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.

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.

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.

Cell-Penetrating and Enzyme-Responsive Peptides for Targeted Cancer Therapy: Role of Arginine Residue Length on Cell Penetration and In Vivo Systemic Toxicity

For the improved delivery of cancer therapeutics and imaging agents, the conjugation of cell-penetrating peptides (CPPs) increases the cellular uptake and water solubility of agents. Among the various CPPs, arginine-rich peptides have been the most widely used. Combining CPPs with enzyme-responsive peptides presents an innovative strategy to target specific intracellular enzymes in cancer cells and when combined with the appropriate click chemistry can enhance theranostic drug delivery through the formation of intracellular self-assembled nanostructures. However, one drawback of CPPs is their high positive charge which can cause nonspecific binding, leading to off-target accumulation and potential toxicity. Hence, balancing cell-specific penetration, toxicity, and biocompatibility is essential for future clinical efficacy. We synthesized six cancer-specific, legumain-responsive RnAANCK peptides containing one to six arginine residues, with legumain being an asparaginyl endopeptidase that is overexpressed in aggressive prostate tumors. When conjugated to Alexa Fluor 488, R1-R6AANCK peptides exhibited a concentration- and time-dependent cell penetration in prostate cancer cells, which was higher for peptides with higher R values, reaching a plateau after approximately 120 min. Highly aggressive DU145 prostate tumor cells, but not less aggressive LNCaP cells, self-assembled nanoparticles in the cytosol after the cleavage of the legumain-specific peptide. The in vivo biocompatibility was assessed in mice after the intravenous injection of R1-R6AANCK peptides, with concentrations ranging from 0.0125 to 0.4 mmol/kg. The higher arginine content in R4-6 peptides showed blood and urine indicators for the impairment of bone marrow, liver, and kidney function in a dose-dependent manner, with instant hemolysis and morbidity in extreme cases. These findings underscore the importance of designing peptides with the optimal arginine residue length for a proper balance of cell-specific penetration, toxicity, and in vivo biocompatibility.

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.

FluoMALDI Microscopy: Matrix Co-Crystallization Simultaneously Enhances Fluorescence and MALDI Imaging

Here, the authors report that co-crystallization of fluorophores with matrix-assisted laser desorption/ionization (MALDI) imaging matrices significantly enhances fluorophore brightness up to 79-fold, enabling the amplification of innate tissue autofluorescence. This discovery facilitates FluoMALDI, the imaging of the same biological sample by both fluorescence microscopy and MALDI imaging. The approach combines the high spatial resolution and specific labeling capabilities of fluorescence microscopy with the inherently multiplexed, versatile imaging capabilities of MALDI imaging. This new paradigm simplifies registration by avoiding physical changes between fluorescence and MALDI imaging, allowing to image the exact same cells in tissues with both modalities. Matrix-fluorophore co-crystallization also facilitates applications with insufficient fluorescence brightness. The authors demonstrate  feasibility of FluoMALDI imaging with endogenous and exogenous fluorophores and autofluorescence-based FluoMALDI of brain and kidney tissue sections. FluoMALDI will advance structural-functional microscopic imaging in cell biology, biomedicine, and pathology.

Evaluating differences in optical properties of indolent and aggressive murine breast tumors using quantitative diffuse reflectance spectroscopy

We used diffuse reflectance spectroscopy to quantify tissue absorption and scattering-based parameters in similarly sized tumors derived from a panel of four isogenic murine breast cancer cell lines (4T1, 4T07, 168FARN, 67NR) that are each capable of accomplishing different steps of the invasion-metastasis cascade. We found lower tissue scattering, increased hemoglobin concentration, and lower vascular oxygenation in indolent 67NR tumors incapable of metastasis compared with aggressive 4T1 tumors capable of metastasis. Supervised learning statistical approaches were able to accurately differentiate between tumor groups and classify tumors according to their ability to accomplish each step of the invasion-metastasis cascade. We investigated whether the inhibition of metastasis-promoting genes in the highly metastatic 4T1 tumors resulted in measurable optical changes that made these tumors similar to the indolent 67NR tumors. These results demonstrate the potential of diffuse reflectance spectroscopy to noninvasively evaluate tumor biology and discriminate between indolent and aggressive tumors.

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.

RaMALDI: Enabling simultaneous Raman and MALDI imaging of the same tissue section

Multimodal tissue imaging techniques that integrate two complementary modalities are powerful discovery tools for unraveling biological processes and identifying biomarkers of disease. Combining Raman spectroscopic imaging (RSI) and matrix-assisted laser-desorption/ionization (MALDI) mass spectrometry imaging (MSI) to obtain fused images with the advantages of both modalities has the potential of providing spatially resolved, sensitive, specific biomolecular information, but has so far involved two separate sample preparations, or even consecutive tissue sections for RSI and MALDI MSI, resulting in images with inherent disparities. We have developed RaMALDI, a streamlined, integrated, multimodal imaging workflow of RSI and MALDI MSI, performed on a single tissue section with one sample preparation protocol. We show that RaMALDI imaging of various tissues effectively integrates molecular information acquired from both RSI and MALDI MSI of the same sample, which will drive discoveries in cell biology, biomedicine, and pathology, and advance tissue diagnostics.

Hyperspectral Raman Imaging for Automated Recognition of Human Renal Amyloid

In the clinical setting, routine identification of the main types of tissue amyloid deposits, light-chain amyloid (AL) and serum amyloid A (AA), is based on histochemical staining; rarer types of amyloid require mass spectrometry analysis. Raman spectroscopic imaging is an analytical tool, which can be used to chemically map, and thus characterize, the molecular composition of fluid and solid tissue. In this proof-of-concept study, we tested the feasibility of applying Raman spectroscopy combined with artificial intelligence to detect and characterize amyloid deposits in unstained frozen tissue sections from kidney biopsies with pathologic diagnosis of AL and AA amyloidosis and control biopsies with no amyloidosis (NA). Raman hyperspectral images, mapped in a 2D grid-like fashion over the tissue sections, were obtained. Three machine learning-assisted analysis models of the hyperspectral images could accurately distinguish AL (types λ and κ), AA, and NA 93-100% of the time. Although very preliminary, these findings illustrate the potential of Raman spectroscopy as a technique to identify, and possibly, subtype renal amyloidosis.

Primary breast tumor induced extracellular matrix remodeling in premetastatic lungs

The premetastatic niche hypothesis proposes an active priming of the metastatic site by factors secreted from the primary tumor prior to the arrival of the first cancer cells. We investigated several extracellular matrix (ECM) structural proteins, ECM degrading enzymes, and ECM processing proteins involved in the ECM remodeling of the premetastatic niche. Our in vitro model consisted of lung fibroblasts, which were exposed to factors secreted by nonmalignant breast epithelial cells, nonmetastatic breast cancer cells, or metastatic breast cancer cells. We assessed ECM remodeling in vivo in premetastatic lungs of female mice growing orthotopic primary breast tumor xenografts, as compared to lungs of control mice without tumors. Premetastatic lungs contained significantly upregulated Collagen (Col) Col4A5, matrix metalloproteinases (MMPs) MMP9 and MMP14, and decreased levels of MMP13 and lysyl oxidase (LOX) as compared to control lungs. These in vivo findings were consistent with several of our in vitro cell culture findings, which showed elevated Col14A1, Col4A5, glypican-1 (GPC1) and decreased Col5A1 and Col15A1 for ECM structural proteins, increased MMP2, MMP3, and MMP14 for ECM degrading enzymes, and decreased LOX, LOXL2, and prolyl 4-hydroxylase alpha-1 (P4HA1) for ECM processing proteins in lung fibroblasts conditioned with metastatic breast cancer cell media as compared to control. Taken together, our data show that premetastatic priming of lungs by primary breast tumors resulted in significant ECM remodeling which could facilitate metastasis by increasing interstitial fibrillar collagens and ECM stiffness (Col14A1), disruptions of basement membranes (Col4A5), and formation of leaky blood vessels (MMP2, MMP3, MMP9, and MMP14) to promote metastasis.

Quantum Plexcitonic Sensing

While fundamental to quantum sensing, quantum state control has been traditionally limited to extreme conditions. This restricts the impact of the practical implementation of quantum sensing on a broad range of physical measurements. Plexcitons, however, provide a promising path under ambient conditions toward quantum state control and thus quantum sensing, owing to their origin from strong plasmon-exciton coupling. Herein, we harness plexcitons to demonstrate quantum plexcitonic sensing by strongly coupling excitonic particles to a plasmonic hyperbolic metasurface. As compared to classical sensing in the weak-coupling regime, our model of quantum plexcitonic sensing performs at a level that is ∼40 times more sensitive. Noise-modulated sensitivity studies reinforce the quantum advantage over classical sensing, featuring better sensitivity, smaller sensitivity uncertainty, and higher resilience against optical noise. The successful demonstration of quantum plexcitonic sensing opens the door for a variety of physical, chemical, and biological measurements by leveraging strongly coupled plasmon-exciton systems.

Noninvasive morpho-molecular imaging reveals early therapy-induced senescence in human cancer cells

Anticancer therapy screening in vitro identifies additional treatments and improves clinical outcomes. Systematically, although most tested cells respond to cues with apoptosis, an appreciable portion enters a senescent state, a critical condition potentially driving tumor resistance and relapse. Conventional screening protocols would strongly benefit from prompt identification and monitoring of therapy-induced senescent (TIS) cells in their native form. We combined complementary all-optical, label-free, and quantitative microscopy techniques, based on coherent Raman scattering, multiphoton absorption, and interferometry, to explore the early onset and progression of this phenotype, which has been understudied in unperturbed conditions. We identified TIS manifestations as early as 24 hours following treatment, consisting of substantial mitochondrial rearrangement and increase of volume and dry mass, followed by accumulation of lipid vesicles starting at 72 hours. This work holds the potential to affect anticancer treatment research, by offering a label-free, rapid, and accurate method to identify initial TIS in tumor cells.

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.

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.

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.

Plasmonic Nanodiamonds

While nitrogen-vacancy (NV) centers in diamonds have emerged as promising solid-state quantum emitters for sensing applications, the tantalizing possibility of coupling them with photonic or broadband plasmonic nanostructures to create ultrasensitive biolabels has not been fully realized. Indeed, it remains technologically challenging to create free-standing hybrid diamond-based imaging nanoprobes with enhanced brightness and high temporal resolution. Herein, we leverage the bottom-up DNA self-assembly to develop hybrid free-standing plasmonic nanodiamonds, which feature a closed plasmonic nanocavity completely encapsulating a single nanodiamond. Correlated single nanoparticle spectroscopical characterizations suggest that the plasmonic nanodiamond displays dramatically and simultaneously enhanced brightness and emission rate. We believe that they hold huge potential to serve as a stable solid-state single-photon source and could serve as a versatile platform to study nontrivial quantum effects in biological systems with enhanced spatial and temporal resolution.

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.

DNA-Patched Nanoparticles for the Self-Assembly of Colloidal Metamaterials

Colloidal metamaterials are highly desired artificial materials that recapitulate the structure of simple molecules. They exhibit exceptional functionalities conferred by the organization of and specific interaction among constituent elements. Harvesting such exquisite attributes for potential applications necessitates establishing precise control over their structural configuration with high precision. Yet, creating molecule-like small clusters of colloidal metamaterials remains profoundly challenging, as a lack of regioselectively encoded surface chemical heterogeneity prevents specific recognition interactions. Herein, we report a new strategy by harnessing magnetic-bead-assisted DNA cluster transferring to create discretely DNA cluster-patched nanoparticles for the self-assembly of colloidal metamaterials. This strategy affords broad generalizability and scalability for robustly patching DNA clusters on nanoparticles unconstrained by geometrical, dimensional, and compositional complexities commonly encountered in colloidal materials at the nano- and microscale. We direct judiciously patched nanoparticles into a wide variety of nanoassemblies and present a case study demonstrating the distinct metamaterial properties in enhancing the spontaneous emission of diamond nanoparticles. This newly invented strategy is readily implementable and extendable to construct a palette of structurally sophisticated and functionality-explicit architecture, paving the way for nanoscale manipulation of colloidal material functionalities with wide-ranging applications for biological sensing, optical engineering, and catalytic chemistry.

Non-Perturbative Identification and Subtyping of Amyloidosis in Human Kidney Tissue with Raman Spectroscopy and Machine Learning

Amyloids are proteins with characteristic beta-sheet secondary structures that display fibrillary ultrastructural configurations. They can result in pathologic lesions when deposited in human organs. Various types of amyloid protein can be routinely identified in human tissue specimens by special stains, immunolabeling, and electron microscopy, and, for certain forms of amyloidosis, mass spectrometry is required. In this study, we applied Raman spectroscopy to identify immunoglobulin light chain and amyloid A amyloidosis in human renal tissue biopsies and compared the results with a normal kidney biopsy as a control case. Raman spectra of amyloid fibrils within unstained, frozen, human kidney tissue demonstrated changes in conformation of protein secondary structures. By using t-distributed stochastic neighbor embedding (t-SNE) and density-based spatial clustering of applications with noise (DBSCAN), Raman spectroscopic data were accurately classified with respect to each amyloid type and deposition site. To the best of our knowledge, this is the first time Raman spectroscopy has been used for amyloid characterization of ex vivo human kidney tissue samples. Our approach, using Raman spectroscopy with machine learning algorithms, shows the potential for the identification of amyloid in pathologic lesions.

Abstract 5916: Weight loss intervention triggered gut microbiota modulation can positively impact obesity associated endocrine therapy resistance

Background: 41.9% of adult US population is obese and every 5-unit BMI increase is associated with 12 percent increase in breast cancer risk; it is an independent predictor of poor outcomes in breast cancer. Microbiome has emerged as a central regulator of obesity and we recently discovered that microbes can not only potentiate breast cancer growth and metastasis but also affect therapeutic response. We showed weight-loss by Diet/Exercise/Behavioral intervention (POWER-remote trial) exerts favorable effects. Here, we examine molecular underpinnings of poor therapy response in obese state and explore the gut microbiome to mitigate the same.

Methods: We co-implanted lineage determined human White preAdipocytes (hWAT) with MCF7 breast cancer cells in the mammary glands of mice. Changes in tumor microenvironment and tumor cells were evaluated ex situ by RNA sequencing, flow cytometry, Raman spectroscopy, Quantitative phase microscopy, PCR, immunoblotting and functional assays. To inspect the gut microbiome, 16 obese breast cancer patients were subjected to weight loss intervention over 14 weeks. Gut microbiome was analyzed at baseline and post-intervention by microbial shotgun sequencing. Patients were divided into responders and non-responders based on % weight-loss.

Results: We found that human breast cancer cells co-implanted with hWAT formed more aggressive tumors compared to controls. RNA sequencing revealed significant upregulation of multiple oncogenic pathways along with robust innate immune response. Tumor dissociated cells from hWAT-MCF7 tumors showed extensive molecular reprogramming rendering them more proliferative, motile and stem-like along with dramatic physiological changes like increased intercellular lipid accumulation. hWAT-MCF7 cells attained resistance to multiple drugs including Tamoxifen, Fulvestrant, Letrozole and Palbociclib. Simultaneous targeting of multiple signaling pathways using pharmacological inhibitors is infeasible in clinical setting hence we explored the obesity-microbiome axis. Obese breast cancer patients underwent POWER-remote intervention and their gut microbiome analysis showed significant differential expression of many microbes. Based on abundance and metabolite profile, F. prausnitzii was selected as the candidate of interest. On treatment with F. prausnitzii growth media, acquired multi-drug resistance in hWAT-MCF7 cells could be reversed suggesting that increase in gut population of the bacterium could be helpful in sensitizing patients unresponsive to therapy.

Conclusion: Direct interaction between breast cancer cells and WAT rendered them resistant to multiple drugs. Metabolites produced by weight-loss-associated bacterium F. prausnitzii reversed adiposity-induced multi-drug resistance, hence, can potentially have therapeutic benefits in obese breast cancer patients.

Citation Format: Sheetal Parida, Sumit Siddharth, Nethaji Muniraj, Zhenhui Liu, Ishan Barman, Michael Smith, Jenni Sheng, Vered Stearns, Janelle W. Coughlin, Dipali Sharma. Weight loss intervention triggered gut microbiota modulation can positively impact obesity associated endocrine therapy resistance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5916.

Pyramidal hyperbolic metasurfaces enhance spontaneous emission of nitrogen-vacancy centers in nanodiamond

Nitrogen-vacancy (NV) centers in nanodiamond hold great promise for creating superior biological labels and quantum sensing methods. Yet, inefficient photon generation and extraction from excited NV centers restricts the achievable sensitivity and temporal resolution. Herein, we report an entirely complementary route featuring pyramidal hyperbolic metasurface to modify the spontaneous emission of NV centers. Fabricated using nanosphere lithography, the metasurface consists of alternatively stacked silica-silver thin films configured in a pyramidal fashion, and supports both spectrally broadband Purcell enhancement and spatially extended intense local fields owing to the hyperbolic dispersion and plasmonic coupling. The enhanced photophysical properties are manifested as a simultaneous amplification to the spontaneous decay rate and emission intensity of NV centers. We envision the reported pyramidal metasurface could serve as a versatile platform for creating chip-based ultrafast single-photon sources and spin-enhanced quantum biosensing strategies, as well as aiding in further fundamental understanding of photoexcited species in condensed phases.

Lithium in breast milk transiently affects the renal electrolytic balance of infants

BACKGROUND

The use of lithium during breast-feeding has not been comprehensively investigated in humans due to concerns about lithium toxicity.

PROCEDURE

We analyzed lithium in the kidneys of nursed pups of lithium medicated mothers, using analytical spectroscopy in a novel rat model. The mothers were healthy rats administered lithium via gavage (1000 mg/day Li₂ CO₃ per 50 kg body weight).

RESULTS

Lithium was detected in the breast milk, and in the blood of pups (0.08 mM), of lithium-exposed dams at post-natal day 18 (P18), during breast-feeding. No lithium was detected after breast-feeding, at P25 (4 days after cessation of nursing). The lithium pups blood had elevated urea nitrogen at P18 and reduced total T₄ at P18 and P25, indicating a longer-term effect on the kidneys and the thyroid gland. Multivariate machine-learning analysis of spectroscopy data collected from the excised kidneys of pups showed elevated potassium in lithium-exposed animals both during- and after breast-feeding. The elevated renal potassium was associated with low nephrin expression in the kidneys measured immunohistochemically during breast-feeding. After lithium exposure is stopped, the filtration of lithium from the kidneys reverses these effects. Our study showed that breastfeeding during lithium use has an effect on the kidneys of the offspring in rats.

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.

Membrane Ruffling is a Mechanosensor of Extracellular Fluid Viscosity

Cell behaviour is affected by the physical forces and mechanical properties of the cells and of their microenvironment. The viscosity of extracellular fluid - a component of the cellular microenvironment - can vary by orders of magnitude, but its effect on cell behaviour remains largely unexplored. Using bio-compatible polymers to increase the viscosity of the culture medium, we characterize how viscosity affects cell behaviour. We find that multiple types of adherent cells respond in an unexpected but similar manner to elevated viscosity. In a highly viscous medium, cells double their spread area, exhibit increased focal adhesion formation and turnover, generate significantly greater traction forces, and migrate nearly two times faster. We observe that when cells are immersed in regular medium, these viscosity-dependent responses require an actively ruffling lamellipodium - a dynamic membrane structure at the front of the cell. We present evidence that cells utilize membrane ruffling to sense changes in extracellular fluid viscosity and to trigger adaptive responses.

DNA-POINT: DNA Patterning of Optical Imprint for Nanomaterials Topography

Engineering well-defined scale-spanning structures through transfer of diverse biomolecules and materials to a surface is of tremendous interest in life sciences research yet remains profoundly challenging. Here, we report a novel method, termed as DNA patterning of optical imprint for nanomaterials topography (DNA-POINT), for rapid photopatterning of large area, geometrically complex surfaces via light-responsive DNA. Our method employs top-down multiphoton-driven patterning of azobenzene-modified DNA strands, offering precise position control of molecules and nanoparticles along the axial plane and a template for bottom-up self-assembly of multiple layers of different chemical composition along the vertical plane. We demonstrate the surface patterning of plasmonic gold nanoparticles, fluorophore-labeled oligonucleotides, and multiple layers consisting of molecule-nanoparticle hybrid patterns into preconceived shapes without compromising on the functionality of the biomolecules. Furthermore, we exhibit scanning mode operation of DNA-POINT, thereby paving the way for maskless and cleanroom-free fast fabrication of biochips for high-throughput diagnostics and biosensing applications.

Raman spectroscopy reveals phenotype switches in breast cancer metastasis

The accurate analytical characterization of metastatic phenotype at primary tumor diagnosis and its evolution with time are critical for controlling metastatic progression of cancer. Here, we report a label-free optical strategy using Raman spectroscopy and machine learning to identify distinct metastatic phenotypes observed in tumors formed by isogenic murine breast cancer cell lines of progressively increasing metastatic propensities. Methods: We employed the 4T1 isogenic panel of murine breast cancer cells to grow tumors of varying metastatic potential and acquired label-free spectra using a fiber probe-based portable Raman spectroscopy system. We used MCR-ALS and random forests classifiers to identify putative spectral markers and predict metastatic phenotype of tumors based on their optical spectra. We also used tumors derived from 4T1 cells silenced for the expression of TWIST, FOXC2 and CXCR3 genes to assess their metastatic phenotype based on their Raman spectra. Results: The MCR-ALS spectral decomposition showed consistent differences in the contribution of components that resembled collagen and lipids between the non-metastatic 67NR tumors and the metastatic tumors formed by FARN, 4T07, and 4T1 cells. Our Raman spectra-based random forest analysis provided evidence that machine learning models built on spectral data can allow the accurate identification of metastatic phenotype of independent test tumors. By silencing genes critical for metastasis in highly metastatic cell lines, we showed that the random forest classifiers provided predictions consistent with the observed phenotypic switch of the resultant tumors towards lower metastatic potential. Furthermore, the spectral assessment of lipid and collagen content of these tumors was consistent with the observed phenotypic switch. Conclusion: Overall, our findings indicate that Raman spectroscopy may offer a novel strategy to evaluate metastatic risk during primary tumor biopsies in clinical patients.

Surface-enhanced Raman scattering: An emerging tool for sensing cellular function

Continuous long-term intracellular imaging and multiplexed monitoring of biomolecular changes associated with key cellular processes remains a challenge for the scientific community. Recently, surface-enhanced Raman scattering (SERS) has been demonstrated as a powerful spectroscopic tool in the field of biology owing to its significant advantages. Some of these include the ability to provide molecule-specific information with exquisite sensitivity, working with small volumes of precious samples, real-time monitoring, and optimal optical contrast. More importantly, the availability of a large number of novel Raman reporters with narrower full width at half maximum (FWHM) of spectral peaks/vibrational modes than conventional fluorophores has created a versatile palette of SERS-based probes that allow targeted multiplex sensing surpassing the detection sensitivity of even fluorescent probes. Due to its nondestructive nature, its applicability has been recognized for biological sensing, molecular imaging, and dynamic monitoring of complex intracellular processes. We critically discuss recent developments in this area with a focus on different applications where SERS has been used for obtaining information that remains elusive for conventional imaging methods. Current reports indicate that SERS has made significant inroads in the field of biology and has the potential to be used for in vivo human applications. This article is categorized under: Diagnostic Tools > In Vitro Nanoparticle-Based Sensing Nanotechnology Approaches to Biology > Nanoscale Systems in Biology Diagnostic Tools > Biosensing Diagnostic Tools > In Vivo Nanodiagnostics and Imaging.

Label-Free Vibrational and Quantitative Phase Microscopy Reveals Remarkable Pathogen-Induced Morphomolecular Divergence in Tumor-Derived Cells

Delineating the molecular and morphological changes that cancer cells undergo in response to extracellular stimuli is crucial for identifying factors that promote tumor progression. Label-free optical imaging offers a potentially promising route for retrieving such single-cell information by generating detailed visualization of the morphology and determining alterations in biomolecular composition. The potential of such nonperturbative morphomolecular microscopy for analyzing microbiota-cancer cell interactions has been surprisingly underappreciated, despite the growing evidence of the critical role of dysbiosis in malignant transformations. Here, using a model system of breast cancer cells, we show that label-free Raman microspectroscopy and quantitative phase microscopy can detect biomolecular and morphological changes in single cells exposed to Bacteroides fragilis toxin (BFT), a toxin secreted by enterotoxigenicB. fragilis. Remarkably, using machine learning to elucidate subtle, but consistent, cellular differences, we found that the morphomolecular differences between BFT-exposed and control breast cancer cells became more accentuated after in vivo passage, corroborating our findings that a short-term BFT exposure imparts a long-term effect on cancer cells and promotes a more invasive phenotype. Complementing more classical labeling techniques, our label-free platform offers a global detection approach with measurements representative of the overall cellular phenotype, paving the way for further investigations into the multifaceted interactions between the cancer cell and the microbiota.

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.

Nanotheranostic Probe Built on Methylene Blue Loaded Cucurbituril [8] and Gold Nanorod: Targeted Phototherapy in Combination with SERS Imaging on Breast Cancer Cells

Recent advancements in a nanoarchitecture platform for safe and effective targeted phototherapy in a synergistic fashion is an absolute necessity in localized cancer therapy. Photothermal and photodynamic therapies (PTT and PDT) are considered as the most promising localized therapeutic intervention for cancer management as they have no long-term side effects and are minimally invasive and affordable. Herein, we have demonstrated a tailor-made nanotheranostic probe in which macrocyclic host cucurbituril [8] (CB[8]) is placed as a glue between two gold nanorods (GNRs) within ∼3 nm gaps in linear nanoassemblies with exquisitely sensitive plasmonics that exert combined phototherapy to investigate the therapeutic progression on human breast cancer cells. Photosensitizer methylene blue was positioned on CB[8] to impart the PDT effect, whereas GNR was responsible for PTT on a single laser trigger ensuring the synchronized phototherapy. Furthermore, the nanoconstruct was tagged with targeting anti-Her2 monoclonal antibody (MB-CB[8]@GNR-anti-Her2) for localized PTT and PDT on Her2 positive SKBR3 cells, subsequent cellular recognition by surface-enhanced Raman spectroscopy (SERS) platform, and further assessment of the combined intracellular phototherapy. Hence, the current strategy is definitely marked as a proof-of-concept straightforward approach that implies the perfect nature of the combined phototherapy to achieve an efficient cancer treatment.

Raman Spectroscopy and Machine Learning Reveals Early Tumor Microenvironmental Changes Induced by Immunotherapy

Cancer immunotherapy provides durable clinical benefit in only a small fraction of patients, and identifying these patients is difficult due to a lack of reliable biomarkers for prediction and evaluation of treatment response. Here, we demonstrate the first application of label-free Raman spectroscopy for elucidating biomolecular changes induced by anti-CTLA4 and anti-PD-L1 immune checkpoint inhibitors (ICI) in the tumor microenvironment (TME) of colorectal tumor xenografts. Multivariate curve resolution-alternating least squares (MCR-ALS) decomposition of Raman spectral datasets revealed early changes in lipid, nucleic acid, and collagen content following therapy. Support vector machine classifiers and random forests analysis provided excellent prediction accuracies for response to both ICIs and delineated spectral markers specific to each therapy, consistent with their differential mechanisms of action. Corroborated by proteomics analysis, our observation of biomolecular changes in the TME should catalyze detailed investigations for translating such markers and label-free Raman spectroscopy for clinical monitoring of immunotherapy response in cancer patients. SIGNIFICANCE: This study provides first-in-class evidence that optical spectroscopy allows sensitive detection of early changes in the biomolecular composition of tumors that predict response to immunotherapy with immune checkpoint inhibitors.

Advancing Raman spectroscopy from research to clinic: Translational potential and challenges

Raman spectroscopy has emerged as a non-invasive and versatile diagnostic technique due to its ability to provide molecule-specific information with ultrahigh sensitivity at near-physiological conditions. Despite exhibiting substantial potential, its translation from optical bench to clinical settings has been impacted by associated limitations. This perspective discusses recent clinical and biomedical applications of Raman spectroscopy and technological advancements that provide valuable insights and encouragement for resolving some of the most challenging hurdles.

Raman and quantitative phase imaging allow morpho-molecular recognition of malignancy and stages of B-cell acute lymphoblastic leukemia

Acute lymphoblastic leukemia (ALL) is one of the most common malignancies that account for nearly one-third of all pediatric cancers. The current diagnostic assays are time-consuming, labor-intensive, and require expensive reagents. Here, we report a label-free approach featuring diffraction phase imaging and Raman microscopy that can retrieve both morphological and molecular attributes for label-free optical phenotyping of individual B cells. By investigating leukemia cell lines of early and late stages along with the healthy B cells, we show that phase images can capture subtle morphological differences among the healthy, early, and late stages of leukemic cells. By exploiting its biomolecular specificity, we demonstrate that Raman microscopy is capable of accurately identifying not only different stages of leukemia cells but also individual cell lines at each stage. Overall, our study provides a rationale for employing this hybrid modality to screen leukemia cells using the widefield QPI and using Raman microscopy for accurate differentiation of early and late-stage phenotypes. This contrast-free and rapid diagnostic tool exhibits great promise for clinical diagnosis and staging of leukemia in the near future.

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.

Lithium from breast-milk inhibits thyroid iodine uptake and hormone production, which are remedied by maternal iodine supplementation

BACKGROUND

Lithium is especially taken as a maintenance medication for Bipolar Disorder. In women with bipolar disorder, lithium is often effective during postpartum period, but breast-feeding for medicated mothers is controversial because of harmful effects for her child. At present, the biological mechanisms of lithium are not well-understood, affecting its usage and overall health implications.

PROCEDURE

We developed a rat lithium and breast-feeding model at human therapeutic levels to study the effects of lithium exposure through breast-milk on pups' thyroid function. Novel laser analytical spectroscopy, along with traditional blood and immunohistochemical tests, were applied to further investigate the mechanisms behind the thyroid dysfunction. Maternal iodine supplementation was evaluated as a therapeutic method to address the pups' thyroid dysfunction.

RESULTS

Pups exposed to lithium via breastmilk, even with the dam on a sub-therapeutic level, experienced weight gain, reduced blood thyroxine (T₄ ), and elevated blood urea nitrogen, indicating effects on thyroid and kidney function. We show that lithium inhibited iodine uptake by thyroid follicles, initiating a mechanism that reduced iodination of tyrosine, thyroglobulin cleavage, and thyroid hormone production. Importantly, infant thyroid function can be significantly improved by administering supplementary iodine to the medicated dam's diet during breast-feeding.

CONCLUSION

These results elucidate the mechanisms of lithium in thyroid function, provide valuable information on use postpartum, and suggest a clinically applicable remedy to side-effects. The results are particularly important for patients (and their infants) who respond well to lithium and need, or choose, to breast-feed.

Role of Aqueous-Phase Calcination in Synthesis of Ultra-Stable Dye-Embedded Fluorescent Nanoparticles for Cellular Probing

Fluorescence imaging is a major driver of discovery in biology, and an invaluable asset in clinical diagnostics. To overcome quenching limitations of conventional fluorescent dyes and further improve intensity, nanoparticle-based constructs have been the subject of intense investigation, and within this realm, dye-doped silica-coated nanoparticles have garnered significant attention. Despite their growing popularity in research, fluorescent silica nanoparticles suffer from a significant flaw. The degradation of these nanoparticles in biological media by hydrolytic dissolution is underreported, leading to serious misinterpretations, and limiting their applicability for live cell and in vivo imaging. Here, the development of an ultra-stable, dye-embedded, silica-coated metal nanoparticle is reported, and its superior performance in long-term live cell imaging is demonstrated. While conventional dye-doped silica nanoparticles begin to degrade within an hour in aqueous media, by leveraging a modified liquid calcination process, this new construct is shown to be stable for at least 24 h. The stability of this metal-enhanced fluorescent probe in biologically relevant temperatures and media, and its demonstrated utility for cell imaging, paves the way for its future adoption in biomedical research.

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.

Spectroscopy-Assisted Label-free Molecular Analysis of Live Cell Surface with Vertically Aligned Plasmonic Nanopillars

A generalized label-free platform for surface-selective molecular sensing in living cells can transform the ability to examine complex events in the cell membrane. While vertically aligned semiconductor and metal-semiconductor hybrid nanopillars have rapidly surfaced for stimulating and probing the intracellular environment, the potential of such constructs for selectively interrogating the cell membrane is surprisingly underappreciated. In this work, a new platform, entitled nano-PROD (nano-pillar based Raman optical detection), enables molecular recording by probing fundamental vibrational modes of membrane constituents of cells adherent on a large-area silver-coated silicon nanopillar substrate fabricated using a precursor solution-based nanomanufacturing process. It is shown that the nano-PROD platform sustains live cells in near-physiological conditions, which can be directly profiled using surface-enhanced Raman spectroscopy due to the confined electromagnetic field enhancement. The experimental results highlight the utility of the platform in probing specific cell surface markers for accurately recognizing the phenotypically identical prostate cancer cells, differing only in prostate-specific membrane antigen expression. Due to its tunability, nano-PROD has the promise to be readily extendable to other applications that can leverage its unique combination of nanoscale topographic features and molecular sensing capabilities, from stain-free cytopathology inspection to understanding spatio-mechanical regulation in membrane receptor function.

Coarse Raman and optical diffraction tomographic imaging enable label-free phenotyping of isogenic breast cancer cells of varying metastatic potential

Identification of the metastatic potential represents one of the most important tasks for molecular imaging of cancer. While molecular imaging of metastases has witnessed substantial progress as an area of clinical inquiry, determining precisely what differentiates the metastatic phenotype has proven to be more elusive. In this study, we utilize both the morphological and molecular information provided by 3D optical diffraction tomography and Raman spectroscopy, respectively, to propose a label-free route for optical phenotyping of cancer cells at single-cell resolution. By using an isogenic panel of cell lines derived from MDA-MB-231 breast cancer cells that vary in their metastatic potential, we show that 3D refractive index tomograms can capture subtle morphological differences among the parental, circulating tumor cells, and lung metastatic cells. By leveraging its molecular specificity, we demonstrate that coarse Raman microscopy is capable of rapidly mapping a sufficient number of cells for training a random forest classifier that can accurately predict the metastatic potential of cells at a single-cell level. We also perform multivariate curve resolution alternating least squares decomposition of the spectral dataset to demarcate spectra from cytoplasm and nucleus, and test the feasibility of identifying metastatic phenotypes using the spectra only from the cytoplasmic and nuclear regions. Overall, our study provides a rationale for employing coarse Raman mapping to substantially reduce measurement time thereby enabling the acquisition of reasonably large training datasets that hold the key for label-free single-cell analysis and, consequently, for differentiation of indolent from aggressive phenotypes.

Silver-coated silicon nanowire platform discriminates genomic DNA from normal and malignant human epithelial cells using label-free Raman spectroscopy

Genomic deoxyribonucleic acid (DNA) stores and carries the information required to maintain and replicate cellular life. While much efforts have been devoted in decoding the sequence of DNA basis to detect the genetic mutations related to cancer disease, it is becoming clear that physical properties, like structural conformation, stiffness and shape, can play an important role to recognize DNA modifications. Here, silver-coated silicon nanowires (Ag/SiNWs) are exploited as Raman spectroscopic platform to easily discriminate healthy and cancer genomic DNA, extracted from human normal skin and malignant melanoma cells, respectively. In particular, aqueous DNA droplets are directly deposited onto a forest of Ag/SiNWs and Raman maps are acquired after sample dehydration. By applying principal component analysis (PCA) to the Raman spectra collected within the droplets, healthy and cancer cell DNA can be distinguished without false negative identifications and with few false positive results (< 2%). The discrimination occurs regardless the analysis of specific DNA sequencing, but through Raman bands strictly related to the interfacing of the DNA and the NWs. The observed phenomenon can be ascribed to conformational differences and/or diverse charge properties between healthy and cancer cell DNA determining a different arrangement of the molecules adsorbed onto the NWs upon water evaporation. The unique interaction with DNA and facile fabrication technology make Ag/SiNWs an effective platform for a robust, rapid and label-free cancer diagnosis, as well as a potential tool to investigate physical properties of DNA.

Silver-Coated Disordered Silicon Nanowires Provide Highly Sensitive Label-Free Glycated Albumin Detection through Molecular Trapping and Plasmonic Hotspot Formation

Glycated albumin (GA) is rapidly emerging as a robust biomarker for screening and monitoring of diabetes. To facilitate its rapid, point-of-care measurements, a label-free surface-enhanced Raman spectroscopy (SERS) sensing platform is reported that leverages the specificity of molecular vibrations and signal amplification on silver-coated silicon nanowires (Ag/SiNWs) for highly sensitive and reproducible quantification of GA. The simulations and experimental measurements demonstrate that the disordered orientation of the nanowires coupled with the wicking of the analyte molecules during the process of solvent evaporation facilitates molecular trapping at the generated plasmonic hotspots. Highly sensitive detection of glycated albumin is shown with the ability to visually detect spectral features at as low as 500 × 10^-9 m, significantly below the physiological range of GA in body fluids. Combined with chemometric regression models, the spectral data recorded on the Ag/SiNWs also allow accurate prediction of glycated concentration in mixtures of glycated and non-glycated albumin in proportions that reflect those in the bloodstream.

A Programmable DNA-Silicification-Based Nanocavity for Single-Molecule Plasmonic Sensing

Plasmonic nanocavities are highly desirable for optical sensing because of their singular ability to confine light into deep subwavelength volumes. Yet, it remains profoundly challenging to fabricate structurally resilient nanocavities with high fidelity, and to obtain direct, noninvasive visualization of the plasmonic hotspots within such constructs. Herein, highly precise and robust nanocavities, entitled DNA-silicified template for Raman optical beacon (DNA-STROBE), are engineered by using silicified DNA scaffolds for spatial organization of discrete plasmonic nanoparticles. In addition to substantially enhancing structural stability and chemical inertness, DNA silicification significantly improves nanogap control, resulting simultaneously in large and controlled local electromagnetic field enhancement. The ultrasmall mode volume of the DNA-STROBE constructs promotes single-molecule occupancy enabling surface-enhanced Raman spectroscopy (SERS) observations of single-molecule activity even at elevated background concentration, significantly relaxing the restrictive pico- to nanomolar molecular concentration condition typically required for such investigations. Additionally, leveraging super-resolution SERS measurements allows noninvasive and diffraction-unlimited spatial profiling of otherwise unresolvable plasmonic hotspots. The highly programmable and reproducible nature of the DNA-STROBE, coupled with its quantitative label-free molecular readouts, provides a versatile platform with applications across the spectrum of nanophotonics and biomedical sciences.

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.

Molecular Radiative Energy Shifts under Strong Oscillating Fields

Coherent manipulation of light-matter interactions is pivotal to the advancement of nanophotonics. Conventionally, the non-resonant optical Stark effect is harnessed for band engineering by intense laser pumping. However, this method is hindered by the transient Stark shifts and the high-energy laser pumping which, by itself, is precluded as a nanoscale optical source due to light diffraction. As an analog of photons in a laser, surface plasmons are uniquely positioned to coherently interact with matter through near-field coupling, thereby, providing a potential source of electric fields. Herein, the first demonstration of plasmonic Stark effect is reported and attributed to a newly uncovered energy-bending mechanism. As a complementary approach to the optical Stark effect, it is envisioned that the plasmonic Stark effect will advance fundamental understanding of coherent light-matter interactions and will also provide new opportunities for advanced optoelectronic tools, such as ultrafast all-optical switches and biological nanoprobes at lower light power levels.

Laser induced breakdown spectroscopy with machine learning reveals lithium-induced electrolyte imbalance in the kidneys

Lithium is a major psychiatric medication, especially as long-term maintenance medication for Bipolar Disorder. Despite its effectiveness, lithium has side-effects, such as on renal function. In this study, lithium was administered to adult rats. This animal model of renal function was validated by measuring blood lithium, urea nitrogen (BUN), and thyroxine (T₄) using inductively-coupled plasma mass spectrometry and enzyme-linked immunosorbent assay. The kidneys were analyzed by laser induced breakdown spectroscopy (LIBS) with 1064 nm ablation and 300-900 nm detection. Principal components analysis (PCA), radial visualization, and random forest classification were performed on the LIBS spectra for multi-element prediction and classification. Lithium at 0.34 mmol/L was detected in the blood of lithium treated subjects only. BUN was increased (6.6 vs. 5.3 mmol/L) and T₄ decreased (58.12 vs. 51.4 mmol/L) in the blood of lithium subjects compared with controls, indicating renal abnormalities. LIBS detected lithium at 2.3 mmol/kg in the kidneys of lithium subjects only. Calcium was also observed to be reduced in lithium subjects, compared with controls. Subsequent PCA observed a change in the balance of sodium and potassium in the kidneys. These are key electrolytes in the body. Importantly, partial least squares regression showed that standard clinical measurements, such as the blood tests, can be used to predict kidney electrolyte measurements, which typically cannot be performed in humans. Overall, lithium accumulates in the kidneys and adversely affects renal function. The effects are likely related to electrolyte imbalance. LIBS with machine learning analysis has potential to improve clinical management of renal side-effects in patients on lithium medication.