Targeted Single-Cell RNA and DNA Sequencing With Fluorescence-Activated Droplet Merger.

Analyzing every cell in a diverse sample provides insight into population-level heterogeneity, but abundant cell types dominate the analysis and rarer populations are scarcely represented in the data. To focus on specific cell types, the current paradigm is to physically isolate subsets of interest prior to analysis; however, it remains difficult to isolate and then single-cell sequence such populations because of compounding losses. Here, we describe an alternative approach that selectively merges cells with reagents to achieve enzymatic reactions without having to physically isolate cells. We apply this technique to perform single-cell transcriptome and genome sequencing of specific cell subsets. Our method for analyzing heterogeneous populations obviates the need for pre- or post-enrichment and simplifies single-cell workflows, making it useful for other applications in single-cell biology, combinatorial chemical synthesis, and drug screening

Targeted Single-Cell RNA and DNA Sequencing With Fluorescence-Activated Droplet Merger.

Analyzing every cell in a diverse sample provides insight into population-level heterogeneity, but abundant cell types dominate the analysis and rarer populations are scarcely represented in the data. To focus on specific cell types, the current paradigm is to physically isolate subsets of interest prior to analysis; however, it remains difficult to isolate and then single-cell sequence such populations because of compounding losses. Here, we describe an alternative approach that selectively merges cells with reagents to achieve enzymatic reactions without having to physically isolate cells. We apply this technique to perform single-cell transcriptome and genome sequencing of specific cell subsets. Our method for analyzing heterogeneous populations obviates the need for pre- or post-enrichment and simplifies single-cell workflows, making it useful for other applications in single-cell biology, combinatorial chemical synthesis, and drug screening

MAFG-driven astrocytes promote CNS inflammation

Multiple sclerosis is a chronic inflammatory disease of the CNS$^{1}$. Astrocytes contribute to the pathogenesis of multiple sclerosis$^{2}$, but little is known about the heterogeneity of astrocytes and its regulation. Here we report the analysis of astrocytes in multiple sclerosis and its preclinical model experimental autoimmune encephalomyelitis (EAE) by single-cell RNA sequencing in combination with cell-specific Ribotag RNA profiling, assay for transposase-accessible chromatin with sequencing (ATAC-seq), chromatin immunoprecipitation with sequencing (ChIP-seq), genome-wide analysis of DNA methylation and in vivo CRISPR-Cas9-based genetic perturbations. We identified astrocytes in EAE and multiple sclerosis that were characterized by decreased expression of NRF2 and increased expression of MAFG, which cooperates with MAT2α to promote DNA methylation and represses antioxidant and anti-inflammatory transcriptional programs. Granulocyte-macrophage colony-stimulating factor (GM-CSF) signalling in astrocytes drives the expression of MAFG and MAT2α and pro-inflammatory transcriptional modules, contributing to CNS pathology in EAE and, potentially, multiple sclerosis. Our results identify candidate therapeutic targets in multiple sclerosis

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

HIV silencing and cell survival signatures in infected T cell reservoirs

Rare CD4 T cells that contain HIV under antiretroviral therapy represent an important barrier to HIV cure1-3, but the infeasibility of isolating and characterizing these cells in their natural state has led to uncertainty about whether they possess distinctive attributes that HIV cure-directed therapies might exploit. Here we address this challenge using a microfluidic technology that isolates the transcriptomes of HIV-infected cells based solely on the detection of HIV DNA. HIV-DNA+ memory CD4 T cells in the blood from people receiving antiretroviral therapy showed inhibition of six transcriptomic pathways, including death receptor signalling, necroptosis signalling and antiproliferative Gα12/13 signalling. Moreover, two groups of genes identified by network co-expression analysis were significantly associated with HIV-DNA+ cells. These genes (n = 145) accounted for just 0.81% of the measured transcriptome and included negative regulators of HIV transcription that were higher in HIV-DNA+ cells, positive regulators of HIV transcription that were lower in HIV-DNA+ cells, and other genes involved in RNA processing, negative regulation of mRNA translation, and regulation of cell state and fate. These findings reveal that HIV-infected memory CD4 T cells under antiretroviral therapy are a distinctive population with host gene expression patterns that favour HIV silencing, cell survival and cell proliferation, with important implications for the development of HIV cure strategies