TEDMED 2017
Steve McCarroll: How data is helping us unravel the mysteries of the brain
史帝夫麥卡羅: 資料如何協助我們解開大腦之謎
Filmed:
Readability: 4.8
1,529,178 views
遺傳學家史帝夫麥卡羅想要為人體內的所有細胞建立圖解導覽，讓我們能夠了解特定（特別是大腦中的）基因如何運作的精確細節資訊。他在這場出色的演說中分享他團隊的進展，包括「液滴定序」這發明，讓科學家可以用前所未有的規模分析個別細胞。他也描述了這項研究如何能帶來治療心理疾病（如精神分裂症）的新方法。
Steve McCarroll - Geneticist
Steve McCarroll is conducting groundbreaking research on the causes of mental illness. Full bio
Steve McCarroll is conducting groundbreaking research on the causes of mental illness. Full bio
Double-click the English transcript below to play the video.
00:12
Nine years ago,
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九年前，
00:14
my sister discovered lumps
in her neck and arm
in her neck and arm
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我姐姐發現她的頸部
和手臂上有腫塊，
和手臂上有腫塊，
經診斷為癌症。
00:17
and was diagnosed with cancer.
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00:20
From that day, she started to benefit
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她自那天起開始受惠於
科學對於癌症的了解。
00:24
from the understanding
that science has of cancer.
that science has of cancer.
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00:28
Every time she went to the doctor,
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每次她去看醫生，
他們測量了特定的分子，
00:30
they measured specific molecules
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從中得到的資訊可以知道她的狀況
00:32
that gave them information
about how she was doing
about how she was doing
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及接下來要做什麼。
00:35
and what to do next.
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00:38
New medical options
became available every few years.
became available every few years.
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每過數年，都有新的
醫療方法可以選用。
醫療方法可以選用。
00:43
Everyone recognized
that she was struggling heroically
that she was struggling heroically
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大家都意識到她在艱苦地奮力掙脫
某種生物疾病。
00:47
with a biological illness.
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00:50
This spring, she received
an innovative new medical treatment
an innovative new medical treatment
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今年春天，她接受
一項創新醫學治療的
一項創新醫學治療的
臨床試驗。
00:54
in a clinical trial.
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它戲劇化地擊退了她的癌症。
00:55
It dramatically knocked back her cancer.
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00:59
Guess who I'm going to spend
this Thanksgiving with?
this Thanksgiving with?
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猜猜我今年的感恩節
要跟誰一起過？
要跟誰一起過？
我那快活的姐姐，
01:02
My vivacious sister,
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她做的運動比我還多，
01:04
who gets more exercise than I do,
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01:06
and who, like perhaps
many people in this room,
many people in this room,
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她也和這間房間中許多人一樣，
越來越會去談這種致命的疾病，
01:09
increasingly talks about a lethal illness
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用過去式來談。
01:12
in the past tense.
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01:14
Science can, in our lifetimes --
even in a decade --
even in a decade --
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科學能夠在我們的一生中
——甚至在十年內——
——甚至在十年內——
改變「患上一種特定疾病」的意義。
01:18
transform what it means
to have a specific illness.
to have a specific illness.
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01:24
But not for all illnesses.
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但並非所有疾病都是如此。
01:27
My friend Robert and I
were classmates in graduate school.
were classmates in graduate school.
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我和我的朋友勞勃
是研究所的同學。
是研究所的同學。
勞勃很聰明，
01:31
Robert was smart,
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但隨著每個月過去，
01:32
but with each passing month,
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他的思考似乎變得越來越零亂。
01:34
his thinking seemed to become
more disorganized.
more disorganized.
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01:38
He dropped out of school,
got a job in a store ...
got a job in a store ...
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他退學了，在一間店
找到一份工作……
找到一份工作……
但連那工作也變得太複雜。
01:41
But that, too, became too complicated.
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01:44
Robert became fearful and withdrawn.
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勞勃變得害怕和退縮。
01:48
A year and a half later,
he started hearing voices
he started hearing voices
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一年半後，他開始有幻聽，
相信有人在跟蹤他。
01:50
and believing that people
were following him.
were following him.
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醫生診斷出他得了精神分裂症，
01:52
Doctors diagnosed him with schizophrenia,
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他們給了他最好的藥。
01:55
and they gave him
the best drug they could.
the best drug they could.
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那種藥讓他的幻聽情況減緩了，
01:57
That drug makes the voices
somewhat quieter,
somewhat quieter,
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02:00
but it didn't restore his bright mind
or his social connectedness.
or his social connectedness.
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但卻無法恢復他聰明的大腦
或是他的社會連結。
或是他的社會連結。
02:06
Robert struggled to remain connected
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勞勃非常努力地維持
和學校、工作，
及朋友世界的連結。
及朋友世界的連結。
02:08
to the worlds of school
and work and friends.
and work and friends.
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他慢慢遠離了，
02:11
He drifted away,
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現今，我不知道他身在何方。
02:12
and today I don't know where to find him.
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02:15
If he watches this,
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如果他看到這場演說，
02:17
I hope he'll find me.
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我希望他會找到我。
02:22
Why does medicine have
so much to offer my sister,
so much to offer my sister,
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為什麼醫學能幫助我姐姐這麼多，
02:27
and so much less to offer
millions of people like Robert?
millions of people like Robert?
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對於數百萬名像勞勃這樣的人，
能幫上的忙卻少很多？
能幫上的忙卻少很多？
需求就在那裡。
02:32
The need is there.
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世界衛生組織估計，大腦疾病
02:34
The World Health Organization
estimates that brain illnesses
estimates that brain illnesses
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如精神分裂症、躁鬱症，
和重度憂鬱症，
和重度憂鬱症，
02:37
like schizophrenia, bipolar disorder
and major depression
and major depression
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是世界上造成失去數年生命
以及工作的最大原因。
以及工作的最大原因。
02:41
are the world's largest cause
of lost years of life and work.
of lost years of life and work.
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02:47
That's in part because these illnesses
often strike early in life,
often strike early in life,
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有部分原因是因為這些疾病
經常出現在人生早期，
經常出現在人生早期，
在許多方面，是人生輝煌時期，
02:51
in many ways, in the prime of life,
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比如剛完成學業時，
剛開始職涯時，
剛開始職涯時，
02:53
just as people are finishing
their educations, starting careers,
their educations, starting careers,
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建立關係和家庭時。
02:58
forming relationships and families.
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這些疾病可能會導致自殺；
03:00
These illnesses can result in suicide;
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03:03
they often compromise one's ability
to work at one's full potential;
to work at one's full potential;
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經常影響患者的工作能力，
讓他們無法盡展潛能；
讓他們無法盡展潛能；
03:09
and they're the cause of so many
tragedies harder to measure:
tragedies harder to measure:
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這些疾病也造成
許多更難測量的悲劇：
許多更難測量的悲劇：
失去人際關係和連結，
03:13
lost relationships and connections,
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錯過追求夢想和理想的機會。
03:15
missed opportunities
to pursue dreams and ideas.
to pursue dreams and ideas.
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03:19
These illnesses limit human possibilities
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這些疾病以我們無法測量的方式
限制了人類的可能性。
03:22
in ways we simply cannot measure.
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03:27
We live in an era in which
there's profound medical progress
there's profound medical progress
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在我們所處的時代，
醫學的許多面向
都有著顯著的進步。
都有著顯著的進步。
03:31
on so many other fronts.
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我姐姐的癌症故事
就是個很好的例子，
就是個很好的例子，
03:33
My sister's cancer story
is a great example,
is a great example,
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心臟疾病亦然。
03:35
and we could say the same
of heart disease.
of heart disease.
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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。
數百萬計個心臟病發和中風案例。
03:38
Drugs like statins will prevent
millions of heart attacks and strokes.
millions of heart attacks and strokes.
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03:43
When you look at these areas
of profound medical progress
of profound medical progress
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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，
醫學進展的領域，
03:46
in our lifetimes,
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會發現它們有個共通之處：
03:47
they have a narrative in common:
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03:50
scientists discovered molecules
that matter to an illness,
that matter to an illness,
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科學家發現分子
對某種疾病的影響，
對某種疾病的影響，
他們開發出一些方法來偵測
和測量人體中的那些分子，
和測量人體中的那些分子，
03:54
they developed ways to detect
and measure those molecules in the body,
and measure those molecules in the body,
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04:00
and they developed ways
to interfere with those molecules
to interfere with those molecules
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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。
來干預那些分子。
04:03
using other molecules -- medicines.
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04:05
It's a strategy that has worked
again and again and again.
again and again and again.
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這個策略一直有效。
04:11
But when it comes to the brain,
that strategy has been limited,
that strategy has been limited,
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但牽涉到大腦時，
這種策略就會受限了，
這種策略就會受限了，
因為現今，我們對於
大腦如何運作，
大腦如何運作，
04:15
because today, we don't know
nearly enough, yet,
nearly enough, yet,
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所知還是不夠多。
04:19
about how the brain works.
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04:22
We need to learn which of our cells
matter to each illness,
matter to each illness,
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我們需要了解每一種疾病
和我們的哪些細胞有關，
和我們的哪些細胞有關，
以及每一種疾病和
那些細胞中的哪些分子有關。
那些細胞中的哪些分子有關。
04:26
and which molecules in those cells
matter to each illness.
matter to each illness.
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04:31
And that's the mission
I want to tell you about today.
I want to tell you about today.
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那是我今天想要跟各位談的使命。
04:34
My lab develops technologies
with which we try to turn the brain
with which we try to turn the brain
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我的實驗室開發一些技術，
試圖用這些技術把大腦
試圖用這些技術把大腦
轉為一個大數據問題。
04:38
into a big-data problem.
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在我成為生物學家之前，
我的工作和電腦及數學有關，
我的工作和電腦及數學有關，
04:40
You see, before I became a biologist,
I worked in computers and math,
I worked in computers and math,
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我學到的一課是：
04:43
and I learned this lesson:
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04:46
wherever you can collect vast amounts
of the right kinds of data
of the right kinds of data
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只要你能夠收集到
大量恰當的資料，
大量恰當的資料，
關於運作一個系統的資料，
04:50
about the functioning of a system,
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04:53
you can use computers in powerful new ways
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你就可以用強大的
新方式來使用電腦，
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。
並了解它是如何運作的。
04:57
to make sense of that system
and learn how it works.
and learn how it works.
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現今，大數據方法正在轉變
05:00
Today, big-data approaches
are transforming
are transforming
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我們經濟中越來越大的部分，
05:02
ever-larger sectors of our economy,
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而在生物學和醫學方面，
這些方法也能產生同樣的轉變。
這些方法也能產生同樣的轉變。
05:05
and they could do the same
in biology and medicine, too.
in biology and medicine, too.
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但你得要找到對的資料。
05:08
But you have to have
the right kinds of data.
the right kinds of data.
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你得要找到關於對的事物的資料。
05:11
You have to have data
about the right things.
about the right things.
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05:13
And that often requires
new technologies and ideas.
new technologies and ideas.
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而那通常會需要新科技和想法。
05:18
And that is the mission that animates
the scientists in my lab.
the scientists in my lab.
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就是這個使命使我的
實驗室裡的科學家充滿活力。
實驗室裡的科學家充滿活力。
今天，我想分享兩個關於
我們工作上的簡短故事。
我們工作上的簡短故事。
05:23
Today, I want to tell you
two short stories from our work.
two short stories from our work.
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05:27
One fundamental obstacle we face
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在嘗試將大腦轉為大數據問題時，
我們所面對的一個根本障礙
05:30
in trying to turn the brain
into a big-data problem
into a big-data problem
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05:33
is that our brains are composed of
and built from billions of cells.
and built from billions of cells.
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在於我們的大腦是由
數十億個細胞所組成和建成的。
數十億個細胞所組成和建成的。
05:39
And our cells are not generalists;
they're specialists.
they're specialists.
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而我們的細胞並不是
通才，它們是專家。
通才，它們是專家。
就像人類工作時一樣，
05:43
Like humans at work,
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它們專攻數千種不同的細胞職業，
05:45
they specialize into thousands
of different cellular careers,
of different cellular careers,
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或細胞類型。
05:50
or cell types.
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05:52
In fact, each of
the cell types in our body
the cell types in our body
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事實上，在我們身體內的
每一種細胞類型
每一種細胞類型
都很可能成為一場
生動的 TED 演講，
生動的 TED 演講，
05:55
could probably give a lively TED Talk
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討論它的工作是什麼。
05:57
about what it does at work.
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06:00
But as scientists,
we don't even know today
we don't even know today
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但，身為科學家，
我們現今甚至還不知道
我們現今甚至還不知道
有多少細胞類型存在，
06:02
how many cell types there are,
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而且我們不知道大部分
那些演講的題目會是什麼。
那些演講的題目會是什麼。
06:04
and we don't know what the titles
of most of those talks would be.
of most of those talks would be.
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06:11
Now, we know many
important things about cell types.
important things about cell types.
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現在，我們知道許多
關於細胞類型的重要資訊。
關於細胞類型的重要資訊。
它們在大小和形狀上
可以有很大的差異。
可以有很大的差異。
06:14
They can differ dramatically
in size and shape.
in size and shape.
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這個細胞會對一個分子
做出反應，另一個細胞卻不會。
做出反應，另一個細胞卻不會。
06:17
One will respond to a molecule
that the other doesn't respond to,
that the other doesn't respond to,
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它們會製造出不同的分子。
06:21
they'll make different molecules.
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但科學在很大程度上
06:23
But science has largely
been reaching these insights
been reaching these insights
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是以特設的方式達到這些見解，
06:26
in an ad hoc way, one cell type at a time,
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一次研究一個細胞類型，
一次研究一個分子。
一次研究一個分子。
06:29
one molecule at a time.
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06:31
We wanted to make it possible to learn
all of this quickly and systematically.
all of this quickly and systematically.
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我們想快速、有系統地了解全部。
06:37
Now, until recently, it was the case
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直到近期，如果你想記錄
06:39
that if you wanted to inventory
all of the molecules
all of the molecules
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出現在部分大腦
或任何器官中的所有分子，
或任何器官中的所有分子，
06:42
in a part of the brain or any organ,
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06:45
you had to first grind it up
into a kind of cellular smoothie.
into a kind of cellular smoothie.
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你得要先把它磨成
一種細胞奶昔。
一種細胞奶昔。
06:50
But that's a problem.
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但那就是問題。
06:52
As soon as you've ground up the cells,
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一旦你研磨了細胞，
你就只能研究細胞的平均內容，
06:55
you can only study the contents
of the average cell --
of the average cell --
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而不是個別的細胞。
06:58
not the individual cells.
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07:01
Imagine if you were trying to understand
how a big city like New York works,
how a big city like New York works,
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想像一下，如果你嘗試了解
紐約般的大城市是怎樣運作 ，
紐約般的大城市是怎樣運作 ，
但你卻只能透過檢視
關於紐約一般居民的
關於紐約一般居民的
07:04
but you could only do so
by reviewing some statistics
by reviewing some statistics
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07:07
about the average resident of New York.
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一些統計數字來了解。
07:10
Of course, you wouldn't learn very much,
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當然，你無法了解非常多，
因為所有有趣、重要，
且讓人興奮的東西
且讓人興奮的東西
07:12
because everything that's interesting
and important and exciting
and important and exciting
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都存在於多樣性
以及特別專門化當中。
以及特別專門化當中。
07:15
is in all the diversity
and the specializations.
and the specializations.
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至於我們的細胞，也是一樣的。
07:18
And the same thing is true of our cells.
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我們研究大腦想要
研究的不是細胞奶昔，
研究的不是細胞奶昔，
07:21
And we wanted to make it possible to study
the brain not as a cellular smoothie
the brain not as a cellular smoothie
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而是細胞水果沙拉，
07:25
but as a cellular fruit salad,
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07:28
in which one could generate
data about and learn from
data about and learn from
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我們可以從每一片水果
產生資料並從中學習。
07:30
each individual piece of fruit.
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07:34
So we developed
a technology for doing that.
a technology for doing that.
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所以我們開發了
一項科技來做這件事。
一項科技來做這件事。
接下來你們會看到
一段關於它的影片。
一段關於它的影片。
07:36
You're about to see a movie of it.
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07:41
Here we're packaging
tens of thousands of individual cells,
tens of thousands of individual cells,
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在這裡，我們把數萬個
個別的細胞包裝在一起，
個別的細胞包裝在一起，
成為它自己的小水滴，
07:45
each into its own tiny water droplet
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供它自己的分子分析用。
07:48
for its own molecular analysis.
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07:51
When a cell lands in a droplet,
it's greeted by a tiny bead,
it's greeted by a tiny bead,
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當一個細胞進入一滴水，
一個小珠子會迎接它，
一個小珠子會迎接它，
那個小珠子會傳遞
數百萬個 DNA 條碼分子。
數百萬個 DNA 條碼分子。
07:56
and that bead delivers millions
of DNA bar code molecules.
of DNA bar code molecules.
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08:01
And each bead delivers
a different bar code sequence
a different bar code sequence
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而每一個小珠子會傳遞
一組不同的條碼序列
一組不同的條碼序列
給一個不同的細胞。
08:04
to a different cell.
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08:06
We incorporate the DNA bar codes
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我們將這 DNA 條碼併入
每一個細胞的 RNA 分子。
08:09
into each cell's RNA molecules.
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RNA 分子是它在
製造的分子抄本，
製造的分子抄本，
08:12
Those are the molecular
transcripts it's making
transcripts it's making
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內容是它工作時所用到的特定基因。
08:15
of the specific genes
that it's using to do its job.
that it's using to do its job.
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08:19
And then we sequence billions
of these combined molecules
of these combined molecules
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接著，我們將數十億個
這類的結合分子排序，
這類的結合分子排序，
08:24
and use the sequences to tell us
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用序列來告訴我們
08:27
which cell and which gene
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每個分子是來自
哪個細胞及哪個基因。
哪個細胞及哪個基因。
08:29
every molecule came from.
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08:32
We call this approach "Drop-seq,"
because we use droplets
because we use droplets
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我們把這個方法稱為
「液滴測序」，因為我們用液滴
「液滴測序」，因為我們用液滴
來將細胞分離，供分析用，
08:35
to separate the cells for analysis,
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08:38
and we use DNA sequences
to tag and inventory
to tag and inventory
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且我們用 DNA 序列
來做標籤和目錄，
來做標籤和目錄，
持續追蹤所有資訊。
08:41
and keep track of everything.
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08:44
And now, whenever we do an experiment,
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現在，每當我們做一項實驗時，
我們會分析數萬個個別細胞。
08:46
we analyze tens of thousands
of individual cells.
of individual cells.
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08:51
And today in this area of science,
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現今，在這個科學領域中，
挑戰漸漸變成是在於
08:53
the challenge is increasingly
how to learn as much as we can
how to learn as much as we can
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要如何用這些巨大的資料集
盡可能快一點去多了解一點資訊。
08:58
as quickly as we can
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09:00
from these vast data sets.
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09:04
When we were developing Drop-seq,
people used to tell us,
people used to tell us,
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當我們在開發液滴測序時，
人們會告訴我們：
人們會告訴我們：
「喔，這會讓你們成為每個重大的
大腦專案計畫必找的人。」
大腦專案計畫必找的人。」
09:07
"Oh, this is going to make you guys
the go-to for every major brain project."
the go-to for every major brain project."
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我們並不是這樣看待它的。
09:13
That's not how we saw it.
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人人都產生許多讓人興奮的
資料時，科學才能發揮得最好。
資料時，科學才能發揮得最好。
09:14
Science is best when everyone
is generating lots of exciting data.
is generating lots of exciting data.
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09:20
So we wrote a 25-page instruction book,
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所以我們寫了一本
25 頁的教學手冊，
25 頁的教學手冊，
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。
建立自家的液滴測序系統。
09:23
with which any scientist could build
their own Drop-seq system from scratch.
their own Drop-seq system from scratch.
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在過去兩年，
那本教學手冊的電子檔，
那本教學手冊的電子檔，
09:28
And that instruction book has been
downloaded from our lab website
downloaded from our lab website
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已經被從我們實驗室的
網站下載了五萬次。
網站下載了五萬次。
09:31
50,000 times in the past two years.
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09:35
We wrote software
that any scientist could use
that any scientist could use
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我們寫了一個軟體，
任何科學家都能使用它
任何科學家都能使用它
來分析來自液滴測序實驗的資料，
09:38
to analyze the data
from Drop-seq experiments,
from Drop-seq experiments,
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且這軟體是免費的。
09:41
and that software is also free,
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這軟體在過去兩年間已被
從我們的網站下載了三萬次。
從我們的網站下載了三萬次。
09:43
and it's been downloaded from our website
30,000 times in the past two years.
30,000 times in the past two years.
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09:48
And hundreds of labs have written us
about discoveries that they've made
about discoveries that they've made
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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。
09:53
using this approach.
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現今，這項科技被用來做
人類細胞的圖解導覽。
人類細胞的圖解導覽。
09:54
Today, this technology is being used
to make a human cell atlas.
to make a human cell atlas.
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它將會是人體內
所有細胞類型的圖解導覽，
所有細胞類型的圖解導覽，
09:58
It will be an atlas of all
of the cell types in the human body
of the cell types in the human body
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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。
在做其工作時會用到的特定基因。
10:01
and the specific genes
that each cell type uses to do its job.
that each cell type uses to do its job.
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3680
10:08
Now I want to tell you about
a second challenge that we face
a second challenge that we face
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我現在想告訴各位，
我們在嘗試將大腦轉為
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。
所面臨的第二個挑戰。
10:11
in trying to turn the brain
into a big data problem.
into a big data problem.
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2448
10:13
And that challenge is that
we'd like to learn from the brains
we'd like to learn from the brains
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這個挑戰就是，我們想要從
數十萬個活人的大腦學習。
10:16
of hundreds of thousands of living people.
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10:19
But our brains are not physically
accessible while we're living.
accessible while we're living.
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但在我們還活著的時候，
並不能實體存取我們的大腦。
並不能實體存取我們的大腦。
10:24
But how can we discover molecular factors
if we can't hold the molecules?
if we can't hold the molecules?
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但是如果我們無法存取分子，
我們要如何發現分子因素？
我們要如何發現分子因素？
10:30
An answer comes from the fact that
the most informative molecules, proteins,
the most informative molecules, proteins,
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答案來自一個事實：
資訊最豐富的分子，即蛋白質，
資訊最豐富的分子，即蛋白質，
被編碼在我們的 DNA 中，
10:34
are encoded in our DNA,
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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。
這些指南來製造我們的蛋白質。
10:36
which has the recipes our cells follow
to make all of our proteins.
to make all of our proteins.
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10:41
And these recipes vary
from person to person to person
from person to person to person
188
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這些方法因人而異，
結果造成蛋白質也會因人而異，
10:46
in ways that cause the proteins
to vary from person to person
to vary from person to person
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3342
確切排列的順序有所不同，
10:50
in their precise sequence
190
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及每種細胞製造出
每種蛋白質的量也不同。
每種蛋白質的量也不同。
10:52
and in how much each cell type
makes of each protein.
makes of each protein.
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10:56
It's all encoded in our DNA,
and it's all genetics,
and it's all genetics,
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這些都編碼在我們的
DNA 中，都是遺傳的，
DNA 中，都是遺傳的，
但這不是我們在學校
學到的遺傳學。
學到的遺傳學。
10:59
but it's not the genetics
that we learned about in school.
that we learned about in school.
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2817
11:03
Do you remember big B, little b?
194
651572
1983
你們還記得大 B 和小 b 嗎？
11:06
If you inherit big B, you get brown eyes?
195
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2400
如果你遺傳了大 B，
你就有褐色的眼睛？
你就有褐色的眼睛？
11:09
It's simple.
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1223
這很簡單。
11:11
Very few traits are that simple.
197
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3173
很少有特徵是那麼簡單的。
11:15
Even eye color is shaped by much more
than a single pigment molecule.
than a single pigment molecule.
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4725
即使是眼睛的顏色，也要用到
遠超過單一色素分子來形成。
遠超過單一色素分子來形成。
11:20
And something as complex
as the function of our brains
as the function of our brains
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而我們的大腦功能複雜，
11:25
is shaped by the interaction
of thousands of genes.
of thousands of genes.
200
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由數千個基因交互作用而形成的。
這些基因都因人而異，
11:28
And each of these genes
varies meaningfully
varies meaningfully
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且這些差異都是有意義的，
11:30
from person to person to person,
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且我們每個人都是
那差異的獨特組合。
那差異的獨特組合。
11:32
and each of us is a unique
combination of that variation.
combination of that variation.
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3517
11:37
It's a big data opportunity.
204
685419
2216
這是一個大數據的機會。
11:40
And today, it's increasingly
possible to make progress
possible to make progress
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3176
現今，我們越來越
有可能取得進展，
有可能取得進展，
達到前所未有的規模。
11:43
on a scale that was never possible before.
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2796
人們對遺傳研究的貢獻
11:46
People are contributing to genetic studies
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屢創新高，
11:48
in record numbers,
208
696663
1594
11:51
and scientists around the world
are sharing the data with one another
are sharing the data with one another
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4087
全世界的科學家彼此分享資料，
以加速進展。
11:55
to speed progress.
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11:57
I want to tell you a short story
about a discovery we recently made
about a discovery we recently made
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我想向各位講一個小故事，
內容是我們近期
內容是我們近期
在遺傳學上關於
精神分裂症的發現。
精神分裂症的發現。
12:00
about the genetics of schizophrenia.
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1894
12:03
It was made possible
by 50,000 people from 30 countries,
by 50,000 people from 30 countries,
213
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4596
這個發現是由三十個國家
共五萬人的努力所達成，
共五萬人的努力所達成，
他們貢獻了他們的 DNA
作精神分裂症的遺傳研究。
作精神分裂症的遺傳研究。
12:08
who contributed their DNA
to genetic research on schizophrenia.
to genetic research on schizophrenia.
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4700
12:14
It had been known for several years
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722406
2096
數年來我們已經知道一件事，
即人類基因組對於
精神分裂症風險的最大影響
精神分裂症風險的最大影響
12:16
that the human genome's largest influence
on risk of schizophrenia
on risk of schizophrenia
216
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是來自基因組當中
12:20
comes from a part of the genome
217
728661
1802
為我們免疫系統中的分子
做編碼的那一部分。
做編碼的那一部分。
12:22
that encodes many of the molecules
in our immune system.
in our immune system.
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3344
但我們並不清楚
是哪一個基因在負責。
是哪一個基因在負責。
12:25
But it wasn't clear which gene
was responsible.
was responsible.
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3034
12:29
A scientist in my lab developed
a new way to analyze DNA with computers,
a new way to analyze DNA with computers,
220
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4040
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，
一種新方法，用電腦來分析 DNA，
且他有了很驚人的發現。
12:33
and he discovered something
very surprising.
very surprising.
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3095
他發現一個叫做
「補體成分 4」的基因——
「補體成分 4」的基因——
12:36
He found that a gene called
"complement component 4" --
"complement component 4" --
222
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3251
簡稱為「C4」——
12:40
it's called "C4" for short --
223
748204
1799
12:43
comes in dozens of different forms
in different people's genomes,
in different people's genomes,
224
751036
3889
它在不同人的基因組當中
有數十種不同的形態，
有數十種不同的形態，
而這些不同的形態在我們的大腦中
12:46
and these different forms
make different amounts
make different amounts
225
754949
3197
製造出不同數量的 C4 蛋白質。
12:50
of C4 protein in our brains.
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2242
12:52
And he found that the more
C4 protein our genes make,
C4 protein our genes make,
227
760957
3985
他發現，我們的基因
製造越多的 C4 蛋白質，
製造越多的 C4 蛋白質，
我們得到精神分裂症的
風險就越高。
風險就越高。
12:56
the greater our risk for schizophrenia.
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2112
12:59
Now, C4 is still just one risk factor
in a complex system.
in a complex system.
229
767919
4907
C4 只是一個複雜
系統中的一個風險因子。
系統中的一個風險因子。
這不是大 B，
13:04
This isn't big B,
230
772850
1989
但這是對於一個重要分子的洞見。
13:06
but it's an insight about
a molecule that matters.
a molecule that matters.
231
774863
3557
像 C4 這樣的補體蛋白質
在免疫系統中所扮演的角色，
在免疫系統中所扮演的角色，
13:11
Complement proteins like C4
were known for a long time
were known for a long time
232
779492
3637
13:15
for their roles in the immune system,
233
783153
1953
我們很早就已經知道了。
在免疫系統中，
它們有點像是分子便利貼，
它們有點像是分子便利貼，
13:17
where they act as a kind of
molecular Post-it note
molecular Post-it note
234
785130
2778
上面寫著「吃我」。
13:19
that says, "Eat me."
235
787932
1580
13:22
And that Post-it note
gets put on lots of debris
gets put on lots of debris
236
790475
2667
那便利貼被貼在我們體內的
很多廢物以及死細胞上，
13:25
and dead cells in our bodies
237
793166
2357
13:27
and invites immune cells
to eliminate them.
to eliminate them.
238
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2490
並會邀請免疫細胞來消滅它們。
13:30
But two colleagues of mine found
that the C4 Post-it note
that the C4 Post-it note
239
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3539
但我的兩位同事發現，C4 便利貼
13:35
also gets put on synapses in the brain
240
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3298
也會被貼在大腦內的突觸上，
13:38
and prompts their elimination.
241
806505
1864
提醒要消滅它們。
13:41
Now, the creation and elimination
of synapses is a normal part
of synapses is a normal part
242
809154
3266
突觸的創造和消滅，
本來就是人類發展
和學習的正常部分。
和學習的正常部分。
13:44
of human development and learning.
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1854
我們的大腦時時刻刻
都在創造和消滅突觸。
都在創造和消滅突觸。
13:46
Our brains create and eliminate
synapses all the time.
synapses all the time.
244
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2921
13:49
But our genetic results suggest
that in schizophrenia,
that in schizophrenia,
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但我們的遺傳研究結果指出，
在精神分裂症的情況下，
在精神分裂症的情況下，
消減的過程可能進入高速狀態。
13:52
the elimination process
may go into overdrive.
may go into overdrive.
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13:57
Scientists at many drug companies tell me
they're excited about this discovery,
they're excited about this discovery,
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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，
他們對這項發現感到很興奮，
因為他們多年來一直研究
14:01
because they've been working
on complement proteins for years
on complement proteins for years
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免疫系統中的補體蛋白質，
14:04
in the immune system,
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對於它們如何運作，
已有不少了解。
已有不少了解。
14:05
and they've learned a lot
about how they work.
about how they work.
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14:08
They've even developed molecules
that interfere with complement proteins,
that interfere with complement proteins,
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他們甚至開發出用來
干預補體蛋白質的分子，
干預補體蛋白質的分子，
且他們已經開始在大腦
以及免疫系統中做測試。
以及免疫系統中做測試。
14:12
and they're starting to test them
in the brain as well as the immune system.
in the brain as well as the immune system.
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14:17
It's potentially a path toward a drug
that might address a root cause
that might address a root cause
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這是種潛在的方法，
或能做出治本的藥物，
或能做出治本的藥物，
而不只是治療個別的症狀。
14:21
rather than an individual symptom,
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14:24
and we hope very much that this work
by many scientists over many years
by many scientists over many years
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我們非常希望這項由許多科學家
努力了許多年的研究
努力了許多年的研究
能夠成功。
14:28
will be successful.
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14:31
But C4 is just one example
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但 C4 只是一個例子，
說明的確有可能用資料導向的方法
14:34
of the potential for data-driven
scientific approaches
scientific approaches
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來為存在數百年的
醫學問題開拓新領域。
醫學問題開拓新領域。
14:37
to open new fronts on medical problems
that are centuries old.
that are centuries old.
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14:42
There are hundreds of places
in our genomes
in our genomes
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在我們的基因組當中，
有數百個地方
有數百個地方
會左右得到大腦疾病的風險，
14:44
that shape risk for brain illnesses,
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其中任何一個地方，
都可以引導我們
都可以引導我們
14:47
and any one of them could lead us
to the next molecular insight
to the next molecular insight
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對於重要的分子產生新的洞見。
14:51
about a molecule that matters.
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有數百種細胞類型會
以不同的組合方式使用這些基因。
以不同的組合方式使用這些基因。
14:53
And there are hundreds of cell types that
use these genes in different combinations.
use these genes in different combinations.
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由於我們和其他科學家努力
14:57
As we and other scientists
work to generate
work to generate
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產生出其他需要的資料，
14:59
the rest of the data that's needed
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並盡可能多了解那些資料，
15:01
and to learn all that we can
from that data,
from that data,
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我們希望能夠開拓更多的新領域。
15:04
we hope to open many more new fronts.
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15:08
Genetics and single-cell analysis
are just two ways
are just two ways
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遺傳學和單細胞分析
只是嘗試將大腦
只是嘗試將大腦
轉為大數據問題的兩種方式。
15:13
of trying to turn the brain
into a big data problem.
into a big data problem.
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15:18
There is so much more we can do.
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我們能做的還有更多。
15:21
Scientists in my lab
are creating a technology
are creating a technology
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我實驗室中的科學家
正在創造一項科技，
正在創造一項科技，
快速配對大腦中的突觸連結，
15:24
for quickly mapping the synaptic
connections in the brain
connections in the brain
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來判斷哪些神經元
在和哪些其他神經元交談，
在和哪些其他神經元交談，
15:27
to tell which neurons are talking
to which other neurons
to which other neurons
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以及在一生中或在生病期間
這些對談會如何改變。
這些對談會如何改變。
15:30
and how that conversation changes
throughout life and during illness.
throughout life and during illness.
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15:35
And we're developing a way
to test in a single tube
to test in a single tube
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我們正在開發一種方式，
在單管中做測試，
在單管中做測試，
針對數百個不同的人的基因組，
15:40
how cells with hundreds
of different people's genomes
of different people's genomes
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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。
不同基因組的細胞會有何不同的反應。
15:42
respond differently to the same stimulus.
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15:46
These projects bring together
people with diverse backgrounds
people with diverse backgrounds
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這些專案計畫匯聚了各種人，
他們有不同的背景、
他們有不同的背景、
訓練，及興趣——
15:51
and training and interests --
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生物學、電腦、化學、
15:53
biology, computers, chemistry,
math, statistics, engineering.
math, statistics, engineering.
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數學、統計學、工程學。
16:00
But the scientific possibilities
rally people with diverse interests
rally people with diverse interests
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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。
16:04
into working intensely together.
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16:08
What's the future
that we could hope to create?
that we could hope to create?
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我們希望能創造怎樣的未來？
16:12
Consider cancer.
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想想癌症。
16:14
We've moved from an era of ignorance
about what causes cancer,
about what causes cancer,
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我們從一個對癌症
成因無知的時代——
成因無知的時代——
把癌症普遍地歸因於
16:18
in which cancer was commonly ascribed
to personal psychological characteristics,
to personal psychological characteristics,
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個人心理特徵，
16:26
to a modern molecular understanding
of the true biological causes of cancer.
of the true biological causes of cancer.
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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。
生物的成因有分子層面的了解。
16:32
That understanding today
leads to innovative medicine
leads to innovative medicine
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現今這樣的了解，
帶來了醫學上持續不斷的創新，
16:35
after innovative medicine,
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雖然還有很多工作要做，
16:36
and although there's still
so much work to do,
so much work to do,
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16:39
we're already surrounded by people
who have been cured of cancers
who have been cured of cancers
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但我們身邊有很多人
經已戰勝癌症康復過來，
經已戰勝癌症康復過來，
在上個世代，他們的狀況
會被認為是無法醫治的。
會被認為是無法醫治的。
16:43
that were considered untreatable
a generation ago.
a generation ago.
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16:48
And millions of cancer survivors
like my sister
like my sister
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數百萬個像我姐姐般的
癌症存活者，
癌症存活者，
不再會把他們餘生
視為理所當然，
視為理所當然，
16:51
find themselves with years of life
that they didn't take for granted
that they didn't take for granted
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他們也會找到工作、
16:56
and new opportunities
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喜悅，以及與人連結的新契機，
16:57
for work and joy and human connection.
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17:03
That is the future that we are determined
to create around mental illness --
to create around mental illness --
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我們很有決心想要為
心理疾病創造這樣的未來——
心理疾病創造這樣的未來——
17:08
one of real understanding and empathy
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一個會有真正的了解、同理心，
及無限的可能性的未來。
17:12
and limitless possibility.
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17:15
Thank you.
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謝謝。
（掌聲）
17:16
(Applause)
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ABOUT THE SPEAKER
Steve McCarroll - GeneticistSteve McCarroll is conducting groundbreaking research on the causes of mental illness.
Why you should listen
Steve McCarroll and the scientists in his lab use human genetics, molecular biology and engineering to create new ways of studying the human brain, reveal the ways in which genomes vary from person to person and discover the molecular and cellular processes that underlie brain illness. McCarroll and his team at Harvard have linked schizophrenia to specific gene variations that recruit immune molecules into "pruning" synapses in the brain, a discovery that is leading toward new ways of thinking about the biological basis of schizophrenia and new approaches for discovering medicines.
Prior to leading his lab, McCarroll earned his PhD in neuroscience at the University of California, San Francisco. He is also currently serving as Director of Genetics for the Broad Institute's Stanley Center for Psychiatric Research.
Steve McCarroll | Speaker | TED.com