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

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.