Next Article in Journal
Assessment of the Role of Endothelial and Vascular Smooth Muscle EGFR for Acute Blood Pressure Effects of Angiotensin II and Adrenergic Stimulation in Obese Mice
Previous Article in Journal
Role of Endothelium in Cardiovascular Sequelae of Long COVID
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Biomedicines
Volume 11
Issue 8
10.3390/biomedicines11082240
biomedicines-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 thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Using Stems to Bear Fruit: Deciphering the Role of Alzheimer’s Disease Risk Loci in Human-Induced Pluripotent Stem Cell-Derived Microglia

by
Edward S. Wickstead
Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
Biomedicines 2023, 11(8), 2240; https://doi.org/10.3390/biomedicines11082240
Submission received: 16 June 2023 / Revised: 3 August 2023 / Accepted: 8 August 2023 / Published: 9 August 2023
(This article belongs to the Special Issue Probing the Role of Alzheimer’s Disease Risk Gene Using Human-Induced Pluripotent Stem Cells and Transgenic Animal Models)
Browse Figure
Review Reports Versions Notes

Abstract

:
Alzheimer’s disease (AD) is the most common neurodegenerative disorder globally. In people aged 65 and older, it is estimated that 1 in 9 currently live with the disease. With aging being the greatest risk factor for disease onset, the physiological, social and economic burden continues to rise. Thus, AD remains a public health priority. Since 2007, genome-wide association studies (GWAS) have identified over 80 genomic loci with variants associated with increased AD risk. Although some variants are beginning to be characterized, the effects of many risk loci remain to be elucidated. One advancement which may help provide a patient-focused approach to tackle this issue is the application of gene editing technology and human-induced pluripotent stem cells (hiPSCs). The relatively non-invasive acquisition of cells from patients with known AD risk loci may provide important insights into the pathological role of these risk variants. Of the risk genes identified, many have been associated with the immune system, including ABCA7, CLU, MEF2C, PICALM and TREM2—genes known to be highly expressed in microglia. This review will detail the potential of using hiPSC-derived microglia to help clarify the role of immune-associated genetic risk variants in AD.

1. Introduction to Alzheimer’s Disease

In 2018, Alzheimer’s Disease International estimated that approximately 50 million people live with dementia worldwide, a statistic predicted to triple by 2050 [1]. This was validated by the 2019 Global Burden of Disease study, which estimated that reported dementia cases could rise to 130–175 million in 2050 [2]. In the United States, the 2023 costs of AD or other dementias were estimated at USD 345 billion, with approximately 25% being patient out-of-pocket expenses [3]. In 2019, global societal costs of all-cause dementia were estimated at USD 1.3 trillion, working out to approximately USD 23,000 per patient [4]. Thus, combating global dementia prevalence is an urgent public health and economic priority.
Alzheimer’s disease (AD) is the most common neurodegenerative disease in humans, and the most common form of dementia. It displays an extensive distribution of two core pathologies: extracellular plaques and intraneuronal neurofibrillary tangles (NFTs), consisting of amyloid beta (Aβ) and the microtubule-associated protein tau, respectively. Although histological lesions were first reported over a century ago by Dr. Alois Alzheimer and Dr. Gaetano Perusini, with the support of Dr. Emil Kraepelin [5,6], it was not until 70 years later that these pathologies gathered widespread attention [7,8,9]. Several additional years followed before extensive clinical neuropathological studies supported both plaques and NFTs with probable AD diagnosis [10,11]. In the three decades since, hundreds of promising therapeutics targeted towards Aβ, tau and other pathologies have failed in clinical trials [12,13], resulting in the stagnation of curative research.
More recently, Biogen’s ‘EMERGE’ and ‘ENGAGE’ clinical trials of a Aβ plaque-directed monoclonal antibody, aducanumab [14], resulted in an accelerated approval in 2021 by the Food and Drug Administration (FDA). Though while plaques were reduced in patients, this may have been used as a surrogate end point, with aducanumab’s clinical efficacy in terms of cognitive decline protection being debated within the field [15,16,17]. In comparison, Eisai’s lecanemab—an Aβ-monoclonal antibody that can target protofibrils—appears to hold greater promise for patients in terms of both reduced plaques and protection against cognitive decline [18,19]. While also approved by the FDA via their accelerated approval pathway, similar side effects are present for both antibodies, namely amyloid-related imaging abnormalities and edema [14,19]. These reported side-effects, alongside decades of clinical trial failures, leave us to question: what are we doing wrong? Excluding the potential antibody successes of aducanumab, lecanumab and the future promise of Eli Lilly’s donanemab [20], AD drug development has presented a phase II and phase III clinical trial failure rate of 97% between 2003 and 2022 [12]. As such, novel approaches to further our understanding of this disease remain essential.
Neuroinflammation is now widely accepted to be a central pathology that contributes to the development and progression of AD [21,22,23,24,25,26]. Thus, in parallel to Aβ and tau targeted antibody treatments, future research should consider the clinical potential of immunomodulatory agents as supplementary therapeutics. This review details the evidence supporting a central role of neuroinflammation in the exacerbation of AD, including the identification of multiple single nucleotide polymorphisms (SNPs) via genome-wide association studies (GWAS) which strongly link the immune system to AD risk. This review will also detail why the incorporation of human stem cells and gene editing techniques can help further our understanding of how these variants contribute to a greater risk of AD, and what this might mean for future therapeutic development.

2. Neuroinflammation in AD: Human vs. Murine Microglia

Inflammation is a complex biological system essential in responding to both injury and infection. Although generally a protective process, if response cessation is inhibited, chronic inflammation can result. In the central nervous system (CNS), chronic neuroinflammation has been strongly linked to both the pathogenesis and progression of AD [21,27,28]. Microglia are the resident immune cells of the CNS, displaying a high dynamicity which facilitates constant surveillance within the brain parenchyma [29]. As such, they respond to a diverse array of environmental cues, often highlighted by their defined changes in cellular morphology [30,31]. This finding was initially reported by Pío del Río Hortega more than a century ago, wherein morphology changes were associated with manifesting pathology and disease states [32]. Though, we now know microglial morphology changes alone lack sufficient accuracy to inform us of a particular response or activity state [33]. Our group has detailed the roles of microglia and neuroinflammation in AD in a previous review [34].
For many years, microglia activation was often defined by the biphasic ‘M1′ and ‘M2′ classification, a now defunct system that was initially used for the activation states of peripheral macrophages [35], and the pitfalls of this nomenclature have been discussed elsewhere [36,37]. While this classification still appears in the literature, its use has declined in the past several years. This is an important update. Microglia activation states likely display on a spectrum due to the diverse array of environmental stimuli, ranging from stress and starvation, to infection, sterile disease and physical injury. The extensive diversity and nomenclature of microglia have been detailed previously [38]. Recent transcriptome analyses confirmed this diversity, with at least 9 transcriptionally distinct microglial subclusters reported in both murine and human microglia [39,40].
Despite the report of similar numbers of distinct microglial populations between humans and mice, there is scant overlap of microglial phenotypes between mouse models of AD and human patient microglia. For example, while a neurodegenerative disease (‘DAM’) cellular profile was previously reported in 5XFAD mice [41], human AD microglia (‘HAM’) displayed little transcriptional overlap [42]. Further analysis reported that of the 229 DAM genes previously reported, only 28 were associated with the AD microglial subcluster in human patients, while 49 additional genes not previously reported in animal models were also identified in this patient subcluster [43].
Regardless of the clear differences, the importance of mouse microglial research cannot be understated. Intricate research projects utilizing various forms of microglial culture have helped identify their roles in synaptic plasticity [44], blood–brain barrier permeability [45,46] and synaptic pruning [47,48]. In terms of AD, transgenic mouse models have been crucial in helping us understand the function of proteins that have known AD risk variants, such as TREM2 [49,50]. The association of TREM2 risk variants to AD were first reported in transgenic mouse models [51,52,53] before being validated in human patients [54]. However, advancing technologies will likely facilitate alternative approaches taking center stage in helping us elucidate the modified functions of the gene variants associated with increased AD risk.

3. GWAS Studies and Innate Genetic Hits

Most AD animal models were designed and developed based on familial AD [55], despite approximately 95% of all AD cases being sporadic. Many inherited forms of the disease—often classified as early-onset AD (EOAD)—are caused by familial autosomal dominant mutations in genes responsible for the processing of the amyloid precursor protein (APP), amongst which the PSEN1, PSEN2 and APP genes dominate [56,57,58,59,60]. Sporadic cases are non-heritable and have a comparatively late-onset (LOAD). Although there is no singular cause, there are numerous factors associated with increased LOAD risk, with age being the most common [61]. Sex differences also exist, with a recent multiethnic cohort study reporting that African American, Latino, Japanese American, Filipino and Caucasian women all display a greater risk for AD and related dementias [62].
In the last decade, genome-wide association studies have become significant. Abbreviated as GWAS, these observational studies of genome-wide genetic variants are applied to simultaneously examine millions of genetic variants with the aim of identifying variants which associate with a particular disease. In 2013, two papers reported that a mutation in TREM2 (R47H), an innate immune receptor highly expressed in microglia, was associated with an increased LOAD risk [49,50]. Although this TREM2 mutant appears to display one of the highest odds ratios for increased AD risk (alongside APOE ε4), dozens of other loci have been identified via GWAS. Two recent studies with considerably larger sample sizes doubled the number of identified loci [63,64]. As with previous GWAS [65], these studies implicate microglial genes in both the development and progression of AD. Although GWAS studies have identified dozens of loci which associate with an increased risk of AD, these analyses are most frequently performed in European populations [66]. Due to genetic variances in ethnicity and AD risk, it is unsurprising that different risk loci have been identified in diversified analyses [67,68]. Nevertheless, six independent GWAS studies have collectively identified 101 independent single-nucleotide polymorphism (SNP) variants expressed across 81 loci [63,64,65,69,70,71].
Many of the top immune hits reported by GWAS are highly expressed in human microglia [34], thus emphasizing the importance of these resident immune cells in AD. However, many of these proteins display limited homology to their animal model counterparts. For example, nucleotide and amino acid BLAST alignment of human and murine TREM2 confirms they only share 77% and 70% identity, respectively. This is true for many identified gene hits and has been detailed previously [72]. Further highlighting animal model limitations, several studies have detailed that human microglia can respond very differently to both inflammatory insult and pharmacological activation [73,74]. In response to amyloidosis in 5XFAD mice, microglia upregulate the DAM gene signature while concomitantly dialing down homeostatic gene expression. However, in the human AD brain, microglia increase the expression of both homeostatic genes and AD risk genes not modulated in the murine DAM profile, such as SORL1 [75]. As such, relying on animal models to delineate the functional changes of GWAS-identified risk loci may inhibit our progression in understanding how these variants increase AD risk in human patients.

4. iPSC Technology and iPSC-Derived Microglia

Although much of our understanding of microglial function has been established through insightful and intricate work utilizing primary murine microglia both in vitro and in vivo, the clear distinctions from human microglia limit the effectiveness of any translatability [76]. Functional validation of AD risk loci is crucial. As such, a shift in focus towards human microglia must be a priority. However, acquisition of human microglia remains difficult. Tissue availability is limited, meaning only a small subset of labs can harvest microglia regularly. Additionally, primary microglia are either harvested post-mortem, or from living patients undergoing brain resection surgery for epileptic foci or tumor removal [77,78,79,80]. While harvesting cells in this manner provides opportunities to further our understanding of human microglial physiology, considerations of their phenotypic heterogeneity in relation to patient age, regional localization and disease of patients undergoing these surgeries must be considered and scrutinized [81]. This also applies to post-mortem tissue, wherein the transcriptomes of microglia following tissue resection can rapidly change, including the downregulation of many AD risk genes, including BIN1, SORL1 and MEF2C [79].
As an alternative to using immortalized lines, primary cells from animal models and human microglia from brain excision surgery, recent advances in technology have facilitated a renewed interested in the generation of microglia from human-induced pluripotent stem cells (hiPSCs). Getting around the issue of tissue availability and variation in microglial phenotypes, hiPSCs can be used for both in vitro and xenographic in vivo work to understand cellular physiology in both health and disease. For example, microglial chimeric mouse models have been successfully generated, wherein hiPSC-derived macrophage progenitors are transplanted into neonatal mouse brains. Single-cell RNA sequencing reported that these xenografted cells largely retain human microglial identity and become widely dispersed by 6 months of age, including in both the hippocampus and cerebral cortex, as reported by TMEM119 staining [82]. It has been suggested that TMEM119 is both a reliable and specific marker, able to discriminate resident microglia from peripherally derived macrophages [83,84]. However, recent work suggests that TMEM119 is not exclusive, nor does it stain all microglia. The latter appears especially true under cellular stress conditions, including murine models of both focal stroke and Parkinson’s disease [85]. As such, supplementary staining methods will be important for future work using this model. Despite this issue though, in tandem with murine microglial depletion methods such as the pharmacological inhibition of CSF1R [86,87], the function of humanized microglia can be validated and compared against traditional non-chimeric animals. Pharmacological and genetic studies which report benefits in both systems will likely strengthen the translational potential of any promising future therapeutic target.
Although our understanding of microglial function continues to improve, cellular dysfunction in AD remains to be fully elucidated, despite the identification of dozens of genetic risk loci (Table 1). The use of hiPSCs to decipher the pathological contributions of these risk variants could thus prove invaluable. Following the seminal publication by Muffet and colleagues in 2016 [88], many protocols to produce hiPSC-derived microglia have become available [89,90,91,92,93,94], with others having reviewed some of these protocols previously [95].
The intricate work by Brownjohn and colleagues showcases how hiPSC-derived microglial research can elucidate both the physiological function and pathological disruption of AD risk genes [93]. In this study, fibroblasts were acquired from a patient either homozygous for the TREM2 p.T66M mutation or the TREM2 p.w50C mutation, both of which are missense mutations known to cause frontotemporal dementia-like syndrome and Nasu-Hakola disease, respectively [97]. These cells were then successfully differentiated into hiPSC-microglia, wherein aberrant processing was reported for all TREM2 mutant backgrounds and abnormalities in TREM2 trafficking were reported for homozygous mutant cells. Despite this, hiPSC-microglia displaying these missense mutations responded effectively to inflammatory challenge and remain phagocytically competent. Although the study had limitations due to the low sample number, expansion of this approach could hold importance to understanding the role of not only TREM2 risk variants in AD, but dozens of other risk loci identified in human patients (Figure 1).
These approaches outweigh the simplified use of both murine microglia and immortalized cell lines. However, it remains unlikely that two-dimensional monoculturing techniques will sufficiently recapture the effects of risk loci on the diverse array of functions displayed by endogenous human microglia. Instead, the use of gene editing technology to incorporate risk loci-containing microglia into 3D organoids may be an approach with greater translational potential. Several groups have successfully developed and validated three-dimensional organoid systems from hiPSCs [98,99,100]. Although, microglia are often absent in organoids developed from currently available protocols. Despite this, several groups have reported successful integration of microglia into human brain organoids via co-culturing techniques [91,101]. In addition, to get around the variability that has often plagued organoid research [102,103], newer protocols can reliably generate a diverse array of cell types typical for the human cerebral cortex with close correlation to endogenous tissue [98,104], strengthening the potential use of 3D cerebral organoids for neurodegenerative disease research. However, whether this reliability can be recaptured in organoids expressing hiPSC-derived microglia with known AD risk loci remains to be determined.

5. Future Work

Though hiPSC-derived microglia display some clear benefits over murine models, various factors must be addressed as we move forward. First, several protocols appear to produce cells that largely resemble either fetal or early postnatal rather than mature adult microglia [88,90,91,92,94]. In addition, although the transcriptomes of hiPSC-derived microglia from differing protocols appear comparable, they remain distinct from primary microglia [89]. The development of a gold standard protocol is therefore warranted.
With the parallel expansion of GWAS studies and the advancement of stem cell technologies, harnessing the power of hiPSCs for human microglia AD research has never looked more promising. Continuing with approaches like that adopted by Brownjohn and colleagues, developing hiPSC-derived microglia from patients with AD risk loci, such as those associated with CR1, PICALM and MEF2C, could extend our understanding of how these variants contribute to increased AD risk via both in vitro culture and in vivo chimeric analyses. Of course, the availability of tissue samples from patients with such variants may present a substantial hurdle. As an alternative, gene editing approaches in hiPSCs derived from healthy patients may hold promise. Gene editing with the CRISPR/Cas9 system could be harnessed to induce point mutations which correlate to the AD risk loci identified via GWAS. This approach has been used for the induction of both point and deletion mutations in the APP gene in patient-derived fibroblasts [105] and hiPSC-derived neurons [106]. Protective deletions within APP were incorporated into the APP-KI AD mouse model using the CRISPR/Cas9 system, resulting in significant reductions in Aβ pathology [107]. The CRISPR/Cas9 approach has also been used to generate novel AD animal models, converting endogenous rat and mouse APP into the human version via independent point mutation inclusion, an approach which resulted in the report of new mechanisms of disease development [108]. As such, this technique may become instrumental in furthering our understanding of not only EOAD, but also how LOAD risk loci contribute to increased AD risk.
Interestingly, many of the microglial genetic risk variants identified by GWAS are also expressed in peripheral macrophages. Because peripheral immune cells can infiltrate the brain in the context of responding to neuropathology, it remains possible that some risk loci act through these cells rather than microglia. As such, gene editing technologies used to study AD-associated microglial risk loci should also be introduced into both human primary and hiPSC-derived macrophages to investigate this possibility.

6. Summary

It is estimated that approximately 1 in 9 people over the age of 65 currently live with Alzheimer’s disease. As such, despite the recent advances in clinical trials, AD remains a public health priority. Novel research tools are urgently required to further our understanding of how the dysfunction of specific cellular processes increases our risk of developing this devastating disease. Although GWAS have helped demystify the genetic landscape of AD, extensive work is required to characterize and validate the functional consequence of any disease-related risk loci, many of which associate with microglia. While animal models can help, their translatability remains limited. This especially holds true for mutations in genes wherein mouse or rat orthologues are lacking.
Two exciting additions to the toolkit used to work around this issue are the recent advances in gene editing technology and the development of hiPSC-derived microglia. Hurdles do exist, however. Many differentiation protocols produce microglia which more closely represent either fetal or early postnatal cells in vivo, thus resulting in questions as to whether these cells are suitable to study for age-related neurodegenerative disease. An alternative method which may hold promise would be to facilitate direct transdifferentiation of adult somatic cells into microglia. This approach avoids cellular reprogramming to pluripotency and thus prevents the erasure of age-related cellular features [109,110].

7. Overall Conclusions

Microglia are multi-faceted, highly diverse immune cells which display crucial roles in regulating the homeostasis of the central nervous system. Unfortunately, their dysfunction is associated with a range of neurodegenerative diseases, including AD. Advances in gene editing technology and the use of human-induced pluripotent stem cells hold promise in designing microglial models which more accurately represent cellular dysfunction in human disease. These exciting approaches may provide new avenues to identify novel targets with potential therapeutic promise.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The author declares no conflict of interest.

References

  1. Alzheimer’s Disease International. World Alzheimer Report 2018. The State of the Art of Dementia Research: New Frontiers. Available online: https://www.alzint.org/resource/world-alzheimer-report-2018/ (accessed on 15 June 2023).
  2. GBD 2019 Dementia Forecasting Collaborators. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: An analysis for the Global Burden of Disease Study 2019. Lancet Public Health 2022, 7, e105–e125. [Google Scholar] [CrossRef] [PubMed]
  3. Alzheimer’s Association. 2023 Alzheimer’s Disease Facts and Figures. 2023. Available online: https://www.alz.org/media/documents/alzheimers-facts-and-figures.pdf (accessed on 15 June 2023).
  4. Wimo, A.; Seeher, K.; Cataldi, R.; Cyhlarova, E.; Dielemann, J.L.; Frisell, O.; Guerchet, M.; Jönsson, L.; Malaha, A.K.; Nichols, E.; et al. The worldwide costs of dementia in 2019. Alzheimer’s Dement. 2023, 19, 2865–2873. [Google Scholar] [CrossRef] [PubMed]
  5. Alzheimer, A. Über Eigenartige Krankheitsfälle des Späteren Alters. Z. Gesamte Neurol. Psychiatr. 1911, 4, 356–385. [Google Scholar] [CrossRef]
  6. Kokmen, E. The Early Story of Alzheimer’s Disease: Translation of the Historical Papers by Alois Alzheimer, Oskar Fischer, Francesco Bonfiglio, Emil Kraepelin, and Gaetano Perusini. Mayo Clin Proc. 1988, 63, 217–218. [Google Scholar] [CrossRef]
  7. Grundke-Iqbal, I.; Wisniewski, H.M.; Johnson, A.B.; Terry, R.D.; Iqbal, K. Evidence that Alzheimer neurofibrillary tangles originate from neurotubules. Lancet 1979, 1, 578–580. [Google Scholar] [CrossRef]
  8. Masters, C.L.; Simms, G.; Weinman, N.A.; Multhaup, G.; McDonald, B.L.; Beyreuther, K. Amyloid plaque core protein in Alzheimer disease and Down syndrome. Proc. Natl. Acad. Sci. USA 1985, 82, 4245–4249. [Google Scholar] [CrossRef]
  9. Kosik, K.S.; Joachim, C.L.; Selkoe, D.J. Microtubule-associated protein tau (tau) is a major antigenic component of paired helical filaments in Alzheimer disease. Proc. Natl. Acad. Sci. USA 1986, 83, 4044–4048. [Google Scholar] [CrossRef]
  10. Braak, H.; Braak, E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 1991, 82, 239–259. [Google Scholar] [CrossRef]
  11. Ball, M.; Braak, H.; Coleman, P.; Dickson, D.; Duyckaerts, C.; Gambetti, P.; Hansen, L.; Hyman, B.; Jellinger, K.; Markesbery, W.; et al. Consensus Recommendations for the Postmortem Diagnosis of Alzheimer’s Disease. Neurobiol. Aging 1997, 18, S1–S2. [Google Scholar]
  12. Kim, C.K.; Lee, Y.R.; Ong, L.; Gold, M.; Kalali, A.; Sarkar, J. Alzheimer’s Disease: Key Insights from Two Decades of Clinical Trial Failures. J. Alzheimer’s Dis. 2022, 87, 83–100. [Google Scholar] [CrossRef]
  13. Kehoe, P.G.; Turner, N.; Howden, B.; Jarutyte, L.; Clegg, S.L.; Malone, I.B.; Barnes, J.; Nielsen, C.; Sudre, C.H.; Wilson, A.; et al. Safety and efficacy of losartan for the reduction of brain atrophy in clinically diagnosed Alzheimer’s disease (the RADAR trial): A double-blind, randomised, placebo-controlled, phase 2 trial. Lancet Neurol. 2021, 20, 895–906. [Google Scholar] [CrossRef]
  14. Haeberlein, S.B.; Aisen, P.; Barkhof, F.; Chalkias, S.; Chen, T.; Cohen, S.; Dent, G.; Hansson, O.; Harrison, K.; von Hehn, C.; et al. Two Randomized Phase 3 Studies of Aducanumab in Early Alzheimer’s Disease. J. Prev. Alzheimer’s Dis. 2022, 9, 197–210. [Google Scholar] [CrossRef] [PubMed]
  15. Knopman, D.S.; Jones, D.T.; Greicius, M.D. Failure to demonstrate efficacy of aducanumab: An analysis of the EMERGE and ENGAGE trials as reported by Biogen, December 2019. Alzheimer’s Dement. 2021, 17, 696–701. [Google Scholar] [CrossRef] [PubMed]
  16. Alexander, G.C.; Emerson, S.; Kesselheim, A.S. Evaluation of Aducanumab for Alzheimer Disease: Scientific Evidence and Regulatory Review Involving Efficacy, Safety, and Futility. JAMA 2021, 325, 1717–1718. [Google Scholar] [CrossRef]
  17. Dunn, B.; Stein, P.; Cavazzoni, P. Approval of Aducanumab for Alzheimer Disease—The FDA’s Perspective. JAMA Intern. Med. 2021, 181, 1276–1278. [Google Scholar] [CrossRef] [PubMed]
  18. McDade, E.; Cummings, J.L.; Dhadda, S.; Swanson, C.J.; Reyderman, L.; Kanekiyo, M.; Koyama, A.; Irizarry, M.; Kramer, L.D.; Bateman, R.J. Lecanemab in patients with early Alzheimer’s disease: Detailed results on biomarker, cognitive, and clinical effects from the randomized and open-label extension of the phase 2 proof-of-concept study. Alzheimer’s Res. Ther. 2022, 14, 191. [Google Scholar] [CrossRef]
  19. Van Dyck, C.H.; Swanson, C.J.; Aisen, P.; Bateman, R.J.; Chen, C.; Gee, M.; Kanekiyo, M.; Li, D.; Reyderman, L.; Cohen, S.; et al. Lecanemab in Early Alzheimer’s Disease. N. Engl. J. Med. 2023, 388, 142–143. [Google Scholar] [CrossRef]
  20. Reardon, S. Alzheimer’s drug donanemab: What promising trial means for treatments. Nature 2023, 617, 232–233. [Google Scholar] [CrossRef]
  21. Heneka, M.T.; Carson, M.J.; Khoury El Landreth, G.E.; Brosseron, F.; Feinstein, D.L.; Jacobs, A.H.; Wyss-Coray, T.; Vitorica, J.; Ransohoff, R.M.; Herrup, K.; et al. Neuroinflammation in Alzheimer’s disease. Lancet Neurol. 2015, 14, 388–405. [Google Scholar] [CrossRef] [Green Version]
  22. Sosna, J.; Philipp, S.; Albay, R., III; Reyes-Ruiz, J.M.; Baglietto-Vargas, D.; LaFerla, F.M.; Glabe, C.G. Early long-term administration of the CSF1R inhibitor PLX3397 ablates microglia and reduces accumulation of intraneuronal amyloid, neuritic plaque deposition and pre-fibrillar oligomers in 5XFAD mouse model of Alzheimer’s disease. Mol. Neurodegener. 2018, 13, 11. [Google Scholar] [CrossRef]
  23. Bradshaw, E.M.; Chibnik, L.B.; Keenan, B.T.; Ottoboni, L.; Raj, T.; Tang, A.; Rosenkrantz, L.L.; Imboywa, S.; Lee, M. CD33 Alzheimer’s disease locus: Altered monocyte function and amyloid biology. Nat. Neurosci. 2013, 16, 848–850. [Google Scholar] [CrossRef]
  24. Rangan, P.; Lobo, F.; Parrella, E.; Rochette, N.; Morselli, M.; Stephen, T.-L.; Tagliafico, L.; Persia, A.; Caffa, I.; Monacelli, F.; et al. Fasting-mimicking diet cycles reduce neuroinflammation to attenuate cognitive decline in Alzheimer’s models. Cell Rep. 2022, 40, 111417. [Google Scholar] [CrossRef] [PubMed]
  25. Zhou, Y.; Tada, M.; Cai, Z.; Andhey, P.S.; Swain, A.; Miller, K.R.; Gilfillan, S.; Artyomov, M.N.; Takao, M.; Kakita, A.; et al. Human early-onset dementia caused by DAP12 deficiency reveals a unique signature of dysregulated microglia. Nat. Immunol. 2023, 24, 545–557. [Google Scholar] [CrossRef] [PubMed]
  26. Chen, C.; Liao, J.; Xia, Y.; Liu, X.; Jones, R.; Haran, J.; McCormick, B.; Sampson, T.R.; Alam, A.; Ye, K. Gut microbiota regulate Alzheimer’s disease pathologies and cognitive disorders via PUFA-associated neuroinflammation. Gut 2022, 71, 2233–2252. [Google Scholar] [CrossRef]
  27. Chen, X.; Firulyova, M.; Manis, M.; Herz, J.; Smirnov, I.; Aladyeva, E.; Wang, C.; Bao, X.; Finn, M.B.; Hu, H.; et al. Microglia-mediated T cell infiltration drives neurodegeneration in tauopathy. Nature 2023, 615, 668–677. [Google Scholar] [CrossRef] [PubMed]
  28. Brigas, H.C.; Ribeiro, M.; Coelho, J.E.; Gomes, R.; Gomez-Murcia, V.; Carvalho, K.; Faivre, E.; Costa-Pereira, S.; Darrigues, J.; de Almeida, A.A.; et al. IL-17 triggers the onset of cognitive and synaptic deficits in early stages of Alzheimer’s disease. Cell Rep. 2021, 36, 109574. [Google Scholar] [CrossRef]
  29. Nimmerjahn, A.; Kirchhoff, F.; Helmchen, F. Resting microglial cells are highly dynamic surveillants of brain parenchyma in vivo. Science 2005, 308, 1314–1318. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  30. Wadhwa, M.; Prabhakar, A.; Ray, K.; Roy, K.; Kumari, P.; Jha, P.K.; Kishore, K.; Kumar, S.; Panjwani, U. Inhibiting the microglia activation improves the spatial memory and adult neurogenesis in rat hippocampus during 48 h of sleep deprivation. J. Neuroinflamm. 2017, 14, 222. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  31. Kirkley, K.S.; Popichak, K.A.; Afzali, M.F.; Legare, M.E.; Tjalkens, R.B. Microglia amplify inflammatory activation of astrocytes in manganese neurotoxicity. J. Neuroinflamm. 2017, 14, 99. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  32. Sierra, A.; de Castro, F.; del Río-Hortega, J.; Rafael Iglesias-Rozas, J.; Garrosa, M.; Kettenmann, H. The “Big-Bang” for modern glial biology: Translation and comments on Pío del Río-Hortega 1919 series of papers on microglia. Glia 2016, 64, 1801–1840. [Google Scholar] [CrossRef]
  33. Boche, D.; Perry, V.H.; Nicoll, J.A.R. Review: Activation patterns of microglia and their identification in the human brain. Neuropathol. Appl. Neurobiol. 2013, 39, 3–18. [Google Scholar] [CrossRef] [PubMed]
  34. Wickstead, E.S.; Irving, M.A.; Getting, S.J.; McArthur, S. Exploiting formyl peptide receptor 2 to promote microglial resolution: A new approach to Alzheimer’s disease treatment. FEBS J. 2021, 289, 1801–1822. [Google Scholar] [CrossRef]
  35. Murray, P.J.; Allen, J.E.; Biswas, S.K.; Fisher, E.A.; Gilroy, D.W.; Goerdt, S.; Gordon, S.; Hamilton, J.A.; Ivashkiv, L.B.; Lawrence, T.; et al. Macrophage Activation and Polarization: Nomenclature and Experimental Guidelines. Immunity 2014, 41, 14–20. [Google Scholar] [CrossRef] [PubMed]
  36. Ransohoff, R.M. A polarizing question: Do M1 and M2 microglia exist? Nat. Neurosci. 2016, 19, 987–991. [Google Scholar] [CrossRef] [PubMed]
  37. Martinez, F.O.; Gordon, S. The M1 and M2 paradigm of macrophage activation: Time for reassessment. F1000Prime Rep. 2014, 6, 13. [Google Scholar] [CrossRef] [Green Version]
  38. Paolicelli, R.C.; Sierra, A.; Stevens, B.; Tremblay, M.-E.; Aguzzi, A.; Ajami, B.; Amit, I.; Audinat, E.; Bechmann, I.; Bennett, M.; et al. Microglia states and nomenclature: A field at its crossroads. Neuron 2022, 110, 3458–3483. [Google Scholar] [CrossRef] [PubMed]
  39. Hammond, T.R.; Dufort, C.; Dissing-Olesen, L.; Giera, S.; Young, A.; Wysoker, A. Single-Cell RNA Sequencing of Microglia throughout the Mouse Lifespan and in the Injured Brain Reveals Complex Cell-State Changes. Immunity 2019, 50, 253–271. [Google Scholar] [CrossRef] [Green Version]
  40. Olah, M.; Menon, V.; Habib, N.; Taga, M.F.; Ma, Y.; Yung, C.J.; Cimpean, M.; Khairallah, A.; Coronas-Samano, G.; Sankowski, R.; et al. Single cell RNA sequencing of human microglia uncovers a subset associated with Alzheimer’s disease. Nat. Commun. 2020, 11, 6129. [Google Scholar] [CrossRef]
  41. Keren-Shaul, H.; Spinrad, A.; Weiner, A.; Matcovitch-Natan, O.; Dvir-Szternfeld, R.; Ulland, T.K.; David, E.; Baruch, K.; Lara-Astaiso, D.; Toth, B.; et al. A Unique Microglia Type Associated with Restricting Development of Alzheimer’s Disease. Cell 2017, 169, 1276–1290.e17. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  42. Srinivasan, K.; Friedman, B.A.; Etxeberria, A.; Huntley, M.A.; van der Brug, M.P.; Foreman, O.; Paw, J.S.; Modrusan, Z.; Beach, T.G.; Serrano, G.E.; et al. Alzheimer’s Patient Microglia Exhibit Enhanced Aging and Unique Transcriptional Activation. Cell Rep. 2020, 31, 107843. [Google Scholar] [CrossRef] [PubMed]
  43. Mathys, H.; Davila-Velderrain, J.; Peng, Z.; Gao, F.; Mohammadi, S.; Young, J.Z.; Menon, M.; He, L.; Abdurrob, F.; Jiang, X.; et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 2019, 570, 332–337. [Google Scholar] [CrossRef]
  44. Lim, S.-H.; Park, E.; You, B.; Jung, Y.; Park, A.-R.; Park, S.G.; Lee, J.-R. Neuronal Synapse Formation Induced by Microglia and Interleukin 10. PLoS ONE 2013, 8, e81218. [Google Scholar] [CrossRef] [Green Version]
  45. Yenari, M.A.; Xu, L.; Tang, X.N.; Qiao, Y.; Giffard, R.G. Microglia potentiate damage to blood-brain barrier constituents: Improvement by minocycline in vivo and in vitro. Stroke 2006, 37, 1087–1093. [Google Scholar] [CrossRef] [Green Version]
  46. Shigemoto-Mogami, Y.; Hoshikawa, K.; Sato, K. Activated Microglia Disrupt the Blood-Brain Barrier and Induce Chemokines and Cytokines in a Rat in vitro Model. Front. Cell. Neurosci. 2018, 12, 494. [Google Scholar] [CrossRef]
  47. Scott-Hewitt, N.; Perrucci, F.; Morini, R.; Erreni, M.; Mahoney, M.; Witkowska, A.; Carey, A.; Faggiani, E.; Schuetz, L.T.; Mason, S.; et al. Local externalization of phosphatidylserine mediates developmental synaptic pruning by microglia. EMBO J. 2020, 39, e105380. [Google Scholar] [CrossRef]
  48. Ji, K.; Akgul, G.; Wollmuth, L.P.; Tsirka, S.E. Microglia Actively Regulate the Number of Functional Synapses. PLoS ONE 2013, 8, e56293. [Google Scholar] [CrossRef] [Green Version]
  49. Guerreiro, R.; Wojtas, A.; Bras, J.; Carrasquillo, M.; Rogaeva, E.; Majounie, E.; Cruchaga, C.; Sassi, C.; Kauwe, J.S.K.; Younkin, S.; et al. TREM2 Variants in Alzheimer’s Disease. N. Engl. J. Med. 2013, 368, 117–127. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  50. Jonsson, T.; Stefansson, H.; Steinberg, S.; Jonsdottir, I.; Jonsson, P.V.; Snaedal, J.; Bjornsson, S.; Huttenlocher, J.; Levey, A.I.; Lah, J.J.; et al. Variant of TREM2 Associated with the Risk of Alzheimer’s Disease. N. Engl. J. Med. 2013, 368, 107–116. [Google Scholar] [CrossRef] [PubMed]
  51. Jay, T.R.; Miller, C.M.; Cheng, P.J.; Graham, L.C.; Bemiller, S.; Broihier, M.L.; Xu, G.; Margevicius, D.; Karlo, J.C.; Sousa, G.L.; et al. TREM2 deficiency eliminates TREM2+ inflammatory macrophages and ameliorates pathology in Alzheimer’s disease mouse models. J. Exp. Med. 2015, 212, 287–295. [Google Scholar] [CrossRef]
  52. Ulrich, J.D.; Finn, M.B.; Wang, Y.; Shen, A.; Mahan, T.E.; Jiang, H.; Stewart, F.R.; Piccio, L.; Colonna, M.; Holtzman, D.M. Altered microglial response to Aβ plaques in APPPS1-21 mice heterozygous for TREM2. Mol. Neurodegener. 2014, 9, 20. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Wang, Y.; Cella, M.; Mallinson, K.; Ulrich, J.D.; Young, K.L.; Robinette, M.L.; Gilfillan, S.; Krishnan, G.M.; Sudhakar, S.; Zinselmeyer, B.H.; et al. TREM2 Lipid Sensing Sustains the Microglial Response in an Alzheimer’s Disease Model. Cell 2015, 160, 1061–1071. [Google Scholar] [CrossRef] [Green Version]
  54. Yuan, P.; Condello, C.; Keene, C.D.; Wang, Y.; Bird, T.D.; Paul, S.M.; Luo, W.; Colonna, M.; Baddeley, D.; Grutzendler, J. TREM2 Haplodeficiency in Mice and Humans Impairs the Microglia Barrier Function Leading to Decreased Amyloid Compaction and Severe Axonal Dystrophy. Neuron 2016, 90, 724–739. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  55. Drummond, E.; Wisniewski, T. Alzheimer’s disease: Experimental models and reality. Acta Neuropathol. 2017, 133, 155–175. [Google Scholar] [CrossRef] [Green Version]
  56. Goate, A.; Chartier-Harlin, M.C.; Mullan, M.; Brown, J.; Crawford, F.; Fidani, L.; Giuffra, L.; Haynes, A.; Irving, N.; James, L.; et al. Segregation of a missense mutation in the amyloid precursor protein gene with familial Alzheimer’s disease. Nature 1991, 349, 704–706. [Google Scholar] [CrossRef] [PubMed]
  57. Raux, G. Molecular diagnosis of autosomal dominant early onset Alzheimer’s disease: An update. J. Med. Genet. 2005, 42, 793–795. [Google Scholar] [CrossRef] [Green Version]
  58. Rogaev, E.I.; Sherrington, R.; Rogaeva, E.A.; Levesque, G.; Ikeda, M.; Liang, Y.; Chi, H.; Lin, C.; Holman, K.; Tsuda, T.; et al. Familial Alzheimer’s disease in kindreds with missense mutations in a gene on chromosome 1 related to the Alzheimer’s disease type 3 gene. Nature 1995, 376, 775–778. [Google Scholar] [CrossRef] [PubMed]
  59. Sherrington, R.; Rogaev, E.I.; Liang, Y.; Rogaeva, E.A.; Levesque, G.; Ikeda, M.; Chi, H.; Lin, C.; Li, G.; Holman, K.; et al. Cloning of a gene bearing missense mutations in early-onset familial Alzheimer’s disease. Nature 1995, 375, 754–760. [Google Scholar] [CrossRef]
  60. Sherrington, R.; Froelich, S.; Sorbi, S.; Campion, D.; Chi, H.; Rogaeva, E.A.; Levesque, G.; Rogaev, E.I.; Lin, C.; Liang, Y.; et al. Alzheimer’s disease associated with mutations in presenilin 2 is rare and variably penetrant. Hum. Mol. Genet. 1996, 5, 985–988. [Google Scholar] [CrossRef]
  61. Guerreiro, R.; Bras, J. The age factor in Alzheimer’s disease. Genome Med. 2015, 7, 106. [Google Scholar] [CrossRef] [Green Version]
  62. Lim, U.; Wang, S.; Park, S.; Bogumil, D.; Wu, A.H.; Cheng, I.; Haiman, C.A.; Le Marchand, L.; Wilkens, L.R.; White, L.; et al. Risk of Alzheimer’s disease and related dementia by sex and race/ethnicity: The Multiethnic Cohort Study. Alzheimer’s Dement. 2021, 18, 1625–1634. [Google Scholar] [CrossRef]
  63. Bellenguez, C.; Küçükali, F.; Jansen, I.E.; Kleineidam, L.; Moreno-Grau, S.; Amin, N.; Naj, A.C.; Campos-Martin, R.; Grenier-Boley, B.; Andrade, V.; et al. New insights into the genetic etiology of Alzheimer’s disease and related dementias. Nat. Genet. 2022, 54, 412–436. [Google Scholar] [CrossRef] [PubMed]
  64. Wightman, D.P.; Jansen, I.E.; Savage, J.E.; Shadrin, A.A.; Bahrami, S.; Holland, D.; Rongve, A.; Børte, S.; Winsvold, B.S.; Drange, O.K.; et al. A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer’s disease. Nat. Genet. 2021, 53, 1276–1282. [Google Scholar] [CrossRef]
  65. Jansen, I.E.; Savage, J.E.; Watanabe, K.; Bryois, J.; Williams, D.M.; Steinberg, S.; Sealock, J.; Karlsson, I.K.; Hägg, S.; Athanasiu, L.; et al. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat. Genet. 2019, 51, 404–413. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  66. Mills, M.C.; Rahal, C. The GWAS Diversity Monitor tracks diversity by disease in real time. Nat. Genet. 2020, 52, 242–243. [Google Scholar] [CrossRef]
  67. Kunkle, B.W.; Schmidt, M.; Klein, H.U.; Naj, A.C.; Hamilton-Nelson, K.L.; Larson, E.B.; Evans, D.A.; De Jager, P.; Crane, P.K.; Buxbaum, J.; et al. Novel Alzheimer Disease Risk Loci and Pathways in African American Individuals Using the African Genome Resources Panel: A Meta-analysis. JAMA Neurol. 2021, 78, 102–113. [Google Scholar]
  68. Shigemizu, D.; Mitsumori, R.; Akiyama, S.; Miyashita, A.; Morizono, T.; Higaki, S.; Asanomi, Y.; Hara, N.; Tamiya, G.; Kinoshita, K.; et al. Ethnic and trans-ethnic genome-wide association studies identify new loci influencing Japanese Alzheimer’s disease risk. Transl. Psychiatry 2021, 11, 151. [Google Scholar] [CrossRef] [PubMed]
  69. Marioni, R.E.; Harris, S.E.; Zhang, Q.; McRae, A.F.; Hagenaars, S.P.; Hill, W.D.; Davies, G.; Ritchie, C.W.; Gale, C.R.; Starr, J.M.; et al. GWAS on family history of Alzheimer’s disease. Transl. Psychiatry 2018, 8, 99. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  70. Lambert, J.-C.; Ibrahim-Verbaas, C.A.; Harold, D.; Naj, A.C.; Sims, R.; Bellenguez, C. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat. Genet. 2013, 45, 1452–1458. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  71. Kunkle, B.W.; Grenier-Boley, B.; Sims, R.; Bis, J.C.; Damotte, V.; Naj, A.C.; Boland, A.; Olaso, R.; Garnier, J.G.; Moutet, M.L.; et al. Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nat. Genet. 2019, 51, 414–430. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  72. Hasselmann, J.; Blurton-Jones, M. Human iPSC-derived microglia: A growing toolset to study the brain’s innate immune cells. Glia 2020, 68, 721–739. [Google Scholar] [CrossRef]
  73. Gibbons, H.M.; Smith, A.M.; Teoh, H.H.; Bergin, P.M.; Mee, E.W.; Faull, R.L.; Dragunow, M. Valproic acid induces microglial dysfunction, not apoptosis, in human glial cultures. Neurobiol. Dis. 2010, 41, 96–103. [Google Scholar] [CrossRef]
  74. Landry, R.P.; Jacobs, V.L.; Romero-Sandoval, E.A.; DeLeo, J.A. Propentofylline, a CNS glial modulator does not decrease pain in post-herpetic neuralgia patients: In vitro evidence for differential responses in human and rodent microglia and macrophages. Exp. Neurol. 2012, 234, 340–350. [Google Scholar] [CrossRef] [PubMed]
  75. Zhou, Y.; Song, W.M.; Andhey, P.S.; Swain, A.; Levy, T.; Miller, K.R.; Poliani, P.L.; Cominelli, M.; Grover, S.; Gilfillan, S.; et al. Human and mouse single-nucleus transcriptomics reveal TREM2-dependent and TREM2-independent cellular responses in Alzheimer’s disease. Nat. Med. 2020, 26, 131–142. [Google Scholar] [CrossRef]
  76. Galatro, T.F.; Holtman, I.R.; Lerario, A.M.; Vainchtein, I.D.; Brouwer, N.; Sola, P.R.; Veras, M.M.; Pereira, T.F.; Leite, R.E.P.; Möller, T.; et al. Transcriptomic analysis of purified human cortical microglia reveals age-associated changes. Nat. Neurosci. 2017, 20, 1162–1171. [Google Scholar] [CrossRef] [PubMed]
  77. Mizee, M.R.; Miedema, S.S.M.; van der Poel, M.; Adelia; Schuurman, K.G.; van Strien, M.E.; Melief, J.; Smolders, J.; Hendrickx, D.A.; Heutinck, K.M.; et al. Isolation of primary microglia from the human post-mortem brain: Effects of ante- and post-mortem variables. Acta Neuropathol. Commun. 2017, 5, 66. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  78. Smith, A.M.; Davey, K.; Tsartsalis, S.; Khozoie, C.; Fancy, N.; Tang, S.S.; Liaptsi, E.; Weinert, M.; McGarry, A.; Muirhead, R.C.J.; et al. Diverse human astrocyte and microglial transcriptional responses to Alzheimer’s pathology. Acta Neuropathol. 2021, 143, 75–91. [Google Scholar] [CrossRef]
  79. Gosselin, D.; Skola, D.; Coufal, N.G.; Holtman, I.R.; Schlachetzki, J.C.M.; Sajti, E.; Jaeger, B.N.; O’Connor, C.; Fitzpatrick, C.; Pasillas, M.P.; et al. An environment-dependent transcriptional network specifies human microglia identity. Science 2017, 356, 1248–1259. [Google Scholar] [CrossRef] [Green Version]
  80. Kumar, P.; Lim, A.; Hazirah, S.N.; Chua, C.J.H.; Ngoh, A.; Poh, S.L.; Yeo, T.H.; Lim, J.; Ling, S.; Sutamam, N.B.; et al. Single-cell transcriptomics and surface epitope detection in human brain epileptic lesions identifies pro-inflammatory signaling. Nat. Neurosci. 2022, 25, 956–966. [Google Scholar] [CrossRef]
  81. De Lopes, K.P.; Snijders, G.J.L.; Humphrey, J.; Allan, A.; Sneeboer, M.A.M.; Navarro, E.; Schilder, B.M.; Vialle, R.A.; Parks, M.; Missall, R.; et al. Genetic analysis of the human microglial transcriptome across brain regions, aging and disease pathologies. Nat. Genet. 2022, 54, 4–17. [Google Scholar] [CrossRef]
  82. Xu, R.; Li, X.; Boreland, A.J.; Posyton, A.; Kwan, K.; Hart, R.P. Human iPSC-derived mature microglia retain their identity and functionally integrate in the chimeric mouse brain. Nat. Commun. 2020, 11, 1577. [Google Scholar] [CrossRef] [Green Version]
  83. Bennett, M.L.; Bennett, F.C.; Liddelow, S.A.; Ajami, B.; Zamanian, J.L.; Fernhoff, N.B.; Mulinyawe, S.; Bohlen, C.; Adil, A.; Tucker, A.; et al. New tools for studying microglia in the mouse human, C.N.S. Proc. Natl. Acad. Sci. USA 2016, 113, E1738–E1746. [Google Scholar] [CrossRef] [PubMed]
  84. Satoh, J.-I.; Kino, Y.; Asahina, N.; Takitani, M.; Miyoshi, J.; Ishida, T.; Saito, Y. TMEM119 marks a subset of microglia in the human brain. Neuropathology 2016, 36, 39–49. [Google Scholar] [CrossRef] [PubMed]
  85. Vankriekelsvenne, E.; Chrzanowski, U.; Manzhula, K.; Greiner, T.; Wree, A.; Hawlitschka, A.; Llovera, G.; Zhan, J.; Joost, S.; Schmitz, C.; et al. Transmembrane protein 119 is neither a specific nor a reliable marker for microglia. Glia 2022, 70, 1170–1190. [Google Scholar] [CrossRef] [PubMed]
  86. Green, K.N.; Crapser, J.D.; Hohsfield, L.A. To Kill a Microglia: A Case for CSF1R Inhibitors. Trends Immunol. 2020, 41, 771–784. [Google Scholar] [CrossRef] [PubMed]
  87. Spangenberg, E.; Severson, P.L.; Hohsfield, L.A.; Crapser, J.; Zhang, J.; Burton, E.A.; Zhang, Y.; Spevak, W.; Lin, J.; Phan, N.Y.; et al. Sustained microglial depletion with CSF1R inhibitor impairs parenchymal plaque development in an Alzheimer’s disease model. Nat. Commun. 2019, 10, 3758. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  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] [Green Version]
  89. Dräger, N.M.; Sattler, S.M.; Huang, C.T.-L.; Teter, O.M.; Leng, K.; Hashemi, S.H.; Hong, J.; Aviles, G.; Clelland, C.D.; Zhan, L.; et al. A CRISPRi/a platform in human iPSC-derived microglia uncovers regulators of disease states. Nat. Neurosci. 2022, 25, 1149–1162. [Google Scholar] [CrossRef]
  90. Abud, E.M.; Ramirez, R.N.; Martinez, E.S.; Healy, L.M.; Nguyen, C.H.H.; Newman, S.A. iPSC-Derived Human Microglia-like Cells to Study Neurological Diseases. Neuron 2017, 94, 278–293.e9. [Google Scholar] [CrossRef] [Green Version]
  91. Pandya, H.; Shen, M.J.; Ichikawa, D.M.; Sedlock, A.B.; Choi, Y.; Johnson, K.R.; Kim, G.; Brown, M.A.; Elkhaloun, 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]
  92. Chen, S.-W.; Hung, Y.-S.; Fuh, J.-L.; Chen, N.-J.; Chu, Y.-S.; Chen, S.-C.; Wong, Y.-H. Stem Cell Reports Resource Efficient conversion of human induced pluripotent stem cells into microglia by defined transcription factors. Stem Cell Rep. 2021, 16, 1363–1380. [Google Scholar] [CrossRef] [PubMed]
  93. Brownjohn, P.W.; Smith, J.; Solanki, R.; Lohmann, E.; Houlden, H.; Hardy, J.; Dietmann, S.; Livesey, F.J. Functional Studies of Missense TREM2 Mutations in Human Stem Cell-Derived Microglia. Stem Cell Rep. 2018, 10, 1294–1307. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  94. Douvaras, P.; Sun, B.; Wang, M.; Kruglikov, I.; Lallos, G.; Zimmer, M.; Terrenoire, C.; Zhang, B.; Gandy, S.; Schadt, E.; et al. Directed Differentiation of Human Pluripotent Stem Cells to Microglia. Stem Cell Rep. 2017, 8, 1516–1524. [Google Scholar] [CrossRef] [Green Version]
  95. 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] [PubMed] [Green Version]
  96. Andrews, S.J.; Renton, A.E.; Fulton-Howard, B.; Podlesny-Drabiniok, A.; Marcora, E.; Goate, A.M. The complex genetic architecture of Alzheimer’s disease: Novel insights and future directions. EBioMedicine 2023, 90, 104511. [Google Scholar] [CrossRef] [PubMed]
  97. Dardiotis, E.; Siokas, V.; Pantazi, E.; Dardioti, M.; Rikos, D.; Xiromerisiou, G.; Markou, A.; Papadimitriou, D.; Speletas, M.; Hadjigeorgiou, G.M. A novel mutation in TREM2 gene causing Nasu-Hakola disease and review of the literature. Neurobiol. Aging 2017, 53, 194.e13–194.e22. [Google Scholar] [CrossRef]
  98. Velasco, S.; Kedaigle, A.J.; Simmons, S.K.; Nash, A.; Rocha, M.; Quadrato, G.; Paulsen, B.; Nguyen, L.; Adiconis, X.; Regev, A.; et al. Individual brain organoids reproducibly form cell diversity of the human cerebral cortex. Nature 2019, 570, 523–527. [Google Scholar] [CrossRef]
  99. Lancaster, M.A.; Corsini, N.S.; Wolfinger, S.; Gustafson, E.H.; Phillips, A.W.; Burkard, T.R.; Otani, T.; Livesey, F.J.; Knoblich, J.A. Guided self-organization and cortical plate formation in human brain organoids. Nat. Biotechnol. 2017, 35, 659–666. [Google Scholar] [CrossRef] [Green Version]
  100. Camp, J.G.; Badsha, F.; Florio, M.; Kanton, S.; Gerber, T.; Wilsch-Bräuninger, M.; Lewitus, E.; Sykers, A.; Hevers, W.; Lancaster, M.; et al. Human cerebral organoids recapitulate gene expression programs of fetal neocortex development. Proc. Natl. Acad. Sci. USA 2015, 112, 15672–15677. [Google Scholar] [CrossRef]
  101. Song, L.; Yuan, X.; Jones, Z.; Vied, C.; Miao, Y.; Marzano, M.; Hua, T.; Sang, Q.-X.A.; Guan, J.; Ma, T.; et al. Functionalization of Brain Region-specific Spheroids with Isogenic Microglia-like Cells. Sci. Rep. 2019, 9, 11055. [Google Scholar] [CrossRef] [Green Version]
  102. Andrews, M.G.; Kriegstein, A.R. Challenges of organoid research. Annu. Rev. Neurosci. 2022, 45, 23–39. [Google Scholar] [CrossRef]
  103. Quadrato, G.; Brown, J.; Arlotta, P. The promises and challenges of human brain organoids as models of neuropsychiatric disease. Nat. Med. 2016, 22, 1220–1228. [Google Scholar] [CrossRef] [PubMed]
  104. Uzquiano, A.; Kedaigle, A.J.; Pigoni, M.; Paulsen, B.; Adiconis, X.; Kim, K.; Faits, T.; Nagaraja, S.; Antón-Bolaños, N.; Gerhardinger, C.; et al. Proper acquisition of cell class identity in organoids allows definition of fate specification programs of the human cerebral cortex. Cell 2022, 185, 3770–3788.e27. [Google Scholar] [CrossRef] [PubMed]
  105. György, B.; Lööv, C.; Zaborowski, M.P.; Takeda, S.; Kleinstiver, B.P.; Commins, C.; Kastanenka, K.; Mu, D.; Volak, A.; Giedraitis, V.; et al. CRISPR/Cas9 Mediated Disruption of the Swedish APP Allele as a Therapeutic Approach for Early-Onset Alzheimer’s Disease. Mol. Ther. Nucleic Acids 2018, 11, 429–440. [Google Scholar] [CrossRef] [Green Version]
  106. Sun, J.; Carlson-Stevermer, J.; Das, U.; Shen, M.; Delenclos, M.; Snead, A.M.; Koo, S.Y.; Wang, L.; Qiao, D.; Loi, J.; et al. CRISPR/Cas9 editing of APP C-terminus attenuates β-cleavage and promotes α-cleavage. Nat. Commun. 2019, 10, 53. [Google Scholar] [CrossRef] [Green Version]
  107. Nagata, K.; Takahashi, M.; Matsuba, Y.; Okuyama-Uchimura, F.; Sato, K.; Hashimoto, S.; Saito, T.; Saido, T.C. Generation of App knock-in mice reveals deletion mutations protective against Alzheimer’s disease-like pathology. Nat. Commun. 2018, 9, 1800. [Google Scholar] [CrossRef] [Green Version]
  108. Serneels, L.; T’syen, D.; Perez-Benito, L.; Theys, T.; Holt, M.G.; De Strooper, B. Modeling the β-secretase cleavage site and humanizing amyloid-beta precursor protein in rat and mouse to study Alzheimer’s disease. Mol. Neurodegener. 2020, 15, 60. [Google Scholar] [CrossRef] [PubMed]
  109. Ng, N.; Newbery, M.; Maksour, S.; Dottori, M.; Sluyter, R.; Ooi, L. Transgene and Chemical Transdifferentiation of Somatic Cells for Rapid and Efficient Neurological Disease Cell Models. Front. Cell. Neurosci. 2022, 16, 858432. [Google Scholar] [CrossRef] [PubMed]
  110. Mollinari, C.; Zhao, J.; Lupacchini, L.; Garaci, E.; Merlo, D.; Pei, G. Transdifferentiation: A new promise for neurodegenerative diseases. Cell Death Dis. 2018, 9, 830. [Google Scholar] [CrossRef] [Green Version]
Biomedicines 11 02240 g001
Figure 1. Differentiation of patient somatic cells into iPSC-derived human microglia. (A) iPSCs can be produced from patients who display microglial AD risk loci, facilitating the production of microglia with these mutations. The CRISPR/Cas9 system can also be incorporated for genetic correction, facilitating the development of an isogenic control in parallel to iPSCs acquired from an age-matched healthy donor. (B) Microglial function and associated phenotypes can then be analyzed via a range of techniques including proteomics, transcriptomics and immunometabolic assays. Figure created with BioRender.com.
Figure 1. Differentiation of patient somatic cells into iPSC-derived human microglia. (A) iPSCs can be produced from patients who display microglial AD risk loci, facilitating the production of microglia with these mutations. The CRISPR/Cas9 system can also be incorporated for genetic correction, facilitating the development of an isogenic control in parallel to iPSCs acquired from an age-matched healthy donor. (B) Microglial function and associated phenotypes can then be analyzed via a range of techniques including proteomics, transcriptomics and immunometabolic assays. Figure created with BioRender.com.
Biomedicines 11 02240 g001
Table 1. Alzheimer’s associated genetic variants identified from genome-wide association studies. All risk variants display an odds ratio of ≥0.05. Table was curated using supplementary data from Andrews and colleagues [96].
Table 1. Alzheimer’s associated genetic variants identified from genome-wide association studies. All risk variants display an odds ratio of ≥0.05. Table was curated using supplementary data from Andrews and colleagues [96].
GeneSNPsGWAS Source
ABCA1rs1800978[63]
ABCA7rs12151021, rs3752231, rs3752246, rs4147929[63,64,69,70,71]
ABI3rs616338[63]
ACErs4277405, rs6504163[63,64]
ADAM10rs442495, rs593742[65,69,71]
ADAM17rs72777026[63]
ADAMTS1rs2830489, rs2830500[63,71]
ALPK2rs76726049[65]
ANKHrs112403360[63]
APH1Brs117618017[63,64,65]
APOErs429358[63,64,71]
BIN1rs4663105, rs6733839[63,64,65,69,70,71]
BLNKrs6584063[63]
CASS4rs6014724, rs6024870, rs6069737, rs7274581[63,64,69,70,71]
CELF1rs10838725[70]
CD2APrs7767350, rs9369716, rs9381563, rs9473117, rs10948363[63,64,65,69,70,71]
CD33rs1354106, rs3865444, rs12459419[64,65,69]
CLNKrs4504245, rs6448453, rs6846529[63,64,65]
CLUrs1532278, rs4236673, rs9331896, rs11787077[63,64,65,69,70,71]
CNTNAP2rs114360492[65]
COX7Crs62374257[63]
CR1rs679515, rs2093760, rs4844610, rs6656401[63,64,65,69,70,71]
CSTF1rs6069736[69]
CTSBrs1065712[63]
CTSHrs12592898[63]
CYB561rs138190086[69,71]
DOC2Ars1140239[63]
ECHDC3rs7920721[69,71]
EEDrs3851179[63,71]
EPHA1rs3935067, rs7810606, rs10808026, rs11771145[63,64,65,69,70,71]
FERMT2rs7146179, rs17125924, rs17125944[63,64,69,70,71]
FOXF1rs16941239[63]
GPR141rs2718058[70]
GRNrs5848[63]
HESX1rs184384746[65]
HLA-DQA1rs1846190, rs6605556, rs6931277, rs9271192[63,64,65,70]
HLA-DRB1rs9271058[71]
ICA1rs10952097[63]
IL34rs4985556[63,69]
INPP5Drs7597763, rs10933431, rs35349669[63,64,65,69,70,71]
IQCKrs7185636[71]
MEF2Crs190982[70]
MINDY2rs602602[63,64]
MMErs16824536, rs61762319[63]
MS4A4Ars1582763, rs2081545[63,64,65,69]
MS4A6Ars983392, rs7933202[70,71]
MYO15Ars2242595[63]
NCK2rs115186657, rs143080277[64,65]
NECTIN2rs41289512[65,69]
NYAP1rs12539172[71]
OARD1rs114812713[71]
PICALMrs867611, rs561655, rs10792832[64,65,69,70]
PLCG2rs12444183, rs12446759, rs72824905[63,69]
PRDM7rs56407236[63]
PRKD3rs17020490[63]
PSMC3rs12292911[69]
PTK2Brs28834970, rs73223431[63,70,71]
RASGEF1Crs113706587[63]
SCIMPrs7225151, rs113260531[63,65,69]
SEC61Grs76928645[63]
SHARPINrs34173062[63]
SLC24A4rs7401792, rs10498633, rs12590654, rs12881735[63,64,65,69,70,71]
SORL1rs11218343, rs74685827[63,64,65,69,70,71]
SORT1rs141749679[63]
SPDYE3rs7384878[63,64]
SPI1rs3740688, rs10437655[63,64,71]
SPPL2Ars8025980, rs59685680[63,69]
TMEM121rs7157106, rs10131280[63]
TPCN1rs6489896[63]
TREM2rs75932628, rs143332484[63,71]
TREML2rs9381040, rs60755019[63,69]
TSPAN14rs6586028[63]
UMAD1rs6943429[63]
UNC5CLrs10947943, rs187370608[63,64,65]
USP6NLrs7912495, rs11257238[63,64,65]
WDR12rs139643391[63]
WDR81rs35048651[63]
WNT3rs199515[63]
WWOXrs62039712[71]
ZCWPW1rs1476679[69,70]
ZNF652rs28394864[64,65]
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

Wickstead, E.S. Using Stems to Bear Fruit: Deciphering the Role of Alzheimer’s Disease Risk Loci in Human-Induced Pluripotent Stem Cell-Derived Microglia. Biomedicines 2023, 11, 2240. https://doi.org/10.3390/biomedicines11082240

AMA Style

Wickstead ES. Using Stems to Bear Fruit: Deciphering the Role of Alzheimer’s Disease Risk Loci in Human-Induced Pluripotent Stem Cell-Derived Microglia. Biomedicines. 2023; 11(8):2240. https://doi.org/10.3390/biomedicines11082240

Chicago/Turabian Style

Wickstead, Edward S. 2023. "Using Stems to Bear Fruit: Deciphering the Role of Alzheimer’s Disease Risk Loci in Human-Induced Pluripotent Stem Cell-Derived Microglia" Biomedicines 11, no. 8: 2240. https://doi.org/10.3390/biomedicines11082240

APA Style

Wickstead, E. S. (2023). Using Stems to Bear Fruit: Deciphering the Role of Alzheimer’s Disease Risk Loci in Human-Induced Pluripotent Stem Cell-Derived Microglia. Biomedicines, 11(8), 2240. https://doi.org/10.3390/biomedicines11082240

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