Optimized network inference for immune diseased single cells

Mathematical models are powerful tools that can be used to advance our understanding of complex diseases. Autoimmune disorders such as systemic lupus erythematosus (SLE) are highly heterogeneous and require high-resolution mechanistic approaches. In this work, we present ONIDsc, a single-cell regulatory network inference model designed to elucidate immune-related disease mechanisms in SLE.

ONIDsc enhances SINGE's Generalized Lasso Granger (GLG) causality model used in Single-cell Inference of Networks using Granger ensembles (SINGE) by finding the optimal lambda penalty with cyclical coordinate descent. We benchmarked ONIDsc against existing models and found it consistently outperforms SINGE and other methods when gold standards are generated from chromatin immunoprecipitation sequencing (ChIP-seq) and ChIP-chip experiments. We then applied ONIDsc to three large-scale datasets, one from control patients and the two from SLE patients, to reconstruct networks common to different immune cell types.

ONIDsc identified four gene transcripts: matrix remodelling-associated protein 8 (MXRA8), nicotinamide adenine dinucleotide kinase (NADK), RNA Polymerase III Subunit GL (POLR3GL) and Ultrabithorax Domain Protein 11 (UBXN11) in CD4+ T-lymphocytes, CD8+ Regulatory T-Lymphocytes, CD8+ T-lymphocytes 1 and Low Density Granulocytes that were present in SLE patients but absent in controls.

These genes were significantly related to nicotinate metabolism, ribonucleic acid (RNA) transcription, protein phosphorylation and the Rho family GTPase (RND) 1-3 signaling pathways, previously associated with immune regulation. Our results highlight ONIDsc's potential as a powerful tool for dissecting physiological and pathological processes in immune cells using high-dimensional single-cell data.