直播时间：2024年3月19日（周二）20:00-22:00

直播平台：

科学网APP

https://weibo.com/l/wblive/p/show/1022:2321325013646999945271

（科学网微博直播间链接）

科学网微博

科学网视频号

北京时间3月19日晚八点，iCANX Youth Talks第四十七期邀请到了中国科学院杭州医学研究所Jinzhao Song、武汉大学Hao Yin、宾夕法尼亚大学Jina Ko三位教授主讲，北京大学张海霞教授担任主持人，期待你一起加入这场知识盛宴。

【嘉宾介绍】

Jinzhao Song

中国科学院杭州医学研究所

Smartphone-based Mobile Detection Platform for Molecular Diagnostics: from Infectious Diseases to Cancer

【Abstract】

Rapid, quantitative molecular diagnostics in the field, at home, and in resource poor settings is needed for evidence-based disease management, control, and prevention. Conventional molecular diagnostics requires extensive sample preparation, sophisticated instruments, and trained personnel, restricting their use to centralized laboratories. To overcome the limitations of laboratory-based procedures, we designed a series of simple, inexpensive, hand-held, smartphone-based mobile detection platforms, for rapid, connected, and quantitative detection of pathogens’ nucleic acids. We anticipate that our methods will improve quality of health care in regions lacking sophisticated laboratories and enable patients to assume greater responsibility for their care.

Moreover, we have been expanding our technology platform to incorporate the detection of rare cancer biomarkers in body fluids, enabling cancer screening, individualized therapy, and minimal residual disease (MRD) detection. To overcome the challenge of identifying very low concentrations of mutant alleles within a large background of very similar wild type nucleic acids, we utilize programmable endonucleases, including Argonaute and CRISPR-Cas systems, to selectively cleave wild type alleles in the course of nucleic acid amplification, yielding a significantly improved signal-to-noise ratio.

在野外、家中以及资源匮乏的环境中，我们需要进行快速、定量的分子诊断，以便为基于证据的疾病管理、控制和预防提供支持。传统的分子诊断方法需要复杂的样本制备、精密的仪器和训练有素的人员，这限制了它们在中央实验室的应用。为了克服实验室操作方法的局限性，我们设计了一系列简单、经济、手持式的智能手机移动检测平台，用于快速、联网和定量检测病原体的核酸。我们预计我们的方法将改善缺乏精密实验室的地区的医疗保健质量，并使患者能够承担更大的护理责任。

此外，我们一直在拓展我们的技术平台，以纳入对体液中罕见癌症生物标志物的检测，从而实现癌症筛查、个体化治疗和微量残留病（MRD）检测。为了克服在大量非常相似的野生型核酸背景下识别极低浓度的突变等位基因的挑战，我们利用可编程核酸内切酶，包括Argonaute和CRISPR-Cas系统，在核酸扩增过程中选择性切割野生型等位基因，从而显著提高信噪比。

【BIOGRAPHY】

Jinzhao Song is a Professor at the Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Zhejiang Cancer Hospital. Prior to this role, he served as a Research Assistant Professor at the University of Pennsylvania, School of Engineering and Applied Science. Dr. Song received his Ph.D. from the Institute of Chemistry, Chinese Academy of Sciences (ICCAS). With over 15 years of experience, he has focused on developing next generation molecular diagnostic systems based on isothermal nucleic acid amplification; microfluidic chip that utilize smartphones for detection, analysis, result recording and sharing; and programmable enzymes such as Argonaute and CRISPR-Cas proteins. He has co-authored 51 peer-reviewed papers with H-index 23, 15 of which he was the lead author, and was named as co-inventor in 9 granted patents and 10 patent applications. Dr. Song is a recipient of the NIH K01 Research Scientist Development Award, as well as a PI/co-PI of an R21 NIH grant and two venture incubation programs. His contributions to the field of molecular diagnostics earned him the AACCs "2019 Young Investigator Award for Outstanding Research in Personalized Medicine." Additionally, Dr. Song founded EzDx Technology Inc., an enterprise dedicated to the commercialization and widespread adoption of these cutting-edge diagnostic tools, thereby setting a new standard in healthcare diagnostics and personalized medicine.

Jinzhao Song是中国科学院杭州医学研究所、浙江省肿瘤医院的一名教授。在此之前，他曾担任宾夕法尼亚大学工程与应用科学学院的研究助理教授。宋教授在中国科学院化学研究所获得了博士学位。拥有超过15年的经验，他主要致力于开发基于等温核酸扩增的新一代分子诊断系统；利用智能手机进行检测、分析、结果记录和分享的微流控芯片；以及可编程酶，如Argonaute和CRISPR-Cas蛋白。

宋教授共撰写了51篇经过同行评审的论文，H指数为23，其中15篇为第一作者，并作为共同发明人获得了9项已授权专利和10项专利申请。宋教授荣获了NIH K01研究科学家发展奖，并担任了R21 NIH资助项目以及两项创业孵化项目的主要或共同负责人。他在分子诊断领域的贡献为他赢得了美国临床化学家协会（AACC）颁发的“2019年个性化医学杰出研究青年研究者奖”。

此外，宋教授还创立了EzDx科技有限公司，致力于将这些前沿诊断工具商业化并广泛推广，从而在医疗保健诊断和个性化医学领域树立新的标准。

Hao Yin

武汉大学

Rapid detection of pathogens based on CRISPR technology

【ABSTRACT】

Despite the great potential of CRISPR-based detection, it has not been competitive with other market diagnostics for on-site and in-home testing. We dissect the rate-limiting factors that undermine the performance of Cas12a-, Cas12b- and Cas13a-mediated detection. In one-pot testing, Cas effectors interferes with isothermal amplification by binding to and cleaving the amplicon, or directly degrades the target RNA. We used suboptimal protospacer adjacent motifs (PAM) of Cas12a to enhance the flexibility, speed, sensitivity, and reproducibility of one-pot testing. Moreover, we found that the PAM-interacting domain engineered Cas12b accelerated one-pot testing with 10–10,000-fold improved sensitivity. In parallel, by diminishing the interference of Cas13a with viral RNA, the optimized Cas13a-based assay detected 86 out of 87 SARS-CoV-2 clinical samples at room temperature in 30 minutes with a sensitivity of 0.5 cp μl−1, equal to optimized RT-qPCR assay. The relaxed reaction conditions and improved performance of CRISPR-based assays make them competitive for widespread use in pathogen detection.

尽管CRISPR检测具有巨大应用潜力，但在临床和家庭自检方面与其他市场诊断产品相比，在性能方面尚不具竞争力。我们分析了限制Cas12a、Cas12b和Cas13a介导检测性能的限速因素。在一步法测试中，Cas蛋白通过结合并切割底物，或直接降解靶标RNA的方式干扰了等温扩增，从而使检测整体性能下降。我们利用Cas12a 的非经典PAM序列来提高一步法测试的灵活性、速度、灵敏度和可重复性。此外，我们发现通过工程化Cas12b的PAM相互作用结构域，可以将一步法测试的速度提高10–10,000倍，并提高灵敏度。与此同时，通过减少Cas13a对病毒RNA的干扰，优化的Cas13a检测方法可在室温条件下在30分钟内检测到87个SARS-CoV-2临床样本中的86个，灵敏度为0.5 cp μl−1,和优化好的RT-qPCR方法相当。这些高性能的检测方法使CRISPR在病原体检测中具备竞争力和转化能力。

【BIOGRAPHY】

Hao Yin is a Professor at the Medical Research Institute and Frontier Science Centre for Immunology and Metabolism of Wuhan University. He was selected for the National High-level Talent Youth Program. He completed his undergraduate studies at Nanjing University and obtained his Ph.D. from the University of Colorado Medical Campus in 2010. From 2010 to 2016, he worked as a postdoctoral fellow at the Massachusetts Institute of Technology (MIT), followed by a position as a scientist at Vertex Pharmaceuticals from 2016 to 2018. Since February 2018, he has been leading a research team at Wuhan University. He has published over 50 papers and has been cited more than 10,000 times according to Google Scholar. Ten of his papers have been recognized as highly cited papers, and his research achievements have been covered by mainstream international media and reviewed in Nature series journals. Since joining Wuhan University, his team has focused on gene editing and RNA therapy, developing efficient diagnostic technologies for diseases using CRISPR and new editing methods for site-specific insertion. He has published multiple papers as corresponding author, including in Nature Biomedical Engineering (2020, 2022, 2023), Nature Methods (2022), and Nature Chemical Biology (2024).

殷昊为武汉大学医学研究院和教育部“珠峰计划”免疫和代谢前沿科学中心教授、博士生导师。本科毕业于南京大学，2010年在美国科罗拉多大学医学中心获博士学位，2010-2016年在美国麻省理工学院任博士后，2016-2018在美国福泰制药任研究员，2018年2月起在武汉大学建立研究团队。已在国际刊物发表和接收论文50余篇，被引用约10000余次 (google scholar)，10篇论文入选高被引论文，多项研究成果被国际主流媒体报道和Nature系列杂志专文评述。入职武汉大学以来，殷昊团队聚焦基因编辑和RNA疗法，研发了CRISPR对疾病进行高效诊断技术, 新型基因组定点插入方法等。以通讯作者发表多篇论文，包括 Nature Biomedical Engineering (2020, 2022, 2023)，Nature Methods (2022) , Nature Chemical Biology (2024) 等。

Jina Ko

宾夕法尼亚大学

Single Biomarker Profiling for Medical Diagnostics

【ABSTRACT】

Due to an inherent biological heterogeneity across individuals and within a disease, it is extremely challenging to identify robust biomarkers that can accurately represent molecular status of the body for disease diagnostics. To solve this intractable problem, we have developed microfluidic platforms and molecular tools that enable high throughput, multiplexed profiling of biomarkers (e.g. cells, extracellular vesicles; EV). We achieved high throughput profiling by combining sequencing with parallelization of microchip technologies and droplet microfluidics. We overcame the variability of any individual biomarker between individual patients, by developing tools that can measure multiple markers and we applied machine learning to identify signatures that persist across this variability. To resolve cell and EV heterogeneity, we have recently developed an ultra-fast cycling method for single cell analysis and an ultra-high sensitive microfluidics that can achieve single particle detection sensitivity, enabling individual EV measurements.

由于个体间以及疾病内部的固有生物学异质性，识别能够准确代表身体分子状态以用于疾病诊断的稳健生物标志物极具挑战性。为了解决这一棘手问题，我们开发了微流控平台和分子工具，能够进行高通量、多重生物标志物分析（例如细胞、细胞外囊泡；EV）。我们通过将测序与微芯片技术的并行化以及液滴微流控技术相结合，实现了高通量分析。我们开发了能够测量多个标记物的工具，克服了不同患者之间任何单一生物标志物的变异性，并应用机器学习来识别这些变异性中持续存在的特征。为了解析细胞和EV的异质性，我们最近开发了一种用于单细胞分析的超快速循环方法，以及一种超高灵敏度的微流控技术，可以实现单粒子检测灵敏度，从而能够对单个EV进行测量。

【BIOGRAPHY】

Jina Ko is an Assistant Professor in the Departments of Pathology and Laboratory Medicine and Bioengineering at University of Pennsylvania. She focuses on developing single molecule detection from single extracellular vesicles (EV) and multiplexed molecular profiling to better diagnose diseases and monitor treatment efficacy. Jina graduated from Rice University with a B.S. in Bioengineering and a B.A. in French Studies in 2013 and she earned her Ph.D. in Bioengineering at the University of Pennsylvania in 2018. During her Ph.D., she developed machine learning-based microchip diagnostics that can detect blood-based biomarkers to diagnose pancreatic cancer and traumatic brain injury. For her postdoctoral training, she worked at Massachusetts General Hospital and the Wyss Institute at Harvard University as a Schmidt Science Fellow and a NIH K99/R00 award recipient. Jina developed new methods to profile single cells and single EV with high throughput and multiplexing.

Jina Ko是宾夕法尼亚大学病理学、实验医学与生物工程系的助理教授。她专注于开发单细胞外囊泡（EV）的单分子检测技术和多重分子分析技术，以更好地诊断疾病和监测治疗效果。Jina于2013年毕业于莱斯大学，获得生物工程学士学位和法国研究学士学位，并于2018年在宾夕法尼亚大学获得生物工程博士学位。在攻读博士学位期间，她开发了基于机器学习的微芯片诊断技术，该技术能够检测血液中的生物标志物，以诊断胰腺癌和创伤性脑损伤。在博士后培训期间，她作为施密特科学研究员和NIH K99/R00奖获得者，在麻省总医院和哈佛大学的怀斯研究所工作。金娜开发了高通量、多重分析的新方法，用于分析单个细胞和单个EV。

【主持人】

Haixia Zhang

北京大学