Spatially resolved whole transcriptome profiling in human and mouse tissue using Digital Spatial Profiling Article Swipe
Stephanie M. Zimmerman
,
Robin Fropf
,
Bridget R. Kulasekara
,
Maddy Griswold
,
Oliver K. Appelbe
,
Arya Bahrami
,
Rich Boykin
,
Derek L. Buhr
,
Kit Fuhrman
,
Margaret L. Hoang
,
Quoc Huynh
,
Lesley Isgur
,
Andrew Klock
,
Alecksandr Kutchma
,
Alexa E. Lasley
,
Yan Liang
,
Jill McKay-Fleisch
,
Jeffrey S. Nelson
,
Karen Nguyen
,
Erin Piazza
,
Aric B.E. Rininger
,
Daniel R. Zollinger
,
Michael Rhodes
,
Joseph Beechem
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1101/gr.276206.121
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1101/gr.276206.121
Emerging spatial profiling technology has enabled high-plex molecular profiling in biological tissues, preserving the spatial and morphological context of gene expression. Here, we describe expanding the chemistry for the Digital Spatial Profiling platform to quantify whole transcriptomes in human and mouse tissues using a wide range of spatial profiling strategies and sample types. We designed multiplexed in situ hybridization probes targeting the protein-coding genes of the human and mouse transcriptomes, referred to as the human or mouse Whole Transcriptome Atlas (WTA). Human and mouse WTAs were validated in cell lines for concordance with orthogonal gene expression profiling methods in regions ranging from ∼10–500 cells. By benchmarking against bulk RNA-seq and fluorescence in situ hybridization, we show robust transcript detection down to ∼100 transcripts per region. To assess the performance of WTA across tissue and sample types, we applied WTA to biological questions in cancer, molecular pathology, and developmental biology. Spatial profiling with WTA detected expected gene expression differences between tumor and tumor microenvironment, identified disease-specific gene expression heterogeneity in histological structures of the human kidney, and comprehensively mapped transcriptional programs in anatomical substructures of nine organs in the developing mouse embryo. Digital Spatial Profiling technology with the WTA assays provides a flexible method for spatial whole transcriptome profiling applicable to diverse tissue types and biological contexts.
Related Topics
Concepts
Biology
Profiling (computer programming)
Transcriptome
Computational biology
Gene expression profiling
Genetics
Gene
Gene expression
Computer science
Operating system
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1101/gr.276206.121
- https://genome.cshlp.org/content/32/10/1892.full.pdf
- OA Status
- hybrid
- Cited By
- 44
- References
- 58
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4295752339
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4295752339Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/gr.276206.121Digital Object Identifier
- Title
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Spatially resolved whole transcriptome profiling in human and mouse tissue using Digital Spatial ProfilingWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-09-13Full publication date if available
- Authors
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Stephanie M. Zimmerman, Robin Fropf, Bridget R. Kulasekara, Maddy Griswold, Oliver K. Appelbe, Arya Bahrami, Rich Boykin, Derek L. Buhr, Kit Fuhrman, Margaret L. Hoang, Quoc Huynh, Lesley Isgur, Andrew Klock, Alecksandr Kutchma, Alexa E. Lasley, Yan Liang, Jill McKay-Fleisch, Jeffrey S. Nelson, Karen Nguyen, Erin Piazza, Aric B.E. Rininger, Daniel R. Zollinger, Michael Rhodes, Joseph BeechemList of authors in order
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https://doi.org/10.1101/gr.276206.121Publisher landing page
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https://genome.cshlp.org/content/32/10/1892.full.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://genome.cshlp.org/content/32/10/1892.full.pdfDirect OA link when available
- Concepts
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Biology, Profiling (computer programming), Transcriptome, Computational biology, Gene expression profiling, Genetics, Gene, Gene expression, Computer science, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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44Total citation count in OpenAlex
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2025: 10, 2024: 17, 2023: 14, 2022: 3Per-year citation counts (last 5 years)
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58Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| primary_location.source.host_organization_lineage_names | Cold Spring Harbor Laboratory Press |
| primary_location.license | |
| primary_location.pdf_url | https://genome.cshlp.org/content/32/10/1892.full.pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Genome Research |
| primary_location.landing_page_url | https://doi.org/10.1101/gr.276206.121 |
| publication_date | 2022-09-13 |
| publication_year | 2022 |
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