Living Sample Viability Measurement Methods from Traditional Assays to Nanomotion

Stain-Free Approach to Determine and Monitor Cell Heath Using Supervised and Unsupervised Image-Based Deep Learning

Cell-based medicinal products (CBMPs) are a growing class of therapeutics that promise new treatments for complex and rare diseases. Given the inherent complexity of the whole human cells comprising CBMPs, there is a need for robust and fast analytical methods for characterization, process monitoring, and quality control (QC) testing during their manufacture. Existing techniques to evaluate and monitor cell quality typically constitute labor-intensive, expensive, and highly specific staining assays. In this work, we combine image-based deep learning with flow imaging microscopy (FIM) to predict cell health metrics using cellular morphology "fingerprints" extracted from images of unstained Jurkat cells (immortalized human T-lymphocyte cells). A supervised (i.e., algorithm trained with human-generated labels for images) fingerprinting algorithm, trained on images of unstained healthy and dead cells, provides a robust stain-free, non-invasive, and non-destructive method for determining cell viability. Results from the stain-free method are in good agreement with traditional stain-based cytometric viability measurements. Additionally, when trained with images of healthy cells, dead cells and cells undergoing chemically induced apoptosis, the supervised fingerprinting algorithm is able to distinguish between the three cell states, and the results are independent of specific treatments or signaling pathways. We then show that an unsupervised variational autoencoder (VAE) algorithm trained on the same images, but without human-generated labels, is able to distinguish between samples of healthy, dead and apoptotic cells along with cellular debris based on learned morphological features and without human input. With this, we demonstrate that VAEs are a powerful exploratory technique that can be used as a process monitoring analytical tool.

Accurate and rapid antibiotic susceptibility testing using a machine learning-assisted nanomotion technology platform

Antimicrobial resistance (AMR) is a major public health threat, reducing treatment options for infected patients. AMR is promoted by a lack of access to rapid antibiotic susceptibility tests (ASTs). Accelerated ASTs can identify effective antibiotics for treatment in a timely and informed manner. We describe a rapid growth-independent phenotypic AST that uses a nanomotion technology platform to measure bacterial vibrations. Machine learning techniques are applied to analyze a large dataset encompassing 2762 individual nanomotion recordings from 1180 spiked positive blood culture samples covering 364 Escherichia coli and Klebsiella pneumoniae isolates exposed to cephalosporins and fluoroquinolones. The training performances of the different classification models achieve between 90.5 and 100% accuracy. Independent testing of the AST on 223 strains, including in clinical setting, correctly predict susceptibility and resistance with accuracies between 89.5% and 98.9%. The study shows the potential of this nanomotion platform for future bacterial phenotype delineation.

An Optical Fiber-Based Nanomotion Sensor for Rapid Antibiotic and Antifungal Susceptibility Tests

The emergence of antibiotic and antifungal resistant microorganisms represents nowadays a major public health issue that might push humanity into a post-antibiotic/antifungal era. One of the approaches to avoid such a catastrophe is to advance rapid antibiotic and antifungal susceptibility tests. In this study, we present a compact, optical fiber-based nanomotion sensor to achieve this goal by monitoring the dynamic nanoscale oscillation of a cantilever related to microorganism viability. High detection sensitivity was achieved that was attributed to the flexible two-photon polymerized cantilever with a spring constant of 0.3 N/m. This nanomotion device showed an excellent performance in the susceptibility tests of Escherichia coli and Candida albicans with a fast response in a time frame of minutes. As a proof-of-concept, with the simplicity of use and the potential of parallelization, our innovative sensor is anticipated to be an interesting candidate for future rapid antibiotic and antifungal susceptibility tests and other biomedical applications.

A simplified version of rapid susceptibility testing of bacteria and yeasts using optical nanomotion detection

We present a novel optical nanomotion-based rapid antibiotic and antifungal susceptibility test. The technique consisted of studying the effects of antibiotics or antifungals on the nanometric scale displacements of bacteria or yeasts to assess their sensitivity or resistance to drugs. The technique relies on a traditional optical microscope, a video camera, and custom-made image analysis software. It provides reliable results in a time frame of 2-4 h and can be applied to motile, non-motile, fast, and slowly growing microorganisms. Due to its extreme simplicity and low cost, the technique can be easily implemented in laboratories and medical centers in developing countries.

Bioadhesives with Antimicrobial Properties

Bioadhesives with antimicrobial properties enable easier and safer treatment of wounds as compared to the traditional methods such as suturing and stapling. Composed of natural or synthetic polymers, these bioadhesives seal wounds and facilitate healing while preventing infections through the activity of locally released antimicrobial drugs, nanocomponents, or inherently antimicrobial polers. Although many different materials and strategies are employed to develop antimicrobial bioadhesives, the design of these biomaterials necessitates a prudent approach as achieving all the required properties including optimal adhesive and cohesive properties, biocompatibility, and antimicrobial activity can be challenging. Designing antimicrobial bioadhesives with tunable physical, chemical, and biological properties will shed light on the path for future advancement of bioadhesives with antimicrobial properties. In this review, the requirements and commonly used strategies for developing bioadhesives with antimicrobial properties are discussed. In particular, different methods for their synthesis and their experimental and clinical applications on a variety of organs are reviewed. Advances in the design of bioadhesives with antimicrobial properties will pave the way for a better management of wounds to increase positive clinical outcomes.

Nanomotion technology in combination with machine learning: a new approach for a rapid antibiotic susceptibility test for Mycobacterium tuberculosis

Nanomotion technology is a growth-independent approach that can be used to detect and record the vibrations of bacteria attached to cantilevers. We have developed a nanomotion-based antibiotic susceptibility test (AST) protocol for Mycobacterium tuberculosis (MTB). The protocol was used to predict strain phenotype towards isoniazid (INH) and rifampicin (RIF) using a leave-one-out cross-validation (LOOCV) and machine learning techniques. This MTB-nanomotion protocol takes 21 h, including cell suspension preparation, optimized bacterial attachment to functionalized cantilever, and nanomotion recording before and after antibiotic exposure. We applied this protocol to MTB isolates (n = 40) and were able to discriminate between susceptible and resistant strains for INH and RIF with a maximum sensitivity of 97.4% and 100%, respectively, and a maximum specificity of 100% for both antibiotics when considering each nanomotion recording to be a distinct experiment. Grouping recordings as triplicates based on source isolate improved sensitivity and specificity to 100% for both antibiotics. Nanomotion technology can potentially reduce time-to-result significantly compared to the days and weeks currently needed for current phenotypic ASTs for MTB. It can further be extended to other anti-TB drugs to help guide more effective TB treatment.

Rapid In Vitro Assessment of Antimicrobial Drug Effect Bridging Clinically Relevant Pharmacokinetics: A Comprehensive Methodology

Rapid in vitro assessment of antimicrobial drug efficacy under clinically relevant pharmacokinetic conditions is an essential element of both drug development and clinical use. Here, we present a comprehensive overview of a recently developed novel integrated methodology for rapid assessment of such efficacy, particularly against the emergence of resistant bacterial strains, as jointly researched by the authors in recent years. This methodology enables rapid in vitro assessment of the antimicrobial efficacy of single or multiple drugs in combination, following clinically relevant pharmacokinetics. The proposed methodology entails (a) the automated collection of longitudinal time-kill data in an optical-density instrument; (b) the processing of collected time-kill data with the aid of a mathematical model to determine optimal dosing regimens under clinically relevant pharmacokinetics for single or multiple drugs; and (c) in vitro validation of promising dosing regimens in a hollow fiber system. Proof-of-concept of this methodology through a number of in vitro studies is discussed. Future directions for the refinement of optimal data collection and processing are discussed.

Sensors and Techniques for On-Line Determination of Cell Viability in Bioprocess Monitoring

In recent years, the bioprocessing industry has experienced significant growth and is increasingly emerging as an important economic sector. Here, efficient process management and constant control of cellular growth are essential. Good product quality and yield can only be guaranteed with high cell density and high viability. Whereas the on-line measurement of physical and chemical process parameters has been common practice for many years, the on-line determination of viability remains a challenge and few commercial on-line measurement methods have been developed to date for determining viability in industrial bioprocesses. Thus, numerous studies have recently been conducted to develop sensors for on-line viability estimation, especially in the field of optical spectroscopic sensors, which will be the focus of this review. Spectroscopic sensors are versatile, on-line and mostly non-invasive. Especially in combination with bioinformatic data analysis, they offer great potential for industrial application. Known as soft sensors, they usually enable simultaneous estimation of multiple biological variables besides viability to be obtained from the same set of measurement data. However, the majority of the presented sensors are still in the research stage, and only a few are already commercially available.

Plasmon-enhanced photothermal properties of Au@Ti3C2Tx nanosheets for antibacterial applications

Antibiotic-resistant bacterial strains have become an ever-increasing public concern due to their significant threats to health safety. Nanomaterial-based photothermal treatment has shown potential in antibacterial applications, but many nanomaterials exhibited limited photothermal activity that may compromise their antibacterial efficacies. Herein, we report a novel strategy based on efficient photothermal ablation and physical contact over a supported nanostructure by loading Au nanoparticles (NPs) on few-layered Ti₃C₂T_x nanosheets (NSs) for antibacterial treatment. Ti₃C₂T_x NSs are delaminated via etching and sonication, and act as a reductant for the in situ reduction of HAuCl₄·xH₂O, producing "naked" Au NPs without any stabilizers. Meanwhile, by adjusting the Au/Ti ratio, the size and loading of the Au NPs are finely regulated, thereby providing an ideal model of a surface-clean Au@Ti₃C₂T_x heterostructure for probing the composition-performance relationship. Upon irradiation with visible light, it exhibits synergistically enhanced photothermal conversion efficiency and stability, owing to the localized surface plasmonic resonance effect of Au NP and Au-NS interactions. Moreover, under visible light irradiation for 10 min, the Au@ Ti₃C₂T_x heterostructure exhibits excellent antibacterial activity for Gram-positive S. aureus and Gram-negative E. coli, and kills over 99% bacteria with a low dose of the nanomedicine suspension (50 μg mL^-1). The work demonstrates that the incorporation of transition metal carbides with plasmonic metal nanostructures is an effective strategy to enhance the photothermal antibacterial efficacy.

Nanoprobes for Tumor Theranostics

This Special Issue of Biosensors, entitled "Nanoprobes for Tumor Theranostics", aims to report the research progress of using nanoprobes for the diagnosis and therapy of tumors, and promote their applications [...].