Isolation and Culture of Single Microbial Cells by Laser Ejection Sorting Technology.

Single-cell isolation and cultivation play an important role in studying physiology, gene expression, and functions of microorganisms. A series of single-cell isolation technologies have been developed, among which single-cell ejection technology is one of the most promising. Single-cell ejection technology has applied laser-induced forward transfer (LIFT) techniques to isolate bacteria, but the viability (or recovery rate) of cells after sorting has not been clarified in current research. In this work, to keep the cells alive as long as possible, we propose a three-layer LIFT system (top layer, 25-nm aluminum film; second layer, 3 µm agar media; third layer, liquid containing bacteria) for the isolation and cultivation of single Gram-negative (Escherichia coli), Gram-positive (Lactobacillus rhamnosus GG [LGG]), and eukaryotic (Saccharomyces cerevisiae) microorganisms. The experiment results showed that the average survival rates for ejected pure single cells were 63% for Saccharomyces cerevisiae, 22% for E. coli DH5α, and 74% for LGG. In addition, we successfully isolated and cultured the green fluorescent protein (GFP)-expressing E. coli JM109 from a mixture containing complex communities of soil bacteria by fluorescence signal. The average survival rate of E. coli JM109 was demonstrated to be 25.3%. In this study, the isolated and cultured single colonies were further confirmed by colony PCR and sequencing. Such precise sorting and cultivation techniques of live single microbial cells could be coupled with other microscopic approaches to isolate single microorganisms with specific functions, revealing their roles in the natural community. IMPORTANCE We developed a laser-induced forward transfer (LIFT) technology to accurately isolate single live microbial cells. The cultivation recovery rates of the ejected single cells were 63% for Saccharomyces cerevisiae, 22% for E. coli DH5α, and 74% for Lactobacillus rhamnosus GG (LGG). With coupled LIFT with a fluorescence microscope, we demonstrated that single cells of GFP-expressing E. coli JM109 were sorted according to fluorescence signal from a complex community of soil bacteria and subsequently cultured with 25% cultivation recovery rate. This single-cell live sorting technology could isolate single microbes with specific functions, revealing their roles in the natural community.

Application of Dual-Enhanced Surface-Enhanced Raman Scattering Probe Technology in the Diagnosis of Tumor Cells in Vitro.

With the development of precision medicine, antigen/antibody-targeted therapy has brought great hope to tumor patients; however, the migration of tumor cells, especially a small number of cells flowing into blood or other tissues, remains a clinical challenge. In particular, it is difficult to use functional gold nanomaterials for targeted clinical tumor diagnosis while simultaneously obtaining stable and highly sensitive Raman signals. Therefore, we developed a detection method for functional Au Nanostars (AuNSs) with dual signal enhancement that can specifically track location and obtain high-intensity surface-enhanced Raman scattering (SERS) signals. First, AuNSs with specific optical properties were synthesized and functionalized. The Raman dye 4-mercapto-hydroxybenzoic acid and polyethylene glycol were coupled with the tumor marker, epidermal growth factor receptor, to obtain the targeted SERS probes. In addition, a detection chip was prepared for Raman detection with physical enhancement, exhibiting a 40-times higher signal intensity than that of quartz glass. This study combines physical enhancement and SERS enhancement technologies to achieve dual enhancement, enabling the detection of a highly sensitive and stable Raman signal; this has potential clinical value for antigen/antibody-targeted tumor diagnosis and treatment.

Visible Particle Identification Using Raman Spectroscopy and Machine Learning.

Visible particle identification is a crucial prerequisite step for process improvement and control during the manufacturing of injectable biotherapeutic drug products. Raman spectroscopy is a technology with several advantages for particle identification including high chemical sensitivity, minimal sample manipulation, and applicability to aqueous solutions. However, considerable effort and experience are required to extract and interpret Raman spectral data. In this study, we applied machine learning algorithms to analyze Raman spectral data for visible particle identification in order to minimize expert support and improve data analysis accuracy. We manually prepared ten types of particle standard solutions to simulate the particle types typically observed during manufacturing and established a Raman spectral library with accurate peak assignments for the visible particles. Five classification algorithms were trained using visible particle Raman spectral data. All models had high prediction accuracy of >98% for all types of visible particles. Our results demonstrate that the combination of Raman spectroscopy and machine learning can provide a simple and accurate data analysis approach for visible particle identification.

An ent-Kaurane Diterpenoid Isolated from Rabdosia excisa Suppresses Bcr-Abl Protein Expression in Vitro and in Vivo and Induces Apoptosis of CML Cells.

Chronic myelogenous leukemia (CML) is a disease of the blood stem cells that features the oncoprotein Bcr-Abl. Tyrosine kinase inhibitors (TKIs) are used to treat CML patients, but these have limited efficacy due to the emergence of resistance via genetic mutation. Kamebakaurin is an ent-kaurane diterpenoid that has been isolated from Rabdosia excisa (Maxim.) H.Hara. Herein, we investigate the potential of kamebakaurin as a chemotherapy reagent for the treatment of CML. We conducted in vitro and in vivo biological experiments and found that kamebakaurin potently inhibits cell proliferation, mainly by enhancing cell apoptosis and down-regulating Bcr-Abl protein levels. In addition, kamebakaurin was found to inhibit tumor growth and has no side effects on five internal organs for in vivo experiment. These results suggest that kamebakaurin is a potential anticancer agent and is a key compound for further investigations.

Double Discrete Cosine Transform-Oriented Multi-View Subspace Clustering.

Low-rank tensor representation with the tensor nuclear norm has been rising in popularity in multi-view subspace clustering (MVSC), in which the tensor nuclear norm is commonly implemented using discrete Fourier transform (DFT). Unfortunately, existing DFT-oriented MVSC methods may provide unsatisfactory results since (1) DFT exploits complex arithmetic in the Fourier domain, usually resulting in high tubal tensor rank, and (2) local structural information is rarely considered. To solve these problems, in this paper, we propose a novel double discrete cosine transform (DCT)-oriented multi-view subspace clustering (D2CTMSC) method, in which the first DCT aims to derive the tensor nuclear norm without complex arithmetic while the second DCT aims to explore the local structure of the self-representation tensor, such that the essential low-rankness and sparsity embedding in multi-view features can be thoroughly exploited. Moreover, we design an effective alternating iteration strategy to solve the proposed model. Experimental results on four types of multi-view datasets (News stories, Face images, Scene images, and Generic objects) demonstrate the superiority of the D2CTMSC method compared with DFT-based methods and other state-of-the-art clustering methods.

RCUMP: Residual Completion Unrolling With Mixed Priors for Snapshot Compressive Imaging.

Deep unrolling-based snapshot compressive imaging (SCI) methods, which employ iterative formulas to construct interpretable iterative frameworks and embedded learnable modules, have achieved remarkable success in reconstructing 3-dimensional (3D) hyperspectral images (HSIs) from 2D measurement induced by coded aperture snapshot spectral imaging (CASSI). However, the existing deep unrolling-based methods are limited by the residuals associated with Taylor approximations and the poor representation ability of single hand-craft priors. To address these issues, we propose a novel HSI construction method named residual completion unrolling with mixed priors (RCUMP). RCUMP exploits a residual completion branch to solve the residual problem and incorporates mixed priors composed of a novel deep sparse prior and mask prior to enhance the representation ability. Our proposed CNN-based model can significantly reduce memory cost, which is an obvious improvement over previous CNN methods, and achieves better performance compared with the state-of-the-art transformer and RNN methods. In this work, our method is compared with the 9 most recent baselines on 10 scenes. The results show that our method consistently outperforms all the other methods while decreasing memory consumption by up to 80%.

Contrastive Multi-View Composite Graph Convolutional Networks Based on Contribution Learning for Autism Spectrum Disorder Classification.

The resting-state functional magnetic resonance imaging (rs-fMRI) faithfully reflects the brain activities and thus provides a promising tool for autism spectrum disorder (ASD) classification. Up to now, graph convolutional networks (GCNs) have been successfully applied in rs-fMRI based ASD classification. However, most of these methods were developed based on functional connectivities (FCs) that only reflect low-level correlation between brain regions, without integrating both high-level discriminative knowledge and phenotypic information into classification. Besides, they suffered from the overfitting problem caused by insufficient training samples. To this end, we propose a novel contrastive multi-view composite GCN (CMV-CGCN) for ASD classification using both FCs and HOFCs. Specifically, a pair of graphs are constructed based on the FC and HOFC features of the subjects, respectively, and they share the phenotypic information in the graph edges. A novel contrastive multi-view learning method is proposed based on the consistent representation of both views. A contribution learning mechanism is further incorporated, encouraging the FC and HOFC features of different subjects to have various contribution in the contrastive multi-view learning. The proposed CMV-CGCN is evaluated on 613 subjects (including 286 ASD patients and 327 NCs) from the Autism Brain Imaging Data Exchange (ABIDE). We demonstrate the performance of the method for ASD classification, which yields an accuracy of 75.20% and an area under the curve (AUC) of 0.7338. Experimental results show that our proposed method outperforms state-of-the-art methods on the ABIDE database.

Partial Tubal Nuclear Norm-Regularized Multiview Subspace Learning.

In this article, a unified multiview subspace learning model, called partial tubal nuclear norm-regularized multiview subspace learning (PTN 2 MSL), was proposed for unsupervised multiview subspace clustering (MVSC), semisupervised MVSC, and multiview dimension reduction. Unlike most of the existing methods which treat the above three related tasks independently, PTN 2 MSL integrates the projection learning and the low-rank tensor representation to promote each other and mine their underlying correlations. Moreover, instead of minimizing the tensor nuclear norm which treats all singular values equally and neglects their differences, PTN 2 MSL develops the partial tubal nuclear norm (PTNN) as a better alternative solution by minimizing the partial sum of tubal singular values. The PTN 2 MSL method was applied to the above three multiview subspace learning tasks. It demonstrated that these tasks organically benefited from each other and PTN 2 MSL has achieved better performance in comparison to state-of-the-art methods.

Nonsingular Practical Fixed-Time Adaptive Output Feedback Control of MIMO Nonlinear Systems.

This article studies the nonsingular fixed-time control problem of multiple-input multiple-output (MIMO) nonlinear systems with unmeasured states for the first time. A state observer is designed to solve the problem that system states cannot be measured. Due to the existence of the unknown system nonlinear dynamics, neural networks (NNs) are introduced to approximate them. Then, through the combination of adaptive backstepping recursive technology and adding power integration technology, a nonsingular fixed-time adaptive output feedback control algorithm is proposed, which introduces a filter to avoid the complicated derivation process of the virtual control function. According to the fixed-time Lyapunov stability theory, the practical fixed-time stability of the closed-loop system is proven, which means that all signals of the closed-loop system remain bounded in a fixed time under the proposed algorithm. Finally, the effectiveness of the proposed algorithm is verified by the numerical simulation and practical simulation.

Unsupervised Contrastive Cross-Modal Hashing.

In this paper, we study how to make unsupervised cross-modal hashing (CMH) benefit from contrastive learning (CL) by overcoming two challenges. To be exact, i) to address the performance degradation issue caused by binary optimization for hashing, we propose a novel momentum optimizer that performs hashing operation learnable in CL, thus making on-the-shelf deep cross-modal hashing possible. In other words, our method does not involve binary-continuous relaxation like most existing methods, thus enjoying better retrieval performance; ii) to alleviate the influence brought by false-negative pairs (FNPs), we propose a Cross-modal Ranking Learning loss (CRL) which utilizes the discrimination from all instead of only the hard negative pairs, where FNP refers to the within-class pairs that were wrongly treated as negative pairs. Thanks to such a global strategy, CRL endows our method with better performance because CRL will not overuse the FNPs while ignoring the true-negative pairs. To the best of our knowledge, the proposed method could be one of the first successful contrastive hashing methods. To demonstrate the effectiveness of the proposed method, we carry out experiments on five widely-used datasets compared with 13 state-of-the-art methods. The code is available at https://github.com/penghu-cs/UCCH.

Evaluating Differentiation Status of Mesenchymal Stem Cells by Label-Free Microscopy System and Machine Learning.

Mesenchymal stem cells (MSCs) play a crucial role in tissue engineering, as their differentiation status directly affects the quality of the final cultured tissue, which is critical to the success of transplantation therapy. Furthermore, the precise control of MSC differentiation is essential for stem cell therapy in clinical settings, as low-purity stem cells can lead to tumorigenic problems. Therefore, to address the heterogeneity of MSCs during their differentiation into adipogenic or osteogenic lineages, numerous label-free microscopic images were acquired using fluorescence lifetime imaging microscopy (FLIM) and stimulated Raman scattering (SRS), and an automated evaluation model for the differentiation status of MSCs was built based on the K-means machine learning algorithm. The model is capable of highly sensitive analysis of individual cell differentiation status, so it has great potential for stem cell differentiation research.

Mitochondria spatially and temporally modulate VSMC phenotypes via interacting with cytoskeleton in cardiovascular diseases.

Cardiovascular diseases caused by atherosclerosis (AS) seriously endanger human health, which is closely related to vascular smooth muscle cell (VSMC) phenotypes. VSMC phenotypic transformation is marked by the alteration of phenotypic marker expression and cellular behaviour. Intriguingly, the mitochondrial metabolism and dynamics altered during VSMC phenotypic transformation. Firstly, this review combs VSMC mitochondrial metabolism in three aspects: mitochondrial ROS generation, mutated mitochondrial DNA (mtDNA) and calcium metabolism respectively. Secondly, we summarized the role of mitochondrial dynamics in regulating VSMC phenotypes. We further emphasized the association between mitochondria and cytoskelton via presenting cytoskeletal support during mitochondrial dynamics process, and discussed its impact on their respective dynamics. Finally, considering that both mitochondria and cytoskeleton are mechano-sensitive organelles, we demonstrated their direct and indirect interaction under extracellular mechanical stimuli through several mechano-sensitive signaling pathways. We additionally discussed related researches in other cell types in order to inspire deeper thinking and reasonable speculation of potential regulatory mechanism in VSMC phenotypic transformation.

Pin1/YAP pathway mediates matrix stiffness-induced epithelial-mesenchymal transition driving cervical cancer metastasis via a non-Hippo mechanism.

Cervical cancer metastasis is an important cause of death in cervical cancer. Previous studies have shown that epithelial-mesenchymal transition (EMT) of tumors promotes its invasive and metastatic capacity. Alterations in the extracellular matrix (ECM) and mechanical signaling are closely associated with cancer cell metastasis. However, it is unclear how matrix stiffness as an independent cue triggers EMT and promotes cervical cancer metastasis. Using collagen-coated polyacrylamide hydrogel models and animal models, we investigated the effect of matrix stiffness on EMT and metastasis in cervical cancer. Our data showed that high matrix stiffness promotes EMT and migration of cervical cancer hela cell lines in vitro and in vivo. Notably, we found that matrix stiffness regulates yes-associated protein (YAP) activity via PPIase non-mitotic a-interaction 1 (Pin1) with a non-Hippo mechanism. These data indicate that matrix stiffness of the tumor microenvironment positively regulates EMT in cervical cancer through the Pin1/YAP pathway, and this study deepens our understanding of cervical cancer biomechanics and may provide new ideas for the treatment of cervical cancer.

[Isolation, identification and enzyme characterization of a thermophilic cellulolytic anaerobic bacterium].

OBJECTIVE: To identify a thermophilic bacterium from horse manure to degrade cellulose efficiently, and to enrich microbial resources producing cellulolytic ethanol by co-culturing with thermophilic ethanol producing bacterium. METHODS: We used Hungate anaerobic technique to isolate a strain named as HCp from horse manure mixed culture; its phylogeny was identified through 16S rDNA sequencing. Enzymatic assays were determined using DNS method. RESULTS: The isolated HCp cells were straight with rods size of(0.35-0.50) microm x (2.42-6.40) microm, in the form of single or paring. This strain belongs to a strictly anaerobic Gram-negative bacterium, it is able to form spores, shows motile ability and resistance to neomycin. The strain could degrade filter paper cellulose, cellulose powder, microcrystalline cellulose, cotton wool, rice straw and gelatin, and it was also able to utilize abundant saccharides as substrates such as cellobiose, glucose, xylose, xylan, raffinose, maltose, sorbose, fructose and galactose. The growth pH ranges from 6.5 to 8.5, temperature from 35 to 70 degrees C and concentration of NaCl on cellulose from 0% to 1.0%, while the optima of pH 6.85, 60 degreesC and 0.2% NaCl. Under the optimal growth conditions, the filter paper cellulose degradation rate was up to 90.40% after 10 days. The optimum temperatures for FPA, CMCase, beta-glucosidase and xylanase were 70 degrees C, 70 degrees C, 70 degrees C, and 60 degrees C respectively. CMCase activity was found with high thermal stability. The phylogenetic analysis based on partial 16S rDNA revealed that HCp was close to Acetivibrio cellulolyticus and A. cellulosolvens with 97.5% sequence similarities. CONCLUSION: Strain HCp is thermophilic, efficiently cellulolytic anaerobe. It is able to utilize vast substrates and produce highly thermostable enzymes. It is a potential bacterium that can be used for cellulolytic ethanol production.

Single cell sorting of young yeast based on label-free fluorescence lifetime imaging microscopy.

Saccharomyces cerevisiae is an attractive organism used in the fermentation industry and is an important model organism for virus research. The ability to sort yeast cells is important for diverse applications. Replicative aging of Saccharomyces Cerevisiae is accompanied by metabolic changes that are related to an essential coenzyme, reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H). Here, a single cell sorting method based on fluorescence lifetime imaging microscopy (FLIM) and laser-induced forward transfer (LIFT) was implemented for the first time. The aging level of yeast was determined based on the FLIM by NAD(P)H, which was a label-free and noninvasive method for studying individual cells. Then, young and active yeast cells were sorted by the LIFT system at the single cell level. During the entire experiment, a sterile and humid environment was maintained to ensure the activity of cells. The high viability of sorted cells was achieved by the LIFT combining with FLIM.

Corrigendum to "A novel mechanism of inhibiting in-stent restenosis with arsenic trioxide drug-eluting stent: Enhancing contractile phenotype of vascular smooth muscle cells via YAP pathway" [Bioact. Mater. 6 2 (February 2021) 375-385].

[This corrects the article DOI: 10.1016/j.bioactmat.2020.08.018.].

A novel mechanism of inhibiting in-stent restenosis with arsenic trioxide drug-eluting stent: Enhancing contractile phenotype of vascular smooth muscle cells via YAP pathway.

OBJECTIVE: Arsenic trioxide (ATO or As2O3) has beneficial effects on suppressing neointimal hyperplasia and restenosis, but the mechanism is still unclear. The goal of this study is to further understand the mechanism of ATO's inhibitory effect on vascular smooth muscle cells (VSMCs). METHODS AND RESULTS: Through in vitro cell culture and in vivo stent implanting into the carotid arteries of rabbit, a synthetic-to-contractile phenotypic transition was induced and the proliferation of VSMCs was inhibited by ATO. F-actin filaments were clustered and the elasticity modulus was increased within the phenotypic modulation of VSMCs induced by ATO in vitro. Meanwhile, Yes-associated protein (YAP) nuclear translocation was inhibited by ATO both in vivo and in vitro. It was found that ROCK inhibitor or YAP inactivator could partially mask the phenotype modulation of ATO on VSMCs. CONCLUSIONS: The interaction of YAP with the ROCK pathway through ATO seems to mediate the contractile phenotype of VSMCs. This provides an indication of the clinical therapeutic mechanism for the beneficial bioactive effect of ATO-drug eluting stent (AES) on in-stent restenosis (ISR).

Cell-derived biomimetic nanoparticles as a novel drug delivery system for atherosclerosis: predecessors and perspectives.

Atherosclerosis is a key mechanism underlying the pathogenesis of cardiovascular disease, which is associated with high morbidity and mortality. In the field of precision medicine for the treatment of atherosclerosis, nanoparticle (NP)-mediated drug delivery systems have great potential, owing to their ability to release treatment locally. Cell-derived biomimetic NPs have attracted extensive attention at present due to their excellent targeting to atherosclerotic inflammatory sites, low immunogenicity and long blood circulation time. Here, we review the utility of cell-derived biomimetic NPs, including whole cells, cell membranes and extracellular vesicles, in the treatment of atherosclerosis.

In-situ growth of 3D rosette-like copper nanoparticles on carbon cloth for enhanced sensing of ammonia based on copper electrodissolution.

Copper is an attractive candidate for sensing ammonia. Here, an electrodissolution mechanism for measuring liquid-phase ammonia was developed via a novel three-dimensional rosette-like structure of copper nanoparticles (CuNPs) integrated onto carbon cloth (CuNPs/CC). A one-step hydrothermal synthetic procedure was employed to construct the metallic CuNPs with a stereo rosette-like pattern on flexible CC substrate. The morphology, composition and sensing performance of the as-prepared composite were characterised in detail. The CuNPs/CC composite showed excellent sensing performance to ammonia, which is attributed to the electrodissolution of CuNPs being promoted by ammonia to form a stabilised copper-ammonia complex. This electrochemical response occurs without the electro-oxidation of ammonia, thus avoiding the energy barrier of the N-N bond and the toxicity of N-adsorbates, which is advantageous for ammonia detection. In addition, the sensor also shows very high sensitivity to ammonia with a low detection limit, as well as good anti-interference performance, repeatability and stability. The high accuracy and precision for the quantification of ammonia concentration in a variety of real samples indicate that the CuNPs/CC composition has potential in the development of high-performance ammonia sensors.

2D SnO2 Nanosheets: Synthesis, Characterization, Structures, and Excellent Sensing Performance to Ethylene Glycol.

Two dimensional (2D)SnO2 nanosheets were synthesized by a substrate-free hydrothermal route using sodium stannate and sodium hydroxide in a mixed solvent of absolute ethanol and deionized water at a lower temperature of 130 °C. The characterization results of the morphology, microstructure, and surface properties of the as-prepared products demonstrated that SnO2 nanosheets with a tetragonal rutile structure, were composed of oriented SnO2 nanoparticles with a diameter of 6-12 nm. The X-ray diffraction (XRD) and high-resolution transmission electron microscope (FETEM) results demonstrated that the dominant exposed surface of the SnO2 nanoparticles was (101), but not (110). The growth and formation was supposed to follow the oriented attachment mechanism. The SnO2 nanosheets exhibited an excellent sensing response toward ethylene glycol at a lower optimal operating voltage of 3.4 V. The response to 400 ppm ethylene glycol reaches 395 at 3.4 V. Even under the low concentration of 5, 10, and 20 ppm, the sensor exhibited a high response of 6.9, 7.8, and 12.0 to ethylene glycol, respectively. The response of the SnO2 nanosheets exhibited a linear dependence on the ethylene glycol concentration from 5 to 1000 ppm. The excellent sensing performance was attributed to the present SnO2 nanoparticles with small size close to the Debye length, the larger specific surface, the high-energy exposed facets of the (101) surface, and the synergistic effects of the SnO2 nanoparticles of the nanosheets.