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Primary angiitis of the central nervous system (PACNS) is a rare and heterogeneous inflammatory disease of the CNS vasculature with poorly understood pathophysiology. Comprehensive immune-cell phenotyping revealed increased frequencies of leukocytes in the cerebrospinal fluid (CSF) of PACNS patients compared to patients with multiple sclerosis, ischemic stroke, and somatoform disorders (n = 18 per group). Changes in the intrathecal immune-cell profile were heterogeneous in PACNS. While proportions of T-cell subsets remained unaltered, some PACNS patients showed a shift toward NK- or B cells. Intrathecal immunoglobulin synthesis was observed in a subgroup of PACNS patients with an increased frequency of antibody producing plasma cells. [ABSTRACT FROM AUTHOR]

Background: Distinct lesion topography in relapsing-remitting multiple sclerosis (RRMS) might be due to different antigen presentation and/or trafficking routes of immune cells into the central nervous system (CNS). Objective: To investigate whether distinct lesion patterns in multiple sclerosis (MS) might be associated with a predominance of distinct circulating T-helper cell subset as well as their innate counterparts. Methods: Flow cytometric analysis of lymphocytes derived from the peripheral blood of patients with exclusively cerebral (n = 20) or predominantly spinal (n = 12) disease manifestation. Results: Patients with exclusively cerebral or preferential spinal lesion manifestation were associated with increased proportions of circulating granulocyte-macrophage colony-stimulating factor (GM-CSF) producing TH1 cells or interleukin (IL)-17-producing TH17 cells, respectively. In contrast, proportions of peripheral IL-17/IL-22-producing lymphoid tissue inducer (LTi), the innate counterpart of TH17 cells, were enhanced in RRMS patients with exclusively cerebral lesion topography. Conclusions: Distinct T-helper and T-helper-like innate lymphoid cell (ILC) subsets are associated with different lesion topography in RRMS. [ABSTRACT FROM AUTHOR]

Distinguishing neurosarcoidosis (NS) from multiple sclerosis (MS) remains challenging and available parameters lack discriminatory power. Comprehensive flow cytometry data of blood and CSF leukocytes of patients with NS (n = 24), MS (n = 49) and idiopathic intracranial hypertension (IIH, n = 52) were analyzed by machine learning algorithms. NS featured a specific immune cell pattern with increased activated CD4+ T cells in CSF and increased plasma cells in blood. Combining blood and CSF parameters improved the differentiation. We thereby identify and independently validate a multi-dimensional model of blood and CSF supporting the difficult differential diagnosis between NS and MS. Blood and CSF of NS patients exhibit adistinct features that allow a predictive model to distinguish NS from MS. Parts of the images have been adapted from Servier Medical Art and from SVG SILH available under Creative Commons Attribution License. Unlabelled Image • Activated CD4+ T cells in CSF and plasma cells in blood are increased in NS compared to MS. • The combination of blood and CSF parameters aids distinguishing between NS and MS. • We generated and validated a multi-dimensional model of blood and CSF parameters to discriminate NS from MS. [ABSTRACT FROM AUTHOR]

The central nervous system (CNS) is an immune-privileged compartment that is separated from the circulating blood and the peripheral organs by the blood–brain and the blood–cerebrospinal fluid (CSF) barriers. Transmigration of lymphocyte subsets across these barriers and their activation/differentiation within the periphery and intrathecal compartments in health and autoinflammatory CNS disease are complex. Mathematical models are warranted that qualitatively and quantitatively predict the distribution and differentiation stages of lymphocyte subsets in the blood and CSF. Here, we propose a probabilistic mathematical model that (i) correctly reproduces acquired data on location and differentiation states of distinct lymphocyte subsets under homeostatic and neuroinflammatory conditions, (ii) provides a quantitative assessment of differentiation and transmigration rates under these conditions, (iii) correctly predicts the qualitative behavior of immune-modulating therapies, (iv) and enables simulation-based prediction of distribution and differentiation stages of lymphocyte subsets in the case of limited access to biomaterial. Taken together, this model might reduce future measurements in the CSF compartment and allows for the assessment of the effectiveness of different immune-modulating therapies. [ABSTRACT FROM AUTHOR]