Dominant Isoform in Alternative Splicing in HeLa S3 Cell Line Revealed by Single-cell RNA-seq

The alternative splicing (AS) is one of the most important contributions for increasing the gene's expression biodiversity. However, whether individual isoforms can exhibit substantial differences in gene expression is unclear. Here, we profiled the AS characteristics in the whole transcriptome of 20 HeLa cells at single-cell level. For the most of the AS, they show the pattern of stochasticity among different single cells, but the pattern of dominant isoform usages in a specific cell. The pathway analysis of the differential AS indicates that the cell cycle state might also have a major influence on the isoform usages. We also identify several cancer-related pathways, including WNT signaling and NOTCH signaling. Furthermore, by investigating the potential regulatory network under the AS, several disease-related transcription factors were identified, including FOS, YWHAZ, and STAT3, which might play important roles in cervical cancer. Together, we perform the comprehensive analysis of AS at single-cell level and reveal the AS patterns and potential roles in both normal cellular process and tumor development.

[1]  Roderic Guigó,et al.  Intron-centric estimation of alternative splicing from RNA-seq data , 2012, Bioinform..

[2]  Yin Hu,et al.  Robust detection of alternative splicing in a population of single cells , 2016, Nucleic acids research.

[3]  J. Manley,et al.  Alternative pre-mRNA splicing regulation in cancer: pathways and programs unhinged. , 2010, Genes & development.

[4]  Huanming Yang,et al.  Full-length single-cell RNA-seq applied to a viral human cancer: applications to HPV expression and splicing analysis in HeLa S3 cells , 2015, GigaScience.

[5]  B. Williams,et al.  From single-cell to cell-pool transcriptomes: Stochasticity in gene expression and RNA splicing , 2014, Genome research.

[6]  International Human Genome Sequencing Consortium.  Initial sequencing and analysis of the human genome , 2001, Nature.

[7]  B. Das,et al.  Functional Regulatory Role of STAT3 in HPV16-Mediated Cervical Carcinogenesis , 2013, PloS one.

[8]  S. Horvath,et al.  Single-Cell Transcriptome Analyses Reveal Signals to Activate Dormant Neural Stem Cells , 2015, Cell.

[9]  N. Neff,et al.  Dissecting direct reprogramming from fibroblast to neuron using single-cell RNA-seq , 2016, Nature.

[10]  Sarah A. Teichmann,et al.  Single-cell transcriptomic reconstruction reveals cell cycle and multi-lineage differentiation defects in Bcl11a-deficient hematopoietic stem cells , 2015, Genome Biology.

[11]  Henning Hermjakob,et al.  The Reactome pathway Knowledgebase , 2015, Nucleic acids research.

[12]  B. Frey,et al.  Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing , 2008, Nature Genetics.

[13]  Rona S. Gertner,et al.  Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells , 2013, Nature.

[14]  Alvaro Plaza Reyes,et al.  Single-Cell RNA-Seq Reveals Lineage and X Chromosome Dynamics in Human Preimplantation Embryos , 2016, Cell.

[15]  Guangchuang Yu,et al.  clusterProfiler: an R package for comparing biological themes among gene clusters. , 2012, Omics : a journal of integrative biology.

[16]  Xiao-yan Chen,et al.  Biological significance and therapeutic implication of resveratrol-inhibited Wnt, Notch and STAT3 signaling in cervical cancer cells , 2014, Genes & cancer.

[17]  Lior Pachter,et al.  Fast and accurate single-cell RNA-seq analysis by clustering of transcript-compatibility counts , 2016, Genome Biology.

[18]  C. Ghigna,et al.  Alternative Splicing and Tumor Progression , 2008, Current genomics.

[19]  S. Linnarsson,et al.  Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq , 2015, Science.

[20]  Eric T. Wang,et al.  Alternative Isoform Regulation in Human Tissue Transcriptomes , 2008, Nature.

[21]  Guangchuang Yu,et al.  ReactomePA: an R/Bioconductor package for reactome pathway analysis and visualization. , 2016, Molecular bioSystems.

[22]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[23]  C. Meijer,et al.  Alterations in AP-1 and AP-1 Regulatory Genes during HPV-Induced Carcinogenesis , 2008, Cellular oncology : the official journal of the International Society for Cellular Oncology.

[24]  Henning Hermjakob,et al.  The Reactome pathway knowledgebase , 2013, Nucleic Acids Res..

[25]  J. Yun,et al.  Identification of a gene‐expression signature for predicting lymph node metastasis in patients with early stage cervical carcinoma , 2011, Cancer.

[26]  Yang Yang,et al.  GSK3β mediates the carcinogenic effect of HPV16 in cervical cancer , 2015, Scientific Reports.