Extended Data Fig. 1: RNA sequencing, Transcriptional Regulator Enrichment Analysis (TREA) and previously published TRs.

a. Flow diagram of RNA-seq procedure. To minimize technical differences, we used mice of similar age and genetic background for all experimental disorder models and examined the same anatomical region (thoracic spinal cord). Spinal cord tissue from different experiments were frozen until all experiments were completed. All tissue was then processed at the same time to limit the potential for technical variations. The RiboTag procedure was used to harvest ribosome-associated RNA transcripts specifically from reactive astrocytes¹². RiboTag hemagglutinin (HA) was transgenically-targeted specifically to astrocytes by using well-characterized mGFAP-Cre¹². RNA-sequencing and analysis were conducted under identical conditions. b. Specificity of RiboTag-HA targeting to astrocytes and not microglia or other cells. Two sets of orthogonal (3D) scans from uninjured spinal cord or after spinal cord injury (SCI) with multichannel immunofluorescence for HA targeted to astrocytes plus Sox9 and Iba1 as markers of astrocytes or microglia respectively. The same areas are shown with different fluorescent wavelength filters and different orthogonal slices that demonstrate 3D staining associated with astrocytes or microglia. HA is robustly present in Sox9-positive astrocytes but is not detectable in Iba1-postive microglia in either uninjured cord or after SCI. Absence of HA-targeting to neurons or oligodendrocytes has been demonstrated previously¹². These immunohistochemical comparisons were repeated independently three times with similar results. c. Venn diagrams show that the relative proportions of shared astrocyte reactivity DEGs and TRs identified in spinal cord astrocytes after EAE, SCI or LPS do not detectably differ when using thresholds of FDR < 0.1 or FDR < 0.05. Table shows PCA analysis of DEGs identified in spinal cord astrocytes after EAE, SCI or LPS using thresholds of FDR < 0.1 or FDR < 0.05. The relative locations of the three disorders in PC space when compared to non-reactive astrocytes do not detectably differ when using thresholds of FDR < 0.1 or FDR < 0.05 as reflected in the percent of total vector length and the angles between vectors. d. Flow diagram of Transcriptional Regulator Enrichment Analysis (TREA) procedure for TR identification by upstream analysis of DEGs in reactive astrocytes. To identify TRs of astrocyte gene expression, we applied a conservative, multi-step algorithm that draws on both computationally- and biologically-derived regulator–target gene interaction data from multiple resource databases: i) ChEA:⁴³ transcription factor regulation inferred from integrating genome-wide ChIP-X experiments, ii) JASPAR⁴⁴ and iii) TRANSFAC⁴⁵ transcription factor DNA-binding preferences as position weight matrices, and iv) Ingenuity Pathway Analysis Upstream Regulator Analytic (IPA^®, Qiagen, Valencia, CA). Using these resource databases, TREA identifies TRs implicated in regulating DEGs by interrogating multiple forms of TR–target gene regulatory interactions that include findings from experimental studies involving techniques such as chromatin immunoprecipitation and genetic loss-of-function studies, as well as well-validated predictive computational DNA binding ‘motif’ analytics. In this manner, TR–target gene interactions considered by TREA include traditional direct TR-DNA binding mechanisms, as well as indirect forms of gene expression regulation wherein a TR may act through different types of intermediaries to effect expression of downstream target genes, including chromatin modifiers and other forms of epigenetic regulators. TRs were included if they met either of two criteria: (1) convergence across resource databases; (2) differential gene expression of the TR plus convergence with at least one resource database. Together, these databases allow for interrogation of gene expression datasets for enrichment of downstream targets for approximately 1350 TRs. Resource database output files containing statistically enriched TRs and their downstream astrocyte target gene IDs were processed for TREA using custom Python scripts available at GitHub repository (https://github.com/burdalab/TREA). Final TREA libraries containing significantly enriched TRs and associated astrocyte target genes were then generated for each condition’s DEG dataset. TREA libraries were used for all comparisons of astrocyte TRs and gene expression profiles across disorders and experimental conditions and to generate a resource database of reactive astrocyte TRs and the DEGs that they regulate across a broad spectrum of CNS disorders and conditions. This database can be accessed via an open-source website http://tr.astrocytereactivity.com and has multiple search parameters according to TR, DEG or condition. e. Published astrocyte reactivity TRs plus literature references^{55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113}. *4 of 62 published TRs not predicted in EAE, LPS or SCI.

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