Modeling neurodegenerative disorders in adult somatic cells: A critical review

Abstract

Development of new therapeutic targets for neurodegenerative disorders has been hampered by a reliance on post mortem tissue that is representative of end-stage disease, or on animal models that fail to provide faithful analogs. However, rapid advances in cellular genetic reprogramming, in particular the induction of somatic cells into stem cells, or directly into neurons, has led to intense interest in modeling of human neurodegeneration in vitro. Here, we critically review current methods and recent progress in cellular models of Alzheimer’s disease, Parkinson’s disease and amyotrophic lateral sclerosis. Several challenges are identified, including technical variability, lack of degenerative phenotypes, neurodevelopmental age and establishing ground truths for models of sporadic disease. Recommendations for evaluating neurodegenerative cellular models are proposed along with suggestions for future research.

Neurodegenerative disorders (NDDs) place an enormous burden on affected individuals, their families and the community at large. For example, they are the leading neurological cause of lost disability-adjusted life years in developed countries ( Collins et al., 2011). Yet unlike researchers in the fields of cancer or cardiovascular disease, the tissue of interest (neuronal circuits) is generally inaccessible, except at post mortem. The history of neuropathology is therefore a catalog of end-stage disease with the pivotal upstream biological events, those most likely amenable to therapeutic targeting, obscure. For these reasons, modeling the pathogenesis of NDDs in adult somatic cells has generated much excitement. This brief review aims at a critical assessment of progress toward this goal.

NDDs are characterized by three core features, each with important ramifications for in vitromodels. The first is not simply age-associated onset (generally in later life), but accelerating prevalence with advanced years. In other words, age is their primary risk factor. In the case of Alzheimer’s disease (AD), this is extreme: AD prevalence doubles every five years after the age of 60, and is approximately 10–20 times stronger than any other risk factor ( Brayne, 2007). As will be briefly reviewed below, this makes the issue of “developmental age” when modeling neurons intrinsic to their validity.

The second core criteria is frequency and severity of neuropathology compared to age-matched controls. Unfortunately, distinctions are blurred because there are generally no absolute thresholds when distinguishing normal from abnormal. Indeed, it is very common for healthy elders’ brains to express AD pathology of some degree; hence, it could be argued that a model cell line from “healthy controls” ought to also express some pathology. The underlying point is that clinical phenotype is not decisive: as many as 30% of individuals who die with moderate or severe AD pathology were asymptomatic in life (Mrc, 1998). Rather, it is a matter of degree, in which case those with more severe pathology are at higher risk of clinical symptoms. Accordingly, an in vitro model that is exclusively engineered to express neuropathology does not guarantee a clinical outcome from positive “hits” in drug screening.

An important caveat to NDD pathology is co-morbidity. In this review, for the sake of brevity we are focusing on AD, Parkinson’s disease (PD) and Amyotrophic Lateral Sclerosis (ALS). The defining classical pathology of AD is intracellular hyperphosphorylated tauopathic neurofibrillary tangles (NFTs) and extracellular insoluble amyloid plaques (varying from diffuse to compact to neuritic); for PD, it is intracellular a-synuclein Lewy bodies, and in ALS it is ubiquitin positive cytoplasmic inclusions in anterior horn cells of spinal cord (and in the brain in some cases). However, it is rare for any particular individual to exhibit just one class of pathology. Those diagnosed with AD may have frequent Lewy bodies and those with PD, amyloid plaques. One could say that it is somewhat of a “dirty secret” that NDDs are so promiscuous. Whether neuronal models of NDDs can or should recapitulate this is debatable. Occam’s razor would argue that one or two within-class pathologies is admirable; on the other hand, if, as often argued, such models permit a prized insight into the etiology of human brain disease, then a wild type pathological landscape in all its fecundity is superior.

Finally, NDDs by their nature display progressive neuronal and synaptic loss at an industrial scale. In early AD there is ~10 million lost neurons in the CA1 subfield of the hippocampus alone ( West et al., 1994); in symptomatic PD, 80% of substantia nigra neurons have died, and in ALS there is an estimated 50%-80% culling of motor neurons in the motor cortex, brainstem and spinal cord ( Dauer and Przedborski, 2003; Coan and Mitchell, 2015). Neuronal and synaptic losses are the closest biologic correlate of clinical symptoms ( Crystal et al., 1996; Price et al., 2001), and so this phenotype is arguably one of the most important when evaluating neuronal models of NDDs. Similarly, there are stereotyped patterns in the spatiotemporal evolution of neuropathology and neuronal loss. In AD, it generally starts in the entorhinal and hippocampal areas, then temporal cortex, insula and eventually most cortex ( Braak and Braak, 1998). In sporadic PD, it tends to begin in the brain stem, then midbrain and finally meso- and neocortex ( Braak et al., 2004). For ALS, a complex pattern has been proposed moving from spinal-, brainstem- motor nuclei to prefrontal cortex, then striatum and even hippocampus ( Braak et al., 2013). How this can be modeled in a dish is not clear, especially when the gold standard is often the “pure culture” of a single neuronal sub-type. Perhaps multicellular organoids ( Lancaster et al., 2013; Lancaster and Knoblich, 2014) will be required to model neurodegenerative progression, but current techniques do not permit this.

Basic methods

Induced pluripotent stem cells (iPSCs)

In 2007, Yamanaka and colleagues successfully generated human iPSCs from adult fibroblasts for the first time ( Takahashi et al., 2007). Briefly, retrovirus-mediated transduction of four transcription factors–Oct3/4, Sox2, Klf4 and c-MYC, was shown to be sufficient to reprogram the somatic nucleus to a pluripotent undifferentiated state (Fig. 1). These iPSCs are similar (but not identical) to native human embryonic stem cells (ESCs) in morphology, protein and gene expression, proliferation and telomerase activity.

Current protocols permit generation of stable patient-specific iPSC lines, albeit at low efficiency rates of 0.005% following transduction ( Takahashi et al., 2007 ; Soldner et al., 2009). These reprogrammed cells can be maintained in vitro for comparable time length to human ESCs, with their endogenous genome and its transcriptional feedbacks largely intact ( Takahashi et al., 2007). Their pluripotency is confirmed by the capacity to differentiate into cells of all three germ layers following in vivo teratoma assay. Subsequently, patient-derived iPSCs can be differentiated into different neuronal subtypes by coculture with appropriate growth factors or feeder cells.

Several general problems continue to hamper iPSCs approaches for modeling pathogenic mechanisms. Because the underlying mechanism of nuclear reprogramming is unknown, randomly incorporated factors can and do get spontaneously reactivated post-differentiation, confounding or masking downstream disease phenotypes ( Okita et al., 2007; Sommer et al., 2012). Also, the inclusion of the proto-oncogene c-MYC, and the act of genetic integration itself, may inactivate tumor suppressor genes and increase the likelihood of tumorgenesis ( Byrne, 2008) further confounding phenotypes. To circumvent this issue, non-integrating delivery methods such as adenoviruses ( Zhou and Freed, 2009) and episomal protein delivery ( Kim et al., 2009), or removing c-MYC from the reprogramming mixture itself ( Nakagawa et al., 2008; Soldner et al., 2009) have been investigated, however, all report a further decrease in efficiency. Efficiency of reprogramming is important to disease modeling not only from an experimental feasibility point of view, but also in terms of ability to replicate results widely between laboratories.

More recently, small molecule cocktails added in combination with viral mediated transcription factors have been shown to enhance the efficiency of iPSC reprogramming by up to 200-fold ( Lin et al., 2009; Li et al., 2009). Hou et al. (2013) demonstrated that these small compounds could entirely substitute for the traditional genetic reprogramming factors and was sufficient to generate iPSC from somatic cells through the targeting of specific signaling pathways, an approach that requires replication.

This brings up the key issue of model replicability. Without replicability there can be no validity, and current iPSCs methods suffer from lack of (within-line) clone-to-clone reproducibility ( Narsinh et al., 2011; Thatava et al., 2013) as well as patient-to-patient or synthesized line-to-line reliability ( Schuster et al., 2015; Kyttälä et al., 2016). Further development of transduction methods and systematic replicability are therefore major technical challenges for disease modeling.

Induced neuronal cells (iNs)

While somatic cells can be induced back to a more primitive pluripotent state, reprogramming technology also permits direct conversion (trans-differentiation) of cells from one somatic lineage (e.g., fibroblasts) into another (e.g., neuronal). It is thereby a way of sidestepping the ‘backward’ pluripotent step and moving directly to the cell type of interest. Conceptually, this method is attractive because of potential time and resource efficiency and it avoids the risk of tumorigenesis.

Vierbuchen and colleagues (2010) used a lentiviral vector packaged with five transcription factors (Brn2, Ascl1, Myte1l, Olig2 and Zic1) to directly convert mouse fibroblast into neurons. Eventually, three BAM “master factors” (Brn2, Ascl1 and Myt1l) were identified to be sufficient for the generation of these induced neuron (iN) cells, with serotonergic, dopaminergic and motor subtypes, albeit with efficiency varying widely between 2 and 50% ( Vierbuchen et al., 2010). These may comprise a hierarchical set of neuronal-specific lineage regulators, in which Ascl1 is up-stream and essential for recruitment of Brn2 to its target ( Wapinski et al., 2013). A detailed mechanistic understanding of how BAM-mediated neuronal specification proceeds is still elusive.

Results are more limited when starting with mature adult human tissue. Human iNs have only been able to attain immature neuronal protein expression and morphology ( Pang et al., 2011). However, the addition of NeuroD1 to BAM-factors (BAMN) resulted in trans-differentiation of fetal and adult human fibroblasts to neurons expressing mature neuronal markers NeuN and MAP2. These cells also form functional synapses and integrate into neuronal networks. Pharmacological application of GABA and AMPA inhibitors halted inhibitory and excitatory post-synaptic potentials, respectively ( Pang et al., 2011). The use of BAMN is therefore a promising application for generating functional neurons from adult human fibroblasts.

The use of micro-RNA (miRNA) for reprogramming is yet another alternative approach that is less genomically invasive than using viral vectors. This provides a non-integrating and self-regulating means of converting human somatic cells into iNs without risk to genetic integrity ( Yoo et al., 2011 ; Lau et al., 2014). Several miRNAs have been demonstrated to promote reprogramming by negatively regulating genes involved in the differentiated state and enhance reprogramming efficiency ( Anokye-Danso et al., 2012). miRNA-124 has been a popular choice due to its specific expression in cells of neuronal lineage. When combined with BAM factors, mature neurons expressing MAP2 and NeuN can be produced, displaying synaptic functionality with mainly GABAergic or glutamatergic characteristics ( Lau et al., 2014).

While such non-viral reprogramming methods preserve genetic integrity and are easier to synthesize, modify and standardize, it should be noted that they do not resolve other issues faced by iPSCs or iNs. Further, miRNAs are involved in many cellular processes and thus the exact molecular mechanisms underlying their pro- reprogramming effects are still unclear ( Anokye-Danso et al., 2012). There is also the question of robustness and whether the generated neurons behave (let alone model disease) similarly to neurons of ESC or iPSC origins.

A major disadvantage of directly reprogrammed iNs is that the fate of the neuronal sub-population is difficult to manipulate and once the cells have differentiated into a particular subtype, their fate (and number) is sealed. Like for iPSCs, methods for producing such cells continue to diverge rather than converge ( Ladewig et al., 2012; Liu et al., 2013) and this methodological variability is likely to have a material effect on NDD model outcomes.

Clearly, each reprogramming technology has its own set of advantages and disadvantages and it remains an empirical question as to which strategy is best suited for a particular application. In the absence of a gold standard, a utilitarian approach is recommended: does the model elucidate a veridical disease pathway; does it help develop a new effective treatment?

Key challenges in neurological disease modeling

From an experimental point of view, two fundamentally different strategies can be trialled when modeling NDDs. One is to culture somatic cells from bona fide patients, reprogram these, grow the cells into mature neurons and compare to disease-free healthy control neurons. This strategy is preferred for sporadic/idiopathic disease which comprise>90% of all NDD, since the underlying polygenetic-environmental interaction driving pathogenesis is unknown. The downside of this approach is that the two groups of cells are necessarily non-isogenic. Whether this in fact matters in these complex disorders is open to debate.

A second approach involves attempted rescue of a disease phenotype in familial, genetically-defined NDDs with gene editing ( Soldner et al., 2011; Ryan et al., 2013; Sproul et al., 2014; Ring et al., 2015). Here, the contrast is between isogenic cell lines and hence allows strong conclusions about causality. Yet as mentioned, these revelations can in the best case only generalize to a small minority of NDD patients, particularly familial AD and PD.

A major challenge inherent to all reprogramming approaches is the reversion to some kind of ground state. For iPSCs, reversion is all the way back to an embryonic-like state ( Silva et al., 2008), for iNs, it is a neural progenitor-like state Ring et al. (2012). However, as mentioned at the outset, age is the number one factor driving NDD pathogenesis. Even in the earliest familial cases of ALS, this may be in the late 30s or early 40s. For sporadic cases, it is at least in the 60s.

So what is the “developmental age” of modeled neurons? There remains some debate, not least because age is a multidimensional construct and complicated by reprogamming-related cellular rejuvenation ( Marion et al., 2009; Suhr et al., 2010; Lapasset et al., 2011; Mahmoudi and Brunet, 2012; Miller et al., 2013; Vera and Studer, 2015; Mertens et al., 2015). In this context, age - and lifestyle - associated epigenetic signatures could potentially be lost or altered during the iPSC reprogramming process, but might be retained in directly converted iNs, assuming that these characteristics are similar between fibroblasts and neurons (Sommer et al., 2012; Kyttälä et al., 2016). What ought to be avoided is circular logic: model X recapitulates disease phenotype Y; therefore it is developmentally mature. Simply designing models for a given phenotype is perilous, particularly for the sporadic diseases, in the same way that transgenic mice models of NDDs have produced little clinical advancements ( Wilcock, 2010; Breitner, 2015). A better approach is for neuronal maturity to be evaluated against a panel of indicators (e.g., loss of developmentally immature transcription factors, loss of embryonic neuronal patterning markers, etc.) that are orthogonal to the NDD phenotype.

Linked to this is the fanciful idea that age may be “accelerated,” not least because of the second law of thermodynamics. Rather, researchers can trial the use environmental stressors (oxidants, free radicals, hypoxemia, carboxia, alcohol, etc.) to attempt to make the experiment more tractable, revealing, and realistic given at least 30% of all sporadic AD is attributable to environmental factors (Silva et al., 2008).

Neuronal cell models of AD

Genetic mutations linked to early onset (<60 years of age) familial AD (FAD) include the Amyloid precursor protein (APP), Presenilin 1 (PSEN1) and Presenilin 2 (PSE2) ( Tanzi and Bertram, 2005). These represent<5% of all AD, the rest being sporadic AD (SAD). Several genetic variants have been proposed as SAD susceptibility or risk factors by genome wide association studies ( Lambert et al., 2009; Harold et al., 2009), but results have not been consistent ( Gandhi and Wood, 2010), with the exception of ApoE-ε4 that is common and exhibits a strong effect size ( Tsai et al., 1994).

Overall, human iPSC-derived neurons from FAD patients with PSEN1 and PSEN2 mutations exhibit abnormal levels of Ab42/40 ratio in the absence of changes to tau ( Yagi et al., 2011). iPSCs with APP mutations displayed increased levels of Ab peptides and abnormal Ab42/40 ratio, as well as elevated levels of total tau, and phosphorylated-tau (p-tau) ( Israel et al., 2012; Muratore et al., 2014). Interestingly, while animal models increasingly suggest that tauopathy is a critical partner in Ab toxicity ( Ittner and Götz, 2011), only a small number of iPSC studies have demonstrated such a connection ( Israel et al., 2012; Choi et al., 2014; Kim et al., 2015). It is also notable that to date no AD cell model has demonstrated bona fide neurofibrillary tangles as opposed to incipient tauopathy.

Biochemical changes to Ab42 and Ab40 in modeled cells have been contradictory. Cells derived from a population of FAD patients with APP mutation found elevated Ab42/40 ratio only in the one case with the APP-V717L mutation ( Kondo et al., 2013). It is thereby possible that variability in disease phenotypes can, in part, be due to the specific nature of APP cleavage site dysfunction ( Kondo et al., 2013; Muratore et al., 2014).

In studies of SAD, variability has been the rule rather than the exception. In general, phenotypes have not been replicable between patients ( Israel et al., 2012). By definition, these studies mix patients with different AD-related genetic mutations along with patients where there is no known genotype ( Kondo et al., 2013). SAD can thereby harbour multiple genetic variants ( Israel et al., 2012), many of which are not related to typical FAD mutations ( Duan et al., 2014; Hossini et al., 2015).

Further limiting AD models is the curious neglect to date of key processes such as apoptosis, synaptic loss and synaptic dysfunction. Altered electrophysiological activity and susceptibility to cell death have been reported ( Duan et al., 2014; Honda et al., 2016), but lack of connection with Ab abnormality or tauopathy suggests these are more likely due to cellular immaturity ( Israel et al., 2012; Muratore et al., 2014).

Given the abundance of research in FAD (e.g., DIAN project ( Cairns et al., 2015 ; Su et al., 2016), in the near future it may be interesting to apply gene editing technology on patient-specific iNs or iPSC-derived neurons to examine whether rescue of point mutations can ameliorate disease phenotype or improve cellular function.

Neuronal cells models of PD

Parkinson’s disease (PD) is a chronic and progressive movement disorder caused by dysfunction and eventual cell death of dopaminergic neurons in the substantia nigra and beyond. The mechanism driving cellular degeneration remains elusive; increasingly, studies point to a confluence of mechanisms rather than a single pathway. These include mitochondrial dysfunction, accumulation of toxic a-synuclein, mitophagy and oxidative stress. Several genes implicated in familial PD have been successfully recapitulated in iPSC-derived dopaminergic neurons, including the SNCA (α-synuclein), LRRK2 (leucine-rich repeat kinase 2) and PINK1 (PTEN induced putative kinase 1) mutations (Table 2).

As seen in Table 2, in vitro models of PD have focused so far solely on familial cases of PD, with a limited number of studies demonstrating disease-specific phenotypes and features of dopaminergic degeneration ( Byers et al., 2011; Fernández-Santiago et al., 2015). Nevertheless, these studies have provided valuable insights into potential mechanisms of cellular dysfunction driven by a discrete genetic mutation. Environmental factors such as oxidative damage and neuroinflammation certainly have a significant role in disease pathogenesis, and future studies should begin to incorporate these factors into their models. Where possible, novel disease-related phenotypes expressed by iPSCs should be cross-validated by isogenic post mortem neuropathology ( Hu et al., 2015b). However, this is clearly not always possible in a manageable time frame and so cross validation by neuropathology in non-isogenically matched individuals is justifiable.

Neuronal cells models of ALS

ALS is an adult-onset progressive neurodegenerative disease marked by degeneration of upper and lower motor neurons ( Pasinelli and Brown, 2006). 90% of ALS is of unknown genetic etiology ( Pasinelli and Brown, 2006) and there is no animal model of sporadic ALS. This is in contrast to several transgenic rodent models of familial ALS, particularly the SOD1 gene mutations. While animal models can recapitulate some aspects of the disease phenotype, including degeneration of spinal motor neurons ( Gurney et al., 1994; Bruijn et al., 1997), these have yet to yield an effective treatment. Thus, model systems that allow validation of results from animal models in adult human motor neurons derived from sporadic ALS cases could be critical in bridging this gap. Notable studies in this area are summarized in Table 3.

Early successes in generating patient-specific cellular models of familial ALS may allow researchers to gain a deeper understanding of disease processes in the large majority without familial disease. However, ALS models suffer from many of the same limitations that apply for other NDDs. While modeled cells express markers of neural stem cells and motor neurons, they lack disease-specific phenotypes and do not show age-dependent degenerative features. Promisingly, a study by Liu et al. (2016) showed that induced motor neurons expressed some features of neurodegeneration, suggesting that more representative models are possible. Also, while motor neurons are the main cell type affected in ALS, neuroimaging and pathological studies have implicated non-motor brain areas in ALS, with some patients showing cognitive and behavioral impairment similar to frontotemporal dementia ( Strong and Yang, 2011) along with cortico-spinal tract abnormalities ( Mascalchi et al., 1995). Future studies should aim to address this by looking at multi-cellular culture systems, combining wild type motor neurons with ALS-derived non-motor neurons or glial cells (and vice versa), to further explore non-cell autonomous disease pathways.

Recommendations for evaluating NDD models

Evidence is mixed for new mechanistic insights or novel therapeutic targets based on models of NDD. AD models (either familial or sporadic) have to date been disappointing in terms of mainly recapitulating known pathology. On the other hand, a model of PD that includes neurodegeneration found that epigenetic modification of the LRRK2 gene may generalize from the familial to sporadic context ( Fernández-Santiago et al.(2015), while another model confirmed at post mortem that AKT/GSK-3 beta signaling pathway may be dysregulated, as is the case in AD, further supporting linkages across classes of NDDs. Similarly, a model of ALS has suggested that a previously known pathogenic mechanism in familial disease (TDP-43 proteinopathy) may be relevant in a small subset of sporadic cases ( Burkhardt et al., 2013).

Cellular models of NDD therefore have potential but a critical approach is recommended. Based on this review and the core pathological features of human NDDs introduced at the outset, Box 1 proposes three criteria when evaluating models of neurodegenerative disease.

Future directions

Neuronal modeling of sporadic NDDs is the greatest challenge for the field. Here, because of the absence of any “ground-truth” studies will need to strive for more complex models, perhaps mixing cell types (e.g., tripartite neuronal-glia-vascular interactions), applying enviromimetics (e.g., oxidative stressors) or incorporating 3D architecture (e.g., cerebral organoids). Eventually, models will need to move from considering just single-cell or univariate phenotypic features to multivariate characteristics (e.g., using multi-electrode arrays).

External validation of NDD models will be essential prior to use in pharmacological screening. This can include rescue of known familial defects or comparison against isogenic or non-isogenic post mortem histopathology. Modeling of not only risk factors but also resilience or protective factors is unchartered territory, but likely to produce fascinating outcomes.

Underlying all such advances is production of modeled cells under a systematic, efficient and low variance protocol. If only a small subset of cells express a given phenotype, in a small subset of cell lines, and these are the subject of publications, then the field will flounder. On the other hand, the rate of advance in reprogramming technology is remarkable. If the field can converge on a set of core methods for producing NDD models then it is more likely to thrive.

References

[1] Anokye-Danso F, Snitow M, Morrisey E E (2012). How microRNAs facilitate reprogramming to pluripotency. J Cell Sci, 125(Pt 18): 4179–4187

[2] Boulting G L, Kiskinis E, Croft G F, Amoroso M W, Oakley D H, Wainger B J, Williams D J, Kahler D J, Yamaki M, Davidow L, Rodolfa C T, Dimos J T, Mikkilineni S, MacDermott A B, Woolf C J, Henderson C E, Wichterle H, Eggan K (2011). A functionally characterized test set of human induced pluripotent stem cells. Nat Biotechnol, 29(3): 279–286

[3] Braak H, Braak E 1998. Evolution of neuronal changes in the course of Alzheimer’s disease. In: Jellinger K, Fazekas F, Windisch M (eds.) Ageing and Dementia. Vienna: Springer Vienna

[4] Braak H, Brettschneider J, Ludolph A C, Lee V M, Trojanowski J Q, Del Tredici K (2013). Amyotrophic lateral sclerosis—a model of corticofugal axonal spread. Nat Rev Neurol, 9(12): 708–714

[5] Braak H, Ghebremedhin E, Rüb U, Bratzke H, Del Tredici K (2004). Stages in the development of Parkinson’s disease-related pathology. Cell Tissue Res, 318(1): 121–134

[6] Brayne C (2007). The elephant in the room- healthy brains in later life, epidemiology and public health. Nat Rev Neurosci, 8(3): 233–239

[7] Breitner J C (2015). Comment: Yet another “disconnect” between amyloid and Alzheimer disease? Neurology, 85(8): 698

[8] Bruijn L I, Becher M W, Lee M K, Anderson K L, Jenkins N A, Copeland N G, Sisodia S S, Rothstein J D, Borchelt D R, Price D L, Cleveland D W (1997). ALS-linked SOD1 mutant G85R mediates damage to astrocytes and promotes rapidly progressive disease with SOD1-containing inclusions. Neuron, 18(2): 327–338

[9] Burkhardt M F, Martinez F J, Wright S, Ramos C, Volfson D, Mason M, Garnes J, Dang V, Lievers J, Shoukat-Mumtaz U, Martinez R, Gai H, Blake R, Vaisberg E, Grskovic M, Johnson C, Irion S, Bright J, Cooper B, Nguyen L, Griswold-Prenner I, Javaherian A (2013). A cellular model for sporadic ALS using patient-derived induced pluripotent stem cells. Mol Cell Neurosci, 56: 355–364

[10] Byers B, Cord B, Nguyen H N, Schüle B, Fenno L, Lee P C, Deisseroth K, Langston J W, Pera R R, Palmer T D (2011). SNCA triplication Parkinson’s patient’s iPSC-derived DA neurons accumulate a-synuclein and are susceptible to oxidative stress. PLoS ONE, 6(11): e26159–e26159

[11] Byrne J A (2008). Generation of isogenic pluripotent stem cells. Hum Mol Genet, 17(R1): R37–R41

[12] Cairns N J,Perrin R J , Franklin E E, Carter D, Vincent B, Xie M, Bateman R J, Benzinger T, Friedrichsen K, Brooks W S, Halliday G M, McLean C, Ghetti B, Morris J C, the Alzheimer Disease Neuroimaging Initiative, the Dominantly Inherited Alzheimer Network (2015). Neuropathologic assessment of participants in two multi-center longitudinal observational studies: the Alzheimer Disease Neuroimaging Initiative (ADNI) and the Dominantly Inherited Alzheimer Network (DIAN). Neuropathology, 35(4): 390–400

[13] Choi S H, Kim Y H, Hebisch M, Sliwinski C, Lee S, D’Avanzo C, Chen H, Hooli B, Asselin C, Muffat J, Klee J B, Zhang C, Wainger B J, Peitz M, Kovacs D M, Woolf C J, Wagner S L, Tanzi R E, Kim D Y (2014). A three-dimensional human neural cell culture model of Alzheimer’s disease. Nature, 515(7526): 274–278

[14] Coan G, Mitchell C S (2015). An assessment of possible neuropathology and clinical relationships in 46 Sporadic amyotrophic lateral sclerosis patient autopsies. Neurodegener Dis, 15(5): 301–312

[15] Collins P Y, Patel V, Joestl S S, March D, Insel T R, Daar A S, Anderson W, Dhansay M A, Phillips A, Shurin S, Walport M, Ewart W, Savill S J, Bordin I A, Costello E J, Durkin M, Fairburn C, Glass R I, Hall W, Huang Y, Hyman S E, Jamison K, Kaaya S, Kapur S, Kleinman A, Ogunniyi A, Otero-Ojeda A, Poo M M, Ravindranath V, Sahakian B J, Saxena S, Singer P A, Stein D J, the Scientific Advisory Board and the Executive Committee of the Grand Challenges on Global Mental Health (2011). Grand challenges in global mental health. Nature, 475(7354): 27–30

[16] Crystal H A, Dickson D, Sliwinski M, Masur D, Blau A, Lipton R B (1996). Associations of status and change measures of neuropsychological function with pathologic changes in elderly, originally nondemented subjects. Arch Neurol, 53(1): 82–87

[17] Dauer W, Przedborski S (2003). Parkinson’s disease: mechanisms and models. Neuron, 39(6): 889–909

[18] Duan L, Bhattacharyya B J, Belmadani A, Pan L, Miller R J, Kessler J A (2014). Stem cell derived basal forebrain cholinergic neurons from Alzheimer’s disease patients are more susceptible to cell death. Mol Neurodegener, 9(1): 3–3

[19] Fernández-Santiago R, Carballo-Carbajal I, Castellano G, Torrent R, Richaud Y, Sánchez-Danés A, Vilarrasa-Blasi R, Sánchez-Pla A, Mosquera J L, Soriano J, López-Barneo J, Canals J M, Alberch J, Raya Á, Vila M, Consiglio A, Martín-Subero J I, Ezquerra M, Tolosa E (2015). Aberrant epigenome in iPSC-derived dopaminergic neurons from Parkinson’s disease patients. EMBO Mol Med, 7(12): 1529–1546

[20] Gandhi S, Wood N W (2010). Genome-wide association studies: the key to unlocking neurodegeneration? Nat Neurosci, 13(7): 789–794

[21] Gurney M E, Pu H, Chiu A Y, Dal Canto M C, Polchow C Y, Alexander D D, Caliendo J, Hentati A, Kwon Y W, Deng H X, et (1994). Motor neuron degeneration in mice that express a human Cu,Zn superoxide dismutase mutation. Science, 264(5166): 1772–1775

[22] Harold D, Abraham R, Hollingworth P, Sims R, Gerrish A, Hamshere M L, Pahwa J S, Moskvina V, Dowzell K, Williams A, Jones N, Thomas C, Stretton A, Morgan A R, Lovestone S, Powell J, Proitsi P, Lupton M K, Brayne C, Rubinsztein D C, Gill M, Lawlor B, Lynch A, Morgan K, Brown K S, Passmore P A, Craig D, McGuinness B, Todd S, Holmes C, Mann D, Smith A D, Love S, Kehoe P G, Hardy J, Mead S, Fox N, Rossor M, Collinge J, Maier W, Jessen F, Schürmann B, Heun R, van den Bussche H, Heuser I, Kornhuber J, Wiltfang J, Dichgans M, Frölich L, Hampel H, Hüll M, Rujescu D, Goate A M, Kauwe J S, Cruchaga C, Nowotny P, Morris J C, Mayo K, Sleegers K, Bettens K, Engelborghs S, De Deyn P P, Van Broeckhoven C, Livingston G, Bass N J, Gurling H, McQuillin A, Gwilliam R, Deloukas P, Al-Chalabi A, Shaw C E, Tsolaki M, Singleton A B, Guerreiro R, Mühleisen T W, Nöthen M M, Moebus S, Jöckel K H, Klopp N, Wichmann H E, Carrasquillo M M, Pankratz V S, Younkin S G, Holmans P A, O’Donovan M, Owen M J, Williams J (2009). Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer’s disease. Nat Genet, 41(10): 1088–1093

[23] Honda M, Minami I, Tooi N, Morone N, Nishioka H, Uemura K, Kinoshita A, Heuser J E, Nakatsuji N, Aiba K (2016). The modeling of Alzheimer’s disease by the overexpression of mutant Presenilin 1 in human embryonic stem cells. Biochem Biophys Res Commun, 469(3): 587–592

[24] Hossini A M, Megges M, Prigione A, Lichtner B, Toliat M R, Wruck W, Schröter F, Nuernberg P, Kroll H, Makrantonaki E, Zouboulis C C, Adjaye J (2015). Induced pluripotent stem cell-derived neuronal cells from a sporadic Alzheimer’s disease donor as a model for investigating AD-associated gene regulatory networks. BMC Genomics, 16(1): 84

[25] Hou P, Li Y, Zhang X, Liu C, Guan J, Li H, Zhao T, Ye J, Yang W, Liu K, Ge J, Xu J, Zhang Q, Zhao Y, Deng H (2013). Pluripotent stem cells induced from mouse somatic cells by small-molecule compounds. Science, 341(6146): 651–654

[26] Hu W, Qiu B, Guan W, Wang Q, Wang M, Li W, Gao L, Shen L, Huang Y, Xie G, Zhao H, Jin Y, Tang B, Yu Y, Zhao J, Pei G (2015a). Direct conversion of normal and Alzheimer’s disease human fibroblasts into neuronal cells by small molecules. Cell Stem Cell, 17(2): 204–212

[27] Hu Z, Pu J, Jiang H, Zhong P, Qiu J, Li F, Wang X, Zhang B, Yan Z, Feng J (2015b). Generation of naivetropic induced pluripotent stem cells from Parkinson’s disease patients for high-efficiency genetic manipulation and disease modeling. Stem Cells Dev, 24(21): 2591–2604

[28] Israel M A, Yuan S H, Bardy C, Reyna S M, Mu Y, Herrera C, Hefferan M P, Van Gorp S, Nazor K L, Boscolo F S, Carson C T, Laurent L C, Marsala M, Gage F H, Remes A M, Koo E H, Goldstein L S B (2012). Probing sporadic and familial Alzheimer’s disease using induced pluripotent stem cells. Nature, 482(7384): 216–220

[29] Ittner L M, Götz J (2011). Amyloid-b and tau—a toxic pas de deux in Alzheimer’s disease. Nat Rev Neurosci, 12(2): 65–72

[30] Kim D, Kim C H, Moon J I, Chung Y G, Chang M Y, Han B S, Ko S, Yang E, Cha K Y, Lanza R, Kim K S (2009). Generation of human induced pluripotent stem cells by direct delivery of reprogramming proteins. Cell Stem Cell, 4(6): 472–476

[31] Kim Y H, Choi S H, D’Avanzo C, Hebisch M, Sliwinski C, Bylykbashi E, Washicosky K J, Klee J B, Brüstle O, Tanzi R E, Kim D Y (2015). A 3D human neural cell culture system for modeling Alzheimer’s disease. Nat Protoc, 10(7): 985–1006

[32] Kondo T, Asai M, Tsukita K, Kutoku Y, Ohsawa Y, Sunada Y, Imamura K, Egawa N, Yahata N, Okita K, Takahashi K, Asaka I, Aoi T, Watanabe A, Watanabe K, Kadoya C, Nakano R, Watanabe D, Maruyama K, Hori O, Hibino S, Choshi T, Nakahata T, Hioki H, Kaneko T, Naitoh M, Yoshikawa K, Yamawaki S, Suzuki S, Hata R, Ueno S, Seki T, Kobayashi K, Toda T, Murakami K, Irie K, Klein W L, Mori H, Asada T, Takahashi R, Iwata N, Yamanaka S, Inoue H (2013). Modeling Alzheimer’s disease with iPSCs reveals stress phenotypes associated with intracellular Ab and differential drug responsiveness. Cell Stem Cell, 12(4): 487–496

[33] Kyttälä A, Moraghebi R, Valensisi C, Kettunen J, Andrus C, Pasumarthy K K, Nakanishi M, Nishimura K, Ohtaka M, Weltner J, Van Handel B, Parkkonen O, Sinisalo J, Jalanko A, Hawkins R D, Woods N B, Otonkoski T, Trokovic R (2016). Genetic variability overrides the impact of parental cell type and determines iPSC differentiation potential. Stem Cell Rep, 6(2): 200–212

[34] Ladewig J, Mertens J, Kesavan J, Doerr J, Poppe D, Glaue F, Herms S, Wernet P, Kögler G, Müller F J, Koch P, Brüstle O (2012). Small molecules enable highly efficient neuronal conversion of human fibroblasts. Nat Methods, 9(6): 575–578

[35] Lambert J C,Heath S , Even G, Campion D, Sleegers K, Hiltunen M, Combarros O, Zelenika D, Bullido M J, Tavernier B, Letenneur L, Bettens K, Berr C, Pasquier F, Fiévet N, Barberger-Gateau P,Engelborghs S , De Deyn P, Mateo I, Franck A, Helisalmi S, Porcellini E, Hanon O, de Pancorbo M M, Lendon C, Dufouil C, Jaillard C, Leveillard T, Alvarez V, Bosco P, Mancuso M, Panza F, Nacmias B, Bossù P, Piccardi P, Annoni G, Seripa D, Galimberti D, Hannequin D, Licastro F, Soininen H, Ritchie K, Blanché H, Dartigues J F, Tzourio C, Gut I, Van Broeckhoven C, Alpérovitch A, Lathrop M, Amouyel P, the European Alzheimer’s Disease Initiative Investigators (2009). Genome-wide association study identifies variants at CLU and CR1 associated with Alzheimer’s disease. Nat Genet, 41(10): 1094–1099

[36] Lancaster M A, Knoblich J A (2014). Generation of cerebral organoids from human pluripotent stem cells. Nat Protoc, 9(10): 2329–2340

[37] Lancaster M A, Renner M, Martin C A, Wenzel D, Bicknell L S, Hurles M E, Homfray T, Penninger J M, Jackson A P, Knoblich J A (2013). Cerebral organoids model human brain development and microcephaly. Nature, 501(7467): 373–379

[38] Lapasset L, Milhavet O, Prieur A, Besnard E, Babled A, Aït-Hamou N, Leschik J, Pellestor F, Ramirez J M, De Vos J, Lehmann S, Lemaitre J M (2011). Rejuvenating senescent and centenarian human cells by reprogramming through the pluripotent state. Genes Dev, 25(21): 2248–2253

[39] Lau S, Rylander Ottosson D, Jakobsson J, Parmar M (2014). Direct neural conversion from human fibroblasts using self-regulating and nonintegrating viral vectors. Cell Reports, 9(5): 1673–1680

[40] Li W, Zhou H, Abujarour R, Zhu S, Young Joo J, Lin T, Hao E, Schöler H R, Hayek A, Ding S (2009). Generation of human-induced pluripotent stem cells in the absence of exogenous Sox2. Stem Cells, 27(12): 2992–3000

[41] Lim S M, Choi W J, Oh K W, Xue Y, Choi J Y, Kim S H, Nahm M, Kim Y E, Lee J, Noh M Y, Lee S, Hwang S, Ki C S, Fu X D, Kim S H (2016). Directly converted patient-specific induced neurons mirror the neuropathology of FUS with disrupted nuclear localization in amyotrophic lateral sclerosis. Mol Neurodegener, 11(1): 8

[42] Lin T, Ambasudhan R, Yuan X, Li W, Hilcove S, Abujarour R, Lin X, Hahm H S, Hao E, Hayek A, Ding S (2009). A chemical platform for improved induction of human iPSCs. Nat Methods, 6(11): 805–808

[43] Liras A, Segovia C, Gabán A S (eds.) (2013). Induced Pluripotent Stem Cells: Therapeutic Applications in Monogenic and Metabolic Diseases, and Regulatory and Bioethical Considerations. InTechOpen

[44] Liu M L, Zang T, Zhang C L (2016). Direct lineage reprogramming reveals disease-specific phenotypes of motor neurons from human ALS patients. Cell Reports, 14(1): 115–128

[45] Liu M L, Zang T, Zou Y, Chang J C, Gibson J R, Huber K M, Zhang C L (2013). Small molecules enable neurogenin 2 to efficiently convert human fibroblasts into cholinergic neurons. Nat Commun, 4: 2183

[46] Mahmoudi S, Brunet A (2012). Aging and reprogramming: a two-way street. Curr Opin Cell Biol, 24(6): 744–756

[47] Marion R M, Strati K, Li H, Tejera A, Schoeftner S, Ortega S, Serrano M, Blasco M A (2009). Telomeres acquire embryonic stem cell characteristics in induced pluripotent stem cells. Cell Stem Cell, 4(2): 141–154

[48] Mascalchi M, Salvi F, Valzania F, Marcacci G, Bartolozzi C, Tassinari C A (1995). Corticospinal tract degeneration in motor neuron disease. AJNR Am J Neuroradiol, 16(4 Suppl): 878–880

[49] Mertens J, Paquola A C, Ku M, Hatch E, Böhnke L, Ladjevardi S, McGrath S, Campbell B, Lee H, Herdy J R, Gonçalves J T, Toda T, Kim Y, Winkler J, Yao J, Hetzer M W, Gage F H (2015). Directly reprogrammed human neurons retain aging-associated transcriptomic signatures and reveal age-related nucleocytoplasmic defects. Cell Stem Cell, 17(6): 705–718

[50] Miller J D, Ganat Y M, Kishinevsky S, Bowman R L, Liu B, Tu E Y, Mandal P K, Vera E, Shim J W, Kriks S, Taldone T, Fusaki N, Tomishima M J, Krainc D, Milner T A, Rossi D J, Studer L (2013). Human iPSC-based modeling of late-onset disease via progerin-induced aging. Cell Stem Cell, 13(6): 691–705

[51] Muratore C R, Rice H C, Srikanth P, Callahan D G, Shin T, Benjamin L N, Walsh D M, Selkoe D J, Young-Pearse T L (2014). The familial Alzheimer’s disease APPV717I mutation alters APP processing and Tau expression in iPSC-derived neurons. Hum Mol Genet, 23(13): 3523–3536

[52] Nakagawa M, Koyanagi M, Tanabe K, Takahashi K, Ichisaka T, Aoi T, Okita K, Mochiduki Y, Takizawa N, Yamanaka S (2008). Generation of induced pluripotent stem cells without Myc from mouse and human fibroblasts. Nat Biotechnol, 26(1): 101–106

[53] Narsinh K H, Sun N, Sanchez-Freire V, Lee A S, Almeida P, Hu S, Jan T, Wilson K D, Leong D, Rosenberg J, Yao M, Robbins R C, Wu J C (2011). Single cell transcriptional profiling reveals heterogeneity of human induced pluripotent stem cells. J Clin Invest, 121(3): 1217–1221

[54] Ohta E, Nihira T, Uchino A, Imaizumi Y, Okada Y, Akamatsu W, Takahashi K, Hayakawa H, Nagai M, Ohyama M, Ryo M, Ogino M, Murayama S, Takashima A, Nishiyama K, Mizuno Y, Mochizuki H, Obata F, Okano H 2015. I2020T mutant LRRK2 iPSC-derived neurons in the Sagamihara family exhibit increased Tau phosphorylation through the AKT/GSK-3 signaling pathway. Human Mol Genet, 24(17):4879–4900

[55] Okita K, Ichisaka T, Yamanaka S (2007). Generation of germline-competent induced pluripotent stem cells. Nature, 448(7151): 313–317

[56] Ooi L, Sidhu K, Poljak A, Sutherland G, O’Connor M D, Sachdev P, Münch G (2013). Induced pluripotent stem cells as tools for disease modelling and drug discovery in Alzheimer’s disease. J Neural Transm (Vienna), 120(1): 103–111

[57] Pang Z P, Yang N, Vierbuchen T, Ostermeier A, Fuentes D R, Yang T Q, Citri A, Sebastiano V, Marro S, Südhof T C, Wernig M (2011). Induction of human neuronal cells by defined transcription factors. Nature, 476(7359): 220–223

[58] Pasinelli P, Brown R H (2006). Molecular biology of amyotrophic lateral sclerosis: insights from genetics. Nat Rev Neurosci, 7(9): 710–723

[59] Price J L, Ko A I, Wade M J, Tsou S K, McKeel D W, Morris J C (2001). Neuron number in the entorhinal cortex and CA1 in preclinical Alzheimer disease. Arch Neurol, 58(9): 1395–1402

[60] Ring K L, An M C, Zhang N, O’Brien R N, Ramos E M, Gao F, Atwood R, Bailus B J, Melov S, Mooney S D, Coppola G, Ellerby L M, the RING (2015). Genomic analysis reveals disruption of striatal neuronal development and therapeutic targets in human Hungtinton's disease neural stem cells. Stem Cell Rep, 5(6): 1023–1038

[61] Ring K L, Tong L M, Balestra M E, Javier R, Andrews-Zwilling Y, Li G, Walker D, Zhang W R, Kreitzer A C, Huang Y (2012). Direct reprogramming of mouse and human fibroblasts into multipotent neural stem cells with a single factor. Cell Stem Cell, 11(1): 100–109

[62] Ryan S D, Dolatabadi N, Chan S F, Zhang X, Akhtar M W, Parker J, Soldner F, Sunico C R, Nagar S, Talantova M, Lee B, Lopez K, Nutter A, Shan B, Molokanova E, Zhang Y, Han X, Nakamura T, Masliah E, Yates J R 3rd, Nakanishi N, Andreyev A Y, Okamoto S, Jaenisch R, Ambasudhan R, Lipton S A (2013). Isogenic human iPSC Parkinson’s model shows nitrosative stress-induced dysfunction in MEF2-PGC1a transcription. Cell, 155(6): 1351–1364

[63] Schuster J, Halvardson J, Pilar Lorenzo L, Ameur A, Sobol M, Raykova D, Annerén G, Feuk L, Dahl N (2015). Transcriptome profiling reveals degree of variability in induced pluripotent stem cell lines: Impact for human disease modeling. Cell Reprogram, 17(5): 327–337

[64] Seibler P, Graziotto J, Jeong H, Simunovic F, Klein C, Krainc D (2011). Mitochondrial Parkin recruitment is impaired in neurons derived from mutant PINK1 induced pluripotent stem cells. J Neurosci, 31(16): 5970–5976

[65] Silva J, Barrandon O, Nichols J, Kawaguchi J, Theunissen T W, Smith A (2008). Promotion of reprogramming to ground state pluripotency by signal inhibition. PLoS Biol, 6(10): e253

[66] Soldner F, Hockemeyer D, Beard C, Gao Q, Bell G W, Cook E G, Hargus G, Blak A, Cooper O, Mitalipova M, Isacson O, Jaenisch R (2009). Parkinson’s disease patient-derived induced pluripotent stem cells free of viral reprogramming factors. Cell, 136(5): 964–977

[67] Soldner F, Laganière J, Cheng A W, Hockemeyer D, Gao Q, Alagappan R, Khurana V, Golbe L I, Myers R H, Lindquist S, Zhang L, Guschin D, Fong L K, Vu B J, Meng X, Urnov F D, Rebar E J, Gregory P D, Zhang H S, Jaenisch R (2011). Generation of isogenic pluripotent stem cells differing exclusively at two early onset Parkinson point mutations. Cell, 146(2): 318–331

[68] Sommer C A, Christodoulou C, Gianotti-Sommer A, Shen S S, Sailaja B S, Hezroni H, Spira A, Meshorer E, Kotton D N, Mostoslavsky G (2012). Residual expression of reprogramming factors affects the transcriptional program and epigenetic signatures of induced pluripotent stem cells. PLoS ONE, 7(12): e51711

[69] Sproul A, Jacob S, Paquet D, Ortiz-Virumbrales M, Campos B, Gandy S, Tessier-Lavigne M, Noggle S (2014). Using familial Alzheimer's disease and isogenic control IPSc-derived basal forebrain neurons to model AD. Alzheimers Dement, 10(4): 643–P644

[70] Strong M J, Yang W (2011). The frontotemporal syndromes of ALS. Clinicopathological correlates. J Mol Neurosci, 45(3): 648–655

[71] Su Y, Blazey T M, Owen C J, Christensen J J, Friedrichsen K, Joseph-Mathurin N, Wang Q, Hornbeck R C, Ances B M, Snyder A Z, Cash L A, Koeppe R A, Klunk W E, Galasko D, Brickman A M, McDade E, Ringman J M, Thompson P M, Saykin A J, Ghetti B, Sperling R A, Johnson K A, Salloway S P, Schofield P R, Masters C L, Villemagne V L, Fox N C, Förster S, Chen K, Reiman E M, Xiong C, Marcus D S, Weiner M W, Morris J C, Bateman R J, Benzinger T L, the Dominantly Inherited Alzheimer Network (2016). Quantitative amyloid imaging in autosomal dominant Alzheimer’s disease: Results from the DIAN study group. PLoS ONE, 11(3): e0152082

[72] Suhr S T, Chang E A, Tjong J, Alcasid N, Perkins G A, Goissis M D, Ellisman M H, Perez G I, Cibelli J B (2010). Mitochondrial rejuvenation after induced pluripotency. PLoS ONE, 5(11): e14095

[73] Takahashi K, Tanabe K, Ohnuki M, Narita M, Ichisaka T, Tomoda K, Yamanaka S (2007). Induction of pluripotent stem cells from adult human fibroblasts by defined factors. Cell, 131(5): 861–872

[74] Tanzi R E, Bertram L (2005). Twenty years of the Alzheimer’s disease amyloid hypothesis: a genetic perspective. Cell, 120(4): 545–555

[75] Thatava T, Kudva Y C, Edukulla R, Squillace K, De Lamo J G, Khan Y K, Sakuma T, Ohmine S, Terzic A, Ikeda Y (2013). Intrapatient variations in type 1 diabetes-specific iPS cell differentiation into insulin-producing cells. Mol Ther, 21(1): 228–239

[76] The Medical Research Council Cognitive Function and Ageing Study (MRC CFAS) (1998). Cognitive function and dementia in six areas of England and Wales: the distribution of MMSE and prevalence of GMS organicity level in the MRC CFA Study. Psychol Med, 28(2): 319–335

[77] Tsai M S, Tangalos E G, Petersen R C, Smith G E, Schaid D J, Kokmen E, Ivnik R J, Thibodeau S N (1994). Apolipoprotein E: risk factor for Alzheimer disease. Am J Hum Genet, 54(4): 643–649

[78] Vera E, Studer L (2015). When rejuvenation is a problem: challenges of modeling late-onset neurodegenerative disease. Development, 142(18): 3085–3089

[79] Vierbuchen T, Ostermeier A, Pang Z P, Kokubu Y, Südhof T C, Wernig M (2010). Direct conversion of fibroblasts to functional neurons by defined factors. Nature, 463(7284): 1035–1041

[80] Wapinski O L, Vierbuchen T, Qu K, Lee Q Y, Chanda S, Fuentes D R, Giresi P G, Ng Y H, Marro S, Neff N F, Drechsel D, Martynoga B, Castro D S, Webb A E, Südhof T C, Brunet A, Guillemot F, Chang H Y, Wernig M (2013). Hierarchical mechanisms for direct reprogramming of fibroblasts to neurons. Cell, 155(3): 621–635

[81] West M J, Coleman P D, Flood D G, Troncoso J C (1994). Differences in the pattern of hippocampal neuronal loss in normal ageing and Alzheimer’s disease. Lancet, 344(8925): 769–772

[82] Wilcock D M (2010). The usefulness and challenges of transgenic mouse models in the study of Alzheimer’s disease. CNS Neurol Disord Drug Targets, 9(4): 386–394

[83] Yagi T, Ito D, Okada Y, Akamatsu W, Nihei Y, Yoshizaki T, Yamanaka S, Okano H, Suzuki N (2011). Modeling familial Alzheimer’s disease with induced pluripotent stem cells. Hum Mol Genet, 20(23): 4530–4539

[84] Yoo A S, Sun A X, Li L, Shcheglovitov A, Portmann T, Li Y, Lee-Messer C, Dolmetsch R E, Tsien R W, Crabtree G R (2011). MicroRNA-mediated conversion of human fibroblasts to neurons. Nature, 476(7359): 228–231

[85] Zhou W, Freed C R (2009). Adenoviral gene delivery can reprogram human fibroblasts to induced pluripotent stem cells. Stem Cells, 27(11): 2667–2674

RIGHTS & PERMISSIONS

Higher Education Press and Springer-Verlag Berlin Heidelberg