Analysis of single-cell transcriptome data from a mouse model implicates protein synthesis dysfunction in schizophrenia

Weller, Andrew E.; Ferraro, Thomas N.; Doyle, Glenn A.; Reiner, Benjamin C.; Berrettini, Wade H.; Crist, Richard C.

doi:10.1007/s13258-024-01545-3

Analysis of single-cell transcriptome data from a mouse model implicates protein synthesis dysfunction in schizophrenia

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Published: 31 July 2024

Volume 46, pages 1071–1084, (2024)
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Analysis of single-cell transcriptome data from a mouse model implicates protein synthesis dysfunction in schizophrenia

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Andrew E. Weller ORCID: orcid.org/0000-0002-8697-1339¹,
Thomas N. Ferraro^1,2,
Glenn A. Doyle³,
Benjamin C. Reiner¹,
Wade H. Berrettini¹ &
…
Richard C. Crist¹

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Abstract

Background

Schizophrenia is a mental disorder that causes considerable morbidity, whose risk largely results from genetic factors. Setd1a is a gene implicated in schizophrenia.

Objective

To study the gene expression changes found in heterozygous Setd1a^± knockout mice in order to gain useful insight into schizophrenia pathogenesis.

Methods

We mined a single-cell RNA sequencing (scRNAseq) dataset from the prefrontal cortex (PFC) and striatum of Setd1a^± mice and identified cell type-specific differentially expressed genes (DEGs) and differential transcript usage (DTU). DEGs and genes containing DTU found in each cell type were used to identify affected biological pathways using Ingenuity Pathway Analysis (IPA).

Results

We identified 273 unique DEGs across all cell types in PFC and 675 unique gene peaks containing DTU. In striatum, we identified 327 unique DEGs across all cell types and 8 unique gene peaks containing DTU. Key IPA findings from the analysis of DEGs found in PFC and striatum implicate processes involved in protein synthesis, mitochondrial function, cell metabolism, and inflammation. IPA analysis of genes containing DTU in PFC points to protein synthesis, as well as cellular activities involving intracellular signaling and neurotransmission. One canonical pathway, ‘EIF2 Signaling’, which is involved in the regulation of protein synthesis, was detected in PFC DEGs, striatum DEGs, and PFC genes containing DTU, drawing attention to its importance in schizophrenia pathophysiology.

Conclusion

Processes involving protein synthesis in general and the ‘EIF2 Signaling’ pathway in particular could be targets for the development of new research strategies and biomarkers in schizophrenia.

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Introduction

Schizophrenia is a chronic, disabling mental disorder whose symptoms often begin in adolescence or early adulthood (Eglit et al. 2018; Owen et al. 2016). The risk for developing schizophrenia has a large genetic component, with a twin study estimating the heritability of schizophrenia to be 79% (Hilker et al. 2018). Recent studies show that neuronal dysfunction in schizophrenia is both cellular subtype- and brain region-specific (Skene et al. 2018; Watanabe et al. 2020, 2019). Single-cell RNA sequencing (scRNAseq) is a powerful tool that can be used to examine the transcriptomes of individual cells and further characterizes the role of specific cell types in disease processes (Chehimi et al. 2023).

Given the limitations associated with brain transcriptomic studies in humans, scRNAseq data from preclinical schizophrenia mouse models can be studied alongside research in humans with the goal of connecting the cells and genes with transcriptomic changes that are linked with the biological underpinnings of schizophrenia. One such model is the Setd1a heterozygous knockout (Setd1a^±) mouse line (Chen et al. 2022). The protein encoded by the human gene SETD1A is a component of a histone methyltransferase (HMT) complex which may play a role in schizophrenia pathophysiology (for review see Kranz and Anastassiadis 2020) (Kranz and Anastassiadis 2020)). Analysis of Setd1a^± mice reveals decreased levels of Setd1a protein in cerebral cortex and behaviors that are consistent with those of patients with schizophrenia (Chen et al. 2022). scRNAseq data generated from the cerebral cortex of Setd1a^± mice show that the behavioral phenotypes are associated with DEGs involved primarily in neuron morphogenesis and synaptic function (Chen et al. 2022).

A previous scRNAseq study of Setd1a^± mice yielded important insights into the role of Setd1a in regulating biological processes previously associated with schizophrenia (Chen et al. 2022). In this work, we extended those insights to account for alternative mRNA isoforms by examining differential transcript usage (DTU) using the Sierra program (Patrick et al. 2020). DTU denotes differential polyadenylation site usage. DTU analysis of scRNAseq data from schizophrenia mouse models could confer additional characterization of schizophrenia pathogenesis. Here we report data mining of scRNAseq data from Setd1a^± mice identifies unique sets of cell type-specific DEGs, DTUs, and biological pathway changes in both PFC and striatum from these mice. Our results support the neurodevelopmental basis of schizophrenia and suggest new molecular targets for the development of therapeutics and disease and treatment biomarkers.

Materials and methods

Datasets

We mined the GSE181021 scRNAseq dataset generated using the 10x Genomics 3’ Gene Expression Assay with CellRanger version 3.0.2 (Chen et al. 2022; Zheng et al. 2017). We analyzed data from eight pooled RNA samples, consisting of two biological replicates for each genotype (WT and Setd1a^±) for two brain regions (PFC and striatum). Each pool contained RNA from two mice. These RNA pools came from samples of PFC from 12- to 14-week-old C57BL/6N wild type (WT) male mice (pooled sample 1: GSM5482102; pooled sample 2: GSM5482103), PFC from 12- to 14-week-old Setd1a^± male mice created on a C57BL/6N background (Chen et al. 2022) (pooled sample 3: GSM5482104; pooled sample 4: GSM5482105), striatum from 12- to 14-week-old C57BL/6N WT male mice (pooled sample 5: GSM5482106; pooled sample 6: GSM5482107), and striatum from 12- to 14-week-old Setd1a^± male mice created on a C57BL/6N background (Chen et al. 2022) (pooled sample 7: GSM5482108; pooled sample 8: GSM5482109).

Seurat analysis

We input the barcodes file, matrix of gene-by-cell expression, and genes file for eight samples—four samples from each brain region (PFC, striatum) into the Seurat program (version 4.0.3 (Stuart et al. 2019)) and in doing so, generated eight different Seurat objects. We used the merge method to aggregate the four Seurat objects from each brain region into a single Seurat object for further examination, resulting in one Seurat object for PFC, and one Seurat object for striatum. We implemented quality control (QC) criteria by retaining cells which had at least 800 but less than 2500 genes, and less than 10% of total transcripts from mitochondrial genes. We normalized the data to 10,000 transcripts per cell and found the 2000 genes with the most variable gene expression using the FindVariableFeatures function with the “vst” selection method. Linear transformation was performed, followed by linear dimension reduction with principal component analysis (PCA). We carried out cell clustering using the first 30 principal components (PCs) at resolution = 0.4, creating 18 distinct clusters for PFC and 20 distinct clusters for striatum. Expression of known marker genes was used to identify the cell type for each cluster. Clusters containing gene markers for more than one cell type were removed from our analyses, yielding 13 clusters for PFC and 15 clusters for striatum. The FindMarkers function was used to identify DEGs between the two mouse genotypes, using default statistical settings of Wilcoxon Rank Sum test and Bonferroni correction, but with logfc.threshold = 0. Mitochondrial genes were excluded from the differential expression analysis.

Pearson’s chi-squared contingency table analysis was conducted to evaluate the number of cells in every cell cluster comparing Setd1a^± and WT mice in separate analyses for both PFC and striatum.

Sierra analysis

We used Regtools (Cotto et al. 2023) (https://github.com/griffithlab/regtools) to acquire the transcript junction information in BED (browser extensible data) format from the Setd1a^± PFC, WT PFC, Setd1a^± striatum, and WT striatum BAM alignment files. We created the reference file from the GRCm38/mm10 genome assembly. The whitelist.bc files came from the Setd1a^± PFC, WT PFC, Setd1a^± striatum, and WT striatum barcodes.tsv files acquired from the GSE181021 dataset. We identified peaks utilizing the Sierra R package (version 0.99.26 (Patrick et al. 2020)) individually for each of the Setd1a^± PFC, WT PFC, Setd1a^± striatum, and WT striatum BAM files. We then combined the four files of peaks detected for each brain region in separate analyses using Sierra. For each brain region, we counted peaks for Setd1a^± and WT samples separately and grouped all of these peaks into one file. We conducted peak annotations with the BSgenome.Mmusculus.UCSC.mm10 genome file and transmitted these peaks to a Seurat object. We analyzed DTU from the PFC and striatum data via the DUTest function using the parameter “exp.thres = 0.1”. We filtered DUTest results with an FDR adjusted p-value < 0.05 and log₂ fold change limit of 0.25. We then created a list of gene peaks by means of the DUTest function differentiating the Setd1a^± and WT samples with the indicated statistical limits. We created lists of gene peaks for each cell cluster for the Setd1a^± vs. WT samples for those with an absolute fold change ≥ 2.0 in at least one cell cluster for each brain region.

Ingenuity pathway analysis: canonical pathways

We input DEGs from each cluster for each brain region (PFC, striatum) into IPA (https://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis) (Krämer et al. 2014). Transcripts from mitochondrial DNA genes and transcripts with an unknown Ensembl ID were excluded from this analysis. Similarly, we input genes containing DTU with peaks with an absolute fold change > 2.0 in each cluster into IPA for each brain region. We then carried out canonical pathway analysis which yielded lists of terms with an FDR q-value < 0.05.

Results

Seurat analysis: transcriptomics

The aggregated Setd1a^± and WT data from the PFC revealed 7262 cells in 13 separate clusters relating to distinct cell types (Fig. 1; separate uniform manifold and projection (UMAP) plots of Setd1a^± and WT samples are shown in Supplementary Fig. 1). Of note, we found that a number of clusters were significantly different in size (i.e., number of captured cells that passed QC) between Setd1a^± and WT mice (Fig. 2; Supplementary File 1). Out of 479 total DEGs across all clusters analyzed, 316 were downregulated and 163 were upregulated in Setd1a^± mice relative to WT mice. The 479 total DEGs included 273 unique genes (Supplementary File 3).

Our analysis of the aggregated striatum datasets from Setd1a^± and WT mice revealed 13,986 cells in 15 separate clusters (Fig. 3; separate uniform manifold and projection (UMAP) plots of Setd1a^± and WT samples are shown in Supplementary Fig. 2). As in the PFC, we found that many of the striatum clusters from Setd1a^± mice contained a significantly different number of cells compared to the same clusters from WT mice (Fig. 4; Supplementary File 2). Out of 515 total DEGs across all clusters analyzed, 395 were downregulated and 120 were upregulated in Setd1a^± mice relative to WT mice. The 515 total DEGs included 327 unique genes (Supplementary File 4).

In the PFC, vascular endothelial cells/pericytes, and subtypes of excitatory neurons generated the most DEGs. These were followed by myelin-forming mature oligodendrocytes (MFOLs), oligodendrocyte precursor cells (OPCs), and newly formed oligodendrocytes (NFOLs). Clusters of microglia, inhibitory neurons, and astrocytes generated the fewest DEGs (Fig. 5; Supplementary File 3). In the striatum, subtypes of inhibitory neurons generated the most DEGs. These were followed by MFOLs, vascular endothelial cells/pericytes, OPCs, and astrocytes. Clusters of excitatory neurons, NFOLs, neuroblasts, and microglia generated the fewest DEGs (Fig. 6; Supplementary File 4). The most significant DEGs in the PFC, independent of cluster, are listed in Supplementary File 3. Those in the striatum are listed in Supplementary File 4. Of the DEGs identified, Gm42418, Rpl41, and Ndufa3 appear in 9 clusters in PFC, more than any other DEGs (Table 1). Apoe appears in 7 clusters in striatum (Table 2), the most of any DEG in striatum.

Table 1 Differentially expressed genes (DEGs) detected in more than five clusters in prefrontal cortex (PFC): Setd1a^± vs. wild type (WT). Cluster 0 = Oligodendrocyte precursor cells (OPCs); Cluster 1 = Myelin-forming mature oligodendrocytes (MFOLs); Cluster 2 = Vascular endothelial cells/Pericytes; Cluster 3 = Myelin-forming mature oligodendrocytes (MFOLs); Cluster 4 = Vascular endothelial cells/Pericytes; Cluster 5 = C1ql3 + , Pde1a + , Htr2c- excitatory neurons (Excitatory neuron 1); Cluster 6 = Astrocytes; Cluster 7 = Microglia; Cluster 8 = C1ql3-, Pde1a + , Htr2c- excitatory neurons (Excitatory neuron 2); Cluster 9 = Newly formed oligodendrocytes (NFOLs); Cluster 10 = C1ql3-, Pde1a-, Htr2c + excitatory neurons (Excitatory neuron 3); Cluster 11 = Npy + inhibitory neurons (Inhibitory neuron 1); Cluster 12 = Vascular endothelial cells/Pericytes

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Table 2 Differentially expressed genes (DEGs) detected in more than five clusters in striatum: Setd1a^± vs. wild type (WT). Cluster 0 = Pdyn + , Gucy1a1 + , Ntng1-, Lhx6- inhibitory neurons (Inhibitory neuron 1); Cluster 1 = Pdyn-, Gucy1a1 + , Ntng1-, Lhx6- inhibitory neurons (Inhibitory neuron 2); Cluster 2 = Myelin-forming mature oligodendrocytes (MFOLs); Cluster 3 = Oligodendrocyte precursor cells (OPCs); Cluster 4 = Pdyn-, Gucy1a1-, Ntng1 + , Lhx6- inhibitory neurons (Inhibitory neuron 3); Cluster 5 = Vascular endothelial cells/Pericytes; Cluster 6 = Vascular endothelial cells/Pericytes; Cluster 7 = Myelin-forming mature oligodendrocytes (MFOLs); Cluster 8 = Astrocytes; Cluster 9 = Neuroblasts; Cluster 10 = Pdyn-, Gucy1a1-, Ntng1-, Lhx6 + inhibitory neurons (Inhibitory neuron 4); Cluster 11 = Microglia; Cluster 12 = Newly formed oligodendrocytes (NFOLs); Cluster 13 = Vascular endothelial cells/Pericytes; Cluster 14 = Excitatory neurons (Excitatory neuron 1)

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Sierra analysis: differential transcript usage

Analysis of DTU in PFC cells from Setd1a^± vs. WT mice produced lists of transcripts for every gene found in each cluster which had a log₂FC ≥ 0.25 (Fig. 5; Supplementary File 5). In total, we found 675 unique gene peaks with an absolute fold change > 2.0 in all clusters (Supplementary File 5).

Similarly, analysis of DTU in striatum cells from Setd1a^± vs. WT mice yielded 8 unique gene peaks with an absolute fold change > 2.0 in all clusters (Fig. 6; Supplementary File 6).

Ingenuity pathway analyses: canonical pathways

Ingenuity Pathway Analysis (IPA) of the DEG lists from each cluster identified 15 canonical pathway terms that were found in more than one cell cluster in the PFC dataset (Supplementary File 7), and 25 canonical pathway terms that were found in more than one cell cluster in the striatum dataset (Supplementary File 8). Forty-nine canonical pathway terms were generated in a single cluster in the PFC dataset (Supplementary File 7), and 27 were generated in a single cluster in the striatum dataset (Supplementary File 8). In the PFC, clusters of excitatory neurons had the greatest number of canonical pathway terms that emerged from more than one cluster, followed by vascular endothelial cells/pericytes, NFOLs, and MFOLs, respectively. OPCs, inhibitory neurons, and microglia each had two significant terms. Astrocytes yielded one canonical pathway term. In the striatum, clusters of inhibitory neurons produced the most canonical pathway terms, followed by vascular endothelial cells/pericytes, OPCs, and MFOLs. NFOLs, microglia, astrocytes, neuroblasts, and excitatory neurons did not yield any canonical pathway terms.

IPA of the cluster-specific DTU gene lists produced 29 canonical pathway terms that were detected in more than one cell cluster in the PFC dataset (Supplementary File 9). Sixty-one canonical pathway terms were found in a single cluster in the PFC dataset (Supplementary File 9). Excitatory neurons generated the greatest number of canonical pathway terms that were detected in more than one cluster, followed by inhibitory neurons, NFOLs, and vascular endothelial cells/pericytes. MFOLs and OPCs each generated four canonical pathway terms. Astrocytes and microglia did not yield any canonical pathway terms. In the striatum, there were no canonical pathway terms that appeared in any cell type.

Discussion

This investigation provides additional knowledge of the transcriptomics underlying processes in the Setd1a^± mouse model corresponding to schizophrenia-like phenotypes (Chen et al. 2022). We identified numerous DEGs between mutant and WT mice in specific cell types from the PFC and striatum, some DEGs that are unique and others that confirm prior data (Chen et al. 2022). Moreover, we identified genes with DTU that are novel and that further extend prior work. Pearson’s chi-squared contingency table analysis points out statistically significant differences for several of the cluster size comparisons between the WT and Setd1a^± mice in both PFC and striatum (Fig. 2; Fig. 4; Supplementary File 1; Supplementary File 2). Future research could verify these findings in the Setd1a^± mouse model.

Some of the most statistically significant DEGs in PFC are genes that are already implicated in schizophrenia, such as Sgk1 (Lang et al. 2010; Stertz et al. 2021), Rps29 (Song et al. 2021), Ptprz1 (Buxbaum et al. 2008; Cressant et al. 2017; Fajnerová et al. 2014; Niisato et al. 2005; Takahashi et al. 2011), Mbp (Gouvea et al. 2016; Matthews et al. 2012), Apoe (Gibbons et al. 2011; González-Castro et al. 2015; Haider et al. 2021; Jonas et al. 2019; Sabherwal et al. 2019), and Enpp2 (Herr et al. 2018; Matsuoka et al. 2008). Setd1a^± mice also showed interesting transcriptome alterations in the striatum consistent with prior work on schizophrenia. For example, DEGs again included Apoe (Gibbons et al. 2011), as well as schizophrenia-associated genes like Meg3 (Fallah et al. 2019; Ghafouri-Fard et al. 2021), Malat1 (Ghafouri-Fard et al. 2021; Rusconi et al. 2020; Sabaie et al. 2021), Hspa8 (Bozidis et al. 2014; Kowalczyk et al. 2022), and Rab3a (Blennow et al. 2000; Davidsson et al. 1999).

An innovative feature of this investigation is the DTU analysis of individual cell types of the brain. We recently detected DTU in mouse models relevant to Alzheimer’s disease (Weller et al. 2022a, 2022b). In a similar vein, this study detects DTU pertinent to schizophrenia. A primary observation is that Setd1a deficiency alters transcript usage of many genes in specific cell types, particularly in the PFC, and many of these genes are relevant to schizophrenia. The gene peaks containing DTU found in the most clusters (9) in the PFC are in Ptma, and Hnrnpk (Table 3). The gene peak containing DTU which we found in the most clusters (10) in striatum is in BC004004 (Table 4). Ptma plays a role in DNA-binding transcription factor binding activity and is involved in apoptosis (Samara et al. 2017). Hnrnpk is a DNA/RNA binding protein that regulates many biological processes (Wang et al. 2020b). BC004004 was recently found to impact synaptic plasticity in mouse hippocampus (Yao et al. 2023). Ptma, Hnrnpk, and BC004004 are novel discoveries in the context of schizophrenia. There were other gene peaks containing DTU found across multiple clusters in PFC that have been implicated in schizophrenia. Prkar1a (found in 8 clusters) is a key kinase found in the brain and involved in cAMP signaling (Forero et al. 2016). Hspa8 (found in 6 clusters) was also found as a DEG in striatum (Bozidis et al. 2014; Kowalczyk et al. 2022).

Table 3 Gene peaks containing differential transcript usage (DTU) identified in more than five clusters in prefrontal cortex (PFC): Setd1a^± vs. wild type (WT). Cluster 0 = Oligodendrocyte precursor cells (OPCs); Cluster 1 = Myelin-forming mature oligodendrocytes (MFOLs); Cluster 2 = Vascular endothelial cells/Pericytes; Cluster 3 = Myelin-forming mature oligodendrocytes (MFOLs); Cluster 4 = Vascular endothelial cells/Pericytes; Cluster 5 = C1ql3 + , Pde1a + , Htr2c- excitatory neurons (Excitatory neuron 1); Cluster 6 = Astrocytes; Cluster 7 = Microglia; Cluster 8 = C1ql3-, Pde1a + , Htr2c- excitatory neurons (Excitatory neuron 2); Cluster 9 = Newly formed oligodendrocytes (NFOLs); Cluster 10 = C1ql3-, Pde1a-, Htr2c + excitatory neurons (Excitatory neuron 3); Cluster 11 = Npy + inhibitory neurons (Inhibitory neuron 1); Cluster 12 = Vascular endothelial cells/Pericytes

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Table 4 Gene peaks containing differential transcript usage (DTU) identified in more than five clusters in striatum: Setd1a^± vs. wild type (WT). Cluster 0 = Pdyn + , Gucy1a1 + , Ntng1-, Lhx6- inhibitory neurons (Inhibitory neuron 1); Cluster 1 = Pdyn-, Gucy1a1 + , Ntng1-, Lhx6- inhibitory neurons (Inhibitory neuron 2); Cluster 2 = Myelin-forming mature oligodendrocytes (MFOLs); Cluster 3 = Oligodendrocyte precursor cells (OPCs); Cluster 4 = Pdyn-, Gucy1a1-, Ntng1 + , Lhx6- inhibitory neurons (Inhibitory neuron 3); Cluster 5 = Vascular endothelial cells/Pericytes; Cluster 6 = Vascular endothelial cells/Pericytes; Cluster 7 = Myelin-forming mature oligodendrocytes (MFOLs); Cluster 8 = Astrocytes; Cluster 9 = Neuroblasts; Cluster 10 = Pdyn-, Gucy1a1-, Ntng1-, Lhx6 + inhibitory neurons (Inhibitory neuron 4); Cluster 11 = Microglia; Cluster 12 = Newly formed oligodendrocytes (NFOLs); Cluster 13 = Vascular endothelial cells/Pericytes; Cluster 14 = Excitatory neurons (Excitatory neuron 1)

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Based on our analysis, key schizophrenia genes whose transcript usage is altered in the PFC of Setd1a^± mice include Sox5 (Adkins et al. 2011; Sellmann et al. 2014), Fxr1 (Khlghatyan and Beaulieu 2018), Clvs2 (Balan et al. 2014), Ntrk2 (Gassó et al. 2018; Lin et al. 2013; Mehterov et al. 2022), Nedd4 (Bi et al. 2021; Han et al. 2019; Rubio et al. 2013), and Grin2b (Hu et al. 2016) (Supplementary File 5). The schizophrenia-relevant genes that we detected in striatum cell types via analysis of DTU are Actb (Matthews et al. 2012) and Ywhaq (Forero et al. 2016; Jacobsen et al. 2015) (Supplementary File 6).

Various canonical pathways derived from our DEG analysis in IPA are noteworthy in relation to schizophrenia. In the PFC, one of these pathways, ‘EIF2 Signaling’, involves genes which interact with or modulate eukaryotic initiation factor 2 (EIF2), an initiation factor involved in translation. The ‘EIF2 Signaling’ pathway is connected to findings in schizophrenia patients (English et al. 2015; Gilabert-Juan et al. 2019) and EIF2D is a putative biomarker (Gilabert-Juan et al. 2019). Likewise, the ‘mTOR (mammalian target of rapamycin) Signaling’ and ‘Regulation of eIF4 and p70S6K Signaling’ pathways play a role in protein synthesis modulation (Yang et al. 2008). mTOR is suggested to play a role in the development of psychiatric disorders including schizophrenia (Izumi et al. 2022), and is a potential therapeutic target for development of schizophrenia treatments (Calabrese et al. 2016; Chadha et al. 2021; Cui et al. 2021; Izumi et al. 2022; Montague-Cardoso 2021). It is possible that these pathways promote risk of schizophrenia through related processes given their roles in protein biosynthesis. The pathways ‘Mitochondrial Dysfunction’ and ‘Oxidative Phosphorylation’ relate to nuclear DNA-encoded genes whose role in mitochondria is to produce energy for cellular activities. This finding confirms research implicating mitochondrial pathophysiology in schizophrenia (Bryll et al. 2020; Rajasekaran et al. 2015).

IPA canonical pathways identified in multiple PFC cell types via analysis of lists of genes containing DTU also yielded several provocative findings. The identification of altered activity in the ‘SNARE Signaling Pathway’ is consistent with evidence that SNARE complex dysfunction confers risk to schizophrenia (Katrancha and Koleske 2015; Ramos-Miguel et al. 2015). ‘Opioid Signaling Pathway’ and ‘Neuropathic Pain Signaling in Dorsal Horn Neurons’ confirm prior work which suggests that the kappa opioid system plays a role in the development of both positive and negative symptoms of schizophrenia through its interaction with dopaminergic neurons (Clark and Abi-Dargham 2019), and opioid signaling plays a role in the development of neuropathic pain (Li et al. 2023). These findings highlight a possible role for the opioid system in schizophrenia. ‘Melatonin Signaling’ has been studied in both the pathogenesis and treatment of schizophrenia (Morera-Fumero and Abreu-Gonzalez 2013). ‘Endocannabinoid Neuronal Synapse Pathway’ is relevant to schizophrenia since the endocannabinoid system is recognized as a potential target for pharmacotherapy (Hoffman 2021; Lu and Mackie 2016). ‘Oxytocin Signaling Pathway’, which plays a role in social cognition and bonding, is also a factor under study in schizophrenia (Goh et al. 2021; Jones et al. 2017).

DEG analysis in the striatum revealed additional canonical pathways relevant to schizophrenia. In some cases, we identified alteration of the same pathways as in PFC, such as those related to protein synthesis and mitochondrial pathology. In addition, ‘Clathrin-mediated Endocytosis Signaling’, a process suggested to play a role in schizophrenia pathophysiology, was enriched in striatum DEGs (Föcking et al. 2011; Schubert et al. 2012). Alteration of the ‘Sirtuin Signaling Pathway’ is also an interesting finding given that a SNP in SIRT1, a gene encoding a sirtuin protein, is associated with schizophrenia in a Chinese Han population (Wang et al. 2015). Moreover, another SNP in SIRT1 is suggested as a possible biomarker of depression symptoms in schizophrenia patients (Wang et al. 2020a). Sirtuins are also being studied as potential therapeutic targets in schizophrenia (Leite et al. 2022). Identification of alteration in ‘Glucocorticoid Receptor Signaling’, ‘Immunogenic Cell Death Signaling Pathway’, ‘Role of PKR in Interferon Induction and Antiviral Response’, ‘NOD1/2 Signaling Pathway’, and ‘Natural Killer Cell Signaling’ in the Setd1a^± model provides additional evidence that dysregulation of inflammatory processes in the CNS plays a role in schizophrenia (Dabbah-Assadi et al. 2022; Müller 2018). Of final note, identification of ‘Protein Ubiquitination Pathway’ alteration is consistent with evidence that ubiquitinated protein formation is abnormally elevated in both the brains and erythrocytes of patients with schizophrenia (Bousman et al. 2019). Altered proteasome activity, an implication of changes in protein ubiquitination, is also suggested to be associated with the pathophysiology of schizophrenia (Rubio et al. 2013; Scott et al. 2016). We detected no canonical pathways by analyzing genes containing DTU in striatum, possibly due to the small size of some clusters, reducing statistical power.

The one canonical pathway that overlaps between PFC DEGs, striatum DEGs, and PFC genes containing DTU, ‘EIF2 Signaling’, is related to protein synthesis. The expression levels of the DEGs and genes containing DTU that appear in ‘EIF2 Signaling’ in PFC and striatum are shown on a heat map (Fig. 7). Additional studies investigating the genes implicating the ‘EIF2 Signaling’ pathway should be carried out to determine their relevance to schizophrenia. The canonical pathways which overlap between PFC DEGs and striatum DEGs are involved in protein synthesis, cellular metabolism, mitochondrial function, and immune response/inflammation. The canonical pathways which overlap between striatum DEGs and PFC genes containing DTU are involved in cellular signaling, protein synthesis/degradation, and immune response/inflammation. These data provide broad clues regarding both the biological adaptations in specific types of brain cells in the Setd1a heterozygous knockout mouse and by analogy the nature of schizophrenia. Processes involving protein synthesis in general, and ‘EIF2 Signaling’ in particular, could be targets for the development of new treatments and biomarkers in schizophrenia.

Limitations

The results of this analysis contribute new insights regarding the biological underpinnings of schizophrenia. Nonetheless, there are some limitations. First, the genetic backgrounds of the Setd1a^± and WT mice are slightly different, which is an aspect that could impact DEGs or genes with DTU. Moreover, the number of libraries analyzed for each genotype is small (n = 2). Also, splice variants located more 3’ than the final 100 bases of transcripts cannot be detected. Additionally, our data are correlative, as we did not carry out tests to show contributory effects with behavior or other phenotypes. Likewise, due to the exploratory nature of this analysis, we did not confirm the DEGs or genes with DTU. Finally, using a mouse model for a disorder in humans is a limiting factor, as species differences could be found in mice that are unrelated to human disease.

Conclusion

Studies of single-cell transcriptomic data are valuable in uncovering the biological underpinnings of complex brain diseases like schizophrenia as they foster a more granular understanding than is achievable through using transcriptomic experiments conducted on bulk brain tissue. IPA results from our PFC DEG lists indicate processes related to cell metabolism, immune response/inflammation, mitochondrial function, and protein translation in schizophrenia pathogenesis in many cell types. IPA results from our striatum DEG lists also point to global alteration in mechanisms related to protein translation, protein degradation, mitochondrial function, cell metabolism, and immune response/inflammation. Schizophrenia pathogenesis could result when those mechanisms do not work properly. Our DTU analysis suggests various genes which may contribute to schizophrenia risk. IPA analysis of genes containing DTU in PFC suggests cellular processes including intracellular signaling, neurotransmission, and protein synthesis/degradation are important in schizophrenia development. One canonical pathway, ‘EIF2 Signaling’, involved in the regulation of protein synthesis, was detected in PFC DEGs, striatum DEGs, and PFC genes containing DTU, drawing attention to its relevance. The ‘EIF2 Signaling’ pathway could be researched with the goal of making available new treatments and biomarkers for schizophrenia.

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Acknowledgements

The authors thank the researchers Renchao Chen PhD, Yiqiong Liu PhD, Mohamed Nadhir Djekidel PhD, Yi Zhang PhD, and their colleagues at Harvard Medical School for generating and graciously making publicly available the dataset GSE181021 which was used in our analysis.

Funding

The preparation of this manuscript was supported by a National Institutes of Health grant R01 MH109260 (WHB is PI). AEW was supported by T32 MH014654 (WHB is PI) and by a Tobacco Settlement Act grant from the Pennsylvania Department of Health (WHB is PI). The authors wish to thank those providing support for this work. BCR was supported by a 2017 NARSAD Young Investigator Grant (#26634) from the Brain and Behavior Research Foundation as the Patrick A. Coffer Investigator, funding for which was generously provided by Ronald and Kathy Chandonais.

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Molecular and Neural Basis of Psychiatric Disease Section, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, 125 S. 31 St., Suite 2200, Philadelphia, PA, 19104, US
Andrew E. Weller, Thomas N. Ferraro, Benjamin C. Reiner, Wade H. Berrettini & Richard C. Crist
Department of Biomedical Sciences, Cooper Medical School of Rowan University, Camden, NJ, 08103, US
Thomas N. Ferraro
Fox Chase Cancer Center, Temple University Health System, Philadelphia, PA, 19111, US
Glenn A. Doyle

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Andrew E. Weller
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Benjamin C. Reiner
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Richard C. Crist
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Conceptualization, AEW, TNF, GAD, BCR, RCC, WHB; Methodology, AEW, TNF, GAD, BCR, RCC; Data Curation, AEW, TNF, GAD; Formal Analysis: AEW, TNF, GAD, BCR; Writing—Original Draft, AEW; Writing—Review & Editing, AEW, TNF, GAD, BCR, RCC, WHB; Funding Acquisition, WHB; Supervision, WHB, TNF, GAD, RCC. All authors have read and agreed to the published version of the manuscript.

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BCR received research support from Novo Nordisk and Boehringer Ingelheim that was not used in support of this study. All other authors have no conflicts of interest to report.

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13258_2024_1545_MOESM1_ESM.tif

Supplementary Material 1: Uniform manifold and projection (UMAP) plot of Setd1a± and wild type (WT) prefrontal cortex (PFC) samples distinguished by genotype. The transcriptome profiles of 7262 single cells in PFC were used for unbiased clustering in Seurat and are displayed as a UMAP plot. Cells are color-coded by cluster, which indicates cell type. Cluster 0 = Oligodendrocyte precursor cells (OPCs); Cluster 1 = Myelin-forming mature oligodendrocytes (MFOLs); Cluster 2 = Vascular endothelial cells/Pericytes; Cluster 3 = Myelin-forming mature oligodendrocytes (MFOLs); Cluster 4 = Vascular endothelial cells/Pericytes; Cluster 5 = C1ql3+, Pde1a+,Htr2c- excitatory neurons (Excitatory neuron 1); Cluster 6 = Astrocytes; Cluster 7 = Microglia; Cluster 8 = C1ql3-, Pde1a+, Htr2c- excitatory neurons (Excitatory neuron 2); Cluster 9 = Newly formed oligodendrocytes (NFOLs); Cluster 10 = C1ql3-, Pde1a-, Htr2c+ excitatory neurons (Excitatory neuron 3); Cluster 11 = Npy+ inhibitory neurons (Inhibitory neuron 1); Cluster 12 = Vascular endothelial cells/Pericytes.

13258_2024_1545_MOESM2_ESM.tif

Supplementary Material 2: Uniform manifold and projection (UMAP) plot of Setd1a± and wild type (WT) striatum samples distinguished by genotype. The transcriptome profiles of 13,986 single cells in striatum were used for unbiased clustering in Seurat and are displayed as a UMAP plot. Cells are color-coded by cluster, which indicates cell type. Cluster 0 = Pdyn+,Gucy1a1+, Ntng1-, Lhx6- inhibitory neurons (Inhibitory neuron 1); Cluster 1 = Pdyn-, Gucy1a1+, Ntng1-, Lhx6- inhibitory neurons (Inhibitory neuron 2); Cluster 2 = Myelin-forming mature oligodendrocytes (MFOLs); Cluster 3 = Oligodendrocyte precursor cells (OPCs); Cluster 4 = Pdyn-, Gucy1a1-, Ntng1+, Lhx6- inhibitory neurons (Inhibitory neuron 3); Cluster 5 = Vascular endothelial cells/Pericytes; Cluster 6 = Vascular endothelial cells/Pericytes; Cluster 7 = Myelin-forming mature oligodendrocytes (MFOLs); Cluster 8 = Astrocytes; Cluster 9 = Neuroblasts; Cluster 10 = Pdyn-, Gucy1a1-, Ntng1-, Lhx6+ inhibitory neurons (Inhibitory neuron 4); Cluster 11 = Microglia; Cluster 12 = Newly formed oligodendrocytes (NFOLs); Cluster 13 = Vascular endothelial cells/Pericytes; Cluster 14 = Excitatory neurons (Excitatory neuron 1).

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Weller, A.E., Ferraro, T.N., Doyle, G.A. et al. Analysis of single-cell transcriptome data from a mouse model implicates protein synthesis dysfunction in schizophrenia. Genes Genom 46, 1071–1084 (2024). https://doi.org/10.1007/s13258-024-01545-3

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Received: 07 May 2024
Accepted: 08 July 2024
Published: 31 July 2024
Issue Date: September 2024
DOI: https://doi.org/10.1007/s13258-024-01545-3

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Analysis of single-cell transcriptome data from a mouse model implicates protein synthesis dysfunction in schizophrenia

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