{"id":23371,"date":"2019-07-08T10:54:03","date_gmt":"2019-07-08T17:54:03","guid":{"rendered":"https:\/\/vermont.salk.edu\/?post_type=disclosure&p=23371"},"modified":"2019-12-09T13:34:45","modified_gmt":"2019-12-09T21:34:45","slug":"new-computational-tool-lets-researchers-identify-cells-based-on-their-chromosome-shape","status":"publish","type":"disclosure","link":"https:\/\/www.salk.edu\/zh\/news-release\/new-computational-tool-lets-researchers-identify-cells-based-on-their-chromosome-shape\/","title":{"rendered":"New computational tool lets researchers identify cells based on their chromosome shape"},"content":{"rendered":"

LA JOLLA\u2014In the nucleus of every living cell, long strands of DNA are tightly folded into compact chromosomes. Now, thanks to a new computational approach developed at the Salk Institute, researchers can use the architecture of these chromosome folds to differentiate between types of cells. The information about each cell\u2019s chromosome structure will give scientists a better understanding of how interactions between different regions of DNA play a role in health and disease. The study was published in the Proceedings of the National Academy of Sciences<\/a><\/em>\u00a0the week of July 8, 2019.<\/p>\n

\u201cIn a tissue like liver or heart or brain, there are many diverse cell types we don\u2019t understand yet; this is a new tool to help us look at these cells one at a time,\u201d says Salk Professor and Howard Hughes Medical Institute Investigator \u7ea6\u745f\u592b\u00b7\u57c3\u514b\u5c14<\/a>, who heads Salk\u2019s Genomic Analysis Laboratory.<\/p>\n

\"Human
Human chromosomes imaged during the metaphase stage of cell division. Image courtesy of Dixon Lab.<\/p>\n

Click here<\/a> for a high-resolution image.<\/p>\n

Credit: Salk Institute<\/figcaption><\/figure>\n

Researchers know that a majority of the human genome is made up of regulatory DNA\u2014stretches of DNA that don\u2019t themselves encode proteins, but help control whether, and when, genes are expressed in any given cell. This regulation may occur through physical interactions between a regulatory stretch of DNA and a gene. Regulatory regions, however, might be far from a gene they regulate on a linear strand of DNA. In the process of chromosome assembly, tight, specific connections are formed between genes and regulatory DNA, held closely together in a folded chromosome.<\/p>\n

In 2009, researchers developed Hi-C, a method of probing cells for these chromosomal interactions. Knowing which stretches of DNA physically interact can tell scientists what the effect of a mutation in a regulatory region might be, helping to explain why levels of a protein are altered in a diseased cell. Typically, Hi-C is used on many cells at once and the results capture only an average of the chromosome architecture in a population of cells. This means that if there\u2019s an interaction between two regions of DNA, but it\u2019s only present in a small minority of cells, it won\u2019t show up in a standard Hi-C experiment.<\/p>\n

\u201cIf half of the cells you\u2019re studying have a particular chromosome architecture, you can see that, but you really can\u2019t tell what individual cells are doing,\u201d says Ecker. His group wanted to develop a way to get more detailed single-cell data from Hi-C experiments.<\/p>\n

The problem with applying Hi-C to single cells is that cells of the same type can have variability in their chromosome architecture, says graduate student Jingtian Zhou, first author of the new paper. Moreover, a Hi-C experiment done on a single cell only reveals data on about 5% of the genome. So finding trends\u2014such as concluding that one cell type has a change in chromosome architecture when disease is present\u2014is tricky. But it turns out that those two problems with the data\u2014known as heterogeneity and sparsity\u2014are dealt with by researchers in many diverse fields that have to analyze large data sets, and there are some solutions.<\/p>\n

\u201cWe ended up borrowing algorithms used in computer science and applying them to biological data to help us deal with the sparsity and heterogeneity of single-cell Hi-C results,\u201d says Zhou.<\/p>\n

With this inspiration from computer science, the team developed scHiCluster, an algorithm to sift through Hi-C data from mixed cells and sort the cells into discrete groups based on the similarity of their chromosome interactions. That lets them more easily draw conclusions about what cells are doing when it comes to gene regulation in different biological circumstances.<\/p>\n

They tested the algorithm on previously published sets of Hi-C data, showing that they could correctly sort out different cell types from a mixed dataset. The new approach will come in handy as researchers continue to study how cells in the human body function, and how that function goes awry in disease.<\/p>\n

\u201cIf you take a disease like Alzheimer\u2019s, researchers have found changes in gene expression in some brain cell types,\u201d says Ecker. \u201cBut until now, we didn\u2019t have the ability to easily link those gene expression changes to regions in the genome that control gene transcription.\u201d<\/p>\n

The group plans to generate Hi-C data in single cells in a variety of human tissues. Applying the computational technique to studying single-cell chromosome structures could further facilitate the understanding of gene regulation diversity in different cells types.<\/p>\n

Other researchers on the study were Yusi Chen, Terrence Sejnowski and Jesse Dixon of the Salk Institute; Jianzhu Ma, Chuankai Cheng and Bokan Bao of UC San Diego; and Jian Peng of University of Illinois at Urbana-Champaign.<\/p>\n

The work described in this paper was supported by the National Human Genome Research Institute.<\/p>","protected":false},"featured_media":23373,"template":"","faculty":[42],"disease-research":[333],"class_list":["post-23371","disclosure","type-disclosure","status-publish","has-post-thumbnail","hentry","faculty-joseph-ecker","disease-research-genetics"],"acf":[],"yoast_head":"\nNew computational tool lets researchers identify cells based on their chromosome shape - Salk Institute for Biological Studies<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.salk.edu\/zh\/news-release\/new-computational-tool-lets-researchers-identify-cells-based-on-their-chromosome-shape\/\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"New computational tool lets researchers identify cells based on their chromosome shape - Salk Institute for Biological Studies\" \/>\n<meta property=\"og:description\" content=\"LA JOLLA\u2014In the nucleus of every living cell, long strands of DNA are tightly folded into compact chromosomes. 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