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naivelocus · 8 years ago

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How to build a human cell atlas

Casey Atkins for Nature

Aviv Regev likes to work at the edge of what is possible. In 2011, the computational biologist was collaborating with molecular geneticist Joshua Levin to test a handful of methods for sequencing RNA. The scientists were aiming to push the technologies to the brink of failure and see which performed the best. They processed samples with degraded RNA or vanishingly small amounts of the molecule. Eventually, Levin pointed out that they were sequencing less RNA than appears in a single cell.

To Regev, that sounded like an opportunity. The cell is the basic unit of life and she had long been looking for ways to explore how complex networks of genes operate in individual cells, how those networks can differ and, ultimately, how diverse cell populations work together. The answers to such questions would reveal, in essence, how complex organisms such as humans are built. “So, we're like, 'OK, time to give it a try',” she says. Regev and Levin, who both work at the Broad Institute of MIT and Harvard in Cambridge, Massachusetts, sequenced the RNA of 18 seemingly identical immune cells from mouse bone marrow, and found that some produced starkly different patterns of gene expression from the rest1. They were acting like two different cell subtypes.

That made Regev want to push even further: to use single-cell sequencing to understand how many different cell types there are in the human body, where they reside and what they do. Her lab has gone from looking at 18 cells at a time to sequencing RNA from hundreds of thousands — and combining single-cell analyses with genome editing to see what happens when key regulatory genes are shut down.

The results are already widening the spectrum of known cell types — identifying, for example, two new forms of retinal neuron2 — and Regev is eager to find more. In late 2016, she helped to launch the International Human Cell Atlas, an ambitious effort to classify and map all of the estimated 37 trillion cells in the human body (see 'To build an atlas'). It is part of a growing interest in characterizing individual cells in many different ways, says Mathias Uhlén, a microbiologist at the Royal Institute of Technology in Stockholm: “I actually think it's one of the most important life-science projects in history, probably more important than the human genome.”

Such broad involvement in ambitious projects is the norm for Regev, says Dana Pe'er, a computational biologist at Memorial Sloan Kettering Cancer Center in New York City, who has known Regev for 18 years. “One of the things that makes Aviv special is her enormous bandwidth. I've never met a scientist who thinks so deeply and so innovatively on so many things.”

Undecided

When Regev was an undergraduate at Tel Aviv University in Israel, students had to pick a subject before beginning their studies. But she didn't want to decide. “Too many things were interesting,” she says. Instead, she chose an advanced interdisciplinary programme that would let her look at lots of subjects and skip a bachelor's degree, going straight to a master's.

A turning point in her undergraduate years came under the tutelage of evolutionary biologist Eva Jablonka. Jablonka has pushed a controversial view of evolution that involves epigenetic inheritance, and Regev says she admired her courage and integrity in the face of criticism. “There are many easy paths that you can take, and it's always impressive to see people who choose alternative roads.”

Jablonka's class involved solving complicated genetics problems, which Regev loved. She was drawn to the way in which genetics relies on abstract reasoning to reach fundamental scientific conclusions. “I got hooked on biology very deeply as a result,” she says. “Genes became fascinating, but more so how they work with each other. And the first vehicle in which they work with each other is the cell.”

Regev did a PhD in computational biology under Ehud Shapiro from the Weizmann Institute of Science in Rehovot, Israel. In 2003 she moved to Harvard University's Bauer Center for Genomics Research in Cambridge, through a unique programme that allows researchers to leapfrog the traditional postdoctoral fellowship and start their own lab. “I had my own small group and was completely independent,” she says. That allowed her to define her own research questions, and she focused on picking apart genetic networks by looking at the RNA molecules produced by genes in cells. In 2004, she applied this technique to tumours and found gene-expression patterns that were shared across wildly different types of cancer, as well as some that were more specific, such as a group of genes related to growth inhibition that is suppressed in acute lymphoblastic leukaemias3. By 2006, at the age of 35, she had established her lab at the Broad Institute and the Massachusetts Institute of Technology in Cambridge.

Shattering similarities

At Broad, Regev continued working on how to tease complex information out of RNA sequencing data. In 2009, she published a paper on a type of mouse immune cell called dendritic cells, revealing the gene networks that control how they respond to pathogens4. In 2011, she developed a method that could assemble a complete transcriptome5 — all the RNA being transcribed from the genes in a sample — without using a reference genome, important when an organism's genome has not been sequenced in any great depth.

It was around this time that Levin mentioned the prospect of sequencing the RNA inside a single cell. Up to that point, single-cell genomics had been almost impossible, because techniques weren't sensitive enough to detect the tiny amount of RNA or DNA inside just one cell. But that began to change around 2011.

The study with the 18 immune cells — also dendritic cells — was meant to test the method. “I had kind of insisted that we do an experiment to prove that when we put the same cell types in, everything comes out the same,” says Rahul Satija, Regev's postdoc at the time, who is now at the New York Genome Center in New York City. Instead, he found two very different groups of cell subtypes. Even within one of the groups, individual cells varied surprisingly in their expression of regulatory and immune genes. “We saw so much in this one little snapshot,” Regev recalls.

“I think even right then, Aviv knew,” says Satija. “When we saw those results, they pointed the way forward to where all this was going to go.” They could use the diversity revealed by single-cell genomics to uncover the true range of cell types in an organism, and find out how they were interacting with each other.

In standard genetic sequencing, DNA or RNA is extracted from a blend of many cells to produce an average read-out for the entire population. Regev compares this approach to a fruit smoothie. The colour and taste hint at what is in it, but a single blueberry, or even a dozen, can be easily masked by a carton of strawberries.

By contrast, “single-cell-resolved data is like a fruit salad”, Regev says. “You can distinguish your blueberries from your blackberries from your raspberries from your pineapples and so on.” That promised to expose a range of overlooked cellular variation. Using single-cell genomics to sequence a tumour, biologists could determine which genes were being expressed by malignant cells, which by non-malignant cells and which by blood vessels or immune cells — potentially pointing to better ways to attack the cancer.

The technique holds promise for drug development in many diseases. Knowing which genes a potential drug affects is more useful if there's a way to comprehensively check which cells are actively expressing the gene.

Regev was not the only one becoming enamoured with single-cell analyses on a grand scale. Since at least 2012, scientists have been toying with the idea of mapping all human cell types using these techniques. “The idea independently arose in several areas of the world at the same time,” says Stephen Quake, a bioengineer at Stanford University in California who co-leads the Chan Zuckerberg Biohub. The Biohub, which has been funding various biomedical research projects since September 2016, includes its own cell-atlas project.

The Human Cell Atlas

Around 2014, Regev started giving talks and workshops on cell mapping. Sarah Teichmann, head of cellular genetics at the Wellcome Trust Sanger Institute in Hinxton, UK, heard about Regev's interest and last year asked her whether she would like to collaborate on building an international human cell atlas project. It would include not just genomics researchers, but also experts in the physiology of various tissues and organ systems.

“I would get stressed out of this world, but she doesn't.”

Regev leapt at the chance, and she and Teichmann are now co-leaders of the Human Cell Atlas. The idea is to sequence the RNA of every kind of cell in the body, to use those gene-expression profiles to classify cells into types and identify new ones, and to map how all those cells and their molecules are spatially organized.

The project also aims to discover and characterize all the possible cell states in the human body — mature and immature, exhausted and fully functioning — which will require much more sequencing. Scientists have assumed that there are about 300 major cell types, but Regev suspects that there are many more states and subtypes to explore. The retina alone seems to contain more than 100 subtypes of neuron, Regev says. Currently, consortium members whose labs are already working on immune cells, liver and tumours are coming together to coordinate efforts on these tissues and organs. “This is really early days,” says Teichmann.

In co-coordinating the Human Cell Atlas project, Regev has wrangled a committee of 28 people from 5 continents and helped to organize meetings for more than 500 scientists. “I would get stressed out of this world, but she doesn't,” Jablonka says. “It's fun to have a vision that's shared with others,” Regev says, simply.

It has been unclear how the project would find funding for all its ambitions. But in June, the Chan Zuckerberg Initiative — the philanthropic organization in Palo Alto, California, that funds the Biohub — contributed an undisclosed amount of money and software-engineering support to the Human Cell Atlas data platform, which will be used to store, analyse and browse project data. Teichmann sees the need for data curation as a key reason to focus on a large, centralized effort instead of many smaller ones. “The computational part is at the heart of the project,” she says. “Uniform data processing, data browsing and so on: that's a clear benefit.”

In April, the Chan Zuckerberg Initiative had also accepted applications for one-year pilot projects to test and develop technologies and experimental procedures for the Human Cell Atlas; it is expected to announce which projects it has selected for funding some time soon. The applications were open to everyone, not just scientists who have participated in planning meetings.

Brain drain

Some scientists worry that the atlas will drain both funding and effort from other creative endeavours — a critique aimed at many such international big-science projects. “There's this tension,” says Atray Dixit, a PhD student in Regev's lab. “We know they're going to give us something, and they're kind of low-risk in that sense. But they're really expensive. How do we balance that?”

Developmental biologist Azim Surani at the University of Cambridge, UK, is not sure that the project will adeptly balance quantity and depth of information. With the Human Cell Atlas, “you would have a broad picture rather than a deeper understanding of what the different cell types are” and the relationships between them, he says. “What is the pain-to-gain ratio here?”

Surani also wonders whether single-cell genomics is ready to converge on one big project. “Has the technology reached maturity so that you're making the best use of it?” he asks. For example, tissue desegregation — extracting single cells from tissue without getting a biased sample or damaging the RNA inside — is still very difficult, and it might be better for the field, some say, if many groups were to go off in their own directions to find the best solution to this and other technical challenges.

And there are concerns that the project is practically limitless in scope. “The definition of a cell type is not very clear,” says Uhlén, who is director of the Human Protein Atlas — an effort to catalogue proteins in normal and cancerous human cells that has been running since 2003. There may be a nearly infinite number of cell types to characterize. Uhlén says that the Human Cell Atlas is important and exciting, but adds: “We need to be very clear, what is the endpoint?”

Regev argues that completion is not the only goal. “It's modular: you can break this to pieces,” she says. “Even if you solve a part of a problem, it's still a meaningful solution.” Even if the project just catalogues all the cells in the retina, for example, that's still useful for drug development, she argues. “It lends itself to something that can unfold over time.”

Regev's focus on the Human Cell Atlas has not distracted her from her more detailed studies of specific cell types. Last December, her group was one of three to publish papers6, 7, 8 in which they used the precision gene-editing tool CRISPR–Cas9 to turn off transcription factors and other regulatory genes in large batches of cells, and then used single-cell RNA sequencing to observe the effects. Regev's lab calls its technique Perturb-seq6.

The aim is to unpick genetic pathways very precisely, on a much larger scale than has been possible before, by switching off one or more genes in each cell, then assaying how they influence every other gene. This was possible before, for a handful of genes at a time, but Perturb-seq can work on 1,000 or even 10,000 genes at once. The results can reveal how genes regulate each other; they can also show the combined effects of activating or deactivating multiple genes at once, which can't be predicted from each of the genes alone.

Dixit, a co-first author on the paper, says Regev is indefatigable. She held daily project meetings at 6 a.m. in the weeks leading up to the submission. “I put in this joke sentence at the end of the supplementary methods — a bunch of alliteration just to see if anyone would read that far. She found it,” Dixit says. “It was 3 a.m. the night before we submitted.”

Regev's intensity and focus is accompanied by relentless positivity. “I'm one of the fortunate people who love what they do,” she says. And she still loves cells. “No matter how you look at them, they're just absolutely amazing things.”

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naivelocus · 8 years ago

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Glycobiology: Sweet success

Chemist Lingquan Deng was presenting a poster at a 2015 meeting of the Society for Glycobiology in San Francisco, California, when a career opportunity came knocking.

At the time, Deng was a research fellow studying platelet–bacteria interactions at Johns Hopkins University in Baltimore, Maryland. He had been searching for a faculty position close to Washington DC, where his wife worked as a lawyer.

Illustrations adapted from Getty

Then, a fellow attendee at the meeting told him that his company was hiring and invited him to present his work there. The company was just outside Baltimore and Deng decided that a visit wouldn't hurt. A day after giving his presentation to GlycoMimetics, he received a job offer. And today, he works there as a research scientist, designing assays to evaluate compounds for preclinical testing for disorders such as blood cancers and sickle-cell anaemia. The skills he gained during his PhD and postdoc in synthesizing carbohydrates and studying their interactions with proteins were a perfect match with the job requirements, he says.

Deng is one of a growing number of scientists finding career opportunities studying the biology of glycans — the sugar molecules that often adorn the surface of cells. Glycans are involved in practically every area of biology, from helping cells to communicate to recognizing invading pathogens. But the field has taken a while to get off the ground, in part because glycans are dizzyingly complex and few tools were available to make them accessible to researchers.

But the situation is starting to change as funders have realized the importance of the field and begun to invest in it. In December 2009, the US National Institutes of Health (NIH) National Heart, Lung, and Blood Institute announced a programme to fund resources for studying glycans, as well as training through courses, workshops and annual retreats. In 2012, the US National Research Council warned that ignoring glycans would impair research in biomedicine. A better understanding of glycoscience, it added, would deepen researchers' understanding of cancer, infectious diseases, biofuels, alternative sources of carbohydrate-based energy and the development of new carbohydrate-based materials. And in the United Kingdom, IBCarb, a network of glycoscientists funded by the Biotechnology and Biological Sciences Research Council, hosts regular workshops and training sessions on glycocience research areas. Similar programmes have also sprung up elsewhere in Europe, as well as in Canada, Asia and Australia. Since 2015, the Common Fund of the NIH has awarded 49 grants totalling US$29.2 million as part of its glycoscience programme, for projects aimed at developing affordable methods to synthesize and analyse carbohydrates and at creating databases to store and share the findings.

As a result of all this investment, academic researchers have been empowered to develop services and tools that help to close the skills and technology gap, and glycoscience is emerging as a high-impact and enticing field. And job prospects are growing in industry, too. The global market for glycobiology is expected to double to $50.1 billion by 2021, according to market-research firm BCC Research in Wellesley, Massachusetts. There are now about a dozen glycomics centres around the globe that can conduct screens or create custom reagents using state-of-the-art commercial equipment.

Glycans are so fundamental to biological processes that learning about them will help to give researchers a more comprehensive understanding of biology, even if they don't specialize in glycobiology, says Ajit Varki, who served as Deng's postdoc mentor and co-directs the Glycobiology Research and Training Center at the University of California, San Diego. It can also serve as a bridge into biomedicine for those who do not have a background in the life sciences — as was the case for Deng, who started off his research life with a bachelor's in materials science and engineering. “Glycobiologists are important in most biological drug programmes. It is a growing area,” says Spencer Williams, a carbohydrate chemist at the University of Melbourne in Australia.

" Glycobiologists are important in most biological drug programmes. "

Crazy complexity

Researchers with an understanding of glycans have a wide choice of potential applications. Glycans attach to proteins and lipids through a chemical process known as glycosylation, and in doing so determine human blood type and facilitate the binding of sperm to eggs and mediate immune-cell interactions. Together with these other biomolecules, the glycome — or total set of glycans — forms a crucial interface that transmits signals between the cell's exterior and interior worlds.

Yet getting that understanding is hard. Researchers generally study biomolecules such as DNA and peptides by synthesizing them in the lab and then probing how they react to different circumstances. But DNA and peptides are linear molecules with no branches, and tools for analysing them took off in the 1970s and 1980s. Sugars, however, have numerous branching points and each of those linkages can exhibit left- or right-handed asymmetrical forms depending on the orientation of the attached molecule. They also have exponentially more potential configurations than do DNA or proteins, and that makes them much harder to synthesize in the lab, says Peter Seeberger, a biochemist at the Max Planck Institute of Colloids and Interfaces in Munich, Germany. DNA is made up of four nucleotides (G, A, T and C), so there are theoretically 4,096 possible ways to build a string of six elements, or a 6-mer. Proteins have more building blocks (20 amino acids) and can potentially assemble into 64 million different 6-mers. But 6-mer carbohydrates can adopt 193 billion possible configurations. As a result, tools for synthesizing sugars are about 35 years behind those for DNA and peptides, Seeberger says.

Investigating the biology of the molecules is also difficult. Researchers working on DNA can type the sequences they want into an online order form and receive them a few days later, Williams says. And for protein studies, online services can deliver custom-made antibodies in 6–8 weeks.

Core SOS

Helping to bring people into the field are the increasing availability of core glycomics facilities, or centralized shared labs that offer access to specialized instruments, technologies and services for studying sugars. Decades ago, biologists wanting to know what carbohydrates a particular protein binds to would have to spend years doing tedious biochemistry experiments. Now, a core lab can run a protein sample across hundreds of carbohydrates immobilized on an array and rapidly detect which glycans the protein binds to. That allows researchers to move quickly onto functional studies and “cuts through years of difficult work”, says Williams.

Emory University's core facility in Atlanta, Georgia, has proved crucial for Brian Robinson, a postdoc investigating the role of glycans in development and wound repair. The team there is helping him to harvest glycans from key target tissues and develop custom arrays to determine which glycans bind to several proteins he is studying. His research will help him to understand how those biomolecules regulate human metabolism and immune responses.

Glycan-array services are offered by about a dozen centres worldwide. The data they generate usually enter public databases, so that other researchers who study similar proteins can see the findings. For those wanting insight into the structure of particular carbohydrates, some labs and companies (see 'Extra help') offer mass-spectrometry and analytical chromatography methods. The advances are enabling researchers such as Robinson, an MD-PhD-trained pathologist, to study the biological role of glycans much more rapidly and in enough detail for future medical applications.

Box 1: Extra help

Researchers who want to familiarize themselves with the basics of glycobiology or learn about tools and support for working with glycans can check out these resources.

Science

Essentials of Glycobiology is a textbook available free online (Cold Spring Harbor Laboratory Press, 2009)

Glycomics centres and core labs

Consortium for Functional Glycomics in Boston, Massachusetts

Glycosciences Laboratory, Imperial College London

Emory Comprehensive Glycomics Core in Atlanta, Georgia

Glycotechnology Core Resource, University of California, San Diego

Copenhagen Center for Glycomics

Alberta Glycomics Centre, Edmonton, Canada

Institute for Glycomics, Griffith University, Brisbane, Australia

Japan Consortium for Glycobiology and Glycotechnology Database

New tools

Seeberger and his colleagues estimate that 90% of known molecules in the mammalian glycome can be synthesized from 45 basic structures. The team has managed to produce large quantities of about 40 such structures — now sold by GlycoUniverse, a spin-off company of his institute. The launch of the company reflects the unmet need for such technology and the entrepreneurial opportunities in the field.

To detect sugars and other biomolecules in living tissue, researchers often buy or make antibodies. However, conventional methods tend to work poorly for sugar-specific antibodies — in part because the glycan antigen can be tricky to make. Some labs are therefore working to create carbohydrates that are more likely to trigger an immune response, and thereby make antibodies — a critical research tool — easier to generate.

Carolyn Bertozzi, a chemist at Stanford University in California, has taken a different approach. She and her team feed cells with monosaccharides, or simple sugars, that sneak into biosynthetic pathways and are incorporated into glycans inside the cell.

Then, by chemically attaching tags or fluorescent dyes onto the building blocks of these sugars, researchers can visualize the glycans in their natural environment without needing the hard-to-make antibodies.

For Julia Maxson, a cell biologist at Oregon Health & Science University in Portland, finding glycobiologists to get tips from and bounce ideas off was key. Several years ago, she was trying to determine how a gene mutation causes rare leukaemias. The mutation affects a receptor on the surface of immune cells by disabling where it attaches to a large glycan. Without the sugar, the receptor can trigger the cell to grow uncontrollably — a unusual cancer-causing pathway.

But when Maxson submitted the manuscript for publication, a reviewer wanted clearer evidence for the modification, called O-linked glycosylation. She wondered whether Bertozzi's labelling strategy might help and e-mailed her for advice on how to use it for her research.

With Bertozzi's counsel and a deeper understanding of how sugars can trigger rare leukaemias, Maxson won an NIH fellowship for postdocs transitioning to faculty positions. Today she works with Bertozzi to characterize sugar structures found uniquely on cancer cells. They hope that their findings can lead them to therapeutic strategies that precisely target tumour cells, which could pique industry interest and create research opportunities. Indeed, as scientists get a better handle on studying glycans in the lab, companies are exploring therapeutic ramifications, which should fuel growth in industrial research jobs.

Most biologics — medical products derived from natural sources — are glycosylated, which drives interest in investigating how the sugar structures influence the safety and effectiveness of therapies being developed for cancer and other diseases, Williams says.

Analytical glycobiology is so crucial, in fact, that Deng's boss is looking to hire another researcher with these skills. And just as in his case, Deng says, it looks likely that such a candidate could land a job without even formally applying.

— Nature - Issue - nature.com scie...

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naivelocus · 8 years ago

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Artificial intelligence: AI zooms in on highly influential citations

The number of times a paper is cited is a poor proxy for its impact (see P. Stephan et al. Nature 544, 411–412; 2017). I suggest relying instead on a new metric that uses artificial intelligence (AI) to capture the subset of an author's or a paper's essential and therefore most highly influential citations.

Academics may cite papers for non-essential reasons — out of courtesy, for completeness or to promote their own publications. These superfluous citations can impede literature searches and exaggerate a paper's importance.

The scientific search engine, Semantic Scholar, is the first to automatically identify the subset of a paper's citations in which the paper had a strong impact on the citing work (see http://semanticscholar.org). It further ranks these according to their estimated impact by using machine-learning methods (see go.nature.com/2th2voa). Although still far from perfect, this 'highly influential citations' metric is a substantially better indicator of impact than raw citation counts are. An author's highly influential citation count is simply the sum of the highly influential citations of his or her papers.

This metric and its implementation exemplify the potential of AI to overcome information overload in the research literature.

— Nature - Issue - nature.com scie...

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naivelocus · 8 years ago

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The trickiest family tree in biology

Illustration by Jasiek Krzysztofiak/Nature

For 18 months in the early 1980s, John Sulston spent his days watching worms grow. Working in twin 4-hour shifts each day, Sulston would train a light microscope on a single Caenorhabditis elegans embryo and sketch what he saw at 5-minute intervals, as a fertilized egg morphed into two cells, then four, eight and so on. He worked alone and in silence in a tiny room at the Medical Research Council Laboratory of Molecular Biology in Cambridge, UK, solving a Rubik's cube between turns at the microscope. “I did find myself little distractions,” the retired Nobel prize-winning biologist once recalled.

His hundreds of drawings revealed the rigid choreography of early worm development, encompassing the births of precisely 671 cells, and the deaths of 111 (or 113, depending on the worm’s sex). Every cell could be traced to its immediate forebear and then to the one before that in a series of invariant steps. From these maps and others, Sulston and his collaborators were able to draw up the first, and so far the only, complete ‘cell-lineage tree’ of a multicellular organism1.

Although the desire to record an organism’s development in such exquisite detail preceded Sulston by at least a century, the ability to do so in more-complex animals has been limited. No one could ever track the fates of billions of cells in a mouse or a human with just a microscope and a Rubik’s cube to pass the time. But there are other ways. Revolutions in biologists’ ability to edit genomes and sequence them at the level of a single cell have sparked a renaissance in cell-lineage tracing.

The effort is attracting not just developmental biologists, but also geneticists and technology developers, who are convinced that understanding a cell’s history — where it came from and even what has happened to it — is one of biology’s next great frontiers. The results so far serve up some tantalizing clues to how humans are put together. Individual cells from an organ such as the brain could be related more closely to cells in other organs than to their surrounding tissue, for example. And unlike the undeviating developmental dance of C. elegans, more-complex organisms invoke quite a bit of improvisation and chance, which will undoubtedly complicate efforts to unpick the choreography.

But even incomplete cellular ancestries could be informative. Sulston’s maps paved the way for discoveries surrounding programmed cell death and small, regulatory RNA molecules. New maps could elucidate the role of stem cells in tissue regeneration or help combat cancer — a disease of unharnessed lineage expansion. “There’s a real feeling of a new era,” says Alexander Schier, a developmental biologist at Harvard University in Cambridge, Massachusetts, who is using genome editing to trace the cell-lineage history of zebrafish and other animals.

Reconstructing history

A cell’s history is written in its genome: every mutation acquired that gets passed on to daughter cells serves as a record. In 2005, the computer scientist Ehud Shapiro at the Weizmann Institute of Science in Rehovot, Israel, calculated that researchers could use the natural mutations in individual human cells to piece together how they are related2. He conceived of a corollary (in concept at least) to the C. elegans cell map, which he called the Human Cell Lineage Project. But the field, he says, wasn’t ready. “When we offered this vision, neither the field nor the name of single-cell genomics existed.”

Fast forward a decade, and researchers have developed a suite of powerful tools to probe the biology of lone cells, from their RNA molecules and proteins to their individual and unique genomes. Now, he envisions a way of capturing the developmental course of a human, frame by frame, from fertilized egg to adult. “You want the whole movie with 3D frames from beginning to end,” he says. To make such a film, it’s not even necessary to look at the entire genome. Shapiro’s team is focusing on repetitive stretches of DNA peppered across the genome called microsatellites. These sequences tend to mutate more frequently than other bits of the genome, and his team is working on sequencing tens of thousands of them across the genomes of hundreds of individual human cells to determine how they relate.

“We’re beginning to see the rules of development in normal human beings.”

Christopher Walsh, a neuroscientist and developmental biologist at Boston Childrens Hospital and Harvard Medical School, doubts that researchers will ever reconstruct a complete human cell-lineage map to match that of C. elegans, but even a less than complete tree will pay dividends, he says. “I’ve been studying cell lineage in the cortex for 25 years, and the idea of studying it directly in the human brain was an inconceivable dream. Now it’s a reality.”

In experiments described in 2015, Walsh’s team sequenced the complete genomes of 36 cortical neurons from 3 healthy people who had died and donated their brains to research3. Reconstructing the relationship between the brain cells in an individual revealed that closely related cells can be spread across the cortex, whereas local areas can contain multiple distinct lineages. Successive generations of cells seem to venture far from their ancestral homes. One cortical neuron, for instance, was more closely related to a heart cell from the same person than to three-quarters of the surrounding neurons. “We were not expecting to find that,” Walsh says.

Walsh’s team is trying to understand how mosaicism in the brain — in which some cells harbour different gene variants — affects health. They have identified, for example, forms of epilepsy that occur even when just a small percentage of cells in a tiny brain region carry a disease-causing mutation. And they have found that individual neurons from healthy individuals can bear mutations that would cause seizures and schizophrenia if present more widely. It seems from this work that it matters which cells end up with a mutation. “The lineage basically determines what diseases are possible,” Walsh says.

Other scientists are uncovering records of life’s earliest events in the genomes of adult cells. In experiments published this year4, Michael Stratton, a geneticist at the Wellcome Trust Sanger Institute in Hinxton, UK, and his team sequenced white blood cells from 241 women with breast cancer and looked for mutations found in only a subset of their blood cells. The study revealed mutations that occurred very early in development, perhaps as far back as the two-cell embryo. And they noted that the descendants of these cells do not contribute equally to the blood system of adults. This could be because one cell multiplies more efficiently than the other; or it could, as Stratton suspects, be that by chance one ends up contributing more to a developing fetus than to a placenta or other supporting tissues.

Future studies, Stratton says, will look for bottlenecks in development that limit the contribution of some cell lineages. “We’re beginning to see the rules of development in normal human beings,” he says.

From blobs to barcodes

Jay Shendure, a geneticist at the University of Washington in Seattle, still remembers the day he became fascinated with cellular histories. As a 14-year-old with an interest in biology and computers, he wrote a program that modelled a mass of multiplying cells to impress his uncle, a reconstructive surgeon visiting from India. “He said, ‘This is amazing. One day you’ll do the same thing, and instead of a blob it will be a whole baby,’ ” Shendure recalls.

Nearly a decade later, Shendure was a first-year graduate student working for the Harvard geneticist George Church. Church presented a list of ideas (“all of which, at the time, seemed totally absurd”, Shendure says); one of them was to reconstruct the lineages of many cells at once, in a single experiment. Shendure toiled for six months trying to use DNA-flipping enzymes called recombinases to create a readable record in the genomes of bacteria as they divide. Rather than relying on naturally acquired mutations in the genome, the system would essentially create variants to keep track of.

Shendure eventually switched projects, but he revived the idea a few years ago when graduate students Aaron McKenna and Greg Findlay joined his laboratory in Seattle. They realized that the popular genome-editing tool CRISPR–Cas9 would be ideal for introducing traceable mutations to whatever part of the genome they wanted (see ‘The lines of succession’). Teaming up with Schier’s lab, they unleashed CRISPR–Cas9 in two single-cell zebrafish embryos and instructed it to edit DNA ‘barcode’ sequences that had been engineered into their genomes. They then sequenced these barcodes in cells of an adult animal and used the mutations in them to piece together their lineage5.

The trees they produced show that a small number of early-forming embryonic lineages give rise to the majority of cells in a given organ. More than 98% of one fish’s blood cells, for instance, came from just 5 of the more than 1,000 cell lineages that the team traced. And although these five contributed to other tissues, they did so in much lower proportions. They were almost entirely absent from the muscle cells in the heart, for example, which was mostly built from its own small number of precursors. “It was profoundly surprising to me,” says Shendure. His colleague Schier says he is still trying to make sense of the data.

Jan Philipp Junker, a quantitative developmental biologist at the Max Delbrück Center for Molecular Medicine in Berlin, says that the cell-lineage trees of early embryos probably vary greatly between individuals, and that the dominance of particular lineages observed by Shendure and Schier’s team could be the result of chance events. The cells of an early embryo move around, and only a fraction of them contribute to the final organism, for example. It would be more revealing, he adds, to track later developmental events, such as the formation of the three germ layers that give rise to different organs, because these events are less governed by luck.

Junker and others have developed a bevy of other CRISPR-based techniques for piecing together developmental histories. He and Alexander van Oudenaarden, a systems biologist at Utrecht University in the Netherlands, applied such an approach to track the regeneration of a damaged fin in zebrafish. Regeneration, they discovered, occurred in the same kind of way as development: few of the cell lineages that gave rise to the original fin were lost when it was remade from stem cells. The finding confirmed previous studies, but the CRISPR-based methods allowed the team to trace lineages of thousands of cells in a single experiment6.

Church says his team has used CRISPR to study mouse development and has managed to record the embryonic cell divisions that give rise to the three major germ layers, which form all the body’s organs7. “I don’t think we’re that far away from doing a complete lineage,” he says.

Some researchers strive to know not just how an organism’s cells relate to one another, but what happened to them along the way. Michael Elowitz and Long Cai, both at the California Institute of Technology in Pasadena, have developed a lineage tracer that creates fluorescent probes to help them observe the histories of cells as they develop8. Their method can track whether certain developmental genes have been turned on in the past for a given lineage. On 5 July, Elowitz, along with Shendure and Schier, were awarded a 4-year, US$10 million grant from the Paul G. Allen Frontiers Group to combine their technologies. The trio plan to develop synthetic chromosomes that act as tape recorders for cell-lineage history and molecular events.

Such recordings might allow scientists to tinker with a cell’s development in more delicate ways than current cell-reprogramming techniques allow, says Tim Liu, a synthetic biologist at the Massachusetts Institute of Technology in Cambridge who is also working on a technology to record a cell’s history9. “You might see some version of these recorders being inserted into the cell therapies of the future,” although it won’t be for a while, he cautions. “I’m not going to go and inject my CRISPR recorder into a patient.”

Lineages for life

Cancer is where new lineage-tracing methods are likely to make waves first. “Cancer is a disease of lineage — it’s a disease of stem cells,” says Walsh. One question that researchers are starting to tackle is the origin of metastatic cells, which emerge from the primary tumour and invade sometimes distant organs. They tend to be the hardest tumour cells to vanquish and the ones most likely to kill patients.

A team led by cancer geneticist Nick Navin at the University of Texas MD Anderson Cancer Center in Houston published lineage maps of two colon cancers in May10. The results showed that liver-invading metastatic cells shared many DNA mutations with the primary tumours they came from, suggesting that the metastasis had emerged at a late stage and hadn’t needed a bunch of new mutations to spread. Lineage mapping could also show whether tumours really develop from single cells, as geneticists have argued, or whether they originate from multiple cells, as some imaging studies have suggested. Navin suspects that similar work could be used to direct treatment. His team and others are tracing cancer-cell lineages in patients as they begin taking drugs. They hope these studies can spot resistant lineages, allowing doctors to pick better treatments and switch medicines in time to make a difference.

“Cancer is a disease of lineage — it’s a disease of stem cells.”

At the moment, however, promise in the field far exceeds the reality. And Sulston’s lineage maps of C. elegans still loom large over current efforts. Stephen Quake, a bioengineer at Stanford University in California, devised his own method for tracking cellular ancestry through CRISPR and decided to test it in the worm11. “It’s nice to have a gold standard,” Quake says. He and his team sequenced the cells of a mature animal after CRISPR had mutated its genome during development. The efforts took much less time than the year and a half that Sulston spent with his microscope. But Quake says that the picture they developed was also less than complete. Yes, it captured a key transition in roundworm development — the segregation of cells bound for the intestine and those that give rise to the rest of the body — but it lacked the exquisite detail Sulston observed with his eyes. “I’ll be perfectly blunt. I’m not very impressed with my results,” says Quake, who hadn’t even planned to publish the work until he saw the rush of other papers using similar techniques. “No one has really got it licked yet,” he says.

There is an argument to be made that Sulston set the bar too high with C. elegans. “This whole concept of a lineage tree is very much influenced by this classic work,” says Junker. And that may deserve a rethink.

In fish, mice and humans, no two individuals’ cell lineage trees are likely to look exactly the same, and each probably changes throughout the individual’s lifetime, as tissues repair and regenerate themselves. Junker and others hope that the new techniques will allow biologists to ask questions about the variability in lineage trees — between individuals, between their organs and as they age. As Schier puts it: “We don’t know how many ways there are to make a heart.”

It is that vast unknown that could make such work transformative, says Elowitz: “It would change the kinds of questions you could ask.” Sulston’s map led biologists into uncharted territory, says Schier, and this could do the same. “We can’t tell you what exactly we’re going to find, but there is a sense that we’re going to find some new continents out there.”

— Nature - Issue - nature.com scie...

#Nature - Issue - nature.com scie...#The trickiest family tree in biology

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naivelocus · 8 years ago

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Single-cell biology

Illustration by Jasiek Krzysztofiak/Nature; Images: Morphart Creation/Shutterstock, Jezper/Shutterstock

Cell theory, the concept of the cell as the basic unit of life, is a cornerstone of biology. But despite nearly 180 years under biologists' microscopes, cells are still enigmatic. This special issue examines how researchers are trying to learn about the nature of cells — how many different kinds exist, what they do and how they change over time — by looking at them singly.

A News Feature details the fruits of lineage tracing, which reveals how complex organisms are built from one, then two, then four seemingly identical cells of an embryo. The diversity of cell type and activity turns out to be much greater than conventional studies on populations of cells could reveal. That's why scientists such as Aviv Regev at the Broad Institute in Cambridge, Massachusetts, are taking part in a massive effort to catalogue every sort of cell in the human body.

Such work could have important therapeutic implications. Keeping track of the differences between cells in a heterogeneous tumour could guide treatment. Or scientists could improve ways to scrutinize the vast array of immune cells that fight infections or cause inflammation, say immunologists Amir Giladi and Ido Amit at the Weizmann Institute of Science in Rehovot, Israel.

And single-cell studies will continue to illuminate the inner life of the cell itself. Takashi Nagano at the Babraham Institute near Cambridge, UK, and his colleagues look at how the genome of a mouse cell is packaged and oriented in three dimensions throughout a division cycle in a research paper. Such efforts presage a real-time 3D view of genome interaction, say Robert Beagrie at the Medical Research Council Molecular Haematology Unit in Oxford, UK, and Ana Pombo at the Max Delbrück Center for Molecular Medicine in Berlin, in a News and Views article.

More and more scientists are jumping into single-cell analysis, which spans classical cell biology, developmental biology, genomics and computational biology. And as the technologies to study single cells expand, they will require sophisticated analytical tools to tame and make sense of results. Just as the proliferation of cell theory powered extraordinary advances in biology, it is clear that single-cell analysis will open new vistas for scientists to explore.

— Nature - Issue - nature.com scie...

#Nature - Issue - nature.com scie...#Single-cell biology

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naivelocus · 8 years ago

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UK scientists hope for softened Brexit after shock election result

Toby Melville/Reuters

Theresa May's plan to increase her Conservative party's majority ahead of Brexit negotiations has backfired.

The United Kingdom’s general election on 8 June has thrown the country's politics into disarray — but scientists trying to divine meaning from the chaos hope that the result will ultimately benefit their nation’s research ties with the European Union.

The Conservative government had called the election in an effort to stretch its slim majority, which would have given it a firmer mandate to negotiate Brexit, the United Kingdom’s split from the EU. The party, under the leadership of Theresa May, was aiming for a ‘hard’ Brexit — placing priorities on ending the free movement of EU citizens to the United Kingdom, cutting immigration and taking the country out of the EU’s single market. That stance alarmed scientists: it seemed likely also to cut the United Kingdom out of EU research programmes and dent the easy movement of scientists to and from the EU.

But the party actually lost seats — ending up eight short of an overall majority, although still the largest single bloc in the British parliament. After the result, which is termed a ‘hung’ parliament because no party has an outright majority, May said that she would form a government anyway, relying on the support of Northern Ireland’s Democratic Unionist Party, a relatively minor player in UK politics (see 'Uneasy partners'). Her cabinet remains largely the same: Greg Clark is still in charge of the Department for Business, Energy and Industrial Strategy — which has ultimate responsibility for research — and Jo Johnson is still science minister. The pro-Brexit politician Michael Gove has been appointed as environment minister.

Although the situation remains uncertain, the result suggests that the government has lost its mandate for the strict Brexit deal for which May had been aiming, says Paul Nightingale, deputy director of the Science Policy Research Unit at the University of Sussex in Brighton, UK. The result “makes it more likely that the UK will have a softer Brexit and will stay within EU science programmes”, says Nightingale, who was speaking in a personal capacity.

Any softening of the United Kingdom’s position on Brexit is “obviously good for our chances of staying in EU funding programmes”, agrees Kieron Flanagan, a science-policy researcher at Alliance Manchester Business School, UK. “Planning to ameliorate the effects of Brexit on science and research should be the number one goal,” he adds.

“Science has a lot to lose from a hard Brexit. So the prospect of a minority government yielding a softer Brexit is likely to appeal to science leaders who have been pushing to retain a range of EU benefits,” says Sarah Main, executive director.

Uneasy partners

The Democratic Unionist Party's controversial views.

The Conservative Party is relying on an informal agreement with Northern Ireland’s Democratic Unionist Party (DUP) to form the next government — which has put a spotlight on some DUP views. The party has a policy to block women’s access to abortion; a Member of Parliament who has called the Paris climate pact “totally flawed and pointless”; and a sizeable minority membership who think that creationism should be taught in science classes.

But researchers needn’t worry that such stances will affect UK laws: other politicians would veto them, says Kieron Flanagan, a science-policy researcher at Alliance Manchester Business School, UK. The DUP does advocate for a softer Brexit, however: it wants to maintain an open border with the Republic of Ireland after Britain leaves the European Union.

Laura Castells Navarro

“The result may provide hope that a hard Brexit can’t be pursued with such vigour as before,” says Anne Glover, a biologist at the University of Aberdeen, UK, who was formerly the European Commission’s chief scientific adviser. “The optimist in me hopes that the hung parliament we seem to have at this stage might end in a rethink on Brexit, perhaps a delay,” she says. “If Brexit is pursued, research needs the closest possible deal we have to the one we have now.”

‘Still in a mess’

Senior Conservatives say that the result could weaken the United Kingdom’s hand in negotiations with the EU, which were set to begin on 19 June. “We had an evidence-free EU referendum, and now we have a negotiating party who, by their own admission, think their negotiating position has been weakened,” says Glover. “Not much cause for celebration — the future of research in the UK is still in a mess.”

Although May will, for now, remain Britain’s prime minister, her long-term position as leader could be in doubt. “May will limp along, but politically, she is toast,” says Nightingale. If she does ultimately go, then the Conservative government’s direction might shift under new leadership. “I don’t think there will be any change in alignment in the science budget and industry strategy,” he adds. Still, May’s trademark stance on restricting the number of foreign students coming to the country as part of large cuts to UK immigration could also be softened, Nightingale notes. Universities and some politicians in May’s own party have opposed the idea that overseas students should count in immigration quotas.

Ahead of the election, all three of the nation’s main national parties had pledged to raise funding for science, an unusual consensus that reflects the difficulties that UK research could face as a consequence of Brexit. Reaching an agreement on science spending becomes more important when governing is reliant on collaboration and cross-party consensus, says Graeme Reid, a science-policy researcher at University College London. The role of science minister becomes even more crucial in such an environment, he says, because the minister needs to provide a sense of direction, and work across political lines.

Although opposition party Labour failed to win a majority of votes or seats, it did much better than expected. Commentators speculate that a high turnout among young people and a wider drift away from the UK Independence Party were behind the rise. Labour, like the Conservatives, has pledged to follow through plans to leave the EU, but has said that it will seek to stay part of EU research programmes. The third national party, the Liberal Democrats, opposes leaving the EU: it increased its number of seats but did not see a large surge.

Among individual results, biochemist Julian Huppert failed to win back his seat as a Liberal Democrat MP, which he lost in 2015. And Conservative Nicola Blackwood lost her seat in Oxford West and Abingdon. Blackwood chaired the House of Commons Science and Technology Committee in 2015—2016, and had been “wonderful at reaching out to the science community to engage a broader range of people and coming up with innovative new ways of drawing science advice into parliamentary debate”, says Reid.

— Nature - Issue - nature.com scie...

#Nature - Issue - nature.com scie...#UK scientists hope for softened Brexit after shock election res

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naivelocus · 8 years ago

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Complexity: Decoding deep similarities

Scale: The Universal Laws of Growth, Innovation, Sustainability, and the Pace of Life, in Organisms, Cities, Economies, and Companies

Geoffrey West Penguin: 2017. ISBN: 9781594205583

Buy this book: US UK Japan

Michael H/Getty

New York City is in some respects a scaled-up version of smaller settlements.

Plato and Aristotle stand proxy for two types of scientist: those who see similarities, patterns and universals, and those who see differences, variation and specifics. Both viewpoints are needed, but they are hostage to disciplinary and cultural fashions. It's probably safe to say that physics stresses the former, biology the latter.

Geoffrey West — a theoretical physicist and erstwhile head of that mecca of multidisciplinary complexity studies, the Santa Fe Institute in New Mexico — takes a Platonic view. In Scale, he explains mathematical relationships between the size and properties of many complex systems, natural and human, that are more often subjected to an Aristotelian scrutiny of particulars — living organisms, cities, companies, economies. Each has properties predictable from their scale: an elephant is a scaled-up mouse; New York City, a scaled-up Santa Fe; Walmart, a scaled-up version of your local grocery.

Scaled up how? Elephants don't have fur and whiskers; New York has (to my knowledge) no skyscrapers made from adobe. Instead, these relationships are apparent in measures of what one might call performance or metabolism — rates of wealth creation, say, and of energy consumption. Crucially, the link isn't linear. Owing to economies of scale (efficiencies that arise from increased size), New York has half the per-capita carbon footprint of Santa Fe, even though the former feels grimy and gridlocked and the latter eco-friendly. As a result, such scaling relationships tend to obey power laws: a parameter will increase in proportion to size raised to some exponent. The canonical example is that metabolic rate scales as (body mass)3/4.

So, for cities, size determines (among other things) the pace of life. Anyone can get a sense of that by trotting along New York's mammoth thoroughfare Fifth Avenue or strolling the pueblo-style streets of Santa Fe. And it is quantitatively revealed in the statistics. As West says, in big cities “diseases spread faster, businesses are born and die more often, commerce is transacted more rapidly, and people even walk faster”. The average walking speed in cities with more than one million inhabitants is nearly double that in towns of a few thousand.

For living things, it's the reverse: if an animal is small, then, with very rare exceptions, it lives fast and dies young. Those two factors, as measured by heart rate and lifespan, balance out so precisely that all creatures, from pygmy shrews to whales, have equal lifespans when accounted in number of heartbeats: about one billion of them. West also discusses the inevitable laws of growth, ageing and death, both in living and human-made systems — why, say, companies have life cycles but cities persist.

If you find such regularities astonishing, you're a Platonist at heart. If you shrug, feeling that they reveal little about pygmy shrews or what makes Shanghai so captivating, you're an Aristotelian. There is a suspicion, often heatedly voiced, that the universality of the laws West and others have uncovered can't tell us anything very interesting about any one system.

That criticism has long been levelled at those who posit that mathematical laws govern aspects of life, arguably starting with biologist D'Arcy Wentworth Thompson and his magisterial book On Growth and Form, published 100 years ago (see P. Ball Nature 494, 32–33; 2013). But a recognition of mathematical principles in biology and social science — in particular, an acknowledgement that simple laws underpin complex growth, form and dynamics — has been growing over the past several decades. Computer algorithms have helped tremendously in that process. Since the 1980s, the Santa Fe Institute has been hugely influential, inspiring similar complexity hubs from Singapore to Arizona, Amsterdam and Vienna.

Yet this broader view of complex human systems has been slow to enter the mainstream, and its absence has had some troubling consequences. The narrow focus on growth, turnover and gross domestic product as indicators of economic well-being, for example, has led to unsustainable growth coupled to environmental despoliation and climate change. That point has been made before, but West brings it home with particular clarity.

He notes that although thermodynamic flows — rates of fossil-fuel consumption, say, and consequent entropy production — are central to socio-economic progress, discussion of that hardly figures in economics textbooks: “Remarkably, concepts like energy and entropy, metabolism and carrying capacity have not found their way into mainstream economics.” If these are acknowledged at all, it tends to be by economists such as Julian Simon and Paul Romer, who have argued that human ingenuity will solve any problems. Ideas, however, are themselves the product of complex social systems — vitally dependent on institutions, opportunities, equality, liberty and the spiritual health of societies.

This blind belief in innovation as a panacea is, as West points out, often coupled to misapprehension or even denial of the costs of open-ended growth, such as climate change. At best, such issues are swept under the carpet as “externalities”, market failures that pose a nuisance for economic accounting. An economics afflicted by such attitudes is not even a dismal science; it's a pseudoscience.

Likewise, cities have often been regarded as if they are mechanical entities that can be arbitrarily redesigned, rather than, as urban theorist Lewis Mumford argued, being more like living organisms, constantly adapting and evolving. West particularly highlights the pioneering ideas of another urban theorist, Jane Jacobs. Her advocacy of the organic approach to urbanism in the 1950s and 1960s invoked the idea of self-organization, now so central to the science of complex systems, before that language even existed (see A. Williams Nature 537, 614–615; 2016).

West is too canny to imagine that universal laws of size and growth say all that needs saying about such systems. But Scale, a grand synthesis of topics he has studied for several decades, makes an important and eloquent case for their significance in an ecology of the natural and human world — and in understanding whether the two can fit together. He calls this “a grand unified theory of sustainability”, allowing “quantitative, predictive, mechanistic” parsing of that relationship. West has no prescription for what such a theory might look like. It surely won't be built bottom-up from an Aristotelian assemblage of details; neither will it reduce simply to a series of scaling laws.

Much of what Scale contains has been popularized before, but West manages to reveal the deeper principles on which these regularities rest. These are inevitable aspects of complex systems. You can ignore them, but you can't escape them.

— Nature - Issue - nature.com scie...

#Nature - Issue - nature.com scie...#Complexity: Decoding deep similarities

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