During early development, tissues and organs begin to bloom through the shifting, splitting, and growing of many thousands of cells.<\/p>\n
A team of MIT engineers has now developed a way to predict, minute by minute, how individual cells will fold, divide, and rearrange during a fruit fly\u2019s earliest stage of growth. The new method may one day be applied to predict the development of more complex tissues, organs, and organisms. It could also help scientists identify cell patterns that correspond to early-onset diseases, such as asthma and cancer.<\/p>\n
In a study appearing today in the journal Nature Methods, the team presents a new deep-learning model that learns, then predicts, how certain geometric properties of individual cells will change as a fruit fly develops. The model records and tracks properties such as a cell\u2019s position, and whether it is touching a neighboring cell at a given moment.<\/p>\n
The team applied the model to videos of developing fruit fly embryos, each of which starts as a cluster of about 5,000 cells. They found the model could predict, with 90 percent accuracy, how each of the 5,000 cells would fold, shift, and rearrange, minute by minute, during the first hour of development, as the embryo morphs from a smooth, uniform shape into more defined structures and features.<\/p>\n
\u201cThis very initial phase is known as gastrulation, which takes place over roughly one hour, when individual cells are rearranging on a time scale of minutes,\u201d says study author Ming Guo, associate professor of mechanical engineering at MIT. \u201cBy accurately modeling this early period, we can start to uncover how local cell interactions give rise to global tissues and organisms.\u201d<\/p>\n
The researchers hope to apply the model to predict the cell-by-cell development in other species, such zebrafish and mice. Then, they can begin to identify patterns that are common across species. The team also envisions that the method could be used to discern early patterns of disease, such as in asthma. Lung tissue in people with asthma looks markedly different from healthy lung tissue. How asthma-prone tissue initially develops is an unknown process that the team\u2019s new method could potentially reveal.<\/p>\n
\u201cAsthmatic tissues show different cell dynamics when imaged live,\u201d says co-author and MIT graduate student Haiqian Yang. \u201cWe envision that our model could capture these subtle dynamical differences and provide a more comprehensive representation of tissue behavior, potentially improving diagnostics or drug-screening assays.\u201d<\/p>\n
The study\u2019s co-authors are Markus Buehler, the\u00a0McAfee Professor of Engineering in MIT\u2019s Department of Civil and Environmental Engineering; George Roy and Tomer Stern of the University of Michigan; and Anh Nguyen and Dapeng Bi of Northeastern University.<\/p>\n
Points and foams<\/strong><\/p>\n Scientists typically model how an embryo develops in one of two ways: as a point cloud, where each point represents an individual cell as point that moves over time; or as a \u201cfoam,\u201d which represents individual cells as bubbles that shift and slide against each other, similar to the bubbles in shaving foam.<\/p>\n Rather than choose between the two approaches, Guo and Yang embraced both.<\/p>\n \u201cThere\u2019s a debate about whether to model as a point cloud or a foam,\u201d Yang says. \u201cBut both of them are essentially different ways of modeling the same underlying graph, which is an elegant way to represent living tissues. By combining these as one graph, we can highlight more structural information, like how cells are connected to each other as they rearrange over time.\u201d<\/p>\n At the heart of the new model is a \u201cdual-graph\u201d structure that represents a developing embryo as both moving points and bubbles. Through this dual representation, the researchers hoped to capture more detailed geometric properties of individual cells, such as the location of a cell\u2019s nucleus, whether a cell is touching a neighboring cell, and whether it is folding or dividing at a given moment in time.<\/p>\n As a proof of principle, the team trained the new model to \u201clearn\u201d how individual cells change over time during fruit fly gastrulation.<\/p>\n \u201cThe overall shape of the fruit fly at this stage is roughly an ellipsoid, but there are gigantic dynamics going on at the surface during gastrulation,\u201d Guo says. \u201cIt goes from entirely smooth to forming a number of folds at different angles. And we want to predict all of those dynamics, moment to moment, and cell by cell.\u201d<\/p>\n Where and when<\/strong><\/p>\n For their new study, the researchers applied the new model to high-quality videos of fruit fly gastrulation taken by their collaborators at the University of Michigan. The videos are one-hour recordings of developing fruit flies, taken at single-cell resolution. What\u2019s more, the videos contain labels of individual cells\u2019 edges and nuclei \u2014 data that are incredibly detailed and difficult to come by.<\/p>\n \u201cThese videos are of extremely high quality,\u201d Yang says. \u201cThis data is very rare, where you get submicron resolution of the whole 3D volume at a pretty fast frame rate.\u201d<\/p>\n The team trained the new model with data from three of four fruit fly embryo videos, such that the model might \u201clearn\u201d how individual cells interact and change as an embryo develops. They then tested the model on an entirely new fruit fly video, and found that it was able to predict with high accuracy how most of the embryo\u2019s 5,000 cells changed from minute to minute.<\/p>\n Specifically, the model could predict properties of individual cells, such as whether they will fold, divide, or continue sharing an edge with a neighboring cell, with about 90 percent accuracy.<\/p>\n \u201cWe end up predicting not only whether these things will happen, but also when,\u201d Guo says. \u201cFor instance, will this cell detach from this cell seven minutes from now, or eight? We can tell when that will happen.\u201d<\/p>\n The team believes that, in principle, the new model, and the dual-graph approach, should be able to predict the cell-by-cell development of other multiceullar systems, such as more complex species, and even some human tissues and organs. The limiting factor is the availability of high-quality video data.<\/p>\n \u201cFrom the model perspective, I think it\u2019s ready,\u201d Guo says. \u201cThe real bottleneck is the data. If we have good quality data of specific tissues, the model could be directly applied to predict the development of many more structures.\u201d<\/p>\n This work is supported, in part, by the U.S. National Institutes of Health.<\/p>\n","protected":false},"excerpt":{"rendered":"During early development, tissues and organs begin to bloom through the shifting, splitting, and growing of many thousands…\n","protected":false},"author":2,"featured_media":234045,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[261],"tags":[291,289,290,47353,18,45387,124467,124466,19,17,124465,124464,124468,42622,82],"class_list":{"0":"post-234044","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-artificial-intelligence","8":"tag-ai","9":"tag-artificial-intelligence","10":"tag-artificialintelligence","11":"tag-cell-imaging","12":"tag-eire","13":"tag-embryo-development","14":"tag-fruit-fly","15":"tag-haiqian-yang","16":"tag-ie","17":"tag-ireland","18":"tag-ming-guo","19":"tag-mit-meche","20":"tag-multicellular-development","21":"tag-neural-networks","22":"tag-technology"},"share_on_mastodon":{"url":"https:\/\/pubeurope.com\/@ie\/115723944798690828","error":""},"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/posts\/234044","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/comments?post=234044"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/posts\/234044\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/media\/234045"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/media?parent=234044"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/categories?post=234044"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/tags?post=234044"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}