An Isotope-Labeled Single-Cell Raman Spectroscopy Approach for Tracking the Physiological Evolution Trajectory of Bacteria toward Antibiotic Resistance

Raman-Activated, Interactive Sorting of Isotope-Labeled Bacteria

Due to its high spatial resolution, Raman microspectroscopy allows for the analysis of single microbial cells. Since Raman spectroscopy analyzes the whole cell content, this method is phenotypic and can therefore be used to evaluate cellular changes. In particular, labeling with stable isotopes (SIPs) enables the versatile use and observation of different metabolic states in microbes. Nevertheless, static measurements can only analyze the present situation and do not allow for further downstream evaluations. Therefore, a combination of Raman analysis and cell sorting is necessary to provide the possibility for further research on selected bacteria in a sample. Here, a new microfluidic approach for Raman-activated continuous-flow sorting of bacteria using an optical setup for image-based particle sorting with synchronous acquisition and analysis of Raman spectra for making the sorting decision is demonstrated, showing that active cells can be successfully sorted by means of this microfluidic chip.

Sensing the Bactericidal and Bacteriostatic Antimicrobial Mode of Action Using Raman Deuterium Stable Isotope Probing (DSIP) in Escherichia coli

The mode of action of antibiotics can be broadly classified as bacteriostatic and bactericidal. The bacteriostatic mode leads to the arrested growth of the cells, while the bacteriocidal mode causes cell death. In this work, we report the applicability of deuterium stable isotope probing (DSIP) in combination with Raman spectroscopy (Raman DSIP) for discriminating the mode of action of antibiotics at the community level. Escherichia coli, a well-known model microbe, was used as an organism for the study. We optimized the concentration of deuterium oxide required for metabolic activity monitoring without compromising the microbial growth. Our findings suggest that changes in the intensity of the C-D band in the high-wavenumber region could serve as a quantifiable marker for determining the antibiotic mode of action. This can be used for early identification of the antibiotic's mode of action. Our results explore the new perspective that supports the utility of deuterium-based vibrational tags in the field of clinical spectroscopy. Understanding the antibiotic's mode of action on bacterial cells in a short and objective manner can significantly enhance the clinical management abilities of infectious diseases and may also help in personalized antimicrobial therapy.

Photo-induced Oriented Crystallization of Intracellular Nanocrystals Based on Phase Separation for Diagnostic Bioimaging and Analysis

Biomineral crystals form complex nonequilibrium structures based on the multistep nucleation theory, via transient amorphous precursors. However, the intricate nature of the biological system results in the inconsistent frequency of nucleation and crystallization, which making it problematic to obtain homogeneous nanocrystals, limits their application in biomedicine. Here, it is reported that homogeneous nanocrystals of photoinduced oriented crystallization with protein coronas are based on intracellular liquid-liquid phase separation for in situ analysis and mapping of surface-enhanced Raman spectroscopy (SERS). Near-infrared light promotes the formation of intracellular dense phases, accelerates the nucleation of gold atoms at secondary structure sites of proteins, and promotes the growth of crystals. Homogeneous gold nanocrystals with stable SERS signals can be used to analysis different cell cycles and acquire in situ molecular information of metastatic tumor cells. Of note are tag molecule is embedded in protein coronas of gold nanocrystals to enable the mapping of patient tumor tissue samples and the portable recognition of tumor cells. Thus, this study proposes a new strategy for biomineralization of intracellular homogeneous gold nanocrystals and its potential application.

Identification of Microorganism in Infected Wounds by Positively Charged Selective Sensor Array and Deep Learning Algorithm

Microorganism are ubiquitous and intimately connected with human health and disease management. The accurate and fast identification of pathogenic microorganisms is especially important for diagnosing infections. Herein, three tetraphenylethylene derivatives (S-TDs: TBN, TPN, and TPI) featuring different cationic groups, charge numbers, emission wavelengths, and hydrophobicities were successfully synthesized. Benefiting from distinct cell wall binding properties, S-TDs were collectively utilized to create a sensor array capable of imaging various microorganisms through their characteristic fluorescent signatures. Furthermore, the interaction mechanism between S-TDs and different microorganisms was explored by calculating the binding energy between S-TDs and cell membrane/wall constituents, including phospholipid bilayer and peptidoglycan. Using a combination of the fluorescence sensor array and a deep learning model of residual network (ResNet), readily differentiation of Gram-negative bacteria (G-), Gram-positive bacteria (G+), fungi, and their mixtures was achieved. Specifically, by extensive training of two ResNet models with large quantities of images data from 14 kinds of microorganism stained with S-TDs, identification of microorganism was achieved at high-level accuracy: over 92.8% for both Gram species and antibiotic-resistant species, with 90.35% accuracy for the detection of mixed microorganism in infected wound. This novel method provides a rapid and accurate method for microbial classification, potentially aiding in the diagnosis and treatment of infectious diseases.

Illuminating the Tiny World: A Navigation Guide for Proper Raman Studies on Microorganisms

Raman spectroscopy is an emerging method for the identification of bacteria. Nevertheless, a lot of different parameters need to be considered to establish a reliable database capable of identifying real-world samples such as medical or environmental probes. In this review, the establishment of such reliable databases with the proper design in microbiological Raman studies is demonstrated, shining a light into all the parts that require attention. Aspects such as the strain selection, sample preparation and isolation requirements, the phenotypic influence, measurement strategies, as well as the statistical approaches for discrimination of bacteria, are presented. Furthermore, the influence of these aspects on spectra quality, result accuracy, and read-out are discussed. The aim of this review is to serve as a guide for the design of microbiological Raman studies that can support the establishment of this method in different fields.

Chemical Nose Strategy with Metabolic Labeling and "Antibiotic-Responsive Spectrum" Enables Accurate and Rapid Pathogen Identification

The worldwide antimicrobial resistance (AMR) dilemma urgently requires rapid and accurate pathogen phenotype discrimination and antibiotic resistance identification. The conventional protocols are either time-consuming or depend on expensive instrumentations. Herein, we demonstrate a metabolic-labeling-assisted chemical nose strategy for phenotyping classification and antibiotic resistance identification of pathogens based on the "antibiotic-responsive spectrum" of different pathogens. d-Amino acids with click handles were metabolically incorporated into the cell wall of pathogens for further clicking with dibenzocyclooctyne-functionalized upconversion nanoparticles (DBCO-UCNPs) in the presence/absence of six types of antibiotics, which generates seven-channel sensing responses. With the assistance of machine learning algorithms, eight types of pathogens, including three types of antibiotic-resistant bacteria, can be well classified and discriminated in terms of microbial taxonomies, Gram phenotypes, and antibiotic resistance. The present metabolic-labeling-assisted strategy exhibits good anti-interference capability and improved discrimination ability rooted in the unique sensing mechanism. Sensitive identification of pathogens with 100% accuracy from artificial urinary tract infection samples at a concentration as low as 105 CFU/mL was achieved. Pathogens outside of the training set can also be discriminated well. This clearly demonstrated the potential of the present strategy in the identification of unknown pathogens in clinical samples.

In situ oxidative polymerization of platinum(iv) prodrugs in pore-confined spaces of CaCO3 nanoparticles for cancer chemoimmunotherapy

Drug resistance and metastases are the leading causes of death in clinics. To overcome this limitation, there is an urgent need for new therapeutic agents and drug formulations that are able to therapeutically intervene by non-traditional mechanisms. Herein, the physical adsorption and oxidative polymerization of Pt(iv) prodrugs in pore-confined spaces of CaCO₃ nanoparticles is presented, and the nanomaterial surface was coated with DSPE-PEG₂₀₀₀-Biotin to improve aqueous solubility and tumor targeting. While the nanoparticle scaffold remained stable in an aqueous solution, it quickly degraded into Ca²⁺ in the presence of acid and into cisplatin in the presence of GSH. The nanoparticles were found to interact in cisplatin-resistant non-small lung cancer cells by a multimodal mechanism of action involving mitochondrial Ca²⁺ overload, dual depletion of GSH, nuclear DNA platination, and amplification of ROS and lipid peroxide generation, resulting in triggering cell death by a combination of apoptosis, ferroptosis and immunogenic cell death in vitro and in vivo. This study could present a novel strategy for the treatment of drug-resistant and metastatic tumors and therefore overcome the limitations of currently used therapeutic agents in the clinics.

Lysogenic bacteriophages encoding arsenic resistance determinants promote bacterial community adaptation to arsenic toxicity

Emerging evidence from genomics gives us a glimpse into the potential contribution of lysogenic bacteriophages (phages) to the environmental adaptability of their hosts. However, it is challenging to quantify this kind of contribution due to the lack of appropriate genetic markers and the associated controllable environmental factors. Here, based on the unique transformable nature of arsenic (the controllable environmental factor), a series of flooding microcosms was established to investigate the contribution of arsM-bearing lysogenic phages to their hosts' adaptation to trivalent arsenic [As(III)] toxicity, where arsM is the marker gene associated with microbial As(III) detoxification. In the 15-day flooding period, the concentration of As(III) was significantly increased, and this elevated As(III) toxicity visibly inhibited the bacterial population, but the latter quickly adapted to As(III) toxicity. During the flooding period, some lysogenic phages re-infected new hosts after an early burst, while others persistently followed the productive cycle (i.e., lytic cycle). The unique phage-host interplay contributed to the rapid spread of arsM among soil microbiota, enabling the quick recovery of the bacterial community. Moreover, the higher abundance of arsM imparted a greater arsenic methylation capability to soil microbiota. Collectively, this study provides experimental evidence for lysogenic phages assisting their hosts in adapting to an extreme environment, which highlights the ecological perspectives on lysogenic phage-host mutualism.