TITLE: Fully defined human pluripotent stem cell-derived microglia and tri-culture system model C3 production in Alzheimer’s disease
AUTHORS: Sudha R. Guttikonda, Lisa Sikkema, Jason Tchieu, Nathalie Saurat, Ryan Walsh, Oliver Harschnitz, Gabriele Ciceri, Marjolein Sneeboer, Linas Mazutis, Manu Setty, Paul Zumbo, Doron Betel, Lot D. de Witte, Dana Pe'er, Lorenz Studer

[DESCRIPTION]
<div class = "text-blocks"><div class = "text-block">Aberrant inflammation in the CNS has been implicated as a major player in the pathogenesis of human neurodegenerative disease. We developed a new approach to derive microglia from human pluripotent stem cells (hPSCs) and built a defined hPSC-derived tri-culture system containing pure populations of hPSC-derived microglia, astrocytes, and neurons to dissect cellular cross-talk along the neuroinflammatory axis in vitro. We used the tri-culture system to model neuroinflammation in Alzheimer’s disease with hPSCs harboring the APPSWE+/+ mutation and their isogenic control. We found that complement C3, a protein that is increased under inflammatory conditions and implicated in synaptic loss, is potentiated in tri-culture and further enhanced in APPSWE+/+ tri-cultures due to microglia initiating reciprocal signaling with astrocytes to produce excess C3. Our study defines the major cellular players contributing to increased C3 in Alzheimer’s disease and presents a broadly applicable platform to study neuroinflammation in human disease.</div></div>

[STEPS]
?. [Derivation of microglia from hPSCs]
Dissociate hPSCs maintained in Essential 8 medium by Accutase to obtain a single-cell suspension.
?. [Derivation of microglia from hPSCs]
Plate a total of 60,000 cells per cm2 in E8 medium containing activin A (R&D 338-AC; ), BMP4 (R&D; ), CHIR 99021 (Tocris; ) and ROCK inhibitor (Y-27632; ) onto Matrigel-coated plates.
?. [Derivation of microglia from hPSCs]
After , change medium to Essential 6 medium containing activin A (), BMP4 () and IWP2 (Selleck;  ).
10
40
?. [Derivation of microglia from hPSCs]
On day 2, change medium to Essential 6 medium containing activin A (), BMP4 (), IWP2 (  ) and FGF2 (R&D; ).
10
40
20
?. [Derivation of microglia from hPSCs]
On day 3, dissociate cultures with Accutase and replate at 60,000 cells per cm2 in Essential 6 medium containing vascular endothelial growth factor (VEGF; R&D) (), FGF2 () and ROCK inhibitor (Y-27632; ).
15
5
?. [Derivation of microglia from hPSCs]
On day 4, Remove the ROCK inhibitor and change medium to Essential 6 with VEGF () and FGF2 ().
?. [Derivation of microglia from hPSCs]
On days 5 and 6, Feed cultures with Essential 6 medium containing VEGF (), FGF2 (), SCF () and IL-6 ().
?. [Derivation of microglia from hPSCs]
On days 7 and 9, change medium to Essential 6 with SCF (), IL-6 (), TPO () and IL-3 ().
?. [Derivation of microglia from hPSCs]
On day 10, collect the cells in suspension and either (1) co-culture with cortical neurons in Neurobasal medium containing B-27 supplement, l-glutamine and BDNF, ascorbic acid, GDNF, cAMP and IL-34 () and M-CSF () for  for direct transition to microglia, or (2) culture in RPMI with 10% FBS, l-glutamine, and penicillin–streptavidin with IL-34 () and M-CSF () for – until cells were adherent and elongated to transition to primitive macrophages.
?. [Derivation of microglia from hPSCs]
For serum-free culture, harvest cells in suspension on day 10 and culture in 75% IMDM, 25% F12 medium containing B-27 supplement, l-glutamine and IL-34 () and M-CSF () for –.
?. [Derivation of microglia from hPSCs]
Co-culture transitioned macrophages with cortical neurons with the addition of IL-34 and M-CSF for  for upregulation of microglial-specific markers.
?. [Derivation of microglia from hPSCs]
Use induced pluripotent stem cells (iPSC) lines to test reproducibility of the microglial differentiation (DBR1, IFNAR1, IL-10RB and STAT1) are each separately derived from patient fibroblasts and are reprogrammed using a nonintegrating Sendai viral vector in the Notarangelo laboratory at the National Institutes of Health (NIH).
?. [Cortical neuron protocol]
Dissociate hPSCs with Accutase and plate at 200,000 cells per cm2 onto Matrigel-coated plates in Essential 8 medium with ROCK inhibitor (Y-27632; ).
?. [Cortical neuron protocol]
Treat cells with Essential 6 medium containing LDN193189 () and SB431542 () for , with the addition of XAV939 () for the first  of differentiation.
?. [Cortical neuron protocol]
Feed cultures with N2 medium with 1:1,000 B-27 supplement for an additional week to allow the development of neural progenitor cells.
?. [Cortical neuron protocol]
Dissociate neural progenitor cells and replate on poly-l-ornithine/fibronectin/laminin-coated plates and maintain in neurobasal medium, BDNF, ascorbic acid, GDNF, cAMP, l-glutamine and B-27 supplement for neuronal differentiation and maturation.
?. [Astrocyte protocol]
Differentiate hPSCs into astrocytes according to the method by Tchieu et al.
?. [Astrocyte protocol]
Briefly, pulse cortical neural stem cells with nuclear factor IA through an inducible lentiviral construct for , after which CD44+ progenitors are sorted and replated and maintained in astrocyte induction medium containing N2, heparin-binding EGF-like growth factor () and leukemia inhibitory factor () for a minimum of .
?. [Fluorescence-activated cell sorting analysis]
Dissociate cells with Accutase for  and resuspend in FACS buffer containing 1% BSA,  EDTA,  DNase I and Normocin in PBS.
?. [Fluorescence-activated cell sorting analysis]
Wash cells and incubate in FACS buffer with antibody for  at  in the dark.
on ice
4 °C
?. [Fluorescence-activated cell sorting analysis]
Wash cells and resuspend in FACS buffer and strained through  caps to eliminate cell clumps.
?. [Fluorescence-activated cell sorting analysis]
Perform data collection on a BD LSR II and FACSDiva v8. Gating (Supplementary Fig. 2), and complete subsequent analysis using FlowJo software v10.5.3.
?. [Droplet-based single-cell RNA-sequencing library preparation and sequencing]
Prepare four samples for single-cell sequencing at different days of the microglial differentiation: ‘day 6’ at day 6 of differentiation, ‘day 10’ at day 10 of differentiation, ‘day 10 suspension’, which only included cells in suspension at day 10 of differentiation, and ‘microglia’, which included end-stage microglial cells cultured with neurons for 14 d.
?. [Droplet-based single-cell RNA-sequencing library preparation and sequencing]
Prepare ‘Day-6’ and ‘day-10’ samples by treating cultures with Accutase for  to achieve a single-cell suspension.
?. [Droplet-based single-cell RNA-sequencing library preparation and sequencing]
Prepare ‘Day-10 suspension’ by collecting and straining cells in suspension through a  filter to achieve a single-cell suspension.
?. [Droplet-based single-cell RNA-sequencing library preparation and sequencing]
Prepare the ‘microglia’ sample by sorting co-culture models of neurons and microglia for expression of CX3CR1.
?. [Droplet-based single-cell RNA-sequencing library preparation and sequencing]
Resuspend all samples were at 1,000 cells per µl in FACS buffer before sequencing.
?. [Droplet-based single-cell RNA-sequencing library preparation and sequencing]
Perform single-cell sequencing using 10x Genomics Chromium Single Cell 3′ Library & Gel bead Kit V2 according to the manufacturer’s protocol.
?. [Droplet-based single-cell RNA-sequencing library preparation and sequencing]
Add an input of 8,700 cells to each 10x channel.
?. [Droplet-based single-cell RNA-sequencing library preparation and sequencing]
Sequence libraries on an Illumina NovaSeq device.
?. [Single-cell RNA-sequencing data preprocessing]
Process scRNA-seq data using the SEQC processing pipeline.
SEQC generates a cells-by-genes count matrix after read alignment, multimapping read resolution and cell barcode and unique molecular identifier correction.
?. [Single-cell RNA-sequencing data preprocessing]
SEQC included 3 steps:
?. [Single-cell RNA-sequencing data preprocessing]
Normalize counts for library size by dividing each gene molecule count by the total number of molecules detected in the cell, then multiplying by 10,000 to convert the original counts to transcripts per 10,000 reads.
?. [Single-cell RNA-sequencing data preprocessing]
Log transform data using natural log and a pseudocount of 1.
?. [Single-cell RNA-sequencing data preprocessing]
Step 1: Remove putative empty droplets based on the cumulative distribution of molecule counts for each barcode.
?. [Single-cell RNA-sequencing data preprocessing]
Step 2: Derive putative apoptotic cells based on >20% of molecules from the mitochondria.
?. [Single-cell RNA-sequencing data preprocessing]
Step 3: Identify low-complexity cells as cells where the detected molecules are aligned to a small subset of genes.
The number of cells per sample after SEQC processing was 5,253, 4,320, 5,555 and 4,961, and median library sizes were 19,195, 4,039, 10,126 and 16,716 molecules per cell (day 6, microglia, day 10 and day-10 suspension, respectively.
?. [Cell filtering]
For each sample, cluster cells using the PhenoGraph clustering algorithm.
?. [Cell filtering]
Remove clusters of cells with low numbers of detected genes (~200) as putative empty droplets.
?. [Cell filtering]
Remove clusters with high mitochondrial RNA and a low number of detected genes as putative dying cells.
?. [Cell filtering]
Remove four clusters not pertaining to hematopoietic differentiation, including two early mesoderm clusters that expressed low levels of MESP1 and PDGFRA but not KDR, PECAM1 or CDH5, one cluster belonging to the cardiac lineage expressing NKX2.5 and ISL1, and one cluster belonging to mature endothelial cells.
?. [Nearest neighbor graph construction]
Use principal components to calculate Euclidean distances between cells.
?. [Nearest neighbor graph construction]
Use an adaptive Gaussian kernel to convert Euclidean distances between cells’ k-nearest neighbors into affinities, as described by Haghverdi et al.
By using a Gaussian kernel, affinities between cells decrease exponentially with their distance, thereby increasing affinity to nearby cells and decreasing affinity to distant cells compared to the original Euclidean distances. Moreover, by using kernels with cell-adapted widths, differences in densities across regions of the data manifold are accounted for.
?. [Nearest neighbor graph construction]
Use nearest neighbor graphs as a basis for force-directed graph layouts and diffusion map embeddings.
?. [Clustering and force-directed graph layout]
Pool data from day 6, day 10 and day-10 suspension samples for trajectory modeling.
?. [Clustering and force-directed graph layout]
Perform principal-component analysis on the data, and select the first 20 principal components for further analyses, to reduce noise due to the high degree of dropouts in scRNA-seq.
?. [Clustering and force-directed graph layout]
Calculate force-directed graph layouts using the ForceAtlas2 algorithm54, based on the 30-nearest neighbors graph of the data that is constructed as described above.
?. [Clustering and force-directed graph layout]
Perform clustering with PhenoGraph, using default parameters.
?. [Diffusion map embedding]
To approximate the low-dimensional data manifold representing the differentiation trajectory,  construct a diffusion map embedding using an adaptive Gaussian kernel-based nearest neighbor graph (k=20; described above).
?. [Diffusion map embedding]
Construction of a diffusion map is a nonlinear method to recapitulate the low-dimensional structure underlying high-dimensional observations.
?. [Diffusion map embedding]
Select the first four diffusion components of the diffusion map for trajectory modeling.
?. [Diffusion map embedding]
Convert the diffusion distances between cells (that is, the Euclidean distances between cells in the ‘diffusion map space’) subsequently into pseudotime distances between individual cells as described by Haghverdi et al.
While distances in standard diffusion maps are related to a random Markov walk of length 1 along the edges of the ‘affinity graph’, diffusion distances in multiscale space generalize over random walks of all lengths, thereby better capturing phenotypic similarities and differences between cells.
?. [Trajectory characterization]
Use Palantir to further characterize the trajectory.
Palantir is a tool that, using pseudotime distances, identifies trajectory end points (‘terminal cells’) in data of differentiating cells and, moreover, measures entropy in cell phenotypes to measure their plasticity (‘differentiation potential’) and commitment to specific cell fates (‘branch probability’).
?. [Trajectory characterization]
As the input approximate start cell of the Palantir trajectory, use a random cell from the CDH5-high, KDR-high and PECAM1-high hemogenic endothelium clusters.
The number of neighbors was set to k=20, and the number of diffusion components was set to 4. For all other parameters, default settings were used.
?. [Calculation of gene trends over pseudotime]
To recover expression trends of individual genes over pseudotime, impute our processed count matrixusing MAGIC.
MAGIC is a method to denoise the cell count matrix and fill in zeros due to dropouts, by sharing information across similar cells via data diffusion. MAGIC is run with the number of neighbors set to k=40, the random walk length t=6, and further settings as default.
?. [Calculation of gene trends over pseudotime]
Based on the imputed count matrix, calculate gene trends using generalized additive models as describedby Setty et al.
?. [Calculation of gene trends over pseudotime]
Calculate Spearman correlations between unimputed gene counts and pseudotime (Supplementary Fig. 2b) per branch.
For each branch, only those cells that had a branch probability equal to or higher than the branchprobability at the start of the trajectory are included.
?. [Diffusion distance distributions]
To investigate whether cells in the microglia sample were still in the process of differentiating, inspect the distribution of pairwise diffusion distances among the cells.
In accordance with single-cell studies of cell differentiation, we assumed that differentiation happens asynchronously: different cells are assumed to be at different stages of differentiation at one point in time. Therefore, the transcriptomes of differentiating cells are expected to lie on an elongated manifold. In contrast, cells with a homogeneous phenotype are thought to be centered around one (multidimensional) mode, thus lying on an approximately sphere-shaped ‘manifold’. The distribution of pairwise distances between cells, calculated over multiple dimensions, is informative as to the shape of the manifold: if this distribution is unimodal, it suggests that the cells lie on aspherical manifold.
?. [Diffusion distance distributions]
Use a multiscaled diffusion map embedding (described earlier) of the cells to calculate pairwise distances between the cells.
Multiscaled diffusion maps are thought to capture the shape of the manifold well, while reducing levels of noise as compared to the nonembedded data.
?. [Diffusion distance distributions]
Calculate distances for different numbers (4–9) of diffusion components, to show robustness of the results to the number of components selected.
?. [Comparison to mouse gene signatures]
For comparison to previously published mouse signatures of EMPs and PMACs25, translate signature mouse gene names that had a one-to-one mouse–human ortholog (as defined by Ensembl BioMarts59) to human gene names.
All other genes were excluded from the analysis. We further excluded genes for which no transcripts were detected in our data. Heat maps showing pseudotime expression trends of signature genes (Figs. 2d and 3i) are based on MAGIC56 imputed expression values.
?. [Comparison to mouse gene signatures]
Normalize imputed gene expression for each gene to range between 0 and 1.
?. [Comparison to mouse gene signatures]
Include all cells that had a minimum myeloid branch probability of 0.1 in the heat map.
?. [Comparison to mouse gene signatures]
Order cells by pseudotime, and cluster genes using centroid clustering.
?. [Comparison to mouse gene signatures]
For the macrophage gene signature, append cells from the microglia sample to the trajectory cells and given an artificial pseudotime of 1.1.
?. [Integration into single-cell mouse embryogenesis atlas]
For integration of trajectory data into a recently published single-cell transcriptomics atlas of mouse gastrulationand early organogenesis, use data of all cells of the hematoendothelial lineage (15,875 cells).
Only genes with a one-to-one mouse–human ortholog (as described earlier) are included in the analysis. To further restrict organism-related bias, the gene set is limited to genes that were highly variable in the reference mouse data.
?. [Integration into single-cell mouse embryogenesis atlas]
Define highly variable genes as described by Satija et al.
The final number of genes included was 1,356.
?. [Integration into single-cell mouse embryogenesis atlas]
To perform further batch correction, use a fast implementation of mutual nearest neighbors batch correction: fastMNN (https://rdrr.io/github/LTLA/batchelor/man/fastMNN.html) performs batch correction on the principal-component matrix instead of the gene expression matrix.
?. [Integration into single-cell mouse embryogenesis atlas]
Use the first 20 principal components of the pooled data for batch correction.
?. [Integration into single-cell mouse embryogenesis atlas]
Perform batch correction among samples from within the same time point, after which batch correction is performed between time points.
The sample order used for fastMNN is: mouse data, late to early, then the human day-10 and day-10 suspension samples (pooled), and finally the day-6 sample.
?. [Integration into single-cell mouse embryogenesis atlas]
Calculate force-directed graph layout as described earlier.
?. [Integration into single-cell mouse embryogenesis atlas]
Construct a graph of the clusters of mouse and human data using PAGA.
?. [Integration into single-cell mouse embryogenesis atlas]
Use only graph edges with a weight of 0.2 or higher for the force-directed layout of the graph.
?. [Data analysis platform]
Use the SCANPY platform (v1.4) for data analysis.
?. [Immunohistochemistry, live/dead assay and high-content imaging]
Fix cells in 4% paraformaldehyde for  at , permeabilize with 0.1% Triton for , wash with 0.2% Tween-20 in PBS for  and block with 5% donkey serum in 0.2% Tween-20 in PBS for .
4 Room temperature
?. [Immunohistochemistry, live/dead assay and high-content imaging]
Dilute primary antibodies in blocking solution and incubate with the sample at
4 °C
?. [Immunohistochemistry, live/dead assay and high-content imaging]
Dilute secondary antibodies (Alexa Fluor 488, 555 and 647) in blocking solution and incubate with the sample at for .
4 Room temperature
?. [Immunohistochemistry, live/dead assay and high-content imaging]
Use DAPI stain to identify cell nuclei.
?. [Immunohistochemistry, live/dead assay and high-content imaging]
Perform the live/dead assay with CC3, with the control of hPSC-derived cortical neurons incubated with 70% methanol for .
Use ImageExpress Micro Confocal High-Content Imaging System to quantify microglial cell numbers in culture.
?. [Immunohistochemistry, live/dead assay and high-content imaging]
Take nine fields at ×5 magnification to scan an entire 96-well culture well.
?. [Engulfment of synaptic proteins imaging]
Co-culture microglia with day-70+ neurons on culture dishes (Ibidi) for up to  and stain with PSD95 and IBA1 ( in total).
?. [Engulfment of synaptic proteins imaging]
Image cultures on the Leica SP8 confocal microscope equipped with white-light laser technology and standard argon lasers (458, 476, 488, 496 and 514 nm) at ×40 magnification.
?. [Engulfment of synaptic proteins imaging]
Process and analyze data with Imaris v9.2: a surface volume mask is generated in the IBA1 channel, within which another mask is generated for the PSD95 channel to determine the volume of PSD95 inclusions/volume of IBA1 in a given z-stack.
?. [Phagocytosis assay and surveying assay]
For the phagocytosis assay, incubate microglial cells or astrocyte controls with Zymosan A bioparticles conjugated with Alexa Fluor 488 for  in an Olympus VivaView fluorescence incubator microscope.
?. [Phagocytosis assay and surveying assay]
For the surveying assay, infect microglial cells with a lentiviral construct expressing GFP and co-culture with day-50 cortical neurons for , then incubate in an Olympus VivaView fluorescence incubator microscope for .
?. [Phagocytosis assay and surveying assay]
Compile time-lapse imaging at  per frame.
?. [RNA sequencing]
Sort approximately 50,000-100,000 hPSC-derived microglia from three different hPSC lines, H1, H9 and the WT iPSC line SA241-1, from neuronal co-cultures by expression of CX3CR1.
?. [RNA sequencing]
Extract RNA using the Zymo RNA Micro Kit.
?. [RNA sequencing]
Obtain RNA from primary human microglia after sorting postmortem tissue from the frontal and temporal lobes from patients aged 60-77 years.
?. [RNA sequencing]
Submit all samples to the Memorial Sloan Kettering Cancer Center (MSKCC) Integrated Genomics Core for paired-end SMARTER sequencing and 30-40 million reads.
?. [RNA sequencing]
Perform analysis via a standard pipeline through the MSKCC Bioinformatics core: FASTQ files were mapped using the rnaSTAR aligner.
?. [RNA sequencing]
Process Output SAM files using PICARD tools.
?. [RNA sequencing]
Process the mapped reads using HTSeq to compute a raw expression count matrix, which is then processed using DESeq from R/BioConductor to analyze differential expression between samples.
?. [Primary human microglia]
Provide brain tissue by the Netherlands Brain Bank (NBB).
Informed consent for brain autopsy, the use of tissue and use of clinical information is obtained premortem. The procedures of the NBB are in accordance with all national laws and the procedures have been approved by the ethics committee of the VU University Medical Center (Amsterdam, Netherlands).
?. [Primary human microglia]
Collect brain tissues from one male and three female donors without a history of a neurological or psychiatric disorder in the age range of 60–77 years.
?. [Primary human microglia]
Isolate microglia from the medial frontal gyrus and temporal superior gyrus as described before.
?. [Primary human microglia]
In short, collect -of tissue within  after death and store in Hibernate medium , and start the isolation procedure within - after autopsy.
2 g
10 g
on ice
?. [Primary human microglia]
Generate a single-cell suspension by dissociating the tissue mechanically through a metal sieve, followed by an enzymatic digestion using collagenase type I (; Worthington) and DNase I (; Roche).
3000
?. [Primary human microglia]
Purify microglia using a Percoll gradient and positive selection using CD11b-conjugated magnetic microbeads (Miltenyi Biotec).
?. [Primary human microglia]
Lyse Microglia in RLT buffer (QIAGEN), and extract RNA according to the protocol provided by the RNeasy Mini Kit (QIAGEN)
?. [Gene ontology analysis and gene-set enrichment analysis]
Perform GO analysis using http://geneontology.org.
The analysis type is PANTHER overrepresentation test (released 04 April 2020), the reference list was Homo Sapiens (all genes in database), the annotation dataset was GO biological processcomplete, and the test type is Fisher with false discovery rate correction.
?. [Gene ontology analysis and gene-set enrichment analysis]
Perform GSEA using the GSEA software (v4.0.3) from the Broad Institute, using the clusters defined in Matcovitch-Natan et al. defining microglial developmental stages.
?. [Comparison with publicly available microglial datasets]
Integrate publicly available datasets from the following studies according to the methodology by Grubman et al. before MDS.
The following datasets were included: Muffat et al. (GSE85839, 8 samples), Douvaras et al. (GSE97744, 14 samples), Abud et al. (GSE89189, 12 samples), Ormel et al. (GSE102335, 9 samples) and Galatro et al. (GSE99074, 38 samples).
?. [Comparison with publicly available microglial datasets]
Integrate a total of 17 samples from our study (13 hPSC-derived microglia and 4 adult primary microglia).
?. [Comparison with publicly available microglial datasets]
Briefly, convert count matrices to log2 reads per kilobase million, and remove the differences between batches using the removeBatchEffect from limma, specifying each dataset as a batch, as well as specifying common groups as design (adultmg, hPSC-microglia and fetal microglia).
?. [Comparison with publicly available microglial datasets]
Perform MDS using the plotMDS function from limma.
?. [Tri-culture system, co-culture ratios and LPS assay]
Differentiate cortical neurons from hPSCs and replate at 200,000 cells per cm2 on plates coated with poly-l-ornithine/fibronectin/laminin and allow to mature for - in NB/BAGC.
?. [Tri-culture system, co-culture ratios and LPS assay]
Dissociate astrocytes that differentiated from hPSCs with Accutase for - and then plate on top of the neurons at 25,000 cells per cm2 and are allowed to settle for in NB/BAGC.
?. [Tri-culture system, co-culture ratios and LPS assay]
Dissociate microglia that differentiated from hPSCs with Accutase for  and plate on topof the astrocyte/neuron culture at 50,000 cells per cm2 in NB/BAGC with IL-34 () and M-CSF ().
?. [Tri-culture system, co-culture ratios and LPS assay]
Change medium every other day with fresh addition of IL-34 and M-CSF.
Other co-cultures included microglia and neurons (50,000 microglia per cm2 and 200,000 neurons per cm2), astrocytes and neurons (25,000 astrocytes per cm2 and 200,000 neurons per cm2) and neurons only (200,000 neurons per cm2).
?. [Tri-culture system, co-culture ratios and LPS assay]
After culture for a minimum of ,  add LPS at  for .
?. [Tri-culture system, co-culture ratios and LPS assay]
Collect culture medium and spun down at  for .
Centrifuge: 2000 33
?. [Tri-culture system, co-culture ratios and LPS assay]
Freeze the supernatant at  until further analysis.
-80 °C
?. [Cytokine ELISA]
Following the manufacturer’s protocols, analyze culture supernatants for C3 and C1Q using the Millipore Luminex Multiplex Kit on the FlexMap 3D system.
?. [Cytokine ELISA]
Send supernatants from the ±LPS assay, as well as the C3+/- assay on WT and C3 KO microglia, to Eve Technologies for multiplexed analysis of 14 inflammatory cytokines using the human high-sensitivity T cell discovery array 14-plex.
?. [Conditioned medium assays]
Harvest medium from hPSC-derived microglia–neuron co-cultures after  to represent MCM.
?. [Conditioned medium assays]
Add MCM to astrocyte-neuron co-cultures for  before the cells are harvested for C3 expression analysis by qPCR.
?. [Conditioned medium assays]
Harvest medium from hPSC-derived astrocyte–neuron co-cultures after  to ACM.
?. [Conditioned medium assays]
Add ACM to microglia-neuron co-cultures for  before the cells are harvested for C3 expression analysis by qPCR.
?. [Cytokine addition assays]
Add human C3 (Millipore; ) to microglia-neuron or astrocyte-neuron co-cultures for  before cells are harvested for C3 expression analysis.
?. [Cytokine addition assays]
Add human TNF-α (R&D; ) and IL-6 (R&D; ) to astrocyte-neuron co-cultures for  before cells are harvested for C3 expression analysis.
?. [Cell line engineering]
CRISPR–Cas9 knockout of C3. The PX458 vector65 containing the guide 5′ TCTGCACTATCCAGGTA 3′ is nucleofected into H1 hESCs.
?. [Cell line engineering]
Sort cells on the basis of GFP expression and culture as single-cell clones in E8 medium with the cloneR supplement.
?. [Cell line engineering]
Pick clones onto replicate plates, and extract genomic DNA using Bradley Lysis Buffer and Proteinase K treatment.
?. [Cell line engineering]
Amplify a 450-bp PCR product around the guide RNA cut site, and screen the clones for indels by Sanger sequencing.
?. [Cell line engineering]
Pick and expand, karyotype and differentiate clones with indels subsequently into microglia for further validation by ELISA for a lack of C3 protein secretion.
?. [Cell line engineering]
Generate AD lines. APPSWE+/+ and WT iCas9 lines using the guide RNA and oligonucleotides from Paquet et al. in an H9 background.
?. [Primers]
Primers used for qPCR included: C1QA (QIAGEN QT00997745), C3 (F 5′ AAAAGGGGCGCAACAAGTTC 3′, R 5′ GATGCCTTCCGGGTTCTCAA 3′), CX3CR1 (F 5′ TGGGGCCTTCACCATGGAT 3′, R 5′ GCCAATGGCAAAGATGACGGAG 3′), TMEM119 (F 5′ CTTCCTGGATGGGATAGTGGAC 3′, R 5′ GCACAGACGATGAACATCAGC 3′), P2RY12 (F 5′ AAGAGCACTCAAGACTTTAC 3′, R 5′ GGGTTTGAATGTATCCAGTAAG 3′) and GPR34 (F 5′ GAAGACAATGAGAAGTCATACC 3′, R 5′ TGTTGCTGAGAAGTTTTGTG 3′).
?. [Statistics and reproducibility]
All data presented in this study are representative of at least three independent experiments, including IF panels, ELISA results and FACS data. Western blots and gel electrophoresis are repeated at least twice. No statistical methods are used to predetermine sample sizes, but oursample sizes are similar to those reported in previous publications. Cells are randomly assigned into experimental culture groups (TRI, M/N, A/N and N) after accounting for their respective genotypes (APPSWE+/+, WT and C3KO). Data collection and analysis are not performed blind to the conditions of the experiments because all quantified data are collected in an automated fashion (ELISA, FACS and ImageExpress) such that bias would not be introduced. Data distribution is assumed to be normal, but this was not formally tested. Data are presented as the mean±s.d. All statistical analyses are performed using GraphPad Prism v.8.0.1: one-way ANOVA with Tukey’s post hoc or Sidak’s tests to compare multiple groups, two-way ANOVA with Tukey’s or Bonferroni post hoc tests to compare multiple groups with two independent variables, and an unpairedtwo-tailed Student’s t-test to compare two groups. Statistical differences are considered significant with P(female) and 84 (female) years.