Work Description

Title: Seq-Scope processed datasets for liver and colon results (RDS) and H&E images Open Access Deposited

h
Attribute Value
Methodology
  • Read alignment was performed using STAR/STARsolo 2.7.5c ( https://github.com/alexdobin/STAR/blob/master/docs/STARsolo.md), from which the digital gene expression (DGE) matrix was generated. Data binning was performed by dividing the imaging space into 100 um2 (10 um-sided) square grids and collapsing all HDMI-UMI information into one barcode per grid. The binned and processed DGE matrix was analyzed in the Seurat v4 package ( https://github.com/satijalab/seurat/). Multiscale analysis was employed to fine tune the annotation. In addition, we employed sliding windows analysis; after the initial 10 um grid sampling, the grid was shifted both horizontally and vertically with 5 um, 2 um or 1 um intervals, producing 4, 25 and 100 times more data, respectively, sampled from the same Seq-Scope results. Then the original 10 um grid dataset was used to guide these sliding windows datasets to perform high-resolution cell type annotation. Details of the analysis could be found in our paper (see "citations to related materials").
Description
  • There are three experimental outputs from Seq-Scope. (1) High definition map coordinate identifier (HDMI) sequence, tile and spatial coordinate information from 1st-Seq, (2) HDMI sequence, coupled with cDNA sequence from 2nd-Seq, and (3) Histological image obtained from Hematoxylin and Eosin (H&E) staining of the tissue slice. (1) and (2) were uploaded to GEO ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE169706). (3) is deposited here. In addition, this deposit includes the processed RDS (single R object) data files.
Creator
Depositor
  • leeju@umich.edu
Contact information
Discipline
Funding agency
  • National Institutes of Health (NIH)
Keyword
Citations to related material
  • Chun-Seok Cho, Jingyue Xi, Sung-Rye Park, Jer-En Hsu, Myungjin Kim, Goo Jun, Hyun-Min Kang, Jun Hee Lee “Seq-Scope: Submicrometer-resolution spatial transcriptomics for single cell and subcellular studies” (preprint) bioRxiv https://doi.org/10.1101/2021.01.25.427807
  • Related data sets in NCBI’s Gene Expression Omnibus (GEO) repository: Cho C, Xi J, Si Y, Lee JH, Kang HM, Park S, Hsu J, Kim M, Jun G “Seq-Scope: Submicrometer-resolution spatial barcoding technology that enables microscopic examination of tissue transcriptome at single cell and subcellular levels” https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE169706
Resource type
Last modified
  • 11/18/2022
Published
  • 04/21/2021
DOI
  • https://doi.org/10.7302/cjfe-wa35
License
To Cite this Work:
Chun-Seok Cho, Jingyue Xi, Hyun Min Kang, Jun Hee Lee. (2021). Seq-Scope processed datasets for liver and colon results (RDS) and H&E images [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/cjfe-wa35

Relationships

This work is not a member of any user collections.

Files (Count: 43; Size: 10.3 GB)

Date: Apr 19, 2021

Dataset Title: Seq-Scope processed datasets for liver and colon results (RDS), H&E images and method videos

Dataset Creators: Chun-Seok Cho, Jingyue Xi, Yichen Si, Sung-Rye Park, Jer-En Hsu, Myungjin Kim, Goo Jun, Hyun Min Kang & Jun Hee Lee

Dataset Contact: Jun Hee Lee leeju@umich.edu

Funding: The work was supported by the NIH (T32AG000114 to C.S.C., K01AG061236 to M.K, U01HL137182 to H.M.K., J.X. and Y.S, R01DK114131 and R01DK102850 to J.H.L., P30AG024824, P30AG013283, P30DK034933, P30DK089503, P30CA046592, P30AR069620, and U2CDK110768), the Chan Zuckerberg Initiative (to H.M.K.), the Frankel Cardiovascular Center Inaugural grant (to J.H.L. and M.K.), an American Association for the Study of Liver Diseases pilot research award (to J.H.L. and H.M.K.), Mcubed initiatives (to M.K., H.M.K. and J.H.L.), Glenn Foundation grants (to J.H.L.), Taiwan Government Scholarship for Overseas Study (to J.E.H.), Frankel Cardiovascular Center Inaugural Grant Award (to J.H.L. and M.K.), and the ADVANCE Program and the Michigan Translational Research and Commercialization for Life Sciences (MTRAC) Program (to J.H.L.), funded by the Michigan Economic Development Corporation (MEDC).

Overview: There are three experimental outputs from Seq-Scope. (1) High definition map coordinate identifier (HDMI) sequence, tile and spatial coordinate information from 1st-Seq, (2) HDMI sequence, coupled with cDNA sequence from 2nd-Seq, and (3) Histological image obtained from Hematoxylin and Eosin (H&E) staining of the tissue slice. (1) and (2) were uploaded to GEO (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE169706). (3) will be included in the current deposit. In addition, the current deposit will also include the processed RDS (single R object) data files.

Methodology: Read alignment was performed using STAR/STARsolo 2.7.5c (https://github.com/alexdobin/STAR/blob/master/docs/STARsolo.md), from which the digital gene expression (DGE) matrix was generated. Data binning was performed by dividing the imaging space into 100 um2 (10 um-sided) square grids and collapsing all HDMI-UMI information into one barcode per grid. The binned and processed DGE matrix was analyzed in the Seurat v4 package (https://github.com/satijalab/seurat/). Multiscale analysis was employed to fine tune the annotation. In addition, we employed sliding windows analysis; after the initial 10 um grid sampling, the grid was shifted both horizontally and vertically with 5 um, 2 um or 1 um intervals, producing 4, 25 and 100 times more data, respectively, sampled from the same Seq-Scope results. Then the original 10 um grid dataset was used to guide these sliding windows datasets to perform high-resolution cell type annotation. Details of the analysis could be found in our paper (see "related publications" below).

Files contained here:

Colon_10um_annotated.rds
>>> Colon dataset binned with 10um-sided square grid.
Colon_5um_2112_anchored.rds
>>> Colon dataset binned with 5um-sided square grid. Annotation was guided by 10um grid dataset.
Colon_2112_SW4X_anchored.rds
>>> Colon dataset processed through sliding windows with 5um intervals. Tile 2112. Annotation was guided by 10um grid dataset.
Colon_2112_topleft_SW25X_anchored.RDS
>>> Colon dataset processed through sliding windows with 2um intervals. Part of Tile 2112. Annotation was guided by 10um grid dataset.
Colon_2112_inset_SW100X_anchored.RDS
>>> Colon dataset processed through sliding windows with 1um intervals. Part of Tile 2112. Annotation was guided by 10um grid dataset.
Liver_normal_10um_annotated.rds
>>> Normal liver dataset binned with 10um-sided square grid
Liver_normal_Segmentation_Hepatocytes.rds
>>> Normal liver dataset binned according to H&E-based single hepatocyte segmentation.
Liver_TD_10um_annotated.rds
>>> TD liver dataset binned with 10um-sided square grid
Liver_TD2117_SW4X_anchored.RDS
>>> TD liver dataset processed through sliding windows with 5um intervals. Tile 2117. Annotation was guided by 10um grid dataset.
Liver_TD2118_SW4X_anchored.RDS
>>> TD liver dataset processed through sliding windows with 5um intervals. Tile 2118. Annotation was guided by 10um grid dataset.
Liver_TD2117_bottom_SW25X_anchored.RDS
>>> TD liver dataset processed through sliding windows with 2um intervals. Part of Tile 2117. Annotation was guided by 10um grid dataset.
Liver_TD2118_middle_SW25X_anchored.RDS
>>> TD liver dataset processed through sliding windows with 2um intervals. Part of Tile 2118. Annotation was guided by 10um grid dataset.
DKO*.jpg
>>> Raw H&E images for TD liver.
wt*.jpg
>>> Raw H&E images for Normal liver.
Colon*.jpg
>>> Raw H&E images for Normal colon.
MiSeq-DraI-100pM-mbcore-RD2-revHDMIs-pos-uniq.txt
>>> 1st-Seq coordinate information for liver dataset
MiSeq-DraI-100pM-mbcore-RD4-revHDMIs-pos-uniq.txt
>>> 1st-Seq coordinate information for colon dataset
DraI-100pM-mbcore-RD2.fastq.gz
>>> 1st-Seq raw MiSeq FASTQ output file for liver dataset
DraI_100pM_RD4.fastq.gz
>>> 1st-Seq raw MiSeq FASTQ output file for colon dataset

Related publication(s):
Cho CS, Xi J, Si Y, Park SR, Hsu JE, Kim M, Jun G, Kang HM, Lee JH. Microscopic examination of spatial transcriptome using Seq-Scope. Cell. 2021 Jun 24;184(13):3559-3572.e22. doi: 10.1016/j.cell.2021.05.010. Epub 2021 Jun 10. PubMed PMID: 34115981; PubMed Central PMCID: PMC8238917.

Chun-Seok Cho, Jingyue Xi, Sung-Rye Park, Jer-En Hsu, Myungjin Kim, Goo Jun, Hyun-Min Kang, Jun Hee Lee "Seq-Scope: Submicrometer-resolution spatial transcriptomics for single cell and subcellular studies" (preprint) bioRxiv https://doi.org/10.1101/2021.01.25.427807

Related data sets in NCBI Gene Expression Omnibus (GEO) repository: Cho C, Xi J, Si Y, Lee JH, Kang HM, Park S, Hsu J, Kim M, Jun G "Seq-Scope: Submicrometer-resolution spatial barcoding technology that enables microscopic examination of tissue transcriptome at single cell and subcellular levels" https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE169706

Use and Access:
This data set is made available under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)

Download All Files (To download individual files, select them in the “Files” panel above)

Total work file size of 10.3 GB is too large to download directly. Consider using Globus (see below).

Files are ready   Download Data from Globus
Best for data sets > 3 GB. Globus is the platform Deep Blue Data uses to make large data sets available.   More about Globus

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.