Generating single-cell gene expression profiles for high-resolution spatial transcriptomics based on cell boundary images Article Swipe
Bohan Zhang
,
Mei Li
,
Qiang Kang
,
Zhonghan Deng
,
Hua Qin
,
Kui Su
,
Xiuwen Feng
,
Lichuan Chen
,
Huanlin Liu
,
Shuangsang Fang
,
Yong Zhang
,
Yuxiang Li
,
Susanne Brix
,
Xun Xu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.46471/gigabyte.110
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.46471/gigabyte.110
In spatially resolved transcriptomics, Stereo-seq facilitates the analysis of large tissues at the single-cell level, offering subcellular resolution and centimeter-level field-of-view. Our previous work on StereoCell introduced a one-stop software using cell nuclei staining images and statistical methods to generate high-confidence single-cell spatial gene expression profiles for Stereo-seq data. With advancements allowing the acquisition of cell boundary information, such as cell membrane/wall staining images, we updated our software to a new version, STCellbin. Using cell nuclei staining images, STCellbin aligns cell membrane/wall staining images with spatial gene expression maps. Advanced cell segmentation ensures the detection of accurate cell boundaries, leading to more reliable single-cell spatial gene expression profiles. We verified that STCellbin can be applied to mouse liver (cell membranes) and Arabidopsis seed (cell walls) datasets, outperforming other methods. The improved capability of capturing single-cell gene expression profiles results in a deeper understanding of the contribution of single-cell phenotypes to tissue biology. Availability & Implementation The source code of STCellbin is available at https://github.com/STOmics/STCellbin.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.46471/gigabyte.110
- https://gigabytejournal.com/admin/apis/public/article_shell/uploads/article_files/Gigabyte/Gigabyte/2024/gigabyte110/gigabyte110.pdf
- OA Status
- gold
- Cited By
- 3
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391968295
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4391968295Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.46471/gigabyte.110Digital Object Identifier
- Title
-
Generating single-cell gene expression profiles for high-resolution spatial transcriptomics based on cell boundary imagesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-02-19Full publication date if available
- Authors
-
Bohan Zhang, Mei Li, Qiang Kang, Zhonghan Deng, Hua Qin, Kui Su, Xiuwen Feng, Lichuan Chen, Huanlin Liu, Shuangsang Fang, Yong Zhang, Yuxiang Li, Susanne Brix, Xun XuList of authors in order
- Landing page
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https://doi.org/10.46471/gigabyte.110Publisher landing page
- PDF URL
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https://gigabytejournal.com/admin/apis/public/article_shell/uploads/article_files/Gigabyte/Gigabyte/2024/gigabyte110/gigabyte110.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://gigabytejournal.com/admin/apis/public/article_shell/uploads/article_files/Gigabyte/Gigabyte/2024/gigabyte110/gigabyte110.pdfDirect OA link when available
- Concepts
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Transcriptome, Cell, Boundary (topology), Computational biology, Gene, Gene expression, Expression (computer science), Biology, Genetics, Computer science, Mathematics, Programming language, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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23Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.staining | 33, 62, 76, 82 |
| abstract_inverted_index.verified | 109 |
| abstract_inverted_index.version, | 71 |
| abstract_inverted_index.STCellbin | 78, 111, 159 |
| abstract_inverted_index.available | 161 |
| abstract_inverted_index.capturing | 133 |
| abstract_inverted_index.datasets, | 125 |
| abstract_inverted_index.detection | 94 |
| abstract_inverted_index.profiles. | 107 |
| abstract_inverted_index.spatially | 1 |
| abstract_inverted_index.STCellbin. | 72 |
| abstract_inverted_index.Stereo-seq | 4, 47 |
| abstract_inverted_index.StereoCell | 25 |
| abstract_inverted_index.capability | 131 |
| abstract_inverted_index.expression | 44, 87, 106, 136 |
| abstract_inverted_index.introduced | 26 |
| abstract_inverted_index.membranes) | 119 |
| abstract_inverted_index.phenotypes | 148 |
| abstract_inverted_index.resolution | 17 |
| abstract_inverted_index.Arabidopsis | 121 |
| abstract_inverted_index.acquisition | 53 |
| abstract_inverted_index.boundaries, | 98 |
| abstract_inverted_index.facilitates | 5 |
| abstract_inverted_index.single-cell | 13, 41, 103, 134, 147 |
| abstract_inverted_index.statistical | 36 |
| abstract_inverted_index.subcellular | 16 |
| abstract_inverted_index.Availability | 152 |
| abstract_inverted_index.advancements | 50 |
| abstract_inverted_index.contribution | 145 |
| abstract_inverted_index.information, | 57 |
| abstract_inverted_index.segmentation | 91 |
| abstract_inverted_index.membrane/wall | 61, 81 |
| abstract_inverted_index.outperforming | 126 |
| abstract_inverted_index.understanding | 142 |
| abstract_inverted_index.Implementation | 154 |
| abstract_inverted_index.field-of-view. | 20 |
| abstract_inverted_index.high-confidence | 40 |
| abstract_inverted_index.centimeter-level | 19 |
| abstract_inverted_index.transcriptomics, | 3 |
| abstract_inverted_index.https://github.com/STOmics/STCellbin. | 163 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 14 |
| citation_normalized_percentile.value | 0.72986709 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |