Reconstructing Boolean network ensembles from single-cell data for unraveling dynamics in the aging of human hematopoietic stem cells
Regulatory dependencies in molecular networks are the basis of dynamic behaviors affecting the phenotypical landscape. With the advance of high throughput technologies, the detail of omics data has arrived at the single-cell level. Nevertheless, new strategies are required to reconstruct regulatory...
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doaj-02ac09438f424fe7b43b675397ba34ea2021-10-02T04:00:46ZengElsevierComputational and Structural Biotechnology Journal2001-03702021-01-011953215332Reconstructing Boolean network ensembles from single-cell data for unraveling dynamics in the aging of human hematopoietic stem cellsJulian D. Schwab0Nensi Ikonomi1Silke D. Werle2Felix M. Weidner3Hartmut Geiger4Hans A. Kestler5Institute of Medical Systems Biology, Ulm University, Albert-Einstein-Allee 11, Ulm 89081, GermanyInstitute of Medical Systems Biology, Ulm University, Albert-Einstein-Allee 11, Ulm 89081, GermanyInstitute of Medical Systems Biology, Ulm University, Albert-Einstein-Allee 11, Ulm 89081, GermanyInstitute of Medical Systems Biology, Ulm University, Albert-Einstein-Allee 11, Ulm 89081, GermanyInstitute of Molecular Medicine, Ulm University, Albert-Einstein-Allee 11, Ulm 89081, GermanyInstitute of Medical Systems Biology, Ulm University, Albert-Einstein-Allee 11, Ulm 89081, Germany; Corresponding author.Regulatory dependencies in molecular networks are the basis of dynamic behaviors affecting the phenotypical landscape. With the advance of high throughput technologies, the detail of omics data has arrived at the single-cell level. Nevertheless, new strategies are required to reconstruct regulatory networks based on populations of single-cell data. Here, we present a new approach to generate populations of gene regulatory networks from single-cell RNA-sequencing (scRNA-seq) data. Our approach exploits the heterogeneity of single-cell populations to generate pseudo-timepoints. This allows for the first time to uncouple network reconstruction from a direct dependency on time series measurements. The generated time series are then fed to a combined reconstruction algorithm. The latter allows a fast and efficient reconstruction of ensembles of gene regulatory networks. Since this approach does not require knowledge on time-related trajectories, it allows us to model heterogeneous processes such as aging. Applying the approach to the aging-associated NF-κB signaling pathway-based scRNA-seq data of human hematopoietic stem cells (HSCs), we were able to reconstruct eight ensembles, and evaluate their dynamic behavior. Moreover, we propose a strategy to evaluate the resulting attractor patterns. Interaction graph-based features and dynamic investigations of our model ensembles provide a new perspective on the heterogeneity and mechanisms related to human HSCs aging.http://www.sciencedirect.com/science/article/pii/S2001037021003974Single-cell network reconstructionBoolean network ensemblesEnsemble dynamic analysesHSCAgingNF-κB |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Julian D. Schwab Nensi Ikonomi Silke D. Werle Felix M. Weidner Hartmut Geiger Hans A. Kestler |
spellingShingle |
Julian D. Schwab Nensi Ikonomi Silke D. Werle Felix M. Weidner Hartmut Geiger Hans A. Kestler Reconstructing Boolean network ensembles from single-cell data for unraveling dynamics in the aging of human hematopoietic stem cells Computational and Structural Biotechnology Journal Single-cell network reconstruction Boolean network ensembles Ensemble dynamic analyses HSC Aging NF-κB |
author_facet |
Julian D. Schwab Nensi Ikonomi Silke D. Werle Felix M. Weidner Hartmut Geiger Hans A. Kestler |
author_sort |
Julian D. Schwab |
title |
Reconstructing Boolean network ensembles from single-cell data for unraveling dynamics in the aging of human hematopoietic stem cells |
title_short |
Reconstructing Boolean network ensembles from single-cell data for unraveling dynamics in the aging of human hematopoietic stem cells |
title_full |
Reconstructing Boolean network ensembles from single-cell data for unraveling dynamics in the aging of human hematopoietic stem cells |
title_fullStr |
Reconstructing Boolean network ensembles from single-cell data for unraveling dynamics in the aging of human hematopoietic stem cells |
title_full_unstemmed |
Reconstructing Boolean network ensembles from single-cell data for unraveling dynamics in the aging of human hematopoietic stem cells |
title_sort |
reconstructing boolean network ensembles from single-cell data for unraveling dynamics in the aging of human hematopoietic stem cells |
publisher |
Elsevier |
series |
Computational and Structural Biotechnology Journal |
issn |
2001-0370 |
publishDate |
2021-01-01 |
description |
Regulatory dependencies in molecular networks are the basis of dynamic behaviors affecting the phenotypical landscape. With the advance of high throughput technologies, the detail of omics data has arrived at the single-cell level. Nevertheless, new strategies are required to reconstruct regulatory networks based on populations of single-cell data. Here, we present a new approach to generate populations of gene regulatory networks from single-cell RNA-sequencing (scRNA-seq) data. Our approach exploits the heterogeneity of single-cell populations to generate pseudo-timepoints. This allows for the first time to uncouple network reconstruction from a direct dependency on time series measurements. The generated time series are then fed to a combined reconstruction algorithm. The latter allows a fast and efficient reconstruction of ensembles of gene regulatory networks. Since this approach does not require knowledge on time-related trajectories, it allows us to model heterogeneous processes such as aging. Applying the approach to the aging-associated NF-κB signaling pathway-based scRNA-seq data of human hematopoietic stem cells (HSCs), we were able to reconstruct eight ensembles, and evaluate their dynamic behavior. Moreover, we propose a strategy to evaluate the resulting attractor patterns. Interaction graph-based features and dynamic investigations of our model ensembles provide a new perspective on the heterogeneity and mechanisms related to human HSCs aging. |
topic |
Single-cell network reconstruction Boolean network ensembles Ensemble dynamic analyses HSC Aging NF-κB |
url |
http://www.sciencedirect.com/science/article/pii/S2001037021003974 |
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