Abstract
Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental polygenic disorder that affects more than 5% of children and adolescents around the world. Genetic and environmental factors play important roles in ADHD etiology, which leads to a wide range of clinical outcomes and biological phenotypes across the population. Brain maturation delays of a 4-year lag are commonly found in patients, when compared to controls of the same age. Possible differences in cellular growth rates might reflect the clinical observations in ADHD patients. However, the cellular mechanisms are still not elucidated. To test this hypothesis, we analysed the proliferation of induced pluripotent stem cells (iPSCs) and neural stem cells (NSCs) derived from male children and adolescents diagnosed with ADHD and with genetic predisposition to it (assessed using polygenic risk scores), as well as their respective matched controls. In the current pilot study, it was noticeable that NSCs from the ADHD group proliferate less than controls, while no differences were seen at the iPSC developmental stage. Our results from two distinct proliferation methods indicate that the functional and structural delays found in patients might be associated with these in vitro phenotypic differences, but start at a distinct neurodevelopmental stage. These findings are the first ones in the field of disease modelling of ADHD and might be crucial to better understand the pathophysiology of this disorder.
Similar content being viewed by others
Avoid common mistakes on your manuscript.
Introduction
Attention-deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders with a worldwide prevalence of over 5% affecting children and adolescents, from which ca. 60% of the cases persist into adulthood (Sharma and Couture 2014; Banaschewski et al. 2017). Clinicians estimate brain maturational delays of about 3 to 5 years, with ADHD patients displaying difficulties in behaviour control and attentive behaviour (Posner et al. 2020; Shaw et al. 2007). These symptoms elicit challenges in everyday life in terms of academic performance and social function (Abrahão and Elias 2021). While the etiology is still not fully understood, multiple genetic factors have been discovered to be strongly associated with ADHD (Palladino et al. 2019; Wood and Neale 2010). Genome wide association studies (GWAS) have revealed risk loci in ADHD, involving genes encoded for brain-related processes such as synapse formation, neuronal development, and plasticity, indicating maturational processes being affected (Demontis et al. 2019). Moreover, studies report abnormalities in brain structure and delayed maturational processes in regions associated with cognitive processes in ADHD patients (Hoogman et al. 2019; Shaw et al. 2007). In consistency to these findings, meta-analysis of gene variants displays negative correlations of intracranial volume, showing a significant overlap between genetics and brain volume measures in ADHD (Klein et al. 2019). ADHD is considered a multifactorial disorder, demonstrating phenotypic and genetic variability, signifying the need for personalized research (Franke et al. 2009; Koi 2021). Therefore, it is of importance to recreate the disorder in an accurate model that recapitulates the genetic background of individuals (Green et al. 2022; Faraone and Larsson 2019). Induced pluripotent stem cell (iPSC) technology displays a relevant tissue model, as cell reprogramming can be conducted on non-invasively obtained somatic cells from patients (Wiegand and Banerjee 2019; Yde Ohki et al. 2022). Human iPSCs allows the research of different developmental stages from iPSCs to mature neurons, mimicking embryonic neurogenesis (Compagnucci et al. 2014).
Neurogenesis is a complex process mediating stem cell proliferation and differentiation relevant for expansion and cellular specification of the brain, from which a tightly regulated timeline plays a critical role (Andrews and Nowakowski 2019). During healthy brain development, neural stem cells (NSCs) are driven to coordinate the interplay between self-renewal, further differentiation to intermediate progenitor cells, and direct differentiation to neurons (Guillemot 2005). Furthermore, NSCs display an important intermediate for the rapid expansion of the brain during neurogenesis (Andrews and Nowakowski 2019; Rajan et al. 2021). An imbalance in early brain development can lead to detrimental consequences for individuals causing neurodevelopmental disorders, such as schizophrenia, autism spectrum disorders (ASD) and ADHD (Courchesne et al. 2020; Friedman and Rapoport 2015; Pantelis et al. 2005).
The current study aims to elucidate neural development in ADHD by evaluating proliferation rates in iPSCs and iPSC-derived NSCs, representing important developmental stages throughout embryonic neurogenesis. Our hypothesis encompasses the proliferation rate being attenuated in ADHD indicating developmental delays as suggested by previous publications (Berger et al. 2013; Dark et al. 2018). To assess the proliferation in iPSCs and NSCs, impedance-based analysis using the xCELLigence settings, with measurements taken throughout 5 days, was considered (Grünblatt et al. 2018). To validate these results, further growth rate analysis using WST-1 assays for 4 days was taken into account. These findings will support previous assumptions of developmental delays at a cellular level and furthermore allow a fundamental approach to further investigate potential treatments in ADHD.
Methods
Recruitment of participants
Children and adolescents with ADHD (responding to MPH treatment) and healthy controls (from 6 to 18 years old) were recruited by the Department of Child and Adolescent Psychiatry and Psychotherapy (KJPP) of the University of Zurich (UZH). Inclusion and exclusion criteria were described in previous publications of our group (Grossmann et al. 2021; Yde Ohki et al. 2021). Details about the participants involved in this study (3 ADHD patients versus 3 healthy controls) can be found in Supplementary Table 1.
After recruitment, salivary DNA samples from patients and controls were submitted to genotyping, as described in previous publications (Grossmann et al. 2021; Yde Ohki et al. 2021). Individual polygenic risk scores (PRS) were calculated as an indicator of genetic predisposition to ADHD, using the software PLINK 1.9 (Chang et al. 2015). Individual PRS were computed using the PRS-auto method with EUR super-population from 1000 Genomes as the linkage disequilibrium reference panel and based on the genome-wide significant risk loci data from Demontis et al. (Demontis et al. 2019; Ge et al. 2019).
Generation and culture of iPSCs
Plucked hair-derived keratinocytes and peripheral mononuclear blood cells (PBMCs) from ADHD patients and healthy controls were collected and reprogrammed into iPSCs using Sendai virus transduction. Quality control (QC) from generated iPSCs was performed as reported in previous publications (Grossmann et al. 2021; Yde Ohki et al. 2021). The QC of iPSC lines K015 c1 and c9, which have not been included in these previous reports, is shown in Supplementary Fig. 1.
Quality control of iPSC-derived NSCs
When NSCs reached p5, NSCs underwent gene and protein expression analysis through RT-qPCR and immunocytochemistry assays, respectively. In the former, the expression of NSC genes PAX6, SOX2 and NES (Nestin) was investigated, using ACTB and C5orf18 (REEP5) as reference genes. The technical procedure was performed as described in Grossmann et al. and Yde Ohki et al., and the primers used are available in Supplementary Table 2 (Grossmann et al. 2021; Yde Ohki et al. 2021).
For immunocytochemistry, protein expression of the classical markers FOXG1, NESTIN, SOX2 and TUJ1 was observed (Supplementary Fig. 2). Details about the primary and secondary antibodies used in this protocol may be found in Supplementary Table 3.
Once the NSCs reached around 90–100% of confluence in 96 wells, the media was removed, and the cells were carefully washed with PBS 1X. Paraformaldehyde (PFA) at 4% was added and cells were incubated for 20 min at room temperature (RT). After incubation, PFA was aspirated properly discarded in special waste and the wells were rinsed 3 times with PBS 1X for 5 min each. After the last washing step, blocking buffer (0.1% Triton™ X-100, 1% BSA in PBS) was added to the wells, which were incubated for 30 min at RT.
In the meantime, the primary antibody mixtures were prepared in blocking buffer on ice. The following antibody combinations can be considered: FOXG1/NESTIN and TUJ1/SOX2. After the incubation period, the antibody combinations were transferred accordingly to the wells, which were incubated overnight at 4 °C.
On the following day, primary antibodies were removed and the cells, washed 3 times with PBS 1X for 5 min each. Next, the NSCs were washed with blocking buffer for 5 min. A secondary antibody mixture was then prepared and added to cells, which were incubated for 30 min in the dark at RT.
To finalize the procedure, the cells were rinsed 3 times with PBS 1X for 5 min each. After the last step, one drop of fluorescence mounting medium (Agilent) was added to each well. Images were subsequently acquired using an IX81 microscope (Olympus) and the cellSens Dimension software (version 3.1) (Olympus).
Assessment of cell proliferation of iPSCs and NSCs via xCELLigence real-time impedance cell analysis
Human iPSCs that were generated and checked for QC in our study were tested in xCELLigence. Real-time impedance-based cell proliferation of generated iPSCs and NSC was measured using the xCELLigence® real-time cell analysis (RTCA) system (ACEA Biosciences).
Each iPSC line was seeded in 12 replicates onto E-96 plates (OLS® BIO) coated with Vitronectin 5 μg/mL (Gibco™) beforehand and stored overnight at 4 °C. Baseline was measured during 1 min after temperature equilibration with Essential 8™ Flex medium (E8 Flex, Gibco™) for 30 min at 37 °C and 5% CO2. iPSCs were counted using Countess™ II FL Automated Cell Counter (Thermo Fisher Scientific™) and seeded at a concentration of 25′000 cells/well. For NSCs, 15′000 cells/well were seeded onto Geltrex (Gibco™) -coated E-96 plates, diluted 1:100 in DMEM/F12 (Gibco™), and cultivated in Neural Expansion Media (NEM; PSC Neural Induction Medium, Gibco™).
For both cell types, media change started one day after seeding the cells and was performed every other day by replacing the medium with fresh E8 Flex or NEM, respectively. Cell proliferation was automatically measured every hour within 5 days. Analysis was performed using RTCA Software (version 2.0) and the graph was generated using the GraphPad Prism software (version 9.0).
Growth rate was determined according to a Malthusian growth model using a least squares method to fit the non-linear regression curve on the cell index (CI) data during the exponential growth period defined from 24 h to CImax set as the first peak followed by 5 steps decline (R Statistical Software v. 4.1.2). Outliers between the replicates were removed according to the interquartile range (IQR) method. For iPSCs, at least 9 replicates per experiment were analysed, while for NSCs, at least 3 replicates were analysed in two independent experiments each. Mean of the wells were plotted per experiment.
Assessment of cell proliferation of iPSCs and NSCs via WST-1 assays
Using the same cell concentrations per well as in xCELLigence, 25′000 iPSCs /well were seeded onto Vitronectin-coated 96-well plates (final concentration of 5 μg/mL in PBS 1x (Gibco™)). On the other hand, NSCs were seeded at a final concentration of 15′000 cells/well onto Geltrex-coated 96 wells (1:100 dilution in DMEM/F12). Cells were seeded in triplicates on 8 different plates for each time point (24 h, 42 h, 48 h, 66 h, 72 h, 90 h, 96 h and 114 h) in E8 Flex or NEM. For both iPSCs and NSCs, the media were completely refreshed every other day.
At each time-point 10 µL of WST-1 tetrazolium salt (Sigma) was added in each well and the plate was incubated for 4 h in a humidified atmosphere at 37 °C, 5% CO2. Four hours later, the absorbance was measured for each sample using the Mithras2 LB 943 Multimode Reader from Berthold Technologies at 440 nm with a reference wavelength at 630 nm. For iPSCs, this read-out was normalized against the background control (wells without cells) considered as blank. Each experiment was performed in triplicates, which were verified for outliers at each time-point by the z-score cleaning method. For iPSCs, three independent experiments were performed, in which the slope of the log2 transformed curve generated from 24 h to maximal absorbance was considered after applying a linear regression model (R Statistical Software v. 4.1.2). For NSCs, two independent experiments were performed and growth rates were computed using a linear regression from 24 h to maximal absorbance (GraphPad Prism software (version 9.0)).
For cell viability analysis, the mean of absorbance values from 96 and 114 h post-seeding (hps) per experiment were plotted.
Data analysis
Statistical tests and graphs were performed using the GraphPad Prism software (version 9.0). When data were normally distributed but with unequal standard deviations, two-tailed unpaired t tests with Welch’s correction were applied. On the other hand, two-tailed unpaired nonparametric Mann–Whitney tests were applied for data that were not normally distributed. A p value of at least 0.05 was considered significant.
Results
In the current pilot study, we investigated only cell lines derived from males, in which the patients were those with positive PRS z-score for ADHD, while the controls had negative PRS for ADHD genetic predisposition (See Supplementary Table 1). Individual growth rates for each iPSC line are depicted in Fig. 1C, D, while Fig. 2C, D depicts individual NSCs.
In the results for iPSC proliferation, we observed no significant differences in growth rates between ADHD and control groups measured using the xCELLigence (Fig. 1A; two-tailed unpaired t test with Welch’s correction, t(18.23) = 0.2514, p = 0.8043), while WST-1 assays have confirmed the non-significant finding (Fig. 1B; Mann–Whitney U = 110, p = 0.3807). On the other hand, growth rates from ADHD NSCs were significantly lower than controls in the real-time impedance method xCELLigence (Fig. 2A; two-tailed unpaired t test with Welch’s correction, t(11.88) = 2.187, p = 0.0495), as well as for the WST-1 method (Fig. 2B; two-tailed unpaired t test with Welch’s correction, t(16.74) = 2.846, p = 0.0113).
When NSC WST-1 experiments were analysed at each time-point separately, differences in the measurements were found for the latest time-points: 96 (Fig. 3A; two-tailed unpaired t test with Welch’s correction, t(19.86) = 2.755, p = 0.0123) and 114 h post-seeding (Fig. 3B; Mann–Whitney U = 19, p = 0.0056). At both time-points, ADHD NSCs seem to show lower metabolic rates than controls. Individual absorbance values are presented in Fig. 3C, D for 96 and 114 hps, respectively.
Discussion
Growth rates are one of the key measurements to assess specific phenotypes at the cellular and molecular levels in several neuropsychiatric conditions. Imbalances between NSC proliferation and differentiation have been previously associated with neurodevelopmental disorders (Ernst 2016). As supporting evidence show, genes that might be related with these two cellular processes also seem to be involved in those disorders, such as MECP2 in Rett syndrome (Li et al. 2014), ODC1 for schizophrenia and other neuropsychiatric disorders (Prokop et al. 2021) and UBE3A in Angelman’s syndrome (Mishra et al. 2009). In this pilot study, we aimed to investigate differences in cell growth rates of two distinct neurodevelopmental stages: iPSCs and NSCs from ADHD patients.
Even though iPSCs of ADHD patients and controls do not display any differences in our pilot study, ADHD NSCs proliferate significantly less than controls. Additionally, the cellular metabolic rate in ADHD seems to be lower than in controls, as already seen in patients in terms of hypometabolism and structural brain maturational delays (Zametkin et al. 1990; Rubia 2007); however, this will need further confirmation using additional metabolic measures, such as mitochondria activity or LDH assays. ADHD is a disorder marked by structural and functional brain maturational delays that might include and involve decreased cortical thinning (Shaw et al. 2006; Hoogman et al. 2017). Functional connectivity and executive functioning are also found to be impaired in ADHD (Wang et al. 2020; Zhang et al. 2020). These clinical observations might be associated with the differences found in the growth of our NSCs, as they are often discussed as part of an imbalance between proliferation and differentiation through molecular mechanisms that are still unknown.
The differences between iPSC and NSC phenotypes in vitro might be attributed to the fact that dynamic changes in transcriptomic and proteomic profiles are temporarily required throughout neural differentiation (Burke et al. 2020; Varderidou-Minasian et al. 2020; Kuruş et al. 2021). These observations led us to hypothesize that the phenotypic differences in terms of growth rates tend to happen in a more advanced developmental stage.
Interestingly, the same outcome was found in another neurodevelopmental disorder: Pitt-Hopkins Syndrome (PTHS). Papes et al. have shown reduced proliferation of iPSC-derived PTHS NSCs, while no differences were seen between the proliferation of PTHS and control iPSCs (Papes et al. 2022).
In accordance, defects in NSC proliferation have been reported in other neuropsychiatric disorders. For instance, iPSC-derived neural cells from autism spectrum disorder (ASD) patients were previously assessed. In this case, NSC from patients were proliferating more than controls, which might be contributing to the macrocephaly clinically observed in ASD (Marchetto et al. 2017). On this report, the Wnt signalling pathway was pointed out as a contributor to this altered phenotype, due to its role in NSC homeostasis by regulating proliferation and differentiation (Lie et al. 2005; Marchetto et al. 2017). Considering the same rationale, multiple evidence to distinct alterations in this pathway has been linked to ADHD pathogenesis (Yde Ohki et al. 2020) and needs to be further investigated at the cellular level.
In schizophrenia, NSCs from patients have been found to proliferate less than controls (Reif et al. 2006). As reviewed by Räsänen and colleagues, the observed decreased proliferation of this cell type could be involved with a premature neuronal differentiation, which could also have been associated with the Wnt signalling (Räsänen et al. 2022).
Here, we report a preliminary novel finding that might be of interest to the ADHD field. Using iPSC-derived neural cell lines allows us to maintain the genetic background of ADHD patients, while reproducing ex vivo cellular phenotypes. Despite the validity and strong contribution of animal models to ADHD research, the iPSC technology offers an advantageous alternative to the use of these models, which would not be able to represent the human polygenic profile of ADHD and its symptoms in a high fidelity manner, as well as human commercially available neural cell lines (e.g., SH-SY5Y neuroblastoma cells). However, the use of iPSC-derived 3D brain organoids would be helpful to study in the future the ADHD pathophysiology in a more complex and heterogeneous scenario.
Limitations
Although our study is resourceful for the initial studies in ADHD disease modelling, there are some limitations i.e. the small sample size, and studying only males. However, already in this preliminary study, a significant difference in growth was observed. Nevertheless, we aim to overcome this in future studies, by adding further individuals (including females) for more gender balanced and highly powered study.
Regarding age differences, participants from ADHD and control groups were not perfectly age matched, which could be considered a limitation. Up to this date, as multiple studies have shown, the irreversibility of age-related epigenetic identity from somatic cells through the reprogramming process is still debatable, as reviewed by (Simpson et al. 2021). In our data, no correlation between the age of participants and growth rates in both methods for both cell types was observed (Supplementary Figs. 3 and 4).
Following the same rationale, differences between iPSC lines coming from distinct somatic cell types may also be considered a discussion topic (Ohi et al. 2011; Sareen et al. 2014). Our analysis have shown that no differences were seen between the proliferation of iPSCs and NSCs originating from PBMCs or keratinocytes for WST-1 and xCELLigence (Supplementary Figs. 5 and 6).
We also need to take into account that only patients with positive ADHD PRS z-scores (as well as controls with negative z-scores) are being assessed in the current preliminary study. Considering the fact that this disorder is a spectrum, patients with low genetic liability to ADHD must also be investigated, as well as respective controls with high genetic predisposition. Balancing ADHD and control groups in this context would also be essential to understand the interplay between genetic and environmental risk factors in this disorder.
Regarding techniques, here we used two different methodologies to assess proliferation. Although WST-1 assays can be performed in distinct time-points to analyse how metabolically active cells are over time based on metabolism (Scarcello et al. 2020; Ghasemi et al. 2021; Aslantürk 2018), they only reflect the metabolic rate of a cell culture and do not directly quantify its exact number of viable cells (Berridge et al. 2005), while measuring in real-time proliferation of one single well across time-points is not possible, as opposed to xCELLigence (Stefanowicz-Hajduk and Ochocka 2020). Therefore, different technical approaches result in different variabilities, in which xCELLigence gives more stable results compared to the WST-1, which needs different wells for each time-point measurements.
Colorimetric WST-1 assays are commonly used to measure cell viability, by quantifying the amount of formazan dye derived from the enzymatic cleavage of tetrazolium salts (in this case, WST-1) in viable cells, indirectly measuring mitochondrial activity (Berridge et al. 2005). Therefore, higher absorbance values indicate higher metabolic activity that could be explained by a higher number of cells or higher metabolic rates from the same amount of cells. Given that proliferation rates are significantly lower in ADHD NSCs by two different methods, the first option was to be expected. However, the second possibility cannot be completely ruled out. Thus, being aware of the technical differences, as well as the advantages and disadvantages of each method, is necessary to take into consideration.
Conclusion
Up to this date, no studies revealing growth differences between cells derived from ADHD and healthy controls have been published. Although further investigation about the differentiation potential of NSCs would be required to elucidate possible discrepancies between ADHD and control lines, our pioneer findings already point out that imbalances in proliferation of patient-specific NSCs might be involved with brain maturational delays found in ADHD patients, in a cell-type dependent manner. To test this hypothesis, we are currently enlarging our sample size and balancing groups for gender and genetic predisposition to ADHD.
Data availability
The data presented are available upon request.
References
Abrahão ALB, dos Elias LC (2021) Students with ADHD: social skills, behavioral problems, academic performance, and family resources. Psico-USF 26(3):545–557. https://doi.org/10.1590/1413-82712021260312
Andrews MG, Nowakowski TJ (2019) Human brain development through the lens of cerebral organoid models. Brain Res 1725:146470. https://doi.org/10.1016/j.brainres.2019.146470
Aslantürk ÖS (2018) In vitro cytotoxicity and cell viability assays: principles, advantages, and disadvantages. In: Marcelo LL, Sonia S (eds) Genotoxicity–a predictable risk to our actual world. InTech, London, UK
Banaschewski T, Becker K, Döpfner M, Holtmann M, Rösler M, Romanos M (2017) Attention-deficit/hyperactivity disorder. Deutsches Arzteblatt Int 114(9):149–159. https://doi.org/10.3238/arztebl.2017.0149
Berger I, Slobodin O, Aboud M, Melamed J, Cassuto H (2013) Maturational delay in ADHD: evidence from CPT. Front Human Neurosci 7:691. https://doi.org/10.3389/fnhum.2013.00691
Berridge MV, Herst PM, Tan AS (2005) Tetrazolium dyes as tools in cell biology: new insights into their cellular reduction. Biotech Ann Rev 11:127–152. https://doi.org/10.1016/S1387-2656(05)11004-7
Burke EE, Chenoweth JG, Shin JH, Collado-Torres L, Kim S-K, Micali N et al (2020) Dissecting transcriptomic signatures of neuronal differentiation and maturation using iPSCs. Nat Commun 11(1):462. https://doi.org/10.1038/s41467-019-14266-z
Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ (2015) Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaSci 4:7. https://doi.org/10.1186/s13742-015-0047-8
Compagnucci C, Nizzardo M, Corti S, Zanni G, Bertini E (2014) In vitro neurogenesis: development and functional implications of iPSC technology. Cell Mol Life Sci 71(9):1623–1639. https://doi.org/10.1007/s00018-013-1511-1
Courchesne E, Gazestani VH, Lewis NE (2020) Prenatal origins of ASD: the when, what, and how of ASD development. Trends Neurosci 43(5):326–342. https://doi.org/10.1016/j.tins.2020.03.005
Dark C, Homman-Ludiye J, Bryson-Richardson RJ (2018) The role of ADHD associated genes in neurodevelopment. Dev Biol 438(2):69–83. https://doi.org/10.1016/j.ydbio.2018.03.023
Demontis D, Walters RK, Martin J, Mattheisen M, Als TD, Agerbo E et al (2019) Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat Genetics 51(1):63–75. https://doi.org/10.1038/s41588-018-0269-7
Ernst C (2016) Proliferation and differentiation deficits are a major convergence point for neurodevelopmental disorders. Trends Neurosci 39(5):290–299. https://doi.org/10.1016/j.tins.2016.03.001
Faraone SV, Larsson H (2019) Genetics of attention deficit hyperactivity disorder. Mol Psychiatry 24(4):562–575. https://doi.org/10.1038/s41380-018-0070-0
Franke B, Neale BM, Faraone SV (2009) Genome-wide association studies in ADHD. Hum Genet 126(1):13–50. https://doi.org/10.1007/s00439-009-0663-4
Friedman LA, Rapoport JL (2015) Brain development in ADHD. Curr Opin Neurobiol 30:106–111. https://doi.org/10.1016/j.conb.2014.11.007
Ge T, Chen C-Y, Ni Y, Feng Y-CA, Smoller JW (2019) Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat Commun 10(1):1776. https://doi.org/10.1038/s41467-019-09718-5
Ghasemi M, Turnbull T, Sebastian S, Kempson I (2021) The MTT assay: utility, limitations, pitfalls, and interpretation in bulk and single-cell analysis. Int J Mol Sci. https://doi.org/10.3390/ijms222312827
Green A, Baroud E, DiSalvo M, Faraone SV, Biederman J (2022) Examining the impact of ADHD polygenic risk scores on ADHD and associated outcomes: a systematic review and meta-analysis. J Psychiatric Res 155:49–67. https://doi.org/10.1016/j.jpsychires.2022.07.032
Grossmann L, Yde Ohki CM, Döring C, Hoffmann P, Herms S, Werling AM et al (2021) Generation of integration-free induced pluripotent stem cell lines from four pediatric ADHD patients. Stem Cell Res 53:102268. https://doi.org/10.1016/j.scr.2021.102268
Grünblatt E, Bartl J, Walitza S (2018) Methylphenidate enhances neuronal differentiation and reduces proliferation concomitant to activation of Wnt signal transduction pathways. Transl Psychiatry 8(1):51. https://doi.org/10.1038/s41398-018-0096-8
Guillemot F (2005) Cellular and molecular control of neurogenesis in the mammalian telencephalon. Curr Opin Cell Biol 17(6):639–647. https://doi.org/10.1016/j.ceb.2005.09.006
Hoogman M, Bralten J, Hibar DP, Mennes M, Zwiers MP, Schweren LSJ et al (2017) Subcortical brain volume differences in participants with attention deficit hyperactivity disorder in children and adults: a cross-sectional mega-analysis. Lancet Psychiatry 4(4):310–319. https://doi.org/10.1016/S2215-0366(17)30049-4
Hoogman M, Muetzel R, Guimaraes JP, Shumskaya E, Mennes M, Zwiers MP et al (2019) Brain imaging of the cortex in ADHD: a coordinated analysis of large-scale clinical and population-based samples. Am J Psychiatry 176(7):531–542. https://doi.org/10.1176/appi.ajp.2019.18091033
Klein M, Walters RK, Demontis D, Stein JL, Hibar DP, Adams HH et al (2019) Genetic markers of ADHD-related variations in intracranial volume. Am J Psychiatry 176(3):228–238. https://doi.org/10.1176/appi.ajp.2018.18020149
Koi P (2021) Genetics on the neurodiversity spectrum: genetic, phenotypic and endophenotypic continua in autism and ADHD. Stud Hist Phil Sci 89:52–62. https://doi.org/10.1016/j.shpsa.2021.07.006
Kuruş M, Akbari S, Eskier D, Bursalı A, Ergin K, Erdal E, Karakülah G (2021) Transcriptome dynamics of human neuronal differentiation from iPSC. Front Cell Dev Biol 9:727747. https://doi.org/10.3389/fcell.2021.727747
Li H, Zhong X, Chau KF, Santistevan NJ, Guo W, Kong G et al (2014) Cell cycle-linked MeCP2 phosphorylation modulates adult neurogenesis involving the Notch signalling pathway. Nat Commun 5:5601. https://doi.org/10.1038/ncomms6601
Lie D-C, Colamarino SA, Song H-J, Désiré L, Mira H, Consiglio A et al (2005) Wnt signalling regulates adult hippocampal neurogenesis. Nature 437(7063):1370–1375. https://doi.org/10.1038/nature04108
Marchetto MC, Belinson H, Tian Y, Freitas BC, Fu C, Vadodaria K et al (2017) Altered proliferation and networks in neural cells derived from idiopathic autistic individuals. Mol Psychiatry 22(6):820–835. https://doi.org/10.1038/mp.2016.95
Mishra A, Godavarthi SK, Jana NR (2009) UBE3A/E6-AP regulates cell proliferation by promoting proteasomal degradation of p27. Neurobiol Dis 36(1):26–34. https://doi.org/10.1016/j.nbd.2009.06.010
Ohi Y, Qin H, Hong C, Blouin L, Polo JM, Guo T et al (2011) Incomplete DNA methylation underlies a transcriptional memory of somatic cells in human iPS cells. Nat Cell Biol 13(5):541–549. https://doi.org/10.1038/ncb2239
Yde Ohki CM, Grossmann L, Christian D, Per H, Stefan H, Maria WA et al (2021) Generation of integration-free induced pluripotent stem cells from healthy individuals. Stem Cell Res 53:102269. https://doi.org/10.1016/j.scr.2021.102269
Yde Ohki CM, McNneill RV, Nieberler M, Radtke F, Kittel-Schneider S, Grünblatt E (2022) Promising developments in the use of induced pluripotent stem cells in research of ADHD. Curr Topics Behav Neurosci 57:483–501. https://doi.org/10.1007/7854_2022_346
Palladino VS, McNeill R, Reif A, Kittel-Schneider S (2019) Genetic risk factors and gene-environment interactions in adult and childhood attention-deficit/hyperactivity disorder. Psychiatric Genet 29(3):63–78. https://doi.org/10.1097/YPG.0000000000000220
Pantelis C, Yücel M, Wood SJ, Velakoulis D, Sun D, Berger G et al (2005) Structural brain imaging evidence for multiple pathological processes at different stages of brain development in schizophrenia. Schizophr Bull 31(3):672–696. https://doi.org/10.1093/schbul/sbi034
Papes F, Camargo AP, de Souza JS, Carvalho VMA, Szeto RA, LaMontagne E et al (2022) Transcription Factor 4 loss-of-function is associated with deficits in progenitor proliferation and cortical neuron content. Nat Commun 13(1):2387. https://doi.org/10.1038/s41467-022-29942-w
Posner J, Polanczyk GV, Sonuga-Barke E (2020) Attention-deficit hyperactivity disorder. Lancet 395(10222):450–462. https://doi.org/10.1016/S0140-6736(19)33004-1
Prokop JW, Bupp CP, Frisch A, Bilinovich SM, Campbell DB, Vogt D et al (2021) Emerging role of ODC1 in neurodevelopmental disorders and brain development. Genes. https://doi.org/10.3390/genes12040470
Rajan A, Ostgaard CM, Lee C-Y (2021) Regulation of neural stem cell competency and commitment during indirect neurogenesis. Int J Mol Sci. https://doi.org/10.3390/ijms222312871
Räsänen N, Tiihonen J, Koskuvi M, Lehtonen Š, Koistinaho J (2022) The iPSC perspective on schizophrenia. Trends Neurosci 45(1):8–26. https://doi.org/10.1016/j.tins.2021.11.002
Reif A, Fritzen S, Finger M, Strobel A, Lauer M, Schmitt A, Lesch K-P (2006) Neural stem cell proliferation is decreased in schizophrenia, but not in depression. Mol Psychiatry 11(5):514–522. https://doi.org/10.1038/sj.mp.4001791
Rubia K (2007) Neuro-anatomic evidence for the maturational delay hypothesis of ADHD. Proc Natl Acad Sci USA 104(50):19663–19664. https://doi.org/10.1073/pnas.0710329105
Sareen D, Saghizadeh M, Ornelas L, Winkler MA, Narwani K, Sahabian A et al (2014) Differentiation of human limbal-derived induced pluripotent stem cells into limbal-like epithelium. Stem Cells Transl Med 3(9):1002–1012. https://doi.org/10.5966/sctm.2014-0076
Scarcello E, Lambremont A, Vanbever R, Jacques PJ, Lison D (2020) Mind your assays: misleading cytotoxicity with the WST-1 assay in the presence of manganese. PLoS ONE. https://doi.org/10.1371/journal.pone.0231634
Sharma A, Couture J (2014) A review of the pathophysiology, etiology, and treatment of attention-deficit hyperactivity disorder (ADHD). Ann Pharmacothe 48(2):209–225. https://doi.org/10.1177/1060028013510699
Shaw P, Lerch J, Greenstein D, Sharp W, Clasen L, Evans A et al (2006) Longitudinal mapping of cortical thickness and clinical outcome in children and adolescents with attention-deficit/hyperactivity disorder. Arch General Psychiatry 63(5):540–549. https://doi.org/10.1001/archpsyc.63.5.540
Shaw P, Eckstrand K, Sharp W, Blumenthal J, Lerch JP, Greenstein D et al (2007) Attention-deficit/hyperactivity disorder is characterized by a delay in cortical maturation. Proc Natl Acad Sci USA 104(49):19649–19654. https://doi.org/10.1073/pnas.0707741104
Simpson DJ, Olova NN, Chandra T (2021) Cellular reprogramming and epigenetic rejuvenation. Clin Epigenetics 13(1):170. https://doi.org/10.1186/s13148-021-01158-7
Stefanowicz-Hajduk J, Ochocka JR (2020) Real-time cell analysis system in cytotoxicity applications: usefulness and comparison with tetrazolium salt assays. Toxicol Reports 7:335–344. https://doi.org/10.1016/j.toxrep.2020.02.002
Varderidou-Minasian S, Verheijen BM, Schätzle P, Hoogenraad CC, Pasterkamp R, Jeroen; Altelaar, Maarten, (2020) Deciphering the proteome dynamics during development of neurons derived from induced pluripotent stem cells. J Proteome Res 19(6):2391–2403. https://doi.org/10.1021/acs.jproteome.0c00070
Wang M, Hu Z, Liu Lu, Li H, Qian Q, Niu H (2020) Disrupted functional brain connectivity networks in children with attention-deficit/hyperactivity disorder: evidence from resting-state functional near-infrared spectroscopy. Neurophotonics 7(1):15012. https://doi.org/10.1117/1.NPh.7.1.015012
Wiegand C, Banerjee I (2019) Recent advances in the applications of iPSC technology. Curr Opin Biotechnol 60:250–258. https://doi.org/10.1016/j.copbio.2019.05.011
Wood AC, Neale MC (2010) Twin studies and their implications for molecular genetic studies: endophenotypes integrate quantitative and molecular genetics in ADHD research. J Am Acad Child Adolesc Psychiatry 49(9):874–883. https://doi.org/10.1016/j.jaac.2010.06.006
Yde Ohki CM, Grossmann L, Alber E, Dwivedi T, Berger G, Werling AM et al (2020) The stress-Wnt-signaling axis: a hypothesis for attention-deficit hyperactivity disorder and therapy approaches. Transl Psychiatry 10(1):315. https://doi.org/10.1038/s41398-020-00999-9
Zametkin AJ, Nordahl TE, Gross M, King AC, Semple WE, Rumsey J et al (1990) Cerebral glucose metabolism in adults with hyperactivity of childhood onset. N Engl J Med 323(20):1361–1366. https://doi.org/10.1056/NEJM199011153232001
Zhang H, Zhao Y, Cao W, Cui D, Jiao Q, Lu W et al (2020) Aberrant functional connectivity in resting state networks of ADHD patients revealed by independent component analysis. BMC Neurosci 21(1):39. https://doi.org/10.1186/s12868-020-00589-x
Acknowledgements
We would like to especially thank Dr. Anna Maria Werling and Dr. Christian Döring for recruiting patients and controls, the University of Zurich for providing funding to this project via the Fonds für wissenschaftliche Zwecke im Interesse der Heilung von Geisteskrankheiten, Dr. Per Hoffmann and Dr. Stefan Herms for providing the DNA genotyping data for saliva and iPSC samples and Dr. Lukasz Smigielski for the PRS calculation.
Funding
Open access funding provided by University of Zurich.
Author information
Authors and Affiliations
Contributions
CMYO generated and characterized iPSCs and NSCs. CMYO, AB, SR and MR performed experiments. CMYO, NMW, SR and AB performed data analysis. CMYO, NMW and AB wrote the manuscript. SW participated in the recruitment of participants and reviewed the manuscript. EG designed, conceptualized and supervised the project and reviewed the manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no potential conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Yde Ohki, C.M., Walter, N.M., Bender, A. et al. Growth rates of human induced pluripotent stem cells and neural stem cells from attention-deficit hyperactivity disorder patients: a preliminary study. J Neural Transm 130, 243–252 (2023). https://doi.org/10.1007/s00702-023-02600-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00702-023-02600-1