Cellular migration is a ubiquitous feature that brings brain cells into appropriate spatial relationships over time; and it helps in the formation of a functional brain. We studied the migration patterns of induced pluripotent stem cell-derived neural precursor cells (NPCs) from individuals with familial bipolar disorder (BD) in comparison with healthy controls. The BD patients also had morphological brain abnormalities evident on magnetic resonance imaging. Time-lapse analysis of migrating cells was performed, through which we were able to identify several parameters that were abnormal in cellular migration, including the speed and directionality of NPCs. We also performed transcriptomic analysis to probe the mechanisms behind the aberrant cellular phenotype identified. Our analysis showed the downregulation of a network of genes, centering on EGF/ERBB proteins. The present findings indicate that collective, systemic dysregulation may produce the aberrant cellular phenotype, which could contribute to the functional and structural changes in the brain reported for bipolar disorder.
Bipolar disorder (BD) is a severe disabling psychiatric illness with a genetic basis and neurodevelopmental origins (Gandal et al., 2018). Many of the identified genes in BD risk are implicated in neurodevelopmental processes and variations in brain morphology (Mühleisen et al., 2018; Ganesh et al., 2019; Dai et al., 2020; Ithal et al., 2021). Multiple studies have documented abnormalities in brain structure in BD (Magioncalda and Martino, 2021), including smaller brain size, reduced cortical gray and white matter (Ching et al., 2020), cortical thinning (Hibar et al., 2018) and decreased numbers of interneurons in the cerebral cortex and hippocampus (Harrison et al., 2020). Cortical plasticity also mediates the structural alterations and cognitive changes, seen over the life span in those with BD (Van Rheenen et al., 2020). Such brain changes in BD have also been shown to be predicted by genetic risk (Abé et al., 2020).
One method to interrogate cellular alterations related to brain abnormalities is to study induced pluripotent stem cells (IPSCs) derived from patients in whom changes were detected by brain imaging. Although a direct link is difficult, this could help to identify potential contributory mechanisms to brain abnormalities. Previous IPSC-based studies have uncovered that BD pathogenesis is associated with differences in (1) expression of ion channels and membrane-bound receptors in neurons (Chen et al., 2014), (2) neurogenesis and expression of genes of the WNT signaling pathway (Madison et al., 2015), and (3) mitochondrial abnormalities in patient-derived neurons (Mertens et al., 2015) as well as in neural precursors (Paul et al., 2020; Osete et al., 2021).
Previous attempts to integrate human brain imaging with IPSC experiments in psychiatry (Johnstone et al., 2019; Vasistha et al., 2019) evaluated persons from multiple affected families with schizophrenia. Johnstone et al. (2019) showed reduction of cortical brain volumes with abnormal proliferation of neural precursor cells (NPCs), whereas Vasistha et al. (2019) showed oligodendrocyte proliferation and morphology deficits in individuals who had white matter alterations in the brain using diffusion tensor imaging. In addition, there is indirect evidence on the role of neuronal migration defects in BD (Uribe and Wix, 2012; Tabarés-Seisdedos et al., 2006). However, there is no direct evidence on the migration defects that could contribute to bipolar disorder.
Here, we performed a pilot study using IPSC-derived NPCs from a single family with two BD patients who had abnormal magnetic resonance imaging (MRI) scans. We have previously reported rare damaging variants related to cellular migration in these patients (Paul et al., 2020; Table S1). We hypothesized that there would be migration abnormalities in IPSC-derived NPCs of these patients. We found that both patient-derived NPCs displayed greater quasi-Brownian randomness in migration patterns, unlike the relatively directed movements in the control NPCs. Transcriptome analysis revealed expression changes in several genes known to regulate cellular migration, implicating the EGF/ERBB signaling pathway in neural cell migration during brain development.
Morphological changes observed in MRI scans of brains of BD patients
In the scans of patient B1, we found that all tissue-specific brain volumes were below the 5th centile, demonstrating a clear deviation from age-related trends. In B2, lower gray matter (cortical at the 10th centile and subcortical at the ∼1/3rd centile) was detected, but the white matter volumes (∼50th centile) did not differ from age-related trends (Fig. 1A).
Aberrant cellular migration in patient-derived neural precursors
Cells from the control lines (C1, C2 and C3) migrated in a directed manner; whereas cells from the BD patient-derived lines (B1 and B2) moved in a random (Brownian) manner throughout the duration of the experiment (Fig. 1B,C; see Fig. 1D for representative paths of cells). The patient-derived lines showed mixed patterns in the direction of migration: cells migrated in circular tracks as well as back and forth, in addition to some that migrated in a directed pattern (Fig. 1D; Movies 1-5). Quantitative analysis confirmed these visually observed differences between the migration trajectories of the controls and the patient-derived lines (Fig. 1E). The values of the mean-squared displacement (MSD) exponent α were 1.56 [with a 95% confidence interval (c.i.) of 1.54 to 1.58], 1.61 (95% c.i. of 1.59 to 1.62) and 1.70 (95% c.i. of 1.69 to 1.71) for the three control lines. This indicates that control cells show a high degree of directionality with some amount of randomness. In contrast, the MSD exponent α values were 1.20 (95% c.i. of 1.18 to 1.22) and 1.26 (95% c.i. of 1.24 to 1.28) for the B1 and B2 lines, respectively, suggesting a trajectory that was closer to a Brownian pattern of movement (Fig. 1E). As the confidence intervals of the estimates of the MSD exponents of the control and patient-derived lines do not overlap, we conclude that the difference between the two groups is statistically significant. The overall displacement did not differ significantly (Fig. 1F). Additionally, although cells from the B1 line migrated at a faster speed, group-wise comparison indicated that migration speeds did not differ significantly from those of cells derived from healthy controls (Fig. 1G).
Transcriptome analysis revealed differential expression of RNA transcripts related to cell migration
Our analysis for the migration-related genes (n=290) in the transcriptome data revealed that there were 61 transcripts in B1 and 58 in B2 that were differentially expressed, in comparison with those in C1. Of these, 29 transcripts were dysregulated across both patient-derived lines (Fig. 2B,C). The expression level of LAMA1 and NRG2 was confirmed using real-time quantitative PCR and was found to be downregulated (Fig. S2). Functional interaction of the 29 genes using STRING (https://string-db.org/) highlighted a densely interconnected network of proteins centered on ERBB proteins (Fig. 2D, Fig. S1).
The BD patients in this study were chosen from a dense family, who had multiple rare damaging variants implicated in cellular migration, and had structural abnormalities noted on brain MRI scans (Fig. 1A). Cellular migration analysis showed that although patient-derived NPCs showed a random trajectory, the NPCs from healthy controls migrated towards other cells (directed/ballistic movement) (Fig. 1C-E). Transcriptome analysis (Fig. 2A) showed that numerous migration-related genes were dysregulated in patient-derived NPCs (Fig. 2B,C).
The results of STRING analysis (Fig. 2D; Fig. S1) revealed a network centered at ERBB proteins. This network includes several proteins including tyrosine protein kinases, which function as cell-surface receptors for neuregulins, EGF and other ligands. They are involved in organogenesis, including that of the brain, during which they regulate many functions including cell proliferation, differentiation, migration and apoptosis. Proteins that are part of the EGF/ERBB network (Fig. S1) fulfill different roles in the migration of neural precursors. EGFR, PLCG2 and PRKCA, for instance, regulate migration via calcium signaling (Büttner et al., 2018). Inhibition of ERBB receptors can lead to suppression of cancer cell migration (Momeny et al., 2017) and inhibition of NRG1/ERBB2 signaling reduces migration of human glioma cells (Ritch et al., 2003).
The trajectories of migrating neural precursors lay the foundations of the developing central nervous system (Rahimi-Balaei et al., 2018; Barber et al., 2015). The speed and direction of migrating cells can alter the regional cellular make-up, and, therefore, wiring of cortical areas. These observations at the level of individual cells may alter collective behavior, e.g. tissue organization, brain disease and behavior. Furthermore, stochasticity is inherent in all biological systems, and its characterization may help us decipher local scale interactions among individuals, whether organisms or cells (Jhawar et al., 2020; Jhawar and Guttal, 2020; Brückner et al., 2019). An understanding of how single cells move, their inherent stochasticity and interaction can be crucial for understanding phenomena across scales, from tissue organization to wound healing and repair, and organization within the brain (Davidson et al., 2021; Otsuki and Brand, 2020; Zinner et al., 2020; Silva et al., 2019).
Overall, our previous work (Paul et al., 2020) and current analysis indicate that there are identifiable cellular abnormalities in NPCs derived from BD patients. These cells proliferate faster and exhibit aberrant migration patterns. Whether these could, in part, be responsible for the deviations in gray and white matter noted on MRI is a matter of conjecture at this point. It is also important to note that these findings are based on a small set of patients from one family, with absence of a family control. We further plan to conduct similar analysis in NPCs generated from a larger number of BD patients, and compare with not just healthy controls, but also familial controls. Such experiments would be important to establish the generalizability of the present findings. This work could also be extended to an investigation in three-dimensional organoids (mini-brains) to examine organizational aspects and functionality in more detail, currently in progress in our lab. The integration of deep clinical phenotyping with IPSC models in such sample sets will be crucial to understand the pathobiology of BD, and the mechanisms of recovery and resilience.
MATERIALS AND METHODS
The NPCs from two BD patients from a multiplex family, and three healthy control lines were used for experiments (pedigree and clinical details in Paul et al., 2020). Details of clinical assessments (Viswanath et al., 2018), magnetic resonance imaging (MRI) (Bhalerao et al., 2021; Holla et al., 2018; Parekh and Naik, 2021), generation of NPCs and their cellular characterization have been described earlier (Mukherjee et al., 2019; Paul et al., 2020). The study was approved by the ethics committee of the National Institute of Mental Health and Neurosciences, Bengaluru, India, and conforms to the ethical norms and standards in the Declaration of Helsinki.
Global brain volumes (gray and white matter) were calculated using the FreeSurfer software suite (v6.0). Individualized centiles for each tissue class were calculated using out-of-sample log likelihood estimation against the bootstrapped model parameters obtained from the expected age-related trends from a large aggregated database of reference brain volumes (Bethlehem et al., 2022). This database included normative brain volumes of Indian subjects for ages 6-60 years (Bethlehem et al., 2022; Holla et al., 2020).
Assessment of migratory capacity of neural precursor cells
Cells were seeded in ibidi Culture-Insert 2-well in μ-dish (81176, ibidi, Germany), at 85-90% confluency. Passage numbers for NPCs ranged between P10-P19. Movement of cells across the 500-μm gap in the ibidi dish was recorded in time-lapse images every 15 min for 15 h with an Olympus microscope equipped with a camera (Hamamatsu) using a 10× dry objective. The experiments for each cell line were done in three biological replicates. Migration of NPCs was tracked for 12 h, in a 37°C humidified chamber with 5% CO2 and quantified using ImageJ (Version IJ1.46r) with the plugin ‘Manual Tracking’.
Quantitative movement analysis of cellular migration
The paths of migrating NPCs were tracked with the help of x- and y-coordinates from the images taken. From the trajectories of each cell, we estimated the average speed (over 15 min intervals) and displacement between the initial and final locations (after 15 h). The box plot (Fig. 1F) shows the displacement of 100 cells (cumulative from three experiments) for each cell line.
From the displacement, we also computed mean-squared displacement (MSD) as a function of time separation between any two points along the trajectory of a cell. The MSD exponent α, which determines the functional relationship between MSD and the time lag (τ), given by MSD(τ )∼τα, was also calculated. When α=1, the cells are said to exhibit Brownian motion (or diffusive motion), whereas deviation from this value – termed anomalous diffusion values – of α>1 represents super-diffusive motion (or if α<1, sub-diffusive motion). A value of α=2 implies that cells are migrating via a highly persistent or ballistic motion (Dieterich et al., 2008; Gal et al., 2013).
RNA-sequencing of the NPCs was performed on the Illumina Hi-Seq platform. Genes which showed >1-fold difference with false discovery rate-adjusted P<0.05 were considered differentially expressed, as detailed elsewhere (Paul et al., 2020). As we observed migration deficits in patient-derived lines, we performed a targeted analysis focusing on genes already implicated in cellular migration. The gene list was extracted from the ConsensusPathDB analysis (Release 34) (Kamburov et al., 2011, 2009) (keywords ‘migration’ and ‘motility’). In addition, the data from published references on genes related to migration were included (Snel et al., 2000; Suyama et al., 2003; Simpson et al., 2008; Wu et al., 2008; Bavamian et al., 2015; Madison et al., 2015; Buchsbaum and Cappello, 2019; Szklarczyk et al., 2019) to create a collated list of migration related genes (n=290).
We wrote our own custom code in the statistical programming platform R to compute the speed, displacement, MSD and the MSD exponent. In addition, to compute the MSD exponent α and its confidence intervals, we performed a simple linear regression between log-transformed MSD and τ, using the function lm in R. The slope of the resulting regression is an estimate of α. If the mean of the estimate of α of control lines did not overlap with the confidence intervals of α of the patient lines, we concluded that these two groups were statistically different with P<0.05. We also performed two tailed paired t-test to compare migration speeds between control- and patient-derived NPC lines.
Total RNA was extracted using the RNeasy Mini Kit (74104, QIAGEN). cDNA synthesis was performed with 1.5 μg of RNA using High-Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific, 4368814). Sample cDNA and ‘no template control’ were run in triplicates with the QuantStudio 6 Flex Real-time qPCR system (Thermo Fisher Scientific). The quantitative PCR reaction was carried out with SYBR Green Master Mix (Takyon Low ROX) and 0.5 μM of each primer. The relative gene expression was estimated as previously described (Paul et al., 2020) and normalized to the housekeeping gene UBC.
The following primer sequences were used: LAMA1 Forward, 5′-GAGCATGGAGAGATTCATACATC-3′; LAMA1 Reverse, 5′-GGTCATGAGATCTGCATTGA-3′; NRG2 Forward, 5′-GCAACGGCAGAAAGAACTCA-3′; NRG2 Reverse, 5′-CTTCCCCAGGATGTTCTCGG-3′; UBC Forward, 5′-CTGGAAGATGGTCGTACCCTG-3′; and UBC Reverse, 5′-GGTCTTGCCAGTGAGTGTCT-3′.
The authors would like to thank Dr Jitesh Jhawar, postdoctoral researcher, Department of Collective Behavior, Max Planck Institute of Animal Behavior, University of Konstanz, for his valuable suggestions on ImageJ analysis; Dr Ravi Muddashetty and Dr Dasaradhi Palakodeti for providing computational facilities for transcriptome analysis; Ms Chitra B. and Mr Mallappa M. for technical support. We are grateful to the participants and their families for their cooperation, as well as to clinicians and staff at NIMHANS for their assistance.
At the time of writing, the members of the Accelerator program for Discovery in Brain disorders using Stem cells (ADBS) Consortium were: Biju Viswanath, Naren P. Rao, Janardhanan C. Narayanaswamy, Palanimuthu T. Sivakumar, Arun Kandasamy, Muralidharan Kesavan, Urvakhsh Meherwan Mehta, Ganesan Venkatasubramanian, John P. John, Meera Purushottam, Odity Mukherjee, Bhupesh Mehta, Thennarasu Kandavel, B. Binukumar, Jitender Saini, Deepak Jayarajan, A. Shyamsundar, Sydney Moirangthem, K. G. Vijay Kumar, Jayant Mahadevan, Bharath Holla, Jagadisha Thirthalli, Prabha S. Chandra, Bangalore N. Gangadhar, Pratima Murthy, Vivek Benegal, Mathew Varghese, Janardhan Y. C. Reddy and Sanjeev Jain from the National Institute of Mental Health and Neurosciences (NIMHANS), India; Mitradas M. Panicker, Upinder S. Bhalla and Raghu Padinjat from the National Center for Biological Sciences (NCBS), India; and Sumantra Chattarji and Mahendra Rao from the Institute for Stem Cell Biology and Regenerative Medicine (InStem), India.
Conceptualization: S.K.S., R.S., B.V.; Methodology: S.K.S., P.P., V.G., B.H., H.B., P.B., A.M.V., V.K., M.P., R.S., B.V.; Validation: S.K.S., R.S., B.V.; Formal analysis: S.K.S., V.G., B.H., A.V., K.M., M.P., R.S., B.V.; Investigation: S.K.S., R.S., B.V.; Resources: S.K.S., R.K.N., B.V.; Data curation: S.K.S., R.S.; Writing - original draft: S.K.S.; Writing - review & editing: M.P., S.J., R.S., B.V.; Visualization: S.K.S., R.S., B.V.; Supervision: M.P., S.J., R.S., B.V.; Project administration: B.V.; Funding acquisition: S.J., A.C., B.V.
This work was supported by grants from the Department of Biotechnology, Ministry of Science and Technology, India (BT/PR17316/MED/31/326/2015) for the ‘Accelerator program for discovery in brain disorders using stem cells’ (ADBS); the Department of Science and Technology, Ministry of Science and Technology, India (BT/01/CEIB/11/VI/11/2012) for the ‘Targeted generation and interrogation of cellular models and networks in neuro-psychiatric disorders using candidate genes’; ‘Imaging-genomics approach to identify molecular markers of Lithium response in Bipolar disorder’ through the Department of Science and Technology, Ministry of Science and Technology, India – INSPIRE Faculty Fellowship awarded to B.V. (project number 00671, Code IFA-12-LSBM-44); Science and Engineering Research Board (India) project ‘Dissecting the biology of lithium response in human induced pluripotent stem cell derived neurons from patients with bipolar affective disorder’ (ECR/2016/002076); ‘Deciphering the mechanisms of lithium response in patients with bipolar disorder’ through the The Wellcome Trust DBT India Alliance Intermediate (Clinical and Public Health) Fellowship awarded to B.V. (IA/CPHI/20/1/505266); and Scientific Knowledge for Ageing and Neurological Ailments (SKAN) trust project ‘Bipolar disorder-specific and cross disorder polygenic risk score to predict deviation in the neuro-trajectories across lifespan, and cellular phenotypes’ (SKAN/002/208/2021/01481). The results of this work have been partially presented as a poster at XXVIIIth World Congress of Psychiatry Genetics, 2020, and also received the Hugh Gurling award 2020. Deposited in PMC for immediate release.
The authors declare no competing or financial interests.