Abstract
The concept of drug discovery through stem cell biology is based on technological developments whose genesis is now coincident. The first is automated cell microscopy with concurrent advances in image acquisition and analysis, known as high content screening (HCS). The second is patient-derived stem cells for modeling the cell biology of brain diseases. HCS has developed from the requirements of the pharmaceutical industry for high throughput assays to screen thousands of chemical compounds in the search for new drugs. HCS combines new fluorescent probes with automated microscopy and computational power to quantify the effects of compounds on cell functions. Stem cell biology has advanced greatly since the discovery of genetic reprograming of somatic cells into induced pluripotent stem cells (iPSCs). There is now a rush of papers describing their generation from patients with various diseases of the nervous system. Although the majority of these have been genetic diseases, iPSCs have been generated from patients with complex diseases (schizophrenia and sporadic Parkinson’s disease). Some genetic diseases are also modeled in embryonic stem cells (ESCs) generated from blastocysts rejected during in vitro fertilization. Neural stem cells have been isolated from post-mortem brain of Alzheimer’s patients and neural stem cells generated from biopsies of the olfactory organ of patients is another approach. These “olfactory neurosphere-derived” cells demonstrate robust disease-specific phenotypes in patients with schizophrenia and Parkinson’s disease. HCS is already in use to find small molecules for the generation and differentiation of ESCs and iPSCs. The challenges for using stem cells for drug discovery are to develop robust stem cell culture methods that meet the rigorous requirements for repeatable, consistent quantities of defined cell types at the industrial scale necessary for HCS.
INTRODUCTION
The last decades have seen development and applications in cellular neuroscience of DNA, RNA, and protein analysis technologies that provide large volumes of information, getting away from the traditional methods that follow the activities of single genes or single cell types. The application of DNA sequencing, RNA microarrays and higher throughput protein mass spectrometry is allowing examination of biological systems in all their complexity. What has been missing for cell biologists is the ability to interrogate cell functions at the same scale and level of complexity. This is being addressed by the developments in automated technologies developed by pharmaceutical companies to screen massive compound libraries for the discovery of new drug leads. Until recently this high throughput screening, with robotic control and automated data capture and analysis of experiments in 96-, 384-, and 1,536-well plates, centered on enzyme and receptor assays but increasingly, interest is turning to cell-based assays that capture the complexity of the environment in which drugs will actually operate. There has been a move to drug screening based on cellular outcomes (e.g., cancer cell apoptosis, inhibition of growth) rather than predicted mechanism (e.g., enzyme inhibition, receptor antagonism). This led to the concept and development of “high content screening” (HCS) that combines advances in fluorescence labeling of cells, robotic and automated microscopy, and automated image analysis that brings the analysis of cell functions to the high throughput formats of multiwell plates (). For the neuroscientist this technology opens up new frontiers in the ability to manipulate many experimental variables simultaneously in highly controlled experiments. For example, the effects of multiple drugs at multiple doses could be tested simultaneously on several cell types. With the many fluorescent reporter methods now available it is possible to follow three or four cellular events in the same experiment in hundreds of cells per well. With many well established platforms available, HCS opens the door to neuroscience for large scale, high throughput cell function analyses for understanding cell functions in health and disease.
In neuroscience, the discovery of the genetic causes of familial diseases has driven understanding of the functions of individual genes and proteins in cell function in the nervous system and the effects of mutations on brain function. Identification of a candidate gene is followed by genetically modified cell and mouse models to identify the functions of the identified gene at cell and systems level. Mouse models have been important for elucidating protein and gene functions but they often do not recapitulate human disease because the mice lack the human genetic background and the introduced human genes are acting in a non-human cellular context. Immortalized human cell lines have a more relevant genetic background but, being derived from tumors, they may not reflect a normal cellular context. Patient-derived primary cells might be a solution for both drawbacks but they cannot usually be maintained for very long in culture and finding an accessible cell type for brain diseases is problematic. Stem cell technologies may have the solution to these drawbacks.
There is an emerging interest in using stem cells to understand the cellular bases of human diseases. There is an imperative here, especially for neurological diseases and conditions. Large pharmaceutical companies are withdrawing from investing in neuroscience research because of the failure of the current paradigms to convert findings in animal models to drugs for human disease (). There are obviously many reasons why animal models, genetic or otherwise, are not proving useful for predicting human responses to drugs. On the other hand, there are good reasons to expect that human stem cells might be useful, if they can be derived from patients with a disease and, if they are the cell types that are affected by the disease. This is clearly a niche that stem cells have the developmental abilities to occupy. Patient-derived stem cells could be used to identify cell functions that are altered by disease and thereby provide a target for drug discovery. Assays can then be developed for HCS to use the patient-derived stem cells to screen large drug libraries for therapeutic activity.
HIGH THROUGHPUT SCREENING
Increasingly in the last 20 years pharmaceutical companies have developed high throughput technologies to screen large chemical libraries of natural products and synthetic compounds for activities against selected enzymes and receptors, candidate targets for diseases of interest (; Figure 1). These high throughput technologies arose from the desire of pharmaceutical companies to test all theoretical chemical compounds (~100 million) against all theoretical human biological targets, estimated from the number genes (~20,000) or proteins (~200,000), indicating that as many as 1012 assays would be needed to identify all interactions between chemicals and targets (). These technologies are pharmacology on a large scale, initially using 96-well plates and now routinely 384- and 1,536-well plates. The imperative is to provide a means to screen very large compound libraries in order to find the very small percentage of lead compounds that are active in a selected assay. High throughput screening is historically based on solution based enzyme and receptor assays using scintillation, absorbance, fluorescence, and chemiluminescence with the aim of finding highly specific chemical interactions with individual biological targets (). High throughput screening is also used in cell-based assays, typically to monitor activation of cell surface receptors through subsequent transduction pathways, transcription events, cell proliferation, or cell death. These assays use multiwell plates and monitor colorimetric, fluorescence, luminance, or absorbance within each well. With developments in genetic technologies and the understanding provided by the Human Genome Project, intracellular events are available for high throughput screening through the development genetic fluorescent reporter assays. Cell lines have genes for luminescent or fluorescent proteins, like luciferase or green fluorescent protein (GFP), spliced into reporter systems to read out activation or inhibition of specific genes or proteins of interest (). High throughput technologies have advanced the development of robotic automation for cell culture, assays, and compound library storage; automated and multipurpose plate readers; fluorescence dyes and reporter systems; computational power, and automated data storage and analysis. It currently takes about 1 week to screen 10,000 compounds against a target and 1–3 months to screen a library of one million compounds (). High throughput screening has been successful in delivering numerous drugs from discovery through to clinical use and the market starting from chemical libraries of 200,000–500,000 compounds ().
FIGURE 1
HIGH CONTENT SCREENING
High content screening is a further development in which the principles of high throughput screening are applied to the analysis of individual cells through the use of automated microscopy and image analysis (; Figure 2). This allows quantitative analyses of components of cells such as spatio-temporal distributions of individual proteins, cytoskeletal structures, vesicles, and organelles when challenged with chemical compounds. HCS can be used to monitor activation or inhibition of individual proteins and protein–protein interactions as well as allowing analysis of broader changes in biological processes and cell functions. Recent advances in the range of fluorescent probes for biological processes, functions, and cell components have combined with developments in fluorescence microscopy to give the cell biologists many new ways to understand cell functions in health and disease. These cell-centric developments have converged with high throughput concepts and with developments in automated microscopy and image analysis to evolve into the new technological synthesis of high throughput screening in which cell-based assays are conducted in multiwell plate formats. High content technologies are now used to screen chemical libraries for drug discovery as well as genome-wide RNA interference libraries to probe gene functions (). One of the advantages of HCS is the ability to apply different criteria to selected cells in the population to account for the heterogeneity of the cell population. For example, a chemical compound of interfering RNA may only act on cells in a particular stage of the cell cycle. Well-based assays provide a readout from the whole cell population, a mixture of cells in different phases of the cell cycle, whereas with cell-based HCS and appropriate markers, cells at identified phases of the cell cycle could be assessed independently. Thus HCS allows quantitative analysis of complex and heterogeneous cell cultures containing multiple cell types. Another advantage of HCS technologies is the ability to make multiple independent and quantitative measurements from single cells of interest. For example, transmitted light might be used to assess morphology; three or more fluorophores might be used to identify molecular components, structures, or organelles; and image analysis might be used to quantify spatial relationships between the fluorescent reporters under control conditions or when challenged with chemical compounds. With assays performed in multiwell plates there is opportunity to scale up experiments to include replicates, concentration–response curves, and parallel assessments of different cells or compounds. Modern instruments equipped with incubators and confocal optics have the ability to investigate cells over time in three spatial dimensions. These technologies will advance further and become cheaper allowing HCS principles to be applied increasingly by academic labs and not restricted to large pharmaceutical companies.
FIGURE 2
In the neurosciences, there is now an opportunity and challenge to combine patient-derived, disease-specific stem cells with HCS technologies with the aim of finding new drugs for brain diseases and conditions. This is not a simple aspiration because the majority of brain diseases are a result of complex genetic and environmental risk factors. Furthermore, brain diseases are usually just that, “brain” diseases and not “cell” diseases in the sense that cancers are. Nonetheless, it is possible that most brain diseases result from identifiable cellular dysfunctions such as those identified in monogenic disease. Such mutations tell us that specific brain dysfunctions can be manifest in specific cell types and pathways, despite universal genetic mutation. This gives hope that cellular models will shed light on the molecular and cellular mechanisms of emergent properties (e.g., cognition, emotion) evident when the brain functions as a whole.
DISEASE-SPECIFIC PLURIPOTENT STEM CELL MODELS OF NEUROLOGICAL DISEASE
The analysis of gene function through gain- or loss-of-function in cell, fly, and mouse models has been very instructive in elucidating functions of genes and proteins but less successful in providing models that predict drug efficacy in human diseases. An example is the failure of the superoxide dismutase transgenic mouse model for amyotrophic lateral sclerosis that has yielded multiple compounds that are therapeutic in mouse but not humans (e.g., creatine;
Disease-specific pluripotent stem cells include human embryonic stem cells (ESCs) with genetic or chromosomal disorders derived from surplus blastocysts during in vitro fertilization and pre-implantation genetic diagnosis (
The list of neurological diseases and conditions for which ESCs or iPSCs have been derived is largely limited to monogenic diseases including Charcot–Marie–Tooth disease type 1A, Down syndrome-trisomy 21, familial amyotrophic lateral sclerosis, familial dysautonomia, familial Parkinson’s disease, Fragile X syndrome, Friedreich ataxia, Gaucher’s disease, Huntington’s disease, Rett syndrome, Spinal muscular atrophy, spinocerebellar ataxia types 2 and 7, and X-linked adrenoleukodystrophy (
It is a challenge to translate pluripotent cells into robust disease models (
PATIENT-DERIVED OLFACTORY STEM CELLS AS MODELS FOR NEUROLOGICAL DISEASES
Published studies of ESCs and iPSCs as disease models are all confined to small numbers of cell lines from patients and controls. This makes it difficult to generalize from these case–control studies to the general population. Variability in the reprograming process and epigenetic status makes it essential that several clones from several individuals are compared to confirm that a “disease-phenotype” is not confounded by individual differences among case or control cell lines (
Another approach to modeling diseases is to sample patient-derived adult stem cells. Neural progenitor cells were isolated from post-mortem brain from Alzheimer’s patients and healthy controls (
There is a multipotent adult stem cell resident in the olfactory mucosa, the organ of smell in the nose (
The olfactory mucosa comprises the superficial epithelium and the underlying lamina propria separated by a basement membrane. Within the epithelium are basal cells among which are the multipotent stem cells that can regenerate all the cell types of the epithelium including the sensory neurons as well as other non-neural supporting and gland cells (
Parkinson’s patient-derived ONS cells showed gene expression and functional differences indicating dysfunctions in pathways involved mitochondrial metabolism and oxidative stress (
Olfactory neurosphere-derived cells are also proving useful for understanding monogenic diseases. Hereditary spastic paraplegia (HSP) is an autosomal dominant disease affecting the long spinal axons from the motor cortex to the lower motor neurons in the spinal cord. ONS cells from patients with HSP were similar in many basic cell functions to ONS cells from healthy controls despite dysregulation of expression of 60% of the genome, indicating a high level of homeostatic regulation in response to dominant mutations in SPAST, which codes for a microtubule severing protein (
PATIENT-DERIVED OLFACTORY STEM CELLS FOR DRUG DISCOVERY
Patient-derived ONS cells have several advantages for HCS for drug lead identification. They are cheap to grow and maintain, growing in standard cell culture conditions with no expensive growth factors after the neurosphere-forming stage. As ONS cells they can be grown for at least 16 passages without significant change in gene expression thus demonstrating minimal phenotypic change and without change in karyotype (unpublished observations). ONS cells are derived from neural tissue and can obviously show disease-specific phenotypes relevant to the neurological diseases from which the donors suffer. Proof-of-principle analyses have shown that brain diseases “ain a dish” can be ameliorated by drug treatment. For example, Parkinson’s patient-derived ONS cell functions were restored to control-derived cell levels by treatment with L-sulforaphane, an agonist of NRF2 (
These experiments show that disease-associated dysfunctions in olfactory cells can be ameliorated by candidate chemical compounds acting on targets known to be disrupted in the patient-derived cells compared to controls. The next challenge is to see whether ONS cells are useful for screening libraries of compounds. They have some of the necessary characteristics such as ease of generation, low cost, robust and repeatable growth characteristics and predictable phenotype. These properties make them useful for building up banks of cells that will allow assessment of variability of cell biology across a wider population of patients and controls, to discriminate disease-specific differences from individual differences in complex diseases like Parkinson’s disease and schizophrenia.
FUTURE PROSPECTS
One of the challenges for the field is to develop robust and repeatable protocols for producing the large quantities of specified neurons or glia that are required for high throughput screening. For ESCs and iPSCs differentiating protocols exist for making different types of neurons, such as dopaminergic neurons, cortical neurons, and motor neurons (
The concept of drug discovery through patient-derived stem cell models of brain diseases is attractive but has many other challenges apart from the practical issues of cost and reliable production. Concerns are raised about the epigenetic status of iPSCs and ESCs – epigenetic status is variably altered by reprograming and by culture methods (
FIGURE 3

Choices of stem cells to model brain diseases. Different criteria guide choices of cells to model brain diseases. ESCs derived during pre-implantation genetic diagnosis are useful for monogenic diseases. Patient-derived adult cells are useful for genetic and sporadic diseases, with the advantage of an associated clinical history. ESCs and iPSCs take many months to generate, validate, and then to produce neurons and glia but have the advantage of being highly proliferative and pluripotent. iPSCs and induced neurons require reprograming with genes, proteins or drugs, whereas ESCs and ONS cells do not. ONS cells and induced neurons may retain the methylation status of differentiated cells whereas ESCs and iPSCs do not. All methods introduce variability associated with cell culture but iPSCs and induced neurons may be more variable because of clonal selection due to the low efficiencies of the induction processes. ESC and ONS cell production average inter-clonal variation across large populations.
Other developments in reprograming will affect this future. It is now possible to generate neurons directly from skin fibroblasts (
Statements
Acknowledgments
This work was funded in part by grants from the Hereditary Spastic Paraplegia Research Foundation Inc., the National Health and Medical Research Council of Australia, and the Australian Government Department of Health and Ageing.
Conflict of interest
The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
REFERENCES
1
AbrahamsenG.FanY.MatigianN.WaliG.BelletteB.SutharsanR.et al. (2013). A patient-derived stem cell model of hereditary spastic paraplegia with SPAST mutations.Dis. Model. Mech.6489–502.
2
ArnoldS. E.HanL. Y.MobergP. J.TuretskyB. I.GurR. E.TrojanowskiJ. Q.et al. (2001). Dysregulation of olfactory receptor neuron lineage in schizophrenia.Arch. Gen. Psychiatry58829–835.
3
BooneN.LoriodB.BergonA.SbaiO.Formisano-TrezinyC.GabertJ.et al. (2010). Olfactory stem cells, a new cellular model for studying molecular mechanisms underlying familial dysautonomia.PLoS ONE 5:e15590. 10.1371/journal.pone.0015590
4
BrennandK. J.SimoneA.JouJ.Gelboin-BurkhartC.TranN.SangarS.et al. (2011). Modelling schizophrenia using human induced pluripotent stem cells.Nature473221–225.
5
ChambersS. M.FasanoC. A.PapapetrouE. P.TomishimaM.SadelainM.StuderL. (2009). Highly efficient neural conversion of human ES and iPS cells by dual inhibition of SMAD signaling.Nat. Biotechnol.27275–280.
6
CookA. L.VitaleA. M.RavishankarS.MatigianN.SutherlandG. T.ShanJ.et al. (2011). NRF2 activation restores disease related metabolic deficiencies in olfactory neurosphere-derived cells from patients with sporadic Parkinson’s disease.PLoS ONE 6:e21907. 10.1371/journal.pone.0021907
7
DelormeB.NivetE.GaillardJ.HauplT.RingeJ.DevezeA.et al. (2010). The human nose harbors a niche of olfactory ectomesenchymal stem cells displaying neurogenic and osteogenic properties.Stem Cells Dev.19853–866.
8
FanY.AbrahamsenG.McgrathJ. J.Mackay-SimA. (2012). Altered cell cycle dynamics in schizophrenia.Biol. Psychiatry71129–135.
9
FanY.AbrahamsenG.MillsR.CalderonC. C.TeeJ. Y.LeytonL.et al. (2013). Focal adhesion dynamics are altered in schizophrenia.Biol. Psychiatry 10.1016/j.biopsych.2013.01.020 [Epub ahead of print].
10
FeronF.PerryC.HirningM. H.McgrathJ.Mackay-SimA. (1999). Altered adhesion, proliferation and death in neural cultures from adults with schizophrenia.Schizophr. Res.40211–218.
11
FeronF.PerryC.McgrathJ. J.Mackay-SimA. (1998). New techniques for biopsy and culture of human olfactory epithelial neurons.Arch. Otolaryngol. Head Neck Surg.124861–866.
12
FerreroI.MazziniL.RustichelliD.GunettiM.MareschiK.TestaL.et al. (2008). Bone marrow mesenchymal stem cells from healthy donors and sporadic amyotrophic lateral sclerosis patients.Cell Transplant.17255–266.
13
GhanbariH. A.GhanbariK.HarrisP. L.JonesP. K.KubatZ.CastellaniR. J.et al. (2004). Oxidative damage in cultured human olfactory neurons from Alzheimer’s disease patients.Aging Cell341–44.
14
GroeneveldG. J.VeldinkJ. H.Van Der TweelI.KalmijnS.BeijerC.De VisserM.et al. (2003). A randomized sequential trial of creatine in amyotrophic lateral sclerosis.Ann. Neurol.53437–445.
15
GrskovicM.JavaherianA.StruloviciB.DaleyG. Q. (2011). Induced pluripotent stem cells – opportunities for disease modelling and drug discovery.Nat. Rev. Drug Discov.10915–929.
16
HoepkenH. H.GispertS.AzizovM.KlinkenbergM.RicciardiF.KurzA.et al. (2008). Parkinson patient fibroblasts show increased alpha-synuclein expression.Exp. Neurol.212307–313.
17
IlaniT.Ben-ShacharD.StrousR. D.MazorM.SheinkmanA.KotlerM.et al. (2001). A peripheral marker for schizophrenia: increased levels of D3 dopamine receptor mRNA in blood lymphocytes.Proc. Natl. Acad. Sci. U.S.A.98625–628.
18
IsraelM. A.YuanS. H.BardyC.ReynaS. M.MuY.HerreraC.et al. (2012). Probing sporadic and familial Alzheimer’s disease using induced pluripotent stem cells.Nature482216–220.
19
JuopperiT. A.SongH.MingG. L. (2011). Modeling neurological diseases using patient-derived induced pluripotent stem cells.Future Neurol.6363–373.
20
KlivenyiP.FerranteR. J.MatthewsR. T.BogdanovM. B.KleinA. M.AndreassenO. A.et al. (1999). Neuroprotective effects of creatine in a transgenic animal model of amyotrophic lateral sclerosis.Nat. Med.5347–350.
21
LeeG.PapapetrouE. P.KimH.ChambersS. M.TomishimaM. J.FasanoC. A.et al. (2009). Modelling pathogenesis and treatment of familial dysautonomia using patient-specific iPSCs.Nature461402–406.
22
LeungC. T.CoulombeP. A.ReedR. R. (2007). Contribution of olfactory neural stem cells to tissue maintenance and regeneration.Nat. Neurosci.10720–726.
23
LovellM. A.GeigerH.Van ZantG. E.LynnB. C.MarkesberyW. R. (2006). Isolation of neural precursor cells from Alzheimer’s disease and aged control postmortem brain.Neurobiol. Aging27909–917.
24
MacarronR.BanksM. N.BojanicD.BurnsD. J.CirovicD. A.GaryantesT.et al. (2011). Impact of high-throughput screening in biomedical research.Nat. Rev. Drug Discov.10188–195.
25
Mackay-SimA. (2012). Patient-derived olfactory stem cells: new models for brain diseases.Stem Cells302361–2365.
26
Mackay-SimA.KittelP. (1991a). Cell dynamics in the adult mouse olfactory epithelium: a quantitative autoradiographic study.J. Neurosci.11979–984.
27
Mackay-SimA.KittelP. W. (1991b). On the life span of olfactory receptor neurons.Eur. J. Neurosci.3209–215.
28
MarJ. C.MatigianN. A.Mackay-SimA.MellickG. D.SueC. M.SilburnP. A.et al. (2011). Variance of gene expression identifies altered network constraints in neurological disease.PLoS Genet. 7:e1002207. 10.1371/journal.pgen.1002207
29
MartinM. A.MolinaJ. A.Jimenez-JimenezF. J.Benito-LeonJ.Orti-ParejaM.CamposY.et al. (1996). Respiratory-chain enzyme activities in isolated mitochondria of lymphocytes from untreated Parkinson’s disease patients.Grupo-Centro de Trastornos del Movimiento. Neurology461343–1346.
30
MatigianN.AbrahamsenG.SutharsanR.CookA. L.VitaleA. M.NouwensA.et al. (2010). Disease-specific, neurosphere-derived cells as models for brain disorders.Dis. Model. Mech.3785–798.
31
MatigianN. A.MccurdyR. D.FeronF.PerryC.SmithH.FilippichC.et al. (2008). Fibroblast and lymphoblast gene expression profiles in schizophrenia: are non-neural cells informative?PLoS ONE3:e2412. 10.1371/journal.pone.0002412
32
MauryY.GauthierM.PeschanskiM.MartinatC. (2012). Human pluripotent stem cells for disease modelling and drug screening.Bioessays3461–71.
33
McCurdyR. D.FeronF.PerryC.ChantD. C.McleanD.MatigianN.et al. (2006). Cell cycle alterations in biopsied olfactory neuroepithelium in schizophrenia and bipolar I disorder using cell culture and gene expression analyses.Schizophr. Res.82163–173.
34
MoreiraP. I.HarrisP. L.ZhuX.SantosM. S.OliveiraC. R.SmithM. A.et al. (2007). Lipoic acid and N-acetyl cysteine decrease mitochondrial-related oxidative stress in Alzheimer disease patient fibroblasts.J. Alzheimers Dis.12195–206.
35
MurrellW.BushellG. R.LiveseyJ.McgrathJ.MacdonaldK. P.BatesP. R.et al. (1996). Neurogenesis in adult human.Neuroreport71189–1194.
36
MurrellW.FeronF.WetzigA.CameronN.SplattK.BelletteB.et al. (2005). Multipotent stem cells from adult olfactory mucosa.Dev. Dyn.233496–515.
37
PackardA.SchnittkeN.RomanoR. A.SinhaS.SchwobJ. E. (2011). DeltaNp63 regulates stem cell dynamics in the mammalian olfactory epithelium.J. Neurosci.318748–8759.
38
PedrosaE.SandlerV.ShahA.CarrollR.ChangC.RockowitzS.et al. (2011). Development of patient-specific neurons in schizophrenia using induced pluripotent stem cells.J. Neurogenet.2588–103.
39
RajamohanD.MatsaE.KalraS.CrutchleyJ.PatelA.GeorgeV.et al. (2013). Current status of drug screening and disease modelling in human pluripotent stem cells.Bioessays35281–298.
40
RonnettG. V.LeopoldD.CaiX.HoffbuhrK. C.MosesL.HoffmanE. P.et al. (2003). Olfactory biopsies demonstrate a defect in neuronal development in Rett’s syndrome.Ann. Neurol.54206–218.
41
SchnabelJ. (2008). Neuroscience: standard model.Nature454682–685.
42
SoldnerF.HockemeyerD.BeardC.GaoQ.BellG. W.CookE. G.et al. (2009). Parkinson’s disease patient-derived induced pluripotent stem cells free of viral reprogramming factors.Cell136964–977.
43
StadtfeldM.HochedlingerK. (2010). Induced pluripotency: history, mechanisms, and applications.Genes Dev.242239–2263.
44
StefanovaV. T.GrifoJ. A.HansisC. (2012). Derivation of novel genetically diverse human embryonic stem cell lines.Stem Cells Dev.211559–1570.
45
StewartR.KozlovS.MatigianN.WaliG.GateiM.SutharsanR.et al. (2013). A patient-specific olfactory stem cell disease model for ataxia-telangiectasia.Hum. Mol. Genet. 10.1093/hmg/ddt101 [Epub ahead of print].
46
SundbergS. A. (2000). High-throughput and ultra-high-throughput screening: solution- and cell-based approaches.Curr. Opin. Biotechnol.1147–53.
47
TakahashiH.MerckenM.HondaT.SaitoY.MurayamaM.SongS.et al. (1999). Impaired proteolytic processing of presenilin-1 in chromosome 14-linked familial Alzheimer’s disease patient lymphocytes.Neurosci. Lett.260121–124.
48
VierbuchenT.OstermeierA.PangZ. P.KokubuY.SudhofT. C.WernigM. (2010). Direct conversion of fibroblasts to functional neurons by defined factors.Nature4631035–1041.
49
VitaleA. M.MatigianN. A.RavishankarS.BelletteB.WoodS. A.WolvetangE. J.et al. (2012). Variability in the generation of induced pluripotent stem cells: importance for disease modelling.Stem Cells Trans. Med.1641–650.
50
WangL.LockstoneH. E.GuestP. C.LevinY.PalotasA.PietschS.et al. (2010). Expression profiling of fibroblasts identifies cell cycle abnormalities in schizophrenia.J. Proteome Res.9521–527.
51
WolozinB.LeschP.LebovicsR.SunderlandT. (1993). A.E.Bennett Research Award 1993. Olfactory neuroblasts from Alzheimer donors: studies on APP processing and cell regulation. Biol. Psychiatry34824–838.
52
ZanellaF.LorensJ. B.LinkW. (2010). High content screening: seeing is believing.Trends Biotechnol.28237–245.
53
ZhangZ.WangX.WangS. (2008). Isolation and characterization of mesenchymal stem cells derived from bone marrow of patients with Parkinson’s disease.In Vitro Cell. Dev. Biol. Anim.44169–177.
54
ZhuH.LenschM. W.CahanP.DaleyG. Q. (2011). Investigating monogenic and complex diseases with pluripotent stem cells.Nat. Rev. Genet.12266–275.
Summary
Keywords
embryonic stem cells, induced pluripotent stem cells, olfactory stem cells, olfactory neurosphere-derived cells, high content screening
Citation
Mackay-Sim A (2013) Patient-derived stem cells: pathways to drug discovery for brain diseases. Front. Cell. Neurosci. 7:29. doi: 10.3389/fncel.2013.00029
Received
01 November 2012
Accepted
06 March 2013
Published
27 March 2013
Volume
7 - 2013
Edited by
Clare Parish, Florey Neuroscience Institute, Australia
Reviewed by
Rheinallt Parri, Aston University, UK; Lachlan Thompson, Florey Neuroscience Institute, Australia
Copyright
© Mackay-Sim.
This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.
*Correspondence: Alan Mackay-Sim, National Centre for Adult Stem Cell Research, Eskitis Institute for Cell and Molecular Therapies, Griffith University, Brisbane, QLD 4111, Australia. e-mail: a.mackay-sim@griffith.edu.au
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