Plasma Exosome Analysis for Protein Mutation Identification Using a Combination of Raman Spectroscopy and Deep Learning.

Protein mutation detection using liquid biopsy can be simply performed periodically, making it easy to detect the occurrence of newly emerging mutations rapidly. However, it has low diagnostic accuracy since there are more normal proteins than mutated proteins in body fluids. To increase the diagnostic accuracy, we analyzed plasma exosomes using nanoplasmonic spectra and deep learning. Exosomes, a promising biomarker, are abundant in plasma and stably carry intact proteins originating from mother cells. However, the mutated exosomal proteins cannot be detected sensitively because of the subtle changes in their structure. Therefore, we obtained Raman spectra that provide molecular information about structural changes in mutated proteins. To extract the unique features of the protein from complex Raman spectra, we developed a deep-learning classification algorithm with two deep-learning models. Consequently, controls with wild-type proteins and patients with mutated proteins were classified with high accuracy. As a proof of concept, we discriminated the lung cancer patients with mutations in the epidermal growth factor receptor (EGFR), L858R, E19del, L858R + T790M, and E19del + T790M, from controls with an accuracy of 0.93. Moreover, the protein mutation status of the patients with primary (E19del, L858R) and secondary (+T790M) mutations was clearly monitored. Overall, our technique is expected to be applied as a novel method for companion diagnostic and treatment monitoring.

Permanent Anticoagulation Blood-Vessel by Mezzo-Sized Double Re-Entrant Structure.

Having a permanent omniphobicity on the inner surface of the tube can bring enormous advantages, such as reducing resistance and avoiding precipitation during mass transfer. For example, such a tube can prevent blood clotting when delivering blood composed of complex hydrophilic and lipophilic compounds. However, it is very challenging to fabricate micro and nanostructures inside a tube. To overcome these, a wearability and deformation-free structural omniphobic surface is fabricated. The omniphobic surface can repel liquids by its "air-spring" under the structure, regardless of surface tension. Furthermore, it is not lost an omniphobicity under physical deformation like curved or twisted. By using these properties, omniphobic structures on the inner wall of the tube by the "roll-up" method are fabricated. Fabricated omniphobic tubes still repels liquids, even complex liquids like blood. According to the ex vivo blood tests for medical usage, the tube can reduce thrombus formation by 99%, like the heparin-coated tube. So, the surface will soon replace typical coating-based medical surfaces or anticoagulation blood vessels.

Artificial Intelligence-Based Major Depressive Disorder (MDD) Diagnosis Using Raman Spectroscopic Features of Plasma Exosomes.

In vitro diagnosis using biomarkers for major depressive disorder (MDD) can offer considerable advantages in overcoming the lack of objective tests for depression and treating more patients. Plasma exosomes can be novel biomarkers for MDD based on their ability to pass through the blood-brain barrier and offer brain-related information. Here, we demonstrate a novel and precise MDD diagnosis using deep learning analysis and surface-enhanced Raman spectroscopy (SERS) of plasma exosomes. Our system is implemented based on 28,000 exosome SERS signals, providing sample-wise prediction results. Notably, this approach shows remarkable performance in predicting 70 test samples unused in the training step, with an area under the curve (AUC) of 0.939, a sensitivity of 91.4%, and a specificity of 88.6%. In addition, we confirm that the diagnostic scores were correlated with the degree of depression. These results show the utility of exosomes as novel biomarkers for MDD diagnosis and suggest a novel approach for prescreening techniques for psychiatric disorders.

Single test-based diagnosis of multiple cancer types using Exosome-SERS-AI for early stage cancers.

Early cancer detection has significant clinical value, but there remains no single method that can comprehensively identify multiple types of early-stage cancer. Here, we report the diagnostic accuracy of simultaneous detection of 6 types of early-stage cancers (lung, breast, colon, liver, pancreas, and stomach) by analyzing surface-enhanced Raman spectroscopy profiles of exosomes using artificial intelligence in a retrospective study design. It includes classification models that recognize signal patterns of plasma exosomes to identify both their presence and tissues of origin. Using 520 test samples, our system identified cancer presence with an area under the curve value of 0.970. Moreover, the system classified the tumor organ type of 278 early-stage cancer patients with a mean area under the curve of 0.945. The final integrated decision model showed a sensitivity of 90.2% at a specificity of 94.4% while predicting the tumor organ of 72% of positive patients. Since our method utilizes a non-specific analysis of Raman signatures, its diagnostic scope could potentially be expanded to include other diseases.

GCC2 as a New Early Diagnostic Biomarker for Non-Small Cell Lung Cancer.

No specific markers have been identified to detect non-small cell lung cancer (NSCLC) cell-derived exosomes circulating in the blood. Here, we report a new biomarker that distinguishes between cancer and non-cancer cell-derived exosomes. Exosomes isolated from patient plasmas at various pathological stages of NSCLC, NSCLC cell lines, and human pulmonary alveolar epithelial cells isolated using size exclusion chromatography were characterized. The GRIP and coiled-coil domain-containing 2 (GCC2) protein, involved in endosome-to-Golgi transport, was identified by proteomics analysis of NSCLC cell line-derived exosomes. GCC2 protein levels in the exosomes derived from early-stage NSCLC patients were higher than those from healthy controls. Receiver operating characteristic curve analysis revealed the diagnostic sensitivity and specificity of exosomal GCC2 to be 90% and 75%, respectively. A high area under the curve, 0.844, confirmed that GCC2 levels could effectively distinguish between the exosomes. These results demonstrate GCC2 as a promising early diagnostic biomarker for NSCLC.

Tumor microenvironmental cytokines bound to cancer exosomes determine uptake by cytokine receptor-expressing cells and biodistribution.

Metastatic spread of a cancer to secondary sites is a coordinated, non-random process. Cancer cell-secreted vesicles, especially exosomes, have recently been implicated in the guidance of metastatic dissemination, with specific surface composition determining some aspects of organ-specific localization. Nevertheless, whether the tumor microenvironment influences exosome biodistribution has yet to be investigated. Here, we show that microenvironmental cytokines, particularly CCL2, decorate cancer exosomes via binding to surface glycosaminoglycan side chains of proteoglycans, causing exosome accumulation in specific cell subsets and organs. Exosome retention results in changes in the immune landscape within these organs, coupled with a higher metastatic burden. Strikingly, CCL2-decorated exosomes are directed to a subset of cells that express the CCL2 receptor CCR2, demonstrating that exosome-bound cytokines are a crucial determinant of exosome-cell interactions. In addition to the finding that cytokine-conjugated exosomes are detected in the blood of cancer patients, we discovered that healthy subjects derived exosomes are also associated with cytokines. Although displaying a different profile from exosomes isolated from cancer patients, it further indicates that specific combinations of cytokines bound to exosomes could likewise affect other physiological and disease settings.

A Proteomic Approach to Understand the Clinical Significance of Acute Myeloid Leukemia-Derived Extracellular Vesicles Reflecting Essential Characteristics of Leukemia.

Extracellular vesicle (EV) proteins from acute myeloid leukemia (AML) cell lines were analyzed using mass spectrometry. The analyses identified 2450 proteins, including 461 differentially expressed proteins (290 upregulated and 171 downregulated). CD53 and CD47 were upregulated and were selected as candidate biomarkers. The association between survival of patients with AML and the expression levels of CD53 and CD47 at diagnosis was analyzed using mRNA expression data from The Cancer Genome Atlas database. Patients with higher expression levels showed significantly inferior survival than those with lower expression levels. ELISA results of the expression levels of CD53 and CD47 from EVs in the bone marrow of patients with AML at diagnosis and at the time of complete remission with induction chemotherapy revealed that patients with downregulated CD53 and CD47 expression appeared to relapse less frequently. Network model analysis of EV proteins revealed several upregulated kinases, including LYN, CSNK2A1, SYK, CSK, and PTK2B. The potential cytotoxicity of several clinically applicable drugs that inhibit these kinases was tested in AML cell lines. The drugs lowered the viability of AML cells. The collective data suggest that AML cell-derived EVs could reflect essential leukemia biology.

Dual size-exclusion chromatography for efficient isolation of extracellular vesicles from bone marrow derived human plasma.

Isolation of pure extracellular vesicles (EVs), especially from blood, has been a major challenge in the field of EV research. The presence of lipoproteins and soluble proteins often hinders the isolation of high purity EVs upon utilization of conventional separation methods. To circumvent such problems, we designed a single-step dual size-exclusion chromatography (dSEC) column for effective isolation of highly pure EVs from bone marrow derived human plasma. With an aim to select appropriate column design parameters, we analyzed the physiochemical properties of the major substances in bone marrow derived plasma, which include EVs, lipoproteins, and soluble proteins. Based on these findings, we devised a novel dSEC column with two different types of porous beads sequentially stacked each other for efficient separation of EVs from other contaminants. The newly developed dSEC columns exhibited better performance in isolating highly pure EVs from AML plasma in comparison to conventional isolation methods.

Extracellular Vesicle Identification Using Label-Free Surface-Enhanced Raman Spectroscopy: Detection and Signal Analysis Strategies.

Extracellular vesicles (EVs) have been widely investigated as promising biomarkers for the liquid biopsy of diseases, owing to their countless roles in biological systems. Furthermore, with the notable progress of exosome research, the use of label-free surface-enhanced Raman spectroscopy (SERS) to identify and distinguish disease-related EVs has emerged. Even in the absence of specific markers for disease-related EVs, label-free SERS enables the identification of unique patterns of disease-related EVs through their molecular fingerprints. In this review, we describe label-free SERS approaches for disease-related EV pattern identification in terms of substrate design and signal analysis strategies. We first describe the general characteristics of EVs and their SERS signals. We then present recent works on applied plasmonic nanostructures to sensitively detect EVs and notable methods to interpret complex spectral data. This review also discusses current challenges and future prospects of label-free SERS-based disease-related EV pattern identification.

Protein Quantification and Imaging by Surface-Enhanced Raman Spectroscopy and Similarity Analysis.

Protein quantification techniques such as immunoassays have been improved considerably, but they have several limitations, including time-consuming procedures, low sensitivity, and extrinsic detection. Because direct surface-enhanced Raman spectroscopy (SERS) can detect intrinsic signals of proteins, it can be used as an effective detection method. However, owing to the complexity and reliability of SERS signals, SERS is rarely adopted for quantification without a purified target protein. This study reports an efficient and effective direct SERS-based immunoassay (SERSIA) technique for protein quantification and imaging. SERSIA relies on the uniform coating of gold nanoparticles (GNPs) on a target-protein-immobilized substrate by simple centrifugation. As centrifugation induces close contact between the GNPs and target proteins, the intrinsic signals of the target protein can be detected. For quantification, the protein levels in a cell lysate are estimated using similarity analysis between antibody-only and protein-conjugated samples. This method reliably estimates the protein level at a sub-picomolar detection limit. Furthermore, this method enables quantitative imaging of immobilized protein at a micrometer range. Because this technique is fast, sensitive, and requires only one type of antibody, this approach can be a useful method to detect proteins in biological samples.

Early-Stage Lung Cancer Diagnosis by Deep Learning-Based Spectroscopic Analysis of Circulating Exosomes.

Lung cancer has a high mortality rate, but an early diagnosis can contribute to a favorable prognosis. A liquid biopsy that captures and detects tumor-related biomarkers in body fluids has great potential for early-stage diagnosis. Exosomes, nanosized extracellular vesicles found in blood, have been proposed as promising biomarkers for liquid biopsy. Here, we demonstrate an accurate diagnosis of early-stage lung cancer, using deep learning-based surface-enhanced Raman spectroscopy (SERS) of the exosomes. Our approach was to explore the features of cell exosomes through deep learning and figure out the similarity in human plasma exosomes, without learning insufficient human data. The deep learning model was trained with SERS signals of exosomes derived from normal and lung cancer cell lines and could classify them with an accuracy of 95%. In 43 patients, including stage I and II cancer patients, the deep learning model predicted that plasma exosomes of 90.7% patients had higher similarity to lung cancer cell exosomes than the average of the healthy controls. Such similarity was proportional to the progression of cancer. Notably, the model predicted lung cancer with an area under the curve (AUC) of 0.912 for the whole cohort and stage I patients with an AUC of 0.910. These results suggest the great potential of the combination of exosome analysis and deep learning as a method for early-stage liquid biopsy of lung cancer.

Wrapping AgCl Nanostructures with Trimetallic Nanomeshes for Plasmon-Enhanced Catalysis and in Situ SERS Monitoring of Chemical Reactions.

Selective chemical control of multiple reactions is incredibly important for the fabrication of sophisticated nanostructures for functional applications. A representative example is the synthesis of plasmonic nanomaterial-silver chloride (AgCl) conjugates, where metal ions should be selectively reduced into metallic nanostructures for plasmon-enhanced catalytic activity, while the reducible AgCl nanomaterials remain intact despite the presence of a chemical reductant. In addition to the selectively controlled reduction, the plasmonic nanostructures should be appropriately designed for the high stability and photoefficiency of catalysts. In this study, we demonstrate how AgCl nanocubes and nanospheres could be comprehensively wrapped by plasmonic three-dimensional nanomesh structures consisting of gold, silver, and palladium by the selective reduction of their ionic precursors while the AgCl nanostructures remain intact. Complete trimetallic wrapping provided the absorption of visible light, while the porosity of the nanomesh structures exposed the photocatalytic AgCl surface to catalyze desired reactions. Platinum in place of palladium was examined to demonstrate the versatility of the wrapping scheme, resulting in an extraordinary catalytic activity. Importantly, the detailed chemical mechanism behind the trimetallic wrapping of the AgCl nanostructures was systematically investigated to understand the roles of each reaction component in controlling the chemical selectivity. The synthesized AgCl-trimetal nanoconjugates excellently exhibit both metal-based and plasmon-enhanced catalytic properties for the removal of environmentally harmful Cr6+. Moreover, their applications as surface-enhanced Raman-scattering (SERS) probes for the in situ monitoring of catalytic reduction in real-time and as single-nanoparticle SERS probes for molecular detection are thoroughly demonstrated.

Aqueous synthesis of highly monodisperse sub-100 nm AgCl nanospheres/cubes and their plasmonic nanomesh replicas as visible-light photocatalysts and single SERS probes.

Despite the distinctive electrochemical and photocatalytic properties of nanostructured silver chloride (AgCl), the shape- and size-dependence of their properties have not been thoroughly investigated to date. The most substantial reason responsible for this incomplete study and the subsequent limited applications is the failure in controlling the structure of AgCl nanomaterials, mainly owing to the challenging synthetic conditions including organic phase and high reaction temperature. In this work, we reported a rapid one-pot room-temperature aqueous synthesis of highly monodisperse sub-100 nm AgCl nanomaterials with various shapes and sizes by controlling the precursor (Ag+ and AuCl4 -) ratios. The remaining unreacted metal precursors (Ag+ and AuCl4 -) used to produce AgClNC were subsequently reduced by ascorbic acid on the surface of the synthesized AgCl nanomaterials to form Ag/Au bimetallic nanomesh structures (AgClNC#AuAgCMs and SMs). After the removal of the AgCl nanotemplates, only nanomesh structures (AuAgCMs and SMs) were obtained. Importantly, we successfully decreased the size of the AgCl nanomaterials which were replicated into bimetallic spherical and cubic nanomesh structures that were small enough (∼100 nm) to show intense surface-plasmon-absorption bands. Based on these unique chemical and physical properties, we could take advantage of the plasmonic photocatalysis properties of the complex comprising semiconducting AgCl/metallic nanomesh replica for the complete removal of the environmentally harmful Cr6+ in the presence of sacrificial agents such as formic acid. Finally, the novel bimetallic nanomesh structures proved themselves to exhibit intense surface-enhanced Raman scattering properties in a single-particle enhancing the electromagnetic field.

Identification of Newly Emerging Influenza Viruses by Detecting the Virally Infected Cells Based on Surface Enhanced Raman Spectroscopy and Principal Component Analysis.

Rapid diagnosis and quarantine of influenza virus mutant-infected people is critical to contain the fatal viral infection spread because effective antiviral drugs are normally not available. Conventional methods, however, cannot be used for the diagnosis because these methods need predefined labels, likely also unavailable for just emerging viruses. Here, we propose label-free identification of cells infected with different influenza viruses based on surface-enhanced Raman spectroscopy (SERS) and principal component analysis (PCA). Viral envelope proteins that are displayed on the surface of cells after infection of influenza viruses were targeted for this identification. Cells that expressed the envelope proteins of A/WSN/33 H1N1 or A/California/04/2009 H1N1 influenza viruses produced distinct SERS signals. Cells that displayed combinations of the envelope proteins from these two viral variants, an indication of emergence of a new virus, also generated characteristic SERS patterns. However, the cell's own surface proteins often hindered the identification of virally infected cells by producing SERS peaks similar to viral ones. PCA of the obtained SERS patterns could effectively capture the virus-specific signal components from the jumbled SERS peaks. Our study demonstrates a potential of combination of SERS and PCA to identify newly emerging influenza viruses through sensing the cells infected with the viruses.

Flexible and Stable Omniphobic Surfaces Based on Biomimetic Repulsive Air-Spring Structures.

In artificial biological circulation systems such as extracorporeal membrane oxygenation, surface wettability is a critical factor in blood clotting problems. Therefore, to prevent blood from clotting, omniphobic surfaces are required to repel both hydrophilic and oleophilic liquids and reduce surface friction. However, most omniphobic surfaces have been fabricated by combining chemical reagent coating and physical structures and/or using rigid materials such as silicon and metal. It is almost impossible for chemicals to be used in the omniphobic surface for biomedical devices due to durability and toxicity. Moreover, a flexible and stable omniphobic surface is difficult to be fabricated by using conventional rigid materials. This study demonstrates a flexible and stable omniphobic surface by mimicking the re-entrant structure of springtail's skin. Our surface consists of a thin nanohole membrane on supporting microstructures. This structure traps air under the membrane, which can repel the liquid on the surface like a spring and increase the contact angle regardless of liquid type. By theoretical wetting model and simulation, we confirm that the omniphobic property is derived from air trapped in the structure. Also, our surface well maintains the omniphobicity under a highly pressurized condition. As a proof of our concept and one of the real-life applications, blood experiments are performed with our flat and curved surfaces and the results including contact angle, advancing/receding angles, and residuals show significant omniphobicity. We hope that our omniphobic surface has a significant impact on blood-contacting biomedical applications.

Correlation between Cancerous Exosomes and Protein Markers Based on Surface-Enhanced Raman Spectroscopy (SERS) and Principal Component Analysis (PCA).

Exosomes, which are nanovesicles secreted by cells, are promising biomarkers for cancer diagnosis and prognosis, based on their specific surface protein compositions. Here, we demonstrate the correlation of nonsmall cell lung cancer (NSCLC) cell-derived exosomes and potential protein markers by unique Raman scattering profiles and principal component analysis (PCA) for cancer diagnosis. On the basis of surface enhanced Raman scattering (SERS) signals of exosomes from normal and NSCLC cells, we extracted Raman patterns of cancerous exosomes by PCA and clarified specific patterns as unique peaks through quantitative analysis with ratiometric mixtures of cancerous and normal exosomes. The unique peaks correlated well with cancerous exosome ratio ( R2 > 90%) as the unique Raman band of NSCLC exosome. To examine the origin of the unique peaks, we compared these unique peaks with characteristic Raman bands of several exosomal protein markers (CD9, CD81, EpCAM, and EGFR). EGFR had 1.97-fold similarity in Raman profiles than other markers, and it showed dominant expression against the cancerous exosomes in an immunoblotting result. We expect that these results will contribute to studies on exosomal surface protein markers for diagnosis of cancers.

The Potential of Exosomes Derived from Chronic Myelogenous Leukaemia Cells as a Biomarker.

BACKGROUND/AIM: Exosomes, derived from chronic myelogenous leukaemia (CML) cells, can be used as biomarkers and new targets for the detection of the BCR-ABL transcript. This study aimed to identify these possibilities. MATERIALS AND METHODS: Human CML cell line-derived exosomes and CML-patients-derived exosomes were isolated with a size-exclusion chromatography column and ExoQuick™ exosome precipitation solution, respectively. Isolated exosomes were analysed by nested PCR to detect the BCR-ABL transcript. RESULTS: Exosomes derived from the two human CML cell lines yielded a 250-bp band. RNA sequence analysis revealed 99% sequence homology with the partial mRNA for the human BCR-ABL chimeric protein. This ~250-bp band was also observed in the exosomes derived from patients with CML. However, only patients at the blast and accelerated phases showed the exosomal BCR-ABL transcript. CONCLUSION: CML-derived exosomes could act as novel targets for the detection of the BCR-ABL transcript.

In-Plate and On-Plate Structural Control of Ultra-Stable Gold/Silver Bimetallic Nanoplates as Redox Catalysts, Nanobuilding Blocks, and Single-Nanoparticle Surface-Enhanced Raman Scattering Probes.

Noble metal bimetallic nanomaterials have attracted a great deal of attention owing to the strong correlation between their morphology and chemical and physical properties. Even though the synthetic strategies for controlling the shapes of monometallic nanomaterials such as gold (Au) and silver (Ag) are well-developed, limited advances have been made with Au/Ag bimetallic nanomaterials to date. In this work, we demonstrate a highly complex in-plate and on-plate structural control of Au/Ag bimetallic nanoplates (Au/AgBNPLs) in contrast to conventional, simply structured, 1D and 2D, branched, and polyhedral nanomaterials. The polymer used in the synthesis of seeds plays a critical role in controlling the structure of the Au/AgBNPLs. The Au/AgBNPLs exhibit exceptionally high chemical stability against various chemical etchants and a versatile catalytic reactivity with biologically and environmentally relevant chemical species. Significantly, the reversible assembly formation of the Au/AgBNPLs is demonstrated by carrying out the surface-functionalization of the materials with thiol DNA, emphasizing the potential applications of the Au/AgBNPLs in various diagnostic and therapeutic purposes. Finally, the surface-enhanced Raman scattering (SERS) properties of the Au/AgBNPLs are experimentally and theoretically investigated, demonstrating a substantial potential of the Au/AgBNPLs as single-nanoparticle SERS probes. Electron microscopy, UV-vis spectroscopy, selected area electron diffraction (SAED), and energy-dispersive X-ray (EDX) spectroscopy are employed to analyze the structure and composition of the Au/AgBNPLs at the atomic level.

Identification of Newly Emerging Influenza Viruses by Surface-Enhanced Raman Spectroscopy.

In this work, we demonstrate in situ virus identification based on surface-enhanced Raman scattering (SERS). We hypothesized that newly emerging influenza viruses possess surface proteins and lipids that can generate distinctive Raman signals. To test this hypothesis, SERS signals were measured from the surface of a noninfluenza virus, two different influenza viruses, and a genetically shuffled influenza virus. To ensure the safety for experimenters we constructed nonreplicating pseudotyped viruses that display main influenza virus surface components. Pseudotype with influenza virus components produced enhanced Raman peaks, on gold nanoparticles, that are easily distinguishable from those of pseudotype with a noninfluenza virus component, vesicular stomatitis virus G protein (VSVG). Furthermore, virus with the surface components of a newly emerging influenza strain, A/California/04/2009 (H1N1), generated Raman peaks different from those of viruses with components of the conventional laboratory-adapted influenza strain, A/WSN/33 (H1N1). Interestingly, the virus simultaneously displaying surface components of both influenza strains, a model mutant with genome reassortment, also produced a Raman signal pattern that is clearly distinguishable from those of each strain. This work highlights that SERS can provide a powerful label-free strategy to quickly identify newly emerging and potentially fatal influenza viruses.

In-situ nanospectroscopic pH monitoring by plasmon resonance energy transfer (PRET).

Real-time detection of pH value in a living cell is in central importance for research about cells and diseases. In spite of the great advances in science and technology, pH measurement in a living cell is still limited in spatial resolution, in-situ detection, and intracellular monitoring. Here, we designed a nanoscale pH meter by Plasmon Resonance Energy Transfer (PRET). In order to highly sensitively measure pH with nanoscale spatial resolution, we choose 80 nm spherical gold nanoparticle (GNP) and phenol red which is commonly used in cell media for pH determination. The resonance energy of GNP is transferred to phenol red because the scattering intensity of GNP is overlapped with the second absorption intensity of phenol red at near 560 nm. Meanwhile, the absorption intensity of phenol red molecules is changing with pH value of the solution. For that reason, the intensity of PRET from GNP to phenol red molecules also changes by the acidity of phenol red solution. Then we can detect pH values with nanoscale spatial resolution through the Rayleigh scattering intensity of GNP. As we changed pH value from 6.0 to 9.0, the scattering intensity of GNP is decreased because the absorbance of phenol red at 560 nm wavelength is increased with increasing pH value. The Gaussian peak of a difference in Rayleigh scattering spectra of GNP between pH 6.0 and pH 9.0 indicates exactly the same as UV-vis spectral difference between basic and acidic phenol red solution. We expect that this pH measuring technique has a significant impact on the pH detection of living cells with nanoscale, and it can make possibility to image the cell structure by pH variation.