Antifouling nanoplatform for controlled attachment of E. coli.

Biofouling is the most common cause of bacterial contamination in implanted materials/devices resulting in severe inflammation, implant mobilization, and eventual failure. Since bacterial attachment represents the initial step toward biofouling, developing synthetic surfaces that prevent bacterial adhesion is of keen interest in biomaterials research. In this study, we develop antifouling nanoplatforms that effectively impede bacterial adhesion and the consequent biofilm formation. We synthesize the antifouling nanoplatform by introducing silicon (Si)/silica nanoassemblies to the surface through ultrafast ionization of Si substrates. We assess the effectiveness of these nanoplatforms in inhibiting Escherichia coli (E. coli) adhesion. The findings reveal a significant reduction in bacterial attachment on the nanoplatform compared to untreated silicon, with bacteria forming smaller colonies. By manipulating physicochemical characteristics such as nanoassembly size/concentration and nanovoid size, we further control bacterial attachment. These findings suggest the potential of our synthesized nanoplatform in developing biomedical implants/devices with improved antifouling properties. .

Self-Functionalized Superlattice Nanosensor Enables Glioblastoma Diagnosis Using Liquid Biopsy.

Glioblastoma (GBM), the most aggressive and lethal brain cancer, is detected only in the advanced stage, resulting in a median survival rate of 15 months. Therefore, there is an urgent need to establish GBM diagnosis tools to identify the tumor accurately. The clinical relevance of the current liquid biopsy techniques for GBM diagnosis remains mostly undetermined, owing to the challenges posed by the blood-brain barrier (BBB) that restricts biomarkers entering the circulation, resulting in the unavailability of clinically validated circulating GBM markers. GBM-specific liquid biopsy for diagnosis and prognosis of GBM has not yet been developed. Here, we introduce extracellular vesicles of GBM cancer stem cells (GBM CSC-EVs) as a previously unattempted, stand-alone GBM diagnosis modality. As GBM CSCs are fundamental building blocks of tumor initiation and recurrence, it is desirable to investigate these reliable signals of malignancy in circulation for accurate GBM diagnosis. So far, there are no clinically validated circulating biomarkers available for GBM. Therefore, a marker-free approach was essential since conventional liquid biopsy relying on isolation methodology was not viable. Additionally, a mechanism capable of trace-level detection was crucial to detecting the rare GBM CSC-EVs from the complex environment in circulation. To break these barriers, we applied an ultrasensitive superlattice sensor, self-functionalized for surface-enhanced Raman scattering (SERS), to obtain holistic molecular profiling of GBM CSC-EVs with a marker-free approach. The superlattice sensor exhibited substantial SERS enhancement and ultralow limit of detection (LOD of attomolar 10-18 M concentration) essential for trace-level detection of invisible GBM CSC-EVs directly from patient serum (without isolation). We detected as low as 5 EVs in 5 µL of solution, achieving the lowest LOD compared to existing SERS-based studies. We have experimentally demonstrated the crucial role of the signals of GBM CSC-EVs in the precise detection of glioblastoma. This was evident from the unique molecular profiles of GBM CSC-EVs demonstrating significant variation compared to noncancer EVs and EVs of GBM cancer cells, thus adding more clarity to the current understanding of GBM CSC-EVs. Preliminary validation of our approach was undertaken with a small amount of peripheral blood (5 µL) derived from GBM patients with 100% sensitivity and 97% specificity. Identification of the signals of GBM CSC-EV in clinical sera specimens demonstrated that our technology could be used for accurate GBM detection. Our technology has the potential to improve GBM liquid biopsy, including real-time surveillance of GBM evolution in patients upon clinical validation. This demonstration of liquid biopsy with GBM CSC-EV provides an opportunity to introduce a paradigm potentially impacting the current landscape of GBM diagnosis.

DNA Methylation Signatures of Tumor-Associated Natural Killer Cells with Self-Functionalized Nanosensor Enable Colorectal Cancer Diagnosis.

Natural killer (NK) cells undergo multiple DNA genomic alterations, especially methylation-based modifications that affect activation and function. Several epigenetic modifier markers have been targeted for immunotherapy to date, but the possibility of cancer diagnosis using NK cell's DNA has been overlooked. Here, we investigated the potential use of NK cell DNA genome modifications as markers for the diagnosis of colorectal cancer (CRC) and validated their efficacy in CRC patients. Using Raman spectroscopy as the detection methodology, we identified CRC-specific methylation signatures by comparing CRC-interacted NK cells to healthy circulating NK cells. Subsequently, we identified methylation-dependent alterations in these NK cell populations. These markers were then utilized by a machine learning algorithm to develop a diagnostic model with predictive capabilities. The diagnostic prediction model accurately differentiated CRC patients from normal controls. Our findings demonstrated the utility of NK DNA markers in the diagnosis of CRC.

Mapping Immune-Tumor Bidirectional Dialogue Using Ultrasensitive Nanosensors for Accurate Diagnosis of Lung Cancer.

Lung cancer is one of the most common cancers with high mortality worldwide despite the development of molecularly targeted therapies and immunotherapies. A significant challenge in managing lung cancer is the accurate diagnosis of cancerous lesions owing to the lack of sensitive and specific biomarkers. The current procedure necessitates an invasive tissue biopsy for diagnosis and molecular subtyping, which presents patients with risk, morbidity, anxiety, and high false-positive rates. The high-risk diagnostic approach has highlighted the need to search for a reliable, low-risk noninvasive diagnostic approach to capture lung cancer heterogeneity precisely. The immune interaction profile of lung cancer is driven by immune cells' distinctive, precise interactions with the tumor microenvironment. Here, we hypothesize that immune cells, particularly T cells, can be used for accurate lung cancer diagnosis by exploiting the distinctive immune-tumor interaction by detecting the immune-diagnostic signature. We have developed an ultrasensitive T-sense nanosensor to probe these specific diagnostic signatures using the physical synthesis process of multiphoton ionization. Our research employed predictive in vitro models of lung cancers, cancer-associated T cells (PCAT, MCAT) and CSC-associated T cells (PCSCAT, MCSCAT), from primary and metastatic lung cancer patients to reveal the immune-diagnostic signature and uncover the molecular, functional, and phenotypic separation between patient-derived T cells (PDT) and healthy samples. We demonstrated this by adopting a machine learning model trained with SERS data obtained using cocultured T cells with preclinical models (CAT, CSCAT) of primary (H69AR) and metastatic lung cancer (H1915). Interrogating these distinct signatures with PDT captured the complexity and diversity of the tumor-associated T cell signature across the patient population, exposing the clinical feasibility of immune diagnosis in an independent cohort of patient samples. Thus, our predictive approach using T cells from the patient peripheral blood showed a highly accurate diagnosis with a specificity and sensitivity of 94.1% and 100%, respectively, for primary lung cancer and 97.9% and 94.4% for metastatic lung cancer. Our results prove that the immune-diagnostic signature developed in this study could be used as a clinical technology for cancer diagnosis and determine the course of clinical management with T cells.

Label-Free Saliva Test for Rapid Detection of Coronavirus Using Nanosensor-Enabled SERS.

The recent COVID-19 pandemic has highlighted the inadequacies of existing diagnostic techniques and the need for rapid and accurate diagnostic systems. Although molecular tests such as RT-PCR are the gold standard, they cannot be employed as point-of-care testing systems. Hence, a rapid, noninvasive diagnostic technique such as Surface-enhanced Raman scattering (SERS) is a promising analytical technique for rapid molecular or viral diagnosis. Here, we have designed a SERS- based test to rapidly diagnose SARS-CoV-2 from saliva. Physical methods synthesized the nanostructured sensor. It significantly increased the detection specificity and sensitivity by ~ten copies/mL of viral RNA (~femtomolar concentration of nucleic acids). Our technique combines the multiplexing capability of SERS with the sensitivity of novel nanostructures to detect whole virus particles and infection-associated antibodies. We have demonstrated the feasibility of the test with saliva samples from individuals who tested positive for SARS-CoV-2 with a specificity of 95%. The SERS-based test provides a promising breakthrough in detecting potential mutations that may come up with time while also preparing the world to deal with other pandemics in the future with rapid response and very accurate results.

Dual-Purpose 3D-Silica Nanostructure Matrix for Rapid Epigenetic Reprogramming of Tumor Cell to Cancer Stem Cell Spheroid.

Cancer stem cells (CSCs), a rare subpopulation responsible for tumorigenesis and therapeutic resistance, are difficult to characterize and isolate. Conventional method of growing CSCs takes up to 2-8 weeks inhibiting the rate of research. Therefore, rapid reprogramming (RR) of tumor cells into CSCs is crucial to accelerate the stem cell oncology research. The current RR techniques cannot be utilized for CSC RR due to many limitations posed due to isolation requirements resulting in loss of vital data. Hence, a technique that can induce CSC RR without the need for isolation procedures is needed. Here, fabrication of a 3D-silica nanostructured extracellular matrix for RR and in situ monitoring is reported. The RR is tested using three preclinical cancer models. The 3D matrix and a zeta potential study confirm an intense material-cellular interaction resulting in the enhanced expressions of surface and epigenetic biomarkers. Cancer cells require only 3-day period to form CSC spheroids with 3D-silica extracellular matrix. Real-time single-cell monitoring of the methylene blue-induced photodynamic demonstrates the dual functionality. To the authors' knowledge, this is the first study to demonstrate a CSC epigenetic reprogramming using nanostructures. These findings may pave the path for accelerating the stem cell research in oncology.

DEEP Surveillance of Brain Cancer Using Self-Functionalized 3D Nanoprobes for Noninvasive Liquid Biopsy.

Brain cancers, one of the most fatal malignancies, require accurate diagnosis for guided therapeutic intervention. However, conventional methods for brain cancer prognosis (imaging and tissue biopsy) face challenges due to the complex nature and inaccessible anatomy of the brain. Therefore, deep analysis of brain cancer is necessary to (i) detect the presence of a malignant tumor, (ii) identify primary or secondary origin, and (iii) find where the tumor is housed. In order to provide a diagnostic technique with such exhaustive information here, we attempted a liquid biopsy-based deep surveillance of brain cancer using a very minimal amount of blood serum (5 µL) in real time. We hypothesize that holistic analysis of serum can act as a reliable source for deep brain cancer surveillance. To identify minute amounts of tumor-derived material in circulation, we synthesized an ultrasensitive 3D nanosensor, adopted SERS as a diagnostic methodology, and undertook a DEEP neural network-based brain cancer surveillance. Detection of primary and secondary tumor achieved 100% accuracy. Prediction of intracranial tumor location achieved 96% accuracy. This modality of using patient sera for deep surveillance is a promising noninvasive liquid biopsy tool with the potential to complement current brain cancer diagnostic methodologies.

Cancer Stem Cell Derived Extracellular Vesicles with Self-Functionalized 3D Nanosensor for Real-Time Cancer Diagnosis: Eliminating the Roadblocks in Liquid Biopsy.

Liquid biopsy for determining the presence of cancer and the underlying tissue of origin is crucial to overcome the limitations of existing tissue biopsy and imaging-based techniques by capturing critical information from the dynamic tumor heterogeneity. A newly emerging liquid biopsy with extracellular vesicles (EVs) is gaining momentum, but its clinical relevance is in question due to the biological and technical challenges posed by existing technologies. The biological barriers of existing technologies include the inability to generate fundamental details of molecular structure, chemical composition as well as functional variations in EVs by gathering simultaneous information on multiple intra-EV molecules, unavailability of holistic qualitative analysis, in addition to the inability to identify tissue of origin. Technological barriers include reliance on EV isolation with a few labeled biomarkers, resulting in the inability to generate comprehensive information on the disease. A more favorable approach would be to generate holistic information on the disease without the use of labels. Such a marker-free diagnosis is impossible with the existing liquid biopsy due to the unavailability clinically validated cancer stem cells (CSC)-specific markers and dependence of existing technologies on EV isolation, undermining the clinical relevance of EV-based liquid biopsy. Here, CSC EVs were employed as an independent liquid biopsy modality. We hypothesize that tracking the signals of CSCs in peripheral blood with CSC EVs will provide a reliable solution for accurate cancer diagnosis, as CSC are the originators of tumor contributing to tumor heterogeneity. We report nanoengineered 3D sensors of extremely small nano-scaled probes self-functionalized for SERS, enabling integrative molecular and functional profiling of otherwise undetectable CSC EVs. A substantially enhanced SERS and ultralow limit of detection (10 EVs per 10 µL) were achieved. This was attributed to the efficient probe-EV interaction due to the 3D networks of nanoprobes, ensuring simultaneous detection of multiple EV signals. We experimentally demonstrate the crucial role of CSC EVs in cancer diagnosis. We then completed a pilot validation of this modality for cancer detection as well as for identification of the tissue of origin. An artificial neural network distinguished cancer from noncancer with 100% sensitivity and 100% specificity for three hard to detect cancers (breast, lung, and colorectal cancer). Binary classification to distinguish one tissue of origin against all other achieved 100% accuracy, while simultaneous identification of all three tissues of origin with multiclass classification achieved up to 79% accuracy. This noninvasive tool may complement existing cancer diagnostics, treatment monitoring as well as longitudinal disease monitoring by validation with a large cohort of clinical samples.

Molecular Crosstalk between T Cells and Tumor Uncovers GBM-Specific T Cell Signatures in Blood: Noninvasive GBM Diagnosis Using Immunosensors.

Glioblastoma (GBM) is the most common and aggressive stage IV brain cancer with a poor prognosis and survival rate. The blood-brain barrier (BBB) in GBM prevents the entry and exit of biomarkers, limiting its treatment options. Hence, GBM diagnosis is pivotal for timely clinical management. Currently, there exists no clinically validated biomarker for GBM diagnosis. T cells exhibit the potential to escape a leaky BBB in GBM patients. These T cells infiltrating the GBM interact with the heterogeneous population of tumor cells, display a symbiotic interaction resulting in intertwined molecular crosstalk, and display a GBM-associated signature while entering the peripheral circulation. Therefore, we hypothesize that studying these distinct molecular changes is critical to enable T cells to be a diagnostic marker for accurate detection of GBM from patient blood. We demonstrated this by utilizing the phenotypic and immunological landscape changes in T cells associated with glioblastoma tumors. GBM exhibits a high level of heterogeneity with diverse subtypes of cells within the tumor, enabling immune infiltration and different degrees of interactions with the tumor. To accurately detect these subtle molecular differences in T cells, we designed an immunosensor with a high detection sensitivity and repeatability. Hence in this study, we investigated the characteristic behavior of T cells to establish two preclinically validated biomarkers: GBM-associated T cells (GBMAT) and GBM stem cell-associated T cells (GSCAT). A comprehensive investigation was conducted by mimicking the tumor microenvironment in vitro by coculturing T cells with cancer cells and cancer stem cells to study the distinct variation in GBMAT and GSCAT. Preclinical investigation of T cells from GBM patient blood shows similar characteristics to our established biomarkers (GBMAT, GSCAT). Further evaluating the relative attributes of T cells in patient blood and tissue biopsy confirms the infiltrating ability of T cells across the BBB. A pilot validation using a SERS-based machine learning algorithm was accomplished by training the model with GBMAT and GSCAT as diagnostic markers. Using GBMAT as a biomarker, we achieved a sensitivity and specificity of 93.3% and 97.4%, respectively, whereas applying GSCAT yielded a sensitivity and specificity of 100% and 98.7%, respectively. We also validated this diagnostic methodology by using conventional biological assays to study the change in expression levels of T cell surface markers (CD4 and CD8) and cytokine levels in T cells (IL6, IL10, TNFα, INFγ) from GBM patients. This study introduces T cells as GBM-specific immune biomarkers to diagnose GBM using patient liquid biopsy. This preclinical validation study presents a better translatability into clinical reality that will enable rapid and noninvasive glioblastoma detection from patient blood.

Minimally invasive detection of cancer using metabolic changes in tumor-associated natural killer cells with Oncoimmune probes.

Natural Killer (NK) cells, a subset of innate immune cells, undergo cancer-specific changes during tumor progression. Therefore, tracking NK cell activity in circulation has potential for cancer diagnosis. Identification of tumor associated NK cells remains a challenge as most of the cancer antigens are unknown. Here, we introduce tumor-associated circulating NK cell profiling (CNKP) as a stand-alone cancer diagnostic modality with a liquid biopsy. Metabolic profiles of NK cell activation as a result of tumor interaction are detected with a SERS functionalized OncoImmune probe platform. We show that the cancer stem cell-associated NK cell is of value in cancer diagnosis. Through machine learning, the features of NK cell activity in patient blood could identify cancer from non-cancer using 5uL of peripheral blood with 100% accuracy and localization of cancer with 93% accuracy. These results show the feasibility of minimally invasive cancer diagnostics using circulating NK cells.

Holistic Analysis of Glioblastoma Stem Cell DNA Using Nanoengineered Plasmonic Metasensor for Glioblastoma Diagnosis.

The clinical relevance of liquid biopsy for glioblastoma (GBM) remains undetermined due to practical and biological limitations such as absence of a reliable GBM-specific biomarker, trace levels in circulation due to the blood-brain-barrier, and lack of a sensitive method to detect the trace levels of biomarkers. It is hypothesized that GBM stem cell (GSC)-associated cell free DNA can function as reliable biomarker for GBM because it accounts for tumor heterogeneity and provide accurate molecular information about the cancer. An integrative methodology is used for GBM diagnosis by utilizing the sub-single molecular sensitivity of nanoengineered plasmonic metasensors for real-time genomic profiling of GSC DNA. The nanoengineered metasensors can detect the rare circulating GSC-DNA accurately from just 5 µL of blood and the test can be performed in under 10 min. Analysis of clinical serum samples from GBM patients and healthy volunteers demonstrates that the technology yielded an accurate classification of GBM in an independent validation cohort (accuracy 98.3%, specificity 100%). The methodology detects GBM-signatures from the patient blood rapidly within the half-life period of cfDNA in circulation, non-invasively and amplification-free with a high diagnostic accuracy. With clinical validation, this methodology can evolve as a clinically viable diagnostic tool for fatal and hard-to-detect cancer like GBM.

Glioblastoma Associated Natural Killer Cell EVs Generating Tumour-Specific Signatures: Noninvasive GBM Liquid Biopsy with Self-Functionalized Quantum Probes.

Diagnosis of glioblastoma (GBM) poses a recurring struggle due to many factors, including the presence of the blood-brain barrier (BBB) in addition to the significant tumor heterogeneity. Natural killer (NK) cells of the innate immune system are the primary immune surveillance mechanism for GBM and identify GBM tumors without any previous sensitization. The metabolic reprogramming of NK cells during GBM association is expected to be reflected in its extracellular vesicles. Therefore, tracking the activity of NK cell vesicles in circulation (circulating immune vesicles, CIVs) has great potential for accurate GBM diagnosis. However, identification GBM associated CIVs in circulation is immensely challenging as there is no availability of clinically validated GBM-specific circulating biomarkers. Here, we present GBM associated CIV profiling for noninvasive GBM diagnosis. We investigated the feasibility of using the signals derived from GBM associated CIVs as a de novo methodology for GBM diagnosis. An ultrasensitive sensor and a marker-free approach were essential for the detection of rare signals of GBM associated CIVs. For this purpose, we designed GBM ImmunoProfiler platform using scalable ultrafast laser multiphoton ionization mechanism and adopted surface enhanced Raman spectroscopy (SERS) ensuring simultaneous detection of multiple CIV signals to identify GBM. We experimentally demonstrated that GBM associated CIVs carry unique, tumor-specific signals. The features of GBM associated CIVs were explored through machine learning identifying its similarity with GBM patient blood (without cell isolation) using a very small amount of peripheral blood (5 µL) with 96.82% sensitivity and 100% specificity. In addition, we demonstrated that a tumor associated CIV profile can classify between multiple brain cancer types (astrocytoma, oligodendroglioma, and glioblastoma). We also experimentally demonstrated significant variation in the immune checkpoint protein expression (PDL-1 and CTLA-4) between GBM associated CIVs and uninteracted CIVs. Preclinical analysis with serum specimens of GBM patients showed the possibility of using our technology for minimally invasive GBM diagnosis. With clinical validation, our technology has potential to improve GBM diagnostics with a useful, minimally invasive GBM liquid biopsy.

Cancer Stem Cell DNA Enabled Real-Time Genotyping with Self-Functionalized Quantum Superstructures-Overcoming the Barriers of Noninvasive cfDNA Cancer Diagnostics.

Cancer diagnosis and determining its tissue of origin are crucial for clinical implementation of personalized medicine. Conventional diagnostic techniques such as imaging and tissue biopsy are unable to capture the dynamic tumor landscape. Although circulating tumor DNA (ctDNA) shows promise for diagnosis, the clinical relevance of ctDNA remains largely undetermined due to several biological and technical complexities. Here, cancer stem cell-ctDNA is used to overcome the biological complexities like the inability for molecular analysis of ctDNA and dependence on ctDNA concentration rather than the molecular profile. Ultrasensitive quantum superstructures overcome the technical complexities of trace-level detection and rapid diagnosis to detect ctDNA within its short half-life. Activation of multiple surface enhanced Raman scattering mechanisms of the quantum superstructures achieved a very high enhancement factor (1.35 × 1011 ) and detection at ultralow concentration (10-15 M) with very high reliability (RSD: 3-12%). Pilot validation with clinical plasma samples from an independent validation cohort achieved a diagnosis sensitivity of ≈95% and specificity of 83%. Quantum superstructures identified the tissue of origin with ≈75-86% sensitivity and ≈92-96% specificity. With large scale clinical validation, the technology can develop into a clinically useful liquid biopsy tool improving cancer diagnostics.

Nanoplatform to Investigate Tumor-Initiating Cancer Stem Cells: Breaking the Diagnostic Barrier.

Drug-resistant capacity in a small population of tumor-initiating cancer stem cells (tiCSCs) can be due to aberrant epigenetic changes. However, currently available conventional detection methods are inappropriate and cannot be applied to investigate the scarce population (tiCSCs). In addition, selective inhibitor drugs are shown to reverse epigenetic changes; however, each cancer type is discrete. Hence, it is essential to probe the resultant changes in tiCSCs even after therapy. Therefore, we have developed a multimode nanoplatform to investigate tiCSCs, detect epigenetic changes, and subsequently explore their transformation signals following drug therapy. We performed this by developing a surface-enhanced Raman scattering (SERS)-active nanoplatform integrated with n-dopant using an ultrafast laser ionization technique. The dopant functionalization enhances Raman scattering ability and permits label-free analysis of biomarkers in tiCSCs with the resolution down to the cellular level. Here, we investigated epigenetic biomarkers of tiCSCs in pancreatic and lung cancers. An extended study using inhibitor drugs demonstrates an unexpected increase of tiCSCs from lung cancer; this difference can be attributed to transformation changes in lung tiCSC. Thus, our work brings new insight into the differentiation abilities of CSCs upon epigenetic reversal, emphasizing unique perceptions in cancer treatment.

Self-functional gold nanoprobes for intra-nuclear epigenomic monitoring of cancer stem-like cells.

Cancer epigenomic-environment is a core center of a tumor's genetic and epigenetic configuration. Surveying epigenomic-environment of cancer stem-like cells (CSC) is vital for developing novel diagnostic methods and improving current therapies since CSCs are among the most challenging clinical hurdles. To date, there exists no technique which can successfully monitor the epigenomics of CSC. Here, we have developed unique sub-10 nm Self-functional Gold Nanoprobes (GNP) as a CSC epigenomic monitoring platform that can easily maneuver into the nucleus while not producing any conformal changes to the genomic DNA. The GNP was synthesized using physical synthesis method of pulsed laser multiphoton ionization, which enabled the shrinking of GNP to 2.69 nm which helped us achieve two critical parameters for epigenomics monitoring: efficient nuclear uptake (98%) without complex functionalization and no conformational nuclear changes. The GNP efficiently generated SERS for structural, functional, molecular epigenetics, and nuclear proteomics in preclinical models of breast and lung CSCs. To the best of knowledge, this study is first to utilize the intranuclear epigenomic signal to distinguish between CSC from different tissues with >99% accuracy and specificity. Our findings are anticipated to help advance real-time epigenomics surveillance technologies such as nucleus-targeted drug surveillance and epigenomic prognosis and diagnostics.

Boosting the sub-cellular biomolecular cancer signals by self-functionalized tag-free nano sensor.

Surface Enhanced Raman Scattering (SERS)-based sub-cellular cancer diagnosis can simultaneously obtain multiple biomolecular signals crucial in diagnostic platform for a heterogeneous disease like cancer. But, SERS-probes being typically tagged with chemical functionalization demonstrate limitations due to adverse biocompatibility, ineffective cellular internalization, SERS-signal quenching and spectral contamination. Although, tag-free SERS-probes overcome these limitations; complexity in spectral interpretation and detection insensitivity make it disadvantageous. In this study, we have exploited the inherent charges of cellular biomolecules and introduced self-functionalized complementary charged, tag-free SERS nano probes for biomolecule-specific investigation. Extremely small nano probes (sub 10 nm), synthesized with multiphoton ionization were functionalized with charge by physical synthesis without any ligands or chemical processes. The probes demonstrated significant SERS (EF~106) with analyte molecules (4ATP & 4MBA). Multifold signal boost was achieved for the signals of cellular components - amplification of ~7 fold for DNA, ~16 fold for proteins and ~24 fold for lipids with the commentary charged nano probes as compared to the neutral nano probes. The signal boost was attributed to the efficient delivery of extremely small, complementary charged probes to the cellular biomolecules of interest enabling simultaneous detection of sub-cellular biomolecules such as DNA, proteins and lipids and with high reproducibility. Cancer classification and investigation of drug resistance in cancer with single cell sensitivity was demonstrated. Such biomolecule-specific investigation of cancer from intact cells will open pathways for comprehensive cancer diagnosis.

Predicting Metastasis from Cues of Metastatic Cancer Stem-like Cells-3D-Ultrasensitive Metasensor at a Single-Cell Level.

Metastasis is the primary reason for treatment failure and cancer-related deaths. Hence forecasting the disease in its primary state can advance the prognosis. However, existing techniques fail to reveal the tumor heterogeneity or its evolutionary cascades; hence they are not feasible to predict the onset of metastatic cancer. The key to metastasis originates from the primary tumor cells, evolving by inheriting multistep sequential cue signals. We have identified this specific population, termed metastatic cancer stem-like cells (MCSCs), to foresee cancer's ability to metastasize. An invasive property renders MCSCs nonadherent, summoning a powerful technique to forecast metastasis. Thus, we have generated an ultrasensitive 3D-metasensor to efficiently capture and investigate MCSCs and magnify the vital premetastatic signals from a single cell. We developed 3D-metasensor by an ultrafast laser ionization technique, consisting of self-assembled three-dimensionally organized nanoprobes incorporated with dopant functionalities. This distinct methodology establishes attachment with nonadherent MCSCs, elevates Raman activity, and enables probing of consequent signals (metabolic, proliferation, and metastatic) specifically altered in MCSCs. Extensive analysis using prediction tools-the area under the curve (AUC) and principal component analysis (PCA)-revealed high sensitivity (100%) and specificity (80%) to differentiate the MCSCs from other populations. Further, investigation reveals that the cue signal level from MCSCs of primary cancer is analogous to MCSCs from higher-level tumors, disclosing the relative dependence to estimate the primary tumor's capacity to metastasize. Multiple spectrum evaluation using the metasensor pinpoint the dynamic cues in MCSCs predict the onset of metastasis; thus, exploring these metastasis hallmarks can enhance prognosis and revolutionize therapy strategies.

Prediction of Cancer Stem Cell Fate by Surface-Enhanced Raman Scattering Functionalized Nanoprobes.

Cancer stem cells (CSCs) are the fundamental building blocks of cancer dissemination, so it is desirable to develop a technique to predict the behavior of CSCs during tumor initiation and relapse. It will provide a powerful tool for pathological prognosis. Currently, there exists no method of such prediction. Here, we introduce nickel-based functionalized nanoprobe facilitated surface enhanced Raman scattering (SERS) for prediction of cancer dissemination by undertaking CSC-based surveillance. SERS profiling of CSCs of various cell lines (breast cancer, cervical cancer, and lung cancer) was compared with their cancer counterparts for the prediction of prognosis, with statistical significance of single-cell sensitivity. The single-cell sensitivity is critical as even a few CSCs are capable of initiating a tumor. Intermediate states of CSC transmutation to cancer cells and its reverse were monitored, and nanoprobe-assisted SERS profiling was undertaken. We experimentally demonstrated that the quasi-intermediate CSC states have dissimilar profiles during the transformation from cancer to CSC and vice versa enabling statistical differentiation without ambiguity. It was also observed that molecular signatures of these opposite pathways are cancer-type specific. This observation provided additional clarity to the current understanding of relatively unfamiliar quasi-intermediate states; making it possible to predict CSC dissemination for variety of cancers with ∼99% accuracy. Nano probe-based prediction of CSC fate is a powerful prediction tool for ultrasensitive prognosis of malignancy in a complex environment. Such CSC-based cancer prognosis has never been proposed before. This prediction technique has potential to provide insights for cancer diagnosis and prognosis as well as for obtaining information instrumental in designing of meaningful CSC-based cancer therapeutics.

Quantum scale organic semiconductors for SERS detection of DNA methylation and gene expression.

Cancer stem cells (CSC) can be identified by modifications in their genomic DNA. Here, we report a concept of precisely shrinking an organic semiconductor surface-enhanced Raman scattering (SERS) probe to quantum size, for investigating the epigenetic profile of CSC. The probe is used for tag-free genomic DNA detection, an approach towards the advancement of single-molecule DNA detection. The sensor detected structural, molecular and gene expression aberrations of genomic DNA in femtomolar concentration simultaneously in a single test. In addition to pointing out the divergences in genomic DNA of cancerous and non-cancerous cells, the quantum scale organic semiconductor was able to trace the expression of two genes which are frequently used as CSC markers. The quantum scale organic semiconductor holds the potential to be a new tool for label-free, ultra-sensitive multiplexed genomic analysis.

Probing Cancer Metastasis at a Single-Cell Level with a Raman-Functionalized Anionic Probe.

Cancer metastasis is the primary reason for cancer-related deaths, yet there is no technique capable of detecting it due to cancer pathogenesis. Current cancer diagnosis methods evaluate tumor samples as a whole/pooled sample process loses heterogeneous information in the metastasis state. Hence, it is not suitable for metastatic cancer detection. In order to gain complete information on metastasis, it is desirable to develop a nondestructive detection method that can evaluate metastatic cells with sensitivity down to single-cell resolution. Here we demonstrated self-functionalized anionic quantum probes for in vitro metastatic cancer detection at a single-cell concentration. We achieved this by incorporating a nondestructive SERS ability within the generated probes by integrating anionic surface species and NIR plasmon resonance. To the best of our knowledge, this was the first time that metastatic cancer cells were detected through their neoplastic transformations. With reliable diagnostic information at the single-cell sensitivity in an in vitro state, we successfully discriminated against cancer malignancy states.