TU Darmstadt / ULB / TUbiblio

Stabilized Reconstruction of Signaling Networks from Single-Cell Cue-Response Data

Kumar, S. and Lun, X.-K. and Bodenmiller, B. and Rodriguez Martinez, M. and Koeppl, H. (2020):
Stabilized Reconstruction of Signaling Networks from Single-Cell Cue-Response Data.
In: Scientific reports, 10, p. 1233. Springer Nature, ISSN 2045-2322,
DOI: 10.1038/s41598-019-56444-5,
[Article]

Abstract

Inferring cell-signaling networks from high-throughput data is a challenging problem in systems biology. Recent advances in cytometric technology enable us to measure the abundance of a large number of proteins at the single-cell level across time. Traditional network reconstruction approaches usually consider each time point separately, resulting thus in inferred networks that strongly vary across time. To account for the possibly time-invariant physical couplings within the signaling network, we extend the traditional graphical lasso with an additional regularizer that penalizes network variations over time. ROC evaluation of the method on in silico data showed higher reconstruction accuracy than standard graphical lasso. We also tested our approach on single-cell mass cytometry data of IFNγ-stimulated THP1 cells with 26 phospho-proteins simultaneously measured. Our approach recapitulated known signaling relationships, such as connection within the JAK/STAT pathway, and was further validated in characterizing perturbed signaling network with PI3K, MEK1/2 and AMPK inhibitors.

Item Type: Article
Erschienen: 2020
Creators: Kumar, S. and Lun, X.-K. and Bodenmiller, B. and Rodriguez Martinez, M. and Koeppl, H.
Title: Stabilized Reconstruction of Signaling Networks from Single-Cell Cue-Response Data
Language: English
Abstract:

Inferring cell-signaling networks from high-throughput data is a challenging problem in systems biology. Recent advances in cytometric technology enable us to measure the abundance of a large number of proteins at the single-cell level across time. Traditional network reconstruction approaches usually consider each time point separately, resulting thus in inferred networks that strongly vary across time. To account for the possibly time-invariant physical couplings within the signaling network, we extend the traditional graphical lasso with an additional regularizer that penalizes network variations over time. ROC evaluation of the method on in silico data showed higher reconstruction accuracy than standard graphical lasso. We also tested our approach on single-cell mass cytometry data of IFNγ-stimulated THP1 cells with 26 phospho-proteins simultaneously measured. Our approach recapitulated known signaling relationships, such as connection within the JAK/STAT pathway, and was further validated in characterizing perturbed signaling network with PI3K, MEK1/2 and AMPK inhibitors.

Journal or Publication Title: Scientific reports
Journal volume: 10
Publisher: Springer Nature
Divisions: 18 Department of Electrical Engineering and Information Technology
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Bioinspired Communication Systems
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications
Date Deposited: 18 Dec 2019 10:34
DOI: 10.1038/s41598-019-56444-5
Export:
Suche nach Titel in: TUfind oder in Google
Send an inquiry Send an inquiry

Options (only for editors)
Show editorial Details Show editorial Details