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.

Noninvasive Monitoring of Blood Glucose with Raman Spectroscopy.

The successful development of a noninvasive blood glucose sensor that can operate reliably over sustained periods of time has been a much sought after but elusive goal in diabetes management. Since diabetes has no well-established cure, control of elevated glucose levels is critical for avoiding severe secondary health complications in multiple organs including the retina, kidney and vasculature. While fingerstick testing continues to be the mainstay of blood glucose detection, advances in electrochemical sensing-based minimally invasive approaches have opened the door for alternate methods that would considerably improve the quality of life for people with diabetes. In the quest for better sensing approaches, optical technologies have surfaced as attractive candidates as researchers have sought to exploit the endogenous contrast of glucose, notably its absorption, scattering, and polarization properties. Vibrational spectroscopy, especially spontaneous Raman scattering, has exhibited substantial promise due to its exquisite molecular specificity and minimal interference of water in the spectral profiles acquired from the blood-tissue matrix. Yet, it has hitherto been challenging to leverage the Raman scattering signatures of glucose for prediction in all but the most basic studies and under the least demanding conditions. In this Account, we discuss the newly developed array of methodologies that address the key challenges in measuring blood glucose accurately using Raman spectroscopy and unlock new prospects for translation to sustained noninvasive measurements in people with diabetes. Owing to the weak intensity of spontaneous Raman scattering, recent research has focused on enhancement of signals from the blood constituents by designing novel excitation-collection geometries and tissue modulation methods while our attempts have led to the incorporation of nonimaging optical elements. Additionally, invoking mass transfer modeling into chemometric algorithms has not only addressed the physiological lag between the actual blood glucose and the measured interstitial fluid glucose values but also offered a powerful tool for predictive measurements of hypoglycemia. This framework has recently been extended to provide longitudinal tracking of glucose concentration without necessitating extensive a priori concentration information. These findings are advanced by the results of recent glucose tolerance studies in human subjects, which also hint at the need for designing nonlinear calibration models that can account for subject-to-subject variations in skin heterogeneity and hematocrit levels. Together, the emerging evidence underscores the promise of a blood withdrawal-free optical platform-featuring a combination of high-throughput Raman spectroscopic instrumentation and data analysis of subtle variations in spectral expression-for diabetes screening in the clinic and, ultimately, for personalized monitoring.

Exploring Morphological and Biochemical Linkages in Fungal Growth with Label-Free Light Sheet Microscopy and Raman Spectroscopy.

Imaging tip growth in fungal hyphae is highly warranted to unravel the molecular mechanism of this extraordinarily precise and localized phenomenon. In situ probing of fungal cultures, however, have been challenging due to their inherent complexity and light penetration issues associated with conventional optical imaging. In this work, we report a label-free approach using a combination of light sheet microscopy and Raman spectroscopy to obtain concomitant morphological and biochemical information from the growing specimen. We show that the variance in morphology in the symbiotic fungus Piriformospora indica are rooted in the underlying differences in chemical composition in the specific growth zones. Our findings suggest that this potent two-pronged approach can comprehensively characterize growth areas and elucidate microbe interactions in still developing colonies with high sensitivity and multiplexing capability.

Rapid Identification of Biotherapeutics with Label-Free Raman Spectroscopy.

Product identification is a critical and required analysis for biotheraputics. In addition to regulatory requirements for identity testing on final drug products, in-process identity testing is implemented to reduce business risks associated with fill operations and can also be used as a tool against counterfeiting. Biotherapeutics, in particular monoclonal antibodies, represent a challenging cohort for identity determination because of their similarity in chemical structure. Traditional methods used for product identification can be time and labor intensive, creating a need for quick, inexpensive and reliable methods of drug identification. Here, driven by its molecular-specific and nonperturbative nature, we present Raman spectroscopy as an alternate analytical tool for identity testing. By exploiting subtle differences in vibrational modes of the biologics, we have developed partial least-squares-discriminant analysis derived decision algorithms that offer excellent differentiation capability using spontaneous Raman spectra as well as label-free plasmon-enhanced Raman spectra. Coupled with the robustness to spurious correlations due to its high information content, our results highlight the potential of Raman spectroscopy as a powerful method for rapid, on-site biotherapeutic product identification.

Real-time fingerprinting of structural isomers using laser induced breakdown spectroscopy.

Laser induced breakdown spectroscopy (LIBS) has surfaced as an attractive alternative to mass spectrometry and wet chemistry methods for chemical identification, driven by its real-time, label-free nature. Rapid analysis needs, especially in high-energy materials and pharmaceutical compounds, have further fueled an increasing number of refinements in LIBS. Yet, isomers are seldom identifiable by LIBS as they generate nearly identical spectra. Here we employ a suite of chemometric approaches to exploit the subtle, but reproducible, differences in LIBS spectra acquired from structural isomers, a set of pyrazoles, to develop a sensitive and reliable segmentation method. We also investigate the possible mechanistic principles (causation) behind such spectral variations and confirm their statistically significant nature that empowers the excellent classification performance.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Mapping the genetic basis of breast microcalcifications and their role in metastasis.

Breast cancer screening and early stage diagnosis is typically performed by X-ray mammography, which detects microcalcifications. Despite being one of the most reliable features of nonpalpable breast cancer, the processes by which these microcalcifications form are understudied and largely unknown. In the current work, we have investigated the genetic drivers for the formation of microcalcifications in breast cancer cell lines, and have investigated their involvement in disease progression. We have shown that stable silencing of the Osteopontin (OPN) gene decreased the formation of hydroxyapatite in MDA-MB-231 breast cancer cells in response to osteogenic cocktail. In addition, OPN silencing reduced breast cancer cell migration. Furthermore, breast cancer cells that had spontaneously metastasized to the lungs in a mouse model of breast cancer had largely elevated OPN levels, while circulating tumor cells in the same mouse model contained intermediately increased OPN levels as compared to parental cells. The observed dual roles of the OPN gene reveal the existence of a direct relationship between calcium deposition and the ability of breast cancer cells to metastasize to distant organs, mediated by common genetic factors.

Label-Free Raman Spectroscopy Detects Stromal Adaptations in Premetastatic Lungs Primed by Breast Cancer.

Recent advances in animal modeling, imaging technology, and functional genomics have permitted precise molecular observations of the metastatic process. However, a comprehensive understanding of the premetastatic niche remains elusive, owing to the limited tools that can map subtle differences in molecular mediators in organ-specific microenvironments. Here, we report the ability to detect premetastatic changes in the lung microenvironment, in response to primary breast tumors, using a combination of metastatic mouse models, Raman spectroscopy, and multivariate analysis of consistent patterns in molecular expression. We used tdTomato fluorescent protein expressing MDA-MB-231 and MCF-7 cells of high and low metastatic potential, respectively, to grow orthotopic xenografts in athymic nude mice and allow spontaneous dissemination from the primary mammary fat pad tumor. Label-free Raman spectroscopic mapping was used to record the molecular content of premetastatic lungs. These measurements show reliable distinctions in vibrational features, characteristic of the collageneous stroma and its cross-linkers as well as proteoglycans, which uniquely identify the metastatic potential of the primary tumor by recapitulating the compositional changes in the lungs. Consistent with histological assessment and gene expression analysis, our study suggests that remodeling of the extracellular matrix components may present promising markers for objective recognition of the premetastatic niche, independent of conventional clinical information. Cancer Res; 77(2); 247-56. ©2016 AACR.