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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.


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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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