiate cell invasion with peripheral softening associated with early metastatic dissemination. This exemplar of live-cell 3D mechanotyping supports that invasion increases cell deformability in a 3D context, illustrating the power of multimodal mechano-microscopy for quantitative mechanobiology in situ.

Spatially offset optical coherence tomography: Leveraging multiple scattering for high-contrast imaging at depth in turbid media.

The penetration depth of optical coherence tomography (OCT) reaches well beyond conventional microscopy; however, signal reduction with depth leads to rapid degradation of the signal below the noise level. The pursuit of imaging at depth has been largely approached by extinguishing multiple scattering. However, in OCT, multiple scattering substantially contributes to image formation at depth. Here, we investigate the role of multiple scattering in OCT image contrast and postulate that, in OCT, multiple scattering can enhance image contrast at depth. We introduce an original geometry that completely decouples the incident and collection fields by introducing a spatial offset between them, leading to preferential collection of multiply scattered light. A wave optics-based theoretical framework supports our experimentally demonstrated improvement in contrast. The effective signal attenuation can be reduced by more than 24 decibels. Notably, a ninefold enhancement in image contrast at depth is observed in scattering biological samples. This geometry enables a powerful capacity to dynamically tune for contrast at depth.

UVA Hyperspectral Light-Sheet Microscopy for Volumetric Metabolic Imaging: Application to Preimplantation Embryo Development.

Cellular metabolism is a key regulator of energetics, cell growth, regeneration, and homeostasis. Spatially mapping the heterogeneity of cellular metabolic activity is of great importance for unraveling the overall cell and tissue health. In this regard, imaging the endogenous metabolic cofactors, nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and flavin adenine dinucleotide (FAD), with subcellular resolution and in a noninvasive manner would be useful to determine tissue and cell viability in a clinical environment, but practical use is limited by current imaging techniques. In this paper, we demonstrate the use of phasor-based hyperspectral light-sheet (HS-LS) microscopy using a single UVA excitation wavelength as a route to mapping metabolism in three dimensions. We show that excitation solely at a UVA wavelength of 375 nm can simultaneously excite NAD(P)H and FAD autofluorescence, while their relative contributions can be readily quantified using a hardware-based spectral phasor analysis. We demonstrate the potential of our HS-LS system by capturing dynamic changes in metabolic activity during preimplantation embryo development. To validate our approach, we delineate metabolic changes during preimplantation embryo development from volumetric maps of metabolic activity. Importantly, our approach overcomes the need for multiple excitation wavelengths, two-photon imaging, or significant postprocessing of data, paving the way toward clinical translation, such as in situ, noninvasive assessment of embryo viability.

Experimentally unsupervised deconvolution for light-sheet microscopy with propagation-invariant beams.

Deconvolution is a challenging inverse problem, particularly in techniques that employ complex engineered point-spread functions, such as microscopy with propagation-invariant beams. Here, we present a deep-learning method for deconvolution that, in lieu of end-to-end training with ground truths, is trained using known physics of the imaging system. Specifically, we train a generative adversarial network with images generated with the known point-spread function of the system, and combine this with unpaired experimental data that preserve perceptual content. Our method rapidly and robustly deconvolves and super-resolves microscopy images, demonstrating a two-fold improvement in image contrast to conventional deconvolution methods. In contrast to common end-to-end networks that often require 1000-10,000s paired images, our method is experimentally unsupervised and can be trained solely on a few hundred regions of interest. We demonstrate its performance on light-sheet microscopy with propagation-invariant Airy beams in oocytes, preimplantation embryos and excised brain tissue, as well as illustrate its utility for Bessel-beam LSM. This method aims to democratise learned methods for deconvolution, as it does not require data acquisition outwith the conventional imaging protocol.

Does artificial intelligence have a role in the IVF clinic?

The success of IVF has remained stagnant for a decade. The focus of a great deal of research is to improve on the current ~30% success rate of IVF. Artificial intelligence (AI), or machines that mimic human intelligence, has been gaining traction for its potential to improve outcomes in medicine, such as cancer diagnosis from medical images. In this commentary, we discuss whether AI has the potential to improve fertility outcomes in the IVF clinic. Based on existing research, we examine the potential of adopting AI within multiple facets of an IVF cycle, including egg/sperm and embryo selection, as well as formulation of an IVF treatment regimen. We discuss both the potential benefits and concerns of the patient and clinician in adopting AI in the clinic. We outline hurdles that need to be overcome prior to implementation. We conclude that AI has an important future in improving IVF success.

Enhancing Resistance of Silk Fibroin Material to Enzymatic Degradation by Cross-Linking Both Crystalline and Amorphous Domains.

Silk fibroin (SF) membranes are finding widespread use as biomaterial scaffolds in a range of tissue engineering applications. The control over SF scaffold degradation kinetics is usually driven by the proportion of SF crystalline domains in the formulation, but membranes with a high ß-sheet content are brittle and still contain amorphous domains, which are highly susceptible to enzymatic degradation. In this work, photo-cross-linking of SF using a ruthenium-based method, and with the addition of glycerol, was used to generate robust and flexible SF membranes for long-term tissue engineering applications requiring slow degradation of the scaffolds. The resulting mechanical properties, protein secondary structure, and degradation rate were investigated. In addition, the cytocompatibility and versatility of porous micropatterning of SF films were assessed. The photo-cross-linking reduced the enzymatic degradation of SF in vitro without interfering with the ß-sheet content of the SF material, while adding glycerol to the composition grants flexibility to the membranes. By combining these methods, the membrane resistance to protease degradation was significantly enhanced compared to either method alone, and the SF mechanical properties were not impaired. We hypothesize that photo-cross-linking protects the SF amorphous regions from enzymatic degradation and complements the natural protection offered by ß-sheets in the crystalline region. Overall, this approach presents broad utility in tissue engineering applications that require a long-term degradation profile and mechanical support.

Three-dimensional imaging of cell and extracellular matrix elasticity using quantitative micro-elastography.

Recent studies in mechanobiology have revealed the importance of cellular and extracellular mechanical properties in regulating cellular function in normal and disease states. Although it is established that cells should be investigated in a three-dimensional (3-D) environment, most techniques available to study mechanical properties on the microscopic scale are unable to do so. In this study, for the first time, we present volumetric images of cellular and extracellular elasticity in 3-D biomaterials using quantitative micro-elastography (QME). We achieve this by developing a novel strain estimation algorithm based on 3-D linear regression to improve QME system resolution. We show that QME can reveal elevated elasticity surrounding human adipose-derived stem cells (ASCs) embedded in soft hydrogels. We observe, for the first time in 3-D, further elevation of extracellular elasticity around ASCs with overexpressed TAZ; a mechanosensitive transcription factor which regulates cell volume. Our results demonstrate that QME has the potential to study the effects of extracellular mechanical properties on cellular functions in a 3-D micro-environment.

Handheld volumetric manual compression-based quantitative microelastography.

Compression optical coherence elastography (OCE) typically requires a mechanical actuator to impart a controlled uniform strain to the sample. However, for handheld scanning, this adds complexity to the design of the probe and the actuator stroke limits the amount of strain that can be applied. In this work, we present a new volumetric imaging approach that utilizes bidirectional manual compression via the natural motion of the user's hand to induce strain to the sample, realizing compact, actuator-free, handheld compression OCE. In this way, we are able to demonstrate rapid acquisition of three-dimensional quantitative microelastography (QME) datasets of a tissue volume (6 × 6 × 1 mm3 ) in 3.4 seconds. We characterize the elasticity sensitivity of this freehand manual compression approach using a homogeneous silicone phantom and demonstrate comparable performance to a benchtop mounted, actuator-based approach. In addition, we demonstrate handheld volumetric manual compression-based QME on a tissue-mimicking phantom with an embedded stiff inclusion and on freshly excised human breast specimens from both mastectomy and wide local excision (WLE) surgeries. Tissue results are coregistered with postoperative histology, verifying the capability of our approach to measure the elasticity of tissue and to distinguish stiff tumor from surrounding soft benign tissue.

Diagnostic Accuracy of Quantitative Micro-Elastography for Margin Assessment in Breast-Conserving Surgery.

Inadequate margins in breast-conserving surgery (BCS) are associated with an increased likelihood of local recurrence of breast cancer. Currently, approximately 20% of BCS patients require repeat surgery due to inadequate margins at the initial operation. Implementation of an accurate, intraoperative margin assessment tool may reduce this re-excision rate. This study determined, for the first time, the diagnostic accuracy of quantitative micro-elastography (QME), an optical coherence tomography (OCT)-based elastography technique that produces images of tissue microscale elasticity, for detecting tumor within 1 mm of the margins of BCS specimens. Simultaneous OCT and QME were performed on the margins of intact, freshly excised specimens from 83 patients undergoing BCS and on dissected specimens from 7 patients undergoing mastectomy. The resulting three-dimensional images (45 × 45 × 1 mm) were coregistered with postoperative histology to determine tissue types present in each scan. Data from 12 BCS patients and the 7 mastectomy patients served to build a set of images for reader training. One hundred and fifty-four subimages (10 × 10 × 1 mm) from the remaining 71 BCS patients were included in a blinded reader study, which resulted in 69.0% sensitivity and 79.0% specificity using OCT images, versus 92.9% sensitivity and 96.4% specificity using elasticity images. The quantitative nature of QME also facilitated development of an automated reader, which resulted in 100.0% sensitivity and 97.7% specificity. These results demonstrate high accuracy of QME for detecting tumor within 1 mm of the margin and the potential for this technique to improve outcomes in BCS. SIGNIFICANCE: An optical imaging technology probes breast tissue elasticity to provide accurate assessment of tumor margin involvement in breast-conserving surgery.

Analysis of spatial resolution in phase-sensitive compression optical coherence elastography.

Optical coherence elastography (OCE) is emerging as a method to image the mechanical properties of tissue on the microscale. However, the spatial resolution, a main advantage of OCE, has not been investigated and is not trivial to evaluate. To address this, we present a framework to analyze resolution in phase-sensitive compression OCE that incorporates the three main determinants of resolution: mechanical deformation of the sample, detection of this deformation using optical coherence tomography (OCT), and signal processing to estimate local axial strain. We demonstrate for the first time, through close correspondence between experiment and simulation of structured phantoms, that resolution in compression OCE is both spatially varying and sample dependent, which we link to the discrepancies between the model of elasticity and the mechanical deformation of the sample. We demonstrate that resolution is dependent on factors such as feature size and mechanical contrast. We believe that the analysis of image formation provided by our framework can expedite the development of compression OCE.