Nine years ago, my sister discovered lumps in her neck and arm and was diagnosed with cancer. From that day, she started to benefit from the understanding that science has of cancer. Every time she went to the doctor, they measured specific molecules that gave them information about how she was doing and what to do next. New medical options became available every few years. Everyone recognized that she was struggling heroically with a biological illness. This spring, she received an innovative new medical treatment in a clinical trial. It dramatically knocked back her cancer. Guess who I'm going to spend this Thanksgiving with? My vivacious sister, who gets more exercise than I do, and who, like perhaps many people in this room, increasingly talks about a lethal illness in the past tense. Science can, in our lifetimes -- even in a decade -- transform what it means to have a specific illness.
﻿9年前， 我妹妹在她的脖子和 手臂上发现了肿块， 随后她被诊断出患有癌症。 从那天起，她开始受益于 关于癌症的科学理论。 每次她去看医生， 医生们会通过测量她体内的特定分子 来获得她的生理状态信息， 并决定接下来应该做什么。 每隔几年就会有新的医疗手段可供选择。 每个人都意识到她在英勇地 与一种生理疾病作斗争。 今年春天，她在一次临床试验中接受了 一种新的疗法。 该疗法显著地击退了她的癌症。 猜猜我今年要跟谁一起过感恩节？ 我那活泼的妹妹， 她的运动量比我都多， 而她，可能跟在座的很多人一样， 会越来越多地谈论过去的 那种致命疾病。 科学可以在我们的有生之年—— 甚至在十年之内—— 改变患特定疾病的含义。
But not for all illnesses. My friend Robert and I were classmates in graduate school. Robert was smart, but with each passing month, his thinking seemed to become more disorganized. He dropped out of school, got a job in a store ... But that, too, became too complicated. Robert became fearful and withdrawn. A year and a half later, he started hearing voices and believing that people were following him. Doctors diagnosed him with schizophrenia, and they gave him the best drug they could. That drug makes the voices somewhat quieter, but it didn't restore his bright mind or his social connectedness. Robert struggled to remain connected to the worlds of school and work and friends. He drifted away, and today I don't know where to find him. If he watches this, I hope he'll find me.
但不是所有的疾病。 我和罗伯特是研究生同学。 罗伯特很聪明， 但随着时间的流逝， 他的思考似乎变得杂乱无章。 他退了学，在一家商店找了份工作。 但那里的环境也让他 觉得越来越难以应付。 罗伯特变得恐惧和孤僻。 一年半后，他开始听到声音， 确信有人在尾随他。 医生诊断他患有精神分裂症， 给了他当时能提供的最好的药物。 这个药物让他脑中的声音变轻了， 但并没有恢复他聪明的头脑或社会联系。 罗伯特很难与学校，工作和朋友的 世界保持连接。 他走失了， 今天我都不知道去哪里找到他。 如果他看到这个演讲， 我希望他会来找我。
Why does medicine have so much to offer my sister, and so much less to offer millions of people like Robert? The need is there. The World Health Organization estimates that brain illnesses like schizophrenia, bipolar disorder and major depression are the world's largest cause of lost years of life and work. That's in part because these illnesses often strike early in life, in many ways, in the prime of life, just as people are finishing their educations, starting careers, forming relationships and families. These illnesses can result in suicide; they often compromise one's ability to work at one's full potential; and they're the cause of so many tragedies harder to measure: lost relationships and connections, missed opportunities to pursue dreams and ideas. These illnesses limit human possibilities in ways we simply cannot measure.
为什么医学可以对我妹妹 有如此大的帮助， 但对像罗伯特那样的 数百万人却无能为力呢？ 需求就在那里。 世界卫生组织估计 像精神分裂症，双相情感障碍和 重度抑郁症之类的大脑疾病 是世界上导致寿命折损和 工作能力丧失的最主要原因。 一定程度上是因为这些疾病 经常发生在生命早期， 往往也是生命的黄金时期， 就在人们完成学业，开始职业发展， 形成稳定关系和家庭的时期。 这些疾病会引发自杀； 它们经常会导致人们无法 充分发挥自己的潜能； 它们也导致了很多难以估量的悲剧： 失去人际关系， 错过追求梦想和实现理想的机会。 这些疾病限制了人类的可能性， 我们却无法衡量这种损失。
We live in an era in which there's profound medical progress on so many other fronts. My sister's cancer story is a great example, and we could say the same of heart disease. Drugs like statins will prevent millions of heart attacks and strokes. When you look at these areas of profound medical progress in our lifetimes, they have a narrative in common: scientists discovered molecules that matter to an illness, they developed ways to detect and measure those molecules in the body, and they developed ways to interfere with those molecules using other molecules -- medicines. It's a strategy that has worked again and again and again. But when it comes to the brain, that strategy has been limited, because today, we don't know nearly enough, yet, about how the brain works. We need to learn which of our cells matter to each illness, and which molecules in those cells matter to each illness. And that's the mission I want to tell you about today.
我们生活的时代，在很多其他领域 已经实现了巨大的医疗进步。 我妹妹的故事 就是一个很好的例子， 对于心脏病，我们同样可以做到。 像他汀类的药物可以预防 数百万例心脏病发作和中风。 当你留心观察我们生活中 这些有着深远的 医疗进步的领域， 它们都有一个共同的特点： 科学家发现了与疾病有关的分子， 发明了检测和测量体内分子的方法， 并开发了用其他分子，也就是药物， 来干扰这些分子的方法。 这是一种不断重复的策略。 但涉及到大脑时， 这个策略的作用受到了限制， 因为今天，我们对大脑如何工作的 了解还很有限。 我们需要知道哪个细胞与疾病有关， 这些细胞中的哪些分子 对哪种疾病起到了关键作用。 这就是我今天要向各位介绍的使命。
My lab develops technologies with which we try to turn the brain into a big-data problem. You see, before I became a biologist, I worked in computers and math, and I learned this lesson: wherever you can collect vast amounts of the right kinds of data about the functioning of a system, you can use computers in powerful new ways to make sense of that system and learn how it works. Today, big-data approaches are transforming ever-larger sectors of our economy, and they could do the same in biology and medicine, too. But you have to have the right kinds of data. You have to have data about the right things. And that often requires new technologies and ideas. And that is the mission that animates the scientists in my lab.
我的实验室开发了可以把大脑问题 转变为大数据问题的技术。 在成为生物学家前， 我的工作围绕着电脑和数学， 从中我有了这样的收获： 只要你能收集到关于某个系统功能的 大量正确的数据， 你就可以在电脑上用强大的新方法 搞清楚该系统及其工作原理。 今天，大数据方法正在改变 我们经济中规模越来越大的部门， 它们也可以在生物和医学上有所作为。 但你必须得有正确的数据。 你必须得到真正想要的数据。 而这通常依赖于新的技术和想法。 这就是我实验室里的科学家们的使命。
Today, I want to tell you two short stories from our work. One fundamental obstacle we face in trying to turn the brain into a big-data problem is that our brains are composed of and built from billions of cells. And our cells are not generalists; they're specialists. Like humans at work, they specialize into thousands of different cellular careers, or cell types.
今天，我想要告诉各位 我们工作中的两个小故事。 在试图把大脑转化为大数据问题时， 摆在我们面前的一个基本障碍是， 我们的大脑由数十亿细胞组成。 这些细胞不是多面手，它们是专家。 就如人们在工作中一样， 它们分别擅长于 成千上万不同的细胞职业， 或细胞类型。
In fact, each of the cell types in our body could probably give a lively TED Talk about what it does at work. But as scientists, we don't even know today how many cell types there are, and we don't know what the titles of most of those talks would be. Now, we know many important things about cell types. They can differ dramatically in size and shape. One will respond to a molecule that the other doesn't respond to, they'll make different molecules. But science has largely been reaching these insights in an ad hoc way, one cell type at a time, one molecule at a time. We wanted to make it possible to learn all of this quickly and systematically.
事实上，我们可以围绕 身体的每个细胞类型 在TED上做一场有关它们工作原理的 生动的演讲。 但作为科学家，我们今天甚至还不知道 总共有多少细胞类型， 也不知道大部分演讲的标题是什么。 我们已经了解了关于细胞类型的 很多重要的信息。 它们的大小和形状有很大的差异。 有些会对某个分子 产生反应，另一些则不会， 它们会制造不同的分子。 但是科学在很大程度上 是以一种特别的方式 来得到这些见解的， 一次一种细胞类型， 一次一种分子。 我们希望能够快速、 系统地学习所有这些知识。
Now, until recently, it was the case that if you wanted to inventory all of the molecules in a part of the brain or any organ, you had to first grind it up into a kind of cellular smoothie. But that's a problem. As soon as you've ground up the cells, you can only study the contents of the average cell -- not the individual cells. Imagine if you were trying to understand how a big city like New York works, but you could only do so by reviewing some statistics about the average resident of New York. Of course, you wouldn't learn very much, because everything that's interesting and important and exciting is in all the diversity and the specializations. And the same thing is true of our cells. And we wanted to make it possible to study the brain not as a cellular smoothie but as a cellular fruit salad, in which one could generate data about and learn from each individual piece of fruit.
一直以来，如果你想要 对大脑或任何器官的所有分子 进行编目， 你得先把这些细胞研磨成 奶昔一样的浆状。 但这就是问题了。 一旦你已经把细胞磨碎了， 你就只能在平均水平上研究细胞—— 无法得到单个细胞的信息。 假设你想搞清楚像纽约 这样的大城市是如何运转的， 但只能通过查看纽约居民的 平均统计数据。 当然，这样一来你得到的 信息就很有限了， 因为有趣，重要，让人激动的一切 都蕴藏在多样性和专门化中。 我们的细胞也同样如此。 我想要让研究大脑不像研究奶昔那样， 而像研究水果沙拉， 这样就能从每一片水果中 得到数据进行学习。
So we developed a technology for doing that. You're about to see a movie of it. Here we're packaging tens of thousands of individual cells, each into its own tiny water droplet for its own molecular analysis. When a cell lands in a droplet, it's greeted by a tiny bead, and that bead delivers millions of DNA bar code molecules. And each bead delivers a different bar code sequence to a different cell. We incorporate the DNA bar codes into each cell's RNA molecules. Those are the molecular transcripts it's making of the specific genes that it's using to do its job. And then we sequence billions of these combined molecules and use the sequences to tell us which cell and which gene every molecule came from.
于是我们为此开发了一种技术。 下面展示的就是关于它的影片。 我们打包了成千上万的单个细胞， 每一个都拥有包裹自身的小水滴， 以用来做自身的分子分析。 当一个细胞降落在一个小液滴上时， 就会接触到一个小珠子， 而这个珠子能传递 数百万个DNA条码分子。 每一个珠子都向不同的细胞 传递不同的条形码序列。 我们将DNA条码整合到 每个细胞的RNA分子中。 这些是它用来完成工作的 特定基因的分子转录信息。 然后我们对数十亿的 组合分子进行测序， 并利用这些序列来了解 每个分子分别来自于 哪个细胞和哪个基因。
We call this approach "Drop-seq," because we use droplets to separate the cells for analysis, and we use DNA sequences to tag and inventory and keep track of everything. And now, whenever we do an experiment, we analyze tens of thousands of individual cells. And today in this area of science, the challenge is increasingly how to learn as much as we can as quickly as we can from these vast data sets.
我们称这种方法为“液滴测序”， 因为我们使用液滴 分离细胞来做分析， 我们使用DNA序列来标记、编目 和追踪所有信息。 每次做实验， 我们都会分析数以万计的单细胞。 当今，在这个科学领域， 我们面临的挑战是如何尽可能多， 且尽可能快地从这些 海量数据集中学习。
When we were developing Drop-seq, people used to tell us, "Oh, this is going to make you guys the go-to for every major brain project." That's not how we saw it. Science is best when everyone is generating lots of exciting data. So we wrote a 25-page instruction book, with which any scientist could build their own Drop-seq system from scratch. And that instruction book has been downloaded from our lab website 50,000 times in the past two years. We wrote software that any scientist could use to analyze the data from Drop-seq experiments, and that software is also free, and it's been downloaded from our website 30,000 times in the past two years. And hundreds of labs have written us about discoveries that they've made using this approach. Today, this technology is being used to make a human cell atlas. It will be an atlas of all of the cell types in the human body and the specific genes that each cell type uses to do its job.
当我们发明液滴测序时，人们告诉我们， “哦，这将使你们的工作成为 每个主要大脑项目的首选。” 我们不是这样看的。 当每个人都产生大量令人兴奋的 数据时，科学就是最好的手段。 于是我们写了25页的指南， 任何科学家都可以借此从零开始 开发他们自己的液滴测序技术。 这个指南过去两年在我们实验室网站的 下载次数为5万次。 每个科学家还可以用我们编写的软件 来分析通过液滴测序得到的实验数据， 而这个软件也是免费的， 过去两年在我们的网站被下载了3万次。 数百家实验室给我们写信， 介绍了他们使用这种方法 得到的发现。 今天，这项技术已经被用来 制作人类细胞图谱。 它是人体所有细胞类型， 以及每个用来完成 其工作的细胞类型的 特定基因的图谱。
Now I want to tell you about a second challenge that we face in trying to turn the brain into a big data problem. And that challenge is that we'd like to learn from the brains of hundreds of thousands of living people. But our brains are not physically accessible while we're living. But how can we discover molecular factors if we can't hold the molecules? An answer comes from the fact that the most informative molecules, proteins, are encoded in our DNA, which has the recipes our cells follow to make all of our proteins. And these recipes vary from person to person to person in ways that cause the proteins to vary from person to person in their precise sequence and in how much each cell type makes of each protein. It's all encoded in our DNA, and it's all genetics, but it's not the genetics that we learned about in school.
现在我想谈一下把大脑问题 转变为大数据问题所面临第二个挑战。 那就是，我们需要研究 成千上万活人的大脑。 但是我们还没有办法接触活体大脑。 如果我们不能控制分子， 要如何发现分子因子呢？ 答案来自于信息最丰富的分子，蛋白质， 它们编码在我们的DNA中， DNA携带了我们的细胞所遵循的食谱， 用来制造我们所有的蛋白质。 这些食谱的内容因人而异， 所制造的蛋白质会根据不同人的 精确序列而变化， 而且每个细胞类型对每种 蛋白质的影响程度不同。 这些信息全都编码在我们的 DNA中，都是可遗传的， 但这不是我们在学校学到的遗传。
Do you remember big B, little b? If you inherit big B, you get brown eyes? It's simple. Very few traits are that simple. Even eye color is shaped by much more than a single pigment molecule. And something as complex as the function of our brains is shaped by the interaction of thousands of genes. And each of these genes varies meaningfully from person to person to person, and each of us is a unique combination of that variation. It's a big data opportunity. And today, it's increasingly possible to make progress on a scale that was never possible before. People are contributing to genetic studies in record numbers, and scientists around the world are sharing the data with one another to speed progress.
你还记得大B，小b吗？ 如果继承了大B，你就有棕色的眼睛？ 原理很简单。 很少有这样简单的特征。 即便塑造眼睛颜色的因素也要 比单一色素分子多很多。 像我们大脑功能那样复杂的东西 是由上千个基因的相互作用塑造的。 每一个基因在人与人之间 都有显著的差异， 我们每个人都是这种变异的独特组合。 这是大数据的机会。 今天，我们越来越有可能 在史无前例的规模上取得进展。 参与遗传研究的人数 创下了记录， 全球各地的科学家彼此分享数据 以加速取得进展。
I want to tell you a short story about a discovery we recently made about the genetics of schizophrenia. It was made possible by 50,000 people from 30 countries, who contributed their DNA to genetic research on schizophrenia. It had been known for several years that the human genome's largest influence on risk of schizophrenia comes from a part of the genome that encodes many of the molecules in our immune system. But it wasn't clear which gene was responsible. A scientist in my lab developed a new way to analyze DNA with computers, and he discovered something very surprising. He found that a gene called "complement component 4" -- it's called "C4" for short -- comes in dozens of different forms in different people's genomes, and these different forms make different amounts of C4 protein in our brains. And he found that the more C4 protein our genes make, the greater our risk for schizophrenia.
我想通过一个简短的故事 介绍一下我们最近 在精神分裂遗传学方面的发现。 该发现包含了来自30多个 国家的5万人贡献的DNA， 用来进行精神分裂的遗传研究。 很多年前我们就知道， 人类基因组对患上 精神分裂症风险的最大影响 来自我们的部分基因组， 这些基因组编码了我们 免疫系统中的很多分子。 但目前还不清楚哪个基因起了作用。 我实验室的科学家开发了一个 使用电脑分析DNA的新方法， 他发现了一些让人惊讶的事情。 他发现了一个被称为 补体成分4的基因—— 简称为C4—— 在不同人的基因组中 有几十种不同的形式， 这些不同的形式会产出我们大脑中 不同数量的C4蛋白质。 他发现我们的基因 产生的C4蛋白质越多， 患精神分裂的风险就越高。
Now, C4 is still just one risk factor in a complex system. This isn't big B, but it's an insight about a molecule that matters. Complement proteins like C4 were known for a long time for their roles in the immune system, where they act as a kind of molecular Post-it note that says, "Eat me." And that Post-it note gets put on lots of debris and dead cells in our bodies and invites immune cells to eliminate them. But two colleagues of mine found that the C4 Post-it note also gets put on synapses in the brain and prompts their elimination. Now, the creation and elimination of synapses is a normal part of human development and learning. Our brains create and eliminate synapses all the time. But our genetic results suggest that in schizophrenia, the elimination process may go into overdrive.
目前，C4只是一个 复杂系统中的风险因素之一。 这不是大B， 但这是一个对重要分子的洞察。 像C4那样的补体分子因它们 在免疫系统中的角色 很早就被人了解， 它们扮演着类似便利贴的角色， 写着，“吃我”。 这些便利贴被放在我们身体的 很多废弃物和死细胞上， 邀请免疫细胞去清除它们。 但我们的两个同事发现C4便利贴 也被贴到了大脑的突触上面， 促进了这些突触连接消失。 突触的创造和消除是 人类发展和学习的 正常部分。 我们的大脑一直在创造和消除突触。 但我们的遗传研究结果表明， 在精神分裂过程中， 这个清除可能在超速运行。
Scientists at many drug companies tell me they're excited about this discovery, because they've been working on complement proteins for years in the immune system, and they've learned a lot about how they work. They've even developed molecules that interfere with complement proteins, and they're starting to test them in the brain as well as the immune system. It's potentially a path toward a drug that might address a root cause rather than an individual symptom, and we hope very much that this work by many scientists over many years will be successful.
很多医药公司的科学家告诉我， 他们对这个发现感到非常兴奋， 因为他们在免疫系统的 补体分子上已经 花费了数年功夫， 对这些分子的工作原理 也有了更深入的了解。 他们甚至开发了分子来干预补体分子， 并在大脑和免疫系统中进行测试。 这可能是一种去除根本病因的药物， 而不只针对单个症状， 我们非常希望许多科学家 多年来所做的工作 能够成功。
But C4 is just one example of the potential for data-driven scientific approaches to open new fronts on medical problems that are centuries old. There are hundreds of places in our genomes that shape risk for brain illnesses, and any one of them could lead us to the next molecular insight about a molecule that matters. And there are hundreds of cell types that use these genes in different combinations. As we and other scientists work to generate the rest of the data that's needed and to learn all that we can from that data, we hope to open many more new fronts. Genetics and single-cell analysis are just two ways of trying to turn the brain into a big data problem.
但C4只是数据驱动的 科学方法的一个例子， 有可能在存在了几个世纪的 医疗问题上开辟新的战线。 在我们的基因组中有数百个地方 存在影响大脑疾病的风险， 它们中的任何一个都能带给我们 关于下一个重要分子的洞见。 有数百种细胞类型在不同的 组合中使用这些基因。 我和其他科学家合作生成了 能够让我们获得所有信息的 余下的部分数据， 我们希望开辟更多的新战线。 遗传和单细胞分析只是试图将大脑 转化为大数据问题的两种方式。
There is so much more we can do. Scientists in my lab are creating a technology for quickly mapping the synaptic connections in the brain to tell which neurons are talking to which other neurons and how that conversation changes throughout life and during illness. And we're developing a way to test in a single tube how cells with hundreds of different people's genomes respond differently to the same stimulus. These projects bring together people with diverse backgrounds and training and interests -- biology, computers, chemistry, math, statistics, engineering. But the scientific possibilities rally people with diverse interests into working intensely together.
我们能做的事情太多了。 我实验室的科学家正在开发一种技术 来快速绘制大脑中的突触连接， 以辨别哪些神经元在 与其他神经元交流， 以及这些交流在衰老和 疾病中是如何变化的。 我们正在开发一种方法， 在单管道中测试 包含上百种人类基因的细胞 如何对同样的刺激做出不同的反应。 这些项目将拥有不同背景， 不同教育和兴趣—— 如生物、计算机、化学、数学、 统计学、工程学的人吸引到一起。 科学的可能性让 兴趣各异的人聚集到一起， 共同努力工作。
What's the future that we could hope to create? Consider cancer. We've moved from an era of ignorance about what causes cancer, in which cancer was commonly ascribed to personal psychological characteristics, to a modern molecular understanding of the true biological causes of cancer. That understanding today leads to innovative medicine after innovative medicine, and although there's still so much work to do, we're already surrounded by people who have been cured of cancers that were considered untreatable a generation ago. And millions of cancer survivors like my sister find themselves with years of life that they didn't take for granted and new opportunities for work and joy and human connection. That is the future that we are determined to create around mental illness -- one of real understanding and empathy and limitless possibility.
我们期待的未来是什么样呢？ 想想癌症。 我们已经走出对癌症致因 一无所知的时代， 那时癌症常被归因于 个人的心理特征， 而今天，对引发癌症真正的生物学原因， 我们已经有了现代分子层面的认识。 这种认识引领了 不断创新的医学， 尽管仍然有很多的工作要做， 我们周围已经有很多人的癌症被治愈了， 而在一代人以前，这些癌症还 被认为是无药可治的。 数百万像我妹妹那样的癌症幸存者 发现自己拥有了意外得到的 若干年生命，以及工作，快乐 和建立人际关系的新机遇。 这就是我们决心围绕精神疾病 去创造的未来—— 一个充满着真正的理解、共情 和无限可能的未来。
Thank you.
谢谢。
(Applause)
（鼓掌）