A genome-wide in vivo CRISPR screen identifies essential regulators of T cell migration to the CNS in a multiple sclerosis model
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Multiple sclerosis (MS) involves the infiltration of autoreactive T cells into the CNS, yet we lack a comprehensive understanding of the signaling pathways that regulate this process. Here, we conducted a genome-wide in vivo CRISPR screen in a rat MS model and identified 5 essential brakes and 18 essential facilitators of T cell migration to the CNS. While the transcription factor ETS1 limits entry to the CNS by controlling T cell responsiveness, three functional modules, centered around the adhesion molecule α4-integrin, the chemokine receptor CXCR3 and the GRK2 kinase, are required for CNS migration of autoreactive CD4+ T cells. Single-cell analysis of T cells from individuals with MS confirmed that the expression of these essential regulators correlates with the propensity of CD4+ T cells to reach the CNS. Our data thus reveal key regulators of the fundamental step in the induction of MS lesions.
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Created: 28th Jun 2024 at 08:35
Last updated: 11th Oct 2024 at 07:54
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Projects: Published Datasets, Unpublished Datasets
Institutions: LMU Klinikum
Projects: Published Datasets, Unpublished Datasets
Institutions: LMU
Please visit the 'Related items' tab within the profile page to explore associated studies in more detail.
Projects: Published Datasets, Unpublished Datasets
Institutions: LMU Klinikum
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This project serves as a centralized repository for omics datasets published by research groups within the SyNergy Cluster. It encompasses investigations such as proteomics and transcriptomics, which are further divided into individual studies led by SyNergy members. Each study is linked to relevant publications, assays and data files (with links to external repositories).
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Public web page: Not specified
Organisms: Mus musculus, Rattus norvegicus, Homo sapiens, Macaca mulatta, Sus scrofa, Danio rerio
Submitter: Rainer Malik
Studies: A genome-wide in vivo CRISPR screen identifies essential regulators of T..., Adult neural stem cell activation in mice is regulated by the day/night ..., Diet triggers specific responses of hypothalamic astrocytes in time and ..., Direct neuronal reprogramming of NDUFS4 patient cells identifies the unf..., Distinct molecular profiles of skull bone marrow in health and neurologi..., Early adaptive immune activation detected in monozygotic twins with prod..., Heterogeneity of neurons reprogrammed from spinal cord astrocytes by the..., High-calorie diets uncouple hypothalamic oxytocin neurons from a gut-to-..., Histone Deacetylase 9 Activates IKK to Regulate Atherosclerotic Plaque V..., Innate Immune Pathways Promote Oligodendrocyte Progenitor Cell Recruitme..., Innate immune memory after brain injury drives inflammatory cardiac dysf..., MicroRNAs from extracellular vesicles as a signature for Parkinson's dis..., Microglia in white matter aging, Molecular diversity of diencephalic astrocytes reveals adult astrogenesi..., Multiomic ALS signatures highlight subclusters and sex differences sugge..., Multi‐omic landscaping of human midbrains identifies disease‐relevant mo..., Myelin degeneration in leucodystrophies, Oligodendrocytes in AD models, Oligodendrocytes in white matter aging, Parkinson's disease motor symptoms rescue by CRISPRa‐reprogramming astro..., Peripheral expression of brain-penetrant progranulin rescues pathologies..., Phagocyte-mediated synapse removal in cortical neuroinflammation is prom..., Shared inflammatory glial cell signature after stab wound injury, reveal..., Skin and gut imprinted helper T cell subsets exhibit distinct functional..., Spatial Transcriptomics-correlated Electron Microscopy maps transcriptio..., Spatial centrosome proteome of human neural cells uncovers disease-relev..., T cells modulate the microglial response to brain ischemia, Twin study identifies early immunological and metabolic dysregulation of...
Assays: Expression profiling: Bulk RNA-seq (human), Expression profiling: Bulk RNA-seq (human), Expression profiling: Bulk RNA-seq (human), Expression profiling: Bulk RNA-seq (human) + smallRNA-seq (human), Expression profiling: Bulk RNA-seq (mouse), Expression profiling: Bulk RNA-seq (mouse), Expression profiling: Bulk RNA-seq (mouse), Expression profiling: Bulk RNA-seq (mouse), Expression profiling: Bulk RNA-seq (mouse), Expression profiling: Bulk RNA-seq (mouse), Expression profiling: Bulk RNA-seq (mouse), Expression profiling: Bulk RNA-seq (mouse), Expression profiling: Bulk RNA-seq (mouse), Expression profiling: Bulk RNA-seq (mouse), Expression profiling: Bulk RNA-seq (mouse), Expression profiling: Bulk RNA-seq (rat), Expression profiling: MERFISH Spatial Transcriptomics (mouse), Expression profiling: Microarray (zebrafish), Expression profiling: RiboTag-mRNA-seq (mouse), Expression profiling: Small RNA-seq (human), Expression profiling: Spatial Transcriptomics (mouse), Expression profiling: Spatial Transcriptomics correlated Electron Micros..., Expression profiling: scRNA-seq (human), Expression profiling: scRNA-seq (human), Expression profiling: scRNA-seq (human), Expression profiling: scRNA-seq (human), Expression profiling: scRNA-seq (human) (Day 20), Expression profiling: scRNA-seq (human) (Day 5), Expression profiling: scRNA-seq (mouse), Expression profiling: scRNA-seq (mouse), Expression profiling: scRNA-seq (mouse), Expression profiling: scRNA-seq (mouse), Expression profiling: scRNA-seq (mouse), Expression profiling: scRNA-seq (mouse), Expression profiling: scRNA-seq (mouse), Expression profiling: scRNA-seq (mouse), Expression profiling: scRNA-seq (mouse), Expression profiling: scRNA-seq (mouse), Expression profiling: scRNA-seq (mouse), Expression profiling: scRNA-seq (mouse), Expression profiling: scRNA-seq (mouse), Expression profiling: scRNA-seq (mouse) + Bulk RNA-seq (mouse), Expression profiling: small RNA-seq (human), Expression profiling: small RNA-seq (mouse), Genome binding/occupancy profiling: Bulk ATAC-seq (mouse), Genome binding/occupancy profiling: Bulk ATAC-seq (mouse), Genome binding/occupancy profiling: CUT&Tag sequencing (mouse), Genome binding/occupancy profiling: snATAC-seq (mouse), Genome wide (GW) and validation CRISPR screens (rat)
Snapshots: No snapshots
Submitter: Rainer Malik
Assay type: CRISPR Screen
Technology type: Technology Type
Investigation: Transcriptomics (Published)
Organisms: Rattus norvegicus
SOPs: No SOPs
Data files: Genome wide (GW) and validation CRISPR screens ...
Snapshots: No snapshots
Submitter: Aditi Methi
Assay type: Transcriptomics
Technology type: Sequencing
Investigation: Transcriptomics (Published)
Organisms: Homo sapiens
SOPs: No SOPs
Data files: scRNAseq of blood and CSF CD4+ T cells from MS ...
Snapshots: No snapshots
Submitter: Aditi Methi
Assay type: Transcriptomics
Technology type: Sequencing
Investigation: Transcriptomics (Published)
Organisms: Rattus norvegicus
SOPs: No SOPs
Data files: Effect of Grk2 (spleen, rat) or Ets1 (spleen an...
Snapshots: No snapshots
To study the effect of the Grk2 or Ets1 KOs in rat and human CD4+ T cells, in vivo or in vitro respectively, we generated KO CD4+ T cell lines by CRISPR-RNP nucleofection (with an gRNA targeting the gene locus) or control (with a non-targeting gRNA) 3' bulk RNAseq was conducted on CD4+ T-MBP rat cells isolated form spleen or spinal cord parenchyma three days after co-transfer of control and KO cells into the animal. Cells were FACS sorted based on their fluorescent protein reporters, BFP for ...
Creators: None
Submitter: Rainer Malik
Investigations: Transcriptomics (Published)
To identify genes driving encephalitogenic CD4+ T cell migration into the CNS, we performed a genome-wide CRISPR screen and a subsequent validation screen. For the genome-wide CRISPR screen, up to 4 sgRNA per gene and 800 non targeted controls were included, for a total of 87690 individual sgRNAs, and for the validation screen, up to 6 sgRNA per gene and 241 non targeted controls for a total of 12000 individual sgRNAs. Comparison of sgRNA distribution by pairwise comparisons across blood, spleen, ...
Creators: None
Submitter: Rainer Malik
Investigations: Transcriptomics (Published)
To study the regulation of candidate genes from our study in human cells, we analyzed CD4+ T cells from blood and CSF of MA patients and age and sex matched idiopathic intracranial hypertension controls We analyzed 40845 cells in control blood, 807 cells in control CSF, 29749 cells in MS blood and 15768 cells in MS CSF. Analysis of overlapping TCR sequences in blood and CSF CD4+ T cells of the same transcript, transcriptomic characterization of the cell clusters and analysis of the behaviour of ...
Creators: None
Submitter: Rainer Malik
Investigations: Transcriptomics (Published)
Abstract (Expand)
Authors: A. Kendirli, C. de la Rosa, K. F. Lammle, K. Eglseer, I. J. Bauer, V. Kavaka, S. Winklmeier, L. Zhuo, C. Wichmann, L. A. Gerdes, T. Kumpfel, K. Dornmair, E. Beltran, M. Kerschensteiner, N. Kawakami
Date Published: 4th Oct 2023
Publication Type: Journal
PubMed ID: 37709997
Citation: Nat Neurosci. 2023 Oct;26(10):1713-1725. doi: 10.1038/s41593-023-01432-2. Epub 2023 Sep 14.