Identification and quantification of alternative polyadenylation sites in single cell RNA-seq data using scPAISO Article Swipe
Yongjie Liu
,
Peiwen Xiong
,
Songyang Li
,
Xinjia Liu
,
Tao Liu
,
Qinglan Yang
,
Shuting Wu
,
Hongyan Peng
,
Yana Li
,
Lingling Zhang
,
Yafei Deng
,
Yong Wei Zhu
,
Junping Wang
,
Youcai Deng
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.08.20.669565
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.08.20.669565
s Alternative polyadenylation (APA) is a critical posttranscriptional mechanism that generates transcriptomic diversity through the production of mRNA isoforms with distinct 3’ UTRs or coding sequences. Current APA analysis based on single-cell RNA sequencing (scRNA-seq) for establishing cell type-specific APA landscapes primarily rely on Read2 data, which lacks precise cleavage site (CS) information. This limitation restricts their ability to achieve precise de novo mapping of polyadenylation sites (PASs). Here, we present s ingle- c ell P oly A denylation ISO form quantification (scPAISO), a computational pipeline designed for de novo identification of PAS and quantification of PAS isoforms in scRNA-seq data, by leveraging the often-discarded Read1 from 3’ tag-based scRNA-seq protocols. Unlike existing tools, scPAISO directly captures mRNA 3’ end cleavage sites, enabling superior performance in motif enrichment (stronger AAUAAA signal) and peak precision (sharper PAS peaks). Moreover, the smaller peak widths enhance the spatial resolution, enabling more accurate detection of closely spaced PASs in the genome. By integrating Read1 and Read2 data, scPAISO achieves isoform-level quantification with an assignment accuracy exceeding 95%. We demonstrate the robustness of scPAISO in identifying PASs and quantifying APA events across diverse biological contexts, including hematopoiesis, systemic sclerosis (SSc), and mouse tissues. We identified stage-specific 3’ UTR lengthening in hematopoietic progenitors, global 3’ UTR remodeling in SSc and tissue-specific polyadenylation (PA) preference along with RNA-binding proteins in mice. Overall, scPAISO represents a significant advancement in the analysis of APA at single-cell resolution and provides a powerful tool for exploring the regulatory landscape of APA, offering new insights into transcriptome complexity and gene regulation in both health and disease.
Related Topics
Concepts
Identification (biology)
Polyadenylation
RNA-Seq
Computational biology
Computer science
Biology
RNA
Genetics
Transcriptome
Gene
Gene expression
Ecology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1101/2025.08.20.669565
- https://www.biorxiv.org/content/biorxiv/early/2025/08/25/2025.08.20.669565.full.pdf
- OA Status
- green
- References
- 68
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413504429
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4413504429Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2025.08.20.669565Digital Object Identifier
- Title
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Identification and quantification of alternative polyadenylation sites in single cell RNA-seq data using scPAISOWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-08-25Full publication date if available
- Authors
-
Yongjie Liu, Peiwen Xiong, Songyang Li, Xinjia Liu, Tao Liu, Qinglan Yang, Shuting Wu, Hongyan Peng, Yana Li, Lingling Zhang, Yafei Deng, Yong Wei Zhu, Junping Wang, Youcai DengList of authors in order
- Landing page
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https://doi.org/10.1101/2025.08.20.669565Publisher landing page
- PDF URL
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https://www.biorxiv.org/content/biorxiv/early/2025/08/25/2025.08.20.669565.full.pdfDirect link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
- OA URL
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https://www.biorxiv.org/content/biorxiv/early/2025/08/25/2025.08.20.669565.full.pdfDirect OA link when available
- Concepts
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Identification (biology), Polyadenylation, RNA-Seq, Computational biology, Computer science, Biology, RNA, Genetics, Transcriptome, Gene, Gene expression, EcologyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.landscape | 247 |
| abstract_inverted_index.mechanism | 8 |
| abstract_inverted_index.precision | 133 |
| abstract_inverted_index.primarily | 41 |
| abstract_inverted_index.restricts | 55 |
| abstract_inverted_index.scRNA-seq | 99, 109 |
| abstract_inverted_index.sclerosis | 193 |
| abstract_inverted_index.tag-based | 108 |
| abstract_inverted_index.(scPAISO), | 82 |
| abstract_inverted_index.assignment | 169 |
| abstract_inverted_index.biological | 188 |
| abstract_inverted_index.complexity | 255 |
| abstract_inverted_index.denylation | 78 |
| abstract_inverted_index.enrichment | 127 |
| abstract_inverted_index.identified | 199 |
| abstract_inverted_index.landscapes | 40 |
| abstract_inverted_index.leveraging | 102 |
| abstract_inverted_index.limitation | 54 |
| abstract_inverted_index.preference | 217 |
| abstract_inverted_index.production | 15 |
| abstract_inverted_index.protocols. | 110 |
| abstract_inverted_index.regulation | 258 |
| abstract_inverted_index.regulatory | 246 |
| abstract_inverted_index.remodeling | 210 |
| abstract_inverted_index.represents | 226 |
| abstract_inverted_index.resolution | 237 |
| abstract_inverted_index.robustness | 176 |
| abstract_inverted_index.sequences. | 25 |
| abstract_inverted_index.sequencing | 33 |
| abstract_inverted_index.(scRNA-seq) | 34 |
| abstract_inverted_index.Alternative | 1 |
| abstract_inverted_index.RNA-binding | 220 |
| abstract_inverted_index.advancement | 229 |
| abstract_inverted_index.demonstrate | 174 |
| abstract_inverted_index.identifying | 180 |
| abstract_inverted_index.integrating | 158 |
| abstract_inverted_index.lengthening | 203 |
| abstract_inverted_index.performance | 124 |
| abstract_inverted_index.quantifying | 183 |
| abstract_inverted_index.resolution, | 145 |
| abstract_inverted_index.significant | 228 |
| abstract_inverted_index.single-cell | 31, 236 |
| abstract_inverted_index.establishing | 36 |
| abstract_inverted_index.information. | 52 |
| abstract_inverted_index.progenitors, | 206 |
| abstract_inverted_index.computational | 84 |
| abstract_inverted_index.hematopoietic | 205 |
| abstract_inverted_index.isoform-level | 165 |
| abstract_inverted_index.transcriptome | 254 |
| abstract_inverted_index.type-specific | 38 |
| abstract_inverted_index.hematopoiesis, | 191 |
| abstract_inverted_index.identification | 90 |
| abstract_inverted_index.quantification | 81, 94, 166 |
| abstract_inverted_index.stage-specific | 200 |
| abstract_inverted_index.transcriptomic | 11 |
| abstract_inverted_index.often-discarded | 104 |
| abstract_inverted_index.polyadenylation | 2, 65, 215 |
| abstract_inverted_index.tissue-specific | 214 |
| abstract_inverted_index.posttranscriptional | 7 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5100698549 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 14 |
| corresponding_institution_ids | https://openalex.org/I139660479, https://openalex.org/I4210132733 |
| citation_normalized_percentile.value | 0.34781366 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |