Single-cell gene regulatory network analysis reveals new melanoma cell states and transition trajectories during phenotype switching

AbstractMelanoma is notorious for its cellular heterogeneity, which is at least partly due to its ability to transition between alternate cell states. Similarly to EMT, melanoma cells with a melanocytic phenotype can switch to a mesenchymal-like phenotype. However, scattered emerging evidence indicates that additional, intermediate state(s) may exist. In order to search for such new melanoma states and decipher their underlying gene regulatory network (GRN), we extensively studied ten patient-derived melanoma cultures by single-cell RNA-seq of >39,000 cells. Although each culture exhibited a unique transcriptome, we identified shared gene regulatory networks that underlie the extreme melanocytic and mesenchymal cell states, as well as one (stable) intermediate state. The intermediate state was corroborated by a distinct open chromatin landscape and governed by the transcription factors EGR3, NFATC2, and RXRG. Single-cell migration assays established that this “transition” state exhibits an intermediate migratory phenotype. Through a dense time-series sampling of single cells and dynamic GRN inference, we unraveled the sequential and recurrent arrangement of transcriptional programs at play during phenotype switching that ultimately lead to the mesenchymal cell state. We provide the scRNA-Seq data with 39,263 melanoma cells on our SCope platform and the ATAC-seq data on a UCSC hub to jointly serve as a resource for the melanoma field. Together, this exhaustive analysis of melanoma cell state diversity indicates that additional states exists between the two extreme melanocytic and mesenchymal-like states. The GRN we identified may serve as a new putative target to prevent the switch to mesenchymal cell state and thereby, acquisition of metastatic and drug resistant potential.

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A scalable SCENIC workflow for single-cell gene regulatory network analysis

Nature Protocols ◽

10.1038/s41596-020-0336-2 ◽

2020 ◽

Vol 15 (7) ◽

pp. 2247-2276 ◽

Cited By ~ 15

Author(s):

Bram Van de Sande ◽

Christopher Flerin ◽

Kristofer Davie ◽

Maxime De Waegeneer ◽

Gert Hulselmans ◽

...

Keyword(s):

Network Analysis ◽

Single Cell ◽

Gene Regulatory Network ◽

Regulatory Network ◽

Gene Regulatory ◽

Cell Gene

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MKG: a mutual information based method to infer single cell gene regulatory network

10.1109/bibm52615.2021.9669880 ◽

2021 ◽

Author(s):

Yanping Zeng ◽

Xuhua Yan ◽

Zhenlan Liang ◽

Ruiqing Zheng ◽

Min Li

Keyword(s):

Mutual Information ◽

Single Cell ◽

Gene Regulatory Network ◽

Regulatory Network ◽

Gene Regulatory ◽

Cell Gene

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scSGL: Signed Graph Learning for Single-Cell Gene Regulatory Network Inference

10.1101/2021.07.08.451697 ◽

2021 ◽

Author(s):

Abdullah Karaaslanli ◽

Satabdi Saha ◽

Selin Aviyente ◽

Tapabrata Maiti

Keyword(s):

Single Cell ◽

Gene Regulatory Network ◽

Regulatory Network ◽

Gene Networks ◽

Signed Graph ◽

Reconstruction Algorithms ◽

Gene Regulatory Network Inference ◽

Graph Learning ◽

Gene Regulatory ◽

Cell Gene

Elucidating the topology of gene regulatory networks (GRN) from large single-cell RNA sequencing (scRNAseq) datasets, while effectively capturing its inherent cell-cycle heterogeneity, is currently one of the most pressing problems in computational systems biology. Recently, graph learning (GL) approaches based on graph signal processing (GSP) have been developed to infer graph topology from signals defined on graphs. However, existing GL methods are not suitable for learning signed graphs, which represent a characteristic feature of GRNs, as they account for both activating and inhibitory relationships between genes. To this end, we propose a novel signed GL approach, scSGL, that incorporates the similarity and dissimilarity between observed gene expression data to construct gene networks. The proposed approach is formulated as a non-convex optimization problem and solved using an efficient ADMM framework. In our experiments on simulated and real single cell datasets, scSGL compares favorably with other single cell gene regulatory network reconstruction algorithms.

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scTenifoldKnk: An Efficient Virtual Knockout Tool for Gene Function Predictions via Single-Cell Gene Regulatory Network Perturbation

SSRN Electronic Journal ◽

10.2139/ssrn.3872938 ◽

2021 ◽

Author(s):

Daniel Osorio ◽

Yan Zhong ◽

Guanxun Li ◽

Qian Xu ◽

Yongjian Yang ◽

...

Keyword(s):

Single Cell ◽

Gene Regulatory Network ◽

Gene Function ◽

Regulatory Network ◽

Gene Regulatory ◽

Cell Gene

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PAGeneRN

Data Analytics in Medicine ◽

10.4018/978-1-7998-1204-3.ch055 ◽

2020 ◽

pp. 1052-1075 ◽

Cited By ~ 1

Author(s):

Dina Elsayad ◽

A. Ali ◽

Howida A. Shedeed ◽

Mohamed F. Tolba

Keyword(s):

Gene Expression ◽

Data Analysis ◽

Network Analysis ◽

Gene Regulatory Network ◽

Gene Expression Data ◽

Regulatory Network ◽

Regulatory Networks ◽

Expression Data ◽

Gene Expression Data Analysis ◽

Gene Regulatory

The gene expression analysis is an important research area of Bioinformatics. The gene expression data analysis aims to understand the genes interacting phenomena, gene functionality and the genes mutations effect. The Gene regulatory network analysis is one of the gene expression data analysis tasks. Gene regulatory network aims to study the genes interactions topological organization. The regulatory network is critical for understanding the pathological phenotypes and the normal cell physiology. There are many researches that focus on gene regulatory network analysis but unfortunately some algorithms are affected by data size. Where, the algorithm runtime is proportional to the data size, therefore, some parallel algorithms are presented to enhance the algorithms runtime and efficiency. This work presents a background, mathematical models and comparisons about gene regulatory networks analysis different techniques. In addition, this work proposes Parallel Architecture for Gene Regulatory Network (PAGeneRN).

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SIN-KNO: A method of gene regulatory network inference using single-cell transcription and gene knockout data

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720019500355 ◽

2019 ◽

Vol 17 (06) ◽

pp. 1950035

Author(s):

Huiqing Wang ◽

Yuanyuan Lian ◽

Chun Li ◽

Yue Ma ◽

Zhiliang Yan ◽

...

Keyword(s):

Gene Expression ◽

Steady State ◽

Single Cell ◽

Gene Regulatory Network ◽

Regulatory Network ◽

Gene Knockout ◽

Cell Heterogeneity ◽

State Information ◽

Gene Regulatory ◽

Cell Data

As a tool of interpreting and analyzing genetic data, gene regulatory network (GRN) could reveal regulatory relationships between genes, proteins, and small molecules, as well as understand physiological activities and functions within biological cells, interact in pathways, and how to make changes in the organism. Traditional GRN research focuses on the analysis of the regulatory relationships through the average of cellular gene expressions. These methods are difficult to identify the cell heterogeneity of gene expression. Existing methods for inferring GRN using single-cell transcriptional data lack expression information when genes reach steady state, and the high dimensionality of single-cell data leads to high temporal and spatial complexity of the algorithm. In order to solve the problem in traditional GRN inference methods, including the lack of cellular heterogeneity information, single-cell data complexity and lack of steady-state information, we propose a method for GRN inference using single-cell transcription and gene knockout data, called SINgle-cell transcription data-KNOckout data (SIN-KNO), which focuses on combining dynamic and steady-state information of regulatory relationship contained in gene expression. Capturing cell heterogeneity information could help understand the gene expression difference in different cells. So, we could observe gene expression changes more accurately. Gene knockout data could observe the gene expression levels at steady-state of all other genes when one gene is knockout. Classifying the genes before analyzing the single-cell data could determine a large number of non-existent regulation, greatly reducing the number of regulation required for inference. In order to show the efficiency, the proposed method has been compared with several typical methods in this area including GENIE3, JUMP3, and SINCERITIES. The results of the evaluation indicate that the proposed method can analyze the diversified information contained in the two types of data, establish a more accurate gene regulation network, and improve the computational efficiency. The method provides a new thinking for dealing with large datasets and high computational complexity of single-cell data in the GRN inference.

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