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

譯者: Lilian Chiu 審譯者: congmei Han

Nine years ago,

九年前，

my sister discovered lumps in her neck and arm

我姐姐發現她的頸部 和手臂上有腫塊，

and was diagnosed with cancer.

經診斷為癌症。

From that day, she started to benefit

她自那天起開始受惠於

from the understanding that science has of cancer.

科學對於癌症的了解。

Every time she went to the doctor,

每次她去看醫生，

they measured specific molecules

他們測量了特定的分子，

that gave them information about how she was doing

從中得到的資訊可以知道她的狀況

and what to do next.

及接下來要做什麼。

New medical options became available every few years.

每過數年，都有新的 醫療方法可以選用。

Everyone recognized that she was struggling heroically

大家都意識到她在艱苦地奮力掙脫

with a biological illness.

某種生物疾病。

This spring, she received an innovative new medical treatment

今年春天，她接受 一項創新醫學治療的

in a clinical trial.

臨床試驗。

It dramatically knocked back her cancer.

它戲劇化地擊退了她的癌症。

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

猜猜我今年的感恩節 要跟誰一起過？

我那快活的姐姐，

My vivacious sister,

who gets more exercise than I do,

她做的運動比我還多，

and who, like perhaps many people in this room,

她也和這間房間中許多人一樣，

increasingly talks about a lethal illness

越來越會去談這種致命的疾病，

in the past tense.

用過去式來談。

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

科學能夠在我們的一生中 ——甚至在十年內——

transform what it means to have a specific illness.

改變「患上一種特定疾病」的意義。

But not for all illnesses.

但並非所有疾病都是如此。

My friend Robert and I were classmates in graduate school.

我和我的朋友勞勃 是研究所的同學。

Robert was smart,

勞勃很聰明，

but with each passing month,

但隨著每個月過去，

his thinking seemed to become more disorganized.

他的思考似乎變得越來越零亂。

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

他退學了，在一間店 找到一份工作……

But that, too, became too complicated.

但連那工作也變得太複雜。

Robert became fearful and withdrawn.

勞勃變得害怕和退縮。

A year and a half later, he started hearing voices

一年半後，他開始有幻聽，

and believing that people were following him.

相信有人在跟蹤他。

Doctors diagnosed him with schizophrenia,

醫生診斷出他得了精神分裂症，

and they gave him the best drug they could.

他們給了他最好的藥。

That drug makes the voices somewhat quieter,

那種藥讓他的幻聽情況減緩了，

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

但卻無法恢復他聰明的大腦 或是他的社會連結。

Robert struggled to remain connected

勞勃非常努力地維持

to the worlds of school and work and friends.

和學校、工作， 及朋友世界的連結。

他慢慢遠離了，

He drifted away,

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

現今，我不知道他身在何方。

If he watches this,

如果他看到這場演說，

I hope he'll find me.

我希望他會找到我。

Why does medicine have so much to offer my sister,

為什麼醫學能幫助我姐姐這麼多，

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

對於數百萬名像勞勃這樣的人， 能幫上的忙卻少很多？

The need is there.

需求就在那裡。

The World Health Organization estimates that brain illnesses

世界衛生組織估計，大腦疾病

like schizophrenia, bipolar disorder and major depression

如精神分裂症、躁鬱症， 和重度憂鬱症，

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

是世界上造成失去數年生命 以及工作的最大原因。

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

有部分原因是因為這些疾病 經常出現在人生早期，

in many ways, in the prime of life,

在許多方面，是人生輝煌時期，

just as people are finishing their educations, starting careers,

比如剛完成學業時， 剛開始職涯時，

forming relationships and families.

建立關係和家庭時。

These illnesses can result in suicide;

這些疾病可能會導致自殺；

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

經常影響患者的工作能力， 讓他們無法盡展潛能；

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

這些疾病也造成 許多更難測量的悲劇：

lost relationships and connections,

失去人際關係和連結，

missed opportunities to pursue dreams and ideas.

錯過追求夢想和理想的機會。

These illnesses limit human possibilities

這些疾病以我們無法測量的方式

in ways we simply cannot measure.

限制了人類的可能性。

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

在我們所處的時代，

醫學的許多面向 都有著顯著的進步。

on so many other fronts.

My sister's cancer story is a great example,

我姐姐的癌症故事 就是個很好的例子，

and we could say the same of heart disease.

心臟疾病亦然。

像施德丁這類藥物，能夠預防 數百萬計個心臟病發和中風案例。

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

When you look at these areas of profound medical progress

如果來探討一下

我們一生中這些有著重大 醫學進展的領域，

in our lifetimes,

they have a narrative in common:

會發現它們有個共通之處：

scientists discovered molecules that matter to an illness,

科學家發現分子 對某種疾病的影響，

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

他們開發出一些方法來偵測 和測量人體中的那些分子，

and they developed ways to interfere with those molecules

他們還開發出一些方法，

用其他分子——即藥物—— 來干預那些分子。

using other molecules -- medicines.

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

這個策略一直有效。

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

但牽涉到大腦時， 這種策略就會受限了，

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

因為現今，我們對於 大腦如何運作，

about how the brain works.

所知還是不夠多。

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

我們需要了解每一種疾病 和我們的哪些細胞有關，

and which molecules in those cells matter to each illness.

以及每一種疾病和 那些細胞中的哪些分子有關。

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

那是我今天想要跟各位談的使命。

My lab develops technologies with which we try to turn the brain

我的實驗室開發一些技術， 試圖用這些技術把大腦

into a big-data problem.

轉為一個大數據問題。

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

在我成為生物學家之前， 我的工作和電腦及數學有關，

and I learned this lesson:

我學到的一課是：

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

只要你能夠收集到 大量恰當的資料，

about the functioning of a system,

關於運作一個系統的資料，

you can use computers in powerful new ways

你就可以用強大的 新方式來使用電腦，

去合理解釋那系統， 並了解它是如何運作的。

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

Today, big-data approaches are transforming

現今，大數據方法正在轉變

ever-larger sectors of our economy,

我們經濟中越來越大的部分，

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

而在生物學和醫學方面， 這些方法也能產生同樣的轉變。

But you have to have the right kinds of data.

但你得要找到對的資料。

You have to have data about the right things.

你得要找到關於對的事物的資料。

And that often requires new technologies and ideas.

而那通常會需要新科技和想法。

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

就是這個使命使我的 實驗室裡的科學家充滿活力。

Today, I want to tell you two short stories from our work.

今天，我想分享兩個關於 我們工作上的簡短故事。

One fundamental obstacle we face

在嘗試將大腦轉為大數據問題時，

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

我們所面對的一個根本障礙

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

在於我們的大腦是由 數十億個細胞所組成和建成的。

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

而我們的細胞並不是 通才，它們是專家。

Like humans at work,

就像人類工作時一樣，

they specialize into thousands of different cellular careers,

它們專攻數千種不同的細胞職業，

or cell types.

或細胞類型。

In fact, each of the cell types in our body

事實上，在我們身體內的 每一種細胞類型

could probably give a lively TED Talk

都很可能成為一場 生動的 TED 演講，

about what it does at work.

討論它的工作是什麼。

But as scientists, we don't even know today

但，身為科學家， 我們現今甚至還不知道

how many cell types there are,

有多少細胞類型存在，

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

而且我們不知道大部分 那些演講的題目會是什麼。

Now, we know many important things about cell types.

現在，我們知道許多 關於細胞類型的重要資訊。

They can differ dramatically in size and shape.

它們在大小和形狀上 可以有很大的差異。

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

這個細胞會對一個分子 做出反應，另一個細胞卻不會。

they'll make different molecules.

它們會製造出不同的分子。

But science has largely been reaching these insights

但科學在很大程度上

是以特設的方式達到這些見解，

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

一次研究一個細胞類型， 一次研究一個分子。

one molecule at a time.

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

我們想快速、有系統地了解全部。

Now, until recently, it was the case

直到近期，如果你想記錄

that if you wanted to inventory all of the molecules

出現在部分大腦 或任何器官中的所有分子，

in a part of the brain or any organ,

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

你得要先把它磨成 一種細胞奶昔。

But that's a problem.

但那就是問題。

As soon as you've ground up the cells,

一旦你研磨了細胞，

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

你就只能研究細胞的平均內容，

not the individual cells.

而不是個別的細胞。

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

想像一下，如果你嘗試了解 紐約般的大城市是怎樣運作 ，

but you could only do so by reviewing some statistics

但你卻只能透過檢視 關於紐約一般居民的

about the average resident of New York.

一些統計數字來了解。

Of course, you wouldn't learn very much,

當然，你無法了解非常多，

because everything that's interesting and important and exciting

因為所有有趣、重要， 且讓人興奮的東西

is in all the diversity and the specializations.

都存在於多樣性 以及特別專門化當中。

And the same thing is true of our cells.

至於我們的細胞，也是一樣的。

我們研究大腦想要 研究的不是細胞奶昔，

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

but as a cellular fruit salad,

而是細胞水果沙拉，

in which one could generate data about and learn from

我們可以從每一片水果

each individual piece of fruit.

產生資料並從中學習。

So we developed a technology for doing that.

所以我們開發了 一項科技來做這件事。

You're about to see a movie of it.

接下來你們會看到 一段關於它的影片。

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

在這裡，我們把數萬個 個別的細胞包裝在一起，

each into its own tiny water droplet

成為它自己的小水滴，

for its own molecular analysis.

供它自己的分子分析用。

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

當一個細胞進入一滴水， 一個小珠子會迎接它，

and that bead delivers millions of DNA bar code molecules.

那個小珠子會傳遞 數百萬個 DNA 條碼分子。

And each bead delivers a different bar code sequence

而每一個小珠子會傳遞 一組不同的條碼序列

to a different cell.

給一個不同的細胞。

We incorporate the DNA bar codes

我們將這 DNA 條碼併入

into each cell's RNA molecules.

每一個細胞的 RNA 分子。

Those are the molecular transcripts it's making

RNA 分子是它在 製造的分子抄本，

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

內容是它工作時所用到的特定基因。

And then we sequence billions of these combined molecules

接著，我們將數十億個 這類的結合分子排序，

and use the sequences to tell us

用序列來告訴我們

which cell and which gene

每個分子是來自 哪個細胞及哪個基因。

every molecule came from.

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

我們把這個方法稱為 「液滴測序」，因為我們用液滴

to separate the cells for analysis,

來將細胞分離，供分析用，

and we use DNA sequences to tag and inventory

且我們用 DNA 序列 來做標籤和目錄，

and keep track of everything.

持續追蹤所有資訊。

And now, whenever we do an experiment,

現在，每當我們做一項實驗時，

we analyze tens of thousands of individual cells.

我們會分析數萬個個別細胞。

And today in this area of science,

現今，在這個科學領域中，

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

挑戰漸漸變成是在於

要如何用這些巨大的資料集

as quickly as we can

盡可能快一點去多了解一點資訊。

from these vast data sets.

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

當我們在開發液滴測序時， 人們會告訴我們：

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

「喔，這會讓你們成為每個重大的 大腦專案計畫必找的人。」

That's not how we saw it.

我們並不是這樣看待它的。

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

人人都產生許多讓人興奮的 資料時，科學才能發揮得最好。

So we wrote a 25-page instruction book,

所以我們寫了一本 25 頁的教學手冊，

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

任何科學家都可以用它來從無到有 建立自家的液滴測序系統。

在過去兩年， 那本教學手冊的電子檔，

And that instruction book has been downloaded from our lab website

50,000 times in the past two years.

已經被從我們實驗室的 網站下載了五萬次。

We wrote software that any scientist could use

我們寫了一個軟體， 任何科學家都能使用它

to analyze the data from Drop-seq experiments,

來分析來自液滴測序實驗的資料，

and that software is also free,

且這軟體是免費的。

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

這軟體在過去兩年間已被 從我們的網站下載了三萬次。

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

有數百間實驗室已寫信告訴我們

有關他們採用這個方法後的發現。

using this approach.

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

現今，這項科技被用來做 人類細胞的圖解導覽。

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

它將會是人體內 所有細胞類型的圖解導覽，

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

另外還包含每一種細胞類型 在做其工作時會用到的特定基因。

Now I want to tell you about a second challenge that we face

我現在想告訴各位， 我們在嘗試將大腦轉為

大數據問題時 所面臨的第二個挑戰。

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

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

這個挑戰就是，我們想要從

of hundreds of thousands of living people.

數十萬個活人的大腦學習。

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

但在我們還活著的時候， 並不能實體存取我們的大腦。

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

但是如果我們無法存取分子， 我們要如何發現分子因素？

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

答案來自一個事實： 資訊最豐富的分子，即蛋白質，

are encoded in our DNA,

被編碼在我們的 DNA 中，

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

DNA 中有指南，我們的細胞會遵照 這些指南來製造我們的蛋白質。

And these recipes vary from person to person to person

這些方法因人而異，

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

結果造成蛋白質也會因人而異，

in their precise sequence

確切排列的順序有所不同，

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

及每種細胞製造出 每種蛋白質的量也不同。

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

這些都編碼在我們的 DNA 中，都是遺傳的，

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

但這不是我們在學校 學到的遺傳學。

Do you remember big B, little b?

你們還記得大 B 和小 b 嗎？

If you inherit big B, you get brown eyes?

如果你遺傳了大 B， 你就有褐色的眼睛？

It's simple.

這很簡單。

Very few traits are that simple.

很少有特徵是那麼簡單的。

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

即使是眼睛的顏色，也要用到 遠超過單一色素分子來形成。

And something as complex as the function of our brains

而我們的大腦功能複雜，

is shaped by the interaction of thousands of genes.

由數千個基因交互作用而形成的。

And each of these genes varies meaningfully

這些基因都因人而異，

from person to person to person,

且這些差異都是有意義的，

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

且我們每個人都是 那差異的獨特組合。

It's a big data opportunity.

這是一個大數據的機會。

And today, it's increasingly possible to make progress

現今，我們越來越 有可能取得進展，

on a scale that was never possible before.

達到前所未有的規模。

People are contributing to genetic studies

人們對遺傳研究的貢獻

in record numbers,

屢創新高，

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

全世界的科學家彼此分享資料，

to speed progress.

以加速進展。

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

我想向各位講一個小故事， 內容是我們近期

about the genetics of schizophrenia.

在遺傳學上關於 精神分裂症的發現。

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

這個發現是由三十個國家 共五萬人的努力所達成，

who contributed their DNA to genetic research on schizophrenia.

他們貢獻了他們的 DNA 作精神分裂症的遺傳研究。

It had been known for several years

數年來我們已經知道一件事，

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

即人類基因組對於 精神分裂症風險的最大影響

comes from a part of the genome

是來自基因組當中

that encodes many of the molecules in our immune system.

為我們免疫系統中的分子 做編碼的那一部分。

But it wasn't clear which gene was responsible.

但我們並不清楚 是哪一個基因在負責。

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

我實驗室中的一位科學家開發出了 一種新方法，用電腦來分析 DNA，

and he discovered something very surprising.

且他有了很驚人的發現。

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

他發現一個叫做 「補體成分 4」的基因——

it's called "C4" for short --

簡稱為「C4」——

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

它在不同人的基因組當中 有數十種不同的形態，

and these different forms make different amounts

而這些不同的形態在我們的大腦中

製造出不同數量的 C4 蛋白質。

of C4 protein in our brains.

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

他發現，我們的基因 製造越多的 C4 蛋白質，

the greater our risk for schizophrenia.

我們得到精神分裂症的 風險就越高。

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

C4 只是一個複雜 系統中的一個風險因子。

This isn't big B,

這不是大 B，

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

但這是對於一個重要分子的洞見。

Complement proteins like C4 were known for a long time

像 C4 這樣的補體蛋白質 在免疫系統中所扮演的角色，

for their roles in the immune system,

我們很早就已經知道了。

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

在免疫系統中， 它們有點像是分子便利貼，

that says, "Eat me."

上面寫著「吃我」。

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

那便利貼被貼在我們體內的

and dead cells in our bodies

很多廢物以及死細胞上，

and invites immune cells to eliminate them.

並會邀請免疫細胞來消滅它們。

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

但我的兩位同事發現，C4 便利貼

also gets put on synapses in the brain

也會被貼在大腦內的突觸上，

and prompts their elimination.

提醒要消滅它們。

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

突觸的創造和消滅，

本來就是人類發展 和學習的正常部分。

of human development and learning.

Our brains create and eliminate synapses all the time.

我們的大腦時時刻刻 都在創造和消滅突觸。

But our genetic results suggest that in schizophrenia,

但我們的遺傳研究結果指出， 在精神分裂症的情況下，

the elimination process may go into overdrive.

消減的過程可能進入高速狀態。

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

許多藥品公司的科學家告訴我， 他們對這項發現感到很興奮，

because they've been working on complement proteins for years

因為他們多年來一直研究

免疫系統中的補體蛋白質，

in the immune system,

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

對於它們如何運作， 已有不少了解。

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

他們甚至開發出用來 干預補體蛋白質的分子，

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

且他們已經開始在大腦 以及免疫系統中做測試。

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

這是種潛在的方法， 或能做出治本的藥物，

rather than an individual symptom,

而不只是治療個別的症狀。

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

我們非常希望這項由許多科學家 努力了許多年的研究

will be successful.

能夠成功。

But C4 is just one example

但 C4 只是一個例子，

of the potential for data-driven scientific approaches

說明的確有可能用資料導向的方法

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

來為存在數百年的 醫學問題開拓新領域。

There are hundreds of places in our genomes

在我們的基因組當中， 有數百個地方

that shape risk for brain illnesses,

會左右得到大腦疾病的風險，

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

其中任何一個地方， 都可以引導我們

對於重要的分子產生新的洞見。

about a molecule that matters.

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

有數百種細胞類型會 以不同的組合方式使用這些基因。

As we and other scientists work to generate

由於我們和其他科學家努力

the rest of the data that's needed

產生出其他需要的資料，

and to learn all that we can from that data,

並盡可能多了解那些資料，

we hope to open many more new fronts.

我們希望能夠開拓更多的新領域。

Genetics and single-cell analysis are just two ways

遺傳學和單細胞分析 只是嘗試將大腦

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

轉為大數據問題的兩種方式。

There is so much more we can do.

我們能做的還有更多。

Scientists in my lab are creating a technology

我實驗室中的科學家 正在創造一項科技，

for quickly mapping the synaptic connections in the brain

快速配對大腦中的突觸連結，

to tell which neurons are talking to which other neurons

來判斷哪些神經元 在和哪些其他神經元交談，

and how that conversation changes throughout life and during illness.

以及在一生中或在生病期間 這些對談會如何改變。

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

我們正在開發一種方式， 在單管中做測試，

針對數百個不同的人的基因組，

how cells with hundreds of different people's genomes

respond differently to the same stimulus.

來了解在相同的刺激下 不同基因組的細胞會有何不同的反應。

These projects bring together people with diverse backgrounds

這些專案計畫匯聚了各種人， 他們有不同的背景、

and training and interests --

訓練，及興趣——

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

生物學、電腦、化學、

數學、統計學、工程學。

But the scientific possibilities rally people with diverse interests

但科學的可能性把各種興趣的人

集合起來一起認真地工作。

into working intensely together.

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

我們希望能創造怎樣的未來？

Consider cancer.

想想癌症。

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

我們從一個對癌症 成因無知的時代——

in which cancer was commonly ascribed to personal psychological characteristics,

把癌症普遍地歸因於

個人心理特徵，

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

到現今，我們對於癌症的真正 生物的成因有分子層面的了解。

That understanding today leads to innovative medicine

現今這樣的了解，

帶來了醫學上持續不斷的創新，

after innovative medicine,

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

雖然還有很多工作要做，

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

但我們身邊有很多人 經已戰勝癌症康復過來，

that were considered untreatable a generation ago.

在上個世代，他們的狀況 會被認為是無法醫治的。

And millions of cancer survivors like my sister

數百萬個像我姐姐般的 癌症存活者，

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

不再會把他們餘生 視為理所當然，

他們也會找到工作、

and new opportunities

for work and joy and human connection.

喜悅，以及與人連結的新契機，

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

我們很有決心想要為 心理疾病創造這樣的未來——

one of real understanding and empathy

一個會有真正的了解、同理心，

and limitless possibility.

及無限的可能性的未來。

Thank you.

謝謝。

(Applause)

（掌聲）