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Identification of astrocyte regulators by nucleic acid cytometry


Multiple sclerosis is a chronic inflammatory disease of the central nervous system1. Astrocytes are heterogeneous glial cells that are resident in the central nervous system and participate in the pathogenesis of multiple sclerosis and its model experimental autoimmune encephalomyelitis2,3. However, few unique surface markers are available for the isolation of astrocyte subsets, preventing their analysis and the identification of candidate therapeutic targets; these limitations are further amplified by the rarity of pathogenic astrocytes. Here, to address these challenges, we developed focused interrogation of cells by nucleic acid detection and sequencing (FIND-seq), a high-throughput microfluidic cytometry method that combines encapsulation of cells in droplets, PCR-based detection of target nucleic acids and droplet sorting to enable in-depth transcriptomic analyses of cells of interest at single-cell resolution. We applied FIND-seq to study the regulation of astrocytes characterized by the splicing-driven activation of the transcription factor XBP1, which promotes disease pathology in multiple sclerosis and experimental autoimmune encephalomyelitis4. Using FIND-seq in combination with conditional-knockout mice, in vivo CRISPR–Cas9-driven genetic perturbation studies and bulk and single-cell RNA sequencing analyses of samples from mouse experimental autoimmune encephalomyelitis and humans with multiple sclerosis, we identified a new role for the nuclear receptor NR3C2 and its corepressor NCOR2 in limiting XBP1-driven pathogenic astrocyte responses. In summary, we used FIND-seq to identify a therapeutically targetable mechanism that limits XBP1-driven pathogenic astrocyte responses. FIND-seq enables the investigation of previously inaccessible cells, including rare cell subsets defined by unique gene expression signatures or other nucleic acid markers.

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Fig. 1: Development of FIND-seq to study rare astrocyte subsets.
Fig. 2: FIND-seq analysis of XBP1-driven astrocytes.
Fig. 3: NR3C2–NCOR2 signalling limits disease-promoting astrocyte responses.
Fig. 4: XBP1 limits NR3C2–NCOR2 signalling in EAE and multiple sclerosis.

Data availability

Genomic data have been deposited in the GEO database under accession number GSE198971. All other data are available from the corresponding authors on reasonable request. Correspondence and request for materials should be addressed to F.J.Q. or A.R.A. Source data are provided with this paper.


  1. Baecher-Allan, C., Kaskow, B. J. & Weiner, H. L. Multiple sclerosis: mechanisms and immunotherapy. Neuron 97, 742–768 (2018).

    Article  CAS  Google Scholar 

  2. Lee, H.-G., Wheeler, M. A. & Quintana, F. J. Function and therapeutic value of astrocytes in neurological diseases. Nat. Rev. Drug Discovery 21, 339–358 (2022).

    Article  CAS  Google Scholar 

  3. Linnerbauer, M., Wheeler, M. A. & Quintana, F. J. Astrocyte crosstalk in cns inflammation. Neuron 108, 608–622 (2020).

    Article  CAS  Google Scholar 

  4. Wheeler, M. A. et al. Environmental control of astrocyte pathogenic activities in CNS inflammation. Cell 176, 581–596.e518 (2019).

    Article  CAS  Google Scholar 

  5. Börner, K. et al. Anatomical structures, cell types and biomarkers of the human reference atlas. Nat. Cell Biol. 23, 1117–1128 (2021).

    Article  ADS  Google Scholar 

  6. Ginhoux, F., Yalin, A., Dutertre, C. A. & Amit, I. Single-cell immunology: past, present, and future. Immunity 55, 393–404 (2022).

    Article  CAS  Google Scholar 

  7. Rozenblatt-Rosen, O. et al. The human tumor atlas network: charting tumor transitions across space and time at single-cell resolution. Cell 181, 236–249 (2020).

    Article  CAS  Google Scholar 

  8. Cugurra, A. et al. Skull and vertebral bone marrow are myeloid cell reservoirs for the meninges and CNS parenchyma. Science 373, eabf7844 (2021).

    Article  CAS  Google Scholar 

  9. Giladi, A. et al. Cxcl10+ monocytes define a pathogenic subset in the central nervous system during autoimmune neuroinflammation. Nat. Immunol. 21, 525–534 (2020).

    Article  CAS  Google Scholar 

  10. Grigg, J. B. et al. Antigen-presenting innate lymphoid cells orchestrate neuroinflammation. Nature 600, 707–712 (2021).

    Article  ADS  CAS  Google Scholar 

  11. Hiltensperger, M. et al. Skin and gut imprinted helper T cell subsets exhibit distinct functional phenotypes in central nervous system autoimmunity. Nat. Immunol. 22, 880–892 (2021).

    Article  CAS  Google Scholar 

  12. Jordão, M. J. C. et al. Single-cell profiling identifies myeloid cell subsets with distinct fates during neuroinflammation. Science 363, eaat7554 (2019).

    Article  Google Scholar 

  13. Khakh, B. S. & Deneen, B. The emerging nature of astrocyte diversity. Annu. Rev. Neurosci. 42, 187–207 (2019).

    Article  CAS  Google Scholar 

  14. Sofroniew, M. V. Astrocyte reactivity: subtypes, states, and functions in cns innate immunity. Trends Immunol. 41, 758–770 (2020).

    Article  CAS  Google Scholar 

  15. Absinta, M. et al. A lymphocyte–microglia–astrocyte axis in chronic active multiple sclerosis. Nature 597, 709–714 (2021).

    Article  ADS  CAS  Google Scholar 

  16. Chao, C. C. et al. Metabolic control of astrocyte pathogenic activity via cpla2-mavs. Cell 179, 1483–1498.e1422 (2019).

    Article  CAS  Google Scholar 

  17. Escartin, C. et al. Reactive astrocyte nomenclature, definitions, and future directions. Nat. Neurosci. 24, 312–325 (2021).

    Article  CAS  Google Scholar 

  18. Mayo, L. et al. Regulation of astrocyte activation by glycolipids drives chronic cns inflammation. Nat. Med. 20, 1147–1156 (2014).

    Article  CAS  Google Scholar 

  19. Rothhammer, V. et al. Microglial control of astrocytes in response to microbial metabolites. Nature 557, 724–728 (2018).

    Article  ADS  CAS  Google Scholar 

  20. Rothhammer, V. et al. Type I interferons and microbial metabolites of tryptophan modulate astrocyte activity and central nervous system inflammation via the aryl hydrocarbon receptor. Nat. Med. 22, 586–597 (2016).

    Article  CAS  Google Scholar 

  21. Sanmarco, L. M. et al. Gut-licensed IFNγ+ NK cells drive LAMP1+TRAIL+ anti-inflammatory astrocytes. Nature 590, 473–479 (2021).

    Article  ADS  CAS  Google Scholar 

  22. Wheeler, M. A. et al. Mafg-driven astrocytes promote cns inflammation. Nature 578, 593–599 (2020).

    Article  ADS  CAS  Google Scholar 

  23. Habib, N. et al. Disease-associated astrocytes in alzheimer’s disease and aging. Nat. Neurosci. 23, 701–706 (2020).

    Article  CAS  Google Scholar 

  24. Hasel, P., Rose, I. V. L., Sadick, J. S., Kim, R. D. & Liddelow, S. A. Neuroinflammatory astrocyte subtypes in the mouse brain. Nat. Neurosci. 24, 1475–1487 (2021).

    Article  CAS  Google Scholar 

  25. Amamoto, R. et al. Probe-seq enables transcriptional profiling of specific cell types from heterogeneous tissue by rna-based isolation. eLife 8, e51452 (2019).

    Article  CAS  Google Scholar 

  26. Eastburn, D. J., Sciambi, A. & Abate, A. R. Ultrahigh-throughput mammalian single-cell reverse-transcriptase polymerase chain reaction in microfluidic drops. Anal. Chem. 85, 8016–8021 (2013).

    Article  CAS  Google Scholar 

  27. Eastburn, D. J., Sciambi, A. & Abate, A. R. Identification and genetic analysis of cancer cells with pcr-activated cell sorting. Nucleic Acids Res. 42, e128 (2014).

    Article  Google Scholar 

  28. Calfon, M. et al. Ire1 couples endoplasmic reticulum load to secretory capacity by processing the XBP-1 mRNA. Nature 415, 92–96 (2002).

    Article  ADS  CAS  Google Scholar 

  29. Clark, I. C. et al. HIV silencing and cell survival signatures in infected T cell reservoirs. Nature (2023).

  30. Clark, I. C., Thakur, R. & Abate, A. R. Concentric electrodes improve microfluidic droplet sorting. Lab Chip 18, 710–713 (2018).

    Article  CAS  Google Scholar 

  31. Smith, H. L. et al. Astrocyte unfolded protein response induces a specific reactivity state that causes non-cell-autonomous neuronal degeneration. Neuron 105, 855–866.e855 (2020).

    Article  CAS  Google Scholar 

  32. Glimcher, L. H., Lee, A. H. & Iwakoshi, N. N. Xbp-1 and the unfolded protein response (UPR). Nat. Immunol. 21, 963–965 (2020).

    Article  CAS  Google Scholar 

  33. Lee, A. H., Iwakoshi, N. N. & Glimcher, L. H. Xbp-1 regulates a subset of endoplasmic reticulum resident chaperone genes in the unfolded protein response. Mol. Cell. Biol. 23, 7448–7459 (2003).

    Article  CAS  Google Scholar 

  34. Arzalluz-Luque, A. & Conesa, A. Single-cell RNAseq for the study of isoforms—how is that possible? Genome Biol. 19, 110 (2018).

    Article  Google Scholar 

  35. Buen Abad Najar, C. F., Yosef, N. & Lareau, L. F. Coverage-dependent bias creates the appearance of binary splicing in single cells. eLife 9, e54603 (2020).

    Article  Google Scholar 

  36. Picelli, S. et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10, 1096–1098 (2013).

    Article  CAS  Google Scholar 

  37. Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

    Article  CAS  Google Scholar 

  38. Clark, I. C. et al. Barcoded viral tracing of single-cell interactions in central nervous system inflammation. Science 372, eabf1230 (2021).

    Article  CAS  Google Scholar 

  39. Glass, C. K. & Saijo, K. Nuclear receptor transrepression pathways that regulate inflammation in macrophages and t cells. Nat. Rev. Immunol. 10, 365–376 (2010).

    Article  CAS  Google Scholar 

  40. Geller, D. S. et al. Activating mineralocorticoid receptor mutation in hypertension exacerbated by pregnancy. Science 289, 119–123 (2000).

    Article  ADS  CAS  Google Scholar 

  41. Ruzzo, E. K. et al. Inherited and de novo genetic risk for autism impacts shared networks. Cell 178, 850–866.e826 (2019).

    Article  CAS  Google Scholar 

  42. Hetz, C. et al. Unfolded protein response transcription factor XBP-1 does not influence prion replication or pathogenesis. Proc. Natl Acad. Sci. USA 105, 757–762 (2008).

    Article  ADS  CAS  Google Scholar 

  43. Srinivasan, R. et al. New transgenic mouse lines for selectively targeting astrocytes and studying calcium signals in astrocyte processes in situ and in vivo. Neuron 92, 1181–1195 (2016).

    Article  CAS  Google Scholar 

  44. Anderson, M. A. et al. Astrocyte scar formation aids central nervous system axon regeneration. Nature 532, 195–200 (2016).

    Article  ADS  CAS  Google Scholar 

  45. John Lin, C. C. et al. Identification of diverse astrocyte populations and their malignant analogs. Nat. Neurosci. 20, 396–405 (2017).

    Article  CAS  Google Scholar 

  46. Saijo, K. et al. A Nurr1/CoREST pathway in microglia and astrocytes protects dopaminergic neurons from inflammation-induced death. Cell 137, 47–59 (2009).

    Article  CAS  Google Scholar 

  47. Shaked, I. et al. Transcription factor Nr4a1 couples sympathetic and inflammatory cues in CNS-recruited macrophages to limit neuroinflammation. Nat. Immunol. 16, 1228–1234 (2015).

    Article  CAS  Google Scholar 

  48. Clarisse, D., Deng, L., de Bosscher, K. & Lother, A. Approaches towards tissue-selective pharmacology of the mineralocorticoid receptor. Br. J. Pharmacol. 179, 3235–3249 (2021).

    Article  Google Scholar 

  49. Ayata, P. et al. Epigenetic regulation of brain region-specific microglia clearance activity. Nat. Neurosci. 21, 1049–1060 (2018).

    Article  CAS  Google Scholar 

  50. Wendeln, A. C. et al. Innate immune memory in the brain shapes neurological disease hallmarks. Nature 556, 332–338 (2018).

    Article  ADS  CAS  Google Scholar 

  51. Boulay, A. C. et al. Translation in astrocyte distal processes sets molecular heterogeneity at the gliovascular interface. Cell Discov. 3, 17005 (2017).

    Article  CAS  Google Scholar 

  52. Magnusson, J. P. et al. Activation of a neural stem cell transcriptional program in parenchymal astrocytes. eLife 9, e59733 (2020).

    Article  CAS  Google Scholar 

  53. Yan, Z., Clark, I. C. & Abate, A. R. Rapid encapsulation of cell and polymer solutions with bubble-triggered droplet generation. Macromol. Chem. Phys. 218, 1600297 (2017).

    Article  Google Scholar 

  54. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal (2011).

  55. Dobin, A. et al. Star: Ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  Google Scholar 

  56. Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from rna-seq data with or without a reference genome. BMC Bioinf. 12, 323 (2011).

    Article  CAS  Google Scholar 

  57. Soneson, C., Love, M. I. & Robinson, M. D. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Res 4, 1521 (2015).

    Article  Google Scholar 

  58. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  Google Scholar 

  59. Zhu, A., Ibrahim, J. G. & Love, M. I. Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Bioinformatics 35, 2084–2092 (2019).

    Article  CAS  Google Scholar 

  60. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    Article  ADS  CAS  Google Scholar 

  61. Kramer, A., Green, J., Pollard, J. Jr. & Tugendreich, S. Causal analysis approaches in ingenuity pathway analysis. Bioinformatics 30, 523–530 (2014).

    Article  Google Scholar 

  62. Grant, C. E., Bailey, T. L. & Noble, W. S. FIMO: scanning for occurrences of a given motif. Bioinformatics 27, 1017–1018 (2011).

    Article  CAS  Google Scholar 

  63. Bailey, T. L., Johnson, J., Grant, C. E. & Noble, W. S. The meme suite. Nucleic Acids Res. 43, W39–W49 (2015).

    Article  CAS  Google Scholar 

  64. Sandelin, A., Alkema, W., Engstrom, P., Wasserman, W. W. & Lenhard, B. JASPAR: An open-access database for eukaryotic transcription factor binding profiles. Nucleic Acids Res. 32, D91–D94 (2004).

    Article  CAS  Google Scholar 

  65. Hagemann-Jensen, M. et al. Single-cell RNA counting at allele and isoform resolution using Smart-seq3. Nat. Biotechnol. 38, 708–714 (2020).

    Article  CAS  Google Scholar 

  66. Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).

    Article  CAS  Google Scholar 

  67. Melsted, P., Ntranos, V. & Pachter, L. The barcode, UMI, set format and BUStools. Bioinformatics 35, 4472–4473 (2019).

    Article  CAS  Google Scholar 

  68. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

    Article  CAS  Google Scholar 

  69. Lee, Y., Messing, A., Su, M. & Brenner, M. GFAP promoter elements required for region-specific and astrocyte-specific expression. Glia 56, 481–493 (2008).

    Article  Google Scholar 

  70. Chen, E. Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinf. 14, 128 (2013).

    Article  Google Scholar 

  71. Kuleshov, M. V. et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44, W90–W97 (2016).

    Article  CAS  Google Scholar 

  72. Polman, C. H. et al. Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann. Neurol. 69, 292–302 (2011).

    Article  Google Scholar 

  73. Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    Article  CAS  Google Scholar 

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This work was supported by grants 1U01AI129206, UM1AI126611, NS087867, ES025530, ES032323, AI126880 and AI149699 from the National Institutes of Health, by the Chan Zuckerberg Biohub, by Wellcome Leap, by the NMSS and the Progressive MS Alliance. I.C.C. was supported by the NIH (K22AI152644, DP2AI154435). M.A.W. was supported by the NIH (1K99NS114111, F32NS101790 and 1R01MH130458-01), a training grant from the NIH and Dana-Farber Cancer Institute (T32CA207201), a travelling neuroscience fellowship from the Program in Interdisciplinary Neuroscience at the Brigham and Women’s Hospital and the Women’s Brain Initiative at the Brigham and Women’s Hospital. H.-G.L. was supported by a Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2021R1A6A3A14039088). C.M.P. was supported by a fellowship from FAPESP BEPE (2019/13731-0). We thank L. Glimcher for providing Xbp1fl/fl mice; all members of the Quintana and Abate laboratories for helpful advice and discussions; and the NeuroTechnology Studio at Brigham and Women’s Hospital for providing instrument access. The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

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Authors and Affiliations



I.C.C., M.A.W., E.A.B., F.J.Q. and A.R.A. designed research. I.C.C., M.A.W., H.-G.L., L.M.S., C.M.P., S.T., S.W.S., G.S., A.R.H., M.C., C.F.A., D.M.A., J.M.R. and F.G. performed experiments. I.C.C., M.A.W., Z.L., F.J.Q. and A.R.A. analysed data. D.C.D., S.E.J.Z. and A.P. provided unique materials and/or discussed findings. I.C.C., M.A.W., F.J.Q. and A.R.A. wrote the paper with input from the co-authors. F.J.Q. and A.R.A. directed and supervised the study.

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Correspondence to Francisco J. Quintana or Adam R. Abate.

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Nature thanks Lawrence Steinman and the other, anonymous reviewers for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Analysis of rare cells by FIND-seq.

(a) Featureplots of Aqp4 and Edem1 expression in cells isolated from the mouse CNS during EAE reanalyzed from ref. 22. (b) Violin plots of Aqp4 and Edem1 by cluster from astrocytes isolated from the EAE CNS in a dataset re-analyzed from ref. 22. (c) Micrograph images of agarose beads containing captured nucleic acids from encapsulated cells. (d) Droplet cytometry plots showing an estimate of the number of cells captured (>1 million) in the bead volume shown in (c). (e) Negative control of droplet cytometry SYBR fluorescence.

Extended Data Fig. 2 Droplet formation and capture of mRNA by FIND-seq.

(a) Schematic of bubble trigger device that drives air-triggered droplet formation during cell encapsulation. (b) Air-triggered droplet formation enables kHz generation of beads from viscous molten agarose. Images from time-lapse videos of agarose jet breakup. (c) Functionalization of agarose with allyl groups is used to directly link acrydited primers. (d) Microscope images demonstrating successful conjugation of polyT primers to agarose with polyA-FAM probes. (e) Quantification of agarose-bound polyT.

Extended Data Fig. 3 Optimization of first-strand synthesis of agarose captured mRNA.

(a) Steric capture of cellular genomic DNA inside the agarose matrix. Left: brightfield image of agarose gels. Right: SYBR green fluorescence of stained genomes inside agarose gels. Cells are loaded at limiting dilution to ensure single-cell encapsulation; approximately one in every ten agarose hydrogels contains a cell. (b) Estimated number of cells per drop based on Poisson statistics for microfluidic loading during FIND-seq. (c) Quantification of live cells by flow cytometry using AmCyan live/dead cell dye. n = 4 mice. (d) Whole transcriptome amplification (WTA) of cDNA covalently attached to agarose beads shows full length material is captured and reverse transcribed. (e) WTA yield as a function of PCR cycle number. (f) Optimization of cDNA capture with buffer composition, enzyme, template switch oligonucleotide concentration and additives (6 mM Mg2+, 1M betaine, and 7.5% PEG-8000). (g) Quantification of the percent mitochondrial reads in bulk FIND-seq data for each replicate. n = 22 samples. (h) Calculation of score per cell from astrocytes derived from scRNA-seq from ref. 22 or each bulk FIND-seq replicate for the pathway GOBP: Execution phase of apoptosis (GO: 0097194).

Extended Data Fig. 4 Agarose bead re-injection and sorting for single-cell detection of nucleic acid markers.

(a) Schematic of microfluidic for re-injection of agarose-captured genomes. (b) Microscope image of single agarose hydrogel beads inside droplets with measured size for each droplet and agarose sphere. (c) Optimization of droplet detection PCR to preserve cDNA quality during thermocycling. (d) Detection of a single-copy HIV genomic DNA target by FIND-seq in infected human JLat cells, but not in uninfected Jurkat control cells, as a proof-of-concept experiment testing FIND-seq sensitivity and specificity. The DNA target was amplified using TaqMan PCR in the FIND-seq workflow (Step 3 in Fig. 1d) followed by detection by droplet cytometry (Step 4 in Fig. 1f). (e) Schematic of microfluidic for droplet sorting using a concentric dielectrophoretic design. (f) Micrographs of droplet sorting. Top: time-lapse images from a droplet sorting video showing droplet deflection into the collection channel. Bottom: In the absence of FPGA sort-triggering, droplets are maintained, via bias oil flow, in the outer waste channel.

Extended Data Fig. 5 Benchmarking FIND-seq as a technology.

(a) Quantification of percent aquaporin-4-expressing cells in naïve mice by antibody-based flow cytometry (n = 4); FIND-seq (n = 3), or scRNA-seq reanalyzed from ref. 22. (b) Validation of FIND-seq specificity and sensitivity through the analysis of AQP4+ and AQP4- cells isolated by flow cytometry and subjected to FIND-seq. Percentages show number of cells expressing Aqp4 in each population based on 70% bead loading. (c) PCA plot of bulk FIND-seq analysis of Aqp4+Edem1- cells. (d) Comparison of FIND-seq detection sensitivity with comparable technologies. (e) Correlation of raw expression counts per gene between bulk FIND-seq-sorted Aqp4+Edem1+ cells and Edem1+ astrocytes extracted from droplet-based scRNA-seq data that we previously reported in22. (f) Quantification of fraction of duplicate reads across bulk RNA-seq platforms. (g) Quantification of Ern1 in bulk FIND-seq data as a function of EAE and Edem1 expression. (h) Calculation of a signature score for transcripts enriched in astrocyte endfeet as reported by ref. 51, analyzed in astrocytes from ref. 22 and bulk FIND-seq.

Extended Data Fig. 6 Single-cell analysis by FIND-seq.

(a) Visualization of a single droplet in flat-bottom microwell plates. Refraction of light at well edges obscures imaging. This is solved by sorting directly into hexadecane. HFE oil sinks, forming a convex shape that forces droplets to the center so that they can be imaged. (b) The percentage of reads mapping to mouse or human cells after single-cell sorting cell mixtures. Mouse (3T3) and human (JLat) cells were mixed 1:100, sorted based on a TaqMan PCR targeting JLat cells, and the transcriptome was sequenced. (c) Quality control analyses for scFIND-seq. (d) Marker genes of Aqp4+Edem1+/– cells from naïve or EAE mice analyzed by scFIND-seq. (e) Elbow plot of principal components detected by scFIND-seq.

Extended Data Fig. 7 In vivo screening of FIND-seq-identified candidate regulators of XBP1+ astrocytes.

(a) Predicted upstream regulator analysis showing Nr3c2 and Xbp1 from bulk FIND-seq data using Qiagen IPA. Differentially expressed genes were used as input and the overlap with the regulon controlled by each molecule was computed. Fisher’s exact test. (b) Left: Prediction of Nr3c2 as an upstream regulator in Aqp4+Edem1+ cells analyzed by FIND-seq during EAE using Qiagen IPA as in (a). Fisher’s exact test. Right: Identification of an NR3C2 motif by SeqPos in genes downregulated in Aqp4+Edem1+ versus Aqp4+Edem1- cells in EAE. (c) UMAP plot of Aqp4+Edem1+ cells from EAE mice analyzed by scFIND-seq. (de) Prediction of upstream regulators (d) and pathway analysis (e) based on Qiagen IPA in Cluster 1 astrocytes analyzed from Aqp4+Edem1+ cells in EAE mice shown in (c). Fisher’s exact test. (f) Schematic of lentiviral vector containing a sgRNA targeting candidate genes and spCas9 under the control of a Gfap promoter. EAE disease progression in mice transduced with sgScrmbl and candidate sgRNA lentiviruses. n = 4–5 mice per condition.

Extended Data Fig. 8 Control analyses of Nr3c2 and Ncor2 knockdown.

(a) Left: Upstream regulator analysis of RNA-seq data by Qiagen IPA from sgNr3c2-targeted versus sgScrmble-targeted mice shows NR3C2 downregulation. Fisher’s exact test. Right: Validation of NR3C2 knockdown by immunostaining quantification. n = 6 images per group from n = 3 mice. Unpaired two-sided t-test. (bc) FACS analysis of total CD4+ cells or FoxP3+, IFN𝛾+, IL17+, and IL10+ CD4 cell subsets in the (b) the spleen and (c) CNS. (d) Validation of NCOR2 knockdown by immunostaining. n = 6 images per group from n = 3 mice. Unpaired two-sided t-test. (ef) FACS analysis of total CD4+ cells or FoxP3+, IFN𝛾 +, IL17+, and GM-CSF+ CD4 cell subsets in the (e) the spleen and (f) CNS.

Extended Data Fig. 9 Control analysis of cell subsets from Xbp1WT and Xbp1Astro mice.

(a) FACS analysis of astrocytes and microglia in the CNS. n = 3 per group. (b) Validation of XBP1 KO by immunostaining. n = 6 images from n = 3 mice per group. Unpaired two-sided t-test. (cd) FACS analysis of total CD4+ cells or FoxP3+, IFN𝛾+, and GM-CSF+ CD4+ T cell subsets from the (c) the spleen and (d) CNS of Xbp1WT and Xbp1Astro mice. (e) Pathways analyzed by pre-ranked gene set enrichment analysis (GSEA) of genes from RNA-seq data comparing Xbp1WT and Xbp1Astro mice.

Extended Data Fig. 10 Analysis of chromatin accessibility in genes responsive to mineralocorticoid signaling as a function of XBP1 expression in astrocytes.

(ab) Re-analysis of ATAC-seq data on bulk flow cytometry-sorted astrocytes that we reported in ref. 4, showing increased chromatin accessibility in NR3C2 responsive genes (a) and in Nr3c2 and Ncor2 (b) as a function of Gfap-specific shRNA-driven lentiviral knockdown of Xbp1.

Extended Data Table 1 FIND-seq practical considerations

Supplementary information

Reporting Summary

Supplementary Data 1

CAD file for the bubble trigger microfluidic device.

Supplementary Data 2

CAD file for the co-flow re-injection microfluidic device.

Supplementary Data 3

CAD file for the droplet sorter microfluidic device.

Supplementary Table 1

Normalized count matrix of bulk FIND-seq data comparing Aqp4+Edem1+/− cells from naive or EAE mice.

Supplementary Table 2

Gene expression by cluster of scFIND-seq data of Aqp4+Edem1+/− cells from naive or EAE mice.

Supplementary Table 3

Differential expression analysis of bulk RNA-seq data of astrocytes isolated from sgNr3c2 mice relative to sgScrmbl mice.

Supplementary Table 4

Differential expression analysis of bulk RNA-seq data of astrocytes isolated from sgNcor2 mice relative to sgScrmbl mice.

Supplementary Table 5

Differential expression analysis of bulk RNA-seq data of astrocytes isolated from Aldh-creERT2;Xbp1fl/fl KO mice relative to Xbp1fl/fl mice.

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Clark, I.C., Wheeler, M.A., Lee, HG. et al. Identification of astrocyte regulators by nucleic acid cytometry. Nature 614, 326–333 (2023).

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