Joint profiling of gene expression and chromatin accessibility of amphioxus development at single cell resolution

Abstract Vertebrate evolution was accompanied with two rounds of whole genome duplication followed by functional divergence in terms of regulatory circuits and gene expression patterns. As a basal and slow-evolving chordate species, amphioxus is an ideal paradigm for exploring the origin and evolution of vertebrates. Single cell sequencing has been widely employed to construct the developmental cell atlas of several key species of vertebrates (human, mouse, zebrafish and frog) and tunicate (sea squirts). Here, we performed single-nucleus RNA sequencing (snRNA-seq) and single-cell assay for transposase accessible chromatin sequencing (scATAC-seq) for different stages of amphioxus (covering embryogenesis and adult tissues). With the datasets generated we constructed the developmental tree for amphioxus cell fate commitment and lineage specification, and revealed the underlying key regulators and genetic regulatory networks. The generated data were integrated into an online platform, AmphioxusAtlas, for public access at http://120.79.46.200:81/AmphioxusAtlas.

Download Full-text

ON CONSTRUCTION OF STOCHASTIC GENETIC NETWORKS BASED ON GENE EXPRESSION SEQUENCES

International Journal of Neural Systems ◽

10.1142/s0129065705000256 ◽

2005 ◽

Vol 15 (04) ◽

pp. 297-310 ◽

Cited By ~ 37

Author(s):

WAI-KI CHING ◽

MICHAEL M. NG ◽

ERIC S. FUNG ◽

TATSUYA AKUTSU

Keyword(s):

Gene Expression ◽

Regulatory Networks ◽

Expression Patterns ◽

Boolean Networks ◽

Genetic Regulatory Networks ◽

Efficient Estimation ◽

Estimation Methods ◽

Series Data ◽

Model Parameters ◽

Proposed Model

Reconstruction of genetic regulatory networks from time series data of gene expression patterns is an important research topic in bioinformatics. Probabilistic Boolean Networks (PBNs) have been proposed as an effective model for gene regulatory networks. PBNs are able to cope with uncertainty, corporate rule-based dependencies between genes and discover the sensitivity of genes in their interactions with other genes. However, PBNs are unlikely to use directly in practice because of huge amount of computational cost for obtaining predictors and their corresponding probabilities. In this paper, we propose a multivariate Markov model for approximating PBNs and describing the dynamics of a genetic network for gene expression sequences. The main contribution of the new model is to preserve the strength of PBNs and reduce the complexity of the networks. The number of parameters of our proposed model is O(n2) where n is the number of genes involved. We also develop efficient estimation methods for solving the model parameters. Numerical examples on synthetic data sets and practical yeast data sequences are given to demonstrate the effectiveness of the proposed model.

Download Full-text

A single cell brain atlas in human Alzheimer’s disease

10.1101/628347 ◽

2019 ◽

Cited By ~ 4

Author(s):

Alexandra Grubman ◽

Gabriel Chew ◽

John F. Ouyang ◽

Guizhi Sun ◽

Xin Yi Choo ◽

...

Keyword(s):

Gene Expression ◽

Transcription Factor ◽

Single Cell ◽

Cell Fate ◽

Expression Patterns ◽

Cell Types ◽

Gene Expression Patterns ◽

Cell Type ◽

Web Resource ◽

Cell Type Specific

AbstractAlzheimer’s disease (AD) is a heterogeneous disease that is largely dependent on the complex cellular microenvironment in the brain. This complexity impedes our understanding of how individual cell types contribute to disease progression and outcome. To characterize the molecular and functional cell diversity in the human AD brain we utilized single nuclei RNA- seq in AD and control patient brains in order to map the landscape of cellular heterogeneity in AD. We detail gene expression changes at the level of cells and cell subclusters, highlighting specific cellular contributions to global gene expression patterns between control and Alzheimer’s patient brains. We observed distinct cellular regulation of APOE which was repressed in oligodendrocyte progenitor cells (OPCs) and astrocyte AD subclusters, and highly enriched in a microglial AD subcluster. In addition, oligodendrocyte and microglia AD subclusters show discordant expression of APOE. Integration of transcription factor regulatory modules with downstream GWAS gene targets revealed subcluster-specific control of AD cell fate transitions. For example, this analysis uncovered that astrocyte diversity in AD was under the control of transcription factor EB (TFEB), a master regulator of lysosomal function and which initiated a regulatory cascade containing multiple AD GWAS genes. These results establish functional links between specific cellular sub-populations in AD, and provide new insights into the coordinated control of AD GWAS genes and their cell-type specific contribution to disease susceptibility. Finally, we created an interactive reference web resource which will facilitate brain and AD researchers to explore the molecular architecture of subtype and AD-specific cell identity, molecular and functional diversity at the single cell level.HighlightsWe generated the first human single cell transcriptome in AD patient brainsOur study unveiled 9 clusters of cell-type specific and common gene expression patterns between control and AD brains, including clusters of genes that present properties of different cell types (i.e. astrocytes and oligodendrocytes)Our analyses also uncovered functionally specialized sub-cellular clusters: 5 microglial clusters, 8 astrocyte clusters, 6 neuronal clusters, 6 oligodendrocyte clusters, 4 OPC and 2 endothelial clusters, each enriched for specific ontological gene categoriesOur analyses found manifold AD GWAS genes specifically associated with one cell-type, and sets of AD GWAS genes co-ordinately and differentially regulated between different brain cell-types in AD sub-cellular clustersWe mapped the regulatory landscape driving transcriptional changes in AD brain, and identified transcription factor networks which we predict to control cell fate transitions between control and AD sub-cellular clustersFinally, we provide an interactive web-resource that allows the user to further visualise and interrogate our dataset.Data resource web interface:http://adsn.ddnetbio.com

Download Full-text

Comprehensive analysis of retinal development at single cell resolution identifies NFI factors as essential for mitotic exit and specification of late-born cells

10.1101/378950 ◽

2018 ◽

Cited By ~ 14

Author(s):

Brian S. Clark ◽

Genevieve L. Stein-O’Brien ◽

Fion Shiau ◽

Gabrielle H. Cannon ◽

Emily Davis ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Cell Fate ◽

Regulatory Networks ◽

Developmental Stages ◽

Cell Types ◽

Developmental Competence ◽

Cellular Heterogeneity ◽

Retinal Cell ◽

Control Of Gene Expression

SUMMARYPrecise temporal control of gene expression in neuronal progenitors is necessary for correct regulation of neurogenesis and cell fate specification. However, the extensive cellular heterogeneity of the developing CNS has posed a major obstacle to identifying the gene regulatory networks that control these processes. To address this, we used single cell RNA-sequencing to profile ten developmental stages encompassing the full course of retinal neurogenesis. This allowed us to comprehensively characterize changes in gene expression that occur during initiation of neurogenesis, changes in developmental competence, and specification and differentiation of each of the major retinal cell types. These data identify transitions in gene expression between early and late-stage retinal progenitors, as well as a classification of neurogenic progenitors. We identify here the NFI family of transcription factors (Nfia, Nfib, and Nfix) as genes with enriched expression within late RPCs, and show they are regulators of bipolar interneuron and Müller glia specification and the control of proliferative quiescence.

Download Full-text

Algorithmic Reconstruction of GBM Network Complexity

10.1101/2021.09.21.461255 ◽

2021 ◽

Author(s):

Abicumaran Uthamacumaran ◽

Morgan Craig

Keyword(s):

Gene Expression ◽

Transcription Factors ◽

Single Cell ◽

Cell Fate ◽

Data Science ◽

Expression Patterns ◽

State Transitions ◽

Gene Expression Patterns ◽

Cell Fate Decisions ◽

Study Gene Expression

Glioblastoma (GBM) is a complex disease that is difficult to treat. Establishing the complex genetic interactions regulating cell fate decisions in GBM can help to shed light on disease aggressivity and improved treatments. Networks and data science offer novel approaches to study gene expression patterns from single-cell datasets, helping to distinguish genes associated with control of differentiation and thus aggressivity. Here, we applied a host of data theoretic techniques, including clustering algorithms, Waddington landscape reconstruction, trajectory inference algorithms, and network approaches, to compare gene expression patterns between pediatric and adult GBM, and those of adult GSCs (glioma-derived stem cells) to identify the key molecular regulators of the complex networks driving GBM/GSC and predict their cell fate dynamics. Using these tools, we identified critical genes and transcription factors coordinating cell state transitions from stem-like to mature GBM phenotypes, including eight transcription factors (OLIG1/2, TAZ, GATA2, FOXG1, SOX6, SATB2, YY1) and four signaling genes (ATL3, MTSS1, EMP1, and TPT1) as clinically targetable novel putative function interactions differentiating pediatric and adult GBMs from adult GSCs. Our study is among the first to provide strong evidence of the applicability of complex systems approaches for reverse-engineering gene networks from patient-derived single-cell datasets and inferring their complex dynamics, bolstering the search for new clinically- relevant targets in GBM.

Download Full-text

How to build regulatory networks from single-cell gene expression data

Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics ◽

10.1145/3388440.3414213 ◽

2020 ◽

Author(s):

Aditya Pratapa ◽

Amogh P. Jalihal ◽

Jeffrey N. Law ◽

Aditya Bharadwaj ◽

T. M. Murali

Keyword(s):

Gene Expression ◽

Single Cell ◽

Gene Expression Data ◽

Regulatory Networks ◽

Expression Data ◽

Cell Gene Expression ◽

Cell Gene

Download Full-text

Identifying cell types from single-cell data based on similarities and dissimilarities between cells

BMC Bioinformatics ◽

10.1186/s12859-020-03873-z ◽

2021 ◽

Vol 22 (S3) ◽

Author(s):

Yuanyuan Li ◽

Ping Luo ◽

Yi Lu ◽

Fang-Xiang Wu

Keyword(s):

Gene Expression ◽

Single Cell ◽

Spectral Clustering ◽

Incidence Matrix ◽

Expression Patterns ◽

Cell Types ◽

Clustering Method ◽

Different Types ◽

Cell Data ◽

Spectral Clustering Method

Abstract Background With the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research. However, the clustering of cell types becomes more complex with the mutual penetration between different types of cells and the instability of gene expression. One way of overcoming this problem is to group similar, related single cells together by the means of various clustering analysis methods. Although some methods such as spectral clustering can do well in the identification of cell types, they only consider the similarities between cells and ignore the influence of dissimilarities on clustering results. This methodology may limit the performance of most of the conventional clustering algorithms for the identification of clusters, it needs to develop special methods for high-dimensional sparse categorical data. Results Inspired by the phenomenon that same type cells have similar gene expression patterns, but different types of cells evoke dissimilar gene expression patterns, we improve the existing spectral clustering method for clustering single-cell data that is based on both similarities and dissimilarities between cells. The method first measures the similarity/dissimilarity among cells, then constructs the incidence matrix by fusing similarity matrix with dissimilarity matrix, and, finally, uses the eigenvalues of the incidence matrix to perform dimensionality reduction and employs the K-means algorithm in the low dimensional space to achieve clustering. The proposed improved spectral clustering method is compared with the conventional spectral clustering method in recognizing cell types on several real single-cell RNA-seq datasets. Conclusions In summary, we show that adding intercellular dissimilarity can effectively improve accuracy and achieve robustness and that improved spectral clustering method outperforms the traditional spectral clustering method in grouping cells.

Download Full-text

GATA-targeted compounds modulate cardiac subtype cell differentiation in dual reporter stem cell line

Stem Cell Research & Therapy ◽

10.1186/s13287-021-02259-z ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Mika J. Välimäki ◽

Robert S. Leigh ◽

Sini M. Kinnunen ◽

Alexander R. March ◽

Ana Hernández de Sande ◽

...

Keyword(s):

Gene Expression ◽

Progenitor Cells ◽

Cell Fate ◽

Reporter Gene ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Cell Fate Decisions ◽

Dual Reporter ◽

Gene Regulatory ◽

Reporter Gene Assays

AbstractBackgroundPharmacological modulation of cell fate decisions and developmental gene regulatory networks holds promise for the treatment of heart failure. Compounds that target tissue-specific transcription factors could overcome non-specific effects of small molecules and lead to the regeneration of heart muscle following myocardial infarction. Due to cellular heterogeneity in the heart, the activation of gene programs representing specific atrial and ventricular cardiomyocyte subtypes would be highly desirable. Chemical compounds that modulate atrial and ventricular cell fate could be used to improve subtype-specific differentiation of endogenous or exogenously delivered progenitor cells in order to promote cardiac regeneration.MethodsTranscription factor GATA4-targeted compounds that have previously shown in vivo efficacy in cardiac injury models were tested for stage-specific activation of atrial and ventricular reporter genes in differentiating pluripotent stem cells using a dual reporter assay. Chemically induced gene expression changes were characterized by qRT-PCR, global run-on sequencing (GRO-seq) and immunoblotting, and the network of cooperative proteins of GATA4 and NKX2-5 were further explored by the examination of the GATA4 and NKX2-5 interactome by BioID. Reporter gene assays were conducted to examine combinatorial effects of GATA-targeted compounds and bromodomain and extraterminal domain (BET) inhibition on chamber-specific gene expression.ResultsGATA4-targeted compounds 3i-1000 and 3i-1103 were identified as differential modulators of atrial and ventricular gene expression. More detailed structure-function analysis revealed a distinct subclass of GATA4/NKX2-5 inhibitory compounds with an acetyl lysine-like domain that contributed to ventricular cells (%Myl2-eGFP+). Additionally, BioID analysis indicated broad interaction between GATA4 and BET family of proteins, such as BRD4. This indicated the involvement of epigenetic modulators in the regulation of GATA-dependent transcription. In this line, reporter gene assays with combinatorial treatment of 3i-1000 and the BET bromodomain inhibitor (+)-JQ1 demonstrated the cooperative role of GATA4 and BRD4 in the modulation of chamber-specific cardiac gene expression.ConclusionsCollectively, these results indicate the potential for therapeutic alteration of cell fate decisions and pathological gene regulatory networks by GATA4-targeted compounds modulating chamber-specific transcriptional programs in multipotent cardiac progenitor cells and cardiomyocytes. The compound scaffolds described within this study could be used to develop regenerative strategies for myocardial regeneration.

Download Full-text