Next Article in Journal
Genome-Wide Identification of the ARF Gene Family in Safflower (Carthamus tinctorius L.) and Their Response Patterns to Exogenous Hormone Treatments
Previous Article in Journal
Epithelial–Mesenchymal Transitions Leading to Conceptus Adhesion in Ruminants: Early Pregnancy Events in Cattle
Previous Article in Special Issue
PIEZO2 Proton Affinity and Availability May Also Regulate Mechanical Pain Sensitivity, Drive Central Sensitization and Neurodegeneration
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJMS
Volume 26
Issue 8
10.3390/ijms26083774
ijms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Current Development of iPSC-Based Modeling in Neurodegenerative Diseases

by
Xiangge Guo
1,†,
Xumeng Wang
1,†,
Jiaxuan Wang
1,
Min Ma
1,2,* and
Qian Ren
1,3,4,*
1
Department of Human Anatomy, Hebei Medical University, Shijiazhuang 050017, China
2
Human Brain Bank, Hebei Medical University, Shijiazhuang 050017, China
3
The Key Laboratory of Neural and Vascular Biology, Ministry of Education, Hebei Medical University, Shijiazhuang 050017, China
4
Hebei Key Laboratory of Neurodegenerative Disease Mechanism, Hebei Medical University, Shijiazhuang 050017, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Int. J. Mol. Sci. 2025, 26(8), 3774; https://doi.org/10.3390/ijms26083774
Submission received: 7 March 2025 / Revised: 8 April 2025 / Accepted: 9 April 2025 / Published: 16 April 2025
(This article belongs to the Special Issue Molecular Research on Neurodegenerative Diseases 4.0)
Browse Figure
Versions Notes

Abstract

Over the past two decades, significant advancements have been made in the induced pluripotent stem cell (iPSC) technology. These developments have enabled the broader application of iPSCs in neuroscience, improved our understanding of disease pathogenesis, and advanced the investigation of therapeutic targets and methods. Specifically, optimizations in reprogramming protocols, coupled with improved neuronal differentiation and maturation techniques, have greatly facilitated the generation of iPSC-derived neural cells. The integration of the cerebral organoid technology and CRISPR/Cas9 genome editing has further propelled the application of iPSCs in neurodegenerative diseases to a new stage. Patient-derived or CRISPR-edited cerebral neurons and organoids now serve as ideal disease models, contributing to our understanding of disease pathophysiology and identifying novel therapeutic targets and candidates. In this review, we examine the development of iPSC-based models in neurodegenerative diseases, including Alzheimer’s disease, Parkinson’s disease, and Huntington’s disease.

1. Introduction

Since the advent of induced pluripotent stem cells (iPSCs) in 2006, two decades of advancements have enabled the widespread application of iPSCs, their differentiated cells and organoids in modeling neurological diseases, elucidating pathogenic mechanisms, and advancing cell-based therapies [1]. Previous reviews have extensively discussed the advantages of iPSC-derived patient-specific in vitro cellular models over traditional animal models and human embryonic stem cells, particularly in disease-specific applications [1,2,3]. However, significant limitations persist in recapitulating disease-specific pathological phenotypes and mechanistic pathways using iPSC-derived cells and organoids [3,4,5]. For instance, heterogeneity and dosage variability remain critical challenges due to differences in cellular/tissue sources, induction protocols, and culture conditions [6]. Furthermore, the existing induction protocols still face technical complexities and cannot indefinitely generate all desired human cell types [7,8].
To address these challenges, researchers have continuously improved iPSC generation methods and differentiation protocols. Simultaneously, the expanding application of the CRISPR/Cas9 gene-editing technology has enabled precise genetic modifications in iPSCs, their differentiated cells, and even organoids, while preserving patient-specific genetic backgrounds [9,10]. These advances have elevated research on disease-specific model establishment, mechanistic investigation, and high-throughput drug screening to new heights. This review systematically elaborates on the recent progress in iPSC induction and iPSC differentiation into neurons, glial cells, and organoids, while examining the applications of these cellular and organoid systems as in vitro disease models for neurodegenerative disorders.

2. Generation of iPSCs

2.1. Delivery Methods

The initial generation of iPSCs relied on the transient expression of exogenous transcription factors [2]. Conventional delivery methods involve retroviral and lentiviral vectors to deliver transcription factors in somatic cells [11]. While these retroviral and lentiviral systems are highly efficient and robust, they carry a significant risk of transgene reactivation post-reprogramming [12]. With advancements in gene delivery technologies, a diverse array of reprogramming methods has been developed. Currently, the most commonly employed strategies include non-integrating viral approaches—such as adenovirus, Sendai virus, and protein delivery—and non-viral methods, including mRNA transfection, PiggyBac transposons, minicircle vectors, and episomal plasmids [2,11,13]. Each of these approaches has its own set of advantages and limitations (Table 1). For example, adenovirus vectors, while associated with a lower risk of transgene reactivation, exhibit suboptimal reprogramming efficiency, making them unsuitable for clinical applications [11,13]. In contrast, Sendai virus-based reprogramming is considered safer and more efficient, as the RNA virus can be eliminated from iPSCs, reducing the risk of genomic integration [11,13]. DNA-based reprogramming methods, such as episomal plasmids, PiggyBac transposons, and minicircle vectors, offer distinct benefits. PiggyBac and minicircle vectors are associated with reduced risks of genomic instability and mutations. However, these methods are limited by low reprogramming efficiency and the restricted availability of transducible somatic cell types [14,15,16]. Episomal plasmids, containing EBNA-1 and OriP sequences from Epstein–Barr virus, represent an alternative DNA-based method. While daily transfection is required to enhance reprogramming efficiency, episomal plasmids circumvent the risk of genomic integration. Moreover, they are cost-effective, easier to produce, and more straightforward to use compared to PiggyBac and minicircle vectors [17,18]. In addition to DNA-based approaches, RNA delivery has emerged as another viable approach to induce pluripotency [19]. This method demonstrates a lower mutagenic risk and a higher efficiency; however, its application has so far been limited to reprogramming human fibroblasts and peripheral blood cells [20,21].

2.2. Reprogramming Factors

Takahashi and Yamanaka initially established iPSCs by overexpressing the four transcription factors, OCT3/4, SOX2, KLF4, and c-MYC (OSKM), in mouse fibroblasts [22]. The four factors, often referred to as Yamanaka factors, perform distinct yet complementary roles: OCT3/4, SOX2, and KLF4 are essential for maintaining pluripotency and inhibiting differentiation, while c-MYC enhances reprogramming efficiency and promotes cell proliferation [23]. In addition to OSKM, various other combinations of transcription factors have been identified to induce iPSC reprogramming (Table 2). For example, OCT3/4, SOX2, NANOG, and LIN28 represent an alternative set of transcription factors, where NANOG plays a pivotal role in stem cell self-renewal, and LIN28 regulates RNA modification and expression [24]. Additionally, transcription factors such as GLIS1, NR5A2, and SALL4 can substitute for c-MYC or OCT3/4, or complement OSKM to enhance the reprogramming efficiency and improve the pluripotent state of the cells [25,26,27].
Recent studies have further demonstrated that epigenome modifiers and miRNA manipulation can significantly enhance iPSC reprogramming efficiency [2] (Table 2). Chemical approaches have also been developed to regulate the pluripotency and differentiation of iPSCs. These methods typically target growth factor receptors or downstream kinases that modulate intracellular signaling pathways during differentiation. In 2011, Yu et al. reported the CHALP cocktail, a combination of six small molecules: CHIR99021 (a GSK3β inhibitor), PD0325901 (a MEK inhibitor), human leukemia inhibitory factor (LIF), A-83-01 (a TGF-β/activin/nodal receptor inhibitor), basic fibroblast growth factor (bFGF), and HA-100 (a ROCK inhibitor), which collectively enhance reprogramming efficiency [28]. Another chemical cocktail protocol includes cyclic pifithrin-α (a P53 inhibitor), A-83-01, CHIR99021, thiazovivin, sodium butyrate (NaB), and PD0325901, which has shown particular efficacy in reprogramming human urine-derived cells (hUCs) [29]. These cocktails exert complex biological effects, such as promoting ground-state pluripotency and facilitating iPSC production from neural progenitor cells [30].
Additionally, specific chemicals, including DNA methyltransferase inhibitors (such as 5′azacytidine (AZA) or valproic acid (VPA)) and histone deacetylase inhibitors (such as trichostatin A, suberoylanilide hydroxamic acid), have been shown to enhance reprogramming efficiency [30,31,32] (Table 2). For example, AZA or VPA can achieve reprogramming without the introduction of the oncogenes c-Myc and Klf4, and the efficiency is significantly increased. Trichostatin A and suberoylanilide hydroxamic acid can improve reprogramming efficiency by more than two-fold under OSKM conditions. Furthermore, reports have shown that the small molecule combination BIX-01294 (a G9a histone methyltransferase inhibitor) and BayK8644 (an L-type calcium channel agonist) enable reprogramming of Oct4/Klf4-transduced fibroblasts. These chemical approaches hold significant promise for advancing stem cell research and accelerating applications in regenerative medicine.

2.3. Somatic Cell Sources

Due to the accessibility and rapid proliferation of fibroblasts, iPSCs were initially successfully induced from mouse fibroblasts. Currently, in addition to human and mouse cells, fibroblasts from various mammalian species, including pigs, rabbits, monkeys, and horses, have been successfully reprogrammed into iPSCs [33,34,35,36,37]. Among human somatic cells, skin fibroblasts remain a primary source for iPSCs [38]. However, the invasive nature of skin biopsies often limits their practicality and patient acceptance.
With advancements in reprogramming technologies, iPSCs can now be derived from various somatic cell types beyond skin fibroblasts [38,39,40]. Peripheral blood mononuclear cells (PBMCs), including T cells, B cells, and monocytes, are particularly valuable due to their accessibility through non-invasive collection methods, making them a promising resource for the generation of iPSC, especially for clinical applications [41,42,43,44,45,46,47]. Keratinocytes, isolated from skin or hair, also provide a non-invasive option for iPSC generation [48]. Mesenchymal stem cells (MSCs), derived from tissues such as bone marrow, adipose tissue, and teeth, are another accessible and widely utilized source, particularly in studies involving regeneration and differentiation [38,49]. Renal epithelial cells isolated from urine represent one of the most convenient and non-invasive sources for iPSC induction [39,50]. Neural stem cells (NSCs) and neural progenitor cells (NPCs), given their inherent pluripotency, have also been successfully reprogrammed into iPSCs, facilitating research in neurological disease modeling [51]. Additionally, cells from other sources, such as liver, stomach, and cord blood, have demonstrated viability for iPSC induction, further expanding the applications of iPSCs in both research and therapeutic contexts [52,53].
Although nearly all somatic cells exhibit the potential for reprogramming into iPSCs, their accessibility and reprogramming efficiency vary significantly. Therefore, careful selection of an appropriate cell source, reprogramming factors, and delivery method is critical for the successful establishment of iPSCs, particularly for disease modeling and subsequent applications (Table 3). Notably, iPSCs retain the epigenetic memory of their original somatic cell type, which can influence both the reprogramming efficiency and the differentiation potential [54]. This retained epigenetic status plays a pivotal role in determining the functional capacity of iPSCs and must be carefully considered in both research and clinical settings.

3. Neural Differentiation of iPSCs

In the pathogenesis of neurodegenerative diseases, neurons, glial cells, and other cell types play pivotal roles. However, obtaining these cells has long posed a significant challenge for researchers. The advent of iPSCs has greatly alleviated the difficulties associated with accessing these cell types. Currently, various neural cells derived from iPSCs have been widely utilized in numerous fields of neuroscience. Particularly in the application as in vitro disease models, iPSC-derived neural cells have demonstrated broad potential.
Obtaining neurons and other relevant cells from patients with neurodegenerative diseases has traditionally posed significant challenges, making it difficult to replicate pathological processes in vitro. However, iPSCs provide a promising solution due to their ease of acquisition, extensive proliferative and differentiation capacities, and minimal ethical concerns [39]. Furthermore, iPSCs retain the genetic background of donor tissues and organs, enabling them to accurately model human physiological and pathological characteristics in vitro [55]. As a result, iPSC-derived neurons have become widely utilized for investigating the underlying mechanisms of neurodegenerative diseases and for screening potential therapeutic targets and drugs (Figure 1 and Table 4).

3.1. Neural Stem Cells

Neural differentiation from iPSCs typically involves inducing iPSCs into NPCs or NSCs using one of three main strategies: embryoid body (EB) formation, co-culture on neural-inducing feeder layers, or dual SMAD inhibition [56,57]. These derived NSCs/NPCs can be expanded in adherent cultures or as floating neurospheres and subsequently treated with specific growth factors to drive differentiation into various neuronal subtypes [57]. Additionally, some direct induction methods bypass the NSC/NPC stage, enabling direct neural induction [3]. Regardless of the approach, iPSC-derived neurons offer unprecedented opportunities for the development of disease models and high-throughput drug screening.

3.2. Neurons

As described above, most neuronal cell types can be induced through the neural rosettes and NSC/NPC stages, followed by the addition of specific growth factors or small molecules to further guide their differentiation into specialized neuronal subtypes. For example, supplementation with brain-derived neurotrophic factor (BDNF), glial cell line-derived neurotrophic factor (GDNF), cyclic adenosine monophosphate (cAMP), forskolin, or retinoic acid (RA), as well as activators of WNT and Sonic Hedgehog (SHH) signaling pathways, can promote the generation of diverse neuronal types, including dopaminergic neurons, GABAergic neurons, and glutamatergic neurons [3,58,59,60,61,62,63].
In addition to protocols that recapitulate developmental differentiation processes in vitro, functional induced neurons (iNs) can also be obtained through direct conversion methods from iPSCs, or even directly from somatic cells [64,65,66,67,68,69,70,71,72,73,74]. These methods significantly shorten the differentiation time, lower costs, and simplify procedures [71]. For example, direct conversion of iPSCs to iNs can be achieved by overexpressing specific neurodevelopmental transcription factors such as BRN2, ASCL1, and MYT1L or NGN2 [67]. Furthermore, overexpression of ASCL1 and DLX2 in iPSCs has been shown to efficiently generate GABAergic neurons [68,69]. Similarly, a combination of SMAD and SHH inhibition with the overexpression of transcription factor NGN2 can directly induce the formation of mature glutamatergic neurons [70]. Additionally, neurons can also be obtained directly from somatic cells through trans-differentiation. For instance, fibroblasts can be reprogrammed into iNs by overexpressing BRN2, ASCL1, MYT1L, and NEUROD1 [73]. Other studies have demonstrated that combinations of certain microRNAs (e.g., miR-9/9*, miR-124) and transcription factors (e.g., MYT1L, NEUROD2) can convert fibroblasts into iNs [64,65,66,74].

3.3. Astrocytes

Astrocytes are the most abundant glial cells in the human brain [75]. Several protocols have been established to differentiate iPSCs into astrocytes [57,75,76,77]. The predominant strategies involve differentiating iPSCs through the NSC/NPC or oligodendrocyte progenitor cell (OPC) stages, followed by further maturation into astrocytes using a combination of growth factors and small molecules. For instance, the differentiation of NSCs/NPCs into astrocytes can be accelerated by using a combination of ciliary neurotrophic factor (CNTF), bone morphogenetic proteins (BMP), FGF2, and fetal bovine serum (FBS) [76,78,79]. Similarly, a mixture of N2, B27-RA, BMP4, and FGF2 has also been reported to facilitate the differentiation of OPCs or NPCs into astrocytes [80]. Beyond strategies that undergo physiological developmental stages, direct generation of astrocytes from iPSCs has been demonstrated by inducing the expression of transcription factors such as NFIA or NFIA in combination with SOX10 [79,80,81,82]. Additionally, direct conversion of fibroblasts into astrocytes has been achieved through the overexpression of NFIA, NFIB, and SOX9 [72]. However, it is important to note that astrocytes exhibit significant plasticity, which limits the ability of in vitro-cultured astrocytes to fully replicate their in vivo counterparts. Moreover, variability between cell lines derived using different differentiation protocols remains a notable challenge that warrants further investigation [83,84].

3.4. Microglia

Microglia, as the innate immune cells of the central nervous system (CNS), play a critical role in neural development, homeostasis, and repair [85]. Similar to iPSC-derived neurons and astrocytes, microglia can be differentiated from iPSCs using various established protocols. The most widely used approach involves initially inducing iPSCs into mesoderm progenitor cells, followed by directed differentiation into microglia. These approaches typically begin with the addition of specific cytokines such as BMP4, activin A, FGF2, and vascular endothelial growth factor A (VEGF-A) to promote the formation of yolk sac embryoid bodies (EBs) or hematopoietic progenitor cells (HPCs). Subsequently, cells are exposed to factors such as interleukin 34 (IL-34), TGF-β, and cell survival factors (CSF) to generate microglia-like cells [85,86,87,88,89]. These protocols aim to recapitulate the in vivo developmental process of microglia. However, they have certain limitations, including multiple steps, high technical complexity, and relatively low differentiation efficiency [89]. Alternative methods have been developed to directly differentiate iPSCs into microglia by introducing key transcription factors such as PU.1 and interferon regulatory factor 8 (IRF8) [90,91]. Additionally, co-culture systems with astrocytes or neurons can provide essential microenvironmental factors to facilitate further differentiation into microglia-like cells [89,92]. It is important to note that microglia-like cells generated in vitro often exhibit phenotypic and functional differences from their in vivo counterparts. Variations in functional characteristics such as phagocytic activity and inflammatory responses are observed across different differentiation protocols [85,87,89]. Nevertheless, mesoderm-derived microglia tend to closely resemble primary microglia and are considered more suitable for modeling neurodegenerative diseases and investigating immune responses within the CNS [89].

3.5. Oligodendrocytes

The primary function of oligodendrocytes is to produce myelin, which protects neurons and axons while maintaining their connectivity. Demyelination of neurons is a common pathological feature observed in neurodegenerative diseases [93]. Consequently, oligodendrocytes derived from iPSCs hold significant potential for studying neurodegenerative disorders. The differentiation of oligodendrocytes typically follows a stepwise process: iPSCs are first induced into NPCs, then differentiated into oligodendrocyte progenitor cells (OPCs), and finally matured into functional oligodendrocytes [94]. During this process, growth factors such as BMP4, FGF2, EGF, and PDGF are used to promote the transition from NPCs to OPCs [94,95]. The subsequent addition of myelinogenic factors, including triiodothyronine (T3) and insulin-like growth factor 1 (IGF-1), facilitates the maturation of OPCs into oligodendrocytes [96,97]. These methods demonstrate a relatively high induction efficiency and yield oligodendrocytes with stable functionality. However, the protocols are complex, time-consuming, and technically challenging.
Alternatively, the direct overexpression of transcription factors such as SOX10, OLIG2, and NKX6.2 in iPSCs has been shown to expedite differentiation into OPCs [98,99]. This approach significantly shortens the differentiation timeline, but the introduction of transcription factors may cause phenotypic instability [94]. Furthermore, the application of small molecules, such as SMAD inhibitors and ROCK inhibitors, combined with such factors as RA, EGF, FGF2, and SHH, has also been shown to drive NPCs toward oligodendrocyte differentiation [94,97]. Recent studies have reported that combining IGF-1 with promyelinating agents such as clemastine and ketoconazole enhances the differentiation of OPCs into mature oligodendrocytes [100,101]. Additionally, co-culture systems involving OPCs with mature neurons and astrocytes can provide essential microenvironmental factors, such as CX3CL1 and TGF-β, which are required during the differentiation process. These systems promote the generation of mature oligodendrocytes with enhanced myelination capacity [102,103]. Although these strategies are effective in producing oligodendrocytes with higher maturation levels, challenges remain in achieving high cell purity.

3.6. Brain Organoids

Although iPSC-derived cells effectively model various functional and structural abnormalities at the cellular level in vitro, these culture systems are limited in their ability to replicate the complex features of cell–cell interactions and microenvironmental cues present in vivo [104]. Brain organoids, three-dimensional (3D) structures that spontaneously self-organize into adequately differentiated cell types, have emerged as powerful tools for simulating human brain development in vitro [105,106,107]. Compared to traditional two-dimensional (2D) cell culture systems, 3D brain organoids better mimic the in vivo environment and are thus more suitable for modeling neurobiological processes of the human brain. As a result, brain organoids have become indispensable tools for investigating the pathogenesis of neurodegenerative diseases and for performing high-throughput drug screening [108,109,110].
Current protocols for generating brain organoids from iPSCs can be broadly categorized into non-guided and guided differentiation methods [108,111,112,113,114]. The non-guided differentiation method relies on the cells’ intrinsic morphogenetic potential rather than on exogenous inductive factors. This method begins with the suspension culture of iPSCs to form EBs, which then develop into neuroectodermal-like structures. These structures are encapsulated in Matrigel and maintained in a 3D culture system using a bioreactor, allowing them to gradually differentiate into brain organoids composed of diverse cell types [105,115]. Brain organoids generated through this method typically exhibit multiple brain-like regions (e.g., forebrain, midbrain) and high cellular diversity, enabling the simulation of developmental characteristics across different brain regions. However, due to the absence of exogenous inducers, this method lacks precise control over cell differentiation and brain region specification, resulting in poor repeatability [105,115,116].
In contrast, the guided differentiation method employs exogenous factors to direct cell fate. After forming EBs in a standard suspension culture, a dual SMAD inhibitor is added to promote neural lineage differentiation. Subsequently, region-specific morphogenic and neurotrophic factors are introduced, and the cells are transferred to a Matrigel-encapsulated 3D culture system for long-term cultivation, enabling the formation of region-specific organoids, such as cortical, hippocampal, or midbrain organoids [108,111,112,113,114,116]. For example, forebrain neural precursor cells can be generated by inhibiting the WNT signaling pathway using IWR-1 and retinoic acid (RA), combined with FGF2 and EGF to maintain proliferation and drive differentiation into cortical organoids [117,118]. Similarly, adding SHH and FGF8 during the neuroectodermal stage can induce midbrain progenitor cells, while the addition of BDNF, GDNF, ascorbic acid, and cAMP promotes the formation of midbrain organoids containing functional dopaminergic neurons [119,120,121]. Hippocampal organoids can be generated by inhibiting WNT, TGF-β, SHH, and BMP signaling pathways to produce hippocampus-patterned NPCs (hpNPCs), followed by exposure to WNT3A, BDNF, cAMP, and ascorbic acid, yielding hippocampal organoids containing VGLUT1+, NeuN+, and glutamatergic neurons [122,123,124].
Additionally, other protocols have been established to generate various specialized brain organoids, including cerebellar organoids, retinal organoids, and choroid plexus organoids, further expanding the applications of 3D brain models in neurobiological research [104,106,109,125]. Notably, despite significant advancements, brain organoids still face challenges in fully recapitulating the physiological features of the human brain, primarily due to the lack of vascularization and cellular heterogeneity [109,110].

3.7. Assembloids

Assembloids are 3D models formed by integrating multiple organoids or cell types, allowing the study of complex cellular interactions in disease models, including neurodegenerative disorders. These models enable the simulation of neural migration, axon guidance, and circuit formation, mimicking both inter-regional and intra-regional cell–cell interactions within the nervous system. Assembloids have been particularly useful in studying interactions between neural and non-neural cells, such as vascular and immune cells, thereby enhancing the physiological relevance of in vitro systems. The integration of multiple organoid types into assembloids has advanced understanding of developmental processes and disease mechanisms, offering a promising tool for disease modeling, including those for neurodegenerative diseases [126].

4. Neurodegenerative Disease Modeling with Patient-Derived iPSCs

Induced pluripotent stem cells (iPSCs) and their derivatives possess an identical genome to that of the donor patient, making patient-derived iPSCs invaluable as in vitro models for studying neurodegenerative diseases that previously lacked appropriate models. While iPSCs have the potential to investigate any neurodegenerative disease, this review specifically focuses on three prevalent conditions: Alzheimer’s disease (AD), Parkinson’s disease (PD), and Huntington’s disease (HD). Although the majority of these cases are sporadic, iPSC-derived cells from patient iPSCs often exhibit phenotypes consistent with familial cases, despite differences in the underlying molecular mechanisms [2]. The establishment of such models is critical for elucidating the precise pathogenesis of these diseases and for the development of targeted therapeutic strategies for both sporadic and familial forms.
However, several limitations must be considered when applying the iPSC technology to study these diseases. A key challenge is the variability in the maturity and functionality of the differentiated somatic cells, which can lead to inconsistencies in experimental observations. This variability is observed even in iPSCs derived from the same individual, complicating the accurate assessment of disease phenotypes. Such differences may arise from genetic variations between clones, epigenetic modifications, the source of iPSCs, or the presence of residual transgenes in each iPSC clone [54,127]. Addressing these issues is crucial to improving the quality of iPSC-based disease models and increasing their reliability for research purposes.

4.1. Alzheimer’s Disease

In the case of AD, the majority of cases are sporadic AD (sAD), accounting for approximately 95%, while familial AD (fAD) represents only about 5%, where fAD is typically associated with mutations in the amyloid precursor protein (APP), PSEN1, and PSEN2 genes. In contrast, the APOE4 allele is a primary risk factor in sAD, with approximately 65–80% of sAD patients carrying this allele, while the APOE2 allele is considered protective. Additionally, over 20 risk genes associated with AD, such as SORL1 and TREM2, have been identified. A key pathological hallmark of AD is the deposition of β-amyloid (Aβ) peptides. The formation of Aβ plaques (primarily Aβ42) involves the sequential cleavage of APP by β- and γ-secretase, while α-secretase cleavage of APP has a protective effect. PSEN1 and PSEN2 constitute the catalytic core of γ-secretase. Another significant pathological characteristic of AD is the aggregation of the Tau protein in neurons, leading to the formation of neurofibrillary tangles. Hyperphosphorylation of Tau, driven by kinases such as glycogen synthase kinase 3β (GSK-3β) and cyclin-dependent kinase 5 (CDK5), results in neuronal tangles. However, the specific mechanisms underlying these pathological features, as well as the interplay between phosphorylated Tau (p-Tau) and Aβ, remain unclear. Consequently, iPSCs carrying these genetic mutations offer powerful tools for investigating these mechanisms [128,129,130].
Most researchers focus on neurons derived from iPSCs of AD patients, particularly those with mutations in APP, PSEN1, PSEN2, and APOE4, associated with either fAD or sAD. AD neurons derived from iPSCs replicate key pathological features of AD, including Aβ accumulation and Tau hyperphosphorylation [131,132,133]. These neurons also exhibit pathological phenotypes such as activation of GSK3β, abnormal neuronal electrophysiological activity, increased oxidative stress, elevated reactive oxygen species (ROS) production, endoplasmic reticulum dysfunction, and mitochondrial abnormalities [134,135,136,137,138]. AD neurons show a heightened sensitivity to Aβ42 compared to healthy individuals, and treatment with β- or γ-secretase inhibitors has been shown to downregulate Aβ secretion and p-Tau levels [128,131,133,135,139,140]. These findings establish neurons derived from AD patients as robust in vitro models for investigating AD mechanisms and drug discovery (Table 5).
In addition to neurons, researchers have utilized other iPSC-derived in vitro models for AD, including NSCs/NPCs, microglia, astrocytes, and oligodendrocytes. Studies on AD-NSCs reveal that NSCs from fAD or sAD patients with APOE and PSEN1 mutations express low levels of APP and Aβ without notable morphological differences. However, their proliferation and self-renewal capabilities are significantly impaired, and they exhibit elevated expression of neurodevelopmental genes such as MAPT, CD24, and STMN2 [131,141,142]. These findings suggest that while AD-NSCs may not display the AD pathology, their compromised proliferation and differentiation functions indicate that AD-NSCs may serve as a valuable in vitro model for studying early-stage AD and environmental risk factors (Table 5).
AD-derived microglia display diminished phagocytic capacity for Aβ and Tau oligomers, alongside increased neuroinflammation [85,143,144]. Astrocytes derived from fAD (PSEN1) and sAD (APOE3/4) patients exhibit morphological alterations, increased Aβ42 release, impaired clearance capabilities, dysregulated cytokine production, calcium homeostasis imbalance, and elevated ROS levels [145,146,147,148]. Although relatively few studies have focused on AD iPSC-derived oligodendrocytes, evidence suggests they suffer from morphological, proliferative, and functional impairments, impacting their neuronal support and myelin formation capabilities [149] (Table 5).
To investigate the interactions between cell types and the formation of pathological features in complex tissue environments, 3D brain organoids derived from iPSCs have gained significant attention. Studies have reported that introducing pathogenic mutations in APP or PSEN1 into NPCs and differentiating them into brain organoids can reveal Aβ deposition and neurofibrillary tangle formation [150]. Brain organoids derived from fAD and sAD iPSCs show increased Aβ production, enhanced Tau phosphorylation, and endoplasmic reticulum abnormalities [151,152,153,154,155]. Furthermore, these pathological changes are modifiable through pharmacological and environmental interventions. Thus, AD brain organoids are highly valuable for studying cell–cell interactions, the mechanisms underlying pathological structure formation, and the effects of therapeutic strategies in a 3D context (Table 5).
Numerous studies have focused on establishing iPSC-based Alzheimer’s disease (AD) models using the CRISPR gene editing technology. For instance, CRISPR/Cas9-generated astrocytes carrying APP or PSEN1 mutations exhibit increased Aβ production, oxidative stress, and impaired neuronal function [145,156]. Neurons engineered to express APOE4 via gene editing demonstrate a significantly elevated Aβ production, heightened tau phosphorylation, and degeneration of GABAergic neurons [157]. Furthermore, microglia with CRISPR/Cas9-introduced TREM2 R47H mutations show reduced lipid droplet accumulation, altered plaque reactivity, and modified APOE secretion patterns [158].

4.2. Parkinson’s Disease

Parkinson disease (PD) is the second most common neurodegenerative disease after Alzheimer’s disease. Approximately 10% of PD patients are diagnosed with familial Parkinson’s disease (fPD), while the remaining cases are classified as sporadic Parkinson’s disease (sPD) [159]. The primary pathological hallmark of PD is the progressive loss of dopaminergic (DA) neurons, accompanied by the accumulation of α-synuclein and the formation of Lewy bodies [160]. Mutations in genes such as SNCA, PARK2, PINK1, and LRRK2 are closely associated with the onset of both fPD and sPD, although the precise roles of these mutations in PD pathogenesis remain unclear [2,161]. Therefore, the use of patient-derived DA neurons with these genetic mutations offers a powerful platform for elucidating the mechanisms underlying PD and screening potential therapeutic agents.
In fPD, DA neurons derived from PD-iPSCs carrying mutations in SNCA, LRRK2, PARK2, PINK1, and the β-glucocerebrosidase gene (GBA1) exhibit characteristic pathological features of PD [2,5,159,162]. SNCA encodes α-synuclein, and studies have consistently shown that DA neurons derived from patients with SNCA triplications or gene multiplication exhibit elevated α-synuclein protein levels, increased sensitivity to oxidative and endoplasmic reticulum (ER) stress, mitochondrial dysfunction, synaptic loss, and increased neuronal death [163,164,165,166,167,168,169]. Similarly, DA neurons harboring the SNCA–A53T mutation display abnormal α-synuclein aggregation, mitochondrial dysfunction, and increased apoptosis [170,171]. Mutations in the LRRK2 gene are another common cause of fPD [172]. Studies have reported that LRRK2-mutant DA neurons exhibit elevated SNCA transcription, mitochondrial dysfunction, oxidative stress, increased apoptosis, and impaired neuronal homeostasis [173,174,175,176,177,178,179] (Table 6).
Although PD primarily affects individuals over 65 years of age, approximately 5–10% of cases are classified as early-onset PD (EOPD) [180]. Mutations in PARK2 and PINK1 are strongly associated with EOPD [181]. DA neurons derived from PARK2 and PINK1 iPSCs exhibit typical PD-like pathological features, including α-synuclein accumulation, defects in mitochondrial autophagy, elevated ROS levels, and increased sensitivity to oxidative stress [182,183,184,185]. These pathological features can be ameliorated using pharmacological agents such as coenzyme Q10 and rapamycin [186]. While sPD accounts for the majority of PD cases, studies have also identified α-synuclein accumulation and epigenetic changes in sPD-derived DA neurons, often associated with single-gene mutations such as LRRK2, although such studies remain limited. Additionally, rarer forms of PD, such as those caused by GBA1 and VPS35 mutations, exhibit similar pathological phenotypes, including α-synuclein accumulation, mitochondrial damage, and elevated ROS production in DA neurons [105,187,188]. These iPSC-derived cells provide invaluable in vitro models for investigating PD pathogenesis and screening potential therapies (Table 6).
Beyond DA neurons, iPSC-derived astrocytes, microglia, and neural stem cells have also garnered significant attention. For instance, astrocytes derived from LRRK2-mutant iPSCs exhibit α-synuclein accumulation, dysregulated autophagy, abnormal mitochondrial morphology and activity, and reduced viability, leading to oxidative stress and degeneration of DA neurons [189,190,191]. Similarly, SNCA- and LRRK2-mutant iPSC-derived microglia display elevated α-synuclein levels, impaired phagocytic capacity, and increased inflammation, ultimately affecting DA neuron function and morphology [191,192,193]. Furthermore, neural stem cells derived from LRRK2-mutant iPSCs exhibit reduced differentiation efficiency and developmental defects [194,195] (Table 6).
Most current in vitro models of PD primarily rely on 2D cell cultures, which lack the complex cellular interactions and multilayered structural features necessary for accurately modeling disease pathology and evaluating drug efficacy. To address these limitations, 3D brain organoids have emerged as a promising tool in PD research [104]. For example, LRRK2-mutant DA neurons cultured in a 3D environment exhibited increased cell death, reduced differentiation potential, and decreased dendritic complexity compared to controls, and these phenotypes can be partially rescued by inhibiting LRRK2 [196]. Similarly, studies have demonstrated a significant reduction in DA neuron populations and neurodevelopmental defects in LRRK2-mutant iPSC-derived organoids [197]. These findings highlight 3D organoids as advanced in vitro models for elucidating PD pathogenesis and developing novel therapeutic strategies (Table 6).
CRISPR-edited iPSC models have been instrumental in studying PD pathogenesis, with key modifications targeting several disease-associated genes. SNCA models through intron-4 enhancer SNP editing or triplication correction demonstrate reduced α-synuclein aggregation and improved neuronal differentiation [163,198]. LRRK2 G2019S mutation correction restores neurite length and reduces phospho-α-synuclein levels while normalizing the astrocyte lysosomal function [189,199]. GBA N370S mutation correction, achieved through precise editing that avoids the GBAP1 pseudogene, improves glucocerebrosidase activity and reduces α-synuclein accumulation [200,201]. PINK1/PRKN single and double knockout models reveal mitophagy dysfunction, increased oxidative stress, and PRKN-specific lysosomal impairment [202,203]. Additionally, CHCHD2/CHCHD10 knockout models show compromised mitochondrial respiration and motor neuron vulnerability [204]. These models collectively highlight the importance of mitochondrial/lysosomal pathways in PD and demonstrate how 3D systems better recapitulate pathology than traditional 2D cultures. The development of these genetically precise iPSC models has significantly advanced our understanding of PD mechanisms and provided valuable platforms for therapeutic development.

4.3. Huntington’s Disease

Huntington’s disease (HD) is an autosomal dominant hereditary neurodegenerative disorder characterized by progressive motor dysfunction, cognitive decline, and psychiatric disturbances. HD is caused by an abnormal expansion of the CAG trinucleotide repeat within exon 1 of the huntington gene (HTT) located on chromosome 4. The length of CAG repeats is inversely correlated with the age of disease onset and directly correlated with disease severity. Pathologically, HD is defined by hallmark features including the aggregation of mutant HTT, progressive striatal degeneration with the selective loss of medium spiny neurons (MSNs), disruptions in neurotransmitter homeostasis, mitochondrial dysfunction, and prominent extrapyramidal motor abnormalities, such as chorea and dystonia [205].
Recent advances in the generation of in vitro models from HD-iPSCs have provided significant insights into HD pathogenesis (Table 7). Notably, the HD Consortium successfully differentiated 14 HD-iPSC lines, encompassing both early-onset and late-onset cases, into NPCs/NSCs, forebrain neurons, and striatal-like neurons. These HD-derived cells express mutated HTT with expanded CAG repeats, and transcriptomic analyses reveal dysregulation in genes associated with key pathways, including signal transduction, cell cycle, axonal guidance, neuronal development, cytoskeleton organization, cell adhesion, and cellular metabolism. These molecular changes are consistent with the known transcriptional abnormalities in HD and are accompanied by phenotypic features such as aberrant electrophysiological properties and increased apoptosis [206].
In a subsequent larger-scale study involving over 100 HD-iPSC lines, astrocytes differentiated from HD-iPSCs exhibited a vacuolation phenotype, which was also observed in primary lymphocytes from HD patients [207]. Additionally, studies have demonstrated that HD-iPSC-derived NPCs, neurons, and glial cells display increased vulnerability to BDNF withdrawal, a phenomenon similar to that observed in HD animal models [208]. Among the various pathological features of HD, the loss of GABAergic MSNs in the striatum is particularly significant. Several studies have reported that GABA MSN-like neurons derived from HD-iPSCs recapitulate the key pathological features of HD, including mutated HTT aggregation, increased lysosomal and autophagosomal activity, nuclear indentations, caspase activation, and exacerbated neuronal cell death during aging [209,210,211] (Table 7).
Collectively, these findings highlight the utility of HD-iPSC-derived cell lines in modeling the diverse pathological processes and temporal progression of HD. These in vitro models provide a robust platform for investigating disease mechanisms and offer valuable opportunities for identifying and testing potential treatments for HD.
CRISPR-edited iPSC models have significantly advanced Huntington’s disease (HD) research by enabling precise genetic modifications and providing insights into disease mechanisms and potential therapies. Key modifications in the HTT gene include CAG repeat expansion, where 72 repeats showed caspase activation under stress and 97 repeats induced disease-associated phenotypes [212]. Repeat contraction using engineered Cas9 (NGN PAM) reduced pathogenic repeats [213], while allele-specific knockout through a dual gRNA approach selectively eliminated mutant HTT [214]. These genetic modifications reveal key phenotypic findings: mutant HTT expression leads to transcriptional dysregulation, impaired neurotrophic factor transport, and increased caspase activity, while genetic correction rescues neuronal viability, striatal differentiation capacity, and cellular stress responses.

4.4. Rare Neurodegenerative Diseases

In addition to AD, PD, and HD, iPSC-based models are also commonly used in the study of spinocerebellar ataxia (SCA) [215,216,217] and spinal muscular atrophy (SMA) [218,219,220,221], with patient-derived iPSCs exhibiting disease-specific phenotypes and vulnerabilities. For example, studies have shown that Purkinje cells in the SCA6 model exhibit susceptibility to thyroid hormone depletion-induced neurite degeneration [215]. In SMA models, survival motor neuron (SMN) protein levels in motor neurons are reduced, with degenerating neurites and delayed neural differentiation [218,219,220,221]. SMA-derived astrocytes show abnormal morphology, reduced neurotrophic factor secretion, and elevated calcium flux, which may contribute to motor neuron damage [222]. In Alexander disease models, GFAP mutations lead to protein aggregation in astrocytes, prompting these glial cells to secrete higher levels of inflammatory cytokines and other molecules, which inhibit myelination and trigger immune responses [223,224].

5. Conclusions

Looking forward, the continuous advancement of the iPSC technology has opened new avenues for research into neurodegenerative diseases. Studies utilizing iPSC-derived neurons, neural stem cells, glial cells, and brain organoids have provided valuable insights, particularly for disease phenotypes that manifest only in mature neurons after progressing through intermediate neural progenitor stages. Despite these advancements, the establishment of in vitro models using iPSCs remains economically challenging. This is largely attributed to the low density and limited reprogramming efficiency of fibroblasts and PBMCs, which are commonly used as source materials for iPSC generation. These limitations pose significant barriers to the large-scale application of iPSC-derived cells and organoids in drug discovery for neurodegenerative diseases. To overcome these challenges, researchers have increasingly explored direct reprogramming approaches as a promising alternative.
In summary, iPSC-based in vitro models, such as neurons and organoids, enable researchers to recapitulate neurodegenerative diseases in diverse and physiologically relevant forms, deepening our understanding of disease mechanisms. These models hold immense promise for advancing the development of targeted therapies and novel drugs, offering renewed hope for the prevention and treatment of neurodegenerative diseases.

Author Contributions

Conceptualization, Q.R. and M.M.; writing—original draft preparation, X.G., X.W. and J.W.; figure preparation, X.G. and Q.R.; writing—review and editing, Q.R. and M.M.; visualization, Q.R.; supervision, Q.R.; project administration, Q.R.; funding acquisition, Q.R. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Natural Science Foundation of China (81901088), the Hundred Talents Program of Hebei Province (E2019050019), the Hebei Provincial Natural Science Foundation’s Precision Medicine Joint Fund’s Cultivation Project (H2022206562, H2022206511), and the Key Laboratory of Neural and Vascular Biology of the Ministry of Education of China (NV20230011).

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Yoshida, Y.; Yamanaka, S. Induced Pluripotent Stem Cells 10 Years Later: For Cardiac Applications. Circ. Res. 2017, 120, 1958–1968. [Google Scholar] [CrossRef] [PubMed]
  2. Karagiannis, P.; Takahashi, K.; Saito, M.; Yoshida, Y.; Okita, K.; Watanabe, A.; Inoue, H.; Yamashita, J.K.; Todani, M.; Nakagawa, M.; et al. Induced Pluripotent Stem Cells and Their Use in Human Models of Disease and Development. Physiol. Rev. 2019, 99, 79–114. [Google Scholar] [CrossRef] [PubMed]
  3. Tabar, V.; Studer, L. Pluripotent stem cells in regenerative medicine: Challenges and recent progress. Nat. Rev. Genet. 2014, 15, 82–92. [Google Scholar] [CrossRef] [PubMed]
  4. Mungenast, A.E.; Siegert, S.; Tsai, L.-H. Modeling Alzheimer’s Disease with Human Induced Pluripotent Stem (iPS) Cells. Mol. Cell. Neurosci. 2016, 73, 13–31. [Google Scholar] [CrossRef]
  5. Chang, C.Y.; Ting, H.C.; Liu, C.A.; Su, H.L.; Chiou, T.W.; Lin, S.Z.; Harn, H.J.; Ho, T.J. Induced Pluripotent Stem Cell (iPSC)-Based Neurodegenerative Disease Models for Phenotype Recapitulation and Drug Screening. Molecules 2020, 25, 2000. [Google Scholar] [CrossRef]
  6. Akter, M.; Ding, B. Modeling Movement Disorders via Generation of hiPSC-Derived Motor Neurons. Cells 2022, 11, 3796. [Google Scholar] [CrossRef]
  7. Soubannier, V.; Maussion, G.; Chaineau, M.; Sigutova, V.; Rouleau, G.; Durcan, T.M.; Stifani, S. Characterization of human iPSC-derived astrocytes with potential for disease modeling and drug discovery. Neurosci. Lett. 2020, 731, 135028. [Google Scholar] [CrossRef]
  8. Yamanaka, S. Pluripotent stem cell-based cell therapy-Promise and challenges. Cell Stem Cell 2020, 27, 523–531. [Google Scholar] [CrossRef]
  9. Tian, R.; Gachechiladze, M.A.; Ludwig, C.H.; Laurie, M.T.; Hong, J.Y.; Nathaniel, D.; Prabhu, A.V.; Fernandopulle, M.S.; Patel, R.; Abshari, M.; et al. CRISPR Interference-Based Platform for Multimodal Genetic Screens in Human iPSC-Derived Neurons. Neuron 2019, 104, 239–255.e12. [Google Scholar] [CrossRef]
  10. Hendriks, D.; Clevers, H.; Artegiani, B. CRISPR-Cas Tools and Their Application in Genetic Engineering of Human Stem Cells and Organoids. Cell Stem Cell 2020, 27, 705–731. [Google Scholar] [CrossRef]
  11. Haridhasapavalan, K.K.; Borgohain, M.P.; Dey, C.; Saha, B.; Narayan, G.; Kumar, S.; Thummer, R.P. An insight into non-integrative gene delivery approaches to generate transgene-free induced pluripotent stem cells. Gene 2019, 686, 146–159. [Google Scholar] [CrossRef] [PubMed]
  12. Okita, K.; Ichisaka, T.; Yamanaka, S. Generation of germline-competent induced pluripotent stem cells. Nature 2007, 448, 313–317. [Google Scholar] [CrossRef] [PubMed]
  13. Malik, N.; Rao, M.S. A review of the methods for human iPSC derivation. Methods Mol. Biol. 2013, 997, 23–33. [Google Scholar]
  14. Woltjen, K.; Michael, I.P.; Mohseni, P.; Desai, R.; Mileikovsky, M.; Hamalainen, R.; Cowling, R.; Wang, W.; Liu, P.; Gertsenstein, M.; et al. PiggyBac transposition reprograms fibroblasts to induced pluripotent stem cells. Nature 2009, 458, 766–770. [Google Scholar] [CrossRef]
  15. Kawaguchi, T.; Tsukiyama, T.; Kimura, K.; Matsuyama, S.; Minami, N.; Yamada, M.; Imai, H. Generation of Naïve Bovine Induced Pluripotent Stem Cells Using PiggyBac Transposition of Doxycycline- Inducible Transcription Factors. PLoS ONE 2015, 10, e0135403. [Google Scholar] [CrossRef]
  16. Jia, F.; Wilson, K.D.; Sun, N.; Gupta, D.M.; Huang, M.; Li, Z.; Panetta, N.J.; Chen, Z.Y.; Robbins, R.C.; Kay, M.A.; et al. A nonviral minicircle vector for deriving human iPS cells. Nat. Methods 2010, 7, 197–199. [Google Scholar] [CrossRef]
  17. Yu, J.; Hu, K.; Smuga-Otto, K.; Tian, S.; Stewart, R.; Slukvin, I.I.; Thomson, J.A. Human induced pluripotent stem cells free of vector and transgene sequences. Science 2009, 324, 797–801. [Google Scholar] [CrossRef]
  18. Okita, K.; Nakagawa, M.; Hyenjong, H.; Ichisaka, T.; Yamanaka, S. Generation of mouse induced pluripotent stem cells without viral vectors. Science 2008, 322, 949–953. [Google Scholar] [CrossRef]
  19. Warren, L.; Manos, P.D.; Ahfeldt, T.; Loh, Y.H.; Li, H.; Lau, F.; Ebina, W.; Mandal, P.K.; Smith, Z.D.; Meissner, A.; et al. Highly efficient reprogramming to pluripotency and directed differentiation of human cells with synthetic modified mRNA. Cell Stem Cell 2010, 7, 618–630. [Google Scholar] [CrossRef]
  20. Subramanyam, D.; Lamouille, S.; Judson, R.L.; Liu, J.Y.; Bucay, N.; Derynck, R.; Blelloch, R. Multiple targets of miR-302 and miR-372 promote reprogramming of human fibroblasts to induced pluripotent stem cells. Nat. Biotechnol. 2011, 29, 443–448. [Google Scholar] [CrossRef]
  21. Anokye-Danso, F.; Trivedi, C.M.; Juhr, D.; Gupta, M.; Cui, Z.; Tian, Y.; Zhang, Y.; Yang, W.; Gruber, P.J.; Epstein, J.A. Highly efficient miRNA-mediated reprogramming of mouse and human somatic cells to pluripotency. Cell Stem Cell 2011, 8, 376–388. [Google Scholar] [CrossRef] [PubMed]
  22. Takahashi, K.; Yamanaka, S. Induction of pluripotent stem cells from embryonic and adult fibroblast cultures by defined factors. Cell 2006, 126, 663–676. [Google Scholar] [CrossRef] [PubMed]
  23. Nakagawa, M.; Koyanagi, M.; Tanabe, K.; Takahashi, K.; Ichisaka, T.; Aoi, T.; Okita, K.; Mochiduki, Y.; Takizawa, N.; Yamanaka, S. Generation of induced pluripotent stem cells without Myc frome mouse and human fibroblasts. Nat. Biotechnol. 2008, 26, 101–106. [Google Scholar] [CrossRef] [PubMed]
  24. Yu, J.; Vodyanik, M.A.; Smuga-Otto, K.; Antosiewicz-Bourget, J.; Frane, J.L.; Tian, S.; Nie, J.; Jonsdottir, G.A.; Ruotti, V.; Stewart, R.; et al. Induced pluripotent stem cell lines derived from human somatic cells. Science 2007, 318, 1917–1920. [Google Scholar] [CrossRef]
  25. Tsubooka, N.; Ichisaka, T.; Okita, K.; Takahashi, K.; Nakagawa, M.; Yamanaka, S. Roles of Sall4 in the generation of pluripotent stem cells from blastocysts and fibroblasts. Genes Cells 2009, 14, 683–694. [Google Scholar] [CrossRef]
  26. Maekawa, M.; Yamaguchi, K.; Nakamura, T.; Shibukawa, R.; Kodanaka, I.; Ichisaka, T.; Kawamura, Y.; Mochizuki, H.; Goshima, N.; Yamanaka, S. Direct reprogramming of somatic cells is promoted by maternal transcription factor Glis1. Nature 2011, 474, 225–229. [Google Scholar] [CrossRef]
  27. Heng, J.C.; Feng, B.; Han, J.; Jiang, J.; Kraus, P.; Ng, J.H.; Orlov, Y.L.; Huss, M.; Yang, L.; Lufkin, T.; et al. The nuclear receptor Nr5a2 can replace Oct4 in the reprogramming of murine somatic cells to pluripotent cells. Cell Stem Cell 2010, 6, 167–174. [Google Scholar] [CrossRef]
  28. Yu, J.; Chau, K.F.; Vodyanik, M.A.; Jiang, J.; Jiang, Y. Efficient feeder-free episomal reprogramming with small molecules. PLoS ONE 2011, 6, e17557. [Google Scholar] [CrossRef]
  29. Li, D.; Wang, L.; Hou, J.; Shen, Q.; Chen, Q.; Wang, X.; Du, J.; Cai, X.; Shan, Y.; Zhang, T.; et al. Optimized Approaches for Generation of Integration-free iPSCs from Human Urine-Derived Cells with Small Molecules and Autologous Feeder. Stem Cell Rep. 2016, 6, 717–728. [Google Scholar] [CrossRef]
  30. Liu, G.; David, B.T.; Trawczynski, M.; Fessler, R.G. Advances in Pluripotent Stem Cells: History, Mechanisms, Technologies, and Applications. Stem Cell Rev. Rep. 2020, 16, 3–32. [Google Scholar] [CrossRef]
  31. Gore, A.; Li, Z.; Fung, H.L.; Young, J.E.; Agarwal, S.; Antosiewicz-Bourget, J.; Canto, I.; Giorgetti, A.; Israel, M.A.; Kiskinis, E.; et al. Somatic coding mutations in human induced pluripotent stem cells. Nature 2011, 471, 63–67. [Google Scholar] [CrossRef] [PubMed]
  32. Kobayashi, T.; Yamaguchi, T.; Hamanaka, S.; Kato-Itoh, M.; Yamazaki, Y.; Ibata, M.; Sato, H.; Lee, Y.S.; Usui, J.; Knisely, A.S.; et al. Generation of rat pancreas in mouse by interspecific blastocyst injection of pluripotent stem cells. Cell 2010, 142, 787–799. [Google Scholar] [CrossRef] [PubMed]
  33. Neira, J.A.; Conrad, J.V.; Rusteika, M.; Chu, L.F. The progress of induced pluripotent stem cells derived from pigs: A mini review of recent advances. Front. Cell Dev. Biol. 2024, 12, 1371240. [Google Scholar] [CrossRef]
  34. Harding, J.; Roberts, R.M.; Mirochnitchenko, O. Large animal models for stem cell therapy. Stem Cell Res. Ther. 2013, 4, 23. [Google Scholar] [CrossRef]
  35. Honda, A.; Hirose, M.; Hatori, M.; Matoba, S.; Miyoshi, H.; Inoue, K.; Ogura, A. Generation of induced pluripotent stem cells in rabbits: Potential experimental models for human regenerative medicine. J. Biol. Chem. 2010, 285, 31362–31369. [Google Scholar] [CrossRef]
  36. Liu, H.; Zhu, F.; Yong, J.; Zhang, P.; Hou, P.; Li, H.; Jiang, W.; Cai, J.; Liu, M.; Cui, K.; et al. Generation of induced pluripotent stem cells from adult rhesus monkey fibroblasts. Cell Stem Cell 2008, 3, 587–590. [Google Scholar] [CrossRef]
  37. Nagy, K.; Sung, H.K.; Zhang, P.; Laflamme, S.; Vincent, P.; Agha-Mohammadi, S.; Woltjen, K.; Monetti, C.; Michael, I.P.; Smith, L.C.; et al. Induced pluripotent stem cell lines derived from equine fibroblasts. Stem Cell Rev. 2011, 7, 693–702. [Google Scholar] [CrossRef]
  38. Raab, S.; Klingenstein, M.; Liebau, S.; Linta, L. A Comparative View on Human Somatic Cell Sources for iPSC Generation. Stem Cells Int. 2014, 2014, 768391. [Google Scholar] [CrossRef]
  39. Ray, A.; Joshi, J.M.; Sundaravadivelu, P.K.; Raina, K.; Lenka, N.; Kaveeshwar, V.; Thummer, R.P. An Overview on Promising Somatic Cell Sources Utilized for the Efficient Generation of Induced Pluripotent Stem Cells. Stem Cell Rev. Rep. 2021, 17, 1954–1974. [Google Scholar] [CrossRef]
  40. Cerneckis, J.; Cai, H.; Shi, Y. Induced pluripotent stem cells (iPSCs): Molecular mechanisms of induction and applications. Signal Transduct. Target. Ther. 2024, 9, 112. [Google Scholar] [CrossRef]
  41. Staerk, J.; Dawlaty, M.M.; Gao, Q.; Maetzel, D.; Hanna, J.; Sommer, C.A.; Mostoslavsky, G.; Jaenisch, R. Reprogramming of human peripheral blood cells to induced pluripotent stem cells. Cell Stem Cell 2010, 7, 20–24. [Google Scholar] [CrossRef] [PubMed]
  42. Loh, Y.H.; Hartung, O.; Li, H.; Guo, C.; Sahalie, J.M.; Manos, P.D.; Urbach, A.; Heffner, G.C.; Grskovic, M.; Vigneault, F.; et al. Reprogramming of T cells from human peripheral blood. Cell Stem Cell 2010, 7, 15–19. [Google Scholar] [CrossRef] [PubMed]
  43. Loh, Y.H.; Agarwal, S.; Park, I.H.; Urbach, A.; Huo, H.; Heffner, G.C.; Kim, K.; Miller, J.D.; Ng, K.; Daley, G.Q. Generation of induced pluripotent stem cells from human blood. Blood 2009, 113, 5476–5479. [Google Scholar] [CrossRef] [PubMed]
  44. Ye, Z.; Zhan, H.; Mali, P.; Dowey, S.; Williams, D.M.; Jang, Y.Y.; Dang, C.V.; Spivak, J.L.; Moliterno, A.R.; Cheng, L. Human-induced pluripotent stem cells from blood cells of healthy donors and patients with acquired blood disorders. Blood 2009, 114, 5473–5480. [Google Scholar] [CrossRef]
  45. Merling, R.K.; Sweeney, C.L.; Choi, U.; De Ravin, S.S.; Myers, T.G.; Otaizo-Carrasquero, F.; Pan, J.; Linton, G.; Chen, L.; Koontz, S.; et al. Transgene-free iPSCs generated from small volume peripheral blood nonmobilized CD34+ cells. Blood 2013, 121, e98–e107. [Google Scholar] [CrossRef]
  46. Mack, A.A.; Kroboth, S.; Rajesh, D.; Wang, W.B. Generation of Induced Pluripotent Stem Cells from CD34+ Cells across Blood Drawn from Multiple Donors with Non-Integrating Episomal Vectors. PLoS ONE 2011, 6, e27956. [Google Scholar] [CrossRef]
  47. de Leeuw, V.C.; van Oostrom, C.T.M.; Imholz, S.; Piersma, A.H.; Hessel, E.V.S.; Dollé, M.E.T. Going Back and Forth: Episomal Vector Reprogramming of Peripheral Blood Mononuclear Cells to Induced Pluripotent Stem Cells and Subsequent Differentiation into Cardiomyocytes and Neuron-Astrocyte Co-cultures. Cell. Reprogramming 2020, 22, 300–310. [Google Scholar] [CrossRef]
  48. Zhang, Y.; Hu, W.; Ma, K.; Zhang, C.; Fu, X. Reprogramming of Keratinocytes as Donor or Target Cells Holds Great Promise for Cell Therapy and Regenerative Medicine. Stem Cell Rev. Rep. 2019, 15, 680–689. [Google Scholar] [CrossRef]
  49. Sun, N.; Panetta, N.J.; Gupta, D.M.; Wilson, K.D.; Lee, A.; Jia, F.; Hu, S.; Cherry, A.M.; Robbins, R.C.; Longaker, M.T.; et al. Feeder-free derivation of induced pluripotent stem cells from adult human adipose stem cells. Proc. Natl. Acad. Sci. USA 2009, 106, 15720–15725. [Google Scholar] [CrossRef]
  50. Zhou, T.; Benda, C.; Duzinger, S.; Huang, Y.; Li, X.; Li, Y.; Guo, X.; Cao, G.; Chen, S.; Hao, L.; et al. Generation of Induced Pluripotent Stem Cells from Urine. J. Am. Soc. Nephrol. 2011, 22, 1221–1228. [Google Scholar] [CrossRef]
  51. Eminli, S.; Utikal, J.; Arnold, K.; Jaenisch, R.; Hochedlinger, K. Reprogramming of neural progenitor cells into induced pluripotent stem cells in the absence of exogenous Sox2 expression. Stem Cells 2008, 26, 2467–2474. [Google Scholar] [CrossRef] [PubMed]
  52. Aoi, T.; Yae, K.; Nakagawa, M.; Ichisaka, T.; Okita, K.; Takahashi, K.; Chiba, T.; Yamanaka, S. Generation of pluripotent stem cells from adult mouse liver and stomach cells. Science 2008, 321, 699–702. [Google Scholar] [CrossRef] [PubMed]
  53. Okita, K.; Yamakawa, T.; Matsumura, Y.; Sato, Y.; Amano, N.; Watanabe, A.; Goshima, N.; Yamanaka, S. An efficient nonviral method to generate integration-free human-induced pluripotent stem cells from cord blood and peripheral blood cells. Stem Cells 2013, 31, 458–466. [Google Scholar] [CrossRef]
  54. Kim, K.; Doi, A.; Wen, B.; Ng, K.; Zhao, R.; Cahan, P.; Kim, J.; Aryee, M.J.; Ji, H.; Ehrlich, L.I.; et al. Epigenetic memory in induced pluripotent stem cells. Nature 2010, 467, 285–290. [Google Scholar] [CrossRef] [PubMed]
  55. Singh, V.K.; Kumar, N.; Kalsan, M.; Saini, A.; Chandra, R. Mechanism of Induction: Induced Pluripotent Stem Cells (iPSCs). J. Stem Cells 2015, 10, 43–62. [Google Scholar] [PubMed]
  56. Chambers, S.M.; Fasano, C.A.; Papapetrou, E.P.; Tomishima, M.; Sadelain, M.; Studer, L. Highly efficient neural conversion of human ES and iPS cells by dual inhibition of SMAD signaling. Nat. Biotechnol. 2009, 27, 275–280. [Google Scholar] [CrossRef]
  57. Barak, M.; Fedorova, V.; Pospisilova, V.; Raska, J.; Vochyanova, S.; Sedmik, J.; Hribkova, H.; Klimova, H.; Vanova, T.; Bohaciakova, D. Human iPSC-Derived Neural Models for Studying Alzheimer’s Disease: From Neural Stem Cells to Cerebral Organoids. Stem Cell Rev. Rep. 2022, 18, 792–820. [Google Scholar] [CrossRef]
  58. Elkabetz, Y.; Panagiotakos, G.; Al Shamy, G.; Socci, N.D.; Tabar, V.; Studer, L. Human ES cell-derived neural rosettes reveal a functionally distinct early neural stem cell stage. Genes Dev. 2008, 22, 152–165. [Google Scholar] [CrossRef]
  59. Koch, P.; Opitz, T.; Steinbeck, J.A.; Ladewig, J.; Brüstle, O. A rosette-type, self-renewing human ES cell-derived neural stem cell with potential for in vitro instruction and synaptic integration. Proc. Natl. Acad. Sci. USA 2009, 106, 3225–3230. [Google Scholar] [CrossRef]
  60. Zhang, S.C.; Wernig, M.; Duncan, I.D.; Brüstle, O.; Thomson, J.A. In vitro differentiation of transplantable neural precursors from human embryonic stem cells. Nat. Biotechnol. 2001, 19, 1129–1133. [Google Scholar] [CrossRef]
  61. Pang, Z.P.; Yang, N.; Vierbuchen, T.; Ostermeier, A.; Fuentes, D.R.; Yang, T.Q.; Citri, A.; Sebastiano, V.; Marro, S.; Südhof, T.C.; et al. Induction of human neuronal cells by defined transcription factors. Nature 2011, 476, 220–223. [Google Scholar] [CrossRef] [PubMed]
  62. Zhang, Y.; Pak, C.; Han, Y.; Ahlenius, H.; Zhang, Z.; Chanda, S.; Marro, S.; Patzke, C.; Acuna, C.; Covy, J.; et al. Rapid Single-Step Induction of Functional Neurons from Human Pluripotent Stem Cells. Neuron 2013, 78, 785–798. [Google Scholar] [CrossRef] [PubMed]
  63. Flitsch, L.J.; Laupman, K.E.; Brüstle, O. Transcription Factor-Based Fate Specification and Forward Programming for Neural Regeneration. Front. Cell. Neurosci. 2020, 14, 121. [Google Scholar] [CrossRef]
  64. Huh, C.J.; Zhang, B.; Victor, M.B.; Dahiya, S.; Batista, L.F.; Horvath, S.; Yoo, A.S. Maintenance of age in human neurons generated by microRNA-based neuronal conversion of fibroblasts. eLife 2001, 5, e18648. [Google Scholar] [CrossRef]
  65. Yoo, A.S.; Sun, A.X.; Li, L.; Shcheglovitov, A.; Portmann, T.; Li, Y.; Lee-Messer, C.; Dolmetsch, R.E.; Tsien, R.W.; Crabtree, G.R. MicroRNA-mediated conversion of human fibroblasts to neurons. Nature 2011, 476, 228–231. [Google Scholar] [CrossRef]
  66. Mertens, J.; Paquola, A.C.M.; Ku, M.; Hatch, E.; Bohnke, L.; Ladjevardi, S.; McGrath, S.; Campbell, B.; Lee, H.; Herdy, J.R.; et al. Directly Reprogrammed Human Neurons Retain Aging-Associated Transcriptomic Signatures and Reveal Age-Related Nucleocytoplasmic Defects. Cell Stem Cell 2015, 17, 705–718. [Google Scholar] [CrossRef]
  67. Cintrón-Colón, A.F.; Almeida-Alves, G.; Boynton, A.M.; Spitsbergen, J.M. GDNF synthesis, signaling, and retrograde transport in motor neurons. Cell Tissue Res. 2020, 382, 47–56. [Google Scholar] [CrossRef]
  68. Lepski, G.P.; Jannes, C.E.; Nikkhah, G.; Bischofberger, J. cAMP promotes the differentiation of neural progenitor cells in vitro via modulation of voltage-gated calcium channels. Front. Cell. Neurosci. 2013, 7, 155. [Google Scholar] [CrossRef]
  69. Jang, S.; Cho, H.H.; Cho, Y.B.; Park, J.S.; Jeong, H.S. Functional neural differentiation of human adipose tissue-derived stem cells using bFGF and forskolin. BMC Cell Biol. 2010, 11, 25. [Google Scholar] [CrossRef]
  70. Janesick, A.; Wu, S.C.; Blumberg, B. Retinoic acid signaling and neuronal differentiation. Cell. Mol. Life Sci. 2015, 72, 1559–1576. [Google Scholar] [CrossRef]
  71. Busskamp, V.; Lewis, N.E.; Guye, P.; Ng, A.H.; Shipman, S.L.; Byrne, S.M.; Sanjana, N.E.; Murn, J.; Li, Y.; Li, S.; et al. Rapid neurogenesis through transcriptional activation in human stem cells. Mol. Syst. Biol. 2014, 10, 760. [Google Scholar] [CrossRef] [PubMed]
  72. Caiazzo, M.; Giannelli, S.; Valente, P.; Lignani, G.; Carissimo, A.; Sessa, A.; Colasante, G.; Bartolomeo, R.; Massimino, L.; Ferroni, S.; et al. Direct conversion of fibroblasts into functional astrocytes by defined transcription factors. Stem Cell Rep. 2015, 4, 25–36. [Google Scholar] [CrossRef] [PubMed]
  73. Marro, S.; Pang, Z.P.; Yang, N.; Tsai, M.C.; Qu, K.; Chang, H.Y.; Südhof, T.C.; Wernig, M. Direct Lineage Conversion of Terminally Differentiated Hepatocytes to Functional Neurons. Cell Stem Cell 2011, 9, 374–382. [Google Scholar] [CrossRef] [PubMed]
  74. Ambasudhan, R.; Talantova, M.; Coleman, R.; Yuan, X.; Zhu, S.; Lipton, S.A.; Ding, S. Direct reprogramming of adult human fibroblasts to functional neurons under defined conditions. Cell Stem Cell 2011, 9, 113–118. [Google Scholar] [CrossRef]
  75. Zhang, W.; Jiao, B.; Zhou, M.; Zhou, T.; Shen, L. Modeling Alzheimer’s Disease with Induced Pluripotent Stem Cells: Current Challenges and Future Concerns. Stem Cells Int. 2016, 2016, 7828049. [Google Scholar] [CrossRef]
  76. Tcw, J.; Wang, M.; Pimenova, A.A.; Hartley, B.J.; Lacin, E.; Machlovi, S.I.; Abdelaal, R.; Karch, C.M.; Phatnani, H.; Slesinger, P.A.; et al. An Efficient Platform for Astrocyte Differentiation from Human Induced Pluripotent Stem Cells. Stem Cell Rep. 2017, 9, 600–614. [Google Scholar] [CrossRef]
  77. Byun, J.S.; Lee, C.O.; Oh, M.; Cha, D.; Kim, W.K.; Oh, K.J.; Bae, K.H.; Lee, S.C.; Han, B.S. Rapid differentiation of astrocytes from human embryonic stem cells. Neurosci. Lett. 2020, 716, 134681. [Google Scholar] [CrossRef]
  78. Ren, B.; Dunaevsky, A. Modeling Neurodevelopmental and Neuropsychiatric Diseases with Astrocytes Derived from Human-Induced Pluripotent Stem Cells. Int. J. Mol. Sci. 2021, 22, 1692. [Google Scholar] [CrossRef]
  79. Canals, I.; Ginisty, A.; Quist, E.; Timmerman, R.; Fritze, J.; Miskinyte, G.; Monni, E.; Hansen, M.G.; Hidalgo, I.; Bryder, D.; et al. Rapid and efficient induction of functional astrocytes from human pluripotent stem cells. Nat. Methods 2018, 15, 693–696. [Google Scholar] [CrossRef]
  80. Tchieu, J.; Calder, E.L.; Guttikonda, S.R.; Gutzwiller, E.M.; Aromolaran, K.A.; Steinbeck, J.A.; Goldstein, P.A.; Studer, L. NFIA is a gliogenic switch enabling rapid derivation of functional human astrocytes from pluripotent stem cells. Nat. Biotechnol. 2019, 37, 267–275. [Google Scholar] [CrossRef]
  81. Janssen, K.; Bahnassawy, L.; Kiefer, C.; Korffmann, J.; Terstappen, G.C.; Lakics, V.; Cik, M.; Reinhardt, P. Generating Human iPSC-Derived Astrocytes with Chemically Defined Medium for In vitro Disease Modeling. In Cell-Based Assays Using iPSCs for Drug Development and Testing; Mandenius, C.F., Ross, J., Eds.; Methods in Molecular Biology; Springer: New York, NY, USA, 2019; Volume 1994, pp. 31–39. [Google Scholar]
  82. Raman, S.; Srinivasan, G.; Brookhouser, N.; Nguyen, T.; Henson, T.; Morgan, D.; Cutts, J.; Brafman, D.A. A Defined and Scalable Peptide-Based Platform for the Generation of Human Pluripotent Stem Cell-Derived Astrocytes. ACS Biomater. Sci. Eng. 2020, 6, 3477–3490. [Google Scholar] [CrossRef] [PubMed]
  83. Foo, L.C.; Allen, N.J.; Bushong, E.A.; Ventura, P.B.; Chung, W.S.; Zhou, L.; Cahoy, J.D.; Daneman, R.; Zong, H.; Ellisman, M.H.; et al. Development of a method for the purification and culture of rodent astrocytes. Neuron 2011, 71, 799–811. [Google Scholar] [CrossRef] [PubMed]
  84. Sun, W.; McConnell, E.; Pare, J.F.; Xu, Q.; Chen, M.; Peng, W.; Lovatt, D.; Han, X.; Smith, Y.; Nedergaard, M. Glutamate-dependent neuroglial calcium signaling differs between young and adult brain. Science 2013, 399, 197–200. [Google Scholar] [CrossRef] [PubMed]
  85. Abud, E.M.; Ramirez, R.N.; Martinez, E.S.; Healy, L.M.; Nguyen, C.H.H.; Newman, S.A.; Yeromin, A.V.; Scarfone, V.M.; Marsh, S.E.; Fimbres, C.; et al. iPSC-Derived Human Microglia-like Cells to Study Neurological Diseases. Neuron 2017, 94, 278–293.e9. [Google Scholar] [CrossRef]
  86. Ijaz, L.; Nijsure, M.; Fossati, V. Human Pluripotent Stem Cell Differentiation to Microglia. In Induced Pluripotent Stem (iPS) Cells; Nagy, A., Turksen, K., Eds.; Methods in Molecular Biology; Humana: New York, NY, USA, 2021; Volume 2454. [Google Scholar]
  87. McQuade, A.; Coburn, M.; Tu, C.H.; Hasselmann, J.; Davtyan, H.; Blurton-Jones, M. Development and validation of a simplified method to generate human microglia from pluripotent stem cells. Mol. Neurodegener. 2018, 13, 67. [Google Scholar] [CrossRef]
  88. Muffat, J.; Li, Y.; Yuan, B.; Mitalipova, M.; Omer, A.; Corcoran, S.; Bakiasi, G.; Tsai, L.H.; Aubourg, P.; Ransohoff, R.M.; et al. Efficient derivation of microglia- like cells from human pluripotent stem cells. Nat. Med. 2016, 22, 1358–1367. [Google Scholar] [CrossRef]
  89. Speicher, A.M.; Wiendl, H.; Meuth, S.G.; Pawlowski, M. Generating microglia from human pluripotent stem cells: Novel in vitro models for the study of neurodegeneration. Mol. Neurodegener. 2019, 14, 46. [Google Scholar] [CrossRef]
  90. Cakir, B.; Tanaka, Y.; Kiral, F.R.; Xiang, Y.; Dagliyan, O.; Wang, J.; Lee, M.; Greaney, A.M.; Yang, W.S.; duBoulay, C.; et al. Expression of the transcription factor, P.U.1 induces the generation of microglia-like cells in human cortical organoids. Nat. Commun. 2022, 13, 430. [Google Scholar] [CrossRef]
  91. Kierdorf, K.; Erny, D.; Goldmann, T.; Sander, V.; Schulz, C.; Perdiguero, E.G.; Wieghofer, P.; Heinrich, A.; Riemke, P.; Hölscher, C.; et al. Microglia emerge from erythromyeloid precursors via Pu.1- and Irf8-dependent pathways. Nat. Neurosci. 2013, 16, 273–280. [Google Scholar] [CrossRef]
  92. Pandya, H.; Shen, M.J.; Ichikawa, D.M.; Sedlock, A.B.; Choi, Y.; Johnson, K.R.; Kim, G.; Brown, M.A.; Elkahloun, A.G.; Maric, D.; et al. Differentiation of human and murine induced pluripotent stem cells to microglia-like cells. Nat. Neurosci. 2017, 20, 753–759. [Google Scholar] [CrossRef]
  93. Kuhn, S.; Gritti, L.; Crooks, D.; Dombrowski, Y. Oligodendrocytes in Development, Myelin Generation and Beyond. Cells 2019, 8, 1424. [Google Scholar] [CrossRef] [PubMed]
  94. Goldman, S.A.; Kuypers, N.J. How to make an oligodendrocyte. Development 2015, 142, 3983–3995. [Google Scholar] [CrossRef] [PubMed]
  95. Dai, Z.-M.; Sun, S.; Wang, C.; Huang, H.; Hu, X.; Zhang, Z.; Lu, Q.R.; Qiu, M. Stage-Specific Regulation of Oligodendrocyte Development by Wnt/β-Catenin Signaling. J. Neurosci. 2014, 34, 8467–8473. [Google Scholar] [CrossRef] [PubMed]
  96. Douvaras, P.; Fossati, V. Generation and isolation of oligodendrocyte progenitor cells from human pluripotent stem cells. Nat. Protoc. 2015, 10, 1143–1154. [Google Scholar] [CrossRef]
  97. Shi, B.; Ding, J.; Liu, Y.; Zhuang, X.; Zhuang, X.; Chen, X.; Fu, C. ERK1/2 pathway-mediated differentiation of IGF- 1-transfected spinal cord-derived neural stem Cells into oligodendrocytes. PLoS ONE 2014, 9, e106038. [Google Scholar] [CrossRef]
  98. Ehrlich, M.; Mozafari, S.; Glatza, M.; Starost, L.; Velychko, S.; Hallmann, A.L.; Cui, Q.L.; Schambach, A.; Kim, K.P.; Bachelin, C.; et al. Rapid and efficient generation of oligodendrocytes from human induced pluripotent stem cells using transcription factors. Proc. Natl. Acad. Sci. USA 2017, 114, E2243–E2252. [Google Scholar] [CrossRef]
  99. Chanoumidou, K.; Hernández-Rodríguez, B.; Windener, F.; Thomas, C.; Stehling, M.; Mozafari, S.; Albrecht, S.; Ottoboni, L.; Antel, J.; Kim, K.P.; et al. One-step Reprogramming of Human Fibroblasts into Oligodendrocyte-like Cells by SOX10, OLIG2, and NKX6.2. Stem Cell Rep. 2021, 16, 771–783. [Google Scholar] [CrossRef]
  100. Madhavan, M.; Nevin, Z.S.; Shick, H.E.; Garrison, E.; Clarkson-Paredes, C.; Karl, M.; Clayton, B.L.L.; Factor, D.C.; Allan, K.C.; Barbar, L.; et al. Induction of myelinating oligodendrocytes in human cortical spheroids. Nat. Methods 2018, 15, 700–706. [Google Scholar] [CrossRef]
  101. Hubler, Z.; Allimuthu, D.; Bederman, I.; Elitt, M.S.; Madhavan, M.; Allan, K.C.; Shick, H.E.; Garrison, E.; T Karl, M.; Factor, D.C.; et al. Accumulation of 8, 9-unsaturated sterols drives oligodendrocyte formation and remyelination. Nature 2018, 560, 372–376. [Google Scholar] [CrossRef]
  102. Swire, M.; Ffrench-Constant, C. Oligodendrocyte-Neuron Myelinating Coculture. In Oligodendrocytes; Lyons, D., Kegel, L., Eds.; Methods in Molecular Biology; Humana Press: New York, NY, USA, 2019; Volume 1936. [Google Scholar]
  103. Sim, F.J.; McClain, C.R.; Schanz, S.J.; Protack, T.L.; Windrem, M.S.; Goldman, S.A. CD140a identifies a population of highly myelinogenic, migration-competent and efficiently engrafting human oligodendrocyte progenitor cells. Nat. Biotechnol. 2011, 29, 934–941. [Google Scholar] [CrossRef]
  104. Abe-Fukasawa, N.; Otsuka, K.; Aihara, A.; Itasaki, N.; Nishino, T. Novel 3D Liquid Cell Culture Method for Anchorage-independent Cell Growth, Cell Imaging and Automated Drug Screening. Sci. Rep. 2018, 8, 3627. [Google Scholar] [CrossRef] [PubMed]
  105. Lancaster, M.A.; Renner, M.; Martin, C.A.; Wenzel, D.; Bicknell, L.S.; Hurles, M.E.; Homfray, T.; Penninger, J.M.; Jackson, A.P.; Knoblich, J.A. Cerebral organoids model human brain development and microcephaly. Nature 2013, 501, 373–379. [Google Scholar] [CrossRef] [PubMed]
  106. Kelava, I.; Lancaster, M.A. Dishing out mini-brains: Current progress and future prospects in brain organoid research. Dev. Biol. 2016, 420, 199–209. [Google Scholar] [CrossRef] [PubMed]
  107. Manganelli, M.; Mazzoldi, E.L.; Ferraro, R.M.; Pinelli, M.; Parigi, M.; Aghel, S.A.M.; Bugatti, M.; Collo, G.; Stocco, G.; Vermi, W.; et al. Progesterone receptor is constitutively expressed in induced Pluripotent Stem Cells (iPSCs). Stem Cell Rev. Rep. 2024, 20, 2303–2317. [Google Scholar] [CrossRef]
  108. Corsini, N.S.; Knoblich, J.A. Human organoids: New strategies and methods for analyzing human development and disease. Cell 2022, 185, 2756–2769. [Google Scholar] [CrossRef]
  109. Koo, B.; Choi, B.; Park, H.; Yoon, K.J. Past, Present, and Future of Brain Organoid Technology. Mol. Cells 2019, 42, 617–627. [Google Scholar]
  110. Fatehullah, A.; Tan, S.H.; Barker, N. Organoids as an in vitro model of human development and disease. Nat. Cell Biol. 2016, 18, 246–254. [Google Scholar] [CrossRef]
  111. Kim, J.; Koo, B.K.; Knoblich, J.A. Human organoids: Model systems for human biology and medicine. Nat. Rev. Mol. Cell Biol. 2020, 21, 571–584. [Google Scholar] [CrossRef]
  112. Schutgens, F.; Clevers, H. Human organoids: Tools for understanding biology and treating diseases. Annu. Rev. Pathol. 2020, 15, 211–234. [Google Scholar] [CrossRef]
  113. Hofer, M.; Lutolf, M.P. Engineering organoids. Nat. Rev. Mater. 2021, 6, 402–420. [Google Scholar] [CrossRef]
  114. Rossi, G.; Manfrin, A.; Lutolf, M.P. Progress and potential in organoid research. Nat. Rev. Genet. 2018, 19, 671–687. [Google Scholar] [CrossRef] [PubMed]
  115. Kyrousi, C.; Cappello, S. Using brain organoids to study human neurodevelopment, evolution and disease. Wiley Interdiscip. Rev. Dev. Biol. 2020, 9, e347. [Google Scholar] [CrossRef]
  116. Lancaster, M.A.; Knoblich, J.A. Organogenesis in a dish: Modeling development and disease using organoid technologies. Science 2014, 345, 1247125. [Google Scholar] [CrossRef] [PubMed]
  117. Cederquist, G.Y.; Asciolla, J.J.; Tchieu, J.; Walsh, R.M.; Cornacchia, D.; Resh, M.D.; Studer, L. Specification of positional identity in forebrain organoids. Nat. Biotechnol. 2019, 37, 436–444. [Google Scholar] [CrossRef]
  118. Qian, X.; Su, Y.; Adam, C.D.; Deutschmann, A.U.; Pather, S.R.; Goldberg, E.M.; Su, K.; Li, S.; Lu, L.; Jacob, F.; et al. Sliced human cortical organoids for modeling distinct cortical layer formation. Cell Stem Cell 2020, 26, 766–781.e769. [Google Scholar] [CrossRef]
  119. Abbott, J.; Tambe, M.; Pavlinov, I.; Farkhondeh, A.; Nguyen, H.N.; Xu, M.; Pradhan, M.; York, T.; Might, M.; Baumgärtel, K.; et al. Generation and characterization of NGLY1 patient-derived midbrain organoids. Front. Cell Dev. Biol. 2023, 11, 1039182. [Google Scholar] [CrossRef]
  120. Sabate-Soler, S.; Nickels, S.L.; Saraiva, C.; Berger, E.; Dubonyte, U.; Barmpa, K.; Lan, Y.J.; Kouno, T.; Jarazo, J.; Robertson, G.; et al. Microglia integration into human midbrain organoids leads to increased neuronal maturation and functionality. Glia 2022, 70, 1267–1288. [Google Scholar] [CrossRef]
  121. Jo, J.; Xiao, Y.; Sun, A.X.; Cukuroglu, E.; Tran, H.D.; Göke, J.; Tan, Z.Y.; Saw, T.Y.; Tan, C.P.; Lokman, H.; et al. Midbrain-like Organoids from Human Pluripotent Stem Cells Contain Functional Dopaminergic and Neuromelanin-Producing Neurons. Cell Stem Cell 2016, 19, 248–257. [Google Scholar] [CrossRef]
  122. Ciarpella, F.; Zamfir, R.G.; Campanelli, A.; Pedrotti, G.; Di Chio, M.; Bottani, E.; Decimo, I. Generation of mouse hippocampal brain organoids from primary embryonic neural stem cells. STAR Protoc. 2023, 4, 102413. [Google Scholar] [CrossRef]
  123. Sarkar, A.; Mei, A.; Paquola, A.C.M.; Stern, S.; Bardy, C.; Klug, J.R.; Kim, S.; Neshat, N.; Kim, H.J.; Ku, M.; et al. Efficient Generation of CA3 Neurons from Human Pluripotent Stem Cells Enables Modeling of Hippocampal Connectivity In Vitro. Cell Stem Cell 2018, 22, 684–697.e9. [Google Scholar] [CrossRef]
  124. Sakaguchi, H.; Kadoshima, T.; Soen, M.; Narii, N.; Ishida, Y.; Ohgushi, M.; Takahashi, J.; Eiraku, M.; Sasai, Y. Generation of functional hippocampal neurons from self-organizing human embryonic stem cell-derived dorsomedial telencephalic tissue. Nat. Commun. 2015, 6, 8896. [Google Scholar] [CrossRef] [PubMed]
  125. Nakano, T.; Ando, S.; Takata, N.; Kawada, M.; Muguruma, K.; Sekiguchi, K.; Saito, K.; Yonemura, S.; Eiraku, M.; Sasai, Y. Self-formation of optic cups and storable stratified neural retina from human ESCs. Cell Stem Cell 2012, 10, 771–785. [Google Scholar] [CrossRef] [PubMed]
  126. Onesto, M.M.; Kim, J.I.; Pasca, S.P. Assembloid models of cell-cell interaction to study tissue and disease biology. Cell Stem Cell 2024, 31, 1563–1573. [Google Scholar] [CrossRef]
  127. Kim, K.; Zhao, R.; Doi, A.; Ng, K.; Unternaehrer, J.; Cahan, P.; Huo, H.; Loh, Y.H.; Aryee, M.J.; Lensch, M.W.; et al. Donor cell type can influence the epigenome and differentiation potential of human induced pluripotent stem cells. Nat. Biotechnol. 2011, 29, 1117–1119, Erratum in Nat. Biotechnol. 2012, 30, 112. [Google Scholar] [CrossRef]
  128. Hossini, A.M.; Megges, M.; Prigione, A.; Lichtner, B.; Toliat, M.R.; Wruck, W.; Schröter, F.; Nuernberg, P.; Kroll, H.; Makrantonaki, E.; et al. Induced pluripotent stem cell-derived neuronal cells from a sporadic Alzheimer’s disease donor as a model for investigating AD-associated gene regulatory networks. BMC Genom. 2015, 16, 84. [Google Scholar]
  129. Young, J.E.; Boulanger-Weill, J.; Williams, D.A.; Woodruff, G.; Buen, F.; Revilla, A.C.; Herrera, C.; Israel, M.A.; Yuan, S.H.; Edland, S.D.; et al. Elucidating molecular phenotypes caused by the SORL1 Alzheimer’s disease genetic risk factor using human induced pluripotent stem cells. Cell Stem Cell 2015, 16, 373–385. [Google Scholar] [CrossRef]
  130. Knupp, A.; Mishra, S.; Martinez, R.; Braggin, J.E.; Szabo, M.; Kinoshita, C.; Hailey, D.W.; Small, S.A.; Jayadev, S.; Young, J.E. Depletion of the AD Risk Gene SORL1 Selectively Impairs Neuronal Endosomal Traffic Independent of Amyloidogenic APP Processing. Cell Rep. 2020, 31, 107719. [Google Scholar] [CrossRef]
  131. Koch, P.; Tamboli, I.Y.; Mertens, J.; Wunderlich, P.; Ladewig, J.; Stüber, K.; Esselmann, H.; Wiltfang, J.; Brustle, O.; Walter, J. Presenilin-1 L166P mutant human pluripotent stem cell–derived neurons exhibit partial loss of γ-secretase activity in endogenous Amyloid-β generation. Am. J. Pathol. 2012, 180, 2404–2416. [Google Scholar] [CrossRef]
  132. Yagi, T.; Ito, D.; Okada, Y.; Akamatsu, W.; Nihei, Y.; Yoshizaki, T.; Yamanaka, S.; Okano, H.; Suzuki, N. Modeling familial Alzheimer’s disease with induced pluripotent stem cells. Hum. Mol. Genet. 2011, 20, 4530–4539. [Google Scholar] [CrossRef]
  133. Moore, S.; Evans, L.D.; Andersson, T.; Portelius, E.; Smith, J.; Dias, T.B.; Saurat, N.; McGlade, A.; Kirwan, P.; Blennow, K.; et al. APP metabolism regulates tau proteostasis in human cerebral cortex neurons. Cell Rep. 2015, 11, 689–696. [Google Scholar] [CrossRef]
  134. Hu, N.W.; Corbett, G.T.; Moore, S.; Klyubin, I.; O’Malley, T.T.; Walsh, D.M.; Livesey, F.J.; Rowan, M.J. Extracellular Forms of Aβ and Tau from iPSC Models of Alzheimer’s Disease Disrupt Synaptic Plasticity. Cell Rep. 2018, 23, 1932–1938. [Google Scholar] [CrossRef]
  135. Israel, M.A.; Yuan, S.H.; Bardy, C.; Reyna, S.M.; Mu, Y.; Herrera, C.; Hefferan, M.P.; Van Gorp, S.; Nazor, K.L.; Boscolo, F.S.; et al. Probing sporadic and familial Alzheimer’s disease using induced pluripotent stem cells. Nature 2012, 482, 216–220. [Google Scholar] [CrossRef] [PubMed]
  136. Zhao, J.; Fu, Y.; Yamazaki, Y.; Ren, Y.; Davis, M.D.; Liu, C.C.; Lu, W.; Wang, X.; Chen, K.; Cherukuri, Y.; et al. APOE4 exacerbates synapse loss and neurodegeneration in Alzheimer’s disease patient iPSC-derived cerebral organoids. Nat. Commun. 2020, 11, 5540. [Google Scholar] [CrossRef] [PubMed]
  137. Birnbaum, J.H.; Wanner, D.; Gietl, A.F.; Saake, A.; Kündig, T.M.; Hock, C.; Nitsch, R.M.; Tackenberg, C. Oxidative stress and altered mitochondrial protein expression in the absence of amyloid-β and tau pathology in iPSC-derived neurons from sporadic Alzheimer’s disease patients. Stem Cell Res. 2018, 27, 121–130. [Google Scholar] [CrossRef]
  138. Elsworthy, R.J.; King, M.C.; Grainger, A.; Fisher, E.; Crowe, J.A.; Alqattan, S.; Ludlam, A.; Hill, D.E.J.; Aldred, S. Amyloid-β Precursor Protein Processing and Oxidative Stress are Altered in Human iPSC-Derived Neuron and Astrocyte Co-Cultures Carrying Pre- senillin-1 Gene Mutations Following Spontaneous Differentiation. Mol. Cell. Neurosci. 2021, 114, 103631. [Google Scholar] [CrossRef]
  139. Armijo, E.; Gonzalez, C.; Shahnawaz, M.; Flores, A.; Davis, B.; Soto, C. Increased susceptibility to Aβ toxicity in neuronal cultures derived from familial Alzheimer’s disease (PSEN1-A246E) induced pluripotent stem cells. Neurosci. Lett. 2017, 639, 74–81. [Google Scholar] [CrossRef]
  140. Ochalek, A.; Mihalik, B.; Avci, H.X.; Chandrasekaran, A.; Téglási, A.; Bock, I.; Giudice, M.L.; Táncos, Z.; Molnár, K.; László, L.; et al. Neurons derived from sporadic Alzheimer’s disease iPSCs reveal elevated TAU hyper-phosphorylation, increased amyloid levels, and GSK3B activation. Alzheimer’s Res. Ther. 2017, 9, 90. [Google Scholar] [CrossRef]
  141. Meyer, K.; Feldman, H.M.; Lu, T.; Drake, D.; Lim, E.T.; Ling, K.H.; Bishop, N.A.; Pan, Y.; Seo, J.; Lin, Y.T.; et al. REST and Neural Gene Network Dysregulation in iPSC Models of Alzheimer’s Disease. Cell Rep. 2019, 26, 1112–1127.e9. [Google Scholar] [CrossRef]
  142. Yang, J.; Zhao, H.; Ma, Y.; Shi, G.; Song, J.; Tang, Y.; Li, S.; Li, T.; Liu, N.; Tang, F.; et al. Early pathogenic event of Alzheimer’s disease documented in iPSCs from patients with PSEN1 mutations. Oncotarget 2017, 8, 7900–7913. [Google Scholar] [CrossRef]
  143. Lin, Y.T.; Seo, J.; Gao, F.; Feldman, H.M.; Wen, H.L.; Penney, J.; Cam, H.P.; Gjoneska, E.; Raja, W.K.; Cheng, J.; et al. APOE4 causes widespread molecular and cellular alterations associated with Alzheimer’s disease phenotypes in human iPSC-derived brain cell types. Neuron 2018, 98, 1141–1154.e7. [Google Scholar] [CrossRef]
  144. Claes, C.; Daele, J.V.D.; Boon, R.; Schouteden, S.; Colombo, A.; Monasor, L.S.; Fiers, M.; Ordovás, L.; Nami, F.; Bohrmann, B.; et al. Human stem cell–derived monocytes and microglia-like cells reveal impaired amyloid plaque clearance upon heterozygous or homozygous loss of TREM2. Alzheimer’s Dement. 2019, 15, 453–464. [Google Scholar] [CrossRef] [PubMed]
  145. Oksanen, M.; Petersen, A.J.; Naumenko, N.; Puttonen, K.; Lehtonen, Š.; Olivé, M.G.; Shakirzyanova, A.; Leskelä, S.; Sarajärvi, T.; Viitanen, M.; et al. PSEN1 Mutant iPSC-Derived Model Reveals Severe Astrocyte Pathology in Alzheimer’s Disease. Stem Cell Rep. 2017, 9, 1885–1897. [Google Scholar] [CrossRef] [PubMed]
  146. Jones, V.C.; Atkinson-Dell, R.; Verkhratsky, A.; Mohamet, L. Aberrant iPSC-derived human astrocytes in Alzheimer’s disease. Cell Death Dis. 2017, 8, e2696. [Google Scholar] [CrossRef] [PubMed]
  147. Fong, L.K.; Yang, M.M.; Dos Santos Chaves, R.; Reyna, S.M.; Langness, V.F.; Woodruff, G.; Roberts, E.A.; Young, J.E.; Goldstein, L.S.B. Full-length amyloid precursor protein regulates lipoprotein metabolism and amyloid-β clearance in human astrocytes. J. Biol. Chem. 2018, 293, 11341–11357. [Google Scholar] [CrossRef]
  148. Sienski, G.; Narayan, P.; Bonner, J.M.; Kory, N.; Boland, S.; Arczewska, A.A.; Ralvenius, W.T.; Akay, L.; Lockshin, E.; He, L.; et al. APOE4 disrupts intracellular lipid homeostasis in human iPSC-derived glia. Sci. Transl. Med. 2021, 13, eaaz4564. [Google Scholar] [CrossRef]
  149. Zou, P.; Wu, C.; Liu, T.C.; Duan, R.; Yang, L. Oligodendrocyte progenitor cells in Alzheimer’s disease: From physiology to pathology. Transl. Neurodegener. 2003, 12, 52. [Google Scholar] [CrossRef]
  150. Choi, S.H.; Kim, Y.H.; Hebisch, M.; Sliwinski, C.; Lee, S.; D’Avanzo, C.; Chen, H.; Hooli, B.; Asselin, C.; Muffat, J.; et al. A three-dimensional human neural cell culture model of Alzheimer’s disease. Nature 2014, 515, 274–278. [Google Scholar] [CrossRef]
  151. Alić, I.; Goh, P.A.; Murray, A.; Portelius, E.; Gkanatsiou, E.; Gough, G.; Mok, K.Y.; Koschut, D.; Brunmeir, R.; Yeap, Y.J.; et al. Patient-specific Alzheimer-like pathology in trisomy 21 cerebral organoids reveals BACE2 as a gene dose-sensitive AD suppressor in human brain. Mol. Psychiatry 2020, 26, 5766–5788. [Google Scholar] [CrossRef]
  152. Gonzalez, C.; Armijo, E.; Bravo-Alegria, J.; Becerra-Calixto, A.; Mays, C.E.; Soto, C. Modeling amyloid beta and tau pathology in human cerebral organoids. Mol. Psychiatry 2018, 23, 2363–2374. [Google Scholar] [CrossRef]
  153. Pavoni, S.; Jarray, R.; Nassor, F.; Guyot, A.C.; Cottin, S.; Rontard, J.; Mikol, J.; Mabondzo, A.; Deslys, J.P.; Yates, F. Small-molecule induction of Aβ-42 peptide production in human cerebral organoids to model Alzheimer’s disease associated phenotypes. PLoS ONE 2018, 13, e0209150. [Google Scholar] [CrossRef]
  154. Raja, W.K.; Mungenast, A.E.; Lin, Y.T.; Ko, T.; Abdurrob, F.; Seo, J.; Tsai, L.H. Self-Organizing 3D Human Neural Tissue Derived from Induced Pluripotent Stem Cells Recapitulate Alzheimer’s Disease Phenotypes. PLoS ONE 2016, 11, e0161969. [Google Scholar] [CrossRef] [PubMed]
  155. Park, J.C.; Jang, S.Y.; Lee, D.; Lee, J.; Kang, U.; Chang, H.; Kim, H.J.; Han, S.H.; Seo, J.; Choi, M.; et al. A logical network-based drug-screening platform for Alzheimer’s disease representing pathological features of human brain organoids. Nat. Commun. 2021, 12, 280. [Google Scholar] [CrossRef] [PubMed]
  156. Ye, T.; Duan, Y.; Tsang, H.W.S.; Xu, H.; Chen, Y.; Cao, H.; Chen, Y.; Fu, A.K.Y.; Ip, N.Y. Efficient manipulation of gene dosage in human iPSCs using CRISPR/Cas9 nickases. Commun. Biol. 2021, 4, 195. [Google Scholar] [CrossRef]
  157. Wang, C.; Najm, R.; Xu, Q.; Jeong, D.E.; Walker, D.; Balestra, M.E.; Yoon, S.Y.; Yuan, H.; Li, G.; Miller, Z.A.; et al. Gain of toxic apolipoprotein E4 effects in human iPSC-derived neurons is ameliorated by a smallmolecule structure corrector. Nat. Med. 2018, 24, 647–657. [Google Scholar] [CrossRef]
  158. Claes, C.; Danhash, E.P.; Hasselmann, J.; Chadarevian, J.P.; Shabestari, S.K.; England, W.E.; Lim, T.E.; Hidalgo, J.L.S.; Spitale, R.C.; Davtyan, H.; et al. Plaque-associated human microglia accumulate lipid droplets in a chimeric model of Alzheimer’s disease. Mol. Neurodegener. 2021, 16, 50. [Google Scholar] [CrossRef]
  159. Bose, A.; Petsko, G.A.; Studer, L. Induced pluripotent stem cells: A tool for modeling Parkinson’s disease. Trends Neurosci. 2022, 45, 608–620. [Google Scholar] [CrossRef]
  160. Marques, O.; Outeiro, T.F. Alpha-synuclein: From secretion to dysfunction and death. Cell Death Dis. 2012, 3, e350. [Google Scholar] [CrossRef]
  161. Torrent, R.; De Angelis Rigotti, F.; Dell’Era, P.; Memo, M.; Raya, A.; Consiglio, A. Using iPS Cells toward the Understanding of Parkinson’s Disease. J. Clin. Med. 2015, 4, 548–566. [Google Scholar] [CrossRef]
  162. Hallett, P.J.; Deleidi, M.; Astradsson, A.; Smith, G.A.; Cooper, O.; Osborn, T.M.; Sundberg, M.; Moore, M.A.; Perez-Torres, E.; Brownell, A.L.; et al. Successful function of autologous iPSC-derived dopamine neurons following transplantation in a non-human primate model of Parkinson’s disease. Cell Stem Cell 2015, 16, 269–274. [Google Scholar] [CrossRef]
  163. Devine, M.J.; Ryten, M.; Vodicka, P.; Thomson, A.J.; Burdon, T.; Houlden, H.; Cavaleri, F.; Nagano, M.; Drummond, N.J.; Taanman, J.-W.; et al. Parkinson’s disease induced pluripotent stem cells with triplication of the alpha-synuclein locus. Nat. Commun. 2011, 2, 440. [Google Scholar] [CrossRef]
  164. Byers, B.; Cord, B.; Nguyen, H.N.; Schüle, B.; Fenno, L.; Lee, P.C.; Deisseroth, K.; Langston, J.W.; Pera, R.R.; Palmer, T.D. SNCA triplication Parkinson’s patient’s iPSC-derived DA neurons accumulate alpha-synuclein and are susceptible to oxidative stress. PLoS ONE 2011, 6, e26159. [Google Scholar] [CrossRef] [PubMed]
  165. Heman-Ackah, S.M.; Manzano, R.; Hoozemans, J.J.M.; Scheper, W.; Flynn, R.; Haerty, W.; Cowley, S.A.; Bassett, A.R.; Wood, M.J.A. Alpha-synuclein induces the unfolded protein response in Parkinson’s disease SNCA triplication iPSC-derived neurons. Hum. Mol. Genet. 2017, 26, 4441–4450. [Google Scholar] [CrossRef] [PubMed]
  166. Prots, I.; Grosch, J.; Brazdis, R.M.; Simmnacher, K.; Veber, V.; Havlicek, S.; Hannappel, C.; Krach, F.; Krumbiegel, M.; Schütz, O.; et al. Alpha-Synuclein oligomers induce early axonal dysfunction in human iPSC-based models of synucleinopathies. Proc. Natl. Acad. Sci. USA 2018, 115, 7813–7818. [Google Scholar] [CrossRef] [PubMed]
  167. Ludtmann, M.H.R.; Angelova, P.R.; Horrocks, M.H.; Choi, M.L.; Rodrigues, M.; Baev, A.Y.; Berezhnov, A.V.; Yao, Z.; Little, D.; Banushi, B.; et al. Alpha-synuclein oligomers interact with ATP synthase and open the permeability transition pore in Parkinson’s disease. Nat. Commun. 2018, 9, 2293. [Google Scholar] [CrossRef]
  168. Chung, C.Y.; Khurana, V.; Auluck, P.K.; Tardiff, D.F.; Mazzulli, J.R.; Soldner, F.; Baru, V.; Lou, Y.; Freyzon, Y.; Cho, S.; et al. Identification and rescue of alpha-synuclein toxicity in Parkinson patient-derived neurons. Science 2013, 342, 983–987. [Google Scholar] [CrossRef]
  169. Zambon, F.; Cherubini, M.; Fernandes, H.J.R.; Lang, C.; Ryan, B.J.; Volpato, V.; Bengoa-Vergniory, N.; Vingill, S.; Attar, M.; Booth, H.D.E.; et al. Cellular alpha-synuclein pathology is associated with bioenergetic dysfunction in Parkinson’s iPSC-derived dopamine neurons. Hum. Mol. Genet. 2019, 28, 2001–2013. [Google Scholar] [CrossRef]
  170. Kouroupi, G.; Taoufik, E.; Vlachos, I.S.; Tsioras, K.; Antoniou, N.; Papastefanaki, F.; Chroni-Tzartou, D.; Wrasidlo, W.; Bohl, D.; Stellas, D.; et al. Defective synaptic connectivity and axonal neuropathology in a human iPSC-based model of familial Parkinson’s disease. Proc. Natl. Acad. Sci. USA 2017, 114, E3679–E3688. [Google Scholar] [CrossRef]
  171. Ryan, S.D.; Dolatabadi, N.; Chan, S.F.; Zhang, X.; Akhtar, M.W.; Parker, J.; Soldner, F.; Sunico, C.R.; Nagar, S.; Talantova, M.; et al. Isogenic human iPSC Parkinson’s model shows nitrosative stress-induced dysfunction in MEF2-PGC1 transcription. Cell 2013, 155, 1351–1364, Erratum in Cell 2013, 155, 1652–1653. [Google Scholar] [CrossRef]
  172. Cookson, M.R. The role of leucine-rich repeat kinase 2 (LRRK2) in Parkinson’s disease. Nat. Rev. Neurosci. 2010, 11, 791–797. [Google Scholar] [CrossRef]
  173. Ho, G.P.H.; Ramalingam, N.; Imberdis, T.; Wilkie, E.C.; Dettmer, U.; Selkoe, D.J. Upregulation of Cellular Palmitoylation Mitigates alpha-Synuclein Accumulation and Neurotoxicity. Mov. Disord. 2021, 36, 348–359. [Google Scholar] [CrossRef]
  174. Nguyen, H.N.; Byers, B.; Cord, B.; Shcheglovitov, A.; Byrne, J.; Gujar, P.; Kee, K.; Schüle, B.; Dolmetsch, R.E.; Langston, W.; et al. LRRK2 mutant iPSC-derived DA neurons demonstrate increased susceptibility to oxidative stress. Cell Stem Cell 2011, 8, 267–280. [Google Scholar] [CrossRef] [PubMed]
  175. Sánchez-Danés, A.; Richaud-Patin, Y.; Carballo-Carbajal, I.; Jiménez-Delgado, S.; Caig, C.; Mora, S.; Di Guglielmo, C.; Ezquerra, M.; Patel, B.; Giralt, A.; et al. Disease-specific phenotypes in dopamine neurons from human iPS-based models of genetic and sporadic Parkinson’s disease. EMBO Mol. Med. 2012, 4, 380–395. [Google Scholar] [CrossRef] [PubMed]
  176. Reinhardt, P.; Schmid, B.; Burbulla, L.F.; Schöndorf, D.C.; Wagner, L.; Glatza, M.; Höing, S.; Hargus, G.; Heck, S.A.; Dhingra, A.; et al. Genetic correction of a LRRK2 mutation in human iPSCs links parkinsonian neurodegeneration to ERK-dependent changes in gene expression. Cell Stem Cell 2013, 12, 354–367. [Google Scholar] [CrossRef] [PubMed]
  177. Orenstein, S.J.; Kuo, S.H.; Tasset, I.; Arias, E.; Koga, H.; Fernandez-Carasa, I.; Cortes, E.; Honig, L.S.; Dauer, W.; Consiglio, A.; et al. Interplay of LRRK2 with chaperone-mediated autophagy. Nat. Neurosci. 2013, 16, 394–406. [Google Scholar] [CrossRef]
  178. Sheng, Z.H.; Cai, Q. Mitochondrial transport in neurons: Impact on synaptic homeostasis and neurodegeneration. Nat. Rev. Neurosci. 2012, 13, 77–93. [Google Scholar] [CrossRef]
  179. Bono, F.; Mutti, V.; Devoto, P.; Bolognin, S.; Schwamborn, J.C.; Missale, C.; Fiorentini, C. Impaired dopamine D3 and nicotinic acetylcholine receptor membrane localization in iPSCs-derived dopaminergic neurons from two Parkinson’s disease patients carrying the LRRK2 G2019S mutation. Neurobiol. Aging 2021, 99, 65–78. [Google Scholar] [CrossRef]
  180. Wickremaratchi, M.M.; Knipe, M.D.; Sastry, B.S.; Morgan, E.; Jones, A.; Salmon, R.; Weiser, R.; Moran, M.; Davies, D.; Ebenezer, L.; et al. The motor phenotype of Parkinson’s disease in relation to age at onset. Mov. Disord. 2011, 26, 457–463. [Google Scholar] [CrossRef]
  181. de Lau, L.M.; Breteler, M.M. Epidemiology of Parkinson’s disease. Lancet Neurol. 2006, 5, 525–535. [Google Scholar] [CrossRef]
  182. Jiang, H.; Ren, Y.; Yuen, E.Y.; Zhong, P.; Ghaedi, M.; Hu, Z.; Azabdaftari, G.; Nakaso, K.; Yan, Z.; Feng, J. Parkin controls dopamine utilization in human midbrain dopaminergic neurons derived from induced pluripotent stem cells. Nat. Commun. 2012, 3, 668. [Google Scholar] [CrossRef]
  183. Imaizumi, Y.; Okada, Y.; Akamatsu, W.; Koike, M.; Kuzumaki, N.; Hayakawa, H.; Nihira, T.; Kobayashi, T.; Ohyama, M.; Sato, S.; et al. Mitochondrial dysfunction associated with increased oxidative stress and alpha-synuclein accumulation in PARK2 iPSC-derived neurons and postmortem brain tissue. Mol. Brain 2012, 5, 35. [Google Scholar] [CrossRef]
  184. Chung, S.Y.; Kishinevsky, S.; Mazzulli, J.R.; Graziotto, J.; Mrejeru, A.; Mosharov, E.V.; Puspita, L.; Valiulahi, P.; Sulzer, D.; Milner, T.A.; et al. Parkin and PINK1 Patient iPSC-Derived Midbrain Dopamine Neurons Exhibit Mitochondrial Dysfunction and alpha-Synuclein Accumulation. Stem Cell Rep. 2016, 7, 664–677. [Google Scholar] [CrossRef] [PubMed]
  185. Oh, C.K.; Sultan, A.; Platzer, J.; Dolatabadi, N.; Soldner, F.; McClatchy, D.B.; Diedrich, J.K.; Yates, J.R., 3rd; Ambasudhan, R.; Nakamura, T.; et al. S-Nitrosylation of PINK1 Attenuates PINK1/Parkin-Dependent Mitophagy in hiPSC-Based Parkinson’s Disease Models. Cell Rep. 2017, 21, 2171–2182. [Google Scholar] [CrossRef] [PubMed]
  186. Cooper, O.; Seo, H.; Andrabi, S.; Guardia-Laguarta, C.; Graziotto, J.; Sundberg, M.; McLean, J.R.; Carrillo-Reid, L.; Xie, Z.; Osborn, T.; et al. Pharmacological rescue of mitochondrial deficits in iPSC-derived neural cells from patients with familial Parkinson’s disease. Sci. Transl. Med. 2012, 4, 141ra190. [Google Scholar] [CrossRef] [PubMed]
  187. Ohuchi, K.; Funato, M.; Kato, Z.; Seki, J.; Kawase, C.; Tamai, Y.; Ono, Y.; Nagahara, Y.; Noda, Y.; Kameyama, T.; et al. Established Stem Cell Model of Spinal Muscular Atrophy Is Applicable in the Evaluation of the Efficacy of Thyrotropin-Releasing Hormone Analog. Stem Cells Transl. Med. 2016, 5, 152–163. [Google Scholar] [CrossRef]
  188. Kaufmann, M.; Schuffenhauer, A.; Fruh, I.; Klein, J.; Thiemeyer, A.; Rigo, P.; Gomez-Mancilla, B.; Heidinger-Millot, V.; Bouwmeester, T.; Schopfer, U.; et al. High-Throughput Screening Using iPSC-Derived Neuronal Progenitors to Identify Compounds Counteracting Epigenetic Gene Silencing in Fragile X Syndrome. J. Biomol. Screen. 2015, 20, 1101–1111. [Google Scholar] [CrossRef]
  189. Angelique di Domenico, A.; Carola, G.; Calatayud, C.; Pons-Espinal, M.; Muñoz, J.P.; Richaud-Patin, Y.; Fernandez-Carasa, I.; Gut, M.; Faella, A.; Parameswaran, J.; et al. Patient-specific iPSC-Derived astrocytes contribute to non-cell-autonomous neurodegeneration in Parkinson’s disease. Stem Cell Rep. 2019, 12, 213–229. [Google Scholar] [CrossRef]
  190. Mamais, A.; Kluss, J.H.; Bonet-Ponce, L.; Landeck, N.; Langston, R.G.; Smith, N.; Beilina, A.; Kaganovich, A.; Ghosh, M.C.; Pellegrini, L.; et al. Mutations in LRRK2 linked to Parkinson disease sequester Rab8a to damaged lysosomes and regulate transferrin-mediated iron uptake in microglia. PLoS Biol. 2021, 19, e3001480, Erratum in PLoS Biol. 2022, 20, e3001621. [Google Scholar] [CrossRef]
  191. Ramos-Gonzalez, P.; Mato, S.; Chara, J.C.; Verkhratsky, A.; Matute, C.; Cavaliere, F. Astrocytic atrophy as a pathological feature of Parkinson’s disease with LRRK2 mutation. NPJ Park. Dis. 2021, 7, 31. [Google Scholar] [CrossRef]
  192. Panagiotakopoulou, V.; Ivanyuk, D.; De Cicco, S.; Haq, W.; Arsic, A.; Yu, C.; Messelodi, D.; Oldrati, M.; Schondorf, D.C.; Perez, M.J.; et al. Interferon-gamma signaling synergizes with LRRK2 in neurons and microglia derived from human induced pluripotent stem cells. Nat. Commun. 2020, 11, 5163. [Google Scholar] [CrossRef]
  193. Haenseler, W.; Zambon, F.; Lee, H.; Vowles, J.; Rinaldi, F.; Duggal, G.; Houlden, H.; Gwinn, K.; Wray, S.; Luk, K.C.; et al. Excess alpha-synuclein compromises phagocytosis in iPSC-derived macrophages. Sci. Rep. 2017, 7, 9003. [Google Scholar] [CrossRef]
  194. Liu, G.H.; Qu, J.; Suzuki, K.; Nivet, E.; Li, M.; Montserrat, N.; Yi, F.; Xu, X.; Ruiz, S.; Zhang, W.; et al. Progressive degeneration of human neural stem cells caused by pathogenic LRRK2. Nature 2012, 491, 603–607. [Google Scholar] [CrossRef] [PubMed]
  195. Tolosa, E.; Botta-Orfila, T.; Morato, X.; Calatayud, C.; Ferrer-Lorente, R.; Marti, M.J.; Fernández, M.; Gaig, C.; Raya, A.; Consiglio, A.; et al. MicroRNA alterations in iPSC-derived dopaminergic neurons from Parkinson disease patients. Neurobiol. Aging 2018, 69, 283–291. [Google Scholar] [CrossRef] [PubMed]
  196. Bolognin, S.; Fossepre, M.; Qing, X.; Jarazo, J.; Scancar, J.; Moreno, E.L.; Nickels, S.L.; Wasner, K.; Ouzren, N.; Walter, J.; et al. 3D Cultures of Parkinson’s Disease-Specific Dopaminergic Neurons for High Content Phenotyping and Drug Testing. Adv. Sci. 2019, 6, 1800927. [Google Scholar] [CrossRef] [PubMed]
  197. Smits, L.M.; Reinhardt, L.; Reinhardt, P.; Glatza, M.; Monzel, A.S.; Stanslowsky, N.; Rosato-Siri, M.D.; Zanon, A.; Antony, P.M.; Bellmann, J.; et al. Modeling Parkinson’s disease in midbrain-like organoids. NPJ Park. Dis. 2019, 5, 5. [Google Scholar] [CrossRef]
  198. Soldner, F.; Stelzer, Y.; Shivalila, C.S.; Abraham, B.J.; Latourelle, J.C.; Barrasa, M.I.; Goldmann, J.; Myers, R.H.; Young, R.A.; Jaenisch, R. Parkinson-associated risk variant in distal enhancer of α-synuclein modulates target gene expression. Nature 2016, 533, 95–99. [Google Scholar] [CrossRef]
  199. Qing, X.; Walter, J.; Jarazo, J.; Arias-Fuenzalida, J.; Hillje, A.L.; Schwamborn, J.C. CRISPR/Cas9 and piggyBacmediated footprint-free LRRK2-G2019S knock-in reveals neuronal complexity phenotypes and α-Synuclein modulation in dopaminergic neurons. Stem Cell Res. 2017, 24, 44–50. [Google Scholar] [CrossRef]
  200. Sonninen, T.-M.; Hämäläinen, R.H.; Koskuvi, M.; Oksanen, M.; Shakirzyanova, A.; Wojciechowski, S.; Puttonen, K.; Naumenko, N.; Goldsteins, G.; Laham-Karam, N.; et al. Metabolic alterations in Parkinson’s disease astrocytes. Sci. Rep. 2020, 10, 14474. [Google Scholar] [CrossRef]
  201. Hanss, Z.; Boussaad, I.; Jarazo, J.; Schwamborn, J.C.; Kruger, R. Quality control strategy for CRISPR-Cas9-based gene editing complicated by a pseudogene. Front. Genet. 2020, 10, 1297. [Google Scholar] [CrossRef]
  202. Ahfeldt, T.; Ordureau, A.; Bell, C.; Sarrafha, L.; Sun, C.; Piccinotti, S.; Grass, T.; Parfitt, G.M.; Paulo, J.A.; Yanagawa, F.; et al. Pathogenic pathways in early-onset autosomal recessive Parkinson’s disease discovered using isogenic human dopaminergic neurons. Stem Cell Rep. 2020, 14, 75–90. [Google Scholar] [CrossRef]
  203. Chen, C.X.-Q.; You, Z.; Abdian, N.; Sirois, J.; Shlaifer, I.; Tabatabaei, M.; Boivin, M.N.; Gaborieau, L.; Karamchandani, J.; Beitel, L.K.; et al. Generation of homozygous PRKN, PINK1 and double PINK1/PRKN knockout cell lines from healthy induced pluripotent stem cells using CRISPR/Cas9 editing. Stem Cell Res. 2022, 62, 102806. [Google Scholar] [CrossRef]
  204. Harjuhaahto, S.; Rasila, T.S.; Molchanova, S.M.; Woldegebriel, R.; Kvist, J.; Konovalova, S.; Sainio, M.T.; Pennonen, J.; Torregrosa-Muñumer, R.; Ibrahim, H.; et al. ALS and Parkinson’s disease genes CHCHD10 and CHCHD2 modify synaptic transcriptomes in human iPSC-derived motor neurons. Neurobiol. Dis. 2020, 141, 104940. [Google Scholar] [CrossRef] [PubMed]
  205. Ross, C.A.; Aylward, E.H.; Wild, E.J.; Langbehn, D.R.; Long, J.D.; Warner, J.H.; Scahill, R.I.; Leavitt, B.R.; Stout, J.C.; Paulsen, J.S.; et al. Huntington disease: Natural history, biomarkers and prospects for therapeutics. Nat. Rev. Neurol. 2014, 10, 204–216. [Google Scholar] [CrossRef] [PubMed]
  206. HD iPSC Consortium. Induced pluripotent stem cells from patients with Huntington’s disease show CAG-repeat-expansion-associated phenotypes. Cell Stem Cell 2012, 11, 264–278. [Google Scholar] [CrossRef]
  207. Juopperi, T.A.; Kim, W.R.; Chiang, C.H.; Yu, H.; Margolis, R.L.; Ross, C.A.; Ming, G.L.; Song, H. Astrocytes generated from patient induced pluripotent stem cells recapitulate features of Huntington’s disease patient cells. Mol. Brain 2012, 5, 17. [Google Scholar] [CrossRef]
  208. Mattis, V.B.; Tom, C.; Akimov, S.; Saeedian, J.; Østergaard, M.E.; Southwell, A.L.; Doty, C.N.; Ornelas, L.; Sahabian, A.; Lenaeus, L.; et al. HD iPSC-derived neural progenitors accumulate in culture and are susceptible to BDNF withdrawal due to glutamate toxicity. Hum. Mol. Genet. 2015, 24, 3257–3271. [Google Scholar] [CrossRef]
  209. Nekrasov, E.D.; Vigont, V.A.; Klyushnikov, S.A.; Lebedeva, O.S.; Vassina, E.M.; Bogomazova, A.N.; Chestkov, I.V.; Semashko, T.A.; Kiseleva, E.; Suldina, L.A.; et al. Manifestation of Huntington’s disease pathology in human induced pluripotent stem cell-derived neurons. Mol. Neurodegener. 2016, 11, 27. [Google Scholar] [CrossRef]
  210. An, M.C.; Zhang, N.; Scott, G.; Montoro, D.; Wittkop, T.; Mooney, S.; Melov, S.; Ellerby, L.M. Genetic correction of Huntington’s disease phenotypes in induced pluripotent stem cells. Cell Stem Cell 2012, 11, 253–263. [Google Scholar] [CrossRef]
  211. Jeon, I.; Lee, N.; Li, J.Y.; Park, I.H.; Park, K.S.; Moon, J.; Shim, S.H.; Choi, C.; Chang, D.J.; Kwon, J.; et al. Neuronal properties, in vivo effects, and pathology of a Huntington’s disease patient-derived induced pluripotent stem cells. Stem Cells 2012, 30, 2054–2062. [Google Scholar] [CrossRef]
  212. Zhang, N.; An, M.C.; Montoro, D.; Ellerby, L.M. Characterization of human Huntington’s disease cell model from induced pluripotent stem cells. PLoS Curr. 2010, 2, RRN1193. [Google Scholar] [CrossRef]
  213. Oura, S.; Noda, T.; Morimura, N.; Hitoshi, S.; Nishimasu, H.; Nagai, Y.; Nureki, O.; Ikawa, M. Precise CAG repeat contraction in a Huntington’s disease mouse model is enabled by gene editing with SpCas9-NG. Commun. Biol. 2021, 4, 771. [Google Scholar] [CrossRef]
  214. Shin, J.W.; Kim, K.-H.; Chao, M.J.; Atwal, R.S.; Gillis, T.; MacDonald, M.E.; Gusella, J.F.; Lee, J.M. Permanent inactivation of Huntington’s disease mutation by personalized allele-specific CRISPR/Cas9. Hum. Mol. Genet. 2016, 25, 4566–4576. [Google Scholar] [CrossRef] [PubMed]
  215. Ishida, Y.; Kawakami, H.; Kitajima, H.; Nishiyama, A.; Sasai, Y.; Inoue, H.; Muguruma, K. Vulnerability of Purkinje cells generated from spinocerebellar ataxia type 6 patient-derived iPSCs. Cell Rep. 2016, 17, 1482–1490. [Google Scholar] [CrossRef] [PubMed]
  216. Koch, P.; Breuer, P.; Peitz, M.; Jungverdorben, J.; Kesavan, J.; Poppe, D.; Doerr, J.; Ladewig, J.; Mertens, J.; Tuting, T.; et al. Excitation-induced ataxin-3 aggregation in neurons from patients with Machado-Joseph disease. Nature 2011, 480, 543–546. [Google Scholar] [CrossRef]
  217. Ou, Z.; Luo, M.; Niu, X.; Chen, Y.; Xie, Y.; He, W.; Song, B.; Xian, Y.; Fan, D.; OuYang, S.; et al. Autophagy Promoted the degradation of mutant ATXN3 in neurally differentiated spinocerebellar Ataxia-3 Human induced pluripotent stem cells. Biomed. Res. Int. 2016, 2016, 6701793. [Google Scholar] [CrossRef]
  218. Ebert, A.D.; Yu, J.; Rose, F.F., Jr.; Mattis, V.B.; Lorson, C.L.; Thomson, J.A.; Svendsen, C.N. Induced pluripotent stem cells from a spinal muscular atrophy patient. Nature 2009, 457, 277–280. [Google Scholar] [CrossRef]
  219. Fuller, H.R.; Mandefro, B.; Shirran, S.L.; Gross, A.R.; Kaus, A.S.; Botting, C.H.; Morris, G.E.; Sareen, D. Spinal Muscular atrophy patient iPSC-Derived motor neurons have reduced expression of proteins important in neuronal development. Front. Cell. Neurosci. 2015, 9, 506. [Google Scholar] [CrossRef]
  220. Lin, X.; Li, J.J.; Qian, W.J.; Zhang, Q.J.; Wang, Z.F.; Lu, Y.Q.; Dong, E.L.; He, J.; Wang, N.; Ma, L.X.; et al. Modeling the differential phenotypes of spinal muscular atrophy with high-yield generation of motor neurons from human induced pluripotent stem cells. Oncotarget 2017, 8, 42030–42042. [Google Scholar] [CrossRef]
  221. Yoshida, M.; Kitaoka, S.; Egawa, N.; Yamane, M.; Ikeda, R.; Tsukita, K.; Amano, N.; Watanabe, A.; Morimoto, M.; Takahashi, J.; et al. Modeling the early phenotype at the neuromuscular junction of spinal muscular atrophy using patient-derived iPSCs. Stem Cell Rep. 2015, 4, 561–568. [Google Scholar] [CrossRef]
  222. McGivern, J.V.; Patitucci, T.N.; Nord, J.A.; Barabas, M.A.; Stucky, C.L.; Ebert, A.D. Spinal muscular atrophy astrocytes exhibit abnormal calcium regulation and reduced growth factor production. Glia 2013, 61, 1418–1428. [Google Scholar] [CrossRef]
  223. Kondo, T.; Funayama, M.; Miyake, M.; Tsukita, K.; Era, T.; Osaka, H.; Ayaki, T.; Takahashi, R.; Inoue, H. Modeling Alexander disease with patient iPSCs reveals cellular and molecular pathology of astrocytes. Acta Neuropathol. Commun. 2016, 4, 69. [Google Scholar] [CrossRef]
  224. Li, L.; Tian, E.; Chen, X.; Chao, J.; Klein, J.; Qu, Q.; Sun, G.; Sun, G.; Huang, Y.; Warden, C.D.; et al. GFAP Mutations in astrocytes impair oligodendrocyte progenitor proliferation and myelination in an hiPSC model of alexander disease. Cell Stem Cell 2018, 23, 239–251.e6. [Google Scholar] [CrossRef]
Ijms 26 03774 g001
Figure 1. In addition to fibroblasts, PBMCs, and NSCs, a wide range of somatic cell sources can be reprogrammed in vitro using methods such as OSKM or chemical cocktails to establish iPSCs. These iPSCs can then be further differentiated into various neurons and organoids, which are widely utilized as disease models in the study of neurodegenerative disorders.
Figure 1. In addition to fibroblasts, PBMCs, and NSCs, a wide range of somatic cell sources can be reprogrammed in vitro using methods such as OSKM or chemical cocktails to establish iPSCs. These iPSCs can then be further differentiated into various neurons and organoids, which are widely utilized as disease models in the study of neurodegenerative disorders.
Ijms 26 03774 g001
Table 1. Advantages and disadvantages of different delivery methods in the reprogramming process of iPSCs.
Table 1. Advantages and disadvantages of different delivery methods in the reprogramming process of iPSCs.
MethodsAdvantagesDisadvantagesReferences
Retroviral and lentiviral vectorsHighly efficient and robustRisk of transgene reactivation[12]
Adenoviral vectorsLower risk of transgene reactivationLow reprogramming efficiency; unsuitable for clinical use[11,13]
Sendai virusHigher efficiency; RNA virus can be completely removedStill involves challenges in clinical application[11,13]
PiggyBacLower risk of genomic instability and mutationsLow reprogramming efficiency; limited cell sources[14,15,16]
Minicircle vectorsLower risk of genomic instability and mutationsLow reprogramming efficiency; limited cell sources[14,15,16]
Episomal plasmidsNo genomic integration risk; cost-effective and easy to useRequires daily transfection; moderate efficiency[17,18]
RNA DeliveryLower mutagenic risk; high efficiencyLimited to specific cell types (e.g., fibroblasts, peripheral blood cells)[19,20,21]
Table 2. Reprogramming factor combinations and their functional roles.
Table 2. Reprogramming factor combinations and their functional roles.
FactorsFunctionsReferences
Yamanaka factorsOCT3/4, SOX2, KLF4, c-MYCOCT3/4, SOX2, and KLF4 maintain pluripotency and inhibit differentiation
c-MYC enhances reprogramming efficiency and promotes cell proliferation		[23]
Alternative factor combinationsOCT3/4, SOX2, NANOG, LIN28NANOG maintains self-renewal of stem cells
LIN28 regulates RNA modification and expression		[24]
Enhancement or complementary factorsGLIS1, NR5A2, SALL4Substitute for c-MYC or OCT3/4 to improve reprogramming efficiency and stabilize cell states[25,26,27]
Chemical cocktails(1) CHALP cocktail:
CHIR99021, PD0325901, LIF, A-83-01, bFGF, HA-100		Enhances reprogramming efficiency[2,28]
(2) Alternative chemical cocktail:
cyclic pifithrin-α, A-83-01, CHIR99021, thiazovivin, NaB, PD0325901		Significantly enhances reprogramming efficiency, particularly in hUCs[2,29]
Epigenetic modulatorsDNA methyltransferase inhibitors,
histone deacetylase inhibitors		Regulate DNA methylation, histone acetylation, and the expression of pluripotency-associated genes to enhance reprogramming efficiency[30,31,32]
Table 3. Somatic cell sources and their advantages.
Table 3. Somatic cell sources and their advantages.
Cell TypeSourcesAdvantages
FibroblastsSkinReadily accessible and widely used
PBMCsBloodNon-invasive; useful for clinical applications
KeratinocytesSkin or hairNon-invasive and readily accessible
MSCsBone marrow, adipose tissue, teethAbundant and frequently used in regenerative research
Renal epithelial cellsUrineHighly convenient and non-invasive
NSCs and NPCsBrain tissueInherent pluripotency; useful for neurological applications
Liver cellsLiver tissueExpands potential applications
Stomach cellsStomach tissueExpands potential applications
Cord blood cellsCord bloodReadily available from umbilical cord; useful in neonatal research
Table 4. Neuronal cell/organoid differentiation methods and their limitations.
Table 4. Neuronal cell/organoid differentiation methods and their limitations.
Differentiated Cell/Organoid TypeDifferentiation MethodsLimitationsReferences
NSCs/NPCs
(1)
EB formation;
(2)
Co-culture on neural inducing feeders;
(3)
Dual SMAD inhibition
Requires intermediate steps;
limited differentiation efficiency		[3,56,57]
Neurons
(e.g., dopaminergic, GABAergic, glutamatergic)
(1)
NSC/NPC stages with growth factors (BDNF, GDNF, CAMP, RA);
(2)
Direct conversion using transcription factors (BRN2, ASCL1, MYT1L, NGN2)
Complex protocols; variability in differentiation outcomes; phenotypic instability with direct conversion[3,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74]
Astrocytes
(1)
NSC/NPC or OPC stages with CNTF, BMP, FGF2, FBS;
(2)
Direct induction using NFlA, NFIB, SOX9
Significant plasticity; variability across protocols; in vitro astrocytes may not fully replicate in vivo properties[72,75,76,77,78,79,80,81,82,83,84]
Microglia
(1)
Mesoderm progenitor cells with BMP4, activin A, FGF2, VEGF-A;
(2)
Direct induction using PU.1, IRF8
High technical complexity; multiple steps; phenotypic and functional differences from in vivo counterparts[85,86,87,88,89,90,91,92]
Oligodendrocytes
(1)
Stepwise differentiation: NPC → OPC (BMP4, FGF2, PDGF, EGF) → oligodendrocytes (T3, IGF-1);
(2)
Direct induction with SOX10, OLIG2, NKX6.2
Time-consuming; phenotypic instability with direct transcription factor induction; challenges in achieving purity[93,94,95,96,97,98,99,100,101,102,103]
Brain organoids
(e.g., cortical, hippocampal,
midbrain, cerebellar)		Non-guided (intrinsic morphogenetic potential or guided (dual SMAD inhibition, region-specific factors such as SHH, RA, WNT inhibitors)Lack of vascularization and heterogeneity; difficulty in precisely controlling differentiation and region specificity[104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125]
Table 5. AD pathological features of different iPSC-derived models.
Table 5. AD pathological features of different iPSC-derived models.
iPSC-Derived ModelAD’s Pathological FeaturesReferences
Neurons
–
Aβ accumulation
–
Hyperphosphorylated Tau (p-Tau)
–
GSK3β activation
–
Abnormal electrophysiological activity
–
Increased oxidative stress and ROS production
–
ER dysfunction
–
Mitochondrial abnormalities
–
Heightened sensitivity to Aβ42
–
Reduced Aβ secretion and p-Tau levels via β-/γ-secretase inhibitors
[128,131,132,133,134,135,136,137,138,139,140]
NSCs/NPCs
–
Low APP and Aβ expression
–
Significantly impaired proliferation and self-renewal capacity
–
Elevated expression of neurodevelopment-related genes (e.g., MAPT, CD24, STMN2)
[131,141,142]
Microglia
–
Reduced phagocytic capacity for the Aβ and Tau oligomers
–
Enhanced neuroinflammation
[85,143,144]
Astrocytes
–
Morphological alterations
–
Increased Aβ42 release
–
Impaired clearance capacity
–
Dysregulated cytokine production
–
Calcium homeostasis imbalance
–
Elevated ROS levels
[145,146,147,148]
Oligodendrocytes
–
Morphological defects
–
Impaired proliferation and functionality
–
Reduced neuronal support capacity
–
Defective myelination
[149]
3D brain organoids
–
Aβ deposition
–
Neurofibrillary tangle formation
–
Enhanced Tau phosphorylation
–
ER abnormalities
–
Pathological phenotypes modifiable by pharmacological/environmental interventions
[150,151,152,153,154,155]
Table 6. PD pathological features of different iPSC-derived models.
Table 6. PD pathological features of different iPSC-derived models.
iPSC-Derived ModelPD’s Pathological FeaturesReferences
DA neurons
–
α-Synuclein accumulation
–
Mitochondrial dysfunction
–
Increased oxidative/ER stress sensitivity
–
Synaptic loss
–
Neuronal death
–
Elevated apoptosis
–
Impaired neuronal homeostasis
–
Defects in mitochondrial autophagy (e.g., PARK2/PINK1 mutants)
–
Elevated ROS levels
–
Epigenetic changes (sPD models)
[2,5,105,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188]
Astrocytes
–
α-Synuclein accumulation
–
Dysregulated autophagy
–
Abnormal mitochondrial morphology/activity
–
Reduced cell viability
–
Oxidative stress-induced DA neuron degeneration
[189,190,191]
Microglia
–
Elevated α-synuclein levels
–
Impaired phagocytic capacity
–
Enhanced neuroinflammation
–
Disrupted DA neuron morphology/function
[191,192,193]
Neural stem cells
–
Reduced differentiation efficiency
–
Developmental defects
[194,195]
3D brain organoids
–
Increased DA neuron death
–
Reduced differentiation potential
–
Decreased dendritic complexity
–
Neurodevelopmental defects
–
Pathological phenotypes rescued by LRRK2 inhibition
[104,196,197]
Table 7. HD pathological features of different iPSC-derived models.
Table 7. HD pathological features of different iPSC-derived models.
iPSC-Derived ModelHD’s Pathological FeaturesReferences
Neural progenitor/stem cells (NPCs/NSCs)
–
Expression of the mutant HTT protein, CAG repeat expansion
–
Transcriptomic dysregulation
–
Aberrant electrophysiological properties
–
Increased apoptosis
–
Enhanced susceptibility to BDNF withdrawal
[206,207,208]
Forebrain neurons
–
Mutant HTT aggregation
–
Synaptic dysfunction
–
Transcriptomic dysregulation
[206]
Striatal-like GABAergic neurons (MSNs)
–
Mutant HTT aggregation
–
Increased lysosomal and autophagosomal activity
–
Nuclear indentations
–
Caspase activation
–
Exacerbated neuronal death during aging
–
Mitochondrial dysfunction (indirectly linked)
[209,210,211]
Astrocytes
–
Vacuolation phenotype
–
Enhanced susceptibility to BDNF withdrawal
–
Impaired neuronal support functions
[207,208]
Glial cells (general)
–
Enhanced susceptibility to BDNF withdrawal
[208]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Guo, X.; Wang, X.; Wang, J.; Ma, M.; Ren, Q. Current Development of iPSC-Based Modeling in Neurodegenerative Diseases. Int. J. Mol. Sci. 2025, 26, 3774. https://doi.org/10.3390/ijms26083774

AMA Style

Guo X, Wang X, Wang J, Ma M, Ren Q. Current Development of iPSC-Based Modeling in Neurodegenerative Diseases. International Journal of Molecular Sciences. 2025; 26(8):3774. https://doi.org/10.3390/ijms26083774

Chicago/Turabian Style

Guo, Xiangge, Xumeng Wang, Jiaxuan Wang, Min Ma, and Qian Ren. 2025. "Current Development of iPSC-Based Modeling in Neurodegenerative Diseases" International Journal of Molecular Sciences 26, no. 8: 3774. https://doi.org/10.3390/ijms26083774

APA Style

Guo, X., Wang, X., Wang, J., Ma, M., & Ren, Q. (2025). Current Development of iPSC-Based Modeling in Neurodegenerative Diseases. International Journal of Molecular Sciences, 26(8), 3774. https://doi.org/10.3390/ijms26083774

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop