Brain Immunoinformatics: A Symmetrical Link between Informatics, Wet Lab and the Clinic
Abstract
:1. Introduction
2. Literature Search and Field Definition
- Original publications and methods that contributed to breakthrough observations in the field (result-oriented selection approach), such as the TYROBP role in Alzheimer’s disease;
- Well-renowned public databases that set a new cornerstone for data science (data-driven approach);
- Outstanding longitudinal works that reshaped the doctrines of neuroimmunology in the last decade, such as the innovative classification of microglial cell activation and the development of image reconstruction tools (longitudinal approach).
3. Big Data-Omics in Neuroimmunoinformatics
3.1. Genome-Wide Studies
3.2. Single-Cell Studies
3.3. Expression Quantitative Trait Loci
3.4. Epigenetics, ChIP-Seq, and ATAC-Seq
4. Microglial “Next-Generation” Classification: From “Resting” to “Active”, to “Polarized”, to “De Novo Classified”
Examples of Microglial Sensotypes
- Homeostatic/resting sensotype or “sensing” MG: is the sensotype that differentiates MG from MΦ in the healthy brain, based on gene expression. Such differentiation is morphologically not possible. Transcripts such as Hexb, Tmem119, Siglech, P2ry12, and Olfm13 are unique to mice MG, distinguishing them from brain MΦ [4,28,55,56]. Cx3cr1 is a gene with strong MG expression, suggested as a signature gene for MG, despite controversies [57]. Micro-RNAs (miRNA) such as miRNA99a, miRNA125b-5p, and miRNA342-3p are expressed only in MG and not in other myeloid cells in murine models [57], whereas the fingerprint of human versus murine MG is still a challenging research terrain [58]. Evidence converges towards Tyrobp as an important sensing factor [56], PU.1 as the most decisive transcription factor, and TGFβ as the most robust lineage upstream stimulator of human MG [4,51].
- Induced human MG (iMG): informatics has supported stem-cell technology and provided tools to characterize iMG in vitro, either in an austere environment or in co-culture with h-iPSC organoids. Besides co-culture induced changes such as SIGLECH upregulation and Tmem119/Tyrobp downregulation, scRNA-seq technology allowed for spotting iMG sensome differences between ventral and dorsal h-iPSC organoids, with significant overexpression of inflammatory genes such as TNFa, IL6, and TREM2 in the ventral organoid iMG [62].
- Xenografted human MG (xMG) in mouse brain chimeras; Xu et al. applied scRNA-seq to sensotype xenografted human brain microglia in MG-depleted mouse brain chimeras [63]. The xMG sensome was evidenced to retain microglial (TMEM119, P2Ry12, SALL1, and OLFML3) and hominid (SPP1, A2M, and C3) traits as xenograft [58].
5. Machine Learning for Prediction of Protein, Cellular, and Network Interactions
5.1. Protein-Epitope Affinity Prediction, Cytokine-Receptor Interactions, and Epitope Prediction
5.2. Cell-Cell Interactions and Multiscale Network Modeling
5.3. Probabilistic and Causal Gene Regulatory Networks
6. Machine Learning in Neuropathology and Immunoprofiling
6.1. Microglial Segmentation and Counting
6.2. Cell Arborization Analysis
6.3. Automated Cell Arbor Tracing
6.4. End-to-End Solutions for Microglia Characterization: Interfaces for Implementation by Biologists and Non-Computer Scientists
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Database | URL | Content |
---|---|---|
Sequencing | ||
Glial heterogeneity | www.glia-network.de/index.php/databases.html (accessed on 1 July 2021) | RNA-seq databases of glial cells |
BrainRNAseq | www.BrainRNAseq.org (accessed on 1 July 2021) | RNA-seq database of glial cells |
ImMunoGeneTics | www.imgt.org (accessed on 1 July 2021) | sequence, genome, structure and monoclonal antibodies database |
MSigDB, Molecular Signature DataBase | www.gsea-msigdb.org/gsea/msigdb (accessed on 1 July 2021) | Gene Set Enrichment Analysis database |
GTEx, Genotype Tissue-Expression repository | www.gtexportal.org/home/ (accessed on 1 July 2021) | eQTL database |
ImmPort and ImmuneSpace | www.immuneprofiling.org (accessed on 1 July 2021) | cross-essay human immunological data, including computational interface |
IEDB, Immune Epitope DataBase | www.iedb.org/ (accessed on 1 July 2021) | antibody and T cell epitopes studied in humans, non-human primates, and other animal species in the context of infectious disease, allergy, autoimmunity, and transplantation |
ImmGen | www.immgen.org/ (accessed on 1 July 2021) | microarray dissection of gene expression and its regulation in the immune system of the mouse |
InnateDB | www.innatedb.com/ (accessed on 1 July 2021) | genes, proteins, experimentally-verified interactions, and signaling pathways involved in the innate immune response of humans, mice, and bovines to microbial infection |
Morphology | ||
Neuromorph | www.neuromorpho.org (accessed on 1 July 2021) | morphology, tracing reconstructions of neurons, astroglia, and microglia |
FARSight | farsight-toolkit.ee.uh.edu/wiki/Main_Page (accessed on 1 July 2021) | Toolkit for Python, cell arborization visualization, analysis, and quantitation applying unsupervised clustering |
FindMyCells | www.findmycells.org/index.html (accessed on 1 July 2021) | Deep learning tool for single-cell detection |
Assay for Transposase-Accessible Chromatin Using Sequencing (ATAC-Seq) | Method for Determining Chromatin Accessibility Across the Genome by Sequencing Regions of Open Chromatin |
Automated Segmentation Algorithm (ASA) Stereology | Stereology with an integrated automated segmentation algorithm for cell recognition |
Blood-brain barrier | Anatomical and functional blood vessels “seal” that keeps harmful substances from reaching the brain |
Chromatin Immunoprecipitation and DNAseq (ChIPseq) | Chromatin Immunoprecipitation (ChIP) and next-generation sequencing (ChIPseq) explores interactions between DNA, histones, and transcription factors |
Cytometry by Time of Flight (CyTOF) | CyTOF is a hybrid method of mass spectrometry and flow cytometry, based on isotope reporters |
Epigenetics | Heritable changes in gene expression that take place without altering DNA sequence or modifications of the chromatin environment |
Epigenome Wide Association studies (EWAS) | An epigenome-wide association study (EWAS) is an examination of a genome-wide set of quantifiable epigenetic marks, such as DNA methylation, in different individuals to derive associations between epigenetic variation and a particular identifiable phenotype/trait |
Expression and methylation Quantitative Trait Loci (eQTL, meQTL) | Genomic loci that explain the variation in RNA expression |
Genome-Wide Association Studies (GWAS) | A genome-wide association study (GWAS) associates genetic variations with diseases. The method involves a population-wide genome screening and isolates genetic markers with possible disease predictive value |
Genome-Wide Transcriptional Profiling (GWTP) | Unbiased, hypothesis-free approach that associates genetic changes with disparate states of the immune system to construct genotype-phenotype associations |
h-iPSC organoids | Organoids are in vitro cultured three-dimensional structures that recapitulate key aspects of in vivo organs. They can be established from pluripotent stem cells (PSC) and induced adult stem cells (iPSC). The abbreviation “h-” speaks for the human PSC origin |
Human Leukocyte Antigen (HLA) type | HLA are proteins that are located on the surface of the white blood cells and other tissues in the body. There are three general groups of HLA: HLA-A, HLA-B, and HLA-DR |
Lacunarity and fractal dimension | The fractal dimension represents the roughness (hence texture) Lacunarity is a measure of gaps between (the fractal) objects |
Lineage trajectories | Clonal lineage tracing of stem cells to define the outcome of differentiation |
Microglial sensome | Proteomics, genomics, and epigenomics defining microglial reactive states |
Microglial sensotypes | Newly-introduced categorization of microglia based on the deep sequencing profile (sensome) as a response to stimuli |
Micro-RNAs (miRNA) | A microRNA (abbreviated miRNA) is a small single-stranded non-coding RNA molecule (containing about 22 nucleotides) found in plants, animals, and some viruses that functions in RNA silencing and post-transcriptional regulation of gene expression |
Neuroimmunoinformatics | Immunoinformatics of the central neural system |
Neuroinformatics | Neuroinformatics is the field that combines informatics and neuroscience. Neuroinformatics is related to neuroscience data and information processing by artificial neural networks |
Probabilistic causal network models | More popular with the name “Bayesian networks”, probabilistic causal networks provide a mathematical model for inferring causal relationships among molecular and higher-order phenotypes. Bayesian networks represent the most popular subcategory of probabilistic causal networks |
PU.1 | Transcription factor that activates gene expression during myeloid and B-lymphocyte development |
SALL1 | Transcriptional regulator encoding gene allocated in microglia |
Signature gene libraries | Cell type-specific gene clusters |
Single-cell profile deconvolution | Predict cell-specific profiles from large cell populations using machine learning and signature gene libraries |
T-helper cell | Helper T cell, also called the CD4+ cell, T helper cell, or helper T lymphocyte, a type of white blood cell and key mediator of the immune function |
Ventral and dorsal h-iPSC organoids | Organoids deriving from ventral and dorsal neurotube stem cells |
Xenografting | Xenotransplantation or heterologous transplant, the transplantation of living cells, tissues or organs from one species to another. |
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Papageorgiou, I.; Bittner, D.; Psychogios, M.N.; Hadjidemetriou, S. Brain Immunoinformatics: A Symmetrical Link between Informatics, Wet Lab and the Clinic. Symmetry 2021, 13, 2168. https://doi.org/10.3390/sym13112168
Papageorgiou I, Bittner D, Psychogios MN, Hadjidemetriou S. Brain Immunoinformatics: A Symmetrical Link between Informatics, Wet Lab and the Clinic. Symmetry. 2021; 13(11):2168. https://doi.org/10.3390/sym13112168
Chicago/Turabian StylePapageorgiou, Ismini, Daniel Bittner, Marios Nikos Psychogios, and Stathis Hadjidemetriou. 2021. "Brain Immunoinformatics: A Symmetrical Link between Informatics, Wet Lab and the Clinic" Symmetry 13, no. 11: 2168. https://doi.org/10.3390/sym13112168