Single-Cell RNA Sequencing in Parkinson's Disease

No association between genetically predicted vitamin D levels and Parkinson's disease

BACKGROUND

Parkinson's disease (PD) is a neurodegenerative disorder, primarily characterized by motor impairments. Vitamin D has several regulatory functions in nerve cell survival and gene expression via its receptors. Although research has shown that vitamin D deficiency is prevalent among PD patients, the causal link to PD risk remains unclear. This study aims to investigate the causal relationship between vitamin D and PD using a bidirectional two-sample Mendelian randomization (MR) analysis method.

METHODS

This study applied a bidirectional two-sample MR analysis to explore the causal link between vitamin D and PD. We selected statistically significant single nucleotide polymorphisms (SNPs) related to 25-hydroxyvitamin D (25(OH)D) as instrumental variables (IVs), ensuring no association with known confounders. The analysis used GWAS data from over 1.2 million Europeans across four major published datasets, elucidating the genetic correlation between vitamin D levels and PD.

RESULTS

We identified 148 instrumental SNPs associated with 25(OH)D. After adjustment for confounding-related SNPs, 131 SNPs remained in the analysis. Data from three PD cohorts revealed no significant correlation between 25(OH)D levels and PD risk using the IVW method (Pcohort1 = 0.365, Pcohort2 = 0.525, Pcohort3 = 0.117). The reverse MR analysis indicated insufficient evidence of PD causing decreased vitamin D levels (P = 0.776).

CONCLUSION

This is the first study to use bidirectional MR across three PD cohorts to investigate the causal relationship between vitamin D and PD. The results indicate that vitamin D levels are not significantly causally related to PD risk at the genetic level. Therefore, future studies should exercise caution when investigating the relationship between vitamin D levels and PD risk. While no direct causal link exists between vitamin D levels and PD, this does not preclude the potential of vitamin D levels as a biomarker for PD diagnosis. Furthermore, larger-scale longitudinal studies are necessary to evaluate the diagnostic and predictive value of vitamin D levels in PD.

Targeting Glucose Metabolism: A Novel Therapeutic Approach for Parkinson's Disease

Glucose metabolism is essential for the maintenance and function of the central nervous system. Although the brain constitutes only 2% of the body weight, it consumes approximately 20% of the body's total energy, predominantly derived from glucose. This high energy demand of the brain underscores its reliance on glucose to fuel various functions, including neuronal activity, synaptic transmission, and the maintenance of ion gradients necessary for nerve impulse transmission. Increasing evidence shows that many neurodegenerative diseases, including Parkinson's disease (PD), are associated with abnormalities in glucose metabolism. PD is characterized by the progressive loss of dopaminergic neurons in the substantia nigra, accompanied by the accumulation of α-synuclein protein aggregates. These pathological features are exacerbated by mitochondrial dysfunction, oxidative stress, and neuroinflammation, all of which are influenced by glucose metabolism disruptions. Emerging evidence suggests that targeting glucose metabolism could offer therapeutic benefits for PD. Several antidiabetic drugs have shown promise in animal models and clinical trials for mitigating the symptoms and progression of PD. This review explores the current understanding of the association between PD and glucose metabolism, emphasizing the potential of antidiabetic medications as a novel therapeutic approach. By improving glucose uptake and utilization, enhancing mitochondrial function, and reducing neuroinflammation, these drugs could address key pathophysiological mechanisms in PD, offering hope for more effective management of this debilitating disease.

New evidence of vascular defects in neurodegenerative diseases revealed by single cell RNA sequencing

Neurodegenerative diseases (NDs) involve the progressive loss of neuronal structure or function in the brain and spinal cord. Despite their diverse etiologies, NDs manifest similar pathologies. Emerging research identifies vascular defects as a previously neglected hallmark of NDs. The development and popularization of single-cell RNA sequencing (scRNA-seq) technologies have significantly advanced our understanding of brain vascular cell types and their molecular characteristics, including gene expression changes at the single-cell level in NDs. These unprecedented insights deepen our understanding of the pathogenic mechanisms underlying NDs. However, the occurrence and role of vascular defects in disease progression remain largely unexplored. In this paper, we systematically summarize recent advances in the structure and organization of the central nervous system vasculature in mice, healthy individuals, and patients with NDs, focussing primarily on disease-specific alterations in vascular cell types or subtypes. Combining scRNA-seq with pathology evidence, we propose that vascular defects, characterized by disruptions in cell types and structural integrity, may serve as common early features of NDs. Finally, we discuss several pathways through which vascular defects in NDs lead to neuronal degeneration. A deeper understanding of the causes and contributions of vascular defects to NDs aids in elucidating the pathogenic mechanisms and developing meaningful therapeutic interventions.

MicroRNA-Targeted Gene Regulation in Salivary Gland Tissue of De Novo Parkinson's Disease Patients

Although α-synucleinopathy has been confirmed in the submandibular gland (SMG) tissue of Parkinson's disease (PD) patients, in-depth disease-related molecular research, such as tissue-specific transcriptional signals, has not been performed. In the present study, disease-relevant tissue-specific transcriptional signals in SMG tissue from PD patients were investigated to identify potential diagnostic, prognostic, and pathophysiologic biomarkers. Here, seven de novo drug-naïve PD patients and six age- and sex-matched individuals without neurological or psychological diseases were enrolled. Total RNA sequencing (RNA-seq) and total small RNA-seq (smRNA-seq) were performed on SMG tissue and blood samples, with 26 RNA-seq and 26 smRNA-seq samples used for the final analysis. Differentially expressed genes (DEGs) and microRNAs in SMG tissue and blood from PD patients were obtained and their functional integration and interaction network were analyzed. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that the DEGs interacted with cytokine-, inflammation-, and immune-related pathways. Synphilin-1 expression was significantly downregulated in SMG tissue of PD patients, and α-synuclein expression did not significantly differ between PD patients and controls in either SMG tissue or blood. Fifteen tissue-specific miRNA signals in SMG tissue were identified that showed better diagnostic ability compared with those in blood samples. The correlation between DEGs and environmental factors appeared altered in PD patients. The results indicated the DEGs and microRNA signatures identified in SMG tissue may be promising diagnostic and prognostic biomarkers. These molecular insights offer potential avenues for the development of novel therapeutic strategies targeting the underlying disease mechanisms in PD patients.

Single-cell sequencing of the substantia nigra reveals microglial activation in a model of MPTP

Background

N-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) is a neurotoxin widely used to induce PD models, but the effect of MPTP on the cells and genes of PD has not been fully elucidated.

Methods

Single-nucleus RNA sequencing was performed in the Substantia Nigra (SN) of MPTP mice. UMAP analysis was used for the dimensionality reduction visualization of the SN in the MPTP mice. Known marker genes highly expressed genes in each cluster were used to annotate most clusters. Specific Differentially Expressed Genes (DEGs) and PD risk genes analysis were used to find MPTP-associated cells. GO, KEGG, PPI network, GSEA and CellChat analysis were used to reveal cell type-specific functional alterations and disruption of cell-cell communication networks. Subset reconstruction and pseudotime analysis were used to reveal the activation status of the cells, and to find the transcription factors with trajectory characterized.

Results

Initially, we observed specific DEGs and PD risk genes enrichment in microglia. Next, We obtained the functional phenotype changes in microglia and found that IGF, AGRN and PTN pathways were reduced in MPTP mice. Finally, we analyzed the activation state of microglia and revealed a pro-inflammatory trajectory characterized by transcription factors Nfe2l2 and Runx1.

Conclusion

Our work revealed alterations in microglia function, signaling pathways and key genes in the SN of MPTP mice.

Single-Cell Sequencing Technology and Its Application in the Study of Central Nervous System Diseases

The mammalian central nervous system consists of a large number of cells, which contain not only different types of neurons, but also a large number of glial cells, such as astrocytes, oligodendrocytes, and microglia. These cells are capable of performing highly refined electrophysiological activities and providing the brain with functions such as nutritional support, information transmission and pathogen defense. The diversity of cell types and individual differences between cells have brought inspiration to the study of the mechanism of central nervous system diseases. In order to explore the role of different cells, a new technology, single-cell sequencing technology has emerged to perform specific analysis of high-throughput cell populations, and has been continuously developed. Single-cell sequencing technology can accurately analyze single-cell expression in mixed-cell populations and collect cells from different spatial locations, time stages and types. By using single-cell sequencing technology to compare gene expression profiles of normal and diseased cells, it is possible to discover cell subsets associated with specific diseases and their associated genes. Therefore, scientists can understand the development process, related functions and disease state of the nervous system from an unprecedented depth. In conclusion, single-cell sequencing technology provides a powerful technology for the discovery of novel therapeutic targets for central nervous system diseases.

Unveiling the regulatory of miR-101-3p on ZNF746 in a Parkinson's disease cell model: Implications for therapeutic targeting

In this study, we explored the regulatory role of microRNA miR-101-3p on the zinc finger protein 746 (ZNF746), also known as PARIS, which is implicated in both sporadic and familial forms of Parkinson's disease. In a Parkinson's disease cell model, utilizing SH-SY5Y cells treated with 1-methyl-4-phenylpyridine (MPP+), we observed that miR-101-3p was downregulated, while ZNF746 was upregulated. To investigate the direct impact of miR-101-3p on ZNF746, our team conducted overexpression experiments, successfully reversing ZNF746's expression at both the mRNA and protein levels, as confirmed through quantitative PCR and western blotting. We also performed luciferase assays, providing compelling evidence that ZNF746 is a direct target of miR-101-3p. Additionally, we noted that miR-101-3p overexpression resulted in increased expression of PGC1α, a gene targeted by ZNF746. Functionally, we assessed the implications of miR-101-3p overexpression through MTS assays and flow cytometry, revealing significant promotion of cell viability, inhibition of ROS production, and reduced apoptosis in the Parkinson's disease cell model. In conclusion, this study highlights the role of miR-101-3p in regulating ZNF746 expression and suggests its potential as a therapeutic target for Parkinson's disease. These findings provide valuable molecular insights that could pave the way for innovative treatment strategies in combating this debilitating neurodegenerative disorder.

Unravelling cell type-specific responses to Parkinson's Disease at single cell resolution

Parkinson's Disease (PD) is the second most common neurodegenerative disorder. The pathological hallmark of PD is loss of dopaminergic neurons and the presence of aggregated α-synuclein, primarily in the substantia nigra pars compacta (SNpc) of the midbrain. However, the molecular mechanisms that underlie the pathology in different cell types is not currently understood. Here, we present a single nucleus transcriptome analysis of human post-mortem SNpc obtained from 15 sporadic Parkinson's Disease (PD) cases and 14 Controls. Our dataset comprises ∼84K nuclei, representing all major cell types of the brain, allowing us to obtain a transcriptome-level characterization of these cell types. Importantly, we identify multiple subpopulations for each cell type and describe specific gene sets that provide insights into the differing roles of these subpopulations. Our findings reveal a significant decrease in neuronal cells in PD samples, accompanied by an increase in glial cells and T cells. Subpopulation analyses demonstrate a significant depletion of tyrosine hydroxylase (TH) enriched astrocyte, microglia and oligodendrocyte populations in PD samples, as well as TH enriched neurons, which are also depleted. Moreover, marker gene analysis of the depleted subpopulations identified 28 overlapping genes, including those associated with dopamine metabolism (e.g., ALDH1A1, SLC6A3 & SLC18A2). Overall, our study provides a valuable resource for understanding the molecular mechanisms involved in dopaminergic neuron degeneration and glial responses in PD, highlighting the existence of novel subpopulations and cell type-specific gene sets.

The involvement of α-synucleinopathy in the disruption of microglial homeostasis contributes to the pathogenesis of Parkinson's disease

The intracellular deposition and intercellular transmission of α-synuclein (α-syn) are shared pathological characteristics among neurodegenerative disorders collectively known as α-synucleinopathies, including Parkinson's disease (PD). Although the precise triggers of α-synucleinopathies remain unclear, recent findings indicate that disruption of microglial homeostasis contributes to the pathogenesis of PD. Microglia play a crucial role in maintaining optimal neuronal function by ensuring a homeostatic environment, but this function is disrupted during the progression of α-syn pathology. The involvement of microglia in the accumulation, uptake, and clearance of aggregated proteins is critical for managing disease spread and progression caused by α-syn pathology. This review summarizes current knowledge on the interrelationships between microglia and α-synucleinopathies, focusing on the remarkable ability of microglia to recognize and internalize extracellular α-syn through diverse pathways. Microglia process α-syn intracellularly and intercellularly to facilitate the α-syn neuronal aggregation and cell-to-cell propagation. The conformational state of α-synuclein distinctly influences microglial inflammation, which can affect peripheral immune cells such as macrophages and lymphocytes and may regulate the pathogenesis of α-synucleinopathies. We also discuss ongoing research efforts to identify potential therapeutic approaches targeting both α-syn accumulation and inflammation in PD. Video Abstract.

Single-cell transcriptomic and genomic changes in the aging human brain

Aging brings dysregulation of various processes across organs and tissues, often stemming from stochastic damage to individual cells over time. Here, we used a combination of single-nucleus RNA-sequencing and single-cell whole-genome sequencing to identify transcriptomic and genomic changes in the prefrontal cortex of the human brain across life span, from infancy to centenarian. We identified infant-specific cell clusters enriched for the expression of neurodevelopmental genes, and a common down-regulation of cell-essential homeostatic genes that function in ribosomes, transport, and metabolism during aging across cell types. Conversely, expression of neuron-specific genes generally remains stable throughout life. We observed a decrease in specific DNA repair genes in aging, including genes implicated in generating brain somatic mutations as indicated by mutation signature analysis. Furthermore, we detected gene-length-specific somatic mutation rates that shape the transcriptomic landscape of the aged human brain. These findings elucidate critical aspects of human brain aging, shedding light on transcriptomic and genomics dynamics.

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.

Single-Cell Transcriptional Landscape Reveals the Regulatory Network and Its Heterogeneity of Renal Mitochondrial Damages in Diabetic Kidney Disease

Diabetic kidney disease (DKD) is one of the common chronic microvascular complications of diabetes in which mitochondrial disorder plays an important role in its pathogenesis. The current study delved into the single-cell level transcriptome heterogeneity of mitochondrial homeostasis in db/db mice, an animal model for study of type 2 diabetes and DKD, with single-cell RNA sequencing (scRNA-Seq) and bulk RNA-seq analyses. From the comprehensive dataset comprising 13 meticulously captured and authenticated renal cell types, an unsupervised cluster analysis of mitochondria-related genes within the descending loop of Henle, collecting duct principal cell, endothelial, B cells and macrophage, showed that they had two types of cell subsets, i.e., health-dominant and DKD-dominant clusters. Pseudotime analysis, cell communication and transcription factors forecast resulted in identification of the hub differentially expressed genes between these two clusters and unveiled that the hierarchical regulatory network of receptor-TF-target genes was triggered by mitochondrial degeneration. Furthermore, the collecting duct principal cells were found to be regulated by the decline of Fzd7, which contributed to the impaired cellular proliferation and development, apoptosis and inactive cell cycle, as well as diminished capacity for material transport. Thereby, both scRNA-Seq and bulk RNA-Seq data from the current study elucidate the heterogeneity of mitochondrial disorders among distinct cell types, particularly in the collecting duct principal cells and B cells during the DKD progression and drug administration, which provide novel insights for better understanding the pathogenesis of DKD.

Integrative multi-omics and systems bioinformatics in translational neuroscience: A data mining perspective

Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information, with its application to neuroscience termed neuroinformatics. Data mining of omics datasets has enabled the generation of new hypotheses based on differentially regulated biological molecules associated with disease mechanisms, which can be tested experimentally for improved diagnostic and therapeutic targeting of neurodegenerative diseases. Importantly, integrating multi-omics data using a systems bioinformatics approach will advance the understanding of the layered and interactive network of biological regulation that exchanges systemic knowledge to facilitate the development of a comprehensive human brain profile. In this review, we first summarize data mining studies utilizing datasets from the individual type of omics analysis, including epigenetics/epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, and spatial omics, pertaining to Alzheimer's disease, Parkinson's disease, and multiple sclerosis. We then discuss multi-omics integration approaches, including independent biological integration and unsupervised integration methods, for more intuitive and informative interpretation of the biological data obtained across different omics layers. We further assess studies that integrate multi-omics in data mining which provide convoluted biological insights and offer proof-of-concept proposition towards systems bioinformatics in the reconstruction of brain networks. Finally, we recommend a combination of high dimensional bioinformatics analysis with experimental validation to achieve translational neuroscience applications including biomarker discovery, therapeutic development, and elucidation of disease mechanisms. We conclude by providing future perspectives and opportunities in applying integrative multi-omics and systems bioinformatics to achieve precision phenotyping of neurodegenerative diseases and towards personalized medicine.

Reactive astrogliosis in the era of single-cell transcriptomics

Reactive astrogliosis is a reaction of astrocytes to disturbed homeostasis in the central nervous system (CNS), accompanied by changes in astrocyte numbers, morphology, and function. Reactive astrocytes are important in the onset and progression of many neuropathologies, such as neurotrauma, stroke, and neurodegenerative diseases. Single-cell transcriptomics has revealed remarkable heterogeneity of reactive astrocytes, indicating their multifaceted functions in a whole spectrum of neuropathologies, with important temporal and spatial resolution, both in the brain and in the spinal cord. Interestingly, transcriptomic signatures of reactive astrocytes partially overlap between neurological diseases, suggesting shared and unique gene expression patterns in response to individual neuropathologies. In the era of single-cell transcriptomics, the number of new datasets steeply increases, and they often benefit from comparisons and integration with previously published work. Here, we provide an overview of reactive astrocyte populations defined by single-cell or single-nucleus transcriptomics across multiple neuropathologies, attempting to facilitate the search for relevant reference points and to improve the interpretability of new datasets containing cells with signatures of reactive astrocytes.

Sparse representation learning derives biological features with explicit gene weights from the Allen Mouse Brain Atlas

Unsupervised learning methods are commonly used to detect features within transcriptomic data and ultimately derive meaningful representations of biology. Contributions of individual genes to any feature however becomes convolved with each learning step, requiring follow up analysis and validation to understand what biology might be represented by a cluster on a low dimensional plot. We sought learning methods that could preserve the gene information of detected features, using the spatial transcriptomic data and anatomical labels of the Allen Mouse Brain Atlas as a test dataset with verifiable ground truth. We established metrics for accurate representation of molecular anatomy to find sparse learning approaches were uniquely capable of generating anatomical representations and gene weights in a single learning step. Fit to labeled anatomy was highly correlated with intrinsic properties of the data, offering a means to optimize parameters without established ground truth. Once representations were derived, complementary gene lists could be further compressed to generate a low complexity dataset, or to probe for individual features with >95% accuracy. We demonstrate the utility of sparse learning as a means to derive biologically meaningful representations from transcriptomic data and reduce the complexity of large datasets while preserving intelligible gene information throughout the analysis.

Spatial Transcriptome Profiling of Mouse Hippocampal Single Cell Microzone in Parkinson's Disease

The hippocampus is an important part of the limbic system in the human brain that has essential roles in spatial navigation and cognitive functions. It is still unknown how gene expression changes in single-cell in different spatial locations of the hippocampus of Parkinson's disease. The purpose of this study was to analyze the gene expression features of single cells in different spatial locations of mouse hippocampus, and to explore the effects of gene expression regulation on learning and memory mechanisms. Here, we obtained 74 single-cell samples from different spatial locations in a mouse hippocampus through microdissection technology, and used single-cell RNA-sequencing and spatial transcriptome sequencing to visualize and quantify the single-cell transcriptome features of tissue sections. The results of differential expression analysis showed that the expression of Sv2b, Neurod6, Grp and Stk32b genes in a hippocampus single cell at different locations was significantly different, and the marker genes of CA1, CA3 and DG subregions were identified. The results of gene function enrichment analysis showed that the up-regulated differentially expressed genes Tubb2a, Eno1, Atp2b1, Plk2, Map4, Pex5l, Fibcd1 and Pdzd2 were mainly involved in neuron to neuron synapse, vesicle-mediated transport in synapse, calcium signaling pathway and neurodegenerative disease pathways, thus affecting learning and memory function. It revealed the transcriptome profile and heterogeneity of spatially located cells in the hippocampus of PD for the first time, and demonstrated that the impaired learning and memory ability of PD was affected by the synergistic effect of CA1 and CA3 subregions neuron genes. These results are crucial for understanding the pathological mechanism of the Parkinson's disease and making precise treatment plans.

Single-cell analysis of gene expression in the substantia nigra pars compacta of a pesticide-induced mouse model of Parkinson's disease

Exposure to pesticides in humans increases the risk of Parkinson’s disease (PD), but the mechanisms remain poorly understood. To elucidate these pathways, we dosed C57BL/6J mice with a combination of the pesticides maneb and paraquat. Behavioral analysis revealed motor deficits consistent with PD. Single-cell RNA sequencing of substantia nigra pars compacta revealed both cell-type-specific genes and genes expressed differentially between pesticide and control, including Fam241b, Emx2os, Bivm, Gm1439, Prdm15, and Rai2. Neurons had the largest number of significant differentially expressed genes, but comparable numbers were found in astrocytes and less so in oligodendrocytes. In addition, network analysis revealed enrichment in functions related to the extracellular matrix. These findings emphasize the importance of support cells in pesticide-induced PD and refocus our attention away from neurons as the sole agent of this disorder.

The role of noncoding RNAs in Parkinson's disease: biomarkers and associations with pathogenic pathways

The discovery of various noncoding RNAs (ncRNAs) and their biological implications is a growing area in cell biology. Increasing evidence has revealed canonical and noncanonical functions of long and small ncRNAs, including microRNAs, long ncRNAs (lncRNAs), circular RNAs, PIWI-interacting RNAs, and tRNA-derived fragments. These ncRNAs have the ability to regulate gene expression and modify metabolic pathways. Thus, they may have important roles as diagnostic biomarkers or therapeutic targets in various diseases, including neurodegenerative disorders, especially Parkinson's disease. Recently, through diverse sequencing technologies and a wide variety of bioinformatic analytical tools, such as reverse transcriptase quantitative PCR, microarrays, next-generation sequencing and long-read sequencing, numerous ncRNAs have been shown to be associated with neurodegenerative disorders, including Parkinson's disease. In this review article, we will first introduce the biogenesis of different ncRNAs, including microRNAs, PIWI-interacting RNAs, circular RNAs, long noncoding RNAs, and tRNA-derived fragments. The pros and cons of the detection platforms of ncRNAs and the reproducibility of bioinformatic analytical tools will be discussed in the second part. Finally, the recent discovery of numerous PD-associated ncRNAs and their association with the diagnosis and pathophysiology of PD are reviewed, and microRNAs and long ncRNAs that are transported by exosomes in biofluids are particularly emphasized.

Dopamine in Health and Disease

The neurotransmitter dopamine (DA) is generally associated with Parkinson's disease (PD) [...].

Analyzing Modern Biomolecules: The Revolution of Nucleic-Acid Sequencing - Review

Recent developments have revolutionized the study of biomolecules. Among them are molecular markers, amplification and sequencing of nucleic acids. The latter is classified into three generations. The first allows to sequence small DNA fragments. The second one increases throughput, reducing turnaround and pricing, and is therefore more convenient to sequence full genomes and transcriptomes. The third generation is currently pushing technology to its limits, being able to sequence single molecules, without previous amplification, which was previously impossible. Besides, this represents a new revolution, allowing researchers to directly sequence RNA without previous retrotranscription. These technologies are having a significant impact on different areas, such as medicine, agronomy, ecology and biotechnology. Additionally, the study of biomolecules is revealing interesting evolutionary information. That includes deciphering what makes us human, including phenomena like non-coding RNA expansion. All this is redefining the concept of gene and transcript. Basic analyses and applications are now facilitated with new genome editing tools, such as CRISPR. All these developments, in general, and nucleic-acid sequencing, in particular, are opening a new exciting era of biomolecule analyses and applications, including personalized medicine, and diagnosis and prevention of diseases for humans and other animals.