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Alzheimer’s Risk Gene TREM2 Determines Functional Properties of New Type of Human iPSC-Derived Microglia

Reich, Marvin, Paris, Iñaki, Ebeling, Martin, Dahm, Nadine, Schweitzer, Christophe, Reinhardt, Dieter, Schmucki, Roland, Prasad, Megana, Köchl, Fabian, Leist, Marcel, Cowley, Sally A., Zhang, Jitao David, Patsch, Christoph, Gutbier, Simon, Britschgi, Markus·2021·Frontiers in Immunology
DOI 10.3389/fimmu.2020.617860PMC PMC7887311
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Abstract

Microglia are key in the homeostatic well-being of the brain and microglial dysfunction has been implicated in neurodegenerative disorders such as Alzheimer’s disease (AD). Due to the many limitations to study microglia in situ or isolated for large scale drug discovery applications, there is a high need to develop robust and scalable human cellular models of microglia with reliable translatability to the disease. Here, we describe the generation of microglia-like cells from human induced pluripotent stem cells (iPSC) with distinct phenotypes for mechanistic studies in AD. We started out from an established differentiation protocol to generate primitive macrophage precursors mimicking the yolk sac ontogeny of microglia. Subsequently, we tested 36 differentiation conditions for the cells in monoculture where we exposed them to various combinations of media, morphogens, and extracellular matrices. The optimized protocol generated robustly ramified cells expressing key microglial markers. Bulk mRNA sequencing expression profiles revealed that compared to cells obtained in co-culture with neurons, microglia-like cells derived from a monoculture condition upregulate mRNA levels for Triggering Receptor Expressed On Myeloid Cells 2 (TREM2), which is reminiscent to the previously described disease-associated microglia. TREM2 is a risk gene for AD and an important regulator of microglia. The regulatory function of TREM2 in these cells was confirmed by comparing wild type with isogenic TREM2 knock-out iPSC microglia. The TREM2-deficient cells presented with stronger increase in free cytosolic calcium upon stimulation with ATP and ADP, as well as stronger migration towards complement C5a, compared to TREM2 expressing cells. The functional differences were associated with gene expression modulation of key regulators of microglia. In conclusion, we have established and validated a work stream to generate functional human iPSC-derived microglia-like cells by applying a directed and neuronal co-culture independent differentiation towards functional phenotypes in the context of AD. These cells can now be applied to study AD-related disease settings and to perform compound screening and testing for drug discovery.

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Figure 1 Approaches employed to differentiate human iPSC towards microglia-like cells. (A) Schematic diagram of the protocols to generate different microglia-like cells from human iPSC. Relevant growth factors and durations (blue) of differentiation steps are indicated. The upper part depicts the differentiation of iPSCs into “myeloid factories” that produce macrophage precursor cells (pre-Macs) (27). For the generation of monocultured iPSC-derived microglia, pre-Macs were differentiated in the presence of IL-34, M-CSF, and TGF-β1. The lower part depicts the differentiation steps from iPSCs to neural stem cells and further to neurons. For the generation of neural co-culture-derived microglia, pre-Macs were seeded on top of the neuron/astrocyte cultures. (B) Representative images of immunostaining of iPSC microglia after 24 days differentiation in monoculture. Monoculture-derived microglia-like cells stain positive for Iba1 (cyan), P2Y12 (yellow), and TREM2 (red). Cellular nuclei were labeled with Hoechst 33342 (blue). (C) Immunostaining of microglia-like cells, neurons and astrocytes after 14 days differentiation in co-culture. Representative images are shown from three biological replicates. Microglia-like cells are detected by Iba1 (orange), astrocytes by GFAP (green), and neurons by TuJ1 (red), respectively. Cellular nuclei were labeled with Hoechst 33342 (blue).
Figure 1 Approaches employed to differentiate human iPSC towards microglia-like cells. (A) Schematic diagram of the protocols to generate different microglia-like cells from human iPSC. Relevant growth factors and durations (blue) of differentiation steps are indicated. The upper part depicts the differentiation of iPSCs into “myeloid factories” that produce macrophage precursor cells (pre-Macs) (27). For the generation of monocultured iPSC-derived microglia, pre-Macs were differentiated in the presence of IL-34, M-CSF, and TGF-β1. The lower part depicts the differentiation steps from iPSCs to neural stem cells and further to neurons. For the generation of neural co-culture-derived microglia, pre-Macs were seeded on top of the neuron/astrocyte cultures. (B) Representative images of immunostaining of iPSC microglia after 24 days differentiation in monoculture. Monoculture-derived microglia-like cells stain positive for Iba1 (cyan), P2Y12 (yellow), and TREM2 (red). Cellular nuclei were labeled with Hoechst 33342 (blue). (C) Immunostaining of microglia-like cells, neurons and astrocytes after 14 days differentiation in co-culture. Representative images are shown from three biological replicates. Microglia-like cells are detected by Iba1 (orange), astrocytes by GFAP (green), and neurons by TuJ1 (red), respectively. Cellular nuclei were labeled with Hoechst 33342 (blue).CC BY · PubMed Central
Figure 2 Comparison of gene expression profiles in mono- and co-culture-derived microglia. (A) Principal component analysis of complete expression profiles of macrophage precursors (pre-MACS), mono- and co-cultured microglia-like cells. The lack of any intersection between the ellipses (95% confidence interval) indicates a clear statistically significant separation between the gene patterns of the different cell types. (B) MA plot to visualize gene expression differences between mono- and co-culture-derived microglia-like cells. The MA plot is based on gene expression levels measured in log2(rpkm), the logarithm to the base of two of the reads per kilobase of transcript per million reads sequenced. The x-axis shows, for every gene, the average expression value between the two conditions, on the y-axis is, for every gene, the difference between the two expression levels. Some strongly affected genes are highlighted in yellow, with gene names specified. (C) Radar plot to visualize changes in gene expression in microglial modules. The red circle indicates the reference level as detected in co-cultured microglia. Peaks outside the red circle indicate higher expression in monoculture microglia and modules closer to the center indicate higher expression in microglia generated in co-culture with neurons. (D) Heatmap of the genes belonging to the DAM or interferon module in co- and monoculture derived microglia. Expression values were standardized and depicted on a z-scale with red indicating high and blue low expression, respectively. The five red and five blue boxes on top of the heatmap indicate the two different culture conditions, respectively, and that each culture condition was performed in five independent experiments. The downregulation of the “interferon” module and the upregulation of “DAM” modules is consistent between different experiments as indicated by the similar gene expression patterns between different biological replicates. This confirms the robustness of our new protocol.
Figure 2 Comparison of gene expression profiles in mono- and co-culture-derived microglia. (A) Principal component analysis of complete expression profiles of macrophage precursors (pre-MACS), mono- and co-cultured microglia-like cells. The lack of any intersection between the ellipses (95% confidence interval) indicates a clear statistically significant separation between the gene patterns of the different cell types. (B) MA plot to visualize gene expression differences between mono- and co-culture-derived microglia-like cells. The MA plot is based on gene expression levels measured in log2(rpkm), the logarithm to the base of two of the reads per kilobase of transcript per million reads sequenced. The x-axis shows, for every gene, the average expression value between the two conditions, on the y-axis is, for every gene, the difference between the two expression levels. Some strongly affected genes are highlighted in yellow, with gene names specified. (C) Radar plot to visualize changes in gene expression in microglial modules. The red circle indicates the reference level as detected in co-cultured microglia. Peaks outside the red circle indicate higher expression in monoculture microglia and modules closer to the center indicate higher expression in microglia generated in co-culture with neurons. (D) Heatmap of the genes belonging to the DAM or interferon module in co- and monoculture derived microglia. Expression values were standardized and depicted on a z-scale with red indicating high and blue low expression, respectively. The five red and five blue boxes on top of the heatmap indicate the two different culture conditions, respectively, and that each culture condition was performed in five independent experiments. The downregulation of the “interferon” module and the upregulation of “DAM” modules is consistent between different experiments as indicated by the similar gene expression patterns between different biological replicates. This confirms the robustness of our new protocol.CC BY · PubMed Central
Figure 3 Differences in gene expression in wild type and TREM2 KO monoculture-derived iPSC microglia. (A) Principal component analysis of complete expression profile of monoculture derived microglia-like cells generated from wild type (WT) and TREM2 knockout (KO) iPSC lines. The lack of intersection between the ellipses (95% confidence interval) indicate a statistically significant separation between the gene patterns of the two cell types while the fact that the ellipses seem to touch each other indicate only a minimal separation in the PCA. (B) MA plot to visualize gene expression differences between WT and TREM2 KO monoculture iPSC microglia. The MA plot is based on gene expression levels measured in log2(rpkm), the logarithm to the base of two of the reads per kilobase of transcript per million reads sequenced. The x-axis shows, for every gene, the average expression value between the two conditions, on the y-axis is, for every gene, the difference between the two expression levels. Genes highlighted in yellow are part of the TREM2-dependent DAM signature genes according to Keren-Shaul et al. (38). (C) Radar plot to visualize changes in gene expression in microglial modules. The red circle indicates the reference level as detected in WT iPSC microglia. Peaks outside the red circle indicate higher expression in TREM2 KO microglia and modules closer to the center indicate higher expression in microglia generated from the WT iPSC line. (D) Heatmap of the genes belonging to the DAM module in WT and KO monoculture microglia. Expression values were standardized and depicted as z-scores with red indicating high and blue low expression, respectively. The five red and five blue boxes on top of the heatmap indicate the two different cell types, respectively, and that each cell type was analyzed in five independent experiments. The gene expression patterns for each cell type appear consistent between different experiments. This confirms the robustness of our new protocol for WT and TREM2 KO iPSC microglia.
Figure 3 Differences in gene expression in wild type and TREM2 KO monoculture-derived iPSC microglia. (A) Principal component analysis of complete expression profile of monoculture derived microglia-like cells generated from wild type (WT) and TREM2 knockout (KO) iPSC lines. The lack of intersection between the ellipses (95% confidence interval) indicate a statistically significant separation between the gene patterns of the two cell types while the fact that the ellipses seem to touch each other indicate only a minimal separation in the PCA. (B) MA plot to visualize gene expression differences between WT and TREM2 KO monoculture iPSC microglia. The MA plot is based on gene expression levels measured in log2(rpkm), the logarithm to the base of two of the reads per kilobase of transcript per million reads sequenced. The x-axis shows, for every gene, the average expression value between the two conditions, on the y-axis is, for every gene, the difference between the two expression levels. Genes highlighted in yellow are part of the TREM2-dependent DAM signature genes according to Keren-Shaul et al. (38). (C) Radar plot to visualize changes in gene expression in microglial modules. The red circle indicates the reference level as detected in WT iPSC microglia. Peaks outside the red circle indicate higher expression in TREM2 KO microglia and modules closer to the center indicate higher expression in microglia generated from the WT iPSC line. (D) Heatmap of the genes belonging to the DAM module in WT and KO monoculture microglia. Expression values were standardized and depicted as z-scores with red indicating high and blue low expression, respectively. The five red and five blue boxes on top of the heatmap indicate the two different cell types, respectively, and that each cell type was analyzed in five independent experiments. The gene expression patterns for each cell type appear consistent between different experiments. This confirms the robustness of our new protocol for WT and TREM2 KO iPSC microglia.CC BY · PubMed Central
Figure 4 Differential mitochondrial respiratory activity in TREM2 KO versus wild type iPSC microglia. WT and TREM2 KO monoculture iPSC microglia-like cells were re-plated after 14 days of differentiation into fibronectin pre-coated seahorse assay plates (A–E). (A) The baseline oxygen consumption rate (OCR) of WT and TREM2 KO cells was assessed, followed by the sequential injection of different indicated mitochondrial electron transport chain complex inhibitors (oligomycin and a combination of rotenone and antimycin A) and the mitochondrial uncoupler carbonyl cyanide-4-(trifluoromethoxy)phenylhydrazone (FCCP). OCR was measured three times initially and after each injection. (B) The basal respiration was calculated by subtracting the OCR of non-mitochondrial respiration (OCR after rotenone/antimycin A injection) from the baseline OCR. (C) The spare respiratory capacity results from the subtraction of the basal respiration from the maximal respiration [(OCR after FCCP injection) – (non-mitochondrial respiration)]. (D) ATP production is the OCR of the basal respiration with the proton leak subtracted. (E) The proton leak is the remaining OCR after injection with oligomycin without non-mitochondrial respiration. n=3; Data shown as mean with standard deviation. Statistical analysis was performed using t-test. (ns, p ≥ 0.05; ***p ≤ 0.001; ****p ≤ 0.0001).
Figure 4 Differential mitochondrial respiratory activity in TREM2 KO versus wild type iPSC microglia. WT and TREM2 KO monoculture iPSC microglia-like cells were re-plated after 14 days of differentiation into fibronectin pre-coated seahorse assay plates (A–E). (A) The baseline oxygen consumption rate (OCR) of WT and TREM2 KO cells was assessed, followed by the sequential injection of different indicated mitochondrial electron transport chain complex inhibitors (oligomycin and a combination of rotenone and antimycin A) and the mitochondrial uncoupler carbonyl cyanide-4-(trifluoromethoxy)phenylhydrazone (FCCP). OCR was measured three times initially and after each injection. (B) The basal respiration was calculated by subtracting the OCR of non-mitochondrial respiration (OCR after rotenone/antimycin A injection) from the baseline OCR. (C) The spare respiratory capacity results from the subtraction of the basal respiration from the maximal respiration [(OCR after FCCP injection) – (non-mitochondrial respiration)]. (D) ATP production is the OCR of the basal respiration with the proton leak subtracted. (E) The proton leak is the remaining OCR after injection with oligomycin without non-mitochondrial respiration. n=3; Data shown as mean with standard deviation. Statistical analysis was performed using t-test. (ns, p ≥ 0.05; ***p ≤ 0.001; ****p ≤ 0.0001).CC BY · PubMed Central
Figure 5 Functional differences in TREM2 KO versus wild type iPSC microglia. (A–C) Intracellular calcium kinetics following different stimuli. (A) Intracellular calcium levels upon stimulation with different concentrations of ATP. Maximum measured fluorescence is indicated as relative fluorescence units (RFU), which are baseline corrected (n = 3). Data shown as mean with standard error of the mean. Statistical analysis of two data sets was performed using a two-way ANOVA. (B) Intracellular calcium levels upon treatment with different concentrations of ADP. Maximum measured fluorescence is indicated as relative fluorescence units (RFU), which are baseline corrected (n = 3). Data shown as mean with standard error of the mean Statistical analysis of two data sets was performed using a two-way ANOVA(****p < 0.0001). (C) Intracellular calcium levels upon treatment of different concentrations of complement component 5a (C5a). Maximum measured fluorescence is indicated as relative fluorescence units (RFU), which are baseline corrected (n = 3). Data shown as mean with standard error of the mean. Statistical analysis of two data sets was performed using a two-way ANOVA (****p < 0.0001). (D) Migration speed of WT and TREM2 KO monoculture microglia in presence or absence of 1 ng/ml C5a as chemoattractant in the bottom compartment. Migration speed was calculated from the increase in iPSC microglia occupied area over time in the bottom compartment of the transwell and expressed relative to untreated WT control. Statistical analysis was performed using t-test for PBS and complement C5a condition, respectively (***p < 0.001). (E) Representative images of WT and TREM2 KO iPSC microglia incubated for 2 h with pH-rodo labeled Aß-coated beads. (F) Quantification of pHrodo positive microglia after 2 h incubation with pHrodo labeled Aß-coated beads. n = 3; Data shown as mean with standard deviation. Statistical analysis was performed using t-test (***p < 0.001).
Figure 5 Functional differences in TREM2 KO versus wild type iPSC microglia. (A–C) Intracellular calcium kinetics following different stimuli. (A) Intracellular calcium levels upon stimulation with different concentrations of ATP. Maximum measured fluorescence is indicated as relative fluorescence units (RFU), which are baseline corrected (n = 3). Data shown as mean with standard error of the mean. Statistical analysis of two data sets was performed using a two-way ANOVA. (B) Intracellular calcium levels upon treatment with different concentrations of ADP. Maximum measured fluorescence is indicated as relative fluorescence units (RFU), which are baseline corrected (n = 3). Data shown as mean with standard error of the mean Statistical analysis of two data sets was performed using a two-way ANOVA(****p < 0.0001). (C) Intracellular calcium levels upon treatment of different concentrations of complement component 5a (C5a). Maximum measured fluorescence is indicated as relative fluorescence units (RFU), which are baseline corrected (n = 3). Data shown as mean with standard error of the mean. Statistical analysis of two data sets was performed using a two-way ANOVA (****p < 0.0001). (D) Migration speed of WT and TREM2 KO monoculture microglia in presence or absence of 1 ng/ml C5a as chemoattractant in the bottom compartment. Migration speed was calculated from the increase in iPSC microglia occupied area over time in the bottom compartment of the transwell and expressed relative to untreated WT control. Statistical analysis was performed using t-test for PBS and complement C5a condition, respectively (***p < 0.001). (E) Representative images of WT and TREM2 KO iPSC microglia incubated for 2 h with pH-rodo labeled Aß-coated beads. (F) Quantification of pHrodo positive microglia after 2 h incubation with pHrodo labeled Aß-coated beads. n = 3; Data shown as mean with standard deviation. Statistical analysis was performed using t-test (***p < 0.001).CC BY · PubMed Central

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Metadata

Authors
Reich, M., Paris, I., Ebeling, M., Dahm, N., Schweitzer, C., Reinhardt, D., Schmucki, R., Prasad, M., Köchl, F., Leist, M., Cowley, S. A., Zhang, J. D., Patsch, C., Gutbier, S., & Britschgi, M.
Venue
Frontiers in Immunology
Publisher
Frontiers Media SA
Published
2021-02-03
Volume
11
ISSN
1664-3224
DOI
10.3389/fimmu.2020.617860
PMC
PMC7887311
Cited by
49
References
36
Licence
creativecommons.org/licenses/by/4.0/ (vor)

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