A single-cell level drug-resistant bacteria rapid drug sensitivity detection chip, system and method

Abstract

The application relates to a single-cell-level drug-resistant bacteria rapid drug sensitivity detection chip, a system and a method, a single-cell-level drug-resistant bacteria rapid drug sensitivity detection chip and method based on transcription response, and belongs to the field of molecular diagnosis and microfluidic technology, in particular to a microfluidic chip for rapid detection and typing of drug-resistant bacteria and a detection method thereof. The application provides a high-sensitivity, rapid and single-bacterium-level detection method for rapid identification, activity state determination and dead bacteria differentiation of drug-resistant bacteria.

Description

Technical Field

[0001] This invention relates to the field of molecular diagnostics and microfluidic detection technology, and in particular to a rapid drug susceptibility detection chip and method for single-cell-level drug-resistant bacteria based on transcriptional response. Specifically, it relates to a rapid analysis system for drug-resistant bacteria that integrates a microfluidic digital chip, short-term antibiotic stimulation, and CRISPR-Cas13a RNA detection. Background Technology

[0002] Drug-resistant bacterial infections have become a significant challenge in global public health. According to the World Health Organization's (WHO) *Antimicrobial Resistance Global Report*, more than 1.27 million people die annually worldwide each year directly from drug-resistant bacterial infections, with related deaths exceeding 4.9 million. It is projected that by 2050, drug-resistant bacterial infections could cause approximately 10 million deaths globally each year, potentially surpassing the harm caused by cancer. In clinical infections, the detection rate of drug-resistant strains, such as *Escherichia coli*, *Klebsiella pneumoniae*, *Acinetobacter baumannii*, *Pseudomonas aeruginosa*, and methicillin-resistant Staphylococcus aureus (MRSA), continues to rise. Multidrug-resistant (MDR) and extensively drug-resistant (XDR) bacteria are showing a rapid growth trend, particularly in intensive care units (ICUs), urinary tract infections, bloodstream infections, and respiratory infections. Since clinical anti-infective treatment is highly dependent on drug sensitivity results, establishing a rapid and accurate drug resistance detection system is crucial for reducing mortality and antibiotic overuse.

[0003] Currently, the gold standard method in clinical practice remains traditional culture combined with antimicrobial susceptibility testing (AST). This method typically involves multiple steps, including sample culture, colony purification, drug incubation, and result interpretation, with an overall testing cycle generally ranging from 24 to 72 hours, and sometimes even 5 to 7 days for some slow-growing pathogens. During this period, clinicians often have to use broad-spectrum antibiotics empirically, which can easily lead to: antibiotic overuse, further screening and spread of drug-resistant bacteria, delays in patient treatment, and an increased risk of nosocomial infections. Studies show that if sepsis patients do not receive effective anti-infective treatment within 6 hours of infection, their risk of death can increase by approximately 7% to 10%. Therefore, the speed of antimicrobial susceptibility testing has become a key factor affecting clinical prognosis. In recent years, molecular detection technologies such as PCR, qPCR, LAMP, and high-throughput sequencing have been widely used in pathogen detection and antimicrobial resistance gene analysis. These technologies can complete nucleic acid detection within hours, significantly improving detection efficiency compared to traditional culture methods. However, existing molecular detection methods mainly identify the antimicrobial resistance genes themselves, rather than directly reflecting the true functional antimicrobial resistance status of bacteria, and therefore still have the following problems: they cannot distinguish between live and dead bacteria. Dead bacterial residues of DNA or RNA may still generate amplification signals, leading to false positives; the presence of resistance genes does not equate to phenotypic resistance. Some strains may carry resistance genes but not express them; conversely, there are also phenotypic resistance mechanisms that do not depend on classical resistance genes; single-cell resolution is lacking. Traditional large-sample testing is essentially a population-average signal analysis, unable to identify low-abundance resistance subpopulations; and it cannot capture the dynamic transcriptional response process under antibiotic stimulation.

[0004] Clinical infection samples are typically not homogeneous bacterial populations, exhibiting significant phenotypic heterogeneity. Studies have found that within a large number of susceptible bacteria, less than 1% may exist as drug-resistant subgroups or persistent cells. In traditional population detection, the background signal from the death of a large number of susceptible bacteria can mask the stress transcription signals of a few drug-resistant bacteria, creating a so-called "masking effect." This phenomenon can easily lead to false-sensitivity in test results, resulting in clinical misdiagnosis. Meanwhile, although digital microfluidic chips and single-cell analysis technologies developed in recent years can achieve physical cell isolation, most research still focuses on DNA amplification, protein detection, or metabolic analysis, with limited research on the dynamic changes in RNA under antibiotic stimulation. In particular, a mature and complete technological system is currently lacking for simultaneously achieving: live-dead bacterial differentiation; drug resistance phenotype determination; drug resistance heterogeneity analysis; and rapid high-throughput detection at the single-bacterial level. On the other hand, the CRISPR-Cas13a system, due to its RNA-specific recognition and collateral cleavage amplification capabilities, has been widely used in RNA detection in recent years. Currently, the CRISPR-Cas13a system is mostly used for viral RNA detection or routine nucleic acid analysis, and there is still a lack of complete technical solutions that combine it with single-cell microfluidic chips, antibiotic stimulation systems, and digital single-bacterial analysis.

[0005] Therefore, there is an urgent need to develop a novel microfluidic drug-resistant bacteria detection system that integrates single-cell physical isolation, short-term antibiotic stimulation, dynamic RNA transcription response analysis, CRISPR-Cas13a high-sensitivity detection, and digital high-throughput analysis to achieve rapid, accurate, and single-strain-level functional analysis of drug-resistant bacteria. Summary of the Invention

[0006] The purpose of this invention is to address the problems of existing drug-resistant bacteria detection technologies, such as long detection cycles, inability to reflect the true phenotypic drug resistance status, inability to distinguish between live and dead bacteria, and lack of single-cell resolution. The invention proposes a rapid drug susceptibility detection chip and method for single-cell-level drug-resistant bacteria based on transcriptional response.

[0007] This invention further provides a high-throughput detection system that integrates microfluidic digital chip, short-term antibiotic stimulation, and CRISPR-Cas13aRNA detection to achieve rapid, accurate, single-strain-level analysis of clinically resistant bacteria.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: a rapid drug susceptibility detection chip for single-cell-level drug-resistant bacteria based on transcriptional response, comprising:

[0009] I: Microfluidic digital chip module, the chip including an antibiotic incubation area, an RNA detection area, a micropore array area, and a microfluidic channel;

[0010] II: Single-bacterial capture module, used to disperse the bacteria to be tested into multiple independent microwells according to the Poisson distribution;

[0011] III: Antibiotic short-term stimulation module for antibiotic incubation of single bacteria in individual microwells;

[0012] IV: CRISPR-Cas13a RNA detection module, which includes Cas13a protein, RNA detection primers, crRNA and fluorescent reporter probe;

[0013] V: Fluorescence signal acquisition module, used for digital scanning of RNA detection signals in each microwell;

[0014] VI: Data analysis module, used to determine the status of drug-resistant bacteria, sensitive bacteria, and dead bacteria based on RNA expression intensity.

[0015] As a preferred embodiment of the present invention, the diameter of a single pore in the micropore array is 50-100 μm, preferably 80±3 μm; the pore depth is 30-80 μm; and the volume of a single pore is 1-10 nL.

[0016] As a preferred embodiment of the present invention, the chip material is PDMS, CYTOP or a combination thereof, and the chip surface is treated with PEG blocking, BSA blocking or silanization to reduce non-specific adsorption of RNA.

[0017] In a preferred embodiment of the present invention, the antibiotic incubation area and the RNA detection area are physically isolated by a microvalve, capillary channel, or high-barrier microstructure to prevent antibiotic diffusion into the RNA detection system. The antibiotic includes one or more of β-lactams, aminoglycosides, quinolones, carbapenems, and glycopeptides, preferably one or more of ceftriaxone, gentamicin, levofloxacin, meropenem, and vancomycin.

[0018] As a preferred embodiment of the present invention, the CRISPR-Cas13a RNA detection module employs a dual-target detection strategy, including:

[0019] (1) Detection of conserved sequence RNA for determining live or dead bacteria;

[0020] (2) RNA detection of stress system for drug resistance analysis.

[0021] More preferably, the conserved sequence RNA includes 16S rRNA; the stress system RNA includes one or more of recA, sulA, groEL, dnaK, or SOS repair system-related RNA.

[0022] In a preferred embodiment of the present invention, the fluorescent reporter probe is an ssRNA or ssDNA probe with a fluorescent group labeled at the 5′ end and a quencher group attached to the 3′ end; after Cas13a is activated by the target RNA, it bypasses the fluorescent probe, causing the fluorescent group and the quencher group to separate and generate a fluorescent signal.

[0023] As a preferred embodiment of the present invention, the antibiotic incubation time in the short-term antibiotic stimulation module is 5 to 30 minutes, preferably 10 to 20 minutes; the incubation temperature is 35 to 39°C.

[0024] As a preferred embodiment of the present invention, the chip includes a multi-channel parallel antibiotic shunt structure, wherein at least one channel is an antibiotic-free control channel.

[0025] The present invention also discloses a rapid drug susceptibility detection system for single-cell-level drug-resistant bacteria based on transcriptional response, which includes the microfluidic digital chip and detection device described in the present invention.

[0026] The detection device includes a housing, a constant temperature incubation module, a fluorescence excitation module, a fluorescence acquisition module, and a data analysis module.

[0027] Furthermore, the isothermal incubation module is used to maintain a stable temperature for the antibiotic-stimulated reaction; the fluorescence excitation module is used to excite the CRISPR reaction to generate fluorescence; and the fluorescence acquisition module is used to acquire the fluorescence intensity of each microwell and perform digital analysis.

[0028] This invention also discloses a method for rapid drug susceptibility detection of single-cell-level drug-resistant bacteria using the aforementioned detection chip, comprising the following steps:

[0029] (1) Sample pretreatment: Filtering, dilution and microfluidic loading of clinical samples;

[0030] (2) Single-bacterial capture: Single bacteria are independently separated through a micro-well array;

[0031] (3) Short-term antibiotic incubation: Add antibiotics to the microwells to stimulate the response;

[0032] (4) RNA detection: The incubated sample is introduced into the RNA detection area and RNA detection is performed using the CRISPR-Cas13a system;

[0033] (5) Fluorescence acquisition and data analysis: Scan and classify the fluorescence signals of each microwell to obtain the proportion of drug-resistant bacteria, the proportion of sensitive bacteria and the proportion of dead bacteria.

[0034] Furthermore, the sample to be tested in the method is an original clinical sample that has not undergone concentration and enrichment treatment, and can be directly used for single-bacterial detection without pre-culture and concentration / enrichment treatment.

[0035] Furthermore, in susceptible bacteria, stress RNA expression was upregulated 2–5 times after antibiotic stimulation; in drug-resistant bacteria, stress RNA expression was upregulated less than 1.2 times; and in dead bacteria, 16S rRNA signal decreased by more than 80%.

[0036] Furthermore, the entire testing process is completed within 2 hours, preferably within 90 minutes.

[0037] The beneficial effects of this invention are:

[0038] (1) Single-cell detection: Single cell separation is achieved through digital microfluidic micropores, avoiding the signal averaging problem caused by traditional population detection;

[0039] (2) Rapid detection, from sample loading to result output can be completed within 90 minutes;

[0040] (3) It can distinguish between live bacteria, dead bacteria and drug-resistant bacteria, thus improving the accuracy of detection;

[0041] (4) Achieving highly sensitive RNA detection by utilizing the CRISPR-Cas13a bypass cleavage mechanism;

[0042] (5) It can identify low-abundance drug-resistant subgroups and realize drug resistance heterogeneity analysis;

[0043] (6) The chip has high-throughput parallel detection capability and is suitable for rapid clinical drug sensitivity analysis. Attached Figure Description

[0044] The present invention will be further described below with reference to the accompanying drawings.

[0045] Figure 1 This is a top view of the overall structure of the microfluidic chip of the present invention;

[0046] Figure 2 This is an exploded view of the microfluidic chip hierarchical structure of the present invention.

[0047] 101—Chip packaging top cover layer, 102—Microfluidic channel layer, 103—Chip substrate layer, 201—Sample input port, 202—Reagent input port, 203—Main shunt channel, 204—CRISPR detection solution channel, 205—Sample pretreatment area, 301—Microvalve isolation structure, 302—Antibiotic incubation area, 303—Single-bacterial microwell array, 304—Digital detection unit;

[0048] Figure 3 This is a cross-sectional view of the microfluidic chip structure of the present invention;

[0049] Figure 4 This is a schematic diagram of the chip's functional partitioning and microvalve connection mechanism according to the present invention;

[0050] Figure 5 This is a cross-sectional view of the single hole structure of the present invention;

[0051] Figure 6 This is a graph showing the fluorescence detection results of the present invention. Detailed Implementation

[0052] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, but the scope of protection of the present invention is not limited to the following embodiments.

[0053] Example 1

[0054] A rapid drug susceptibility detection chip for single-cell-level drug-resistant bacteria based on transcriptional response includes: a chip body, a sample input structure, an antimicrobial drug dispensing structure, a single-cell confined microwell array, a nucleic acid detection structure, a microvalve isolation structure, and a fluorescence signal acquisition structure;

[0055] The chip body includes a chip packaging cover layer, a microfluidic functional layer and a chip substrate layer. The microfluidic functional layer is disposed between the chip packaging cover layer and the chip substrate layer. The layers are bonded together to form an integrated microfluidic chip structure.

[0056] The sample input structure is disposed on the top cover layer of the chip package and is connected to the main fluid transport channel in the microfluidic functional layer, for introducing the sample to be tested into the chip.

[0057] The antibacterial drug distribution structure is disposed within the microfluidic functional layer and connected to the main fluid transport channel. The antibacterial drug distribution structure includes multiple parallel diversion microchannels for delivering different antibacterial drugs to the corresponding drug stimulation detection branches.

[0058] The single-strain confined microwell array is located downstream of each drug stimulation detection branch and is connected to the diversion microchannel. The single-strain confined microwell array includes multiple regularly arranged confined micro-reaction chambers, which are used to disperse the bacteria to be tested into multiple independent microwells according to the Poisson distribution to form a digital single-strain reaction unit.

[0059] The nucleic acid detection structure is set in the corresponding detection area of ​​the single-strain confined microwell array, including a CRISPR-Cas detection system, crRNA and fluorescent reporter probe, for detecting target RNA after stimulation by antibacterial drugs;

[0060] The microvalve isolation structure is disposed between the antibacterial drug dispensing structure and the nucleic acid detection structure, and is located in the corresponding region of the microfluidic functional layer. The microvalve isolation structure includes a thermosensitive microsphere material, which undergoes volume or phase change under temperature change conditions, thereby achieving physical isolation between different detection areas and preventing antibacterial drug diffusion and cross-contamination.

[0061] The fluorescence signal acquisition structure is positioned above the single-strain confined microwell array and includes a fluorescence excitation module and a fluorescence acquisition module. It is used to acquire the fluorescence signals of each digitized single-strain reaction unit and output bacterial drug resistance phenotype analysis results based on the fluorescence intensity distribution, the proportion of positive reaction units, and the digitization analysis results.

[0062] Example 2

[0063] This embodiment provides a rapid drug susceptibility detection chip for single-cell-level drug-resistant bacteria based on transcriptional response. Its structure includes a chip encapsulation cover layer 101, a microfluidic channel layer 102, and a chip base layer 103. The chip encapsulation cover layer 101 is provided with a sample input port 201 and a reagent input port 202. The sample input port 201 is used to introduce the bacterial solution to be tested, and the reagent input port 202 is used to introduce antibiotic stimulation solution, CRISPR detection solution, and buffer solution.

[0064] The microfluidic channel layer 102 is internally provided with a main shunt channel 203, a microvalve isolation structure 301, and an antibiotic incubation area 302. The main shunt channel 203 is used to realize the transport and distribution of samples and reagents; the microvalve isolation structure 301 is used to realize liquid isolation between the antibiotic incubation area and the RNA detection area, so as to avoid the antibiotic from interfering with the CRISPR detection system.

[0065] The chip's functional layer includes a single-bacterial microwell array 303 and a digital detection unit 304. The single-bacterial microwell array 303 disperses the bacteria to be tested into multiple independent microwells according to a Poisson distribution, thereby forming a digital single-cell reaction system. The microwells have a diameter of 80±3μm, a depth of 50μm, a spacing of 200μm, and a single-well volume of approximately 5nL.

[0066] The chip body is fabricated using a composite structure of polydimethylsiloxane (PDMS) and glass. The chip substrate layer is made of glass, and the microfluidic channel layer is constructed using PDMS. The PDMS is formulated using Sylgard 184 PDMS prepolymer and a curing agent (Dow Corning, USA), with a prepolymer to curing agent mass ratio of 5:1 to 15:1, preferably 10:1. The microfluidic channels are fabricated using a SU-8 photolithography mold, with a channel width of 100–300 μm and a depth of 40–80 μm. The overall chip dimensions are 75 mm × 45 mm × 5 mm. The chip surface is sealed with PEG and BSA, with a PEG concentration of 2% (w / v) and a BSA concentration of 1% (w / v) to reduce non-specific RNA adsorption.

[0067] Example 3

[0068] This embodiment provides a CRISPR-Cas13a detection system for detecting RNA from drug-resistant bacteria. The detection system includes Cas13a protein, crRNA, RNA detection primers, RNA fluorescent reporter probes, and reaction buffer.

[0069] The concentrations of Cas13a protein were 200 nM, each crRNA was 400 nM, and the RNA fluorescent reporter probe was 1000 nM. The reaction buffer was 1×NEB r2.1 buffer, consisting of 50 mM NaCl, 10 mM Tris-HCl, 10 mM MgCl2, and 100 μg / mL BSA.

[0070] The added RNA detection primers include viable bacterial marker RNA detection primers and stress response RNA detection primers. 16S rRNA detection primers and stress response RNA detection primers are added simultaneously to the reaction system for the concurrent detection of viable bacterial status and drug resistance transcriptional response signals.

[0071] The final concentration of each RNA detection primer is 0.2–1 μM; more preferably, the final concentration of each forward and reverse primer is 0.4 μM.

[0072] The 16S rRNA detection primer sequences are as follows: forward primer F: 5′-AGAGTTTGATCCTGGCTCAG-3′; reverse primer R: 5′-GGTTACCTTGTTACGACTT-3′.

[0073] The corresponding crRNA recognition sequence is as follows: 5′-GAUUUACCGCGGCUGCUGGCA-3′.

[0074] The added RNA detection primers include 16S rRNA detection primers and stress response RNA detection primers, as well as corresponding crRNAs.

[0075] The stress-response RNAs are recA RNA and sulA RNA. The primer sequences for detecting recA stress-response RNA are as follows:

[0076] Forward primer F:

[0077] 5′-ATGCGTTATCGACGAAACG-3′;

[0078] Reverse primer R:

[0079] 5′-CGCTTTACCAGCTCCAGTC-3′.

[0080] The corresponding crRNA recognition sequence is as follows:

[0081] 5′-UCUACCGCUUCGACGACAUCG-3′.

[0082] The primer sequences for detecting sulA stress response RNA are as follows:

[0083] Forward primer F:

[0084] 5′-TGCGATGTCGAACTGGATGA-3′;

[0085] Reverse primer R:

[0086] 5′-CGTTGAGCTGCTTCTTCAGT-3′.

[0087] The corresponding crRNA recognition sequence is as follows:

[0088] 5′-GAUCGCUUCGACUGGCUAUG-3′.

[0089] The RNA fluorescent reporter probe sequence corresponding to 16S rRNA is as follows:

[0090] 5′-FAM-UUUUU-BHQ1-3′.

[0091] The RNA fluorescent reporter probe sequences corresponding to stress-response RNA are as follows:

[0092] 5′-Cy5-UUUUU-BHQ2-3′.

[0093] When Cas13a recognizes 16S rRNA or stress-responsive RNA via crRNA, its bypass cleavage activity is activated, further cleaving the corresponding RNA fluorescent reporter probe, separating the fluorescent group from the quencher group and generating a fluorescent signal.

[0094] Among them, drug-sensitive bacteria showed a significant increase in the expression levels of stress-response RNAs such as recA, sulA, groEL, and dnaK after antibiotic stimulation, with a marked increase in red fluorescence signal, accompanied by a gradual decrease in the green fluorescence signal of 16S rRNA; drug-resistant bacteria showed a smaller change in the fluorescence of stress-response RNA after antibiotic stimulation, while the fluorescence signal of 16S rRNA remained stable; inactivated or dead bacteria showed a significant decrease in the fluorescence signals of both 16S rRNA and stress-response RNA due to RNA degradation.

[0095] Example 4

[0096] Rapid drug susceptibility testing of single-cell-level drug-resistant bacteria was performed using the detection chip described in Example 1 and the CRISPR-Cas13a detection system described in Example 2.

[0097] The sample pretreatment area is used to enrich bacteria, filter impurities, and replace buffer solutions in clinical urine, blood, or sputum samples to reduce the interference of host cells, proteins, and debris impurities on subsequent RNA detection.

[0098] Preferably, the sample pretreatment area is equipped with a microcolumn filtration structure and an inertial microfluidic enrichment structure to trap large particulate impurities and enrich bacterial cells. The pretreated sample is then transported through a main diversion channel to the antibiotic distribution area and a single-cell microwell array for single-cell drug sensitivity testing.

[0099] First, clinical urine, blood, or sputum samples are introduced into the sample pretreatment area. After microstructure filtration and PBS buffer replacement, the bacterial concentration is adjusted to 10. 3 ~10 5 CFU / mL.

[0100] Subsequently, the processed bacterial solution is introduced into the chip through the sample input port 201 and delivered to the single-bacterial microarray 303 in the antibiotic incubation area 302 via the main diversion channel 203. The sample concentration is controlled based on the Poisson distribution, so that the single-bacterial occupancy rate reaches 40% to 50% and the multi-bacterial occupancy rate is less than 5%.

[0101] Ceftriaxone, gentamicin, levofloxacin, and meropenem solutions were added to different antibiotic incubation zones 302 via a microfluidic channel. The final concentration of the antibiotics was 1 to 4 times the clinical MIC concentration, and short-term stimulation incubation was performed at 37°C for 10 to 20 minutes.

[0102] After antibiotic stimulation, the reaction solution containing single bacteria and stress-response RNA is switched via a microvalve and enters the fluorescence detection area through a microfluidic channel, where it reacts with Cas13a, crRNA, and the RNA fluorescent reporter probe. The CRISPR-Cas13a detection system is then introduced into the digital detection unit 304 via the CRISPR detection solution channel 204 and reacted at 37°C for 10 minutes.

[0103] When the target RNA is present, Cas13a is activated and cleaves the RNA fluorescent reporter probe, thereby releasing a fluorescent signal.

[0104] The chip was then scanned using a CMOS dual-channel fluorescence imaging system. The excitation wavelength of the 16S rRNA detection channel was 488 nm and the emission wavelength was 520 nm. The excitation wavelength of the stress response RNA detection channel was 640 nm and the emission wavelength was 680 nm.

[0105] The intensity of green viable bacterial fluorescence signal and red stress fluorescence signal in each digitized single-strain reaction unit was statistically analyzed using digital image analysis software. The bacterial drug sensitivity was then analyzed based on the changes in the dual-channel fluorescence ratio, the proportion of positive reaction units, and the two-dimensional heterogeneous scatter distribution results.

[0106] Among them, drug-sensitive bacteria, after antibiotic stimulation, showed a 2-5 fold increase in the red fluorescence signal of stress-response RNAs such as recA, sulA, groEL, and dnaK, while the green fluorescence signal of 16S rRNA decreased to 20%-40% of the initial level; drug-resistant bacteria, after antibiotic stimulation, showed a change of less than 1.2 fold in the fluorescence of stress-response RNA, while the 16S rRNA fluorescence signal remained above 70% of the initial level; inactivated or dead bacteria, due to RNA degradation, showed a decrease of more than 80% in both the fluorescence signals of 16S rRNA and stress-response RNA.

[0107] Based on the dual-channel fluorescence intensity distribution, red-green fluorescence ratio, positive well ratio, and dynamic threshold clustering analysis results of each digital single-strain reaction unit, the system can determine the bacterial survival status, drug sensitivity, drug resistance phenotype, and drug resistance heterogeneity.

[0108] Example 5

[0109] The invention was validated using 50 clinical infection samples, including 20 Escherichia coli infection samples, 15 Klebsiella pneumoniae infection samples, 10 Pseudomonas aeruginosa infection samples, and 5 Staphylococcus aureus infection samples.

[0110] After the clinical samples were digitally distributed using a chip, 16S rRNA and stress response RNA were detected by dual-channel CRISPR-Cas13a fluorescence, and a two-dimensional heterogeneity scatter plot of single cells was constructed.

[0111] The results of this invention were compared with those of traditional drug sensitivity culture. The results showed that the sensitivity of the drug sensitivity determination was 95.2%, the specificity was 97.8%, the overall consistency rate was 93.6%, the limit of detection reached 1 CFU / μL, and the overall detection time was controlled within 90 minutes.

[0112] Among them, drug-sensitive bacteria showed a significant increase in red fluorescence signal of stress response RNA after stimulation with antibiotics, while the green fluorescence signal of 16S rRNA was significantly reduced; drug-resistant bacteria maintained a high 16S rRNA fluorescence intensity and showed little change in stress response RNA.

[0113] Furthermore, single-cell two-dimensional fluorescence heterogeneity analysis can identify tolerant subgroups and bacteria under excessive stress that are difficult to distinguish using traditional drug susceptibility testing.

[0114] Compared with the traditional culture method of 24 to 72 hours, the present invention significantly shortens the drug sensitivity detection time and realizes the joint analysis of single-cell bacterial survival status, drug stress response and drug resistance heterogeneity.

[0115] Example 6

[0116] To improve the stability and fluorescence detection sensitivity of the CRISPR-Cas13a detection system, different buffer solutions and signal enhancers were optimized.

[0117] CRISPR-Cas13a detection systems were constructed using PBS buffer, Tris-HCl buffer, 1×NEB r2.1 buffer, and 1×NEB r3.1 buffer, respectively. E. coli RNA carrying the blaKPC resistance gene was used as the detection target, and fluorescence detection was performed after reacting at 37℃ for 10 min.

[0118] Experimental results showed that the Cas13a bypass cleavage activity was strongest in the 1×NEB r2.1 buffer system, with an average fluorescence intensity of 2680±115 a.u., which was significantly higher than that of the PBS buffer group (1320±96 a.u.), the Tris-HCl buffer group (1580±104 a.u.), and the NEB r3.1 buffer group (2140±110 a.u.).

[0119] Furthermore, the CRISPR-Cas13a detection system employed an optimized buffer formulation: 1×NEB r2.1 buffer, comprising 50 mM NaCl, 10 mM Tris-HCl, 10 mM MgCl2, and 100 μg / mL BSA; L-proline was added as a stabilizer, with a final L-proline concentration of 0.5 M and a final BSA concentration of 100 μg / mL. Results showed:

[0120] After adding L-proline alone, the average fluorescence intensity of the system increased to 3510±126 a.u., which is about 31.0% higher than that of the group without the enhancement agent;

[0121] After adding BSA alone, the average fluorescence intensity increased to 3890±138 a.u., which is about 45.1% higher than the group without the enhancer.

[0122] With the addition of L-proline and BSA, the average fluorescence intensity was further increased to 4720±165 a.u., which is about 76.1% higher than the group without the enhancement agent.

[0123] Further testing on low-concentration drug-resistant bacterial samples revealed that, at 10 CFU / mL, the combined enhancement group still obtained a clearly distinguishable fluorescence signal, while the fluorescence signal of the group without enhancement was close to the background value.

[0124] The results showed that L-proline could improve the stability of Cas13a protein and enhance its bypass cleavage activity, while BSA could reduce the non-specific adsorption of enzymes and RNA on the chip surface. The combined use of the two could significantly improve the fluorescence signal intensity of the CRISPR-Cas13a detection system and the detection sensitivity of low-concentration drug-resistant bacterial RNA.

[0125] Example 7

[0126] Different antibiotic stimulation times were tested. The results showed that for bacterial samples with a concentration of 100 CFU / mL, significant changes in stress RNA could be observed after 10 minutes of antibiotic stimulation; for samples with a low concentration of 10 CFU / mL, the stimulation time needed to be extended to 20 minutes.

[0127] Therefore, the overall detection time of this invention can be controlled within 60 to 90 minutes.

[0128] All parts not covered in this invention are the same as or can be implemented using existing technologies.

Claims

1. A rapid drug susceptibility detection chip for single-cell-level drug-resistant bacteria based on transcriptional response, characterized in that, include: Single-strain capture module, antibiotic short-term stimulation module, CRISPR-Cas13a RNA detection module, microfluidic channel, fluorescence signal acquisition module and data analysis module; The single-bacterial capture module is used to disperse the bacteria to be tested into multiple independent microwells according to the Poisson distribution; A short-term antibiotic stimulation module for incubating single bacteria with antibiotics in individual microwells; The CRISPR-Cas13a RNA detection module includes Cas13a protein, RNA detection primers, crRNA, and fluorescent reporter probes. The fluorescence signal acquisition module is used to digitally scan the RNA detection signals from each microwell. The data analysis module is used to determine the status of drug-resistant bacteria, sensitive bacteria, and dead bacteria based on RNA expression intensity.

2. A rapid drug susceptibility detection chip for single-cell-level drug-resistant bacteria based on transcriptional response, characterized in that, include: The chip body, sample input structure, antimicrobial drug dispensing structure, single-strain confined microwell array, nucleic acid detection structure, microvalve isolation structure, and fluorescence signal acquisition structure; The chip body includes a chip packaging cover layer, a microfluidic functional layer and a chip substrate layer. The microfluidic functional layer is disposed between the chip packaging cover layer and the chip substrate layer. The layers are bonded together to form an integrated microfluidic chip structure. The sample input structure is disposed on the chip package cover layer and is connected to the main fluid transport channel in the microfluidic functional layer, for introducing the sample to be tested into the chip; The antibacterial drug distribution structure is disposed within the microfluidic functional layer and connected to the main fluid transport channel. The antibacterial drug distribution structure includes multiple parallel diversion microchannels for delivering different antibacterial drugs to the corresponding drug stimulation detection branches. The microvalve isolation structure is a thermosensitive microsphere isolation structure. The thermosensitive microsphere isolation structure is disposed within the microfluidic functional layer. The thermosensitive microsphere isolation structure is used to enclose and isolate the single-bacterial microwell array to form mutually independent digital detection units. The single-bacterial confined microwell array is disposed downstream of each drug stimulation detection branch and is connected to the shunt microchannel. The single-bacterial confined microwell array includes multiple regularly arranged confined micro-reaction chambers, which are used to disperse the bacteria to be tested into multiple independent microwells according to the Poisson distribution to form digital single-bacterial reaction units. The nucleic acid detection structure is set in the corresponding detection area of ​​the single-strain confined microwell array, including the CRISPR-Cas detection system, for detecting target RNA after stimulation by antibacterial drugs; The thermosensitive microsphere isolation structure is disposed between the antibacterial drug dispensing structure and the nucleic acid detection structure, and is located in the corresponding area of ​​the microfluidic functional layer. It is used to achieve closed isolation between different detection areas to prevent the diffusion of antibacterial drugs and cross-contamination. The thermosensitive microsphere isolation structure includes a thermoresponsive microsphere material, which undergoes volume or phase change under temperature variations, thereby achieving closed isolation of the micropore array detection area. The fluorescence signal acquisition structure is positioned above the single-strain confined micropore array and includes a fluorescence excitation module and a fluorescence acquisition module for acquiring fluorescence signals from each digitized single-strain reaction unit.

3. The rapid drug susceptibility detection chip for single-cell-level drug-resistant bacteria based on transcriptional response according to claim 2, characterized in that: The micropores in the single-strain microporous array have a diameter of 80±3μm, a depth of 30~80μm, and a volume of 1~10nL. The micropores in the single-strain microwell array are arranged in a matrix to form multiple digital detection areas, and each digital detection area corresponds to a different antibiotic detection channel. The microfluidic channel layer includes a main shunt channel and multiple branch channels. The main shunt channel is connected to the sample input structure, the antibiotic dispensing structure, and the single-bacterial microwell array, respectively. The microvalve isolation structure is a thermosensitive microsphere isolation structure; The CRISPR-Cas detection system includes Cas13a protein, RNA detection primers and corresponding crRNA, and a fluorescent reporter probe; the fluorescent reporter probe is a single-stranded RNA probe or a single-stranded DNA probe with a fluorescent group and a quencher group connected to each end respectively. The fluorescence signal acquisition structure is used to acquire the fluorescence signals of each digitized single-strain reaction unit, and outputs bacterial resistance phenotype analysis results based on the fluorescence intensity distribution, the proportion of positive reaction units, and the digitization analysis results.

4. The rapid drug susceptibility detection chip for single-cell-level drug-resistant bacteria based on transcriptional response according to claim 1, characterized in that: The RNA fluorescent reporter probe sequence is as follows: 5′-FAM-UUUUU-BHQ1-3′; RNA detection primers include primers for detecting live bacterial marker RNA and primers for detecting stress response RNA; The live bacterial marker RNA includes 16S rRNA; The stress response RNAs include SOS repair-related RNAs and cellular stress-related RNAs, including one or more of recA, sulA, lexA, umuD, groEL, and dnaK.

5. The rapid drug susceptibility detection chip for single-cell-level drug-resistant bacteria based on transcriptional response according to claim 1, characterized in that: Primers and corresponding crRNA for 16S rRNA detection; primers and corresponding crRNA for stress response RNA detection; The primers for detecting stress response RNA include one or more of the following: recA, sulA, groEL, dnaK, and SOS repair system-related RNA primers, as well as the corresponding crRNA.

6. The rapid drug susceptibility detection chip for single-cell-level drug-resistant bacteria based on transcriptional response according to claim 1, characterized in that: The concentration of the Cas13a protein is 100–300 nM; the concentration of each crRNA is 200–500 nM; the concentration of the fluorescent reporter probe is 500–1500 nM; and the final concentration of each RNA detection primer is 0.2–1 μM. The chip body material includes PDMS, glass, or a combination thereof; The chip surface is treated with PEG blocking, BSA blocking, or silanization to reduce non-specific RNA adsorption; The antibiotic distribution structure includes multiple parallel antibiotic detection channels, at least one of which is an antibiotic-free control channel; the antibiotics include one or more of β-lactams, aminoglycosides, quinolones, carbapenems, and glycopeptides.

7. A rapid drug susceptibility testing system for single-cell-level drug-resistant bacteria using a detection chip, characterized in that, Used for non-disease treatment and diagnosis, including: The constant temperature incubation module is used to maintain the temperature for antibiotic-induced response. The fluorescence excitation module is used to excite the CRISPR detection system to generate a fluorescence signal; The fluorescence acquisition module is used to acquire fluorescence images of each digitized single-strain reaction unit; The data analysis module is used to analyze bacterial drug resistance status based on fluorescence intensity; The operating temperature of the constant temperature incubation module is 35–39°C. The data analysis module is used to jointly analyze the single-cell fluorescence dynamics changes of 16S rRNA and stress response RNA, the proportion of digital positive reaction units and threshold distribution characteristics, so as to determine the bacterial survival status, drug sensitivity and drug resistance heterogeneity.

8. A method for rapid drug susceptibility testing of single-cell-level drug-resistant bacteria using the detection chip described in any one of claims 1 to 6, characterized in that, For use in non-disease treatment and diagnosis, including the following steps: S1: Filter, dilute and preprocess the sample to be tested; S2: The pretreated sample is introduced into the single-bacterial microwell array in the antibiotic incubation area, so that the bacteria to be tested are dispersed into multiple independent microwells according to the Poisson distribution. S3: Add different antibiotics to different detection areas for short-term stimulation and incubation; S4: Introduce the CRISPR-Cas detection system into the RNA detection structure to detect the target RNA; S5: Collect fluorescence signals of 16S rRNA and stress response RNA in each digital single-strain reaction unit, and output drug sensitivity analysis results based on fluorescence intensity distribution, positive reaction unit ratio and dynamic threshold analysis results.

9. A method for rapid drug susceptibility testing of single-cell-level drug-resistant bacteria using the detection chip according to any one of claims 8, characterized in that, For use in non-disease treatment and diagnosis, including the following steps: In step S2, the sample to be tested is the original clinical sample that has not undergone concentration and enrichment treatment; The antibiotic stimulation time in step S3 is 10-20 minutes; The CRISPR-Cas detection system described in step S4 includes Cas13a protein, RNA detection primers, crRNA, and a fluorescent reporter probe; In step S5, After antibiotic stimulation, drug-sensitive bacteria showed a 2-5 fold increase in the red fluorescence signal of their recA, sulA, groEL, and dnaK stress-response RNAs, while the green fluorescence signal of their 16S rRNA decreased to 20%-40% of the initial level. Drug-resistant bacteria, after antibiotic stimulation, showed a less than 1.2 fold change in the fluorescence of their stress-response RNAs, while maintaining more than 70% of the initial level of their 16S rRNA fluorescence signal. Inactivated or dead bacteria, due to RNA degradation, showed a decrease of more than 80% in both the fluorescence signals of their 16S rRNAs and stress-response RNAs. Based on the dual-channel fluorescence intensity distribution, red-green fluorescence ratio, positive well ratio, and dynamic threshold clustering analysis results of each digital single-strain reaction unit, the system can determine the bacterial survival status, drug sensitivity, drug resistance phenotype, and drug resistance heterogeneity.

10. A device for rapid drug susceptibility testing of single-cell-level drug-resistant bacteria using a detection chip according to claim 8 or 9.

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