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MINI-EX : integrative inference of single-cell gene regulatory networks in plants

Camilla Ferrari (UGent) , Nicolás Manosalva Pérez (UGent) and Klaas Vandepoele (UGent)
(2022) MOLECULAR PLANT. 15(11). p.1807-1824
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Abstract
Multicellular organisms, such as plants, are characterized by highly specialized and tightly regulated cell populations, establishing specific morphological structures and executing distinct functions. Gene regulatory networks (GRNs) describe condition-specific interactions of transcription factors (TFs) regulating the expression of target genes, underpinning these specific functions. As efficient and validated methods to identify cell-type-specific GRNs from single-cell data in plants are lacking, limiting our understanding of the organization of specific cell types in both model species and crops, we developed MINI-EX (Motif-Informed Network Inference based on single-cell EXpression data), an integrative approach to infer cell-type-specific networks in plants. MINI-EX uses single-cell transcriptomic data to define expression-based networks and integrates TF motif information to filter the inferred regulons, resulting in networks with increased accuracy. Next, regulons are assigned to different cell types, leveraging cell-specific expression, and candidate regulators are prioritized using network centrality measures, functional annotations, and expression specificity. This embedded prioritization strategy offers a unique and efficient means to unravel signaling cascades in specific cell types controlling a biological process of interest. We demonstrate the stability of MINI-EX toward input data sets with low number of cells and its robustness toward missing data, and show that it infers state-of-the-art networks with a better performance compared with other related single-cell network tools. MINI-EX successfully identifies key regulators controlling root development in Arabidopsis and rice, leaf development in Arabidopsis, and ear development in maize, enhancing our understanding of cell-type-specific regulation and unraveling the roles of different regulators controlling the development of specific cell types in plants.
Keywords
gene regulatory network, single-cell RNA-seq, systems biology, transcription factors

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Citation

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MLA
Ferrari, Camilla, et al. “MINI-EX : Integrative Inference of Single-Cell Gene Regulatory Networks in Plants.” MOLECULAR PLANT, vol. 15, no. 11, 2022, pp. 1807–24, doi:10.1016/j.molp.2022.10.016.
APA
Ferrari, C., Manosalva Pérez, N., & Vandepoele, K. (2022). MINI-EX : integrative inference of single-cell gene regulatory networks in plants. MOLECULAR PLANT, 15(11), 1807–1824. https://doi.org/10.1016/j.molp.2022.10.016
Chicago author-date
Ferrari, Camilla, Nicolás Manosalva Pérez, and Klaas Vandepoele. 2022. “MINI-EX : Integrative Inference of Single-Cell Gene Regulatory Networks in Plants.” MOLECULAR PLANT 15 (11): 1807–24. https://doi.org/10.1016/j.molp.2022.10.016.
Chicago author-date (all authors)
Ferrari, Camilla, Nicolás Manosalva Pérez, and Klaas Vandepoele. 2022. “MINI-EX : Integrative Inference of Single-Cell Gene Regulatory Networks in Plants.” MOLECULAR PLANT 15 (11): 1807–1824. doi:10.1016/j.molp.2022.10.016.
Vancouver
1.
Ferrari C, Manosalva Pérez N, Vandepoele K. MINI-EX : integrative inference of single-cell gene regulatory networks in plants. MOLECULAR PLANT. 2022;15(11):1807–24.
IEEE
[1]
C. Ferrari, N. Manosalva Pérez, and K. Vandepoele, “MINI-EX : integrative inference of single-cell gene regulatory networks in plants,” MOLECULAR PLANT, vol. 15, no. 11, pp. 1807–1824, 2022.
@article{8772213,
  abstract     = {{Multicellular organisms, such as plants, are characterized by highly specialized and tightly regulated cell populations, establishing specific morphological structures and executing distinct functions. Gene regulatory networks (GRNs) describe condition-specific interactions of transcription factors (TFs) regulating the expression of target genes, underpinning these specific functions. As efficient and validated methods to identify cell-type-specific GRNs from single-cell data in plants are lacking, limiting our understanding of the organization of specific cell types in both model species and crops, we developed MINI-EX (Motif-Informed Network Inference based on single-cell EXpression data), an integrative approach to infer cell-type-specific networks in plants. MINI-EX uses single-cell transcriptomic data to define expression-based networks and integrates TF motif information to filter the inferred regulons, resulting in networks with increased accuracy. Next, regulons are assigned to different cell types, leveraging cell-specific expression, and candidate regulators are prioritized using network centrality measures, functional annotations, and expression specificity. This embedded prioritization strategy offers a unique and efficient means to unravel signaling cascades in specific cell types controlling a biological process of interest. We demonstrate the stability of MINI-EX toward input data sets with low number of cells and its robustness toward missing data, and show that it infers state-of-the-art networks with a better performance compared with other related single-cell network tools. MINI-EX successfully identifies key regulators controlling root development in Arabidopsis and rice, leaf development in Arabidopsis, and ear development in maize, enhancing our understanding of cell-type-specific regulation and unraveling the roles of different regulators controlling the development of specific cell types in plants.}},
  author       = {{Ferrari, Camilla and Manosalva Pérez, Nicolás and Vandepoele, Klaas}},
  issn         = {{1674-2052}},
  journal      = {{MOLECULAR PLANT}},
  keywords     = {{gene regulatory network,single-cell RNA-seq,systems biology,transcription factors}},
  language     = {{eng}},
  number       = {{11}},
  pages        = {{1807--1824}},
  title        = {{MINI-EX : integrative inference of single-cell gene regulatory networks in plants}},
  url          = {{http://doi.org/10.1016/j.molp.2022.10.016}},
  volume       = {{15}},
  year         = {{2022}},
}
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