Protein molecules and their interactions via protein-protein interactions (PPIs) are at the
core of cellular functions. While such global PPI networks have been useful for analyzing
gene function and effects of genetic variants, they do not resolve tissue and cell-typespecific
interactions. Here we leverage recent advances in single-cell RNA sequencing
(scRNA-seq) to reconstruct cell-type-specific PPI networks across different tissues to
enable a context-sensitive analysis of immune cells’ gene-protein pathways. Targeting B
cells, T cells, and macrophage cells as a proof-of-principle, we used scRNA-seq data
across different tissues from the Tabula Muris mouse consortium. We mapped the
protein-coding DEGs to a protein-protein interaction network database (STRING v.11).
Topological and global similarity analysis of the networks revealed distinct properties
between tissues highlighting tissue-specific behaviors for each cell type. For example, we
found that degree and clustering coefficients distributions were tissue-specific. Different
cell types and tissues displayed specific characteristics, and in particular, the splenic PPI
networks were different compared to other analyzed tissues for all the immune cell types
examined. For example, the pairwise comparison of the Jaccard index for node similarity
and the mantel test correlation analysis showed that the spleen’ node and PPI networks
are more different than any other tissues for each cell type examined. The physiological
and anatomical properties that distinguish the spleen from other examined tissues might
explain why the splenic PPI networks tend to be less similar compared to other tissues.
The cell-type-specific network analyses using the different distance measures between
the adjacency matrices on the hub nodes such as Euclidean, Manhattan, Jaccard, and
Hamming distances showed a macrophage-specific behavior not observed in B cells and
T cells, confirming their lineage differences. Finally, we explored the rewiring of selected
hub nodes and transcription factors in the PPI networks along with their biological
enrichments to validate our observations. The suggested biological validity of our results
confirms the relevance of data-driven reconstruction of these context-sensitive networks
using more advanced network inference algorithms. In conclusion, scRNA-seq enables
the reconstruction of global unspecific PPI networks into cell and tissue-specific
networks, thereby providing an increased resolution of the biological context.
| Identifer | oai:union.ndltd.org:kaust.edu.sa/oai:repository.kaust.edu.sa:10754/669004 |
| Date | 04 1900 |
| Creators | Althobaiti, Atheer |
| Contributors | Tegner, Jesper, Biological and Environmental Sciences and Engineering (BESE) Division, Gao, Xin, Arold, Stefan T. |
| Source Sets | King Abdullah University of Science and Technology |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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