MICA - Developing novel single-cell multiplexing methods to identify drug targets for the treatment of multiple myeloma

多发性骨髓瘤是一种骨髓癌症，在英国每年有超过5700名新患者受到影响。目前对治疗的反应率各不相同，平均生存年龄仅为4-5年，10年生存率仅为~3%。很大一部分治疗失败是由于多药耐药的出现，多药耐药是由构成我们DNA的核苷酸的变化（突变）引起的。RNA是DNA的一种，主要作为信使执行DNA的指令。表观遗传学是在DNA序列上添加信息，可以改变细胞的行为。这是通过向核苷酸添加各种化学修饰或“标记”来实现的。从历史上看，科学家们通过捣碎组织碎片并进行生物测量来研究复杂的疾病生物学，以找出疾病的原因。这类似于将水果混合到冰沙中，然后把它交给其他人，让他们通过玻璃杯来计算成分。有些水果可能很容易识别，而另一些则可能无法识别。最近，科学家们开发了一种技术，可以让我们更容易地通过更精细地观察其各个部分来重建奶昔。这些“单细胞”技术使我们能够一次一个细胞地观察组织的组成部分，并使我们能够找出可能导致疾病的原因。有了这些信息，我们就可以设计出更好的针对疾病的药物。在这项研究中，我将开发新的单细胞技术，能够捕获比现有技术更多的生物读数。这项技术将被应用于了解DNA、RNA和表观遗传机制，这些机制有助于骨癌多发性骨髓瘤（MM）的发展。我与一家生物技术公司合作，在我的实验室里开发了这样一种技术，能够同时从同一个细胞中测量RNA和DNA。这项研究的第一个目标是扩展这项技术的效用，这样我就可以增加不同类型的测量。例如，我将开发一种方法，可以同时测量RNA和DNA上的表观遗传标记。接下来，我将使用这项技术来调查MM疾病。具体来说，我会问以下几个问题：1)为什么患者会发展MM？2)为什么患者会对目前的临床治疗产生耐药性？最终，我研究的主要目标是更好地了解MM疾病生物学，以便我们可以开发新的药物来治疗这种不治之症。为了弄清楚耐药性发生的原因，我们需要弄清楚使用我们的单细胞技术产生的数据模式。考虑到在这个项目中产生的大量数据，我将使用我作为软件开发人员的技能来生成计算机代码，使这个过程自动化。最后，这段代码的输出将允许我生成疾病地图，然后使用该地图确定治疗MM患者的药物靶标。为什么这个项目有更大的成功机会？很少有方法可以同时观察多种生物测量。我将在这项研究中开发的基于我们独特的单细胞技术的检测方法，将提供对导致MM和耐药性发展的复杂机制的前所未有的理解。这项及时的技术将使我能够确定MM的新药物靶点，然后将在牛津多发性骨髓瘤研究中心的临床前模型中进行跟进，并最终转化为临床。

Multiple Myeloma is a bone marrow cancer that affects over 5,700 new patients a year in the UK. Current response rates to treatment are varied and there is a mean survival age of only 4-5 years, with a 10-year survival rate of only ~3%. A significant proportion of treatment failure is due to the emergence of multi-drug resistance, which arise because of changes (mutations) in the nucleotides that form the building blocks of our DNA. RNA is a type of DNA and mostly acts as a messenger to carry out the instructions of DNA. Epigenetics is the addition of information on top of the DNA sequence that can modify the behaviour of a cell. This is achieved by adding a variety of chemical modifications or "marks" to the nucleotides.Historically, scientists have investigated complex disease biology by mashing up pieces of tissue and taking biological measurements to work out the causes of disease. This is akin to blending fruit into a smoothie and then giving it to someone else to work out the ingredients by looking through the glass. Some fruit may be easy to identify, while others may be impossible to recognise. More recently, scientists have developed technologies that have allowed us to reconstruct the smoothie more easily by looking at its individual parts in finer detail. These "single-cell" technologies allow us to look at the components of the tissue one cell at a time and permit us to work out what may be causing disease. With this information we can design better drugs that target disease. In this fellowship, I will develop novel single-cell technology that are capable of capturing many more biological readouts than previously possible with currently technologies. This technology will then be applied to understand DNA, RNA and epigenetic mechanisms that contribute to the development of a bone cancer called Multiple Myeloma (MM). In partnership with a biotechnology company, I have developed such a technology within in my lab that is capable of measuring RNA and DNA simultaneously from the same single cell. The first aim of this fellowship will be to extend the utility of this technology so that I can increase the different types of measurements that are possible. For example, I will develop a method that makes it possible to measure both RNA and the epigenetic marks on the DNA. Next, I will use this technology to investigate MM disease. Specifically, I will ask the following questions: 1) why do patients develop MM? and 2) why do patients develop resistance to current clinical therapies? Ultimately, the main goal of my research is to better understand MM disease biology so we can develop new medicines to treat this incurable disease.In order to work out why drug resistance occurs we need to make sense of the patterns of data that are generated using our single-cell technologies. Given the large amount of data generated during this project, I will use my skills as a software developer to generate computer code that will automate this process. Ultimately, outputs from this code will allow me to generate a map of disease that I can then use to identify drug targets for treating MM patients. Why does this project have a greater chance of success? Very few methods have been developed that can look at more than one biological measurement simultaneously. The assays that I will develop during this fellowship, which are based on our unique single-cell technology, will provide and unparalleled understanding of the complex mechanisms that contribute to the development of MM and drug resistance. This timely technology will allow me to identify novel drug targets for MM, which will then be followed up in pre-clinical models within the Oxford Centre for Multiple Myeloma Research and ultimately translated towards the clinic.