Svensson, V., Vento-Tormo, R. & Teichmann, S. A. Exponential scaling of single-cell RNA-seq in the past decade. Nat. Protoc. 13, 599–604 (2018).
Treutlein, B. et al. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509, 371–375 (2014).
Zeisel, A. et al. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015).
[+]
Svensson, V., Vento-Tormo, R. & Teichmann, S. A. Exponential scaling of single-cell RNA-seq in the past decade. Nat. Protoc. 13, 599–604 (2018).
Treutlein, B. et al. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509, 371–375 (2014).
Zeisel, A. et al. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015).
Chen, R., Wu, X., Jiang, L. & Zhang, Y. Single-cell RNA-Seq reveals hypothalamic cell diversity. Cell Rep. 18, 3227–3241 (2017).
Wu, Y. E., Pan, L., Zuo, Y., Li, X. & Hong, W. Detecting activated cell populations using single-cell RNA-Seq. Neuron 96, 313–329.e6 (2017).
Villani, A.-C. et al. Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors. Science 356, eaah4573 (2017).
Zhong, S. et al. A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex. Nature 555, 524–528 (2018).
Crow, M. & Gillis, J. Co-expression in single-cell analysis: Saving grace or original sin? Trends Genet. 34, 823–831 (2018).
Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).
Saelens, W., Cannoodt, R., Todorov, H. & Saeys, Y. A comparison of single-cell trajectory inference methods. Nat. Biotechnol. 37, 547–554 (2019).
La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).
Jia, G. et al. Single cell RNA-seq and ATAC-seq analysis of cardiac progenitor cell transition states and lineage settlement. Nat. Commun. 9, 4877 (2018).
Su, X. et al. Single-cell RNA-Seq analysis reveals dynamic trajectories during mouse liver development. BMC Genomics 18, 946 (2017).
Guo, F. et al. Single-cell multi-omics sequencing of mouse early embryos and embryonic stem cells. Cell Res. 27, 967–988 (2017).
Le, J. et al. Single-cell RNA-Seq mapping of human thymopoiesis reveals lineage specification trajectories and a commitment spectrum in T cell development. Immunity 52, 1105–1118.e9 (2020).
Jerber, J. et al. Population-scale single-cell RNA-seq profiling across dopaminergic neuron differentiation. Nat. Genet. 53, 304–312 (2021).
Pratapa, A., Jalihal, A. P., Law, J. N., Bharadwaj, A. & Murali, T. M. Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data. Nat. Methods 17, 147–154 (2020).
Westoby, J., Artemov, P., Hemberg, M. & Ferguson-Smith, A. Obstacles to detecting isoforms using full-length scRNA-seq data. Genome Biol. 21, 74 (2020).
Arzalluz-Luque, Á. & Conesa, A. Single-cell RNAseq for the study of isoforms—how is that possible? Genome Biol. 19, 110 (2018).
Ntranos, V., Yi, L., Melsted, P. & Pachter, L. A discriminative learning approach to differential expression analysis for single-cell RNA-seq. Nat. Methods 16, 163–166 (2019).
Qiu, X. et al. Single-cell mRNA quantification and differential analysis with Census. Nat. Methods 14, 309–315 (2017).
Song, Y. et al. Single-cell alternative splicing analysis with expedition reveals splicing dynamics during neuron differentiation. Mol. Cell 67, 148–161.e5 (2017).
Huang, Y. & Sanguinetti, G. BRIE: transcriptome-wide splicing quantification in single cells. Genome Biol. 18, 123 (2017).
Wu, X., Liu, T., Ye, C., Ye, W. & Ji, G. scAPAtrap: Identification and quantification of alternative polyadenylation sites from single-cell RNA-seq data. Brief. Bioinform. 2020, 1–15 (2020).
Hu, Y., Wang, K. & Li, M. Detecting differential alternative splicing events in scRNA-seq with or without Unique Molecular Identifiers. PLoS Comput. Biol. 16, e1007925 (2020).
Patrick, R. et al. Sierra: Discovery of differential transcript usage from polyA-captured single-cell RNA-seq data. Genome Biol. 21, 1–27 (2020).
Booeshaghi, A. S. et al. Isoform cell-type specificity in the mouse primary motor cortex. Nature 598, 195–199 (2021).
Byrne, A. et al. Nanopore long-read RNAseq reveals widespread transcriptional variation among the surface receptors of individual B cells. Nat. Commun. 8, 16027 (2017).
Volden, R. et al. Improving nanopore read accuracy with the R2C2 method enables the sequencing of highly multiplexed full-length single-cell cDNA. Proc. Natl Acad. Sci. USA 115, 9726–9731 (2018).
Gupta, I. et al. Single-cell isoform RNA sequencing characterizes isoforms in thousands of cerebellar cells. Nat. Biotechnol. 36, 1197–1202 (2018).
Joglekar, A. et al. A spatially resolved brain region- and cell type-specific isoform atlas of the postnatal mouse brain. Nat. Commun. 12, 463 (2021).
Tian, L. et al. Comprehensive characterization of single-cell full-length isoforms in human and mouse with long-read sequencing. Genome Biol. 22, 310 (2021).
Feng, H. et al. Complexity and graded regulation of neuronal cell-type–specific alternative splicing revealed by single-cell RNA sequencing. Proc. Natl Acad. Sci. USA 118, e2013056118 (2021).
Becht, E., Zhao, E., Amezquita, R. & Gottardo, R. Aggregating transcript-level analyses for single-cell differential gene expression. Nat. Methods 17, 583–585 (2020).
Yi, L., Pimentel, H., Bray, N. L. & Pachter, L. Gene-level differential analysis at transcript-level resolution. Genome Biol. 19, 53 (2018).
Soneson, C. & Robinson, M. D. Bias, robustness and scalability in single-cell differential expression analysis. Nat. Methods 15, 255–261 (2018).
Shalek, A. K. et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 498, 236–240 (2013).
Liu, W. & Zhang, X. Single-cell alternative splicing analysis reveals dominance of single transcript variant. Genomics 112, 2418–2425 (2020).
Buen Abad Najar, C. F., Yosef, N. & Lareau, L. F. Coverage-dependent bias creates the appearance of binary splicing in single cells. eLife 9, 1–23 (2020).
Tilgner, H. et al. Microfluidic isoform sequencing shows widespread splicing coordination in the human transcriptome. Genome Res. 28, 231–242 (2018).
Tilgner, H. et al. Comprehensive transcriptome analysis using synthetic long-read sequencing reveals molecular co-association of distant splicing events. Nat. Biotechnol. 33, 736–742 (2015).
Skinnider, M. A., Squair, J. W. & Foster, L. J. Evaluating measures of association for single-cell transcriptomics. Nat. Methods 16, 381–386 (2019).
Tasic, B. et al. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci. 19, 335–346 (2016).
Tasic, B. et al. Shared and distinct transcriptomic cell types across neocortical areas. Nature 563, 72–78 (2018).
Wyman, D. et al. A technology-agnostic long-read analysis pipeline for transcriptome discovery and quantification. Preprint at bioRxiv https://doi.org/10.1101/672931 (2019).
Soneson, C., Matthes, K. L., Nowicka, M., Law, C. W. & Robinson, M. D. Isoform prefiltering improves performance of count-based methods for analysis of differential transcript usage. Genome Biol. 17, 12 (2016).
Tardaguila, M. et al. SQANTI: Extensive characterization of long-read transcript sequences for quality control in full-length transcriptome identification and quantification. Genome Res. 28, 396–411 (2018).
Van den Berge, K. et al. Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications. Genome Biol. 19, 24 (2018).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2009).
Chen, S. & Mar, J. C. Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression data. BMC Bioinform. 19, 232 (2018).
Iacono, G., Massoni-Badosa, R. & Heyn, H. Single-cell transcriptomics unveils gene regulatory network plasticity. Genome Biol. 20, 110 (2019).
Brennecke, P. et al. Accounting for technical noise in single-cell RNA-seq experiments. Nat. Methods 10, 1093–1095 (2013).
Grün, D., Kester, L. & van Oudenaarden, A. Validation of noise models for single-cell transcriptomics. Nat. Methods 11, 637–640 (2014).
Raj, A. & van Oudenaarden, A. Nature, nurture, or chance: Stochastic gene expression and its consequences. Cell 135, 216–226 (2008).
Langfelder, P., Zhang, B. & Horvath, S. Defining clusters from a hierarchical cluster tree: The dynamic tree cut package for R. Bioinformatics 24, 719–720 (2008).
Pimentel, R. S., Niewiadomska-Bugaj, M. & Wang, J.-C. Association of zero-inflated continuous variables. Stat. Probab. Lett. 96, 61–67 (2015).
Erb, I. & Notredame, C. How should we measure proportionality on relative gene expression data? Theory Biosci. 135, 21–36 (2016).
Zhang, X., Xu, C. & Yosef, N. Simulating multiple faceted variability in single cell RNA sequencing. Nat. Commun. 10, 2611 (2019).
Vitting-Seerup, K. & Sandelin, A. IsoformSwitchAnalyzeR: Analysis of changes in genome-wide patterns of alternative splicing and its functional consequences. Bioinforma. Oxf. Engl. 35, 4469–4471 (2019).
de la Fuente, L. et al. tappAS: A comprehensive computational framework for the analysis of the functional impact of differential splicing. Genome Biol. 21, 119 (2020).
Zhu, K., Wang, Y., Liu, L., Li, S. & Yu, W. Long non-coding RNA MBNL1-AS1 regulates proliferation, migration, and invasion of cancer stem cells in colon cancer by interacting with MYL9 via sponging microRNA-412-3p. Clin. Res. Hepatol. Gastroenterol. 44, 101–114 (2020).
Lee, K.-Y., Chang, H.-C., Seah, C. & Lee, L.-J. Deprivation of muscleblind-like proteins causes deficits in cortical neuron distribution and morphological changes in dendritic spines and postsynaptic densities. Front. Neuroanat. 13, 75 (2019).
Wang, P.-Y., Chang, K.-T., Lin, Y.-M., Kuo, T.-Y. & Wang, G.-S. Ubiquitination of MBNL1 is required for its cytoplasmic localization and function in promoting neurite outgrowth. Cell Rep. 22, 2294–2306 (2018).
Sta Maria, N. S. et al. Mbnl1 and Mbnl2 regulate brain structural integrity in mice. Commun. Biol. 4, 1342 (2021).
Derrick, B., White, P. & Toher, D. Parametric and non-parametric tests for the comparison of two samples which both include paired and unpaired observations. J. Mod. Appl. Stat. Methods 18, 2–23 (2019).
Supek, F., Bošnjak, M., Škunca, N. & Šmuc, T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS One 6, e21800 (2011).
MacNicol, M. C., Cragle, C. E. & MacNicol, A. M. Context-dependent regulation of Musashi-mediated mRNA translation and cell cycle regulation. Cell Cycle 10, 39–44 (2011).
Okano, H., Imai, T. & Okabe, M. Musashi: A translational regulator of cell fate. J. Cell Sci. 115, 1355–1359 (2002).
Li, H. J., Haque, Z. K., Chen, A. & Mendelsohn, M. RIF-1, a novel nuclear receptor corepressor that associates with the nuclear matrix. J. Cell. Biochem. 102, 1021–1035 (2007).
Tang, S. J., Meulemans, D., Vazquez, L., Colaco, N. & Schuman, E. A role for a rat homolog of staufen in the transport of RNA to neuronal dendrites. Neuron 32, 463–475 (2001).
Gleghorn, M. L., Gong, C., Kielkopf, C. L. & Maquat, L. E. Staufen1 dimerizes through a conserved motif and a degenerate dsRNA-binding domain to promote mRNA decay. Nat. Struct. Mol. Biol. 20, 515–524 (2013).
McInnes, L., Healy, J. & Melville, J. UMAP: Uniform manifold approximation and projection for dimension reduction. Preprint at https://arxiv.org/abs/1802.03426 (2020).
Masood, M., Grimm, S., El-Bahrawy, M. & Yagüe, E. TMEFF2: A transmembrane proteoglycan with multifaceted actions in cancer and disease. Cancers 12, E3862 (2020).
Jen, Y.-H. L., Musacchio, M. & Lander, A. D. Glypican-1 controls brain size through regulation of fibroblast growth factor signaling in early neurogenesis. Neural Dev. 4, 33 (2009).
Fu, X. D. & Ares, M. Context-dependent control of alternative splicing by RNA-binding proteins. Nat. Rev. Genet. 15, 689–701 (2014).
Saha, A. et al. Co-expression networks reveal the tissue-specific regulation of transcription and splicing. Genome Res. 27, 1843–1858 (2017).
Aghamirzaie, D., Collakova, E., Li, S. & Grene, R. CoSpliceNet: a framework for co-splicing network inference from transcriptomics data. BMC Genomics 17, 845 (2016).
Zhang, P., Southey, B. R. & Rodriguez-Zas, S. L. Co-expression networks uncover regulation of splicing and transcription markers of disease. EPiC Ser. Comput. 70, 119–128 (2020).
Chau, K. et al. Isoform transcriptome of developing human brain provides new insights into autism. Cell Rep. 36, 109631 (2021).
Vu, T. N. et al. Isoform-level gene expression patterns in single-cell RNA-sequencing data. Bioinformatics 10, 1–9 (2018).
Yap, K., Xiao, Y., Friedman, B. A., Je, H. S. & Makeyev, E. V. Polarizing the neuron through sustained co-expression of alternatively spliced isoforms. Cell Rep. 15, 1316–1328 (2016).
Ma, J. et al. Comprehensive expression-based isoform biomarkers predictive of drug responses based on isoform co-expression networks and clinical data. Genomics 112, 647–658 (2020).
Ma, J.-Q. et al. Differential alternative splicing genes and isoform regulation networks of rapeseed (Brassica napus L.) infected with Sclerotinia sclerotiorum. Genes 11, 784 (2020).
Bray, N. The power of 3′ UTRs. Nat. Rev. Neurosci. https://doi.org/10.1038/s41583-018-0011-6 (2018).
Bae, B. & Miura, P. Emerging roles for 3′ UTRs in neurons. Int. J. Mol. Sci. 21, 3413 (2020).
Picelli, S. et al. Tn5 transposase and tagmentation procedures for massively scaled sequencing projects. Genome Res. 24, 2033–2040 (2014).
Dobin, A. et al. STAR: Ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Li, B. & Dewey, C. N. RSEM: Accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinform. 12, 323 (2011).
Tarazona, S. et al. Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package. Nucleic Acids Res. 43, gkv711 (2015).
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).
Venables, W. & Ripley, B. Modern Applied Statistics with S (Springer, 2002).
Fox, J. & Weisberg, S. An R Companion to Applied Regression (Sage, 2019).
Young, M. D., Wakefield, M. J., Smyth, G. K. & Oshlack, A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. 11, R14 (2010).
Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).
O’Leary, N. A. et al. Reference sequence (RefSeq) database at NCBI: Current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 44, D733–D745 (2016).
Arzalluz-Luque, A. acorde: Unraveling functionally interpretable networks of isoform co-usage from single cell data. GitHub https://doi.org/10.5281/zenodo.6341636 (2022).
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Salguero-García, Pedro
