This year, our team developed a universal diagnostic platform targeting microRNAs, combining polymerase self-enhancement and CRISPR two-step reaction, which realized rapid and direct diagnosis of multiple cancers with high specificity and sensitivity. We have successfully constructed such a diagnostic system not only from a wet-experiment perspective, but also made significant progress on dry experiments. Our development process and the platform itself will provide valuable insights and experiences for other iGEM teams in the future.
The working mechanism of our designed molecular computing system can be categorized into the steps of recognition, amplification, reporting and decision making:
- Recognition
The 3' end sequence of the Rep strand is complementary to the miRNA target. Therefore, the Rep strand can specifically bind to the target and complete the recognition of the input signal. - Amplification
The miRNA-bound Rep strand undergoes a polymerase-mediated strand displacement reaction in the presence of Bst polymerase, displacing the H strand.The 3' terminal sequence of the DDSD long probe strand is complementary to the free H strand. The 3' end sequence of the DDSD long probe chain is complementary to the free H chain, so the free H chain can specifically bind to the DDSD long probe chain, and the polymerase-mediated chain displacement reaction also occurs under the action of Bst polymerase, displacing one I chain and a specific number of wt chains.
(Schematic diagram of the primary amplification system)
- Reporter
1) Polymerase self-enhanced binding chain displacement system: the 3' end sequence of the quenching probe RP chain of this system is complementary to the fluorescent probe F chain, in which the F chain is modified with a fluorescent group and the RP chain is modified with a quenching group, and the fluorescence signals are quenched due to the combination of the two chains bringing the fluorescent and quenching groups in close proximity to each other. wt chains are also designed to be complementary to the 3' end of the RP chain, and the wt chains are also designed to be complementary to the 3' end of the RP chain, which are complementary to the 3' end of the RP chain. The wt chain is also designed to be complementary to the 3' end of the RP chain and is longer than the F chain, so it can undergo a toe-mediated chain displacement reaction with the reporter probe, displacing the F chain and restoring fluorescence.
2) Polymerase self-enhanced binding CRISPR system: the reporter probe of this system is DNA modified with fluorescent group at one end and quenching group at the other end. in the initial state, the fluorescent group and quenching group are close to each other, and the fluorescence is quenched. The wt strand displaced in the previous step can co-activate cas12A with crRNA, triggering the non-specific DNA endonuclease paracrine activity of cas12A, which cuts the reporter probe into two parts, and the fluorescent group and the quenching group will be dispersed in the solution, and the fluorescence is restored.
(Schematic of secondary magnification)
- Decision Making
1) qPCR instrument reporting system (non-qPCR reaction): after obtaining the fluorescence signal intensity, a pre-determined threshold is utilized to determine the assay result.
2) Single-molecule microscope reporting system: single-molecule fluorescence super-resolution microscope on the number of fluorescence points for continuous dynamic shooting capture target quantitative conversion of fluorescence points, using the team developed single-molecule fluorescence processing program for analysis and processing, to obtain the accurate fluorescence signal intensity, with the pre-determined threshold to determine the detection results.
Our team has developed a method to rapidly screen multiple cancer microRNA targets.
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Initially, we calculated the correlation coefficients between the expression of each microRNA in each cancer and whether the patient had cancer, and screened the targets with Top5 correlation coefficients.
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Subsequently, we used machine learning algorithms to test the detection effect of the targets, which were evaluated with AUC, confusion matrix and other metrics to verify the validity of the targets.
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Since microRNAs show consistent expression patterns across multiple cancer types, our method is not only applicable to specific types of cancers, but is also expected to be generalized for pan-cancer studies. This means that the method can be used to identify microRNA targets that are shared across different cancer types, thus providing new perspectives on pan-cancer diagnostic and therapeutic strategies.
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In addition, it supports in-depth exploration of the mechanisms of cancer inhibition and carcinogenesis.
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The development of this technology is expected to save time and resources, make significant contributions to the field of medical and biological target research, and promote the improvement of human health.
Our team has performed dynamic modeling of the molecular computation process and verified the theoretical feasibility of a diagnostic system based on molecular computation.
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Experimental guidance and optimization: Dynamic modeling deepens the understanding of molecular computational processes, provides guidance for experimental design, and helps optimize experimental conditions and improve experimental efficiency.
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Resource sharing and knowledge dissemination: the research results can be used as a reference to support the design and implementation of molecular computation experiments by other teams, which promotes knowledge sharing and scientific cooperation.
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Cost-effectiveness and sustainability: Dynamic modeling helps to allocate and utilize experimental resources more efficiently, reduce the cost of trial and error, enhance the success rate of experiments, and promote the development of research projects in a more feasible and sustainable direction.
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Based on the concept of automated processing and data visualization, our team has designed and implemented a single-molecule fluorescence image processing program. Through image preprocessing, enhancement and feature extraction techniques, it realizes automatic identification and quantification of the brightness and contrast of bright areas in single-molecule fluorescence images, so as to effectively locate and analyze single-molecule events in biological samples.
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Greatly improves the ability to process complex single-molecule fluorescence images, simplifies the data analysis process, and saves valuable time and human resources.
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Improves data reliability and consistency by automating and standardizing image processing to reduce manual errors.
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By open-sourcing the program and sharing resources in the iGEM community, not only promotes the free flow of scientific knowledge, but also encourages the spirit of openness and cooperation in the research community, which contributes to a more inclusive and collaborative research environment.
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Online platform education: We have conducted science education through Beep and WeChat, popularizing the core concepts and technological advances of synthetic biology, attracting a large number of students and the public, and broadening the boundaries of science education.
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Science popularization activities and lectures: We carried out a series of science education activities for communities and primary and secondary schools in Beijing and other places, popularizing the applications of synthetic biology in medicine and health, environmental protection, and agricultural improvement through small synthetic biology experiments and games, and enhancing the public's scientific literacy.