Dissection of the polygenic architecture of neuronal Aβ production using a large sample of individual iPSC lines derived from Alzheimer's disease patients

Deep joint learning diagnosis of Alzheimer's disease based on multimodal feature fusion

Alzheimer's disease (AD) is an advanced and incurable neurodegenerative disease. Genetic variations are intrinsic etiological factors contributing to the abnormal expression of brain function and structure in AD patients. A new multimodal feature fusion called "magnetic resonance imaging (MRI)-p value" was proposed to construct 3D fusion images by introducing genes as a priori knowledge. Moreover, a new deep joint learning diagnostic model was constructed to fully learn images features. One branch trained a residual network (ResNet) to learn the features of local pathological regions. The other branch learned the position information of brain regions with different changes in the different categories of subjects' brains by introducing attention convolution, and then obtained the discriminative probability information from locations via convolution and global average pooling. The feature and position information of the two branches were linearly interacted to acquire the diagnostic basis for classifying the different categories of subjects. The diagnoses of AD and health control (HC), AD and mild cognitive impairment (MCI), HC and MCI were performed with data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The results showed that the proposed method achieved optimal results in AD-related diagnosis. The classification accuracy (ACC) and area under the curve (AUC) of the three experimental groups were 93.44% and 96.67%, 89.06% and 92%, and 84% and 81.84%, respectively. Moreover, a total of six novel genes were found to be significantly associated with AD, namely NTM, MAML2, NAALADL2, FHIT, TMEM132D and PCSK5, which provided new targets for the potential treatment of neurodegenerative diseases.

Enrichment of Allelic Editing Outcomes by Prime Editing in Induced Pluripotent Stem Cells

Gene editing in human induced pluripotent stem (iPS) cells with programmable nucleases facilitates reliable disease models, but methods using double-strand break repair often produce random on-target by-products. Prime editing (PE) combines Cas9 nickase with reverse transcriptase and PE guide RNA (pegRNA) encoding a repair template to reduce by-products. We implemented a GMP-compatible protocol for transfecting Cas9- or PE-2A-mCherry plasmids to track and fractionate human iPS cells based on PE expression level. We compared the editing outcomes of Cas9- and PE-based methods in a GFP-to-BFP conversion assay at the HEK3 benchmark locus and at the APOE Alzheimer's risk locus, revealing superior precision of PE at high expression levels. Moreover, sorting cells for PE expression level influenced allelic editing outcomes at the target loci. We expect that our findings will aid in the creation of gene-edited human iPS cells with intentional heterozygous and homozygous genotypes.

Identifying the earliest-occurring clinically targetable precursors of late-onset Alzheimer's disease

Most cases of Alzheimer's disease (AD) are late-onset dementias (LOAD). However, research on AD is predominantly of early-onset disease (EOAD). The determinants of EOAD, gene variants of APP and presenilin proteins, are not the basic precursors of LOAD. Rather, multiple other genes and associated cellular processes underlie risk for LOAD. These determinants could be modified in individuals at risk for LOAD well before signs and symptoms appear. Studying brain cells produced from patient-derived induced-pluripotent-stem-cells (iPSC), in culture, will be instrumental in developing such interventions. This paper summarises evidence accrued from iPSC culture models identifying the earliest occurring clinically targetable determinants of LOAD. Results obtained and replicated, thus far, suggest that abnormalities of bioenergetics, lipid metabolism, digestive organelle function and inflammatory activity are primary processes underlying LOAD. The application of cell culture platforms will become increasingly important in research and also on LOAD detection, assessment, and treatment in the years ahead.

Strategies for modeling aging and age-related diseases

The ability to reprogram patient-derived-somatic cells to IPSCs (Induced Pluripotent Stem Cells) has led to a better understanding of aging and age-related diseases like Parkinson's, and Alzheimer's. The established patient-derived disease models mimic disease pathology and can be used to design drugs for aging and age-related diseases. However, the age and genetic mutations of the donor cells, the employed reprogramming, and the differentiation protocol might often pose challenges in establishing an appropriate disease model. In this review, we will focus on the various strategies for the successful reprogramming and differentiation of patient-derived cells to disease models for aging and age-related diseases, emphasizing the accuracy in the recapitulation of disease pathology and ways to overcome the limitations of its potential application in cell replacement therapy and drug development.

Induced pluripotent stem cells (iPSCs): molecular mechanisms of induction and applications

The induced pluripotent stem cell (iPSC) technology has transformed in vitro research and holds great promise to advance regenerative medicine. iPSCs have the capacity for an almost unlimited expansion, are amenable to genetic engineering, and can be differentiated into most somatic cell types. iPSCs have been widely applied to model human development and diseases, perform drug screening, and develop cell therapies. In this review, we outline key developments in the iPSC field and highlight the immense versatility of the iPSC technology for in vitro modeling and therapeutic applications. We begin by discussing the pivotal discoveries that revealed the potential of a somatic cell nucleus for reprogramming and led to successful generation of iPSCs. We consider the molecular mechanisms and dynamics of somatic cell reprogramming as well as the numerous methods available to induce pluripotency. Subsequently, we discuss various iPSC-based cellular models, from mono-cultures of a single cell type to complex three-dimensional organoids, and how these models can be applied to elucidate the mechanisms of human development and diseases. We use examples of neurological disorders, coronavirus disease 2019 (COVID-19), and cancer to highlight the diversity of disease-specific phenotypes that can be modeled using iPSC-derived cells. We also consider how iPSC-derived cellular models can be used in high-throughput drug screening and drug toxicity studies. Finally, we discuss the process of developing autologous and allogeneic iPSC-based cell therapies and their potential to alleviate human diseases.

Directed Differentiation of Neurons from Human iPSCs for Modeling Neurological Disorders

Human-induced pluripotent stem cell (hiPSC) technology has enabled comprehensive human cell-based disease modeling in vitro. Due to limited accessibility of primary human neurons as well as species-specific divergence between human and rodent brain tissues, hiPSC-derived neurons have become a popular tool for studying neuronal biology in a dish. Here, we provide methods for transcription factor-driven directed differentiation of neurons from hiPSCs via a neural progenitor cell (NPC) intermediate. Doxycycline-inducible expression of neuron fate-determining transcription factors neurogenin 2 (NGN2) and achaete-scute homolog 1 (ASCL1) enables rapid and controllable differentiation of human neurons for disease modeling applications. The provided method is also designed to improve the reproducibility of human neuron differentiation by reducing the batch-to-batch variation of NPC differentiation and lentiviral transduction.

Few-shot prediction of amyloid β accumulation from mainly unpaired data on biomarker candidates

The pair-wise observation of the input and target values obtained from the same sample is mandatory in any prediction problem. In the biomarker discovery of Alzheimer's disease (AD), however, obtaining such paired data is laborious and often avoided. Accumulation of amyloid-beta (Aβ) in the brain precedes neurodegeneration in AD, and the quantitative accumulation level may reflect disease progression in the very early phase. Nevertheless, the direct observation of Aβ is rarely paired with the observation of other biomarker candidates. To this end, we established a method that quantitatively predicts Aβ accumulation from biomarker candidates by integrating the mostly unpaired observations via a few-shot learning approach. When applied to 5xFAD mouse behavioral data, the proposed method predicted the accumulation level that conformed to the observed amount of Aβ in the samples with paired data. The results suggest that the proposed model can contribute to discovering Aβ predictability-based biomarkers.

Urine-derived cells from the aged donor for the 2D/3D modeling of neural cells via iPSCs

Human neural cell models derived from induced pluripotent stem cells (iPSCs) have been widely accepted to model various neurodegenerative diseases such as Alzheimer's disease (AD) in vitro. Although the most common sources of iPSCs are fibroblasts and peripheral blood mononuclear cells, the collection of these cells is invasive. To reduce the donor's burden, we propose the use of urine-derived cells (UDCs), which can be obtained non-invasively from a urine sample. However, the collection of UDCs from elderly donors suffering from age-related diseases such as AD has not been reported, and it is unknown whether these UDCs from the donor aged over 80 years old can be converted into iPSCs and differentiated into neural cells. In this study, we reported a case of using the UDCs from the urine sample of an 89-year-old AD patient, and the UDCs were successfully reprogrammed into iPSCs and differentiated into neural cells in four different ways: (i) the dual SMAD inhibition with small-molecules via the neural progenitor precursor stage, (ii) the rapid induction method using transient expression of Ngn2 and microRNAs without going through the neural progenitor stage, (iii) the cortical brain organoids for 3D culture, and (iv) the human astrocytes. The accumulation of phosphorylated Tau proteins, which is a pathological hallmark of AD, was examined in the neuronal models generated from the UDCs of the aged donor. The application of this cell source will broaden the target population for disease modeling using iPS technology.

BMP4-SMAD1/5/9-RUNX2 pathway activation inhibits neurogenesis and oligodendrogenesis in Alzheimer's patients' iPSCs in senescence-related conditions

In addition to increasing β-amyloid plaque deposition and tau tangle formation, inhibition of neurogenesis has recently been observed in Alzheimer's disease (AD). This study generated a cellular model that recapitulated neurogenesis defects observed in patients with AD, using induced pluripotent stem cell lines derived from sporadic and familial AD (AD iPSCs). AD iPSCs exhibited impaired neuron and oligodendrocyte generation when expression of several senescence markers was induced. Compound screening using these cellular models identified three drugs able to restore neurogenesis, and extensive morphological quantification revealed cell-line- and drug-type-dependent neuronal generation. We also found involvement of elevated Sma- and Mad-related protein 1/5/9 (SMAD1/5/9) phosphorylation and greater Runt-related transcription factor 2 (RUNX2) expression in neurogenesis defects in AD. Moreover, BMP4 was elevated in AD iPSC medium during neural differentiation and cerebrospinal fluid of patients with AD, suggesting a BMP4-SMAD1/5/9-RUNX2 signaling pathway contribution to neurogenesis defects in AD under senescence-related conditions.

CDiP technology for reverse engineering of sporadic Alzheimer's disease

Alzheimer's disease (AD) is a neurodegenerative disease that causes cognitive impairment for which neither treatable nor preventable approaches have been confirmed. Although genetic factors are considered to contribute to sporadic AD, for the majority of AD patients, the exact causes of AD aren't fully understood. For AD genetics, we developed cellular dissection of polygenicity (CDiP) technology to identify the smallest unit of AD, i.e., genetic factors at a cellular level. By CDiP, we found potential therapeutic targets, a rare variant for disease stratification, and polygenes to predict real-world AD by using the real-world data of AD cohort studies (Alzheimer's Disease Neuroimaging Initiative: ADNI and Japanese Alzheimer's Disease Neuroimaging Initiative: J-ADNI). In this review, we describe the components and results of CDiP in AD, induced pluripotent stem cell (iPSC) cohort, a cell genome-wide association study (cell GWAS), and machine learning. And finally, we discuss the future perspectives of CDiP technology for reverse engineering of sporadic AD toward AD eradication.