Introduction
The elderly population has continued to expand in recent years, and without the accompanying healthspan growth, it has led to economic and social burdens.[1] Aging is the major risk factor for most chronic diseases, such as cancer, diabetes, cardiovascular disease, dementia, arthritis, sarcopenia, and renal dysfunction, that seriously affect the well-being of the elderly.[2] Therefore, studying the underlying mechanisms of human aging and developing interventions targeting the aging process have become the priorities for research worldwide. Considering the biological relevance and practicality, various model systems have been selected to investigate the cellular and molecular mechanisms underlying aging, identify potential aging intervention targets, and guide the development of candidate drugs for delaying human aging. These model systems include animals, such as nematodes, rodents, non-human primates, and cells of human origins.[3–8]
Human aging is a complex physiological process.[2] Delineating the etiology and pathophysiology of aging, therefore, requires experimental models that can recapitulate multifaceted pathophysiology and clinical manifestations of human aging.[9] However, it is worth noticing that the maximal lifespan varies greatly across species and different species have evolved different molecular mechanisms to control their lifespans.[10,11] Many biological processes are very different between the human and other organisms, such as brain development,[12] immune response,[13] and drug metabolism.[14,15] For instance, most laboratory animals have higher rates of pharmacokinetics than humans.[16–18] Cells, regardless of established cell lines or recently isolated primary cells, are traditionally cultured in vitro as two-dimensional (2D) monolayers with a limited capacity for physiologic representation.[19] For example, extracellular deposition of amyloidogenic peptides, a typical characteristic of Alzheimer’s disease (AD), cannot be recapitulated in induced pluripotent stem cells (iPSCs)–derived neurons grown as monolayer.[20] Thus, traditional animal and cell culture models cannot fully reflect the aging-associated physiological and pathological states of humans. These limitations of classical models have hindered the translation of basic research findings into clinical applications.
Compared with the models mentioned above, the recently developed organoid system has been recognized for its great potential in translational medicine. Organoids are referred to as “Preclinical Models of Human Disease” because they largely recapitulate the physiological cellular compositions and preserve human regulatory pathways.[19,21] Organoids address many limitations of cells grown in the traditional 2D culture and bridge the gap between animal models and human beings.[22] For instance, organoids have higher physiological complexity compared to 2D cell culture, are more accessible compared to animals, and better simulate human physiology.[22] Therefore, to a certain extent, the organoid system has the potential to become a more reliable aging research model (Fig. 1).
;)
Figure 1.: Comparison of organoids with other model systems. We compare the advantages and disadvantages of the 2D cell culture, Mus musculus, non-human primates, and human organoids used in biological research. Compared with other models, human organoids are highly anticipated for their ability to more realistically mimic the physiological and pathological features of the human body. Different tags are used to represent different relative scores, and they are best suitable (dark green tick), partly suitable (yellow circle), not suitable (red cross). Unpublished data.
An organoid is “a collection of organ-specific cell types derived from stem cells or organ progenitors, self-organized through cell sorting and spatially restricted lineage commitment like in vivo.”[23] Organoids can be generated from either PSCs, including embryonic stem cells (ESCs) and iPSCs, or adult stem cells (ASCs) and are called PSC-derived organoids and ASC-derived organoids, respectively.[22] PSC-derived organoids are generated by stepwise differentiation protocols, which recapitulate the signals during gastrulation and organogenesis with various growth factors or inhibitors to initiate the development of different germ layers (endoderm, mesoderm, and ectoderm). On the other hand, ASC-derived organoids are generated by culturing ASCs in the presence of niche factors, a cocktail of growth factors that maintain ASCs in an undifferentiated state while permitting differentiation.[22,24] Although most organoids can be derived from both PSCs and ASCs, certain organoids, like the brain ones, can only be generated from PSCs.[22,25,26] In 2009, Hans Clevers’s group generated an intestinal organoid with self-organizing crypt-villus structures from a single mouse Lgr5-expressing intestinal stem cells in vitro.[27] This was the first reported three-dimensional (3D) organoid culture from a single ASC, which laid the foundation for many subsequent organoids works. These include the generation of ASC- or PSC-derived neuroectoderm (eg, brain[28] and retina[29]) and mesendoderm (eg, heart,[30,31] stomach,[32] intestine,[33–36] liver,[37–41] pancreas,[42–46] lung,[47–49] and kidney[50–53]). Established on top of the stem cell technology,[54–56] the reprogramming technology,[57–60] and the existing knowledge of organogenesis, organoid technologies continue to develop by integrating new technologies.[25,51,61–63] Researchers can now mimic the 3D architecture, the cell-type composition, and the function of a wide range of tissues and organs with the organoid culture. The organoid system has emerged as a powerful and pivotal research platform for both developmental biology research and clinical applications, such as disease modeling, drug discovery, biobanking, precision medicine, and regenerative medicine.[25]
The history of organoid research has been well described by others (Fig. 2).[24,64] In this review, we focus on discussing recent implementations of organoid technologies in aging studies. We highlight the value of organoids as model systems in both mechanistic and translational research for human aging and aging-related disease and point out key challenges that remain to be addressed.
Figure 2.: The history of organoid research. Timeline of milestones for the organoid methodologies. hESCs=human embryonic stem cells, PSC=pluripotent stem cells.
Database retrieval strategy
Literature review was electronically performed using PubMed database. The following combinations of key words were used to initially select the articles: aging model; organoid; organoid and aging; brain organoid; ASCs and aging. Most of the selected articles (90% of all references) were published from 2013 to 2023. The articles included in this review were selected based on their relevance to the topic.
Applications of organoids in aging research
The combination of iPSCs possessing disease-related mutations with organoid technologies provides a promising platform for simulating the pathophysiology of several aging-related diseases,[65] including neurodegenerative diseases (NDDs),[66,67] arthritis,[68–70] diabetes,[71–73] and cancer.[74,75] Although PSC-derived organoids generated by stepwise differentiation protocols usually resemble fetal-stage tissues and are mainly used to study organogenesis and early developmental events, organoids generated from PSCs carrying aging-related genetic variations exhibit accelerated emergence of aging-related pathophysiology (Fig. 3A). For instance, many brain organoids of NDDs well reproduce the pathological phenotypes of these diseases, such as amyloid aggregation, hyperphosphorylated tau protein, and endosome abnormalities in familial AD.[67] iPSC-derived organoids have higher maturity, more complex culture environment, and intercellular interaction, which thus can reproduce more complex and diverse pathological features of degenerative diseases. For example, in AD iPSC-derived organoids, amyloid β (Aβ) plaques occur spontaneously.[76] However, iPSC-derived neurons from AD patients without exogenous induction did not produce Aβ plaques, possibly due to the diffusion of secreted Aβ into the medium due to 2D culture conditions, reflecting a defect in the 2D cell model.[77] The establishment of iPSC-derived organoids thus provides a great opportunity to recapitulate the progression of aging-related diseases in vitro, allowing researchers to explore the early events of diseases and discover early disease-associated molecular signatures and markers that can be ideal targets for therapies.[78,79] Moreover, drug screenings using organoid models have provided us with much new information, especially for diseases with complicated pathogenesis. For instance, integrating mathematical modeling and human brain organoids from iPSCs seems to be a more effective way to identify potential and optimal drug targets for each risk factor of AD.[80]
Figure 3.: Application of organoids in aging research. (A) PSC-derived organoids are potent tools in modeling aging-related diseases, such as neurodegeneration. (B) ASC-derived organoids reflect the homeostatic or regenerative conditions of their tissue of origin. Identifying and isolating ASCs from young and old donors to generate ASC-derived organoids in vitro will improve our understanding of the molecular mechanism underlying stem cell exhaustion and guide the identification of novel targets for intervening stem cell aging. (C) Other organoids cultured in vitro, especially those established with microfluidic systems, well simulate the aging processes caused by inflammation and external stress. Moreover, the rapid development of organ-on-chip will greatly promote the study of multi-organ pathologies and systemic aging. ASC=adult stem cell, PSC=pluripotent stem cells. Unpublished data.
Identifying and isolating ASCs from young and old donors to generate organoids in vitro is feasible and has gradually become a common approach to studying aging (Fig. 3B). Stem cell exhaustion is considered the ultimate culprit for cellular and organismal aging.[81] Functional attrition of ASCs during aging reduces tissue repair and regeneration potentials systemically, one of the manifestations of degenerative aging.[82,83] Accordingly, cells from aged mice and humans have been reported to have reduced organoid formation efficiency.[84–87] Although it is still unclear whether organoids generated from ASC isolated from old donors manifest hallmarks of aging in the absence of the aging niche, several aging-related markers can be reproduced in such organoids. Thus, organoids derived from aged individuals are potent models for studying the molecular mechanisms underlying stem cell aging.
Some tissues and organs, such as skin and joints, cannot be mimicked in vitro with PSC- or ASC-derived organoids. One of the standard methods to resolve this problem is co-culturing multiple cell types on scaffolds (Fig. 3C).[88] Moreover, aging is a systemic process accompanied by the degeneration of various tissues and organs. The pathophysiology of some aging-related diseases is also highly relevant to inter-organ crosstalk. The organ-on-a-chip technology uses microfabricated cell culture devices that combine biomaterial technologies, microfluidic systems, and tissue engineering and can simulate the crosstalk between different tissues and organs.[89] Despite its current limitations,[90] the organ-on-chip technology has been employed to study several aging-related diseases, such as rheumatoid arthritis,[91] hypertensive nephropathy,[92] diabetes,[71] and neuromuscular diseases[93] (Fig. 3C). With the advances of new technologies, the organ-on-chip technology will likely become a powerful tool for studying systemic milieus that regulate the aging process.
Below, we will describe the current applications of organoids in aging research in detail.
Pluripotent stem cell–derived organoids
Brain organoids
Human brain organoids are derived from PSCs, like ESCs or iPSCs. Previous in vitro human brain models had established approaches for stimulating self-organization and inducing differentiation of PSCs.[94–99] Building upon these works, in 2013, Lancaster et al[28] first established a human PSC-derived whole-brain organoid employing Matrigel to support the 3D organization of the organoid without using patterning growth factors. Identities of various brain regions, including the forebrain, the midbrain, the hindbrain, and the retinal tissues, were recognized in this whole-brain organoid. To generate dorsal forebrain organoids[100] and ventral forebrain organoids,[101] the Pasca group exposed embryoid bodies to neural induction and differentiation factors. Following that is the generation of various region-specific organoids, including the midbrain organoid,[102] the cerebellum organoid,[103] the striatal organoid,[104] the choroid plexus organoid,[105] the spinal cord organoid,[106] and the hypothalamic arcuate organoid.[107] Excitingly, different brain region-specific organoids can fuse to form assemblies, which provides models for elucidating the interactions between brain regions.[108] A recent study demonstrates that the cerebral-cortex organoid and the spinal cord organoid can also fuse with the skeletal muscle-organoid to simulate the cortico-motor circuit.[109]
In the past decade, researchers have introduced multiple approaches from diverse aspects to optimize the culture condition for brain organoids. For example, forming embryoid bodies from single-cell suspensions can reduce variabilities between batches[110,111] and slice cultures can minimize internal necrosis caused by hypoxia and nutrient deficiencies.[111] The advances in 3D-printed mini-bioreactors can further increase the throughput of organoid production.[112]
The brain organoids used for modeling NDDs are generated primarily using iPSCs derived from patients,[20,66] and only a proportion of them are derived from genetically modified ESCs. Brain organoids containing genetic variations associated with NDDs have become a promising model for studying the early pathological features of these diseases (Fig. 4). Findings from these studies will support the development of clinical diagnoses and interventions.
Figure 4.: Brain organoids modeling NDDs. iPSCs-derived whole-brain organoid or region-specific brain organoid can well recapitulate the key pathological characteristics of many NDDs, such as AD, PD, A-T, and HD. Brain organoid models facilitate the elucidation of the molecular features of NDDs at the early stage of disease progression and the subsequent development of early intervention drugs. AD=Alzheimer’s disease, APOE4=apolipoprotein E4, APP=amyloid precursor protein, A-T=ataxia-telangiectasia, ATM=ataxia telangiectasia-mutated gene, Aβ=amyloid β-protein, cGAS-STING=the cyclic GMP-AMP synthase (cGAS)–stimulator of interferon genes (STING) pathway, FTD=frontotemporal dementia, HD=Huntington disease, HTT=huntingtin, iPSC=induced pluripotent stem cells, LRRK2-G2019S=leucine-rich repeat kinase 2-G2019S, NDDs=neurodegenerative diseases, PARK7=Parkinson disease 7, PD=Parkinson disease, PITRM1=pitrilysin metallopeptidase 1, PSEN1=Presenilin 1. Unpublished data.
Modeling AD with brain organoids
Both whole-brain and forebrain organoids can be used to model AD. Brain organoids derived from iPSCs generated from AD patients spontaneously develop disease-associated pathologies, such as amyloid aggregation, hyperphosphorylation of Tau proteins (pTau), endosome abnormalities, and cellular apoptosis (Fig. 4),[113,114] that can hardly be reproduced in 2D culture models.[76] Besides, the apolipoprotein E gene (APOE)-mutated brain organoids also display AD-associated phenotypes, such as increased α-synuclein, impaired synaptic functions, decreased glucocerebrosidase levels, and accumulated lipid droplets.[115] Remarkably, exogenous APOE2/3, but not APOE4, partially rescues these phenotypes, suggesting the positive effects of APOE2/3 in preventing AD. The deletion of the mitochondrial enzyme pitrilysin metallopeptidase 1 gene (PITRM1) in brain organoids also spontaneously leads to the development of AD pathological features, suggesting a link between mitochondrial dysfunctions and AD.[116] With the extension of the culture period, brain organoids can display aging-related pathological features. The intervention with β and γ secretase inhibitors reduces Aβ production first and then the tau hyperphosphorylation in these brain organoids.[113] Exposing brain organoids to human serum, a culture condition mimicking the blood-brain barrier leakage in AD patients, leads to synaptic loss and neural network impairments in these brain organoids.[117] Serum activates glycogen synthase kinase (GSK) pathways and elevates Aβ and pTau levels in human brain organoids, suggesting that GSK is a potential target for AD interventions.
Impairment in astrocytes has been implicated in various NDDs, especially in AD. However, astrocytes can hardly be differentiated into brain organoids,[100,118] limiting the application of this approach in NDD modeling.[119] Recently, a chimeric brain organoid system has been developed to accelerate the production of functional astrocytes. By generating chimeric brain organoids containing neurons and astrocytes with different APOE allelotypes (APOE3 or APOE4), chimeric cerebral organoids carrying APOE4 are found to show elevated Aβ and p-Tau levels, increased lipid droplet formation, and increased accumulation of cholesterol in neurons. This chimeric organoid system provides a unique opportunity to dissect cell-type–specific roles in AD pathogenesis.[120] In order to delve into the mechanism of tau pathology in the whole-brain organoids of microtubule-associated protein tau (MAPT) mutations, mature brain organoids can be digested into individual neurons. Morphological and functional abnormalities of axons as well as mislocalization and reduced phosphorylation levels of the mutant tau proteins in the neurons can be found.[121] Moreover, the electrophysiology platform for brain organoids has been established. With this platform, AD organoids are found to abnormally increase the frequency of spontaneous action potentials (Fig. 4). Inhibitor screens show that the NMDA-type glutamate receptors (NMDAR) antagonist NitroSynapsin can rescue this abnormal neural activity.[122] The organoid-based high-throughput screening system holds promises for identifying and validating new drugs for AD treatments.[80]
Modeling Parkinson’s disease with brain organoids
Parkinson’s disease (PD) is a neurodegenerative disorder that affects mainly the dopaminergic neurons in the midbrain, and midbrain dopaminergic neurons (mDANs). Organoids that recapitulate hallmarks of PD, such as the degradation of mDANs and the aggregation of α-synuclein, can be generated from either iPSCs derived from PD patients or genetically modified human ESCs (Fig. 4).[123–125] Several studies have unveiled neurodevelopmental defects in PD organoid models, such as impaired WNT-LMX1A autoregulatory pathway and upregulated neural progenitor cell marker FOXA2 in mDANs.[123,124] Mechanistic studies have revealed molecular targets for delaying the progression of PD in various mutant backgrounds. The WNT-LMX1A signals are impaired in organoids carrying DNAJC6 mutations, which result in the production of mDANs with reduced expressions of midbrain markers and shrunken cell bodies.[124] In LRRK2-G2019S mutant organoids, the expression of TXNIP encoding a thiol-oxidoreductase is reported to be significantly induced. Knocking down TXNIP significantly reduces the aggregation of α-synuclein.[125] In PARK7-mutant PD organoids, a defect in the U1-dependent splicing of the PARK7 transcript is identified, which causes the malfunction of mitochondria and the loss of dopaminergic neurons.[126] With the ability to recapitulate early pathological features of PD, the midbrain organoid provides a powerful platform for identifying effective preventive strategies for PD. For example, combinatorial treatment with small molecules that promote exon inclusion and protein expression can restore the dopaminergic neuron loss in PARK7-mutant midbrain organoids. In addition, the neural progenitor cells isolated from midbrain organoids can serve as stem cell repertoire for generating functional mDANs for PD treatment.[127]
Modeling other NDDs with brain organoids
With the use of patient iPSCs-derived organoids, NDDs research has rapidly progressed. Using the state-of-art single-cell transcriptomic approach in combination with biological assays, researchers analyzed the cerebral organoid slice model for amyotrophic lateral sclerosis overlapping with frontotemporal dementia. They identified DNA damage in both astrocytes and neurons and the potential of GSK2606414 in rescuing this phenotype.[128] In a brain organoid model for Ataxia-telangiectasia (A-T), inhibiting the cGAS-STING pathway delays the aging processes by suppressing the senescence-associated secretory phenotype and the subsequent astrocyte senescence and neurodegeneration.[129] This result suggests the cGAS-STING pathway as a therapeutic target for A-T. In a Huntington’s disease organoid model, neurodevelopmental defects caused by the abnormal length of CAG repeats in the huntingtin gene (HTT) can be rescued by inhibiting a downstream effector of the mutant HTT.[130] IPSCs-derived cerebellar organoid of spinocerebellar ataxia type 3 (SCA3), the second most common polyglutamine disease after Huntington’s disease, has been observed spontaneous ataxin-3 aggregates in neurons, and the SCA3 disease-associated phenotypic abnormalities in cerebellar neurons can be reversed by the genetic correction.[131] With the advances in organoid technologies, we are optimistic that the use of brain organoids can provide us with valuable knowledge of molecular mechanisms underlying NDDs and illuminate novel ideas for developing targeted therapy.
Adult stem cell-derived organoids
Gastrointestinal organoids
Even with the emergence of iPSC technologies, most gastrointestinal organoids like intestinal, gastric, liver, and pancreatic ones are still derived from ASC. This is likely because these tissues harbor large populations of proliferating stem-like cells.[128,132] With the declination of the number and functionality of stem cells during aging, tissues and organs lose the ability to maintain homeostasis or recover from injuries (Fig. 5).[133] Enhancing the regenerative capacity of aging stem cells will promote healthy aging.[134] Therefore understanding the mechanism underlying stem cell aging is the foundation for developing effective approaches for anti-aging therapies.
Figure 5.: Study on ASC-derived organoid assisted stem cell exhaustion with aging. The ability of repairing tissue damages and regeneration decreases with aging because of declination of stem cells activities. The advantages of ASC-derived organoids that can reconstruct the homeostatic or regenerative conditions of their tissue of origin have been greatly reflected. Organoids generated from old ASCs show the molecular characteristics of stem cell aging in vivo, such as accumulation of SA-β-gal and age-associated epigenetic signatures. Besides, some means of aging intervention, including calorie, restriction, NAD+ supplementation and others, have also been evaluated in ASC-derived organoids. ASC=adult stem cell, mTOR=mammalian target of rapamycin, NAD+=nicotinamide adenine dinucleotide, npGH=non-pituitary growth hormone, SA-β-Gal=senescence-associated β-galactosidase. Unpublished data.
The stem cell niche is a specialized microenvironment where stem cells reside in animals that mediate stem cell self-renewal and regulates stem cell functions. It consists of niche cells, cellular matrix, and secreted soluble factors.[135,136] Compared with 2D cell cultures, assessing the proliferation and differentiation ability of ASCs in 3D organoid cultures is more viable, as organoids partly replicate the complex cellular interactions in tissues and the molecular environments that stem cells are exposed to in vivo.[137]
ASCs from old donors have been used to generate organoids for aging research (Fig. 5). For example, intestinal epithelial organoids have been established with stem cells from mice at various stages of life, and those generated from aged mice accumulate the senescent marker SA-β-gal and increase expressions of Cdkn1a (p21) and Cdkn2a (p16).[138] To evaluate the use of human organoids for aging studies, DNA methylation rates of organoids derived from human intestines were assessed and found to maintain the regional-specific and age-associated epigenetic signatures of their tissues of origin.[139] Similarly, ex vivo long-term-cultured mouse colon-derived organoids also mimic epigenetic features of in vivo aging, especially global methylation changes including hypermethylation at promoters and hypomethylation in intronic and intergenic regions. The aging-like spontaneous DNA hypermethylation at promoters promotes BrafV600E-induced transformation.[140] Age-associated increase in non-pituitary growth hormone (npGH) and p16 levels and decrease in telomere length are observed in intestinal organoids cultured for up to 4 months.[141] Moreover, investigations of age-induced changes in niche-stem cell communications reveal that the induction of notum in aged Paneth cells, the niche cells supporting the intestinal stem cells, inhibits WNT signaling in stem cells and abrogates their regenerative potentials.[87]
Based on many research models, many intervention strategies have been exploited to delay aging.[142–145] ASC-derived gastrointestinal organoids have also been employed in assessing aging interventions like fasting,[146] calorie, restriction,[147] NAD+ supplementation,[138,148] and inhibition of mammalian target of rapamycin.[84,87] Using the crypts organoids as models, 24-hour fasting has been shown to enhance intestinal stem cell functions by activating fatty acid oxidation programs. Exogenously supplying palmitic acid or inducing the fatty acid oxidation program with PPARδ agonists largely restores the aged intestinal stem cell functions in the organoids.[146]
Therefore, ASC-derived organoids from different age groups are good research models for aging. However, it is difficult to obtain a large number of high-quality ASCs of different ages. Although ASCs can be isolated from normal or malignant human tissues by surgical resection or biopsy, the quality and quantity of tissues can hardly be guaranteed. In addition, since some ASCs (eg, intestinal stem cells) are low in content, small in size, possess a high nuclear-to-cytoplasmic ratio, and lack reliable surface markers, isolating ASCs directly from tissues is also one of the current technical bottlenecks.[136,149] However, with the development of various advanced technologies such as microdissection based on high-resolution laser capture and fluorescence-activated cell sorting, the technology to identify and isolate ASCs[150] will improve, and be better applied in organoid research.
Other organoids
Skin organoids
Different from organoids derived from PSCs or ASCs discussed above, skin organoids are generated by co-culturing skin fibroblasts and keratinocytes on unique scaffolds in vitro. Skin aging is caused by a combination of intrinsic and extrinsic factors, which eventually damage the structural integrity and physiological function of the skin.[151] Skin organoids generated using fibroblasts exposed to mitomycin C can acquire characteristics similar to those of the old skin, such as decreased filaggrin expression, impaired epidermal differentiation, and reduced elastin and collagen, have been established.[88] Skin aging model can also be generated by extending the skin culture time to 120 days.[152] Prolonging the time of organoid culture in vitro as much as possible or accelerating the aging of organoids through external factors seem to be two more feasible methods to induce aging. Although limitations such as the heterogeneity among cell lines used in skin organoids still exist, skin organoids provide a novel 3D model to investigate the molecular and cellular mechanisms of skin aging.
Organ-on-chip
Unlike organoids that self-organize to develop into complex structures, organ-on-chip is a platform that utilizes bioengineering tools to assemble matured tissue constructs into functional units of an organ in microfabricated cell culture devices.[153] For example, the glomerular filtration barrier, the functional structure of the glomerulus, has been reconstructed by culturing mouse podocytes and glomerular endothelial cells on the opposite sides of an extracellular matrix–coated membrane on the microfluidic chip. It has been used to reproduce the clinical manifestations of hypertensive nephropathy by applying high-perfusion flows.[91] Moreover, tissue responses to inflammation, which is also known as a stress factor causing aging, have been successfully simulated. For instance, a 3D synovium-on-a-chip system consisting of patient-derived primary synovial organoids and embedded optical sensor arrays has been developed to investigate the impact of inflammation on the development of rheumatoid arthritis. The synovial organoids used on the chip have very similar structures to the synovial tissue in vivo, containing a compact outermost layer and a highly organized sublining layer with an interconnected synoviocyte network. Exposing the 3D synovial organoids to the pro-inflammatory cytokines tumor necrosis factor-α significantly changes the tissue structure, which can be identified with the light scatter technology.[91]
Aging is a systemic degeneration process that simultaneously affects multiple tissues and organs. The onset and progression of some aging-related diseases are also mediated by inter-organ crosstalk.[154] In combination with organoids, organ-on-a-chip will be a powerful tool for studying multi-organ pathologies.[25] Recapitulating these interactions via multiple mini-organs on a chip provides an unprecedented model to study the organ crosstalk in vitro and shed light on the systemic mechanisms of aging.
Limitations
This review highlighted studies in brain and gastrointestinal organoids to review the progress that has been made recently in the application of organoids for aging research and discuss current limitations in technologies and new technologies to look ahead. Comparatively, the description of other organoid models is not well-extended. Relevant publications and possible deviations are not comprehensively retrieved due to the authors’ inadequate experience and expertise.
Conclusion and challenges
The emergence of organoid models well recapitulates key pathological features of aging-related diseases that cannot be well mimicked in 2D cell culture systems. Compared with 2D cell culture systems, organoids mimic the 3D structures of real organs. Organoids stand out as they better recapitulate the complexity of organs while maintaining the accessibility of the traditional cell culture models. Gene mutations or factor stimulations have accelerated the onset of aging-related phenotypes, making organoids a powerful tool for studying aging. Additionally, organoid culture reproduces tissue degeneration and the eventual aging process in vitro, allowing us to study the cellular and molecular features of aging at different stages and discover early molecular pathways associated with aging. Combined with advanced technologies such as single-cell sequencing, spatial transcriptomic analysis and cell-type–specific genetic manipulation, organoid models will further help us analyze senescence-related spatiotemporal changes of different cell types and discover aging-sensitive cells.[155,156]
Although organoids have the potential to recapitulate key pathological features of aging-related diseases, recent studies have revealed limitations of current organoid systems in simulating in vivo conditions. First, compared with human organs, organoids are relatively immature. Single-cell transcriptomic analyses demonstrate that cell subtype specification is lacking from the human forebrain organoids.[157] Pushing the in vitro cortical organoids to a physiological state that resembles postnatal tissue is extremely time-consuming.[158] From the technical perspective, growing organoids to a size and shape comparable to human organs and extending the culture time of organoids for aging studies are still challenging. Thus, the immaturity of organoids, compared with adult tissues, hampers the use of organoids in modeling aging. Methods to improve organoid maturation and structure have been developed, such as bioprinting techniques, bioengineering, extracellular matrix modulation, vascularization, and organ-on-a-chip, enabling extended culture times and higher resemblance to real organs in the future.[25,61,159] Second, the organoid culture does not fully recapitulate the in vivo environment that organs are exposed to. Thorough investigations are needed to unveil the impact of extracellular matrix and growth factor selection on organoid formations and functions. The absence of vascular networks and immune systems in the current organoid culture restricts the use of organoids in studying multifactorial diseases, such as microglia in the brain.[160] Third, organoids are highly variable from batch to batch, which increases the importance of using in vivo systems to validate findings from using organoids in vitro.
Another question is how far or what aspects of aging can be mimicked by organoids. The emergence of senescent phenotypes in organoids can be accelerated by genetic mutation or stimulation with exogenous factors. For example, the study of progeria syndromes, especially Hutchinson-Gilford progeria syndrome, has boosted the study of human aging due to the similarities between progeria syndromes and biological aging.[161–163] In addition, external stressors, such as oxidative stress (such as H2O2 and hyperoxia), genotoxic stress (such as ultraviolet light and γ-irradiation), oncogene activation,[164,165] can be used to manipulate organoid cultures to investigate environmental effects on aging. Organoids thus provide a versatile platform to study the intrinsic and extrinsic drivers of aging and its underlying cellular and molecular mechanisms.
Moreover, advancements in analytical methods, such as single-cell sequencing and genome engineering, will enhance our understanding of aging.[166–168] Recently, genetic screens have been proven to be powerful tools for discovering many new key genes, such as a novel microcephaly-causing gene in brain organoids.[169] Using a similar strategy, more novel aging-associated genes can be discovered by combining genetic screening with aging organoids.[170] In general, the application of organoids in aging research is promising. It will deepen our understanding of aging, uncover potential molecular markers for the clinical evaluation of aging intervention and further suggest strategies for combatting aging and aging-related diseases.
Acknowledgments
We apologize for not citing all important relevant studies in this review to comply with manuscript length limitations. All the figures were created with BioRender.com.
Author contributions
All authors participated in the literature search, manuscript writing, and review, and approved the final version of the manuscript.
Financial support
This work was supported by the National Key Research and Development Program of China (Nos. 2020YFA0804000, 2022YFA1103700, 2020YFA0112200, 2021YFF1201005), the National Natural Science Foundation of China (Nos. 81921006, 82125011, 92149301, 92168201, 91949209, 92049304, 92049116, 32121001, 82192863), the Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDA16000000), CAS Project for Young Scientists in Basic Research (Nos. YSBR-076, YSBR-012), the Program of the Beijing Natural Science Foundation (No. Z190019), Youth Innovation Promotion Association of CAS (No. E1CAZW0401), the Informatization Plan of Chinese Academy of Sciences (Nos. CAS-WX2021SF-0301, CAS-WX2022SDC-XK14, CAS-WX2021SF-0101), and the Tencent Foundation (No. 2021-1045).
Data availability statement
Not applicable.
Conflicts of interest
There are no conflicts of interest.
References
[1]. Partridge L, Deelen J, Slagboom PE. Facing up to the global challenges of ageing. Nature 2018;561:45–56. doi: 10.1038/s41586-018-0457-8.
[2]. Cai Y, Song W, Li J, et al. The landscape of
aging. Sci China Life Sci 2022;65:2354–2454. doi: 10.1007/s11427-022-2161-3.
[3]. Taormina G, Ferrante F, Vieni S, et al. Longevity: lesson from model organisms. Genes (Basel) 2019;10:518. doi: 10.3390/genes10070518.
[4]. Mikuła-Pietrasik J, Pakuła M, Markowska M, et al. Nontraditional systems in
aging research: an update. Cell Mol Life Sci 2021;78:1275–1304. doi: 10.1007/s00018-020-03658-w.
[5]. Liu GH, Qu J, Suzuki K, et al. Progressive degeneration of human neural stem cells caused by pathogenic LRRK2. Nature 2012;491:603–607. doi: 10.1038/nature11557.
[6]. Fu L, Xu X, Ren R, et al. Modeling xeroderma pigmentosum associated neurological pathologies with patients-derived iPSCs. Protein Cell 2016;7:210–221. doi: 10.1007/s13238-016-0244-y.
[7]. Suzuki K, Yu C, Qu J, et al. Targeted gene correction minimally impacts whole-genome mutational load in human-disease-specific induced pluripotent
stem cell clones. Cell
Stem Cell 2014;15:31–36. doi: 10.1016/j.stem.2014.06.016.
[8]. Wang S, Min Z, Ji Q, et al. Rescue of premature
aging defects in Cockayne syndrome stem cells by CRISPR/Cas9-mediated gene correction. Protein Cell 2020;11:1–22. doi: 10.1007/s13238-019-0623-2.
[9]. Doss MX, Sachinidis A. Current challenges of iPSC-based
disease modeling and therapeutic implications. Cells 2019;8:403. doi: 10.3390/cells8050403.
[10]. Tian X, Seluanov A, Gorbunova V. Molecular mechanisms determining lifespan in short- and long-lived species. Trends Endocrinol Metab 2017;28:722–734. doi: 10.1016/j.tem.2017.07.004.
[11]. Cagan A, Baez-Ortega A, Brzozowska N, et al. Somatic mutation rates scale with lifespan across mammals. Nature 2022;604:517–524. doi: 10.1038/s41586-022-04618-z.
[12]. Benito-Kwiecinski S, Giandomenico SL, Sutcliffe M, et al. An early cell shape transition drives evolutionary expansion of the human forebrain. Cell 2021;184:2084–2102.e19. doi: 10.1016/j.cell.2021.02.050.
[13]. Seok J, Warren HS, Cuenca AG, et al. Genomic responses in mouse models poorly mimic human inflammatory diseases. Proc Natl Acad Sci U S A 2013;110:3507–3512. doi: 10.1073/pnas.1222878110.
[14]. Zhou M, Luo J, Chen M, et al. Mouse species-specific control of hepatocarcinogenesis and metabolism by FGF19/FGF15. J Hepatol 2017;66:1182–1192. doi: 10.1016/j.jhep.2017.01.027.
[15]. Fujiwara R, Yoda E, Tukey RH. Species differences in drug glucuronidation: Humanized UDP-glucuronosyltransferase 1 mice and their application for predicting drug glucuronidation and drug-induced toxicity in humans. Drug Metab Pharmacokinet 2018;33:9–16. doi: 10.1016/j.dmpk.2017.10.002.
[16]. Inoue T, Nitta K, Sugihara K, et al. CYP2C9-catalyzed metabolism of S-warfarin to 7-hydroxywarfarin in vivo and in vitro in chimeric mice with humanized liver. Drug Metab Dispos 2008;36:2429–2433. doi: 10.1124/dmd.108.022830.
[17]. Sanoh S, Horiguchi A, Sugihara K, et al. Predictability of metabolism of ibuprofen and naproxen using chimeric mice with human hepatocytes. Drug Metab Dispos 2012;40:2267–2272. doi: 10.1124/dmd.112.047555.
[18]. Vanhooren V, Libert C. The mouse as a model organism in
aging research: usefulness, pitfalls and possibilities. Ageing Res Rev 2013;12:8–21. doi: 10.1016/j.arr.2012.03.010.
[19]. Li M, Izpisua Belmonte JC. Organoids — preclinical models of human disease. Reply. N Engl J Med 2019;380:1982. doi: 10.1056/NEJMc1903253.
[20]. Grenier K, Kao J, Diamandis P. Three-dimensional modeling of human neurodegeneration: brain organoids coming of age. Mol Psychiatry 2020;25:254–274. doi: 10.1038/s41380-019-0500-7.
[21]. Kaluthantrige Don F, Huch M. Organoids, where we stand and where we go. Trends Mol Med 2021;27:416–418. doi: 10.1016/j.molmed.2021.03.001.
[22]. Kim J, Koo BK, Knoblich JA. Human organoids: model systems for human biology and medicine. Nat Rev Mol Cell Biol 2020;21:571–584. doi: 10.1038/s41580-020-0259-3.
[23]. Lancaster MA, Knoblich JA. Organogenesis in a dish: modeling development and disease using
organoid technologies. Science 2014;345:1247125. doi: 10.1126/science.1247125.
[24]. Corrò C, Novellasdemunt L, Li VSW. A brief history of organoids. Am J Physiol Cell Physiol 2020;319:C151–C165. doi: 10.1152/ajpcell.00120.2020.
[25]. Rossi G, Manfrin A, Lutolf MP. Progress and potential in
organoid research. Nat Rev Genet 2018;19:671–687. doi: 10.1038/s41576-018-0051-9.
[26]. Fan P, Wang Y, Lu K, et al. Modeling maternal cholesterol exposure reveals a reduction of neural progenitor proliferation using human cerebral organoids. Life Med 2022. doi:10.1093/lifemedi/lnac034. [Epub ahead of print].
[27]. Sato T, Vries RG, Snippert HJ, et al. Single Lgr5 stem cells build crypt-villus structures in vitro without a mesenchymal niche. Nature 2009;459:262–265. doi: 10.1038/nature07935.
[28]. Lancaster MA, Renner M, Martin CA, et al. Cerebral organoids model human brain development and microcephaly. Nature 2013;501:373–379. doi: 10.1038/nature12517.
[29]. Cowan CS, Renner M, De Gennaro M, et al. Cell types of the human retina and its organoids at single-cell resolution. Cell 2020;182:1623–1640.e34. doi: 10.1016/j.cell.2020.08.013.
[30]. Hofbauer P, Jahnel SM, Papai N, et al. Cardioids reveal self-organizing principles of human cardiogenesis. Cell 2021;184:3299–3317.e22. doi: 10.1016/j.cell.2021.04.034.
[31]. Zhang F, Qiu H, Dong X, et al. Single-cell atlas of multilineage cardiac organoids derived from human induced pluripotent stem cells. Life Med 2022;1:179–195. doi:10.1093/lifemedi/lnac002.
[32]. McCracken KW, Catá EM, Crawford CM, et al. Modelling human development and disease in pluripotent stem-cell-derived gastric organoids. Nature 2014;516:400–404. doi: 10.1038/nature13863.
[33]. Fujii M, Matano M, Toshimitsu K, et al. Human intestinal organoids maintain self-renewal capacity and cellular diversity in niche-inspired culture condition. Cell
Stem Cell 2018;23:787–793.e6. doi: 10.1016/j.stem.2018.11.016.
[34]. Tallapragada NP, Cambra HM, Wald T, et al. Inflation-collapse dynamics drive patterning and morphogenesis in intestinal organoids. Cell
Stem Cell 2021;28:1516–1532.e14. doi: 10.1016/j.stem.2021.04.002.
[35]. Lukonin I, Serra D, Meylan LC, et al. Phenotypic landscape of intestinal
organoid regeneration. Nature 2020;586:275–280. doi: 10.1038/s41586-020-2776-9.
[36]. Serra D, Mayr U, Boni A, et al. Self-organization and symmetry breaking in intestinal
organoid development. Nature 2019;569:66–72. doi: 10.1038/s41586-019-1146-y.
[37]. Prior N, Inacio P, Huch M. Liver organoids: from basic research to therapeutic applications. Gut 2019;68:2228–2237. doi: 10.1136/gutjnl-2019-319256.
[38]. Broutier L, Mastrogiovanni G, Verstegen MM, et al. Human primary liver cancer-derived
organoid cultures for
disease modeling and drug screening. Nat Med 2017;23:1424–1435. doi: 10.1038/nm.4438.
[39]. Hendriks D, Artegiani B, Hu H, et al. Establishment of human fetal hepatocyte organoids and CRISPR-Cas9-based gene knockin and knockout in
organoid cultures from human liver. Nat Protoc 2021;16:182–217. doi: 10.1038/s41596-020-00411-2.
[40]. Aloia L, McKie MA, Vernaz G, et al. Epigenetic remodelling licences adult cholangiocytes for
organoid formation and liver regeneration. Nat Cell Biol 2019;21:1321–1333. doi: 10.1038/s41556-019-0402-6.
[41]. He J, Cui H, Shi X, et al. Functional hepatobiliary organoids recapitulate liver development and reveal essential drivers of hepatobiliary cell fate determination. Life Med 2022;1:345–358. doi:10.1093/lifemedi/lnac055.
[42]. Boj SF, Hwang CI, Baker LA, et al.
Organoid models of human and mouse ductal pancreatic cancer. Cell 2015;160:324–338. doi: 10.1016/j.cell.2014.12.021.
[43]. Broutier L, Andersson-Rolf A, Hindley CJ, et al. Culture and establishment of self-renewing human and mouse adult liver and pancreas 3D organoids and their genetic manipulation. Nat Protoc 2016;11:1724–1743. doi: 10.1038/nprot.2016.097.
[44]. Marsee A, Roos FJM, Verstegen MMA, et al. Building consensus on definition and nomenclature of hepatic, pancreatic, and biliary organoids. Cell
Stem Cell 2021;28:816–832. doi: 10.1016/j.stem.2021.04.005.
[45]. Yoshihara E, O’Connor C, Gasser E, et al. Immune-evasive human islet-like organoids ameliorate diabetes. Nature 2020;586:606–611. doi: 10.1038/s41586-020-2631-z.
[46]. Wang D, Wang J, Bai L, et al. Long-term expansion of pancreatic islet organoids from resident Procr+ progenitors. Cell 2020;180:1198–1211.e19. doi: 10.1016/j.cell.2020.02.048.
[47]. Hai J, Zhang H, Zhou J, et al. Generation of genetically engineered mouse lung
organoid models for squamous cell lung cancers allows for the study of combinatorial immunotherapy. Clin Cancer Res 2020;26:3431–3442. doi: 10.1158/1078-0432.CCR-19-1627.
[48]. Salahudeen AA, Choi SS, Rustagi A, et al. Progenitor identification and SARS-CoV-2 infection in human distal lung organoids. Nature 2020;588:670–675. doi: 10.1038/s41586-020-3014-1.
[49]. Han Y, Duan X, Yang L, et al. Identification of SARS-CoV-2 inhibitors using lung and colonic organoids. Nature 2021;589:270–275. doi: 10.1038/s41586-020-2901-9.
[50]. Wu HJ, Uchimura K, Donnelly EL, et al. Comparative analysis and refinement of human PSC-derived kidney
organoid differentiation with single-cell transcriptomics. Cell
Stem Cell 2018;23:869–881.e8. doi: 10.1016/j.stem.2018.10.010.
[51]. Lawlor KT, Vanslambrouck JM, Higgins JW, et al. Cellular extrusion bioprinting improves kidney
organoid reproducibility and conformation. Nat Mater 2021;20:260–271. doi: 10.1038/s41563-020-00853-9.
[52]. Homan KA, Gupta N, Kroll KT, et al. Flow-enhanced vascularization and maturation of kidney organoids in vitro. Nat Methods 2019;16:255–262. doi: 10.1038/s41592-019-0325-y.
[53]. Czerniecki SM, Cruz NM, Harder JL, et al. High-throughput screening enhances kidney
organoid differentiation from human pluripotent stem cells and enables automated multidimensional phenotyping. Cell
Stem Cell 2018;22:929–940.e4. doi: 10.1016/j.stem.2018.04.022.
[54]. Evans M. Origin of mouse embryonal carcinoma cells and the possibility of their direct isolation into tissue culture. J Reprod Fertil 1981;62:625–631. doi: 10.1530/jrf.0.0620625.
[55]. Martin GR. Isolation of a pluripotent cell line from early mouse embryos cultured in medium conditioned by teratocarcinoma stem cells. Proc Natl Acad Sci U S A 1981;78:7634–7638. doi: 10.1073/pnas.78.12.7634.
[56]. Thomson JA, Itskovitz-Eldor J, Shapiro SS, et al. Embryonic
stem cell lines derived from human blastocysts. Science 1998;282:1145–1147. doi: 10.1126/science.282.5391.1145.
[57]. Takahashi K, Yamanaka S. Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell 2006;126:663–676. doi: 10.1016/j.cell.2006.07.024.
[58]. Takahashi K, Tanabe K, Ohnuki M, et al. Induction of pluripotent stem cells from adult human fibroblasts by defined factors. Cell 2007;131:861–872. doi: 10.1016/j.cell.2007.11.019.
[59]. Yu J, Vodyanik MA, Smuga-Otto K, et al. Induced pluripotent
stem cell lines derived from human somatic cells. Science 2007;318:1917–1920. doi: 10.1126/science.1151526.
[60]. Shi Y, Inoue H, Wu JC, et al. Induced pluripotent
stem cell technology: a decade of progress. Nat Rev Drug Discov 2017;16:115–130. doi: 10.1038/nrd.2016.245.
[61]. Yi SA, Zhang Y, Rathnam C, et al. Bioengineering approaches for the advanced
organoid research. Adv Mater 2021;33:e2007949. doi: 10.1002/adma.202007949.
[62]. Brandenberg N, Hoehnel S, Kuttler F, et al. High-throughput automated
organoid culture via stem-cell aggregation in microcavity arrays. Nat Biomed Eng 2020;4:863–874. doi: 10.1038/s41551-020-0565-2.
[63]. Nikolaev M, Mitrofanova O, Broguiere N, et al. Homeostatic mini-intestines through scaffold-guided
organoid morphogenesis. Nature 2020;585:574–578. doi: 10.1038/s41586-020-2724-8.
[64]. Simian M, Bissell MJ. Organoids: a historical perspective of thinking in three dimensions. J Cell Biol 2017;216:31–40. doi: 10.1083/jcb.201610056.
[65]. Liu GH, Ding Z, Izpisua Belmonte JC. iPSC technology to study human
aging and
aging-related disorders. Curr Opin Cell Biol 2012;24:765–774. doi: 10.1016/j.ceb.2012.08.014.
[66]. Chang Y, Kim J, Park H, et al. Modelling neurodegenerative diseases with 3D brain organoids. Biol Rev Camb Philos Soc 2020;95:1497–1509. doi: 10.1111/brv.12626.
[67]. Wray S. Modelling neurodegenerative disease using brain organoids. Semin Cell Dev Biol 2021;111:60–66. doi: 10.1016/j.semcdb.2020.05.012.
[68]. Bhamidipati K, Wei K. Precision medicine in rheumatoid arthritis. Best Pract Res Clin Rheumatol 2022;36:101742. doi: 10.1016/j.berh.2022.101742.
[69]. Singh YP, Moses JC, Bhardwaj N, et al. Overcoming the dependence on animal models for osteoarthritis therapeutics — the promises and prospects of in vitro models. Adv Healthc Mater 2021;10:e2100961. doi: 10.1002/adhm.202100961.
[70]. Jalal S, Dastidar S, Tedesco FS. Advanced models of human skeletal muscle differentiation, development and disease: three-dimensional cultures, organoids and beyond. Curr Opin Cell Biol 2021;73:92–104. doi: 10.1016/j.ceb.2021.06.004.
[71]. Tsakmaki A, Fonseca Pedro P, Bewick GA. Diabetes through a 3D lens:
organoid models. Diabetologia 2020;63:1093–1102. doi: 10.1007/s00125-020-05126-3.
[72]. Zhang X, Ma Z, Song E, et al. Islet
organoid as a promising model for diabetes. Protein Cell 2022;13:239–257. doi: 10.1007/s13238-021-00831-0.
[73]. Wimmer RA, Leopoldi A, Aichinger M, et al. Human blood vessel organoids as a model of diabetic vasculopathy. Nature 2019;565:505–510. doi: 10.1038/s41586-018-0858-8.
[74]. Veninga V, Voest EE. Tumor organoids: opportunities and challenges to guide precision medicine. Cancer Cell 2021;39:1190–1201. doi: 10.1016/j.ccell.2021.07.020.
[75]. Bleijs M, van de Wetering M, Clevers H, et al. Xenograft and
organoid model systems in cancer research. EMBO J 2019;38:e101654. doi: 10.15252/embj.2019101654.
[76]. Choi SH, Kim YH, Hebisch M, et al. A three-dimensional human neural cell culture model of Alzheimer’s disease. Nature 2014;515:274–278. doi: 10.1038/nature13800.
[77]. Israel MA, Yuan SH, Bardy C, et al. Probing sporadic and familial Alzheimer’s disease using induced pluripotent stem cells. Nature 2012;482:216–220. doi: 10.1038/nature10821.
[78]. Bowles KR, Silva MC, Whitney K, et al. ELAVL4, splicing, and glutamatergic dysfunction precede neuron loss in MAPT mutation cerebral organoids. Cell 2021;184:4547–4563.e17. doi: 10.1016/j.cell.2021.07.003.
[79]. Zhang W, Qu J, Suzuki K, et al. Concealing cellular defects in pluripotent stem cells. Trends Cell Biol 2013;23:587–592. doi: 10.1016/j.tcb.2013.07.001.
[80]. Park JC, Jang SY, Lee D, 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. doi: 10.1038/s41467-020-20440-5.
[81]. LĂłpez-OtĂn C, Blasco MA, Partridge L, et al. The hallmarks of
aging. Cell 2013;153:1194–1217. doi: 10.1016/j.cell.2013.05.039.
[82]. Cai Y, Wang S, Qu J, et al. Rejuvenation of tissue stem cells by intrinsic and extrinsic factors. Stem Cells Transl Med 2022;11:231–238. doi: 10.1093/stcltm/szab012.
[83]. Liu B, Qu J, Zhang W, et al. A
stem cell aging framework, from mechanisms to interventions. Cell Rep 2022;41:111451. doi: 10.1016/j.celrep.2022.111451.
[84]. He D, Wu H, Xiang J, et al. Gut
stem cell aging is driven by mTORC1 via a p38 MAPK-p53 pathway. Nat Commun 2020;11:37. doi: 10.1038/s41467-019-13911-x.
[85]. Yan Z, Yin H, Brochhausen C, et al. Aged tendon stem/progenitor cells are less competent to form 3D tendon organoids due to cell autonomous and matrix production deficits. Front Bioeng Biotechnol 2020;8:406. doi: 10.3389/fbioe.2020.00406.
[86]. Nalapareddy K, Nattamai KJ, Kumar RS, et al. Canonical Wnt signaling ameliorates
aging of intestinal stem cells. Cell Rep 2017;18:2608–2621. doi: 10.1016/j.celrep.2017.02.056.
[87]. Pentinmikko N, Iqbal S, Mana M, et al. Notum produced by Paneth cells attenuates regeneration of aged intestinal epithelium. Nature 2019;571:398–402. doi: 10.1038/s41586-019-1383-0.
[88]. Diekmann J, Alili L, Scholz O, et al. A three-dimensional skin equivalent reflecting some aspects of in vivo aged skin. Exp Dermatol 2016;25:56–61. doi: 10.1111/exd.12866.
[89]. Schneider MR, Oelgeschlaeger M, Burgdorf T, et al. Applicability of organ-on-chip systems in toxicology and pharmacology. Crit Rev Toxicol 2021;51:540–554. doi: 10.1080/10408444.2021.1953439.
[90]. Piergiovanni M, Leite SB, Corvi R, et al. Standardisation needs for organ on chip devices. Lab Chip 2021;21:2857–2868. doi: 10.1039/D1LC00241D.
[91]. Rothbauer M, Höll G, Eilenberger C, et al. Monitoring tissue-level remodelling during inflammatory arthritis using a three-dimensional synovium-on-a-chip with non-invasive light scattering biosensing. Lab Chip 2020;20:1461–1471. doi: 10.1039/c9lc01097a.
[92]. Zhou M, Zhang X, Wen X, et al. Development of a functional glomerulus at the organ level on a chip to mimic hypertensive nephropathy. Sci Rep 2016;6:31771. doi: 10.1038/srep31771.
[93]. Osaki T, Uzel SGM, Kamm RD. Microphysiological 3D model of amyotrophic lateral sclerosis (ALS) from human iPS-derived muscle cells and optogenetic motor neurons. Sci Adv 2018;4:eaat5847. doi: 10.1126/sciadv.aat5847.
[94]. Ying QL, Stavridis M, Griffiths D, et al. Conversion of embryonic stem cells into neuroectodermal precursors in adherent monoculture. Nat Biotechnol 2003;21:183–186. doi: 10.1038/nbt780.
[95]. Watanabe K, Kamiya D, Nishiyama A, et al. Directed differentiation of telencephalic precursors from embryonic stem cells. Nat Neurosci 2005;8:288–296. doi: 10.1038/nn1402.
[96]. Eiraku M, Watanabe K, Matsuo-Takasaki M, et al. Self-organized formation of polarized cortical tissues from ESCs and its active manipulation by extrinsic signals. Cell
Stem Cell 2008;3:519–532. doi: 10.1016/j.stem.2008.09.002.
[97]. Zhang SC, Wernig M, Duncan ID, et al. In vitro differentiation of transplantable neural precursors from human embryonic stem cells. Nat Biotechnol 2001;19:1129–1133. doi: 10.1038/nbt1201-1129.
[98]. Chambers SM, Fasano CA, Papapetrou EP, et al. Highly efficient neural conversion of human ES and iPS cells by dual inhibition of SMAD signaling. Nat Biotechnol 2009;27:275–280. doi: 10.1038/nbt.1529.
[99]. Eiraku M, Takata N, Ishibashi H, et al. Self-organizing optic-cup morphogenesis in three-dimensional culture. Nature 2011;472:51–56. doi: 10.1038/nature09941.
[100]. Paşca AM, Sloan SA, Clarke LE, et al. Functional cortical neurons and astrocytes from human pluripotent stem cells in 3D culture. Nat Methods 2015;12:671–678. doi: 10.1038/nmeth.3415.
[101]. Sloan SA, Andersen J, Paşca AM, et al. Generation and assembly of human brain region-specific three-dimensional cultures. Nat Protoc 2018;13:2062–2085. doi: 10.1038/s41596-018-0032-7.
[102]. Jo J, Xiao Y, Sun AX, et al. Midbrain-like organoids from human pluripotent stem cells contain functional dopaminergic and neuromelanin-producing neurons. Cell
Stem Cell 2016;19:248–257. doi: 10.1016/j.stem.2016.07.005.
[103]. Muguruma K, Nishiyama A, Kawakami H, et al. Self-organization of polarized cerebellar tissue in 3D culture of human pluripotent stem cells. Cell Rep 2015;10:537–550. doi: 10.1016/j.celrep.2014.12.051.
[104]. Miura Y, Li MY, Birey F, et al. Generation of human striatal organoids and cortico-striatal assembloids from human pluripotent stem cells. Nat Biotechnol 2020;38:1421–1430. doi: 10.1038/s41587-020-00763-w.
[105]. Pellegrini L, Bonfio C, Chadwick J, et al. Human CNS barrier-forming organoids with cerebrospinal fluid production. Science 2020;369:eaaz5626. doi: 10.1126/science.aaz5626.
[106]. Lee JH, Shin H, Shaker MR, et al. Production of human spinal-cord organoids recapitulating neural-tube morphogenesis. Nat Biomed Eng 2022;6:435–448. doi: 10.1038/s41551-022-00868-4.
[107]. Huang WK, Wong SZH, Pather SR, et al. Generation of hypothalamic arcuate organoids from human induced pluripotent stem cells. Cell
Stem Cell 2021;28:1657–1670.e10. doi: 10.1016/j.stem.2021.04.006.
[108]. Birey F, Andersen J, Makinson CD, et al. Assembly of functionally integrated human forebrain spheroids. Nature 2017;545:54–59. doi: 10.1038/nature22330.
[109]. Andersen J, Revah O, Miura Y, et al. Generation of functional human 3D cortico-motor assembloids. Cell 2020;183:1913–1929.e26. doi: 10.1016/j.cell.2020.11.017.
[110]. Yoon SJ, Elahi LS, PaĹźca AM, et al. Reliability of human cortical
organoid generation. Nat Methods 2019;16:75–78. doi: 10.1038/s41592-018-0255-0.
[111]. Giandomenico SL, Sutcliffe M, Lancaster MA. Generation and long-term culture of advanced cerebral organoids for studying later stages of neural development. Nat Protoc 2021;16:579–602. doi: 10.1038/s41596-020-00433-w.
[112]. Qian X, Jacob F, Song MM, et al. Generation of human brain region-specific organoids using a miniaturized spinning bioreactor. Nat Protoc 2018;13:565–580. doi: 10.1038/nprot.2017.152.
[113]. Raja WK, Mungenast AE, Lin YT, et al. Self-organizing 3D human neural tissue derived from induced pluripotent stem cells recapitulate Alzheimer’s disease phenotypes. PLoS One 2016;11:e0161969. doi: 10.1371/journal.pone.0161969.
[114]. Gonzalez C, Armijo E, Bravo-Alegria J, et al. Modeling amyloid beta and tau pathology in human cerebral organoids. Mol Psychiatry 2018;23:2363–2374. doi: 10.1038/s41380-018-0229-8.
[115]. Zhao J, Lu W, Ren Y, et al. Apolipoprotein E regulates lipid metabolism and α-synuclein pathology in human iPSC-derived cerebral organoids. Acta Neuropathol 2021;142:807–825. doi: 10.1007/s00401-021-02361-9.
[116]. Pérez MJ, Ivanyuk D, Panagiotakopoulou V, et al. Loss of function of the mitochondrial peptidase PITRM1 induces proteotoxic stress and Alzheimer’s disease-like pathology in human cerebral organoids. Mol Psychiatry 2021;26:5733–5750. doi: 10.1038/s41380-020-0807-4.
[117]. Chen X, Sun G, Tian E, et al. Modeling sporadic Alzheimer’s disease in human brain organoids under serum exposure. Adv Sci (Weinh) 2021;8:e2101462. doi: 10.1002/advs.202101462.
[118]. Sloan SA, Darmanis S, Huber N, et al. Human astrocyte maturation captured in 3D cerebral cortical spheroids derived from pluripotent stem cells. Neuron 2017;95:779–790.e6. doi: 10.1016/j.neuron.2017.07.035.
[119]. Chukwurah E, Osmundsen A, Davis SW, et al. All together now: modeling the interaction of neural with non-neural systems using
organoid models. Front Neurosci 2019;13:582. doi: 10.3389/fnins.2019.00582.
[120]. Huang S, Zhang Z, Cao J, et al. Chimeric cerebral organoids reveal the essentials of neuronal and astrocytic APOE4 for Alzheimer’s tau pathology. Signal Transduct Target Ther 2022;7:176. doi: 10.1038/s41392-022-01006-x.
[121]. Nakamura M, Shiozawa S, Tsuboi D, et al. Pathological progression induced by the frontotemporal dementia-associated R406W Tau Mutation in Patient-Derived iPSCs.
Stem Cell Rep 2019;13:684–699. doi: 10.1016/j.stemcr.2019.08.011.
[122]. Ghatak S, Dolatabadi N, Gao R, et al. NitroSynapsin ameliorates hypersynchronous neural network activity in Alzheimer hiPSC models. Mol Psychiatry 2021;26:5751–5765. doi: 10.1038/s41380-020-0776-7.
[123]. Smits LM, Reinhardt L, Reinhardt P, et al. Modeling Parkinson’s disease in midbrain-like organoids. NPJ Parkinsons Dis 2019;5:5. doi: 10.1038/s41531-019-0078-4.
[124]. Wulansari N, Darsono WHW, Woo HJ, et al. Neurodevelopmental defects and neurodegenerative phenotypes in human brain organoids carrying Parkinson’s disease-linked DNAJC6 mutations. Sci Adv 2021;7:eabb1540. doi: 10.1126/sciadv.abb1540.
[125]. Kim H, Park HJ, Choi H, et al. Modeling G2019S-LRRK2 sporadic Parkinson’s disease in 3D midbrain organoids.
Stem Cell Rep 2019;12:518–531. doi: 10.1016/j.stemcr.2019.01.020.
[126]. Boussaad I, Obermaier CD, Hanss Z, et al. A patient-based model of RNA mis-splicing uncovers treatment targets in Parkinson’s disease. Sci Transl Med 2020;12:eaau3960. doi: 10.1126/scitranslmed.aau3960.
[127]. Kim SW, Woo HJ, Kim EH, et al. Neural stem cells derived from human midbrain organoids as a stable source for treating Parkinson’s disease: Midbrain
organoid-NSCs (Og-NSC) as a stable source for PD treatment. Prog Neurobiol 2021;204:102086. doi: 10.1016/j.pneurobio.2021.102086.
[128]. Szebényi K, Wenger LMD, Sun Y, et al. Human ALS/FTD brain
organoid slice cultures display distinct early astrocyte and targetable neuronal pathology. Nat Neurosci 2021;24:1542–1554. doi: 10.1038/s41593-021-00923-4.
[129]. Aguado J, Chaggar HK, Gómez-Inclán C, et al. Inhibition of the cGAS-STING pathway ameliorates the premature
senescence hallmarks of Ataxia-Telangiectasia brain organoids.
Aging Cell 2021;20:e13468. doi: 10.1111/acel.13468.
[130]. Conforti P, Besusso D, Bocchi VD, et al. Faulty neuronal determination and cell polarization are reverted by modulating HD early phenotypes. Proc Natl Acad Sci U S A 2018;115:E762–E771. doi: 10.1073/pnas.1715865115.
[131]. Song G, Ma Y, Gao X, et al. CRISPR/Cas9-mediated genetic correction reverses spinocerebellar ataxia 3 disease-associated phenotypes in differentiated cerebellar neurons. Life Med 2022;1:27–44. doi: 10.1093/lifemedi/lnac020.
[132]. Clevers H. STEM CELLS. What is an adult
stem cell? Science 2015;350:1319–1320. doi: 10.1126/science.aad7016.
[133]. Goodell MA, Rando TA. Stem cells and healthy
aging. Science 2015;350:1199–1204. doi: 10.1126/science.aab3388.
[134]. Cai Y, Ji Z, Wang S, et al. Genetic enhancement: a new avenue to combat
aging-related diseases. Life Med 2022;1:307–318. doi:10.1093/lifemedi/lnac054.
[135]. Oh J, Lee YD, Wagers AJ.
Stem cell aging: mechanisms, regulators and therapeutic opportunities. Nat Med 2014;20:870–880. doi: 10.1038/nm.3651.
[136]. Xie H, Zhao S, Liu S.
Aging of human adult stem cells. Adv Exp Med Biol 2018;1086:105–115. doi: 10.1007/978-981-13-1117-8_7.
[137]. Murrow LM, Weber RJ, Gartner ZJ. Dissecting the
stem cell niche with
organoid models: an engineering-based approach. Development 2017;144:998–1007. doi: 10.1242/dev.140905.
[138]. Uchida R, Saito Y, Nogami K, et al. Epigenetic silencing of Lgr5 induces
senescence of intestinal epithelial organoids during the process of
aging. NPJ
Aging Mech Dis 2018;5:1. doi: 10.1038/s41514-018-0031-5.
[139]. Lewis SK, Nachun D, Martin MG, et al. DNA methylation analysis validates organoids as a viable model for studying human intestinal
aging. Cell Mol Gastroenterol Hepatol 2020;9:527–541. doi:10.1016/j.jcmgh.2019.11.013.
[140]. Tao Y, Kang B, Petkovich DA, et al.
Aging-like spontaneous epigenetic silencing facilitates Wnt activation, stemness, and Braf(V600E)-induced tumorigenesis. Cancer Cell 2019;35:315–328.e6. doi: 10.1016/j.ccell.2019.01.005.
[141]. Chesnokova V, Zonis S, Apostolou A, et al. Local non-pituitary growth hormone is induced with
aging and facilitates epithelial damage. Cell Rep 2021;37:110068. doi:10.1016/j.celrep.2021.110068.
[142]. Ma S, Sun S, Geng L, et al. Caloric restriction reprograms the single-cell transcriptional landscape of rattus norvegicus
aging. Cell 2020;180:984–1001.e22. doi: 10.1016/j.cell.2020.02.008.
[143]. Liu Z, Li W, Geng L, et al. Cross-species metabolomic analysis identifies uridine as a potent regeneration promoting factor. Cell Discov 2022;8:6. doi: 10.1038/s41421-021-00361-3.
[144]. Wang K, Liu H, Hu Q, et al. Epigenetic regulation of
aging: implications for interventions of
aging and diseases. Signal Transduct Target Ther 2022;7:374. doi: 10.1038/s41392-022-01211-8.
[145]. Geng L, Zhang B, Liu H, et al. A comparative study of metformin and nicotinamide riboside in alleviating tissue
aging in rats. Life Med 2022. doi:10.1093/lifemedi/lnac045. [Epub ahead of print].
[146]. Mihaylova MM, Cheng CW, Cao AQ, et al. Fasting activates fatty acid oxidation to enhance intestinal
stem cell function during homeostasis and
aging. Cell
Stem Cell 2018;22:769–778.e4. doi: 10.1016/j.stem.2018.04.001.
[147]. Igarashi M, Guarente L. mTORC1 and SIRT1 cooperate to foster expansion of gut adult stem cells during calorie restriction. Cell 2016;166:436–450. doi: 10.1016/j.cell.2016.05.044.
[148]. Igarashi M, Miura M, Williams E, et al. NAD(+) supplementation rejuvenates aged gut adult stem cells.
Aging Cell 2019;18:e12935. doi: 10.1111/acel.12935.
[149]. Bhartiya D. Pluripotent stem cells in adult tissues: struggling to be acknowledged over two decades.
Stem Cell Rev Rep 2017;13:713–724. doi: 10.1007/s12015-017-9756-y.
[150]. Bhartiya D, Shaikh A, Anand S, et al. Endogenous, very small embryonic-like stem cells: critical review, therapeutic potential and a look ahead. Hum Reprod Update 2016;23:41–76. doi: 10.1093/humupd/dmw030.
[151]. Franco AC, Aveleira C, Cavadas C. Skin
senescence: mechanisms and impact on whole-body
aging. Trends Mol Med 2022;28:97–109. doi: 10.1016/j.molmed.2021.12.003.
[152]. Dos Santos M, Metral E, Boher A, et al. In vitro 3-D model based on extending time of culture for studying chronological epidermis
aging. Matrix Biol 2015;47:85–97. doi: 10.1016/j.matbio.2015.03.009.
[153]. Vunjak-Novakovic G, Ronaldson-Bouchard K, Radisic M. Organs-on-a-chip models for biological research. Cell 2021;184:4597–4611. doi: 10.1016/j.cell.2021.08.005.
[154]. Sun Y, Li Q, Kirkland JL. Targeting senescent cells for a healthier longevity: the roadmap for an era of global
aging. Life Med 2022;1:103–119. doi: 10.1093/lifemedi/lnac030.
[155]. He X, Memczak S, Qu J, et al. Single-cell omics in ageing: a young and growing field. Nat Metab 2020;2:293–302. doi: 10.1038/s42255-020-0196-7.
[156].
Aging Atlas Consortium
Aging Atlas: a multi-omics database for
aging biology. Nucleic Acids Res 2021;49:D825–D830. doi: 10.1093/nar/gkaa894.
[157]. Bhaduri A, Andrews MG, Mancia Leon W, et al. Cell stress in cortical organoids impairs molecular subtype specification. Nature 2020;578:142–148. doi: 10.1038/s41586-020-1962-0.
[158]. Gordon A, Yoon SJ, Tran SS, et al. Long-term maturation of human cortical organoids matches key early postnatal transitions. Nat Neurosci 2021;24:331–342. doi: 10.1038/s41593-021-00802-y.
[159]. Garreta E, Kamm RD, Chuva de Sousa Lopes SM, et al. Rethinking
organoid technology through bioengineering. Nat Mater 2021;20:145–155. doi: 10.1038/s41563-020-00804-4.
[160]. Li B, Li Y, Li Z, et al. Human microglia development in the embryonic brain. Life Med 2022;1:55–57. doi: 10.1093/lifemedi/lnac029.
[161]. Kubben N, Misteli T. Shared molecular and cellular mechanisms of premature ageing and ageing-associated diseases. Nat Rev Mol Cell Biol 2017;18:595–609. doi: 10.1038/nrm.2017.68.
[162]. Wu Z, Zhang W, Song M, et al. Differential
stem cell aging kinetics in Hutchinson-Gilford progeria syndrome and Werner syndrome. Protein Cell 2018;9:333–350. doi: 10.1007/s13238-018-0517-8.
[163]. Zhang W, Li J, Suzuki K, et al.
Aging stem cells. A Werner syndrome
stem cell model unveils heterochromatin alterations as a driver of human
aging. Science 2015;348:1160–1163. doi: 10.1126/science.aaa1356.
[164]. Mohamad Kamal NS, Safuan S, Shamsuddin S, et al.
Aging of the cells: insight into cellular
senescence and detection methods. Eur J Cell Biol 2020;99:151108. doi: 10.1016/j.ejcb.2020.151108.
[165]. Ott C, Jung T, Grune T, et al. SIPS as a model to study age-related changes in proteolysis and aggregate formation. Mech Ageing Dev 2018;170:72–81. doi: 10.1016/j.mad.2017.07.007.
[166]. Zhang H, Li J, Ren J, et al. Single-nucleus transcriptomic landscape of primate hippocampal
aging. Protein Cell 2021;12:695–716. doi: 10.1007/s13238-021-00852-9.
[167]. Leng SX, Pawelec G. Single-cell immune atlas for human
aging and frailty. Life Med. 2022;1:67–70. doi: 10.1093/lifemedi/lnac013.
[168]. Zhang Y, Zheng Y, Wang S, et al. Single-nucleus transcriptomics reveals a gatekeeper role for FOXP1 in primate cardiac
aging. Protein Cell 2022. doi:10.1093/procel/pwac038.
[169]. Esk C, Lindenhofer D, Haendeler S, et al. A human tissue screen identifies a regulator of ER secretion as a brain-size determinant. Science 2020;370:935–941. doi: 10.1126/science.abb5390.
[170]. Wang W, Zheng Y, Sun S, et al. A genome-wide CRISPR-based screen identifies KAT7 as a driver of cellular
senescence. Sci Transl Med 2021;13:eabd2655. doi: 10.1126/scitranslmed.abd2655.
