Computational Construction of Gene Regulatory Network and Trajectory of Developing Midbrain Dopaminergic Neurons Perturbed by Insecticides Open Access

He, Yajun (Spring 2021)

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Background: Embryonic exposure to environmental chemicals, such as insecticides, can cause developmental defects that increase individuals’ susceptibility to neurological diseases in both childhood and adulthood. Abnormal development of the midbrain dopamine circuit appears to underlie several neurological disorders, including autism spectrum disorder and attention deficit hyperactivity disorder. Epidemiological evidence has identified associations between exposure to insecticides during critical periods of brain development and neurological deficits. However, how exactly midbrain neurodevelopment is disrupted by insecticides is poorly understood.

Objective: As the development of dopaminergic (DA) neurons is underpinned by an elaborate network of transcription factors, we aim to characterize and construct the gene regulatory network (GRN) that orchestrates DA neural differentiation and migration to understand their developmental trajectories that may be perturbed by insecticides.

Method: Integrated bioinformatics and machine-learning approaches were used to analyze existing single-cell RNA sequencing data of developing mouse midbrain (La Manno et al., 2016). We firstly used R package Seurat to conduct quality control, dimension reduction, clustering, and differential expression analysis to obtain DA neuron subtypes. Then, we used pyScenic to generate a co-expression matrix according to Spearman correlation, and motif enrichment analysis using RcisTarget to obtain pruned regulons and TFs-genes regulation network. Lastly, we used scVelo to calculate RNA velocity and latent time to derive the developmental trajectories.

Results: 1901 cells were grouped into 15 cell types and visualized in 2D t-SNE and 3D UMAP map. Cell types were annotated according to the expression profile of marker genes. Immature DA neurons were sub-clustering into 4 types: NbDA, DA0, DA1 and DA2. pyScenic’s output from 9 selected cell types gave us a complex network containing 603 transcription factors, 13691 target genes and 52538 edges. After cross referencing with literature, an organized, minimal network was obtained with 25 transcription factors. RNA velocity analysis produced trajectories of DA lineage from Prog to NbM, NbDA, DA0, DA1 and DA2.

Conclusion: We characterized the variety of developing midbrain DA neurons and derived a minimal GRN that underpins the normal midbrain DA development. Such GRN can be used to model the aberrations of DA neuron developmental trajectory by insecticides in future. 

Table of Contents


1.    Anatomical location, projection and function of midbrain DA neuron

2.    Midbrain DA neurodevelopment process

2.1 Morphological development

2.2 Molecular network underlying the development

3.    Effects of insecticides on midbrain DA neurodevelopment

3.1 Toxicity phenotype: sensitive time window

3.2 Molecular changes

4.    Understanding the mechanism of normal mDA neuronal development that can be perturbed by insecticides

4.1 Molecular network and developmental trajectory

4.2 How insecticides may perturb the network and trajectory

4.3 A computational modeling approach is necessary to map and simulate the TF network and trajectory

4.4 Utilizing scRNA-seq to map the TF network and facilitate modeling

·     scRNA-seq could inform neuron subtypes during DA development

·     scRNA-seq could infer network structure

·     scRNA-seq could infer pseudo-time and latent time



1.    Mouse developing midbrain scRNA-seq data source

2.    Analysis using Seurat

3.    Network inference using Scenic

4.    RNA velocity analysis

a.    bioinformatics pipeline to obtain loom file

b.    scVelo for RNA velocity, latent time and parameter calculation

5.    Integrating scenic network with literature



1.    Identification of neural cell types in developing mouse midbrain

2.    Inference of TF network in developing mouse midbrain

3.    mDA developmental trajectories






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