A bio-microfluidic chip device and its use in detecting microorganism motility patterns

Abstract

The present application provides a biological microfluidic chip device and a detection system contained therein, which can distinguish non-motile bacteria, swimming bacteria or cluster bacteria, and further be used for disease diagnosis, biomarker, target bacteria screening, drug-resistant bacteria screening, and cluster inhibitor screening according to the detection results. The detection and separation method involved in the present application is proved to be convenient and very valuable, which can further assist in diagnosis and treatment. The whole device is relatively miniaturized and portable, and can be detected at any convenient time.

Description

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 is an important characteristic, not only enhancing bacterial cell function but also being related to host health. Bacterial swarming and swimming are two main types of movement driven by flagella. Bacterial swarming is defined as multicellular movement on a moist surface mediated by flagella. During swarming, bacterial cells sometimes elongate, intertwine with each other via flagella, and form rafts. In contrast, bacterial swimming is movement in a liquid, 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 movement, and when a mixture of bacteria is present.

[0003] In the prior art, patent literature reports some findings on the detection of cell motility properties. For example, US509386A6 discloses an apparatus and method for determining the motility and other properties of cells 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. US20190030192 uses a circular capsule to measure the motility 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 movement 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 motility, 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 motility can be identified as non-motile, swimming, or clustered based on movement patterns within a confined space. Although swimming and clustering bacteria exhibit similar collective movement patterns in open spaces, they show different movement patterns within confined spaces of a specific size. Non-motile 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] Throughout the following description and claims, certain terms are used 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, such as 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 targets can be used as test samples. In some cases, modifying solid test samples to form liquid media or release targets (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] See diagrams of different motion modes Figure 10 As shown Invention Details

[0025] The first aspect of this invention provides a microfluidic biochip.

[0026] Microfluidic biochips 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 used in biochips 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 implementation method

[0035] A biochip, 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.

[0036] A biochip, 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.

[0037] A biochip, 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.

[0038] A top view of the PDMS chip is shown below. Figure 1a As shown, Figure 1b A side perspective view of the PDMS biochip is shown. A second aspect of the invention further provides a method for fabricating the biochip.

[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 plate is sliced ​​into pieces to form a biochip.

[0040] Specifically, biochips are fabricated using photolithography, which employs ultraviolet light exposure to create micropores of the desired size and shape to fabricate bacterial implantation and culture devices. The stainless steel photomask used for photolithography is shown in the figure. The photomask is divided into nine regions, each 0.9 cm x 0.9 cm in size (see Figure 1). Figure 6-7 Each region A has its own periodically repeating circular transparent array of 90x90. This mask fits perfectly onto common 2-inch silicon wafer substrates. It can be used to fabricate photoresist structures or to grow permanent structures using conventional growth equipment such as magnetron sputtering with metal targets. The micro / nano structures fabricated using this mask can then be used to create a mold of PDMS material, yielding 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.

[0044] The detection system includes a biochip, a culture plate, and an imaging device; optionally, it further includes an incubator. When using the detection system to detect bacterial motility, the biochip is placed on a semi-solid plate, so that the bacteria to be tested are sandwiched between a PDMS sheet and 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, for example, 20x, 30x, or 40x.

[0052] The camera can be an optical camera or a digital camera, such as a ThorLabs Kiralux CS505MU camera. A fourth aspect of this invention provides a method for identifying bacterial motility.

[0053] 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 bacterial motility, 5 μl of the sample is inoculated onto a soft agar plate (exemplarily, a bacterial suspension and PBS can be mixed to a concentration of 100 mg / ml before being spotted onto a culture dish for incubation). The culture 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 Wait 5-10 minutes to allow the 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 patterns of the confined bacteria can be observed under a microscope. If no movement is observed, it means the sample does not contain active bacteria. If the sample contains clusters of bacteria, such as... Figure 2a As shown, the motion pattern within the micropores will be a single vortex, either clockwise or counterclockwise. If the sample contains only motile bacteria and not clustered bacteria, the motion pattern will be turbulent (chaotic motion), such as... Figure 2bAs shown. The constrained motion was captured by a microscope camera and processed using computer software (such as the MATLAB PIV toolkit) to calculate various parameters in the velocity field.

[0054] 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.

[0055] A microfluidic chip with a specific channel structure is used to detect the movement behavior of bacteria under different environmental conditions.

[0056] 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.

[0057] 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.

[0058] Calculation formula:

[0059] (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).

[0060] (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.

[0061] (3) Acceleration calculation, used to distinguish different types of motion modes (such as rapid start, stop or turn): Where a: acceleration (μm / s²) 2 ), Δv: velocity change (μm / s), Δt: time interval (s).

[0062] (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 ).

[0063] (5) Coverage calculation,

[0064] (6) Diffusion rate calculation, Where Δr: diffusion distance at the edge of the bacterial community (μm), Δt: time interval (s).

[0065] (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;

[0066] 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.

[0067] (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.

[0068] 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.

[0069] Table 1

[0070]

[0071] Taking bacteria A and B as examples, detection and calculation were performed, and the data shown in Table 2 were obtained.

[0072] Table 2

[0073]

[0074]

[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. 2Population 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 (Coverage 60%), vorticity 1.8: relatively high, indicating significant collective rotational behavior, more consistent with swarming characteristics. Preliminary conclusion:

[0080] Bacterial B's movement pattern is swarming.

[0081] 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 single-vortex motion, 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 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 [link to relevant documentation]. Figure 11B .

[0082] 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 assessment can be made by combining dynamic diffusion rate analysis, multi-region diffusion analysis, and machine learning models. See [link to relevant documentation]. Figure 11C .

[0083] Verification of micropore size

[0084] The inventors simulated the confined movement of clustered and planktonic cells under circular confinement 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 with a minimum disk size of 30 μm, and under a confinement size of 90 μm, the disk filled by clustered cells transformed into multiple vortices (see results). Figure 8 The vertical axis is Ф), thus proving that a better micropore diameter range of at least 30μm-90μm can be achieved, but a better range can be 40-90μm.

[0085] The inventors also verified the effect of restricting cluster movement using micropores of different depths (18μm, 22μm, 40μm) within a micropore with a pore size of 74μm. Each group had 5 samples, and the values ​​represent the mean and standard deviation (see results). Figure 9 The results show 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.

[0086] Methods to improve development and tracing

[0087] 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.

[0088] Tracer beads are coated into the holes. In the case of clustered movement, the tracer beads will exhibit a single vortex (…). Figure 3a During swimming, the tracer beads will exhibit chaotic motion. Figure 3b The tracer beads can be made of various materials and sizes, such as polystyrene, silica, PMMA, magnetic particles, fluorescently labeled particles, liposomes, and metal particles.

[0089] Tracer microgears can also be used to display motion patterns within holes. Figure 4a -b). The tracer microgear can be "J-shaped" or any other shape or size / thickness and different materials / properties (e.g., magnetic).

[0090] Mucin-coated surfaces can be used to study motility; 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 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).

[0091] Chemotaxis promotes further screening

[0092] 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.

[0093] The fifth aspect of this invention provides applications for microfluidic chips.

[0094] The use of microfluidic biochips in detecting the movement of microbial clusters in samples; the samples are environmental samples, such as contaminated samples, such as soil, plants, feces, vomit, food, sputum, urine, wounds / secretions, etc., but the results obtained are not necessarily directly related to diseases.

[0095] The use of microfluidic biochips in target bacterial screening, drug-resistant bacterial screening, and screening for cluster inhibitors is described, but the test results are not used for disease diagnosis.

[0096] A method for assisting in the diagnosis, treatment, and management of diseases by detecting the state of microbial cluster movement using a microfluidic biochip; 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).

[0097] Beneficial effects

[0098] 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 for testing bacterial motility in complex mixtures, existing methods 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 time-consuming and labor-intensive. This new method is ideal if researchers or clinicians require rapid results within a given clinical sample, such as within 24 hours, regarding the presence of swarming bacteria.

[0099] A comparison between the technique for identifying clustered / swimming / non-motorized bacteria in this invention and traditional bacterial motility testing methods (see [link]). Figure 5 Left image: Compared to traditional methods, the PDMS biochip method can distinguish bacterial motility types in complex mixtures in three steps and reduce testing time by 10 times. Right image: Traditional methods require separate testing of swimming (blue arrow) and swarming motility (green arrow), and swarming testing is very time-consuming, requiring at least 4 days.

[0100] 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

[0101] Figure 1a Top view of PDMS biochip

[0102] Figure 1b Side perspective view of PDMS biochip

[0103] Figure 2a Single vortex motion mode

[0104] Figure 2b Chaotic motion pattern

[0105] Figure 3a The tracer bead will present a single vortex.

[0106] Figure 3b The tracer beads will exhibit chaotic motion.

[0107] Figure 4a The tracer microgear exhibits a single vortex

[0108] Figure 4bThe tracer microgear exhibits chaotic motion.

[0109] Figure 5 Comparison of techniques for identifying swarming / swimming / non-motile bacteria with traditional methods for detecting bacterial motility

[0110] Figure 6 Mask design drawing

[0111] Figure 7 Mask design drawing

[0112] Figure 8 Microfluidic biochip micropore size gradient experiment

[0113] Figure 9 Microfluidic biochip micropore depth gradient experiment

[0114] Figure 10 Diagrams of different motion modes

[0115] Figure 11A Microfluidic chips used in tablets (Figure)

[0116] Figure 11B Microfluidic chip analysis confirmed the pattern as a centralized cluster motion (microscopic image).

[0117] Figure 11C Bacterial movement pattern generated by Matlab PIV

[0118] Figure 12 ROC curves of swarming exercise in IBD patients and healthy individuals 24-28 hours later

[0119] Figure 13 ROC curves of swarming exercise in IBD patients and healthy individuals after 48 hours Detailed Implementation

[0120] This invention can be applied to detection in various fields and is not particularly limited thereto. The following are just examples, providing several typical application scenarios.

[0121] I. The biochip of this invention can be used for disease diagnosis and as a biomarker.

[0122] 1. Biomarkers for inflammatory bowel disease can be used to aid in diagnosis.

[0123] 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).

[0124] Bacterial swarming can serve as a specific marker of inflammatory bowel disease. The inventors conducted a series of experimental screenings, the experimental protocols of which are as follows:

[0125] Fecal sample preparation

[0126] 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.

[0127] Specific methods:

[0128] Perform cluster motion analysis

[0129] 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.

[0130] 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.

[0131] 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 cluster assay. The results are shown in […]. Figure 13 As shown, fecal samples from the DSS group exhibited significant clustering movements. The biochip of this invention was used to verify Ф, and the result was greater than 0.8.

[0132] Validation of porcine biomarkers: Clustering determination (72 hours) of fecal samples collected from pigs with and without inflammatory bowel disease (IBD). Based on clustering scores: 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), feces 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 Ф, with results greater than 0.8.

[0133] 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.

[0134] 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 incubation. The method for identifying bacterial motility used in the fourth aspect of the invention was used for calculation. Samples with concentrated cluster motility were recorded along with all samples. Further subdivisions were made into CD (34 cases) and UC (45 cases) groups. The results are shown in Tables 3 and 4. Figure 12-13 As shown in the figure. The results demonstrate a high correlation between concentrated group movement and IBD patients. It can be used for biomarker prediction in human IBD, but it does not play a decisive role in biomarkers for further subdivision of CD and UC groups.

[0135] Table 3

[0136]

[0137] Table 4

[0138] CD 47.06％ 58.82％ 58.82％ 34 UC 44.44％ 57.78％ 66.67％ 45

[0139] The method for calculating the average swarming percentage (Mean Swarming%):

[0140] Within-group mean: The mean of the cluster proportions for all samples is used to obtain the Mean Swarming in the table.

[0141] The relationship between the average cluster ratio and the vortex order parameter Ф:

[0142] High Ф value: indicates that bacterial movement exhibits a distinct vortex pattern.

[0143] Low Ф value: diffusion-dominated.

[0144] The high clustering ratio and large Ф value of the IBD group indicate that its clustering motion is accompanied by orderly group vortex motion.

[0145] 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.

[0146] Comprehensive analysis

[0147] Table 5

[0148] Health comparison 11.67％ Low 0.2 IBD patients 41.62％ high 0.8

[0149] in conclusion

[0150] The high clustering rate, high velocity field, and Ф value in IBD patients collectively indicate that the inflammatory environment promotes bacterial synergistic motility.

[0151] 2. Urinary tract infection (UTI) testing

[0152] 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.

[0153] 3. Gastrointestinal (GI) stress (and dysbiosis) detection

[0154] 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.

[0155] We have shown that these bacterial isolates consistently protect the body from inflammation.

[0156] 4. Rapid diagnosis of foodborne illnesses: The main foodborne pathogens are swarming motile bacteria. These can be detected in vomit, feces, or ingested food.

[0157] II. Used for disease biomarker screening

[0158] 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.

[0159] III. Used for laboratory research on bacterial motility

[0160] 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.

[0161] IV. Used for screening drug-resistant bacteria

[0162] 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).

[0163] V. Microbial Detection in Pollution Sources

[0164] The pollution source can be environmental samples, such as soil, plants, feces, vomit, food, sputum, urine, wounds / secretions, etc.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] VI. Used for testing cluster inhibitors

[0169] 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.

[0170] 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, characterized in that, The detection system detects and calculates the bacteria's movement speed, directionality coefficient, acceleration, population density, coverage, diffusion velocity, vorticity, and vortex step parameters to determine which type of bacteria it belongs to: Twitching, Gliding, Sliding, Swarming, or Swimming. Twitching motion pattern, velocity range 0.01-2 μm / s, directionality coefficient 0.1-0.5, acceleration range 1-10 μm / s², population density 0.001-10 bacterial counts / μm², coverage 1%-10%, diffusion velocity 0.1-1 μm / s, vorticity 0.1-0.5; Gliding motion pattern, velocity range 2-10 μm / s, directionality coefficient 0.5-0.9, acceleration range 0-1 μm / s², population density 0.01-0.1 bacteria / μm², coverage 10%-100%, diffusion velocity 0.5-5 μm / s, vorticity 0-0.5; Sliding motion mode, velocity range 0.01 - 1 μm / s, directionality coefficient 0.1 - 0.5, acceleration range 0 - 1 μm / s², population density 0.1 - 1 bacterial count / μm², coverage 10% - 100%, diffusion velocity 0.1 - 2 μm / s, vorticity 0−0.5; Swarming motion pattern, speed range 10 - 50 μm / s, directionality coefficient 0.7 - 1.0, acceleration range 0.5 - 5 μm / s², population density 1 - 10 bacterial counts / μm², coverage 10% - 100%, diffusion velocity 10 - 50 μm / s, vorticity 1.0 - 2.0; Swimming motion mode, speed range 20 - 50 μm / s, directionality coefficient 0.8 - 1.0, acceleration range 50 - 200 μm / s², population density 0.1 - 1 bacterial count / μm², coverage <10%, diffusion velocity 10 - 50 μm / s, vorticity 2.0−5.0; The parameter calculation methods for the above motion modes are as follows: Calculation of bacterial movement speed Where v: the speed of bacterial movement, in μm / s; Δd: the displacement of bacteria within the time interval, in μm; Δt: the time interval, in s; Directional coefficient calculation Where D is the directionality coefficient, ranging from 0 ≤ D ≤ 1. The closer the value is to 1, the more directional the movement is; the closer it is to 0, the more random the movement is. Acceleration calculation, Where a: acceleration, in μm / s², Δv: change in velocity, in μm / s, Δt: time interval, in s; Population density calculation Where ρ: population density, in units of bacteria number / μm², N: number of bacteria, and A: area of ​​the observation area, in units of μm²; Coverage calculation ; Diffusion rate calculation Where Δr: the diffusion distance at the edge of the bacterial community, in μm; Δt: the time interval, in s; vorticity calculation , where v x and v y These 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; Through microscopic imaging and image analysis techniques, it is possible to obtain v x and v y ;Calculate using numerical differentiation methods and Calculate the vorticity at each location according to the formula; Furthermore, once the bacteria are determined to be in a swarming motion pattern, the following formula is used for further determination and calculation of vortex step parameters. , 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 exhibiting concentrated cluster movement, and when Ф is less than 0.8, it is identified as exhibiting non-concentrated cluster movement. The detection system includes a microfluidic biochip with multiple arranged micropore structures made of transparent material, a semi-solid culture plate, and an imaging device. When using the detection system to detect bacterial motility, the biochip is placed on the 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 sheet and the soft agar surface. Then, the imaging system is used to detect the motility parameters of the bacteria and calculate to determine their motility mode. The water activity of the semi-solid culture plate is between 0.90 and 0.999, which allows the sample to form a layer with a thickness of 10–40 μm on the culture medium. The semi-solid culture plate was prepared using 1% w / v tryptone, 0.5% w / v yeast, 0.5% w / v sodium chloride, 0.5% w / v agar and 20 ml deionized water; The microfluidic biochip has a pore size of 30µm-200µm and a micropore depth of 10-50µm. The method described is not intended for the diagnosis of diseases.

2. The method according to claim 1, wherein the microfluidic biochip has a pore size of 50-80µm and a micropore depth of 20-30µm.

3. The method according to claim 1, wherein the microfluidic biochip is made of materials including polydimethylsiloxane, silicon, glass, poly(methyl methacrylate), polycarbonate, polyimide, cyclic olefin copolymer or natural polymer.

4. The method according to claim 1, wherein the water activity of the semi-solid culture plate is 0.

995.

5. Use of the method of claim 1 in detecting microorganisms in a sample, wherein the sample is soil, plant, feces, vomit, food, sputum, urine, or wound secretions.

6. The use of the method of claim 1 in screening for target bacteria and screening for cluster inhibitors.

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