bioRxivNeurosciencePreprint
Epigenetic memory astrocytes are likely an artifact of immune cell contamination
Abstract
Innate immune memory, in which prior immune stimuli can "train" certain immune cells to respond more aggressively to subsequent challenges, is crucial for immune system plasticity in disease. Lee et al. [1] recently described a similar kind of immune memory state in astrocytes which they termed "epigenetic memory astrocytes". The discovery of astrocytes with immune memory could have tremendous importance in understanding and treating neurological disease. However, the RNA-seq data and in vitro experiments presented by Lee et al. to claim astrocytes possess pro-inflammatory immune memory show signs of immune cell contamination. Further, astrocyte-specific knockout of Ep300, the purported epigenetic regulator of this memory, did not reduce expression of any memory astrocyte signature genes. The FIND-seq signature used to verify the presence of epigenetic memory astrocytes in experimental autoimmune encephalomyelitis (EAE) also shows signs of immune cell contamination, and the cells identified as memory astrocytes in previously published EAE single-cell RNA-seq data are misannotated macrophages. Lastly, we find the purported epigenetic memory astrocytes identified in single-nucleus RNA-seq data of multiple sclerosis (MS) tissue are an artifact of ambient RNA, low quality nuclei, and non-astrocyte contamination. We conclude that the epigenetic memory astrocyte signature is likely driven by immune cell contamination and the existence of astrocyte immunological memory is insufficiently evidenced. We caution that astrocyte transcriptomic, epigenomic, and functional assays must take care to exclude contamination by immune cells, especially when evaluating the potential of astrocytes to perform immunological functions.
- organism: mouse
- assay: bulk RNA-seq
- assay: spatial transcriptomics
- research focus: benchmarking
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 6
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