Skip to main content
Neuronal Signaling logoLink to Neuronal Signaling
. 2022 Apr 8;6(1):NS20210054. doi: 10.1042/NS20210054

Cell models for Down syndrome-Alzheimer’s disease research

Yixing Wu 1, Nicole R West 2,3, Anita Bhattacharyya 3,4,, Frances K Wiseman 1,5,
PMCID: PMC8996251  PMID: 35449591

Abstract

Down syndrome (DS) is the most common chromosomal abnormality and leads to intellectual disability, increased risk of cardiac defects, and an altered immune response. Individuals with DS have an extra full or partial copy of chromosome 21 (trisomy 21) and are more likely to develop early-onset Alzheimer’s disease (AD) than the general population. Changes in expression of human chromosome 21 (Hsa21)-encoded genes, such as amyloid precursor protein (APP), play an important role in the pathogenesis of AD in DS (DS-AD). However, the mechanisms of DS-AD remain poorly understood. To date, several mouse models with an extra copy of genes syntenic to Hsa21 have been developed to characterise DS-AD-related phenotypes. Nonetheless, due to genetic and physiological differences between mouse and human, mouse models cannot faithfully recapitulate all features of DS-AD. Cells differentiated from human-induced pluripotent stem cells (iPSCs), isolated from individuals with genetic diseases, can be used to model disease-related cellular and molecular pathologies, including DS. In this review, we will discuss the limitations of mouse models of DS and how these can be addressed using recent advancements in modelling DS using human iPSCs and iPSC-mouse chimeras, and potential applications of iPSCs in preclinical studies for DS-AD.

Keywords: Alzheimers disease, Down syndrome, induced pluripotent stem cells

Introduction

Overview on Down syndrome neurodevelopment

Trisomy of human chromosome 21 (Hsa21) was first discovered as the underlying cause of Down syndrome (DS, Ts21) in 1959 [1,2] and is the most common genetic cause of intellectual disability, affecting approximately 1 in 700 live births [3–5]. Hsa21, first sequenced in 2000, is the smallest human autosome and makes up ∼1–1.5% of the human genome [5]. Overexpression of Hsa21 genes and non-coding elements alters prenatal development of the brain, however, some effects do not appear until later in life [6–8]. Aberrant neurodevelopment in DS leads to overall smaller brain volumes and structural defects in cerebral cortex and cerebellum, affecting cognitive functions such as attention, learning, memory, and motor function to varying degrees [6,8–10]. A reduction in brain volume is detected as early as 15 gestational weeks in foetuses with DS, and by adulthood, brains of individuals with DS are ∼20% smaller than controls when corrected for their reduced body size [11,12]. While it is clear from studies of post-mortem tissue that this smaller volume is primarily due to a reduction in the number of neurons, we have a poor understanding of the causal underlying cellular deficits [13–22]. Further, the molecular mechanisms driving these anatomical abnormalities are largely unknown, which has resulted in potential treatments to enhance cognition in infants and children with DS that target symptoms rather than the basis of the disorder [10]. Importantly, it is not known whether or how these initial neurodevelopmental deficits may affect the progression of AD pathology in DS.

Overview on Alzheimer’s disease

According to the World Health Organization (WHO), Alzheimer’s disease (AD) contributes to 60–70% of the dementia cases worldwide [23]. AD causes progressive loss of memory and reduction in cognitive function that leads to dementia and ultimately death [24]. Brain atrophy due to neural and synaptic loss is also detectable in AD patients [25]. Presence of the neuropathological hallmarks amyloid-β (Aβ) plaques and neurofibrillary tangles (NFTs), formed from misfolded microtubule-associated protein tau (MAPT), are necessary for disease diagnosis [26].

Although more than 90% of AD cases are late-onset (LOAD) and sporadic (sAD) with no known causal mutations [27], several disease-related mutations in the genes encoding, amyloid precursor protein (APP), presenilin 1 (PSEN1) and presenilin 2 (PSEN2) cause early-onset AD (EOAD). APP can be processed by amyloidogenic or non-amyloidogenic pathways. In the amyloidogenic pathway, APP is cleaved in a two-step process to form Aβ. PSEN1 and 2 are subunits of the γ-secretase complex that catalyses the second cleavage step of APP yielding Aβ [28]. Mutations in PSEN1 and PSEN2 cause an increase in Aβ production or result in a shift in the Aβ40/Aβ42 ratio favouring the formation of pathogenic aggregates [29], which drives AD development. Genetic association studies have identified several risk genes involved in multiple pathways for EOAD and LOAD [30], including most significantly the ε4 allele of the apolipoprotein E (APOE) [31,32] and the more recently identified chromosome 21-encoded gene, ADAM metallopeptidase with thrombospondin type 1 motif 1 (ADAMTS1) [33]. Despite the aetiology of AD not being fully understood, it is widely accepted that it is a complex disease that affects multiple cell types in the brain [34] and that immune response, endocytosis, lipid transport and vesicle trafficking modulate disease development [33,35].

The association between AD and DS

People with DS have an extremely high risk of developing AD with extensive Aβ plaque accumulation occurring in most individuals by age 40 [36–38]. By the age of 60, approximately two-thirds of individuals with DS will have developed clinical dementia [39] (Figure 1). The pattern of cognitive decline is similar in individuals who have Alzheimer’s disease in Down syndrome (DS-AD) compared with AD, although occurring earlier in DS-AD [40], and individuals with DS-AD develop seizures more frequently than other forms of AD [41]. Triplication of a dosage-sensitive gene or genes on Hsa21 likely plays an important role in the pathogenesis of AD. APP is located on Hsa21 and duplication of APP in the absence of DS leads to EOAD [42,43]. Moreover, individuals with DS who do not have a third copy of APP do not develop AD neuropathology or dementia [44,45]. Thus, the additional copy of APP plays a central role in DS-AD. The pattern and type of Aβ accumulation in individuals with DS is similar to people with EOAD and LOAD, although occurrence of cerebral amyloid angiopathy is higher in DS-AD than EOAD and LOAD [46–50] (Figure 1). In recent years, whether other genes on Hsa21 also have roles in AD pathogenesis has been studied. Several Hsa21-encoded proteins are thought to be potential candidates for this altered biology, including dual-specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A) that phosphorylates tau [51,52] and APP [53], Synaptojanin 1 (SYNJ1) that is involved in endocytosis and membrane trafficking [54], β-Secretase 2 (BACE2) – a putative Aβ-degrading protease [55] and Cystatin B (CSTB), an endogenous inhibitor of cysteine cathepsins [56,57].

Figure 1. Schematic of the development of AD neuropathology and dementia in individuals who have DS.

Figure 1

People who have DS first develop Aβ deposition, NFTs and then go on to develop dementia in middle age.

While chromosome 21 genes account for the majority of differentially expressed genes in DS, genes on other chromosomes are also differentially expressed and may also play a role in DS-AS progression [58]. Lockstone et al. found that APOE, while not an Hsa21 gene, is up-regulated in DS [58]. Recently, Bejanin et al. screened for the prevalence of the APOE ε4 AD-risk allele in 464 adults with DS [59]. They reported that 20.9% of individuals with DS had the APOE ε4 allele. These individuals had earlier cognitive decline and earlier clinical symptoms of AD compared with the 79.1% of DS individuals without the APOE ε4 allele [59], similar to findings in the general population and previous reports in individuals with DS [60–66]. Exploring the mechanistic roles of APOE isoforms and other non-Hsa21 genes in the pathogenesis of AD in DS is important for developing effective treatments for DS-AD.

DS-AD mouse models and human tissue

Uses and limitations of DS-AD mouse models

Mouse models overexpressing causal mutations of familial Alzheimer’s disease (fAD) are widely used in AD research, and recapitulate aspects of disease pathology [67], although differences in human and mouse biology limit the use of these systems for some key aspects of disease; most notably AD-neuroinflammation [68]. Moreover, compared with AD models, it is more challenging to generate DS mouse models because of the genetic complexity of the disorder and since orthologue genes of Hsa21 are located on regions of three mouse chromosomes (Mmu10, Mmu16 and Mmu17) [69]. However, to date, several DS mouse models have been developed [70,71] and have been used to study aspects of DS-AD (Figure 2).

Figure 2. Schematic illustration of DS mouse models.

Figure 2

The regions of Mmu10 (purple), Mmu17 (orange) and Mmu16 (blue) that are homologous with Hsa21 (long arm) as indicated. The content of the transchromosome 21 in the Tc1 and TcMAC21 models with deletions and key rearrangements as indicated. The Tc1 mouse model has a human centromere (red circle). The TcMAC21, Ts65Dn and Ts66Yah have mouse centromeres (blue circle). The region Mmu16 with an additional copy in the Ts65Dn, Ts66Yah, Dp1Yey, Dp1Tyb, Dp2Tyb, Dp3Tyb and Dp9Tyb as indicated. The duplication of Mmu17 in the Dp(17)3Yey and the duplication of Mmu10 in Dp(10)2Yey as shown. The approximate human APP gene position is shown in bold, the TcMAC21, Ts65Dn, Ts66Yah, Dp1Yey, Dp1Tyb and Dp9Tyb models carry an additional copy of APP/App.

One of the first mouse models of DS was the Ts65Dn [72] which has a partial extra copy of Mmu16 and is trisomic for approximately 55% of Hsa21 orthologous genes [73,74]. Ts65Dn mice exhibit learning impairment, locomotor hyperactivity, neurodegeneration and neuroinflammation [74,75], representing a number of the features of DS and AD. Using the Ts65Dn, Salehi et al. found that an increased level of App contributes to cholinergic neurodegeneration in the basal forebrain by disrupting NGF transport, providing insight into this feature of DS-AD [76]. Similarly, Garcia-Cerro et al. used the Ts65Dn to demonstrate the role of three copies of Dyrk1A in modulation of APP/Aβ biology [53] and Yin et al. used a pharmacological approach, targeting the kinase, to investigate changes of Tau biology in the model [77]. Moreover, use of an anti-Aβ vaccine in the Ts65Dn model alleviated some DS-AD-related phenotypes, demonstrating the importance of the peptide in disease mechanism [78]. The Ts65Dn model carries extra copies of some genes that are not orthologues of Hsa21 genes [73] and phenotypic drift has occurred in the mouse likely because of its complex genetic background limiting the utility of this model for future research [79].

More recently, a series of mouse models with extra copies of Mmu10, Mmu16 and Mmu17 genes, that are orthologous with Hsa21 have been generated including; Dp1Tyb, Dp2Tyb, Dp3Tyb and the Dp1Yey; Dp2Yey; Dp3Yey known as the DP16/10/17 ‘triple’ mouse model [80–83]. A recent study by Tosh et al. used segmental duplication mouse models (Dp2Tyb, Dp3Tyb, Dp2Yey and Dp3Yey) to understand which regions of Hsa21 can modulate Aβ aggregation [84]. The study identified that an extra copy of the genes located between Mir802 and Zbtb21 was sufficient to increase Aβ aggregation in vivo. However, these models lack some Hsa21 orthologues and cannot fully recapitulate trisomy of Hsa21 [84]. Moreover, Aβ plaques or aggregates do not form in the brains of models which carry an additional copy of the mouse App gene [75,81], likely because of differences in the biology of mouse and human APP/Aβ caused by key differences in the amino acid sequence between the species. Indeed, partial humanisation of mouse and rat App using knock-in approaches lead to a closer recapitulation of AD biology [85,86], and in the future such approaches may also lead to improved DS-AD rodent models.

The Tc1 ‘humanised’ transchromosomic mouse [formally called Tc(Hsa21)1TybEmcf], that carries an extra copy of approximately 75% of Hsa21 genes, was published in 2005 [87,88]. Tc1 mice show human DS-related defects in synaptic plasticity, cerebellar granule neurons and altered heart development [88]. Importantly, this model does not carry an extra copy of APP due to a rearrangement within the transchromosome [52,87], making Tc1 a useful tool for studying the role of other Hsa21 genes, independently of the triplication of APP, in the pathogenesis of AD. Using this approach Wiseman et al. demonstrated that Hsa21 genes other than APP increase Aβ deposition and exacerbate AD-related cognitive deficits [89]. However, during mouse development, random loss of the additional chromosome leads to mosaicism, limiting the ability to correlate genotype and phenotype in this system [88,90]. This model also lacks an additional copy of ∼25% of Hsa21 genes, such that it cannot be used to study the role of these missing genes in DS-AD [71,87].

Recently, a non-mosaic, transchromic DS mouse model, TcMAC21, was generated by cloning the long arm of Hsa21 as a mouse artificial chromosome [91]. TcMAC21 manifests DS-related features such as defects in memory, learning and synaptic plasticity, heart and craniofacial development as well as haematological abnormalities [91], making it by far the most genetically complete DS mouse model. Of note, TcMAC21 has elevated APP protein in the brain, but despite carrying an additional copy of human APP, Aβ plaques are not detected in the model [91], consistent with previous reports that humanisation of App is not sufficient to cause substantial Aβ accumulation in mice [92]. Further characterisation of this line and crossing it with mouse models of AD pathology will be needed to study plaque-associated DS-AD phenotypes.

Although DS mouse models have provided many insights into the causation and pathophysiology of both DS and AD, they are unable to fully reflect the human disorder because of the complex nature of genetic, transcriptional and translational regulation of human biology as well as the physiological and developmental differences between mouse and human [93–97]. In particular, comparative studies have indicated differences in neurotransmitter mechanisms between mouse and humans [98], and that some AD-specific patterns of gene expression are not recapitulated in the mouse [99], despite an overall good conservation of cell type. Moreover, differences between human and mouse astrocyte and microglia biology [99,100] may have particular implications for the modelling of neurodegenerative disease. Thus, although many aspects of DS and AD biology can be effectively modelled in mouse, additional research tools that capture key aspects of human biology that are not reproduced in rodents are also required to undertake research in these important areas.

Uses and limitations of human tissue in DS-AD studies

Human tissue from individuals with DS and AD has long been an important source for immunohistochemical, biochemical and, more recently, transcriptomic analysis providing information about DS-AD-associated pathological changes. In the last decade, sequencing and genetics-based studies have elucidated the effects of full or partial copy of chromosome 21 (trisomy 21) on brain development [10,58,101,102], as well as AD-related pathology.

Histology and biochemistry of AD-related phenotypes in DS

By studying post-mortem brain samples from individuals with DS across the lifespan, the pattern of Aβ plaque and NFTs formation has been determined to be broadly similar to that which occurs in AD, albeit commencing several decades earlier [103–107]. Aβ deposition is first seen in the parahippocampal gyrus in children with DS [36]. Loss of neurons in the entorhinal cortex occurs in both DS-AD and AD [108,109]. Coskun et al. show that mutations in mitochondrial DNA accumulate with age and are increased in DS-AD brains compared with age-matched controls [110] consistent with reports from AD in the general population [110,111]. Wilcock et al. analysed the expression of microglia markers in DS, DS-AD, and sAD tissue [112], revealing that elevated neuroinflammation occurs in the brains of people who have DS and unique neuroinflammatory phenotypes and microglia activation states occur in the DS-AD brain [112]. Additional studies have supported this seminal finding, showing differences in microglia morphology and cytokine profiles in the brains of people who have DS and DS-AD [113,114]. Notably altered cytokine changes predict cognitive decline DS-AD [115], consistent with reports of microglia activation correlating with increased tau across Braak stages in AD [116]. Further studies are needed to gain a better understanding of the contribution of different brain cell types to DS-AD pathology and cognitive decline.

Transcriptomic studies to elucidate mechanism

The expression of genes throughout the genome is altered in the brain of people who have DS [58,101,102,117–121]. Gene expression profiling of foetus through adult post-mortem DS tissue has revealed that many, but not all, Hsa21 genes are up-regulated [58,101,102,117–121]. While triplication of APP is thought to be a main driver of DS-AD, Lockstone et al. found no evidence of increased APP abundance in the brain of adults who had DS [58]. In contrast, more recent studies have shown robust up-regulation of APP transcript and protein in the brains of individuals with DS and DS-AD [122,123]. The expression of other Hsa21 genes, including DYRK1A, ADAMTS1, BACE2, RCAN1, and non-Hsa21 genes of interest, including APOE and NOTCH2, is also increased in the brains of adults who have DS [58]. Using single-nucleus RNA-sequencing technology, Palmer et al. carried out a transcriptomics study in post-mortem prefrontal cortex from individuals with DS and euploid controls [123]. Consistent with recent histological and biochemical studies [113,114], this showed changes to microglia biology in both young and middle-aged adults who had DS and suggested a significant change in the ratio of inhibitory and excitatory neurons caused by trisomy of Hsa21 [123]. Further comparative single-nuclei RNA-sequencing studies of tissues from individuals who have DS and DS-AD (and equivalent tissues from the general population with and without AD) will provide critical new insights into how neurodevelopment and neurodegeneration are altered by trisomy of chromosome 21.

Challenges and future approaches

Despite the significant information provided by studies of human post-mortem tissues, this research approach has a number of limitations. Although post-mortem tissue is typically matched by age, sex and post-mortem interval, it is not possible to account for all environmental differences that may affect phenotypes of interest. In addition, technical differences, such as fixation, method of processing the tissue or freezing the tissue, can affect results, making it difficult to compare findings from different studies and material sourced from different brain banks. Limited information on cellular processes can be obtained using post-mortem samples, and it is highly challenging to test molecular and cellular hypotheses as these provide information only at a static timepoint. Moreover, it is still challenging to obtain sufficient samples, both because of ethical constraints (such as ensuring appropriate informed consent from people who have an intellectual disability) and historical issues with accurate clinical diagnosis of dementia and mild cognitive impairment (MCI) in people with DS [124]. In particular, obtaining brain material from adults with DS that have not yet developed AD pathology is highly challenging because of the early development of pathology and can hamper adequate statistical power for many research questions. In 2013, The Academy of Medical Sciences released a report calling for increased collection of tissue at international biobanks [125]. Lawrence et al. surveyed U.K. researchers and determined their motivation for choice of tissue was availability of clinical data as well as sourcing from local tissue banks [124]. Further tissue banking from individuals who have DS or DS-AD who have undergone clinical phenotyping during their lifetime will help alleviate limitations of access to tissue.

Cell models (non-pluripotent stem cells)

Cellular models can be used to address the limitations of animal preclinical models and human tissue studies, facilitating hypothesis-testing in a genetically and physiologically relevant system. Immortalised human cell lines and cells derived from affected individuals are commonly used to model and study cellular and molecular mechanisms in disorders and diseases, including DS and AD (Table 1). Human brain microvascular endothelial cells (hBMECs), human cerebral microvascular endothelial cells (hCMECs), human neuroblastoma cells (SHSY-5Y, SK-N-MC), human embryonic kidney cells (HEK293), human teratocarcinoma cells (NTera 2 or NT2/D1) and human lung cancer cells (CALU-3) are among the human cell lines used to screen potential therapeutics and have been valuable in understanding how overexpression of Hsa21 genes affects proliferation, differentiation, oxidative stress, Aβ accumulation, tau pathology and cell death in both DS and AD [126–141].

Table 1. Cellular Models used in DS and DS-AD research.

Cell line/model Source Use References
hBMECs Human brain microvascular endothelial cells Mimic the BBB [134,135,38]
hCMECs Human cerebral microvascular endothelial cells Mimic the BBB [136,137]
SHSY-5Y Human neuroblastoma; subcloned from SK-N-MC cells Neural-like [127–129,132,137,139,140]
SK-N-MC Human neuroblastoma Neural-like [138]
HEK293 Human embryonic kidney cell 293 Fundamental biological processes [130,131]
NTera or NT2/D1 Human teratocarcinoma Resemble neural precursor cells [133]
CALU-3 Human lung adenocarcinoma Mimic the nasal–brain barrier [141]
Primary Cultures Fibroblasts, astrocytes, neurons and neural stem/progenitor cells Individual specific and disease relevant [142–155]
hESCs Human embryonic stem cells derived from blastocysts Differentiate into cell types of interest; maintain genetic background of donor [169–171]
iPSCs Induced pluripotent stem cells reprogrammed from somatic cells Differentiate into cell types of interest; maintain genetic background of donor [55,180–231,234–242]
Organoids iPSC-derived 3D model 3D culture; differentiate into cell types of interest; maintain genetic background of donor [55,204,208,212,244,246–250]
Induced neurons (iNs) iPSCs and somatic cells directly reprogrammed to neurons Retain age-markers and genetic background of donor [196,272–275]

A reduction in the GABAA α3 subunit was detected in the hippocampus of DS foetal tissue [127]. To understand this feature, SH-SY5Y cells, which have neural origins, were treated with Aβ leading to a reduction in the GABAA α3 subunit, suggesting that Aβ may play a role in regulating GABAA receptor subunits [127]. Similarly, Krishtal et al. used SH-SY5Y cells to show that Aβ treatment caused neurite abnormalities, activated caspases, and caused cell death [128]. Moreover, increased APP expression in SH-SY5Y cells led to enhanced susceptibility to oxidative stress and cell death [129]. SH-SY5Y cells have also been used to investigate the role of vitamin A in neural differentiation because vitamin A deficiency is associated with AD and DS and induces neural differentiation by regulating mitochondrial morphology and function [139]. SH-SY5Y cells used to study RCAN1 and oxidative stress revealed that inhibition of RCAN1 reduces oxidative stress and apoptosis [140].

Non-neural HEK293 cells overexpressing MAPT formed pTau aggregates, which can be rescued by inhibition of kinase, glycogen synthase kinase 3 (GSK3), implicating GSK3 in the formation of pTau [130]. Notable changes in GSK3 activity have been reported in the Tc1 mouse models [52]. HEK293 cells overexpressing DYRK1A have hyperphosphorylated acetyl transferase, p300, and CREB-binding protein (CBP), revealing that DYRK1A may play a role in regulating enhancer activity and gene expression [131]. DYRK1A overexpression in SH-SY5Y cells reduced proliferation, and the sustained overexpression-induced cell cycle exit and premature neuronal differentiation, defects consistent with those seen in other trisomy 21 cellular models [132]. hBMECs and hCMECs are used to mimic the blood–brain barrier (BBB) and have been used as a model to study the BBB permeability to Aβ, BBB dysfunction and neuroinflammation, and to test uptake of potential AD therapeutics [134–138]. Quercetin, a potential AD therapy with low BBB permeability, was encased in liposomes with RMP-7 and lactoferrin. The liposome construct was permeable to the hBMEC BBB model, and Quercetin alleviated Aβ neurotoxicity in SK-N-MC cells [138]. Similar approaches may be used to understand how trisomy 21 impacts the BBB in DS-AD.

In summary, while these immortalised cells can easily be cultured and manipulated to study cellular defects that may be altered in DS and AD, these models do not carry trisomy 21 but only alter one or a few genes of interest, thus limiting them from fully recapitulating DS-AD biology.

Cells derived from individuals with DS

Primary cell cultures of fibroblasts, neurons, astrocytes, and neural progenitor/stem cells derived from tissue of individuals who have DS, retain trisomy 21 and have revealed phenotypes associated with neurodegeneration, cell stress, and AD development.

Proteomics and transcriptomics of trisomy 21 primary fibroblasts have shown that Hsa21-encoded mRNAs and proteins are increased an average of approximately 1.5-fold and expression of other non-Hsa21 gene products is also altered, thus modelling a key aspect of DS biology [142]. Aneuploidy-associated stress response in cells leads to impaired cell proliferation, mitochondrial dysfunction, increased ROS, disrupted protein homoeostasis, trafficking deficits, accumulation of protein aggregates, and premature senescence in these cells, thus providing a system in which this key DS-AD relevant biology can be understood and potential treatments investigated [142–149].

Primary neurons and astrocytes can be derived from post-mortem foetal brain tissue and those from DS show increased ROS and undergo apoptosis compared with control cells [150] as well as dysfunctional mitochondria and altered processing of APP, leading to accumulation of insoluble Aβ [151]. With the capability to be differentiated into specific neural subtypes and glial cells, foetal tissue-derived neural stem cells (NSCs) can be used to study developmentally relevant disease mechanisms and pathology, which may overlap with neurodegenerative mechanisms. For example, altered synaptic pruning pathways impact both development and neuron degeneration in AD and DS-AD [152]. Trisomy 21 cultures reveal aberrant development of DS neurons, which may play a role in susceptibility to AD pathology later in life [14,153–155].

Pluripotent stem cell models of DS-AD

With the ability to be differentiated into many disease-relevant cells, human pluripotent stem cells (PSCs) are unmatched in their ability to model diseases and can also be used as a source of human cells for testing of therapeutics [156–167] (Figure 3). Human embryonic stem cells (hESCs) were successfully derived and cultured from human blastocysts in 1998 [168]. hESCs have since been derived from early embryos with aneuploidies, including trisomy 21 [169–171] and have developmental defects, including a reduction in pluripotency regulators leading to premature neuronal differentiation and increased cell death, consistent with mechanisms shown in other trisomy cell models as well as phenotypes seen in individuals with DS [169,170,172].

Figure 3. Schematic illustration of DS-AD cell models.

Figure 3

Patient-derived hESCs or hiPSCs are first patterned toward NSCs. They are then differentiated into neural progenitor cells and further differentiated into different cell types (astrocytes, neurons, and oligodendrocytes). Induced neurons skip progenitor stages by directly reprograming somatic cells into neurons. These new techniques and models are enhancing the research of DS-AD and have the potential for developing efficient treatments. Created with BioRender.com.

The use of hESCs in research is ethically controversial since they are derived from an early-stage human embryo [173–175]. Further, access to embryos with trisomy 21 is difficult, such that only limited DS and DS-AD research has been undertaken using hESCs. As an alternative, human somatic cells can be reprogrammed by introducing specific transcription factors (Oct3/4, Sox2, c-Myc, and Klf4; or, Oct3/4, Sox2, Nanog, and Lin28) that return the somatic cells to an undifferentiated, hESC-like state [176–179]. These induced pluripotent stem cells (iPSCs) have become an invaluable resource in research to model AD, DS, and DS-AD [180–185].

In 2011, iPSCs were first derived from individuals with autosomal-dominant, early-onset fAD caused by mutations in PSEN1 and PSEN2 [186] and subsequently from fAD individuals with a duplication of APP and individuals with sAD [187]. Neurons differentiated from these iPSCs recapitulate AD pathogenic features such as accumulation of Aβ [188,189] and increased pTau and GSK-3β validating these cells as an AD model [186,187]. For example, basal forebrain cholinergic neurons (BFCNs) are prone to degeneration in both DS and AD and have been differentiated from AD iPSCs to identify underlying cellular and molecular mechanisms of their vulnerability [190–192]. AD iPSCs have been used to understand the roles of AD-risk genes and the underlying mechanisms contributing to the onset and progression of the disease [193–201].

While these models have contributed significant knowledge of the pathophysiological mechanisms of the disease, a major limitation with 2D models is the inability to recapitulate all aspects of disease pathogenesis. Notably, these in vitro systems do not facilitate the development of extracellular Aβ plaques. Moreover, they do not fully replicate all of the age-dependent pathological features, and they also lack the complex interaction of multiple cell types, which are suggested to have a major role in AD development [202]. While AD iPSCs have been used extensively to elucidate underlying mechanisms of the disease, Israel et al. found iPSC lines generated from individuals with sAD and fAD with an APP duplication did not all display the same phenotypes [187]. Similarly, Kondo et al. found that seven AD iPSC lines did not recapitulate the same phenotypes [189], illustrating the underlying variability in this model system likely because of genetic differences between individuals.

iPSCs were first derived from cells from two individuals with DS in 2008 and retained trisomy 21, validating iPSC technology as a tool to study DS [203]. Subsequent studies generated trisomy iPSCs from both banked cells and directly from donor samples [55,185,203–231]. In early iPSC studies, disorder-specific cells were typically compared with an age- and sex-matched control. Inherent genetic human variation between controls and disorder made it hard to distinguish differences caused by the disorder from underlying genetic differences between individuals. The generation of isogenic pairs of trisomy and euploid iPSCs from mosaic trisomy 21 cells addressed this limitation [218,226]. However, mosaicism is rare and occurs in 2–4% of individuals with DS [232,233], limiting the generation of isogenic iPSC pairs by this approach. Another strategy to generate DS and control lines with limited genetic variability is to derive iPSCs from monozygotic twins discordant for DS [220,234]. Silencing of one copy of chromosome 21 in trisomy 21 iPSCs can also be accomplished [221,229]. One strategy is to co-opt function of XIST, the X-inactivation gene, in the DYRK1A locus on chromosome 21, allowing the XIST non-coding RNA to coat the chromosome and silence it [221]. When one copy of chromosome 21 was silenced in trisomy 21 iPSCs, proliferation and neural rosette formation defects were rescued [221]. These strategies provide models to study gene expression changes without confounds of genetic and epigenetic background.

Although iPSCs can be differentiated into various cell types, much of the trisomy 21 iPSC research has generated cells of the nervous system to investigate underlying mechanisms of intellectual disability. Trisomy 21 iPSC-derived neural progenitor cells (NPCs) and neurons have revealed deficits in cellular and molecular processes of neural development and maturation, as a result of extra copies of Hsa21 genes. Trisomy 21 NPCs have deficits in proliferation, differentiation, and migration [204,205,220–223,229]. Trisomy 21 neurons differentiated from NPCs have fewer processes, a reduced area, increased vulnerability to oxidative stress, and synaptic defects [213,224–226]. Furthermore, trisomy 21 NPCs differentiated into fewer neurons but more astrocytes and oligodendrocytes compared with controls, suggesting deficits in neurogenesis and a shift in the timing of the neuron–glial switch [154,219]. Compared with isogenic controls, trisomy 21 cells have decreased numbers of synapses, exhibit slower proliferation of neural progenitors, develop more double-stranded DNA breaks, and have increased Aβ levels, number of mitochondria, and markers of oxidative stress [218,226]. Transcriptomic analysis of iPSC-derived cells reveals that an additional copy of Hsa21 causes the differential expression of genes throughout the genome. Pathway analysis indicates changes in embryonic development, organ development, nervous system development, and cell adhesion along with reduced proliferation and increased apoptosis modelled in this system [220,226,229,234,235].

Trisomy 21 iPSC models have also been used to study the early pathogenic phenotypes associated with AD [55,217,218,224,236–240]. Trisomy 21 iPSC-derived neurons and hESC-derived neurons, develop AD pathology including Aβ and pTau accumulation [187,189,224,241,242]. Trisomy 21 iPSC-derived cortical neurons have increased insoluble Aβ, accumulate amyloid deposits [217,224], have increased hyperphosphorylated tau, and show that tau dissociates from axonal microtubules and relocalises to the cell body and dendrites, which are key pathological hallmarks of AD [217,224]. Ovchinnikov et al. used CRISPR methodology to delete the additional copy of APP in Trisomy 21 iPSCs and to up-regulate APP in euploid cells, showing the additional copy of APP is responsible for increased Aβ and the altered Aβ42/40 ratio that occurs in this model but is not responsible for tau-related phenotypes or increased apoptosis [213]. While iPSCs have been valuable in understanding DS and AD, neurons differentiated from iPSCs are functionally immature and do not retain age markers, limiting their use as a model for age-related aspects of AD [243].

Three-dimensional cell cultures

While monolayer cultures provide insight into disease onset, progression, and drug discovery, they fail to recapitulate the dimensionality and complex circuitry of the brain. Three-dimensional organoid cultures derived from PSCs better model the brain in vitro and have been used to model AD phenotypes. With the overexpression of APP or PSEN1 with fAD mutations, organoids accumulate Aβ plaques and aggregates of phosphorylated tau along with revealing that GSK3 regulates Aβ-mediated tau phosphorylation [244]. 3D organoid cultures of neurons respond to the addition of exogenous Aβ whereas 2D neuron cultures do not [245]. Kim et al. report Aβ aggregation after 6 weeks of differentiation and tau pathology after 10–14 weeks using organoids that overexpress APP or PSEN1 with fAD mutations [246]. Using fAD patient-derived iPSCs with an APP duplication or mutation in PSEN1, Raja et al. found Aβ aggregation, hyperphosphorylated tau, and endosome abnormalities occur in an age-dependent manner in self-organising organoids [247]. To elucidate effects of glial cell types, Park et al. used a 3D triculture of AD-derived neurons and astrocytes with adult microglia in which Aβ and pTau accumulate and there is neuroinflammatory activity [248]. Thus, these 3D models exhibit features of AD that 2D cultures cannot.

Cerebral organoids generated from trisomy 21 iPSCs are smaller in size with decreased proliferation and fewer cortical neurons [55,204]. The DSCAM/PAK1 pathway, which regulates proliferation and is more active in DS, can be regulated with CRISPR interference (CRISPRi) and help normalise the size of the organoids [204]. Epigenetic ageing measured by Horvath clock DNA methylation is accelerated in DS organoids [249], concordant with the accelerated ageing hallmarks observed in DS tissue [250]. Recent work from Xu et al., indicated that the Hsa21-encoded OLIG2 transcription factor causes an overproduction of progenitor cells and GABAergic interneurons [208]. Organoids will likely be more prevalent for assessing neurodevelopmental defects in DS in the future.

Recently, DS organoids have been used to study DS-AD. Organoids generated from iPSCs with fAD mutations or trisomy 21 accumulate structures similar to Aβ plaques and NFTs [212]. Similarly, Alić et al. reported Aβ deposits, hyperphosphorylated tau, and premature neuron loss in organoids derived from trisomy 21 iPSCs [55]. 3D organoids provide a better structural model of the brain and result in more mature cells, potentially making them a better model for DS-AD.

Induced neurons

A key limitation of iPSC-derived cells is that they are developmentally immature, presenting a challenge to reflect age-dependent pathological features when modelling age-related diseases, such as AD. To better model age-related diseases, induced neurons (iNs) are directly reprogrammed into neurons from an affected individual’s somatic cells or iPSCs, skipping the NPCs stage [251,252]. Different neuron subtypes, including dopaminergic, motor, excitatory, inhibitory, serotonergic, cholinergic, and peripheral sensory neurons [236,251,253–265] induced by overexpressing specific combinations of transcription factors can currently be generated [266]. iNs that are converted directly from somatic cells maintain the individual’s epigenetic background at the time of cell collection, making them a valuable model for studying age-related neurodegeneration [267–271]. Mertens et al. report that AD iNs retain age markers of the donor individual, have a down-regulation of mature neuronal markers, and have up-regulation of immature neuron and progenitor-like pathways [196]. AD iPSC-derived neurons had no significant disease-related transcriptome signatures [196], corroborating earlier findings that excitatory iNs retain age-related signatures compared with iPSC-derived neurons from the same individuals [272]. Wang et al. used iNs for high-throughput screening to identify potential a drug candidate for AD that would lower tau [273]. Trisomy 21 iNs have the characteristic overexpression of Hsa21 genes at both the RNA and protein level, along with increased Aβ and pTau, increased synaptic vesicle release, and dysregulation of axonal transport [274]. Trisomy 21 iNs also show aneuploidy-associated stress response, dysregulated protein homoeostasis, up-regulation of the endoplasmic reticulum stress pathway, and increased cell death [275]. Treatment of iNs with 4-phenylbutyrate decreased protein aggregates and reduced cell apoptosis in the Ts21 iNs, suggesting that the aneuploidy stress may be a target for neurodegeneration in DS and DS-AD [275]. As a relatively new model, iNs have thus far yielded limited data on disease onset and progression in AD and DS-AD. Moreover, currently isogenic controls for Trisomy 21 iNs are lacking, and further refinement of this technology will ensure its utility to study DS and DS-AD.

Potential applications

Mouse – iPSC chimera

Mouse – iPSC chimeric models have been used to study both DS and AD fundamental mechanisms. This approach permits the long-term growth of human cells and favours the development of complex synaptic architecture. Moreover, this combinatorial system negates the limitation of non-physiological oxygen concentrations in in vitro cellular systems while permitting the modelling of human-specific biology. Typically, iPSC-derived precursor cells are injected into the brain of recipient animals, but recently a more mature cell population isolated from organoids has been used [208]. In some systems Rag2−/− and/or Il2rγ−/− mice are used to facilitate long-term maintenance of engraftment of cells by suppression of the recipient’s natural immune response to the introduced human cells, a technique first developed for hematopoietic system chimeras [276].

This chimeric approach has been used to demonstrate trisomy 21-specific changes in dendritic stability and neuronal activity [211]. Human neuronal engraftment was also used to study the role of the Hsa21 gene OLIG2 in trisomy 21-associated learning and memory deficits via the gene’s role in GABAergic neuronal development, as had been previously reported in mice [208,277]. In AD research, a similar approach was used to understand how human neurons respond to the accumulation of Aβ [278]. In more recent years these techniques have been developed to permit the engraftment of other cell types, most notably microglia, addressing limitations of current mouse models to recapitulate key features of AD neuroinflammation. Successful long-term engraftment of this cell type, necessary to understand ageing effects, requires that the recipient mouse is both immunocompromised (Rag2−/−Il2rγ−/−) and also expresses human CSF1 (macrophage differentiation cytokine) [279]. This approach has been used to identify species-specific differences in the response of microglia to Aβ and further elucidate the role of the AD-risk gene TREM2 [279].

Notably, these model systems are highly complex and the proliferation, survival, and differentiation of human cells after injection can vary significantly between studies with each human graft containing a different mixture of cell types [280,281]. Moreover, typically in these systems, the mouse cells are not fully replaced by the engrafted human cells which only compose a small fraction of the total brain. Mosaicism may limit the manifestation and interpretation of phenotypes in these models. Depletion of the key cell type of interest in the recipient animal could be used to mitigate this limitation. For example, diphtheria toxin receptor (DTR) expression in the lineage of interest could be utilized to ablate the cells and create a niche which can then be populated by engrafted iPSCs [282].

iPSCs use in drug screening

Although numerous promising results of AD treatment have been obtained in animal models, there are very few medications available to treat patients, and those that are available have poor efficacy. For example, the efficacy of the recently FDA-approved immunotherapy drug aducanumab that targets Aβ is questionable [283,284]. Progress using animal model-driven drug screening approaches is very slow, with large failure rates, reflecting the limitations of these models. Primary human cells can therefore be an attractive option for drug screening [285,286]. However, due to the post-mitotic nature of many types of primary cells such as neurons and invasive procedures of cell extraction, accessing and obtaining enough primary cells can be challenging [287]. Cells differentiated from iPSCs derived from patients are a useful model for drug screening because of the patient-specific genetic background, ability to engineer isogenic controls, and ability to produce large numbers of cells [287]. Using cortical neurons differentiated from AD patient iPSCs, Kondo et al. conducted an anti-Aβ drug screen and identified a combination of compounds that may be useful for treating the earliest stages of AD [288]. More recently, through deleting one copy of Hsa21 gene BACE2 by CRISPR-Cas9 in AD pathology-free cerebral organoids differentiated from human trisomy 21 iPSCs, Alić et al. reported an induction of AD pathology, demonstrating that BACE2 has a protective role against AD, which could be a therapeutic target [55]. These findings also indicate that DS organoids can be a useful tool for hypothesis-free drug screening [55].

Although the use of iPSCs in drug screening has begun to identify potential drugs targeting AD, limitations of this model should not be ignored. For instance, since iPSCs are reprogrammed cells, they are epigenetically and phenotypically young and unable to well model all aspects of age-related neurodegenerative diseases, such as LOAD [287,289]. Moreover, maintenance and differentiation of iPSCs as well as validating cells differentiated from iPSCs is costly and requires a significant amount of effort [290]. Lastly, culture conditions and passage number can significantly affect phenotype, data consistency, and reproducibility [291].

Stem cell therapies

With advancements in stem cell culture, human stem cells have become a focus of potential transplantation therapies for neurological disorders [292]. NSCs from foetal tissue transplanted into an AD mouse model reduced amyloid plaques via recruitment of activated microglia and improved performance on hippocampus-related memory tasks [293]. hESCs differentiated into BFCNs have been shown to ameliorate memory and learning deficits when transplanted into AD mouse models, showing that this subset of neurons plays a critical role and could be the target of potential therapeutics for neurological disorders, including DS and AD [294–296].

While transplants have been successful in mouse models and have provided insight into disease mechanisms, there is no evidence that current transplants are beneficial in humans. Lacking online regulation, clinics are marketing stem cell therapies with remarkable outcomes that lack results and evidence from well-controlled trials [297]. Recently, a clinic in India claimed to have successfully used stem cell transplants to treat DS in up to 14 individuals [298]. However, it is currently unknown if or how stem cells can be used to treat the genetic disorder, making it unlikely that this treatment will be beneficial but will likely put these individuals at risk of transplant-related side effects [298]. In another report, doctors injected hESCs into a child with DS, who presented with deficits in speech, motor skills and had delayed developmental milestones [299]. The report claims the child had improvements in understanding, recognition, and muscle tone and that the hESCs could have induced normal neurogenesis in the brain improving the deficits resulting from DS. However, there were no controls used in this study and no data to suggest the correction of neurogenesis [299]. Advertisements and studies claiming beneficial results of stem cell therapies can mislead individuals and their families looking for treatment options. Until we have a better understanding of the underlying mechanisms of these conditions and how to correct these alterations, cell transplants are not a beneficial treatment for DS or DS-AD in humans.

Conclusion

Less than two decades since human iPSCs were first introduced [176–178,203], the field of disease modelling has been revolutionised and is fast developing. Compared with other preclinical models such as mouse, patient-derived iPSCs have a number of advantages for the study of human disease mechanisms. Most importantly, compared with animal studies these human-derived systems conserve fundamental human genetics and biology that may not be recapitulated in preclinical model species (such as mice and rats), thus research either in vitro or in combinatorial chimeric systems is likely to have high translational relevance. Moreover, iPSCs are relatively easy to obtain and have fewer ethical concerns compared with other models, such as foetal tissue, hESCs and animals [300]. Notably, in vitro iPSC research has considerable 3Rs (Replacement, Reduction, and Refinement) benefits and is likely to significantly reduce the number of animals used in medical research but not completely replace the need for in vivo research [301]. In DS-AD research, key applications include the understanding of the role of glial cells in disease pathogenesis, as key aspects of both astrocytes and microglia biology differ between mouse and human. In vitro iPSC and organoid research are also important for the replacement of in vivo research that has a particularly high animal welfare burden, such as the study of hyperexcitability and seizures in DS-AD.

However, due to the immature nature of iPSC-derived cells, it is challenging to reflect age-dependent pathological features when modelling age-related diseases, such as AD. Additionally, the majority of AD iPSC models contain fAD causal mutations which are a relatively rare cause of the disease [302]. Moreover, although the problem of heterogeneity between disease modelling and healthy control iPSCs has been largely addressed by generating isogenic controls through genome editing such as by the use of CRISPR-Cas9 technology, off-target effects of gene editing and the key role of epigenetic variations should not be ignored [303]. Despite the considerable achievements in DS-AD modelling using iPSCs, this new model of disease is still in its early stages and will have numerous obstacles to overcome. In the foreseeable future, exploring mechanisms of DS-AD will be dependent on both animal and cell models. Nevertheless, with the continuous development of techniques such as genome editing, mouse-iPSC chimeras, 3D cell culture, and multiomics, iPSC-based studies will shed more light on discovering the pathomechanisms of DS-AD and provide an efficient and reliable platform for translational medicine.

Funding

This work was supported by the U.K. Dementia Research Institute, which receives its funding from DRI Ltd, funded by the U.K. Medical Research Council, Alzheimer’s Society and Alzheimer’s Research U.K. [grant number UKDRI-1014 (to F.K.W)]; the Alzheimer’s Research U.K. Senior Research Fellowship [grant number ARUK-SRF2018A-001 (to F.K.W)]; the Royal Society Research Grant [grant number RGS\R1\211219 (to F.K.W)]; the Wisconsin Partnership Program New Investigator Program, University of Wisconsin Alzheimer’s Disease Research Center REC Scholar Award (to A.B.); the Jerome LeJeune Foundation [grant number #1910 (to A.B.)]; the National Institute of Child Health and Human Development [grant numbers 1R01HD106197, U54 HD090256, P50HD105353 (to A.B.)]; and the Wisconsin Alumni Research Foundation (to A.B.).

Abbreviations

AD

Alzheimer’s disease

ADAMTS1

ADAM metallopeptidase with thrombospondin type 1 motif 1

APOE

apolipoprotein E

APP

amyloid precursor protein

amyloid-β

BACE2

β-Secretase 2

BBB

blood–brain barrier

BFCN

basal forebrain cholinergic neuron

Cas9

CRISPR-associated protein 9

CRISPR

clustered regularly interspaced short palindromic repeats

DS

Down syndrome

DS-AD

Alzheimer’s disease in Down syndrome

DYRK1A

dual-specificity tyrosine phosphorylation-regulated kinase 1A

EOAD

early-onset AD

fAD

familial Alzheimer’s disease

GSK3

glycogen synthase kinase 3

hBMEC

human brain microvascular endothelial cell

hCMEC

human cerebral microvascular endothelial cell

HEK293

human embryonic kidney cell

hESC

human embryonic stem cell

Hsa21

human chromosome 21

GABA

gamma-aminobutyric acid

iN

induced neuron

iPSC

induced pluripotent stem cell

LOAD

late-onset AD

MAPT

microtubule-associated protein tau

NFT

neurofibrillary tangle

NGF

nerve growth factor

Mmu

Mus musculus chromosome

NPC

neural progenitor cell

NSC

neural stem cell

PSC

pluripotent stem cell

PSEN1/2

presenilin 1/2

sAD

sporadic AD

Trisomy 21

full or partial copy of chromosome 21

Contributor Information

Anita Bhattacharyya, Email: bhattacharyy@waisman.wisc.edu.

Frances K. Wiseman, Email: f.wiseman@ucl.ac.uk.

Competing Interests

The authors declare that there are no competing interests associated with the manuscript.

Open Access

Open access for this article was enabled by the participation of MRC Laboratory of Molecular Biology in an all-inclusive Read & Publish agreement with Portland Press and the Biochemical Society under a transformative agreement with JISC.

References

  • 1.Lejeune J., Gauthier M. and Turpin R. (1959) Human chromosomes in tissue cultures. C. R. Hebd. Seances Acad. Sci. 248, 602–603 [PubMed] [Google Scholar]
  • 2.Lejeune J., Gautier M. and Turpin R. (1959) Study of somatic chromosomes from 9 mongoloid children. C. R. Hebd. Seances Acad. Sci. 248, 1721–1722 [PubMed] [Google Scholar]
  • 3.Hassold T. and Hunt P. (2001) To err (meiotically) is human: the genesis of human aneuploidy. Nat. Rev. Genet. 2, 280–291 10.1038/35066065 [DOI] [PubMed] [Google Scholar]
  • 4.Fidler D.J. and Nadel L. (2007) Education and children with Down syndrome: neuroscience, development, and intervention. Ment. Retard. Dev. Disabil. Res. Rev. 13, 262–271 10.1002/mrdd.20166 [DOI] [PubMed] [Google Scholar]
  • 5.Hattori M.et al. (2000) The DNA sequence of human chromosome 21. Nature 405, 311–319 10.1038/35012518 [DOI] [PubMed] [Google Scholar]
  • 6.Dierssen M. (2012) Down syndrome: the brain in trisomic mode. Nat. Rev. Neurosci. 13, 844–858 10.1038/nrn3314 [DOI] [PubMed] [Google Scholar]
  • 7.Antonarakis S.E.et al. (2020) Down syndrome. Nat. Rev. Dis. Primers 6, 9 10.1038/s41572-019-0143-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Haydar T.F. and Reeves R.H. (2012) Trisomy 21 and early brain development. Trends Neurosci. 35, 81–91 10.1016/j.tins.2011.11.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Pinter J.D.et al. (2001) Neuroanatomy of Down’s syndrome: a high-resolution MRI study. Am. J. Psychiatry 158, 1659–1665 10.1176/appi.ajp.158.10.1659 [DOI] [PubMed] [Google Scholar]
  • 10.Baburamani A.A.et al. (2019) New approaches to studying early brain development in Down syndrome. Dev. Med. Child Neurol. 61, 867–879 10.1111/dmcn.14260 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Guihard-Costa A.-M.et al. (2006) Biometry of face and brain in fetuses with trisomy 21. Pediatr. Res. 59, 33–38 10.1203/01.pdr.0000190580.88391.9a [DOI] [PubMed] [Google Scholar]
  • 12.Kemper T.L. (1991) Down Syndrome, in Normal and Altered States of Function(Peters A. and Jones E.G., eds), pp. 511–526, Springer, Boston, MA, U.S.A. [Google Scholar]
  • 13.Ross M.H., Galaburda A.M. and Kemper T.L. (1984) Down’s syndrome: is there a decreased population of neurons? Neurology 34, 909–916 10.1212/WNL.34.7.909 [DOI] [PubMed] [Google Scholar]
  • 14.Golden J.A. and Hyman B.T. (1994) Development of the superior temporal neocortex is anomalous in trisomy 21. J. Neuropathol. Exp. Neurol. 53, 513–520 10.1097/00005072-199409000-00011 [DOI] [PubMed] [Google Scholar]
  • 15.Davidoff L.M. (1928) The brain in mongolian idiocy: a report of ten cases. Arch. Neurol. Psychiatry 20, 1229–1257 10.1001/archneurpsyc.1928.02210180080004 [DOI] [Google Scholar]
  • 16.Becker L.et al. (1991) Growth and development of the brain in Down syndrome. Prog. Clin. Biol. Res. 373, 133–152 [PubMed] [Google Scholar]
  • 17.Kesslak J.P.et al. (1994) Magnetic resonance imaging analysis of age‐related changes in the brains of individuals with Down’s syndrome. Neurology 44, 1039–1039 10.1212/WNL.44.6.1039 [DOI] [PubMed] [Google Scholar]
  • 18.Schmidt-Sidor B.et al. (1990) Brain growth in Down syndrome subjects 15 to 22 weeks of gestational age and birth to 60 months. Clin. Neuropathol. 9, 181–190 [PubMed] [Google Scholar]
  • 19.Wisniewski K.E. (1990) Down syndrome children often have brain with maturation delay, retardation of growth, and cortical dysgenesis. Am. J. Med. Genet. 37, 274–281 10.1002/ajmg.1320370755 [DOI] [PubMed] [Google Scholar]
  • 20.Crome L.S.J., Stern J., 1967, Pathology of mental retardation, Little, Brown and Company, Boston, Mass, USA. [Google Scholar]
  • 21.Benda C. (1946) Mongolism and cretinism. Mongolism and cretinism, Grune and Stratton, New York, USA. [Google Scholar]
  • 22.Colon E.J. (1972) The structure of the cerebral cortex in Down’s syndrome: a quantitative analysis. Neuropediatrics 3 362–376 10.1055/s-0028-1091775 [DOI] [Google Scholar]
  • 23.Dementia (2020) Fact sheets. https://www.who.int/news-room/fact-sheets/detail/dementia
  • 24.Burns A. and Iliffe S. (2009) Alzheimer’s disease. BMJ 338, b158 10.1136/bmj.b158 [DOI] [PubMed] [Google Scholar]
  • 25.Serrano-Pozo A.et al. (2011) Neuropathological alterations in Alzheimer disease. Cold Spring Harb. Perspect. Med. 1, 1-24 10.1101/cshperspect.a006189 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Alzheimer A.et al. (1995) An English translation of Alzheimer’s 1907 paper, “Uber eine eigenartige Erkankung der Hirnrinde”. Clin. Anat. 8, 429–431 10.1002/ca.980080612 [DOI] [PubMed] [Google Scholar]
  • 27.Harman D. (2006) Alzheimer’s disease pathogenesis. Ann. N.Y. Acad. Sci. 1067, 454–460 10.1196/annals.1354.065 [DOI] [PubMed] [Google Scholar]
  • 28.Sun X., Chen W.-D. and Wang Y.-D. (2015) β-Amyloid: the key peptide in the pathogenesis of Alzheimer’s disease. Front. Pharmacol. 6, 1-9 10.3389/fphar.2015.00221 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Weggen S. and Beher D. (2012) Molecular consequences of amyloid precursor protein and presenilin mutations causing autosomal-dominant Alzheimer’s disease. Alzheimers Res. Ther. 4, 9 10.1186/alzrt107 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Bertram L. and Tanzi R.E. (2019) Alzheimer disease risk genes: 29 and counting. Nat. Rev. Neurol. 15, 191–192 10.1038/s41582-019-0158-4 [DOI] [PubMed] [Google Scholar]
  • 31.Farrer L.A.et al. (1997) Effects of age, sex, and ethnicity on the association between apolipoprotein E genotype and Alzheimer disease: a meta-analysis. JAMA 278, 1349–1356 10.1001/jama.1997.03550160069041 [DOI] [PubMed] [Google Scholar]
  • 32.Harold D.et al. (2009) Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer’s disease. Nat. Genet. 41, 1088–1093 10.1038/ng.440 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Kunkle B.W.et al. (2019) Genetic meta-analysis of diagnosed Alzheimer's disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nat. Genet. 51, 414–430 10.1038/s41588-019-0358-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.De Strooper B. and Karran E. (2016) The cellular phase of Alzheimer’s disease. Cell 164, 603–615 10.1016/j.cell.2015.12.056 [DOI] [PubMed] [Google Scholar]
  • 35.Karch C.M. and Goate A.M. (2015) Alzheimer’s disease risk genes and mechanisms of disease pathogenesis. Biol. Psychiatry 77, 43–51 10.1016/j.biopsych.2014.05.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Leverenz J.B. and Raskind M.A. (1998) Early amyloid deposition in the medial temporal lobe of young Down syndrome patients: a regional quantitative analysis. Exp. Neurol. 150, 296–304 10.1006/exnr.1997.6777 [DOI] [PubMed] [Google Scholar]
  • 37.Mann D.M. (1988) Alzheimer’s disease and Down’s syndrome. Histopathology 13, 125–137 10.1111/j.1365-2559.1988.tb02018.x [DOI] [PubMed] [Google Scholar]
  • 38.Wiseman F.K.et al. (2015) A genetic cause of Alzheimer disease: mechanistic insights from Down syndrome. Nat. Rev. Neurosci. 16, 564–574 10.1038/nrn3983 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.McCarron M.et al. (2014) A prospective 14-year longitudinal follow-up of dementia in persons with Down syndrome. J. Intellect. Disabil. Res. 58, 61–70 10.1111/jir.12074 [DOI] [PubMed] [Google Scholar]
  • 40.Carmona-Iragui M.et al. (2021) Diagnostic and prognostic performance and longitudinal changes in plasma neurofilament light chain concentrations in adults with Down syndrome: a cohort study. Lancet Neurol. 20, 605–614 10.1016/S1474-4422(21)00129-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Altuna M., Gimenez S. and Fortea J. (2021) Epilepsy in Down syndrome: a highly prevalent comorbidity. J. Clin. Med. 10, 1-17 10.3390/jcm10132776 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Sleegers K.et al. (2006) APP duplication is sufficient to cause early onset Alzheimer’s dementia with cerebral amyloid angiopathy. Brain 129, 2977–2983 10.1093/brain/awl203 [DOI] [PubMed] [Google Scholar]
  • 43.Rovelet-Lecrux A.et al. (2006) APP locus duplication causes autosomal dominant early-onset Alzheimer disease with cerebral amyloid angiopathy. Nat. Genet. 38, 24–26 10.1038/ng1718 [DOI] [PubMed] [Google Scholar]
  • 44.Prasher V.P.et al. (1998) Molecular mapping of Alzheimer-type dementia in Down’s syndrome. Ann. Neurol. 43, 380–383 10.1002/ana.410430316 [DOI] [PubMed] [Google Scholar]
  • 45.Doran E.et al. (2017) Down Syndrome, Partial Trisomy 21, and absence of Alzheimer’s disease: the role of APP. J. Alzheimers Dis. 56, 459–470 10.3233/JAD-160836 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Abrahamson E.E.et al. (2019) Neuropathological correlates of amyloid PET imaging in Down syndrome. Dev. Neurobiol. 79, 750–766 10.1002/dneu.22713 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Hartley S.L.et al. (2014) Cognitive functioning in relation to brain amyloid-beta in healthy adults with Down syndrome. Brain 137, 2556–2563 10.1093/brain/awu173 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Mak E.et al. (2019) Longitudinal trajectories of amyloid deposition, cortical thickness, and tau in Down syndrome: a deep-phenotyping case report. Alzheimers Dement. (Amst.) 11, 654–658 10.1016/j.dadm.2019.04.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Zammit M.D.et al. (2020) Amyloid accumulation in Down syndrome measured with amyloid load. Alzheimers Dement. (Amst.) 12, e12020 10.1002/dad2.12020 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Mann D.M.A.et al. (2018) Patterns and severity of vascular amyloid in Alzheimer’s disease associated with duplications and missense mutations in APP gene, Down syndrome and sporadic Alzheimer’s disease. Acta Neuropathol. 136, 569–587 10.1007/s00401-018-1866-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Woods Y.L.et al. (2001) The kinase DYRK phosphorylates protein-synthesis initiation factor eIF2Bepsilon at Ser539 and the microtubule-associated protein tau at Thr212: potential role for DYRK as a glycogen synthase kinase 3-priming kinase. Biochem. J. 355, 609–615 10.1042/bj3550609 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Sheppard O.et al. (2012) Altered regulation of tau phosphorylation in a mouse model of down syndrome aging. Neurobiol. Aging 33, 828.e31–828.e44 10.1016/j.neurobiolaging.2011.06.025 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Garcia-Cerro S.et al. (2017) Normalizing the gene dosage of Dyrk1A in a mouse model of Down syndrome rescues several Alzheimer’s disease phenotypes. Neurobiol. Dis. 106, 76–88 10.1016/j.nbd.2017.06.010 [DOI] [PubMed] [Google Scholar]
  • 54.Cossec J.C.et al. (2012) Trisomy for synaptojanin1 in Down syndrome is functionally linked to the enlargement of early endosomes. Hum. Mol. Genet. 21, 3156–3172 10.1093/hmg/dds142 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Alić I.et al. (2021) Patient-specific Alzheimer-like pathology in trisomy 21 cerebral organoids reveals BACE2 as a gene dose-sensitive AD suppressor in human brain. Mol. Psychiatry, 26, 5766–5788, 10.1038/s41380-020-0806-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Yang D.S.et al. (2011) Reversal of autophagy dysfunction in the TgCRND8 mouse model of Alzheimer's disease ameliorates amyloid pathologies and memory deficits. Brain 134, 258–277 10.1093/brain/awq341 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Wu Y.et al. (2021) The effects of Cstb duplication on APP/amyloid-β pathology and cathepsin B activity in a mouse model. PLoS ONE 16, e0242236 10.1371/journal.pone.0242236 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Lockstone H.E.et al. (2007) Gene expression profiling in the adult Down syndrome brain. Genomics 90, 647–660 10.1016/j.ygeno.2007.08.005 [DOI] [PubMed] [Google Scholar]
  • 59.Bejanin A.et al. (2021) Association of apolipoprotein E ɛ4 allele with clinical and multimodal biomarker changes of Alzheimer disease in adults with Down syndrome. JAMA Neurol. 78, 937–947 10.1001/jamaneurol.2021.1893 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Coppus A.M.et al. (2008) The impact of apolipoprotein E on dementia in persons with Down’s syndrome. Neurobiol. Aging 29, 828–835 10.1016/j.neurobiolaging.2006.12.013 [DOI] [PubMed] [Google Scholar]
  • 61.Deb S.et al. (2000) APOE epsilon 4 influences the manifestation of Alzheimer’s disease in adults with Down’s syndrome. Br. J. Psychiatry 176, 468–472 10.1192/bjp.176.5.468 [DOI] [PubMed] [Google Scholar]
  • 62.Hithersay R.et al. (2019) Association of dementia with mortality among adults with Down syndrome older than 35 years. JAMA Neurol. 76, 152–160 10.1001/jamaneurol.2018.3616 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Hyman B.T.et al. (1995) Quantitative analysis of senile plaques in Alzheimer disease: observation of log-normal size distribution and molecular epidemiology of differences associated with apolipoprotein E genotype and trisomy 21 (Down syndrome). Proc. Natl. Acad. Sci. U.S.A. 92, 3586–3590 10.1073/pnas.92.8.3586 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64.Patel A.et al. (2011) Association of variants within APOE, SORL1, RUNX1, BACE1 and ALDH18A1 with dementia in Alzheimer’s disease in subjects with Down syndrome. Neurosci. Lett. 487, 144–148 10.1016/j.neulet.2010.10.010 [DOI] [PubMed] [Google Scholar]
  • 65.Prasher V.P.et al. (2008) Significant effect of APOE epsilon 4 genotype on the risk of dementia in Alzheimer’s disease and mortality in persons with Down syndrome. Int. J. Geriatr. Psychiatry 23, 1134–1140 10.1002/gps.2039 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.Silverman W.P.et al. (2013) Intellectual disability, mild cognitive impairment, and risk for dementia. J. Policy Pract. Intellect. Disabil. 10, 245–251, 10.1111/jppi.12042 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 67.Esquerda-Canals G.et al. (2017) Mouse models of Alzheimer’s disease. J. Alzheimers Dis. 57, 1171–1183 10.3233/JAD-170045 [DOI] [PubMed] [Google Scholar]
  • 68.Friedman B.A.et al. (2018) Diverse brain myeloid expression profiles reveal distinct microglial activation states and aspects of Alzheimer’s disease not evident in mouse models. Cell Rep. 22, 832–847 10.1016/j.celrep.2017.12.066 [DOI] [PubMed] [Google Scholar]
  • 69.Davisson M.T.et al. (2001) Evolutionary breakpoints on human chromosome 21. Genomics 78, 99–106 10.1006/geno.2001.6639 [DOI] [PubMed] [Google Scholar]
  • 70.Tybulewicz V.L.J. and Fisher E.M.C. (2006) New techniques to understand chromosome dosage: mouse models of aneuploidy. Hum. Mol. Genet. 15, R103–R109 10.1093/hmg/ddl179 [DOI] [PubMed] [Google Scholar]
  • 71.Choong X.Y.et al. (2015) Dissecting Alzheimer disease in Down syndrome using mouse models. Front. Behav. Neurosci. 9, 1–24 10.3389/fnbeh.2015.00268 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 72.Davisson M.T., Schmidt C. and Akeson E.C. (1990) Segmental trisomy of murine chromosome 16: a new model system for studying Down syndrome. Prog. Clin. Biol. Res. 360, 263–280 [PubMed] [Google Scholar]
  • 73.Duchon A.et al. (2011) Identification of the translocation breakpoints in the Ts65Dn and Ts1Cje mouse lines: relevance for modeling Down syndrome. Mamm. Genome 22, 674–684 10.1007/s00335-011-9356-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 74.Reeves R.H.et al. (1995) A mouse model for Down syndrome exhibits learning and behaviour deficits. Nat. Genet. 11, 177–184 10.1038/ng1095-177 [DOI] [PubMed] [Google Scholar]
  • 75.Holtzman D.M.et al. (1996) Developmental abnormalities and age-related neurodegeneration in a mouse model of Down syndrome. Proc. Natl. Acad. Sci. U.S.A. 93, 13333–13338 10.1073/pnas.93.23.13333 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 76.Salehi A.et al. (2006) Increased App expression in a mouse model of Down’s syndrome disrupts NGF transport and causes cholinergic neuron degeneration. Neuron 51, 29–42 10.1016/j.neuron.2006.05.022 [DOI] [PubMed] [Google Scholar]
  • 77.Yin X.et al. (2017) Dyrk1A overexpression leads to increase of 3R-tau expression and cognitive deficits in Ts65Dn Down syndrome mice. Sci. Rep. 7, 619 10.1038/s41598-017-00682-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 78.Belichenko P.V.et al. (2016) An anti-β-amyloid vaccine for treating cognitive deficits in a mouse model of Down syndrome. PLoS ONE 11, e0152471 10.1371/journal.pone.0152471 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 79.Shaw P.R.et al. (2020) Longitudinal neuroanatomical and behavioral analyses show phenotypic drift and variability in the Ts65Dn mouse model of Down syndrome. Dis. Model Mech. 13, 10.1242/dmm.046243 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 80.Li Z.et al. (2007) Duplication of the entire 22.9 Mb human chromosome 21 syntenic region on mouse chromosome 16 causes cardiovascular and gastrointestinal abnormalities. Hum. Mol. Genet. 16, 1359–1366 10.1093/hmg/ddm086 [DOI] [PubMed] [Google Scholar]
  • 81.Yu T.et al. (2010) A mouse model of Down syndrome trisomic for all human chromosome 21 syntenic regions. Hum. Mol. Genet. 19, 2780–2791 10.1093/hmg/ddq179 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 82.Duchon A.et al. (2008) Inducing segmental aneuploid mosaicism in the mouse through targeted asymmetric sister chromatid event of recombination. Genetics 180, 51–59 10.1534/genetics.108.092312 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 83.Raveau M.et al. (2012) The App-Runx1 region is critical for birth defects and electrocardiographic dysfunctions observed in a Down syndrome mouse model. PLoS Genet. 8, e1002724 10.1371/journal.pgen.1002724 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 84.Tosh J.L.et al. (2021) Genetic dissection of down syndrome-associated alterations in APP/amyloid-β biology using mouse models. Sci. Rep. 11, 5736 10.1038/s41598-021-85062-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 85.Serneels L.et al. (2020) Modeling the β-secretase cleavage site and humanizing amyloid-beta precursor protein in rat and mouse to study Alzheimer's disease. Mol. Neurodegener. 15, 60 10.1186/s13024-020-00399-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 86.Baglietto-Vargas D.et al. (2021) Generation of a humanized Aβ expressing mouse demonstrating aspects of Alzheimer’s disease-like pathology. Nat. Commun. 12, 2421 10.1038/s41467-021-22624-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 87.Gribble S.M.et al. (2013) Massively parallel sequencing reveals the complex structure of an irradiated human chromosome on a mouse background in the Tc1 model of Down Syndrome. PLoS ONE 8, e60482 10.1371/journal.pone.0060482 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 88.Doherty A.et al. (2005) An aneuploid mouse strain carrying human chromosome 21 with Down syndrome phenotypes. Science 309, 2033 10.1126/science.1114535 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 89.Wiseman F.K.et al. (2018) Trisomy of human chromosome 21 enhances amyloid-β deposition independently of an extra copy of APP. Brain 141, 2457–2474 10.1093/brain/awy159 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 90.Roper R.J. and Reeves R.H. (2006) Understanding the basis for Down syndrome phenotypes. PLoS Genet. 2, e50 10.1371/journal.pgen.0020050 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 91.Kazuki Y.et al. (2020) A non-mosaic transchromosomic mouse model of Down syndrome carrying the long arm of human chromosome 21. eLife 9, 1–29 10.7554/eLife.56223 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 92.Saito T.et al. (2014) Single App knock-in mouse models of Alzheimer’s disease. Nat. Neurosci. 17, 661–663 10.1038/nn.3697 [DOI] [PubMed] [Google Scholar]
  • 93.Zhao X. and Bhattacharyya A. (2018) Human models are needed for studying human neurodevelopmental disorders. Am. J. Hum. Genet. 103, 829–857 10.1016/j.ajhg.2018.10.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 94.Drummond E. and Wisniewski T. (2017) Alzheimer’s disease: experimental models and reality. Acta Neuropathol. (Berl.) 133, 155–175 10.1007/s00401-016-1662-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 95.Perlman R.L. (2016) Mouse models of human disease: an evolutionary perspective. Evol. Med. Public Health 2016, 170–176 10.1093/emph/eow014 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 96.Jankowsky J.L. and Zheng H. (2017) Practical considerations for choosing a mouse model of Alzheimer’s disease. Mol. Neurodegener. 12, 89 10.1186/s13024-017-0231-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 97.Yue F.et al. (2014) A comparative encyclopedia of DNA elements in the mouse genome. Nature 515, 355–364 10.1038/nature13992 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 98.Hodge R.D.et al. (2019) Conserved cell types with divergent features in human versus mouse cortex. Nature 573, 61–68 10.1038/s41586-019-1506-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 99.Li J.et al. (2021) Conservation and divergence of vulnerability and responses to stressors between human and mouse astrocytes. Nat. Commun. 12, 3958 10.1038/s41467-021-24232-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 100.Geirsdottir L.et al. (2019) Cross-species single-cell analysis reveals divergence of the primate microglia program. Cell 179, 1609–1622, e16. 10.1016/j.cell.2019.11.010 [DOI] [PubMed] [Google Scholar]
  • 101.Mao R.et al. (2003) Global up-regulation of chromosome 21 gene expression in the developing down syndrome brain. Genomics 81, 457–467 10.1016/S0888-7543(03)00035-1 [DOI] [PubMed] [Google Scholar]
  • 102.Olmos-Serrano J.L.et al. (2016) Down syndrome developmental brain transcriptome reveals defective oligodendrocyte differentiation and myelination. Neuron 89, 1208–1222 10.1016/j.neuron.2016.01.042 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 103.Motte J. and Williams R.S. (1989) Age-related changes in the density and morphology of plaques and neurofibrillary tangles in Down syndrome brain. Acta Neuropathol. (Berl.) 77, 535–546 10.1007/BF00687256 [DOI] [PubMed] [Google Scholar]
  • 104.Lemere C.et al. (1996) Sequence of deposition of heterogeneous amyloid b-peptides and APO E in Down syndrome: implications for initial events in amyloid plaque formation. Neurobiol. Dis. 3, 16–32 10.1006/nbdi.1996.0003 [DOI] [PubMed] [Google Scholar]
  • 105.Iwatsubo T.et al. (1995) Amyloid protein A deposition A 42 43 precedes A 40 in down Syndrome. Ann. Neurol. 37, 294–299 10.1002/ana.410370305 [DOI] [PubMed] [Google Scholar]
  • 106.Hof P.R.et al. (1995) Age-related distribution of neuropathologic changes in the cerebral cortex of patients with Down’s syndrome: quantitative regional analysis and comparison with Alzheimer’s disease. Arch. Neurol. 52, 379–391 10.1001/archneur.1995.00540280065020 [DOI] [PubMed] [Google Scholar]
  • 107.LeVine H. IIIet al. (2017) Down syndrome: age-dependence of PiB binding in postmortem frontal cortex across the lifespan. Neurobiol. Aging 54, 163–169 10.1016/j.neurobiolaging.2017.03.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 108.Sadowski M.et al. (1999) Entorhinal cortex of aged subjects with Down's syndrome shows severe neuronal loss caused by neurofibrillary pathology. Acta Neuropathol. 97, 156–164 10.1007/s004010050968 [DOI] [PubMed] [Google Scholar]
  • 109.Gómez-Isla T.et al. (1996) Profound loss of layer II entorhinal cortex neurons occurs in very mild Alzheimer’s disease. J. Neurosci. 16, 4491–4500 10.1523/JNEUROSCI.16-14-04491.1996 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 110.Coskun P.E.et al. (2010) Systemic mitochondrial dysfunction and the etiology of Alzheimer’s disease and down syndrome dementia. J. Alzheimers Dis. 20, S293–S310 10.3233/JAD-2010-100351 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 111.Wang W.et al. (2020) Mitochondria dysfunction in the pathogenesis of Alzheimer’s disease: recent advances. Mol. Neurodegener. 15, 30 10.1186/s13024-020-00376-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 112.Wilcock D.M.et al. (2015) Down syndrome individuals with Alzheimer’s disease have a distinct neuroinflammatory phenotype compared to sporadic Alzheimer’s disease. Neurobiol. Aging 36, 2468–2474 10.1016/j.neurobiolaging.2015.05.016 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 113.Flores-Aguilar L.et al. (2020) Evolution of neuroinflammation across the lifespan of individuals with Down syndrome. Brain 143, 3653–3671 10.1093/brain/awaa326 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 114.Martini A.C.et al. (2020) Distribution of microglial phenotypes as a function of age and Alzheimer’s disease neuropathology in the brains of people with Down syndrome. Alzheimers Dement. (Amst.) 12, e12113 10.1002/dad2.12113 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 115.Iulita M.F.et al. (2016) An inflammatory and trophic disconnect biomarker profile revealed in Down syndrome plasma: Relation to cognitive decline and longitudinal evaluation. Alzheimers Dement. 12, 1132–1148 10.1016/j.jalz.2016.05.001 [DOI] [PubMed] [Google Scholar]
  • 116.Pascoal T.A.et al. (2021) Microglial activation and tau propagate jointly across Braak stages. Nat. Med. 27, 1592–1599 10.1038/s41591-021-01456-w [DOI] [PubMed] [Google Scholar]
  • 117.Aït Yahya-Graison E.et al. (2007) Classification of human chromosome 21 gene-expression variations in Down syndrome: impact on disease phenotypes. Am. J. Hum. Genet. 81, 475–491 10.1086/520000 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 118.Mao R.et al. (2005) Primary and secondary transcriptional effects in the developing human Down syndrome brain and heart. Genome Biol. 6, R107 10.1186/gb-2005-6-13-r107 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 119.Pelleri M.C.et al. (2018) Integrated quantitative transcriptome maps of human trisomy 21 tissues and cells. Front. Genet. 9, 125–125 10.3389/fgene.2018.00125 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 120.Hosoda R.et al. (1998) Quantification of modified amyloid β peptides in Alzheimer disease and Down syndrome brains. J. Neuropathol. Exp. Neurol. 57, 1089–1095 10.1097/00005072-199811000-00012 [DOI] [PubMed] [Google Scholar]
  • 121.FitzPatrick D.R.et al. (2002) Transcriptome analysis of human autosomal trisomy. Hum. Mol. Genet. 11, 3249–3256 10.1093/hmg/11.26.3249 [DOI] [PubMed] [Google Scholar]
  • 122.Sawa M.et al. (2021) Impact of increased APP gene dose in Down syndrome and the Dp16 mouse model. Alzheimers Dement. 1–32 10.1002/alz.12463 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 123.Palmer C.R.et al. (2021) Altered cell and RNA isoform diversity in aging Down syndrome brains. Proc. Natl. Acad. Sci. U.S.A. 118, 47, 1–11 10.1073/pnas.2114326118 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 124.Lawrence E.et al. (2020) The barriers and motivators to using human tissues for research: the views of UK-based biomedical researchers. Biopreserv. Biobank. 18, 266–273 10.1089/bio.2019.0138 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 125. (2013) Realising the potential of stratified medicine. Academy of Medical Sciences. Available from: https://acmedsci.ac.uk/viewFile/51e915f9f09fb.pdf [Google Scholar]
  • 126.Dubey S.K.et al. (2019) Recent expansions on cellular models to uncover the scientific barriers towards drug development for Alzheimer’s disease. Cell. Mol. Neurobiol. 39, 181–209 10.1007/s10571-019-00653-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 127.Milenkovic I.et al. (2018) GABA (A) receptor subunit deregulation in the hippocampus of human foetuses with Down syndrome. Brain Struct. Funct. 223, 1501–1518 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 128.Krishtal J.et al. (2017) In situ fibrillizing amyloid-beta 1-42 induces neurite degeneration and apoptosis of differentiated SH-SY5Y cells. PLoS ONE 12, e0186636 10.1371/journal.pone.0186636 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 129.Matsumoto K.et al. (2006) Overexpression of amyloid precursor protein induces susceptibility to oxidative stress in human neuroblastoma SH-SY5Y cells. J. Neural Transm. 113, 125–135 10.1007/s00702-005-0318-0 [DOI] [PubMed] [Google Scholar]
  • 130.Houck A.L., Hernández F. and Ávila J. (2016) A simple model to study tau pathology. J. Exp. Neurosci. 10, 31–38 10.4137/JEN.S25100 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 131.Li S.et al. (2018) DYRK1A interacts with histone acetyl transferase p300 and CBP and localizes to enhancers. Nucleic Acids Res. 46, 11202–11213 10.1093/nar/gky754 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 132.Soppa U.et al. (2014) The Down syndrome-related protein kinase DYRK1A phosphorylates p27(Kip1) and Cyclin D1 and induces cell cycle exit and neuronal differentiation. Cell Cycle 13, 2084–2100 10.4161/cc.29104 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 133.Tokuhiro S.et al. (1998) The presenilin 1 mutation (M146V) linked to familial Alzheimer’s disease attenuates the neuronal differentiation of NTera 2 cells. Biochem. Biophys. Res. Commun. 244, 751–755 10.1006/bbrc.1998.8336 [DOI] [PubMed] [Google Scholar]
  • 134.Mackic J.B.et al. (1998) Human blood-brain barrier receptors for Alzheimer’s 1- 40. Asymmetrical binding, endocytosis, and transcytosis at the apical side of brain microvascular endothelial cell monolayer. J. Clin. Invest. 102, 734–743 10.1172/JCI2029 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 135.Bachmeier C., Mullan M. and Paris D. (2010) Characterization and use of human brain microvascular endothelial cells to examine β-amyloid exchange in the blood-brain barrier. Cytotechnology 62, 519–529 10.1007/s10616-010-9313-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 136.Festoff B.W.et al. (2016) HMGB1 and thrombin mediate the blood-brain barrier dysfunction acting as biomarkers of neuroinflammation and progression to neurodegeneration in Alzheimer’s disease. J. Neuroinflammation 13, 194 10.1186/s12974-016-0670-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 137.Freese C.et al. (2014) A novel blood-brain barrier co-culture system for drug targeting of Alzheimer’s disease: establishment by using acitretin as a model drug. PLoS ONE 9, e91003 10.1371/journal.pone.0091003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 138.Kuo Y.-C. and Tsao C.-W. (2017) Neuroprotection against apoptosis of SK-N-MC cells using RMP-7- and lactoferrin-grafted liposomes carrying quercetin. Int. J. Nanomed. 12, 2857–2869 10.2147/IJN.S132472 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 139.Mu Q.et al. (2018) RIP140/PGC-1α axis involved in vitamin A-induced neural differentiation by increasing mitochondrial function. Artif. Cells Nanomed. Biotechnol. 46, 806–816 10.1080/21691401.2018.1436552 [DOI] [PubMed] [Google Scholar]
  • 140.Lim S.et al. (2017) Lycopene inhibits regulator of calcineurin 1-mediated apoptosis by reducing oxidative stress and down-regulating Nucling in neuronal cells. Mol. Nutr. Food Res. 61, 1600530 10.1002/mnfr.201600530 [DOI] [PubMed] [Google Scholar]
  • 141.Zheng X.et al. (2015) Intranasal H102 peptide-loaded liposomes for brain delivery to treat Alzheimer’s disease. Pharm. Res. 32, 3837–3849 10.1007/s11095-015-1744-9 [DOI] [PubMed] [Google Scholar]
  • 142.Hwang S.et al. (2021) Consequences of aneuploidy in human fibroblasts with trisomy 21. Proc. Natl. Acad. Sci. U.S.A. 118, 1–12 10.1073/pnas.2014723118 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 143.Gimeno A.et al. (2014) Decreased cell proliferation and higher oxidative stress in fibroblasts from Down Syndrome fetuses. Preliminary study. Biochim. Biophys. Acta Mol. Basis Dis. 1842, 116–125 10.1016/j.bbadis.2013.10.014 [DOI] [PubMed] [Google Scholar]
  • 144.Piccoli C.et al. (2012) Chronic pro-oxidative state and mitochondrial dysfunctions are more pronounced in fibroblasts from Down syndrome foeti with congenital heart defects. Hum. Mol. Genet. 22, 1218–1232 10.1093/hmg/dds529 [DOI] [PubMed] [Google Scholar]
  • 145.Cataldo A.M.et al. (2008) Down syndrome fibroblast model of Alzheimer-related endosome pathology: accelerated endocytosis promotes late endocytic defects. Am. J. Pathol. 173, 370–384 10.2353/ajpath.2008.071053 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 146.Bordi M.et al. (2019) mTOR hyperactivation in Down Syndrome underlies deficits in autophagy induction, autophagosome formation, and mitophagy. Cell Death Dis. 10, 563 10.1038/s41419-019-1752-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 147.Jiang Y.et al. (2019) Lysosomal dysfunction in Down syndrome is APP-dependent and mediated by APP-βCTF (C99). J. Neurosci. 39, 5255–5268 10.1523/JNEUROSCI.0578-19.2019 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 148.Colacurcio D.J.et al. (2018) Dysfunction of autophagy and endosomal-lysosomal pathways: roles in pathogenesis of Down syndrome and Alzheimer’s disease. Free Radic. Biol. Med. 114, 40–51 10.1016/j.freeradbiomed.2017.10.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 149.Jiang Y.et al. (2010) Alzheimer's-related endosome dysfunction in Down syndrome is Abeta-independent but requires APP and is reversed by BACE-1 inhibition. Proc. Natl. Acad. Sci. U.S.A. 107, 1630–1635 10.1073/pnas.0908953107 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 150.Busciglio J. and Yankner B.A. (1995) Apoptosis and increased generation of reactive oxygen species in Down’s syndrome neurons in vitro. Nature 378, 776–779 10.1038/378776a0 [DOI] [PubMed] [Google Scholar]
  • 151.Busciglio J.et al. (2002) Altered metabolism of the amyloid beta precursor protein is associated with mitochondrial dysfunction in Down’s syndrome. Neuron 33, 677–688 10.1016/S0896-6273(02)00604-9 [DOI] [PubMed] [Google Scholar]
  • 152.Lu J.et al. (2011) Generation of neural stem cells from discarded human fetal cortical tissue. J. Vis Exp. 10.3791/2681 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 153.Yin X.J., Ju R. and Feng Z.C. (2006) [Experimental study on growth, proliferation and differentiation of neural stem cell from subventricular zone of human fetal brain at different gestational age]. Zhonghua Er Ke Za Zhi 44, 500–504 [PubMed] [Google Scholar]
  • 154.Bhattacharyya A.et al. (2009) A critical period in cortical interneuron neurogenesis in down syndrome revealed by human neural progenitor cells. Dev. Neurosci. 31, 497–510 10.1159/000236899 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 155.Esposito G.et al. (2008) Genomic and functional profiling of human Down syndrome neural progenitors implicates S100B and aquaporin 4 in cell injury. Hum. Mol. Genet. 17, 440–457 10.1093/hmg/ddm322 [DOI] [PubMed] [Google Scholar]
  • 156.Avior Y., Sagi I. and Benvenisty N. (2016) Pluripotent stem cells in disease modelling and drug discovery. Nat. Rev. Mol. Cell Biol. 17, 170–182 10.1038/nrm.2015.27 [DOI] [PubMed] [Google Scholar]
  • 157.Bai X. (2020) Stem cell-based disease modeling and cell therapy. Cells 9, 2193 10.3390/cells9102193 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 158.Siller R.et al. (2013) Modelling human disease with pluripotent stem cells. Curr. Gene Ther. 13, 99–110 10.2174/1566523211313020004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 159.Sharma A.et al. (2020) Multi-lineage human iPSC-derived platforms for disease modeling and drug discovery. Cell Stem Cell 26, 309–329 10.1016/j.stem.2020.02.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 160.Tang S.et al. (2016) Patient-specific induced pluripotent stem cells for disease modeling and phenotypic drug discovery. J. Med. Chem. 59, 2–15 10.1021/acs.jmedchem.5b00789 [DOI] [PubMed] [Google Scholar]
  • 161.Singh V.K.et al. (2015) Induced pluripotent stem cells: applications in regenerative medicine, disease modeling, and drug discovery. Front. Cell Dev. Biol. 3, 1–18 10.3389/fcell.2015.00002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 162.Colman A. and Dreesen O. (2009) Pluripotent stem cells and disease modeling. Cell Stem Cell 5, 244–247 10.1016/j.stem.2009.08.010 [DOI] [PubMed] [Google Scholar]
  • 163.Cao L.et al. (2015) Induced pluripotent stem cells for disease modeling and drug discovery in neurodegenerative diseases. Mol. Neurobiol. 52, 244–255 10.1007/s12035-014-8867-6 [DOI] [PubMed] [Google Scholar]
  • 164.Marchetto M.C.et al. (2011) Induced pluripotent stem cells (iPSCs) and neurological disease modeling: progress and promises. Hum. Mol. Genet. 20, R109–R115 10.1093/hmg/ddr336 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 165.Rubin L.L. (2008) Stem cells and drug discovery: the beginning of a new era? Cell 132, 549–552 10.1016/j.cell.2008.02.010 [DOI] [PubMed] [Google Scholar]
  • 166.Grskovic M.et al. (2011) Induced pluripotent stem cells — opportunities for disease modelling and drug discovery. Nat. Rev. Drug Discov. 10, 915–929 10.1038/nrd3577 [DOI] [PubMed] [Google Scholar]
  • 167.Chang C.Y.et al. (2020) Induced pluripotent stem cell (iPSC)-based neurodegenerative disease models for phenotype recapitulation and drug screening. Molecules 25, 1–21 10.3390/molecules25082000 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 168.Thomson J.A. (1998) Embryonic stem cell lines derived from human blastocysts. Science 282, 1145–1147 10.1126/science.282.5391.1145 [DOI] [PubMed] [Google Scholar]
  • 169.Biancotti J.-C.et al. (2010) Human embryonic stem cells as models for aneuploid chromosomal syndromes. Stem Cells 28, 1530–1540 10.1002/stem.483 [DOI] [PubMed] [Google Scholar]
  • 170.Halevy T.et al. (2016) Molecular characterization of down syndrome embryonic stem cells reveals a role for RUNX1 in neural differentiation. Stem Cell Rep. 7, 777–786 10.1016/j.stemcr.2016.08.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 171.Dumevska B.et al. (2016) Derivation of Trisomy 21 affected human embryonic stem cell line Genea053. Stem Cell Res. 16, 500–502 10.1016/j.scr.2016.02.003 [DOI] [PubMed] [Google Scholar]
  • 172.Canzonetta C.et al. (2008) DYRK1A-dosage imbalance perturbs NRSF/REST levels, deregulating pluripotency and embryonic stem cell fate in Down syndrome. Am. J. Hum. Genet. 83, 388–400 10.1016/j.ajhg.2008.08.012 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 173.Wert G.D. and Mummery C. (2003) Human embryonic stem cells: research, ethics and policy. Hum. Reprod. 18, 672–682 10.1093/humrep/deg143 [DOI] [PubMed] [Google Scholar]
  • 174.Lo B. and Parham L. (2009) Ethical issues in stem cell research. Endocr. Rev. 30, 204–213 10.1210/er.2008-0031 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 175.King N.M.P. and Perrin J. (2014) Ethical issues in stem cell research and therapy. Stem Cell Res. Ther. 5, 85 10.1186/scrt474 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 176.Takahashi K.et al. (2007) Induction of pluripotent stem cells from adult human fibroblasts by defined factors. Cell 131, 861–872 10.1016/j.cell.2007.11.019 [DOI] [PubMed] [Google Scholar]
  • 177.Yu J.et al. (2007) Induced pluripotent stem cell lines derived from human somatic cells. Science 318, 1917–1920 10.1126/science.1151526 [DOI] [PubMed] [Google Scholar]
  • 178.Park I.-H.et al. (2008) Generation of human-induced pluripotent stem cells. Nat. Protoc. 3, 1180–1186 10.1038/nprot.2008.92 [DOI] [PubMed] [Google Scholar]
  • 179.Park I.-H.et al. (2008) Reprogramming of human somatic cells to pluripotency with defined factors. Nature 451, 141–146 10.1038/nature06534 [DOI] [PubMed] [Google Scholar]
  • 180.Mungenast A.E., Siegert S. and Tsai L.H. (2016) Modeling Alzheimer’s disease with human induced pluripotent stem (iPS) cells. Mol. Cell. Neurosci. 73, 13–31 10.1016/j.mcn.2015.11.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 181.Arber C., Lovejoy C. and Wray S. (2017) Stem cell models of Alzheimer’s disease: progress and challenges. Alzheimers Res. Ther. 9, 42 10.1186/s13195-017-0268-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 182.Essayan-Perez S.et al. (2019) Modeling Alzheimer’s disease with human iPS cells: advancements, lessons, and applications. Neurobiol. Dis. 130, 104503 10.1016/j.nbd.2019.104503 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 183.Gough G.et al. (2020) Modeling Down syndrome in cells: from stem cells to organoids. Prog. Brain Res. 251, 55–90 10.1016/bs.pbr.2019.10.003 [DOI] [PubMed] [Google Scholar]
  • 184.Zhang T.et al. (2020) [Progress of research on induced pluripotent stem cell models for Down syndrome]. Zhonghua Yi Xue Yi Chuan Xue Za Zhi 37, 1183–1185 [DOI] [PubMed] [Google Scholar]
  • 185.Brigida A.L. and Siniscalco D. (2016) Induced pluripotent stem cells as a cellular model for studying Down Syndrome. J. Stem Cells Regen. Med. 12, 54–60 10.46582/jsrm.1202009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 186.Yagi T.et al. (2011) Modeling familial Alzheimer’s disease with induced pluripotent stem cells. Hum. Mol. Genet. 20, 4530-4539, 4530–4539 10.1093/hmg/ddr394 [DOI] [PubMed] [Google Scholar]
  • 187.Israel M.A.et al. (2012) Probing sporadic and familial Alzheimer’s disease using induced pluripotent stem cells. Nature 482, 216–220 10.1038/nature10821 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 188.Chang K.-H.et al. (2019) Modeling Alzheimer’s disease by induced pluripotent stem cells carrying APP D678H mutation. Mol. Neurobiol. 56, 3972–3983 10.1007/s12035-018-1336-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 189.Kondo T.et al. (2013) Modeling Alzheimer’s disease with iPSCs reveals stress phenotypes associated with intracellular Aβ and differential drug responsiveness. Cell Stem Cell 12, 487–496 10.1016/j.stem.2013.01.009 [DOI] [PubMed] [Google Scholar]
  • 190.Duan L.et al. (2014) Stem cell derived basal forebrain cholinergic neurons from Alzheimer’s disease patients are more susceptible to cell death. Mol. Neurodegener. 9, 3 10.1186/1750-1326-9-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 191.Ma S.et al. (2020) Aging-relevant human basal forebrain cholinergic neurons as a cell model for Alzheimer's disease. Mol. Neurodegener. 15, 61 10.1186/s13024-020-00411-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 192.Muñoz S.S.et al. (2020) A simple differentiation protocol for generation of induced pluripotent stem cell-derived basal forebrain-like cholinergic neurons for Alzheimer’s disease and frontotemporal dementia disease modeling. Cells 9, 2018 10.3390/cells9092018 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 193.Hernández-Sapiéns M.A.et al. (2020) A three-dimensional Alzheimer’s disease cell culture model using iPSC-derived neurons carrying A246E mutation in PSEN1. Front. Cell. Neurosci. 14, 151 10.3389/fncel.2020.00151 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 194.Israel M.A. and Goldstein L.S. (2011) Capturing Alzheimer’s disease genomes with induced pluripotent stem cells: prospects and challenges. Genome Med. 3, 49 10.1186/gm265 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 195.Liu S.et al. (2020) Reconstruction of Alzheimer’s disease cell model in vitro via extracted peripheral blood molecular cells from a sporadic patient. Stem Cells Int. 2020, 1–10 10.1155/2020/8897494 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 196.Mertens J.et al. (2021) Age-dependent instability of mature neuronal fate in induced neurons from Alzheimer’s patients. Cell Stem Cell 28, 1533–1548 10.1016/j.stem.2021.04.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 197.Meyer K.et al. (2019) REST and neural gene network dysregulation in iPSC models of Alzheimer’s disease. Cell Rep. 26, 1112.e9–1127.e9 10.1016/j.celrep.2019.01.023 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 198.Papadimitriou C.et al. (2018) 3D culture method for Alzheimer’s disease modeling reveals interleukin-4 rescues Aβ42-induced loss of human neural stem cell plasticity. Dev. Cell 46, 85.e8–101.e8 10.1016/j.devcel.2018.06.005 [DOI] [PubMed] [Google Scholar]
  • 199.Penney J., Ralvenius W.T. and Tsai L.-H. (2020) Modeling Alzheimer’s disease with iPSC-derived brain cells. Mol. Psychiatry 25, 148–167 10.1038/s41380-019-0468-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 200.Sullivan S.E. and Young-Pearse T.L. (2017) Induced pluripotent stem cells as a discovery tool for Alzheimer’s disease. Brain Res. 1656, 98–106 10.1016/j.brainres.2015.10.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 201.Woodruff G.et al. (2013) The Presenilin-1 ΔE9 mutation results in reduced γ-secretase activity, but not total loss of PS1 function, in isogenic human stem cells. Cell Rep. 5, 974–985 10.1016/j.celrep.2013.10.018 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 202.Slanzi A.et al. (2020) In vitro models of neurodegenerative diseases. Front. Cell Dev. Biol. 8, 1–18 10.3389/fcell.2020.00328 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 203.Park I.-H.et al. (2008) Disease-specific induced pluripotent stem cells. Cell 134, 877–886 10.1016/j.cell.2008.07.041 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 204.Tang X.Y.et al. (2021) DSCAM/PAK1 pathway suppression reverses neurogenesis deficits in iPSC-derived cerebral organoids from patients with Down syndrome. J. Clin. Invest. 131, 1–17 10.1172/JCI135763 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 205.Czermiński J.T. and Lawrence J.B. (2020) Silencing trisomy 21 with XIST in neural stem cells promotes neuronal differentiation. Dev. Cell 52, 294.e3–308.e3 10.1016/j.devcel.2019.12.015 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 206.Ponroy Bally B.et al. (2020) Human iPSC-derived Down syndrome astrocytes display genome-wide perturbations in gene expression, an altered adhesion profile, and increased cellular dynamics. Hum. Mol. Genet. 29, 785–802 10.1093/hmg/ddaa003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 207.Laan L.et al. (2020) DNA methylation changes in Down syndrome derived neural iPSCs uncover co-dysregulation of ZNF and HOX3 families of transcription factors. Clin. Epigenetics 12, 9 10.1186/s13148-019-0803-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 208.Xu R.et al. (2019) OLIG2 Drives abnormal neurodevelopmental phenotypes in human iPSC-based organoid and chimeric mouse models of Down Syndrome. Cell Stem Cell 24, 908.e8–926.e8 10.1016/j.stem.2019.04.014 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 209.Sobol M.et al. (2019) Transcriptome and proteome profiling of neural induced pluripotent stem cells from individuals with Down Syndrome disclose dynamic dysregulations of key pathways and cellular functions. Mol. Neurobiol. 56, 7113–7127 10.1007/s12035-019-1585-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 210.Chiang J.C.et al. (2018) Trisomy silencing by XIST normalizes Down syndrome cell pathogenesis demonstrated for hematopoietic defects in vitro. Nat. Commun. 9, 5180 10.1038/s41467-018-07630-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 211.Real R.et al. (2018) In vivo modeling of human neuron dynamics and Down syndrome. Science 362, 10.1126/science.aau1810 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 212.Gonzalez C.et al. (2018) Modeling amyloid beta and tau pathology in human cerebral organoids. Mol. Psychiatry 23, 2363–2374 10.1038/s41380-018-0229-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 213.Ovchinnikov D.A.et al. (2018) The impact of APP on Alzheimer-like pathogenesis and gene expression in Down Syndrome iPSC-derived neurons. Stem Cell Rep. 11, 32–42 10.1016/j.stemcr.2018.05.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 214.Araujo B.H.S.et al. (2018) Down Syndrome iPSC-derived astrocytes impair neuronal synaptogenesis and the mTOR pathway in vitro. Mol. Neurobiol. 55, 5962–5975 10.1007/s12035-017-0818-6 [DOI] [PubMed] [Google Scholar]
  • 215.Cao S.Y.et al. (2017) Enhanced derivation of human pluripotent stem cell-derived cortical glutamatergic neurons by a small molecule. Sci. Rep. 7, 3282 10.1038/s41598-017-03519-w [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 216.Hu Y.et al. (2016) Directed differentiation of basal forebrain cholinergic neurons from human pluripotent stem cells. J. Neurosci. Methods 266, 42–49 10.1016/j.jneumeth.2016.03.017 [DOI] [PubMed] [Google Scholar]
  • 217.Chang C.-Y.et al. (2015) N-butylidenephthalide attenuates Alzheimer’s disease-like cytopathy in Down Syndrome induced pluripotent stem cell-derived neurons. Sci. Rep. 5, 8744 10.1038/srep08744 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 218.Murray A.et al. (2015) Brief report: isogenic induced pluripotent stem cell lines from an adult with mosaic down syndrome model accelerated neuronal ageing and neurodegeneration. Stem Cells 33, 2077–2084 10.1002/stem.1968 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 219.Chen C.et al. (2014) Role of astroglia in Down’s syndrome revealed by patient-derived human-induced pluripotent stem cells. Nat. Commun. 5, 4430 10.1038/ncomms5430 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 220.Hibaoui Y.et al. (2014) Modelling and rescuing neurodevelopmental defect of Down syndrome using induced pluripotent stem cells from monozygotic twins discordant for trisomy 21. EMBO Mol. Med. 6, 259–277 10.1002/emmm.201302848 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 221.Jiang J.et al. (2013) Translating dosage compensation to trisomy 21. Nature 500, 296–300 10.1038/nature12394 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 222.Briggs J.A.et al. (2013) Integration-free induced pluripotent stem cells model genetic and neural developmental features of down syndrome etiology. Stem Cells 31, 467–478 10.1002/stem.1297 [DOI] [PubMed] [Google Scholar]
  • 223.Lu H.E.et al. (2013) Modeling neurogenesis impairment in Down syndrome with induced pluripotent stem cells from Trisomy 21 amniotic fluid cells. Exp. Cell. Res. 319, 498–505 10.1016/j.yexcr.2012.09.017 [DOI] [PubMed] [Google Scholar]
  • 224.Shi Y.et al. (2012) A human stem cell model of early Alzheimer’s disease pathology in Down syndrome. Sci. Transl. Med. 4, 124ra29 10.1126/scitranslmed.3003771 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 225.Huo H.-Q.et al. (2018) Modeling Down Syndrome with patient iPSCs reveals cellular and migration deficits of GABAergic neurons. Stem Cell Rep. 10, 1251–1266 10.1016/j.stemcr.2018.02.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 226.Weick J.P.et al. (2013) Deficits in human trisomy 21 iPSCs and neurons. Proc. Natl. Acad. Sci. U.S.A. 110, 9962–9967 10.1073/pnas.1216575110 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 227.Maclean G.A.et al. (2012) Altered hematopoiesis in trisomy 21 as revealed through in vitro differentiation of isogenic human pluripotent cells. Proc. Natl. Acad. Sci. U.S.A. 109, 17567–17572 10.1073/pnas.1215468109 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 228.Mou X.et al. (2012) Generation of disease-specific induced pluripotent stem cells from patients with different karyotypes of Down syndrome. Stem Cell Res. Ther. 3, 14 10.1186/scrt105 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 229.Li L.B.et al. (2012) Trisomy correction in Down syndrome induced pluripotent stem cells. Cell Stem Cell 11, 615–619 10.1016/j.stem.2012.08.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 230.Kawatani K.et al. (2021) A human isogenic iPSC-derived cell line panel identifies major regulators of aberrant astrocyte proliferation in Down syndrome. Commun. Biol. 4, 1–15 10.1038/s42003-021-02242-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 231.Inoue M.et al. (2019) Autonomous trisomic rescue of Down syndrome cells. Lab. Invest. 99, 885–897 10.1038/s41374-019-0230-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 232.Papavassiliou P.et al. (2015) Mosaicism for trisomy 21: a review. Am. J. Med. Genet. A 167, 26–39 10.1002/ajmg.a.36861 [DOI] [PubMed] [Google Scholar]
  • 233.Papavassiliou P.et al. (2009) The phenotype of persons having mosaicism for trisomy 21/Down syndrome reflects the percentage of trisomic cells present in different tissues. Am. J. Med. Genet. A 149A, 573–583 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 234.Hibaoui Y.et al. (2014) Data in brief: transcriptome analysis of induced pluripotent stem cells from monozygotic twins discordant for trisomy 21. Genom. Data 2, 226–229 10.1016/j.gdata.2014.07.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 235.Gonzales P.K.et al. (2018) Transcriptome analysis of genetically matched human induced pluripotent stem cells disomic or trisomic for chromosome 21. PLoS ONE 13, e0194581 10.1371/journal.pone.0194581 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 236.Ambasudhan R.et al. (2011) Direct reprogramming of adult human fibroblasts to functional neurons under defined conditions. Cell Stem Cell 9, 113–118 10.1016/j.stem.2011.07.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 237.Salehi A., Ashford J.W. and Mufson E.J. (2016) The link between Alzheimer’s disease and Down syndrome. A historical perspective. Curr. Alzheimer Res. 13, 2–6 10.2174/1567205012999151021102914 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 238.Snyder H.M.et al. (2020) Further understanding the connection between Alzheimer’s disease and Down syndrome. Alzheimers Dement. 16, 1065–1077 10.1002/alz.12112 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 239.Hartley D.et al. (2015) Down syndrome and Alzheimer’s disease: common pathways, common goals. Alzheimers Dement. 11, 700–709 10.1016/j.jalz.2014.10.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 240.Karmiloff-Smith A.et al. (2016) The importance of understanding individual differences in Down syndrome. F1000Res. 5, F1000, Faculty Rev-389 10.12688/f1000research.7506.1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 241.Yang J.et al. (2016) Induced pluripotent stem cells in Alzheimer’s disease: applications for disease modeling and cell-replacement therapy. Mol. Neurodegener. 11, 39–39 10.1186/s13024-016-0106-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 242.Dashinimaev E.B.et al. (2017) Neurons derived from induced pluripotent stem cells of patients with Down Syndrome reproduce early stages of Alzheimer’s disease type pathology in vitro. J. Alzheimers Dis. 56, 835–847 10.3233/JAD-160945 [DOI] [PubMed] [Google Scholar]
  • 243.Berry B.J.et al. (2018) Advances and current challenges associated with the use of human induced pluripotent stem cells in modeling neurodegenerative disease. Cells Tissues Organs 205, 331–349 10.1159/000493018 [DOI] [PubMed] [Google Scholar]
  • 244.Choi S.H.et al. (2014) A three-dimensional human neural cell culture model of Alzheimer’s disease. Nature 515, 274–278 10.1038/nature13800 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 245.Zhang D.et al. (2014) A 3D Alzheimer’s disease culture model and the induction of P21-activated kinase mediated sensing in iPSC derived neurons. Biomaterials 35, 1420–1428 10.1016/j.biomaterials.2013.11.028 [DOI] [PubMed] [Google Scholar]
  • 246.Kim Y.H.et al. (2015) A 3D human neural cell culture system for modeling Alzheimer’s disease. Nat. Protoc. 10, 985–1006 10.1038/nprot.2015.065 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 247.Raja W.K.et al. (2016) Self-organizing 3D human neural tissue derived from induced pluripotent stem cells recapitulate Alzheimer’s disease phenotypes. PLoS ONE 11, e0161969 10.1371/journal.pone.0161969 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 248.Park J.et al. (2018) A 3D human triculture system modeling neurodegeneration and neuroinflammation in Alzheimer's disease. Nat. Neurosci. 21, 941–951 10.1038/s41593-018-0175-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 249.Hoshino A.et al. (2019) Synchrony and asynchrony between an epigenetic clock and developmental timing. Sci. Rep. 9, 3770–3770 10.1038/s41598-019-39919-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 250.Horvath S.et al. (2015) Accelerated epigenetic aging in Down syndrome. Aging Cell 14, 491–495 10.1111/acel.12325 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 251.Zhang Y.et al. (2013) Rapid single-step induction of functional neurons from human pluripotent stem cells. Neuron 78, 785–798 10.1016/j.neuron.2013.05.029 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 252.Vierbuchen T.et al. (2010) Direct conversion of fibroblasts to functional neurons by defined factors. Nature 463, 1035–1041 10.1038/nature08797 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 253.Yang N.et al. (2011) Induced neuronal cells: how to make and define a neuron. Cell Stem Cell 9, 517–525 10.1016/j.stem.2011.11.015 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 254.Chanda S.et al. (2014) Generation of induced neuronal cells by the single reprogramming factor ASCL1. Stem Cell Rep. 3, 282–296 10.1016/j.stemcr.2014.05.020 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 255.Pfisterer U.et al. (2011) Direct conversion of human fibroblasts to dopaminergic neurons. Proc. Natl. Acad. Sci. U.S.A. 108, 10343–10348 10.1073/pnas.1105135108 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 256.Caiazzo M.et al. (2011) Direct generation of functional dopaminergic neurons from mouse and human fibroblasts. Nature 476, 224–227 10.1038/nature10284 [DOI] [PubMed] [Google Scholar]
  • 257.Yoo A.S.et al. (2011) MicroRNA-mediated conversion of human fibroblasts to neurons. Nature 476, 228–231 10.1038/nature10323 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 258.Torper O.et al. (2013) Generation of induced neurons via direct conversion in vivo. Proc. Natl. Acad. Sci. U.S.A. 110, 7038–7043 10.1073/pnas.1303829110 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 259.Lee H.et al. (2020) Sequentially induced motor neurons from human fibroblasts facilitate locomotor recovery in a rodent spinal cord injury model. eLife 9, e52069 10.7554/eLife.52069 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 260.Son E.Y.et al. (2011) Conversion of mouse and human fibroblasts into functional spinal motor neurons. Cell Stem Cell 9, 205–218 10.1016/j.stem.2011.07.014 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 261.Marro S.et al. (2011) Direct lineage conversion of terminally differentiated hepatocytes to functional neurons. Cell Stem Cell 9, 374–382 10.1016/j.stem.2011.09.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 262.Mertens J.et al. (2018) Aging in a dish: iPSC-derived and directly induced neurons for studying brain aging and age-related neurodegenerative diseases. Annu. Rev. Genet. 52, 271–293 10.1146/annurev-genet-120417-031534 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 263.Vadodaria K.C.et al. (2016) Generation of functional human serotonergic neurons from fibroblasts. Mol. Psychiatry 21, 49–61 10.1038/mp.2015.161 [DOI] [PubMed] [Google Scholar]
  • 264.Liu M.-L.et al. (2013) Small molecules enable neurogenin 2 to efficiently convert human fibroblasts into cholinergic neurons. Nat. Commun. 4, 2183–2183 10.1038/ncomms3183 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 265.Blanchard J.W.et al. (2015) Selective conversion of fibroblasts into peripheral sensory neurons. Nat. Neurosci. 18, 25–35 10.1038/nn.3887 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 266.Pang Z.P.et al. (2011) Induction of human neuronal cells by defined transcription factors. Nature 476, 220–223 10.1038/nature10202 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 267.Mollinari C.et al. (2018) Transdifferentiation: a new promise for neurodegenerative diseases. Cell Death Dis. 9, 830 10.1038/s41419-018-0891-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 268.Mollinari C. and Merlo D. (2021) Direct reprogramming of somatic cells to neurons: pros and cons of chemical approach. Neurochem. Res. 46, 1330–1336 10.1007/s11064-021-03282-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 269.D’Souza G.X.et al. (2021) The application of in vitro-derived human neurons in neurodegenerative disease modeling. J. Neurosci. Res. 99, 124–140 10.1002/jnr.24615 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 270.Hulme A.J.et al. (2022) Making neurons, made easy: the use of Neurogenin-2 in neuronal differentiation. Stem Cell Rep. 17, 14–34 10.1016/j.stemcr.2021.11.015 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 271.Lagomarsino V.N.et al. (2021) Stem cell-derived neurons reflect features of protein networks, neuropathology, and cognitive outcome of their aged human donors. Neuron 109, 3402.e9–3420.e9 10.1016/j.neuron.2021.08.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 272.Mertens J.et al. (2015) Directly reprogrammed human neurons retain aging-associated transcriptomic signatures and reveal age-related nucleocytoplasmic defects. Cell Stem Cell 17, 705–718 10.1016/j.stem.2015.09.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 273.Wang C.et al. (2017) Scalable production of iPSC-derived human neurons to identify tau-lowering compounds by high-content screening. Stem Cell Rep. 9, 1221–1233 10.1016/j.stemcr.2017.08.019 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 274.Wu C.I.et al. (2022) APP and DYRK1A regulate axonal and synaptic vesicle protein networks and mediate Alzheimer’s pathology in trisomy 21 neurons. Mol. Psychiatry 1–20 10.1038/s41380-022-01454-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 275.Hirata K.et al. (2020) 4-Phenylbutyrate ameliorates apoptotic neural cell death in Down syndrome by reducing protein aggregates. Sci. Rep. 10, 14047 10.1038/s41598-020-70362-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 276.Goldman J.P.et al. (1998) Enhanced human cell engraftment in mice deficient in RAG2 and the common cytokine receptor gamma chain. Br. J. Haematol. 103, 335–342 10.1046/j.1365-2141.1998.00980.x [DOI] [PubMed] [Google Scholar]
  • 277.Chakrabarti L.et al. (2010) Olig1 and Olig2 triplication causes developmental brain defects in Down syndrome. Nat. Neurosci. 13, 927–934 10.1038/nn.2600 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 278.Espuny-Camacho I.et al. (2017) Hallmarks of Alzheimer’s disease in stem-cell-derived human neurons transplanted into mouse brain. Neuron 93, 1066.e8–1081.e8 10.1016/j.neuron.2017.02.001 [DOI] [PubMed] [Google Scholar]
  • 279.Hasselmann J.et al. (2019) Development of a chimeric model to study and manipulate human microglia in vivo. Neuron 103, 1016.e10–1033.e10 10.1016/j.neuron.2019.07.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 280.Manley W.F. and Anderson S.A. (2019) Dosage counts: correcting Trisomy-21-related phenotypes in human organoids and xenografts. Cell Stem Cell 24, 835–836 10.1016/j.stem.2019.05.009 [DOI] [PubMed] [Google Scholar]
  • 281.Mancuso R.et al. (2019) Stem-cell-derived human microglia transplanted in mouse brain to study human disease. Nat. Neurosci. 22, 2111–2116 10.1038/s41593-019-0525-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 282.Saito M.et al. (2001) Diphtheria toxin receptor-mediated conditional and targeted cell ablation in transgenic mice. Nat. Biotechnol. 19, 746–750 10.1038/90795 [DOI] [PubMed] [Google Scholar]
  • 283.Walsh S.et al. (2021) Aducanumab for Alzheimer’s disease? BMJ 374, n1682 10.1136/bmj.n1682 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 284.Knopman D.S. and Perlmutter J.S. (2021) Prescribing aducanumab in the face of meager efficacy and real risks. Neurology 97, 545 10.1212/WNL.0000000000012452 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 285.Nolan G.P. (2007) What’s wrong with drug screening today. Nat. Chem. Biol. 3, 187–191 10.1038/nchembio0407-187 [DOI] [PubMed] [Google Scholar]
  • 286.Hunsberger J.G.et al. (2015) Induced pluripotent stem cell models to enable in vitro models for screening in the central nervous system. Stem Cells Dev. 24, 1852–1864 10.1089/scd.2014.0531 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 287.Qian L. and Julia T.C.W. (2021) Human iPSC-based modeling of central nerve system disorders for drug discovery. Int. J. Mol. Sci. 22, 1203 10.3390/ijms22031203 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 288.Kondo T.et al. (2017) iPSC-based compound screening and in vitro trials identify a synergistic anti-amyloid β combination for Alzheimer’s disease. Cell Rep. 21, 2304–2312 10.1016/j.celrep.2017.10.109 [DOI] [PubMed] [Google Scholar]
  • 289.Miller J.D.et al. (2013) Human iPSC-based modeling of late-onset disease via progerin-induced aging. Cell Stem Cell 13, 691–705 10.1016/j.stem.2013.11.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 290.Sullivan S.et al. (2018) Quality control guidelines for clinical-grade human induced pluripotent stem cell lines. Regen. Med. 13, 859–866 10.2217/rme-2018-0095 [DOI] [PubMed] [Google Scholar]
  • 291.Volpato V.et al. (2018) Reproducibility of molecular phenotypes after long-term differentiation to human iPSC-derived neurons: a multi-site omics study. Stem Cell Rep. 11, 897–911 10.1016/j.stemcr.2018.08.013 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 292.Alessandrini M.et al. (2019) Stem cell therapy for neurological disorders. S. Afr. Med. J. 109, 70–77 10.7196/SAMJ.2019.v109i8b.14009 [DOI] [PubMed] [Google Scholar]
  • 293.McGinley L.M.et al. (2018) Human neural stem cell transplantation improves cognition in a murine model of Alzheimer’s disease. Sci. Rep. 8, 14776 10.1038/s41598-018-33017-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 294.Liu Y.et al. (2013) Medial ganglionic eminence-like cells derived from human embryonic stem cells correct learning and memory deficits. Nat. Biotechnol. 31, 440–447 10.1038/nbt.2565 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 295.Bissonnette C.J.et al. (2011) The controlled generation of functional basal forebrain cholinergic neurons from human embryonic stem cells. Stem Cells 29, 802–811 10.1002/stem.626 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 296.Yue W.et al. (2015) ESC-derived basal forebrain cholinergic neurons ameliorate the cognitive symptoms associated with Alzheimer’s disease in mouse models. Stem Cell Rep. 5, 776–790 10.1016/j.stemcr.2015.09.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 297.Berger I.et al. (2016) Global distribution of businesses marketing stem cell-based interventions. Cell Stem Cell 19, 158–162 10.1016/j.stem.2016.07.015 [DOI] [PubMed] [Google Scholar]
  • 298.Coghlan A. (2017) Clinic claims it has used stem cells to treat Down’s syndrome. New Scientist 3111, mg23331113-900 [Google Scholar]
  • 299.Shroff G. (2016) Human embryonic stem cells in the treatment of patients with Down Syndrome: a case report. J. Med. Cases 2016, 123–125 10.14740/jmc2455w [DOI] [Google Scholar]
  • 300.Ebert A.D., Liang P. and Wu J.C. (2012) Induced pluripotent stem cells as a disease modeling and drug screening platform. J. Cardiovasc. Pharmacol. 60, 408–416 10.1097/FJC.0b013e318247f642 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 301.Aerts L.et al. (2022) Do we still need animals? Surveying the role of animal-free models in Alzheimer’s and Parkinson’s disease research. EMBO J e110002, 10.15252/embj.2021110002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 302.Rowland H.A., Hooper N.M. and Kellett K.A.B. (2018) Modelling sporadic Alzheimer’s disease using induced pluripotent stem cells. Neurochem. Res. 43, 2179–2198 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 303.Doss M.X. and Sachinidis A. (2019) Current challenges of iPSC-based disease modeling and therapeutic implications. Cells 8, , 10.3390/cells8050403 [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from Neuronal Signaling are provided here courtesy of Portland Press Ltd

RESOURCES