How data is helping us unravel the mysteries of the brain | Steve McCarroll

翻译人员: jacks peng 校对人员: Yolanda Zhang

Nine years ago,

9年前，

my sister discovered lumps in her neck and arm

我妹妹在她的脖子和 手臂上发现了肿块，

and was diagnosed with cancer.

随后她被诊断出患有癌症。

From that day, she started to benefit

从那天起，她开始受益于

from the understanding that science has of cancer.

关于癌症的科学理论。

Every time she went to the doctor,

每次她去看医生，

they measured specific molecules

医生们会通过测量她体内的特定分子

that gave them information about how she was doing

来获得她的生理状态信息，

and what to do next.

并决定接下来应该做什么。

New medical options became available every few years.

每隔几年就会有新的医疗手段可供选择。

Everyone recognized that she was struggling heroically

每个人都意识到她在英勇地

with a biological illness.

与一种生理疾病作斗争。

This spring, she received an innovative new medical treatment

今年春天，她在一次临床试验中接受了

in a clinical trial.

一种新的疗法。

It dramatically knocked back her cancer.

该疗法显著地击退了她的癌症。

Guess who I'm going to spend this Thanksgiving with?

猜猜我今年要跟谁一起过感恩节？

My vivacious sister,

我那活泼的妹妹，

who gets more exercise than I do,

她的运动量比我都多，

and who, like perhaps many people in this room,

而她，可能跟在座的很多人一样，

increasingly talks about a lethal illness

会越来越多地谈论过去的

in the past tense.

那种致命疾病。

Science can, in our lifetimes -- even in a decade --

科学可以在我们的有生之年—— 甚至在十年之内——

transform what it means to have a specific illness.

改变患特定疾病的含义。

But not for all illnesses.

但不是所有的疾病。

My friend Robert and I were classmates in graduate school.

我和罗伯特是研究生同学。

Robert was smart,

罗伯特很聪明，

but with each passing month,

但随着时间的流逝，

his thinking seemed to become more disorganized.

他的思考似乎变得杂乱无章。

He dropped out of school, got a job in a store ...

他退了学，在一家商店找了份工作。

But that, too, became too complicated.

但那里的环境也让他 觉得越来越难以应付。

Robert became fearful and withdrawn.

罗伯特变得恐惧和孤僻。

A year and a half later, he started hearing voices

一年半后，他开始听到声音，

and believing that people were following him.

确信有人在尾随他。

Doctors diagnosed him with schizophrenia,

医生诊断他患有精神分裂症，

and they gave him the best drug they could.

给了他当时能提供的最好的药物。

That drug makes the voices somewhat quieter,

这个药物让他脑中的声音变轻了，

but it didn't restore his bright mind or his social connectedness.

但并没有恢复他聪明的头脑或社会联系。

Robert struggled to remain connected

罗伯特很难与学校，工作和朋友的

to the worlds of school and work and friends.

世界保持连接。

He drifted away,

他走失了，

and today I don't know where to find him.

今天我都不知道去哪里找到他。

If he watches this,

如果他看到这个演讲，

I hope he'll find me.

我希望他会来找我。

Why does medicine have so much to offer my sister,

为什么医学可以对我妹妹 有如此大的帮助，

and so much less to offer millions of people like Robert?

但对像罗伯特那样的 数百万人却无能为力呢？

The need is there.

需求就在那里。

The World Health Organization estimates that brain illnesses

世界卫生组织估计

like schizophrenia, bipolar disorder and major depression

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

are the world's largest cause of lost years of life and work.

是世界上导致寿命折损和 工作能力丧失的最主要原因。

That's in part because these illnesses often strike early in life,

一定程度上是因为这些疾病 经常发生在生命早期，

in many ways, in the prime of life,

往往也是生命的黄金时期，

just as people are finishing their educations, starting careers,

就在人们完成学业，开始职业发展，

forming relationships and families.

形成稳定关系和家庭的时期。

These illnesses can result in suicide;

这些疾病会引发自杀；

they often compromise one's ability to work at one's full potential;

它们经常会导致人们无法 充分发挥自己的潜能；

and they're the cause of so many tragedies harder to measure:

它们也导致了很多难以估量的悲剧：

lost relationships and connections,

失去人际关系，

missed opportunities to pursue dreams and ideas.

错过追求梦想和实现理想的机会。

These illnesses limit human possibilities

这些疾病限制了人类的可能性，

in ways we simply cannot measure.

我们却无法衡量这种损失。

We live in an era in which there's profound medical progress

我们生活的时代，在很多其他领域

on so many other fronts.

已经实现了巨大的医疗进步。

My sister's cancer story is a great example,

我妹妹的故事 就是一个很好的例子，

and we could say the same of heart disease.

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。

Drugs like statins will prevent millions of heart attacks and strokes.

When you look at these areas of profound medical progress

当你留心观察我们生活中 这些有着深远的

in our lifetimes,

医疗进步的领域，

they have a narrative in common:

它们都有一个共同的特点：

scientists discovered molecules that matter to an illness,

科学家发现了与疾病有关的分子，

they developed ways to detect and measure those molecules in the body,

发明了检测和测量体内分子的方法，

and they developed ways to interfere with those molecules

并开发了用其他分子，也就是药物，

using other molecules -- medicines.

来干扰这些分子的方法。

It's a strategy that has worked again and again and again.

这是一种不断重复的策略。

But when it comes to the brain, that strategy has been limited,

但涉及到大脑时， 这个策略的作用受到了限制，

because today, we don't know nearly enough, yet,

因为今天，我们对大脑如何工作的

about how the brain works.

了解还很有限。

We need to learn which of our cells matter to each illness,

我们需要知道哪个细胞与疾病有关，

and which molecules in those cells matter to each illness.

这些细胞中的哪些分子 对哪种疾病起到了关键作用。

And that's the mission I want to tell you about today.

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

我的实验室开发了可以把大脑问题

into a big-data problem.

转变为大数据问题的技术。

You see, before I became a biologist, I worked in computers and math,

在成为生物学家前， 我的工作围绕着电脑和数学，

and I learned this lesson:

从中我有了这样的收获：

wherever you can collect vast amounts of the right kinds of data

只要你能收集到关于某个系统功能的

about the functioning of a system,

大量正确的数据，

you can use computers in powerful new ways

你就可以在电脑上用强大的新方法

to make sense of that system and learn how it works.

搞清楚该系统及其工作原理。

Today, big-data approaches are transforming

今天，大数据方法正在改变

ever-larger sectors of our economy,

我们经济中规模越来越大的部门，

and they could do the same in biology and medicine, too.

它们也可以在生物和医学上有所作为。

But you have to have the right kinds of data.

但你必须得有正确的数据。

You have to have data about the right things.

你必须得到真正想要的数据。

And that often requires new technologies and ideas.

而这通常依赖于新的技术和想法。

And that is the mission that animates the scientists in my lab.

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.

今天，我想要告诉各位 我们工作中的两个小故事。

One fundamental obstacle we face

在试图把大脑转化为大数据问题时，

in trying to turn the brain into a big-data problem

摆在我们面前的一个基本障碍是，

is that our brains are composed of and built from billions of cells.

我们的大脑由数十亿细胞组成。

And our cells are not generalists; they're specialists.

这些细胞不是多面手，它们是专家。

Like humans at work,

就如人们在工作中一样，

they specialize into thousands of different cellular careers,

它们分别擅长于 成千上万不同的细胞职业，

or cell types.

或细胞类型。

In fact, each of the cell types in our body

事实上，我们可以围绕 身体的每个细胞类型

could probably give a lively TED Talk

在TED上做一场有关它们工作原理的

about what it does at work.

生动的演讲。

But as scientists, we don't even know today

但作为科学家，我们今天甚至还不知道

how many cell types there are,

总共有多少细胞类型，

and we don't know what the titles of most of those talks would be.

也不知道大部分演讲的标题是什么。

Now, we know many important things about cell types.

我们已经了解了关于细胞类型的 很多重要的信息。

They can differ dramatically in size and shape.

它们的大小和形状有很大的差异。

One will respond to a molecule that the other doesn't respond to,

有些会对某个分子 产生反应，另一些则不会，

they'll make different molecules.

它们会制造不同的分子。

But science has largely been reaching these insights

但是科学在很大程度上 是以一种特别的方式

in an ad hoc way, one cell type at a time,

来得到这些见解的， 一次一种细胞类型，

one molecule at a time.

一次一种分子。

We wanted to make it possible to learn all of this quickly and systematically.

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

一直以来，如果你想要

that if you wanted to inventory all of the molecules

对大脑或任何器官的所有分子

in a part of the brain or any organ,

进行编目，

you had to first grind it up into a kind of cellular smoothie.

你得先把这些细胞研磨成 奶昔一样的浆状。

But that's a problem.

但这就是问题了。

As soon as you've ground up the cells,

一旦你已经把细胞磨碎了，

you can only study the contents of the average cell --

你就只能在平均水平上研究细胞——

not the individual cells.

无法得到单个细胞的信息。

Imagine if you were trying to understand how a big city like New York works,

假设你想搞清楚像纽约 这样的大城市是如何运转的，

but you could only do so by reviewing some statistics

但只能通过查看纽约居民的

about the average resident of New York.

平均统计数据。

Of course, you wouldn't learn very much,

当然，这样一来你得到的 信息就很有限了，

because everything that's interesting and important and exciting

因为有趣，重要，让人激动的一切

is in all the diversity and the specializations.

都蕴藏在多样性和专门化中。

And the same thing is true of our cells.

我们的细胞也同样如此。

And we wanted to make it possible to study the brain not as a cellular smoothie

我想要让研究大脑不像研究奶昔那样，

but as a cellular fruit salad,

而像研究水果沙拉，

in which one could generate data about and learn from

这样就能从每一片水果中

each individual piece of fruit.

得到数据进行学习。

So we developed a technology for doing that.

于是我们为此开发了一种技术。

You're about to see a movie of it.

下面展示的就是关于它的影片。

Here we're packaging tens of thousands of individual cells,

我们打包了成千上万的单个细胞，

each into its own tiny water droplet

每一个都拥有包裹自身的小水滴，

for its own molecular analysis.

以用来做自身的分子分析。

When a cell lands in a droplet, it's greeted by a tiny bead,

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，

and that bead delivers millions of DNA bar code molecules.

而这个珠子能传递 数百万个DNA条码分子。

And each bead delivers a different bar code sequence

每一个珠子都向不同的细胞

to a different cell.

传递不同的条形码序列。

We incorporate the DNA bar codes

我们将DNA条码整合到

into each cell's RNA molecules.

每个细胞的RNA分子中。

Those are the molecular transcripts it's making

这些是它用来完成工作的

of the specific genes that it's using to do its job.

特定基因的分子转录信息。

And then we sequence billions of these combined molecules

然后我们对数十亿的 组合分子进行测序，

and use the sequences to tell us

并利用这些序列来了解

which cell and which gene

每个分子分别来自于

every molecule came from.

哪个细胞和哪个基因。

We call this approach "Drop-seq," because we use droplets

我们称这种方法为“液滴测序”， 因为我们使用液滴

to separate the cells for analysis,

分离细胞来做分析，

and we use DNA sequences to tag and inventory

我们使用DNA序列来标记、编目

and keep track of everything.

和追踪所有信息。

And now, whenever we do an experiment,

每次做实验，

we analyze tens of thousands of individual cells.

我们都会分析数以万计的单细胞。

And today in this area of science,

当今，在这个科学领域，

the challenge is increasingly how to learn as much as we can

我们面临的挑战是如何尽可能多，

as quickly as we can

且尽可能快地从这些

from these vast data sets.

海量数据集中学习。

When we were developing Drop-seq, people used to tell us,

当我们发明液滴测序时，人们告诉我们，

"Oh, this is going to make you guys the go-to for every major brain project."

“哦，这将使你们的工作成为 每个主要大脑项目的首选。”

That's not how we saw it.

我们不是这样看的。

Science is best when everyone is generating lots of exciting data.

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。

So we wrote a 25-page instruction book,

于是我们写了25页的指南，

with which any scientist could build their own Drop-seq system from scratch.

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。

And that instruction book has been downloaded from our lab website

这个指南过去两年在我们实验室网站的

50,000 times in the past two years.

下载次数为5万次。

We wrote software that any scientist could use

每个科学家还可以用我们编写的软件

to analyze the data from Drop-seq experiments,

来分析通过液滴测序得到的实验数据，

and that software is also free,

而这个软件也是免费的，

and it's been downloaded from our website 30,000 times in the past two years.

过去两年在我们的网站被下载了3万次。

And hundreds of labs have written us about discoveries that they've made

数百家实验室给我们写信， 介绍了他们使用这种方法

using this approach.

得到的发现。

Today, this technology is being used to make a human cell atlas.

今天，这项技术已经被用来 制作人类细胞图谱。

It will be an atlas of all of the cell types in the human body

它是人体所有细胞类型， 以及每个用来完成

and the specific genes that each cell type uses to do its job.

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

现在我想谈一下把大脑问题

in trying to turn the brain into a big data problem.

转变为大数据问题所面临第二个挑战。

And that challenge is that we'd like to learn from the brains

那就是，我们需要研究

of hundreds of thousands of living people.

成千上万活人的大脑。

But our brains are not physically accessible while we're living.

但是我们还没有办法接触活体大脑。

But how can we discover molecular factors if we can't hold the molecules?

如果我们不能控制分子， 要如何发现分子因子呢？

An answer comes from the fact that the most informative molecules, proteins,

答案来自于信息最丰富的分子，蛋白质，

are encoded in our DNA,

它们编码在我们的DNA中，

which has the recipes our cells follow to make all of our proteins.

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。

And these recipes vary from person to person to person

这些食谱的内容因人而异，

in ways that cause the proteins to vary from person to person

所制造的蛋白质会根据不同人的

in their precise sequence

精确序列而变化，

and in how much each cell type makes of each protein.

而且每个细胞类型对每种 蛋白质的影响程度不同。

It's all encoded in our DNA, and it's all genetics,

这些信息全都编码在我们的 DNA中，都是可遗传的，

but it's not the genetics that we learned about in school.

但这不是我们在学校学到的遗传。

Do you remember big B, little b?

你还记得大B，小b吗？

If you inherit big B, you get brown eyes?

如果继承了大B，你就有棕色的眼睛？

It's simple.

原理很简单。

Very few traits are that simple.

很少有这样简单的特征。

Even eye color is shaped by much more than a single pigment molecule.

即便塑造眼睛颜色的因素也要 比单一色素分子多很多。

And something as complex as the function of our brains

像我们大脑功能那样复杂的东西

is shaped by the interaction of thousands of genes.

是由上千个基因的相互作用塑造的。

And each of these genes varies meaningfully

每一个基因在人与人之间

from person to person to person,

都有显著的差异，

and each of us is a unique combination of that variation.

我们每个人都是这种变异的独特组合。

It's a big data opportunity.

这是大数据的机会。

And today, it's increasingly possible to make progress

今天，我们越来越有可能

on a scale that was never possible before.

在史无前例的规模上取得进展。

People are contributing to genetic studies

参与遗传研究的人数

in record numbers,

创下了记录，

and scientists around the world are sharing the data with one another

全球各地的科学家彼此分享数据

to speed progress.

以加速取得进展。

I want to tell you a short story about a discovery we recently made

我想通过一个简短的故事 介绍一下我们最近

about the genetics of schizophrenia.

在精神分裂遗传学方面的发现。

It was made possible by 50,000 people from 30 countries,

该发现包含了来自30多个 国家的5万人贡献的DNA，

who contributed their DNA to genetic research on schizophrenia.

用来进行精神分裂的遗传研究。

It had been known for several years

很多年前我们就知道，

that the human genome's largest influence on risk of schizophrenia

人类基因组对患上 精神分裂症风险的最大影响

comes from a part of the genome

来自我们的部分基因组，

that encodes many of the molecules in our immune system.

这些基因组编码了我们 免疫系统中的很多分子。

But it wasn't clear which gene was responsible.

但目前还不清楚哪个基因起了作用。

A scientist in my lab developed a new way to analyze DNA with computers,

我实验室的科学家开发了一个 使用电脑分析DNA的新方法，

and he discovered something very surprising.

他发现了一些让人惊讶的事情。

He found that a gene called "complement component 4" --

他发现了一个被称为 补体成分4的基因——

it's called "C4" for short --

简称为C4——

comes in dozens of different forms in different people's genomes,

在不同人的基因组中 有几十种不同的形式，

and these different forms make different amounts

这些不同的形式会产出我们大脑中

of C4 protein in our brains.

不同数量的C4蛋白质。

And he found that the more C4 protein our genes make,

他发现我们的基因 产生的C4蛋白质越多，

the greater our risk for schizophrenia.

患精神分裂的风险就越高。

Now, C4 is still just one risk factor in a complex system.

目前，C4只是一个 复杂系统中的风险因素之一。

This isn't big B,

这不是大B，

but it's an insight about a molecule that matters.

但这是一个对重要分子的洞察。

Complement proteins like C4 were known for a long time

像C4那样的补体分子因它们 在免疫系统中的角色

for their roles in the immune system,

很早就被人了解，

where they act as a kind of molecular Post-it note

它们扮演着类似便利贴的角色，

that says, "Eat me."

写着，“吃我”。

And that Post-it note gets put on lots of debris

这些便利贴被放在我们身体的

and dead cells in our bodies

很多废弃物和死细胞上，

and invites immune cells to eliminate them.

邀请免疫细胞去清除它们。

But two colleagues of mine found that the C4 Post-it note

但我们的两个同事发现C4便利贴

also gets put on synapses in the brain

也被贴到了大脑的突触上面，

and prompts their elimination.

促进了这些突触连接消失。

Now, the creation and elimination of synapses is a normal part

突触的创造和消除是 人类发展和学习的

of human development and learning.

正常部分。

Our brains create and eliminate synapses all the time.

我们的大脑一直在创造和消除突触。

But our genetic results suggest that in schizophrenia,

但我们的遗传研究结果表明， 在精神分裂过程中，

the elimination process may go into overdrive.

这个清除可能在超速运行。

Scientists at many drug companies tell me they're excited about this discovery,

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，

because they've been working on complement proteins for years

因为他们在免疫系统的 补体分子上已经

in the immune system,

花费了数年功夫，

and they've learned a lot about how they work.

对这些分子的工作原理 也有了更深入的了解。

They've even developed molecules that interfere with complement proteins,

他们甚至开发了分子来干预补体分子，

and they're starting to test them in the brain as well as the immune system.

并在大脑和免疫系统中进行测试。

It's potentially a path toward a drug that might address a root cause

这可能是一种去除根本病因的药物，

rather than an individual symptom,

而不只针对单个症状，

and we hope very much that this work by many scientists over many years

我们非常希望许多科学家 多年来所做的工作

will be successful.

能够成功。

But C4 is just one example

但C4只是数据驱动的 科学方法的一个例子，

of the potential for data-driven scientific approaches

有可能在存在了几个世纪的

to open new fronts on medical problems that are centuries old.

医疗问题上开辟新的战线。

There are hundreds of places in our genomes

在我们的基因组中有数百个地方

that shape risk for brain illnesses,

存在影响大脑疾病的风险，

and any one of them could lead us to the next molecular insight

它们中的任何一个都能带给我们

about a molecule that matters.

关于下一个重要分子的洞见。

And there are hundreds of cell types that use these genes in different combinations.

有数百种细胞类型在不同的 组合中使用这些基因。

As we and other scientists work to generate

我和其他科学家合作生成了

the rest of the data that's needed

能够让我们获得所有信息的

and to learn all that we can from that data,

余下的部分数据，

we hope to open many more new fronts.

我们希望开辟更多的新战线。

Genetics and single-cell analysis are just two ways

遗传和单细胞分析只是试图将大脑

of trying to turn the brain into a big data problem.

转化为大数据问题的两种方式。

There is so much more we can do.

我们能做的事情太多了。

Scientists in my lab are creating a technology

我实验室的科学家正在开发一种技术

for quickly mapping the synaptic connections in the brain

来快速绘制大脑中的突触连接，

to tell which neurons are talking to which other neurons

以辨别哪些神经元在 与其他神经元交流，

and how that conversation changes throughout life and during illness.

以及这些交流在衰老和 疾病中是如何变化的。

And we're developing a way to test in a single tube

我们正在开发一种方法， 在单管道中测试

how cells with hundreds of different people's genomes

包含上百种人类基因的细胞

respond differently to the same stimulus.

如何对同样的刺激做出不同的反应。

These projects bring together people with diverse backgrounds

这些项目将拥有不同背景，

and training and interests --

不同教育和兴趣——

biology, computers, chemistry, math, statistics, engineering.

如生物、计算机、化学、数学、 统计学、工程学的人吸引到一起。

But the scientific possibilities rally people with diverse interests

科学的可能性让 兴趣各异的人聚集到一起，

into working intensely together.

共同努力工作。

What's the future that we could hope to create?

我们期待的未来是什么样呢？

Consider cancer.

想想癌症。

We've moved from an era of ignorance about what causes cancer,

我们已经走出对癌症致因 一无所知的时代，

in which cancer was commonly ascribed to personal psychological characteristics,

那时癌症常被归因于 个人的心理特征，

to a modern molecular understanding of the true biological causes of cancer.

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。

That understanding today leads to innovative medicine

这种认识引领了

after innovative medicine,

不断创新的医学，

and although there's still so much work to do,

尽管仍然有很多的工作要做，

we're already surrounded by people who have been cured of cancers

我们周围已经有很多人的癌症被治愈了，

that were considered untreatable a generation ago.

而在一代人以前，这些癌症还 被认为是无药可治的。

And millions of cancer survivors like my sister

数百万像我妹妹那样的癌症幸存者

find themselves with years of life that they didn't take for granted

发现自己拥有了意外得到的

and new opportunities

若干年生命，以及工作，快乐

for work and joy and human connection.

和建立人际关系的新机遇。

That is the future that we are determined to create around mental illness --

这就是我们决心围绕精神疾病 去创造的未来——

one of real understanding and empathy

一个充满着真正的理解、共情

and limitless possibility.

和无限可能的未来。

Thank you.

谢谢。

(Applause)

（鼓掌）