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Article

Excitatory Neurons Derived from Human-Induced Pluripotent Stem Cells Show Transcriptomic Differences in Alzheimer’s Patients from Controls

by
Ram Sagar
1,2,
Ioannis Azoidis
1,2,
Cristina Zivko
1,2,
Ariadni Xydia
1,2,
Esther S. Oh
2,3,4,
Paul B. Rosenberg
2,4,
Constantine G. Lyketsos
2,4,
Vasiliki Mahairaki
1,2,* and
Dimitrios Avramopoulos
1,2,*
1
Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
2
The Richman Family Precision Medicine Center of Excellence in Alzheimer’s Disease, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
3
Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21224, USA
4
Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
*
Authors to whom correspondence should be addressed.
Cells 2023, 12(15), 1990; https://doi.org/10.3390/cells12151990
Submission received: 29 June 2023 / Revised: 27 July 2023 / Accepted: 28 July 2023 / Published: 2 August 2023
(This article belongs to the Special Issue iPS Cells (iPSCs) for Modelling and Treatment of Human Diseases 2022)
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Abstract

:
The recent advances in creating pluripotent stem cells from somatic cells and differentiating them into a variety of cell types is allowing us to study them without the caveats associated with disease-related changes. We generated induced Pluripotent Stem Cells (iPSCs) from eight Alzheimer’s disease (AD) patients and six controls and used lentiviral delivery to differentiate them into excitatory glutamatergic neurons. We then performed RNA sequencing on these neurons and compared the Alzheimer’s and control transcriptomes. We found that 621 genes show differences in expression levels at adjusted p < 0.05 between the case and control derived neurons. These genes show significant overlap and directional concordance with genes reported from a single-cell transcriptome study of AD patients; they include five genes implicated in AD from genome-wide association studies and they appear to be part of a larger functional network as indicated by an excess of interactions between them observed in the protein–protein interaction database STRING. Exploratory analysis with Uniform Manifold Approximation and Projection (UMAP) suggests distinct clusters of patients, based on gene expression, who may be clinically different. Our research outcomes will enable the precise identification of distinct biological subtypes among individuals with Alzheimer’s disease, facilitating the implementation of tailored precision medicine strategies.

1. Introduction

Alzheimer’s disease (AD) and dementia globally affect more than 55 million people, and the cases are projected to triple by 2050 [1], causing a major and worsening public health crisis.
Even with the newly approved drugs (lecanemab and donanemab), there is no clinically effective treatment for AD. Lecanemab only minimally delays progression of AD symptoms, and does not fully treat the disease. The newly approved drug lecanemab only minimally delays progression of AD symptoms, and does not fully treat the disease. Lacking disease therapies that reverse or substantially delay progression of AD symptoms, it is important to continue to improve our understanding of its biology including both genetic and environmental contributions. Despite significant progress in the AD genetics, including the identification of three genes (PSEN1, PSEN2, and APP) [2,3,4] that cause familial AD, the discovery of APOE as a major risk factor [5,6] and the identification of over 30 other genes with smaller but significant contributions [7,8,9,10], the etiological treatments currently approved have not shown substantial improvements in managing the disease progress. A promising solution to this problem may be the application of precision medicine to allow for a better match between patients and candidate treatments. At the Johns Hopkins Richman Family Precision Medicine Center of Excellence in Alzheimer’s Disease (JH-AD-PMC), we are continuously looking for new ways to achieve this goal.
One of the most promising ways to accurately characterize patients in order to identify biomarkers that can help towards the goal of precision medicine is the study of their biological materials. Among the samples and materials most likely to provide answers are the cells directly involved in the disease under study. For diseases of the human brain such as AD, this poses a significant problem, as there are multiple limitations in the study of brain cells. The brain is not easily accessible and cannot be sampled without risk. Even if sampled, a mix of cells would be acquired (neurons, astrocytes, microglia etc.) at varying relative abundance. Further, these cells would have been subjected not only to the ongoing disease process, but also to the medications used against it and to many unknown environmental variables. Recent advances in cell engineering have opened new possibilities for brain tissue at the individual level. Accessible cells such as skin fibroblasts or peripheral blood mononuclear cells (PMNCs) can be easily acquired and then reprogrammed into iPSCs, erasing the epigenetic marks of differentiation, and allowing new differentiation to a variety of cell types [11,12,13,14]. Induced PSCs were first established more than a decade ago using the four Yamanaka transcription factors (OCT3/4, SOX2, KLF4, c-MYC) to reprogram mouse adult fibroblast cells [15]. Since then, different technological advances have contributed to reliably establish human iPSCs from different cells, including new combinations of transcription factors, using small molecules, or reprogramming with episomal vectors [16]. Advances in differentiation methods have allowed the generation of multiple cell types from iPSCs, including many types of neuronal and glial cells [17,18] that resemble in vivo counterparts; these have become a popular tool for preclinical disease modeling in scientific research [17,19]. At the same time, advances in DNA sequencing allow for the accurate characterization of the transcriptomic state of cells and tissues, a window into their metabolic state and genomic make-up.
Here we investigate whether cells derived from iPSC of individual AD patients show transcriptomic differences when compared to cells derived from cognitively unimpaired individuals, after they have been first reprogrammed to pluripotency followed by differentiation into excitatory glutamatergic neurons. Such differences could not only be useful in predicting disease, but also in categorizing patients into different evidence-based clusters that might predict their course and response to treatment, a big step forward for precision medicine in AD.

2. Materials and Methods

2.1. Patients and Controls

Blood from patients (JHU-AD-01 to -06 and JHU-AD-08), as well as from healthy individuals (JHU-WT-03, -04, -07, and -08) was collected through the Johns Hopkins Alzheimer’s Disease Research Center (ADRC) and the Johns Hopkins Memory and Alzheimer’s Treatment Center (MATC). Blood from patient JHU-AD-07 was provided to us from collaborators through the ongoing S-CitAD clinical trial (ClinicalTrials.gov Identifier: NCT03108846). Patients recruited in the latter study had AD dementia, Mini-Mental State Examination (MMSE) scores of 5–26, and met criteria for agitation syndrome [20]. Adult skin fibroblasts from cognitively unimpaired individuals (JHU-WT-01 and -05) were obtained from the Coriell Institute for Medical Research [21]. AD patients had a mean age of 71.4 years and controls had a mean age of 71.7 years. Table 1 shows the age and sex of all participants. The participants were overwhelmingly female with only 3 males among the patients. Blood was collected by venipuncture into yellow-top tubes (containing trisodium citrate, citric acid, and dextrose) and shipped to the Johns Hopkins Genetics Core facility for the isolation of PMNCs which were then stored in liquid nitrogen until used. All patients had sporadic AD.
This study was approved by the Johns Hopkins Institutional Review Board (IRB).

2.2. Induced Pluripotent Stem Cell (iPSC) Generation, Culture and Maintenance

Peripheral blood mononuclear cells (PMNCs) were isolated from the blood samples of the individuals who consented to their participation in the study. They were then reprogrammed to iPSCs by using a transient expression method (nucleofection) involving three plasmid vectors (MOS, MMK and GBX) under feeder-free/xeno-free culture on 4D Nucleofector (Lonza, Basel, Switzerland) [12,22,23,24]. Generated iPSCs were characterized by immunocytochemistry for pluripotency markers, including Nanog, OCT4 and TRA-1-60, flow cytometry, and karyotyping. Established cell lines were cultured (250 K cells/well in 6 well plate) on vitronectin-coated tissue culture plates in Essential-8 (E8) medium with 10 uM Rock inhibitor (Y-27632) during seeding and then maintained in E8 medium till 80–90% confluency [12,23].

2.3. Lentivirus Transduction of iPSCs with NgN2

To acquire a high yield of functional neurons, we transduced the generated human iPSCs with Ngn2 and rTTA expressing lentivirus (lentivirus was purchased by Cellomics Technology, Arbutus, MD, USA). We followed an established protocol for the generation of induced neuronal cells in 21 days [18,25]. Once we established iPSCs confluency at 40–50%, the cells were transduced with Ngn2 (1.5 uL) and rtTA (1.5 uL) expressing lentivirus. Further polybrene (1 ug/mL, Santa Cruz, CA, USA) was added at the time of transduction and cells were incubated for 6 h at 37 °C. After 6 h, culture medium was replaced with fresh E8 medium and cells were incubated for 24 h or until 90% confluency was achieved. Cells were hereafter passaged only in rock inhibitor supplemented E8 medium. Transduced cells (250 k cells/well in a 6-well plate) were used for neuronal differentiation in this study.

2.4. Neuronal Differentiation of Ngn2 Transduced iPSCs

The iPSCs were differentiated into glutamatergic excitatory neurons as described in a previously published protocol [18,25,26]. Ngn2-transduced cells (250 K cells/well) were plated on vitronectin coated 6-well culture plates. After 48 h, culture medium was replaced with fresh E8 medium. To start the differentiation, Doxycycline was added to induce Ngn2 expression (Day 0) using the iN-N2 induction medium {DMEM/F12 (Gibco, Grand Island, NY, USA), N-2 supplement (Gibco, Grand Island, NY, USA), D-Glucose (Millipore Sigma, St. Louis, MO, USA), 2-β-mercaptoethanol (Gibco, Grand Island, NY, USA), Primocin (Invivogen, San Diego, CA, USA), BDNF (10 ng/mL, Peprotech, Cranbury, NJ, USA), NT3 (10 ng/mL, Peprotech, Cranbury, NJ, USA), Laminin (200 ng/mL, Millipore Sigma, St. Louis, MO, USA), and Doxycycline (2 µg/mL, Sigma-Aldrich, St. Louis, MO, USA). On Day 1, the induction medium was supplemented with puromycin (2 µg/mL) for 24 h for the selection of transduced cells. On Day 2, surviving cells were passaged on Matrigel-coated 6-well plates at a concentration of 500 K cells/well in neural differentiation medium (Neurobasal medium (Gibco, Grand Island, NY, USA), B27 supplement (Gibco, Grand Island, NY, USA), Glutamax (1% v/v, Gibco, Grand Island, NY, USA), Penicillin-Streptomycin (Pen/Strep, 5000 units/mL and 5000 µg/mL respectively, Gibco, Grand Island, NY, USA), D-Glucose (Sigma-Aldrich, St. Louis, MO, USA), BDNF (10 ng/mL, Peprotech, Cranbury, NJ, USA), NT3 (10 ng/mL, Peprotech, Cranbury, NJ, USA), Laminin (200 ng/mL, Millipore Sigma, St. Louis, MO, USA), and Doxycycline (2 µg/mL, Sigma-Aldrich, St. Louis, MO, USA). On Day 4, 50% of the medium was replaced with Neural maturation medium (Neurobasal medium A (Gibco, Grand Island, NY, USA), B27 (Gibco, Grand Island, NY, USA), Glutamax (1% v/v, Gibco, Grand Island, NY, USA), Pen/Strep, Glucose Pyruvate mix (1:100, final concentration of 5 mM glucose and 10 mM sodium pyruvate), BDNF (10 ng/mL, Peprotech, Cranbury, NJ, USA), NT3 (10 ng/mL, Peprotech, Cranbury, NJ, USA), Laminin (200 ng/mL, Millipore Sigma, St. Louis, MO, USA), and Doxycycline (2 µg/mL, Sigma-Aldrich, St. Louis, MO, USA) supplemented with 4 µM of Cytosineβ-D-arabinofuranoside hydrochloride (Ara-C, Sigma-Aldrich, St. Louis, MO, USA) to inhibit the non-neuronal cell proliferation. From Day 6, 70% of neuronal maturation medium was changed every other day until Day 12. From Day 15, non-doxycycline supplemented maturation medium was used to replace 50% of the medium every 48 h thereafter until Day 21. Mature neuronal cells were collected on Day 21 for RNA isolation and transcriptomic analysis.

2.5. Immunocytochemistry Staining for Pluripotency

Immunocytochemistry staining was performed to confirm the pluripotency by using nucleus markers (OCT4 and NANOG) and TRA-1-60 antibodies were used for surface markers. We cultured the iPSCs in the 12-well plates (60,000 cells/well) for 3 days, then cells were quickly washed with PBS and 4% PFA (paraformaldehyde) was used for fixation up to 20 min in 4 °C. Cells were washed with PBS and incubated with blocking buffer with 10% goat serum for 1 h. Primary antibodies OCT4 (#SC-9081), NANOG (#BD-560482), and TRA-1-60 (#MAB4360) were used for staining overnight at 4 °C. Next day cells were washed with PBS and incubated at 37 °C with secondary antibodies and counterstained with DAPI for 20 min at 4 °C.

2.6. Flow Cytometry

In order to validate the pluripotency of the cells, the flow cytometry analysis was completed on a BD LSR Fortessa Analyzer (BD Biosciences, Franklin Lakes, NJ, USA) and data were analyzed by using flowJo software (v10.8.1). Induced PSCs were dissociated into single cells with TrypLE (Gibco, Grand Island, NY, USA) and washed with BD-FACS staining buffer (Thermofischer, #00422257, Carlsbad, CA, USA). Then cells were resuspended in the BD-FACS staining buffer and labelled through anti-human TRA-1-60 antibody (Millipore Sigma, St. Louis, MO, USA) and anti-mouse IgM control, PE conjugated (#IC015P), and incubated at 4 °C for 40 min. After incubation, 2 mL of PBS was added in the labelled cells and centrifuged for 5 min at 1500 RPM. Finally, a 200 uL FACS-buffer was used to resuspend the cell and analyzed in BD-Fortessa analyzer (BD, Biosciences, San Jose, CA, USA).

2.7. RNA Extraction and Quality Control

Total RNA was isolated from neuronal cell pellets using the Quick-RNA MiniPrep Kit (Zymo Research #R1054) according to the manufacturer’s protocol. Total RNA was quantified using Nanodrop (Thermo Scientific, Waltham, MA, USA). Total RNA of final concentration (400 ng) was aliquoted and shipped to Novogene (Novogene Corporation Inc., Sacramento, CA, USA) for 150 bp paired-end RNA sequencing. Once all the samples passed the primary quality control, library preparation was initiated. See the previously published paper from our group for the descriptive methodology of RNA sequencing and analysis [26,27].

2.8. RNA Sequencing and Data Analysis

Next-generation sequencing was outsourced to Novogene Corporation Inc. (Sacramento, CA, USA). The 12 libraries passed Novogene’s quality control and were sequenced in one batch. The company returned to us .fastq files including on average 44.9 million reads per sample with a maximum of 52.7 million and a minimum of 39.8 million. In order to analyze the samples, the 150 bp paired-end reads were aligned to the human reference genome GRCH38 using the package Hisat2 (version 2.2.1) [28], then SAMtools (version 1.1.4) [29] was utilized to produce the corresponding BAM files, and stringtie (version 2.1.7) [30] was used to assemble RNAseq alignments into potential transcripts and estimate their abundance according to GRCh38 human genome annotations [31]. Subsequently, raw counts were computed via the Bioconductor package tximport [32]. Only transcripts with at least 10 reads across all samples were considered for further analysis. The Bioconductor package DESeq2 was used for deferential gene expression analysis [33] and adjusted p (Padj) was calculated using the Benjamini–Hochberg adjustment.

2.9. Bioinformatics Analysis

To identify possible outliers due to experimental conditions, we performed principal component analysis using read counts as input and the ggplot2 and ggfortify R packages. For UMAP analysis, we used the R functions prcomp and umap.
To validate the results through comparison with other datasets, the differentially expressed genes (DEG) were filtered by the adjusted p-values by DEseq2 (FDR 0.05, 0.1 and 0.2 for our dataset) and intersected with the data reported by Mathys et al. [34] for excitatory cortical neurons (FDR between 5 × 10−5, 5 × 10−6, and 5 × 10−7 for the Mathys dataset due to the larger number of positives).
To confirm that the DEGs were a valid gene set that could potentially indicate functional differences between AD and control-derived neurons, we utilized the STRING database of protein–protein interactions [35] version 11.5 (https://string-db.org/, accessed on 3 May 2023) to determine whether these genes exhibited more frequent interactions than predicted by chance. Input genes were those with adjusted p-value < 0.05 of which 485 were recognized by STRING. The default parameters were used in the STRING interface, including a medium interaction confidence (0.4) and restricting to only the query genes without an additional level of interactors, as suggested by the authors for meaningful edge (interaction) enrichment results.

3. Results

3.1. Generation of iPSCs and Excitatory Neurons

Human iPSCs were generated through PMNCs acquired from the Johns Hopkins Core facility. Cells were reprogrammed through the transient expression method by using episomal vectors. Nucleofector cells were grown in the culture for a minimum of two weeks to develop iPSC-like colonies [12,23]. The morphological identity of the iPSCs was captured by brightfield microscopy (Figure 1a). The immunofluorescence staining with nucleic markers (NANOG and OCT4) and surface marker (TRA-1-60) confirmed pluripotency which was also validated by flow cytometry for TRA-1-60 positive cells (Figure 1c). The functional pluripotency of the iPSCs was assessed by the in vitro trilineage differentiation into three germ layers as previously reported [22]. We did not observe any chromosomal abnormality by karyotyping.
To assess the excitatory neuronal identity of the cells produced by Ngn2 induction, we used the RNA sequencing results to assess the expression of the expected marker genes. A list of genes and their expression levels is shown in Table 2. We note that we, as well as others, have used this protocol repeatedly and consistently to obtain cells with transcription profiles resembling those of excitatory neurons [17,25,26,27,36,37]. For comparison, we provide Figure 2, a heatmap for the expression of these genes in human excitatory neurons from the single-cell expression database available by the Allen Brain Atlas (https://celltypes.brain-map.org/rnaseq/human_m1_10x, accessed on 26 July 2023).

3.2. RNA Sequencing

We received an average of 45.1 million paired reads per sample (min = 39.8, max = 52.7) with an error rate of 0.03%, of which at least 96.3% had a Phred score of 20 and at least 90.1 of those had a Phred score of 30. Principal component analysis (PCA) using all genes and the Fragments Per Kbp per million (FPKM) showed that sample AD06 was an outlier, likely the result of unknown differences during cell growth and differentiation, and it was removed from further analysis (Figure 3). The remaining samples were then used for differential expression (DE) analysis. This analysis identified 621/22,011 DE genes at Padj < 0.05, 204 higher in AD, and 417 lower.

3.3. Bioinformatics Analysis

The main question of this project is whether the transcriptome of neuronal cells derived from AD patients shows detectable differences as compared to those derived from the controls, making them appropriate for the study of the disease and potentially the identification of biomarkers. Having identified a significant number of genes changing expression, we set out to test whether these genes are a meaningful set and related to AD rather than a collection of random genes from experimental noise. Therefore, we asked three specific questions: Are the identified genes related to each other? Do they overlap with genes identified by comparable in vivo studies? Do they contain genes that are known to be involved in Alzheimer’s disease? Lastly, we investigated whether the transcriptome of neurons derived from patients and controls could potentially be used for meaningful classification of individuals to facilitate precision medicine.

3.4. Dysregulated Genes Are Part of a Gene Network

To explore whether the observed set of dysregulated genes is an apparently random collection or a functionally meaningful set, we queried the STRING database which systematically collects and integrates physical interactions and functional associations. Additionally, for the interactive visualization of these interactions in a provided gene set, it allows the assessment of excess of edges (connections) between the nodes (proteins/genes) compared to a random set. Of the 485 genes corresponding to known proteins at Padj < 0.05, there were 801 interactions, compared to an expected 662 (p-enrichment 9 × 10−8). The genes corresponding to the interacting proteins were enriched for those expressed in the brain (FDR = 3 × 10−5).

3.5. Validation through Comparison with External Data

As opposed to most transcriptomic studies of AD that examine bulk tissue from the brain, our data come from a more homogeneous population of cells after reprogramming and differentiation to excitatory neurons. We sought an appropriate dataset to examine the validity of our results by comparing the identified differentially regulated genes. No published dataset fully matched our conditions, yet one recent study performed single-cell sequencing providing data specific to excitatory neurons from the prefrontal cortex of 48 individuals with varying degrees of AD pathology (Mathys et al. [34] from their Supplementary Table 3, tab Ex, no pathology vs. pathology). Due to our small sample size, we examined genes with a corrected p-value of <0.05, 0.1, and 0.2 in our study, while we examined p-value thresholds <5 × 10−5, 5 × 10−6, and 5 × 10−7, for the Mathys et al. [34] study which reported many more positive results (Table 3). We consistently found a significant excess of overlap from that expected by chance (lowest p = 3.5 × 10−5). We also consistently observed significantly more concordance in the direction of change than that expected by chance (Table 3), reaching a binomial test p-value of 3.2 × 10−4. The enrichment exceeded 1.5-fold and the concordance rate was 75% for some thresholds. These results suggest that many of the genes we found to have expression differences mirror changes observed in disease despite our small sample size and despite coming from blood cells that have been reprogrammed and undergone differentiation. It further suggests that the genes driving this overlap represent primary expression changes related to disease risk and not secondary changes due to the disease itself and/or the prescribed medications.
Many genes related to AD were among the differentially expressed (DE) genes in AD patients versus the control-derived samples. A complete list of genes with p-values is provided in Supplementary Table S1. Notably, among genes at p-adjusted <0.05 was APOE that was expressed less in patient-derived neurons, with an additional four genes identified by large GWAS for AD: CD2AP, RBFOX1, TMEM132C, and NPAS2. A complete list of genes with all normalized counts in excitatory neurons (RNAseq) for all individuals included in the analysis is also provided in Supplementary Table S2.

3.6. Use of Transcriptome Data as a Patient Classification Tool

Having shown that iPSC-derived neurons from individuals with AD show transcriptome differences from controls, we performed exploratory analysis to determine whether the transcriptome of such cells may also be used to cluster affected individuals in groups that might be helpful in determining the course and/or best treatment options, a major goal of precision medicine. Given our small sample size, we did not expect statistical support, but perhaps an indication of whether this might be a useful path going forward. To determine whether the AD patient transcriptome could be used as a multidimensional variable for patient classification, we used Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP), a technique that can be used to visualize patterns of clustering in high-dimensional data [38] that is frequently used for the analysis of single-cell data. Figure 4 shows the results of this analysis. We observed that the seven patients appeared to form two clusters on the UMAP1 axis, one containing patients 1, 3, 5, and 8 and the other containing patients 2, 4, and 7. We compared cognitive data between clusters for the individuals with detailed clinical data (all but AD_06 and AD_08). Interestingly, despite having similar clinical dementia ratings (CDR) in the CDR scale [39], the two groups were significantly different in age at examination but despite that the younger group, had consistently lower performance in letter fluency [40] “Number of words” test and on the Mini-Mental State Examination (MMSE) [41] (Table 4). These differences did not reach statistical significance, as would be expected by the very small numbers. Moreover, while the three male patients do cluster together, AD_01 who is female, also clusters in the middle of them, making it unlikely that this is a sex effect. Given that the patient sample alone is small, this is not unlikely to be by chance.

4. Discussion

We generated iPSCs from 14 individuals and after differentiation to excitatory neurons, we found that the patients with AD and the controls showed significant transcriptomic differences. The genes involved appear functionally connected to each other and overlap with genes identified in prior in vivo analysis by another research group, supporting the validity of our results. This is an important observation as it supports the use of measurements from patient-derived cells differentiated into neurons for the identification of biomarkers for AD. Further, our exploratory patient-only analysis of clustering suggested two possible clusters, indicating it might be possible to group patients into biologically meaningful groups that may advance precision medicine.
The genes differing between the cases and controls included APOE, a very well-established AD risk gene that was found to be expressed lower in patient-derived cells, and others previously linked to AD (CD2AP, RBFOX1, TMEM132C, NPAS2). CD2AP, whose levels were decreased in the patient-derived cells, is a scaffolding molecule reported to be associated with AD [42]. Its mRNA levels have also been found to be decreased in peripheral lymphocytes of sporadic AD patients and CD2AP loss of function has been linked to enhanced Aβ production, Tau-induced neurotoxicity, abnormal neurite structure modulation, and reduced blood–brain barrier integrity [43]. RBFOX1, whose levels were increased in the patient derived cells is another AD associated gene [44], linked to multiple additional psychiatric traits [45]. It is an RNA-binding protein that regulates alternative splicing [46] including that of APP [47] and variants near it also regulate gene co-expression modules in the aging human brain [48]. TMEM132C, whose levels were increased in the patient-derived cells, encodes a neural adhesion molecule associated with AD [44] which is also independently associated with cognitive impairment in a hypotensive population [49] and with high-altitude adaptation in Tibetans. NPAS2, whose levels were decreased in the patient-derived cells, is a circadian rhythm gene, a system that has been linked to many diseases including dementia [50]. Interestingly, NPAS2 has also been implicated in prion diseases [51,52] and is a regulator of genes controlling inflammation in AD [53].
The highest ranked genes in our study, HS6ST2 and FBXO2, have also been previously linked to AD. HS6ST2, along with its isoform HS3ST2, have been found to increase in AD and HS3ST2, which through further study, was found to be critical for the abnormal phosphorylation of tau [54]. Most notably, FBXO2 has been found to regulate amyloid precursor protein levels and processing [55] and alter cognitive behavior in mice [56].
While there seem to be biologically relevant differences between the transcriptomes of iPSC-derived neurons from patients and controls, our sample is too small to reach conclusions about the possible classification of patients based on these transcriptomes. Our exploratory analysis using UMAP, a tool for visualizing the proximity of different samples in a multidimensional space (transcriptome) in two dimensions, suggests two possible clusters. Interestingly the clusters showed some clinical differences, with one cluster (AD_2, and 4) having a lower age at presentation, showing similar clinical dementia ratings, but showing potentially worse scores in MMSE and the “Number of words” test. We expect that with larger samples more of these differences will emerge that might reflect patient subgroups and determine the usefulness of this clustering approach for precision medicine. Incorporated in our mainstream analysis of patients for precision medicine, this could become a powerful tool. Despite the small sample size, our positive results indicate that the differences we observed are likely only the tip of the iceberg of information we might achieve with large patient samples when studying iPSC-derived excitatory neurons. These preliminary findings suggest that this methodology has the potential for better understanding AD mechanisms and identifying potential targets for treatment, as well as better understanding the heterogeneity of mechanisms within AD.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cells12151990/s1, Table S1: Complete list of the differentially expressed (DE) genes in AD patient versus control-derived samples with p-values; Table S2: Normalized counts in excitatory neurons (RNAseq) for patients and healthy individuals used in the transcriptomic studies.

Author Contributions

Conceptualization, V.M. and D.A.; methodology, R.S.; software, I.A. and D.A.; validation, I.A., V.M. and D.A.; formal analysis, I.A. and D.A.; investigation, R.S., I.A., C.Z. and A.X.; resources, E.S.O. and P.B.R. and C.G.L.; data curation, I.A., D.A. and V.M.; writing—original draft preparation, D.A. and V.M.; writing—review and editing, R.S., I.A., C.Z., A.X., E.S.O., P.B.R., C.G.L., V.M. and D.A.; visualization, R.S., I.A., V.M. and D.A.; supervision, V.M. and D.A.; project administration, V.M.; funding acquisition, V.M. and C.G.L. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the R21 AG067016 NIH grant and the Johns Hopkins Richman Family Precision Medicine Center of Excellence in Alzheimer’s Disease including significant contributions from the Richman Family Foundation, the Rick Sharp Alzheimer’s Foundation, and the Sharp Family Foundation. Support for collection of the blood sample was provided by R01AG052510 to the S-CitAD study.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board (or Ethics Committee) of Johns Hopkins (IRB00038456; 6/17/2014 and NA_00045104; 10/28/2014).

Informed Consent Statement

Be submitted to the Gene Expression Omnibus database.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Nichols, E.; Steinmetz, J.D.; Vollset, S.E.; Fukutaki, K.; Chalek, J.; Abd-Allah, F.; Abdoli, A.; Abualhasan, A.; Abu-Gharbieh, E.; Akram, T.T.; et al. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: An analysis for the Global Burden of Disease Study 2019. Lancet Public Health 2022, 7, e105–e125. [Google Scholar] [CrossRef] [PubMed]
  2. Goate, A.; Chartier-Harlin, M.C.; Mullan, M.; Brown, J.; Crawford, F.; Fidani, L.; Giuffra, L.; Haynes, A.; Irving, N.; James, L. Segregation of a missense mutation in the amyloid precursor protein gene with familial Alzheimer’s disease. Nature 1991, 349, 704–706. [Google Scholar] [CrossRef] [PubMed]
  3. Sherrington, R.; Rogaev, E.I.; Liang, Y.; Rogaeva, E.A.; Levesque, G.; Ikeda, M.; Chi, H.; Lin, C.; Li, G.; Holman, K.; et al. Cloning of a gene bearing missense mutations in early-onset familial Alzheimer’s disease. Nature 1995, 375, 754–760. [Google Scholar] [CrossRef] [PubMed]
  4. Levy-Lahad, E.; Wijsman, E.M.; Nemens, E.; Anderson, L.; Goddard, K.A.; Weber, J.L.; Bird, T.D.; Schellenberg, G.D. A familial Alzheimer’s disease locus on chromosome 1. Science 1995, 269, 970–973. [Google Scholar] [CrossRef]
  5. Strittmatter, W.J.; Saunders, A.M.; Schmechel, D.; Pericak-Vance, M.; Enghild, J.; Salvesen, G.S.; Roses, A.D. Apolipoprotein E: High-avidity binding to beta-amyloid and increased frequency of type 4 allele in late-onset familial Alzheimer disease. Proc. Natl. Acad. Sci. USA 1993, 90, 1977–1981. [Google Scholar] [CrossRef]
  6. Reiman, E.M.; Webster, J.A.; Myers, A.J.; Hardy, J.; Dunckley, T.; Zismann, V.L.; Joshipura, K.D.; Pearson, J.V.; Hu-Lince, D.; Huentelman, M.J.; et al. GAB2 alleles modify Alzheimer’s risk in APOE epsilon4 carriers. Neuron 2007, 54, 713–720. [Google Scholar] [CrossRef] [Green Version]
  7. Wightman, D.P.; Jansen, I.E.; Savage, J.E.; Shadrin, A.A.; Bahrami, S.; Holland, D.; Rongve, A.; Børte, S.; Winsvold, B.S.; Drange, O.K.; et al. A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer’s disease. Nat. Genet. 2021, 53, 1276–1282. [Google Scholar] [CrossRef]
  8. Myint, L.; Wang, R.; Boukas, L.; Hansen, K.D.; Goff, L.A.; Avramopoulos, D. A screen of 1,049 schizophrenia and 30 Alzheimer’s-associated variants for regulatory potential. Am. J. Med Genet. Part B Neuropsychiatr. Genet. 2020, 183, 61–73. [Google Scholar] [CrossRef]
  9. A Physician Surplus?|NEJM. Available online: https://www.nejm.org/doi/full/10.1056/NEJM198810063191419 (accessed on 19 July 2023).
  10. Bellenguez, C.; Küçükali, F.; Jansen, I.E.; Kleineidam, L.; Moreno-Grau, S.; Amin, N.; Naj, A.C.; Campos-Martin, R.; Grenier-Boley, B.; Andrade, V.; et al. New insights into the genetic etiology of Alzheimer’s disease and related dementias. Nat. Genet. 2022, 54, 412–436. [Google Scholar] [CrossRef]
  11. Takahashi, K.; Tanabe, K.; Ohnuki, M.; Narita, M.; Ichisaka, T.; Tomoda, K.; Yamanaka, S. Induction of pluripotent stem cells from adult human fibroblasts by defined factors. Cell 2007, 131, 861–872. [Google Scholar] [CrossRef] [Green Version]
  12. Chou, B.-K.; Mali, P.; Huang, X.; Ye, Z.; Dowey, S.N.; Resar, L.M.; Zou, C.; Zhang, Y.A.; Tong, J.; Cheng, L. Efficient human iPS cell derivation by a non-integrating plasmid from blood cells with unique epigenetic and gene expression signatures. Cell Res. 2011, 21, 518–529. [Google Scholar] [CrossRef] [PubMed]
  13. Liu, G.; David, B.T.; Trawczynski, M.; Fessler, R.G. Advances in Pluripotent Stem Cells: History, Mechanisms, Technologies, and Applications. Stem Cell Rev. Rep. 2020, 16, 3–32. [Google Scholar] [CrossRef] [Green Version]
  14. Nishikawa, S.-I.; Goldstein, R.A.; Nierras, C.R. The promise of human induced pluripotent stem cells for research and therapy. Nat. Rev. Mol. Cell Biol. 2008, 9, 725–729. [Google Scholar] [CrossRef] [PubMed]
  15. Takahashi, K.; Yamanaka, S. Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell 2006, 126, 663–676. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Shi, Y.; Inoue, H.; Wu, J.C.; Yamanaka, S. Induced pluripotent stem cell technology: A decade of progress. Nat. Rev. Drug Discov. 2017, 16, 115–130. [Google Scholar] [CrossRef] [PubMed]
  17. Das, D.; Feuer, K.; Wahbeh, M.; Avramopoulos, D. Modeling Psychiatric Disorder Biology with Stem Cells. Curr. Psychiatry Rep. 2020, 22, 24. [Google Scholar] [CrossRef] [PubMed]
  18. Das, D.; Peng, X.; Lam, A.-T.N.; Bader, J.S.; Avramopoulos, D. Transcriptome analysis of human induced excitatory neurons supports a strong effect of clozapine on cholesterol biosynthesis. Schizophr. Res. 2021, 228, 324–326. [Google Scholar] [CrossRef]
  19. Karagiannis, P.; Takahashi, K.; Saito, M.; Yoshida, Y.; Okita, K.; Watanabe, A.; Inoue, H.; Yamashita, J.K.; Todani, M.; Nakagawa, M.; et al. Induced Pluripotent Stem Cells and Their Use in Human Models of Disease and Development. Physiol. Rev. 2019, 99, 79–114. [Google Scholar] [CrossRef]
  20. Ehrhardt, S.; Porsteinsson, A.P.; Munro, C.A.; Rosenberg, P.B.; Pollock, B.G.; Devanand, D.P.; Mintzer, J.; Rajji, T.K.; Ismail, Z.; Schneider, L.S.; et al. Escitalopram for agitation in Alzheimer’s disease (S-CitAD): Methods and design of an investigator-initiated, randomized, controlled, multicenter clinical trial. Alzheimer’s Dement. 2019, 15, 1427–1436. [Google Scholar] [CrossRef]
  21. Mahairaki, V.; Ryu, J.; Peters, A.; Chang, Q.; Li, T.; Park, T.S.; Burridge, P.W.; Talbot, C.C.; Asnaghi, L.; Martin, L.J.; et al. Induced Pluripotent Stem Cells from Familial Alzheimer’s Disease Patients Differentiate into Mature Neurons with Amyloidogenic Properties. Stem Cells Dev. 2014, 23, 2996–3010. [Google Scholar] [CrossRef]
  22. Das, D.; Li, J.; Liu, S.; Oh, E.; Cheng, L.; Lyketsos, C.; Mahairaki, V. Generation and characterization of a novel human iPSC line from a resilient Alzheimer’s disease patient. Stem Cell Res. 2020, 48, 101979. [Google Scholar] [CrossRef] [PubMed]
  23. Chou, B.-K.; Gu, H.; Gao, Y.; Dowey, S.N.; Wang, Y.; Shi, J.; Li, Y.; Ye, Z.; Cheng, T.; Cheng, L. A facile method to establish human induced pluripotent stem cells from adult blood cells under feeder-free and xeno-free culture conditions: A clinically compliant approach. Stem Cells Transl. Med. 2015, 4, 320–332. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Yu, J.; Hu, K.; Smuga-Otto, K.; Tian, S.; Stewart, R.; Slukvin, I.I.; Thomson, J.A. Human Induced Pluripotent Stem Cells Free of Vector and Transgene Sequences. Science 2009, 324, 797–801. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Zhang, Y.; Pak, C.; Han, Y.; Ahlenius, H.; Zhang, Z.; Chanda, S.; Marro, S.; Patzke, C.; Acuna, C.; Covy, J.; et al. Rapid single-step induction of functional neurons from human pluripotent stem cells. Neuron 2013, 78, 785–798. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Lam, A.-T.N.; Peng, X.; Das, D.; Bader, J.S.; Avramopoulos, D. Transcriptomic data of Clozapine-treated Ngn2-induced Human Excitatory Neurons. Data Brief 2021, 35, 106897. [Google Scholar] [CrossRef] [PubMed]
  27. Wahbeh, M.H.; Peng, X.; Bacharaki, S.; Hatzimanolis, A.; Dimitrakopoulos, S.; Wohler, E.; Yang, X.; Yovo, C.; Maher, B.J.; Sobreira, N.; et al. A Missense Variant in CASKIN1′s Proline-Rich Region Segregates with Psychosis in a Three-Generation Family. Genes 2023, 14, 177. [Google Scholar] [CrossRef]
  28. Kim, D.; Paggi, J.M.; Park, C.; Bennett, C.; Salzberg, S.L. Salzberg Graph-Based Genome Alignment and Genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 2019, 37, 907–915. [Google Scholar] [CrossRef]
  29. Li, H.; Handsaker, B.; Wysoker, A.; Fennell, T.; Ruan, J.; Homer, N.; Marth, G.; Abecasis, G.; Durbin, R. The Sequence Alignment/Map format and SAMtools. Bioinformatics 2009, 25, 2078–2079. [Google Scholar] [CrossRef] [Green Version]
  30. Pertea, M.; Pertea, G.M.; Antonescu, C.M.; Chang, T.-C.; Mendell, J.T.; Salzberg, S.L. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 2015, 33, 290–295. [Google Scholar] [CrossRef] [Green Version]
  31. Pertea, M.; Kim, D.; Pertea, G.M.; Leek, J.T.; Salzberg, S.L. Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nat. Protoc. 2016, 11, 1650–1667. [Google Scholar] [CrossRef]
  32. Soneson, C.; Love, M.I.; Robinson, M.D. Robinson. Differential analyses for RNA-seq: Transcript-level estimates improve gene-level inferences. F1000Research 2015, 4, 1521. [Google Scholar] [CrossRef]
  33. Love, M.I.; Huber, W.; Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014, 15, 550. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Mathys, H.; Davila-Velderrain, J.; Peng, Z.; Gao, F.; Mohammadi, S.; Young, J.Z.; Menon, M.; He, L.; Abdurrob, F.; Jiang, X.; et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 2019, 570, 332–337. [Google Scholar] [CrossRef] [PubMed]
  35. Szklarczyk, D.; Kirsch, R.; Koutrouli, M.; Nastou, K.; Mehryary, F.; Hachilif, R.; Gable, A.L.; Fang, T.; Doncheva, N.T.; Pyysalo, S.; et al. The STRING database in 2023: Protein–protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 2022, 51, D638–D646. [Google Scholar] [CrossRef] [PubMed]
  36. Feuer, K.L.; Peng, X.; Yovo, C.; Avramopoulos, D. DPYSL2/CRMP2 isoform B knockout in human iPSC-derived glutamatergic neurons confirms its role in mTOR signaling and neurodevelopmental disorders. Mol. Psychiatry 2023, 1–10. [Google Scholar] [CrossRef]
  37. Ho, S.-M.; Hartley, B.J.; Tcw, J.; Beaumont, M.; Stafford, K.; Slesinger, P.A.; Brennand, K.J. Rapid Ngn2-induction of excitatory neurons from hiPSC-derived neural progenitor cells. Methods 2016, 101, 113–124. [Google Scholar] [CrossRef] [Green Version]
  38. McInnes, L.; Healy, J.; Melville, J. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv 2020. [Google Scholar] [CrossRef]
  39. Morris, J.C. The Clinical Dementia Rating (CDR): Current version and scoring rules. Neurology 1993, 43, 2412–2414. [Google Scholar] [CrossRef]
  40. Borkowski, J.G.; Benton, A.L.; Spreen, O. Word fluency and brain damage. Neuropsychologia 1967, 5, 135–140. [Google Scholar] [CrossRef]
  41. Folstein, M.F.; Folstein, S.E.; McHugh, P.R. ‘Mini-mental state’. A practical method for grading the cognitive state of patients for the clinician. J. Psychiatr. Res. 1975, 12, 189–198. [Google Scholar] [CrossRef]
  42. Lambert, J.C.; Ibrahim-Verbaas, C.A.; Harold, D.; Naj, A.C.; Sims, R.; Bellenguez, C.; DeStafano, A.L.; Bis, J.C.; Beecham, G.W.; Grenier-Boley, B.; et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat. Genet. 2013, 45, 1452–1458. [Google Scholar] [CrossRef] [Green Version]
  43. Tao, Q.-Q.; Chen, Y.-C.; Wu, Z.-Y. The role of CD2AP in the Pathogenesis of Alzheimer’s Disease. Aging Dis. 2019, 10, 901–907. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Herold, C.; Hooli, B.V.; Mullin, K.; Liu, T.; Roehr, J.T.; Mattheisen, M.; Parrado, A.R.; Bertram, L.; Lange, C.; Tanzi, R.E. Family-based Association Analyses of Imputed Genotypes Reveal Genome-Wide Significant Association of Alzheimer’s disease with OSBPL6, PTPRG and PDCL3. Mol. Psychiatry 2016, 21, 1608–1612. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. O’Leary, A.; Fernàndez-Castillo, N.; Gan, G.; Yang, Y.; Yotova, A.Y.; Kranz, T.M.; Grünewald, L.; Freudenberg, F.; Antón-Galindo, E.; Cabana-Domínguez, J.; et al. Behavioural and functional evidence revealing the role of RBFOX1 variation in multiple psychiatric disorders and traits. Mol. Psychiatry 2022, 27, 4464–4473. [Google Scholar] [CrossRef] [PubMed]
  46. Ponthier, J.L.; Schluepen, C.; Chen, W.; Lersch, R.A.; Gee, S.L.; Hou, V.C.; Lo, A.J.; Short, S.A.; Chasis, J.A.; Winkelmann, J.C.; et al. Fox-2 splicing factor binds to a conserved intron motif to promote inclusion of protein 4.1R alternative exon 16. J. Biol. Chem. 2006, 281, 12468–12474. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. Alam, S.; Suzuki, H.; Tsukahara, T. Alternative splicing regulation of APP exon 7 by RBFox proteins. Neurochem. Int. 2014, 78, 7–17. [Google Scholar] [CrossRef]
  48. Yang, H.-S.; White, C.C.; Klein, H.-U.; Yu, L.; Gaiteri, C.; Ma, Y.; Felsky, D.; Mostafavi, S.; Petyuk, V.A.; Sperling, R.A.; et al. Genetics of Gene Expression in the Aging Human Brain Reveal TDP-43 Proteinopathy Pathophysiology. Neuron 2020, 107, 496–508.e6. [Google Scholar] [CrossRef]
  49. Chen, Y.-C.; Liu, Y.-L.; Tsai, S.-J.; Kuo, P.-H.; Huang, S.-S.; Lee, Y.-S. LRRTM4 and PCSK5 Genetic Polymorphisms as Markers for Cognitive Impairment in A Hypotensive Aging Population: A Genome-Wide Association Study in Taiwan. J. Clin. Med. 2019, 8, 1124. [Google Scholar] [CrossRef] [Green Version]
  50. Lamont, E.W.; Legault-Coutu, D.; Cermakian, N.; Boivin, D.B. The role of circadian clock genes in mental disorders. Dialogues Clin. Neurosci. 2007, 9, 333–342. [Google Scholar] [CrossRef]
  51. Sanchez-Juan, P.; Bishop, M.T.; Aulchenko, Y.S.; Brandel, J.-P.; Rivadeneira, F.; Struchalin, M.; Lambert, J.-C.; Amouyel, P.; Combarros, O.; Sainz, J.; et al. Genome-wide study links MTMR7 gene to variant Creutzfeldt-Jakob risk. Neurobiol. Aging 2012, 33, 1487.e21–1487.e28. [Google Scholar] [CrossRef]
  52. E Lloyd, S.; Mead, S.; Collinge, J. Genetics of prion diseases. Curr. Opin. Genet. Dev. 2013, 23, 345–351. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Lananna, B.V.; McKee, C.A.; King, M.W.; Del-Aguila, J.L.; Dimitry, J.M.; Farias, F.H.G.; Nadarajah, C.J.; Xiong, D.D.; Guo, C.; Cammack, A.J.; et al. Chi3l1/YKL-40 is controlled by the astrocyte circadian clock and regulates neuroinflammation and Alzheimer’s disease pathogenesis. Sci. Transl. Med. 2020, 12, eaax3519. [Google Scholar] [CrossRef] [PubMed]
  54. Sepulveda-Diaz, J.E.; Alavi Naini, S.M.; Huynh, M.B.; Ouidja, M.O.; Yanicostas, C.; Chantepie, S.; Villares, J.; Lamari, F.; Jospin, E.; van Kuppevelt, T.H.; et al. HS3ST2 expression is critical for the abnormal phosphorylation of tau in Alzheimer’s disease-related tau pathology. Brain 2015, 138, 1339–1354. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  55. Atkin, G.; Hunt, J.; Minakawa, E.; Sharkey, L.; Tipper, N.; Tennant, W.; Paulson, H.L. F-box only protein 2 (Fbxo2) regulates amyloid precursor protein levels and processing. J. Biol. Chem. 2014, 289, 7038–7048. [Google Scholar] [CrossRef] [Green Version]
  56. Williams, J.B.; Cao, Q.; Wang, W.; Lee, Y.-H.; Qin, L.; Zhong, P.; Ren, Y.; Ma, K.; Yan, Z. Inhibition of histone methyltransferase Smyd3 rescues NMDAR and cognitive deficits in a tauopathy mouse model. Nat. Commun. 2023, 14, 91. [Google Scholar] [CrossRef]
Cells 12 01990 g001
Figure 1. Characterization of iPSCs: (a) Brightfield microscope shows the iPSC morphology (Scale bars: 400 μm). (b) Fluorescent immunostaining by using pluripotency markers (TRA-1-60, OCT4, Nanog) (Scale bars: 100 μm). (c) Flow-cytometry shows the purity of the iPSCs by pluripotency marker (TRA-1-60+ cells).
Figure 1. Characterization of iPSCs: (a) Brightfield microscope shows the iPSC morphology (Scale bars: 400 μm). (b) Fluorescent immunostaining by using pluripotency markers (TRA-1-60, OCT4, Nanog) (Scale bars: 100 μm). (c) Flow-cytometry shows the purity of the iPSCs by pluripotency marker (TRA-1-60+ cells).
Cells 12 01990 g001
Cells 12 01990 g002
Figure 2. Expression of marker genes in human brain. Heatmap of the expression of the genes in Table 2 in human excitatory neurons from the single-cell expression database available from the Allen Brain Atlas. For details on subtypes and single cells, see the Allen Brain Atlas database.
Figure 2. Expression of marker genes in human brain. Heatmap of the expression of the genes in Table 2 in human excitatory neurons from the single-cell expression database available from the Allen Brain Atlas. For details on subtypes and single cells, see the Allen Brain Atlas database.
Cells 12 01990 g002
Cells 12 01990 g003
Figure 3. Principal component analysis (PCA) using all genes on AD and control samples.
Figure 3. Principal component analysis (PCA) using all genes on AD and control samples.
Cells 12 01990 g003
Cells 12 01990 g004
Figure 4. Identification of AD patients’ clusters by UMAP.
Figure 4. Identification of AD patients’ clusters by UMAP.
Cells 12 01990 g004
Table 1. Diagnosis and demographics of patients and healthy individuals used in the transcriptomic studi-s.
Table 1. Diagnosis and demographics of patients and healthy individuals used in the transcriptomic studi-s.
Tracking IDPhenotypeAgeGenderSource of iPSC
JHU-AD-01AD68FPBMC
JHU-AD-02AD60FPBMC
JHU-AD-03AD70MPBMC
JHU-AD-04AD62FPBMC
JHU-AD-05AD72MPBMC
JHU-AD-06AD55FPBMC
JHU-AD-07AD89FPBMC
JHU-AD-08AD81MPBMC
JHU-WT-01Normal56FSkin Fibroblast
JHU-WT-03Normal81FPBMC
JHU-WT-04Normal85FPBMC
JHU-WT-05Normal56FSkin Fibroblast
JHU-WT-07Normal88FPBMC
JHU-WT-08Normal84FPBMC
Table 2. Expression of excitatory neuronal markers in iPSC-derived neurons.
Table 2. Expression of excitatory neuronal markers in iPSC-derived neurons.
MarkerGene NameIdentityPFKM
NeuNRBFOX3Neuronal22.5
MAP2MAP2Neuronal197
160 kDa neurofilament MediumNEFMNeuronal2428
200 kDa neurofilament HeavyNEFHNeuronal6.5
SynaptophysinSYPNeuronal207
PSD95DLG4Neuronal134
TUJ1TUBB3Neuronal1384
vGluT1SLC17A7Glutamatergic24
vGluT2SLC17A6Glutamatergic107
NMDAR1GRIN1Glutamatergic5.4
GlutaminaseGLSGlutamatergic29.3
Glutamine synthetaseGLULGlutamatergic27
Table 3. Comparison of identified differentially regulated genes with external data.
Table 3. Comparison of identified differentially regulated genes with external data.
SC Study Threshold p (N Genes)Our Study Threshold p (N Genes)Overlap Fold Excess over Expected𝓧 Test p ValueConcordance %Concordance NBinomial p-Value
0.00005 (1331)0.05 (394)461.060n.s.72%330.002
0.000005 (899)0.05 (394)361.228n.s.75%270.002
0.00005 (1331)0.1 (765)1091.2930.00319650760%650.0275
0.000005 (899)0.1 (765)861.5110.000035105562%530.02
0.00005 (1331)0.2 (1547)2001.1730.01018637359%1180.0065
0.000005 (899)0.2 (1547)1461.2680.00136146964%940.00032
0.0000005 (632)0.2 (1547)1001.2360.0197440163%630.006
Table 4. Clinical differences of the identified patient clusters. Sum of FAS is the sum of number of words starting from F, A, and S. MMSE is the score on the MMSE including serial 7s.
Table 4. Clinical differences of the identified patient clusters. Sum of FAS is the sum of number of words starting from F, A, and S. MMSE is the score on the MMSE including serial 7s.
PatientAge-at-ExaminationSum of FASMMSEOverall CDR RatingCDR Sum of Boxes
AD-462171814.5
AD-26023170.53.5
AD-16848240.52.5
AD-37047250.53
AD-572271918.5
t-test p0.010.1080.1230.7890.81
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Sagar, R.; Azoidis, I.; Zivko, C.; Xydia, A.; Oh, E.S.; Rosenberg, P.B.; Lyketsos, C.G.; Mahairaki, V.; Avramopoulos, D. Excitatory Neurons Derived from Human-Induced Pluripotent Stem Cells Show Transcriptomic Differences in Alzheimer’s Patients from Controls. Cells 2023, 12, 1990. https://doi.org/10.3390/cells12151990

AMA Style

Sagar R, Azoidis I, Zivko C, Xydia A, Oh ES, Rosenberg PB, Lyketsos CG, Mahairaki V, Avramopoulos D. Excitatory Neurons Derived from Human-Induced Pluripotent Stem Cells Show Transcriptomic Differences in Alzheimer’s Patients from Controls. Cells. 2023; 12(15):1990. https://doi.org/10.3390/cells12151990

Chicago/Turabian Style

Sagar, Ram, Ioannis Azoidis, Cristina Zivko, Ariadni Xydia, Esther S. Oh, Paul B. Rosenberg, Constantine G. Lyketsos, Vasiliki Mahairaki, and Dimitrios Avramopoulos. 2023. "Excitatory Neurons Derived from Human-Induced Pluripotent Stem Cells Show Transcriptomic Differences in Alzheimer’s Patients from Controls" Cells 12, no. 15: 1990. https://doi.org/10.3390/cells12151990

APA Style

Sagar, R., Azoidis, I., Zivko, C., Xydia, A., Oh, E. S., Rosenberg, P. B., Lyketsos, C. G., Mahairaki, V., & Avramopoulos, D. (2023). Excitatory Neurons Derived from Human-Induced Pluripotent Stem Cells Show Transcriptomic Differences in Alzheimer’s Patients from Controls. Cells, 12(15), 1990. https://doi.org/10.3390/cells12151990

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