Reactive astrocyte nomenclature, definitions, and future directions

Abstract

Reactive astrocytes are astrocytes undergoing morphological, molecular, and functional remodeling in response to injury, disease, or infection of the CNS. Although this remodeling was first described over a century ago, uncertainties and controversies remain regarding the contribution of reactive astrocytes to CNS diseases, repair, and aging. It is also unclear whether fixed categories of reactive astrocytes exist and, if so, how to identify them. We point out the shortcomings of binary divisions of reactive astrocytes into good-vs-bad, neurotoxic-vs-neuroprotective or A1-vs-A2. We advocate, instead, that research on reactive astrocytes include assessment of multiple molecular and functional parameters-preferably in vivo-plus multivariate statistics and determination of impact on pathological hallmarks in relevant models. These guidelines may spur the discovery of astrocyte-based biomarkers as well as astrocyte-targeting therapies that abrogate detrimental actions of reactive astrocytes, potentiate their neuro- and glioprotective actions, and restore or augment their homeostatic, modulatory, and defensive functions.

Fig. 1 |. Multivariate assessment of reactive astrocytes.

a, Reactive astrocyte proliferation in the vicinity of blood vessels assessed by co-staining for BrdU (green; arrows), DAPI (blue), GFAP (white), and CD31 (red) after stab injury of the mouse cortex. Scale bar, 15 μm. Unpublished image from authors S.S. and M.G. b, Human cortical protoplasmic astrocytes in a surgical specimen injected with Lucifer yellow (arrow, injection site) that traverses the gap junctions into neighboring astrocytes. Scale bar, 45 μm. Courtesy of Drs. Xu, Sosunov, and McKhann, Columbia University Department of Neurosurgery. c, Event-based determination of Ca²⁺ responses in a GCaMP6-expressing astrocyte (surrounded by a dashed line) in mouse cortical slices using astrocyte quantitative analysis (AQuA). Colors indicate AQuA events occurring in a single 1-s frame of a 5-min movie. Scale bar, 10 μm. d, Activation of the transcription factor STAT3 (green) assessed by nuclear accumulation in GFAP+ reactive astrocytes (red) surrounding an amyloid plaque (blue, arrow) in a mouse AD model. Scale bar, 20 μm. Adapted from ref. , Society for Neuroscience. e, ScRNAseq in the remission phase of a mouse model of MS reveals several transcriptional astrocyte clusters. These astrocyte sub-populations may be validated with spatial transcriptomics, as shown in f in a model of AD. Adapted from ref. , Nature Publishing Group. f, Distribution of 87 astrocytic (green), neuronal (red), microglial (yellow), and oligodendroglial (blue) genes with in situ multiplex gene sequencing in a coronal section from a mouse model of AD. The method ‘reads’ barcodes of antisense DNA probes that simultaneously target numerous mRNAs. Scale bar, 800 μm. Boxed area is magnified in bottom image, showing 6E10⁺ amyloid-β plaques (white; arrows). Adapted from ref. , Cell Press.

Fig. 2 |. Workflow for the identification of key variables shaping astrocyte reactivity using multidimensional analyses.

a, Variables to measure in individual experiments. Although at present it is unrealistic to measure all in the same experiment, it will in most cases be possible to measure at least two or three. b, Variables to record in individual experiments. In some experiments, all or most of these variables are kept constant and are not compared, but they should all be recorded to allow for future comparison across experiments and studies. c, Individual studies will generate multidimensional datasets of reactive astrocytes that can be organized in matrices containing all outcome measures of variables assessed in a (for example, -omics data, functional measurements). One matrix may be generated for each condition listed in b using data obtained in a. Determining whether such states are equivalent to fixed categories rather than temporary changes due to the dynamic nature of cell functioning requires cross-comparison among studies or longitudinal studies, paired with statistical analyses, as in d. d, Multidimensional data analysis and clustering statistics of weighted scores from datasets (a) across different contexts (b) represented in matrices (c) allow identification of functional vectors (V) driving astrocyte reactivity in different contexts. A high score and a low score in each vector represent gain and loss of function, respectively. The graph shows a hypothetical plot of simulated multivariate datasets from a (each dot represents one dataset or sample) obtained in different contexts (b), depicted in different colors. Astrocytes with shared features segregate together along three axes according to the predominance of the function represented in each vector. A state is defined by where the dataset(s) are placed in the V1–3 space. The analysis can be n-dimensional, but for visual clarity, we show a 3D scenario.