Characterizing Cellular Differentiation Potency and Waddington Landscape Via Energy Indicator

Overview

Journal Research (Wash D C)

Specialty Biology

Date 2023 May 24

PMID 37223479

Authors

Hanshuang Li

Chunshen Long

Yan Hong

Liaofu Luo

Yongchun Zuo

Affiliations

Soon will be listed here.

Abstract

The precise characterization of cellular differentiation potency remains an open question, which is fundamentally important for deciphering the dynamics mechanism related to cell fate transition. We quantitatively evaluated the differentiation potency of different stem cells based on the Hopfield neural network (HNN). The results emphasized that cellular differentiation potency can be approximated by Hopfield energy values. We then profiled the Waddington energy landscape of embryogenesis and cell reprogramming processes. The energy landscape at single-cell resolution further confirmed that cell fate decision is progressively specified in a continuous process. Moreover, the transition of cells from one steady state to another in embryogenesis and cell reprogramming processes was dynamically simulated on the energy ladder. These two processes can be metaphorized as the motion of descending and ascending ladders, respectively. We further deciphered the dynamics of the gene regulatory network (GRN) for driving cell fate transition. Our study proposes a new energy indicator to quantitatively characterize cellular differentiation potency without prior knowledge, facilitating the further exploration of the potential mechanism of cellular plasticity.

Citing Articles

Metabolic Objectives and Trade-Offs: Inference and Applications.

Lin D, Khattar S, Chandrasekaran S Metabolites. 2025; 15(2).

PMID: 39997726 PMC: 11857637. DOI: 10.3390/metabo15020101.

Reconstructing Waddington Landscape from Cell Migration and Proliferation.

Han Y, Chen B, Bi Z, Zhang J, Hu Y, Bian J Interdiscip Sci. 2025; .

PMID: 39775538 DOI: 10.1007/s12539-024-00686-z.

Identification of CCR7 and CBX6 as key biomarkers in abdominal aortic aneurysm: Insights from multi-omics data and machine learning analysis.

Yong X, Hu X, Kang T, Deng Y, Li S, Yu S IET Syst Biol. 2024; 18(6):250-260.

PMID: 39602349 PMC: 11665846. DOI: 10.1049/syb2.12106.

A composite scaling network of EfficientNet for improving spatial domain identification performance.

Zhao Y, Long C, Shang W, Si Z, Liu Z, Feng Z Commun Biol. 2024; 7(1):1567.

PMID: 39587274 PMC: 11589849. DOI: 10.1038/s42003-024-07286-z.

Inference and analysis of cell-cell communication of non-myeloid circulating cells in late sepsis based on single-cell RNA-seq.

Tao Y, Li M, Liu C IET Syst Biol. 2024; 18(6):218-226.

PMID: 39578684 PMC: 11665843. DOI: 10.1049/syb2.12109.

References

Fard A, Ragan M . Quantitative Modelling of the Waddington Epigenetic Landscape. Methods Mol Biol. 2019; 1975:157-171. DOI: 10.1007/978-1-4939-9224-9_7. View

Bendall S, Davis K, Amir E, Tadmor M, Simonds E, Chen T . Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell. 2014; 157(3):714-25. PMC: 4045247. DOI: 10.1016/j.cell.2014.04.005. View

Miao Z, Moreno P, Huang N, Papatheodorou I, Brazma A, Teichmann S . Putative cell type discovery from single-cell gene expression data. Nat Methods. 2020; 17(6):621-628. DOI: 10.1038/s41592-020-0825-9. View

Li H, Song M, Yang W, Cao P, Zheng L, Zuo Y . A Comparative Analysis of Single-Cell Transcriptome Identifies Reprogramming Driver Factors for Efficiency Improvement. Mol Ther Nucleic Acids. 2020; 19:1053-1064. PMC: 7015826. DOI: 10.1016/j.omtn.2019.12.035. View

Schiebinger G, Shu J, Tabaka M, Cleary B, Subramanian V, Solomon A . Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming. Cell. 2019; 176(4):928-943.e22. PMC: 6402800. DOI: 10.1016/j.cell.2019.01.006. View

Maetschke S, Ragan M . Characterizing cancer subtypes as attractors of Hopfield networks. Bioinformatics. 2014; 30(9):1273-9. DOI: 10.1093/bioinformatics/btt773. View

Hopfield J . Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci U S A. 1982; 79(8):2554-8. PMC: 346238. DOI: 10.1073/pnas.79.8.2554. View

Karagiannis P, Yamanaka S . The fate of cell reprogramming. Nat Methods. 2014; 11(10):1006-8. DOI: 10.1038/nmeth.3109. View

Banerji C, Miranda-Saavedra D, Severini S, Widschwendter M, Enver T, Zhou J . Cellular network entropy as the energy potential in Waddington's differentiation landscape. Sci Rep. 2013; 3:3039. PMC: 3807110. DOI: 10.1038/srep03039. View

10.

Iturbide A, Torres-Padilla M . A cell in hand is worth two in the embryo: recent advances in 2-cell like cell reprogramming. Curr Opin Genet Dev. 2020; 64:26-30. DOI: 10.1016/j.gde.2020.05.038. View

11.

Pertea M, Kim D, Pertea G, Leek J, Salzberg S . Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nat Protoc. 2016; 11(9):1650-67. PMC: 5032908. DOI: 10.1038/nprot.2016.095. View

12.

Mascetti V, Pedersen R . Contributions of Mammalian Chimeras to Pluripotent Stem Cell Research. Cell Stem Cell. 2016; 19(2):163-175. PMC: 5366358. DOI: 10.1016/j.stem.2016.07.018. View

13.

Anders S, Pyl P, Huber W . HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics. 2014; 31(2):166-9. PMC: 4287950. DOI: 10.1093/bioinformatics/btu638. View

14.

Herman J, Sagar , Grun D . FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data. Nat Methods. 2018; 15(5):379-386. DOI: 10.1038/nmeth.4662. View

15.

Lang A, Li H, Collins J, Mehta P . Epigenetic landscapes explain partially reprogrammed cells and identify key reprogramming genes. PLoS Comput Biol. 2014; 10(8):e1003734. PMC: 4133049. DOI: 10.1371/journal.pcbi.1003734. View

16.

Cheadle C, Cho-Chung Y, Becker K, Vawter M . Application of z-score transformation to Affymetrix data. Appl Bioinformatics. 2004; 2(4):209-17. View

17.

Wang Y, Yuan P, Yan Z, Yang M, Huo Y, Nie Y . Single-cell multiomics sequencing reveals the functional regulatory landscape of early embryos. Nat Commun. 2021; 12(1):1247. PMC: 7902657. DOI: 10.1038/s41467-021-21409-8. View

18.

Takahashi K, Yamanaka S . Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell. 2006; 126(4):663-76. DOI: 10.1016/j.cell.2006.07.024. View

19.

Dotson G, Ryan C, Chen C, Muir L, Rajapakse I . Cellular reprogramming: Mathematics meets medicine. Wiley Interdiscip Rev Syst Biol Med. 2020; :e1515. PMC: 8867497. DOI: 10.1002/wsbm.1515. View

20.

Szedlak A, Sims S, Smith N, Paternostro G, Piermarocchi C . Cell cycle time series gene expression data encoded as cyclic attractors in Hopfield systems. PLoS Comput Biol. 2017; 13(11):e1005849. PMC: 5711035. DOI: 10.1371/journal.pcbi.1005849. View