Skip to content
2000
image of Analysis of Alternative Splicing Heterogeneity during Early Stages of Mouse Embryonic Development

Abstract

Introduction

Pre-mRNA alternative splicing (AS) is a prevalent phenomenon in mammals, playing a crucial role in various biological processes such as embryonic development, tissue differentiation, and disease pathogenesis. Despite the advancements in single-cell RNA sequencing (scRNA-seq) technology, the extent of AS heterogeneity at the transcript level during early mouse embryonic development remains largely unexplored.

Methods

The BRIE2 and expedition were employed to identify and quantify splicing events. Cell clustering was performed with Scanpy based on Percent Spliced In (PSI) values and gene expression levels. Then, marker AS events and differential AS events were detected by the Wlicocon rank-sum test and BRIE2's Mode-2 quantification mode. GO and KEGG enrichment analysis were conducted by ClusterProfiler.

Results

The results suggested substantial heterogeneity in AS events and elucidated PSI values as a critical index of cell heterogeneity during early mouse embryonic development, shedding light on the regulatory mechanisms underlying these processes. By examining marker and differential AS events, the study provided a comprehensive understanding of the dynamic changes in splicing patterns throughout early mouse embryonic development.

Discussion

This study revealed the heterogeneity of AS and elucidated its implications during early mouse embryonic development by analyzing AS at the single-cell level. However, the results are theoretical and lack experimental validation.

Conclusion

The findings offer critical insights into studying mouse embryonic development from the perspective of RNA cellular heterogeneity, emphasizing the importance of AS in shaping cellular diversity and developmental processes.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/cbio/10.2174/0115748936376211250429102627
2025-05-14
2025-08-18

  • From This Site

    /content/journals/cbio/10.2174/0115748936376211250429102627
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Wang E.T. Sandberg R. Luo S. Khrebtukova I. Zhang L. Mayr C. Kingsmore S.F. Schroth G.P. Burge C.B. Alternative isoform regulation in human tissue transcriptomes. Nature 2008 456 7221 470 476 10.1038/nature07509 18978772
    [Google Scholar]
  2. Graveley B.R. Alternative splicing: increasing diversity in the proteomic world. Trends Genet. 2001 17 2 100 107 10.1016/S0168‑9525(00)02176‑4 11173120
    [Google Scholar]
  3. Tian G.G. Li J. Wu J. Alternative splicing signatures in preimplantation embryo development. Cell Biosci. 2020 10 1 33 10.1186/s13578‑020‑00399‑y 32175076
    [Google Scholar]
  4. Chen J. Li Y. Chen G. Shi T. Single-cell analysis of alternative splicing and gene regulatory network reveals remarkable expression and regulation dynamics during early embryonic development. Research Square 2021 Preprint, 15 Feb 2021. 10.21203/rs.3.rs‑190947/v1
    [Google Scholar]
  5. Zheng L. Liu D. Li Y.A. Yang S. Liang Y. Xing Y. Zuo Y. RaacFold: A webserver for 3D visualization and analysis of protein structure by using reduced amino acid alphabets. Nucleic Acids Res. 2022 50 W1 W633 W638 10.1093/nar/gkac415 35639512
    [Google Scholar]
  6. Chen J. Weiss W.A. Alternative splicing in cancer: Implications for biology and therapy. Oncogene 2015 34 1 1 14 10.1038/onc.2013.570 24441040
    [Google Scholar]
  7. Han H. Irimia M. Ross P.J. Sung H.K. Alipanahi B. David L. Golipour A. Gabut M. Michael I.P. Nachman E.N. Wang E. Trcka D. Thompson T. O’Hanlon D. Slobodeniuc V. Barbosa-Morais N.L. Burge C.B. Moffat J. Frey B.J. Nagy A. Ellis J. Wrana J.L. Blencowe B.J. MBNL proteins repress ES-cell-specific alternative splicing and reprogramming. Nature 2013 498 7453 241 245 10.1038/nature12270 23739326
    [Google Scholar]
  8. Yeo G.W. Xu X. Liang T.Y. Muotri A.R. Carson C.T. Coufal N.G. Gage F.H. Alternative splicing events identified in human embryonic stem cells and neural progenitors. PLOS Comput. Biol. 2007 3 10 e196 10.1371/journal.pcbi.0030196 17967047
    [Google Scholar]
  9. Salomonis N. Schlieve C.R. Pereira L. Wahlquist C. Colas A. Zambon A.C. Vranizan K. Spindler M.J. Pico A.R. Cline M.S. Clark T.A. Williams A. Blume J.E. Samal E. Mercola M. Merrill B.J. Conklin B.R. Alternative splicing regulates mouse embryonic stem cell pluripotency and differentiation. Proc. Natl. Acad. Sci. USA 2010 107 23 10514 10519 10.1073/pnas.0912260107 20498046
    [Google Scholar]
  10. Roy A. Wang G. Iskander D. O’Byrne S. Elliott N. O’Sullivan J. Buck G. Heuston E.F. Wen W.X. Meira A.R. Hua P. Karadimitris A. Mead A.J. Bodine D.M. Roberts I. Psaila B. Thongjuea S. Transitions in lineage specification and gene regulatory networks in hematopoietic stem/progenitor cells over human development. Cell Rep. 2021 36 11 109698 10.1016/j.celrep.2021.109698 34525349
    [Google Scholar]
  11. Popescu D.M. Botting R.A. Stephenson E. Green K. Webb S. Jardine L. Calderbank E.F. Polanski K. Goh I. Efremova M. Acres M. Maunder D. Vegh P. Gitton Y. Park J.E. Vento-Tormo R. Miao Z. Dixon D. Rowell R. McDonald D. Fletcher J. Poyner E. Reynolds G. Mather M. Moldovan C. Mamanova L. Greig F. Young M.D. Meyer K.B. Lisgo S. Bacardit J. Fuller A. Millar B. Innes B. Lindsay S. Stubbington M.J.T. Kowalczyk M.S. Li B. Ashenberg O. Tabaka M. Dionne D. Tickle T.L. Slyper M. Rozenblatt-Rosen O. Filby A. Carey P. Villani A.C. Roy A. Regev A. Chédotal A. Roberts I. Göttgens B. Behjati S. Laurenti E. Teichmann S.A. Haniffa M. Decoding human fetal liver haematopoiesis. Nature 2019 574 7778 365 371 10.1038/s41586‑019‑1652‑y 31597962
    [Google Scholar]
  12. Aizarani N. Saviano A. Sagar Mailly L. Durand S. Herman J.S. Pessaux P. Baumert T.F. Grün D. A human liver cell atlas reveals heterogeneity and epithelial progenitors. Nature 2019 572 7768 199 204 10.1038/s41586‑019‑1373‑2 31292543
    [Google Scholar]
  13. Eze U.C. Bhaduri A. Haeussler M. Nowakowski T.J. Kriegstein A.R. Single-cell atlas of early human brain development highlights heterogeneity of human neuroepithelial cells and early radial glia. Nat. Neurosci. 2021 24 4 584 594 10.1038/s41593‑020‑00794‑1 33723434
    [Google Scholar]
  14. Hu B. Zheng L. Long C. Song M. Li T. Yang L. Zuo Y. EmExplorer: A database for exploring time activation of gene expression in mammalian embryos. Open Biol. 2019 9 6 190054 10.1098/rsob.190054 31164042
    [Google Scholar]
  15. Giustacchini A. Thongjuea S. Barkas N. Woll P.S. Povinelli B.J. Booth C.A.G. Sopp P. Norfo R. Rodriguez-Meira A. Ashley N. Jamieson L. Vyas P. Anderson K. Segerstolpe Å. Qian H. Olsson-Strömberg U. Mustjoki S. Sandberg R. Jacobsen S.E.W. Mead A.J. Single-cell transcriptomics uncovers distinct molecular signatures of stem cells in chronic myeloid leukemia. Nat. Med. 2017 23 6 692 702 10.1038/nm.4336 28504724
    [Google Scholar]
  16. Psaila B. Wang G. Rodriguez-Meira A. Li R. Heuston E.F. Murphy L. Yee D. Hitchcock I.S. Sousos N. O’Sullivan J. Anderson S. Senis Y.A. Weinberg O.K. Calicchio M.L. Iskander D. Royston D. Milojkovic D. Roberts I. Bodine D.M. Thongjuea S. Mead A.J. Single-cell analyses reveal megakaryocyte-biased hematopoiesis in myelofibrosis and identify mutant clone-specific targets. Mol. Cell 2020 78 3 477 492.e8 10.1016/j.molcel.2020.04.008 32386542
    [Google Scholar]
  17. Louka E. Povinelli B. Rodriguez-Meira A. Buck G. Wen W.X. Wang G. Sousos N. Ashley N. Hamblin A. Booth C.A.G. Roy A. Elliott N. Iskander D. de la Fuente J. Fordham N. O’Byrne S. Inglott S. Norfo R. Salio M. Thongjuea S. Rao A. Roberts I. Mead A.J. Heterogeneous disease-propagating stem cells in juvenile myelomonocytic leukemia. J. Exp. Med. 2021 218 2 e20180853 10.1084/jem.20180853 33416891
    [Google Scholar]
  18. Patel A.P. Tirosh I. Trombetta J.J. Shalek A.K. Gillespie S.M. Wakimoto H. Cahill D.P. Nahed B.V. Curry W.T. Martuza R.L. Louis D.N. Rozenblatt-Rosen O. Suvà M.L. Regev A. Bernstein B.E. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 2014 344 6190 1396 1401 10.1126/science.1254257 24925914
    [Google Scholar]
  19. Regev A. Teichmann S.A. Lander E.S. Amit I. Benoist C. Birney E. Bodenmiller B. Campbell P. Carninci P. Clatworthy M. Clevers H. Deplancke B. Dunham I. Eberwine J. Eils R. Enard W. Farmer A. Fugger L. Göttgens B. Hacohen N. Haniffa M. Hemberg M. Kim S. Klenerman P. Kriegstein A. Lein E. Linnarsson S. Lundberg E. Lundeberg J. Majumder P. Marioni J.C. Merad M. Mhlanga M. Nawijn M. Netea M. Nolan G. Pe’er D. Phillipakis A. Ponting C.P. Quake S. Reik W. Rozenblatt-Rosen O. Sanes J. Satija R. Schumacher T.N. Shalek A. Shapiro E. Sharma P. Shin J.W. Stegle O. Stratton M. Stubbington M.J.T. Theis F.J. Uhlen M. van Oudenaarden A. Wagner A. Watt F. Weissman J. Wold B. Xavier R. Yosef N. Human Cell Atlas Meeting Participants The Human Cell Atlas. eLife 2017 6 e27041 10.7554/eLife.27041 29206104
    [Google Scholar]
  20. Ma L. Hernandez M.O. Zhao Y. Mehta M. Tran B. Kelly M. Rae Z. Hernandez J.M. Davis J.L. Martin S.P. Kleiner D.E. Hewitt S.M. Ylaya K. Wood B.J. Greten T.F. Wang X.W. Tumor Cell Biodiversity Drives Microenvironmental Reprogramming in Liver Cancer. Cancer Cell 2019 36 4 418 430.e6 10.1016/j.ccell.2019.08.007 31588021
    [Google Scholar]
  21. Loo L. Simon J.M. Xing L. McCoy E.S. Niehaus J.K. Guo J. Anton E.S. Zylka M.J. Single-cell transcriptomic analysis of mouse neocortical development. Nat. Commun. 2019 10 1 134 10.1038/s41467‑018‑08079‑9 30635555
    [Google Scholar]
  22. Hammond T.R. Dufort C. Dissing-Olesen L. Giera S. Young A. Wysoker A. Walker A.J. Gergits F. Segel M. Nemesh J. Marsh S.E. Saunders A. Macosko E. Ginhoux F. Chen J. Franklin R.J.M. Piao X. McCarroll S.A. Stevens B. Single-Cell RNA Sequencing of Microglia throughout the Mouse Lifespan and in the Injured Brain Reveals Complex Cell-State Changes. Immunity 2019 50 1 253 271.e6 10.1016/j.immuni.2018.11.004 30471926
    [Google Scholar]
  23. Gupta I. Collier P.G. Haase B. Mahfouz A. Joglekar A. Floyd T. Koopmans F. Barres B. Smit A.B. Sloan S.A. Luo W. Fedrigo O. Ross M.E. Tilgner H.U. Single-cell isoform RNA sequencing characterizes isoforms in thousands of cerebellar cells. Nat. Biotechnol. 2018 36 12 1197 1202 10.1038/nbt.4259 30320766
    [Google Scholar]
  24. Paik D.T. Tian L. Lee J. Sayed N. Chen I.Y. Rhee S. Rhee J.W. Kim Y. Wirka R.C. Buikema J.W. Wu S.M. Red-Horse K. Quertermous T. Wu J.C. Large-Scale Single-Cell RNA-Seq Reveals Molecular Signatures of Heterogeneous Populations of Human Induced Pluripotent Stem Cell-Derived Endothelial Cells. Circ. Res. 2018 123 4 443 450 10.1161/CIRCRESAHA.118.312913 29986945
    [Google Scholar]
  25. Davie K. Janssens J. Koldere D. De Waegeneer M. Pech U. Kreft Ł. Aibar S. Makhzami S. Christiaens V. Bravo González-Blas C. Poovathingal S. Hulselmans G. Spanier K.I. Moerman T. Vanspauwen B. Geurs S. Voet T. Lammertyn J. Thienpont B. Liu S. Konstantinides N. Fiers M. Verstreken P. Aerts S. A Single-Cell Transcriptome Atlas of the Aging Drosophila Brain. Cell 2018 174 4 982 998.e20 10.1016/j.cell.2018.05.057 29909982
    [Google Scholar]
  26. Zhu Y. Bai L. Ning Z. Fu W. Liu J. Jiang L. Fei S. Gong S. Lu L. Deng M. Yi M. Deep Learning for Clustering Single-cell RNA-seq Data. Curr. Bioinform. 2024 19 3 193 210 10.2174/1574893618666221130094050
    [Google Scholar]
  27. Zheng L. Liang P. Long C. Li H. Li H. Liang Y. He X. Xi Q. Xing Y. Zuo Y. EmAtlas: A comprehensive atlas for exploring spatiotemporal activation in mammalian embryogenesis. Nucleic Acids Res. 2023 51 D1 D924 D932 10.1093/nar/gkac848 36189903
    [Google Scholar]
  28. Zhang Y. Yang Y. Ren L. Zhan M. Sun T. Zou Q. Zhang Y. Predicting intercellular communication based on metabolite-related ligand-receptor interactions with MRCLinkdb. BMC Biol. 2024 22 1 152 10.1186/s12915‑024‑01950‑w 38978014
    [Google Scholar]
  29. Stegle O. Teichmann S.A. Marioni J.C. Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet. 2015 16 3 133 145 10.1038/nrg3833 25628217
    [Google Scholar]
  30. Li H. Long C. Hong Y. Luo L. Zuo Y. Characterizing cellular differentiation potency and waddington landscape via energy indicator. Research. 2023 6 0118 10.34133/research.0118
    [Google Scholar]
  31. Long C. Li H. Liang P. Chao L. Hong Y. Zhang J. Xi Q. Zuo Y. Deciphering the decisive factors driving fate bifurcations in somatic cell reprogramming. Mol. Ther. Nucleic Acids 2023 34 102044 10.1016/j.omtn.2023.102044 37869261
    [Google Scholar]
  32. Wang J. Yuan L. Prospects of identifying alternative splicing events from single-cell RNA sequencing data. Curr. Bioinform. 2024 19 9 845 850 10.2174/0115748936279561231214072041
    [Google Scholar]
  33. Picelli S. Faridani O.R. Björklund Å.K. Winberg G. Sagasser S. Sandberg R. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 2014 9 1 171 181 10.1038/nprot.2014.006 24385147
    [Google Scholar]
  34. Macosko E.Z. Basu A. Satija R. Nemesh J. Shekhar K. Goldman M. Tirosh I. Bialas A.R. Kamitaki N. Martersteck E.M. Trombetta J.J. Weitz D.A. Sanes J.R. Shalek A.K. Regev A. McCarroll S.A. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 2015 161 5 1202 1214 10.1016/j.cell.2015.05.002 26000488
    [Google Scholar]
  35. Chen G. Ning B. Shi T. Single-cell RNA-seq technologies and related computational data analysis. Front. Genet. 2019 10 317 10.3389/fgene.2019.00317 31024627
    [Google Scholar]
  36. Picelli S. Björklund Å.K. Faridani O.R. Sagasser S. Winberg G. Sandberg R. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 2013 10 11 1096 1098 10.1038/nmeth.2639 24056875
    [Google Scholar]
  37. Song Y. Botvinnik O.B. Lovci M.T. Kakaradov B. Liu P. Xu J.L. Yeo G.W. Single-cell alternative splicing analysis with expedition reveals splicing dynamics during neuron differentiation. Mol. Cell 2017 67 1 148 161.e5 10.1016/j.molcel.2017.06.003 28673540
    [Google Scholar]
  38. Huang Y. Sanguinetti G. Using BRIE to detect and analyze splicing isoforms in scRNA-seq data. Methods Mol. Biol. 2019 1935 175 185 10.1007/978‑1‑4939‑9057‑3_12 30758827
    [Google Scholar]
  39. Huang Y. Sanguinetti G. BRIE2: computational identification of splicing phenotypes from single-cell transcriptomic experiments. Genome Biol. 2021 22 1 251 10.1186/s13059‑021‑02461‑5 34452629
    [Google Scholar]
  40. Olivieri J.E. Dehghannasiri R. Salzman J. The SpliZ generalizes ‘percent spliced in’ to reveal regulated splicing at single-cell resolution. Nat. Methods 2022 19 3 307 310 10.1038/s41592‑022‑01400‑x 35241832
    [Google Scholar]
  41. Wen W.X. Mead A.J. Thongjuea S. MARVEL: An integrated alternative splicing analysis platform for single-cell RNA sequencing data. Nucleic Acids Res. 2023 51 5 e29 10.1093/nar/gkac1260 36631981
    [Google Scholar]
  42. Fan X. Zhang X. Wu X. Guo H. Hu Y. Tang F. Huang Y. Single-cell RNA-seq transcriptome analysis of linear and circular RNAs in mouse preimplantation embryos. Genome Biol. 2015 16 1 148 10.1186/s13059‑015‑0706‑1 26201400
    [Google Scholar]
  43. Mohammed H. Hernando-Herraez I. Savino A. Scialdone A. Macaulay I. Mulas C. Chandra T. Voet T. Dean W. Nichols J. Marioni J.C. Reik W. Single-cell landscape of transcriptional heterogeneity and cell fate decisions during mouse early gastrulation. Cell Rep. 2017 20 5 1215 1228 10.1016/j.celrep.2017.07.009 28768204
    [Google Scholar]
  44. Argelaguet R. Clark S.J. Mohammed H. Stapel L.C. Krueger C. Kapourani C.A. Imaz-Rosshandler I. Lohoff T. Xiang Y. Hanna C.W. Smallwood S. Ibarra-Soria X. Buettner F. Sanguinetti G. Xie W. Krueger F. Göttgens B. Rugg-Gunn P.J. Kelsey G. Dean W. Nichols J. Stegle O. Marioni J.C. Reik W. Multi-omics profiling of mouse gastrulation at single-cell resolution. Nature 2019 576 7787 487 491 10.1038/s41586‑019‑1825‑8 31827285
    [Google Scholar]
  45. Andrews S. FastQC: A quality control tool for high throughput sequence data. Bioinformatics 2010 26 15 1968 1971
    [Google Scholar]
  46. Krueger F. Trim Galore!: A wrapper around Cutadapt and FastQC to consistently apply adapter and quality trimming to FastQ files, with extra functionality for RRBS data. Babraham Institute 2015
    [Google Scholar]
  47. Kim D. Langmead B. Salzberg S.L. HISAT: A fast spliced aligner with low memory requirements. Nat. Methods 2015 12 4 357 360 10.1038/nmeth.3317 25751142
    [Google Scholar]
  48. Pertea M. Pertea G.M. Antonescu C.M. Chang T.C. Mendell J.T. Salzberg S.L. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 2015 33 3 290 295 10.1038/nbt.3122 25690850
    [Google Scholar]
  49. Dobin A. Davis C.A. Schlesinger F. Drenkow J. Zaleski C. Jha S. Batut P. Chaisson M. Gingeras T.R. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 2013 29 1 15 21 10.1093/bioinformatics/bts635 23104886
    [Google Scholar]
  50. Li B. Dewey C.N. RSEM: Accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 2011 12 1 323 10.1186/1471‑2105‑12‑323 21816040
    [Google Scholar]
  51. Wolf F.A. Angerer P. Theis F.J. SCANPY: Large-scale single-cell gene expression data analysis. Genome Biol. 2018 19 1 15 10.1186/s13059‑017‑1382‑0 29409532
    [Google Scholar]
  52. Love M.I. Huber W. Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014 15 12 550 10.1186/s13059‑014‑0550‑8 25516281
    [Google Scholar]
  53. Li H. Long C. Xiang J. Liang P. Li X. Zuo Y. Dppa2/4 as a trigger of signaling pathways to promote zygote genome activation by binding to CG-rich region. Brief. Bioinform. 2021 22 4 bbaa342 10.1093/bib/bbaa342 33316032
    [Google Scholar]
  54. Yu G. Wang L.G. Han Y. He Q.Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 2012 16 5 284 287 10.1089/omi.2011.0118 22455463
    [Google Scholar]
  55. Figueroa-Romero C. Hur J. Bender D.E. Delaney C.E. Cataldo M.D. Smith A.L. Yung R. Ruden D.M. Callaghan B.C. Feldman E.L. Identification of epigenetically altered genes in sporadic amyotrophic lateral sclerosis. PLoS One 2012 7 12 e52672 10.1371/journal.pone.0052672 23300739
    [Google Scholar]
  56. Kanekura K. Suzuki H. Aiso S. Matsuoka M. ER stress and unfolded protein response in amyotrophic lateral sclerosis. Mol. Neurobiol. 2009 39 2 81 89 10.1007/s12035‑009‑8054‑3 19184563
    [Google Scholar]
  57. Park H.R. Yang E.J. Oxidative stress as a therapeutic target in amyotrophic lateral sclerosis: Opportunities and limitations. Diagnostics (Basel) 2021 11 9 1546 10.3390/diagnostics11091546 34573888
    [Google Scholar]
  58. Yang X. Ji Y. Wang W. Zhang L. Chen Z. Yu M. Shen Y. Ding F. Gu X. Sun H. Amyotrophic lateral sclerosis: Molecular mechanisms, biomarkers, and therapeutic strategies. Antioxidants 2021 10 7 1012 10.3390/antiox10071012 34202494
    [Google Scholar]
  59. Khatun J. Gelles J.D. Chipuk J.E. Dynamic death decisions: How mitochondrial dynamics shape cellular commitment to apoptosis and ferroptosis. Dev. Cell 2024 59 19 2549 2565 10.1016/j.devcel.2024.09.004 39378840
    [Google Scholar]
  60. Armstrong J.F. Kaufman M.H. Harrison D.J. Clarke A.R. High-frequency developmental abnormalities in p53-deficient mice. Curr. Biol. 1995 5 8 931 936 10.1016/S0960‑9822(95)00183‑7 7583151
    [Google Scholar]
  61. Sah V.P. Attardi L.D. Mulligan G.J. Williams B.O. Bronson R.T. Jacks T. A subset of p53-deficient embryos exhibit exencephaly. Nat. Genet. 1995 10 2 175 180 10.1038/ng0695‑175 7663512
    [Google Scholar]
  62. Erickson S.L. O’Shea K.S. Ghaboosi N. Loverro L. Frantz G. Bauer M. Lu L.H. Moore M.W. ErbB3 is required for normal cerebellar and cardiac development: A comparison with ErbB2- and heregulin-deficient mice. Development 1997 124 24 4999 5011 10.1242/dev.124.24.4999 9362461
    [Google Scholar]
  63. Xu Q. Xiang Y. Wang Q. Wang L. Brind’Amour J. Bogutz A.B. Zhang Y. Zhang B. Yu G. Xia W. Du Z. Huang C. Ma J. Zheng H. Li Y. Liu C. Walker C.L. Jonasch E. Lefebvre L. Wu M. Lorincz M.C. Li W. Li L. Xie W. SETD2 regulates the maternal epigenome, genomic imprinting and embryonic development. Nat. Genet. 2019 51 5 844 856 10.1038/s41588‑019‑0398‑7 31040401
    [Google Scholar]
  64. Horn D. Fernández-Núñez E. Gomez-Carmona R. Rivera-Barahona A. Nevado J. Schwartzmann S. Biallelic truncating variants in MAPKAPK5 cause a new developmental disorder involving neurological, cardiac, and facial anomalies combined with synpolydactyly. Genet. Med. Official J. Am. Coll. Med. Genetics 2021 23 4 679 688 10.1038/s41436‑020‑01052‑2
    [Google Scholar]
  65. Kageyama S. Liu H. Nagata M. Aoki F. The role of ETS transcription factors in transcription and development of mouse preimplantation embryos. Biochem. Biophys. Res. Commun. 2006 344 2 675 679 10.1016/j.bbrc.2006.03.192 16630543
    [Google Scholar]
  66. Peng M. Keppeke G.D. Tsai L.K. Chang C.C. Liu J.L. Sung L.Y. The IMPDH cytoophidium couples metabolism and fetal development in mice. Cell. Mol. Life Sci. 2024 81 1 210 10.1007/s00018‑024‑05233‑z 38717553
    [Google Scholar]
  67. Zhang Y. Pan X. Shi T. Gu Z. Yang Z. Liu M. Xu Y. Yang Y. Ren L. Song X. Lin H. Deng K. P450Rdb: A manually curated database of reactions catalyzed by cytochrome P450 enzymes. J. Adv. Res. 2024 63 35 42 10.1016/j.jare.2023.10.012 37871773
    [Google Scholar]
  68. Gu J.J. Stegmann S. Gathy K. Murray R. Laliberte J. Ayscue L. Mitchell B.S. Inhibition of T lymphocyte activation in mice heterozygous for loss of the IMPDH II gene. J. Clin. Invest. 2000 106 4 599 606 10.1172/JCI8669 10953035
    [Google Scholar]
  69. Khatri D. Zizioli D. Trivedi A. Borsani G. Monti E. Finazzi D. Overexpression of human mutant PANK2 proteins affects development and motor behavior of zebrafish embryos. Neuromolecular Med. 2019 21 2 120 131 10.1007/s12017‑018‑8508‑8 30141000
    [Google Scholar]
  70. Gao C. Guo X. Xue A. Ruan Y. Wang H. Gao X. High intratumoral expression of eIF4A1 promotes epithelial-to-mesenchymal transition and predicts unfavorable prognosis in gastric cancer. Acta Biochim. Biophys. Sin. (Shanghai) 2020 52 3 310 319 10.1093/abbs/gmz168 32147684
    [Google Scholar]
  71. Kwon J. Jo Y.J. Namgoong S. Kim N.H. Functional roles of hnRNPA2/B1 regulated by METTL3 in mammalian embryonic development. Sci. Rep. 2019 9 1 8640 10.1038/s41598‑019‑44714‑1 31201338
    [Google Scholar]
  72. Mould A.W. Pang Z. Pakusch M. Tonks I.D. Stark M. Carrie D. Mukhopadhyay P. Seidel A. Ellis J.J. Deakin J. Wakefield M.J. Krause L. Blewitt M.E. Kay G.F. Smchd1 regulates a subset of autosomal genes subject to monoallelic expression in addition to being critical for X inactivation. Epigenetics Chromatin 2013 6 1 19 10.1186/1756‑8935‑6‑19 23819640
    [Google Scholar]
  73. McPherson J.P. Sarras H. Lemmers B. Tamblyn L. Migon E. Matysiak-Zablocki E. Hakem A. Azami S.A. Cardoso R. Fish J. Sanchez O. Post M. Hakem R. Essential role for Bclaf1 in lung development and immune system function. Cell Death Differ. 2009 16 2 331 339 10.1038/cdd.2008.167 19008920
    [Google Scholar]
  74. Toro C. Hori R.T. Malicdan M.C.V. Tifft C.J. Goldstein A. Gahl W.A. Adams D.R. Fauni H.B. Wolfe L.A. Xiao J. Khan M.M. Tian J. Hope K.A. Reiter L.T. Tremblay M.G. Moss T. Franks A.L. Balak C. LeDoux M.S. A recurrent de novo missense mutation in UBTF causes developmental neuroregression. Hum. Mol. Genet. 2018 27 4 691 705 10.1093/hmg/ddx435 29300972
    [Google Scholar]
  75. Zhou X. Vize P.D. Amino acid cotransporter SLC3A2 is selectively expressed in the early proximal segment of Xenopus pronephric kidney nephrons. Gene Expr. Patterns 2005 5 6 774 777 10.1016/j.modgep.2005.04.003 15923149
    [Google Scholar]
  76. Shen H. Yang M. Li S. Zhang J. Peng B. Wang C. Chang Z. Ong J. Du P. Mouse totipotent stem cells captured and maintained through spliceosomal repression. Cell 2021 184 11 2843 2859.e20 10.1016/j.cell.2021.04.020 33991488
    [Google Scholar]
  77. Vivori C. Papasaikas P. Stadhouders R. Di Stefano B. Rubio A.R. Balaguer C.B. Generoso S. Mallol A. Sardina J.L. Payer B. Graf T. Valcárcel J. Dynamics of alternative splicing during somatic cell reprogramming reveals functions for RNA-binding proteins CPSF3, hnRNP UL1, and TIA1. Genome Biol. 2021 22 1 171 10.1186/s13059‑021‑02372‑5 34082786
    [Google Scholar]
/content/journals/cbio/10.2174/0115748936376211250429102627
Loading
Analysis of Alternative Splicing Heterogeneity during Early Stages of Mouse Embryonic Development
Bentham Science Publishers ; https://doi.org/10.2174/0115748936376211250429102627
/content/journals/cbio/10.2174/0115748936376211250429102627
/content/journals/cbio/10.2174/0115748936376211250429102627
Loading

Data & Media loading...


  • Article Type:     Research Article
Keywords: differential analysis ; enrichment analysis ; alternative splicing ; Early embryonic development ; cellular heterogeneity ; scRNA-seq

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test