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.

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.

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.

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.

Adaptive Optical Two-Photon Fluorescence Microscopy Probes Cellular Organization of Ocular Lenses In Vivo.

Purpose: The mammalian ocular lens is an avascular multicellular organ that grows continuously throughout life. Traditionally, its cellular organization is investigated using dissected lenses, which eliminates in vivo environmental and structural support. Therefore, in vivo optical imaging methods for studying lenses in their native context in live animals are urgently needed. Methods: Here, we demonstrated that two-photon fluorescence microscopy can visualize lens cells in vivo. To maintain subcellular resolution at depth, we used adaptive optics to correct aberrations owing to ocular and lens tissues, which led to substantial signal and resolution improvements. Results: Imaging lens cells up to 980 µm deep, we observed novel cellular organizations including suture-associated voids, enlarged vacuoles, and large cavities, contrary to the conventional view of a highly ordered organization. We tracked these features longitudinally over weeks and observed the incorporation of new cells during growth. Conclusions: Taken together, noninvasive longitudinal in vivo imaging of lens morphology using adaptive optics two-photon fluorescence microscopy will allow us to observe the development or alterations of lens cellular organization in living animals directly.

Retinal microvascular and neuronal pathologies probed in vivo by adaptive optical two-photon fluorescence microscopy.

The retina, behind the transparent optics of the eye, is the only neural tissue whose physiology and pathology can be non-invasively probed by optical microscopy. The aberrations intrinsic to the mouse eye, however, prevent high-resolution investigation of retinal structure and function in vivo. Optimizing the design of a two-photon fluorescence microscope (2PFM) and sample preparation procedure, we found that adaptive optics (AO), by measuring and correcting ocular aberrations, is essential for resolving putative synaptic structures and achieving three-dimensional cellular resolution in the mouse retina in vivo. Applying AO-2PFM to longitudinal retinal imaging in transgenic models of retinal pathology, we characterized microvascular lesions with sub-capillary details in a proliferative vascular retinopathy model, and found Lidocaine to effectively suppress retinal ganglion cell hyperactivity in a retinal degeneration model. Tracking structural and functional changes at high-resolution longitudinally, AO-2PFM enables microscopic investigations of retinal pathology and pharmacology for disease diagnosis and treatment in vivo.

Adaptive optical two-photon fluorescence microscopy probes cellular organization of ocular lenses in vivo.

The mammalian ocular lens is an avascular multicellular organ that grows continuously throughout life. Traditionally, its cellular organization is investigated using dissected lenses, which eliminates in vivo environmental and structural support. Here, we demonstrated that two-photon fluorescence microscopy (2PFM) can visualize lens cells in vivo. To maintain subcellular resolution at depth, we employed adaptive optics (AO) to correct aberrations due to ocular and lens tissues, which led to substantial signal and resolution improvements. Imaging lens cells up to 980 µm deep, we observed novel cellular organizations including suture-associated voids, enlarged vacuoles, and large cavities, contrary to the conventional view of a highly ordered organization. We tracked these features longitudinally over weeks and observed the incorporation of new cells during growth. Taken together, non-invasive longitudinal in vivo imaging of lens morphology using AO 2PFM will allow us to directly observe the development or alterations of lens cellular organization in living animals.

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.

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.

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.

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.

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.

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.

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.

Identification and Staging of B-Cell Acute Lymphoblastic Leukemia Using Quantitative Phase Imaging and Machine Learning.

Identification and classification of leukemia cells in a rapid and label-free fashion is clinically challenging and thus presents a prime arena for implementing new diagnostic tools. Quantitative phase imaging, which maps optical path length delays introduced by the specimen, has been demonstrated to discern cellular phenotypes based on differential morphological attributes. Rapid acquisition capability and the availability of label-free images with high information content have enabled researchers to use machine learning (ML) to reveal latent features. We developed a set of ML classifiers, including convolutional neural networks, to discern healthy B cells from lymphoblasts and classify stages of B cell acute lymphoblastic leukemia. Here, we show that the average dry mass and volume of normal B cells are lower than those of cancerous cells and that these morphologic parameters increase further alongside disease progression. We find that the relaxed training requirements of a ML approach are conducive to the classification of cell type, with minimal space, training time, and memory requirements. Our findings pave the way for a larger study on clinical samples of acute lymphoblastic leukemia, with the overarching goal of its broader use in hematopathology, where the prospect of objective diagnoses with minimal sample preparation remains highly desirable.

A Fluorescence and Surface-Enhanced Raman Spectroscopic Dual-Modal Aptasensor for Sensitive Detection of Cyanotoxins.

The ability to detect trace analytes without necessitating solid surface attachment or complicated processing steps would facilitate the translation of sensors for monitoring environmental toxins in the field. To address a critical unmet need in fresh water ecology, we have developed a dual-modal aptamer-based biosensor (aptasensor), featuring fluorescence and surface-enhanced Raman spectroscopy (SERS), for sensitive and selective detection of hepatotoxin microcystin-LR (MC-LR). The rational sensor design is based on the high affinity of the cyanine (Cy3) dye-modified complementary DNA (Cy3-cDNA) strand toward the plasmonic gold nanostars (GNSs) in comparison to the Cy3-cDNA/aptamer duplex. The preferential binding of MC-LR toward the MC-LR-specific aptamer triggers the dissociation of Cy3-cDNA/aptamer duplexes, which switches the Cy3's fluorescence "off" and SERS "on" due to the proximity of Cy3 dye to the GNS surface. Both fluorescence and SERS intensities are observed to vary linearly with the MC-LR concentration over the range of investigation. We have achieved high sensitivity and excellent specificity with the aptasensor toward MC-LR, which can be attributed to the fluorescence quenching effect, significant SERS enhancement by the GNSs, and the high affinity of the aptamer toward the MC-LR analytes. We further demonstrate the applicability of the present aptasensor for detection of MC-LR in a diverse set of real water samples with high accuracy and excellent reproducibility. With further refinement, we believe that the aptamer-driven complementary assembly of the SERS and fluorescence sensing constructs can be applied for rapid, multiplexed, and robust measurements of environmental toxins in the field.

Ultrasensitive Detection of Hepatotoxic Microcystin Production from Cyanobacteria Using Surface-Enhanced Raman Scattering Immunosensor.

Microcystin-LR (MC-LR) is considered the most common hazardous toxin produced during harmful algal blooms. In addition to potential risk of long-term exposure to low concentrations in drinking water, acute toxicity due to MC-LR resulting from algal blooms could result in fatalities in rare cases. Although several methods are currently available to detect MC-LR, development of a low-cost, ultrasensitive measurement method would help limit exposure by enabling early detection and continuous monitoring of MC-LR. Here, we develop a surface-enhanced Raman scattering (SERS) spectroscopic immunosensor for detection and quantification of the hepatotoxic MC-LR toxin in aquatic settings with excellent robustness, selectivity, and sensitivity. We demonstrate that the developed SERS sensor can reach a limit of detection (0.014 µg/L) at least 1 order of magnitude lower and display a linear dynamic detection range (0.01 µg/L to 100 µg/L) 2 orders of magnitude wider in comparison to the commercial enzyme-linked immunosorbent assay test. The superior analytical performance of this SERS immunosensor enables monitoring of the dynamic production of MC-LR from a Microcystis aeruginosa culture. We believe that the present method could serve as a useful tool for detection of hepatotoxic microcystin toxins in various aquatic settings such as drinking water, lakes, and reservoirs. Further development of this technique could result in single-cell microcystin resolution or real-time monitoring to mitigate the associated toxicity and economic loss.

Label-Free Raman Spectroscopy Reveals Signatures of Radiation Resistance in the Tumor Microenvironment.

Delay in the assessment of tumor response to radiotherapy continues to pose a major challenge to quality of life for patients with nonresponsive tumors. Here, we exploited label-free Raman spectroscopic mapping to elucidate radiation-induced biomolecular changes in tumors and uncovered latent microenvironmental differences between treatment-resistant and -sensitive tumors. We used isogenic radiation-resistant and -sensitive A549 human lung cancer cells and human head and neck squamous cell carcinoma (HNSCC) cell lines (UM-SCC-47 and UM-SCC-22B, respectively) to grow tumor xenografts in athymic nude mice and demonstrated the molecular specificity and quantitative nature of Raman spectroscopic tissue assessments. Raman spectra obtained from untreated and treated tumors were subjected to chemometric analysis using multivariate curve resolution-alternating least squares (MCR-ALS) and support vector machine (SVM) to quantify biomolecular differences in the tumor microenvironment. The Raman measurements revealed significant and reliable differences in lipid and collagen content postradiation in the tumor microenvironment, with consistently greater changes observed in the radiation-sensitive tumors. In addition to accurately evaluating tumor response to therapy, the combination of Raman spectral markers potentially offers a route to predicting response in untreated tumors prior to commencing treatment. Combined with its noninvasive nature, our findings provide a rationale for in vivo studies using Raman spectroscopy, with the ultimate goal of clinical translation for patient stratification and guiding adaptation of radiotherapy during the course of treatment. SIGNIFICANCE: These findings highlight the sensitivity of label-free Raman spectroscopy to changes induced by radiotherapy and indicate the potential to predict radiation resistance prior to commencing therapy.