Identifying cell-state associated alternative splicing events and their co-regulation

Alternative splicing shapes the transcriptome and contributes to each cell's unique identity, but single-cell RNA sequencing has struggled to capture the impact of alternative splicing. We previously showed that low recovery of mRNAs from single cells led to erroneous conclusions about the cell-to-cell variability of alternative splicing. Here, we present a method, Psix, to confidently identify splicing that changes across a landscape of single cells, using a probabilistic model that is robust against the data limitations of scRNA-seq. Its autocorrelation-inspired approach finds patterns of alternative splicing that correspond to patterns of cell identity, such as cell type or developmental stage, without the need for explicit cell clustering, labeling, or trajectory inference. Applying Psix to data that follow the trajectory of mouse brain development, we identify exons whose alternative splicing patterns cluster into modules of co-regulation. We show that the exons in these modules are enriched for binding by distinct neuronal splicing factors, and that their changes in splicing correspond to changes in expression of these splicing factors. Thus, Psix reveals cell-type-dependent splicing patterns and the wiring of the splicing regulatory networks that control them. Our new method will enable scRNA-seq analysis to go beyond transcription to understand the roles of post-transcriptional regulation in determining cell identity.

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Neurodegenerative diseases: a hotbed for splicing defects and the potential therapies

Translational Neurodegeneration ◽

10.1186/s40035-021-00240-7 ◽

2021 ◽

Vol 10 (1) ◽

Author(s):

Dunhui Li ◽

Craig Stewart McIntosh ◽

Frank Louis Mastaglia ◽

Steve Donald Wilton ◽

May Thandar Aung-Htut

Keyword(s):

Alternative Splicing ◽

Neurodegenerative Diseases ◽

Messenger Rna ◽

Muscular Atrophy ◽

Single Gene ◽

Synaptic Function ◽

Coding Regions ◽

Splicing Defects ◽

The Impact ◽

Splicing Patterns

AbstractPrecursor messenger RNA (pre-mRNA) splicing is a fundamental step in eukaryotic gene expression that systematically removes non-coding regions (introns) and ligates coding regions (exons) into a continuous message (mature mRNA). This process is highly regulated and can be highly flexible through a process known as alternative splicing, which allows for several transcripts to arise from a single gene, thereby greatly increasing genetic plasticity and the diversity of proteome. Alternative splicing is particularly prevalent in neuronal cells, where the splicing patterns are continuously changing to maintain cellular homeostasis and promote neurogenesis, migration and synaptic function. The continuous changes in splicing patterns and a high demand on many cis- and trans-splicing factors contribute to the susceptibility of neuronal tissues to splicing defects. The resultant neurodegenerative diseases are a large group of disorders defined by a gradual loss of neurons and a progressive impairment in neuronal function. Several of the most common neurodegenerative diseases involve some form of splicing defect(s), such as Alzheimer’s disease, Parkinson’s disease and spinal muscular atrophy. Our growing understanding of RNA splicing has led to the explosion of research in the field of splice-switching antisense oligonucleotide therapeutics. Here we review our current understanding of the effects alternative splicing has on neuronal differentiation, neuronal migration, synaptic maturation and regulation, as well as the impact on neurodegenerative diseases. We will also review the current landscape of splice-switching antisense oligonucleotides as a therapeutic strategy for a number of common neurodegenerative disorders.

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The Impact of Pro-Inflammatory Cytokines on Alternative Splicing Patterns in Human Islets

Diabetes ◽

10.2337/db20-0847 ◽

2021 ◽

pp. db200847

Author(s):

Wenting Wu ◽

Farooq Syed ◽

Edward Simpson ◽

Chih-Chun Lee ◽

Jing Liu ◽

...

Keyword(s):

Alternative Splicing ◽

Inflammatory Cytokines ◽

Human Islets ◽

The Impact ◽

Splicing Patterns ◽

Pro Inflammatory Cytokines

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LOOKING FOR EVIDENCE OF DIFFERENTIATION AND COOPERATION: NATURAL MEASURES FOR THE STUDY OF EVOLUTION OF MULTICELLULARITY

Advances in Complex Systems ◽

10.1142/s0219525909002222 ◽

2009 ◽

Vol 12 (03) ◽

pp. 255-271

Author(s):

MORITZ BUCK ◽

CHRYSTOPHER L. NEHANIV

Keyword(s):

Artificial Life ◽

Regulatory Networks ◽

Single Cells ◽

Genetic Regulatory Networks ◽

Free Living ◽

Evolutionary Transitions ◽

A Cell ◽

Communication Ability ◽

Natural Measures ◽

The Impact

The understanding of the evolutionary transitions is a major area of research in artificial life and in biology. We follow an artificial life approach to investigate these phenomena, using a system inspired by Anabaena cyanobacteria (which exhibit rudimentary multicellular differentiation and cooperation) in order to look for evidence of emerging differentiation and multicellular cooperation in colonies of individual cells.We first evolve single free-living cells with the help of a Genetic Algorithm (GA). These cells are controlled with genetic regulatory networks. The single cells are evolved to each perform both of two tasks: an abstraction of house-keeping metabolism and a reproductive cycle. Once such a cell was evolved with the GA, the cell is used to seed the growth of a multicellular filamentous colony, whose constituent cells continue to reproduce and evolve. Two types of colonies generated from the seed cell are studied: one with intercellular communication ability and one without.We introduce and apply new measures for assessing the impact of multicellular interaction on individual reproduction and on life span.The conclusion of these studies shows that the colony with the ability to communicate shows, with the help of our new measures, behaviors that hint at the emergence of early cooperation.

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Design and application of circular RNAs with protein-sponge function

Nucleic Acids Research ◽

10.1093/nar/gkaa1085 ◽

2020 ◽

Vol 48 (21) ◽

pp. 12326-12335

Author(s):

Silke Schreiner ◽

Anna Didio ◽

Lee-Hsueh Hung ◽

Albrecht Bindereif

Keyword(s):

Alternative Splicing ◽

Mammalian Cells ◽

Regulatory Networks ◽

Rna Binding ◽

Molecular Medicine ◽

Circular Rnas ◽

Ca Repeat ◽

New Strategy ◽

Nuclear Ribonucleoprotein ◽

Splicing Patterns

Abstract Circular RNAs (circRNAs) are a class of noncoding RNAs, generated from pre-mRNAs by circular splicing of exons and functionally largely uncharacterized. Here we report on the design, expression, and characterization of artificial circRNAs that act as protein sponges, specifically binding and functionally inactivating hnRNP (heterogeneous nuclear ribonucleoprotein) L. HnRNP L regulates alternative splicing, depending on short CA-rich RNA elements. We demonstrate that designer hnRNP L-sponge circRNAs with CA-repeat or CA-rich sequence clusters can efficiently and specifically modulate splicing-regulatory networks in mammalian cells, including alternative splicing patterns and the cellular distribution of a splicing factor. This new strategy can in principle be applied to any RNA-binding protein, opening up new therapeutic strategies in molecular medicine.

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Take ACTION to characterize the functional identity of single cells

10.1101/081273 ◽

2016 ◽

Author(s):

Shahin Mohammadi ◽

Vikram Ravindra ◽

David F. Gleich ◽

Ananth Grama

Keyword(s):

Regulatory Networks ◽

Single Cells ◽

Survival Rates ◽

Molecular Signatures ◽

Functional Identity ◽

Cell Type ◽

Differential Survival ◽

Transcriptomic Data ◽

Lineage Differentiation ◽

Therapeutic Responses

Single-cell transcriptomic data has the potential to radically redefine our view of cell type identity. Cells that were previously believed to be homogeneous are now clearly distinguishable in terms of their expression phenotype. Methods for automatically characterizing the functional identity of cells, and their associated properties, can be used to uncover processes involved in lineage differentiation as well as sub-typing cancer cells. They can also be used to suggest personalized therapies based on molecular signatures associated with pathology. We develop a new method, called ACTION, to infer the functional identity of cells from their transcriptional profile, classify them based on their dominant function, and reconstruct regulatory networks that are responsible for mediating their identity. Using ACTION, we identify novel Melanoma sub-types with differential survival rates and therapeutic responses, for which we provide biomarkers along with their underlying regulatory networks.

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Impact of Alternative Splicing Variants on Liver Cancer Biology

Cancers ◽

10.3390/cancers14010018 ◽

2021 ◽

Vol 14 (1) ◽

pp. 18

Author(s):

Jose J. G. Marin ◽

Maria Reviejo ◽

Meraris Soto ◽

Elisa Lozano ◽

Maitane Asensio ◽

...

Keyword(s):

Alternative Splicing ◽

Cancer Cells ◽

Cancer Biology ◽

Survival Rates ◽

Splicing Factors ◽

Aberrant Splicing ◽

Splicing Variants ◽

Mrna Maturation ◽

Available Information ◽

The Impact

The two most frequent primary cancers affecting the liver, whose incidence is growing worldwide, are hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA), which are among the five most lethal solid tumors with meager 5-year survival rates. The common difficulty in most cases to reach an early diagnosis, the aggressive invasiveness of both tumors, and the lack of favorable response to pharmacotherapy, either classical chemotherapy or modern targeted therapy, account for the poor outcome of these patients. Alternative splicing (AS) during pre-mRNA maturation results in changes that might affect proteins involved in different aspects of cancer biology, such as cell cycle dysregulation, cytoskeleton disorganization, migration, and adhesion, which favors carcinogenesis, tumor promotion, and progression, allowing cancer cells to escape from pharmacological treatments. Reasons accounting for cancer-associated aberrant splicing include mutations that create or disrupt splicing sites or splicing enhancers or silencers, abnormal expression of splicing factors, and impaired signaling pathways affecting the activity of the splicing machinery. Here we have reviewed the available information regarding the impact of AS on liver carcinogenesis and the development of malignant characteristics of HCC and iCCA, whose understanding is required to develop novel therapeutical approaches aimed at manipulating the phenotype of cancer cells.

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Untangling the effects of cellular composition on coexpression analysis

10.1101/735951 ◽

2019 ◽

Cited By ~ 4

Author(s):

Marjan Farahbod ◽

Paul Pavlidis

Keyword(s):

Regulatory Networks ◽

Large Fraction ◽

Expression Patterns ◽

Cell Types ◽

Cellular Composition ◽

Cell Type ◽

Coexpression Analysis ◽

The Impact ◽

Bulk Tissue ◽

Rna Levels

AbstractBackgroundCoexpression analysis is one of the most widely used methods in genomics, with applications to inferring regulatory networks, predicting gene function, and interpretation of transcriptome profiling studies. Most studies use data collected from bulk tissue, where the effects of cellular composition present a potential confound. However, the impact of composition on coexpression analysis have not been studied in detail. Here we examine this issue for the case of human brain RNA analysis.ResultsWe found that for most genes, differences in expression levels across cell types account for a large fraction of the variance of their measured RNA levels in brain (median R2 = 0.64). We then show that genes that have similar expression patterns across cell types will have correlated RNA levels in bulk tissue, due to the effect of variation in cellular composition. We demonstrate that much of the coexpression in the bulk tissue can be attributed to this effect. We further show how this composition-induced coexpression masks underlying intra-cell-type coexpression observed in single-cell data. Attempt to correct for composition yielded mixed results.ConclusionsThe dominant coexpression signal in brain can be attributed to cellular compositional effects, rather than intra-cell-type regulatory relationships, and this is likely to be true for other tissues. These results have important implications for the relevance and interpretation of coexpression in many applications.

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Evolutionary strategies applied to artificial gene regulatory networks

10.1101/2021.09.28.462218 ◽

2021 ◽

Author(s):

André Luiz de Lucena Moreira ◽

César Rennó-Costa

Keyword(s):

Gene Regulatory Networks ◽

Regulatory Networks ◽

Task Complexity ◽

Single Cells ◽

Single Gene ◽

Evolutionary Strategy ◽

Evolutionary Strategies ◽

Gene Regulatory ◽

Artificial Gene ◽

The Impact

Evolution optimizes cellular behavior throughout sequential generations by selecting the successful individual cells in a given context. As gene regulatory networks (GRNs) determine the behavior of single cells by ruling the activation of different processes - such as cell differentiation and death - how GRNs change from one generation to the other might have a relevant impact on the course of evolution. It is not clear, however, which mechanisms that affect GRNs effectively favor evolution and how. Here, we use a population of computational robotic models controlled by artificial gene regulatory networks (AGRNs) to evaluate the impact of different genetic modification strategies in the course of evolution. The virtual agent senses the ambient and acts on it as a bacteria in different phototaxis-like tasks - orientation to light, phototaxis, and phototaxis with obstacles. We studied how the strategies of gradual and abrupt changes on the AGRNs impact evolution considering multiple levels of task complexity. The results indicated that a gradual increase in the complexity of the performed tasks is beneficial for the evolution of the model. Furthermore, we have seen that larger gene regulatory networks are needed for more complex tasks, with single-gene duplication being an excellent evolutionary strategy for growing these networks, as opposed to full-genome duplication. Studying how GRNs evolved in a biological environment allows us to improve the computational models produced and provide insights into aspects and events that influenced the development of life on earth.

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ArchR: An integrative and scalable software package for single-cell chromatin accessibility analysis

10.1101/2020.04.28.066498 ◽

2020 ◽

Cited By ~ 18

Author(s):

Jeffrey M. Granja ◽

M. Ryan Corces ◽

Sarah E. Pierce ◽

S. Tansu Bagdatli ◽

Hani Choudhry ◽

...

Keyword(s):

Single Cell ◽

Large Scale ◽

Single Cells ◽

Chromatin Accessibility ◽

Cell Type ◽

Biological Meaning ◽

Cell Clustering ◽

Gene Regulatory ◽

Regulatory Landscapes ◽

Scalable Software

ABSTRACTThe advent of large-scale single-cell chromatin accessibility profiling has accelerated our ability to map gene regulatory landscapes, but has outpaced the development of robust, scalable software to rapidly extract biological meaning from these data. Here we present a software suite for single-cell analysis of regulatory chromatin in R (ArchR; www.ArchRProject.com) that enables fast and comprehensive analysis of single-cell chromatin accessibility data. ArchR provides an intuitive, user-focused interface for complex single-cell analyses including doublet removal, single-cell clustering and cell type identification, robust peak set generation, cellular trajectory identification, DNA element to gene linkage, transcription factor footprinting, mRNA expression level prediction from chromatin accessibility, and multi-omic integration with scRNA-seq. Enabling the analysis of over 1.2 million single cells within 8 hours on a standard Unix laptop, ArchR is a comprehensive analytical suite for end-to-end analysis of single-cell chromatin accessibility data that will accelerate the understanding of gene regulation at the resolution of individual cells.

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