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  • New tool aligns data from tissue slices virtually

    New tool aligns data from tissue slices virtually

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    Newswise — SAN FRANCISCO, CA—Imagine a few roughly cut slices of bread on a plate. With just those slices, could you picture, in fine detail, the loaf they came from?

    Now, imagine several thin slices of tissue from, say, a small tumor. You’ve tested which of several genes are active at every point across each slice’s length and width. With that two-dimensional data from just a few slices, could you predict which of the genes are active throughout the entire three-dimensional structure of the tumor? Not easy, right?

    Discerning the 3D makeup of a tumor—or other tissue—using data from just a few slices is a serious computational challenge. But a new method developed at Gladstone Institutes enables researchers to do just that. This approach, published in the journal Nature Methods, could allow for much deeper understanding of biological tissue samples.

    “Without that third dimension, you can miss a lot of what’s happening in tissue,” says Gladstone Senior Investigator Barbara Engelhardt, PhD, senior author of the study. “Putting together slices in 3D space should help us begin to answer questions for which 2D data falls short. For instance, what are the precise boundaries of a tumor? Where have immune cells infiltrated the tumor? Where in the tumor would be best to inject a treatment?”

    The new method, named Gaussian Process Spatial Alignment (GPSA), is not just for tumors. It can be applied to nearly any kind of tissue and any type of data obtained from tissue slices, such as the structure of cells or which genes or proteins are switched on within them—with broad implications for research and medicine.

    Filling in the Blanks

    One of the most widely used ways to understand biological tissue—whether from a patient with an illness or an animal in a lab—is to surgically remove some of the affected tissue and analyze it. In labs around the world, technicians may slice the removed tissue into thin pieces to view under a microscope or to test for the presence of specific molecules that could aid diagnosis, guide treatment, or hint at how well a drug is working.

    However, the time, budget, and computational power needed to analyze each slice means that researchers and doctors are often limited to just a few slices from different parts of the tissue. What’s more, tissue slices become physically warped when they are cut, processed, and analyzed in a lab, making it difficult to discern exactly how the slices line up and fit together within the overall 3D structure of the original tissue.

    “The first step in going from 2D slice data to a full, 3D picture of the tissue is to computationally reverse warping so that we can realign the slices in virtual space,” says Engelhardt, who is also a professor in the Department of Biomedical Data Science at Stanford University.

    To address this challenge, the GPSA method uses what Engelhardt and her team refer to as a two-layer Gaussian process. This statistical approach harnesses data from the 2D tissue slices and, in the first layer, fits the warped 2D slice onto a 3D model of the tissue. In the second layer, GPSA attributes to each point in the 3D model some data collected from the slice, such as what genes are turned on at that point. In this way, GPSA reverses warping virtually and enables a highly precise alignment of the slices.

    During this process, the GPSA model fills in the spaces between slices with predictions of gene or protein expression for every point throughout the tissue, ultimately generating a 3D “atlas” of the tissue.

    “Say you have four slices from different locations in a person’s breast cancer tumor, and for every point on each slice you know which of 20,000 genes are turned on or off,” Engelhardt says. “GPSA creates a fully query-able 3D atlas where, for any single ‘x, y, z’ coordinate, for any of the 20,000 genes, we can dive in and ask: What genes are on and off at this position in the tumor? And how certain are we in this estimate?”

    A Highly Flexible Framework

    With GPSA, researchers can construct tissue atlases with data obtained from slices of inconsistent sizes, using different technologies, and at different scales and levels of resolution. While prior methods require the 3D scaffolds or “coordinate frameworks” to be pre-specified, GPSA estimates this 3D framework from the 2D slices alone when a coordinate framework for the tissue does not yet exist. The new method can also combine multiple types of tissue-slice data—say, both information about which genes are switched on and information about cellular structure—into a single atlas.

    In addition, when applied to slices taken from the same tissue at different points in time, GPSA can generate atlases that predict how every location within the tissue changes over time. In this way, the technique could help deepen understanding of aging, how illnesses progress, or how different tissues develop in a growing organism.

    “Flexibility is one of the main strengths of our new tool,” Engelhardt says.

    She and her team are now conducting analyses to further demonstrate that flexibility. For instance, they have developed a method that could be used by labs on a budget to determine the minimum number of tissue slices needed—and the precise locations where those slices should be cut—for GPSA to construct a tissue atlas with the desired information.

    “The goal is to maximize the insights we can gain from tissue slices, in order to allow researchers and clinicians to deeply query 3D tissues that are well-studied or tumors that are unique to a patient, and ultimately improve healthcare,” Engelhardt says.

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  • Evolution of uniquely human DNA was a balancing act, study concludes

    Evolution of uniquely human DNA was a balancing act, study concludes

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    Newswise — SAN FRANCISCO, CA—January 13, 2023—Humans and chimpanzees differ in only one percent of their DNA. Human accelerated regions (HARs) are parts of the genome with an unexpected amount of these differences. HARs were stable in mammals for millennia but quickly changed in early humans. Scientists have long wondered why these bits of DNA changed so much, and how the variations set humans apart from other primates.

    Now, researchers at Gladstone Institutes have analyzed thousands of human and chimpanzee HARs and discovered that many of the changes that accumulated during human evolution had opposing effects from each other.

    “This helps answer a longstanding question about why HARs evolved so quickly after being frozen for millions of years,” says Katie Pollard, PhD, director of the Gladstone Institute of Data Science and Biotechnology and lead author of the new study published today in Neuron. “An initial variation in a HAR might have turned up its activity too much, and then it needed to be turned down.”

    The findings, she says, have implications for understanding human evolution. In addition—because she and her team discovered that many HARs play roles in brain development—the study suggests that variations in human HARs could predispose people to psychiatric disease.

    “These results required cutting-edge machine learning tools to integrate dozens of novel datasets generated by our team, providing a new lens to examine the evolution of HAR variants,” says Sean Whalen, PhD, first author of the study and senior staff research scientist in Pollard’s lab.

    Enabled by Machine Learning

    Pollard discovered HARs in 2006 when comparing the human and chimpanzee genomes. While these stretches of DNA are nearly identical among all humans, they differ between humans and other mammals. Pollard’s lab went on to show that the vast majority of HARs are not genes, but enhancers— regulatory regions of the genome that control the activity of genes.

    More recently, Pollard’s group wanted to study how human HARs differ from chimpanzee HARs in their enhancer function. In the past, this would have required testing HARs one at a time in mice, using a system that stains tissues when a HAR is active.

    Instead, Whalen input hundreds of known human brain enhancers, and hundreds of other non-enhancer sequences, into a computer program so that it could identify patterns that predicted whether any given stretch of DNA was an enhancer. Then he used the model to predict that a third of HARs control brain development.

    “Basically, the computer was able to learn the signatures of brain enhancers,” says Whalen.

    Knowing that each HAR has multiple differences between humans and chimpanzees, Pollard and her team questioned how individual variants in a HAR impacted its enhancer strength. For instance, if eight nucleotides of DNA differed between a chimpanzee and human HAR, did all eight have the same effect, either making the enhancer stronger or weaker?

    “We’ve wondered for a long time if all the variants in HARs were required for it to function differently in humans, or if some changes were just hitchhiking along for the ride with more important ones,” says Pollard, who is also chief of the division of bioinformatics in the Department of Epidemiology and Biostatistics at UC San Francisco (UCSF), as well as a Chan Zuckerberg Biohub investigator.

    To test this, Whalen applied a second machine learning model, which was originally designed to determine if DNA differences from person to person affect enhancer activity. The computer predicted that 43 percent of HARs contain two or more variants with large opposing effects: some variants in a given HAR made it a stronger enhancer, while other changes made the HAR a weaker enhancer.

    This result surprised the team, who had expected that all changes would push the enhancer in the same direction, or that some “hitchhiker” changes would have no impact on the enhancer at all.

    Measuring HAR Strength

    To validate this compelling prediction, Pollard collaborated with the laboratories of Nadav Ahituv, PhD, and Alex Pollen, PhD, at UCSF. The researchers fused each HAR to a small DNA barcode. Each time a HAR was active, enhancing the expression of a gene, the barcode was transcribed into a piece of RNA. Then, the researchers used RNA sequencing technology to analyze how much of that barcode was present in any cell—indicating how active the HAR had been in that cell.

    “This method is much more quantitative because we have exact barcode counts instead of microscopy images,” says Ahituv. “It’s also much higher throughput; we can look at hundreds of HARs in a single experiment.”

    When the group carried out their lab experiments on over 700 HARs in precursors to human and chimpanzee brain cells, the data mimicked what the machine learning algorithms had predicted.

    “We might not have discovered human HAR variants with opposing effects at all if the machine learning model hadn’t produced these startling predictions,” said Pollard.

    Implications for Understanding Psychiatric Disease

    The idea that HAR variants played tug-of-war over enhancer levels fits in well with a theory that has already been proposed about human evolution: that the advanced cognition in our species is also what has given us psychiatric diseases.

    “What this kind of pattern indicates is something called compensatory evolution,” says Pollard. “A large change was made in an enhancer, but maybe it was too much and led to harmful side effects, so the change was tuned back down over time—that’s why we see opposing effects.”

    If initial changes to HARs led to increased cognition, perhaps subsequent compensatory changes helped tune back down the risk of psychiatric diseases, Pollard speculates. Her data, she adds, can’t directly prove or disprove that idea. But in the future, a better understanding of how HARs contribute to psychiatric disease could not only shed light on evolution, but on new treatments for these diseases.

    “We can never wind the clock back and know exactly what happened in evolution,” says Pollard. “But we can use all these scientific techniques to simulate what might have happened and identify which DNA changes are most likely to explain unique aspects of the human brain, including its propensity for psychiatric disease.”

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    About the Study

    The paper “Machine learning dissection of human accelerated regions in primate neurodevelopment,” was published in the journal Neuron on January 13, 2023.

    Other authors are Kathleen Keough, Alex Williams, Md. Abu Hassan Samee, and Sean Thomas of Gladstone; Fumitaka Inoue, Hane Ryu, Tyler Fair, Eirene Markenscoff-Papadimitrious, Beatriz Alvarado, Orry Elor, Dianne Laboy Cintron, Erik Ullian, Arnold Kriegstein, and John Rubenstein of UC San Francisco; Martin Kircher, Beth Martin, and Jay Shendure of University of Washington; and Robert Krencik of Houston Methodist Research Institute.

    The work was supported by the Schmidt Futures Foundation and the National Institutes of Health (DP2MH122400-01, R35NS097305, FHG011569A, R01MH109907, U01MH116438, UM1HG009408, UM1HG011966, 2R01NS099099).

    About Gladstone Institutes

    To ensure our work does the greatest good, Gladstone Institutes focuses on conditions with profound medical, economic, and social impact—unsolved diseases. Gladstone is an independent, nonprofit life science research organization that uses visionary science and technology to overcome disease. It has an academic affiliation with the University of California, San Francisco.

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