Quantification of single cell-type-specific alternative transcript initiation Article Swipe
Shuhua Fu
,
Parker C. Wilson
,
Bo Zhang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.04.29.651292
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.04.29.651292
Cells can transcribe different isoforms of a gene by using distinct Transcriptional Start Regions (TSRs), which are recognized by RNA-Polymerase II and regulated by cell-type-specific expressed transcription factors, eventually forming tissue and cell-type-specific expression during development. However, how the distinct TSRs are selectively activated in different tissues and cell types remains largely uncharacterized. To better explore the alternative usage of gene TSRs, we developed TSRdetector , a novel bioinformatic method specifically designed to detect the significant usage alteration of gene TSRs in different tissues, cell types, or diseases. TSRdetector can process either scRNA-seq or bulk-RNA-seq transcriptome data, define dominant TSRs, and compute the differential usage of gene TSRs between given conditions. To demonstrate the capacity of TSRdetector , we applied TSRdetector to analyze a 10X snRNA-seq dataset of healthy and diabetic human kidneys, a Smart-seq2 dataset of mouse B-cell differentiation, and bulk-RNA-seq data of pluripotency transition in human ESCs. In all three analyses, TSRdetectors discovered significant alterations in TSR usage, accompanied by the significant remodeling of the epigenetic landscape. Alteration of TSR usage can change the dominant transcript isoform and further affect the major protein products. Interestingly, a large proportion of these alterations of TSR usage in different cell types did not change the overall gene expression, revealing unique transcription regulations that are independent of expression level. In summary, TSRdetector is a user-friendly package to analyze the differential usage of gene TSRs by using both scRNA-seq and bulk-RNA-seq data, and can be used to explore the alternative transcription initiations of genes at the single cell-type level.
Related Topics
Concepts
Biology
Epigenetics
Transcriptome
Gene
Cell type
Gene expression
RNA-Seq
Gene isoform
Transcription factor
Cell
Genetics
Computational biology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2025.04.29.651292
- https://www.biorxiv.org/content/biorxiv/early/2025/05/04/2025.04.29.651292.full.pdf
- OA Status
- green
- References
- 54
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410076011
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4410076011Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2025.04.29.651292Digital Object Identifier
- Title
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Quantification of single cell-type-specific alternative transcript initiationWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-05-04Full publication date if available
- Authors
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Shuhua Fu, Parker C. Wilson, Bo ZhangList of authors in order
- Landing page
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https://doi.org/10.1101/2025.04.29.651292Publisher landing page
- PDF URL
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https://www.biorxiv.org/content/biorxiv/early/2025/05/04/2025.04.29.651292.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
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https://www.biorxiv.org/content/biorxiv/early/2025/05/04/2025.04.29.651292.full.pdfDirect OA link when available
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Biology, Epigenetics, Transcriptome, Gene, Cell type, Gene expression, RNA-Seq, Gene isoform, Transcription factor, Cell, Genetics, Computational biologyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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54Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.TSRs, | 62, 100 |
| abstract_inverted_index.data, | 97, 239 |
| abstract_inverted_index.genes | 251 |
| abstract_inverted_index.given | 110 |
| abstract_inverted_index.human | 132, 148 |
| abstract_inverted_index.large | 189 |
| abstract_inverted_index.major | 184 |
| abstract_inverted_index.mouse | 138 |
| abstract_inverted_index.novel | 68 |
| abstract_inverted_index.these | 192 |
| abstract_inverted_index.three | 152 |
| abstract_inverted_index.types | 50, 200 |
| abstract_inverted_index.usage | 59, 77, 105, 173, 196, 229 |
| abstract_inverted_index.using | 10, 234 |
| abstract_inverted_index.which | 16 |
| abstract_inverted_index.B-cell | 139 |
| abstract_inverted_index.affect | 182 |
| abstract_inverted_index.better | 55 |
| abstract_inverted_index.change | 175, 203 |
| abstract_inverted_index.define | 98 |
| abstract_inverted_index.detect | 74 |
| abstract_inverted_index.during | 35 |
| abstract_inverted_index.either | 92 |
| abstract_inverted_index.level. | 217, 256 |
| abstract_inverted_index.method | 70 |
| abstract_inverted_index.single | 254 |
| abstract_inverted_index.tissue | 31 |
| abstract_inverted_index.types, | 86 |
| abstract_inverted_index.unique | 209 |
| abstract_inverted_index.usage, | 160 |
| abstract_inverted_index.(TSRs), | 15 |
| abstract_inverted_index.Regions | 14 |
| abstract_inverted_index.analyze | 123, 226 |
| abstract_inverted_index.applied | 120 |
| abstract_inverted_index.between | 109 |
| abstract_inverted_index.compute | 102 |
| abstract_inverted_index.dataset | 127, 136 |
| abstract_inverted_index.explore | 56, 245 |
| abstract_inverted_index.forming | 30 |
| abstract_inverted_index.further | 181 |
| abstract_inverted_index.healthy | 129 |
| abstract_inverted_index.isoform | 179 |
| abstract_inverted_index.largely | 52 |
| abstract_inverted_index.overall | 205 |
| abstract_inverted_index.package | 224 |
| abstract_inverted_index.process | 91 |
| abstract_inverted_index.protein | 185 |
| abstract_inverted_index.remains | 51 |
| abstract_inverted_index.tissues | 47 |
| abstract_inverted_index.ABSTRACT | 0 |
| abstract_inverted_index.However, | 37 |
| abstract_inverted_index.capacity | 115 |
| abstract_inverted_index.designed | 72 |
| abstract_inverted_index.diabetic | 131 |
| abstract_inverted_index.distinct | 11, 40 |
| abstract_inverted_index.dominant | 99, 177 |
| abstract_inverted_index.factors, | 28 |
| abstract_inverted_index.isoforms | 5 |
| abstract_inverted_index.kidneys, | 133 |
| abstract_inverted_index.summary, | 219 |
| abstract_inverted_index.tissues, | 84 |
| abstract_inverted_index.activated | 44 |
| abstract_inverted_index.analyses, | 153 |
| abstract_inverted_index.cell-type | 255 |
| abstract_inverted_index.developed | 64 |
| abstract_inverted_index.different | 4, 46, 83, 198 |
| abstract_inverted_index.diseases. | 88 |
| abstract_inverted_index.expressed | 26 |
| abstract_inverted_index.products. | 186 |
| abstract_inverted_index.regulated | 23 |
| abstract_inverted_index.revealing | 208 |
| abstract_inverted_index.scRNA-seq | 93, 236 |
| abstract_inverted_index.snRNA-seq | 126 |
| abstract_inverted_index.Alteration | 170 |
| abstract_inverted_index.Smart-seq2 | 135 |
| abstract_inverted_index.alteration | 78 |
| abstract_inverted_index.discovered | 155 |
| abstract_inverted_index.epigenetic | 168 |
| abstract_inverted_index.eventually | 29 |
| abstract_inverted_index.expression | 34, 216 |
| abstract_inverted_index.landscape. | 169 |
| abstract_inverted_index.proportion | 190 |
| abstract_inverted_index.recognized | 18 |
| abstract_inverted_index.remodeling | 165 |
| abstract_inverted_index.transcribe | 3 |
| abstract_inverted_index.transcript | 178 |
| abstract_inverted_index.transition | 146 |
| abstract_inverted_index.TSRdetector | 65, 89, 117, 121, 220 |
| abstract_inverted_index.accompanied | 161 |
| abstract_inverted_index.alterations | 157, 193 |
| abstract_inverted_index.alternative | 58, 247 |
| abstract_inverted_index.conditions. | 111 |
| abstract_inverted_index.demonstrate | 113 |
| abstract_inverted_index.expression, | 207 |
| abstract_inverted_index.independent | 214 |
| abstract_inverted_index.initiations | 249 |
| abstract_inverted_index.regulations | 211 |
| abstract_inverted_index.selectively | 43 |
| abstract_inverted_index.significant | 76, 156, 164 |
| abstract_inverted_index.TSRdetectors | 154 |
| abstract_inverted_index.bulk-RNA-seq | 95, 142, 238 |
| abstract_inverted_index.development. | 36 |
| abstract_inverted_index.differential | 104, 228 |
| abstract_inverted_index.pluripotency | 145 |
| abstract_inverted_index.specifically | 71 |
| abstract_inverted_index.bioinformatic | 69 |
| abstract_inverted_index.transcription | 27, 210, 248 |
| abstract_inverted_index.transcriptome | 96 |
| abstract_inverted_index.user-friendly | 223 |
| abstract_inverted_index.Interestingly, | 187 |
| abstract_inverted_index.RNA-Polymerase | 20 |
| abstract_inverted_index.Transcriptional | 12 |
| abstract_inverted_index.differentiation, | 140 |
| abstract_inverted_index.uncharacterized. | 53 |
| abstract_inverted_index.cell-type-specific | 25, 33 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.14765431 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |