An Integrative Multi-modal Approach to Define and Diagnose Pediatric Autoimmune Neurological Disorders

Building the largest cerebrospinal fluid immune profile dataset in pediatric autoimmune neurological diseases to enable faster, more accurate diagnosis of MS and related conditions.
Completed Anti-Myelin Oligodendrocyte Glycoprotein Antibody Disease (MOG) Cross-Cutting & Special Populations Multiple Sclerosis Neurologic Diseases Pediatric Autoimmune Diseases Adaptive Immunity B Cell Biology Biological & Mechanistic Biomarker Discovery Disease Subtyping Early Disease Detection Experimental Platforms & Models Human Cohorts Immune Profiling Neuro-Immune Interactions Single Cell Technologies Translational & Clinical

Funding Year: 2023

Project Overview

Rare autoimmune neurological diseases affect 1 to 5 out of every 100,000 children annually, and diagnosing them is challenging because the immune system behaves differently across each condition — particularly at the moment of first attack. This project examined immune cells in cerebrospinal fluid from children with rare autoimmune neurological diseases using flow cytometry and single-cell RNA sequencing, building the largest CSF immune profile collection to date for this population. Key findings include higher frequencies of antibody-secreting cells and lower frequencies of CD14+ myeloid cells in pediatric MS compared to MOGAD and other conditions, leading to a simple, highly effective AMR score that distinguishes MS from MOGAD. An interactive, cloud-based application was also developed to make findings accessible to clinicians and researchers worldwide.

Impact & Innovation

A simple immune score that untangles pediatric neurological disease.

 

By profiling cerebrospinal fluid immune cells at single-cell resolution across the largest pediatric autoimmune neurology cohort to date, this project delivers a two-cell-type diagnostic score that outperforms complex existing classifiers for distinguishing MS from MOGAD.

  • Identifies CSF immune cell patterns — specifically the ASC:CD14+ myeloid cell ratio — as a simple, highly effective diagnostic tool for distinguishing pediatric MS from MOGAD and other acquired demyelinating syndromes
  • Produces an open, interactive cloud-based application enabling clinicians and researchers worldwide to explore and reuse the CSF immune profiling dataset for future discovery
  • Advances the Consortium’s Integrated Data and Discovery Platforms pillar by creating a sharable, multi-modal pediatric neuroimmunology dataset and cloud-based tool that can accelerate diagnosis and research across rare autoimmune neurological conditions
Research Approach

A framework designed for discovery

This project applied flow cytometry, single-cell RNA sequencing, and computational analysis to build a comprehensive immune profile of cerebrospinal fluid from children with rare autoimmune neurological diseases, with the goal of identifying diagnostic immune signatures and developing accessible clinical tools.

Flow cytometry and single-cell RNA sequencing of cerebrospinal fluid immune cells from children with pediatric MS, MOGAD, and other autoimmune neurological conditions; development and validation of the AMR score based on the ASC:CD14+ myeloid cell frequency ratio; receiver operating characteristic analysis comparing AMR to existing diagnostic scores; and development of an interactive, cloud-based Pediatric Cerebrospinal Fluid Immune Profiling application.

The largest collection to date of cerebrospinal fluid samples from children with rare autoimmune neurological diseases, including pediatric MS, MOGAD, and other acquired demyelinating syndromes; single-cell immune profiling datasets; and comparative diagnostic performance datasets validating the AMR score against the neuroflammatory composite score (coNCS).

Identification and validation of a simple, highly effective CSF immune signature for distinguishing pediatric MS from MOGAD and related conditions, with a translational goal of improving early diagnosis and guiding timely treatment. The interactive cloud-based application supports broader clinical adoption and collaborative future research.

Investigators & Institutions

Powering the science

Principal Investigator

Rui Li, MD, PhD, Colton Consortium Member

Research Associate, Department of Neurology, Perelman School of Medicine, University of Pennsylvania

Key Collaborators

Mengyuan Kan, PhD

Research Associate, Department of Neurology, Perelman School of Medicine, University of Pennsylvania

Colton Centers & Institutions

Colton Center for Autoimmunity at the University of Pennsylvania

Perelman School of Medicine

Research Outputs

From insight to impact

Publications

Pediatric cerebrospinal fluid immune profiling distinguishes pediatric-onset multiple sclerosis from other pediatric-onset acute neurological disorders

bioRxiv [Preprint]
Spinoza, DA; Zrzavy, T; Breville, G; Thebault, S; Marefi, A; Mexhitaj, I; Yamashita, LD; Kan, M; Bacchus, M; Legaspi, J; Fernandez, S; Melamed, A; Stubblebine, M; Kim, Y; Martinez, Z; Diorio, C; Schulte-Mecklenbeck, A; Wiendl, H; Rezk, A; Li, R; Narula, S; Waldman, AT; Hopkins, SE; Banwell, B; Bar-Or, A May 2025
Adaptive ImmunityBioinformaticsBiological & MechanisticBiomarker DiscoveryData-Driven & QuantitativeDisease SubtypingEarly Disease DetectionExperimental Platforms & ModelsHuman CohortsImmune ProfilingSingle Cell TechnologiesTranslational & ClinicalCross-Cutting & Special PopulationsMultiple SclerosisNeurologic DiseasesPediatric Autoimmune DiseasesUniversity of Pennsylvania