On the 25th of May ‘The Scientist’ held a symposium concerning the use of stem cells in models of neurological diseases. It saw talks from some of the top researchers in the study of neurology, including representatives from University of Pennsylvania, Yale University, the Neural Stem Cell Institute, the Beckman Research Institute, and Sanford Burnham Prebys Medical Discovery Institute. The presentations covered how they use stem cells for producing models and outlined all of their current investigations and future directions for the use of stem cells in neurological disorders. At present not all neurological diseases can be modelled by transgenic animals and there’s been no jump from testing in stem cells to the clinic as of yet. A collective hope, mentioned in the discussion of this symposium, of the presenting panel is that the future will see more confidence put into stem cell research and the models derived from them. Their aim is to have therapeutics tested on stem cell models accepted for use in clinical studies without the use of animals.
The first speaker was Guo-li Ming from the department of Neuroscience and Psychiatry at UPenn. She is focussed on understanding the molecular mechanisms of neuronal development and dysregulation. She uses both patient-derived iPSCs and mouse models. From the first differentiation into hESCs by the group of James Thomson in 1998 we have seen substantial advances in the use of stem cell technology. These advances have allowed the production of models specific to different regions of the human body. In Guo-li’s lab hESCs and hiPSCs have been previously differentiated into highly enriched 2D human neuronal and glial culture models of the nervous system. Under different culture conditions ESCs and PSCs can be manipulated to give rise to different cells, such as primitive neural stem cells (NSC), which can then make more region specific stem cells that can be used for modelling. These stem cells include forebrain NSCs (these lead to cortical glutamate neurons, cortical GABA interneurons, medium forebrain ChAT and basal forebrain ChAT models), midbrain/hindbrain NSCs (these lead to midbrain DA neurons and hindbrain 5-HT neuron models), spinal NSCs (leading to spinal motor neuron models), and glial cell types such as astrocytes and oligo progenitor cells.
When grown in suspension stem cells can form organ-like structures. For the production of brain organoids there are two strategies:
- Non-Guided Differentiation doesn’t use patterning factors and has many different brain region cells mixed together making a very complex brain organoid.
- Guided Differentiation uses patterning factors. This generates region specific organoids like the cerebral cortex organoid for example. This method can generate multiple organoids which allows the investigation of interactions between brain regions.
These models can be used for the discovery, development and preclinical testing of potential therapeutics for neurological disorders. This is a current area of research for Guo-li and colleagues and they are working on perfecting these models for the study of disease development, such as frontotemporal dementia development, which may unearth target areas for potential therapeutics.
Next up was Kristen Brennand of the department of Psychiatry and Genetics at Yale. She uses the integration of stem cell technology with CRISPR engineering to investigate the effects of patient-specific variants in brain cell classes. From functional genomic analyses they hope to learn how to improve genetic diagnosis of disease prior to symptom onset. A better understanding of the genetic risk factors which can confer disease risk can assist in this and may help to identify better targets for therapeutics (both preventative and precision treatments). The goal of her lab is to find these novel genetic targets for all neurological disorders. Their current work includes understanding how risk variant effects vary with polygenic risk, development, cell type, sex, and environment.
Understanding the interactions between risk variants can inform critical clinical outcomes and drug responses. One way for understanding these is by combining all the risk variants to produce a polygenic risk score. The highly complex genetic risk architecture of schizophrenia (SZ) is not yet fully understood. When looking at the polygenic risk scores they saw a significant group average difference but no significant values for individuals outside the range, from this no individuals could be statistically diagnosed with SZ. Copy number variants and protein-truncating variants were not found in most patients. Common variants show a small risk for SZ which, in aggregate, explains the majority of risk factors in patients. They predict that by revising the polygenic risk scores it will improve diagnostic power, predict clinical trajectories, and discover novel therapeutic targets.
By isolating individual risk variants they are able to uncover the pathway involved. This allowed them to study how their effects vary according to different factors such as cell type, cell environment etc. For SZ they have been studying two specific genes with causal variants, FURIN and SNAP91. Through the use of CRISPR they were able to identify a single SNP that controls both variants and found 20-30 other SNPs that they, as of yet, have not been able to understand the link, if any, to SZ. For FURIN a significant difference was noted between the AA and the GG genotypes, present due to patient-to-patient variability. Half of the donors had SZ and the other half were normal for SZ (these were used as controls). This difference was utilised to test the difference in FURIN expression when everything but the RS4702 SNP is controlled. The two genotypes were grown in parallel. GG neurons were found to have a lower expression of FURIN compared to AA and had reduced neurite outgrowth and altronodal activity. It was also found that when the miR338 binding site is inhibited it also inhibits the SNP effect in a context-dependent eQTL fashion. This is because the SNP is part of that 3’ UTR of that binding site.
Next they looked at the impact of donor genetic background. They did this by profiling the SNPs in all donor samples to determine the phenotypic effects of different backgrounds using RNA seq. From this they identified extremely high and low polygenic risk donor iPSC lines (which they are now editing) and observed that allelic conversion was the same from both high and low donors. The study is in its early stages so there is the possibility that they will see differences in phenotypic development between them.
They used principal component analysis (PCA) to compare isogenic, glutamatergic and GABAergic neurons. The isogenic neurons that differ at only that SNP can be distinguished by the first principal component (PC1). The glutamatergic neurons are linked to synaptic function and GABAergic neurons see more synaptic vessel release.
In their study of SARS-CoV-2 they discovered that the current strain compared to the strain from years ago is more infectious as it has introduced a FURIN cleavage site. They used isogenic lung and neuronal cells to study their susceptibility to SARS-CoV-2 infection. Isogenic lung cells had reduced FURIN expression and infection was reduced in the GG genotype relative to AA. In neurons they noted a reduced susceptibility to infection when the cell exhibited reduced FURIN expression and as a whole they were less susceptible than alveolar cells. As the cells were intentionally infected (i.e. they came from normal donors) in the lab no clinical COVID-19 diagnosis information was available.
Studies of the impact of cell type on distal common variant genotype effect were also performed. The study looked at the relationship between the structure of chromatin loops and gene expression using the CRISPR Cloud9 tool to link 3D chromatin dynamics to gene expression and neuronal activity. They also used Cloud9 to destroy the chromatin loops highly enriched for synaptic proteins. From these they were able to generate subtype specific RNAseq profiles to determine to what extent the genes represent risk variants to psychiatric disorders. In GABAergic neurons they found that, in response to stress, enrichment is in IFN gamma production suggesting a different biological process contributing to disease risk in different brain cell types.
They identified M64 associated with neuronal genes that are predicted to be driven by ATP6V1a as part of the study for causal Alzheimer’s (AZ) drivers. They predicted drugs that would increase ATP6V1a expression in the brain and defined NCH51, which when applied, both increased ATP6V1a expression and neuronal activity. The models for these tests were made using knocked-down post mortem co-expressed M64 neuronal genes driven by ATP61A. An increase in postsynaptic activity was seen for common variant genes linked to SZ. CRISPR was used to up and down regulate these genes. Tests were performed on these common variant models to evaluate the synergistic effects between them. 82% of downstream genes changed as expected, 11% were more up regulated than expected (enriched for all SZ and bipolar rare and common variants linked to disease risk), and 7% were more down regulated than expected (enriched for all the major neurotransmitter classes). Early data is suggesting some pathway-specific effects. Unexpected rare variant additive effects can be distinguished in neurons by NRXN1. They think the phenotypes for this are occurring through two different mechanisms; loss of NRXN1 in dose and the additive effect of mutant isoform activity.
They are optimistic that one day it will be possible to predict patient treatment response based on some of these in vitro models because at present drug screens are possible at the level of RNA and synaptic function.
They currently have many open funded post-op projects on-going involving:
- Splice QTLs regulating microglia function in Alzheimer’s disease
- 3D genome misfolding due to repeat instability in brain disease
- Convergence of autism spectrum disorder (ASD) risk variants
Sally Temple, the Scientific Director of the Neural Stem Cell Institute, then took over to tell us about her experiences with stem cells. Her team use iPSC-derived neural stem cell culture models to develop and discover potential treatments for brain, eye, and spinal cord diseases. They have also been known to make hiPSC-derived organoids for modelling neuropathology. When using stem cells they have to think very carefully about what the models can tell them, as neurological disorders usually arise with age but iPSCs are young and immature. They have been working on solutions for this issue and have produced an encapsulated version of the growth factor, FGF2, that allows growth for more than 1 week compared to a meagre 4 hours unencapsulated. The normal growth factor causes dramatic protein fluctuation which reduces the control over the pluripotent state of the cells. They think with the encapsulated version outcomes are improving. At present they are focussed on two diseases; age-related macular degeneration (AMD) and frontotemporal dementia (FTD).
A main reason for studying AMD is the high statistical rate of it in the world. 1 in 5 over 75 will get AMD, that is around 196 million people across the globe. This causes them to lose their central vision. The RPE cells degrade which leads to photoreceptor cell death. To study this they identified the genes involved in this mechanism, which were ARMS2/HTRA1 (in 100% linkage disequilibrium). They made models using these genes for analysis of RPE. They found differences in inflammatory and complementary factors between control and test models that increased the risk of AMD. This information aligned with GWAS data showing the capabilities of the model. They also did some small molecule drug screening in these models and found that nicotinamide (NAM) was effective in reducing disease-associated proteins in the model. From this they then went on to isolate the RPE cells from patients and controls and exposed them to NAM. The differential expression analysis allowed the identification of genes and pathways involved in this AMD mechanism including the complement pathway but also genes they weren’t aware of.
Tauopathy is caused by abnormal Tau protein in neurons causing tangles and death of cells. The patients display signs of FTD, with behavioral and language disorders. It is caused by mutations in MAPT affecting many cell types including astrocytes, oligodendrocytes, and neurons. As a result of this more complex models are needed. Her lab has designed an iPSC-derived cerebral organoid to fit this purpose. At 20 days they consist of progenitor cells and over a 6 month period they form neurons in the cortex, then deep layer cells develop, and finally upper layer cells form. Astrocytes are also present in neurons at 4 months. All layers are present from 4 months. They started with a set of 3 lines with both mutant and isogenic controls to look at changes over time (2, 4, and 6 months) to understand the impact of MAPT. This showed their maturation over time.
To validate the model they increased the Tau and Phosphotau proteins in V337M MAPT organoids. Compared to the control models they saw an increase in tau and phosphotau, respectively, at 4 months which was exacerbated by 6 months revealing the disease phenotype. By looking at the identity of the cells and following the changes over time in the neuronal cells they found a significant loss of glutamatergic neurons over time. They think this was selective as it wasn’t seen in astrocytes or GABAergic neurons. In a follow-up study of the destabilisation of proteostasis with age they found that these mutant organoids saw perturbations and loss of P62 after 6 months, and indication of alterations to the lysosomal pathway as early as 2 months into organoid growth.
In a pseudo-time analysis they discovered early upregulation of MAPT in mutant organoids and early maturation of synapses particularly in glutamate pathway signalling. This indicated increased sensitivity of the mutant organoids to glutamate toxicity. They also noted significant differences in splicing from control to mutant. From these results they were able to identify a glutamate receptor recycling inhibitor called Apilimod which lowers sensitivity to glutamate. From their tests they identified that it has the ability to prevent the loss of neurons to glutamate as Apilimod diminishes cell death. It is well tolerated so is worth testing as a potential treatment for tauopathies.
Following up from Sally Temple was Yanhong Shi of the Beckman Research Institute of City of Hope, where she is a Professor and Director in the Division of Stem Cell Research at the Department of Developmental and Stem Cell Biology. Dr. Shi spoke about her work regarding stem cell-based modelling of neurological disorders, starting with hiPSC-based disease modelling. This involves the remodelling of patient-derived somatic cells into iPSCs, which are then edited into isogenic controls using CRISPR Cas9 editing, and then differentiated into multiple cell types. These desired cell types, such as neurons, can then be subjected to -omics analysis in order to identify molecular targets for neurological disorders, providing the exciting and unique opportunity of developing personalised medicine for such patients. Dr. Shi then went on to share an example of such a model, regarding Alexander disease (AxD), using hiPSC-derived astrocytes. This proved to be the ideal candidate for this model as AxD is caused by a GFAP mutation, which cannot be recapitulated in animal models. Once again using CRISPR Cas9 editing, they converted the GFAP mutant to the GFAP wildtype, with this forming one of the AxD iPSCs. These AxD iPSCs were then converted into astrocytes, which exhibited GFAP aggregation, which could not be detected in either control used, proving this model could provide a valid platform for such studies.
The next hurdle for Dr. Shi and her team was then to figure out whether these AxD astrocytes could induce myelination defects in 3D nanofibre systems. To conduct this research, they used a 3D co-culture system comprised of the astrocytes with oligodendrocyte progenitor cells on a 3D nanofiber, to mimic neuronal exons. The results were interesting, with similar numbers of GFAP astrocytes in all co-cultures but there was a significant reduction of the oligodendrocytes in co-culture with the AxD astrocytes, compared to co-culture with controls. With this information, they deduced that AxD could in fact induce myelination defects in 3D nanofibre systems.
Next on the list was figuring out how this mechanism works, for which they compared wildtype astrocytes and AxD astrocytes. This analysis revealed a significant increase in genes related to neuroinflammatory responses, including cytokine activity. One molecule in particular, CHI3L1, was found to be significantly upregulated in AxD patient brains, while myelination genes reduced in these patients. From this data, they discovered that CHI3L1 mediates the inhibitory effect of AxD astrocytes on myelination, by making knocked-down CHI3L1 astrocytes and performing a co-culture with nanofibers. Dr. Shi expressed her confidence in using this model for potential future drug screening and therapy for AxD as it covers both the structure and activity of the human brain. You can read more on this study here.
Moving on, Dr. Shi then went on to discuss human Cytomegalovirus (HCMV) induced microcephaly and how this can be modelled using hiPSC-derived brain organoids. HCMV is a leading cause of neurodevelopmental disorders, including microcephaly. Due to the high species specificity of the virus, it is difficult to research in animal models, making it another great candidate for hiPSC-derived brain organoid research models. For this study, they subjected the hiPSC-derived brain organoids to TB40/E to mock infection by HCMV, which was shown to substantially impair the growth of those organoids, particularly in the subventricular area, where substantial thinning of this zone was observed. However, it was also discovered that this can be prevented through incubation with a neutralising antibody that can block the HCMV infection to prevent cortical structure malformation and neural network deficiency in brain organoids. Once again, Dr. Shi indicated that she hopes this model can be used to further investigate HCMV-induced brain malformations and hopefully identify potential antiviral agents further into the future. You can read more about this study here.
Moving onto a more current topic, Dr. Shi explained how a similar model was used to model SARS-CoV-2 susceptibility and response using iPSC derived neural cells and brain organoids. This was extremely relevant as Dr. Shi revealed that more than 30% of hospitalised Covid-19 patients had neurological manifestations, suggesting infection of human brain cells by SARS-CoV-2. For this study, they incubated neurons and astrocytes with SARS-CoV-2, and later discovered Covid-19 positive cells for both, indicating that both cell types can be infected by SARS-CoV-2 in vitro. Also in relation to Covid-19, it was recently discovered that Apolipoprotein E4 (ApoE4), associated with Alzheimer’s disease, was also identified as a risk factor for severe Covid-19 infection. It increased both infection and mortality in those with this genotype, however its causal role is not yet clear. In light of this, Dr. Shi and her team decided to use their hiPSC-derived cell model to investigate this new finding. To begin, they generated isogenic ApoE4 iPSC neurons and infected them with SARS-CoV-2. They then compared their results with infection of ApoE3 neurons, which had much lower infection rates than those of ApoE4, although neurite degeneration was observed in both. From here, they wanted to determine whether Sars-CoV-2 infection could cause neurological defects in positive patients. For this, they examined neurite degradation in E3 and E4 neurons, and discovered neuite degradation in both, with slightly more damage observed in E4 neurons. They also found that E4 astrocytes were more susceptible to Sars-CoV-2 infection than E3 astrocytes, with substantially different responses in both, such as increased percentage of fragmented nuclei in E4 astrocytes, indicating cellular apoptosis. Dr. Shi concluded that this study may be able to provide some insight into why some, but not all, Covid-19 patients may exhibit more severe neurological manifestations. You can read more on this study here. As a final point, Dr. Shi mentioned that she hopes these models can also be used for drug screening and testing of antiviral drugs that may ease and improve the neurological symptoms observed in some Covid-19 patients.
Finally, Dr. Shi briefly touched on her work regarding the combination of iPSCs with gene therapy, in an effort to correct genetic deficiencies. For this study, they have focused on Canavan disease, a neurodevelopmental disease caused by a mutation to the aspartoacylase (ASPA) gene, leading to increased levels of N-acetylaspartic acid (NAA) in the brain. Their model uses patient-derived iPSCs, which are differentiated into neural progenitor cells (NPCs) to which a functioning ASPA gene is then introduced. These functional ASPA iNPCs were then inserted into mouse models, where substantial improvement and rescue was observed. Once again, you can read more on this study here. Dr. Shi hopes that this model can soon be approved for translation into human patient studies, and has the potential to greatly improve their symptoms.
Kimberly Christian from the University of Pennsylvania, then took over to tell us about her use of human iPSCs to model the effects of in utero drug exposure on the developing brain. Recently, Dr. Christian and her team have focused on developing protocols to optimise differentiation of organoids to model specific brain regions, particularly the forebrain, midbrain hippocampus and hypothalamus. During the seminar, she focused on her work surrounding their forebrain cortical organoid model. These organoids mirror foetal brain development at the transcriptional level, which allows for a better understanding of the ongoing activity and maturation of the fetal brain through observation of the organoid model.
In order to look at gene expression, they performed bulk RNA sequencing on these organoids and compared their data to published RNAseq data sets from foetal brain tissue from three different regions. Up to approximately day 54, they found a high correlation between overall gene expression levels in both datasets through the first trimester. When the organoids were cultured longer than this, until about 100 days, there was an even higher correlation of data through the second trimester. This data then led them to investigate the consequences of in utero drug exposure. Dr. Christian addressed the lack of data around the impact that drugs can have on pregnancy, as pregnant women are often considered a vulnerable population and therefore excluded from clinical trials. Most of the current data consists of observational studies performed at birth, looking for adverse effects. However, we did learn that, as of 2019, this rule has since been amended, allowing the pregnant population to now take part in clinical trials. For this study, Dr. Christian and her team focused on the antiretroviral therapy (ART) drugs used to treat people living with HIV. These drugs must be taken throughout pregnancy and prior to conception to prevent mother-to-child-transmission of the virus, making them an ideal candidate for their study.
However, following the publishing of another similar observational study in 2018, regarding the drug Dolutegravir, which suggested its links to increased neural tube defects, the FDA released a safety recommendation for pregnant women to avoid taking this drug prior to conception. This prompted the team to refocus their efforts onto Dolutegravir instead and look at the early stages of organoid formation. For the purpose of their study, they exposed the organoids to different doses of Dolutegravir, focusing particularly on days 20, 40 and 60 of the experiment. Throughout the study, they discovered that Dolutegravir exposure leads to neural rosette thinning and disorganisation, and reduced organoid growth and size led to morphological disruptions. In order to test the specificity of Dolutegravir, Ralteguveir, another integrase inhibitor, was also tested. It was observed that Ralteguveir caused minimal effect on rosette size and structure within the organoids.
Bulk RNASeq was then used to compare the highest dose treated Dolutegravir organoids to Dimethyl Sulfoxide (DMSO) treated organoids, in an effort to gain a better understanding of this mechanism, where upregulated genes are enriched for stress related processes. In the Dolutegravir treated organoids, one particularly enriched gene observed was ATF4, a primary downstream effector of the integrated stress response (ISR) pathway. This was also observed in the drug dose study, where the organoids with the highest dose of Dolutegravir showcased an increased expression of ATF4. Given these results, they then decided to co-culture the ISR inhibitor (ISRIB) with Dolutegravir, where a partial rescue of phenotypes and neural rosette number and defects, in thickness but not diameter, was observed.
They then looked at the downregulated genes in response to Dolutegravir, and it was found that these genes were enriched for neuronal development and neurogenesis. Looking at their earlier data, particularly of their 60 day organoids, they found that this correlated as they observed dose-dependent reduction of neurogenesis in dolutegravir treated organoids. However, it was found that there was no reduction in the percentage of remaining cells in the cell cycle of the Dolutegravir organoids, as was observed in controls with proliferative markers. This once again led back to the ISR pathway, where they suspect that ISR activation is leading to p21-mediated cell cycle delay in phase G1. Upregulation of p21 can lead to cell cycle arrest, and it was found that Dolutegravir increased the proportion of p21 positive cells in these organoids. In light of this, Dr. Christian and her team are now investigating the involvement of the ISR pathway in relation to Dolutegravir pathways, work which is still ongoing.
The hope is that these organoid models can better our understanding of neural development at the very early stages of foetal development, and also may be used to provide complementary data to existing and future preclinical studies, such as drug efficacy and safety. They may also assist in working towards a more personalised medical approach through the use of patient-specific cell lines.
Last to speak was Anne Bang from the Sanford Burnham Prebys Medical Discovery Institute, where she is the Director of Cell Biology at their Conrad Prebys Center for Chemical Genomics. Her group is specifically focused on bringing human iPSCs into the drug discovery process and have worked to develop models that provide the high throughput data necessary for the drug testing and screening of complex models. As part of their efforts to develop these models, Dr. Bang and her team are working on evaluating hiPSC derived neuronal networks on multi-electrode arrays (MEAs).
Dr. Bang began by describing how primary neurons, from rodents or hiPSC neurons, which self-organise into networks which generate spontaneous and organised bursts of activity to resemble aspects of developing brain circuits, can be used to study human neuronal networks. She went on to explain that network bursting, a physiologically relevant pattern of normal brain activity, is a fundamental property of circuit function and crucial not only in development but also to neural plasticity, learning and memory. Many disorders like ASD, SZ, AZ and epilepsy cause dysregulation of synchronized network bursting. She then went on to introduce the MEA systems they are using to model these human neuronal networks, which consists of an axion maestro, with a 48-well format that has 16 planar electrodes in each well. Such a system is beneficial as it allows for in vitro testing and recording over long time periods due to its non-invasive model and psychologically appropriate signalling rates. It also allows for good well-to-well reproducibility, which in turn, allows for good pharmacological screening. In their study, Dr. Bang and her team cultured the hiPSC cortical neurons on MEAs for 3-4 weeks and added primary human astrocytes to improve network consistency. They observed that initially, the plated neurons exhibited tonic uncoordinated firing, but within a few weeks they developed coordinated network bursting across most electrodes. They are also looking at the functional connectivity within the cultures in the hopes of creating a map showing how the neurons in the culture are connected to each other.
Moving on, Dr. Bang went on to discuss excitation and inhibition balance between neurons, and how this defines bursting patterns. As mentioned, many imbalances can be related to many neurological disorders. This balance of excitation and inhibition works through a negative feedback loop, where a loss of feedback inhibition leads to a pathological increased output of firing, as seen in epilepsy, and a gain of feedback inhibition leads to a decreased firing output, as seen in Rett syndrome. Using iGluta neurons, they studied this excitation/inhibition balance in culture by performing a sequence of steps involving:
- GABA receptor blockers to block inhibition.
- AP5, an NMDA receptor blocker, to inhibit glutamate mediated excitation.
- AMPA receptor blockers, which cause a loss of coordinated network bursting.
They determined that network bursting is driven by the AMPA receptors, with some contribution from NMDA receptors and then refined by an inhibitory input. To analyse their data, Dr. Bang’s team first determined the percentage change from baseline activity across selected metrics, and then used a custom R-script to generate a heat map so that their data could be visualised. They looked at 18 different metrics describing activity, bursting, network bursts and synchronicity in culture. Upon analysis, they observed patterns of activity that clustered around drug treatment. Following this work and through the use of their obtained data, Dr. Bang and her team hope to continue to define data signatures and begin to build a detailed collection of benchmark signatures, which can be compared with more complex drugs.
Finally, Dr. Bang went on to discuss neuroplasticity in relation to MEAs, with a particular focus on long-term potentiation (LTP), which she described as a “form of neuroplasticity where a persistent strengthening of synapses based on recent activity produces a long lasting increase in signal between two neurons”. She emphasised the importance of neuroplasticity and the role it plays in learning and memory, particularly in response to injury or experience. Should there be any irregularities in LTP, this can lead to many neurological and cognitive disorders, such as AZ, ASD, SZ, addiction and more. This can be evoked by excitatory input leading to increased Ca2+ in postsynaptic dendritic spines, activating multiple downstream pathways which then lead to increased firing and network bursting, consistent with strengthening of synapse, after several days.
There are at least two phases of LTP maintenance:
- Early phase: short-term, requiring phosphorylation of existing proteins.
- Late phase: long-term, depends on protein transcription and translation.
Dr. Bang and her team used hiPSC-derived midbrain dopaminergic neurons (iDOPA) cells combined with iPSC-derived astrocytes (iAstros) to develop their LTP assay, which consisted of immunofluorescence analysis and bulk RNAseq. This assay confirmed that the iDOPA and iAstro cell population are highly enriched for midbrain dopaminergic neurons, which are relevant to neurodegenerative and neuropsychiatric disease, as well as addictive behaviours. Dr. Bang then went on to discuss chemical LTP (cLTP) assays on iDOPA neurons, which induces canonical activity regulated gene expression and is highly reproducible and useful for the study of both early and late LTP. They observed a rapid phosphorylation of the cAMP response element-binding protein (CREB), with increased expression of immediate early genes, confirming that the assay was effective in replicating early and late phase LTP. Dr. Bang also looked at gene expression changes induced by cLTP using bulk RNASeq analysis in the late phases of LTP. Many changes in gene expression were observed, with 407 upregulated and 143 down regulated transcripts. Using gene ontology analyses, they confirmed that cLTP on iDOPA induces canonical activity regulated gene expression, including regulation of synaptic plasticity, increased kinase binding and high enrichment for synaptically localised proteins. They then performed a comparative study involving activity regulated gene expression in cultured cortical neurons, one study used mouse cortical neurons, another used hiPSC neurons. It was discovered that nearly 40% of the upregulated genes in their own dataset overlapped with these studies, reflecting common canonical activity regulating gene expression. They then deduced that, compared with other systems, cLTP on iDOPA induces both overlapping and non overlapping gene expression. Dr. Bang specified that there are many differences between the studies to which they compared, suggesting their cLTP-MEA system could be a good way of answering other questions regarding differences between activity regulated gene expression and different circuits and types of neurons, especially for humans, as well as analysing neuroplasticity using iPSCs.
As a final point, Dr. Bang expressed her excitement at the body of work and research that has been produced by her and her team. In particular, she mentioned possible future applications, such as using their MEAs to investigate synaptic plasticity of hiPSC derived neurons, finding methods of further developing the maturation of hiPSC neurons, and using their models to investigate iPSC disease models in relation to neuroplasticity.
The seminar ended with a Q&A session in which many great questions were tackled. As such, stress on in vitro cultures was emphasised, as these stresses (glutamate, endoplasmic reticulum stress, oxidative stress etc.) can see a mild increase in expression. Astrocyte benefits were also highlighted. They provide growth factors, keep down glutamate toxicity and are not required for network bursting. For rare variants they recommended the use of isogenic controls whereas common variants can and should use more donors to get the full picture from different ethnicities as iPSC lines are affected by genetics and epigenetics.
AMD – Age-related Macular Degeneration
AMPA – α-Amino-3-hydroxy-5-Methyl-4-isoxazolePropionic Acid receptor
ApoE4 – Apolipoprotein E4
ART – Antiretroviral Therapy
ASD – Autism Spectrum Disorder
ASPA – Aspartoacylase
AxD – Alexander Disease
AZ – Alzheimers
ChAT – Choline Acetyltransferase
cLTP – chemical Long-Term Potentiation
CREB – cAMP Response Element-Binding protein
CRISPR – Clustered Regularly Interspaced Palindromic Repeats
DA – Dopamine
DMSO – Dimethyl Sulfoxide
eQTL – expression Quantitative Trait Loci
FDA – US Food and Drug Administration
FTD – Frontotemporal Dementia
GABA – Gamma Aminobutyric Acid
GFAP – Glial Fibrillary Acidic Protein
GWAS – Genome-Wide Association Study
HCMV – Human Cytomegalovirus
hiPSC – human induced Pluripotent Stem Cells
hECS – human Embryonic Stem Cells/
iAstros – iPSC-derived Astrocytes
iDOPA – iPSC-derived midbrain Dopaminergic neurons
IFN – Interferon
ISR – Integrated Stress Response pathway
ISRIB – Integrated Stress Response pathway Inhibitor
LTP – Long-Term Potentiation
MAPT – Microtubule Associated Protein Tau
MEAs – Multi-Electrode Arrays
NAA – N-Acetylaspartic Acid
NAM – Nicotinamide
NMDA – N-Methyl-D-Aspartate receptor
NPCs – Neural Progenitor Cells
NSC – Neural Stem Cell
PCA – Principal Component Analysis
QTL – Quantitative Trait Loci
RPE – Retinal Pigment Epithelium
SZ – Schizophrenia
SNP – Single Nucleotide Polymorphism
UTR – Untranslated Region
Written by Ellen Towey and Saoirse Foley