Single-cell transcriptomics of human iPSC differentiation dynamics reveal a core molecular network of Parkinson's disease

Single-cell sequencing insights into the transcriptional landscape of Parkinson's disease

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder, with an unknown etiology and no specific treatment. Emerging single-cell and single-nucleus RNA sequencing (sc/snRNA-seq) technologies have become instrumental in unravelling cellular heterogeneity and characterizing molecular signatures at single-cell resolution. Single-cell T cell receptor sequencing (scTCR-seq) and single-cell B cell receptor sequencing (scBCR-seq) technologies provide unprecedented opportunities to explore the immune repertoire diversity. These state-of-the-art technologies have been increasingly applied in PD research in the last five years, offering novel insights into the cellular susceptibilities and complex molecular mechanisms underlying PD pathogenesis. Herein we review recent advances in the applications of sc/snRNA-seq, scTCR-seq and scBCR-seq technologies in various PD models. Moreover, we focus on degenerative neurons, activated neuroglial cells, as well as pro-inflammatory immune cells, exploring their unique transcriptional landscapes in PD, as revealed by single-cell sequencing technologies. Finally, we highlight important challenges and the future directions of single-cell experiments in PD research.

Proteome Dynamics in iPSC-Derived Human Dopaminergic Neurons

Dopaminergic neurons participate in fundamental physiological processes and are the cell type primarily affected in Parkinson's disease. Their analysis is challenging due to the intricate nature of their function, involvement in diverse neurological processes, and heterogeneity and localization in deep brain regions. Consequently, most of the research on the protein dynamics of dopaminergic neurons has been performed in animal cells ex vivo. Here we use iPSC-derived human mid-brain-specific dopaminergic neurons to study general features of their proteome biology and provide datasets for protein turnover and dynamics, including a human axonal translatome. We cover the proteome to a depth of 9409 proteins and use dynamic SILAC to measure the half-life of more than 4300 proteins. We report uniform turnover rates of conserved cytosolic protein complexes such as the proteasome and map the variable rates of turnover of the respiratory chain complexes in these cells. We use differential dynamic SILAC labeling in combination with microfluidic devices to analyze local protein synthesis and transport between axons and soma. We report 105 potentially novel axonal markers and detect translocation of 269 proteins between axons and the soma in the time frame of our analysis (120 h). Importantly, we provide evidence for local synthesis of 154 proteins in the axon and their retrograde transport to the soma, among them several proteins involved in RNA editing such as ADAR1 and the RNA helicase DHX30, involved in the assembly of mitochondrial ribosomes. Our study provides a workflow and resource for the future applications of quantitative proteomics in iPSC-derived human neurons.

The Neuroanatomy of Induced Pluripotent Stem Cells: In Vitro Models of Subcortical Nuclei in Neurodegenerative Disorders

Neuromodulatory subcortical systems (NSSs) are monoaminergic and cholinergic neuronal groups that are markedly and precociously involved in the pathogenesis of many neurodegenerative disorders (NDDs), including Parkinson's and Alzheimer's diseases. In humans, although many tools have been developed to infer information on these nuclei, encompassing neuroimaging and neurophysiological methods, a detailed and specific direct evaluation of their cellular features in vivo has been difficult to obtain until recent years. The development of induced pluripotent stem cell (iPSC) models has allowed research to deeply delve into the cellular and molecular biology of NSS neurons. In fact, iPSCs can be produced easily and non-invasively from patients' fibroblasts or circulating blood monocytes, by de-differentiating those cells using specific protocols, and then be re-differentiated towards neural phenotypes, which may reproduce the specific features of the correspondent brain neurons (including NSS ones) from the same patient. In this review, we summarized findings obtained in the field of NDDs using iPSCs, with the aim to understand how reliably these might represent in vitro models of NSS. We found that most of the current literature in the field of iPSCs and NSSs in NDDs has focused on midbrain dopaminergic neurons in Parkinson's disease, providing interesting results on cellular pathophysiology and even leading to the first human autologous transplantation. Differentiation protocols for noradrenergic, cholinergic, and serotoninergic neurons have also been recently defined and published. Thus, it might be expected that in the near future, this approach could extend to other NSSs and other NDDs.

State of the Art in Sub-Phenotyping Midbrain Dopamine Neurons

Dopaminergic neurons in the ventral tegmental area (VTA) and the substantia nigra pars compacta (SNpc) comprise around 75% of all dopaminergic neurons in the human brain. While both groups of dopaminergic neurons are in close proximity in the midbrain and partially overlap, development, function, and impairments in these two classes of neurons are highly diverse. The molecular and cellular mechanisms underlying these differences are not yet fully understood, but research over the past decade has highlighted the need to differentiate between these two classes of dopaminergic neurons during their development and in the mature brain. This differentiation is crucial not only for understanding fundamental circuitry formation in the brain but also for developing therapies targeted to specific dopaminergic neuron classes without affecting others. In this review, we summarize the state of the art in our understanding of the differences between the dopaminergic neurons of the VTA and the SNpc, such as anatomy, structure, morphology, output and input, electrophysiology, development, and disorders, and discuss the current technologies and methods available for studying these two classes of dopaminergic neurons, highlighting their advantages, limitations, and the necessary improvements required to achieve more-precise therapeutic interventions.

RNA Sequencing in Disease Diagnosis

RNA sequencing (RNA-seq) enables the accurate measurement of multiple transcriptomic phenotypes for modeling the impacts of disease variants. Advances in technologies, experimental protocols, and analysis strategies are rapidly expanding the application of RNA-seq to identify disease biomarkers, tissue- and cell-type-specific impacts, and the spatial localization of disease-associated mechanisms. Ongoing international efforts to construct biobank-scale transcriptomic repositories with matched genomic data across diverse population groups are further increasing the utility of RNA-seq approaches by providing large-scale normative reference resources. The availability of these resources, combined with improved computational analysis pipelines, has enabled the detection of aberrant transcriptomic phenotypes underlying rare diseases. Further expansion of these resources, across both somatic and developmental tissues, is expected to soon provide unprecedented insights to resolve disease origin, mechanism of action, and causal gene contributions, suggesting the continued high utility of RNA-seq in disease diagnosis.

Multi-omics integration of scRNA-seq time series data predicts new intervention points for Parkinson's disease

Parkinson's disease (PD) is a complex neurodegenerative disorder without a cure. The onset of PD symptoms corresponds to 50% loss of midbrain dopaminergic (mDA) neurons, limiting early-stage understanding of PD. To shed light on early PD development, we study time series scRNA-seq datasets of mDA neurons obtained from patient-derived induced pluripotent stem cell differentiation. We develop a new data integration method based on Non-negative Matrix Tri-Factorization that integrates these datasets with molecular interaction networks, producing condition-specific "gene embeddings". By mining these embeddings, we predict 193 PD-related genes that are largely supported (49.7%) in the literature and are specific to the investigated PINK1 mutation. Enrichment analysis in Kyoto Encyclopedia of Genes and Genomes pathways highlights 10 PD-related molecular mechanisms perturbed during early PD development. Finally, investigating the top 20 prioritized genes reveals 12 previously unrecognized genes associated with PD that represent interesting drug targets.

Basal activity of PINK1 and PRKN in cell models and rodent brain

The ubiquitin kinase-ligase pair PINK1-PRKN recognizes and transiently labels damaged mitochondria with ubiquitin phosphorylated at Ser65 (p-S65-Ub) to mediate their selective degradation (mitophagy). Complete loss of PINK1 or PRKN function unequivocally leads to early-onset Parkinson disease, but it is debated whether impairments in mitophagy contribute to disease later in life. While the pathway has been extensively studied in cell culture upon acute and massive mitochondrial stress, basal levels of activation under endogenous conditions and especially in vivo in the brain remain undetermined. Using rodent samples, patient-derived cells, and isogenic neurons, we here identified age-dependent, brain region-, and cell type-specific effects and determined expression levels and extent of basal and maximal activation of PINK1 and PRKN. Our work highlights the importance of defining critical risk and therapeutically relevant levels of PINK1-PRKN signaling which will further improve diagnosis and prognosis and will lead to better stratification of patients for future clinical trials.

Induced-pluripotent stem cells and neuroproteomics as tools for studying neurodegeneration

The investigation of neurodegenerative diseases advanced significantly with the advent of cell-reprogramming technology, leading to the creation of new models of human illness. These models, derived from induced pluripotent stem cells (iPSCs), facilitate the study of sporadic as well as hereditary diseases and provide a comprehensive understanding of the molecular mechanisms involved with neurodegeneration. Through proteomics, a quantitative tool capable of identifying thousands of proteins from small sample volumes, researchers have attempted to identify disease mechanisms by detecting differentially expressed proteins and proteoforms in disease models, biofluids, and postmortem brain tissue. The integration of these two technologies allows for the identification of novel pathological targets within the realm of neurodegenerative diseases. Here, we highlight studies from the past 5 years on the contributions of iPSCs within neuroproteomic investigations, which uncover the molecular mechanisms behind these illnesses.

Region-specific changes in gene expression are associated with cognitive deficits in the alpha-synuclein-induced model of Parkinson's disease: A transcriptomic profiling study

Mild cognitive impairment (MCI) is a common trait of Parkinson's disease (PD), often associated with early motor deficits, eventually evolving to PD with dementia in later disease stages. The neuropathological substrate of MCI is poorly understood, which weakens the development and administration of proper therapies. In an α-synuclein (αSyn)-based model of PD featuring early motor and cognitive impairments, we investigated the transcriptome profile of brain regions involved in PD with cognitive deficits, via a transcriptomic analysis based on RNA sequencing (RNA-seq) technology. Rats infused in the substantia nigra with human α-synuclein oligomers (H-SynOs) developed mild cognitive deficits after three months, as measured by the two-trial recognition test in a Y-maze and the novel object recognition test. RNA-seq analysis showed that 17,436 genes were expressed in the anterior cingulate cortex (ACC) and 17,216 genes in the hippocampus (HC). In the ACC, 51 genes were differentially expressed between vehicle and H-αSynOs treated samples, which showed N= 21 upregulated and N = 30 downregulated genes. In the HC, 104 genes were differentially expressed, the majority of them not overlapping with DEGs in the ACC, with N = 41 upregulated and N = 63 downregulated in H-αSynOs-treated samples. The Gene Ontology (GO) and the Kyoto Encyclopedia of Gene and Genomes (KEGG) analysis, followed by the protein-protein interaction (PPI) network inspection of DEGs, revealed that in the ACC most enriched terms were related with immune functions, specifically with antigen processing/presentation via the major histocompatibility complex (MHC) class II and phagocytosis via CD68, supporting a role for dysregulated immune responses in early PD cognitive dysfunction. Immunofluorescence analysis confirmed the decreased expression of CD68 within microglial cells. In contrast, the most significantly enriched terms in the HC were mainly involved in mitochondrial homeostasis, potassium voltage-gated channel, cytoskeleton and fiber organisation, suggesting that the gene expression in the neuronal population was mostly affected in this region in early disease stages. Altogether results show that H-αSynOs trigger a region-specific dysregulation of gene expression in ACC and HC, providing a pathological substrate for MCI associated with early PD.

The molecular landscape of neurological disorders: insights from single-cell RNA sequencing in neurology and neurosurgery

Single-cell ribonucleic acid sequencing (scRNA-seq) has emerged as a transformative technology in neurological and neurosurgical research, revolutionising our comprehension of complex neurological disorders. In brain tumours, scRNA-seq has provided valuable insights into cancer heterogeneity, the tumour microenvironment, treatment resistance, and invasion patterns. It has also elucidated the brain tri-lineage cancer hierarchy and addressed limitations of current models. Neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis have been molecularly subtyped, dysregulated pathways have been identified, and potential therapeutic targets have been revealed using scRNA-seq. In epilepsy, scRNA-seq has explored the cellular and molecular heterogeneity underlying the condition, uncovering unique glial subpopulations and dysregulation of the immune system. ScRNA-seq has characterised distinct cellular constituents and responses to spinal cord injury in spinal cord diseases, as well as provided molecular signatures of various cell types and identified interactions involved in vascular remodelling. Furthermore, scRNA-seq has shed light on the molecular complexities of cerebrovascular diseases, such as stroke, providing insights into specific genes, cell-specific expression patterns, and potential therapeutic interventions. This review highlights the potential of scRNA-seq in guiding precision medicine approaches, identifying clinical biomarkers, and facilitating therapeutic discovery. However, challenges related to data analysis, standardisation, sample acquisition, scalability, and cost-effectiveness need to be addressed. Despite these challenges, scRNA-seq has the potential to transform clinical practice in neurological and neurosurgical research by providing personalised insights and improving patient outcomes.

Data Mining of Microarray Datasets in Translational Neuroscience

Data mining involves the computational analysis of a plethora of publicly available datasets to generate new hypotheses that can be further validated by experiments for the improved understanding of the pathogenesis of neurodegenerative diseases. Although the number of sequencing datasets is on the rise, microarray analysis conducted on diverse biological samples represent a large collection of datasets with multiple web-based programs that enable efficient and convenient data analysis. In this review, we first discuss the selection of biological samples associated with neurological disorders, and the possibility of a combination of datasets, from various types of samples, to conduct an integrated analysis in order to achieve a holistic understanding of the alterations in the examined biological system. We then summarize key approaches and studies that have made use of the data mining of microarray datasets to obtain insights into translational neuroscience applications, including biomarker discovery, therapeutic development, and the elucidation of the pathogenic mechanisms of neurodegenerative diseases. We further discuss the gap to be bridged between microarray and sequencing studies to improve the utilization and combination of different types of datasets, together with experimental validation, for more comprehensive analyses. We conclude by providing future perspectives on integrating multi-omics, to advance precision phenotyping and personalized medicine for neurodegenerative diseases.

Transcriptome-Powered Pluripotent Stem Cell Differentiation for Regenerative Medicine

Pluripotent stem cells are endless sources for in vitro engineering human tissues for regenerative medicine. Extensive studies have demonstrated that transcription factors are the key to stem cell lineage commitment and differentiation efficacy. As the transcription factor profile varies depending on the cell type, global transcriptome analysis through RNA sequencing (RNAseq) has been a powerful tool for measuring and characterizing the success of stem cell differentiation. RNAseq has been utilized to comprehend how gene expression changes as cells differentiate and provide a guide to inducing cellular differentiation based on promoting the expression of specific genes. It has also been utilized to determine the specific cell type. This review highlights RNAseq techniques, tools for RNAseq data interpretation, RNAseq data analytic methods and their utilities, and transcriptomics-enabled human stem cell differentiation. In addition, the review outlines the potential benefits of the transcriptomics-aided discovery of intrinsic factors influencing stem cell lineage commitment, transcriptomics applied to disease physiology studies using patients' induced pluripotent stem cell (iPSC)-derived cells for regenerative medicine, and the future outlook on the technology and its implementation.

Generation of two human induced pluripotent stem cell lines from fibroblasts of Parkinson’s disease patients carrying the ILE368ASN mutation in PINK1 (LCSBi002) and the R275W mutation in Parkin (LCSBi004)

Mutations in PINK1 and Parkin are two of the main causes of recessive early-onset Parkinson's disease (PD). We generated human induced pluripotent stem cells (hiPSCs) from fibroblasts of a 64-year-old male patient with a homozygous ILE368ASN mutation in PINK1, who experienced disease onset at 33 years, and from fibroblasts of a 61-year-old female patient heterozygous for the R275W mutation in Parkin, who experienced disease onset at 44 years. Array comparative genomic hybridization (aCGH) determined genotypic variation in each line. The cell lines were successfully used to generate midbrain dopaminergic neurons, the neuron type primarily affected in PD.