The effects of biofilms on tumor progression in a 3D cancer-biofilm microfluidic model

Research Progress in the Field of Tumor Model Construction Using Bioprinting: A Review

The development of therapeutic drugs and methods has been greatly facilitated by the emergence of tumor models. However, due to their inherent complexity, establishing a model that can fully replicate the tumor tissue situation remains extremely challenging. With the development of tissue engineering, the advancement of bioprinting technology has facilitated the upgrading of tumor models. This article focuses on the latest advancements in bioprinting, specifically highlighting the construction of 3D tumor models, and underscores the integration of these two technologies. Furthermore, it discusses the challenges and future directions of related techniques, while also emphasizing the effective recreation of the tumor microenvironment through the emergence of 3D tumor models that resemble in vitro organs, thereby accelerating the development of new anticancer therapies.

Organoids as an in vitro model to study human tumors and bacteria

Organoids faithfully replicate the morphological structure, physiological functions, stable phenotype of the source tissue. Recent research indicates that bacteria can significantly influence the initiation, advancement, and treatment of tumors. This article provides a comprehensive review of the applications of organoid technology in tumor research, the relationship between bacteria and the genesis and development of tumors, and the exploration of the impact of bacteria on tumors and their applications in research.

Oxidative stress induced by Etoposide anti-cancer chemotherapy drives the emergence of tumor-associated bacteria resistance to fluoroquinolones

INTRODUCTION

Antibiotic-resistant bacterial infections, such as Pseudomonas aeruginosa and Staphylococcus aureus, are prevalent in lung cancer patients, resulting in poor clinical outcomes and high mortality. Etoposide (ETO) is an FDA-approved chemotherapy drug that kills cancer cells by damaging DNA through oxidative stress. However, it is unclear if ETO can cause unintentional side effects on tumor-associated microbial pathogens, such as inducing antibiotic resistance.

OBJECTIVES

We aimed to show that prolonged ETO treatment could unintendedly confer fluoroquinolone antibiotic resistance to P. aeruginosa, and evaluate the effect of tumor-associated P. aeruginosa on tumor progression.

METHODS

We employed experimental evolution assay to treat P. aeruginosa with prolonged ETO exposure, evaluated the ciprofloxacin resistance, and elucidated the gene mutations by DNA sequencing. We also established a lung tumor-P. aeruginosa bacterial model to study the role of ETO-evolved intra-tumoral bacteria in tumor progression using immunostaining and confocal microscopy.

RESULTS

ETO could generate oxidative stress and lead to gene mutations in P. aeruginosa, especially the gyrase (gyrA) gene, resulting in acquired fluoroquinolone resistance. We further demonstrated using a microfluidic-based lung tumor-P. aeruginosa coculture model that bacteria can evolve ciprofloxacin (CIP) resistance in a tumor microenvironment. Moreover, ETO-induced CIP-resistant (EICR) mutants could form multicellular biofilms which protected tumor cells from ETO killing and enabled tumor progression.

CONCLUSION

Overall, our preclinical proof-of-concept provides insights into how anti-cancer chemotherapy could inadvertently allow tumor-associated bacteria to acquire antibiotic resistance mutations and shed new light on the development of novel anti-cancer treatments based on anti-bacterial strategies.

Microfluidics-based patient-derived disease detection tool for deep learning-assisted precision medicine

Cancer spatial and temporal heterogeneity fuels resistance to therapies. To realize the routine assessment of cancer prognosis and treatment, we demonstrate the development of an Intelligent Disease Detection Tool (IDDT), a microfluidic-based tumor model integrated with deep learning-assisted algorithmic analysis. IDDT was clinically validated with liquid blood biopsy samples (n = 71) from patients with various types of cancers (e.g., breast, gastric, and lung cancer) and healthy donors, requiring low sample volume (∼200 μl) and a high-throughput 3D tumor culturing system (∼300 tumor clusters). To support automated algorithmic analysis, intelligent decision-making, and precise segmentation, we designed and developed an integrative deep neural network, which includes Mask Region-Based Convolutional Neural Network (Mask R-CNN), vision transformer, and Segment Anything Model (SAM). Our approach significantly reduces the manual labeling time by up to 90% with a high mean Intersection Over Union (mIoU) of 0.902 and immediate results (<2 s per image) for clinical cohort classification. The IDDT can accurately stratify healthy donors (n = 12) and cancer patients (n = 55) within their respective treatment cycle and cancer stage, resulting in high precision (∼99.3%) and high sensitivity (∼98%). We envision that our patient-centric IDDT provides an intelligent, label-free, and cost-effective approach to help clinicians make precise medical decisions and tailor treatment strategies for each patient.

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 and Cancer: Interactions and Future Directions for Cancer Therapy

There is a growing body of evidence supporting the significant role of bacterial biofilms in the pathogenesis of various human diseases, including cancer. Biofilms are polymicrobial communities enclosed within an extracellular matrix composed of polysaccharides, proteins, extracellular DNA, and lipids. This complex matrix provides protection against antibiotics and host immune responses, enabling the microorganisms to establish persistent infections. Moreover, biofilms induce anti-inflammatory responses and metabolic changes in the host, further facilitating their survival. Many of these changes are comparable to those observed in cancer cells. This review will cover recent research on the role of bacterial biofilms in carcinogenesis, especially in colorectal (CRC) and gastric cancers, emphasizing the shared physical and chemical characteristics of biofilms and cancer. This review will also discuss the interactions between bacteria and the tumor microenvironment, which can facilitate oncogene expression and cancer progression. This information will provide insight into developing new therapies to identify and treat biofilm-associated cancers, such as utilizing bacteria as delivery vectors, using bacteria to upregulate immune function, or more selectively targeting biofilms and cancer for their shared traits.

Distinct bacterial population dynamics and disease dissemination after biofilm dispersal and disassembly

Microbial communities that form surface-attached biofilms must release and disperse their constituent cells into the environment to colonize fresh sites for continued survival of their species. For pathogens, biofilm dispersal is crucial for microbial transmission from environmental reservoirs to hosts, cross-host transmission, and dissemination of infections across tissues within the host. However, research on biofilm dispersal and its consequences in colonization of fresh sites remain poorly understood. Bacterial cells can depart from biofilms via stimuli-induced dispersal or disassembly due to direct degradation of the biofilm matrix, but the complex heterogeneity of bacterial populations released from biofilms rendered their study difficult. Using a novel 3D-bacterial "biofilm-dispersal-then-recolonization" (BDR) microfluidic model, we demonstrated that Pseudomonas aeruginosa biofilms undergo distinct spatiotemporal dynamics during chemical-induced dispersal (CID) and enzymatic disassembly (EDA), with contrasting consequences in recolonization and disease dissemination. Active CID required bacteria to employ bdlA dispersal gene and flagella to depart from biofilms as single cells at consistent velocities but could not recolonize fresh surfaces. This prevented the disseminated bacteria cells from infecting lung spheroids and Caenorhabditis elegans in on-chip coculture experiments. In contrast, EDA by degradation of a major biofilm exopolysaccharide (Psl) released immotile aggregates at high initial velocities, enabling the bacteria to recolonize fresh surfaces and cause infections in the hosts efficiently. Hence, biofilm dispersal is more complex than previously thought, where bacterial populations adopting distinct behavior after biofilm departure may be the key to survival of bacterial species and dissemination of diseases.

3D Biomimetic Models to Reconstitute Tumor Microenvironment In Vitro: Spheroids, Organoids, and Tumor-on-a-Chip

Decades of efforts in engineering in vitro cancer models have advanced drug discovery and the insight into cancer biology. However, the establishment of preclinical models that enable fully recapitulating the tumor microenvironment remains challenging owing to its intrinsic complexity. Recent progress in engineering techniques has allowed the development of a new generation of in vitro preclinical models that can recreate complex in vivo tumor microenvironments and accurately predict drug responses, including spheroids, organoids, and tumor-on-a-chip. These biomimetic 3D tumor models are of particular interest as they pave the way for better understanding of cancer biology and accelerating the development of new anticancer therapeutics with reducing animal use. Here, the recent advances in developing these in vitro platforms for cancer modeling and preclinical drug screening, focusing on incorporating hydrogels are reviewed to reconstitute physiologically relevant microenvironments. The combination of spheroids/organoids with microfluidic technologies is also highlighted to better mimic in vivo tumors and discuss the challenges and future directions in the clinical translation of such models for drug screening and personalized medicine.

Translocation of Oral Microbiota into the Pancreatic Ductal Adenocarcinoma Tumor Microenvironment

Oral dysbiosis has long been associated with pancreatic ductal adenocarcinoma (PDAC). In this work, we explore the relationship between the oral and tumor microbiomes of patients diagnosed with PDAC. Salivary and tumor microbiomes were analyzed using a variety of sequencing methods, resulting in a high prevalence and relative abundance of oral bacteria, particularly Veillonella and Streptococcus, within tumor tissue. The most prevalent and abundant taxon found within both saliva and tumor tissue samples, Veillonella atypica, was cultured from patient saliva, sequenced and annotated, identifying genes that potentially contribute to tumorigenesis. High sequence similarity was observed between sequences recovered from patient matched saliva and tumor tissue, indicating that the taxa found in PDAC tumors may derive from the mouth. These findings may have clinical implications in the care and treatment of patients diagnosed with PDAC.

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.

Extracellular lipids of Candida albicans biofilm induce lipid droplet formation and decreased response to a topoisomerase I inhibitor in dysplastic and neoplastic oral cells

OBJECTIVE

Some microorganisms, i.e., Candida albicans, have been associated with cancer onset and development, although whether the fungus promotes cancer or whether cancer facilitates the growth of C. albicans is unclear. In this context, microbial-derived molecules can modulate the growth and resistance of cancer cells. This study isolated extracellular lipids (ECL) from a 36-h Candida albicans biofilm incubated with oral dysplastic (DOK) and neoplastic (SCC 25) cells, which were further challenged with the topoisomerase I inhibitor camptothecin (CPT), a lipophilic anti-tumoral molecule.

METHODOLOGY

ECL were extracted from a 36-h Candida albicans biofilm with the methanol/chloroform precipitation method and identified with Nuclear Magnetic Resonance (1H-NMR). The MTT tetrazolium assay measured ECL cytotoxicity in DOK and SCC 25 cells, alamarBlue™ assessed cell metabolism, flow cytometry measured cell cycle, and confocal microscopy determined intracellular features.

RESULTS

Three major classes of ECL of C. albicans biofilm were found: phosphatidylinositol (PI), phosphatidylcholine (PC), and phosphatidylglycerol (PG). The ECL of C. albicans biofilm had no cytotoxic effect on neither cell after 24 hours, with a tendency to disturb the SCC 25 cell cycle profile (without statistical significance). The ECL-induced intracellular lipid droplet (LD) formation on both cell lines after 72 hours. In this context, ECL enhanced cell metabolism, decreased the response to CPT, and modified intracellular drug distribution.

CONCLUSION

The ECL (PI, PC, and PG) of 36-h Candida albicans biofilm directly interacts with dysplastic and neoplastic oral cells, highlighting the relevance of better understanding C. albicans biofilm signaling in the microenvironment of tumor cells.

Fundamentals of utilizing microbes in advanced cancer therapeutics: Current understanding and potential applications

One of the biggest health related issues in the twenty-first century is cancer. The current therapeutic platforms have not advanced enough to keep up with the number of rising cases. The traditional therapeutic approaches frequently fail to produce the desired outcomes. Therefore, developing new and more potent remedies is crucial. Recently, investigating microorganisms as potential anti-cancer treatments have garnered a lot of attention. Tumor-targeting microorganisms are more versatile at inhibiting cancer than the majority of standard therapies. Bacteria preferentially gather and thrive inside tumors, where they can trigger anti-cancer immune responses. They can be further trained to generate and distribute anticancer drugs based on clinical requirements using straightforward genetic engineering approaches. To improve clinical outcomes, therapeutic strategies utilizing live tumor-targeting bacteria can be used either alone or in combination with existing anticancer treatments. On the other hand, oncolytic viruses that target cancer cells, gene therapy via viral vectors, and viral immunotherapy are other popular areas of biotechnological investigation. Therefore, viruses serve as a unique candidate for anti-tumor therapy. This chapter describes the role of microbes, primarily bacteria and viruses in anti-cancer therapeutics. The various approaches to utilizing microbes in cancer therapy are discussed and examples of microorganisms that are now in use or that are undergoing experimental research are briefly discussed. We further point out the hurdles and the prospects of microbes-based remedies for cancer treatment.

A rapid screening platform to coculture bacteria within tumor spheroids

The prevalence of tumor-colonizing bacteria along with advances in synthetic biology are leading to a new generation of living microbial cancer therapies. Because many bacterial systems can be engineered to recombinantly produce therapeutics within tumors, simple and high-throughput experimental platforms are needed to screen the large collections of bacteria candidates and characterize their interactions with cancer cells. Here, we describe a protocol to selectively grow bacteria within the core of tumor spheroids, allowing for their continuous and parallel profiling in physiologically relevant conditions. Specifically, tumor spheroids are incubated with bacteria in a 96-well low-adhesion plate followed by a series of washing steps and an antibiotic selection protocol to confine bacterial growth within the hypoxic and necrotic core of tumor spheroids. This bacteria spheroid coculture (BSCC) system is stable for over 2 weeks, does not require specialized equipment and is compatible with time-lapse microscopy, commercial staining assays and histology that uniquely enable analysis of growth kinetics, viability and spatial distribution of both cellular populations, respectively. We show that the procedure is applicable to multiple tumor cell types and bacterial species by varying protocol parameters and is validated by using animal models. The BSCC platform will allow the study of bacteria-tumor interactions in a continuous manner and facilitate the rapid development of engineered microbial therapies.

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.

Biofilm matrix cloaks bacterial quorum sensing chemoattractants from predator detection

Microbes often secrete high levels of quorum sensing (QS) autoinducers into the environment to coordinate gene expression and biofilm formation, but risk detection and subsequent predation by bacterivorous predators. With such prominent signaling molecules acting as chemoattractants that diffuse into the environment at alarmingly high concentrations, it is unclear if bacterial cells can mask their chemical trails from predator detection. Here, we describe a microbial-based anti-detection adaptation, termed as "biofilm cloak", where the biofilm prey produced biofilm matrix exopolysaccharides that "locked" and reduced the leaching of autoinducers into the milieu, thereby concealing their trails to the detection by the bacterivorous Caenorhabditis elegans nematode. The exopolysaccharides act as common good for the non-producers to hide their autoinducers from predator detection. Deficiency in chemosensory gene odr-10 in mutant animals abrogated their ability to detect autoinducers and migrate toward their prey in a directed manner, which led to lower population growth rate of animals. Hence, restriction of bacterial communication activities to the confinements of biofilms is a novel approach for predator evasion, which plays a fundamental role in shaping ecological dynamics of microbial communities and predator-prey interactions.

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.

Label-free biosensor of phagocytosis for diagnosing bacterial infections

Phagocytic cells recognize and phagocytose invading microbes for destruction. However, bacterial pathogens can remain hidden at low levels from conventional detection or replicate intracellularly after being phagocytosed by immune cells. Current phagocytosis-detection approaches involve flow cytometry or microscopic search for rare bacteria-internalized phagocytes among large populations of uninfected cells, which poses significant challenges in research and clinical settings. Hence it is imperative to develop a rapid, non-disruptive, and label-free phagocytosis detection approach. Using deformability assays and microscopic imaging, we have demonstrated for the first time that the presence of intracellular bacteria in phagocytic blood cells led to aberrant physical properties. Specifically, human monocytes with internalized bacteria of various species were stiffer and larger compared with uninfected monocytes. Taking advantage of these physical differences, a novel microfluidics-based biosensor platform was developed to passively sort, concentrate and quantify rare monocytes with internalized pathogens (MIP) from uninfected monocyte populations for phagocytosis detection. The clinical utility of the MIP platform was demonstrated by enriching and detecting bacteria-internalized monocytes from spiked human blood samples within 1.5 h. Patient-derived clinical isolates were used to validate the utility of the MIP platform further. This proof-of-concept presents a phagocytosis detection platform that could be used to rapidly diagnose microbial infections, especially in bloodstream infections (BSIs), thereby improving the clinical outcomes for point-of-care management.