Systems neuroimmunology: a review of multiomics methodologies to characterize neuroimmunological interactions in spinal and cranial diseases

Abstract

Neuroimmunology plays a critical role in our understanding of the pathophysiological processes that underlie a variety of diseases treated by neurosurgeons, including degenerative disc disease (DDD), glioblastoma (GBM), aneurysmal subarachnoid hemorrhage (aSAH), and others. Compared with traditional methods in neuroimmunology, which study one pathway or gene at a time, emerging multiomics methodologies allow for holistic interrogation of multiple immune-signaling pathways to test hypotheses and the effects of therapeutics at a systems level. In this review, the authors summarize key concepts for gathering and analyzing multiomics data so that neurosurgeons can contribute to the emerging field of systems neuroimmunology. Additionally, they describe 3 use cases, based on original research published by their group and others, that utilize transcriptomic, metabolomic, and proteomic analyses to study immune-signaling pathways in DDD, aSAH, and GBM. Through these use cases, techniques for performing machine learning and network-based analyses to generate new clinical insights from multiomics data are shared. The authors hope that neurosurgeons might use this review as a summary of common tools and principles in systems immunology to better engage in creating the immunotherapies of tomorrow.

Immuno-Modulatory Effects of Intervertebral Disc Cells

“…Their results identified six genes with these characteristics (CHI3L1, KRT19, COL6A2, DPT, TNFAIP6 and COL11A2), two of which were identified as important IDD markers in independent studies. Another group used transcriptomic data collected from lumbar-degenerated IVDs to build gene regulatory networks, finding differentially expressed genes in chemotactic signaling and matrix-degrading pathways that could later be used to help develop novel pharmacological approaches for IDD treatment ( Zamanian et al, 2022 ). Li H. et al (2022) constructed a protein-interaction network as well as a disease-gene interaction network that identified two potential therapeutic drugs, entrectinib and larotrectinib, demonstrating how emerging network models can be leveraged to identify novel treatments.…”

A machine learning and deep learning-based integrated multi-omics technique for leukemia prediction

“…Most extra-cranial solid tumors in children are neuroblastomas, which are extremely prevalent. Gene expression data and CNA data were used to train the supervised classification algorithms, including XGBoost in this study [ 52 ] utilizing various machine-learning approaches. Support Vector Machine (SVM) was used with the GE dataset to get the maximum accuracy of 99 %.…”