When surface-enhanced Raman spectroscopy meets complex biofluids: A new representation strategy for reliable and comprehensive characterization.

BACKGROUND: Surface-enhanced Raman spectroscopy (SERS) has gained increasing importance in molecular detection due to its high specificity and sensitivity. Complex biofluids (e.g., cell lysates and serums) typically contain large numbers of different bio-molecules with various concentrations, making it extremely challenging to be reliably and comprehensively characterized via conventional single SERS spectra due to uncontrollable electromagnetic hot spots and irregular molecular motions. The traditional approach of directly reading out the single SERS spectra or calculating the average of multiple spectra is less likely to take advantage of the full information of complex biofluid systems. RESULTS: Herein, we propose to construct a spectral set with unordered multiple SERS spectra as a novel representation strategy to characterize full molecular information of complex biofluids. This new SERS representation not only contains details from each single spectra but captures the temporal/spatial distribution characteristics. To address the ordering-independent property of traditional chemometric methods (e.g., the Euclidean distance and the Pearson correlation coefficient), we introduce Wasserstein distance (WD) to quantitatively and comprehensively assess the quality of spectral sets on biofluids. WD performs its superiority for the quantitative assessment of the spectral sets. Additionally, WD benefits from its independence of the ordering of spectra in a spectral set, which is undesirable for traditional chemometric methods. With experiments on cell lysates and human serums, we successfully achieve the verification for the reproducibility between parallel samples, the uniformity at different positions in the same sample, the repeatability from multiple tests at one location of the same sample, and the cardinality effect of the spectral set. SERS spectral sets also manage to distinguish different classes of human serums and achieve higher accuracy than the traditional prostate-specific antigen in prostate cancer classification. SIGNIFICANCE: The proposed SERS spectral set is a robust representation approach in accessing full information of biological samples compared to relying on a single or averaged spectra in terms of reproducibility, uniformity, repeatability, and cardinality effect. The application of WD further demonstrates the effectiveness and robustness of spectral sets in characterizing complex biofluid samples, which extends and consolidates the role of SERS.

SERSomes for metabolic phenotyping and prostate cancer diagnosis.

Molecular phenotypic variations in metabolites offer the promise of rapid profiling of physiological and pathological states for diagnosis, monitoring, and prognosis. Since present methods are expensive, time-consuming, and still not sensitive enough, there is an urgent need for approaches that can interrogate complex biological fluids at a system-wide level. Here, we introduce hyperspectral surface-enhanced Raman spectroscopy (SERS) to profile microliters of biofluidic metabolite extraction in 15 min with a spectral set, SERSome, that can be used to describe the structures and functions of various molecules produced in the biofluid at a specific time via SERS characteristics. The metabolite differences of various biofluids, including cell culture medium and human serum, are successfully profiled, showing a diagnosis accuracy of 80.8% on the internal test set and 73% on the external validation set for prostate cancer, discovering potential biomarkers, and predicting the tissue-level pathological aggressiveness. SERSomes offer a promising methodology for metabolic phenotyping.

Digital colloid-enhanced Raman spectroscopy by single-molecule counting.

Quantitative detection of various molecules at very low concentrations in complex mixtures has been the main objective in many fields of science and engineering, from the detection of cancer-causing mutagens and early disease markers to environmental pollutants and bioterror agents1-5. Moreover, technologies that can detect these analytes without external labels or modifications are extremely valuable and often preferred6. In this regard, surface-enhanced Raman spectroscopy can detect molecular species in complex mixtures on the basis only of their intrinsic and unique vibrational signatures7. However, the development of surface-enhanced Raman spectroscopy for this purpose has been challenging so far because of uncontrollable signal heterogeneity and poor reproducibility at low analyte concentrations8. Here, as a proof of concept, we show that, using digital (nano)colloid-enhanced Raman spectroscopy, reproducible quantification of a broad range of target molecules at very low concentrations can be routinely achieved with single-molecule counting, limited only by the Poisson noise of the measurement process. As metallic colloidal nanoparticles that enhance these vibrational signatures, including hydroxylamine-reduced-silver colloids, can be fabricated at large scale under routine conditions, we anticipate that digital (nano)colloid-enhanced Raman spectroscopy will become the technology of choice for the reliable and ultrasensitive detection of various analytes, including those of great importance for human health.

Artificial Intelligence for Surface-Enhanced Raman Spectroscopy.

Surface-enhanced Raman spectroscopy (SERS), well acknowledged as a fingerprinting and sensitive analytical technique, has exerted high applicational value in a broad range of fields including biomedicine, environmental protection, food safety among the others. In the endless pursuit of ever-sensitive, robust, and comprehensive sensing and imaging, advancements keep emerging in the whole pipeline of SERS, from the design of SERS substrates and reporter molecules, synthetic route planning, instrument refinement, to data preprocessing and analysis methods. Artificial intelligence (AI), which is created to imitate and eventually exceed human behaviors, has exhibited its power in learning high-level representations and recognizing complicated patterns with exceptional automaticity. Therefore, facing up with the intertwining influential factors and explosive data size, AI has been increasingly leveraged in all the above-mentioned aspects in SERS, presenting elite efficiency in accelerating systematic optimization and deepening understanding about the fundamental physics and spectral data, which far transcends human labors and conventional computations. In this review, the recent progresses in SERS are summarized through the integration of AI, and new insights of the challenges and perspectives are provided in aim to better gear SERS toward the fast track.

Intracellular metabolic profiling of drug resistant cells by surface enhanced Raman scattering.

BACKGROUND: Intracellular metabolic profiling reveals real-time metabolic information useful for the study of underlying mechanisms of cells in particular conditions such as drug resistance. However, mass spectrometry (MS), one of the leading metabolomics technologies, usually requires a large number of cells and complex pretreatments. Surface enhanced Raman scattering (SERS) has an ultrahigh detection sensitivity and specificity, favorable for metabolomics analysis. However, some targeted SERS methods focus on very limited metabolite without global bioprofiling, and some label-free approaches try to fingerprint the metabolic response based on whole SERS spectral classification, but comprehensive interpretation of biological mechanisms was lacking. (95) RESULTS: We proposed a label-free SERS technique for intracellular metabolic profiling in complex cellular lysates within 3 min. We first compared three kinds of cellular lysis methods and sonication lysis shows the highest extraction efficiency of metabolites. To obtain comprehensive metabolic information, we collected a spectral set for each sample and further qualified them by the Pearson correlation coefficient (PCC) to calculate how many spectra should be acquired at least to gain the adequate information from a statistical and global view. In addition, according to our measurements with 10 pure metabolites, we can understand the spectra acquired from complex cellular lysates of different cell lines more precisely. Finally, we further disclosed the variations of 22 SERS bands in enzalutamide-resistant prostate cancer cells and some are associated with the androgen receptor signaling activity and the methionine salvage pathway in the drug resistance process, which shows the same metabolic trends as MS. (149) SIGNIFICANCE: Our technique has the capability to capture the intracellular metabolic fingerprinting with the optimized lysis approach and spectral set collection, showing high potential in rapid, sensitive and global metabolic profiling in complex biosamples and clinical liquid biopsy. This gives a new perspective to the study of SERS in insightful understanding of relevant biological mechanisms. (54).

Imaging immune checkpoint networks in cancer tissues with supermultiplexed SERS nanoprobes.

Combined immune checkpoint (ICP) inhibitors maximize immune response rates of patients compared to the single-drug treatment strategy in cancer immunotherapy, and prediction of such optimal combinations requires high-throughput imaging techniques and suitable data analysis. In this work, we report a rational strategy for predicting combined drugs of ICP inhibitors based on supermultiplexed surface-enhanced Raman scattering (SERS) imaging and correlation network analysis. To this end, we first built an ultrasensitive and supermultiplexed volume-active SERS (VASERS) nanoprobe platform, where Raman molecules are randomly arranged in 3D volumetric electromagnetic hotspots. By examining various bio-orthogonal Raman molecules with different electronic properties, we developed frequency modulation guidelines and achieved 32 resolvable colors in the Raman-silent region, the largest number of resolvable SERS colors demonstrated to date. We then demonstrated one-shot ten-color imaging of ICPs with high spectral resolution in clinical biopsies of breast cancer tissues, suggesting highly heterogeneous expression patterns of ICPs across tumor subtypes. Through correlation network analysis of these high-throughput Raman data, we investigated co-expression relationships among these ten-panel ICPs in cancer tissues and finally identified a variety of possible ICP combinations for synergistic immunotherapy of breast cancers, which may lead to novel therapeutical insights.

Thermal insulation and antibacterial foam templated from bagasse nanocellulose /nisin complex stabilized Pickering emulsion.

Foam packaging with good thermal insulation and antibacterial properties is promising for cold chain delivery to strengthen food safety. This study reports a novel antibacterial foam with thermal insulation templated from bagasse nanocellulose complex particle-stabilised acrylate epoxy soybean oil (AESO) Pickering emulsions. Nanocellulose/nisin complex particles (N-CNFs) were prepared by loading positively charged nisin onto negatively charged cellulose nanofibrils via electrostatic interactions, that highly enhanced the stability of nanocellulose at the AESO/water interface and imparted the corresponding foam with good antibacterial properties. The results show that the porosity of the foam prepared with N-CNFs increased from 10.9% to 29.9% compared with that of the foam corresponding with bare nanocellulose; the thermal conductivity of the N-CNF foam decreased substantially from 0.431 W/m·K to 0.197 W/m·K. Moreover, the prepared foam exhibited good antibacterial activity, and its bacteriostatic rate against Listeria monocytogenes was 91.33%. The incorporation of antibacterial peptides into nanocellulose has enriched the study of the Pickering emulsion templating method for preparing multifunctional foam materials and is expected to broaden the application of nanocellulose in the field of food packaging.

Density-adjusted liquid-phase microextraction with smartphone digital image colorimetry to determine parathion-methyl in water, fruit juice, vinegar, and fermented liquor.

To achieve rapid and convenient on-site pretreatment and determination of parathion-methyl, a density-adjusted liquid-phase microextraction with smartphone digital image colorimetry was established to detect parathion-methyl in food samples. In this study, the environmentally friendly biomass-derived solvent guaiacol was used as the extractant. Salt and water, as density regulators, realized the two movements (floating-sinking) of the extractant and full contact between the extractant and the sample solution to establish an environmentally friendly, fast, and efficient pretreatment method. Under strong alkaline conditions, parathion-methyl generated a yellow product; then, a smartphone was used to obtain the image of the yellow product for intensity analysis. Parathion-methyl has a good linear relationship in the range of 0.01-1 mg L-1, and the limits of detection and quantification are 0.003 and 0.01 mg L-1, respectively. This method has been successfully applied to the determination of parathion-methyl in spiked water, fruit juice, vinegar, and fermented liquor with a recovery of 91.6-106.5% and a relative standard deviation of 0.6-6.0%. The established density-adjusted liquid phase microextraction with smartphone digital image colorimetry is rapid, convenient, and environmentally friendly for the determination of parathion-methyl in food samples.

Rapid analysis of triazine herbicides in fruit juices using evaporation-assisted dispersive liquid-liquid microextraction with solidification of floating organic droplets and HPLC-DAD.

A rapid and convenient analytical procedure (evaporation-assisted dispersive liquid-liquid microextraction with solidification of floating organic droplets) is advanced for determining the concentrations of triazine herbicide residues (e.g. simazine and atrazine) in fruit juices via HPLC-DAD. The technique involves adding 1-dodecanol (low density) and dichloromethane (high density) to the test solution to act as the extraction and volatile solvents, respectively. Calcium oxide is added to generate heat to accelerate the evaporation of dichloromethane, whereupon the 1-dodecanol quickly disperses into small droplets to complete the microextraction process. Thus, there is no need to use a dispersive solvent and heating equipment is also not required. The floating 1-dodecanol is subsequently frozen using an ice bath to facilitate its separation from the sample. Under optimal conditions (250 µL of 1-dodecanol (extraction solvent), 150 µL of CH2Cl2 (volatile solvent), 1250 mg of CaO, and an extraction time of 60 s) the detection procedure is linear over the range 0.05-5 µg mL-1 (with R > 0.99). The limits of detection (LOD) and quantification (LOQ) were determined to be 0.0022-0.0034 µg mL-1 and 0.0073-0.0113 µg mL-1, respectively. The recovery of simazine and atrazine in three fruit juices ranged between 78.5% and 96.4% with a relative standard deviation <8.2%. Therefore, the proposed approach can be effectively adopted to analyze the triazine herbicide content in fruit juices. The method has been proved to be simple, reliable, and remarkably efficient.

Evidence that exogenous urea acts as a potent cue to alleviate ammonium-inhibition of root system growth of cotton plant (Gossypium hirsutum).

Many plants grown with low-millimolar concentration of NH4 + as a sole nitrogen source develop NH4 + -toxicity symptoms. To date, crucial molecular identities and a practical approach involved in the improvement of plant NH4 + -tolerance remain largely unknown. By phenotyping of upland cotton grown on varied nitrogen forms, we came across a phenomenon that caused sub-millimolar concentrations of urea (e.g., up 50 µM) to repress the growth inhibition of roots and whole plant cultivated in a NH4 + -containing nutrient solution. A growth-recovery assay revealed that the relief in NH4 + -inhibited growth required only a short-term exposure (≧12 h) of the roots to urea, implying that urea could elicit an internal signaling and be involved in antagonizing NH4 + -sensitivity. Intriguingly, split-root experiments demonstrated that low urea occurrence in one root-half could efficaciously stimulate not only supplied root but also the root-half grown in NH4 + -solution without urea, indicating the existence of urea-triggered local and systemic long-distance signaling. In the split-root experiment we also observed high arginase activity, strong arginine reduction and remarkable upregulation of polyamine biosynthesis-related genes (ADC1/2, SPDS and SPMS). Therefore, we suggest that external urea might serve as an effective cue (signal molecule) in an arginine-/polyamine-related process for ameliorating NH4 + -suppressed root growth, providing a novel aspect for deeper exploring and understanding plant NH4 + -tolerance.

Gap-enhanced resonance Raman tags for live-cell imaging.

Surface-enhanced Raman scattering (SERS) nanotags are widely used in the biomedical field including live-cell imaging due to the high specificity from their fingerprint spectrum and the multiplexing capability from the ultra-narrow linewidth. However, long-term live-cell Raman imaging is limited due to the photodamage from a relatively long exposure time and a high laser power, which are needed for acquiring detectable Raman signals. In this work, we attempt to resolve this issue by developing ultrabright gap-enhanced resonance Raman tags (GERRTs), consisting of a petal-like gold core and a silver shell with the near-infrared resonant reporter of IR-780 embedded in between, for long-term and high-speed live-cell imaging. GERRTs exhibit an ultrahigh Raman intensity down to a single-nanoparticle level in aqueous solution and the solid state upon 785 nm excitation, allowing for high-resolution time-lapse live-cell Raman imaging with an exposure time of 1 ms per pixel and a laser power of 50 µW. Under these measurement conditions, we can possibly capture dynamic cellular processes with a high temporal resolution, and track living cells for long periods of time owing to the reduced photodamage to cells. These nanotags open new opportunities for ultrasensitive, low-phototoxic, and long-term live-cell imaging.