{"id":2242,"date":"2019-07-09T23:07:16","date_gmt":"2019-07-09T23:07:16","guid":{"rendered":"https:\/\/goya.everthemes.com\/demo-basic\/?page_id=2242"},"modified":"2024-01-16T12:35:34","modified_gmt":"2024-01-16T12:35:34","slug":"store-locations","status":"publish","type":"page","link":"https:\/\/www.geneethic.com.tr\/?page_id=2242","title":{"rendered":"Spatial Transcriptome \/ Stereo-Seq"},"content":{"rendered":"[vc_row et_full_width=”true” et_row_padding=”true” css=”.vc_custom_1579113527778{padding-top: 120px !important;padding-bottom: 60px !important;background-color: #f6f6f6 !important;}”][vc_column css=”.vc_custom_1684760689814{background-color: #f6f6f6 !important;}”][vc_row_inner][vc_column_inner animation=”animation bottom-to-top”]\t
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Spatial transcriptome-Stereo-seq<\/h3>\n
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Stereo-seq, a spatio-temporal omics technology independently developed by BGI Genomics, captures mRNA from tissue section by stereo chips and restores the spatial context by utilizing the spatial barcode (Coordinate ID, CID), thus establishing a solid research foundation for further understanding the relationship between gene expression, morphology of cells and local environment.<\/p>\n

Stereo-seq is a pioneering tool that achieves Nanoscale Resolution: can theoretically achieve a 100% cell capture rate, obtaining more informative and accurate cell clustering results.<\/p>\n

Stereo-seq provides centimeter-scale panoramic field of view, a maximum field of view of 13 cm x 13 cm, enabling the rendering of a panoramic molecular cell map of organs and life. \u00a0Stereo-seq recognizes the location of the nucleus through fluorescent imaging, and in combination with the algorithm, the expression map can be achieved at approximate single-cell level.<\/p>\n<\/div>\n<\/div>\n[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row et_full_width=”true” et_row_padding=”true” css=”.vc_custom_1579113590633{padding-top: 60px !important;background-color: #f6f6f6 !important;}”][vc_column][vc_row_inner et_column_align=”align-center” content_placement=”middle”][vc_column_inner width=”1\/2″ animation=”animation bottom-to-top” offset=”vc_col-lg-6″][vc_column_text animation=”animation bottom-to-top” css=”.vc_custom_1705408006448{margin-bottom: 0px !important;}”]\"\"[\/vc_column_text][\/vc_column_inner][vc_column_inner width=”1\/2″ animation=”animation bottom-to-top”][vc_column_text css=”.vc_custom_1705408027590{margin-bottom: 0px !important;}”]\n

Data Analysis<\/h3>\n
Stereo-seq Analysis Workflow (SAW) software suite is a set of pipelines bundled to position sequenced reads to their spatial location on the tissue section, quantify spatial gene expression and visually present spatial expression distribution. SAW processes the sequencing data of Stereo-seq to generate spatial gene expression matrices, and then users could take these files as the starting point to perform downstream analysis. SAW includes thirteen essential and suggested pipelines, as well as auxiliary tools for supporting other handy functions.<\/div>\n[\/vc_column_text][\/vc_column_inner][\/vc_row_inner]\t\t
\n\t\t\tNeed a quotation?<\/span>\r\n\r\n\r\n<\/svg>\r\n<\/a>\n\t\t<\/div>\n\t\t\n\t[vc_row_inner et_max_width=”” et_column_align=”align-center” content_placement=”middle”][vc_column_inner offset=”vc_col-lg-6″][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row et_column_align=”align-center” css=”.vc_custom_1579113617984{padding-top: 60px !important;padding-bottom: 60px !important;}”][vc_column width=”1\/2″ animation=”animation bottom-to-top”][vc_tta_accordion active_section=”1″][vc_tta_section title=”Specifications” tab_id=”1557543124640-60cb9bb3-2cc8″][vc_column_text]1. Guaranteed \u226580% of bases with quality score of Q30<\/p>\n

2. DNBSEQ\u2122 PE100 sequencing<\/p>\n

3. Raw data of 1G reads\/sample and SAW analysis are available for delivery[\/vc_column_text][\/vc_tta_section][vc_tta_section title=”Sample Requirements” tab_id=”1557543124710-e0287025-a0eb”][vc_column_text]Fresh frozen samples suggest embedded in Tissue-Teck OCT, to avoid RNA degradation, we recommend performing tissue embedding within 30 minutes after resected and wiped extra fluid.<\/p>\n

The tissue size should not exceed 0.9 cm x 0.9 cm x 2 cm, as the tissue section should not exceed 80% area coverage of the chip.<\/p>\n

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splitMask<\/strong><\/div>\n
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Split Stereo-seq Chip T mask file into several pieces according to CID indexing in the Q4 FASTQ files.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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CIDCount<\/strong><\/div>\n
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Count CIDs in the Stereo-seq Chip T mask file and roughly estimate memory required to do\u00a0mapping.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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Mapping<\/strong><\/div>\n
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Correspond in situ captured sequenced reads recorded in FASTQ(3,4) files by Stereo-seq with their spatial information. It also aligns reads to the reference genome and generates coordination sorted BAM files.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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Merge (optional)<\/strong><\/div>\n
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Combine CID (same as barcodes) listed files with reads count from multiple runs of\u00a0mapping. Only for an analysis that requires to combine multiple pairs of FASTQ.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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Count<\/strong><\/div>\n
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Read BAM files generated from\u00a0mapping\u00a0to perform gene annotation, de-duplication, and gene expression analysis on the aligned reads.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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Register<\/strong><\/div>\n
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Align microscopic tissue staining image with gene expression matrix file (GEF) generated from\u00a0count.\u00a0register\u00a0is an optional pipeline when image fails QC or input image is absent.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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ImageTools<\/strong><\/div>\n
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Convert TIFF images from IPR, such as template-aligned stitched TIFF image, binarized tissue segmentation and cell segmentation images. Optional module when image fails QC or input image is absent.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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TissueCut<\/strong><\/div>\n
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Identify tissue coverage area on the chip and extract gene expression matrix of the corresponding spatial location by taking inputs from both\u00a0count\u00a0and\u00a0register\u00a0or\u00a0count\u00a0pipeline alone.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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spatialCluster:<\/strong><\/div>\n
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Perform clustering analysis for spots (bin200) according to the gene expression matrix of the tissue coverage area generated from\u00a0tissueCut.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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cellCut<\/strong><\/div>\n
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Identify cell nuclei coverage area on the staining image and extract gene expression matrix of the corresponding spatial location by taking inputs from both\u00a0count\u00a0and\u00a0register & imageTools\u00a0pipeline. Optional module when image fails QC or input image is absent.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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cellCorrect<\/strong><\/div>\n
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Adjust cell coverage region based on the aligned cell nuclei segmentation image generated from\u00a0register\u00a0and\u00a0imageTools. Then extract expression matrix of the adjusted cells in cell bin GEF and GEM formats.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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cellCluster<\/strong><\/div>\n
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Perform clustering analysis for cell bins according to the gene expression matrix which is generated from\u00a0cellCorrect. Optional module when image fails QC or input image is absent.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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Saturation<\/strong><\/div>\n
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Calculate sequencing saturation of tissue coverage area based on the file that was used for sampling data generated from\u00a0count.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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Report<\/strong><\/div>\n
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Generate a JSON format statistical summary report that integrates the analysis result from each step, as well as an HTML web analysis report, and shows spatial expression distribution of genes, key statistical metrics, sequencing saturation plots, clustering analysis results. Depending on the image input state and register mode, HTML reports may or may not have cell bin statistical data and image processing key results.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n[\/vc_column_text][\/vc_tta_section][\/vc_tta_accordion][vc_column_text animation=”animation bottom-to-top”]\n

STOmics Validated Tissue List and Risky Sample Types<\/strong><\/a><\/p>\n

\"\"<\/a><\/p>\n[\/vc_column_text][vc_video link=”https:\/\/www.youtube.com\/watch?v=LsIs5LerSTE”][\/vc_column][\/vc_row][vc_row et_full_width=”true” et_row_padding=”true” css=”.vc_custom_1579113623784{padding-top: 60px !important;background-color: #f6f6f6 !important;}”][vc_column][vc_row_inner et_column_align=”align-center” content_placement=”middle”][vc_column_inner animation=”animation bottom-to-top” offset=”vc_col-lg-6″][vc_column_text css=”.vc_custom_1693853529047{padding-right: 10% !important;padding-left: 10% !important;}”]\n

Unique DNBSEQ\u2122 Sequencing Technology<\/h2>\n

BGI’s RNA Sequencing services are typically executed with proprietary DNBSEQ\u2122 sequencing technology platforms, for great sequencing data at some of the lowest costs in the industry. DNBSEQ\u2122 offers advantages in terms of lower amplification error rates and much lower duplication rates. In addition, studies have shown the lower index hopping rate in DNBSEQ\u2122 platforms.[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][vc_row_inner et_max_width=”” et_column_align=”align-center” content_placement=”middle”][vc_column_inner animation=”animation right-to-left” offset=”vc_col-lg-6″]\t

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\n\t\t\t\t\t\"\"\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t<\/div>\n [\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row][vc_column][\/vc_column][\/vc_row]\n","protected":false},"excerpt":{"rendered":"

[vc_row et_full_width=”true” et_row_padding=”true” css=”.vc_custom_1579113527778{padding-top: 120px !important;padding-bottom: 60px !important;background-color: #f6f6f6 !important;}”][vc_column css=”.vc_custom_1684760689814{background-color: #f6f6f6 !important;}”][vc_row_inner][vc_column_inner animation=”animation bottom-to-top”][vc_column_text css=”.vc_custom_1705406699258{margin-bottom: 0px !important;}”] Spatial transcriptome-Stereo-seq Stereo-seq, a spatio-temporal omics technology independently developed by BGI Genomics, captures mRNA from tissue section by stereo chips and restores the spatial context by utilizing the spatial barcode (Coordinate ID,…<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-2242","page","type-page","status-publish","hentry"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.geneethic.com.tr\/index.php?rest_route=\/wp\/v2\/pages\/2242","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.geneethic.com.tr\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.geneethic.com.tr\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.geneethic.com.tr\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.geneethic.com.tr\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=2242"}],"version-history":[{"count":10,"href":"https:\/\/www.geneethic.com.tr\/index.php?rest_route=\/wp\/v2\/pages\/2242\/revisions"}],"predecessor-version":[{"id":4546,"href":"https:\/\/www.geneethic.com.tr\/index.php?rest_route=\/wp\/v2\/pages\/2242\/revisions\/4546"}],"wp:attachment":[{"href":"https:\/\/www.geneethic.com.tr\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2242"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}