Label-free biosensor of phagocytosis for diagnosing bacterial infections

A deformability-based biochip for precise label-free stratification of metastatic subtypes using deep learning

Cellular deformability is a promising biomarker for evaluating the physiological state of cells in medical applications. Microfluidics has emerged as a powerful technique for measuring cellular deformability. However, existing microfluidic-based assays for measuring cellular deformability rely heavily on image analysis, which can limit their scalability for high-throughput applications. Here, we develop a parallel constriction-based microfluidic flow cytometry device and an integrated computational framework (ATMQcD). The ATMQcD framework includes automatic training set generation, multiple object tracking, segmentation, and cellular deformability quantification. The system was validated using cancer cell lines of varying metastatic potential, achieving a classification accuracy of 92.4% for invasiveness assessment and stratifying cancer cells before and after hypoxia treatment. The ATMQcD system also demonstrated excellent performance in distinguishing cancer cells from leukocytes (accuracy = 89.5%). We developed a mechanical model based on power-law rheology to quantify stiffness, which was fitted with measured data directly. The model evaluated metastatic potentials for multiple cancer types and mixed cell populations, even under real-world clinical conditions. Our study presents a highly robust and transferable computational framework for multiobject tracking and deformation measurement tasks in microfluidics. We believe that this platform has the potential to pave the way for high-throughput analysis in clinical applications, providing a powerful tool for evaluating cellular deformability and assessing the physiological state of cells.

Biofilms exacerbate atherogenesis through macrophage-induced inflammatory responses in a fibrous plaque microsystem model

BACKGROUND

Microbes have been implicated in atherosclerosis development and progression, but the impact of bacterial-based biofilms on fibrous plaque rupture remains poorly understood.

RESULTS

Here, we developed a comprehensive atherosclerotic model to reflect the progression of fibrous plaque under biofilm-induced inflammation (FP-I). High expressions of biofilm-specific biomarkers algD, pelA and pslB validated the presence of biofilms. Biofilm promotes the polarization of macrophages towards a pro-inflammatory (M1) phenotype, as demonstrated by an increase in M1 macrophage-specific marker CD80 expression in CD68+ macrophages. The increase in the number of intracellular lipid droplets (LDs) and foam cell percentage highlighted the potential role of biofilms on lipid synthesis or metabolic pathways in macrophage-derived foam cells. In addition, collagen I production by myofibroblasts associated with the fibrous cap was significantly reduced along with the promotion of apoptosis of myofibroblasts, indicating that biofilms affect the structural integrity of the fibrous cap and potentially undermine its strength.

CONCLUSION

We validated the unique role of biofilm-based inflammation in exacerbating fibrous plaque damage in the FP-I model, increasing fibrous plaque instability and risk of thrombosis. Our results lay the foundation for mechanistic studies of the role of biofilms in fibrous plaques, allowing the evaluation of preclinical combination strategies for drug therapy.

STATEMENT OF SIGNIFICANCE

A microsystem-based model was developed to reveal interactions in fibrous plaque during biofilm-induced inflammation (FP-I). Real-time assessment of biofilm formation and its role in fibrous plaque progression was achieved. The presence of biofilms enhanced the expression of pro-inflammatory (M1) specific marker CD80, lipid droplets, and foam cells and reduced anti-inflammatory (M2) specific marker CD206 expression. Fibrous plaque exposure to biofilm-based inflammation reduced collagen I expression and increased apoptosis marker Caspase-3 expression significantly. Overall, we demonstrate the unique role of biofilm-based inflammation in exacerbating fibrous plaque damage in the FP-I model, promoting fibrous plaque instability and enhanced thrombosis risk. Our findings lay the groundwork for mechanistic studies, facilitating the evaluation of preclinical drug combination strategies.

Biofilm Potentiates Cancer-Promoting Effects of Tumor-Associated Macrophages in a 3D Multi-Faceted Tumor Model

Components of the tumor microenvironment (TME), such as tumor-associated macrophages (TAMs), influence tumor progression. The specific polarization and phenotypic transition of TAMs in the tumor microenvironment lead to two-pronged impacts that can promote or hinder cancer development and treatment. Here, a novel microfluidic multi-faceted bladder tumor model (TAM^PIEB ) is developed incorporating TAMs and cancer cells to evaluate the impact of bacterial distribution on immunomodulation within the tumor microenvironment in vivo. It is demonstrated for the first time that biofilm-induced inflammatory conditions within tumors promote the transition of macrophages from a pro-inflammatory M1-like to an anti-inflammatory/pro-tumor M2-like state. Consequently, multiple roles and mechanisms by which biofilms promote cancer by inducing pro-tumor phenotypic switch of TAMs are identified, including cancer hallmarks such as reducing susceptibility to apoptosis, enhancing cell viability, and promoting epithelial-mesenchymal transition and metastasis. Furthermore, biofilms formed by extratumoral bacteria can shield tumors from immune attack by TAMs, which can be visualized through various imaging assays in situ. The study sheds light on the underlying mechanism of biofilm-mediated inflammation on tumor progression and provides new insights into combined anti-biofilm therapy and immunotherapy strategies in clinical trials.

Sandwich-Type Electrochemical Aptasensor for Highly Sensitive and Selective Detection of Pseudomonas Aeruginosa Bacteria Using a Dual Signal Amplification Strategy

An electrochemical aptasensor developed to realize the detection of Pseudomonas aeruginosa (P. aeruginosa) bacteria based on a signal amplification strategy. The carbon screen-printed electrode (CSPE) surface was modified by MIL-101(Cr)/Multi-walled carbon nanotubes (MWCNT), which significantly increased the effective surface area of the electrode, thus resulting in further F23 aptamer immobilization at the surface of the modified electrode. As a result, the P. aeruginosa can be efficiently captured onto the surface of the aptasensor. Moreover, aptamer immobilized on the two-dimensional graphitic carbon nitride complex with silver nanoparticles (AgNPs/c-g-C₃N₄/Apt) was used as an electrochemical signal label, connected to P. aeruginosa bacteria at the modified electrode. This strategy increased the aptamer surface density and the sensitivity for detecting P. aeruginosa. Also, the resultant material was thoroughly characterized using Fourier transform infrared (FT-IR), X-ray diffraction (XRD), thermogravimetric analysis (TGA), field emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM), and Brunauer-Emmett-Teller (BET) analysis techniques. A highly sensitive voltammetric aptasensor for P. aeruginosa detection was obtained via this strategy at the limit of detection of 1 Colony-forming unit (CFU)/mL (σ = 3). Therefore, this proposed strategy with dual signal amplification can be a promising platform for simple, practical, reliable, and sensitive method for P. aeruginosa.

Inertial microfluidics: current status, challenges, and future opportunities

Inertial microfluidics uses the hydrodynamic effects induced at finite Reynolds numbers to achieve passive manipulation of particles, cells, or fluids and offers the advantages of high-throughput processing, simple channel geometry, and label-free and external field-free operation. Since its proposal in 2007, inertial microfluidics has attracted increasing interest and is currently widely employed as an important sample preparation protocol for single-cell detection and analysis. Although great success has been achieved in the inertial microfluidics field, its performance and outcome can be further improved. From this perspective, herein, we reviewed the current status, challenges, and opportunities of inertial microfluidics concerning the underlying physical mechanisms, available simulation tools, channel innovation, multistage, multiplexing, or multifunction integration, rapid prototyping, and commercial instrument development. With an improved understanding of the physical mechanisms and the development of novel channels, integration strategies, and commercial instruments, improved inertial microfluidic platforms may represent a new foundation for advancing biomedical research and disease diagnosis.

Label-free enrichment of human blast cells from whole blood for leukemia monitoring

Liquid biopsy is an alternative to invasive bone marrow biopsy for leukemia detection and management. However, no robust technology is available for enriching leukemic blast cells from the blood. Here, we present a simple and effective protocol for vigorous enrichment of blast cells from whole blood using a one-step microfluidic blast cell biochip (BCB) that exploits distinct cell mechanical properties between diseased and healthy leukocytes. The BCB system achieves higher sensitivity than flow cytometry in detecting blasts.

For complete details on the use and execution of this protocol, please refer to Khoo et al. (2019).

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A label-free biochip to enrich leukemic blasts from liquid biopsy (blood) samples

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Detailed approaches for fabricating the microfluidic blast cell biochip (BCB)

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Detailed steps for preparing blood samples to isolate leukemic blasts from blood

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Operational procedure for biochip enrichment of leukemic blasts from whole blood

Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

A Technique for Rapid Bacterial-Density Enumeration through Membrane Filtration and Differential Pressure Measurements

In this article, we present a microfluidic technique for the rapid enumeration of bacterial density with a syringe filter to trap bacteria and the quantification of the bacterial density through pressure difference measurement across the membrane. First, we established the baseline differential pressure and hydraulic resistance for a filtration membrane by fully wetting the filter with DI water. Subsequently, when bacteria were infused and trapped at the pores of the membrane, the differential pressure and hydraulic resistance also increased. We characterized the infusion time required for the bacterial sample to achieve a normalized hydraulic resistance of 1.5. An equivalent electric-circuit model and calibration data sets from parametric studies were used to determine the general form of a calibration curve for the prediction of the bacterial density of a bacterial sample. As a proof of concept, we demonstrated through blind tests with Escherichia coli that the device is capable of determining the bacterial density of a sample ranging from 7.3 × 10⁶ to 2.2 × 10⁸ CFU/mL with mean and median accuracies of 87.21% and 91.33%, respectively. The sample-to-result time is 19 min for a sample with lower detection threshold, while for higher-bacterial-density samples the measurement time is further shortened to merely 8 min.

Rapid detection of microorganisms in a fish infection microfluidics platform

Inadequate access to clean water is detrimental to human health and aquatic industries. Waterborne pathogens can survive prolonged periods in aquatic bodies, infect commercially important seafood, and resist water disinfection, resulting in human infections. Environmental agencies and research laboratories require a relevant, portable, and cost-effective platform to monitor microbial pathogens and assess their risk of infection on a large scale. Advances in microfluidics enable better control and higher precision than traditional culture-based pathogen monitoring approaches. We demonstrated a rapid, high-throughput fish-based teleost (fish)-microbe (TelM) microfluidic-based device that simultaneously monitors waterborne pathogens in contaminated waters and assesses their infection potential under well-defined settings. A chamber-associated port allows direct access to the animal, while the transparency of the TelM platform enables clear observation of sensor readouts. As proof-of-concept, we established a wound infection model using Pseudomonas aeruginosa-contaminated water in the TelM platform, where bacteria formed biofilms on the wound and secreted a biofilm metabolite, pyoverdine. Pyoverdine was used as fluorescent sensor to correlate P. aeruginosa contamination to infection. The TelM platform was validated with environmental waterborne microbes from marine samples. Overall, the TelM platform can be readily applied to assess microbial and chemical risk in aquatic bodies in resource-constrained settings.

Early Predictor Tool of Disease Using Label-Free Liquid Biopsy-Based Platforms for Patient-Centric Healthcare

Cancer cells undergo phenotypic changes or mutations during treatment, making detecting protein-based or gene-based biomarkers challenging. Here, we used algorithmic analysis combined with patient-derived tumor models to derive an early prediction tool using patient-derived cell clusters from liquid biopsy (LIQBP) for cancer prognosis in a label-free manner. The LIQBP platform incorporated a customized microfluidic biochip that mimicked the tumor microenvironment to establish patient clusters, and extracted physical parameters from images of each sample, including size, thickness, roughness, and thickness per area (n = 31). Samples from healthy volunteers (n = 5) and cancer patients (pretreatment; n = 4) could be easily distinguished with high sensitivity (91.16 ± 1.56%) and specificity (71.01 ± 9.95%). Furthermore, we demonstrated that the multiple unique quantitative parameters reflected patient responses. Among these, the ratio of normalized gray value to cluster size (RGVS) was the most significant parameter correlated with cancer stage and treatment duration. Overall, our work presented a novel and less invasive approach for the label-free prediction of disease prognosis to identify patients who require adjustments to their treatment regime. We envisioned that such efforts would promote the management of personalized patient care conveniently and cost effectively.

Early Predictor Tool of Disease Using Label-Free Liquid Biopsy-Based Platforms for Patient-Centric Healthcare

Simple Summary

We proposed a comprehensive early prediction tool based on liquid biopsy for the label-free phenotypic analysis of cell clusters from clinical samples (n = 31). Our custom algorithm analysis, combined with a microfluidic-based tumor model, was designed to assess and stratify cancer patients in a label-free, cost-effective, and user-friendly way. Multiple quantitative phenotypic parameters (cluster size, thickness, roughness, and thickness per area) were derived from the profiling of the patient-derived cell clusters. Our platform could distinguish healthy donors from pretreatment cancer patients with high sensitivity (91.16 ± 1.56%) and specificity (71.01 ± 9.95%). In addition, the ratio of normalized gray value to cluster size (RGVS) parameter was significantly correlated to treatment duration and cancer stage. In conclusion, our patient-centric, early prediction tool will allow the prognosis of patients in a relatively less invasive manner, which can help clinicians identify diseases or indicate the need for new treatment strategies.

Abstract

Cancer cells undergo phenotypic changes or mutations during treatment, making detecting protein-based or gene-based biomarkers challenging. Here, we used algorithmic analysis combined with patient-derived tumor models to derive an early prediction tool using patient-derived cell clusters from liquid biopsy (LIQBP) for cancer prognosis in a label-free manner. The LIQBP platform incorporated a customized microfluidic biochip that mimicked the tumor microenvironment to establish patient clusters, and extracted physical parameters from images of each sample, including size, thickness, roughness, and thickness per area (n = 31). Samples from healthy volunteers (n = 5) and cancer patients (pretreatment; n = 4) could be easily distinguished with high sensitivity (91.16 ± 1.56%) and specificity (71.01 ± 9.95%). Furthermore, we demonstrated that the multiple unique quantitative parameters reflected patient responses. Among these, the ratio of normalized gray value to cluster size (RGVS) was the most significant parameter correlated with cancer stage and treatment duration. Overall, our work presented a novel and less invasive approach for the label-free prediction of disease prognosis to identify patients who require adjustments to their treatment regime. We envisioned that such efforts would promote the management of personalized patient care conveniently and cost effectively.

Biofilm dispersal induced by mechanical cutting leads to heightened foodborne pathogen dissemination

The biofilm life cycle where bacteria alternate between biofilm and planktonic lifestyles poses major implications in food spoilage and gastrointestinal infections. Recent studies had shown that freshly biofilm-dispersed cells have a unique physiology from planktonic cells, raising the fundamental question if biofilm-dispersed cells and planktonic cells disseminate differently across food surfaces. Mechanical dislodging via cutting can cause biofilm dispersal and eventual food cross-contamination. Here, we showed that biofilm-dispersed bacteria from various foodborne pathogens were transferred from freshly cut surface at a higher rate to the cutting material than that of planktonic bacteria. When the cutting tool was used to cut a fresh surface, more biofilm-dispersed bacteria were disseminated from the cutting tool to the newly cut surface than planktonic bacteria. Our observations were applicable to cutting tools of various materials and cut surfaces, where polystyrene and surfaces with high water content were most susceptible to biofilm transfer, respectively. Simple washing with detergent and mechanical wiping could aid bacterial removal from cutting tools. Our work revealed that biofilm-dispersed cells were transferred at a higher rate than planktonic cells and cutting tool was an important medium for pathogen cross-contamination, thus providing insights in maintaining their cleanliness in food processing industries.