CRISPRi-based screen of autism spectrum disorder risk genes in microglia uncovers roles of ADNP in microglia endocytosis and synaptic pruning

Abstract

Autism Spectrum Disorders (ASD) are a set of neurodevelopmental disorders with complex biology. The identification of ASD risk genes from exome-wide association studies and de novo variation analyses has enabled mechanistic investigations into how ASD-risk genes alter development. Most functional genomics studies have focused on the role of these genes in neurons and neural progenitor cells. However, roles for ASD risk genes in other cell types are largely uncharacterized. There is evidence from postmortem tissue that microglia, the resident immune cells of the brain, appear activated in ASD. Here, we used CRISPRi-based functional genomics to systematically assess the impact of ASD risk gene knockdown on microglia activation and phagocytosis. We developed an iPSC-derived microglia-neuron coculture system and high-throughput flow cytometry readout for synaptic pruning to enable parallel CRISPRi-based screening of phagocytosis of beads, synaptosomes, and synaptic pruning. Our screen identified ADNP, a high-confidence ASD risk genes, as a modifier of microglial synaptic pruning. We found that microglia with ADNP loss have altered endocytic trafficking, remodeled proteomes, and increased motility in coculture.

Fig. 1. A subset of ASD risk genes are expressed by microglia and their perturbation shifts the activation state of iPSC-derived microglia (iTF-Microglia).

A Stacked bar graph comparing the relative expression of 102 ASD risk genes [16] in neurons and microglia. B UMAP derived from single cell transcriptomic data for iTF-Microglia with CRISPRi-mediated knockdowns targeting 53 ASD risk genes involved in gene regulation. Each dot represents a single cell colored by gene target. C UMAP displaying the expression of the microglia marker, IBA1, encoded by AIF1 for individual cells. D (Left) UMAP displaying the results of transcriptomic cluster analysis for which each color represents a unique cluster. (Right) Dot plot showing the expression of each cluster’s top three differentially expressed genes. The color of the dots reflect the relative expression of the gene in a given cluster, and the size of the dots represent the percent of cells expressing the gene in a given cluster. E, F UMAPs displaying the distribution of cells containing sgRNAs targeting 12 specific ASD risk genes or non-targeting controls (NTCs). Blue dots represent the target cell and gray dots represent all other cells. G Stacked bar graph comparing the cluster occupancy by cells containing sgRNAs targeting 12 specific ASD risk genes or NTCs. Bar segments are colored according to cluster identity. H Individual bar graphs for the three activation clusters showing the change in cluster occupancy relative to NTCs. I Scatterplot showing the change in the interferon and chemokine clusters relative to NTCs.

Fig. 2. iPSC-derived microglia (iTF-Microglia) and iPSC-derived neuron (iNeuron) coculture enables monitoring of microglial synaptic pruning.

A Experimental and analytical strategy for measuring iTF-Microglia uptake of synaptic material from iPSC-derived neurons (iNeurons) using flow cytometry. (Top) iNeurons are engineered to express synaptophysin linked to the acid-tolerant green fluorescent protein, Gamillus. iTF-Microglia are engineered to express a nuclear blue fluorescent protein. (Bottom) After coculture and measurement using flow cytometry, iTF-Microglia are identified by their blue fluorescence, and the amount of green fluorescence from uptake of synaptophysin-Gamillus is measured within this iTF-Microglia population. B Representative micrographs of iNeurons expressing synaptophysin-Gamillus (green) costained with an antibody against a presynaptic marker, vGlut1 (magenta, top), or a post-synaptic marker, Homer (magenta, bottom). C Correlation analysis of synaptophysin-Gamillus with vGlut1 or Homer (N = 3 fields of view from 3 wells, bars represent mean +/− standard deviation). D, E Time lapse images of iNeurons expressing synaptophysin-Gamillus (green) and iTF-Microglia expressing a membrane marker, Lck-mApple (gray). D The yellow arrow highlights a synaptophysin-Gamillus punctum that is taken up into a phagocytic cup and beginning to be trafficked toward the iTF-Microglia’s soma. E The yellow arrow highlights a synaptophysin-Gamillus puncta inside a phagocytic cup that is being trafficked toward the iTF-Microglia’s soma. F Representative micrographs of iNeurons engineered to express cytosolic Gamillus or synaptophysin-Gamillus. Nuclei are marked by Hoechst 33342 and displayed in blue. Gamillus is displayed in gray. G Expression of the Gamillus protein measured by total fluorescence intensity per field of view (n = 5 wells, bars represent mean +/− standard deviation, Tukey’s multiple comparisons test). H Uptake of Gamillus by iTF-Microglia in coculture measured by flow cytometry (n = 3 wells, bars represent mean +/− standard deviation, Tukey’s multiple comparisons test). I Uptake of synaptophysin-Gamillus by iTF-Microglia in cocultures untreated, treated with actin polymerization inhibitor, Cytochalasin D, or treated with phosphatidylserine blocker, annexin V, as measured by flow cytometry (n = 5 wells, bars represent mean +/− standard deviation, Tukey’s multiple comparisons test). J Uptake of synaptophysin-Gamillus by iTF-Microglia with TREM2 targeting sgRNAs compared to uptake of synaptophysin-Gamillus by in-well iTF-Microglia with non-targeting control (NTC) sgRNA (n = 5 wells, bars represent mean, connected dots represent NTC and TREM2 knockdown microglia in the same well, multiple paired t-tests).

Fig. 3. Convergent and divergent phagocytosis phenotypes reveal ADNP as a unique genetic regulator of iPSC-derived microglia (iTF-Microglia).

A CRISPRi-based screening strategy to assess the role of ASD risk genes in microglial phagocytosis of various substrates: 1 um beads, iNeuron-derived synaptosomes, and live synaptic material. B–D Volcano plots showing the effect of gene knockdowns (KDs) in iTF-Microglia on uptake of beads (B), iNeuron-derived synaptosomes (C), and live synapses (D) across three screening replicates and their corresponding statistical significance (two-sided Mann–Whitney U-test, false discovery rate (FDR = 0.01). Gene KDs that increase uptake are noted in red, gene KDs that decrease uptake are noted in blue, non-targeting controls (NTCs) are noted in gray, and non-hit gene knockdowns are noted in black. E Heatmap comparing the phagocytosis phenotypes across substrates for each hit gene. Statistically significant phenotypes (FDR < 0.01) are noted with an asterisks. Bolded gene KDs are further analyzed in F. F Heatmap comparing the cluster occupancy of cells with a given gene KD relative to NTCs. Increased cluster occupancy is noted by reds, and decreased cluster occupancy is noted by blues. Gene KDs are grouped by their phagocytosis phenotypes. G–I Individual validation of phagocytosis phenotypes for iTF-Microglia with ADNP KD (N = 3–6 wells, multiple paired t-tests).

Fig. 4. ADNP knockdown (KD) alters iPSC-derived microglia (iTF-Microglia) proteome and interactions with iPSC-derived neurons (iNeurons).

A, B Volcano plots showing the change in abundance for individual surface proteins (A) or whole cell proteins (B) in iTF-Microglia with ADNP KD compared to iTF-Microglia with non-targeting control (NTC) sgRNA as determined by surface protein labeling and mass spectroscopy (N = 3 replicates per cell type). Proteins with statistically significant changes in abundance in iTF-Microglia with ADNP KD are noted in dark gray and those contributing to specific GO Cellular Component pathways in D are correspondingly colored red, blue, or cyan. Proteins with no statistically significant change are noted in light gray (Differentially expressed proteins (DAPs), Padj < 0.05, two-tailed Student’s t-test). Genes whose expression turned on or off with ADNP KD are marked by an X symbol. C Scatterplot comparing protein changes at the surface and whole cell for iTF-Microglia with ADNP KD. Proteins that are significantly differentially expressed in both datasets are noted in green, those exclusive to the whole cell are noted in purple, those exclusive to the cell surface are noted in orange, and those with no significant differential expression are noted in gray. D GO Cellular Component enrichment analyses of DAPs with increased (red) or decreased (blues) expression in iTF-Microglia with ADNP KD (Padj < 0.05). Terms are color-coded such that terms of the same color have many overlapping genes. E (Left) Surveillance area quantification strategy. Time lapse immunofluorescence micrographs of iTF-Microglia expressing a fluorescently tagged membrane protein, Lck-mNeonGreen, are acquired, segmented, and maximum-intensity projections are analyzed to measure surveillance area. (Right) Representative surveillance areas from coculture iTF-Microglia with ADNP KD or NTC. F Single cell surveillance area normalized to initial cell area for monoculture iTF-Microglia with ADNP KD or NTC (N = 16–24 single cells, violin plot area represents the density curve, significance based on NTC vs ADNP factor computed by two-way ANOVA). G Single cell surveillance area normalized to initial cell area for coculture of iTF-Microglia with ADNP KD or NTC with iNeurons (N = 35–59 single cells, violin plot area represents the density curve, t significance based on NTC vs ADNP factor computed by two-way ANOVA).

Fig. 5. ADNP knockdown (KD) affects endocytic trafficking in iPSC-derived microglia (iTF-Microglia).

A Volcano plot showing the change in expression as measured by RNA-sequencing for individual genes in iTF-Microglia with ADNP KD compared to iTF-Microglia with non-targeting control (NTC) sgRNAs. RNA-sequencing data extracted from a CROP-seq dataset, see Methods for detail. Significantly differentially expressed genes (DEGs) are noted in dark gray or color-coded based on their corresponding categorization in enrichment analyses (B) and genes with no statistically significant change are noted in light gray (DEGs, Padj < 0.05, two-tailed Student’s t-test). B GO Cellular Component enrichment analyses of DEGs with increased expression in iTF-Microglia with ADNP KD. Terms representing unique gene sets and with Padj <0.05 are displayed. Terms are color-coded such that terms of the same color have many overlapping genes. C Scatterplot comparing fold changes at the transcriptomic and proteomic levels when comparing iTF-Microglia with ADNP KD to iTF-Microglia with NTC. Some significantly differentially abundant proteins are noted in black and labeled. D Venn diagrams showing the overlap in differentially expressed genes and proteins that are increased or decreased in iTF-Microglia with ADNP KD. E (Top) Schematic of the biology of dextran-Alexa488 endocytosis. (Bottom) Endocytic load within iTF-Microglia with ADNP KD or NTC and with or without endocytosis inhibitor, Dynasore, as measured by flow cytometry (N = 2–3 wells, bars represent mean +/− standard deviation, 2 way ANOVA). F (Top) Schematic of the biology of dextran-pHrodo endocytosis. (Bottom) Endocytic acidification within iTF-Microglia with ADNP KD or NTC and with or without V-ATPase inhibitor, BafilomycinA, as measured by flow cytometry (N = 3 wells, bars represent mean +/− standard deviation, 2 way ANOVA). G (Top) Schematic of the biology of Lysotracker. (Bottom) Acidic vesicles within iTF-Microglia with ADNP KD or NTC and with or without Dynasore and BafilomycinA as measured by flow cytometry (N = 3 wells, bars represent mean +/− standard deviation, 2 way ANOVA). H (Top) Schematic of the biology of lysosensor. (Bottom) Lysosomes within iTF-Microglia with ADNP KD or NTC as measured by flow cytometry (N = 3 wells, bars represent mean +/− standard deviation, two-tailed Student’s t-test). I TREM2 concentration of cell lysates or media collected from iTF-Microglia with ADNP KD or NTC measured using a Homogeneous Time Resolved Fluorescence (HTRF) assay (N = 3 wells, bars represent mean +/− standard deviation, t-test). J Relative abundances measured using the integrated intensity from a cytokine array for proteins that have a trend for differential abundance between iTF-Microglia with NTC or ADNP KD (N = 2 wells, bars represent mean +/− standard deviation).

Fig. 6. ADNP localizes to early endocytic compartments in iPSC-derived microglia (iTF-microglia).

A Representative micrographs of iPSC-derived neurons (iNeurons) and iTF-Microglia showing the localization of ADNP (detected by immunofluorescence, gray) with respect to nuclei demarked by Hoechst 33342 (blue). B Quantification of the percent of nuclei with high ADNP intensity (N = 3–4 wells per cell type, bars represent mean +/− standard deviation). C Representative immunofluorescence micrographs of iTF-Microglia stained with antibodies against ADNP (red) and various endolysosomal organelle markers including CLTC, Rab5A, Rab11A, CD68, and GM130 (green). D Average Pearson correlation between ADNP and individual organelle markers measured per cell for a given field of view (N = 1–4 fields of view from 3 wells, bars represent mean +/− standard deviation). E Average Mander’s overlap for ADNP and individual organelle markers measured per cell for a given field of view (N = 1–4 fields of view from 3 wells, bars represent mean +/− standard deviation).

Fig. 7. Altered endocytosis in microglia is supported by transcriptomic data from human ASD brain tissue.

A Volcano plot showing the change in expression as measured by single cell RNA-sequencing for differentially expressed genes (DEGs) in microglia from postmortem tissue from individuals with ASD compared to neurotypical controls [37] (DEGs, Benjamini & Hochberg corrected p values < 0.01). B GO Cellular Component enrichment analysis of DEGs with increased expression in microglia from postmortem tissue from individuals with ASD. Terms representing unique gene sets and with Padj < 0.05 are displayed. Terms are color-coded such that terms of the same color have many overlapping genes. C Summary model of microglial phenotypes with ADNP loss.