高通量单细胞表达谱分析

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http://phys.org/news/2015-05-ave ... y-barcode-tens.html
  M' k$ ^4 e* U: Z8 r6 jBeyond average: New platforms genetically barcode tens of thousands of cells at7 w1 z9 \+ K- k; {1 V' s) V! a
假如有人给你一杯慕斯让说出里面所有的成分。你也许可以辨别出草莓或酸奶，但是无法辨别其他一些混合配料成分。现在想象这杯慕斯是由20000个细胞组成的，你可以像科学家一样通过科学的手段对这些细胞进行分析，确定里面的分子组成。这些分析会给出一些有用的信息，即组成这杯慕斯的混合细胞的平均组分，不能够确定单个细胞的组分，即不能确定每个细胞的异质性。/ _$ t  ^* r' {1 Q" \, u
当我们需要知道一份人体组织的信息时，这样的平均化分析结果会误导结论的产生。正如你所知道的没有一个“平均”的食物叫做strawbanaspinach-orangegurt。, b5 i# P2 p! J* }
科学家们知道大脑并不是由一种细胞组成的。 Marc Kirschner, the John Franklin Enders University Professor of Systems Biology and chair of the Department of Systems Biology at Harvard Medical School说：“如果你把一大块组织磨碎，然后分析RNA的表达，你将不知道这个结果来自组织中的每个细胞还是个别细胞。假定有一群男人和女人，你算出这个群体的性别是一半男一半女，你不能根据这个结果判断具体某个人是男还是女。
. Q) ?, v. I* S# I问题是要筛选出组织中的特定细胞或者细胞群是一件昂贵而又费时费力的事。Kirschner 和 Steven McCarroll, assistant professor of genetics at HMS最近分别发表了他们实验室的研究成果，即开发了一项高通量测序技术避免细胞混合，能够快速，轻松，经济地得到样品中单一细胞的遗传条形码。因此，面对复杂的组织细胞，科学家们可以分析获得组织中每个细胞的信息，而不是获得一个平均化的分析结果。9 u) K5 V3 |" i6 E" ^
McCarroll, who is also director of genetics for the Stanley Center for Psychiatric Research at the Broad Institute of Harvard and MIT 说：”组织中的不同细胞使用相同的基因组却产生不同的存在形式：形成特定的细胞形态，完成不同的生理功能，对相同的刺激做出不同的反应。这一技术将让科学家了解单细胞水平的生物系统。对于未来的工作，我们感到非常兴奋。”+ Q; O2 R& ^7 y
为了建立他们的系统，两个实验小组都 David Weitz, the Mallinckrodt Professor of Physics and Applied Physics at Harvard's School of Engineering and Applied Sciences 一个微流体领域的先驱进行了合作（一个人同时和两个做相同技术的团队合作，是不是不太合适呀？）。
4 h5 ]# ?2 q7 w  |. b  E他们的成果同时发表在cell杂志上，他们希望他们的技术可以使生命科学工作者能够更深入地了解机体内细胞的组成，比如描绘出大脑细胞的组成，解析干细胞的分化和遗传疾病等问题。
+ p7 G# ?/ t+ O3 ]" Z哈佛的技术开发办公室和研究人员有着密切的合作，他们为各种技术提供专利申请，从而使这些技术能够快速地商业化。
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5 _! D0 j8 }4 M- C“黄色树林分出两条路”* t. D9 S5 R4 f6 v) ]% X
Evan Macosko 和Allon Klein几年前在微流体课上见过面，然后，他们各奔东西。他们接下来的研究是瞒着对方进行的，他们决定开发一种方法来解决一个同一个问题：如何对组织中成千上万个单细胞进行基因表达分析，从而更好地了解组织内的基因表达的复杂性。
; Q; T# S6 a# R, f生命的每个过程都需要基因表达，特定的细胞激活特定的基因。从认知的大脑到受精卵的发育。50多年前科学家们就已经知道了每个细胞的基因表达都是不一样的，就像每个人的指纹，使得皮肤细胞和肝细胞不同，肝细胞和其他细胞也不同。但他们无法对多​​细胞类型组成的样品中的单细胞进行有效的基因表达分析。
7 W* ]3 J5 [# Z! RMacosko, HMS instructor in psychiatry at Massachusetts General Hospital and a Stanley Neuroscience Fellow in the McCarroll lab 想出了一个方法，他称之为Drop-seq。Klein, assistant professor of systems biology at HMS, 建立了另一种方法，他称为indexing droplets for sequencing, or inDrops。+ d" N# m3 C9 n* N- M% f
去年秋天，他们在科学会议上了解了对方的工作。Macosko说：“这就像遇到了自己的分身。他想的问题也是我这两年一直思考的问题。人类可以通过不同的途径解决相同的问题，看到他的成果我觉得非常有意思。”5 w" K- g6 [( `6 h, d+ @5 C
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他们是如何工作的？
# N! f; b1 R  L; O0 o每一个团队开发利用微珠可以携带大量不同的DNA条形码同时进入几十万纳米尺寸的液滴中。因为Weitz's 的加入，两个团队都是用了微流装置将细胞包裹到含有微珠的液滴中，液滴是在一个很小的装配线上形成的，液流沿着发丝宽度的通道前进。珠子上的基因条形码结合每个细胞中的基因，科学家就可以对每个珠子上结合的所有基因进行测序，并且追踪基因表达的单个细胞。
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9 F% I, J, e# c( sMacosko 和 Klein的技术存在一些差异，比如制造珠子的方法不同，并且在实验过程中，液滴的破裂发生在不同的阶段，其他的差异还包括chemistry diverge。但这两种技术的结果是相同的。
. K5 N* Y' Y- V1 v; f: m使用 Drop-seq or inDrops对一批单细胞进行分析后，科学家可以看到样品的基因表达情况，并能对其中的每个单细胞进行分析。他们可以使用软件分析发现基因表达的规律，比如某些细胞具有相似的基因表达，这提供了一种新的分类方法来判定原始组织的细胞组成，并有可能发现新的细胞类型。/ k, D* H: v" w$ d' S5 P: l
目前，研究人员使用其他技术分析96个单细胞基因表达谱需要一天时间并花费几千美元，相比之下使用Drop-seq一天可以分析10000个细胞的表达谱，单价是6.5每份。" a3 B9 t$ h8 F  w
Macosko说：”如果你是一个在考虑一个有趣的问题的生物学家，这种方法可以让你眼前一亮而不让你破产。它终于使单细胞水平的基因表达谱分析变得容易。我认为这是很多不同领域生物学家梦寐以求的技术。”
  q5 P8 y# E  g他们认为他们的技术并不是互相竞争的，有两个可用的选项有利于科学界的发展。
8 W! O* D5 H4 X# v& r( TMacosko说：“每一种方法都有其独特的特点，使得它们有各自的擅长的领域。生物学家将选择最适合他们的技术。”* R' y) X: ]6 a* o( J) ]; d

  s; l1 ?0 X) H: U; P不同的目标
+ w' H4 o0 C/ B+ P$ |2 _4 eMcCarroll，Macosko和他们的同事想用Drop-seq探索大脑。) I6 @6 g# P; M; a
幸运的话，他们将发现新的细胞类型，并将这些细胞定位到大脑结构中，解析大脑的发育和功能，因为很多疾病于此相关。0 A1 c5 I. G/ ~1 K8 ~
他们想要追寻的答案是：是哪些类型的细胞构成了大脑并使其发挥作用？为什么这些类型不同的细胞面对刺激具有不同的功能和反应？哪些细胞的丢失或功能异常会导致精神分裂症，孤独症和脑部其它病症？! g$ Q  d. R8 W0 C% B, n" q; i% p; t
Joshua Sanes, the Jeff C. Tarr Professor of Molecular and Cellular Biology and the Paul J. Finnegan Family Director of the Center for Brain Science at Harvard University and a co-author of the Drop-seq paper 说：“细胞类型分析看上去不是那么诱人，但它奠定绘制神经回路的研究，使我们在未来的某一天将能够探索湿漉漉的大脑是如何产生了想法，情绪和行为的。
5 G/ E8 Y6 m4 n" U- H近期，Sanes在完善小鼠视网膜细胞类型的目录。目前已经发现了一些新的细胞组成类型。. {# Z# S/ U8 k& Z5 p/ I

* D5 i& d) N" n5 a. YKirschner, Klein和他的合作者专注于其他领域，包括干细胞的发育。: ~; f/ d" {* h4 V
Klein 想要知道：“我们最初认为均一的细胞是不是存在一些亚群。”他试图通过对免疫细胞及各种成体干细胞的研究找出答案。 “一个早期发育中的干细胞具有什么性质？是什么赋予这些细胞多能性？是它具有更加可塑性的基因表达还是存在一个特定的表达谱让它与成熟的细胞相区别。它的命运是如何决定的？”
1 y7 q$ j" J. Z- g- PKlein和他的研究团队使用inDrops已经证实先前的发现，即即使是胚胎干细胞也是不一致的。他们在研究样品中发现了以前未被发现的细胞类型，以及细胞的中间阶段，它们可能从一种细胞类型转变为另一种。, q9 o+ p' P( {# @
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虽然两个研究团队 对Drop-seq 和 inDrops将给他们和其他研究者带来的大量研究数据兴奋不已，但是数据的分析是一个大的问题。
1 }! W* t+ L3 z; kKlein说：“我们有成千上万的细胞表达数万个基因，我们不可能同时看20,000个不同的方向来挑出有兴趣的问题，”( R) I1 Q( i# [& [4 R
电脑是一个很好的解决手段，研究团队已经采用新的统计方法。尽管如此， Kirschner已经召集了一些数学和电脑专家开发新的思路来分析和挖掘获得的海量生物信息学数据。) W% {! g  r  |
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; x+ ?7 `" x( |& d5 zBeyond average: New platforms genetically barcode tens of thousands of cells at a time- D" N, ?3 e$ }( a

  l1 A. S! z( w3 [8 M- ^8 ~Imagine someone hands you a smoothie and asks you to identify everything that went into it.
3 h3 T6 p9 W4 {- w+ t9 P3 UYou might be able to discern a hint of strawberry or the tang of yogurt. But overall it tastes like a blend of indiscernible ingredients.& h5 {$ }# V! g. w, g
Now imagine that the smoothie is made of 20,000 ground-up cells from, say, the brain.
- `- f( O; v: T5 s$ t1 h  S! zYou could run tests to determine what molecules are in the sample, which is what scientists do now. That would certainly give you useful information, but it wouldn't tell you which cells those molecules originally came from. It would provide only an average cell profile for the whole smoothie.+ R: U* e0 v2 @' K; ?% \
And when it comes to the tissues in our bodies, averages are almost always misleading. Just as you know there isn't an "average" food called strawbanaspinach-orangegurt, scientists know there isn't just one cell type in the brain.! {" w( Q2 b  ]- D" |2 [/ z" w
"If you take a hunk of tissue and grind it up and analyze the RNA, you have no idea if it represents what every cell in that population is doing or what no cell in the population is doing," said Marc Kirschner, the John Franklin Enders University Professor of Systems Biology and chair of the Department of Systems Biology at Harvard Medical School. "Imagine if you had a population of men and women. If you assume everyone is an average of men and women, you [probably] wouldn't represent a single person in that population."
0 w( z  D% L* j( tThe trouble is, it's expensive, time-consuming and tricky to characterize tissues one cell, or cell type, at a time.
  A! L1 A" O! n0 d; PKirschner and Steven McCarroll, assistant professor of genetics at HMS, reported this week in separate papers that their labs have developed high-throughput techniques to quickly, easily and inexpensively give every cell in a sample a unique genetic barcode before it goes into the blender.
( {- T5 g( j" ?9 `. Y. N3 uAs a result, scientists can analyze complex tissues by profiling each individual cell—no averaging required.+ d- C. A( y0 n+ R+ z# @3 q
"Different cells in a tissue use the same genome in amazingly diverse ways: to engineer specialized cell shapes, accomplish diverse feats of physiology, and mount distinct functional responses to the same stimulus. These techniques will finally let science understand how biological systems operate at that single-cell level," said McCarroll, who is also director of genetics for the Stanley Center for Psychiatric Research at the Broad Institute of Harvard and MIT. "We are so excited about the work ahead.”
7 e0 p# |; p  n  ]* _4 A/ WTo make their tools, both teams collaborated with David Weitz, the Mallinckrodt Professor of Physics and Applied Physics at Harvard's School of Engineering and Applied Sciences and a pioneer in the field of microfluidics.
! m2 {, J: P, ?9 f4 n: U- MThe teams expect that their techniques, published concurrently in the journal Cell, will equip biologists to discover and classify cell types in the body in much greater depth, map cell diversity in complex tissues such as the brain, better understand stem cell differentiation and gain more insights into the genetics of disease.
9 W$ L  [! ^  S- \Harvard's Office of Technology Development has been working closely with the researchers to develop patent applications for various aspects of the technology, all with an eye toward commercialization.
0 r& G! f0 C& A5 p2 }- t0 o0 u'Two roads diverged in a yellow wood'; Z$ Q$ _1 b) o& n" a- H$ m7 ~' i
Evan Macosko and Allon Klein met in a microfluidics class a few years ago. Then they went their separate ways.) a' a0 M; g5 y) t) d  P" P
Unbeknownst to each other, they decided to develop methods to answer the same question: How could they obtain gene expression profiles for thousands of individual cells to better understand the complexity of gene expression within a tissue?
. X# O7 J. n6 b' W% p% j+ o' H2 iGene expression—the pattern of gene activity in a particular cell—underlies every process in biology, from cognition in the brain to development in the egg. Scientists have known for 50 years that gene expression varies from cell to cell like a fingerprint, making skin cells different from liver cells and making some liver cells different from others. But they haven't been able to measure it efficiently at the single-cell level in samples with many cell types.: j# R" ?3 k- e0 `1 R0 e: C4 H
Macosko, HMS instructor in psychiatry at Massachusetts General Hospital and a Stanley Neuroscience Fellow in the McCarroll lab, came up with a technique he called Drop-seq. Klein, assistant professor of systems biology at HMS, devised a method he called indexing droplets for sequencing, or inDrops.3 x! A* A* [: N
Last fall, they learned about each other's work through the scientific conference circuit.
3 o1 V. e0 j3 ^2 O7 F, A$ `9 b7 @8 X"It was kind of like meeting your doppelgänger," said Macosko. "He had been thinking about the same things I had for two years. Human beings have different ways of solving problems, and it was really cool to see how he did it."
# c1 v6 v$ \) Q4 A& \" i& sHow they work
+ b; f* `3 V& v' ]The teams each developed ways of using tiny beads to deliver vast numbers of different DNA barcodes into hundreds of thousands of nanometer-sized water droplets simultaneously.
  r9 b0 i8 M' K4 dThanks to Weitz's expertise, both methods were able to use microfluidic devices to co-encapsulate cells in these droplets along with the beads. The droplets get created in a tiny assembly line, streaming along a channel the width of a human hair.) i5 m: N% p: \% }
The bead barcodes get attached to the genes in each cell, so that scientists can sequence the genes all in one batch and still trace each gene back to the cell it came from." c6 R6 ~  [, ^7 R5 K
Macosko and Klein make their beads in different ways. The droplets get broken up at different steps in the process. Other aspects of the chemistry diverge. But the result is the same." T6 {8 R0 l% M. A
After running a single batch of cells through Drop-seq or inDrops, scientists "can see which genes are expressed in the entire sample—and can sort by each individual cell," said Klein.# @5 ^0 Q- g. v$ P
They can then use computer software to uncover patterns in the mix, including which cells have similar gene expression profiles. That provides a way to classify what cell types were in the original tissue—and to possibly discover new ones." K$ n: P, b! d- b, ?
Current methods allow researchers to generate 96 single-cell expression profiles in a day for several thousand dollars. Drop-seq, by comparison, enables 10,000 profiles a day for 6.5 cents each.
* T/ z- }) a0 ~0 s1 U$ }+ w) B& R/ y"If you're a biologist with an interesting question in mind, this approach could shine a light on the problem without bankrupting you," said Macosko. "It finally makes gene expression profiling on a cell-by-cell level tractable and accessible. I think it's something biologists in a lot of fields will want to use."2 ~* @( N; R+ i4 _3 Z/ }# }* x
Rather than competing with each other, the teams believe that having two options available in Drop-seq and inDrops will benefit the scientific community.
+ o) W" G1 c; [- a6 P- k7 i& S3 ~"Each method has unique elements that makes it better for different applications. Biologists will be able to choose which one is most appropriate for them," said Macosko.
+ U$ r2 {* {/ ~: U$ sDifferent goals: ^8 x. p# P+ g+ b- J6 L
McCarroll, Macosko and their colleagues are excited to explore the brain with Drop-seq.
# [1 o- E+ d4 r5 wWith luck, that will include discovering new cell types, constructing a global architecture of those cell types in the brain and understanding brain development and function as they relate to disease.) I  I0 H4 e- b
Among the questions they want to pursue are: What are all the cell types that make the brain work? How do these cell types vary in their functions and responses to stimuli? What cell populations are missing or malfunctioning in schizophrenia, autism and other disorders of the brain?- c5 E# h- R$ `2 j
Classifying cell types may not sound exciting, said Joshua Sanes, the Jeff C. Tarr Professor of Molecular and Cellular Biology and the Paul J. Finnegan Family Director of the Center for Brain Science at Harvard University and a co-author of the Drop-seq paper, but it lays the foundation for mapping neuronal circuits and one day being able to probe the mystery of how the "wetware" of the brain gives rise to thoughts, emotions and behaviors.
% u6 O) p6 G2 IIn the shorter term, Sanes looks forward to completing a catalog of cell types in the mouse retina. Drop-seq has already revealed several new ones.
; j( S5 t4 [' s9 H. RKirschner, Klein and their colleagues, meanwhile, are keenly interested in other areas, including stem cell development., n: ?/ M( G) d. a* ?3 T) V" }; H! A
"Does a population of cells that we initially think is uniform actually have some substructure?" Klein wants to know; he's trying to find out by studying immune cells and different kinds of adult stem cells. "What is the nature of an early developing stem cell? What endows those cells with a pluripotent state? Is gene expression more plastic or does it have a well-defined state that's different from a more mature cell? How is its fate determined?"
0 c/ r) o6 F- o! a  F. ^Using inDrops, Klein and team have confirmed prior findings that suggest even embryonic stem cells are not uniform. They found previously undiscovered cell types in the population they studied, as well as cells in intermediate stages that they suspect are converting from one type to another.% J. q4 {# K, n( N
Although both teams are excited by the massive amounts of data they and other researchers will obtain from Drop-seq and inDrops, they realize the sheer volume of information poses a problem as well.
7 W' c3 I: R' t. y& o6 e"We have thousands of cells expressing tens of thousands of genes. We can't look in 20,000 directions to pick out interesting features," said Klein.  c' W2 X4 ]6 v8 n- @7 ]5 o) w( I) ~
Machine learning is able to do some of that, and the teams have already employed new statistical techniques. Still, Kirschner has called on mathematicians and computer scientists to develop new ideas about how to analyze and extract useful information about our biology from the mountains of data that are on the horizon.

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Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells7 l) b1 _- L+ b. H. R+ K, x

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4 m& e" x* B. ^, D2 n& O5 YHighly Parallel Genome-wide expression Profiling of Individual Cells Using Nanoliter Droplets