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Research on single cells could help to explain disease progression.

The street-light effect is often used as a criticism in science, because it introduces an observation bias. The concept is based on the old joke about the night-time drunk who looks for his house keys under the light, even though he lost them somewhere else, because thats the only place he can examine.

But if nothing is lost and a street light shines scientific light on a new place, then it would be perverse not to peer underneath. Because thats one of the attractive features of science: discovery and the joy of the unknown. So it is difficult to criticize those scientists who rush to exploit new tools that allow the analysis of single cells. As we describe in a special series of articles this week, advances in the past few years at this technical and computational frontier offer an unprecedented view of what goes on at the cellular level, with implications for everything from genomics and ageing to the treatment of disease.

Some of this science is descriptive and discovery-led. Its nearly 180 years since the cell was first proposed as the most basic individual unit of all life on Earth. Yet most of what we know about how cells work at the molecular and biochemical level comes from studying them not as individuals but as groups. This is problematic: researchers know that tissues, and even apparently homogeneous collections of identical cells, can carry significant differences. These ups and downs are missed when cells are mashed together and assessed. Its a classic downside of the tyranny of the average. But that was where the light was, so thats where scientists looked. And now the unexplored territory inside the cell is ripe for adventure.

As the street light of science starts to focus on individual cells and individual characteristics, so it also becomes a spotlight. For the study of the single cell is not just the territory of discovery it also enables problem-based research. Take cancer. We know that tumours comprise a multitude of vastly different cells, not all of them explicitly cancerous (think of blood and lymph vessels and immune cells). To unravel the ways in which they interact and either fight or maintain tumours has been a major challenge. One way of addressing that is to get more data on all the players, and to extract information from cancer cells about how they developed and what weaknesses they may harbour. And that takes single-cell analysis.

The illumination of this biology of individual cells also shines a light on some interesting cultural differences. To explore this new frontier demands new skills, and so mathematicians and computer scientists are teaming up with cell biologists, developmental biologists and the various systems specialists: immunologists, neuroscientists and others. As they do so, they are bringing with them the more collaborative and open approach seen in their native disciplines. As a result, and unusually for a dynamic and fast-moving field in the life sciences, single-cell biology has seen data, tools and results being shared more readily before publication.

This is hugely positive, and is perhaps a benefit of the otherwise-maligned street-light approach to science. The better the search tools, and the more that scientists work together to improve them, the greater the chance of everyone striking lucky. When the goals and rewards of science are less clear, then perhaps the benefits of cooperation outweigh the risks.

It will be instructive to see whether this interdisciplinary ethos continues, and whether it spreads to other subfields as the impact of big data forces biologists to rethink their approaches and broaden the expertise in their groups and laboratories. One indication might be the open submission and sharing or not of the computer code used to crunch the data presented in journal papers. As this publication pointed out in 2014, the delivery of such code from scientists lags behind that of other forms of data (Nature 514, 536; 2014). The lack of standardization makes it difficult to mandate open sharing of code, but scientists shouldnt use this as an excuse to keep it to themselves.

One sign of how far the field of single-cell analysis has come is that it has its own ambitious some say too ambitious mega-project. The Human Cell Atlas aims to identify the number of cell types and cell states that comprise a person. That ambition, of course, raises a similar question about the street-light effect. People are as individual as cells, so what if a map of cells in one human says little about the cells representation in other humans? At some point were going to have to spread the light around. The effect could be blinding. Or it could be dazzling.

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Biology of single cells shines a light on collaboration - Nature.com

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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 Uhln, 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.

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.

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.

Reporter Shamini Bundell finds out what can be learned from studying cells one by one.

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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.

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.

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 Uhln, 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. Uhln 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 CRISPRCas9 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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How to build a human cell atlas : Nature News & Comment - Nature.com

Scientists at the University of St Andrews have developed an advanced new microscopy technique that could revolutionise our understanding of how immune and cancer cells find their way through the body.

Elastic Resonator Interference Stress Microscopy (ERISM) images the extremely weak mechanical forces that living cells apply when they move, divide, and probe their environment.

As described in Nature Cell Biology today (Monday 19 June 2017), ERISM resolves the tiny forces applied by feet-like structures on the surface of human immune cells.

These feet allow immune cells to find the fastest route to a site of infection in the body.

Similar structures may be responsible for the invasion of cancer cells into healthy tissue and it is planned to use ERISM in the future to learn more about the mechanisms involved in cancer spreading.

The physical effect giving soap bubbles their rainbow-like appearance is a phenomenon called thin-film interference. It is based on interaction of light reflected on either side of a soap film.

The different colours that white light consists of interact with different local thicknesses of the thin film and generate the familiar rainbow patterns.

Effectively the colours are an image of the film thickness at each point on the surface of the soap bubble.

A similar effect can be used to determine the forces exerted by cells. Professor Malte Gather of the School of Physics and Astronomy at St Andrews explained: Our microscope records very high colour resolution images of the light reflected by a thin and soft probe. From these images, we then create a highly accurate map of the thickness of the probe with a mind-blowing precision of one-billionth part of a metre.

If cells apply forces to the probe, the probe thickness changes locally, thus providing information about the position and magnitude of the applied forces.

Although researchers have recorded forces applied by cells before, our interference-based approach gives an unprecedented resolution and in addition provides an internal reference that makes our technique extremely robust and relatively easy to use.

This robustness means that measuring cell forces could soon become a tool in clinical diagnostics. For example, doctors may find that the ERISM method can complement existing techniques to assess the invasiveness of cancer. Work to scale up ERISM for use in the clinic is now under way.

Long-term imaging of cellular forces with high precision by elastic resonator interference stress microscopyby Nils M Kronenberg, Philipp Liehm, Anja Steude, Johanna A Knipper, Jessica G Borger, Giuliano Scarcelli, Kristian Franze, Simon J Powis and Malte C Gather is published on Nature Cell Biologys website. The DOI for this paper is 10.1038/ncb3561.

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Optics of soap bubbles - Scientist Live

Would you be willing to give up the smell of fresh-baked chocolate chip cookies or a pizza right out of the oven if it meant slimming down?

A new study showed that mice that lost their sense of smell didn't gain weight even when they ate the same high-fat diet as mice that could smell and did gain weight.

The mice that retained their sense of smell packed on twice their normal weightwhile the smell-deficient mice didn't gain at all, scientists from the University of California, Berkeley, reported in the journal Cell Metabolism.

A group of mice whose olfactory neurons had been genetically altered to take away their sense of smell were also compared with another group of mice whose sense of smell had been enhanced. The "super-smellers" gained even more weight.

Two mice on the same high-fat diet are shown in the photo. The mouse on the top grew plump but the mouse on the bottom, whose sense of smell was blocked by UC Berkeley researchers, stayed a normal weight.

Andrew Dillin and Celine Riera, UC Berkeley

"This paper is one of the first studies that really shows if we manipulate olfactory inputs, we can actually alter how the brain perceives energy balance, and how the brain regulates energy balance," said study author Cline Riera.

The findings raise questions about whether or not the same would hold true for humans, Riera, a former UC Berkeley postdoctoral fellow now an assistant professor at Cedars-Sinai Medical Center in Los Angeles, told CBS News.

"The cool thing about olfactory nerves is that they are totally unique. They're not in brain, they're in the nose. Maybe in future, we can non-invasively block them in humans. Maybe if you can remove olfaction in the patients for several months, it may help them lose weight," she said.

In an article in Berkeley News, senior study author Andrew Dillin said, "Sensory systems play a role in metabolism. Weight gain isn't purely a measure of the calories taken in; it's also related to how those calories are perceived."

The researchers hope future work in this area could someday benefit patients who are morbidly obese or overweight people with health problems like diabetes.

Dillin, a professor of molecular and cell biology and Howard Hughes Medical Institute investigator, said that if the discovery proves true in humans as well as mice, it could offer new treatment options for obese patients thinking about stomach stapling or bariatric surgery. "For that small group of people, you could wipe out their smell for maybe six months and then let the olfactory neurons grow back, after they've got their metabolic program rewired," he suggested.

Those with food addictions, such as binge-eating disorders, might be helped, too.

Riera said, "We hope to eventually find a way to do that in humans as well, and help them control their addictive behaviors and switch their metabolism to fat burning instead of fat storage."

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Is sense of smell linked to being fatter or thinner? - CBS News

A research team led by Princeton engineers has revealed in remarkable new detail how liquid droplets can develop structure amidst the soup of material found inside a living cell. These droplets, known as membraneless organelles, play critical roles in cellular function and diseases.

The team, a mix of biologists and materials scientists, has shown that surprisingly low concentrations of proteins can readily condense into a droplet that has internal structure, yet is very dilute, consisting mostly of empty, watery space. This liquid scaffolding lets molecules only of certain sizes easily diffuse in and out of the structure, enabling them to perform their vital tasks.

The new insights into the molecular organization inside membraneless organelles will help clarify their contributions to health and when that organization breaks down to certain diseases.

In this study, we have measured important aspects of the protein-to-protein interactions that drive the form and function of a membraneless organelle, said Ming-Tzo Wei, a postdoctoral research associate in the Department of Chemical and Biological Engineering and lead author of the study published June 26 in the journal Nature Chemistry.

Were really starting to understand the molecular-level organization within this membraneless class of cellular structures, saidClifford Brangwynne, an assistant professor ofchemical and biological engineering, senior author of the paper and principal investigator of the Soft Living Matter Group.

At left: Membraneless organelles, called P granules, are shown in green around a cell's nucleus in a flatworm embryo. Middle: A zoom-in of the liquid-like organelles. At right: An artist's impression of a tighter zoom into the P granule, revealing its structure that it is permeable to molecules only of certain sizes, shown in red.

Image courtesy of the researchers

The team collaborated with Rohit Pappu, a biomedical engineer at Washington University in St. Louis, and also includedRodney Priestley, associate professor of chemical and biological engineering, andCraig Arnold, director of thePrinceton Institute for the Science and Technology of Materials.

The researchers focused on a protein type, LAF-1, that joins with other proteins and RNA to form a globular, membraneless organelle called a P granule. In a popularly studied roundworm,Caenorhabditis elegans, the P granules keep the worms sex cells in a prepared state for reproduction.

A set of experiments sought to determine the concentration of LAF-1 inside of a P granule versus the levels of the protein otherwise floating freely within the cell. Knowing the difference would tell the researchers what concentration of the protein is needed to form the structure. A novel technique, called ultrafast-scanning fluorescence correlation spectroscopy, proved critical to the task.

Developed in collaboration with paper co-author Arnold, who also is a professor of mechanical and aerospace engineering, the technique uses a special lens to reduce uncertainty about the size of a volume being scanned by a microscope. As a result, the concentration of proteins fitted with fluorescent tags can be accurately determined in a given space, for instance within a P granule.

Wei took a series of such measurements, along with co-first author Shana Elbaum-Garfinkle, also a postdoctoral research associate in the Department of Chemical and Biological Engineering. In addition, the researchers tracked the motions of molecules in the P granule and observed how interactions with RNA reduced the protein concentration, in effect lowering the granules fluid consistency, or viscosity.

For further insight, the researchers turned to the science of polymers, which are substances composed of many similar, smaller units, like those found in consumer plastic products. LAF-1 is a disordered protein, and can be thought of as a flexible polymer chain. The polymeric nature of LAF-1 allows it to form a scaffold-like network within the droplet. However, unlike with plastics, the teams results indicated that the mesh size, or average size of the gaps between units, is relatively large, three to eight nanometers (billionths of a meter). Molecules larger than this span cannot move throughout the droplet. This result places limits on the kinds of material that the membraneless organelle can interact with inside of a cell, shedding light on its function.

The findings were further validated by a series of computer simulations run by computational biophysicist and co-first author Alex Holehouse, a graduate student working closely with his adviser Pappu of Washington University in St. Louis.

We were able to basically swim inside the organelles to determine how much room is actually available," Pappu said in a news story published by Washington University. "While we expected to see a crowded swimming pool, we found one with plenty of room, and water. Were starting to realize that these droplets are not all going to be the same.

Pappu added that the implications for the work are broad. It is essential to be able to understand how one can regulate the functions of these droplets, Pappu said. If we succeed, the impact could be transformative: its not just cancer, its neurodegeneration, about developmental disorders, and even the fundamentals of cell biology.

The advance required the melding of multiple perspectives and expertise, Brangwynne said.

This study represents a unique collaboration between soft matter and polymer physics, mechanical engineering, computational physics and biology, said Brangwynne. Working together in this way has given us all a real sense of triumph in having helped move science forward.

Additional authors on the paper include Carlos Chih-Hsiung Chen, a research specialist in the Department of Chemical and Biological Engineering, and Marina Feric, formerly of Princeton and now a postdoctoral fellow at the National Institutes of Health. The work was supported by the Princeton Center for Complex Materials, the National Science Foundation, the National Institutes of Health and the Eric and Wendy Schmidt Transformative Technology Fund.

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'Liquid scaffolding': Watery droplets form structures inside cells - Princeton University