Effects of antidepressants on glucocorticoid receptor binding and downstream gene expression

Student: Kate Jones Supervisor: Angela Bithell

I confirm that this is my own work and the use of materials from other sources has been properly and fully acknowledged.

Signature: Date: 16/4/2015

Abstract

Cortisol is a glucocorticoid hormone known to affect the expression of several genes associated with hippocampal neurogenesis, however in many cases it is unknown whether this is through direct binding of the glucocorticoid receptor (GR) to the target gene. In order to investigate this, we established a working chromatin immunoprecipitation (ChIP) procedure to be used in the analysis of GR protein-DNA interactions. Using this methodology in a human hippocampal neural progenitor cell line (HPC03A/07), we found that the GR binds directly to at least one such target gene, Sgk1, and that exposing cells to dexamethasone increases GR binding at this locus when compared to controls. This suggests that up-regulation of Sgk1 may occur in times of stress. Previous studies indicate that Sgk1 plays a role in mediating cortisol-induced reductions in neurogenesis. Thus, understanding how this is activated may help to further understand its role and potential as a therapeutic target. Through immunofluorescence staining, we also observed that sertraline may reduce cortisol-dependant reductions in neuronal proliferation and differentiation in HPC03A/07 cells. This supports the hypothesis that the therapeutic efficacy of sertraline is based on a GR-dependant mechanism in a subset of patients with depression.

Introduction

Depression is a common affective disorder, prevalent in approximately 14% of the global population and contributing significant detrimental effects to patient quality of life, with wider social and economic implications (Mitchell et al., 2009). Whilst several clinical subtypes exist, common symptoms in all forms of major depressive disorder include anhedonia, feelings of guilt, changes in appetite and/or sleep and persistently low mood. (Cizza et al., 2012). The exact cause of depression is not fully understood, however there are several psychological, sociological and biological theories that attempt to address the disorder’s underlying, often multifactorial, pathology.

Biologically, the monoamine theory is perhaps the most well-known pathological cause. The underlying principle is that depressed patients exhibit reduced synaptic transmission of dopamine, noradrenaline and/or serotonin. Originally, it was thought that this was caused by reduced activity of the pre-synaptic monoaminergic receptors, resulting in a subsequent monoaminergic deficit across the synapses. The monoamine theory is supported by the fact that all major classes of antidepressant drugs (mono-amine oxidase inhibitors, tricyclic antidepressants and selective serotonin re-uptake inhibitors (SSRIs)) agonise the effect of at least one monoamine (Morrissette and Stahl, 2015). Furthermore, monoamine antagonists such as reserpine are known to cause depression as a side effect (Antkiewicz-Michaluk et al., 2014). Low levels of serotonin metabolites have also been correlated with depression (Brown and Linnoila, 1990).

However, antidepressant drugs have been shown to increase monoaminergic levels rapidly, yet clinical improvement in patients is not typically seen until after approximately 3 weeks of treatment. To account for this, revised versions of the theory have been proposed. One version suggests that depressed patients exhibit overactive monoaminergic activity and that the delay in clinical efficacy of these antidepressants is caused by the time taken for monoaminergic post-synaptic receptors to become desensitised. The second, more established, hypothesis suggests that depressed patients have more sensitive presynaptic autoreceptors, which are responsible for sending inhibitory signals towards monoamine release. In this theory, antidepressant drugs initially trigger a decrease in monoamine release as the receptors respond and increase inhibitory signalling. However, over time, the receptors become less sensitive and thus monoaminergic release across the synapse is increased (Elhwuegi, 2004).

The monoamine theory of depression is reasonable but not without its assumptions (Sapolsky, 2004). Critically, both models used to explain the delay in a treatment’s clinical effect rely on the concept that neural compensation will occur in response to monoamine neurotransmitter level fluctuations. Additionally, resistance to current pharmacological treatment with drugs that act on this system is significant. Approximately one third of patients exhibit an inadequate response to first-line monotherapy and around 10% of patients experience prolonged states of depression regardless of multiple treatments (Souery et al., 1999, Nierenberg and Amsterdam, 1990).

An emerging, alternative theory suggests that in a subset of patients with depression, dysfunctional hippocampal neurogenesis within the granular layer of the dentate gyrus could be a significant causative factor and might partially dictate individual response to anti-depressive treatment (Boldrini et al., 2012). This has been correlated with observations of lower granular cell layer volumes in depressed patients and increased hippocampal neurogenesis in patients treated with SSRIs (Kempermann, 2002, Kempermann and Kronenberg, 2003). Furthermore, the timing of onset for therapeutic efficacy in SSRIs is similar to the time it takes for the formation of neurones from

their respective neural stem cells. (Ge et al., 2007, Jacobs et al., 2000). Zhao et al showed that neural stem cells were functionally integrated into the hippocampus of mice after 2-3 weeks and were required for the expression of trace memory (Zhao et al., 2006). There is a lack of direct evidence determining the functional role of neurogenesis in humans, however the proliferation rate and relative abundance of these neurones is comparable to that observed in mice. In such studies, further effects on behaviour have been identified and include the ability to separate memories, a feature lacking in some patients with depression and anxiety (Spalding et al., 2013).

Much of the current research on the hypothalamic-pituitary-adrenal (HPA) axis’ involvement in the stress response indicates a two-way relationship between the HPA axis regulation and hippocampal neurogenesis. In the presence of acute physiological stress, the hippocampus signals for increased levels of corticotrophin releasing hormone (CRH), which triggers a cascade of events associated with the HPA axis that lead to increased plasma levels of cortisol (Anacker, 2014, Anacker et al., 2011a, Holsboer et al., 1984). In mice with low levels of hippocampal neurogenesis, increased levels of cortisol were observed following stress, suggesting an increased HPA axis response (Schloesser et al., 2009, Snyder et al., 2011).

Cortisol is a steroid hormone that acts on glucocorticoid (GR) and mineralocorticoid receptors (MR). Both GR and MR are nuclear receptors with widespread expression in the body. Cortisol-GR binding causes dissociation from the GR inhibitory complex, resulting in a conformational shape change that increases the availability of the corresponding nuclear localisation sequence. Subsequent translocation of GR monomers and dimers from the cytoplasm to the nucleus allows the GR to bind to glucocorticoid response elements (GREs) and negative glucocorticoid response elements (nGREs) respectively, leading to gene transcription via modulation of factors that influence chromatin configuration (Herrlich, 2001, Aoyagi and Archer, 2011). Other research also shows translocation to mitochondria where they influence mtDNA gene expression (Du et al., 2009). A previous study by Anacker et al showed that dexamethasone could be used in-vitro instead of cortisol to elicit the same agonist response at the GR (Anacker et al., 2011b).

Recent in-vitro studies using a human hippocampal neural progenitor cells line (HPC03A/07) have shown interesting results related to hippocampal neural progenitor cell (NPC) proliferation and differentiation when exposed to varying levels of dexamethasone and SSRIs. In untreated NPCs, there was a high rate of proliferation but low differentiation. With exposure to sertraline, proliferation was lower but differentiation was higher while in NPCs treated with high concentrations of cortisol (1 µm) alone, proliferation and differentiation remained low. In NPCs exposed to high concentrations of cortisol and sertraline, proliferation and differentiation was high (Anacker et al., 2011b). These findings suggested that to increase hippocampal neurogenesis through the use of sertraline, GR agonist activity by high levels of cortisol are required as a pre-requisite. This was further supported by results of decreased neurogenesis following GR-antagonism with RU486. From a broader perspective this implies that in a subset of patients, the therapeutic effect of sertraline is only initiated in response to a cortisol-induced depressive state.

Furthermore, exposure of progenitor cells to high levels of cortisol and sertraline may result in unique GR phosphorylation and GR-dependent gene expression. Anacker et al found that the PDE4/PKA signalling cascade regulated the effects of sertraline on cell proliferation and differentiation by altering the phosphorylation state of the GR, depending on the combination of conditions used (Anacker et al., 2011b). This was shown by different patterns of phosphorylation at the receptor’s serine residues S203, S211 and S226 when cortisol and sertraline concentrations were varied. The subsequent phosphorylation state of the GR was then demonstrated to mediate the

levels of p27kip1 and p57kip2 gene expression. P27kip 1 and p57kip2 are CDK2 inhibitors which promote the termination of cell division and increase differentiation. Levels of p27kip1 and p57kip2 expression were shown to be consistent with the proliferation and differentiation states of the cells. The concept that target genes of the GR can be selectively activated via the promotor region depending on the specific GR phospho-isoform present is supported from a number of other studies. (Galliher-Beckley et al., 2008, Blind and Garabedian, 2008, Chen et al., 2008, Kumar and Calhoun, 2008, Webster et al., 1997, Anacker et al., 2011b).

Further advances have since been made to identify other specific GR target genes that contribute towards hippocampal neurogenesis both upstream and downstream of the GR. Evidence to date from gene expression analysis suggests that GR transactivation strongly influences the level of expression of the genes p11 and β-arrestin 2 (which relate to the serotonin receptor); CCND1, HDM2 (cell cycle promoting genes) and the stress-responsive genes SGK1, FKBP5, FOXO1 and GADD45B. (Anacker et al., 2013a, Anacker et al., 2013b, Anacker et al., 2011b). However, it is unknown whether the expression of the genes listed above is through direct activation and epigenetic modifications must be considered. Furthermore, we hypothesise that there are other genes linked to hippocampal neurogenesis that are yet to be identified.

Cortisol causes increased expression of SGK1 and is of particular interest to our research. A previous study by Anacker et al identified SGK1 as a key inhibitory mediator of the Hedgehog signalling pathway, which is a major cause of cortisol-induced reductions in neurogenesis (Anacker et al., 2013b). Furthermore, SGK1 increases GR function upstream and regulates genes downstream of the GR that are involved in reducing NPC proliferation. It has been shown to potentiate the effects of GR activation even after cortisol is removed by facilitating GR translocation to the nucleus. SGK1 expression was found to be increased in the hippocampus of both patients with depression and in mice exposed to stress. However, these studies do not fully elucidate the extent of the effects of SSRI treatment, in combination with cortisol, on SGK1 expression in depressed patients.

In this study, we first aimed to develop a robust method to perform chromatin immunoprecipitation (ChIP) assays using DNA from the HPC03A/07 cell line to determine direct target genes of the GR (via direct binding). GR proteins, tagged with corresponding antibodies, will be added to the chromatin samples that were extracted from HPC03A/07 NPCs, treated with varying concentrations of dexamethasone or ethanol, the vehicle control. The antibodies will be used to recognise the amount of GR that is bound to specific gene loci within the chromatin, in the different conditions. We will also conduct qualitative analysis of immunofluorescently labelled HPC03A/07 cells to determine the effects of dexamethasone and sertraline on changes in cellular differentiation and proliferation.

Materials and Methods

Cell culture

This section of the methodology was performed by Dr Bithell. HPC03A/07 is a multipotent, immortalised human foetal hippocampal progenitor cell line used in these experiments. Cells were immortalised using the c-myc-ER™ transgene and treated for 3 days with ethanol (vehicle, EtOH), dexamethasone (Dex) and/or sertraline (Sert) in proliferative conditions (with epidermal growth factor (EGF), fibroblast growth factor 2 (FGF2) and 4-hydroxytamoxifen (4-OHT).They were then differentiated using reduced modified medium without EGF. FGF2, 4-OHT and with removal of other treatments. As an additional control, some cells were left untreated in the proliferative stage. At proliferation day 3 and then after 2 weeks of differentiation, cells were fixed and used for immunofluorescence analysis. For chromatin, cells were treated for 1hr with vehicle or dexamethasone in proliferation conditions before harvesting for chromatin. Further information can be found under Appendix 3H: Tissue Culture Methods for ReNeuron Cells (HPC03A/07).

Chromatin immunoprecipitation (ChIP)

This section of the methodology was performed by Dr Bithell.

Cells were fixed in 1% HCHO in PBS for 5 minutes then quenched in 630 µl of 2M glycine for 5 minutes to produce a final glycine concentration of 0.125M. They were then washed 3 times with ice-cold phosphate buffered saline (PBS), scraped off dishes and centrifuged at 1300rpm for 5 minutes at 4°C. The final PBS wash contained 1x protease inhibitors. Pellets were then re-suspended in ice-cold Lysis Buffer containing protease inhibitor and incubated on ice for 30 minutes. A microfuge was used to spin pellet nuclei at 5000rpm for 10 min at 4°C before re-suspension into shearing buffer with protease inhibitors in 17ml Falcon tubes, stored on ice. Chromatin was then sonicated in cycles of 30 seconds on, 30 seconds off, until the sheared chromatin was within 200-600bp (determined by de-crosslinking 25µl of each sample in 200µl using MilliQ water, 8µl 5M NaCl and 10µg RNase A for 4h at 65°C. 10µl of Proteinase K was then added and samples were incubated for 2hr at 42°C before DNA clean up and gel electrophoresis). The shared chromatin samples were centrifuged at 10,000 rpm at 4°C for 10 minutes and supernatant chromatin removed for use. Protein-G magnetic beads (PGM beads) were first pre-blocked by rotating at 4°C 4x 10mins in mRIPA with 1x protease inhibitors and 1mg/ml Bovine Serum Albumin (BSA), then twice with 1ml mRIPA buffer with 1x protease inhibitors. Chromatin was pre-cleared using 20µl of protein G magnetic (PGM) beads in each chromatin sample (Dex or EtOH) diluted in mRIPA buffer with 1x protease inhibitors and rotated during incubation for 2h at 4°C. 20µg of chromatin was used for each ChIP reaction (36.7µl of Dex or 39.22µl of EtOH chromatin diluted to 100µl in mRIPA/protease inhibitors). PGM beads were then captured and 10µl of sample was retained at this stage for use as input chromatin. ChIP samples were set up in protein LoBind tubes with 2.5µl or 5µl (5µg) of antibody, 377.5µl or 375µl of mRIPA buffer respectively, 100µl of pre-cleared chromatin (20µg) and 20µl of 25x protease inhibitors. The following antibodies were used: anti-REST, rabbit IgG (provided by Millipore); rabbit IgG (negative control provided by Sigma); mouse monoclonal antibody IgG2b (provided by Diagenode) and rabbit pAb (provided by Thermo Scientific Pierce). Samples were incubated at 4°C on a rotator overnight. Pre-blocked PGM beads were added to each rotating sample at 4°C for four hours. Supernatant was removed using a magnetic stand to pellet the beads. 800µl Wash Buffer 1 was added 2x, each for 3mins on a rotator, then the same technique was used for Wash Buffer 2 (1 x 3mins) and 2x 3mins Tris-EDTA (TE). Washed beads were re-suspended in

100µl Elution buffer (also for 10µl input samples). 4µl 5M NaCl and 1µl RNase was then added and samples were incubated at 65°C for 4 hours to de-crosslink.

At room temperature, 2µl Proteinase K (10mg/ml) was added then samples were incubated at 42°C for 2h, then allowed to return to room temperature again. Magnetic beads were re-captured, and supernatant (containing DNA) removed for clean-up. DNA was purified using a Qiagen QIAquick PCR clean up kit according to the manufacturer’s instructions. This involved mixing samples with 5x volume of binding buffer and applying to a column. This was spun at 13000rpm for 30-60s. Flow-through was discarded and 0.75ml wash buffer PE was applied to the column and a further spin at 13000rpm for 30-60s was conducted. Flow-through was discarded again, the column was spun at 13,000rpm for 2mins and the column was placed in a new 1.5ml Eppendorf tube. The DNA was eluted into 50µl of HPLC H2O by centrifugation at 13000rpm for 1min. Samples were stored at -20°C until used in QPCR.

Quantitative Real-time polymerase chain (QPCR) reactions

This section of the methodology was performed by Kate Jones.

ChIP DNA samples (see above) were used in qPCR reactions to investigate changes in GR binding by amplification of bound target sequences. Primers designed to be compatible with Dscam, Gilz, Mt2a, Sgk1 and Slc19a (see Appendix 3A: Forward and Reverse Primer Sequences) were prepared in stock solutions, stored in 1.7ml Eppendorf tubes, to be used in 20µl reactions in 96-well plate assays. Each reaction contained 0.4/1.0µl of 10µM forward primer, 0.4/1.0µl of 10µM reverse primer, 10 µl of 2x SYBR® green dye (Biorad), 2µl of ChIP DNA and 6.6/6.0µl of high-performance liquid chromatography (HPLC) grade water. The reagents were mixed in a master mix without ChIP DNA, aliquotted, and each ChIP DNA sample added, including water/negative controls. Quantitative analysis was based on standard curves for each gene, using the average value of 2 duplicates containing 0.1, 0.3, 1.0, 3.0, 10.0 and 30.0ng/ml. StepOnePlus from Applied Biosystems was used to perform the polymerase chain reaction over 3.5 hours and subsequent analysis was through StepOnePlus software. The polymerase chain reaction was ran over 40 cycles with each step taking 30s. Denaturation took place at 95°C, annealing at 60°C and extension at 72°C.

Immunofluorescence Staining/Immunocytochemistry

This section of the methodology was performed by Dr Bithell and Kate Jones.

Medium was aspirated from the HPC03A/07 culture medium and excess debris was removed through careful rinsing with PBS. 4% paraformaldehyde was added to preserve cells through cross-linking for 10 minutes, then terminated by 3 washes with PBS. Cell membranes were permeabilized using 0.1% TX-100 (triton) in PBS for 5 minutes at room temperature to allow antibodies access to their target intracellular epitopes and then washed again in PBS. 30µl of primary antibody solution was used per coverslip in combination with 10% normal serum in PBS.

The primary antibodies used were 1:1000 TuJ1 mouse IgG2a (beta-III tubulin, from Covance) which recognises microtubules in neurons; 1:200 anti-GFAP mouse IgG1 (Millipore) which is specific to intermediate filaments in astrocytes; 1:1000 Ki67 polyclonal rabbit IgG (Abcam) which is a proliferation marker and 1:60 anti-human Nestin mouse IgG1 (from R&D Systems), an NSC/NPC-specific marker. Samples were incubated without light and at room temperature for 2 hours and washed with 3x PBS to remove unbound antibody. The same steps were also conducted for the secondary antibodies. To examine neuronal differentiation, 1:1000 polyclonal goat anti-rabbit IgG (for use with anti-GFAP) fluorescently tagged with AF-594 and 1:1000 goat anti-mouse monoclonal

IgG2a (for use with TuJ1) fluorescently tagged with AF-488 were used. To examine NPC proliferation, 1:1000 goat anti-rabbit polyclonal IgG was used (to detect Ki67) tagged with AF-594 and 1:1000 goat anti-mouse monoclonal IgG1 (to detect Nestin) fluorescently tagged with AF-488. All goat-derived antibodies were sourced from Invitrogen Life Technologies. Nuclei were counterstained using DAPI (4’,6-Diaminidino-2-phenylindole) diluted from stock solution 1mg/ml to 1:2000 in PBS, applied for one minute then washed off.

Before mounting, PBS was removed onto a tissue and inverted onto the mounting medium Prolong® (Gold) Antifade Reagent, then placed on the cover slip. They were then stored for at least 24 hours without light to allow the mounting medium to cure. Coverslips were imaged using appropriate filter sets on Zeiss Axioimager A1 wide field epifluoresence microscope with Axiovision software at 40x magnification.

Results and Discussion

Establishment of a functional ChIP protocol with REST ChIP

In order to test that the chromatin immunoprecipitation procedure worked, Repressor Element-1 Silencing Transcription Factor (REST) was used a positive control ChIP in combination with the primers for a binding site in the Snap-25 promoter (QR1 primers, where REST binds), and for a site in the coding region of a gene where REST dose not bind (QC3 primers, M4coding). Information about REST’s binding site has already been well-established and to be consistent with previous findings, it was anticipated that the REST ChIPs would show significant relative enrichment at the Snap-25 binding site and minimal enrichment at the M4coding site, to act as positive and negative controls respectively.

In the experiment involving REST and QR1 Snap-25, REST consistently showed enrichment between 35 and 40 fold relative to IgG. In these samples, GR showed a maximum of 3-fold enrichment using either antibody. Comparatively, REST showed very low levels of enrichment at the M4coding region (at 0.5 fold in DEX and 1-fold in EtOH ChIPs). Interestingly, minor enrichment was observed in GR ChIPs (1.5-2 in DEX samples and 2.5 to 3-fold in EtOH). This is likely to be the background level, however it could suggest low level GR binding which requires further study.

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Figure 1. REST binds to QR1 Snap 25 significantly more than GR in both dexamethasone and ethanol samples. The graph shows the average relative enrichment of REST and GR binding at the Snap-25 gene locus relative to IgG, using the results of two biological replicates, each of which used the average of 3 technical triplicate values. Raw values for the technical triplicates can be viewed in Appendix 3G: QR1 Snap 25 Numerical Results Data.

The high degree of enrichment of REST at the RE1 site of the Snap-25 gene, in combination with no enrichment of REST at the M4coding gene, is consistent with what was expected and indicates that the ChIP protocol is functional. This suggests that the quality of the components used within the protocol, such as the chromatin and buffers, were suitable for use in further experiments that investigated changes in GR binding.

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Figure 2. REST shows minimal binding to the m4coding gene. The graph shows the average relative enrichment of REST and GR binding at the M4coding gene locus relative to IgG, using the results of two biological replicates, each which used the average of 3 technical triplicate values. Raw values for the technical triplicates can be viewed in Appendix 3F: M4coding Numerical Results Data.

ChIP assays using chromatin extracted from (HPC03A/07) using different antibodies against GR (from ThermoScientific (Thermo) and Diagenode) with rabbit IgG (rIgG) as a negative control were performed to immunoprecipitate and thus identify direct GR target genes. Cells were treated with either dexamethasone (DEX) or ethanol (EtOH), where dexamethasone mimics cortisol and ethanol acts as a negative vehicle control, to compare the relative effects of dexamethasone treatment. By comparing both conditions, we were able to qualitatively define whether or not protein-DNA binding occurs at a specific locus and then quantitatively examine the relative effect dexamethasone had on the degree of GR binding. This was measured by fold enrichment, relative to IgG. IgG is a non-specific antibody used as a negative control ChIP to determine the threshold level of background noise.

Numerous primer pairs were then used to amplify the isolated, immunoprecipitated strands of genomic DNA for quantitative polymerase chain reaction (q-pcr) analysis. These include: Dscam, Gilz, Mt2a, Sgk1 and Slc19a2. The primer sequences are available in the Appendix 3A: Forward and Reverse Primer Sequences.

Optimisation of ChIP for GR binding in NPCs

Potential GR binding at the Dscam negative control locus

Modified Dscam was used as a negative control to measure the degree of non-specific DNA binding and subsequent immunoprecipitation, although in theory this should not exist. On average, DEX-treated immunoprecipitated with either GR antibody showed approximately 2.5-fold relative enrichment. EtOH-treated samples showed 3-fold and 2.5-fold enrichment for GR Thermo and GR Diagenode respectively. Enrichment at the REST binding site was low indicating specificity towards the GR and supporting the conclusion that the ChIP procedure was functional. There is a significant amount of variability between the biological replicates that target the GR binding site, as shown by the standard error, particularly for ethanol containing samples. The results show that the GR binds to the Dscam gene both in the presence and absence of dexamethasone, although the variability in results makes it difficult to determine the extent of agonist or inhibitory effect that dexamethasone has on GR binding in this case and further studies are needed to elucidate this.

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Figure 3. GR binds to Dscam in the presence or absence of dexamethasone. Graphs show the relative enrichment of GR and REST binding at the DSCAM gene relative to IgG controls. In figure 3A, y values represent the mean average of corresponding y values in figures 3B and 3C. In figure 3B and 3C, y values correspond to the mean average of technical triplicates, excluding results deemed anomalous. Raw values for the technical triplicates can be viewed in Appendix 3B: DSCAM Numerical Results Data.

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GR binding at the Gilz positive control locus may be inhibited by the presence of dexamethasone

Glucocorticoid-induced leucine zipper (Gilz) is a mediator of GR-dependant immunomodulation pathways. Transactivation of the GR increases Gilz transcription and so GR binding at this locus was expected within our experiments. The gene therefore serves as a further positive control. Our results show GR enrichment of the Gilz locus in the absence of dexamethasone and in some samples containing dexamethasone. On average, approximately 2-fold enrichment was observed in GR samples containing dexamethasone, which is deemed non-significant and a 4.5 fold enrichment was seen in samples containing ethanol. This could suggest that dexamethasone has an inhibitory effect on GR binding. REST was not enriched in either conditions (DEX or EtOH), with less than 1-fold in dexamethasone and just over 1-fold in ethanol, though this latter sample also had more variation between biological replicates.

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Figure 4. GR binds to Gilz in the presence or absence of dexamethasone. Graphs show the relative enrichment of GR and REST binding at the Gilz locus relative to IgG controls. GR binds more readily in samples containing ethanol compared to those containing dexamethasone. In figure 4A, y values represent the mean average of corresponding y values in figures 4B and 4C. In figures 4B and 4C, y values correspond to the mean average of technical triplicates, excluding results deemed anomalous. Raw values for the technical triplicates can be viewed in Appendix 3C: GILZ Numerical Results Data.

GR binding at the Mt2a positive control locus may be increased by the presence of dexamethasone

Metallothionein 2A (Mt2a) is a known GR target and thus serves as a positive control in our experiment. Our results consistently show that significant enrichment of the GR at this locus occurs in the presence of dexamethasone, but there is some variability in the ethanol containing samples. The average of the results indicates that dexamethasone increases enrichment between 2.7- and 3.5-fold compared to the control and between 2-2.5-fold in the ethanol vehicle, however further investigation is needed.

REST does not show enrichment in either DEX or EtOH, there is approximately 1-fold average enrichment in both, with low variability between the biological replicates. This is consistent with what is expected of the negative control, as there is no RE1 binding site at the Mt2a locus. This supports the validity of the results of GR binding.

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binding at the Slc19a2 positive control locus may be increased by the presence of dexamethasone

Slc19a2 was used as a positive control, following data of successful GR-binding in A549 human lung cells (So et al., 2007). Significant average enrichment of GR at the Slc19a2 locus was observed in both samples but was higher overall in the presence of dexamethasone, at 5.5-fold, 3-fold, 2-fold and 1.5-fold in the DEX GR Thermo, DEX GR Diagenode, EtOH Thermo and EtOH Diagenode samples respectively. There is a larger distinction in the results when comparing DEX to EtOH in the first biological replicate, with very high relative enrichment values for DEX samples and no enrichment at EtOH. If this result was to be replicated, it could suggest that dexamethasone is required for GR-binding at the Slc19a2 locus. In the second biological replicate, no enrichment is observed except for in EtOH Thermo at approximately 3.2-fold. It is likely that the data from the first biological replicate is more reliable, as this is consistent with expectations of a positive control, however further studies are needed to confirm these findings.

On average, no enrichment of REST at the Slc19a2 locus was observed, which is as expected as it was a negative control. This suggests that despite the variation in results, the ChIP procedure used was appropriate and that error is likely to originate from the PCR reactions.

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Figure 6. GR binds to Slc19a with and without the presence of dexamethasone. Graphs show the relative enrichment of GR and REST binding at the Slc19a gene locus relative to IgG controls. GR binds more readily in samples containing dexamethasone compared to the ethanol control. In figure 6A, y values represent the mean average of corresponding y values in figures 6B and 6C. In figures 6b and 6c, y values correspond to the mean average of technical triplicates, excluding results deemed anomalous. Raw values for the technical triplicates can be viewed in Appendix: Slc19a Numerical Results Data.

GR binding at Sgk1 locus may be dependent on the presence of dexamethasone

Serine/threonine-protein kinase 1 (Sgk1) is a GR target gene associated with mediation of the Hedgehog signalling pathway (Anacker et al., 2013b). GR binding at the Sgk1 locus in NPCs was enriched by approximately 5-fold in DEX treated samples compared with approximately 2-fold in EtOH vehicle controls, suggesting that dexamethasone administration is required for GR binding. Further studies are needed to confirm these findings however, as there is a large amount of variability in the results from biological replicates of DEX treated samples. In the first biological replicate (figure 7B), DEX samples show approximately 7 to 8-fold enrichment compared to 1.5 to 2-fold enrichment in the second (figure 7C), which is not generally considered significant due to being relative to IgG. Minimal enrichment is observed in EtOH samples from both replicates.

REST shows an average of approximately 3-fold and 1-fold enrichment in DEX and EtOH treated samples. The biological replicates of DEX treated samples show approximately 6-fold (figure B) and 0.5-fold (figure C) enrichment. REST was intended for use as a negative control as there is no known RE1 binding site at the Sgk1 locus, therefore the result from the second biological replicate (figure C) is likely to be more accurate. Although the possibility of REST binding should not be excluded, it is noteworthy that most enrichment values, including REST, are higher in the first replicate. This could implicate a more general erroneous result, which is likely to have arisen from errors in the PCR reaction.

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Figure 7. GR binds to Sgk1 with and without the presence of dexamethasone. Graphs show the relative enrichment of GR and REST binding at the Mt2a gene locus relative to IgG controls. GR binds more readily in samples containing dexamethasone compared to the ethanol control. In figure 7A, y values represent the mean average of corresponding y values in figures 7B and 7C. In figures 7B and 7C, y values correspond to the mean average of technical triplicates, excluding results deemed anomalous. Raw values for the technical triplicates can be viewed in Appendix: Sgk1 Numerical Results Data.

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Summary

Results from ChIP data show successful optimisation of the procedure. We are confident that the ChIP procedure is functional due to the Snap-25 and M4coding results being consistent with what is expected of positive and negative controls for REST binding respectively.

Results from the PCR data shows that there may be direct GR binding at the Sgk1 locus, which is dependent on the presence of dexamethasone. The results of the positive controls Mt2a, Slc19a2 and Gilz improve the validity of our experiments by showing that the ChIP and PCR procedures display positive results where expected. At the Dscam negative control locus, our results indicated that some GR binding may be present despite theory suggesting that it should not. Alternatively, the perceived GR binding may be at least partially attributable to the Dscam primer design. In future studies it would advisable to use a different GR negative control locus if possible.

Immunofluorescence imaging of changing morphologies in HPC03A/07 NPCs

Qualitative analysis of cells was then conducted using immunofluorescence labelling to establish the effects of dexamethasone and/or sertraline on cellular proliferation and differentiation. Similar to our previous experiments, ethanol was used as a negative vehicle control. The ethanol concentration was doubled in samples where the intention was to compare the effects of samples containing both sertraline and dexamethasone. This acted as a control for the volumes of sertraline and dexamethasone added, both of which use EtOH as a solvent. A further negative control was used which was untreated with any vehicle other than reduced modified medium (without FGF2/EGF/4OHT).

The effects of dexamethasone and sertraline on neural progenitor cells at proliferation day 3

NPCs were grown in varying conditions for 3 days and labelled with DAPI, Nestin and Ki67 conjugated with different fluorochromes to permit immunoflourescene imaging. The conditions were treatment with either dexamethasone 1µm, sertaline 1µm, dexamethasone 1µm and sertraline 1µm, ethanol 0.1mM or ethanol 0.2mM. A further control was also created whereby the cells received no treatment. Our results show that dexamethasone may have an inhibitory effect on NPC proliferation that is counter-acted to some extent by co-treatment with sertraline.

Figures 8a and 8b: Scale bar: 20µm. The top left panels indicate staining by DAPI (blue); top right: Nestin (green); bottom left: Ki67 (red) and the bottom right panel combines images taken from all filter sets. DAPI has been used to stain all cell nuclei; Ki67 is a non-specific cellular proliferation marker and Nestin stains an intermediate filament found in NSCs/NPCs. The white arrow in top left quadrant of figure 8Ai shows an example of a nucleus in a cell that is not proliferating. The white arrow in the bottom left quadrant of this figure shows a nucleus of a proliferating cell, as indicated by a positive Ki67 stain.

These figures show that dexamethasone slightly decreases NPC proliferation at day 3 compared to the negative vehicle control EtOH. This is shown by increased numbers of Ki67-positive cells in both ethanol samples (figures 8Bi and 8Bii) relative to that observed in figure 8Ai. Sertraline-treated cells also showed increased proliferation compared to dexamethasone. All cells treated with sertraline were shown to be positive for the Ki67 marker (figure 8Aii). Cells that were co-treated with sertraline and dexamethasone showed an intermediate amount of proliferation compared to each stand-alone treatment, with approximately half of the cells stained being Ki67 positive (figure 8Aiii). Control samples and those treated with ethanol alone appeared to be greater in cell number and show similar relative amounts of proliferation compared to those co-treated with dexamethasone and sertraline.

Morphological changes as a result of the different conditions are minimal. In the untreated sample, there appears to be a higher cell count with increased clustering, however the significance of this is unknown.

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The effects of dexamethasone and sertraline on neuronal differentiation at day 14

Following treatment in proliferative conditions, NPCs were allowed to differentiate in the presence of either dexamethasone 1µm, sertaline 1µm, dexamethasone 1µm and sertraline 1µm, ethanol 0.1mM or ethanol 0.2mM. As before, an additional untreated control was also created. Our results suggest that dexamethasone has an inhibitory effect on neuronal differentiation. This could either be a consequence of cell death or an increase in the proportion of cells that differentiate into astrocytes. Treatment with sertraline increased the extent of neuronal differentiation both in the presence and absence of dexamethasone.

Figures 9a and 9b: Scale bar: 20µm. The top left panels indicate staining by DAPI (blue); top right: TuJ1 (green); bottom left: Anti-GFAP (red) and the bottom right panel combines images taken from all filter sets. DAPI has been used to stain all cell nuclei; TuJ1 is stains microtubules contained in neuronal cells and Anti-GFAP stains an intermediate filament found in astrocytes. Figures 9Ai and 9Bi, 9Bii and 9Biii show that differentiation into neurons is reduced in the presence of dexamethasone compared to negative controls, as shown by lower TuJ1 staining. In cells treated with sertraline, neuronal differentiation is increased compared to controls (see figure 9Aii in comparison to figures 9Bi, 9Bii and 9Biii). Co-treatement with sertraline and dexamethasone (figure 9Aii) shows an intermediate amount of neurons in relation to astrocytes.

Furthermore, in 9Ai an example of an asteron (a hybrid cell that contains properties of both astrocytes and neurons) is highlighted (Laywell et al., 2005). The nucleus is shown by the white arrow and can be found in the same position in all quadrants of the figure, indicating that it is both TuJ1 and Anti-GFAP positive. The presence of asterons was not observed in other samples and it is therefore difficult to predict the significance of this.

It is difficult to assess the effects of dexamethasone on neuronal morphology due to the low number present (figure 9Ai). Those that do exist appear to be smaller but with more dendritic regions. This effect seems to be consistent when subsequently comparing cells treated with sertraline alone (figure 9Aii) to those that received cotreatment with sertraline and dexamethasone (figure 9Aiii), whereby in the latter sample there is also generally a higher number of dendritic regions observed in neurons.

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Discussion

Establishing a working ChIP protocol

The first aim of these experiments was to ensure that the procedure used for chromatin immunoprecipitation was fully-functional. To test this, repressor element-1 silencing transcription factor (REST) binding was first validated by measuring the relative enrichment of REST at a gene where it was known to bind as well as one where it was not. As expected, REST showed significant enrichment at the known target genes, averaging at between 35- to 40-fold enrichment in dexamethasone and ethanol-treated HPC03A/07 NPCs. This shows that the procedure used for ChIP in our experiments was successful and that the chromatin and REST antibodies were appropriate. Therefore, errors in experiments that involve GR binding are likely to result from problems with the GR antibodies and/or the quantitative polymerase chain reaction (qpcr).

Both GR antibodies (from Thermo Scientific Pierce and Diagenode) showed a relatively low but significant 3-fold and 2.5-fold enrichment at the M4coding locus in EtOH treated samples from one biological replicate. GR was not expected to bind in this region, which suggests that these antibodies exhibited non-specific binding to some extent. However, the GR binding in the other biological replicate was as expected. It is likely that error involving GR target genes may also be predominantly technical and associated with the qpcr procedure. This is discussed further in the section: Error and further considerations for experiment optimisation.

Overall, we achieved our first aim of successfully optimising the ChIP protocol for use in interrogating putative GR targets.

Establishing the extent of GR-binding at target genes

Previous work by Anacker et al…Known target genes of the GR were shown to have higher relative enrichment values relative to internal controls and the negative control gene, Dscam. Furthermore, we were able to determine the extent of inhibitory and stimulatory effects that dexamethasone had on GR-binding at the target genes and our results are largely consistent with previous findings, however there is significant variability in some results and further studies should be conducted to confirm these.

Of the two validated and GR specific antibodies used, samples that contained GR-Thermo, a rabbit polyclonal antibody, typically showed higher enrichment values for GR. Our results could indicate that GR-Thermo antibody may be more effective at binding to the GR in our ChIP conditions compared to the monoclonal GR-Diagenode antibody. Monoclonal antibodies are often favoured due to their higher specificity towards a single epitope and thus subsequently lower background noise, however in our experiments the use of a polyclonal antibody has been advantageous. This could be due to the fact that it can recognise multiple epitopes on the same GR protein and thus be slightly more resilient against any masking that can occur due to areas of heterochromatin formation. (Lipman et al., 2005). Regardless, both antibodies were adequate and showed consistent enrichment. Thus, our findings show that we have a working protocol to determine GR binding using commercial GR antibodies, which can be used in the future for studies that investigate other gene loci that may be directly activated by the GR.

Possible GR binding at Dscam negative control locus

The Dscam gene is responsible for the formation of Down syndrome cell adhesion molecules and is not known to be a GR target. Modified versions of previously published human Dscam primers were

used in our experiments (see Appendix 3A: Forward and Reverse Primer Sequences) with the intention of improving their efficiency in the PCR to act as a negative control region, based on poor results with previous primers (AB, personal communication). Regardless, the results indicate possible GR binding at the Dscam locus, which the presence of dexamethasone does not appear to significantly influence. This could suggest potential non-specific binding within our experiments, which can result in background enrichment, slightly decreasing the validity of relative quantitative results. Alternatively, GR may in fact bind to this region and thus in future experiments, a better negative region control would need to be selected. To account for this, a range of other controls have also been used in combination and enrichment has been measured relative to IgG instead of through absolute quantification.

Gilz is an effective positive control locus for GR binding that may be inhibited by the presence of dexamethasone

The glucocorticoid-induced leucine zipper (Gilz) gene is well-known as a mediator of glucocorticoid-dependant immunomodulation pathways, yet its widespread expression suggests that it may also have more basic roles (Ayroldi and Riccardi, 2009, Yachi et al., 2007). Murine water-immersion restraint stress tests strongly increased GILZ mRNA expression and subsequent protein expression. Furthermore, it was observed that in mice without adrenal glands, up-regulation of GILZ was ceased, suggesting that the increase in expression was dependant on HPA axis stimulation and subsequent cortisol release. (Yachi et al., 2007) Up-regulation of the gene was observed in several areas of the brain associated with stress, including the hippocampus, in an uneven distribution that suggested specific functional roles. However the exact nature of these roles still remains unclear, particularly in relation to hippocampal neurogenesis. Previous research by Yachi et al observed increased expression specifically in pyramidal neurons of Ammon’s horn and granule neurons from the dentate gyrus as a response to increased HPA activation, but did not examine the effects on differentiation or the formation of these cells from respective precursors (Yachi et al., 2007).

Furthermore, whilst it is known that glucocorticoids increase transcription of Gilz via transactivation of the glucocorticoid receptor, the extent to which individual synthetic glucocorticoids influence gene expression is unknown. Whilst recognised as a dexamethasone-inducible gene, there exists contradictory results arising from dexamethasone suppression tests (Smit et al., 2005, Yachi et al., 2007). Interestingly, other synthetic glucocorticoids such as hydrocortisone and budesonide also had varying effects on GILZ mRNA levels (Smit et al., 2005).

Our results suggest that dexamethasone has inhibitory effects on GR binding at the Gilz loci compared to the ethanol control. If these findings can be replicated in the future, it could suggest that in patients with cortisol-induced depression, there is a lower extent of GR binding in NPCs. Combining this with research discussed previously, this could indicate that increased GR-binding at this loci has an inhibitory effect on GILZ expression and that subsequently, cortisol-induced depression may result in lower GILZ expression. However, this assumes that GR-binding has the same effects on gene expression in both NPCs and fully differentiated neurons. Further studies are needed to confirm our findings of the effects of dexamethasone on GR-binding at the Gilz locus and then, through parallel gene expression analysis, how GR-binding at the Gilz locus affects GILZ gene expression.

The primary purpose of using Gilz in our experiments was as a positive control to aid in the validation of our results. This has been achieved, as shown by consistent enrichment of GR binding at the Gilz locus. Additionally, no enrichment of REST binding at the Gilz locus was observed, which is

also as expected as there is no RE1 binding site. Thus, the use of a REST antibody in all GR binding experiments is as a further negative control.

Mt2a is an effective positive control locus for GR binding that may be stimulated by dexamethasone

Metallothionein 2A (Mt2a) is a gene which functions by binding to heavy metals and plays a role in diseases such as cerebral lymphoma (Yang et al., 2007). There is little evidence linking Mt2a expression to cortisol-mediated depression, although one small study showed that increased Mt2A expression within the anterior cingulate cortex in patients who had committed suicide (Sequeira et al., 2012). GR is known to bind to MT2A locus and MT2A is considered a dexamethasone inducible gene, thus this experiment serves as a positive control. However, binding of GR to the Mt2A locus may be cell specific (So et al., 2007). This could be due to different chromatin states in different cells and the subsequent extent of epigenetic masking. It was unknown whether GR binds to these gene locus in HPC03A/07 cells specifically. T Our results showed enrichment in the presence and absence of dexamethasone, but with a higher enrichment in dexamethasone-treated samples, suggesting that dexamethasone has a stimulatory effect on GR-MT2A binding. However, the variability in the ethanol containing samples suggests that technical sources of error are likely to be present and thus experiments need to be repeated. Regardless, MT2A was a successful positive control in our experiments and GR binding to the gene loci was as predicted.

Slc19a2 is an effective positive control locus for GR binding that may be dependent on the presence of dexamethasone

Slc19a2 is a known, dexamethasone-responsive, human GR target gene that codes for a thiamine transporter protein (So et al., 2007, Aoyagi and Archer, 2011). The GR binding site is positioned upstream of the gene, 136 base pairs from the transcription start site (So et al., 2007). In our experiment, it was intended as a positive control, however previous data on GR-binding specificity in HPC03A/07 cells is lacking. Our results indicate that GR does in fact bind to Slc19a2 in HPC03A/07 cells, however there is significant variation in results when comparing the effects of dexamethasone to the samples containing ethanol alone. No enrichment was observed for REST binding at the locus, confirming that the ChIP procedure used was accurate. Variation in results is likely to be a consequence of technical error associated with the PCR.

On average, dexamethasone appears to be a pre-requisite for GR binding to the Slc19a2 locus. Further studies are needed to quantify the effects of dexamethasone and confirm these findings.

Dexamethasone increases GR binding at the Sgk1 locus, which may directly upregulate SGK1 gene expression

There is a significant amount of literature that suggests that Sgk1, a GR target gene, is involved in the body’s response to stress (Luca et al., 2009). The glucocorticoid binding region is positioned upstream of the gene (So et al., 2007). The hedgehog pathway is responsible for the transmission of information responsible for neural stem cell development within both embryos and adult stem cells. According to research by Anacker et al, SGK1 is necessary for cortisol induced, neurogenic, hedgehog pathway inhibition and thus subsequent decreases in neurogenesis (Anacker et al., 2013a, Anacker et al., 2013b). SGK1 also increased cortisol-dependant activation of the GR by increasing GR translocation to the nucleus and phosphorylation (Anacker et al., 2013b). Previous results from Anacker et al showed that SGK1 expression is increased by activation of the GR receptor. However, direct binding of GR to SGK1 following activation with cortisol or dexamethasone had not been previously proven.

Our results show significant binding of GR at the Sgk1 locus using both GR-targeted antibodies tested. Our results also indicate that GR may therefore up-regulate SGK1 directly, in keeping with the findings from Anacker et al., (Anacker et al., 2013b). Our results also showed a lower but significant amount of binding in ethanol containing samples, suggesting a low degree of GR binding and thus possibly that SGK1 expression is not dependent on dexamethasone activation.

However, there is a significant degree of variability between the biological replicates, which is likely due to technical error associated with the PCR reaction. Notably, the difference in the extent of enrichment of GR binding in dexamethasone treated samples, between the biological replicates, was considerably high. Furthermore, REST binding was also observed at the Sgk1 locus in one biological replicate despite no known RE1 binding site and the use of an optimised ChIP protocol. Therefore, more studies are needed to confirm our findings.

If our findings can be replicated and GR does upregulate SGK1 directly, this may have important implications for the ways in which this gene is utilised as a therapeutic target to treat stress-induced depression.

Qualitative observations of dexamethasone and sertraline’s effects on neural progenitor cell proliferation and neuronal differentiation

Our results are consistent with findings by Anacker et al (Anacker, 2014, Anacker et al., 2013a) suggesting that sertraline mediates both proliferation and differentiation of hippocampal progenitor cells.

We first examined the effects of sertraline and dexamethasone on NPC proliferation and observed that dexamethasone may inhibit this. In cells treated with both dexamethasone and sertraline, this inhibitory effect was reduced, suggesting that sertraline antagonises the effects of dexamethasone. We then observed that in cells allowed to differentiate, those that were treated with dexamethasone exhibited lower differentiation into neurons. This suggested that high levels of dexamethasone were inhibitory towards neuronal differentiation, possibly by encouraging the formation of astrocytes instead. Similarly, the presence of sertraline counter-acted this effect.

Interestingly, hybrid asterons were observed in some samples, although the relationship between differentiation into these cells and levels of dexamethasone and/or sertraline remains unclear. Asterons can be defined as cells that share the same morphology as both astrocytes and neurons. They are hypothesised to be an intermediate step between the differentiation of neurons into astrocytes (Laywell et al., 2005).

Further studies are needed to confirm our findings and quantify the effects described above. These should be conducted on a larger scale using technical triplicates and appropriate numbers of biological replicates. Quantification can then be achieved through standard cell counting methods.

From a broader perspective, our results support the concept that sertraline may exhibit anti-depressant effects outside of those described in the monoaminergic hypothesis. As previously discussed, the combination of high levels of dexamethasone and the presence of sertraline may confer unique phosphorylation states at the GR via PKA signalling mechanisms at serine residues S203, S211 and S226 (Anacker et al., 2011b). This may have significant consequences on subsequent GR-dependent gene expression, as our findings suggest that least one stress-related gene (SGK1) is upregulated directly as a result of GR binding. The phosphorylation state of the GR may influence the extent of GR binding, for example through modification of its compatibility with varying target chromatin states. Future research testing the effects of sertraline and dexamethasone on GR binding

at the SGK1 locus, using our optimised ChIP protocol, may therefore be useful, particularly if ran in parallel to gene expression analysis. It would also be good to assess whether or not GR binds directly to other gene loci using our ChIP protocol such as P27kip 1 and p57kip2 where the phosphorylation state of the GR has already been established to influence expression and whereby the genes have already been linked to the termination of cell division and increases in neuronal differentiation. (Galliher-Beckley et al., 2008, Blind and Garabedian, 2008, Chen et al., 2008, Kumar and Calhoun, 2008, Webster et al., 1997, Anacker et al., 2011b).

Potential errors and further considerations for experiment optimisation

As previously discussed, it is likely that the major sources of error in GR-binding analyses are due to technical mistakes associated with the q-pcr procedure. This includes human error, such as variation in pipetting technique. The percentage error arising from such variables is likely to be high due to the small volumes of solutions used, particularly given that the standard curves were also subject to this error. Another source of human error in the q-pcr reaction could include incomplete thawing of reagents before use, resulting in unevenly distributed concentrations.

There may also be cases of systematic error. The nature of polymerase chain reactions requires every individual reaction to be optimised. The main steps of PCR are denaturation, annealing and extension. Each may be a potential source of error if the time given for each is not carefully considered. For example, not enough time given to the denaturing step can result in incomplete strand separation, hindering the ability to bind to specific primers in the annealing stage. In the annealing stage, too low of a temperature permits non-specific binding. The time given to each step and the number of cycles performed was based upon the number of base pairs in the target sequence. The efficiency of the reactions were then observed based and primers were then optimised accordingly.

The frequency of calibration checks of the qpcr machine was unknown. Additionally, on analysis of the melt curves of some samples, the peak indicating primer dimerization was present in some samples. It may also be possible that some primers bound to non-target regions of DNA, for example due to repeat sequences. M4coding and Snap25 primers were designed individually using Primer 3 to check for possibility of secondary structure formation and thus reduce error from DNA hairpins. However, other primers that were used originated from existing publications to compare results to known data and it may be possible to further optimise these in future experiments.

From the ChIP procedure, it is unlikely that the degree of error was as significant. However one potential source of error in this is slight variation in chromatin size due to timings of each stage, which can affect PCR kinetics.

In future experiments, it may be more cost-effective to conduct genome wide ChIP sequencing analysis of GR binding, if analysing a large number of genes. It may also interesting to run genome-wide gene expression in parallel to this, to further elucidate the direct effects of GR-binding on gene expression. If budget permits, there is also the potential for error in the q-pcr to be reduced through the use of sequence specific fluorescent DNA probes. These are more selective towards the target DNA to be amplified, thus decreasing levels of background noise.

With respect to the immunofluorescence staining experiments, the time-frame in which they were carried out made quantitative analysis through cell counting unfeasible. The images obtained from these experiments may also be subject to sampling bias. Leakage of fluorescence signal into different filters sets may also have been a problem, for example red light sometimes leaked into images viewed under the green filter set. In ideal circumstances, cell counting would allow us to further

elucidate information regarding the effects of dexamethasone and/or sertraline on hippocampal cell proliferation and differentiation. If this were to happen, more biological replicates would be needed to ensure reliable results and subsequent statistical analysis. Use of a confocal microscope may also be preferable due to the ability to control depth of field and thus reduce the amount of background information, particularly in analysis of differentiation patterns where clustering of cells along the z plane was common, however this is unlikely to have had a significant impact on our findings.

Conclusions and future work

We have established an optimal chromatin immunoprecipitation method for use in the analysis of GR-binding in HPC03A/07 cells. The gene of primary interest in our experiment, Sgk1, was found to bind to the GR directly and dexamethasone has a stimulatory effect on the extent of this. Low levels of Sgk1-GR binding were observed in samples only containing ethanol. Due to technical variability, further conclusive studies are needed, however our average results extend upon findings from Anacker (Anacker et al., 2013b). We recommend further studies using our optimised ChIP protocol to investigate GR binding at more gene loci in the presence and absence of dexamethasone and antidepressant treatment. Qualitative analysis of immunofluorescently stained HPC03A/07 showed that dexamethasone reduces proliferation and may inhibit differentiation into neurons, possibly by directing development into astrocytes instead. Sertraline does not significantly increase proliferation or differentiation of cells compared to the negative control, but does appear to counteract the effects of dexamethasone, suggesting that cortisol-induced depression is required for a therapeutic effect on hippocampal neurogenesis. Further studies are needed to quantify the effects of dexamethasone and sertraline on cellular development. It would also be interesting to analyse changes in morphology, such as the number and size of dendrites on neuronal cells. Some samples were also found to contain astroneurons, however their role in depression, if it does exist, is currently elusive and their relationship to dexamethasone and/or sertraline levels is unknown.

Acknowledgements and References

Many thanks to Dr Angela Bithell for her continual support, patience and encouragement throughout the project.

Appendix 1: Outline Proposal

Background:Depression is a common affective disorder characterised by sustained periods of low mood, often combined with symptoms such as anhedonia, guilt, low self-esteem, appetite changes, fatigue, sleep disturbance and impaired concentration [1]. The World Health Organisation (WHO) identifies the disorder as a public health dilemma of global concern [1].There are several psychological, sociological and biological theories that attempt to address the disorder’s underlying cause.Biologically, dysregulation of monoaminergic systems, particularly those that involve serotonin, have long been thought of as key [2]. However, emerging evidence suggests that in a subset of depressive patients, the serotonergic deficit hypothesis is overly simplistic and cannot, in isolation, be responsible. In these patients, it is proposed that a lack of correct hippocampal neurogenesis could be linked to both the cause of depression and response to antidepressant treatments. This is supported by data showing, for example, that the time it takes for selective serotonin re-uptake inhibitors to work is similar to the time required for the formation of functional neurones from their neural progenitor cells [2]. Antidepressants have also been shown to increase hippocampal neurogenesis and substantially decreased rates of neurogenesis have been observed in untreated depressive patients [2] [3]. Based on this evidence, it follows that an understanding of the molecular mechanisms surrounding hippocampal neurogenesis may aid in further developing our overall understanding of depression and neurochemical targets that could be utilised to enhance therapy.Cortisol is a steroid hormone that acts on the glucocorticoid (GR) and mineralocorticoid receptors (MR), which are located in the hippocampus. MRs are typically activated at a wider range of cortisol levels, therefore exhibiting less sensitivity in relation to GRs [4]. GR activation requires higher levels of cortisol and will be the focus of this research. It is thought that in their default state, GRs exist as an inactive form within the cytoplasm. Upon cortisol binding, a conformational shape change causes the receptor protein to be activated and move to the nucleus as a monomer or dimer. Both types are linked to activation of various transcription factors through the binding of the GR to a GR response element in gene promoters, a process known as transactivation. Importantly, the level of transactivation mediates the level of hippocampal neurogenesis and differentiation. Studies have shown that depressed patients typically exhibit higher levels of cortisol, which is inversely proportional to hippocampal neurogenesis [2]. However, the mechanisms underlying this are not fully understood.

Aim: To investigate the effects of cortisol and antidepressant treatment on hippocampal neurogenesis through GR binding and downstream gene expression.

Objectives:Examine neural stem cell GR binding with varying concentrations of glucocorticoid and antidepressant drugs, at specific gene loci.Explore how changes in GR binding determine phenotypic variations in cell morphology, viability and the extent of differentiation and proliferation.(If time permits) Investigate GR binding in alternative cell types, with varying levels of glucocorticoid and antidepressant drugs, using publicly available genome-wide datasets.

References:[1] Marcus M et al (2012), Depression: A Global Crisis. World Federation for Mental Health: Occoquan (Virginia). 6-8.[2] Mahar et al. (2014). Stress, serotonin, and hippocampal neurogenesis in relation to depression and antidepressant effects. Neuroscience & Biobehavioral Reviews. 38, 173-192[3] Boldrini, M et al. (2012). Hippocampal Angiogenesis and Progenitor Cell Proliferation Are Increased with Antidepressant Use in Major Depression. Biological Psychiatry. 72 (7), 562-571

[4] Anacker et al. (2013). Glucocorticoid-Related Molecular Signaling Pathways Regulating Hippocampal Neurogenesis.Neuropsychopharmacology. 38 (5), 872-883

Appendix 2: Response to referee’s comments

Abstract

Typically don’t reference within the abstract. Make clear what your cell line is earlier on.

References were removed and cell line is now specified at the earliest point cells are mentioned

Introduction

Full stops don’t come before a reference Reference citations were amended so that full stops came afterwards

Third paragraph needs referencing Third paragraph was updated with the appropriate reference

Paragraph 8 is confusing – need to clarify results from Anacker 2011 study

Wording of this paragraph has been improved

When say evidence suggests strong involvement of genes including Sgk1, Fkbp5 etc. need to elaborate on what the evidence is

Evidence has been clarified (from gene expression analysis) and elaborated on

Be careful not to confuse DNA with chromatin This has been rectified throughout the textFinal ‘aims’ paragraph needs some re-wording to make clearer the main aims, including to establish a working GR ChIP and to look at changes in GR binding to putative target genes. This is the central idea of the project and has not been done in this model so far

This has been re-worded for clarification and the aims have been added in

Materials and Methods

In cell culture, need to state this was done by AB and more precisely when growth factors were in/out and for how long cells were in proliferation/dex/sert etc. then differentiation.

I have listed at the top of each relevant section who conducted the work. Growth factor conditions were made more specific.

ChIP protocol needs to include how pre-blocking and pre-clearing were done precisely and to include how de-crosslinked ChIP DNA was purified at the end before PCR

I have added this information as suggested in line with other correspondence.

qPCR needs conditions for PCR programme and greater detail

Greater detail has been added using information from the original protocol.

ICC/immunofluorescence needs full details of all primary and secondary antibodies used and the dilution factors as well as explanation of why each marker was used.

Greater detail has been added with help from further correspondence and consulting the original protocol and notes.

Results

Titles for sub-sections need to say what the result is for that section

Titles have been amended.

In all figure legends ensure you say what fold enrichment is relative to (i.e. IgG)

This has now been specified.

‘SI’ should be included as Appendices for the thesisMake sure it is clear that B and C are individual biological replicates and A is the average of the 2 in PCR figures

SI has been changed to appendices (all are contained within Appendix 3)

Make clear why each gene locus was chosen to interrogate

This has now been made clear through specifying which were positive/negative controls ect.

Ensure you summarise the meaning of the result at the end of each sub-section. E.g. if GR enrichment is at Sgk1 in DEX and not EtOH and it is significantly (over 2-fold) above IgG then it tells you that the ChIP is working, that GR binds in response to DEX and is not bound in untreated samples. Also, explain more carefully what you mean when discussing possible sources of error

A small summary has now been given in each sub-section, followed by a larger summary at the end of the results. Sources of error have been clarified.

Summarise all the PCR data briefly at the end before making a sub-heading for the Immuno experiments

A brief sub-section has now been used as a summary.

Immuno bit needs work – needs to be expanded to better describe the experiment done, why and the results. You need to say which markers were used on which cells and why. Then what you see and what this means (can include how it fits with published data is you like)

This has now been extensively added to

Immuno part – all figures need legends to explain what panels show including scale bars. Some annotation would help e.g. arrows to indicate things described in the text. (e.g. asterons, an example of which is shown in Figure 8A, white arrow). Also need to say what asterons are.

Scale bars have now been specified within the main figure legend and explanation added of what is within each image. Each quadrant has been given a subtype (I,ii,iii) and referred to within the text in a logical order. Arrows have been added to highlight a proliferating cell and an asteron.

Discussion

First aim was to check ChIP was working – e.g. chromatin good, buffers good etc. thus used REST. Say what REST is and reference. Then also say next was to determine if this protocol was suitable for GR ChIP using 2 antibodies that were being tested

This has been clarified and discussed more

Do not always say our results support previous findings – in places we are extending those findings to include data they did not have – e.g. Anacker et al., shoed gene expression changes

This has now been stressed.

downstream of dex/GR activation but did not show direct binding of GR to those genes (e.g. Sgk1). You have done that and need to stress that as it is a major finding and novelty of the workAgain need better titles for sub-heading to describe the importance of the findingsBe careful again not to confuse gene expression with GR binding/enrichment. You did not do gene expression analysis

New titles have now been given for sub-sections and I have edited areas where the term gene expression analysis was used inappropriately

Put Sgk1 findings last in the list of loci analysed – it is the most significant. Also, be sure to include what would be your next step in these experiments now that you have a working GR ChIP in the cells (think about drug treatment, loci to analyse etc.)

This has now been swapped with Slc19a2

Sectionon immune/proliferation/differentiation is far too brief and needs to be expanded including bringing in the relevant findings from previous literature and how your data fit with those and the next step (consider also if might look at other cells not just NPCs – what about when they differentiate into neurons?)

This has now been expanded on

Conclusions/future work

Remove bit on asterons or make clearer This has been clarified

Include what would be future work now you have a ChIP protocol or GR (see earlier comment above)

This has now been mentioned but discussed and rationalised in more detail in discussion

Other comments

Human gene names are usually in capital letters italicized (whilst e.g. mouse are usually lower case except the first letter). When talking about expression they are not italicized and when talking about the protein they are capital, not italicized. E.g. human Snap25 would be SNAP25 for the gene and SNAP25 for the mRNA or protein.

These have now been changed

Ensure you mention where experiments or parts thereof were performed by AB, you or together where applicable to be clear

This has now been clarified

Appendix 3

A. Forward and Reverse Primer Sequences

Genes Forward Primer Reverse PrimerSGK1_Luca CCC CTC CCT TCG CTT GTT GGA AGA AGT ACA ATC TGC ATT TCA CTSLC19A2 CCGGAATGTCCATTCAGTTT TCCTGGGCTTCTGATGTCTTGILZ (primer 1) GTGCCTGGAGACCAACTCAT ACCCTTGATGCTGAGCAAGTMT2A GACGATTCGGCTGAGCTAGA AGGGCCTTAGATCGTCAACCDSCAM (2) ACGTTGAACAAACCCATGCT GGTCAACCCAAGGAACTAGQR1 Snap 25 GGGTGCTATTATCCAGGGAAG CAGGCGGCATAAATCAAGTCQC3 m4coding GGCAGTTTGTGGTGGGTAAG GCAGGTAGAAGGCAGCAATG

B. DSCAM Numerical Results Data

DSCAM Sample 1

Sample Name Cт Cт MeanQuantity

Quantity Mean

Quantity SD

DEX_GR_Thermo 32.4183931.9125595

10.05802

5 0.0850795210.02516939

3

DEX_GR_Thermo 31.5247231.9125595

10.10780

2 0.0850795210.02516939

3

DEX_GR_Thermo 31.7945731.9125595

10.08941

2 0.0850795210.02516939

3

DEX_GR_Diagenode 31.3373531.9582977

30.12275

1 0.0841073990.03426680

7

DEX_GR_Diagenode 32.1040531.9582977

3 0.07215 0.0841073990.03426680

7

DEX_GR_Diagenode 32.4334831.9582977

30.05742

1 0.0841073990.03426680

7

DEX_REST 33.9826333.9526901

20.01962

3 0.0200394080.00056636

5

DEX_REST 33.9688333.9526901

20.01981

1 0.0200394080.00056636

5

DEX_REST 33.9066133.9526901

20.02068

4 0.0200394080.00056636

5

DEX_IgG 33.2966133.7829208

40.03156

9 0.0263850220.01495737

8

DEX_IgG 33.0267933.7829208

40.03806

1 0.0263850220.01495737

8

DEX_IgG 35.0253633.7829208

40.00952

5 0.0263850220.01495737

8

EtOH_GR_Thermo 31.6317431.3974456

80.10009

4 0.118533880.01637632

8

EtOH_GR_Thermo 31.3212931.3974456

80.12412

5 0.118533880.01637632

8

EtOH_GR_Thermo 31.2393131.3974456

80.13138

2 0.118533880.01637632

8EtOH_GR_Diagenode 32.12441

32.24609375

0.071139 0.067264192

0.018692363

EtOH_GR_Diagenode 32.72433

32.24609375

0.046938 0.067264192

0.018692363

EtOH_GR_Diagenode 31.88955

32.24609375

0.083715 0.067264192

0.018692363

EtOH_REST 32.99904 32.4676094 0.0388 0.063823462 0.04184911

1

EtOH_REST 31.4678432.4676094

10.11213

6 0.063823462 0.04184911

EtOH_REST 32.9359632.4676094

10.04053

4 0.063823462 0.04184911

EtOH_IgG 31.6195232.0339393

60.10094

6 0.0788108330.02553398

3

EtOH_IgG 31.8741932.0339393

60.08461

1 0.0788108330.02553398

3

EtOH_IgG 32.608132.0339393

60.05087

6 0.0788108330.02553398

3

DEX_Input 26.4774426.3199119

6 3.56393 3.9987964630.61499398

9

DEX_Input 26.1623926.3199119

64.43366

3 3.9987964630.61499398

9

DEX_InputUndetermined

26.31991196

EtOH_Input 26.2903126.6510009

8 4.05749 3.2153599260.75053024

3

EtOH_Input 26.9229826.6510009

8 2.61707 3.2153599260.75053024

3

EtOH_Input 26.7397226.6510009

8 2.97152 3.2153599260.75053024

3

DSCAM Sample 2

Sample Name Cт Cт Mean QuantityQuantity Mean

Quantity SD

DEX_GR_Thermo

DEX_GR_Thermo30.0282020

629.9776191

70.15138822

80.15700171

90.00793867

5

DEX_GR_Thermo29.9270381

929.9776191

7 0.162615210.15700171

90.00793867

5DEX_GR_Diagenode

DEX_GR_Diagenode 30.146230729.9609737

40.13926553

70.16012355

70.02949769

4

DEX_GR_Diagenode29.7757167

829.9609737

40.18098157

60.16012355

70.02949769

4

DEX_REST32.2091865

532.0804901

10.03238002

20.03599264

8 0.00780573

DEX_REST31.7453384

432.0804901

10.04495026

50.03599264

8 0.00780573

DEX_REST32.2869415

332.0804901

10.03064766

30.03599264

8 0.00780573

DEX_IgG30.6115379

331.0148620

60.10021676

90.07722122

20.02115714

6

DEX_IgG31.0622158

131.0148620

60.07286686

50.07722122

20.02115714

6

DEX_IgG31.3708305

431.0148620

6 0.058580030.07722122

20.02115714

6EtOH_GR_Thermo

EtOH_GR_Thermo29.7102527

629.6365585

30.18955680

70.19996860

60.01472451

8

EtOH_GR_Thermo 29.562862429.6365585

3 0.210380420.19996860

60.01472451

8EtOH_GR_Diagenode

30.19615746

29.90525246

0.134434387

0.166838706

0.028133459

EtOH_GR_Diagenode

29.77517891

29.90525246 0.18105042

0.166838706

0.028133459

EtOH_GR_Diagenode

29.74442291

29.90525246

0.185031295

0.166838706

0.028133459

EtOH_REST31.4585266

131.4449901

60.05505753

30.05596889

20.00804368

7

EtOH_REST31.6401996

631.4449901

60.04841969

50.05596889

20.00804368

7

EtOH_REST31.2362403

931.4449901

6 0.064429440.05596889

20.00804368

7

EtOH_IgG31.9576301

631.8903865

80.03868424

50.04061406

90.00272918

5EtOH_IgG

EtOH_IgG31.8231430

131.8903865

80.04254389

60.04061406

90.00272918

5

DEX_Input21.8323993

723.5387344

449.7912712

122.3212413

8 23.9431324

DEX_Input23.9305629

723.5387344

411.2920780

222.3212413

8 23.9431324

DEX_Input24.8532409

723.5387344

45.88037204

722.3212413

8 23.9431324

EtOH_Input23.6667861

923.8236827

9 13.607674612.7475814

84.41967153

5

EtOH_Input24.4248447

423.8236827

97.96108198

212.7475814

84.41967153

5

EtOH_Input23.3794174

223.8236827

916.6739864

312.7475814

84.41967153

5

C. Gilz Numerical Results Data

Gilz Sample 1

Sample Name Cт Cт Mean QuantityQuantity Mean

Quantity SD

DEX_GR_Thermo34.337917

334.1755561

80.08136787

30.11536619

8 0.09650635

DEX_GR_Thermo35.417533

934.1755561

80.04046019

20.11536619

8 0.09650635

DEX_GR_Thermo32.771217

334.1755561

80.22427053

70.11536619

8 0.09650635DEX_GR_Diagenode

33.3435631

33.58509445

0.154851392

0.138934016

0.048387609

DEX_GR_Diagenode

33.1338654

33.58509445

0.177357852

0.138934016

0.048387609

DEX_GR_Diagenode

34.2778549

33.58509445

0.084592804

0.138934016

0.048387609

DEX_REST29.110071

229.1767482

82.39735984

82.29731202

10.09122439

5

DEX_REST29.229715

329.1767482

82.21874499

32.29731202

10.09122439

5

DEX_REST29.190460

229.1767482

82.27583098

42.29731202

10.09122439

5

DEX_IgG 34.55548135.0865211

50.07068169

10.05293646

50.01989862

5

DEX_IgG34.895954

135.0865211

50.05670447

60.05293646

50.01989862

5

DEX_IgG35.808132

235.0865211

50.03142322

20.05293646

50.01989862

5

EtOH_GR_Thermo32.633586

933.0073585

5 0.245161980.20943643

20.09255570

9

EtOH_GR_Thermo32.434875

533.0073585

50.27880510

70.20943643

20.09255570

9

EtOH_GR_Thermo33.953617

133.0073585

5 0.104342230.20943643

20.09255570

9EtOH_GR_Diagenode

34.0514526

34.22140503

0.097940773

0.089692116

0.021824755

EtOH_GR_Diagenode

34.6862755

34.22140503

0.064945236

0.089692116

0.021824755

EtOH_GR_Diagenode 33.926487

34.22140503

0.106190339

0.089692116

0.021824755

EtOH_REST28.531488

428.8430156

73.48612761

52.90330028

50.66250443

5

EtOH_REST28.742574

728.8430156

73.04100894

92.90330028

50.66250443

5

EtOH_REST29.254980

128.8430156

72.18276381

52.90330028

50.66250443

5

EtOH_IgG33.780239

133.7762603

80.11673142

80.11797329

80.01827094

1

EtOH_IgG33.534713

733.7762603

80.13683348

90.11797329

80.01827094

1

EtOH_IgG34.013824

533.7762603

8 0.100354970.11797329

80.01827094

1

DEX_Input27.296100

6 27.33894927.75438976

37.56004333

50.62594091

9

DEX_Input27.235265

7 27.33894928.06575775

17.56004333

50.62594091

9

DEX_Input27.485479

4 27.33894926.85998153

77.56004333

50.62594091

9

EtOH_Input 27.28973227.2832546

27.78641462

37.82008695

60.14999899

3

EtOH_Input27.309028

627.2832546

2 7.689785487.82008695

60.14999899

3

EtOH_Input27.250997

527.2832546

27.98405981

17.82008695

60.14999899

3

Gilz Sample 2

Sample Name Cт Cт Mean QuantityQuantity Mean

Quantity SD

DEX_GR_Thermo30.7630977

630.5587310

80.18003533

80.21024081

10.04271699

1DEX_GR_Thermo

DEX_GR_Thermo30.3543624

930.5587310

80.24044628

40.21024081

10.04271699

1DEX_GR_Diagenode

30.34864998

30.66509819

0.241420597

0.197469279

0.049831588

DEX_GR_Diagenode

30.56147575

30.66509819

0.207655683

0.197469279

0.049831588

DEX_GR_Diagenode

31.08516693

30.66509819

0.143331558

0.197469279

0.049831588

DEX_REST33.1167259

232.6838684

10.03402245

40.04758608

70.01383601

8

DEX_REST32.6585693

432.6838684

10.04705652

60.04758608

70.01383601

8

DEX_REST32.2763137

832.6838684

10.06167928

50.04758608

70.01383601

8

DEX_IgG 31.305570631.7260913

80.12262573

10.09384424

20.02789195

3

DEX_IgG32.1607704

231.7260913

80.06693629

20.09384424

20.02789195

3

DEX_IgG31.7119369

531.7260913

80.09197071

90.09384424

20.02789195

3

EtOH_GR_Thermo29.9892425

530.0308284

8 0.31136474 0.30322513 0.02811465

EtOH_GR_Thermo30.1804962

230.0308284

80.27193868

2 0.30322513 0.02811465

EtOH_GR_Thermo29.9227466

630.0308284

80.32637187

8 0.30322513 0.02811465

EtOH_GR_Diagenode

29.67978477

29.78486443 0.38762176

0.360971779

0.034522232

EtOH_GR_Diagenode 29.9419136

29.78486443

0.321973473

0.360971779

0.034522232

EtOH_GR_Diagenode

29.73289108

29.78486443

0.373320043

0.360971779

0.034522232

EtOH_REST31.2183647

231.7059936

50.13043433

4 0.09606570.03268298

5

EtOH_REST31.7056293

531.7059936

50.09238230

4 0.09606570.03268298

5

EtOH_REST32.1939926

131.7059936

50.06538044

7 0.09606570.03268298

5

EtOH_IgG32.2281265

332.0427780

20.06381957

20.07339477

50.01354138

2EtOH_IgG

EtOH_IgG31.8574256

932.0427780

20.08296997

80.07339477

50.01354138

2

DEX_Input24.5420532

2 24.7970562 14.720969212.5271263

12.85815405

8

DEX_Input24.6575298

3 24.797056213.5654640

212.5271263

12.85815405

8

DEX_Input25.1915836

3 24.79705629.29494476

312.5271263

12.85815405

8

EtOH_Input25.1186618

825.1909313

29.78736209

9 9.310179710.54896652

7

EtOH_Input25.1707630

225.1909313

29.43295669

6 9.310179710.54896652

7

EtOH_Input25.2833671

625.1909313

28.71022033

7 9.310179710.54896652

7

D. Mt2a Numerical Results Data

Mt2a Sample 1

Sample Name CтCт Mean

Quantity

Quantity Mean

Quantity SD

DEX_GR_Thermo 31.33603

31.36954

0.112923

0.113922

0.034621

DEX_GR_Thermo 31.83765

31.36954

0.079812

0.113922

0.034621

DEX_GR_Thermo 30.93495

31.36954

0.149032

0.113922

0.034621

DEX_GR_Diagenode 31.07767

31.3598

0.135022

0.112584

0.02239

DEX_GR_Diagenode 31.66012

31.3598

0.090242

0.112584

0.02239

DEX_GR_Diagenode 31.34161

31.3598

0.112487

0.112584

0.02239

DEX_REST 32.2086532.63227

0.061745

0.048076

0.016073

DEX_REST 32.4538132.63227

0.052113

0.048076

0.016073

DEX_REST 33.2343432.63227

0.03037

0.048076

0.016073

DEX_IgG 34.4480333.83852

0.013116

0.029555

0.031939

DEX_IgG 34.9631733.83852

0.009184

0.029555

0.031939

DEX_IgG 32.1043533.83852

0.066365

0.029555

0.031939

EtOH_GR_Thermo 30.71562

30.81747

0.173452

0.162092

0.014447

EtOH_GR_Thermo 30.77048

30.81747

0.166992

0.162092

0.014447

EtOH_GR_Thermo 30.96633

30.81747

0.145833

0.162092

0.014447

EtOH_GR_Diagenode 30.84647

31.34744

0.15844

0.11625

0.038912

EtOH_GR_Diagenode 31.80261

31.34744

0.081771

0.11625

0.038912

EtOH_GR_Diagenode 31.39325

31.34744

0.108539

0.11625

0.038912

EtOH_REST 31.6790331.97647

0.089069

0.076747

0.028583

EtOH_REST 31.5542131.97647

0.097102

0.076747

0.028583

EtOH_REST 32.6961631.97647

0.044069

0.076747

0.028583

EtOH_IgG 31.2343831.3993

0.121148

0.109441

0.020254

EtOH_IgG 31.7288131.3993

0.086054

0.109441

0.020254

EtOH_IgG 31.234731.3993

0.121122

0.109441

0.020254

DEX_Input 25.5430225.61696

6.212363

5.940785

0.805994

DEX_Input 25.4608225.61696

6.575912

5.940785

0.805994

DEX_Input 25.84703 25.61 5.034 5.940 0.805

696 081 785 994

EtOH_InputUndetermined

25.46935

EtOH_Input 25.5160125.46935

6.329543

6.540607

0.298489

EtOH_Input 25.4226925.46935

6.751671

6.540607

0.298489

Mt2a Sample 2

Sample NameTarget Name Cт Cт Mean Quantity

Quantity Mean

Quantity SD

DEX_GR_Thermo Mt2a 29.8476 29.809 0.277893 0.285975 0.01143DEX_GR_Thermo Mt2a 29.7704 29.809 0.294057 0.285975 0.01143DEX_GR_Thermo Mt2aDEX_GR_Diagenode Mt2a 30.74175 30.72056 0.144374 0.14665 0.003219DEX_GR_Diagenode Mt2aDEX_GR_Diagenode Mt2a 30.69936 30.72056 0.148926 0.14665 0.003219DEX_REST Mt2a 32.36602 32.53336 0.043942 0.039166 0.006754DEX_REST Mt2aDEX_REST Mt2a 32.70069 32.53336 0.03439 0.039166 0.006754DEX_IgG Mt2a 31.15639 31.51998 0.106564 0.083306 0.020854DEX_IgG Mt2a 31.59865 31.51998 0.077081 0.083306 0.020854DEX_IgG Mt2a 31.80492 31.51998 0.066274 0.083306 0.020854EtOH_GR_Thermo Mt2a 29.78892 29.99426 0.290096 0.253233 0.050517EtOH_GR_Thermo Mt2a 29.8671 29.99426 0.273954 0.253233 0.050517EtOH_GR_Thermo Mt2a 30.32676 29.99426 0.19565 0.253233 0.050517EtOH_GR_Diagenode Mt2aEtOH_GR_Diagenode Mt2a 30.04943 29.93988 0.239709 0.26057 0.029501EtOH_GR_Diagenode Mt2a 29.83033 29.93988 0.281431 0.26057 0.029501EtOH_REST Mt2aEtOH_REST Mt2a 31.2969 31.4229 0.096144 0.088043 0.011457EtOH_REST Mt2a 31.5489 31.4229 0.079941 0.088043 0.011457EtOH_IgG Mt2a 32.72152 31.80198 0.03387 0.080032 0.059835EtOH_IgG Mt2a 30.71127 31.80198 0.147633 0.080032 0.059835EtOH_IgG Mt2a 31.97314 31.80198 0.058592 0.080032 0.059835DEX_Input Mt2a 24.52884 24.65135 13.66292 12.54076 1.586975

DEX_Input Mt2a 24.77386 24.65135 11.4186 12.54076 1.586975DEX_Input Mt2aEtOH_Input Mt2a 24.71077 24.81464 11.95854 11.11241 0.985899EtOH_Input Mt2a 24.95094 24.81464 10.02977 11.11241 0.985899EtOH_Input Mt2a 24.78222 24.81464 11.34892 11.11241 0.985899

E. Sgk1 Numerical Results Data

Sgk1 Sample 1

Sample Name CтCт Mean

Quantity

Quantity Mean

Quantity SD

DEX_GR_Thermo

32.01871

32.45957

0.159032

0.119648

0.036216

DEX_GR_Thermo

32.85159

32.45957

0.087778

0.119648

0.036216

DEX_GR_Thermo

32.5084

32.45957

0.112134

0.119648

0.036216

DEX_GR_Diagenode

32.89449

32.79361

0.085132

0.103609

0.063217

DEX_GR_Diagenode

33.59375

32.79361

0.05169

0.103609

0.063217

DEX_GR_Diagenode

31.8926

32.79361

0.174006

0.103609

0.063217

DEX_REST32.50795

32.98707

0.11217

0.084392

0.039283

DEX_REST33.46619

32.98707

0.056615

0.084392

0.039283

DEX_REST

DEX_IgG36.95366

35.79335

0.004701

0.01392

0.011922

DEX_IgG35.94221

35.79335

0.009675

0.01392

0.011922

DEX_IgG34.48417

35.79335

0.027383

0.01392

0.011922

EtOH_GR_ThermoEtOH_GR_Thermo

32.24117

32.34834

0.13569

0.126068

0.013608

EtOH_GR_Thermo

32.45552

32.34834

0.116445

0.126068

0.013608

EtOH_GR_Diagenode

31.41858

31.07673

0.244037

0.320763

0.108507

EtOH_GR_DiagenodeEtOH_GR_Diagenode

30.73488

31.07673

0.397489

0.320763

0.108507

EtOH_REST33.38749

33.2726

0.059885

0.065145

0.005301

EtOH_REST33.27124

33.2726

0.065064

0.065145

0.005301

EtOH_REST33.15907

33.2726

0.070486

0.065145

0.005301

EtOH_IgG32.71308

32.86449

0.096897

0.109937

0.083067

EtOH_IgG31.70624

32.86449

0.198754

0.109937

0.083067

EtOH_IgG34.17416

32.86449

0.034162

0.109937

0.083067

DEX_Input26.17943

26.22619

10.25632

9.92529

0.46815

DEX_Input26.27295

26.22619

9.594258

9.92529

0.46815

DEX_InputEtOH_Input 26.14 26.14 10.51 10.63 1.859

464 291 41 535 575

EtOH_Input26.3878

26.14291

8.839371

10.63535

1.859575

EtOH_Input25.89629

26.14291

12.55259

10.63535

1.859575

Sgk1 Sample 2

Sample Name CтCт Mean

Quantity

Quantity Mean

Quantity SD

DEX_GR_Thermo

29.32795

29.26802

0.177987

0.185304

0.006787

DEX_GR_Thermo

29.2187

29.26802

0.191393

0.185304

0.006787

DEX_GR_Thermo

29.25742

29.26802

0.186531

0.185304

0.006787

DEX_GR_Diagenode

28.95483

28.98848

0.228088

0.228515

0.060567

DEX_GR_Diagenode

29.41338

28.98848

0.168162

0.228515

0.060567

DEX_GR_Diagenode

28.59722

28.98848

0.289294

0.228515

0.060567

DEX_REST31.52379

31.70617

0.041351

0.039289

0.016683

DEX_REST31.09891

31.70617

0.054846

0.039289

0.016683

DEX_REST32.49579

31.70617

0.021671

0.039289

0.016683

DEX_IgG29.95304

30.07588

0.117473

0.112918

0.038151

DEX_IgG29.59955

30.07588

0.148588

0.112918

0.038151

DEX_IgG30.67506

30.07588

0.072695

0.112918

0.038151

EtOH_GR_Thermo

28.54086

28.55823

0.300337

0.296917

0.00482

EtOH_GR_Thermo

28.54753

28.55823

0.299009

0.296917

0.00482

EtOH_GR_Thermo

28.58629

28.55823

0.291403

0.296917

0.00482

EtOH_GR_Diagenode

29.26721

28.90723

0.185321

0.239345

0.052236

EtOH_GR_Diagenode

28.85879

28.90723

0.243124

0.239345

0.052236

EtOH_GR_Diagenode

28.59569

28.90723

0.289588

0.239345

0.052236

EtOH_REST29.70954

29.91558

0.138112

0.125878

0.042509

EtOH_REST30.55767

29.91558

0.078594

0.125878

0.042509

EtOH_REST29.47952

29.91558

0.160929

0.125878

0.042509

EtOH_IgG29.51678

29.6881

0.156993

0.140559

0.014266

EtOH_IgG29.76256

29.6881

0.133329

0.140559

0.014266

EtOH_IgG29.78498

29.6881

0.131356

0.140559

0.014266

DEX_Input23.86912

22.59185

6.702911

30.91397

41.43156

DEX_Input20.16261

22.59185

78.75383

30.91397

41.43156

DEX_Input23.74381

22.59185

7.285161

30.91397

41.43156

EtOH_Input 23.76 23.79 7.185 7.058 0.263

45 201 659 764 182

EtOH_Input23.85722

23.79201

6.756176

7.058764

0.263182

EtOH_Input23.75432

23.79201

7.234455

7.058764

0.263182

F. m4coding Numerical Results Data

M4coding sample 1

Sample Name CтCт Mean Cт SD

Quantity

Quantity Mean

Quantity SD

DEX_GR_Thermo 29.4771230.1109

20.64929

50.49129

70.35351

20.13270

5

DEX_GR_Thermo 30.0809530.1109

20.64929

50.34268

70.35351

20.13270

5

DEX_GR_Thermo 30.7746730.1109

20.64929

5 0.226550.35351

20.13270

5

DEX_GR_Diagenode 30.61519

30.337580.25810

10.24916

3 0.296340.04460

6

DEX_GR_Diagenode 30.10489

30.337580.25810

10.33782

9 0.296340.04460

6

DEX_GR_Diagenode 30.29266

30.337580.25810

10.30202

9 0.296340.04460

6

DEX_REST 32.4299232.5128

10.22402

60.08439

40.08079

10.01042

5

DEX_REST 32.3420432.5128

10.22402

60.08893

60.08079

10.01042

5

DEX_REST 32.7664732.5128

10.22402

60.06904

20.08079

10.01042

5

DEX_IgG 31.24485 31.19050.28663

50.17113

80.17853

30.03111

6

DEX_IgG 30.88058 31.19050.28663

50.21267

90.17853

30.03111

6

DEX_IgG 31.44607 31.19050.28663

5 0.151780.17853

30.03111

6

EtOH_GR_Thermo 30.3327

29.614140.81965

10.29489

90.49136

90.24881

9

EtOH_GR_Thermo 28.7214

29.614140.81965

10.77115

40.49136

90.24881

9

EtOH_GR_Thermo 29.78832

29.614140.81965

10.40805

30.49136

90.24881

9

EtOH_GR_Diagenode 29.90104

29.87544 0.03432

0.3815150.38744

10.00797

2

EtOH_GR_Diagenode 29.83644

29.87544 0.03432

0.3965050.38744

10.00797

2

EtOH_GR_Diagenode 29.88883

29.87544 0.03432

0.3843040.38744

10.00797

2

EtOH_REST 31.07467 31.3796 0.4313 0.18942 0.16053 0.04085

4 6 6 6

EtOH_REST 31.6846231.3796

4 0.43130.13164

60.16053

60.04085

6

EtOH_RESTUndetermined

31.37964 0.4313

EtOH_IgG 30.5724531.4982

4 0.853340.25559

9 0.160750.08443

7

EtOH_IgG 31.6689631.4982

4 0.853340.13288

2 0.160750.08443

7

EtOH_IgG 32.2533131.4982

4 0.853340.09377

1 0.160750.08443

7

DEX_Input 25.3067424.9089

40.38698

45.91340

67.63015

11.73246

1

DEX_Input 24.5337624.9089

40.38698

49.37791

27.63015

11.73246

1

DEX_Input 24.8863124.9089

40.38698

47.59913

67.63015

11.73246

1

EtOH_Input 24.8288424.9924

10.22030

67.86422

77.17327

30.90949

9

EtOH_Input 24.9054924.9924

10.22030

67.51272

27.17327

30.90949

9

EtOH_Input 25.2429224.9924

10.22030

66.14286

87.17327

30.90949

9

G. QR1 Snap25 Numerical Results Data

Sample Name Cт Cт Mean Cт SD QuantityQuantity Mean Quantity SD

DEX_GR_Thermo 31.96854 31.5484 0.906621575 0.185217783 0.267966211 0.162267178

DEX_GR_Thermo 30.50793 31.5484 0.906621575 0.454926014 0.267966211 0.162267178

DEX_GR_Thermo 32.16874 31.5484 0.906621575 0.163754851 0.267966211 0.162267178

DEX_GR_Diagenode 32.40881 32.01583 0.453111708 0.141269535 0.184737682 0.053188469

DEX_GR_Diagenode 32.11848 32.01583 0.453111708 0.168896466 0.184737682 0.053188469

DEX_GR_Diagenode 31.5202 32.01583 0.453111708 0.244047061 0.184737682 0.053188469

DEX_REST 36.95646 35.09836 1.611665249 0.008609516 0.034802753 0.022837803

DEX_REST 34.07943 35.09836 1.611665249 0.050545316 0.034802753 0.022837803

DEX_REST 34.25919 35.09836 1.611665249 0.045253422 0.034802753 0.022837803

DEX_IgG 33.12841 33.50735 0.437235713 0.090736441 0.073560916 0.018758204

DEX_IgG 33.4079 33.50735 0.437235713 0.076402105 0.073560916 0.018758204

DEX_IgG 33.98575 33.50735 0.437235713 0.05354419 0.073560916 0.018758204

EtOH_GR_Thermo 31.76974 31.63173 0.511485517 0.209315181 0.235745788 0.077346541

EtOH_GR_Thermo 31.0654 31.63173 0.511485517 0.322843134 0.235745788 0.077346541

EtOH_GR_Thermo 32.06005 31.63173 0.511485517 0.175079077 0.235745788 0.077346541

EtOH_GR_Diagenode 32.58231 32.18702 0.343037426 0.126967803 0.164240077 0.032373674

EtOH_GR_Diagenode 31.96735 32.18702 0.343037426 0.185353667 0.164240077 0.032373674

EtOH_GR_Diagenode 32.01139 32.18702 0.343037426 0.180398777 0.164240077 0.032373674

EtOH_REST 32.16562 32.94107 0.73724544 0.164068744 0.109103642 0.049938649

EtOH_REST 33.0246 32.94107 0.73724544 0.096720658 0.109103642 0.049938649

EtOH_REST 33.633 32.94107 0.73724544 0.066521525 0.109103642 0.049938649

EtOH_IgG 31.68766 32.10696 0.382613242 0.220156044 0.173335731 0.042039119

EtOH_IgG 32.19607 32.10696 0.382613242 0.161024272 0.173335731 0.042039119

EtOH_IgG 32.43716 32.10696 0.382613242 0.138826877 0.173335731 0.042039119

DEX_Input 26.19505 26.36539 0.177511424 6.460769176 5.841034412 0.632981539

DEX_Input 26.35182 26.36539 0.177511424 5.866744041 5.841034412 0.632981539

DEX_Input 26.54929 26.36539 0.177511424 5.195589542 5.841034412 0.632981539

EtOH_Input 26.52024 26.7127 0.166960314 5.289283276 4.715533733 0.497600526

EtOH_Input 26.79911 26.7127 0.166960314 4.455393314 4.715533733 0.497600526

EtOH_Input 26.81874 26.7127 0.166960314 4.401924133 4.715533733 0.497600526

Sample Name CтCт Mean Cт SD

Quantity

Quantity Mean

Quantity SD

DEX_GR_Thermo30.7987

930.7710

10.02912

10.31538

5 0.321830.00678

3

DEX_GR_Thermo30.7735

230.7710

10.02912

10.32119

8 0.321830.00678

3

DEX_GR_Thermo30.7407

130.7710

10.02912

10.32890

7 0.321830.00678

3

DEX_GR_Diagenode

31.8832231.4938

90.34500

40.14402

10.19465

10.04535

8

DEX_GR_Diagenode

31.3723331.4938

90.34500

40.20835

30.19465

10.04535

8

DEX_GR_Diagenode

31.2261231.4938

90.34500

40.23157

80.19465

10.04535

8

DEX_REST 27.239527.5036

60.35614

14.13186

83.48625

40.83201

6

DEX_REST27.3627

927.5036

60.35614

13.77959

33.48625

40.83201

6

DEX_REST27.9086

927.5036

60.35614

1 2.54733.48625

40.83201

6

DEX_IgG32.2161

432.3826

10.15764

70.11321

90.10082

10.01158

8

DEX_IgG32.4020

732.3826

10.15764

70.09898

10.10082

10.01158

8

DEX_IgG32.5296

332.3826

10.15764

70.09026

30.10082

10.01158

8

EtOH_GR_Thermo30.2903

930.1847

40.12365

10.45544

5 0.492910.04468

4

EtOH_GR_Thermo 30.215130.1847

40.12365

10.48091

8 0.492910.04468

4

EtOH_GR_Thermo30.0487

430.1847

40.12365

10.54236

6 0.492910.04468

4

EtOH_GR_Diagenode

29.9259430.4328

50.44699

70.59270

80.42605

60.14581

9

EtOH_GR_Diagenode

30.6021430.4328

50.44699

70.36355

60.42605

60.14581

9

EtOH_GR_Diagenode

30.7704830.4328

50.44699

70.32190

50.42605

60.14581

9

EtOH_REST26.7146

926.9021

10.28175

16.03800

65.34337

81.02026

2

EtOH_REST 26.7655 26.9021 0.28175 5.82010 5.34337 1.02026

4 1 1 9 8 2

EtOH_REST27.2261

326.9021

10.28175

14.17201

75.34337

81.02026

2

EtOH_IgG31.6925

132.3458

10.61762

20.16530

70.11024

70.04988

4

EtOH_IgG32.4247

532.3458

10.61762

20.09737

20.11024

70.04988

4

EtOH_IgG32.9201

732.3458

10.61762

20.06806

40.11024

70.04988

4

DEX_Input25.4264

925.5215

40.16642

915.3205

414.3708

31.66493

6

DEX_Input25.4244

125.5215

40.16642

915.3435

814.3708

31.66493

6

DEX_Input25.7137

125.5215

40.16642

912.4483

814.3708

31.66493

6

EtOH_Input25.4742

225.6330

90.15650

414.8010

313.2517

61.50059

1

EtOH_Input25.7871

125.6330

90.15650

411.8051

113.2517

61.50059

1

EtOH_Input25.6379

425.6330

90.15650

413.1491

313.2517

61.50059

1

H. Tissue Culture Methods for ReNeuron Cells (HPC03A/07)

Human Cells

Reduced Modified Medium (RMM)

Remove 10 ml of media from a 500 ml bottle of DMEM:F12 with 15 mM HEPES and sodium bicarbonate but without L-glutamine (Sigma: D6421) and add:

0.75 ml Human serum albumin solution (0.03% final concentration)(20% stock, PAA: C11-096)

1.0 ml Human apo-transferrin (100 µgml-1 final concentration)(50 mgml-1 stock, SCIPAC: T100-5)

1.0 ml Putrescine dihydrochloride (16.2 gml-1 final concentration)(8.1mgml-1 stock, Sigma: P5780)

0.25 ml Human recombinant insulin (5 µgml-1 final concentration)(10 mgml-1 stock, Sigma: I9278)

1.0 ml Progesterone (60 ngml-1 final concentration)(30 µgml-1 stock, Sigma: P6149) 5.0 ml L-glutamine (2 mM final concentration)(200 mM stock, Sigma: G7513) 1.0 ml Sodium selenite (40 ngml-1 final concentration)(20 gml-1 stock, Sigma: S9133) 5.0 ml Penicillin/Streptomycin ()

For proliferation, the following components should also be added to make RMM+++:

0.5 ml Human FGF-basic (10 ngml-1 final concentration)(10 gml-1 stock, PeproTech: 100-18B)

100 l Human EGF (20 ngml-1 final concentration)(100 gml-1 stock, PeproTech: AF-100-15) 50 l 4-OHT (100 nM final concentration)(1 mM stock, Sigma: H7904)

Filter the medium with all additional components through a 0.2 M filter unit and store for no longer than 2 weeks at 4C.

NB: Bithell lab there is no requirement to filter as all components are sterile. Make up RMM and only add FGF2/EGF and 4OHT when required

Differentiation Medium (DM)

Remove 10 ml of media from a 500 ml bottle of Neurobasal Medium (Invitrogen: 21103-049) and add: 0.75 ml Human serum albumin solution (0.03% final concentration)(20% stock, PAA: C11-

096) 1.0 ml Human apo-transferrin (100 µgml-1 final concentration)(50 mgml-1 stock, SCIPAC:

T100-5) 1.0 ml Putrescine dihydrochloride (16.2 gml-1 final concentration)(8.1mgml-1 stock, Sigma:

P5780) 0.25 ml Human recombinant insulin (5 µgml-1 final concentration)(10 mgml-1 stock, Sigma:

I9278) 1.0 ml Progesterone (60 ngml-1 final concentration)(30 µgml-1 stock, Sigma: P6149) 5.0 ml L-glutamine (2 mM final concentration)(200 mM stock, Sigma: G7513) 1.0 ml Sodium selenite (40 ngml-1 final concentration)(20 gml-1 stock, Sigma: S9133) 10 ml B27 Supplement (1x final concentration)(50x stock, Invitrogen: 17504-044)

Alternative Differentiation Medium 1 – ‘RMM’

Simply use RMM without FGF2, EGF or 4OHT

Alternative Differentiation Medium 2 – ‘NB:B27’

To Neurobasal medium add 2mM glutamine, 1x Pen/strep and 1x B27 (as used for iPSC-derived hNPCs, 11530536/17504-044, Invitrogen)

Alternative Differentiation Medium 3 – ‘N2:B27’

Make a 50:50 mix of ‘N2 medium’ used for iPSC-derived hNPCs (DMEM:F12:Glutamax (31331-093 Invitrogen), 1x N2 (11520536/17502-048, Invitrogen), 1xNEAA (11140-050, Invitrogen), 1mM glutamine, 1x penstrep) and ‘B27 medium’ (same as NB:B27 above)

Filter the media with all additional components through a 0.2 M filter unit and store for no longer than 2 weeks at 4C.

NB: Bithell lab there is no requirement to filter as all components are sterile. Make up RMM and only add FGF2/EGF and 4OHT when required

Cell culture Plastic and Substrate Preparation

Cells grow on Nunc plasticware coated with laminin at 1 µgcm-2. Cells grown on coverslips coated with PDL and laminin. Laminin stock is 1mg/ml (Sigma L2020). Stock vials should be thawed at 4oC (NOT room temperature) and then stored at 4oC and used within 1 month. To laminin coat, dilute laminin in DMEM:F12 or HBSS in an appropriate volume with the appropriate amount of laminin added for the surface area to be coated. For example, 75µl of laminin in 6 ml of DMEM:F12 or HBSS for a T75 flask, 10µl in 1ml for a well of a 6-well plate or 2µl in 0.5ml for a well of a 24-well plate. Ensure it covers the surface. Incubate at 37oC for a MINIMUM of 2hrs, ideally overnight. Following incubation, wash 3x with 1xPBS or HBSS and do not allow any laminin-coated surface to dry before plating cells onto it (i.e. leave on the last PBS wash until you are ready to plate the cells – the plasticware can happily sit in PBS at room temp or at 37oC until required). For PDL/laminin coating (when plating onto glass), first coat with PDL and then laminin. To PDL coat, thaw a stock vial of PDL (1mg/ml) at room temperature and dilute 1:50 (so to 20µg/ml) in dH2O. Cover the surface and incubate at 37oC for 1- 2hrs (can do overnight but not necessary). Ensure that coverslips do not float (push down if necessary). Wash 2-3x 1xPBS or dH2O before proceeding to laminin coating (as before).

Growth and Passage of ReNeuron Cells

For proliferative cells, split when 70-80% confluent and don’t split more than 1:4. Feed the cells every other day with 10 ml of fresh medium (for a T75 flask, RMM+++). To passage the cells, remove all medium, rinse in 1xPBS then add 2-3ml Accutase (Sigma) at 37oC for ~3 minutes until cells detach. Dilute with 7 ml of DMEM:F12 or HBSS. Spin the cells at 1000 rpm for 5 minutes, remove the supernatant (except for a small volume), flick to resuspent before fully resuspending in RMM + FGF2/EGF/4OHT. Split into newly prepared flasks/plates as required.

Freezing and Thawing ReNeuron Cells

To freeze cells, accutase cells off the plastic (e.g. T75 flask) and pellet by centrifugation as above. Remove almost all the supernatant and resuspend by flicking in the small volume remaining (as above). Resuspend in 1.8ml of warm RMM+++ and add 10% DMSO (0.2ml, D2650, Invitrogen) slowly and with agitation. Fully mix and aliquot into cryovials, 1ml of cells per vial (i.e. 1 T75 will generate 2x 1ml vials for freezing). Transfer to a Mr Frosty freezing vessel at room temperature and place into a -80 oC freezer overnight. The following day, transfer frozen vials to liquid nitrogen for long-term storage.

To thaw a vial of cells ensure that you have prepared a T75 flask with laminin coating (including being washed) and that you have a 15ml tube with 11ml of pre-warmed RMM+++. Place the vial into the 37 oC waterbath until the vial thaws except for a small piece of ice. Transfer into the hood and take <1ml of the 11ml of pre-warmed RMM+++. Add this dropwise into the vial of cells and then remove cells/RMM+++ into the remaining 10ml of RMM+++. You should now have 12ml in total. Transfer this to the prepared T75 flask and place in the incubator. Allow ~1hr for the cells to settle and gently remove and replace the medium with a fresh 10ml of RMM+++ to remove the medium with DMSO. Alternatively, upon thawing, transfer the cells into 9ml of pre-warmed DMEM:F12 and immediately centrifuge at 1000rpm for 5mins and discard the supernatant (to remove

DMSO). Resuspend the pellet as above (for passaging) and fully resuspend into 10ml of RMM++ before transferring into the prepared T75.

ReNeuron Cell Differentiation

For differentiation, grow the cells to approximately 80% confluence before washing the cells twice with plain DMEM:F12 or HBSS then replace with differentiation medium (see DM or alternatives above). Change half of the medium every 2-3 days.

For Treatment with Depression Study Drugs

Cells were plated, treated in proliferative conditions and then differentiated (RMM without FGF2/EGF/4OHT) essentially as described in Anacker et al., with: Dexamethasone (1µM, dlluted 1:10,000 from stock in EtOH), Sertraline (1µM, dlluted 1:10,000 from stock in EtOH), Dexamethasone + Sertraline (1µM each as above) or the appropriate amount of vehicle (EtOH, 1:10,000 for single drug conditions or 1:5,000 for dual drug conditions). Some cells were left in untreated conditions. Some cells from each condition were fixed at Day 3 of proliferation before differentiation and some following 2 weeks of differentiation and processed for immunofluorescence analysis.

References

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