Single-Cell Applications of Next-Generation Sequencing

Next generation sequencing technologies to address aberrant mRNA translation in cancer

In this review, we explore the transformative impact of next generation sequencing technologies in the realm of translatomics (the study of how translational machinery acts on a genome-wide scale). Despite the expectation of a direct correlation between mRNA and protein content, the complex regulatory mechanisms that affect this relationship remark the limitations of standard RNA-seq approaches. Then, the review characterizes crucial techniques such as polysome profiling, ribo-seq, trap-seq, proximity-specific ribosome profiling, rnc-seq, tcp-seq, qti-seq and scRibo-seq. All these methods are summarized within the context of cancer research, shedding light on their applications in deciphering aberrant translation in cancer cells. In addition, we encompass databases and bioinformatic tools essential for researchers that want to address translatome analysis in the context of cancer biology.

Biomarkers in idiopathic pulmonary fibrosis: Current insight and future direction

Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive interstitial lung disease with a dismal prognosis. Early diagnosis, accurate prognosis, and personalized therapeutic interventions are essential for improving patient outcomes. Biomarkers, as measurable indicators of biological processes or disease states, hold significant promise in IPF management. In recent years, there has been a growing interest in identifying and validating biomarkers for IPF, encompassing various molecular, imaging, and clinical approaches. This review provides an in-depth examination of the current landscape of IPF biomarker research, highlighting their potential applications in disease diagnosis, prognosis, and treatment response. Additionally, the challenges and future perspectives of biomarker integration into clinical practice for precision medicine in IPF are discussed.

Unraveling Divergent Transcriptomic Profiles: A Comparative Single-Cell RNA Sequencing Study of Epithelium, Gingiva, and Periodontal Ligament Tissues

The periodontium comprising periodontal ligament (PDL), gingiva, and epithelium play crucial roles in maintaining tooth integrity and function. Understanding tissue cellular composition and gene expression is crucial for illuminating periodontal pathophysiology. This study aimed to identify tissue-specific markers via scRNA-Seq. Primary human PDL, gingiva, and epithelium tissues (n = 7) were subjected to cell hashing and sorting. scRNA-Seq library preparation using 10× Genomics protocol and Illumina sequencing was conducted. The analysis was performed using Cellranger (v3.1.0), with downstream analysis via R packages Seurat (v5.0.1) and SCORPIUS (v1.0.9). Investigations identified eight distinct cellular clusters, revealing the ubiquitous presence of epithelial and gingival cells. PDL cells evolved in two clusters with numerical superiority. The other clusters showed varied predominance regarding gingival and epithelial cells or an equitable distribution of both. The cluster harboring most cells mainly consisted of PDL cells and was present in all donors. Some of the other clusters were also tissue-inherent, while the presence of others was environmentally influenced, revealing variability across donors. Two clusters exhibited genetic profiles associated with tissue development and cellular integrity, respectively, while all other clusters were distinguished by genes characteristic of immune responses. Developmental trajectory analysis uncovered that PDL cells may develop after epithelial and gingival cells, suggesting the inherent PDL cell-dominated cluster as a final developmental stage. This single-cell RNA sequencing study delineates the hierarchical organization of periodontal tissue development, identifies tissue-specific markers, and reveals the influence of environmental factors on cellular composition, advancing our understanding of periodontal biology and offering potential insights for therapeutic interventions.

Revealing Genetic Dynamics: scRNA-seq Unravels Modifications in Human PDL Cells across In Vivo and In Vitro Environments

The periodontal ligament (PDL) is a highly specialized fibrous tissue comprising heterogeneous cell populations of an intricate nature. These complexities, along with challenges due to cell culture, impede a comprehensive understanding of periodontal pathophysiology. This study aims to address this gap, employing single-cell RNA sequencing (scRNA-seq) technology to analyze the genetic intricacies of PDL both in vivo and in vitro. Primary human PDL samples (n = 7) were split for direct in vivo analysis and cell culture under serum-containing and serum-free conditions. Cell hashing and sorting, scRNA-seq library preparation using the 10x Genomics protocol, and Illumina sequencing were conducted. Primary analysis was performed using Cellranger, with downstream analysis via the R packages Seurat and SCORPIUS. Seven distinct PDL cell clusters were identified comprising different cellular subsets, each characterized by unique genetic profiles, with some showing donor-specific patterns in representation and distribution. Formation of these cellular clusters was influenced by culture conditions, particularly serum presence. Furthermore, certain cell populations were found to be inherent to the PDL tissue, while others exhibited variability across donors. This study elucidates specific genes and cell clusters within the PDL, revealing both inherent and context-driven subpopulations. The impact of culture conditions-notably the presence of serum-on cell cluster formation highlights the critical need for refining culture protocols, as comprehending these influences can drive the creation of superior culture systems vital for advancing research in PDL biology and regenerative therapies. These discoveries not only deepen our comprehension of PDL biology but also open avenues for future investigations into uncovering underlying mechanisms.

Molecular Classifications in Gastric Cancer: A Call for Interdisciplinary Collaboration

Gastric cancer (GC) is a heterogeneous disease, often diagnosed at advanced stages, with a 5-year survival rate of approximately 20%. Despite notable technological advancements in cancer research over the past decades, their impact on GC management and outcomes has been limited. Numerous molecular alterations have been identified in GC, leading to various molecular classifications, such as those developed by The Cancer Genome Atlas (TCGA) and the Asian Cancer Research Group (ACRG). Other authors have proposed alternative perspectives, including immune, proteomic, or epigenetic-based classifications. However, molecular stratification has not yet transitioned into clinical practice for GC, and little attention has been paid to alternative molecular classifications. In this review, we explore diverse molecular classifications in GC from a practical point of view, emphasizing their relationships with clinicopathological factors, prognosis, and therapeutic approaches. We have focused on classifications beyond those of TCGA and the ACRG, which have been less extensively reviewed previously. Additionally, we discuss the challenges that must be overcome to ensure their impact on patient treatment and prognosis. This review aims to serve as a practical framework to understand the molecular landscape of GC, facilitate the development of consensus molecular categories, and guide the design of innovative molecular studies in the field.

Unraveling the intricacies of host-pathogen interaction through single-cell genomics

Single-cell genomics provide researchers with tools to assess host-pathogen interactions at a resolution previously inaccessible. Transcriptome analysis, epigenome analysis, and immune profiling techniques allow for a better comprehension of the heterogeneity underlying both the host response and infectious agents. Here, we highlight technological advancements and data analysis workflows that increase our understanding of host-pathogen interactions at the single-cell level. We review various studies that have used these tools to better understand host-pathogen dynamics in a variety of infectious disease contexts, including viral, bacterial, and parasitic diseases. We conclude by discussing how single-cell genomics can advance our understanding of host-pathogen interactions.

Next-Generation sequencing transforming clinical practice and precision medicine

Next-generation sequencing (NGS) has revolutionized the field of genomics and is rapidly transforming clinical diagnosis and precision medicine. This advanced sequencing technology enables the rapid and cost-effective analysis of large-scale genomic data, allowing comprehensive exploration of the genetic landscape of diseases. In clinical diagnosis, NGS has proven to be a powerful tool for identifying disease-causing variants, enabling accurate and early detection of genetic disorders. Additionally, NGS facilitates the identification of novel disease-associated genes and variants, aiding in the development of targeted therapies and personalized treatment strategies. NGS greatly benefits precision medicine by enhancing our understanding of disease mechanisms and enabling the identification of specific molecular markers for disease subtypes, thus enabling tailored medical interventions based on individual characteristics. Furthermore, NGS contributes to the development of non-invasive diagnostic approaches, such as liquid biopsies, which can monitor disease progression and treatment response. The potential of NGS in clinical diagnosis and precision medicine is vast, yet challenges persist in data analysis, interpretation, and protocol standardization. This review highlights NGS applications in disease diagnosis, prognosis, and personalized treatment strategies, while also addressing challenges and future prospects in fully harnessing genomic potential within clinical practice.

Multiple Dimensions of using Mesenchymal Stem Cells for Treating Liver Diseases: From Bench to Beside

Liver diseases impose a huge burden worldwide. Although hepatocyte transplantation has long been considered as a potential strategy for treating liver diseases, its clinical implementation has created some obvious limitations. As an alternative strategy, cell therapy, particularly mesenchymal stem cell (MSC) transplantation, is widely used in treating different liver diseases, including acute liver disease, acute-on-chronic liver failure, hepatitis B/C virus, autoimmune hepatitis, nonalcoholic fatty liver disease, nonalcoholic steatohepatitis, alcoholic liver disease, liver fibrosis, liver cirrhosis, and hepatocellular carcinoma. Here, we summarize the status of MSC transplantation in treating liver diseases, focusing on the therapeutic mechanisms, including differentiation into hepatocyte-like cells, immunomodulating function with a variety of immune cells, paracrine effects via the secretion of various cytokines and extracellular vesicles, and facilitation of homing and engraftment. Some improved perspectives and current challenges are also addressed. In summary, MSCs have great potential in the treatment of liver diseases based on their multi-faceted characteristics, and more accurate mechanisms and novel therapeutic strategies stemming from MSCs will facilitate clinical practice.

Advancements and Applications of Liquid Biopsies in Oncology: A Narrative Review

According to the World Health Organization (WHO), nearly 10 million people died from cancer worldwide in 2020, making it the leading cause of mortality. Liquid biopsies, which provide non-invasive and real-time monitoring of tumor dynamics, have evolved into innovative diagnostic techniques in the field of oncology. Liquid biopsies offer important insights into tumor heterogeneity, treatment response, minimum residual disease identification, and personalized treatment of cancer through the analysis of circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), extracellular vesicles, and microRNAs. They offer several advantages over traditional tissue biopsies, such as being less invasive, more convenient, more representative of tumor heterogeneity and dynamics, and more informative for guiding personalized treatment decisions. Liquid biopsies are being utilized increasingly in clinical oncology, particularly for patients with metastatic disease who require ongoing monitoring and treatment modification. In this narrative review article, we review the latest developments of liquid biopsy technologies, their applications and limitations, and their potential to transform diagnosis, prognosis, and management of cancer patients.

Alternative Splicing, RNA Editing, and the Current Limits of Next Generation Sequencing

The advent of next generation sequencing (NGS) has fostered a shift in basic analytic strategies of a gene expression analysis in diverse pathologies for the purposes of research, pharmacology, and personalized medicine. What was once highly focused research on individual signaling pathways or pathway members has, from the time of gene expression arrays, become a global analysis of gene expression that has aided in identifying novel pathway interactions, the discovery of new therapeutic targets, and the establishment of disease-associated profiles for assessing progression, stratification, or a therapeutic response. But there are significant caveats to this analysis that do not allow for the construction of the full picture. The lack of timely updates to publicly available databases and the "hit and miss" deposition of scientific data to these databases relegate a large amount of potentially important data to "garbage", begging the question, "how much are we really missing?" This brief perspective aims to highlight some of the limitations that RNA binding/modifying proteins and RNA processing impose on our current usage of NGS technologies as relating to cancer and how not fully appreciating the limitations of current NGS technology may negatively affect therapeutic strategies in the long run.

Single-Cell RNA-Sequencing Reveals the Skeletal Cellular Dynamics in Bone Repair and Osteoporosis

The bone is an important organ that performs various functions, and the bone marrow inside the skeleton is composed of a complex intermix of hematopoietic, vascular, and skeletal cells. Current single-cell RNA sequencing (scRNA-seq) technology has revealed heterogeneity and sketchy differential hierarchy of skeletal cells. Skeletal stem and progenitor cells (SSPCs) are located upstream of the hierarchy and differentiate into chondrocytes, osteoblasts, osteocytes, and bone marrow adipocytes. In the bone marrow, multiple types of bone marrow stromal cells (BMSCs), which have the potential of SSPCs, are spatiotemporally located in distinct areas, and SSPCs' potential shift of BMSCs may occur with the advancement of age. These BMSCs contribute to bone regeneration and bone diseases, such as osteoporosis. In vivo lineage-tracing technologies show that various types of skeletal lineage cells concomitantly gather and contribute to bone regeneration. In contrast, these cells differentiate into adipocytes with aging, leading to senile osteoporosis. scRNA-seq analysis has revealed that alteration in the cell-type composition is a major cause of tissue aging. In this review, we discuss the cellular dynamics of skeletal cell populations in bone homeostasis, regeneration, and osteoporosis.

CRISPR-GRANT: a cross-platform graphical analysis tool for high-throughput CRISPR-based genome editing evaluation

BACKGROUD

CRISPR/Cas is an efficient genome editing system that has been widely used for functional genetic studies and exhibits high potential in biomedical translational applications. Indel analysis has thus become one of the most common practices in the lab to evaluate DNA editing events generated by CRISPR/Cas. Several indel analysis tools have been reported, however, it is often required that users have certain bioinformatics training and basic command-line processing capability.

RESULTS

Here, we developed CRISPR-GRANT, a stand-alone graphical CRISPR indel analysis tool, which could be easily installed for multi-platforms, including Linux, Windows, and macOS. CRISPR-GRANT offered a straightforward GUI by simple click-and-run for genome editing analysis of single or pooled amplicons and one-step analysis for whole-genome sequencing without the need of data pre-processing, making it ideal for novice lab scientists. Moreover, it also exhibited shorter run-time compared with tools currently available.

CONCLUSION

Therefore, CRISPR-GRANT is a valuable addition to the current CRISPR toolkits that significantly lower the barrier for wet-lab researchers to conduct indel analysis from large NGS datasets. CRISPR-GRANT binaries are freely available for Linux (above Ubuntu 16.04), macOS (above High Sierra 10.13) and Windows (above Windows 7) at https://github.com/fuhuancheng/CRISPR-GRANT . CRISPR-GRANT source code is licensed under the GPLv3 license and free to download and use.

Entropy sorting of single-cell RNA sequencing data reveals the inner cell mass in the human pre-implantation embryo

A major challenge in single-cell gene expression analysis is to discern meaningful cellular heterogeneity from technical or biological noise. To address this challenge, we present entropy sorting (ES), a mathematical framework that distinguishes genes indicative of cell identity. ES achieves this in an unsupervised manner by quantifying if observed correlations between features are more likely to have occurred due to random chance versus a dependent relationship, without the need for any user-defined significance threshold. On synthetic data, we demonstrate the removal of noisy signals to reveal a higher resolution of gene expression patterns than commonly used feature selection methods. We then apply ES to human pre-implantation embryo single-cell RNA sequencing (scRNA-seq) data. Previous studies failed to unambiguously identify early inner cell mass (ICM), suggesting that the human embryo may diverge from the mouse paradigm. In contrast, ES resolves the ICM and reveals sequential lineage bifurcations as in the classical model. ES thus provides a powerful approach for maximizing information extraction from high-dimensional datasets such as scRNA-seq data.

Glioma Stem Cells: Novel Data Obtained by Single-Cell Sequencing

Glioma is the most common type of primary CNS tumor, composed of cells that resemble normal glial cells. Recent genetic studies have provided insight into the inter-tumoral heterogeneity of gliomas, resulting in the updated 2021 WHO classification of gliomas. Thorough understanding of inter-tumoral heterogeneity has already improved the prognosis and treatment outcomes of some types of gliomas. Currently, the challenge for researchers is to study the intratumoral cell heterogeneity of newly defined glioma subtypes. Cancer stem cells (CSCs) present in gliomas and many other tumors are an example of intratumoral heterogeneity of great importance. In this review, we discuss the modern concept of glioma stem cells and recent single-cell sequencing-driven progress in the research of intratumoral glioma cell heterogeneity. The particular emphasis was placed on the recently revealed variations of the cell composition of the subtypes of the adult-type diffuse gliomas, including astrocytoma, oligodendroglioma and glioblastoma. The novel data explain the inconsistencies in earlier glioma stem cell research and also provide insight into the development of more effective targeted therapy and the cell-based immunotherapy of gliomas. Separate sections are devoted to the description of single-cell sequencing approach and its role in the development of cell-based immunotherapies for glioma.

Insights from Studies of White Adipose Tissue Using Single-Cell Approaches

Technologies allowing studies at single-cell resolution have provided important insights into how different cell populations contribute to tissue function. Application of these methods to white adipose tissue (WAT) has revealed how various metabolic aspects of this organ, such as insulin response, inflammation and tissue expansion, are regulated by specific WAT resident cells, including different subtypes of adipocytes, adipocyte progenitors as well as immune and endothelial cells. In this chapter, we provide an overview of the different technical approaches, their strengths and weaknesses, and summarize how these studies have improved our understanding of WAT function in health and disease.

Smart-RRBS for single-cell methylome and transcriptome analysis

The integration of DNA methylation and transcriptional state within single cells is of broad interest. Several single-cell dual- and multi-omics approaches have been reported that enable further investigation into cellular heterogeneity, including the discovery and in-depth study of rare cell populations. Such analyses will continue to provide important mechanistic insights into the regulatory consequences of epigenetic modifications. We recently reported a new method for profiling the DNA methylome and transcriptome from the same single cells in a cancer research study. Here, we present details of the protocol and provide guidance on its utility. Our Smart-RRBS (reduced representation bisulfite sequencing) protocol combines Smart-seq2 and RRBS and entails physically separating mRNA from the genomic DNA. It generates paired epigenetic promoter and RNA-expression measurements for ~24% of protein-coding genes in a typical single cell. It also works for micro-dissected tissue samples comprising hundreds of cells. The protocol, excluding flow sorting of cells and sequencing, takes ~3 d to process up to 192 samples manually. It requires basic molecular biology expertise and laboratory equipment, including a PCR workstation with UV sterilization, a DNA fluorometer and a microfluidic electrophoresis system.

Ligand-Receptor Interactions Elucidate Sex-Specific Pathways in the Trajectory From Primordial Germ Cells to Gonia During Human Development

The human germ cell lineage originates from primordial germ cells (PGCs), which are specified at approximately the third week of development. Our understanding of the signaling pathways that control this event has significantly increased in recent years and that has enabled the generation of PGC-like cells (PGCLCs) from pluripotent stem cells in vitro. However, the signaling pathways that drive the transition of PGCs into gonia (prospermatogonia in males or premeiotic oogonia in females) remain unclear, and we are presently unable to mimic this step in vitro in the absence of gonadal tissue. Therefore, we have analyzed single-cell transcriptomics data of human fetal gonads to map the molecular interactions during the sex-specific transition from PGCs to gonia. The CellPhoneDB algorithm was used to identify significant ligand–receptor interactions between germ cells and their sex-specific neighboring gonadal somatic cells, focusing on four major signaling pathways WNT, NOTCH, TGFβ/BMP, and receptor tyrosine kinases (RTK). Subsequently, the expression and intracellular localization of key effectors for these pathways were validated in human fetal gonads by immunostaining. This approach provided a systematic analysis of the signaling environment in developing human gonads and revealed sex-specific signaling pathways during human premeiotic germ cell development. This work serves as a foundation to understand the transition from PGCs to premeiotic oogonia or prospermatogonia and identifies sex-specific signaling pathways that are of interest in the step-by-step reconstitution of human gametogenesis in vitro.

Failure of Glial Cell-Line Derived Neurotrophic Factor (GDNF) in Clinical Trials Orchestrated By Reduced NR4A2 (NURR1) Transcription Factor in Parkinson's Disease. A Systematic Review

Parkinson’s disease (PD) is one of the most common neurodegenerative maladies with unforeseen complex pathologies. While this neurodegenerative disorder’s neuropathology is reasonably well known, its etiology remains a mystery, making it challenging to aim therapy. Glial cell-line derived neurotrophic factor (GDNF) remains an auspicious therapeutic molecule for treating PD. Neurotrophic factor derived from glial cell lines is effective in rodents and nonhuman primates, but clinical findings have been equivocal. Laborious exertions have been made over the past few decades to improve and assess GDNF in treating PD (clinical studies). Definitive clinical trials have, however, failed to demonstrate a survival advantage. Consequently, there seemed to be a doubt as to whether GDNF has merit in the potential treatment of PD. The purpose of this cutting edge review is to speculate as to why the clinical trials have failed to meet the primary endpoint. We introduce a hypothesis, “Failure of GDNF in clinical trials succumbed by nuclear receptor-related factor 1 (Nurr1) shortfall.” We demonstrate how Nurr1 binds to GDNF to induce dopaminergic neuron synthesis. Due to its undisputable neuro-protection aptitude, we display Nurr1 (also called Nr4a2) as a promising therapeutic target for PD.