![](\"https://s3.amazonaws.com/4dn-dcic-public/static-pages/InfoBoxes/SPRITE_figA_Original.png\"){kind=icon}
\n\n\n Image source: Quinodoz et. al. Cell 2018, Figure 1A\n
\nSPRITE is a method to detect and quantify genome-wide higher-order interactions that occur simultaneously within the same nucleus. It was first published in 2018, and it aims to address certain limitations of proximity ligation and imaging methods. Compared to proximity ligation methods, this technique does not depend on the ligation of spatially close DNA fragments; therefore, it can detect interactions occurring across larger distances in the genome. Additionally, unlike both methods that can only capture simultaneous interactions between a small number of genomic regions (2-3), this technique is able to capture simultaneous interactions between a larger number of genomic regions.\n
\n\nThe protocol involves cross-linking the cells to form links between physically adjacent DNA regions and other interacting molecules such as RNA and proteins. Then, the cells are lysed, and a restriction enzyme is used to digest the chromatin into multiple fragments. The cross-linked complexes are coupled to magnetic beads. A split-pool tagging strategy is performed that consists of splitting the cross-linked complexes across a 96 well plate, and ligating a tag sequence unique to each well to each molecule. The wells are then pooled and this process is repeated several times. The molecules located in the same complex will stick together throughout the entire split-pool process, resulting in them having the same barcode combination at the end, while the molecules located in other complexes will have their own distinct barcode combinations. The molecules are sequenced, and all the reads containing the same unique barcode combination are grouped together into a cluster. Initial processing results in the generation of a clusters file where each cluster occupies one line that includes the barcode name and genomic alignments of that cluster. This can be used for additional analysis and to create visualizations.\n\nSee Quinodoz et. al. Cell 2018 for more details.\n
", "name": "quinodoz-etal.main-summary", "award": {"@id": "/awards/1U01CA200059-01/", "project": "4DN", "@type": ["Award", "Item"], "name": "1U01CA200059-01", "display_title": "4D NUCLEOME NETWORK DATA COORDINATION AND INTEGRATION CENTER - PHASE I", "uuid": "b0b9c607-f8b4-4f02-93f4-9895b461334b", "description": "DCIC: The goals of the 4D Nucleome (4DN) Data Coordination and Integration Center (DCIC) are to collect, store, curate, display, and analyze data generated in the 4DN Network. We have assembled a team of investigators with a strong track record in analysis of chromatin interaction data, image processing and three-dimensional data visualization, integrative analysis of genomic and epigenomic data, data portal development, large-scale computing, and development of secure and flexible cloud technologies. In Aim 1, we will develop efficient submission pipelines for data and metadata from 4DN data production groups. We will define data/metadata requirements and quality metrics in conjunction with the production groups and ensure that high-quality, well- annotated data become available to the wider scientific community in a timely manner. In Aim 2, we will develop a user-friendly data portal for the broad scientific community. This portal will provide an easy-to-navigate interface for accessing raw and intermediate data files, allow for programmatic access via APIs, and will incorporate novel analysis and visualization tools developed by DCIC as well as other Network members. For computing and storage scalability and cost-effectiveness, significant efforts will be devoted to development and deployment of cloud-based technology. We will conduct tutorials and workshops to facilitate the use of 4DN data and tools by external investigators. In Aim 3, we will coordinate and assist in conducting integrative analysis of the multiple data types. These efforts will examine key questions in higher-order chromatin organization using both sequence and image data, and the tools and algorithms developed here will be incorporated into the data portal for use by other investigators. These three aims will ensure that the data generated in 4DN will have maximal impact for the scientific community.", "status": "current", "center_title": "DCIC - DCIC", "pi": {"error": "no view permissions"}, "principals_allowed": {"view": ["system.Everyone"], "edit": ["group.admin"]}}, "title": "Project Summary", "status": "released", "aliases": ["4dn-dcic-lab:project_summary_sprite"], "options": {"filetype": "html", "collapsible": false, "default_open": true}, "date_created": "2018-12-03T16:11:21.965084+00:00", "section_type": "Page Section", "submitted_by": {"error": "no view permissions"}, "last_modified": {"modified_by": {"error": "no view permissions"}, "date_modified": "2018-12-04T01:59:15.895758+00:00"}, "schema_version": "2", "contributing_labs": [{"correspondence": [{"contact_email": "bWd1dHRtYW5AY2FsdGVjaC5lZHU=", "@id": "/users/ac3920c8-caa6-444b-be8a-48b52a1dcb3e/", "display_title": "Mitchell Guttman"}], "status": "current", "@id": "/labs/mitchell-guttman-lab/", "@type": ["Lab", "Item"], "display_title": "Mitchell Guttman, CALTECH", "uuid": "c17e88b5-912a-496a-acc7-28dd71215a7d", "pi": {"error": "no view permissions"}, "principals_allowed": {"view": ["system.Everyone"], "edit": ["group.admin", "role.lab_submitter", "submits_for.c17e88b5-912a-496a-acc7-28dd71215a7d"]}}], "@id": "/static-sections/cd605246-3d70-44b2-aba0-c6acd6b5caf1/", "@type": ["StaticSection", "UserContent", "Item"], "uuid": "cd605246-3d70-44b2-aba0-c6acd6b5caf1", "principals_allowed": {"view": ["system.Everyone"], "edit": ["group.admin", "role.owner", "userid.e2324f87-0625-4bbc-803b-d47677aebe08"]}, "display_title": "Project Summary", "external_references": [], "content": " Technology Development: SPRITE \n\n\n\nSPRITE is a method to detect and quantify genome-wide higher-order interactions that occur simultaneously within the same nucleus. It was first published in 2018, and it aims to address certain limitations of proximity ligation and imaging methods. Compared to proximity ligation methods, this technique does not depend on the ligation of spatially close DNA fragments; therefore, it can detect interactions occurring across larger distances in the genome. Additionally, unlike both methods that can only capture simultaneous interactions between a small number of genomic regions (2-3), this technique is able to capture simultaneous interactions between a larger number of genomic regions.\n
\n\nThe protocol involves cross-linking the cells to form links between physically adjacent DNA regions and other interacting molecules such as RNA and proteins. Then, the cells are lysed, and a restriction enzyme is used to digest the chromatin into multiple fragments. The cross-linked complexes are coupled to magnetic beads. A split-pool tagging strategy is performed that consists of splitting the cross-linked complexes across a 96 well plate, and ligating a tag sequence unique to each well to each molecule. The wells are then pooled and this process is repeated several times. The molecules located in the same complex will stick together throughout the entire split-pool process, resulting in them having the same barcode combination at the end, while the molecules located in other complexes will have their own distinct barcode combinations. The molecules are sequenced, and all the reads containing the same unique barcode combination are grouped together into a cluster. Initial processing results in the generation of a clusters file where each cluster occupies one line that includes the barcode name and genomic alignments of that cluster. This can be used for additional analysis and to create visualizations.\n\nSee Quinodoz et. al. Cell 2018 for more details.\n
", "filetype": "html", "content_as_html": "\nSPRITE is a method to detect and quantify genome-wide higher-order interactions that occur simultaneously within the same nucleus. It was first published in 2018, and it aims to address certain limitations of proximity ligation and imaging methods. Compared to proximity ligation methods, this technique does not depend on the ligation of spatially close DNA fragments; therefore, it can detect interactions occurring across larger distances in the genome. Additionally, unlike both methods that can only capture simultaneous interactions between a small number of genomic regions (2-3), this technique is able to capture simultaneous interactions between a larger number of genomic regions.\n
\n\nThe protocol involves cross-linking the cells to form links between physically adjacent DNA regions and other interacting molecules such as RNA and proteins. Then, the cells are lysed, and a restriction enzyme is used to digest the chromatin into multiple fragments. The cross-linked complexes are coupled to magnetic beads. A split-pool tagging strategy is performed that consists of splitting the cross-linked complexes across a 96 well plate, and ligating a tag sequence unique to each well to each molecule. The wells are then pooled and this process is repeated several times. The molecules located in the same complex will stick together throughout the entire split-pool process, resulting in them having the same barcode combination at the end, while the molecules located in other complexes will have their own distinct barcode combinations. The molecules are sequenced, and all the reads containing the same unique barcode combination are grouped together into a cluster. Initial processing results in the generation of a clusters file where each cluster occupies one line that includes the barcode name and genomic alignments of that cluster. This can be used for additional analysis and to create visualizations.\n\nSee Quinodoz et. al. Cell 2018 for more details.\n