Research ArticleNeurodegeneration

A human microglia-like cellular model for assessing the effects of neurodegenerative disease gene variants

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Science Translational Medicine  20 Dec 2017:
Vol. 9, Issue 421, eaai7635
DOI: 10.1126/scitranslmed.aai7635
  • Fig. 1 Differentiation of human peripheral blood monocytes into MDMi cells induces a microglial gene expression and functional phenotype.

    Human peripheral blood monocytes from young, healthy subjects were incubated with cytokines and differentiated into monocyte-derived microglia-like (MDMi) cells. (A) The cell type–specific enriched gene expression for MDMi cells, ex vivo human microglia (HuMG), ex vivo murine microglia (P60MG), and human embryonic and induced pluripotent stem cell–derived microglia (ESC/iPSC)–derived microglia (pMGL) were compared. (B) Four genes defined as being microglia-specific in mice were significantly up-regulated in MDMi cells (TGFβR1: ****P < 0.0001; PROS1: ***P = 0.0005; C1QB: ***P = 0.0007; P2RX7: **P = 0.0058) at day 10 of differentiation compared to freshly isolated monocytes and MDM cells from the same five individuals. Gene expression was quantified using RNA sequencing and expressed as fragments per kilobase of transcript per million (FPKM). One-way analysis of variance (ANOVA) with Tukey’s post hoc test. (C) P2RY12 and TMEM119 proteins were more highly expressed in MDMi cells compared to monocytes. (D) MDMi cells functionally mimicked human microglia in response to conditions that led to either an M1 or M2 phenotype. Under M1 conditions, MDMi cells expressed significantly more interleukin-10 (IL-10) mRNA (**P < 0.01) compared to MDM cells from the same individuals. Student’s t test, n = 14. For (B) and (D), each dot represents a biological replicate. Horizontal line denotes the mean. DAPI, 4′,6-diamidino-2-phenylindole.

  • Fig. 2 Genotype-induced differential gene expression is different in MDMi cells compared to monocytes.

    Using a Fluidigm high-throughput quantitative polymerase chain reaction (qPCR) chip, we measured the expression of 94 genes found in loci associated with susceptibility to Alzheimer’s disease (AD), multiple sclerosis (MS), or Parkinson’s disease (PD) in MDMi cells differentiated from the peripheral blood monocytes of 95 young, healthy subjects of European ancestry with genome-wide genotype data available. (A) Manhattan plot of expression quantitative trait locus (eQTL) results for the 94 genes measured in MDMi cells. n = 95 biological replicates per gene. Each dot represents one single-nucleotide polymorphism (SNP); selected SNPs include all of those found within 1 Mb of the transcription start site of the profiled gene. The x axis denotes the physical position of the SNP, and the y axis reports the significance of the SNP’s association with the expression of the nearby gene (eQTL result). The red line highlights the threshold of significance in our analysis. (B) We compared our MDMi eQTL results to those of our previously published monocyte eQTL results derived from n = 211 young, healthy subjects of European ancestry (13). Left: We plotted the top eQTL SNP for each gene in the MDMi data; the x axis reports the absolute value of the effect size (ρ) of the SNP in MDMi cells; the ρ in the monocyte data is presented on the y axis. The threshold of significance [false discovery rate (FDR) < 0.05] is shown by dotted lines in each dimension. The light-red quadrant contains those loci with consistent effects in both cell types, whereas the light-green quadrant contains those loci that had a significant association only in MDMi cells. The light-blue quadrant contains those loci with an effect in monocytes that was not significant in our current MDMi analysis. Right: The best SNP for each locus in the monocyte data. (C) Locus zoom plots highlight the regional distribution of associations in two loci, CD37 and CR1, which have very different eQTL associations in MDMi cells compared to monocytes. Each dot is one SNP in these figures, with the physical position captured on the x axis and the eQTL significance on the y axis. The location of genes in this locus is shown below the SNPs. The top eQTL SNP for the MDMi data is shown as a purple diamond, and the other SNPs are colored by the extent of linkage disequilibrium (r2) with the lead SNP. In the monocyte plots, the SNP colors are defined by the lead MDMi SNP, highlighting the fact that the haplotype driving association in MDMi cells does not have a strong effect in monocytes. MDMi cells n = 95 biological replicates, monocytes n = 211 biological replicates.

  • Fig. 3 Association of disease-specific GWAS SNPs with gene expression in MDMi cells differs from that in monocytes.

    Each gene selected for the Fluidigm experiment was in a locus associated with AD, PD, or MS. (A) Example of a cis-eQTL shared between monocytes and MDMi cells (top). The association of RGS1 expression with the MS risk allele rs1323292G was similar in both cell types. With PTK2B (bottom), the AD-associated rs28834970C risk allele had an effect in monocytes but not in MDMi cells. n = 95 (MDMi), n = 211 (monocytes). (B) A significant eQTL was found in MDMi cells for the PILRB gene (P = 0.00084), illustrated with the AD SNP rs1476679; this was not seen in the monocyte data set (P = 0.19) (top). n = 95 (MDMi cells), n = 211 (monocytes). This finding was replicated in an independent set of 37 individuals for RNA expression (P = 0.0034) using TaqMan PCR (middle row) and 34 individuals for protein expression (P = 0.0112) (bottom row). One-way ANOVA with Tukey’s post hoc test. Each dot represents a biological replicate. Horizontal line denotes the mean. GWAS, genome-wide association studies. Mean fluorescence intensity, MFI.

  • Fig. 4 Significant association in MDMi cells between LRRK2 gene expression and the PD SNP rs7690479.

    (A) The PD GWAS rs76904798T risk allele was associated with increased LRRK2 expression in MDMi cells (right) (P < 8.92 × 10−7); there was a nonsignificant trend in the monocyte data (left) (P < 1.77 × 10−2). (B) Three regional association plots illustrate the colocalization of the PD susceptibility haplotype, tagged by rs76904798, and the eQTL haplotype in MDMi cells but not in monocytes. Top: Data from monocytes: Each dot is one SNP and is colored in relation to the extent of linkage disequilibrium (r2) with the lead PD SNP (rs76904798). The color key is presented to the right of the panels. The x axis presents the physical position of the SNP, and the y axis presents the association between the SNP and the amount of LRRK2 expression. Middle: The same set of SNPs is presented; here, the association P value is derived from the relation of each SNP to LRRK2 expression in MDMi cells. Bottom: The results of the published PD GWAS (44) are presented for the same set of SNPs.

  • Fig. 5 Differential expression of CD33 isoforms in MDMi cells.

    (A) In our model system, we found a significant effect of genotype on CD33 expression. We observed an increase in full-length CD33M mRNA expression (***P = 0.0009) and a decrease in truncated CD33m mRNA expression (****P < 0.0001) in MDMi cells in a dose-dependent fashion relative to the rs3865444C risk allele in the Fluidigm data set. One-way ANOVA with Tukey’s post hoc test, n = 95. (B) Western blot analysis showed a significant effect of genotype on CD33M protein expression in monocytes (*P = 0.034) and MDMi cells (**P = 0.009) from a second set of individuals (n = 16). However, a significant effect of genotype on CD33m protein expression was only observed for MDMi cells (*P = 0.016) and not for monocytes (P = 0.124). A representative Western blot displays the data from four subjects, with each subject’s sample run in a separate lane. The genotype of the subject is included at the top of each lane. (C) The genotype-dependent difference in CD33 surface expression (P = 0.001) and (D) fluorescein isothiocyanate–dextran (DEX) uptake (P = 0.047) were further confirmed in MDMi cells using high content imaging. Each dot represents a biological replicate. Student’s t test was performed for (B) to (D). ns, not significant.

  • Table 1 Disease-associated cis-eQTLs in MDMi cells.
    SNPGeneChromosomeMDMiMDMi P valueMonocyteMonocyte P
    value
    DiseaseMDMi-
    targeted FDR
    rs3865444CD33_SHORT19−0.6881.39 × 10−14−0.254*1.96 × 10−14*AD1.38 × 10−12
    CD33_LONG0.3397.91 × 10−41.30 × 10−2
    rs1476679PILRB7−0.3378.42 × 10−4−0.091.90 × 10−1AD1.30 × 10−2
    rs10838725NUP16011−0.3831.29 × 10−4−0.2414.08 × 10−4AD3.22 × 10−3
    rs76904798LRRK212−0.4798.92 × 10−7−0.1631.77 × 10−2PD2.96 × 10−5
    rs1323292RGS110.5168.62 × 10−80.4866.33 × 10−14MS4.30 × 10−6
    rs701006METTL21B120.3359.14 × 10−40.4134.18 × 10−10MS1.30 × 10−2

    *Total CD33 (includes CD33_Long and CD33_Short).

    Supplementary Materials

    • www.sciencetranslationalmedicine.org/cgi/content/full/9/421/eaai7635/DC1

      Materials and Methods

      Fig. S1. Higher expression of P2RY12 and TMEM119 in MDMi cells compared to monocytes.

      Fig. S2. The proapoptotic gene BAX is increased in monocytes and MDM cells compared to MDMi cells.

      Fig. S3. Many SNPs have differential eQTLs in MDMi cells compared to monocytes.

      Table S1. Differentially expressed genes in MDMi cells compared to bulk brain tissue.

      Table S2. Cis-eQTLs in MDMi cells.

      Table S3. LD-pruned list of cis-eQTLs in MDMi cells.

      References (7277)

    • Supplementary Material for:

      A human microglia-like cellular model for assessing the effects of neurodegenerative disease gene variants

      Katie J. Ryan, Charles C. White, Kruti Patel, Jishu Xu, Marta Olah, Joseph M. Replogle, Michael Frangieh, Maria Cimpean, Phoebe Winn, Allison McHenry, Belinda J. Kaskow, Gail Chan, Nicole Cuerdon, David A. Bennett, Justin D. Boyd, Jaime Imitola, Wassim Elyaman, Philip L. De Jager, Elizabeth M. Bradshaw*

      *Corresponding author. Email: emb2280{at}cumc.columbia.edu

      Published 20 December 2017, Sci. Transl. Med. 9, eaai7635 (2017)
      DOI: 10.1126/scitranslmed.aai7635

      This PDF file includes:

      • Materials and Methods
      • Fig. S1. Higher expression of P2RY12 and TMEM119 in MDMi cells compared to monocytes.
      • Fig. S2. The proapoptotic gene BAX is increased in monocytes and MDM cells compared to MDMi cells.
      • Fig. S3. Many SNPs have differential eQTLs in MDMi cells compared to monocytes.
      • Legends for table S1 to S3
      • References (7277)

      [Download PDF]

      Other Supplementary Material for this manuscript includes the following:

      • Table S1. (Microsoft Excel format). Differentially expressed genes in MDMi cells compared to bulk brain tissue.
      • Table S2. (Microsoft Excel format). Cis-eQTLs in MDMi cells.
      • Table S3. (Microsoft Excel format). LD-pruned list of cis-eQTLs in MDMi cells.

      [Download Tables S1 to S3]

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