Biological micro-flow chip device and application thereof in microorganism movement mode detection

Abstract

The invention provides a biological micro-fluidic chip device and a detection system contained in the biological micro-fluidic chip device, which can distinguish non-motor bacteria, swimming bacteria or cluster bacteria, and can be further applied to disease diagnosis, biomarkers, target bacteria screening, drug-resistant bacteria screening and cluster inhibitor screening according to detection results. The detection and separation method provided by the invention is proved to be convenient and very valuable, and can further assist diagnosis and treatment. The whole device is relatively miniaturized and portable, and detection can be carried out at any time.

Description

Technical Field

[0001] The present invention relates to the field of biochips, and in particular to a bio-microfluidic chip device for detecting microbial movement patterns. Background Art

[0002] Bacterial motility is an important trait that not only enhances bacterial cell function but is also associated with host fitness. Bacterial swarming and swimming are two major types of flagella-driven motility. Bacterial swarming is defined as flagella-mediated multicellular movement on a wet surface. During swarming, bacterial cells sometimes become elongated, entangled with each other by flagellar adhesions, and form rafts. In contrast, bacterial swimming is movement in liquids, where cells are free and independent of each other. However, in some cases, this descriptive definition can be vague and confusing, making it difficult to distinguish swarmers from swimmers when some bacteria can display both types of motility and when bacterial mixtures are present.

[0003] Patents in the prior art have reported on detecting cell motility properties. For example, US Pat. No. 509386A6 discloses an apparatus and method for determining the motility and other characteristics 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 wavelength light by cells and subsequent fluorescence emission. While swimming motility can be detected using fluorescence images in the fluid medium, bacterial swarming cannot be distinguished from swimming motility on a soft agar surface. US Pat. No. 5733736A discloses a motility channel pathogen detector and a method for using the detector for detecting target motile pathogens in potential pathogen test samples, but the device cannot detect bacterial swarming. US Pat. No. 10266867A uses an imaging system to characterize bacterial swimming motility to infer the microbial type. However, this method does not provide swarming information. US Pat. No. 20190291052 describes a microwell for capturing bacteria. Cell density increases as the cells are captured, but the device does not collect any information about cell motility. US Pat. No. 20190030192 uses a circular capsule to measure the motility of a target region with an identification marker. US20070202137 studied motile organisms in a predefined area to screen test molecules, but it did not reveal information about swarming motility. However, using this device, it was not possible to accurately characterize bacterial motility beyond the swimming motility mode.

[0004] Similar reports have been reported in non-patent literature, such as journal articles, such as Be'er et al. (2019), which discussed the statistical properties of bacterial clusters and the behavior of collective motion. However, the article did not describe in detail any confined clustering phenomena and related settings for detection. Wioland et al. (2016) observed that swimming bacteria were confined in connected micro-tracks and exhibited collective motion. There is a coupling between the track width and the bacterial motion pattern, but it is necessary to seal the PDMS with a glass slide and inject the bacteria into the device, which cannot be directly applied to tissues and open surfaces, is limited in use, and the detection is not accurate. Cheong et al. (2015) described a 3D tracking system for bacterial motion 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 closely associate with dense bacterial groups on surfaces. Gurung et al. (2020) reported on microfluidic techniques for separating bacterial cells by chemotaxis (chemotaxis, pH chemotaxis, thermotaxis, rheotaxis, aerotaxis, magnetotaxis, and phototaxis). However, they did not mention any microfluidic devices similar to those contemplated in the present invention. Beppu et al. (2017) studied the locomotion of bacteria in shape-specific PDMS (polydimethylsiloxane) confinement environments. The PDMS sheet confinement device was placed on hard glass, and the duration of the movement did not exceed 10 minutes. However, the article did not demonstrate bacterial swarming, nor did it use a semisolid plate. Wioland et al. (2016, Nature Physics) studied the handedness of bacteria confined within a pattern of interconnected microwells in a microfluidic device. The rotational motion was complemented by interactions between adjacent wells, unlike the independent microwells in the present invention. Similarly, the PDMS was placed on hard glass rather than a sustainable agar surface, and there was no mention of swarming bacteria.

[0005] It can be seen that although the existing technology involves the detection of cell motility performance, it is relatively lacking in the detection of bacterial cluster movement. Moreover, the operation is relatively complicated and time-consuming, and it cannot fully present the bacterial movement pattern. There is currently a lack of a portable device that can fully distinguish bacterial movement patterns, is easy to operate, and takes less time. Summary of the Invention

[0006] The inventors discovered that, unexpectedly, using micropores to restrict the range of bacterial movement can distinguish between different movement patterns such as bacterial swarming and swimming. The swarming type forms a single vortex movement pattern in the micropores, while the planktonic type forms multiple vortices in the micropores. Similar differential behaviors have also been observed for several other Gram-negative bacteria. Therefore, a PDMS microarray was designed to observe the swarming movement of bacteria. Using this method, bacterial motility can be identified as immobile, swimming, or swarming patterns based on the movement pattern within a confined space. Although swimming and swarming bacteria exhibit similar collective movement patterns in open spaces, they exhibit different movement patterns within confined spaces of a specific size. Immobile bacteria do not move, while swimming bacteria move vigorously. Due to the unique arrangement between cells, swarming bacteria exhibit a single vortex movement pattern. The trajectory of the vortex motion or vortex is specific to the surface of the chip to which it is applied, but before using the device involved in the present invention, the difference between swarming and swimming had never been revealed.

[0007] definition

[0008] Throughout the following description and claims, certain terms are used to refer to particular system components and configurations. As those skilled in the art will appreciate, the same component may be referred to by different names. This document does not intend to distinguish between components that differ in name but function. In the following discussion and claims, the terms "include" and "comprising" are used in an open-ended manner and, thus, should be interpreted to mean "including, but not limited to..." "Couple" or "couples" means either an indirect or direct connection. Thus, if a first device or apparatus couples to a second device or apparatus, that connection may be through a direct connection or through an indirect connection via other devices or apparatuses and connections.

[0009] References to relative terms such as "top," "front," "bottom," and "back" are used to provide relative relationships between elements and are not intended to imply any absolute direction. Various features may be arbitrarily drawn in different scales for simplicity and clarity.

[0010] When used with the term "comprising" in the claims and / or description, the use of the word "a" or "an" may mean "one", but it is also consistent with the meaning of "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 error for the device or method being employed to determine the 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, as obtained from a source, or after pretreatment to alter the sample's characteristics. 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, and the like. Test samples may be pretreated prior to use, such as by preparing plasma from blood, diluting viscous fluids, or lysing microorganisms within the sample. Treatment methods may include filtration, precipitation, dilution, distillation, mixing, concentration, inactivation of interfering components, lysis of organisms and / or cells, and the addition of reagents. In addition to physiological fluids, other liquid samples such as water, food, and soil can be used for environmental or food production analyses. Furthermore, solid materials suspected of containing the target can be used as test samples. In some cases, it may be beneficial to modify the solid test sample to form a liquid medium or to release the target (e.g., nucleic acid).

[0013] The term "cluster movement"

[0014] Swarming motility is the collective, directional, and spreading movement of bacteria on a solid surface, driven by the rotational motion of their flagella. Swarming movement, high coverage, and high diffusion rates are associated with high swarm density because bacteria move in a swarm and cover a large area.

[0015] The term "floating motion"

[0016] Swimming Motility is the movement of a single bacterium in a liquid environment, which moves in one direction by relying on the rotation of its flagella. It is an individual movement with a fixed direction.

[0017] The term "twitching movement"

[0018] Twitching motility is the intermittent movement of bacteria through the repeated extension and retraction of Type IV pili and their attachment to solid surfaces or neighboring cells. This movement manifests as short, discontinuous "twitching" movements and is common in Gram-negative bacteria such as Pseudomonas.

[0019] The term "gliding motion"

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

[0021] The term "sliding motion"

[0022] Sliding motility is a form of bacterial movement that relies on the passive diffusion of surface-active substances (such as biosurfactants) secreted by bacteria on low-friction surfaces. This movement is often accompanied by the expansion of the colony edge and is common in some Bacillus and Streptomyces species.

[0023] Schematic diagram of different exercise modes Figure 10 shown Detailed Description of the Invention

[0025] The first aspect of the present invention provides a microfluidic biochip

[0026] The microfluidic biochip is made of a transparent material comprising a plurality of arranged microwells.

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

[0028] As an example, the pore size of the micropores may be 50 μm, 55 μm, 60 μm, 65 μm, 70 μm, 73 μm, 74 μm, 75 μm, 76 μm, etc.

[0029] The number of holes on a PDMS sheet does not need to be particularly limited. The arrangement can be 100x100, 200x200, 300x300, etc., but can also be any combination of micropore integers. It is not limited to being a square with the same length of horizontal and vertical sides, and can also be other geometric shapes that are convenient for operation, such as rectangle, circle, diamond, etc.

[0030] The width and length of the PDMS sheet can be customized to a single side length of 0.1-100 cm, preferably 0.5 cm x 0.5 cm, 1 cm x 1 cm, 1.5 cm x 1.5 cm, 2 cm x 2 cm.

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

[0032] The micropore depth may be 10-50 μm, preferably 18-40 μm, 18-30 μm, 18-25 μm, 20-30 μm. As an example, the micropore depth may be 18 μm, 19 μm, 20 μm, 21 μm, 22 μm, 23 μm, 24 μm, 25 μm, etc.

[0033] The biochip is made of 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 copolymer (COC), natural polymers, cellulose membranes, gels, etc.

[0034] In a specific embodiment,

[0035] A biochip, about 1 cm 2 Large, 0.3mm thick. Each PDMS sheet is a biochip, with a row of microwells on one side of the surface. The circular holes are 50μm in diameter and 22μm deep, with approximately 10,000 microwells on a single PDMS sheet.

[0036] A biochip, about 1 cm 2 Large, 0.3mm thick. Each PDMS sheet is a biochip, with a row of microwells on one side of the surface. The circular holes have a diameter of 74μm and a depth of 22μm, and there are approximately 10,000 microwells on a single PDMS sheet.

[0037] A biochip, about 1 cm 2 Large, 0.3mm thick. Each PDMS sheet is a biochip, with a row of microwells on one side of the surface. The circular holes have a diameter of 65μm and a depth of 20μm, and there are approximately 10,000 microwells on a single PDMS sheet.

[0038] The top view of the PDMS chip is shown in Figure 1a As shown, Figure 1b The second aspect of the present invention further provides a method for preparing a biochip.

[0039] Using soft lithography, PDMS (polydimethylsiloxane) was applied to a silicon wafer and the SU8 microcylinder array was patterned. After demolding, the PDMS sheet was cut into slices to make a biochip.

[0040] Specifically, the biochip is manufactured based on photolithography technology, using ultraviolet exposure technology to form micro-holes of the required size and shape to create a bacterial implantation and culture device. The stainless steel mask used in photolithography is shown in the figure. The mask is divided into nine areas of 0.9cm x 0.9cm (see Figure 6-7 ), each area A has its own periodically repeating circular light-transmitting array of 90 x 90. This mask is perfectly adapted for common 2-inch silicon wafer substrates. It can be used to create photoresist structures and can also be used in conventional growth equipment such as magnetron sputtering to grow permanent structures using metal targets. The micro-nanostructures created with this mask can then be used to re-mold PDMS material to obtain the desired implantation culture device for bacterial culture testing.

[0041] Since the desired thickness of the culture device is generally tens of microns, SU8 or SPR220 photoresists were chosen. The spin coating parameters achieved in the experiment were 500 rpm and a spin time of 40 seconds. After photolithography, a step profiler was used to measure the structure of the SPR220 resin, which had a thickness of 40 μm. The overall morphology was generally consistent with expectations.

[0042] Furthermore, the photoresist material can be replaced with metal materials to further increase the service life.

[0043] The third aspect of the present invention is to provide a detection system

[0044] The detection system includes a biochip, a culture plate, an imaging device, and optionally, an incubator. When using the detection system to detect bacterial motility, the biochip is placed on a semi-solid plate so that the test bacteria are sandwiched between the PDMS sheet and the soft agar surface.

[0045] Culture plates:

[0046] The culture plate is a semisolid plate, preferably a moist semisolid surface such as soft agar, mucous membrane or duct, methylcellulose semisolid culture medium, or beef peptone semisolid agar. The material is not particularly limited; any material that meets the required moisture requirements can be used for the culture plate. Moisture content can be quantified by water activity (Aw), calculated as follows: 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 water activity range for semisolid plates suitable for 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 nutrients, and can be prepared using culture media known in the prior art.

[0048] Furthermore, a preparation component 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. The agar can be replaced with carrageenan, sodium alginate, gelatin, etc. Sterilization is then performed using common sterilization methods such as high-pressure steam sterilization, dry heat sterilization, filtration sterilization, and ultraviolet sterilization. Cooling is performed using cooling methods such as cold water bath cooling, ice cooling, and fan cooling.

[0049] Imaging device

[0050] The microscope is equipped with high-power objectives and a camera.

[0051] The objective lens magnification can be 4 times, 10 times, 20 times, 30 times, 40 times, 50 times, 100 times, preferably 20 times to 40 times, for example 20 times, 30 times, 40 times

[0052] The camera can be an optical camera or a digital camera, for example, a ThorLabs, Kiralux CS505MU camera. The fourth aspect of the present invention is to provide a method for identifying bacterial motility.

[0053] To use the chip involved in the present invention, it is first necessary to prepare samples for bacterial motility test. The test sample can be a single bacterial strain, a bacterial mixture, or a more complex mixture, such as a stool sample. For example, in order to detect the motility type of bacteria, 5μl of the sample is inoculated on a soft agar plate (for example, the bacterial solution and PBS can be mixed to a liquid concentration of 100mg / ml, and then spotted on a culture dish for culture). The culture dish is incubated at 37°C for 4-6 hours (the incubation time may vary depending on the nature of the sample and the surface type) until bacterial colonies are formed on the agar. Place the microbiochip on the edge of the bacterial colony with the patterned side facing down to confine the bacteria to the wells (see Figure 11A ). Wait 5-10 minutes for the bacteria to fill the microwells. Based on the cell density, the bacterial colonies can form a layer of 10-40 μm thickness on the culture medium in a 22 μm deep microwell. When confined between the agar and the PDMS surface, the bacteria tend to swim close to the porous agar surface. For bacteria swimming near the agar surface, the rotating cell body experiences the lateral component of the 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 swarms of bacteria, such as Figure 2a As shown in the figure, the motion pattern in the microwell will be a single vortex in the clockwise or counterclockwise direction. If the sample contains only motile bacteria but not swarming bacteria, its motion pattern will be turbulent (chaotic motion), as shown in the figure. Figure 2bThe restricted 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.

[0054] Images of swarming or swimming under confinement were captured by a microscope camera (ThorLabs, Kiralux CS505MU) and then processed using the particle image velocimetry (PIV) software 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 trajectories in the microfluidic chip, the bacterial movement patterns are quantified.

[0057] The bacterial movement pattern is comprehensively determined by the bacterial movement speed, movement change rate, acceleration, group density, coverage, diffusion speed, vorticity, and vortex order parameters.

[0058] Calculation formula:

[0059] (1) Calculation of bacterial movement speed. The bacterial movement speed is the key parameter to distinguish different movement modes: Where v is the bacterial velocity (μm / s), Δd is the bacterial displacement in the time interval (μm), and Δt is the time interval (s).

[0060] (2) Calculation of the rate of change of movement direction. The rate of change of movement direction is used to quantify the randomness or directionality of bacterial movement: Where D is the directionality coefficient (0≤D≤1). The closer the value is to 1, the more directional the movement is, and the closer the value 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 is acceleration (μm / s 2 ), Δv: velocity change (μm / s), Δt: time interval (s).

[0062] (4) Calculation of population density. Population density refers to the number of bacteria per unit area or unit volume, which can be used to quantify the degree of bacterial aggregation: Where ρ: population density (number of bacteria / μm 2 ), N: number of bacteria, A: area of ​​observation area (μm 2 ).

[0063] (5) Coverage calculation,

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

[0065] (7) Vorticity is the curl of the velocity field, which is used to quantify the rotation intensity of fluid or group motion and can reflect the rotation characteristics of bacterial group motion: Among them, v x and v y are the velocity components of the bacterial population in the x and y directions, respectively, according to; and are the partial derivatives of the velocity components in space;

[0066] Through microscopy imaging and image analysis technology, v x and v y ; Use numerical differentiation methods (such as finite difference method) to calculate and The vorticity at each location was calculated using the formula: The distribution of the average vorticity of the entire bacterial population was calculated.

[0067] (8) Based on the velocity field, the vortex order parameter is calculated by the following Φ function: Among them, v i is the local velocity, t i is the tangential unit vector perpendicular to the radial vector.

[0068] Based on a large number of bacterial detection results, the inventors provide the numerical ranges of the parameters in Table 1 for different motion models to facilitate the calculation of bacterial motion patterns.

[0069] Table 1

[0070]

[0071] Taking bacteria A and bacteria B as examples, we perform detection and calculation and obtain the data shown in Table 2.

[0072] Table 2

[0073]

[0074]

[0075] Analyze the results of bacteria A and bacteria B

[0076] Bacteria A:

[0077] Diffusion speed 0.2μm / s: falls within the diffusion speed range of Twitching and Sliding; combined with other parameters (speed 1μm / s, directionality 0.4, acceleration 0.5μm / s 2, population density 5 bacteria / μm 2 , coverage 10%), and vorticity 0.5, indicating almost no collective rotational behavior, which is more consistent with the characteristics of twitching. Conclusion: The movement mode of bacterium A is twitching.

[0078] Bacteria B:

[0079] Diffusion velocity 20 μm / s: falls within the diffusion velocity range of Swarming and Swimming; combined with other parameters (speed 0 μm / s, directionality 0.83, acceleration 2 μm / s 2 , population density 2 bacteria / μm 2 , coverage 60%), vorticity 1.8: relatively high, indicating the presence of obvious collective rotation behavior, more consistent with the characteristics of swarming. Preliminary conclusions:

[0080] The movement mode of bacteria B is swarming.

[0081] Based on the above parameters, bacteria B have been preliminarily identified as belonging to the swarming mode. Further subdivision is required by calculating the vortex order parameter Φ. If Φ is greater than or equal to 0.8, the movement is considered to be a single vortex, and the movement type is defined as centralized cluster movement, which can be effectively used for disease diagnosis, biomarker verification, target strain isolation, sample testing, etc., while bacteria with Φ less than 0.8 are defined as non-centralized cluster movement. Although they have a certain cluster movement ability, the movement is defined as chaotic movement or swimming movement, which is not sufficient for subsequent disease diagnosis, biomarker verification, target strain isolation, sample testing, etc. Figure 11B .

[0082] Add automatic algorithms to calculate and determine vortices from visual images (frames). For example, you can use the MATLAB PIV toolkit to track the moving bacteria in diluted clusters and swimming organism images for comparison. Furthermore, you can also combine dynamic diffusion velocity analysis, multi-region diffusion analysis, machine learning models, etc. for comprehensive judgment. Figure 11C .

[0083] Verification of micropore size

[0084] The inventors simulated the movement of swarming and swimming under circular confinement conditions of different sizes similar to those in the experiment (Swimming is an active mode of movement of planktonic bacteria, but not all planktonic bacteria have the ability to swim). By assigning an enhanced range of alignment interactions only to swarming cells, the simulation results reflected the experimental results. Both swarming and swimming cells started with a single vortex pattern with a minimum disk size of 30 μm. At a confinement size of 90 μm, the disk filled by swarming cells transformed into multiple vortices (results see Figure 8 , the vertical axis is Φ), which proves that the optimal micropore diameter range is at least 30μm-90μm, but the optimal range can be 40-90μm.

[0085] The inventors also verified the situation of limiting cluster movement in micropores with a pore size of 74 μm by using micropores of different depths (18 μm, 22 μm, and 40 μm). Each group has 5 samples, and the values ​​represent the mean and standard deviation (see the results). Figure 9 , the vertical axis is Ф), 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. If the depth is too deep, for example, greater than 40μm, it will affect the state of cluster movement.

[0086] Methods to improve imaging and tracing

[0087] In order to improve the clarity of the bacterial movement pattern in the observation hole, before the biochip is mounted on the colony, a tracing device that improves the clarity of movement observation can be added to the hole, including but not limited to mucin, tracing beads, tracing micro gears, etc.

[0088] The tracer beads are applied to the wells. In the case of swarming, the tracer beads will appear as a single vortex ( Figure 3a ), in the case of swimming, the tracer beads will exhibit chaotic motion ( Figure 3b ). Tracer beads can be made of polystyrene, silica, PMMA, magnetic particles, fluorescent labeled particles, liposomes, metal particles, and other materials and sizes.

[0089] Tracer microgears can also be used to show movement patterns within a hole ( Figure 4a -b). The tracer micro gears can be "J-shaped" or any other shape or size / thickness and different materials / properties (eg, magnetic).

[0090] Mucin-coated surfaces can be used to study motility. Specifically, secretory mucin and membrane-bound mucin can be used. Specifically, MUC5AC and MUC2 proteins can be used. When used, the mucin is dissolved in a buffer solution (such as PBS) at a concentration of usually 0.1-1 mg / mL. The microporous surface (such as glass, PDMS) is immersed in the mucin solution and incubated for 1-2 hours. The surface is rinsed with buffer to remove unbound mucin. Fluorescently labeled mucin or antibodies are used to verify the coating effect, and atomic force microscopy (AFM) or surface plasmon resonance (SPR) is used to analyze the surface morphology and mucin density.

[0091] Further screening of chemotaxis promotion

[0092] Chemotactic substances such as nutrients and antibiotics can be added to observe the chemotactic behavior of bacteria and isolate target strains. Furthermore, by adding connecting channels between the micropores and micropores of the microfluidic chip, a more preferred target single strain can be screened through biological chemotaxis, chemical chemotaxis, and physical chemotaxis. Specifically, the microfluidic technology of bacterial cells can be used to separate bacterial chemotaxis (pH chemotaxis, thermotaxis, rheology, aerotaxis, magnetotaxis, and phototaxis, etc.), drive the target bacteria to be screened in the first hole, add chemotactic substances or chemotactic conditions in the second hole, and drive the bacterial colony to move from the first hole to the second hole through cluster movement. Thereby, it is used to further screen the target strain.

[0093] The fifth aspect of the present invention provides a use of a microfluidic chip

[0094] The use of microfluidic biochips in detecting the movement of microbial clusters in samples; the samples come from 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 the disease.

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

[0096] A method for assisting in the diagnosis and treatment of diseases by using a microfluidic biochip to detect the state of microbial swarming and drawing conclusions; the diseases are diseases that can be diagnosed by swarming, specifically inflammatory bowel disease, urinary tract infection, gastrointestinal stress, foodborne diseases, active autoimmune diseases, etc. The detected microbial swarming can be used as a biomarker for diseases, for example, inflammatory bowel disease. More specifically, inflammatory bowel disease is further divided into UC (ulcerative colitis) and CD (Crohn's disease).

[0097] Beneficial effects

[0098] The method of combining PDMS biochip + microscope + motion parameter algorithm to distinguish between swarming motion and swimming motion is a new method that has not been disclosed in the prior art. A key advantage of the present invention is detection efficiency. In order to help illustrate the huge improvement in testing bacterial motility from complex mixtures compared to traditional methods, traditional methods used in the prior art detect swarming bacteria in human samples, such as using MALDI-TOF mass spectrometry to identify several swarming bacteria in human body fluids, such as Serratia marcescens and Citrobacter cohnii. However, this conventional method is time-consuming and labor-intensive. If researchers or clinicians expect to obtain rapid results on whether swarming bacteria are present in a given clinical sample within a certain period of time, for example within 24 hours, the new method of the present invention will be very suitable.

[0099] Comparison between the present invention's technology for identifying swarming / swimming / non-motile bacteria and 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 and shorten the testing time by 10 times. Right: Traditional methods require separate testing of swimming (blue arrow) and swarming motility (green arrow). Swarming testing is very time-consuming, requiring at least 4 days.

[0100] The present invention effectively distinguishes non-motile, swimming, and swarming bacteria in a single experimental setup. Because both swimming and swarming are characteristic of bacterial flagellar motility, which is crucial to host health, the detection and separation methods described in this invention are both convenient and valuable, further aiding diagnosis and treatment. The entire device is relatively compact and portable, allowing testing to be performed at any convenient time. BRIEF DESCRIPTION OF THE DRAWINGS

[0101] Figure 1a Top view of PDMS biochip

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

[0103] Figure 2a Single vortex motion mode

[0104] Figure 2b Chaotic motion pattern

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

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

[0107] Figure 4a Tracer micro-gears show a single vortex

[0108] Figure 4bTracer microgears exhibit chaotic motion

[0109] Figure 5 Comparison of swarming / swimming / non-motile bacteria identification technology with traditional bacterial motility detection methods

[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 Schematic diagram of different exercise modes

[0115] Figure 11A Microfluidic chip used on a flat plate

[0116] Figure 11B The microfluidic chip confirmed that it was a centralized cluster movement image (bottom of the mirror)

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

[0118] Figure 12 ROC curve of swarming exercise in IBD patients and healthy people after 24-28 hours

[0119] Figure 13 ROC curve of swarming exercise in IBD patients and healthy people after 48 hours DETAILED DESCRIPTION

[0120] The present invention can be applied in various fields without any special limitation. The following are some typical application scenarios for illustration only.

[0121] 1. The biochip of the present invention can be used for disease diagnosis and as a biomarker

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

[0123] The diagnosis of IBD is usually performed by colonoscopy and biopsy of pathological lesions such as blood or fecal lesions. It is an invasive test that is potentially traumatic to the body, and painless endoscopy requires anesthesia and a caregiver. The microfluidic biochip involved in the present invention does not need to directly contact the human body and can be used as a biomarker of IBD by detecting cluster movement, and can efficiently and quickly diagnose IBD. IBD (Inflammatory Bowel Disease) is mainly divided into two types: UC (Ulcerative Colitis) and CD (Crohn's Disease).

[0124] Bacterial swarming can be used as a specific marker for inflammatory bowel disease. The inventors conducted a series of experimental screening experiments, the experimental plan is as follows:

[0125] Stool sample preparation

[0126] Take a stool sample stored in its original container and gradually thaw the sample Thaw on ice for one hour. Pre-weigh a sterile microcentrifuge tube on a scale and set it to zero (tare). In a fume hood, remove the sample with a sterile toothpick and place it in a microcentrifuge tube. Weigh the stool sample using the same scale used to weigh the sterile microcentrifuge tube in the previous step. Add enough sterile phosphate-buffered saline (PBS) (pH = 7.4, room temperature) to the microcentrifuge tube to achieve a final stool concentration of 100 mg / mL. Spin the sterile pestle in the microcentrifuge tube approximately twenty (20) times to completely homogenize the stool pellet.

[0127] Specific methods:

[0128] Conduct cluster motion analysis

[0129] Vortex the microcentrifuge tube containing the fecal sample for approximately 10 seconds, then inoculate 3 μL of the homogenized fecal solution onto the center surface of a 0.5% LB agar plate. Incubate the plate at 37°C and 40% constant humidity for 8–12 hours. Place a microfluidic chip near the edge of the bacteria and incubate for an additional 5–10 minutes. Observe the bacterial velocity field within the microfluidic chip using an imaging system, and confirm whether swarming is observed using tools such as Matlab PIV.

[0130] Through the above screening method, a strain with strong swarming motility was screened out from the patient's feces. The inventors further found that after the symbiotic swarming bacteria isolated from the feces of colitis mice were amplified in vitro and gavage back into the colitis mice, the colitis of the mice was relieved, while the corresponding mutant strain lacking swarming ability had no such effect, indicating that the protective effect is closely related to the swarming motility phenotype of bacteria.

[0131] Biomarker validation in mice: C57BL / 6 mice (8 weeks old) were exposed to water or DSS water for 7 days (n=4 per group). Fecal samples from the control group (above the red line) and the DSS group (below the red line) were collected for cluster determination. The results are shown in Figure 13 As shown, the fecal samples of the DSS group showed significant clustering. The biochip of the present invention was used to verify the Φ, and the result was greater than 0.8.

[0132] Porcine Biomarker Validation: Clustering assay (72 hours) of fecal samples collected from pigs with and without inflammatory bowel disease. Based on a clustering score of 0, no clustering; 1, swarming within 72 hours; 2, swarming within 48 hours; and 3, swarming within 24 hours or less (control, n=6; IBD, n=7), feces collected from a limited sample of pigs with active porcine intestinal disease also showed an increased tendency for collective spreading and clustering compared to pigs in the control group. Validation of the Φ using the biochip of the present invention resulted in a value greater than 0.8.

[0133] Human biomarker validation: To further verify whether cluster motion parameters can play the same role as biomarkers in humans and can be extended to further subdivide CD and UC, the inventors further validated the human experimental group.

[0134] Two comparative groups, a healthy control group and an IBD patient group (79 cases), were set up. Fecal samples from the experimental group were separated and cultured on plates for observation at two time points: 24-28 hours and 48 hours. The method for identifying bacterial motility used in the fourth aspect of the invention was used for calculation. Samples with results indicating centralized clustering were recorded along with all samples, and further subdivided into CD (34 cases) and UC (45 cases). The results are shown in Tables 3, 4, and 5. Figure 12-13 The results demonstrate a high correlation between clustered motion and IBD patients. This finding suggests that clustered motion can be used as a marker for predicting IBD in humans, but it does not provide a definitive marker for further subdividing CD and UC groups.

[0135] Table 3

[0136]

[0137] Table 4

[0138] Group 16-20h 24-28h 48h Sample Number CD 47.06％ 58.82％ 58.82％ 34 UC 44.44％ 57.78％ 66.67％ 45

[0139] Calculation method of average cluster ratio (Mean Swarming%):

[0140] Mean within group: Take the mean of the cluster proportions of all samples to obtain the Mean Swarming% in the table.

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

[0142] High Ф value: indicates that the bacterial movement presents a clear vortex pattern.

[0143] Low Ф value: diffusion dominated.

[0144] The IBD group had a high clustering ratio and a large Ф value, indicating that its clustering motion was accompanied by an orderly group vortex motion.

[0145] Velocity field strength: The bacterial velocity field strength was determined using tools such as Matlab PIV. High velocity field strength in IBD patients is driven by inflammatory signals, promoting concentrated bacterial clustering. Low velocity field strength in healthy controls demonstrates random diffusion, slow, and disordered movement.

[0146] Comprehensive analysis

[0147] Table 5

[0148] Group Average cluster ratio Velocity field strength Ф value Healthy controls 11.67％ Low 0.2 IBD patients 41.62％ high 0.8

[0149] in conclusion

[0150] The high cluster ratio, high velocity field, and Φ value in IBD patients collectively suggest that the inflammatory environment promotes coordinated bacterial movement.

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

[0152] Over 80% of urinary tract infections are caused by uropathogenic Escherichia coli or other bacteria, including Proteus mirabilis. In community cases, diagnosis is typically made using a urine dipstick and urine culture. However, urine cultures take several days to complete. A positive or negative urine dipstick result can trigger the use of our technology to determine whether Motile Bacteria are abundant. If present, this may trigger early use of broad-spectrum antibiotics. If not, antibiotics may be reserved for cases where urine results are returned or to investigate non-UTI causes of symptoms. Our technology can provide data within 6-8 hours.

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

[0154] Gastrointestinal stress encompasses any pathological condition of the gastrointestinal mucosa. Published data show that motility genes are upregulated in IBD, and bacterial colonization is more prevalent in patients with IBD and polyps. Therefore, our technology could be used clinically in patients with any form of gastrointestinal disease to aid in the diagnosis of specific conditions such as intestinal inflammation or polyps. A second advantage is that the biochip of our invention can isolate the bacteria responsible for colonization at the colonization leading edge. These bacterial isolates have been shown to uniformly protect against inflammation. This is the diagnostic approach of our technology. For patients suffering from gastrointestinal stress, the method of our invention offers both diagnostic and therapeutic benefits.

[0155] We have shown that these bacterial isolates consistently protect against inflammation.

[0156] 4 Rapid diagnosis of foodborne diseases: The main foodborne pathogens are colonizing motile bacteria, which can be detected in vomitus, feces or consumed food.

[0157] 2. Used for disease marker screening

[0158] For example, it can be used as a marker for active autoimmune diseases. In psoriatic arthritis, for example, but not exclusively for other diseases, increased bacterial chemotaxis is associated with intestinal dysbiosis and is considered a marker of disease activity. The detection technology of this invention is faster and less expensive than sequencing methods, allowing the identification of mobile, chemotactic bacteria within hours and providing clinicians with clues to current and developing disease activity.

[0159] 3. Used in laboratory research on bacterial movement

[0160] The detection method of the present invention can be used to study bacterial motility in the laboratory. This technology provides a method for quantitatively identifying bacteria by microbial microarray confinement to define bacterial motility on any surface. PDMS can be modified to separate bacterial cells based on size and motility. This technology provides a quantitative method to define bacterial motility on any surface.

[0161] 4. Screening for drug-resistant bacteria

[0162] The problem of antibiotic resistance, despite the fact that there are now good management systems for antibiotics, still occurs at every level; in many parts of the world, self-prescription is still common. The swarming movement of bacteria indicates that most strains have developed antibiotic resistance. When used on biological materials (feces, sputum, urine isolation and identification of bacteria through microbial chips, wounds / secretions), the technology of the present invention (which obtains results in hours rather than days / weeks for traditional clinical tests such as culture) can detect antibiotic resistance early, and also early detection of epidemic strains (such as E. coli). If there is a choice, this will lead to treatment decisions whether to switch antibiotics or not to use antibiotics. It also helps us to focus on the typing of antibiotic resistance in the population (for example, nursing homes, hospitals and MDR bacteria).

[0163] 5. Microbial Detection in Pollution Sources

[0164] The pollution source may be an environmental sample, such as soil, plants, feces, vomitus, food, sputum, urine, wounds / secretions and other materials.

[0165] Clusters of bacteria can defeat fungal contamination and grape blight. Testing grapevines for clusters of bacteria can ensure safe wine production. Testing grapes for clusters of bacteria can ensure safe wine production.

[0166] Bacillus subtilis is an important soil bacterium that can provide biological control of seedling pathogens. Another application of the present technology is to detect bacterial colonies in greenhouse soil. Elimination of colonies may require the introduction of these cultures into the soil.

[0167] Proteus is a known worldwide marker of fecal contamination and the technology of the present invention can be used to detect this substance in environmental samples.

[0168] 6. Used to test cluster inhibitors

[0169] Laboratories and pharmaceutical companies are interested in developing quantum sensing inhibitors, particularly swarming inhibitors, for various applications (e.g., UTIs), and the present technique quantitatively assesses swarming / swimming inhibition.

[0170] The above uses are only for illustration and are not intended to limit the scope of protection of the present invention.

Claims

1. A bio-microfluidic chip for detecting bacterial movement patterns, characterized by: The microfluidic biochip comprises a plurality of arranged microporous structures and is made of transparent material.

2. The chip according to claim 1, wherein: The chip pore diameter is 30 μm-200 μm, preferably 30-100 μm, 30-90 μm, 40-90 μm, 50-80 μm; the micropore depth can be 10-50 μm, preferably 18-40 μm, 18-30 μm, 18-25 μm, 20-30 μm.

3. The chip according to claim 1-2 is made of thermoplastic materials such as polydimethylsiloxane (PDMS), silicon, glass, poly(methyl methacrylate) (PMMA), polycarbonate (PC), polyimide (PI), cycloolefin copolymer (COC), natural polymers, cellulose membranes, and gels.

4. A detection system for detecting bacterial movement patterns, comprising the chip of claims 1-3, a semi-solid culture plate, and an imaging device. When using the detection system to detect bacterial movement, the biochip is placed on the semi-solid culture plate so that the culture medium fills the micropores of the chip and the test bacteria are sandwiched between the chip sheet and the soft agar surface. The imaging system is then used to detect the movement parameters of the bacteria and determine their movement patterns by calculation. 5 . The system according to claim 4 , wherein the water activity of the semi-solid culture plate is between 0.90-0.999 (Aw), preferably 0.995 (Aw), so that the sample can form a layer with a thickness of 10–40 μm on the culture medium.

6. The system according to claim 5, wherein the semi-solid culture plate adopts at least one moist semi-solid surface selected from soft agar, carrageenan, sodium alginate, gelatin, mucosa, methylcellulose, beef peptone semi-solid agar and the like.

7. The system according to claim 6, 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.

8. A method for detecting bacterial movement patterns using the detection system according to claims 4-7, characterized in that: The movement parameters of bacteria are detected and calculated by an imaging device to determine which type of bacteria they belong to: Twitching, Gliding, Sliding, Swarming, or Swimming.

9. The method according to claim 8, characterized in that The bacterial movement pattern is comprehensively determined by detecting the bacterial movement speed, movement change rate, acceleration, group density, coverage, diffusion speed, vorticity, and vortex order parameters.

10. The method according to claim 9, further comprising determining the bacterial movement pattern by the following parameters: Twitching motion mode, speed range 0.01-2μm / s, directionality 0.1-0.5, acceleration 1-10μm / s 2 Range, population density is 0.001-10 bacteria / μm 2 , coverage 1%-10%, diffusion speed 0.1-1μm / s, vorticity 0.1-0.5; Gliding motion mode, speed range 2-10μm / s, directionality 0.5-0.9, acceleration 0-1μm / s 2 Range, population density is 0.01-0.1 bacteria / μm 2 , coverage 10%-100%, diffusion speed 0.5-5μm / s, vorticity 0-0.5; Sliding motion mode, speed range 0.01-1μm / s, directionality 0.1-0.5, acceleration 0-1μm / s 2 Range, population density is 0.1-1 bacteria / μm 2 , coverage 10%-100%, diffusion speed 0.1-2μm / s, vorticity 0-0.5; Swarming motion mode, speed range 10-50μm / s, directionality 0.7-1.0, acceleration 0.5-5μm / s 2 Range, population density is 1-10 bacteria / μm 2 , coverage 10%-100%, diffusion speed 10-50μm / s, vorticity 1.0-2.0; Swimming mode, speed range 20-50μm / s, directionality 0.8-1.0, acceleration 50-200μm / s 2 Range, population density is 0.1-1 bacteria / μm 2 , coverage <10%, diffusion speed 10-50 μm / s, vorticity 2.0-5.0; The calculation method of the parameters of the above motion modes is as follows: Calculation of bacterial movement speed, Where v is the bacterial velocity (μm / s); Δd is the bacterial displacement in the time interval (μm); Δt is the time interval (s); Calculation of the rate of change of motion direction, Where D is the 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, Where ρ: population density (number of bacteria / μm 2 ), N: number of bacteria, A: area of ​​observation area (μm 2 ); Coverage calculation, Diffusion rate calculation, Where Δr: diffusion distance of the bacterial colony edge (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 are the partial derivatives of the velocity components in space; Through microscopy imaging and image analysis technology, v x and v y ; Use numerical differentiation methods (such as finite difference method) to calculate and , calculate the vorticity at each position according to the formula.

11. The method according to claim 10, when the bacteria are judged to be in swarming motion mode, the following formula is further used to judge and calculate the vortex order parameter: where v i is the 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 judged as centralized cluster movement, and when Ф is less than 0.8, it is judged as non-centralized cluster movement.

12. The microfluidic biochip of claims 1-3, the system of claims 4-7, and the method of claims 8-11, for use in assisting the diagnosis of diseases by detecting the movement of microbial clusters.

13. The use according to claim 12, wherein the disease is a disease associated with diagnosis through cluster movement, including any one of inflammatory bowel disease, urinary tract infection, gastrointestinal stress, foodborne disease, and active autoimmune disease; wherein inflammatory bowel disease also includes ulcerative colitis and Crohn's disease.

14. The microfluidic biochip of claims 1-3, the system of claims 4-7, the method of claims 8-11, and the conclusion of the detected microbial cluster movement can be used as a diagnostic biomarker, for example, as a diagnostic biomarker for inflammatory bowel disease, urinary tract infection, gastrointestinal stress, foodborne diseases, and active autoimmune diseases.

15. Use of the microfluidic biochip of claims 1-3, the system of claims 4-7, and the method of claims 8-11 in detecting microorganisms in environmental samples, screening target bacteria, screening for drug-resistant bacteria, and screening for cluster inhibitors, wherein the detection results are not used for diagnosis of diseases. 16 . The microfluidic biochip according to claim 15 , wherein the environmental sample is a pollution source, and the sample comprises materials such as soil, plants, feces, vomitus, food, sputum, urine, wounds / secretions, etc.

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