Quantitative Detection of Thyroid-Stimulating Hormone in Patient Samples with a Nanomechanical Single-Antibody Spectro-Immunoassay.

Functional disorders of the thyroid remain a global challenge and have profound impacts on human health. Serving as the barometer for thyroid function, thyroid-stimulating hormone (TSH) is considered the single most useful test of thyroid function. However, the prevailing TSH immunoassays rely on two types of antibodies in a sandwich format. The requirement of repeated incubation and washing further complicates the issue, making it unable to meet the requirements of the shifting public health landscape that demands rapid, sensitive, and low-cost TSH tests. Herein, a systematic study is performed to investigate the clinical translational potential of a single antibody-based biosensing platform for the TSH test. The biosensing platform leverages Raman spectral variations induced by the interaction between a TSH antigen and a Raman molecule-conjugated TSH antibody. In conjunction with machine learning, it allows TSH concentrations in various patient samples to be predicted with high accuracy and precision, which is robust against substrate-to-substrate, intra-substrate, and day-to-day variations. It is envisioned that the simplicity and generalizability of this single-antibody immunoassay coupled with the demonstrated performance in patient samples pave the way for it to be widely applied in clinical settings for low-cost detection of hormones, other molecular biomarkers, DNA, RNA, and pathogens.

Engineering vascularized skin-mimetic phantom for non-invasive Raman spectroscopy.

Recent advances in Raman spectroscopy have shown great potential for non-invasive analyte sensing, but the lack of a standardized optical phantom for these measurements has hindered further progress. While many research groups have developed optical phantoms that mimic bulk optical absorption and scattering, these materials typically have strong Raman scattering, making it difficult to distinguish metabolite signals. As a result, solid tissue phantoms for spectroscopy have been limited to highly scattering tissues such as bones and calcifications, and metabolite sensing has been primarily performed using liquid tissue phantoms. To address this issue, we have developed a layered skin-mimetic phantom that can support metabolite sensing through Raman spectroscopy. Our approach incorporates millifluidic vasculature that mimics blood vessels to allow for diffusion akin to metabolite diffusion in the skin. Furthermore, our skin phantoms are mechanically mimetic, providing an ideal model for development of minimally invasive optical techniques. By providing a standardized platform for measuring metabolites, our approach has the potential to facilitate critical developments in spectroscopic techniques and improve our understanding of metabolite dynamics in vivo.

Targeted Enzyme Activity Imaging with Quantitative Phase Microscopy.

Quantitative phase imaging (QPI) is a powerful optical imaging modality for label-free, rapid, and three-dimensional (3D) monitoring of cells and tissues. However, molecular imaging of important intracellular biomolecules such as enzymes remains a largely unexplored area for QPI. Herein, we introduce a fundamentally new approach by designing QPI contrast agents that allow sensitive detection of intracellular biomolecules. We report a new class of bio-orthogonal QPI-nanoprobes for in situ high-contrast refractive index (RI) imaging of enzyme activity. The nanoprobes feature silica nanoparticles (SiO2 NPs) having higher RI than endogenous cellular components and surface-anchored cyanobenzothiazole-cysteine (CBT-Cys) conjugated enzyme-responsive peptide sequences. The nanoprobes specifically aggregated in cells with target enzyme activity, increasing intracellular RI and enabling precise visualization of intracellular enzyme activity. We envision that this general design of QPI-nanoprobes could open doors for spatial-temporal mapping of enzyme activity with direct implications for disease diagnosis and evaluating the therapeutic efficacy.

Label-Free Spectroscopic SARS-CoV-2 Detection on Versatile Nanoimprinted Substrates.

Widespread testing and isolation of infected patients is a cornerstone of viral outbreak management, as underscored during the ongoing COVID-19 pandemic. Here, we report a large-area and label-free testing platform that combines surface-enhanced Raman spectroscopy and machine learning for the rapid and accurate detection of SARS-CoV-2. Spectroscopic signatures acquired from virus samples on metal-insulator-metal nanostructures, fabricated using nanoimprint lithography and transfer printing, can provide test results within 25 min. Not only can our technique accurately distinguish between different respiratory and nonrespiratory viruses, but it can also detect virus signatures in physiologically relevant matrices such as human saliva without any additional sample preparation. Furthermore, our large area nanopatterning approach allows sensors to be fabricated on flexible surfaces allowing them to be mounted on any surface or used as wearables. We envision that our versatile and portable label-free spectroscopic platform will offer an important tool for virus detection and future outbreak preparedness.

A Dual-Modal Single-Antibody Plasmonic Spectro-Immunoassay for Detection of Small Molecules.

Small molecules play a pivotal role in regulating physiological processes and serve as biomarkers to uncover pathological conditions and the effects of therapeutic treatments. However, it remains a significant challenge to detect small molecules given the size as compared to macromolecules. Recently, the newly emerging plasmonic immunoassays based on surface-enhanced Raman scattering (SERS) offer great promise to deliver extraordinary sensitivity. Nevertheless, they are limited by the intrinsic SERS intensity fluctuations associated with the SERS uncertainty principle. The single transducer that relies on the intensity change is also prone to false signals. Additionally, the prevailing sandwich immunoassay format proves less effective towards detecting small molecules. To circumvent these critical issues, a dual-modal single-antibody approach that synergizes both the intensity and shift of the peak-based immunoassay with Raman enhancement, coined as the INSPIRE assay, is developed for small molecules detection. With two independent transduction mechanisms, it allows better prediction of analyte concentration and attenuation of signal artifacts, providing a new and robust strategy for molecular analysis. With a proof-of-concept demonstration for detection of free T4 and testosterone in serum matrix, the authors envision that the INSPIRE assay could be expanded for a wide spectrum of applications in biomedical diagnosis, discovery of new biopharmaceuticals, food safety, and environmental monitoring.

Leveraging Nanomechanical Perturbations in Raman Spectro-Immunoassays to Design a Versatile Serum Biomarker Detection Platform.

While immunoassays are pivotal to medical diagnosis and bioanalytical chemistry, the current landscape of public health has catalyzed an important shift in the requirements of immunoassays that demand innovative solutions. For example, rapid, label-free, and low-cost screening of a given analyte is required to inform the best countermeasures to combat infectious diseases in a timely manner. Yet, the current design of immunoassays cannot accommodate such requirements as constraint by accumulative challenges, such as repeated incubation and washing, and the need of two types of antibodies in the sandwich format. To provide a potential solution, herein, a plasmonic Raman immunoassay with single-antibody, multivariate regression, and shift-of-peak strategies, coined as the PRISM assay, for serum biomarkers detection, is reported. The PRISM assay relies on Raman reporter-antibody conjugates to capture analytes on a plasmonic substrate. The ensuing nanomechanical perturbations to vibration of Raman reporters induce subtle but characteristic spectral changes that encode rich information related to the captured analytes. By fusing Raman spectroscopy and chemometric analysis, both Raman frequency shift- and multivariate regression models for sensitive detection of biomarkers are developed. The PRISM assay is expected to find a wide range of applications in clinical diagnosis, food safety surveillance, and environmental monitoring.

Energy-efficient bandwidth enhancement of Brillouin microwave photonic bandpass filters.

Stimulated Brillouin scattering has been widely utilized to realize frequency-agile narrowband and wideband microwave photonic bandpass filters by primarily utilizing its gain response. However, most demonstrated wideband Brillouin-based filters are limited in operation due to the high-power requirements for bandwidth tailoring. We propose a novel approach to realize wideband reconfigurable, Brillouin-based microwave photonic bandpass filters employing RF interferometry and advanced phase engineering. Demonstrated filters exhibit >20 dB selectivity and >700 MHz bandwidth using only 8 dB peak SBS gain (of intrinsic linewidth 30 MHz), and total optical pump power of only ∼14 dBm. We also demonstrate frequency tunability up to 22 GHz. The filter passband has a very flat and highly linear phase response, thus exhibiting zero group delay which we have experimentally verified by propagating an RF pulse at 10.25 GHz. Furthermore, the filter does not suffer from added Brillouin noise in the passband, which is a major advance compared to conventional Brillouin-based microwave photonic sub-systems. This paper presents simulations, mathematical analysis, and experimental results of the proposed filter. The proposed filter demonstrates a pathway toward power-efficient Brillouin-based microwave photonic filters, utilizing SBS responses, in combination with phase manipulation for advanced filtering operations.

Nanowire Assisted Mechanotyping of Cellular Metastatic Potential.

Nanotechnology has provided tools for next generation biomedical devices which rely on nanostructure interfaces with living cells. In vitro biomimetic structures have enabled observation of cell response to various mechanical and chemical cues, and there is a growing interest in isolating and harnessing the specific cues that three-dimensional microenvironments can provide without the requirement for such culture and the experimental drawbacks associated with it. Here we report a randomly oriented gold coated Si nanowire substrate with patterned hydrophobic-hydrophilic areas for differentiation of isogenic breast cancer cells of varying metastatic potential. When considering synthetic surfaces for the study of cell-nanotopography interfaces, randomly oriented nanowires more closely resemble the isotropic architecture of natural extracellular matrix as compared to currently more widely used vertical nanowire arrays. In the study reported here, we show that primary cancer cells preferably attach to the hydrophilic region of randomly oriented nanowire substrate while secondary cancer cells do not adhere. Using machine learning analysis of fluorescence images, cells were found to spread and elongate on the nanowire substrates as compared to a flat substrate, where they mostly remain round, when neither surface was coated with extracellular matrix (ECM) proteins. Such platforms can not only be used for developing bioassays but also as stepping stones for tissue printing technologies where cells can be selectively patterned at desired locations.

Plexcitonic Quasi-Bound States in the Continuum.

Enhancing light-matter interactions is fundamental to the advancement of nanophotonics and optoelectronics. Yet, light diffraction on dielectric platforms and energy loss on plasmonic metallic systems present an undesirable trade-off between coherent energy exchange and incoherent energy damping. Through judicious structural design, both light confinement and energy loss issues could be potentially and simultaneously addressed by creating bound states in the continuum (BICs) where light is ideally decoupled from the radiative continuum. Herein, the authors present a general framework based on the two-coupled resonances to first conceptualize and then numerically demonstrate a type of quasi-BICs that can be achieved through the interference between two bare resonance modes and is characterized by the considerably narrowed spectral line shape even on lossy metallic nanostructures. The ubiquity of the proposed framework further allows the paradigm to be extended for the realization of plexcitonic quasi-BICs on the same metallic systems. Owing to the topological nature, both plasmonic and plexcitonic quasi-BICs display strong mode robustness against parameters variation, thereby providing an attractive platform to unlock the potential of the coupled plasmon-exciton systems for manipulation of the photophysical properties of condensed phases.

Furin-Mediated Self-Assembly of Olsalazine Nanoparticles for Targeted Raman Imaging of Tumors.

Olsalazine (Olsa) is a broad-spectrum anti-cancer agent acting as a DNA-methylation inhibitor. When conjugated to 2-cyano-6-aminobenzothiazole and a peptide substrate specific for the tumor-overexpressed enzyme furin, it can self-assemble into nanoparticles that can be detected by chemical-exchange saturation-transfer magnetic-resonance imaging (CEST MRI). We report here that these nano-assemblies can also be detected with high specificity in furin-overexpressing tumor cells by Raman spectroscopy with a distinct scattering signature and demonstrate the utility of this sensing mechanism in vitro and in vivo. Our findings suggest that Raman spectroscopy could be used for high-resolution image-guided surgery to precisely delineate tumor margins during and after resection in real-time as well as to determine microscopic tumor invasion and multifocal locoregional tumor spread, which are currently impossible to visualize with available imaging technologies, including CEST MRI.

Microwave power induced resonance shifting of silicon ring modulators for continuously tunable, bandwidth scaled frequency combs.

We demonstrate a technique to continuously tune center frequency and repetition rate of optical frequency combs generated in silicon microring modulators and bandwidth scale them. We utilize a drive frequency dependent, microwave power induced shifting of the microring modulator resonance. In this work, we demonstrate center frequency tunability of frequency combs generated in silicon microring modulators over a wide range (∼8nm) with fixed number of lines. We also demonstrate continuously tunable repetition rates from 7.5GHz to 15GHz. Further, we use this effect to demonstrate a proof-of-principle experiment to bandwidth scale an 8-line (20dB band) comb generated from a single ring modulator driven at 10GHz to a comb with 12 and 15 lines by cascading two and three ring modulators, respectively. This is accomplished by merging widely spaced ring modulator resonances to a common location, thus coupling light simultaneously into multiple cascaded ring modulators.

Cell-TIMP: Cellular Trajectory Inference based on Morphological Parameter.

Cellular morphology, shaped by various genetic and environmental influences, is pivotal to studying experimental cell biology, necessitating precise measurement and analysis techniques. Traditional approaches, which rely on geometric metrics derived from stained images, encounter obstacles stemming from both the imaging and analytical domains. Staining processes can disrupt the cell's natural state and diminish accuracy due to photobleaching, while conventional analysis techniques, which categorize cells based on shape to discern pathophysiological conditions, often fail to capture the continuous and asynchronous nature of biological processes such as cell differentiation, immune responses, and cancer progression. In this work, we propose the use of quantitative phase imaging for morphological assessment due to its label-free nature. For analysis, we repurposed the genomic analysis toolbox to perform trajectory inference analysis purely based on morphology information. We applied the developed framework to study the progression of leukemia and breast cancer metastasis. Our approach revealed a clear pattern of morphological evolution tied to the diseases' advancement, highlighting the efficacy of our method in identifying functionally significant shape changes where conventional techniques falter. This advancement offers a fresh perspective on analyzing cellular morphology and holds significant potential for the broader research community, enabling a deeper understanding of complex biological dynamics.

CellSNAP: a fast, accurate algorithm for 3D cell segmentation in quantitative phase imaging.

Significance: Three-dimensional quantitative phase imaging (QPI) has rapidly emerged as a complementary tool to fluorescence imaging, as it provides an objective measure of cell morphology and dynamics, free of variability due to contrast agents. It has opened up new directions of investigation by providing systematic and correlative analysis of various cellular parameters without limitations of photobleaching and phototoxicity. While current QPI systems allow the rapid acquisition of tomographic images, the pipeline to analyze these raw three-dimensional (3D) tomograms is not well-developed. We focus on a critical, yet often underappreciated, step of the analysis pipeline that of 3D cell segmentation from the acquired tomograms. Aim: We report the CellSNAP (Cell Segmentation via Novel Algorithm for Phase Imaging) algorithm for the 3D segmentation of QPI images. Approach: The cell segmentation algorithm mimics the gemstone extraction process, initiating with a coarse 3D extrusion from a two-dimensional (2D) segmented mask to outline the cell structure. A 2D image is generated, and a segmentation algorithm identifies the boundary in the x-y plane. Leveraging cell continuity in consecutive z-stacks, a refined 3D segmentation, akin to fine chiseling in gemstone carving, completes the process. Results: The CellSNAP algorithm outstrips the current gold standard in terms of speed, robustness, and implementation, achieving cell segmentation under 2 s per cell on a single-core processor. The implementation of CellSNAP can easily be parallelized on a multi-core system for further speed improvements. For the cases where segmentation is possible with the existing standard method, our algorithm displays an average difference of 5% for dry mass and 8% for volume measurements. We also show that CellSNAP can handle challenging image datasets where cells are clumped and marred by interferogram drifts, which pose major difficulties for all QPI-focused AI-based segmentation tools. Conclusion: Our proposed method is less memory intensive and significantly faster than existing methods. The method can be easily implemented on a student laptop. Since the approach is rule-based, there is no need to collect a lot of imaging data and manually annotate them to perform machine learning based training of the model. We envision our work will lead to broader adoption of QPI imaging for high-throughput analysis, which has, in part, been stymied by a lack of suitable image segmentation tools.

Shining Light on Osteoarthritis: Spatially Offset Raman Spectroscopy as a Window into Cartilage Health.

Articular cartilage is a complex tissue, and early detection of osteoarthritis (OA) is crucial for effective treatment. However, current imaging modalities lack molecular specificity and primarily detect late-stage changes. In this study, we propose the use of spatially offset Raman spectroscopy (SORS) for noninvasive, depth-dependent, and molecular-specific diagnostics of articular cartilage. We demonstrate the potential of SORS to penetrate deep layers of cartilage, providing a comprehensive understanding of disease progression. Our SORS measurements were characterized and validated through mechanical and histological techniques, revealing strong correlations between spectroscopic measurements and both Young's modulus and depth of cartilage damage. By longitudinally monitoring enzymatically degraded condyles, we further developed a depth-dependent damage-tracking method. Our analysis revealed distinct components related to sample depth and glycosaminoglycan (GAG) changes, offering a comprehensive picture of cartilage health. Collectively, these findings highlight the potential of SORS as a valuable tool for enhancing OA management and improving patient outcomes.

Label-free plasmonic spectral profiling of serum DNA.

Genetic and epigenetic modifications are linked to the activation of oncogenes and inactivation of tumor suppressor genes. Likewise, the associated molecular alternations can best inform precision medicine for personalized tumor treatment. Therefore, performing characterization of genetic and epigenetic alternations at the molecular level represents a crucial step in early diagnosis and/or therapeutics of cancer. However, the prevailing methods for DNA analysis involve a series of tedious and complicated steps, in which important genetic and epigenetic information could be lost or altered. To provide a potential approach for non-invasive, direct, and efficient DNA analysis, herein, we present a promising strategy for label-free molecular profiling of serum DNA in its pristine form by fusing surface-enhanced Raman spectroscopy with machine learning on a superior plasmonic nanostructured platform. Using DNA methylation and single-point mutation as two case studies, the presented strategy allows a well-balanced sensitive and specific detection of epigenetic and genetic changes at the single-nucleotide level in serum. We envision the presented label-free strategy could serve as a versatile tool for direct molecular profiling in pristine forms of a wide range of biological markers and aid biomedical diagnostics as well as therapeutics.

Optical diffraction tomography and Raman spectroscopy reveal distinct cellular phenotypes during white and brown adipocyte differentiation.

White adipose tissue (WAT) and brown adipose tissue (BAT) are the primary types of fats in humans, and they play prominent roles in energy storage and thermogenesis, respectively. While the mechanisms of terminal adipogenesis are well understood, much remains unknown about the early stages of adipogenic differentiation. Label-free approaches, such as optical diffraction tomography (ODT) and Raman spectroscopy, offer the ability to retrieve morphological and molecular information at the single cell level without the negative effects of photobleaching and system perturbation due to introduction of fluorophores. In this study, we employed 3D ODT and Raman spectroscopy to gain deeper insights into the early stages of differentiation of human white preadipocytes (HWPs) and human brown preadipocytes (HBPs). We utilized ODT to retrieve morphological information, including cell dry mass and lipid mass, and Raman spectroscopy to obtain molecular information about lipids. Our findings reveal that HWPs and HBPs undergo dynamic and differential changes during the differentiation process. Notably, we found that HBPs accumulated lipids more rapidly and had a higher lipid mass than HWPs. Additionally, both cell types experienced an increase and subsequent decrease in cell dry mass during the first seven days, followed by an increase after day 7, which we attribute to the transformation of adipogenic precursors in the early stages. Finally, HBPs had higher lipid unsaturation levels than HWPs for the same differentiation timepoints. The insights gained from our study provide crucial contributions towards the advancement of new therapies for obesity and related diseases.

CellSNAP: A fast, accurate algorithm for 3D cell segmentation in quantitative phase imaging.

Quantitative phase imaging (QPI) has rapidly emerged as a complementary tool to fluorescence imaging, as it provides an objective measure of cell morphology and dynamics, free of variability due to contrast agents. In particular, three-dimensional (3D) tomographic imaging of live cells has opened up new directions of investigation by providing systematic and correlative analysis of various cellular parameters without limitations of photobleaching and phototoxicity. While current QPI systems allow the rapid acquisition of tomographic images, the pipeline to analyze these raw 3D tomograms is not well-developed. This work focuses on a critical, yet often underappreciated, step of the analysis pipeline, that of 3D cell segmentation from the acquired tomograms. The current method employed for such tasks is the Otsu-based 3D watershed algorithm, which works well for isolated cells; however, it is very challenging to draw boundaries when the cells are clumped. This process is also memory intensive since the processing requires computation on a 3D stack of images. We report the CellSNAP (Cell Segmentation via Novel Algorithm for Phase Imaging) algorithm for the segmentation of QPI images, which outstrips the current gold standard in terms of speed, robustness, and implementation, achieving cell segmentation under 2 seconds per cell on a single-core processor. The implementation of CellSNAP can easily be parallelized on a multi-core system for further speed improvements. For the cases where segmentation is possible with the existing standard method, our algorithm displays an average difference of 5% for dry mass and 8% for volume measurements. We also show that CellSNAP can handle challenging image datasets where cells are clumped and marred by interferogram drifts, which pose major difficulties for all QPI-focused segmentation tools. We envision our work will lead to the broader adoption of QPI imaging for high-throughput analysis, which has, in part, been stymied by a lack of suitable image segmentation tools.

Shining Light on Osteoarthritis: Spatially Offset Raman Spectroscopy as a Window into Cartilage Health.

Articular cartilage is a complex tissue, and early detection of osteoarthritis (OA) is crucial for effective treatment. However, current imaging modalities lack molecular specificity and primarily detect late-stage changes. In this study, we propose the use of Spatially Offset Raman Spectroscopy (SORS) for non-invasive, depth-dependent, and molecular-specific diagnostics of articular cartilage. We demonstrate the potential of SORS to penetrate deep layers of cartilage, providing a comprehensive understanding of disease progression. Our SORS measurements were characterized and validated through mechanical and histological techniques, revealing strong correlations between spectroscopic measurements and both Young's modulus and depth of cartilage damage. By longitudinally monitoring enzymatically degraded condyles, we further developed a depth-dependent damage-tracking method. Our analysis revealed distinct components related to sample depth and glycosaminoglycan (GAG) changes, offering a comprehensive picture of cartilage health. Collectively, these findings highlight the potential of SORS as a valuable tool for enhancing OA management and improving patient outcomes.

A Smart Intracellular Self-Assembling Bioorthogonal Raman Active Nanoprobe for Targeted Tumor Imaging.

Inspired by the principle of in situ self-assembly, the development of enzyme-activated molecular nanoprobes can have a profound impact on targeted tumor detection. However, despite their intrinsic promise, obtaining an optical readout of enzyme activity with high specificity in native milieu has proven to be challenging. Here, a fundamentally new class of Raman-active self-assembling bioorthogonal enzyme recognition (nanoSABER) probes for targeted tumor imaging is reported. This class of Raman probes presents narrow spectral bands reflecting their vibrational fingerprints and offers an attractive solution for optical imaging at different bio-organization levels. The optical beacon harnesses an enzyme-responsive peptide sequence, unique tumor-penetrating properties, and vibrational tags with stretching frequencies in the cell-silent Raman window. The design of nanoSABER is tailored and engineered to transform into a supramolecular structure exhibiting distinct vibrational signatures in presence of target enzyme, creating a direct causality between enzyme activity and Raman signal. Through the integration of substrate-specific for tumor-associated enzyme legumain, unique capabilities of nanoSABER for imaging enzyme activity at molecular, cellular, and tissue levels in combination with machine learning models are shown. These results demonstrate that the nanoSABER probe may serve as a versatile platform for Raman-based recognition of tumor aggressiveness, drug accumulation, and therapeutic response.

Utilizing pulse dynamics for non-invasive Raman spectroscopy of blood analytes.

Non-invasive measurement methods offer great benefits in the field of medical diagnostics with molecular-specific techniques such as Raman spectroscopy which is increasingly being used for quantitative measurements of tissue biochemistry in vivo. However, some important challenges still remain for label-free optical spectroscopy to be incorporated into the clinical laboratory for routine testing. In particular, non-analyte-specific variations in tissue properties introduce significant variability of the spectra, thereby preventing reliable calibration. For measurements of blood analytes such as glucose, we propose to decrease the interference from individual tissue characteristics by exploiting the known dynamics of the blood-tissue matrix. We reason that by leveraging the natural blood pulse rhythm, the signals from the blood analytes can be enhanced while those from the static components can be effectively suppressed. Here, time-resolved measurements with subsequent pulse frequency estimation and phase-sensitive detection are proposed to recover the Raman spectra correlated with the dynamic changes at blood-pulse frequency. Pilot in vivo study results are presented to establish the benefits as well as outline the challenges of the proposed method in terms of instrumentation and signal processing.