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发表于 2017-1-5 18:14:38 | 显示全部楼层 |阅读模式
去年研究single cell的时候看到的,翻译了一部分。当做简单的入门好啦 ;)

Report from broadinstitute <Single-cell analysis hits its stride>
[size=1em]“When you are analyzing a single cell it’s kind of like a snowflake,It’s going to be different from every other cell in your system, but even though all snowflakes are different, there are certain things that all snowflakes have in common.The only way you would be able to know that is if you saw thousands and thousands of snowflakes.” - Macosko, Drop-Seq developer
The high throughput capability of Drop-Seq helped researchers identify the new cell types.

Why Drop-seq?
The Human Genome Project gave us an incredible foundation from which to understand our potential genetic repertoire. In order to understand the actual roles of particular genes in disease, however, it is not only critical to identify genes, but also to know in which cells the genes are expressed and when.
[size=1em]“Genetics has gotten very good at finding genetic variation associated with disease. Going from genetic results to biological understanding of disease is the next important challenge,” said Broad Institute member Steve McCarroll, also director of genetics in the Stanley Center for Psychiatric Research at the Broad and an assistant professor of genetics at Harvard Medical School. -Steve McCarroll
Critical to these biological insights is the ability to analyze individual cells roles in health and disease. New advances in single-cell technology and computational analysis by researchers at the Broad are enabling studies with a scale, ease, and affordability not possible before, opening the door for applications increasingly closer to the clinic.
遗传学研究已发现了很多与疾病有关的遗传变异,而下一步挑战则是从这些结果中找到疾病的生物学机制(biological understanding)。而要了解这些生物学机制,势必就需要先分析每个细胞在健康和疾病中发挥的作用。Broad研究者们新研发的单细胞技术和计算分析方法使研究变得无偏倚(scale)、简便与便宜,促进了单细胞分析技术走向临床应用。
The technology to sequence and analyze the genes expressed in a single cell — its transcriptome — has accelerated dramatically in just a few years. In 2012, Broad core institute member Aviv Regev and colleagues published a landmark analysis consisting of data from 18 cells. Last year, they expanded this to analyze hundreds of cells at once. Despite this growth, access to single-cell analysis was limited by the cost and time involved in preparing hundreds of individual cells for sequencing.
早在2012年BroadIn 的Aviv Regev等人发布了一个单细胞测序(转录组)的标志性分析,包含18个细胞数据。去年他们就把此类分析扩展到数百个细胞。尽管研究仍在进步,其发展却因为制备上百个用于测序的单细胞太过昂贵和耗时而受到限制。
McCarroll, Regev, and colleagues sought to change that. “We wanted to be able to chart the many cell types that are in the human body, human disease, and the many states and conditions that they may assume,” said Regev. “That would mean assaying many millions of cells, requiring a radical change from the available technologies where every cell had to be physically isolated from each other on a plastic plate or device.”

how the Drop-seq microfluidic device works
流程视频简述Single-Cell Analysis: Powerful Drops in the Bucket
What at first glance looks like water dripping through pipes is actually a cool new technology for swiftly and efficiently analyzing the gene activity of thousands of individual cells.
- First, a nanoliter-sized droplet of lysis(【生化】细胞[细菌]溶解) buffer containing a bead with a barcoded sequencing primer on its surface slides downward through the straight channel at the top of the screen.
首先,包含bead with barcoded sequencing primer 的细胞溶解缓冲液从纳升为单位的滴头滴下,如图中最上方

- At the same time, fluid containing individual cells flows through the curved channels on either side of the bead-bearing channel—you can catch a fleeting glimpse of a tiny cell in the left-hand channel about 5 seconds into the video.
The two streams (barcoded-bead primers and cells) feed into a single channel where they mix, pass through an oil flow, and get pinched off(夹在一起) into oily drops.
同时包含单细胞的fluid会在curved channel 流出(bead bearing channel左右两侧的)。有条码磁珠的引物和细胞同时流入一个singlechannel,在oil flow 里融合(pinched off),
Most are empty, but some contain a bead or a cell—and a few contain both.
- At the point where the channel takes a hard left, these drops travel over a series of bumps that cause the cell to rupture and release its messenger RNA—an indicator of what genes are active in the cell.
The mRNAs are captured by the primer on the bead from which, after the drops are broken, they can be transcribed, amplified, and sequenced to produce STAMPS (single-cell transcriptomes attached to microparticles). Because each bead contains its own unique barcode that enables swift identification of the transcriptomes of individual cells, this process can be done simultaneously on thousands of cells in a single reaction.
STAMPS: (single-cell transcriptomes attached to microparticles)
The mRNA/microbead complex is called a STAMP, for Single-cell TranscriptomesAttached to MicroParticles. The mRNA is then retrieved and converted into DNA, which is amplified and sequenced. Thousands of STAMPs (corresponding to thousands of cells) can be sequenced in a single reaction.
The STAMP barcode can then be used to determine from which cell each transcript came.

  • 数量大,但是总量小
    Tracking each cell in this new technique is critical. “We are replacing wells on a microtiter酶标板 plate with drops,” said Weitz, who has worked for years to develop single-cell droplets with his group at the Harvard School of Engineering and Applied Sciences. “The problem is that the wells on a microtiter plate have spatial information. We lose that by going to drops and we have to somehow get that information back.” Barcoding the microbeads allows cells to be tracked while running at great scale using very small reaction volumes.
  • captures the heterogeneity of cells in a tissue& identify new cell types
    Single-cell analysis is a powerful experimental approach, in part because information from rare cell types is not lost among the masses. Unlike approaches that analyze DNA from a population of cells at once, single-cell analysis captures the heterogeneity of cells in a tissue. Testing this ability with Drop-Seq, the researchers analyzed more than 45,000 cells from the mouse retina, a well-studied model system with a large number of cell types. From their data, the researchers were able to identify 39 retinal cell types, including known types to show that the method works and several potential new ones. Before Drop-Seq, such an achievement would have taken decades.
    The high throughput capability of Drop-Seq helped researchers identify the new cell types. “When you are analyzing a single cell it’s kind of like a snowflake,” said Macosko. “It’s going to be different from every other cell in your system, but even though all snowflakes are different, there are certain things that all snowflakes have in common. The only way you would be able to know that is if you saw thousands and thousands of snowflakes.”

  • high cost and risk of contamination
    Drop-Seq reduces the cost of single-cell analysis to about six cents per cell — a 500-fold decrease from previous methods. While mechanically or physically separating cells into individual wells of a microtiter plate in other single cell approaches is expensive, Drop-Seq’s nanoliter scale droplets are so tiny that 100 million of them fit in a test tube the size of a person’s thumb. Since individual cells are tracked with microbead barcodes, the whole sample can be pooled(共享?), shrinking the sequencing reaction volume and reducing costs significantly.
Basu also noted that, “we don’t have to worry about contamination as much since everything happens inside drops. The intact cell goes into a drop, it gets lysed(【生,生化,医】细胞溶解;裂解;溶化), barcoded, and only then do you open the drop. At that point, contamination does not really affect it.”
  • Cell location matters a gread deal: Seurat

    • Drop-Seq, and other methods for single cell analysis, usually begins with a dissociated tissue(解离组织), but inside the body a cell’s location matters a great deal. New approaches in computational analysis are now tackling this challenge as well. A team including Rahul Satija, from Regev’s lab, now at New York Genome Center, and Jeff Farrell, from associate member Alex Schier’s group at Harvard University, developed a computational method known as Seurat to analyze gene expression patterns across complex tissues and derive cellular localization.
    • Seurat使用机器学习方法,将单细胞RNA测序数据与每个组织中的RNA原位杂交模式整合,将单细胞map到所属的位置。
      即每个点都有特异的spatial patterning, 正如赛特点彩画中,每个点都有独特模式, evocative!!
      Named after the French impressionist whose pointillist(点彩画家) style is evocative of detailed spatial patterning information, Seurat uses machine learning approaches to map single cells by integrating single-cell RNA sequencing data with RNA in situ hybridization patterns from tissues.
      The team used Seurat to generate a map of spatial patterning in zebrafish embryos based on the transcriptome of over 800 cells. In Nature Biotechnology, they describe their work in the fish, but the approach is applicable to other systems including human tissue samples. For canonical tissues, Seurat may be unbounded in terms of scalability(扩充性) and, Regev said “Drop-Seq is its best friend,“ as parts of the Seurat package and work by Rahul Satija were integral for the analysis of cells and classification of the retina data obtained using Drop-Seq.
      然后他们就用zebrafish embryos,基于转录组产生800个细胞特定的的空间模式图谱。
      对于一些经典组织, Seurat的扩充性极好。
      “Seurat provides a framework in which to tackle the scale and type of data that experimental biologists pose when they use single cell genomics,” Regev said.
      Seurat恰到好处地帮实验生物学家在研究单细胞基因组学时面临的细胞数量少和数据类型(可能是spatial pattern的不一致)的困扰。

Recent fruits
  • Translational studies&workflows: pathways, prediction
    tumor heterogeneity and the factors controlling whether tumors respond to different types of therapies
    Since single-cell analysis can identify alterations in gene activity within a tumor that eludes bulk analysis, if differences are found, the data can provide clues about the molecular pathways involved in tumor response. Also, monitoring expression changes over time in response to a therapy could help predict when cells will fail to respond. “We think that single-cell analysis may help us discern heterogeneity—not simply for its own sake, but for what it tells us about tumor subpopulations, how well they respond to therapy, and how tumor cells evolve to drug resistance.” said Garraway.研究肿瘤异质性,不仅仅是为了学术研究,更是方便与肿瘤亚型人群对治疗的应答和亚型中的肿瘤细胞如何进化为抗药的。
  • Mircrobial genomics: microorganisms&antibiotics
    Another area in which single-cell analysis can have a tremendous impact is microbial genomics. “Being able to discover and understand microbial diversity is continuing to grow in importance,” said Broad core institute member Paul Blainey, particularly as we learn more about the impact of the microbiome – the billions of microorganisms that live in and on the human body – on our health. Additionally, in response to the significant global threat posed by antibiotic resistance, researchers have stepped up efforts leveraging single cell analysis to find new antibiotics and identify antibiotic resistance genes.

populations of people and populations of cell: human cell atlas
With these advances, single-cell analyses at the Broad have expanded to the scale of tens of thousands of cells – up from 18 just three years ago. As Eric Lander, president and director of the Broad Institute noted, this new era of “quantitative biomedicine” will allow us to learn from two types of populations – populations of people and populations of cells. “We need to learn from the natural variation among cells,” he said. “It’s time to start thinking about a complete cell catalog.” Such a “cell atlas” will afford an unbiased way of identifying every cell type in the human body based on things like cell state, environment, lineage, and history. “A human cell atlas is now within reach,” Regev said. “It will require some discipline, methods, and computational approaches that will be scalable, sensitive, and robust. Most significantly it will require a partnership across biology, technology, and medicine.” Such a partnership would be a culmination of the interdisciplinary多学科 advances that have so successfully launched single cell genomics forward. It will cut across experimental, computational, and mathematical approaches and it has the potential to transform our understanding and treatment of human disease.


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发表于 2017-4-8 10:05:58 | 显示全部楼层
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