PMID- 34456686 OWN - NLM STAT- PubMed-not-MEDLINE VI - 15 TI - Astrocyte Heterogeneity in Multiple Sclerosis: Current Understanding and Technical Challenges. PG - 726479 CI - Copyright © 2021 Lo, Skarica, Mansoor, Bhandarkar, Toro and Pitt. LA - eng PT - Journal Article PT - Review PL - Switzerland TA - Front Cell Neurosci JT - Frontiers in cellular neuroscience JID - 101477935 IS - 1662-5102 (Print) LID - 10.3389/fncel.2021.726479 [doi] FAU - Lo, Chih Hung AU - Lo CH AD - Department of Neurology, Yale School of Medicine, New Haven, CT, United States. FAU - Skarica, Mario AU - Skarica M AD - Department of Neuroscience, Yale School of Medicine, New Haven, CT, United States. FAU - Mansoor, Mohammad AU - Mansoor M AD - Department of Neurology, Yale School of Medicine, New Haven, CT, United States. FAU - Bhandarkar, Shaan AU - Bhandarkar S AD - Department of Neurology, Yale School of Medicine, New Haven, CT, United States. FAU - Toro, Steven AU - Toro S AD - Department of Neurology, Yale School of Medicine, New Haven, CT, United States. FAU - Pitt, David AU - Pitt D AD - Department of Neurology, Yale School of Medicine, New Haven, CT, United States. IS - 1662-5102 (Linking) OTO - NOTNLM OT - astrocytes OT - experimental autoimmune encephalomyelitis OT - multiple sclerosis OT - multiplexed imaging OT - single nucleus sequencing PMC - PMC8385194 LR - 20210920 DP - 2021 DEP - 20210811 AB - The emergence of single cell technologies provides the opportunity to characterize complex immune/central nervous system cell assemblies in multiple sclerosis (MS) and to study their cell population structures, network activation and dynamics at unprecedented depths. In this review, we summarize the current knowledge of astrocyte subpopulations in MS tissue and discuss the challenges associated with resolving astrocyte heterogeneity with single-nucleus RNA-sequencing (snRNA-seq). We further discuss multiplexed imaging techniques as tools for defining population clusters within a spatial context. Finally, we will provide an outlook on how these technologies may aid in answering unresolved questions in MS, such as the glial phenotypes that drive MS progression and/or neuropathological differences between different clinical MS subtypes.