Alzheimer’s Disease Risk Loci in hiPSCs-Derived Microglia: Comparison
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Please note this is a comparison between Version 2 by Catherine Yang and Version 1 by Edward S Wickstead.

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).

  • Alzheimer’s disease
  • GWAS
  • iPSC
  • microglia
  • neurodegeneration

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[5][6],6], it was not until 70 years later that these pathologies gathered widespread attention [7,8,9][7][8][9]. Several additional years followed before extensive clinical neuropathological studies supported both plaques and NFTs with probable AD diagnosis [10,11][10][11]. In the three decades since, hundreds of promising therapeutics targeted towards Aβ, tau and other pathologies have failed in clinical trials [12[12][13],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][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][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][14][19].

2. iPSC Technology and iPSC-Derived Microglia

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][20]. It has been suggested that TMEM119 is both a reliable and specific marker, able to discriminate resident microglia from peripherally derived macrophages [83,84][21][22]. 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][23]. 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[24][25],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 the 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][26], many protocols to produce hiPSC-derived microglia have become available [89,90,91,92[27][28][29][30][31][32],93,94], with others having reviewed some of these protocols previously [95][33].
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].
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 [34].
Gene SNPs GWAS Source
ABCA1 rs1800978 [63][35]
ABCA7 rs12151021, rs3752231, rs3752246, rs4147929 [63,64,69,70,71][35][36][37][38][39]
ABI3 rs616338 [63][35]
ACE rs4277405, rs6504163 [63,64][35][36]
ADAM10 rs442495, rs593742 [65,69,71][37][39][40]
ADAM17 rs72777026 [63][35]
ADAMTS1 rs2830489, rs2830500 [63,71][35][39]
ALPK2 rs76726049 [65][40]
ANKH rs112403360 [63][35]
APH1B rs117618017 [63,64,65][35][36][40]
APOE rs429358 [63,64,71][35][36][39]
BIN1 rs4663105, rs6733839 [63,][35]64,[36]65,[69,70,37][38][39]71[40]
BLNK rs6584063 [63][35]
CASS4 rs6014724, rs6024870, rs6069737, rs7274581 [63,64,69,70,71][35][36][37][38][39]
CELF1 rs10838725 [70][38]
CD2AP rs7767350, rs9369716, rs9381563, rs9473117, rs10948363 [63,64,65,69,70,71][35][36][37][38][39][40]
CD33 rs1354106, rs3865444, rs12459419 [64,65,69][36][37][40]
CLNK rs4504245, rs6448453, rs6846529 [63,64,65][35][36][40]
CLU rs1532278, rs4236673, rs9331896, rs11787077 [63,64,65,69,70,71][35][36][37][38][39][40]
CNTNAP2 rs114360492 [65][40]
COX7C rs62374257 [63][35]
CR1 rs679515, rs2093760, rs4844610, rs6656401 [63,64,65,69,70,71][35][36][37][38][39][40]
CSTF1 rs6069736 [69][37]
CTSB rs1065712 [63][35]
CTSH rs12592898 [63][35]
CYB561 rs138190086 [69,71][37][39]
DOC2A rs1140239 [63][35]
ECHDC3 rs7920721 [69,71][37][39]
EED rs3851179 [63,71][35][39]
EPHA1 rs3935067, rs7810606, rs10808026, rs11771145 [63,64,65,69,70,71][35][36][37][38][39][40]
FERMT2 rs7146179, rs17125924, rs17125944 [63,64,69,70,71][35][36][37][38][39]
FOXF1 rs16941239 [63][35]
GPR141 rs2718058 [70][38]
GRN rs5848 [63][35]
HESX1 rs184384746 [65][40]
HLA-DQA1 rs1846190, rs6605556, rs6931277, rs9271192 [63,64,65,70][35][36][38][40]
HLA-DRB1 rs9271058 [71][39]
ICA1 rs10952097 [63][35]
IL34 rs4985556 [63,69][35][37]
INPP5D rs7597763, rs10933431, rs35349669 [63,64,65,69,70,71][35][36][37][38][39][40]
IQCK rs7185636 [71][39]
MEF2C rs190982 [70][38]
MINDY2 rs602602 [63,64][35][36]
MME rs16824536, rs61762319 [63][35]
MS4A4A rs1582763, rs2081545 [63,64,65,69][35][36][37][40]
MS4A6A rs983392, rs7933202 [70,[3871]][39]
MYO15A rs2242595 [63][35]
NCK2 rs115186657, rs143080277 [64,65][36][40]
NECTIN2 rs41289512 [65,69][37][40]
NYAP1 rs12539172 [71][39]
OARD1 rs114812713 [71][39]
PICALM rs867611, rs561655, rs10792832 [64,65,69,[37][3870][36]][40]
PLCG2 rs12444183, rs12446759, rs72824905 [63,69][35][37]
PRDM7 rs56407236 [63][35]
PRKD3 rs17020490 [63][35]
PSMC3 rs12292911 [69][37]
PTK2B rs28834970, rs73223431 [63,[70,3571]][38][39]
RASGEF1C rs113706587 [63][35]
SCIMP rs7225151, rs113260531 [63,65,69][35][37][40]
SEC61G rs76928645 [63][35]
SHARPIN rs34173062 [63][35]
SLC24A4 rs7401792, rs10498633, rs12590654, rs12881735 [63,64,65,69,70,71][35][36][37][38][39][40]
SORL1 rs11218343, rs74685827 [63,64,65,69,70,71][35][36][37][38][39][40]
SORT1 rs141749679 [63][35]
SPDYE3 rs7384878 [63,64][35][36]
SPI1 rs3740688, rs10437655 [63,64,71][35][36][39]
SPPL2A rs8025980, rs59685680 [63,69][35][37]
TMEM121 rs7157106, rs10131280 [63][35]
TPCN1 rs6489896 [63][35]
TREM2 rs75932628, rs143332484 [63,71][35][39]
TREML2 rs9381040, rs60755019 [63,69][35][37]
TSPAN14 rs6586028 [63][35]
UMAD1 rs6943429 [63][35]
UNC5CL rs10947943, rs187370608 [63,64,65][35][36][40]
USP6NL rs7912495, rs11257238 [63,64,65][35][36][40]
WDR12 rs139643391 [63][35]
WDR81 rs35048651 [63][35]
WNT3 rs199515 [63][35]
WWOX rs62039712 [71][39]
ZCWPW1 rs1476679 [69,70][37][38]
ZNF652 rs28394864 [64,65][36][40]
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][31]. 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][41]. 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).
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.
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][42][43][44]. 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][29][45]. In addition, to get around the variability that has often plagued organoid research [102,103][46][47], 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][42][48], 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.

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