Laser Tweezers Raman Spectroscopy Research Articles

Single-cell analysis reveals microbial spore responses to sodium hypochlorite.

Pollution from toxic spores has caused us a lot of problems because spores are extremely resistant and can survive most disinfectants. Therefore, the detection of spore response to disinfectant is of great significance for the development of effective decontamination strategies. In this work, we investigated the effect of 0.5% sodium hypochlorite on the molecular and morphological properties of single spores of Bacillus subtilis using single-cell techniques. Laser tweezers Raman spectroscopy showed that sodium hypochlorite resulted in Ca2+-dipicolinic acid release and nucleic acid denaturation. Atomic force microscopy showed that the surface of treated spores changed from rough to smooth, protein shells were degraded at 10 min, and the permeability barrier was destroyed at 15 min. The spore volume decreased gradually over time. Live-cell imaging showed that the germination and growth rates decreased with increasing treatment time. These results provide new insight into the response of spores to sodium hypochlorite.

Trans-cinnamaldehyde fumigation inhibits Escherichia coli by affecting the mechanism of intracellular biological macromolecules

This study aimed to determine the antibacterial mechanism of cinnamaldehyde fumigation in Escherichia coli (E. coli). Through vapour fumigation, cinnamaldehyde was confirmed to exhibit effective antibacterial activity against E. coli. The minimum inhibitory concentration (MIC) and minimum bacterial concentration (MBC) were 0.25 μL/mL and 0.5 μL/mL, respectively. Based on transmission electron microscopy, the wrinkled bacterial cells observed after fumigation could be related to the leakage of intracellular substances. Laser tweezers Raman spectroscopy revealed changes in the main chain of proteins, the hydrogen bond system and spatial structure, and single- and double-stranded DNA breaks. In addition, breakage of the fatty acyl chain backbone was found to affect the vertical order degree of the lipid bilayer and cell membrane fluidity, thereby inhibiting the growth of E. coli. Overall, our findings indicate that cinnamaldehyde fumigation inhibits E. coli growth by inducing changes in intracellular biological macromolecules.

Rapid and accurate identification of marine bacteria spores at a single-cell resolution by laser tweezers Raman spectroscopy and deep learning.

Marine bacteria have been considered as important participants in revealing various carbon/sulfur/nitrogen cycles of marine ecosystem. Thus, how to accurately identify rare marine bacteria without a culture process is significant and valuable. In this work, we constructed a single-cell Raman spectra dataset from five living bacteria spores and utilized convolutional neural network to rapidly, accurately, nondestructively identify bacteria spores. The optimal CNN architecture can provide a prediction accuracy of five bacteria spore as high as 94.93% ± 1.78%. To evaluate the classification weight of extracted spectra features, we proposed a novel algorithm by occluding fingerprint Raman bands. Based on the relative classification weight arranged from large to small, four Raman bands located at 1518, 1397, 1666, and 1017 cm-1 mostly contribute to producing such high prediction accuracy. It can be foreseen that, LTRS combined with CNN approach have great potential for identifying marine bacteria, which cannot be cultured under normal condition.

Surface tension of single suspended aerosol microdroplets

The surface tension of troposphere aerosols can significantly influence their atmospheric processes and key properties, particularly on the morphology, the phase transition, the activation as cloud condensation nuclei, and the gas-particle partitioning. However, directly measuring the surface tension of single ambient aerosol is quite challenging, due to the limitations of their picolitre volumes and thermal motion. Here, we developed a dual laser tweezers Raman spectroscopy (DLT-RS) system to directly sense the surface tension of single airborne microdroplets (PM10 particles). A pair of aerosol droplets were trapped and driven to coalesce by the laser tweezers. Meanwhile, the backscattering light intensity and bright-field images during the coalescence process were recorded to characterize the aerosol surface tension. A remarkable advantage of directly sensing aerosol surface tension is that the solutes in aerosols are often supersaturated, which is common in atmospheric aerosols but almost unavailable in bulk solutions. We experimentally measured the surface tension of aerosols composed of nitrates or oxalic acid/nitrate mixture. Besides, the variation of surface tension during aerosol aging process was also explored, which brings possible implications on the surface evolution of actual ambient aerosol during their atmospheric lifetime.

A lab-on-a-chip utilizing microwaves for bacterial spore disruption and detection

Bacterial spores are problematic in agriculture, the food industry, and healthcare, with the fallout costs from spore-related contamination being very high. Spores are difficult to detect since they are resistant to many of the bacterial disruption techniques used to bring out the biomarkers necessary for detection. Because of this, effective and practical spore disruption methods are desirable. In this study, we demonstrate the efficiency of a compact microfluidic lab-on-chip built around a coplanar waveguide (CPW) operating at 2.45 GHz. We show that the CPW generates an electric field hotspot of ∼10 kV/m, comparable to that of a commercial microwave oven, while using only 1.2 W of input power and thus resulting in negligible sample heating. Spores passing through the microfluidic channel are disrupted by the electric field and release calcium dipicolinic acid (CaDPA), a biomarker molecule present alongside DNA in the spore core. We show that it is possible to detect this disruption in a bulk spore suspension using fluorescence spectroscopy. We then use laser tweezers Raman spectroscopy (LTRS) to show the loss of CaDPA on an individual spore level and that the loss increases with irradiation power. Only 22% of the spores contain CaDPA after exposure to 1.2 W input power, compared to 71% of the untreated control spores. Additionally, spores exposed to microwaves appear visibly disrupted when imaged using scanning electron microscopy (SEM). Overall, this study shows the advantages of using a CPW for disrupting spores for biomarker release and detection.

Accurate identification of living Bacillus spores using laser tweezers Raman spectroscopy and deep learning

Accurately, rapidly, and noninvasively identifying Bacillus spores can greatly contribute to controlling a plenty of infectious diseases. Laser tweezers Raman spectroscopy (LTRS) has confirmed to be a powerful tool for studying Bacillus spores at a single cell level. In this study, we constructed a single-cell Raman spectra dataset of living Bacillus spores and utilized deep learning approach to accurately, nondestructively identify Bacillus spores. The trained convolutional neural network (CNN) could efficiently extract tiny Raman spectra features of five spore species, and provide a prediction accuracy of specie identification as high as 100 %. Moreover, the spectral feature differences in three Raman bands at 660, 826, and 1017 cm−1 were confirmed to mostly contribute to producing such high prediction accuracy. In addition, optimal CNN model was employed to monitor and identify sporulation process at different metabolic phases in one growth cycle. The obtained average prediction accuracy of metabolic phase identification was approximately 88 %. It can be foreseen that, LTRS combined with CNN approach have great potential for accurately identifying spore species and metabolic phases at a single cell level, and can be gradually extended to perform identification for many unculturable bacteria growing in soil, water, and food.

Hybrid Principal Component Analysis Denoising Enables Rapid, Label-Free Morpho-Chemical Quantification of Individual Nanoliposomes.

Laser tweezers Raman spectroscopy enables multiplexed, quantitative chemical and morphological analysis of individual bionanoparticles such as drug-loaded nanoliposomes, yet it requires minutes-scale acquisition times per particle, leading to a lack of statistical power in typical small-sized data sets. The long acquisition times present a bottleneck not only in measurement time but also in the analytical throughput, as particle concentration (and thus throughput) must be kept low enough to avoid swarm measurement. The only effective way to improve this situation is to reduce the exposure time, which comes at the expense of increased noise. Here, we present a hybrid principal component analysis (PCA) denoising method, where a small number (∼30 spectra) of high signal-to-noise ratio (SNR) training data construct an effective principal component subspace into which low SNR test data are projected. Simulations and experiments prove the method outperforms traditional denoising methods such as the wavelet transform or traditional PCA. On experimental liposome samples, denoising accelerated data acquisition from 90 to 3 s, with an overall 4.5-fold improvement in particle throughput. The denoised data retained the ability to accurately determine complex morphochemical parameters such as lamellarity of individual nanoliposomes, as confirmed by comparison with cryo-EM imaging. We therefore show that hybrid PCA denoising is an efficient and effective tool for denoising spectral data sets with limited chemical variability and that the RR-NTA technique offers an ideal path for studying the multidimensional heterogeneity of nanoliposomes and other micro/nanoscale bioparticles.

Single-cell analysis reveals microbial spore responses to microwave radiation

To determine the effects of microwave radiation at the molecular level as well as on the germination, growth and morphology of dry spores at the single-cell level. Dry Bacillus aryabhattai MCCC 1K02966 spores were microwave-treated at different powers and characterized using single-cell optical technology. As determined by laser tweezers Raman spectroscopy, the Ca[Formula: see text]-dipicolinic acid content increased and nucleic acid denaturation occurred in response to microwave treatment. Live-cell microscopy revealed that the germination and growth rates decreased as the microwave power increased. With respect to morphology, atomic force microscopy (AFM) demonstrated that spores became wrinkled and rough after microwave treatment. Furthermore, spores became smaller as the microwave power increased. Microwave treatment can damage DNA, and high-power microwaves can inhibit the germination of spores and reduce spore volumes. These results provide a new perspective on the responses of living single cells to microwave radiation and demonstrate the application of various new techniques for analyses of microorganisms at the single-cell level.

Detecting the pH-dependent liquid-liquid phase separation of single levitated aerosol microdroplets via laser tweezers-Raman spectroscopy

Ambient atmospheric aerosol particles comprised of various inorganic and organic substances ubiquitously undergo phase transition, such as efflorescence, amorphization, and especially liquid-liquid phase separation (LLPS). Resultant changes of physicochemical properties in aerosols then deeply affect the climate system. However, finely detecting these processes occurring in single aerosol particles, especially under the acidic condition of real atmospheric environment, remains a challenge. In this work, we investigated the pH-dependent phase separation in single levitated microdroplets using a self-developed laser tweezers Raman spectroscopy (LTRS) system. The dynamic process of LLPS in laser-trapped droplets over the course of humidity cycles was detected with the time-resolved cavity-enhanced Raman spectra. These measurements provide the first comprehensive account of the pH-dependent LLPS in single levitated aerosol microdroplets and bring possible implications on phase separation in actual atmospheric particles.

Optical tweezers and Raman spectroscopy for single-cell classification of drug resistance in acute lymphoblastic leukemia.

Laser Tweezers Raman Spectroscopy (LTRS) is a combination of laser tweezers and Raman spectroscopy. It is a physical tool based on the mechanical effects of the laser, which can be used to study single living cells in suspension in a fast and non-destructive way. Our work aims to establish a methodology system based on LTRS to rapidly and non-destructively detect the resistance of acute lymphoblastic leukemia (ALL) cells and to provide a new idea for the evaluation of the resistance of ALL cells. Two specific adriamycin-resistant and parental ALL cells BALL-1 and Nalm6 were included in this study. Adriamycin resistant cells can induce the spectral differences, which can be detected by LTRS initially. To ensure the accuracy of the results, we use the principal components analysis (PCA) as well as the classification and regression trees (CRT) algorithms, which show that the specificity and sensitivity of LTRS are above 90%. In addition, to further clarify the chemoresistance status of ALL cells, we used the CRT models and receiver operating characteristic (ROC) curves which are based on the band data to look for some important bands and band intensity ratios that have strong pointing significance. Our work proves that LTRS analysis combined with multivariate statistical analyses have great potential to be a novel analytical strategy at the single-cell level for rapidly evaluating the chemoresistance status of ALL cells.

Laser tweezers Raman spectroscopy combined with machine learning for diagnosis of Alzheimer’s disease

Alzheimer’s disease (AD) is a common nervous system disease to affect mostly elderly people over the age of 65 years. However, the diagnosis of AD is mainly depend on the imaging examination, clinical assessments and neuropsychological tests, which may get error diagnosis results and are not able to detect early AD. Here, a rapid, non-invasive, and high accuracy diagnostic method for AD especially early AD is provided based on the laser tweezers Raman spectroscopy (LTRS) combined with machine learning algorithms. AD platelets from different 3xTg-AD transgenic rats at different stages of disease are captured to collect high signal-to-noise ratio Raman signals without contact by LTRS, which is then combined with partial least squares discriminant analysis (PLS-DA), support vector machine (SVM) and principal component analysis (PCA)-canonical discriminate function (CDA) for classification. The results show that the normal and diseased platelets at 3-, 6- and 12-month AD are successfully distinguished and the accuracy is 91%, 68% and 97% respectively, which demonstrates the suggested method can provide a precise detection for AD diagnosis at early, middle and advanced stages.

Reference Raman spectrum and mapping of Cryptosporidium parvum oocysts

AbstractCryptosporidium parvum is a protozoan parasite and among the most infectious diarrhea‐causing pathogens, leading to severe health problems for malnourished children and immunocompromised individuals. Outbreaks are common even in developed countries, originating from water or food contamination and resulting in suffering and large costs for society. Therefore, robust, fast, and highly specific detection strategies of Cryptosporidium are needed. Label‐free detection techniques such as Raman spectroscopy have been suggested; however, high‐resolution reported spectra in the literature are limited. In this work, we report reference Raman spectra at 3 cm−1 resolution for viable and inactivated Cryptosporidium oocysts of the species C. parvum, gathered at a single oocyst level using a laser tweezers Raman spectroscopy system. We furthermore provide tentative Raman peak assignments for the Cryptosporidium oocysts, along with Raman mapping of the oocysts' heterogeneous internal structure. Finally, we compare the C. parvum Raman spectrum with other common enterotoxigenic pathogens: Escherichia coli, Vibrio cholerae, Bacillus cereus, and Clostridium difficile. Our results show a significant difference between C. parvum Raman spectra and the other pathogens.

Laser tweezers Raman spectroscopy combined with deep learning to classify marine bacteria

Rapid identification of marine microorganisms is critical in marine ecology, and Raman spectroscopy is a promising means to achieve this. Single cell Raman spectra contain the biochemical profile of a cell, which can be used to identify cell phenotype through classification models. However, traditional classification methods require a substantial reference database, which is highly challenging when sampling at difficult-to-access locations. In this scenario, only a few spectra are available to create a taxonomy model, making qualitative analysis difficult. And the accuracy of classification is reduced when the signal-to-noise ratio of a spectrum is low. Here, we describe a novel method for categorizing microorganisms that combines optical tweezers Raman spectroscopy, Progressive Growing of Generative Adversarial Nets (PGGAN), and Residual network (ResNet) analysis. Using the optical Raman tweezers, we acquired single cell Raman spectra from five deep-sea bacterial strains. We randomly selected 300 spectra from each strain as the database for training a PGGAN model. PGGAN generates a large number of high-resolution spectra similar to the real data for the training of the residual neural network. Experimental validations show that the method enhances machine learning classification accuracy while also reducing the demand for a considerable amount of training data, both of which are advantageous for analyzing Raman spectra of low signal-to-noise ratios. A classification model was built with this method, which reduces the spectra collection time to 1/3 without compromising the classification accuracy.

Rapid and accurate identification of pathogenic bacteria at the single-cell level using laser tweezers Raman spectroscopy and deep learning.

We report a new method for the rapid identification of pathogenic bacterial species at the single-cell level that combines laser tweezers Raman spectroscopy (LTRS) with deep learning (DL). LTRS can accurately measure single-cell Raman spectra (scRS) without destroying and labeling cells. Based on the scRS data, DL rapidly and accurately identifies pathogenic bacteria. We measured scRS of 15 species bacteria using homemade LTRS. For each species, approximately, 160 cells from three different patients were measured, one patient's data were used as test set, and the rest after being augmented was used as training set. A residual network (ResNet) model, trained on the augmented training set, achieved an accuracy of 94.53% on the test set. Moreover, we applied gradient-weighted class activation mapping to visualize the proposed model. Finally, we demonstrated the advantages of ResNet over traditional machine-learning algorithms.

Blood identification at the single-cell level based on a combination of laser tweezers Raman spectroscopy and machine learning.

Laser tweezers Raman spectroscopy (LTRS) combines optical tweezers technology and Raman spectroscopy to obtain biomolecular compositional information from a single cell without invasion or destruction, so it can be used to "fingerprint" substances to characterize numerous types of biological cell samples. In the current study, LTRS was combined with two machine learning algorithms, principal component analysis (PCA)-linear discriminant analysis (LDA) and random forest, to achieve high-precision multi-species blood classification at the single-cell level. The accuracies of the two classification models were 96.60% and 96.84%, respectively. Meanwhile, compared with PCA-LDA and other classification algorithms, the random forest algorithm is proved to have significant advantages, which can directly explain the importance of spectral features at the molecular level.

Synthetic NAC 71-82 Peptides Designed to Produce Fibrils with Different Protofilament Interface Contacts.

Alpha-synucleinopathies are featured by fibrillar inclusions in brain cells. Although α-synuclein fibrils display structural diversity, the origin of this diversity is not fully understood. We used molecular dynamics simulations to design synthetic peptides, based on the NAC 71-82 amino acid fragment of α-synuclein, that govern protofilament contacts and generation of twisted fibrillar polymorphs. Four peptides with structures based on either single or double fragments and capped or non-capped ends were selected for further analysis. We determined the fibrillar yield and the structures from these peptides found in the solution after fibrillisation using protein concentration determination assay and circular dichroism spectroscopy. In addition, we characterised secondary structures formed by individual fibrillar complexes using laser-tweezers Raman spectroscopy. Results suggest less mature fibrils, based on the lower relative β-sheet content for double- than single-fragment peptide fibrils. We confirmed this structural difference by TEM analysis which revealed, in addition to short protofibrils, more elongated, twisted and rod-like fibril structures in non-capped and capped double-fragment peptide systems, respectively. Finally, time-correlated single-photon counting demonstrated a difference in the Thioflavin T fluorescence lifetime profiles upon fibril binding. It could be proposed that this difference originated from morphological differences in the fibril samples. Altogether, these results highlight the potential of using peptide models for the generation of fibrils that share morphological features relevant for disease, e.g., twisted and rod-like polymorphs.

Characterization of Drug Resistance in Chronic Myeloid Leukemia Cells Based on Laser Tweezers Raman Spectroscopy.

Multidrug resistance is highly associated with poor prognosis of chronic myeloid leukemia. This work aims to explore whether the laser tweezers Raman spectroscopy (LTRS) could be practical in separating adriamycin-resistant chronic myeloid leukemia cells K562/adriamycin from its parental cells K562, and to explore the potential mechanisms. Detection of LTRS initially reflected the spectral differences caused by chemoresistance including bands assigned to carbohydrates, amino acid, protein, lipids, and nucleic acid. In addition, principal components analysis as well as the classification and regression trees algorithms showed that the specificity and sensitivity were above 90%. Moreover, the band data-based classification and regression tree model and receiver operating characteristic curve further determined some important bands and band intensity ratios to be reliable indexes in discriminating K562 chemoresistance status. Finally, we highlighted three metabolism pathways correlated with chemoresistance. This work demonstrates that the label-free LTRS analysis combined with multivariate statistical analyses have great potential to be a novel analytical strategy at the single-cell level for rapid evaluation of the chemoresistance status of K562 cells.

Optical design for laser tweezers Raman spectroscopy setups for increased sensitivity and flexible spatial detection

We demonstrate a method to double the collection efficiency in laser tweezers Raman spectroscopy (LTRS) by collecting both the forward-scattered and backscattered light in a single-shot multitrack measurement. Our method can collect signals at different sample volumes, granting both the pinpoint spatial selectivity of confocal Raman spectroscopy and the bulk sensitivity of non-confocal Raman spectroscopy simultaneously. Further, we display that our approach allows for reduced detector integration time and laser power. To show this, we measure the Raman spectra of both polystyrene beads and bacterial spores. For spores, we can trap them at 2.5 mW laser power and acquire a high signal-to-noise ratio power spectrum of the calcium-dipicolinic acid peaks using an integration time of ${2} \times {30}\;{\rm s}$. Thus, our method will enable the monitoring of biological samples sensitive to high intensities for longer times. Additionally, we demonstrate that by a simple modification, we can add polarization sensitivity and retrieve extra biochemical information.

Mode of Action of Disinfection Chemicals on the Bacterial Spore Structure and Their Raman Spectra.

Contamination of toxic spore-forming bacteria is problematic since spores can survive a plethora of disinfection chemicals and it is hard to rapidly detect if the disinfection chemical has inactivated the spores. Thus, robust decontamination strategies and reliable detection methods to identify dead from viable spores are critical. In this work, we investigate the chemical changes of Bacillus thuringiensis spores treated with sporicidal agents such as chlorine dioxide, peracetic acid, and sodium hypochlorite using laser tweezers Raman spectroscopy. We also image treated spores using SEM and TEM to verify if we can correlate structural changes in the spores with changes to their Raman spectra. We found that over 30 min, chlorine dioxide did not change the Raman spectrum or the spore structure, peracetic acid showed a time-dependent decrease in the characteristic DNA/DPA peaks and ∼20% of the spores were degraded and collapsed, and spores treated with sodium hypochlorite showed an abrupt drop in DNA and DPA peaks within 20 min and some structural damage to the exosporium. Structural changes appeared in spores after 10 min, compared to the inactivation time of the spores, which is less than a minute. We conclude that vibrational spectroscopy provides powerful means to detect changes in spores but it might be problematic to identify if spores are live or dead after a decontamination procedure.

Cytological and spectroscopic characteristics of c-KIT N822K mutation in core binding factor acute myeloid leukemia cells.

The frequency of N822K mutation is high in the A-loop region of c-KIT which is highly associated with poor prognosis of core binding factor acute myeloid leukemia. The current work used common assays including cell cycle, apoptosis, clone formation and western blot to perform cytological detection for HL60 (wild type), NB4 (carrying t[15;17] chromosome translocation) and Kasumi-1 (with c-KIT N822K mutation); and meanwhile, the laser tweezers Raman spectroscopy (LTRS) was also used to perform label-free detection of single living cells. The results demonstrated that Kasumi-1 cell line bearing c-KIT N822K mutation has a stable cell cycle, while there was a significant difference between early and late apoptosis within 48 hours. The LTRS detection initially reflected the spectral differences induced by genetic abnormalities and highlighted progressive patterns of DNA and amino acids band contents which were appropriately consistent with that of cell clone ratio and the c-KIT phosphorylation level. It is concluded that methodology of LTRS-based single living cell characterization could be potential and effective to reveal gene mutation-induced cell differentiation.