Figure - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
Comparison between cell concentrations estimated by the number of colonies formed during the CFU measurement and calculated FFT score of different samples sources.
Source publication
Microfluidic devices are an emerging platform for a variety of experiments involving bacterial cell culture, and has advantages including cost and convenience. One inevitable step during bacterial cell culture is the measurement of cell concentration in the channel. The optical density measurement technique is generally used for bacterial growth es...
Citations
... The potential progress of antibiotic susceptibility tests by using a micro-loading chip has been described 118,119,123 , which enables the automatic analysis of morphological changes in single bacteria under various antimicrobial conditions 129 . Recently, microfluidic chips have been used to generate a gradient of antibiotic concentration to determine the minimal inhibitory concentration 130,131 . The combination of highresolution imaging modalities with the fine fluidic controller will facilitate the development of an automated highthroughput monitoring system. ...
Bacterial heterogeneity is pivotal for adaptation to diverse environments, posing significant challenges in microbial diagnostics and therapeutic interventions. Recent advancements in high-resolution optical microscopy have revolutionized our ability to observe and characterize individual bacteria, offering unprecedented insights into their metabolic states and behaviors at the single-cell level. This review discusses the transformative impact of various high-resolution imaging techniques, including fluorescence and label-free imaging, which have enhanced our understanding of bacterial pathophysiology. These methods provide detailed visualizations that are crucial for developing targeted treatments and improving clinical diagnostics. We highlight the integration of these imaging techniques with computational tools, which has facilitated rapid, accurate pathogen identification and real-time monitoring of bacterial responses to treatments. The ongoing development of these optical imaging technologies promises to significantly advance our understanding of microbiology and to catalyze the translation of these insights into practical healthcare solutions.
... Compared to the model presented in Table S4, we used the parameter k rdsd = 0.7762 min -1 as suggested by Santos-Moreno et al. [43] for non-oscillatory circuits. Additionally, the dilution rate was reduced to k d = 0.005 min -1 , as cell division occurs more slowly on a solid surface [50]. The model generated from GRN modeler confirmed that by applying light pulses, the expression of the fluorescent reporters should follow the input level (Figure 6c). ...
Modeling and simulating gene regulatory networks (GRNs) is crucial for understanding biological processes, predicting system behavior, guiding the design of synthetic biological systems, and interpreting experimental data. In synthetic biology, GRNs play a pivotal role in enabling the design and control of complex systems for a wide range of applications. However, GRN simulations can be time-consuming and often require specialized expertise. To make this process more accessible, we developed a user-friendly application with a graphical user interface (GUI), allowing users to create simple phenomenological models without requiring prior programming experience. We demonstrate the versatility of our tool through several examples, including the design of novel oscillator families capable of robust oscillation with an even number of nodes. These complement the well-known repressilator family, which only oscillates with odd-numbered nodes. Furthermore, we showcase how GRN modeler allowed us to develop a light-detecting biosensor in Escherichia coli that can track light intensity over several days, leaving a record in the form of ring patterns in bacterial colonies. In summary, our work empowers biologists to model their systems of interest even without programming expertise.
... Agarose gel is chosen as the scaffold material since the porous medium allows for free diffusion of chemicals in and out of the culturing channel. [43][44][45][46][47][48] The antibiotic gradient is formed in agar gel. Agarose immobilizes bacteria after solidification and prevents growth out its initial area. ...
... The AST result can be detected by counting the cell number under microscope, 48 analyzing the micrographs for acquiring cell density across the gradient [43][44][45]47 or identifying the spectrum characteristics associated with cell density. 46 The profile of cell density along the concentration gradient is plotted. This curve exhibits the dose-dependent cell response and indicates the exact MIC. ...
Antimicrobial resistance is getting serious and becoming a threat to public health worldwide. The improper and excessive use of antibiotics is responsible for this situation. The standard methods used in clinical laboratories, to diagnose bacterial infections, identify pathogens, and determine susceptibility profiles, are time-consuming and labor-intensive, leaving the empirical antimicrobial therapy as the only option for the first treatment. To prevent the situation from getting worse, evidence-based therapy should be given. The choosing of effective drugs requires powerful diagnostic tools to provide comprehensive information on infections. Recent progress in microfluidics is pushing infection diagnosis and antimicrobial susceptibility testing (AST) to be faster and easier. This review summarizes the recent development in microfluidic assays for rapid identification and AST in bacterial infections. Finally, we discuss the perspective of microfluidic-AST to develop the next-generation infection diagnosis technologies.
... They were even able to quantify the dead cells from a large number of viable cells. Kim et al. proposed a new computer-vision-based method to estimate the viability of bacteria in a microfluidic channel using fast Fourier transform to detect the frequency alteration of a microscopic image 68 . By analyzing the frequency of time-lapse images, the regional concentration changes of bacteria cultured under an antibiotic gradient were detected. ...
In this paper, we review the integration of microfluidic chips and computer vision, which has great potential to advance research in the life sciences and biology, particularly in the analysis of cell imaging data. Microfluidic chips enable the generation of large amounts of visual data at the single-cell level, while computer vision techniques can rapidly process and analyze these data to extract valuable information about cellular health and function. One of the key advantages of this integrative approach is that it allows for noninvasive and low-damage cellular characterization, which is important for studying delicate or fragile microbial cells. The use of microfluidic chips provides a highly controlled environment for cell growth and manipulation, minimizes experimental variability and improves the accuracy of data analysis. Computer vision can be used to recognize and analyze target species within heterogeneous microbial populations, which is important for understanding the physiological status of cells in complex biological systems. As hardware and artificial intelligence algorithms continue to improve, computer vision is expected to become an increasingly powerful tool for in situ cell analysis. The use of microelectromechanical devices in combination with microfluidic chips and computer vision could enable the development of label-free, automatic, low-cost, and fast cellular information recognition and the high-throughput analysis of cellular responses to different compounds, for broad applications in fields such as drug discovery, diagnostics, and personalized medicine.
... The nanoprecipitation process's multivariate nature and the extensive laboratory optimization required in microfluidics make it particularly suited to nonlinear modelling, e.g. by means of Artificial Neural Networks (ANN) [25][26][27][28][29][30] . Indeed, as already demonstrated in other industry sectors (e.g. in the semiconductor industry, where the modelling phase is adopted to support the production processes), I the combination of ANN with microfluidics could represent an opportunity in the biotechnology and bioengineering field [31][32][33] , with applications ranging from microfluidic cell culture and flow cytometry [34][35][36] to microchannel design and computational fluid dynamics simulations [ 37 , 38 ] to the estimation of flow parameters in the formation of micro-and nanostructures [39][40][41][42][43] and, not least, to the optimization of microfluidicbased point of care devices [44] . While some studies have already adopted the ANN modelling to optimize formulation parameters in batch nanoprecipitation processes [30] , in the last decade, only a few works have focused on ANN's usability to study the nanoprecipitation process in microfluidics. ...
The engineering of nanoparticles impacts the control of their nano-bio interactions at each level of the delivery pathway. Therefore, optimal nanoparticle physicochemical properties should be identified to favour on-target interactions and deliver efficiently active compounds to a specific target. To date, traditional batch processes do not guarantee the reproducibility of results and low polydispersity index of the nanostructures, while microfluidics has emerged as cost effectiveness, short-production time approach to control the nanoparticle size and size distribution. Several thermodynamic processes have been implemented in microfluidics, such as nanoprecipitation, ionotropic gelation, self-assembly, etc., to produce nanoparticles in a continuous mode and high throughput way. In this work, we show how the Artificial Neural Network (ANN) can be adopted to model the impact of microfluidic parameters (namely, flow rates and polymer concentrations) on the size of the nanoparticles. Promising results have been obtained, with the highest model accuracy reaching 98.9 %, thus confirming the proposed approach's potential applicability for an ANN-guided biopolymer nanoparticle design for biomedical applications. Nanostructures with different degrees of complexity are analysed, and a proof-of-concept machine learning approach is proposed to evaluate Hydrodenticity in biopolymer matrices.
STATEMENT OF SIGNIFICANCE: Size, shape and surface charge determine nano-bio interactions of nanoparticles and their ability to target diseases. The ideal nanoparticle design avoids off-target interactions and favours on-target interactions. So, tools enabling the identification of the optimal nanoparticle physicochemical properties for delivery to a specific target are required. In this work, we evaluate the use of Artificial Neural Network (ANN) to analyse the role of microfluidic parameters in predicting the optimal size of the different hydrogel nanoparticles and their ability to trigger Hydrodenticity.
... Microfluidics applications to separate bacterial cells in unprocessed samples already play a valuable role in single-cell research (22). Certain microfluidic applications have incorporated cultivation and visual estimation of growth, but this is possible only for rapidly growing bacteria (23,24). For clinical sputum samples, microfluidic applications could capture the mycobacteria and reduce the amounts of nonmycobacteria, thus enriching the M. tuberculosis for downstream analyses. ...
Mycobacterium tuberculosis whole-genome sequencing (WGS) is a powerful tool as it can provide data on population diversity, drug resistance, disease transmission, and mixed infections. Successful WGS is still reliant on high concentrations of DNA obtained through M. tuberculosis culture. Microfluidics technology plays a valuable role in single-cell research but has not yet been assessed as a bacterial enrichment strategy for culture-free WGS of M. tuberculosis. In a proof-of-principle study, we evaluated the use of Capture-XT, a microfluidic lab-on-chip cleanup and pathogen concentration platform to enrich M. tuberculosis bacilli from clinical sputum specimens for downstream DNA extraction and WGS. Three of the four (75%) samples processed by the microfluidics application passed the library preparation quality control, compared to only one of the four (25%) samples not enriched by the microfluidics M. tuberculosis capture application. WGS data were of sufficient quality, with mapping depth of ≥25× and 9 to 27% of reads mapping to the reference genome. These results suggest that microfluidics-based M. tuberculosis cell capture might be a promising method for M. tuberculosis enrichment in clinical sputum samples, which could facilitate culture-free M. tuberculosis WGS.
IMPORTANCE Diagnosis of tuberculosis is effective using molecular methods; however, a comprehensive characterization of the resistance profile of Mycobacterium tuberculosis often requires culturing and phenotypic drug susceptibility testing or culturing followed by whole-genome sequencing (WGS). The phenotypic route can take anywhere from 1 to >3 months to result, by which point the patient may have acquired additional drug resistance. The WGS route is a very attractive option; however, culturing is the rate-limiting step. In this original article, we provide proof-of-principle evidence that microfluidics-based cell capture can be used on high-bacillary-load clinical samples for culture-free WGS.
... Particularly for conventional AST, the time-to-result is of the essence for patient care. Therefore, automated microfluidic platforms in conjugation with optical detection and deep learningpowered analysis to rapidly detect bacteria, as well as screening and identifying appropriate antibiotics choices and therapeutic ranges, are driven by clinical needs [178][179][180][181]. ...
Microfluidics is a rapidly growing discipline that involves studying and manipulating fluids at reduced length scale and volume, typically on the scale of micro- or nanoliters. Under the reduced length scale and larger surface-to-volume ratio, advantages of low reagent consumption, faster reaction kinetics, and more compact systems are evident in microfluidics. However, miniaturization of microfluidic chips and systems introduces challenges of stricter tolerances in designing and controlling them for interdisciplinary applications. Recent advances in artificial intelligence (AI) have brought innovation to microfluidics from design, simulation, automation, and optimization to bioanalysis and data analytics. In microfluidics, the Navier–Stokes equations, which are partial differential equations describing viscous fluid motion that in complete form are known to not have a general analytical solution, can be simplified and have fair performance through numerical approximation due to low inertia and laminar flow. Approximation using neural networks trained by rules of physical knowledge introduces a new possibility to predict the physicochemical nature. The combination of microfluidics and automation can produce large amounts of data, where features and patterns that are difficult to discern by a human can be extracted by machine learning. Therefore, integration with AI introduces the potential to revolutionize the microfluidic workflow by enabling the precision control and automation of data analysis. Deployment of smart microfluidics may be tremendously beneficial in various applications in the future, including high-throughput drug discovery, rapid point-of-care-testing (POCT), and personalized medicine. In this review, we summarize key microfluidic advances integrated with AI and discuss the outlook and possibilities of combining AI and microfluidics.
... We demonstrated compatibility with fluorescent dyes for viability (i.e., alamarBlue), but it might also be possible to visually estimate bacteria growth with brightfield microscopy. 36 We also explored whether it was possible to recapitulate our microscale method on the macroscale by immobilizing E. coli in a thin layer of agarose on the bottom of a 96-well plate. While a 20 min exposure could be executed and could achieve similar results (Fig. S2 in the supplementary material), it was difficult to perform all pipetting steps within a shorter time frame (e.g., 10 min). ...
Microfluidic tools are well suited for studying bacteria as they enable the analysis of small colonies or single cells. However, current techniques for studying bacterial response to antibiotics are largely limited to static dosing. Here, we describe a microfluidic device and a method for entrapping and cultivating bacteria in hydrogel plugs. Ring-shaped isolation valves are used to define the shape of the plugs and also to control exposure of the plugs to the surrounding medium. We demonstrate bacterial cultivation, determination of the minimum inhibitory concentration of an antibiotic, and transient dosing of an antibiotic at sub-1-h doses. The transient dosing experiments reveal that at dose durations on the order of minutes, ampicillin's bactericidal effect has both a time and concentration dependency.
... Based on the diffusion behavior, the side channel along the main channel experiences different antibiotic concentrations. [24][25][26] Although this design was previously demonstrated for MIC determination, issues still exist. First, the above methods usually use imaging-based methods, such as bacteria counting or fluorescence labeling, to quantify the effect of antibiotics on the bacteria. ...
Antimicrobial susceptibility testing (AST) is a key measure in clinical microbiology laboratories to enable appropriate antimicrobial administration. During an AST, the determination of the minimum inhibitory concentration (MIC) is an important step in which the bacterial responses to an antibiotic at a series of concentrations obtained in separate bacterial growth chambers or sites are compared. However, the preparation of different antibiotic concentrations is time-consuming and labor-intensive. In this paper, we present a microfluidic device that generates a concentration gradient for antibiotics that is produced by diffusion in the laminar flow regime along a series of lateral microwells to encapsulate bacteria for antibiotic treatment. All the AST preparation steps (including bacterium loading, antibiotic concentration generation, buffer washing, and isolated bacterial growth with an antibiotic) can be performed in a single chip. The viable bacterial cells in each microwell after the antibiotic treatment are then quantified by their surface-enhanced Raman scattering (SERS) signals that are acquired after placing a uniform SERS-active substrate in contact with all the microwells. For proof-of-concept, we demonstrated the AST performance of this system on ampicillin (AMP)-susceptible and -resistant E. coli strains. Compared with the parameters for conventional AST methods, the AST procedure based on this chip requires only 20 μL of bacteria solution and 5 h of operation time. This result indicates that this integrated system can greatly shorten and simplify the tedious and labor-intensive procedures required for current standard AST methods.
... Most existing measurement methods are not suitable for microfluidic equipment with small sample volumes as the level of bacteria in the channel needs to be measured during culturing. Hence, Kim et al. developed an image-based method to assess the growth status of bacteria in microfluidic channels [91]. In this study, bacteria were cultured in a microfluidic device with liquid and agar gel media in two separate channels. ...
... Related to the Figure 9: Deep learning in images. (a) CNN structure estimated bacterial growth in microfluidic channels (reproduced with permission from Ref. [91]). (b) CNN architecture combined with an RNN architecture, which could use the temporal information of a single-cell track to choose the lineage of a stem cell's progeny automatically (reproduced with permission from Ref. [92]). 10 Research aforementioned examples, Nguyen et al., in the group mentioned earlier, built a more sophisticated HER2 + breast tumor microenvironment in the tumor-on-a-chip (Figure 11(a)) [98]. ...
Microfluidic-based organs-on-chips (OoCs) are a rapidly developing technology in biomedical and chemical research and have emerged as one of the most advanced and promising in vitro models. The miniaturization, stimulated tissue mechanical forces, and microenvironment of OoCs offer unique properties for biomedical applications. However, the large amount of data generated by the high parallelization of OoC systems has grown far beyond the scope of manual analysis by researchers with biomedical backgrounds. Deep learning, an emerging area of research in the field of machine learning, can automatically mine the inherent characteristics and laws of “big data” and has achieved remarkable applications in computer vision, speech recognition, and natural language processing. The integration of deep learning in OoCs is an emerging field that holds enormous potential for drug development, disease modeling, and personalized medicine. This review briefly describes the basic concepts and mechanisms of microfluidics and deep learning and summarizes their successful integration. We then analyze the combination of OoCs and deep learning for image digitization, data analysis, and automation. Finally, the problems faced in current applications are discussed, and future perspectives and suggestions are provided to further strengthen this integration.
