A novel stress sensor enables accurate estimation of micro-scale tissue mechanics in quantitative micro-elastography.

Quantitative micro-elastography (QME) is a compression-based optical coherence elastography technique enabling the estimation of tissue mechanical properties on the micro-scale. QME utilizes a compliant layer as an optical stress sensor, placed between an imaging window and tissue, providing quantitative estimation of elasticity. However, the implementation of the layer is challenging and introduces unpredictable friction conditions at the contact boundaries, deteriorating the accuracy and reliability of elasticity estimation. This has largely limited the use of QME to ex vivo studies and is a barrier to clinical translation. In this work, we present a novel implementation by affixing the stress sensing layer to the imaging window and optimizing the layer thickness, enhancing the practical use of QME for in vivo applications by eliminating the requirement for manual placement of the layer, and significantly reducing variations in the friction conditions, leading to substantial improvement in the accuracy and repeatability of elasticity estimation. We performed a systematic validation of the integrated layer, demonstrating >30% improvement in sensitivity and the ability to provide mechanical contrast in a mechanically heterogeneous phantom. In addition, we demonstrate the ability to obtain accurate estimation of elasticity (<6% error compared to <14% achieved using existing QME) in homogeneous phantoms with mechanical properties ranging from 40 to 130 kPa. Furthermore, we show the integrated layer to be more robust, exhibiting increased temporal stability, as well as improved conformity to variations in sample surface topography, allowing for accurate estimation of elasticity over acquisition times 3× longer than current methods. Finally, when applied to ex vivo human breast tissue, we demonstrate the ability to distinguish between healthy and diseased tissue features, such as stroma and cancer, confirmed by co-registered histology, showcasing the potential for routine use in biomedical applications.

Stiffness Mediated-Mechanosensation of Airway Smooth Muscle Cells on Linear Stiffness Gradient Hydrogels.

In obstructive airway diseases such as asthma and chronic obstructive pulmonary disease (COPD), the extracellular matrix (ECM) protein amount and composition of the airway smooth muscle (ASM) is often remodelled, likely altering tissue stiffness. The underlying mechanism of how human ASM cell (hASMC) mechanosenses the aberrant microenvironment is not well understood. Physiological stiffnesses of the ASM were measured by uniaxial compression tester using porcine ASM layers under 0, 5 and 10% longitudinal stretch above in situ length. Linear stiffness gradient hydrogels (230 kPa range) were fabricated and functionalized with ECM proteins, collagen I (ColI), fibronectin (Fn) and laminin (Ln), to recapitulate the above-measured range of stiffnesses. Overall, hASMC mechanosensation exhibited a clear correlation with the underlying hydrogel stiffness. Cell size, nuclear size and contractile marker alpha-smooth muscle actin (αSMA) expression showed a strong correlation to substrate stiffness. Mechanosensation, assessed by Lamin-A intensity and nuc/cyto YAP, exhibited stiffness-mediated behaviour only on ColI and Fn-coated hydrogels. Inhibition studies using blebbistatin or Y27632 attenuated most mechanotransduction-derived cell morphological responses, αSMA and Lamin-A expression and nuc/cyto YAP (blebbistatin only). This study highlights the interplay and complexities between stiffness and ECM protein type on hASMC mechanosensation, relevant to airway remodelling in obstructive airway diseases.

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.

Tumor spheroid elasticity estimation using mechano-microscopy combined with a conditional generative adversarial network.

BACKGROUND AND OBJECTIVES: Techniques for imaging the mechanical properties of cells are needed to study how cell mechanics influence cell function and disease progression. Mechano-microscopy (a high-resolution variant of compression optical coherence elastography) generates elasticity images of a sample undergoing compression from the phase difference between optical coherence microscopy (OCM) B-scans. However, the existing mechano-microscopy signal processing chain (referred to as the algebraic method) assumes the sample stress is uniaxial and axially uniform, such that violation of these assumptions reduces the accuracy and precision of elasticity images. Furthermore, it does not account for prior information regarding the sample geometry or mechanical property distribution. In this study, we investigate the feasibility of training a conditional generative adversarial network (cGAN) to generate elasticity images from phase difference images of samples containing a cell spheroid embedded in a hydrogel. METHODS: To construct the cGAN training and simulated test sets, we generated 30,000 artificial elasticity images using a parametric model and computed the corresponding phase difference images using finite element analysis to simulate compression applied to the artificial samples. We also imaged real MCF7 breast tumor spheroids embedded in hydrogel using mechano-microscopy to construct the experimental test set and evaluated the cGAN using the algebraic elasticity images and co-registered OCM and confocal fluorescence microscopy (CFM) images. RESULTS: Comparison with the simulated test set ground truth elasticity images shows the cGAN produces a lower root mean square error (median: 3.47 kPa, 95 % confidence interval (CI) [3.41, 3.52]) than the algebraic method (median: 4.91 kPa, 95 % CI [4.85, 4.97]). For the experimental test set, the cGAN elasticity images contain features resembling stiff nuclei at locations corresponding to nuclei seen in the algebraic elasticity, OCM, and CFM images. Furthermore, the cGAN elasticity images are higher resolution and more robust to noise than the algebraic elasticity images. CONCLUSIONS: The cGAN elasticity images exhibit better accuracy, spatial resolution, sensitivity, and robustness to noise than the algebraic elasticity images for both simulated and real experimental data.

Analysis of friction in quantitative micro-elastography.

Quantitative micro-elastography (QME) is a compression-based optical coherence elastography technique capable of measuring the mechanical properties of tissue on the micro-scale. As QME requires contact between the imaging window and the sample, the presence of friction affects the accuracy of the estimated elasticity. In previous implementations, a lubricant was applied at the contact surfaces, which was assumed to result in negligible friction. However, recently, errors in the estimation of elasticity caused by friction have been reported. This effect has yet to be characterized and is, therefore, not well understood. In this work, we present a systematic analysis of friction in QME using silicone phantoms. We demonstrate that friction, and, therefore, the elasticity accuracy, is influenced by several experimental factors, including the viscosity of the lubricant, the mechanical contrast between the compliant layer and the sample, and the time after the application of a compressive strain. Elasticity errors over an order of magnitude were observed in the absence of appropriate lubrication when compared to uniaxial compression testing. Using an optimized lubrication protocol, we demonstrate accurate elasticity estimation (<10% error) for nonlinear elastic samples with Young's moduli ranging from 3 kPa to 130 kPa. Finally, using a structured phantom, we demonstrate that friction can significantly reduce mechanical contrast in QME. We believe that the framework established in this study will facilitate more robust elasticity estimations in QME, as well as being readily adapted to understand the effects of friction in other contact elastography techniques.

In vivo characterisation of field pea stem wall thickness using optical coherence tomography.

BACKGROUND: Modern field pea breeding faces a significant challenge in selecting lines with strong stems that resist lodging. Traditional methods of assessing stem strength involve destructive mechanical tests on mature stems after natural senescence, such as measuring stem flexion, stem buckling or the thickness of dry stems when compressed, but these measurements may not correspond to the strength of stems in the living plant. Optical coherence tomography (OCT) can be used as a noncontact and nondestructive method to measure stem wall thickness in living plants by acquiring two- or three-dimensional images of living plant tissue. RESULTS: In this proof-of-principle study, we demonstrated in vivo characterisation of stem wall thickness using OCT, with the measurement corrected for the refractive index of the stem tissue. This in vivo characterisation was achieved through real-time imaging of stems, with an acquisition rate of 13 milliseconds per two-dimensional, cross-sectional OCT image. We also acquired OCT images of excised stems and compared the accuracy of in vivo OCT measurements of stem wall thickness with ex vivo results for 10 plants each of two field pea cultivars, Dunwa and Kaspa. In vivo OCT measurements of stem wall thickness have an average percent error of - 3.1% when compared with ex vivo measurements. Additionally, we performed in vivo measurements of both stem wall thickness and stem width at various internode positions on the two cultivars. The results revealed that Dunwa had a uniform stem wall thickness across different internode positions, while Kaspa had a significantly negative slope of [Formula: see text]0.0198 mm/node. Both cultivars exhibited an increase in stem width along the internode positions; however, Dunwa had a rate of increase of 0.1844 mm/node, which is three times higher than that of Kaspa. CONCLUSIONS: Our study has demonstrated the efficacy of OCT for accurate measurement of the stem wall thickness of live field pea. Moreover, OCT shows that the trends of stem wall thickness and stem width along the internode positions are different for the two cultivars, Dunwa and Kaspa, potentially hinting at differences in their stem strength. This rapid, in vivo imaging method provides a useful tool for characterising physical traits critical in breeding cultivars that are resistant to lodging.

A surgically optimized intraoperative poly(I:C)-releasing hydrogel prevents cancer recurrence.

Recurrences frequently occur following surgical removal of primary tumors. In many cancers, adjuvant therapies have limited efficacy. Surgery provides access to the tumor microenvironment, creating an opportunity for local therapy, in particular immunotherapy, which can induce local and systemic anti-cancer effects. Here, we develop a surgically optimized biodegradable hyaluronic acid-based hydrogel for sustained intraoperative delivery of Toll-like receptor 3 agonist poly(I:C) and demonstrate that it significantly reduces tumor recurrence after surgery in multiple mouse models. Mechanistically, poly(I:C) induces a transient interferon alpha (IFNα) response, reshaping the tumor/wound microenvironment by attracting inflammatory monocytes and depleting regulatory T cells. We demonstrate that a pre-existing IFN signature predicts response to the poly(I:C) hydrogel, which sensitizes tumors to immune checkpoint therapy. The safety, immunogenicity, and surgical feasibility are confirmed in a veterinary trial in canine soft tissue tumors. The surgically optimized poly(I:C)-loaded hydrogel provides a safe and effective approach to prevent cancer recurrence.

Quantitative Micro-Elastography Enables In Vivo Detection of Residual Cancer in the Surgical Cavity during Breast-Conserving Surgery.

Breast-conserving surgery (BCS) is commonly used for the treatment of early-stage breast cancer. Following BCS, approximately 20% to 30% of patients require reexcision because postoperative histopathology identifies cancer in the surgical margins of the excised specimen. Quantitative micro-elastography (QME) is an imaging technique that maps microscale tissue stiffness and has demonstrated a high diagnostic accuracy (96%) in detecting cancer in specimens excised during surgery. However, current QME methods, in common with most proposed intraoperative solutions, cannot image cancer directly in the patient, making their translation to clinical use challenging. In this proof-of-concept study, we aimed to determine whether a handheld QME probe, designed to interrogate the surgical cavity, can detect residual cancer directly in the breast cavity in vivo during BCS. In a first-in-human study, 21 BCS patients were scanned in vivo with the QME probe by five surgeons. For validation, protocols were developed to coregister in vivo QME with postoperative histopathology of the resected tissue to assess the capability of QME to identify residual cancer. In four cavity aspects presenting cancer and 21 cavity aspects presenting benign tissue, QME detected elevated stiffness in all four cancer cases, in contrast to low stiffness observed in 19 of the 21 benign cases. The results indicate that in vivo QME can identify residual cancer by directly imaging the surgical cavity, potentially providing a reliable intraoperative solution that can enable more complete cancer excision during BCS. SIGNIFICANCE: Optical imaging of microscale tissue stiffness enables the detection of residual breast cancer directly in the surgical cavity during breast-conserving surgery, which could potentially contribute to more complete cancer excision.

Smartphone-based optical palpation: towards elastography of skin for telehealth applications.

Smartphones are now integral to many telehealth services that provide remote patients with an improved diagnostic standard of care. The ongoing management of burn wounds and scars is one area in which telehealth has been adopted, using video and photography to assess the repair process over time. However, a current limitation is the inability to evaluate scar stiffness objectively and repeatedly: an essential measurement for classifying the degree of inflammation and fibrosis. Optical elastography detects mechanical contrast on a micrometer- to millimeter-scale, however, typically requires expensive optics and bulky imaging systems, making it prohibitive for wide-spread adoption in telehealth. More recently, a new variant of optical elastography, camera-based optical palpation, has demonstrated the capability to perform elastography at low cost using a standard digital camera. In this paper, we propose smartphone-based optical palpation, adapting camera-based optical palpation by utilizing a commercially available smartphone camera to provide sub-millimeter resolution imaging of mechanical contrast in scar tissue in a form factor that is amenable to telehealth. We first validate this technique on a silicone phantom containing a 5 × 5 × 1 mm3 embedded inclusion, demonstrating comparative image quality between mounted and handheld implementations. We then demonstrate preliminary in vivo smartphone-based optical palpation by imaging a region of healthy skin and two scars on a burns patient, showing clear mechanical contrast between regions of scar tissue and healthy tissue. This study represents the first implementation of elastography on a smartphone device, extending the potential application of elastography to telehealth.

Optical palpation for tumor margin assessment in breast-conserving surgery.

Intraoperative margin assessment is needed to reduce the re-excision rate of breast-conserving surgery. One possibility is optical palpation, a tactile imaging technique that maps stress (force applied across the tissue surface) as an indicator of tissue stiffness. Images (optical palpograms) are generated by compressing a transparent silicone layer on the tissue and measuring the layer deformation using optical coherence tomography (OCT). This paper reports, for the first time, the diagnostic accuracy of optical palpation in identifying tumor within 1 mm of the excised specimen boundary using an automated classifier. Optical palpograms from 154 regions of interest (ROIs) from 71 excised tumor specimens were obtained. An automated classifier was constructed to predict the ROI margin status by first choosing a circle diameter, then searching for a location within the ROI where the circle was ≥ 75% filled with high stress (indicating a positive margin). A range of circle diameters and stress thresholds, as well as the impact of filtering out non-dense tissue regions, were tested. Sensitivity and specificity were calculated by comparing the automated classifier results with the true margin status, determined from co-registered histology. 83.3% sensitivity and 86.2% specificity were achieved, compared to 69.0% sensitivity and 79.0% specificity obtained with OCT alone on the same dataset using human readers. Representative optical palpograms show that positive margins containing a range of cancer types tend to exhibit higher stress compared to negative margins. These results demonstrate the potential of optical palpation for margin assessment.

Camera-based optical palpation.

Optical elastography is undergoing extensive development as an imaging tool to map mechanical contrast in tissue. Here, we present a new platform for optical elastography by generating sub-millimetre-scale mechanical contrast from a simple digital camera. This cost-effective, compact and easy-to-implement approach opens the possibility to greatly expand applications of optical elastography both within and beyond the field of medical imaging. Camera-based optical palpation (CBOP) utilises a digital camera to acquire photographs that quantify the light intensity transmitted through a silicone layer comprising a dense distribution of micro-pores (diameter, 30-100 µm). As the transmission of light through the micro-pores increases with compression, we deduce strain in the layer directly from intensity in the digital photograph. By pre-characterising the relationship between stress and strain of the layer, the measured strain map can be converted to an optical palpogram, a map of stress that visualises mechanical contrast in the sample. We demonstrate a spatial resolution as high as 290 µm in CBOP, comparable to that achieved using an optical coherence tomography-based implementation of optical palpation. In this paper, we describe the fabrication of the micro-porous layer and present experimental results from structured phantoms containing stiff inclusions as small as 0.5 × 0.5 × 1 mm. In each case, we demonstrate high contrast between the inclusion and the base material and validate both the contrast and spatial resolution achieved using finite element modelling. By performing CBOP on freshly excised human breast tissue, we demonstrate the capability to delineate tumour from surrounding benign tissue.

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.

Finger-mounted quantitative micro-elastography.

We present a finger-mounted quantitative micro-elastography (QME) probe, capable of measuring the elasticity of biological tissue in a format that avails of the dexterity of the human finger. Finger-mounted QME represents the first demonstration of a wearable elastography probe. The approach realizes optical coherence tomography-based elastography by focusing the optical beam into the sample via a single-mode fiber that is fused to a length of graded-index fiber. The fiber is rigidly affixed to a 3D-printed thimble that is mounted on the finger. Analogous to manual palpation, the probe compresses the tissue through the force exerted by the finger. The resulting deformation is measured using optical coherence tomography. Elasticity is estimated as the ratio of local stress at the sample surface, measured using a compliant layer, to the local strain in the sample. We describe the probe fabrication method and the signal processing developed to achieve accurate elasticity measurements in the presence of motion artifact. We demonstrate the probe's performance in motion-mode scans performed on homogeneous, bi-layer and inclusion phantoms and its ability to measure a thermally-induced increase in elasticity in ex vivo muscle tissue. In addition, we demonstrate the ability to acquire 2D images with the finger-mounted probe where lateral scanning is achieved by swiping the probe across the sample surface.

Volume Adaptation Controls Stem Cell Mechanotransduction.

Recent studies have found discordant mechanosensitive outcomes when comparing 2D and 3D, highlighting the need for tools to study mechanotransduction in 3D across a wide spectrum of stiffness. A gelatin methacryloyl (GelMA) hydrogel with a continuous stiffness gradient ranging from 5 to 38 kPa was developed to recapitulate physiological stiffness conditions. Adipose-derived stem cells (ASCs) were encapsulated in this hydrogel, and their morphological characteristics and expression of both mechanosensitive proteins (Lamin A, YAP, and MRTFa) and differentiation markers (PPARγ and RUNX2) were analyzed. Low-stiffness regions (∼8 kPa) permitted increased cellular and nuclear volume and enhanced mechanosensitive protein localization in the nucleus. This trend was reversed in high stiffness regions (∼30 kPa), where decreased cellular and nuclear volumes and reduced mechanosensitive protein nuclear localization were observed. Interestingly, cells in soft regions exhibited enhanced osteogenic RUNX2 expression, while those in stiff regions upregulated the adipogenic regulator PPARγ, suggesting that volume, not substrate stiffness, is sufficient to drive 3D stem cell differentiation. Inhibition of myosin II (Blebbistatin) and ROCK (Y-27632), both key drivers of actomyosin contractility, resulted in reduced cell volume, especially in low-stiffness regions, causing a decorrelation between volume expansion and mechanosensitive protein localization. Constitutively active and inactive forms of the canonical downstream mechanotransduction effector TAZ were stably transfected into ASCs. Activated TAZ resulted in higher cellular volume despite increasing stiffness and a consistent, stiffness-independent translocation of YAP and MRTFa into the nucleus. Thus, volume adaptation as a function of 3D matrix stiffness can control stem cell mechanotransduction and differentiation.