TY - JOUR

T1 - Enabling single-cell trajectory network enrichment

AU - Grønning, Alexander G.B.

AU - Oubounyt, Mhaned

AU - Kanev, Kristiyan

AU - Lund, Jesper

AU - Kacprowski, Tim

AU - Zehn, Dietmar

AU - Röttger, Richard

AU - Baumbach, Jan

N1 - Funding Information: J.B. and A.G.B.G. received funding from J.B.’s VILLUM Young Investigator grant no. 13154. The work of J.B. and T.K. was further funded by H2020 project RepoTrial (no. 777111). The work of R.R. and J.B. was partially funded by H2020 project FeatureCloud (no. 826078). J.B. and T.K. are grateful for financial support from BMBF project Sys_Care. M.O. is grateful for financial support from the Collaborative Research Center SFB924. Publisher Copyright: © 2021, The Author(s), under exclusive licence to Springer Nature America, Inc.

PY - 2021

Y1 - 2021

N2 - Single-cell sequencing (scRNA-seq) technologies allow the investigation of cellular differentiation processes with unprecedented resolution. Although powerful software packages for scRNA-seq data analysis exist, systems biology-based tools for trajectory analysis are rare and typically difficult to handle. This hampers biological exploration and prevents researchers from gaining deeper insights into the molecular control of developmental processes. Here, to address this, we have developed Scellnetor; a network-constraint time-series clustering algorithm. It allows extraction of temporal differential gene expression network patterns (modules) that explain the difference in regulation of two developmental trajectories. Using well-characterized experimental model systems, we demonstrate the capacity of Scellnetor as a hypothesis generator to identify putative mechanisms driving haematopoiesis or mechanistically interpretable subnetworks driving dysfunctional CD8 T-cell development in chronic infections. Altogether, Scellnetor allows for single-cell trajectory network enrichment, which effectively lifts scRNA-seq data analysis to a systems biology level.

AB - Single-cell sequencing (scRNA-seq) technologies allow the investigation of cellular differentiation processes with unprecedented resolution. Although powerful software packages for scRNA-seq data analysis exist, systems biology-based tools for trajectory analysis are rare and typically difficult to handle. This hampers biological exploration and prevents researchers from gaining deeper insights into the molecular control of developmental processes. Here, to address this, we have developed Scellnetor; a network-constraint time-series clustering algorithm. It allows extraction of temporal differential gene expression network patterns (modules) that explain the difference in regulation of two developmental trajectories. Using well-characterized experimental model systems, we demonstrate the capacity of Scellnetor as a hypothesis generator to identify putative mechanisms driving haematopoiesis or mechanistically interpretable subnetworks driving dysfunctional CD8 T-cell development in chronic infections. Altogether, Scellnetor allows for single-cell trajectory network enrichment, which effectively lifts scRNA-seq data analysis to a systems biology level.

U2 - 10.1038/s43588-021-00025-y

DO - 10.1038/s43588-021-00025-y

M3 - Journal article

AN - SCOPUS:85125347958

VL - 1

SP - 153

EP - 163

JO - Nature Computational Science

JF - Nature Computational Science

SN - 2662-8457

IS - 2

ER -