Biological microfluidic chip apparatus and use thereof in determination of microbial movement mode

Abstract

Provided are a biological microfluidic chip apparatus and a detection system comprised therein. The biological microfluidic chip apparatus can distinguish non-motile bacteria, swimming bacteria, and swarming bacteria, and on the basis of the determination results, it is further used in disease diagnosis, biomarkers, target bacteria screening, drug-resistant bacteria screening, and swarming inhibitor screening. The determination and separation method involved has been proven to be both convenient and highly valuable, and can further assist in diagnosis and treatment. The entire apparatus is relatively miniaturized and portable, allowing determination to be performed at any convenient time.

Description

A bio-microfluidic chip device and its application in detecting microbial motility patterns Technical Field

[0001] This invention relates to the field of biochips, and more specifically to a biomicrofluidic chip device for detecting microbial motility patterns. Background Technology

[0002] Bacterial motility patterns are an important characteristic, not only enhancing bacterial cell function but also impacting host health. Bacterial swarming and swimming are two primary types of flagellated-driven locomotion. Bacterial swarming is defined as multicellular movement on moist surfaces mediated by flagella. During swarming, bacterial cells sometimes elongate, intertwine with each other via flagella, and form rafts. In contrast, bacterial swimming is a form of locomotion in liquids, where cells are free and independent of each other. However, this descriptive definition can be vague and confusing in certain situations, making it difficult to distinguish between swarmers and swimmers when certain bacteria can exhibit both types of locomotion, and when mixtures of bacteria are present.

[0003] In the prior art, patent literature has reported some detection of cell motility patterns. For example, US509386A6 discloses an apparatus and method for determining cell motility patterns and other properties in a fluid medium. This method utilizes the scattering and transmission of light through the medium, as well as the absorption of shorter wavelengths of light by cells and subsequent fluorescence emission. Swimming motion can be detected using fluorescence images in the fluid medium, but it cannot distinguish bacterial cluster movement from swimming motion on a soft agar surface. US5733736A discloses a motility channel pathogen detector and a method for using the detector to detect target motile pathogens in potential pathogen test samples, but this device cannot detect bacterial cluster movement. US10266867A uses an imaging system to characterize bacterial swimming motion to infer the type of microorganism. However, this method does not provide cluster information. US20190291052 describes a micropore for capturing bacteria, where cell density increases; however, this device cannot collect any information about cell motility patterns. US20190030192 uses a circular capsule to measure the motility patterns of a target area with identification markers. US20070202137 studied motile organisms in a predefined region to screen for test molecules, but it did not reveal information about swarming movement. Furthermore, the device could not accurately characterize bacterial motility beyond swimming patterns.

[0004] Similar reports exist in non-patent literature, such as journal articles. For example, Be'er et al. (2019) discussed the statistical characteristics and collective movement behavior of bacterial clusters. However, the article did not describe in detail any restricted clustering phenomena and related settings for detection. Wioland et al. (2016) observed swimming bacteria confined in connected micro-tracks, exhibiting collective movement. There is a coupling between track width and bacterial movement patterns, but this requires sealing PDMS with a slide and injecting bacteria into the device, limiting its application to tissues and open surfaces, and making detection inaccurate. Cheong et al. (2015) elucidated a 3D tracking system for bacterial movement using phase-contrast microscopy. In our invention, we developed a pseudo-2D system for vision-based testing that can track bacteria swimming in fluids but cannot tightly bind to dense bacterial populations on surfaces. Gurung et al. (2020) discussed microfluidic techniques for separating bacterial cells by chemotaxis (chemotaxis, pH chemotaxis, thermotaxis, rheology, gas chemotaxis, magnetotaxis, and phototaxis); however, they did not mention any microfluidic devices similar to those envisioned in this invention. Beppu et al. (2017) investigated the movement of motile bacteria in a shape-specific PDMS (polydimethylsiloxane) confinement environment, placing the PDMS sheet confinement device on a hard glass surface, with movement lasting no more than 10 minutes; however, this article did not demonstrate bacterial clustering or use a semi-solid plate. Wioland et al. (2016, *Nature Physics*) investigated the orthotropic nature of confined motile bacteria within a pattern of interconnected micropores in a microfluidic device, where rotational movement was supplemented by interactions between adjacent pores. This differs from the independent micropores in this invention; similarly, the PDMS was placed on a hard glass surface rather than a sustainable agar surface, and clustered bacteria were not mentioned.

[0005] It is evident that while existing technologies cover the detection of cell movement patterns, they lack the ability to detect bacterial cluster movement. Furthermore, the operation is relatively complex and time-consuming, and cannot fully present the bacterial movement patterns. Currently, there is a lack of portable devices that can completely distinguish bacterial movement patterns, are easy to operate, and require less time. Summary of the Invention

[0006] The inventors discovered, unexpectedly during experiments, that using micropores to restrict the movement range of bacteria can distinguish between bacterial clustering and swimming patterns. Clustered bacteria form a single vortex movement pattern within the micropores, while planktonic bacteria form multiple vortices. Similar differential behaviors were observed in several other Gram-negative bacteria. Therefore, a PDMS microarray was designed to observe bacterial clustering. Using this method, bacterial movement patterns can be identified as non-motorized, swimming, or clustered based on their movement patterns within a confined space. Although swimming and clustered bacteria exhibit similar collective movement patterns in open spaces, they show different movement patterns within confined spaces of a specific size. Non-motorized bacteria do not move, while swimming bacteria move vigorously. Due to their unique intercellular arrangement, clustered bacteria exhibit a single vortex movement pattern. The trajectory of the vortex or vortex is specific to the surface of the applied chip, but the distinction between clustering and swimming had never been revealed before using the device involved in this invention.

[0007] definition

[0008] Certain terms are used throughout the following description and claims to refer to specific system components and configurations. As those skilled in the art will understand, the same component may be referred to by different names. This document is not intended to distinguish between components with different names but different functions. In the following discussion and claims, the terms “comprising” and “including” are used in an open-ended manner and should therefore be interpreted as “including, but not limited to…”. “Couple” or “couples” means indirect or direct connection. Thus, if a first device or apparatus is coupled to a second device or apparatus, the connection may be through a direct connection or through an indirect connection via other devices or apparatuses.

[0009] References to relative terms such as “top,” “front,” “bottom,” and “rear” are used to provide relative relationships between elements and are not intended to imply any absolute direction. For simplicity and clarity, various features can be drawn at any scale.

[0010] When used with the term "comprising" in the claims and / or specification, the word "a" or "an" may mean "one," but it also has the same meaning as "one or more," "at least one," and "one or more."

[0011] Throughout this application, the term “about” is used to indicate that a value includes the standard deviation of the error of the equipment or method used to determine that value.

[0012] As used herein, the term "sample" or "test sample" generally refers to material suspected of containing one or more target strains. Test samples can be used directly, either obtained from the source or pretreated to alter the characteristics of the sample. Test samples can be derived from any biological source, such as physiological fluids, including blood, interstitial fluid, saliva, lens fluid, cerebrospinal fluid, sweat, urine, breast milk, ascites, mucus, synovial fluid, peritoneal fluid, vaginal secretions, amniotic fluid, etc. Test samples can be pretreated before use, such as preparing plasma from blood, diluting viscous fluids, lysing microorganisms in the sample, etc. Treatment methods may include filtration, precipitation, dilution, distillation, mixing, concentration, inactivation of interfering components, lysis of organisms and / or cells, and addition of reagents. In addition to physiological fluids, other liquid samples such as water, food, soil, etc., can be used for environmental or food production analysis. Furthermore, solid materials suspected of containing the target can be used as test samples. In some cases, modifying solid test samples to form a liquid medium or release the target (e.g., nucleic acids) may be beneficial.

[0013] The term "cluster movement"

[0014] Swarming motion is the movement of many bacteria on a solid surface in the same direction by the rotation of their flagella. It is a collective, directional expansion movement. Swarming motion is characterized by high coverage and high diffusion speed; the population density is high because bacteria move in groups and cover a large area.

[0015] The term "plankton movement"

[0016] Swimming motion is the movement of a single bacterium in a liquid environment, which is a type of individual, directional movement.

[0017] The term "twitching movement"

[0018] Twitching motion is an intermittent movement of bacteria achieved through the repeated contraction and extension of type IV pili and their attachment to solid surfaces or adjacent cells. This movement manifests as short, discontinuous "twitching" displacements and is commonly seen in Gram-negative bacteria such as Pseudomonas.

[0019] The term "gliding motion"

[0020] Gliding motion is a smooth, continuous movement of bacteria on solid surfaces without flagella or pili, achieved through the secretion of mucus or the use of power generated by cell membrane protein complexes. This movement is variable in direction and relatively slow, and is commonly found in myxobacteria and cyanobacteria.

[0021] The term "gliding motion"

[0022] Sliding motion is a mode of movement in which bacteria passively diffuse across low-friction surfaces, relying on their own growth or secreted surfactants (such as biosurfactants). This movement is often accompanied by the expansion of the colony edge and is commonly seen in certain Bacillus and Streptomyces.

[0023] Figure 10 shows a schematic diagram of different motion modes.

[0024] Invention Details

[0025] The first aspect of this invention provides a bio-microfluidic chip for detecting bacterial motility patterns.

[0026] Bio-microfluidic chips are made of a transparent material containing multiple arranged micropores.

[0027] The pore size can be adjusted between 30μm and 200μm according to the characteristics of the microswimming object, preferably 30-100μm, 30-90μm, 40-90μm, or 50-80μm.

[0028] For example, the micropore diameter can be 50μm, 55μm, 60μm, 65μm, 70μm, 73μm, 74μm, 75μm, 76μm, etc.

[0029] The number of holes on a PDMS sheet is not particularly limited. The arrangement can be 100x100, 200x200, 300x300, etc., or any combination of integers of micro-holes. It is not limited to a square with equal lengths of horizontal and vertical sides. Other geometric shapes that are easy to handle, such as rectangles, circles, rhombuses, etc., can also be used.

[0030] The width and length of PDMS sheets can be customized to a single side length of 0.1-100cm, with preferred widths being 0.5cm x 0.5cm, 1cm x 1cm, 1.5cm x 1.5cm, and 2cm x 2cm.

[0031] The curvature of the upper and lower surfaces of the micropore can be non-uniform circles and other shapes such as irregular polygons, allowing for optimization on different surfaces.

[0032] The micropore depth can be 10-50μm, preferably 18-40μm, 18-30μm, 18-25μm, or 20-30μm. For example, the micropore depth can be 18μm, 19μm, 20μm, 21μm, 22μm, 23μm, 24μm, or 25μm.

[0033] The material of the bio-microfluidic chip of this invention is polydimethylsiloxane (PDMS). However, any other transparent material that allows bacteria to move within the micropores can be used as an alternative. Suitable thermoplastic materials include silicon, glass, poly(methyl methacrylate) (PMMA), polycarbonate (PC), polyimide (PI), cyclic olefin copolymers (COC), natural polymers, cellulose membranes, gels, etc.

[0034] In one specific embodiment, the bio-microfluidic chip of the present invention has approximately 1 cm 2 Large, 0.3 mm thick. Each PDMS sheet is a biochip with a row of micropores on one side of its surface. The circular pores are 50 μm in diameter and 22 μm deep, with approximately 10,000 micropores on a single PDMS sheet.

[0035] In one specific embodiment, the bio-microfluidic chip of the present invention has approximately 1 cm 2 Large, 0.3 mm thick. Each PDMS sheet is a biochip with a row of micropores on one side of its surface. The circular pores are 74 μm in diameter and 22 μm deep, with approximately 10,000 micropores on a single PDMS sheet.

[0036] In one specific embodiment, the bio-microfluidic chip of the present invention has approximately 1 cm 2 Large, 0.3mm thick. Each PDMS sheet is a biochip with a row of micropores on one side of its surface. The circular pores are 65μm in diameter and 20μm deep, with approximately 10,000 micropores on a single PDMS sheet.

[0037] The top view of the bio-microfluidic chip of the present invention, namely the PDMS chip, is shown in Figure 1a, and Figure 1b shows a side perspective view of the PDMS biochip.

[0038] The second aspect of this invention further provides a method for preparing a bio-microfluidic chip.

[0039] Using soft lithography, PDMS (polydimethylsiloxane) is applied onto a silicon wafer, and the SU8 micro-cylinder array is patterned. After the PDMS is removed, the PDMS board is sliced ​​into pieces to form a bio-microfluidic chip.

[0040] Specifically, the fabrication of bio-microfluidic chips is based on photolithography, using ultraviolet exposure to form micropores of the desired size and shape to create bacterial implantation and culture devices. The stainless steel photomask used for photolithography is shown in the figure. The photomask is divided into nine regions of 0.9cm x 0.9cm (see Figure 6-7), each region A having its own periodically repeating circular transparent array of 90x90 elements. This photomask is perfectly suited for common 2-inch silicon wafer substrates. It can be used to fabricate photoresist structures or, using conventional growth equipment such as magnetron sputtering, to grow permanent structures using metal targets. The micro / nano structures fabricated using this photomask can then be used to create a mold of PDMS material to obtain the desired implantation and culture devices for bacterial culture testing.

[0041] Since the required thickness for the cultured device is generally in the tens of micrometers, SU8 or SPR220 photoresist was selected. The spin coating parameters obtained in the experiment were 500 rpm and a spin time of 40 s. After photolithography, a profilometer was used to measure the thickness of the SPR220 resist, obtaining a structure with a thickness of 40 μm, and the overall morphology basically met expectations.

[0042] Furthermore, the photoresist material can be replaced with a metallic material to further increase its lifespan.

[0043] The third aspect of this invention is to provide a detection system for detecting bacterial motility patterns.

[0044] The detection system includes a biofluidic chip, a culture plate (preferably a semi-solid culture plate), and an imaging device; optionally, it further includes an incubator. When using the detection system to detect bacterial motility, the biofluidic chip is placed on the semi-solid plate, so that the bacteria to be tested are sandwiched between the biofluidic chip, such as a PDMS sheet, and the culture plate, such as a soft agar surface.

[0045] Culture plate:

[0046] The culture plates are semi-solid plates, preferably with moist semi-solid surfaces such as soft agar, mucous membranes or vessels, methylcellulose semi-solid medium, or beef peptone semi-solid agar. There are no specific limitations on the material; any material that meets the required level of moisture can be used for culture plates. Moisture level can be quantified by water activity (Aw), calculated using the formula: Aw = P / P0, where P is the vapor pressure of water in the sample, and P0 is the vapor pressure of pure water at the same temperature. The suitable water activity range for semi-solid plates with bacterial motility is between 0.90 and 0.999.

[0047] Taking soft agar as an example, the composition of agar can be adjusted, such as different agar concentrations or different nutrient components. It can be prepared using culture media known in existing technologies.

[0048] Furthermore, a preparation composition is provided as an example, but not intended to limit the scope of protection: 1% (w / v) tryptone, 0.5% (w / v) yeast, 0.5% (w / v) sodium chloride, 0.5% (w / v) agar, and 20 ml of deionized water, wherein the agar can be replaced by carrageenan, sodium alginate, gelatin, etc. Then, sterilization is performed using common sterilization methods such as autoclaving, dry heat sterilization, filtration sterilization, and ultraviolet sterilization. Cooling methods are employed, such as cold water bath cooling, ice cooling, and fan cooling.

[0049] Imaging device

[0050] The microscope is equipped with a multi-magnification objective lens and a camera.

[0051] The objective lens magnification can be 4x, 10x, 20x, 30x, 40x, 50x, or 100x, with 20x-40x being preferred, such as 20x, 30x, or 40x.

[0052] The camera can be an optical camera or a digital camera, such as the ThorLabs Kiralux CS505MU camera.

[0053] The fourth aspect of this invention is to provide a method for identifying bacterial motility patterns.

[0054] The method utilizes the aforementioned detection system for detecting bacterial motility patterns.

[0055] To use the chip involved in this invention, a sample for bacterial motility testing must first be prepared. The test sample can be a single bacterial strain, a bacterial mixture, or a more complex mixture, such as a fecal sample. For example, to detect the type of bacterial motility, 5 μl of sample is inoculated onto a soft agar plate (exemplarily, bacterial suspension and PBS can be mixed to a concentration of 100 mg / ml before spotting onto a petri dish for incubation). The petri dish is incubated at 37°C for 4–6 hours (incubation time may vary depending on the nature of the sample and surface type) until bacterial colonies form on the agar. The microchip is placed at the edge of the bacterial colonies, patterned side down, confining the bacteria within the wells (see Figure 11A). After waiting 5-10 minutes to allow bacteria to fill the micropores, based on cell density calculations, bacterial colonies can form a layer 10-40 μm thick on the culture medium in micropores 22 μm deep. When confined between the agar and PDMS surface, bacteria tend to swim closer to the porous agar surface. For bacteria swimming near the agar surface, the rotating cell bodies experience a lateral component of resistance. Next, the movement pattern of the confined bacteria can be observed under a microscope. If no movement is observed, it means that the sample does not contain active bacteria. If the sample contains clusters of bacteria, as shown in Figure 2a, the movement pattern in the micropores will be a single clockwise or counterclockwise vortex. If the sample contains only motile bacteria and not clustered bacteria, its movement pattern is turbulent (chaotic motion), as shown in Figure 2b. The confined motion is captured by a microscope camera and processed using computer software (such as the MATLAB PIV toolkit) to calculate various parameters in the velocity field.

[0056] Images of clusters or swimming under confinement were captured using a microscope camera (ThorLabs, Kiralux CS505MU) and then processed using the Particle Image Velocimetry (PIV) package in MATLAB.

[0057] In methods for identifying bacterial motility patterns, microfluidic chips with specific channel structures are used to detect bacterial motility under different environmental conditions.

[0058] By using microscopy imaging and image analysis techniques, combined with the bacterial movement trajectory in a microfluidic chip, the movement patterns of bacteria can be quantified.

[0059] The movement pattern of bacteria is determined by comprehensively considering parameters such as bacterial movement speed, rate of change of movement, acceleration, population density, coverage, diffusion speed, vorticity, and vortex step.

[0060] Calculation formula:

[0061] (1) Calculation of bacterial motility speed: Bacterial motility speed is a key parameter for distinguishing different motility modes. Where v: the speed of bacterial movement (μm / s), Δd: the displacement of bacteria within the time interval (μm), and Δt: the time interval (s).

[0062] (2) Calculation of the rate of change of movement direction, which is used to quantify the randomness or directionality of bacterial movement: Where D: Directionality coefficient (0≤D≤1), the closer the value is to 1, the more directional the movement is, and the closer it is to 0, the more random the movement is.

[0063] (3) Acceleration calculation, used to distinguish different types of motion modes (such as rapid start, stop or turn):

[0064] Where a: acceleration (μm / s2), Δv: change in velocity (μm / s), and Δt: time interval (s).

[0065] (4) Population density calculation: Population density refers to the number of bacteria per unit area or unit volume, and can be used to quantify the degree of bacterial aggregation. Where ρ: population density (bacterial count / μm) 2 N: number of bacteria, A: area of ​​the observation region (μm) 2 ).

[0066] (5) Coverage calculation,

[0067] (6) Diffusion rate calculation Where Δr: diffusion distance at the edge of the bacterial community (μm), Δt: time interval (s).

[0068] (7) Vorticity is the curl of a velocity field, used to quantify the rotational intensity of fluid or swarm motion, and can reflect the rotational characteristics of bacterial swarm motion: Among them, v x and v y These are the velocity components of the bacterial community in the x and y directions, respectively, according to; and These are the partial derivatives of the velocity components in space;

[0069] Through microscopic imaging and image analysis techniques, it is possible to obtain v x and v y ; Use numerical differentiation methods (such as the finite difference method) to calculate and Calculate the vorticity at each location using the formula. Calculate the average vorticity distribution across the entire bacterial population.

[0070] (8) Based on the velocity field, the vortex order parameters are calculated by the following function Ф: Among them, v i It is a local velocity, t i It is a tangential unit vector perpendicular to the radial vector.

[0071] Based on a large number of bacterial test results, the inventors provided the numerical ranges of each parameter in Table 1 for different motion models, which facilitates the calculation of bacterial motion patterns.

[0072] Table 1

[0073] Taking bacteria A and B as examples, detection and calculation were performed, and the data shown in Table 2 were obtained.

[0074] Table 2

[0075] Analysis of the results for bacteria A and bacteria B

[0076] Bacterium A:

[0077] The diffusion velocity is 0.2 μm / s, falling within the diffusion velocity range of twitching and sliding; combined with other parameters (velocity 1 μm / s, directionality 0.4, acceleration 0.5 μm / s²), the diffusion velocity is within the range of twitching and sliding. 2 Population density 5 bacteria count / μm 2 The coverage was 10%, and the vortex was 0.5, indicating almost no collective rotational behavior, which is more consistent with the characteristics of twitching. Conclusion: The movement pattern of bacteria A is twitching.

[0078] Bacteria B:

[0079] The diffusion velocity of 20 μm / s falls within the diffusion velocity range of swarming and swimming; combined with other parameters (velocity 0 μm / s, directionality 0.83, acceleration 2 μm / s²), the diffusion velocity is within the range of swarming and swimming. 2 Population density 2 bacterial count / μm 2 The high vorticity (60% coverage) and vortex (1.8) indicate significant collective rotational behavior, more consistent with swarming characteristics. Preliminary conclusion: Bacterium B exhibits swarming movement.

[0080] Based on the above parameters, bacteria B belonging to the swarming pattern has been preliminarily identified. Further subdivision is needed by calculating the vortex step parameter Ф. Bacterial communities with Ф greater than or equal to 0.8 are considered to have a single swirling flow, defined as concentrated clustering motion, which can be effectively used for disease diagnosis, biomarker validation, target strain isolation, and sample testing. Bacterial communities with Ф less than 0.8 are defined as having non-concentrated clustering motion; although they have some clustering ability, their motion is defined as chaotic or swimming motion, insufficient for subsequent disease diagnosis, biomarker validation, target strain isolation, and sample testing. See Figure 11B.

[0081] Automated algorithms can be added to visual images (frames) to calculate and identify eddies. For example, the MATLAB PIV toolkit can be used to track and compare moving bacteria in images of diluted clusters and swimming organisms. Furthermore, a comprehensive judgment can be made by combining dynamic diffusion rate analysis, multi-region diffusion analysis, and machine learning models. See Figure 11C.

[0082] Verification of micropore size

[0083] The inventors simulated the movement of clustered and planktonic cells under confined circular conditions of varying sizes, similar to those used in experiments (swimming is an active mode of movement in planktonic bacteria, but not all planktonic bacteria possess swimming capabilities). The simulation results mirrored the experimental findings by assigning only an enhanced range of comparative interactions to clustered cells. Both clustered and swimming cells began with a single vortex pattern at a minimum disk size of 30 μm. Under a confined size of 90 μm, the disk filled by clustered cells transformed into multiple vortices (results shown in Figure 8, with Φ on the ordinate), demonstrating that a superior micropore size range of at least 30 μm–90 μm is achievable, but a more optimal range is 40–90 μm.

[0084] The inventors also verified the effect of using micropores of different depths (18μm, 22μm, 40μm) to restrict cluster movement in micropores with a pore size of 74μm. Each group had 5 samples, and the values ​​represent the mean and standard deviation (the results are shown in Figure 9, with Ф on the vertical axis). The results showed that 18-40μm can effectively reflect the state of cluster movement, 18-25μm and 20-30μm are relatively preferred sizes, and 22μm is a more preferred size. However, if the depth is too deep, for example, greater than 40μm, it will affect the state of cluster movement.

[0085] Methods to improve development and tracing

[0086] To improve the clarity of bacterial movement patterns within the observation wells, tracer devices that enhance the clarity of movement observation can be added to the wells before the biochip is mounted onto the colonies. These devices include, but are not limited to, mucin, tracer beads, and tracer microgears.

[0087] Tracer beads are coated into the pores. In the case of swarming motion, the tracer beads will exhibit a single vortex (Fig. 3a), and in the case of swimming motion, the tracer beads will exhibit chaotic motion (Fig. 3b). The tracer beads can be made of different materials and sizes, such as polystyrene, silica, PMMA, magnetic particles, fluorescently labeled particles, liposomes, and metal particles.

[0088] Tracer microgears can also be used to display motion patterns within a hole (Figures 4a-b). Tracer microgears can be "J-shaped" or any other shape or size / thickness and different materials / properties (e.g., magnetic).

[0089] Mucin-coated surfaces can be used to study locomotion patterns; specifically, secreted mucins and membrane-bound mucins can be used. MUC5AC and MUC2 proteins can be used. In preparation, the mucin is dissolved in a buffer (such as PBS) at a concentration typically of 0.1-1 mg / mL. The microporous surface (such as glass or PDMS) is then immersed in the mucin solution and incubated for 1-2 hours. The surface is then rinsed with buffer to remove unbound mucin. The coating effect is verified using fluorescently labeled mucin or antibodies, and the surface morphology and mucin density are analyzed using atomic force microscopy (AFM) or surface plasmon resonance (SPR).

[0090] Chemotaxis promotes further screening

[0091] Nutrients, antibiotics, and other chemotactic substances can be added to observe bacterial chemotactic behavior and isolate target strains. Furthermore, by adding connecting channels between the micropores of the microfluidic chip, more effective target single strains can be screened through biological chemotaxis, chemotaxis, and physical chemotaxis. Specifically, microfluidic technology can be used to separate bacterial cells based on bacterial chemotaxis (pH chemotaxis, thermotaxis, rheology, gas chemotaxis, magnetotaxis, and phototaxis, etc.). The target bacteria can be driven into the first well, and chemotactic substances or conditions can be added to the second well, driving the bacterial community to move from the first well to the second well through swarming movement. This is then used for further screening of target strains.

[0092] The fifth aspect of this invention provides the use of biomicrofluidic chips.

[0093] The bio-microfluidic chip of the present invention can be used to detect the cluster movement of microorganisms in a sample; the sample is an environmental sample, such as a contaminated sample, such as soil, plants, feces, vomit, food, sputum, urine, wound / secretions, etc., but the results obtained are not necessarily directly related to the disease.

[0094] Biofluidic chips can be used for target bacterial screening, drug-resistant bacterial screening, and cluster inhibitor screening. In specific implementations, the detection results are not used for disease diagnosis.

[0095] In a sixth aspect of the present invention, a method for assisting in the diagnosis, treatment, and diagnosis of diseases is provided.

[0096] In a specific embodiment, this invention provides a method for assisting in the diagnosis, treatment, and management of diseases by detecting the state of microbial cluster movement using a bio-microfluidic chip. The diseases mentioned are those that can be diagnosed through cluster movement, specifically including inflammatory bowel disease, urinary tract infection, gastrointestinal stress, foodborne illness, and active autoimmune diseases. The detected microbial cluster movement can be used as a biomarker for diseases, such as inflammatory bowel disease. More specifically, inflammatory bowel disease is further divided into ulcerative colitis (UC) and Crohn's disease (CD). Beneficial effects

[0097] The method combining PDMS biochips, microscopy, and motion parameter algorithms to distinguish between swarming and swimming movements is a novel approach not disclosed in existing technologies. A key advantage of this invention is its detection efficiency. To illustrate the significant improvement over conventional methods in testing bacterial motility patterns from complex mixtures, conventional methods used in the prior art for detecting swarming bacteria in human samples, such as using MALDI-TOF mass spectrometry to identify several swarming bacteria in human bodily fluids, including *Serratia marcescens* and *Citrobacter kojiculatus*, are both time-consuming and labor-intensive. The novel method of this invention is highly suitable if researchers or clinicians require rapid results within a given clinical sample, such as within 24 hours, regarding the presence of swarming bacteria.

[0098] The present invention compares the technique for identifying clustered / swimming / non-motile bacteria with traditional bacterial motility testing methods (see Figure 5). Left: Compared to traditional methods, the PDMS biochip method can distinguish bacterial motility types in complex mixtures in three steps, reducing testing time by 10 times. Right: Traditional methods require separate testing for swimming (blue arrows) and clustered motility (green arrows), with cluster testing being very time-consuming, requiring at least four days.

[0099] This invention can effectively and clearly distinguish non-motile, swimming, or clustered bacteria in a single experimental setup. Since swimming and clustering are both forms of bacterial flagellation, crucial for host health, the detection and isolation method described in this invention has proven to be both convenient and valuable, further aiding in diagnosis and treatment. The entire device is relatively small and portable, allowing for detection at any convenient time. Attached Figure Description

[0100] Figure 1a shows a top view of the PDMS biochip;

[0101] Figure 1b shows a side perspective view of the PDMS biochip;

[0102] Figure 2a shows a single vortex motion mode;

[0103] Figure 2b shows the chaotic motion pattern;

[0104] Figure 3a shows that the tracer bead will present a single vortex;

[0105] Figure 3b shows that the tracer bead will exhibit chaotic motion;

[0106] Figure 4a shows that the tracer microgear exhibits a single vortex;

[0107] Figure 4b shows the chaotic motion of the tracer microgear;

[0108] Figure 5 shows a comparison between the swarm / swimming / non-motile bacteria identification technique and traditional bacterial motility detection methods;

[0109] Figure 6 shows the mask design;

[0110] Figure 7 shows the mask design;

[0111] Figure 8 shows the micropore size gradient experiment of the microfluidic biochip;

[0112] Figure 9 shows the micropore depth gradient experiment of the microfluidic biochip;

[0113] Figure 10 shows a schematic diagram of different motion modes;

[0114] Figure 11A shows the application of the microfluidic chip on a tablet.

[0115] Figure 11B shows a concentrated cluster motion pattern confirmed by the microfluidic chip (microscopic view);

[0116] Figure 11C shows the bacterial motion pattern generated by Matlab PIV;

[0117] Figure 12 shows the ROC curves of swarming exercise in IBD patients and healthy individuals after 24-28 hours;

[0118] Figure 13 shows the ROC curves of swarming exercise in IBD patients and healthy individuals after 48 hours. Detailed Implementation

[0119] This invention can be applied to detection in various fields and is not particularly limited. These are just examples, and the following are some typical application scenarios.

[0120] Example 1: The biochip of the present invention is used for disease diagnosis and as a biomarker.

[0121] 1. Biomarkers for inflammatory bowel disease can be used to aid in diagnosis.

[0122] Diagnosis of IBD typically involves colonoscopy and biopsy of pathological findings, such as blood or stool lesions. This is an invasive procedure with potential trauma to the body, and painless endoscopy requires anesthesia and a resident's presence. The microfluidic biochip described in this invention does not require direct contact with the human body. It can detect swarming movements, serving as a biomarker for IBD, and can efficiently and rapidly diagnose IBD. IBD (Inflammatory Bowel Disease) is mainly divided into two types: UC (Ulcerative Colitis) and CD (Crohn's Disease).

[0123] Bacterial swarming can serve as a specific marker of inflammatory bowel disease. The inventors conducted a series of screening experiments, the experimental protocol of which is as follows:

[0124] Fecal sample preparation

[0125] Take the fecal sample stored in the original container and thaw it gradually. Thaw on ice for one hour. Pre-weigh a sterile microcentrifuge tube on a balance and set it to 0 (tare). In a fume hood, remove the sample with a sterile toothpick and place it into the microcentrifuge tube. Weigh the stool sample using the same scale used to weigh the sterile microcentrifuge tube in the previous step. Add sufficient sterile phosphate-buffered saline (PBS) (pH 7.4, room temperature) to the microcentrifuge tube to achieve a final stool concentration of 100 mg / mL. Rotate the sterile pestle in the microcentrifuge tube approximately twenty (20) times to completely homogenize the stool particles.

[0126] Specific methods:

[0127] Perform cluster motion analysis

[0128] A microcentrifuge tube containing a fecal sample was vortexed for approximately 10 seconds, followed by inoculation of 3 μL of homogenized fecal solution onto the center surface of a 0.5% LB agar plate. The plate was incubated at 37°C and 40% constant humidity for 8–12 hours. A microfluidic chip was then placed at the edge of the bacteria, and the plate was incubated for another 5–10 minutes. The bacterial velocity field within the microfluidic chip was observed using an imaging system, and calculations were performed using tools such as Matlab PIV to confirm whether the movement was swarming.

[0129] Using the above screening method, a strain with strong swarming ability was screened from the feces of patients. The inventors further discovered that after the symbiotic swarming bacteria isolated from the feces of colitis mice were amplified in vitro and then reinfused into colitis mice via gavage, the colitis mice experienced relief. However, inoculation with the corresponding mutant strain lacking swarming ability did not have this effect, indicating that the protective effect is closely related to the swarming phenotype of the bacteria.

[0130] Validation of mouse biomarkers: C57BL / 6 mice (8 weeks old) were exposed to water or DSS water for 7 days (n=4 per group). Fecal samples were collected from the control group (above the red line) and the DSS group (below the red line) for clustering analysis. The results are shown in Figure 13. The fecal samples from the DSS group showed significant clustering movement. The biochip of this invention was used to verify Ф, and the result was greater than 0.8.

[0131] Validation of porcine biomarkers: Clustering determination (72 hours) of fecal samples collected from pigs with and without inflammatory bowel disease (IBD). Clustering scores were used: 0, no clustering; 1, clustering within 72 hours; 2, clustering within 48 hours; 3, clustering within 24 hours or less (control, n=6; IBD, n=7). Results are shown in Figure 14. Fecal samples collected from a limited sample size of pigs with active porcine intestinal disease also showed an increased tendency for clustering and diffusion compared to the control group. The biochip of this invention was used to validate the Ф value, which was greater than 0.8.

[0132] Human biomarker validation: In order to further validate whether cluster motion parameters can play the same role as biomarkers in humans, and to extend to further subdivision of CD and UC, the inventors further validated the human experimental group.

[0133] Two control groups were set up: a healthy control group and an IBD patient group (79 cases). Fecal samples were isolated from the experimental group and observed at two time points: 24-28 hours and 48 hours after plate culture. The method for identifying bacterial motility patterns used in the fourth aspect of the invention was employed for calculation. Samples exhibiting concentrated clustering motility were recorded along with all samples, and further subdivided into CD (34 cases) and UC (45 cases) groups. The results are shown in Tables 3 and 4, and Figures 12-13. The results demonstrate a high correlation between concentrated clustering motility and IBD patients. It can be used for biomarker prediction in human IBD, but it does not play a decisive role in further subdividing the CD and UC groups.

[0134] Table 3

[0135] Table 4

[0136] The method for calculating the average swarming percentage (Mean Swarming%):

[0137] Within-group mean: The mean of the cluster proportions for all samples is used to obtain the Mean Swarming in the table.

[0138] The relationship between the average cluster ratio and the vortex order parameter Ф:

[0139] High Ф value: indicates that bacterial movement exhibits a distinct vortex pattern.

[0140] Low Ф value: diffusion-dominated.

[0141] The high clustering ratio and large Ф value of the IBD group indicate that its clustering motion is accompanied by orderly group vortex motion.

[0142] Velocity field intensity: Tools such as Matlab PIV were used to determine the bacterial velocity field intensity. High velocity field intensity in IBD patients is driven by inflammatory signals and promotes concentrated clustering of bacteria; low-intensity velocity field in healthy controls shows random diffusion, with slow and disordered movement.

[0143] Comprehensive analysis

[0144] Table 5

[0145] in conclusion

[0146] The high clustering rate, high velocity field, and Ф value in IBD patients collectively indicate that the inflammatory environment promotes bacterial synergistic motility.

[0147] 2. Urinary tract infection (UTI) testing

[0148] Over 80% of urinary tract infection (UTI) cases are caused by urinary tract pathogenic Escherichia coli or other bacteria, including Proteus mirabilis. In community cases, diagnosis is typically made using urine test strips and urine cultures. However, urine cultures can take several days to complete. A positive or negative result on a urine test strip can trigger the use of our technique to check for an abundance of motile bacteria. If present, this may prompt early use of broad-spectrum antibiotics. If absent, antibiotics can be reserved for cases where urine results are returned or for investigating non-UTI symptom causes. The technique of this invention, however, can provide data within 6–8 hours.

[0149] 3. Gastrointestinal (GI) stress (and dysbiosis) detection

[0150] Gastrointestinal stress encompasses any pathological condition of the gastrointestinal mucosa. Published data show that motility genes are upregulated in IBD, and bacterial clusters are more dominant in both IBD and polyp patients. Therefore, our technology can be clinically used to aid in the diagnosis of specific conditions such as intestinal inflammation or polyps in patients with any form of gastrointestinal disease. A second advantage is that the biochip of this invention can isolate the bacteria responsible for clustering at the leading edge of the cluster. These bacterial isolates have been shown to uniformly prevent inflammation. This is the diagnostic approach of our technology. For patients with gastrointestinal stress, the method of this invention can both diagnose and treat the condition.

[0151] We have shown that these bacterial isolates consistently protect the body from inflammation.

[0152] 4. Rapid diagnosis of foodborne illnesses: The main foodborne pathogens are swarming motile bacteria. These can be detected in vomit, feces, or ingested food.

[0153] Example 2: Screening of Disease Biomarkers

[0154] For example, it can be used as a biomarker for active autoimmune diseases. Psoriatic arthritis is an example, but this does not preclude its use in other diseases. It is associated with gut dysbiosis and increased bacterial chemotaxis, and is considered a biomarker for disease activity. The detection technology of this invention is faster and less expensive than sequencing methods, identifying mobile chemotactic bacteria within hours and providing clinicians with clues about current and developing disease activity.

[0155] Example 3: Study of bacterial motility in the laboratory

[0156] The detection method of this invention can be used to study bacterial motility in the laboratory. This technology provides a method for quantitative identification of bacteria through microbial microarray confinement to define bacterial motility on any surface. PDMS can be modified according to size and motility to isolate bacterial cells. This technology provides a quantitative method for defining bacterial motility on any surface.

[0157] Example 4: Screening for drug-resistant bacteria

[0158] Antibiotic resistance remains a problem at every level, despite well-regulated antibiotic practices; self-prescribing is still prevalent in many parts of the world. Clustering of bacteria indicates antibiotic resistance in most strains. When used on biomaterials (feces, sputum, urine isolated via microarrays for bacterial identification, wounds / secretions), the technology of this invention (resulting within hours, rather than days / weeks for traditional clinical testing such as culture) can detect antibiotic resistance early, as well as early detection of epidemic strains (such as E. coli). This leads to treatment decisions, if necessary, regarding switching antibiotics or discontinuing their use. It also helps us focus on the typing of antibiotic resistance in populations (e.g., nursing home, hospital, and MDR bacteria).

[0159] Example 5: Detection of Microorganisms in Pollution Sources

[0160] The pollution source can be environmental samples, such as soil, plants, feces, vomit, food, sputum, urine, wounds / secretions, etc.

[0161] Clusters of bacteria can overcome fungal contamination and grape wilting. Detecting clusters of bacteria on grapevines ensures safe wine production. Detecting the presence of microbial communities in grapes guarantees safe wine production.

[0162] Bacillus subtilis is an important bacterium in soil, capable of biocontrolling seedling pathogens. Another application is using the technology of this invention to detect bacterial communities in greenhouse soils. The disappearance of these communities may necessitate the introduction of these cultures into the soil.

[0163] Proteus mirabilis is a known marker of fecal contamination worldwide. The technology of this invention can be used to detect this substance in environmental samples.

[0164] Example 6: Testing of Cluster Inhibitors

[0165] Laboratories and pharmaceutical companies are interested in developing inhibitors for quantum sensing, particularly cluster inhibitors, for use in various applications (such as UTI). The technique of this invention quantitatively assesses swarm / swimming inhibition.

[0166] The above uses are merely illustrative and are not intended to limit the scope of protection of this invention.

Claims

1. A method for detecting bacterial motility patterns using a detection system for detecting bacterial motility patterns, said detection system comprising a bio-microfluidic chip, a semi-solid culture plate, and an imaging device; The bio-microfluidic chip comprises multiple arranged microporous structures and is made of a transparent material; When using a detection system to detect bacterial motility, the biochip is placed on a semi-solid culture plate, so that the culture medium fills the micropores of the chip, and the bacteria to be tested are sandwiched between the chip and the semi-solid culture plate. Then, an imaging device is used to detect the motility parameters of the bacteria and calculate to determine their motility pattern, thus identifying which type of bacteria they belong to: Twitching, Gliding, Sliding, Swarming, or Swimming.

2. The method of claim 1, wherein, The movement pattern of bacteria is determined by comprehensively detecting parameters such as movement speed, rate of change of movement, acceleration, population density, coverage, diffusion speed, vorticity, and vortex step.

3. The method according to claim 2, further, determines the bacterial motility pattern using the following parameters: Twitching motility pattern, speed range 0.01-2 μm / s, directionality 0.1-0.5, acceleration 1-10 μm / s 2 range, population density 0.001-10 bacteria number / μm 2 , coverage 1%-10%, diffusion speed 0.1-1 μm / s, vorticity 0.1-0.5; Gliding motility pattern, velocity range 2-10 μm / s, directionality 0.5-0.9, acceleration 0-1 μm / s 2 Range, population density 0.01-0.1 bacteria number / μm 2 , coverage 10%-100%, diffusion speed 0.5-5 μm / s, vorticity 0-0.5; Sliding mode of motion, velocity range 0.01-1 μm / s, directionality 0.1-0.5, acceleration 0-1 μm / s 2 range, population density 0.1-1 bacteria number / μm 2 , coverage 10%-100%, diffusion speed 0.1-2 μm / s, vorticity 0-0.5; Swarming motility pattern, speed range 10-50 μm / s, directionality 0.7-1.0, acceleration 0.5-5 μm / s 2 range, population density 1-10 bacteria number / μm 2 , coverage 10%-100%, diffusion speed 10-50 μm / s, vorticity 1.0-2.0; Swimming motility pattern, velocity range 20-50 μm / s, directionality 0.8-1.0, acceleration 50-200 μm / s 2 range, population density 0.1-1 bacteria number / μm 2 , coverage <10%, diffusion velocity 10-50 μm / s, vorticity 2.0-5.0; The calculation methods for the parameters in the above motion modes are as follows: Bacterial motility speed calculation, Where v: the velocity of the bacteria (μm / s); Δd: the displacement of the bacteria within the time interval (μm); Δt: the time interval (s); Motion direction change rate calculation, Where D: Directionality coefficient (0≤D≤1), the closer the value is to 1, the more directional the movement is, and the closer it is to 0, the more random the movement is. acceleration calculation, where a: acceleration (μm / s 2 ), Δv: velocity change (μm / s), Δt: time interval (s); Population density calculation, wherein p: population density (number of bacteria / pm 2 ), N: number of bacteria, A: area of the observation region (pm 2 ); Coverage calculation, Diffusion velocity calculation, Where Δr: diffusion distance at the edge of the bacterial community (μm), Δt: time interval (s); Vorticity calculation, where v x and v y are the velocity components of the bacterial population in the x and y directions, respectively; and These are the partial derivatives of the velocity components in space; By microscopy imaging and image analysis techniques, it is possible to obtain v x and v y ; using numerical differentiation methods (such as finite difference method) to calculate and Calculate the vorticity at each location using the formula.

4. According to the method of claim 3, after the bacteria are determined to belong to the swarming movement mode, the following formula is further used for determination, and the vortex step parameter is calculated. Where v i It is a local velocity, t i It is the tangential unit vector perpendicular to the radial vector; when Ф is greater than or equal to 0.8, the target bacteria can be identified as having a concentrated cluster movement, and when Ф is less than 0.8, it can be identified as having a non-concentrated cluster movement.

5. The method according to any one of claims 1-4, characterized in that, The method assists in disease diagnosis by detecting the movement of microbial clusters.

6. The method according to any one of claims 1-4, characterized in that, The method is used to detect microorganisms in environmental samples, screen for target bacteria, screen for drug-resistant bacteria, and screen for cluster inhibitors; preferably, the detection results are not used for disease diagnosis.

7. The method of claim 6, wherein, The environmental samples are pollution sources, and the samples include materials such as soil, plants, feces, vomit, food, sputum, urine, wounds / secretions, etc.

8. A bio-microfluidic chip for detecting bacterial motility patterns, comprising: The bio-microfluidic chip comprises multiple arranged microporous structures and is made of a transparent material.

9. The bio-microfluidic chip according to claim 8, wherein: The chip aperture is between 30μm and 200μm, preferably 30-100μm, 30-90μm, 40-90μm, or 50-80μm; the micropore depth can be 10-50μm, preferably 18-40μm, 18-30μm, 18-25μm, or 20-30μm.

10. The bio-microfluidic chip according to claim 8 or 9, wherein the material used includes thermoplastic materials such as polydimethylsiloxane (PDMS), silicon, glass, poly(methyl methacrylate) (PMMA), polycarbonate (PC), polyimide (PI), cyclic olefin copolymer (COC), natural polymers, cellulose membranes, and gels.

11. A detection system for detecting bacterial motility patterns, comprising, as described in any one of claims 8-10, a bio-microfluidic chip, a semi-solid culture plate, and an imaging device, wherein when using the detection system to detect bacterial motility, the bio-microfluidic chip is placed on the semi-solid culture plate, such that the culture medium fills the micropores of the chip, and the bacteria to be tested are sandwiched between the bio-microfluidic chip and the semi-solid culture plate, and then the imaging system is used to detect the motility parameters of the bacteria and to determine their motility patterns through calculation.

12. The detection system according to claim 11, wherein the water activity of the semi-solid culture plate is between 0.90 and 0.999 (Aw), preferably 0.995 (Aw), so that the sample can form a layer with a thickness of 10–40 μm on the culture medium.

13. The detection system according to claim 12, wherein the semi-solid culture plate is selected from at least one moist semi-solid surface selected from soft agar, carrageenan, sodium alginate, gelatin, mucosa, methylcellulose, beef peptone semi-solid agar, etc.

14. The detection system according to claim 13, wherein the semi-solid culture plate is prepared with 1% (w / v) tryptone, 0.5% (w / v) yeast, 0.5% (w / v) sodium chloride, 0.5% (w / v) agar and 20 ml of deionized water.

15. The biomicrofluidic chip of any one of claims 8-10, and the system of any one of claims 11-14, for use in the preparation of a diagnostic device, said diagnostic device assisting in the diagnosis of diseases by detecting the movement of microbial clusters.

16. The use according to claim 15, wherein the disease is a disease associated with diagnosis through group movement, including any one of inflammatory bowel disease, urinary tract infection, gastrointestinal stress, foodborne disease, and active autoimmune disease; Preferably, the inflammatory bowel disease includes ulcerative colitis and Crohn's disease.

17. A diagnostic biomarker, said diagnostic biomarker being a conclusion of microbial cluster movement detected by any one of claims 9-10, any one of claims 11-14, or any one of claims 1-7; for example, said diagnostic biomarker being a diagnostic biomarker for inflammatory bowel disease, urinary tract infection, gastrointestinal stress, foodborne illness, or active autoimmune disease.

18. The biomicrofluidic chip of any one of claims 9-10, and the system of any one of claims 11-14, are used in detecting microorganisms in environmental samples, screening for target bacteria, screening for drug-resistant bacteria, and screening for cluster inhibitors; preferably, the detection results are not used for disease diagnosis.

19. The use according to claim 18, wherein the environmental sample is a source of pollution, and the sample comprises materials such as soil, plants, feces, vomit, food, sputum, urine, wounds / secretions, etc.

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