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TED Talks + Video : Science / Brain / Health / Biology, Steve McCarroll / How data is helping us unravel the mysteries of the brain

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

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.

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 Robert became fearful and withdrawn.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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Thank you.

(Applause)


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Nine years ago, my sister discovered lumps in her neck and arm and was diagnosed with cancer. 9年前、姉は首や腕のしこりを発見し、ガンと診断されました。 Девять лет назад у моей сестры обнаружили опухоли на шее и руке, и у нее диагностировали рак. Pred devetimi leti je moja sestra na vratu in roki odkrila bulice in diagnosticirali so ji raka. 九年前,我姐姐在脖子和手臂上發現了腫塊,被診斷出患有癌症。 From that day, she started to benefit from the understanding that science has of cancer. その日から、彼女は科学が癌について持っているという理解から利益を得るようになりました。 A partir desse dia, começou a beneficiar da compreensão que a ciência tem do cancro. С того дня она начала извлекать выгоду из того, что наука знает о раке. O günden itibaren, bilimin kanseri olduğu anlayışından faydalanmaya başladı. 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. 彼女が医者に行くたびに、彼らは特定の分子を測定し、彼女の状態と次に何をすべきかについての情報を提供しました. Sempre que ela ia ao médico, mediam moléculas específicas que lhes davam informações sobre como ela estava e o que fazer a seguir. Vsakič, ko je šla k zdravniku, so izmerili določene molekule, ki so jim dale informacije o njenem stanju in o tem, kaj naj storijo naprej. New medical options became available every few years. Alle paar Jahre wurden neue medizinische Optionen verfügbar. 数年ごとに新しい医療オプションが利用可能になりました。 Новые медицинские возможности становились доступными каждые несколько лет. Everyone recognized that she was struggling heroically with a biological illness. Jeder erkannte, dass sie heldenhaft mit einer biologischen Krankheit zu kämpfen hatte. 誰もが彼女が生物学的病気に英雄的に苦しんでいることを認識していました。 Все признавали, что она героически боролась с биологическим заболеванием. Herkes onun biyolojik bir hastalık ile kahramanca mücadele ettiğini kabul etti. This spring, she received an innovative new medical treatment in a clinical trial. この春、彼女は臨床試験で革新的な新しい治療を受けました。 Esta primavera, recebeu um novo tratamento médico inovador num ensaio clínico. Весной этого года она получила инновационное лечение в ходе клинических испытаний. Bu bahar, klinik bir denemede yenilikçi bir tıbbi tedavi gördü. It dramatically knocked back her cancer. それは彼女の癌を劇的にノックバックしました。 O cancro foi dramaticamente eliminado. Это резко отбросило ее рак. Dramatik olarak kanserini geri aldı. 它极大地击退了她的癌症。 Guess who I'm going to spend this Thanksgiving with? 私がこの感謝祭を一緒に過ごすつもりだと思いますか? Угадай, с кем я проведу День Благодарения? Tahmin et şükran gününü kiminle geçireceğim? 猜猜我要和谁一起过这个感恩节? 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. Mi hermana vivaz, que hace más ejercicio que yo y que, como quizás muchas personas en esta sala, habla cada vez más sobre una enfermedad letal en tiempo pasado. 私よりも運動量が多く、おそらくこの部屋の多くの人々のように、過去形で致命的な病気についてますます話している私の元気な妹。 A minha irmã vivaz, que faz mais exercício do que eu e que, como talvez muitas pessoas nesta sala, fala cada vez mais de uma doença mortal no pretérito perfeito. Моя жизнерадостная сестра, которая занимается спортом больше, чем я, и которая, как, возможно, и многие люди в этом зале, все чаще говорит о смертельной болезни в прошедшем времени. Benden daha fazla egzersiz yapan ve belki de bu odadaki birçok insan gibi, geçmiş zamanlardaki ölümcül bir hastalıktan bahseden capcanlı kız kardeşim. Science can, in our lifetimes -- even in a decade -- transform what it means to have a specific illness. 科学は、私たちの生涯で、たとえ10年であっても、特定の病気を持つことの意味を変えることができます。 Наука может за всю нашу жизнь — даже за десятилетие — изменить то, что значит иметь конкретное заболевание. Bilim, yaşamlarımızda - on yılda bile - belirli bir hastalığa sahip olmanın anlamını değiştirebilir. 科学可以在我们的有生之年——甚至十年内——改变患有某种特定疾病的含义。

But not for all illnesses. しかし、すべての病気に当てはまるわけではありません。 Ancak tüm hastalıklar için değil. My friend Robert and I were classmates in graduate school. Arkadaşım Robert ve ben lisansüstü okulda sınıf arkadaşıydık. Robert was smart, but with each passing month, his thinking seemed to become more disorganized. Robert war schlau, aber mit jedem Monat schien sein Denken unorganisierter zu werden. ロバートは頭が良かったが、月を追うごとに、彼の考えはよりまとまりがなくなったようだった。 Роберт был умен, но с каждым месяцем его мышление становилось все более неорганизованным. Robert akıllıydı, ancak her geçen ay, düşüncesi daha düzensiz görünüyordu. He dropped out of school, got a job in a store ... But that, too, became too complicated. Он бросил школу, устроился работать в магазин... Но и это стало слишком сложно. Okuldan ayrıldı, bir mağazada iş buldu ... Ama bu da çok karmaşık bir hal aldı. Robert became fearful and withdrawn. Robert tornou-se medroso e retraído. Роберт стал боязливым и замкнутым. Robert korkup geri çekildi. A year and a half later, he started hearing voices and believing that people were following him. 1年半後、彼は声を聞き始め、人々が彼をフォローしていると信じ始めました。 Через полтора года он начал слышать голоса и верить, что люди преследуют его. Bir buçuk yıl sonra sesleri duymaya ve insanların onu takip ettiğine inanmaya başladı. 一年半后,他开始听到声音并相信有人在跟踪他。 Doctors diagnosed him with schizophrenia, and they gave him the best drug they could. Врачи диагностировали у него шизофрению и дали ему самое лучшее лекарство, какое только могли. Doktorlar şizofreni tanısı koydu ve ellerinden geldiğince en iyi ilacı verdiler. That drug makes the voices somewhat quieter, but it didn't restore his bright mind or his social connectedness. その薬は声を幾分静かにしますが、それは彼の明るい心や彼の社会的つながりを回復しませんでした。 Este medicamento faz com que as vozes fiquem um pouco mais calmas, mas não lhe devolveu a sua mente brilhante nem a sua ligação social. Это лекарство сделало голоса несколько тише, но не восстановило его ясный ум или социальные связи. Bu ilaç sesleri biraz daha sessiz hale getiriyor, ancak parlak zihnini veya onun sosyal bağını eski haline getirmedi. Robert struggled to remain connected to the worlds of school and work and friends. ロバートは、学校や職場、友人の世界とのつながりを維持するのに苦労しました。 Роберт изо всех сил пытался оставаться на связи с миром школы, работы и друзей. Robert, okul dünyasıyla, işiyle ve arkadaşlarıyla bağlantıda kalmak için mücadele etti. He drifted away, and today I don't know where to find him. 彼は漂流しました、そして今日私は彼をどこで見つけるかわかりません。 Ele foi-se embora e hoje não sei onde o encontrar. Он уплыл, и сегодня я не знаю, где его найти. Uzaklaştı ve bugün onu nerede bulacağımı bilmiyorum. If he watches this, I hope he'll find me. 彼がこれを見たら、彼が私を見つけてくれることを願っています。

Why does medicine have so much to offer my sister, and so much less to offer millions of people like Robert? なぜ薬は私の妹に提供するものがたくさんあり、ロバートのような何百万もの人々に提供するものははるかに少ないのですか? Почему медицина может так много предложить моей сестре и гораздо меньше предложить миллионам людей, подобных Роберту? Neden tıpta kız kardeşime sunacak bu kadar çok şey var ve Robert gibi milyonlarca insana bu kadar az şey sunacak? The need is there. 必要があります。 Необходимость есть. İhtiyaç orada. 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. 世界保健機関は、統合失調症、双極性障害、大うつ病などの脳の病気が、人生と仕事の喪失の世界最大の原因であると推定しています。 По оценкам Всемирной организации здравоохранения, заболевания головного мозга, такие как шизофрения, биполярное расстройство и глубокая депрессия, являются основной причиной потерянных лет жизни и работы в мире. Dünya Sağlık Örgütü, şizofreni, bipolar bozukluk ve majör depresyon gibi beyin hastalıklarının dünyanın yaşam ve iş kaybının yıllar içindeki en büyük nedeni olduğunu tahmin ediyor. 世界卫生组织估计,精神分裂症、双相情感障碍和重度抑郁症等脑部疾病是世界上导致生命和工作损失的最大原因。 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. これは、人々が教育を終え、キャリアを開始し、人間関係や家族を形成しているのと同じように、これらの病気が人生の早い段階で、多くの点で、人生の最盛期にしばしば襲うためです。 Отчасти потому, что эти болезни часто поражают в раннем возрасте, во многих отношениях, в самом расцвете сил, когда люди заканчивают свое образование, начинают карьеру, строят отношения и семьи. Bunun nedeni kısmen bu hastalıkların yaşamın başlarında, birçok yönden, yaşamın ilk döneminde, tıpkı insanların eğitimlerini bitirmesi, kariyerine başlaması, ilişkileri kurması ve aileleri oluşturmasıdır. 部分原因是这些疾病通常在生命的早期,从很多方面来说,在生命的黄金时期发作,就像人们完成学业、开始职业生涯、建立人际关系和家庭一样。 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. これらの病気は自殺につながる可能性があります。彼らはしばしば自分の可能性を最大限に発揮する能力を損なう。そして、それらは測定するのが難しい非常に多くの悲劇の原因です:失われた関係とつながり、夢とアイデアを追求する機会を逃した。 Эти болезни могут привести к самоубийству; они часто ставят под угрозу способность работать в полную силу; и они являются причиной многих трагедий, которые трудно измерить: потерянные отношения и связи, упущенные возможности реализовать мечты и идеи. Bu hastalıklar intiharla sonuçlanabilir; genellikle birisinin tam potansiyelinde çalışabilme yeteneğinden ödün verir; ve onlar, ölçülmesi zor olan birçok trajedi sebebidir: ilişkileri kaybetmek ve bağlantılar kurmak, hayalleri ve fikirleri sürdürmek için kaçırılmış fırsatları. 这些疾病可能导致自杀;它们常常会损害一个人充分发挥潜力的能力;它们是许多难以衡量的悲剧的原因:失去关系和联系,错过追求梦想和想法的机会。 These illnesses limit human possibilities in ways we simply cannot measure. Эти болезни ограничивают человеческие возможности способами, которые мы просто не можем измерить. 这些疾病以我们根本无法衡量的方式限制了人类的可能性。

We live in an era in which there's profound medical progress on so many other fronts. 私たちは、他の多くの面で深刻な医学的進歩がある時代に生きています。 Мы живем в эпоху, когда медицина добилась значительных успехов во многих других областях. Diğer pek çok alanda derin tıbbi gelişmelerin olduğu bir çağda yaşıyoruz. 我们生活在一个在许多其他方面都取得了深刻医学进步的时代。 My sister's cancer story is a great example, and we could say the same of heart disease. История моей сестры с раком — отличный пример, и то же самое можно сказать о болезни сердца. Drugs like statins will prevent millions of heart attacks and strokes. スタチンのような薬は、何百万もの心臓発作や脳卒中を防ぎます. Такие лекарства, как статины, предотвратят миллионы сердечных приступов и инсультов. Statinler gibi ilaçlar milyonlarca kalp krizini ve felci engeller. 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. 私たちの生涯における深刻な医学的進歩のこれらの領域を見ると、それらには共通の物語があります:科学者は病気に重要な分子を発見し、体内でそれらの分子を検出および測定する方法を開発し、干渉する方法を開発しました他の分子を使用しているそれらの分子-薬。 Когда вы смотрите на эти области глубокого медицинского прогресса в нашей жизни, у них есть общее повествование: ученые открыли молекулы, которые имеют значение для болезни, они разработали способы обнаружения и измерения этих молекул в организме, и они разработали способы вмешательства в эти молекулы используют другие молекулы — лекарства. Yaşamlarımızdaki bu derin tıbbi ilerlemenin bu alanlarına baktığınızda, ortak bir anlatıya sahipler: bilim adamları bir hastalığa önem veren molekülleri keşfettiler, vücuttaki bu molekülleri tespit etmek ve ölçmek için yollar geliştirdiler ve müdahale etmek için yollar geliştirdiler. diğer molekülleri kullanan bu moleküller - ilaçlar. 当你观察我们一生中这些取得深远医学进步的领域时,他们有一个共同的叙述:科学家发现了对疾病至关重要的分子,他们开发了检测和测量体内这些分子的方法,并且他们开发了干扰的方法这些分子使用其他分子——药物。 It's a strategy that has worked again and again and again. それは何度も何度も働いてきた戦略です。 Это стратегия, которая работала снова и снова и снова. 这是一个一次又一次奏效的策略。 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. Но когда дело доходит до мозга, эта стратегия имеет ограничения, потому что сегодня мы еще недостаточно знаем о том, как работает мозг. Ancak, beyne gelince, bu strateji sınırlıydı, çünkü bugün, beynin nasıl çalıştığını henüz yeterince bilmiyoruz. We need to learn which of our cells matter to each illness, and which molecules in those cells matter to each illness. 私たちは、どの細胞が各病気に重要であり、それらの細胞のどの分子が各病気に重要であるかを知る必要があります。 Нам нужно узнать, какие из наших клеток важны для каждой болезни и какие молекулы в этих клетках важны для каждой болезни. 我们需要了解我们的哪些细胞与每种疾病有关,以及这些细胞中的哪些分子与每种疾病有关。 And that's the mission I want to tell you about today. Ve bugün size anlatmak istediğim görev bu.

My lab develops technologies with which we try to turn the brain into a big-data problem. 私の研究室では、脳をビッグデータの問題に変えようとする技術を開発しています。 Моя лаборатория разрабатывает технологии, с помощью которых мы пытаемся превратить мозг в проблему больших данных. Laboratuvarım, beyni büyük veri problemine dönüştürmeye çalıştığımız teknolojiler geliştiriyor. 我的实验室开发的技术试图将大脑变成一个大数据问题。 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. 生物学者になる前は、コンピューターと数学の分野で働いていました。この教訓を学びました。システムの機能に関する適切な種類のデータを大量に収集できる場所であればどこでも、コンピューターを強力な新しい方法で使用できます。そのシステムを理解し、それがどのように機能するかを学びます。 Видите ли, до того, как я стал биологом, я занимался компьютерами и математикой, и я усвоил этот урок: везде, где вы можете собрать огромное количество нужных данных о функционировании системы, вы можете использовать компьютеры новыми мощными способами для разобраться в этой системе и узнать, как она работает. Today, big-data approaches are transforming ever-larger sectors of our economy, and they could do the same in biology and medicine, too. 今日、ビッグデータのアプローチは、私たちの経済のかつてないほど大きなセクターを変革しており、生物学や医学でも同じことができる可能性があります。 Сегодня подходы к большим данным преобразуют все более крупные сектора нашей экономики, и они могут сделать то же самое в биологии и медицине. Bugün, büyük veri yaklaşımları ekonomimizin daha büyük sektörlerini dönüştürüyor ve biyoloji ve tıpta da aynısını yapabilirler. But you have to have the right kinds of data. ただし、適切な種類のデータが必要です。 Но у вас должны быть правильные виды данных. Ancak doğru veri türüne sahip olmalısınız. You have to have data about the right things. 正しいことに関するデータが必要です。 您必须拥有有关正确事物的数据。 And that often requires new technologies and ideas. そして、それはしばしば新しい技術とアイデアを必要とします。 And that is the mission that animates the scientists in my lab. そして、それが私の研究室の科学者を活気づける使命です。 Ve bu, laboratuardaki bilim insanlarını canlandıran görev.

Today, I want to tell you two short stories from our work. 今日は、私たちの仕事から 2 つの短い話をお話したいと思います。 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. 脳をビッグデータの問題に変えようとするときに直面する根本的な障害の1つは、脳が数十億の細胞で構成され、構築されていることです。 Одно фундаментальное препятствие, с которым мы сталкиваемся, пытаясь превратить мозг в проблему больших данных, заключается в том, что наш мозг состоит из миллиардов клеток и построен из них. Beyni büyük veri sorununa dönüştürmeye çalışırken karşılaştığımız temel engellerden biri de beynimizin milyarlarca hücreden oluşması ve inşa edilmesidir. And our cells are not generalists; they're specialists. そして私たちの細胞はジェネラリストではありません。彼らはスペシャリストです。 И наши клетки не универсальны; они специалисты. Ve hücrelerimiz genelci değildir; onlar uzman. 我们的细胞不是通才;他们是专家。 Like humans at work, they specialize into thousands of different cellular careers, or cell types. 職場の人間のように、彼らは何千もの異なる細胞のキャリア、または細胞型を専門としています。 Подобно людям на работе, они специализируются на тысячах различных клеточных профессий или типов клеток. İş yerindeki insanlar gibi, binlerce farklı hücresel kariyere veya hücre türüne uzmanlaşıyorlar.

In fact, each of the cell types in our body could probably give a lively TED Talk about what it does at work. 実際、私たちの体の各細胞タイプは、その働きについて活発な TED トークを行う可能性があります。 На самом деле, каждый из типов клеток в нашем теле, вероятно, мог бы выступить на TED Talk о том, что они делают на работе. Aslında, vücudumuzdaki hücre türlerinin her biri muhtemelen işte ne yaptığı hakkında canlı bir TED Konuşması verebilir. 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. しかし、科学者である私たちは、今日、細胞の種類がいくつあるかさえ知りませんし、それらの講演のほとんどのタイトルが何であるかも知りません. Ancak bilim insanları olarak bugün kaç tane hücre tipi olduğunu bilmiyoruz ve bu konuşmaların çoğunun başlığının ne olacağını bilmiyoruz. Now, we know many important things about cell types. Теперь мы знаем много важных вещей о типах клеток. They can differ dramatically in size and shape. Они могут кардинально отличаться по размеру и форме. Boyut ve şekil bakımından çarpıcı biçimde farklılık gösterebilirler. One will respond to a molecule that the other doesn't respond to, they'll make different molecules. 一方は、もう一方が応答しない分子に応答し、異なる分子を作成します。 Один будет реагировать на молекулу, на которую другой не реагирует, они будут создавать другие молекулы. But science has largely been reaching these insights in an ad hoc way, one cell type at a time, one molecule at a time. しかし、科学は主に、一度に1つの細胞型、一度に1つの分子というアドホックな方法でこれらの洞察に到達しています。 Но наука в основном достигала этих открытий ситуативным путем, по одному типу клеток, по одной молекуле за раз. Ancak bilim büyük ölçüde bu içgörülere geçici bir şekilde, bir seferde bir hücre tipi, bir seferde bir molekül olarak ulaşmıştır. We wanted to make it possible to learn all of this quickly and systematically. これらすべてを迅速かつ体系的に学習できるようにしたかったのです。 Мы хотели сделать так, чтобы можно было научиться всему этому быстро и систематически.

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. さて、最近まで、脳の一部や臓器のすべての分子の目録を作成したい場合は、最初にそれを一種の細胞スムージーに粉砕する必要がありました。 До недавнего времени считалось, что если вы хотите провести инвентаризацию всех молекул в части мозга или любого органа, вам нужно сначала измельчить их в нечто вроде клеточного коктейля. Şimdi, yakın zamana kadar, beynin bir kısmındaki veya herhangi bir organdaki tüm moleküllerin envanterini çıkarmak istiyorsanız, önce onu bir tür hücresel yüzlü olarak öğütmek zorunda kaldığınız durumdu. But that's a problem. Но это проблема. As soon as you've ground up the cells, you can only study the contents of the average cell -- not the individual cells. Как только вы измельчите клетки, вы сможете изучать содержимое только средней ячейки, а не отдельных ячеек. 一旦你把细胞磨碎了,你就只能研究平均细胞的内容——而不是单个细胞。 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. ニューヨークのような大都市がどのように機能するかを理解しようとしていると想像してみてください。しかし、ニューヨークの平均的な居住者に関するいくつかの統計を確認することによってのみ理解できます。 Представьте, что вы пытаетесь понять, как устроен такой большой город, как Нью-Йорк, но вы можете сделать это, только просмотрев некоторые статистические данные о среднестатистическом нью-йоркце. 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. もちろん、面白くて重要でエキサイティングなものはすべて多様性と専門分野にあるので、あまり学ぶことはありません。 Конечно, многому не научишься, потому что все интересное, важное и увлекательное есть во всем многообразии и специализациях. Tabii ki çok fazla şey öğrenemezsiniz, çünkü ilginç ve önemli ve heyecan verici olan her şey, çeşitlilik ve uzmanlıkların içindedir. And the same thing is true of our cells. 同じことが私たちの細胞にも当てはまります。 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. そして、私たちは脳を細胞のスムージーとしてではなく、細胞のフルーツサラダとして研究できるようにしたかったのです。そこでは、個々の果物についてのデータを生成し、そこから学ぶことができました。 И мы хотели сделать возможным изучение мозга не как клеточного коктейля, а как клеточного фруктового салата, в котором можно было бы получать данные о каждом отдельном кусочке фрукта и учиться на нем. Ve beyni hücresel bir yüzlü olarak değil, her bir meyve parçasından veri üretip öğrenebileceği hücresel bir meyve salatası olarak incelemeyi mümkün kılmak istedik.

So we developed a technology for doing that. You're about to see a movie of it. あなたはそれの映画を見ようとしています。 Вы собираетесь посмотреть фильм об этом. Here we're packaging tens of thousands of individual cells, each into its own tiny water droplet for its own molecular analysis. ここでは、数万個の個別のセルをパッケージ化しており、それぞれが独自の分子分析のために独自の小さな水滴になっています。 Здесь мы упаковываем десятки тысяч отдельных клеток, каждую в свою крошечную капельку воды для собственного молекулярного анализа. Burada, her biri kendi moleküler analizi için kendi küçük su damlacıklarına yerleştirilen onbinlerce ayrı hücreyi paketliyoruz. When a cell lands in a droplet, it's greeted by a tiny bead, and that bead delivers millions of DNA bar code molecules. 細胞が液滴に着地すると、小さなビーズがそれを迎え、そのビーズは何百万ものDNAバーコード分子を送達します。 Когда клетка попадает в каплю, ее встречает крошечная бусинка, и эта бусинка доставляет миллионы молекул штрих-кода ДНК. Bir hücre bir damlacık içine indiğinde, küçük bir boncuk tarafından karşılanır ve bu boncuk milyonlarca DNA barkod molekülü sunar. 当细胞落在液滴中时,它会受到一个微小的珠子的欢迎,该珠子会传递数百万个 DNA 条形码分子。 And each bead delivers a different bar code sequence to a different cell. また、各ビーズは異なるバーコードシーケンスを異なるセルに配信します。 И каждая бусина доставляет разные последовательности штрих-кода в разные клетки. Ve her boncuk farklı bir hücreye farklı bir barkod dizisi sunar. We incorporate the DNA bar codes into each cell's RNA molecules. DNAバーコードを各細胞のRNA分子に組み込みます。 Мы включаем штрих-коды ДНК в молекулы РНК каждой клетки. DNA barkodlarını her bir hücrenin RNA moleküllerine dahil ediyoruz. Those are the molecular transcripts it's making of the specific genes that it's using to do its job. それらは、その仕事をするために使用している特定の遺伝子から作られている分子転写物です. Это молекулярные транскрипты определенных генов, которые он использует для выполнения своей работы. Bunlar, işini yapmak için kullandığı spesifik genleri ürettiği moleküler transkriptlerdir. 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. そして、何十億ものこれらの結合した分子の配列を決定し、その配列を使用して、各分子がどの細胞とどの遺伝子に由来するかを特定します。 А затем мы секвенируем миллиарды этих объединенных молекул и используем последовательности, чтобы сказать нам, из какой клетки и из какого гена произошла каждая молекула. Ve sonra bu birleşik moleküllerin milyarlarca dizisini dizeriz ve dizileri, her bir molekülün hangi hücreden ve hangi genden geldiğini söylemek için kullanırız.

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. 私たちはこのアプローチを「Drop-seq」と呼んでいます。これは、分析のために液滴を使用して細胞を分離し、DNA シーケンスを使用してタグを付け、インベントリを作成し、すべてを追跡するためです。 Мы называем этот подход «Drop-seq», потому что мы используем капли для разделения клеток для анализа, и мы используем последовательности ДНК для маркировки, инвентаризации и отслеживания всего. Bu yaklaşıma "Drop-seq" diyoruz çünkü analiz için hücreleri ayırmak için damlacıklar kullanıyoruz ve DNA dizilerini etiketlemek, envanter yapmak ve her şeyi takip etmek için kullanıyoruz. And now, whenever we do an experiment, we analyze tens of thousands of individual cells. そして今、私たちは実験を行うたびに、何万もの個々の細胞を分析しています. И теперь всякий раз, когда мы проводим эксперимент, мы анализируем десятки тысяч отдельных клеток. 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. そして今日、科学のこの分野では、これらの膨大なデータセットからできるだけ多くのことをできるだけ早く学ぶ方法がますます課題になっています。 И сегодня в этой области науки все чаще возникает проблема, как узнать как можно больше и как можно быстрее из этих обширных наборов данных. Ve bugün bu bilim alanında, bu büyük veri setlerinden olabildiğince çabuk öğrenebileceğimiz zorluk giderek artmaktadır.

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. 私たちが Drop-seq を開発していたとき、人々はよくこう言いました。 Когда мы разрабатывали Drop-seq, люди обычно говорили нам: «О, это сделает вас, ребята, подходящими для каждого крупного мозгового проекта. " That's not how we saw it. 「それは私たちが見た方法ではありません。 “Gördüğümüz gibi değil. Science is best when everyone is generating lots of exciting data. 科学は、誰もが多くの刺激的なデータを生成しているときに最適です。 So we wrote a 25-page instruction book, with which any scientist could build their own Drop-seq system from scratch. そこで、科学者が独自の Drop-seq システムをゼロから構築できるように、25 ページの説明書を作成しました。 Поэтому мы написали 25-страничную инструкцию, с помощью которой любой ученый мог создать свою собственную систему Drop-seq с нуля. And that instruction book has been downloaded from our lab website 50,000 times in the past two years. そして、その教則本は、過去 2 年間で 50,000 回、研究室の Web サイトからダウンロードされました。 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. そして、何百ものラボが、このアプローチを使用して行った発見について私たちに書いています. Today, this technology is being used to make a human cell atlas. 現在、この技術はヒト細胞アトラスの作成に使用されています。 Сегодня эта технология используется для создания атласа клеток человека. Günümüzde bu teknoloji insan hücre atlası yapmak için kullanılıyor. 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. これは、人体のすべての細胞タイプと、各細胞タイプがその仕事を行うために使用する特定の遺伝子のアトラスになります。 Это будет атлас всех типов клеток человеческого тела и конкретных генов, которые каждый тип клеток использует для выполнения своей работы. İnsan vücudundaki tüm hücre tiplerinin bir atlası ve her hücre tipinin işini yapmak için kullandığı spesifik genler olacaktır.

Now I want to tell you about a second challenge that we face in trying to turn the brain into a big data problem. ここで、脳をビッグデータの問題に変えようとする際に直面する2番目の課題についてお話ししたいと思います。 Теперь я хочу рассказать вам о второй проблеме, с которой мы сталкиваемся, пытаясь превратить мозг в проблему больших данных. Şimdi size beyni büyük bir veri sorununa dönüştürmeye çalışırken karşılaştığımız ikinci bir zorluktan bahsetmek istiyorum. And that challenge is that we'd like to learn from the brains of hundreds of thousands of living people. そして、その課題は、何十万人もの生きている人々の脳から学びたいということです。 Ve bu zorluk, yüz binlerce yaşayan insanın beyinlerinden öğrenmek istiyoruz. But our brains are not physically accessible while we're living. Aber unser Gehirn ist physisch nicht zugänglich, solange wir leben. しかし、私たちが生きている間、私たちの脳に物理的にアクセスすることはできません。 Но наш мозг физически недоступен, пока мы живем. But how can we discover molecular factors if we can't hold the molecules? しかし、分子を保持できない場合、どうすれば分子因子を発見できるでしょうか。 Ama molekülleri tutamazsak moleküler faktörleri nasıl keşfedebiliriz? 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. 答えは、最も有益な分子であるタンパク質が私たちのDNAにコード化されているという事実から来ています。これには、私たちの細胞がすべてのタンパク質を作るために従うレシピがあります。 Ответ исходит из того факта, что наиболее информативные молекулы, белки, закодированы в нашей ДНК, в которой есть рецепты, которым наши клетки следуют, чтобы производить все наши белки. Bir cevap, en bilgilendirici moleküller olan proteinlerin DNA'mızda kodlanmış olmalarından kaynaklanmaktadır, bu da hücrelerimizin tüm proteinlerimizi yapmak için takip ettiği tarifleri içermektedir. 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. そして、これらのレシピは、タンパク質が正確な順序で人から人へと変化するように、そして各細胞型が各タンパク質をどれだけ作るかという点で、人から人へと変化します。 И эти рецепты варьируются от человека к человеку таким образом, что белки различаются от человека к человеку в их точной последовательности и в том, сколько каждый тип клеток производит из каждого белка. Ve bu tarifler, kişiden kişiye, proteinlerin insanlardan insana kesin sekanslarında ve her bir hücre tipinin her bir proteinden ne kadarının yapılacağına göre değişmesine neden olacak şekilde değişir. It's all encoded in our DNA, and it's all genetics, but it's not the genetics that we learned about in school. それはすべて私たちのDNAにコード化されており、すべて遺伝学ですが、学校で学んだ遺伝学ではありません. Все это закодировано в нашей ДНК, и это все генетика, но это не та генетика, о которой мы узнали в школе.

Do you remember big B, little b? If you inherit big B, you get brown eyes? ビッグBを受け継ぐと瞳が茶色に? Büyük B miras alırsan, kahverengi gözlerin olur mu? It's simple. Very few traits are that simple. それほど単純な特性はほとんどありません。 Очень немногие черты настолько просты. Çok az özellik bu kadar basit. Even eye color is shaped by much more than a single pigment molecule. Даже цвет глаз формируется не одной молекулой пигмента. Göz rengi bile tek bir pigment molekülünden çok daha fazla şekillenir. And something as complex as the function of our brains is shaped by the interaction of thousands of genes. そして、私たちの脳の機能と同じくらい複雑なものは、何千もの遺伝子の相互作用によって形作られています. И такая сложная вещь, как функция нашего мозга, формируется взаимодействием тысяч генов. Beynimizin işlevi kadar karmaşık bir şey de binlerce genin etkileşimi ile şekillenir. And each of these genes varies meaningfully from person to person to person, and each of us is a unique combination of that variation. そして、これらの遺伝子のそれぞれは人から人へと意味のある変化をします、そして私たちのそれぞれはその変化のユニークな組み合わせです。 И каждый из этих генов значительно варьируется от человека к человеку, и каждый из нас представляет собой уникальную комбинацию этих вариаций. Ve bu genlerin her biri anlamlı olarak kişiden kişiye değişir ve her birimiz bu varyasyonun benzersiz bir birleşimidir. It's a big data opportunity. それはビッグデータの機会です。 And today, it's increasingly possible to make progress on a scale that was never possible before. そして今日、これまで不可能だった規模で進歩を遂げることがますます可能になっています。 И сегодня становится все более возможным добиться прогресса в таких масштабах, которые раньше были невозможны. People are contributing to genetic studies in record numbers, and scientists around the world are sharing the data with one another to speed progress. 記録的な数の人々が遺伝子研究に貢献しており、世界中の科学者がデータを共有して進歩を加速させています。 Люди вносят свой вклад в генетические исследования в рекордных количествах, а ученые всего мира обмениваются данными друг с другом, чтобы ускорить прогресс. İnsanlar rekor sayılarda genetik çalışmalara katkıda bulunuyorlar ve dünyadaki bilim insanları ilerlemeyi hızlandırmak için verileri birbirleriyle paylaşıyorlar.

I want to tell you a short story about a discovery we recently made about the genetics of schizophrenia. 統合失調症の遺伝学について私たちが最近行った発見について、短い話をしたいと思います。 It was made possible by 50,000 people from 30 countries, who contributed their DNA to genetic research on schizophrenia. それは統合失調症の遺伝子研究に彼らのDNAを貢献した30カ国からの50,000人の人々によって可能になりました。 DNA'larını şizofreni konusunda genetik araştırmalara katkıda bulunan 30 ülkeden 50.000 insan tarafından mümkün kılındı. 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. 統合失調症のリスクに対するヒトゲノムの最大の影響は、免疫系の分子の多くをコードするゲノムの一部に由来することが数年前から知られていました. Уже несколько лет было известно, что наибольшее влияние человеческого генома на риск шизофрении оказывает часть генома, которая кодирует многие молекулы нашей иммунной системы. But it wasn't clear which gene was responsible. しかし、どの遺伝子が原因であるかは明らかではありませんでした。 Но было неясно, какой ген был ответственным. A scientist in my lab developed a new way to analyze DNA with computers, and he discovered something very surprising. 私の研究室の科学者が、コンピューターで DNA を分析する新しい方法を開発し、非常に驚くべきことを発見しました。 Laboratuvarımdaki bir bilim adamı, DNA'yı bilgisayarlarla analiz etmek için yeni bir yöntem geliştirdi ve çok şaşırtıcı bir şey keşfetti. 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. 彼は、「補体成分4」(略して「C4」と呼ばれる)と呼ばれる遺伝子が、さまざまな人々のゲノムに数十の異なる形態で存在し、これらのさまざまな形態が私たちの脳にさまざまな量のC4タンパク質を作ることを発見しました。 Он обнаружил, что ген под названием «компонент комплемента 4» — для краткости он называется «С4» — встречается в десятках различных форм в геномах разных людей, и эти разные формы производят различное количество белка С4 в нашем мозгу. And he found that the more C4 protein our genes make, the greater our risk for schizophrenia. そして彼は、私たちの遺伝子が作るC4タンパク質が多いほど、統合失調症のリスクが高くなることを発見しました. И он обнаружил, что чем больше белка С4 производят наши гены, тем выше риск развития шизофрении.

Now, C4 is still just one risk factor in a complex system. 現在、C4 は依然として複雑なシステムにおけるリスク要因の 1 つにすぎません。 Теперь C4 по-прежнему является лишь одним из факторов риска в сложной системе. Şimdi, C4 hala karmaşık bir sistemde sadece bir risk faktörüdür. This isn't big B, but it's an insight about a molecule that matters. Das ist kein großes B, aber es ist ein Einblick in ein Molekül, das wichtig ist. これは大きなBではありませんが、重要な分子についての洞察です。 Это не большая Б, но это понимание молекулы, которая имеет значение. Bu büyük B değil, ama önemli olan bir molekül hakkında bir içgörü. 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. C4 のような補体タンパク質は、免疫系での役割が長い間知られており、「私を食べてください。 Белки комплемента, такие как С4, давно известны своей ролью в иммунной системе, где они действуют как своего рода молекулярные стикеры, на которых написано: «Съешь меня. C4 gibi tamamlayıcı proteinler, bağışıklık sistemindeki rolleriyle uzun süredir biliniyordu, burada bir tür moleküler Post-it notu yazıyorlar, "Ye beni. " And that Post-it note gets put on lots of debris and dead cells in our bodies and invites immune cells to eliminate them. 「そして、そのポストイットは、私たちの体内のたくさんの破片や死んだ細胞に貼り付けられ、免疫細胞にそれらを排除するように促します. «И эта записка Post-it приклеивается к большому количеству мусора и мертвых клеток в наших телах и побуждает иммунные клетки уничтожать их. But two colleagues of mine found that the C4 Post-it note also gets put on synapses in the brain and prompts their elimination. しかし、私の 2 人の同僚は、C4 ポストイット ノートも脳内のシナプスに配置され、シナプスの排除を促すことを発見しました。 Но два моих коллеги обнаружили, что стикеры C4 Post-it также воздействуют на синапсы в мозгу и способствуют их ликвидации. Ancak iki meslektaşım, C4 Post-it notunun aynı zamanda beyindeki sinapslara büründüğünü ve ortadan kaldırılmasını istediğini buldu. Now, the creation and elimination of synapses is a normal part of human development and learning. 現在、シナプスの生成と除去は、人間の発達と学習の正常な部分です。 Теперь создание и устранение синапсов является нормальной частью человеческого развития и обучения. Şimdi, sinapsların yaratılması ve ortadan kaldırılması, insani gelişmenin ve öğrenmenin normal bir parçasıdır. Our brains create and eliminate synapses all the time. Beyinlerimiz her zaman sinaps oluşturur ve yok eder. But our genetic results suggest that in schizophrenia, the elimination process may go into overdrive. Aber unsere genetischen Ergebnisse deuten darauf hin, dass bei Schizophrenie der Eliminationsprozess auf Hochtouren laufen kann. しかし、私たちの遺伝的結果は、統合失調症では、排除プロセスがオーバードライブになる可能性があることを示唆しています. Но наши генетические результаты говорят о том, что при шизофрении процесс элиминации может идти с перегрузкой. Ancak genetik sonuçlarımız şizofrenide, eleme sürecinin aşırı hızlanabileceğini göstermektedir.

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. 多くの製薬会社の科学者は、免疫系の補体タンパク質に何年も取り組んできて、その働きについて多くのことを学んだので、この発見に興奮していると私に言いました. Ученые многих фармацевтических компаний говорят мне, что они в восторге от этого открытия, потому что они много лет работали над белками комплемента в иммунной системе и многое узнали о том, как они работают. Birçok ilaç şirketindeki bilim adamları bana bu keşiften heyecanlandıklarını söylüyorlar, çünkü bağışıklık sisteminde yıllarca tamamlayıcı proteinler üzerinde çalışıyorlar ve nasıl çalıştıkları hakkında çok şey öğrendiler. 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. 彼らは補体タンパク質に干渉する分子を開発し、免疫系だけでなく脳内でそれらをテストし始めています. Они даже разработали молекулы, которые взаимодействуют с белками комплемента, и начинают тестировать их в мозге, а также в иммунной системе. 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. これは、個々の症状ではなく根本原因に対処する可能性のある薬への道である可能性があり、多くの科学者による長年にわたるこの研究が成功することを強く願っています. Potansiyel bir semptomdan ziyade bir kök nedene değinebilecek bir ilaca doğru bir yol olabilir ve bu çalışmanın uzun yıllar boyunca birçok bilim insanının başarılı olacağını umuyoruz.

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. しかし、C4 は、何世紀にもわたる医学的問題に新たな前線を開くデータ駆動型の科学的アプローチの可能性の一例にすぎません。 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. 私たちのゲノムには、脳疾患のリスクを形成する場所が何百もあり、それらのいずれかが、重要な分子に関する次の分子的洞察につながる可能性があります。 В нашем геноме есть сотни мест, которые формируют риск заболеваний головного мозга, и любое из них может привести нас к следующему молекулярному пониманию важной молекулы. Genomumuzda beyin hastalıkları riskini şekillendiren yüzlerce yer var ve bunların herhangi biri bizi önemli olan bir molekül hakkında bir sonraki moleküler görüşe yönlendirebilir. And there are hundreds of cell types that use these genes in different combinations. そして、これらの遺伝子をさまざまな組み合わせで使用する細胞型が何百もあります。 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. 私たちと他の科学者は、必要な残りのデータを生成し、そのデータからできることをすべて学ぶために取り組んでおり、さらに多くの新しい分野を開くことを望んでいます. По мере того, как мы и другие ученые работают над созданием остальных необходимых данных и изучением всего, что мы можем из этих данных, мы надеемся открыть еще много новых фронтов. Genetics and single-cell analysis are just two ways of trying to turn the brain into a big data problem. 遺伝学と単一細胞解析は、脳をビッグデータの問題に変えようとする 2 つの方法にすぎません。 Генетика и анализ отдельных клеток — это всего лишь два способа превратить мозг в проблему больших данных. Genetik ve tek hücreli analiz, beyni büyük bir veri problemine dönüştürmeye çalışmanın sadece iki yoludur.

There is so much more we can do. Мы можем сделать гораздо больше. Yapabileceğimiz daha çok şey var. 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. 私の研究室の科学者は、脳内のシナプス接続を迅速にマッピングして、どのニューロンが他のどのニューロンと話しているか、そしてその会話が生涯や病気の間にどのように変化するかを知る技術を開発しています. Ученые в моей лаборатории создают технологию для быстрого картирования синаптических связей в мозге, чтобы определить, какие нейроны общаются с какими другими нейронами и как этот диалог меняется на протяжении жизни и во время болезни. 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. そして、何百もの異なる人々のゲノムを持つ細胞が、同じ刺激に対してどのように異なる反応をするかを、単一のチューブでテストする方法を開発しています. И мы разрабатываем способ проверить в одной пробирке, как клетки с геномами сотен разных людей по-разному реагируют на один и тот же раздражитель. These projects bring together people with diverse backgrounds and training and interests -- biology, computers, chemistry, math, statistics, engineering. These projects bring together people with diverse backgrounds and training and interests -- biology, computers, chemistry, math, statistics, engineering. これらのプロジェクトでは、生物学、コンピューター、化学、数学、統計学、工学など、さまざまなバックグラウンド、トレーニング、関心を持つ人々が集まります。 Эти проекты объединяют людей с разным опытом, образованием и интересами — биология, компьютеры, химия, математика, статистика, инженерия. But the scientific possibilities rally people with diverse interests into working intensely together. しかし、科学の可能性は、さまざまな関心を持つ人々を結集させて、熱心に協力して取り組んでいます。 Но научные возможности сплачивают людей с разными интересами для интенсивной совместной работы. Ancak, bilimsel olanaklar, farklı ilgi alanlarına sahip insanları yoğun bir şekilde birlikte çalışmaya itiraz ediyor.

What's the future that we could hope to create? 私たちが望むことができる未来は何ですか? Какое будущее мы могли бы создать? Yaratmayı umabileceğimiz gelecek nedir? Consider cancer. がんを考えてみましょう。 Возьмем рак. Kanseri düşünün. 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. 私たちは、がんが一般的に個人の心理的特性に起因すると考えられていたがんの原因についての無知の時代から、がんの真の生物学的原因の現代の分子的理解へと移行しました. Мы перешли от эпохи незнания того, что вызывает рак, когда рак обычно приписывали личным психологическим характеристикам, к современному молекулярному пониманию истинных биологических причин рака. Kanserin genel olarak kişisel psikolojik özelliklere atfedildiği kansere neyin sebep olduğu konusundaki cehalet çağından kanserin gerçek biyolojik nedenlerinin modern bir moleküler anlayışına geçtik. 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. 今日のこの理解は、革新的な医療に次ぐ革新的な医療につながっています。まだやるべきことはたくさんありますが、私たちの周りには、一世代前には治療不可能と考えられていたがんを治した人々がすでにいます。 Это понимание сегодня приводит к инновационной медицине за инновационной медициной, и хотя предстоит еще так много работы, мы уже окружены людьми, излечившимися от рака, который поколение назад считалось неизлечимым. Bugün bu anlayış, yenilikçi tıbbın ardından yenilikçi tıbbın ortaya çıkmasına neden olmakta ve hala yapılacak çok iş olmasına rağmen, bir kuşak önce tedavi edilemeyen olarak kabul edilen kanserleri tedavi eden insanlar tarafından kuşatıldık. 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. そして、私の妹のような何百万人ものがんサバイバーが、当たり前とは思っていなかった何年もの人生を送り、仕事や喜び、人とのつながりを得る新しい機会を手にしています。 И миллионы выживших после рака, таких как моя сестра, обретают годы жизни, которые они не считали само собой разумеющимися, и новые возможности для работы, радости и человеческого общения. That is the future that we are determined to create around mental illness -- one of real understanding and empathy and limitless possibility. それこそが、私たちが精神疾患を中心に創造しようと決意している未来です。それは、真の理解と共感、そして無限の可能性の 1 つです。 Это будущее, которое мы полны решимости создать вокруг психических заболеваний — будущее настоящего понимания, сочувствия и безграничных возможностей. Gerçek bir anlayış ve empati ve sınırsız olasılıklardan biri olan zihinsel hastalıklar etrafında yaratmaya kararlı olduğumuz gelecek budur.

Thank you. Thank you.

(Applause) (Applause)