IFDlong: an isoform and fusion detector for accurate annotation and quantification of long-read RNA-seq data.

Advancements in long-read transcriptome sequencing (long-RNA-seq) technology have revolutionized the study of isoform diversity. These full-length transcripts enhance the detection of various transcriptome structural variations, including novel isoforms, alternative splicing events, and fusion transcripts. By shifting the open reading frame or altering gene expressions, studies have proved that these transcript alterations can serve as crucial biomarkers for disease diagnosis and therapeutic targets. In this project, we proposed IFDlong, a bioinformatics and biostatistics tool to detect isoform and fusion transcripts using bulk or single-cell long-RNA-seq data. Specifically, the software performed gene and isoform annotation for each long-read, defined novel isoforms, quantified isoform expression by a novel expectation-maximization algorithm, and profiled the fusion transcripts. For evaluation, IFDlong pipeline achieved overall the best performance when compared with several existing tools in large-scale simulation studies. In both isoform and fusion transcript quantification, IFDlong is able to reach more than 0.8 Spearman's correlation with the truth, and more than 0.9 cosine similarity when distinguishing multiple alternative splicing events. In novel isoform simulation, IFDlong can successfully balance the sensitivity (higher than 90%) and specificity (higher than 90%). Furthermore, IFDlong has proved its accuracy and robustness in diverse in-house and public datasets on healthy tissues, cell lines and multiple types of diseases. Besides bulk long-RNA-seq, IFDlong pipeline has proved its compatibility to single-cell long-RNA-seq data. This new software may hold promise for significant impact on long-read transcriptome analysis. The IFDlong software is available at https://github.com/wenjiaking/IFDlong.

Data normalization for addressing the challenges in the analysis of single-cell transcriptomic datasets.

BACKGROUND: Normalization is a critical step in the analysis of single-cell RNA-sequencing (scRNA-seq) datasets. Its main goal is to make gene counts comparable within and between cells. To do so, normalization methods must account for technical and biological variability. Numerous normalization methods have been developed addressing different sources of dispersion and making specific assumptions about the count data. MAIN BODY: The selection of a normalization method has a direct impact on downstream analysis, for example differential gene expression and cluster identification. Thus, the objective of this review is to guide the reader in making an informed decision on the most appropriate normalization method to use. To this aim, we first give an overview of the different single cell sequencing platforms and methods commonly used including isolation and library preparation protocols. Next, we discuss the inherent sources of variability of scRNA-seq datasets. We describe the categories of normalization methods and include examples of each. We also delineate imputation and batch-effect correction methods. Furthermore, we describe data-driven metrics commonly used to evaluate the performance of normalization methods. We also discuss common scRNA-seq methods and toolkits used for integrated data analysis. CONCLUSIONS: According to the correction performed, normalization methods can be broadly classified as within and between-sample algorithms. Moreover, with respect to the mathematical model used, normalization methods can further be classified into: global scaling methods, generalized linear models, mixed methods, and machine learning-based methods. Each of these methods depict pros and cons and make different statistical assumptions. However, there is no better performing normalization method. Instead, metrics such as silhouette width, K-nearest neighbor batch-effect test, or Highly Variable Genes are recommended to assess the performance of normalization methods.

Clinical implications of single cell sequencing for bladder cancer.

Bladder cancer (BC) is the 10th most common cancer worldwide, with about 0.5 million reported new cases and about 0.2 million deaths per year. In this scoping review, we summarize the current evidence regarding the clinical implications of single-cell sequencing for bladder cancer based on PRISMA guidelines. We searched PubMed, CENTRAL, Embase, and supplemented with manual searches through the Scopus, and Web of Science for published studies until February 2023. We included original studies that used at least one single-cell technology to study bladder cancer. Forty-one publications were included in the review. Twenty-nine studies showed that this technology can identify cell subtypes in the tumor microenvironment that may predict prognosis or response to immune checkpoint inhibition therapy. Two studies were able to diagnose BC by identifying neoplastic cells through single-cell sequencing urine samples. The remaining studies were mainly a preclinical exploration of tumor microenvironment at single cell level. Single-cell sequencing technology can discriminate heterogeneity in bladder tumor cells and determine the key molecular properties that can lead to the discovery of novel perspectives on cancer management. This nascent tool can advance the early diagnosis, prognosis judgment, and targeted therapy of bladder cancer.

Endothelial cell ferroptosis influences IDH wild-type glioblastoma growth in recurrent glioblastoma multiforme patients

Glioblastomas are known for their poor clinical prognosis, with recurrent tumors often exhibiting greater invasiveness and faster growth rates compared to primary tumors. To understand the intratumoral changes driving this phenomenon, we employed single-cell sequencing to analyze the differences between two pairs of primary and recurrent glioblastomas. Our findings revealed an upregulation of ferroptosis in endothelial cells within recurrent tumors, identified by the significant overexpression of the NOX4 gene. Further analysis indicated that knocking down NOX4 in endothelial cells reduced the activity of the ferroptosis pathway. Utilizing conditioned media from endothelial cells with lower ferroptosis activity, we observed a decrease in the growth rate of glioblastoma cells. These results highlighted the complex role of ferroptosis within tumors and suggested that targeting ferroptosis in the treatment of glioblastomas requires careful consideration of its effects on endothelial cells, as it may otherwise produce counterproductive outcomes.

Single-cell analysis extracted CAFs-related genes to established online app to predict clinical outcome and radiotherapy prognosis of prostate cancer.

BACKGROUND: Cancer-associated fibroblasts (CAFs) play a significant role in regulating the clinical outcome and radiotherapy prognosis of prostate cancer (PCa). The aim of this study is to identify CAFs-related genes (CAFsRGs) using single-cell analysis and evaluate their potential for predicting the prognosis and radiotherapy prognosis in PCa. METHODS: We acquire transcriptome and single-cell RNA sequencing (scRNA-seq) results of PCa and normal adjacent tissues from The GEO and TCGA databases. The "MCPcounter" and "EPIC" R packages were used to assess the infiltration level of CAFs and examine their correlation with PCa prognosis. ScRNA-seq and differential gene expression analyses were used to extract CAFsRGs. We also applied COX and LASSO analysis to further construct a risk score (CAFsRS) to assess biochemical recurrence-free survival (BRFS) and radiotherapy prognosis of PCa. The predictive efficacy of CAFsRS was evaluated by ROC curves and subgroup analysis. Finally, we integrated the CAFsRS gene signature with relevant clinical features to develop a nomogram, enhancing the predictive accuracy. RESULTS: The abundance of CAFs is associated with a poor prognosis of PCa patients. ScRNA-seq and differential gene expression analysis revealed 323 CAFsRGs. After COX and LASSO analysis, we obtained seven CAFsRGs with prognostic significance (PTGS2, FKBP10, ENG, CDH11, COL5A1, COL5A2, and SRD5A2). Additionally, we established a risk score model based on the training set (n = 257). The ROC curve was used to confirm the performance of CAFsRS (The AUC values for 1, 3 and 5-year survival were determined to be 0.732, 0.773, and 0.775, respectively.). The testing set (n = 129), GSE70770 set (n = 199) and GSE116918 set (n = 248) revealed that the model exhibited exceptional predictive performance. This was also confirmed by clinical subgroup analysis. The violin plot demonstrated a statistically significant disparity in the CAFs infiltrations between the high-risk and low-risk groups of CAFsRS. Further analysis confirmed that both CAFsRS and T stage were independent prognostic factors for PCa. The nomogram was then established and its excellent predictive performance was demonstrated through calibration and ROC curves. Finally, we developed an online prognostic prediction app ( https://sysu-symh-cafsnomogram.streamlit.app/ ) to facilitate the practical application of the nomogram. CONCLUSIONS: The prognostic prediction risk score model we constructed could accurately predict BRFS and radiotherapy prognosis PCa, which can provide new ideas for clinicians to develop personalized PCa treatment and follow-up programs.

Single-cell and single-nuclei RNA sequencing as powerful tools to decipher cellular heterogeneity and dysregulation in neurodegenerative diseases.

Neurodegenerative diseases affect millions of people worldwide and there are currently no cures. Two types of common neurodegenerative diseases are Alzheimer's (AD) and Parkinson's disease (PD). Single-cell and single-nuclei RNA sequencing (scRNA-seq and snRNA-seq) have become powerful tools to elucidate the inherent complexity and dynamics of the central nervous system at cellular resolution. This technology has allowed the identification of cell types and states, providing new insights into cellular susceptibilities and molecular mechanisms underlying neurodegenerative conditions. Exciting research using high throughput scRNA-seq and snRNA-seq technologies to study AD and PD is emerging. Herein we review the recent progress in understanding these neurodegenerative diseases using these state-of-the-art technologies. We discuss the fundamental principles and implications of single-cell sequencing of the human brain. Moreover, we review some examples of the computational and analytical tools required to interpret the extensive amount of data generated from these assays. We conclude by highlighting challenges and limitations in the application of these technologies in the study of AD and PD.

Single-cell RNA-seq analyses show that long non-coding RNAs are conspicuously expressed in Schistosoma mansoni gamete and tegument progenitor cell populations.

Schistosoma mansoni is a flatworm that causes schistosomiasis, a neglected tropical disease that affects over 200 million people worldwide. New therapeutic targets are needed with only one drug available for treatment and no vaccine. Long non-coding RNAs (lncRNAs) are transcripts longer than 200 nucleotides with low or no protein-coding potential. In other organisms, they have been shown as involved with reproduction, stem cell maintenance and drug resistance, and they tend to exhibit tissue-specific expression patterns. S. mansoni expresses thousands of lncRNA genes; however, the cell type expression patterns of lncRNAs in the parasite remain uncharacterized. Here, we have re-analyzed publicly available single-cell RNA-sequencing (scRNA-seq) data obtained from adult S. mansoni to identify the lncRNAs signature of adult schistosome cell types. A total of 8023 lncRNAs (79% of all lncRNAs) were detected. Analyses of the lncRNAs expression profiles in the cells using statistically stringent criteria were performed to identify 74 lncRNA gene markers of cell clusters. Male gamete and tegument progenitor lineages clusters contained most of the cluster-specific lncRNA markers. We also identified lncRNA markers of specific neural clusters. Whole-mount in situ hybridization (WISH) and double fluorescence in situ hybridization were used to validate the cluster-specific expression of 13 out of 16 selected lncRNA genes (81%) in the male and female adult parasite tissues; for one of these 16 gene loci, probes for two different lncRNA isoforms were used, which showed differential isoform expression in testis and ovary. An atlas of the expression profiles across the cell clusters of all lncRNAs detected in our analysis is available as a public website resource (http://verjolab.usp.br:8081). The results presented here give strong support to a tissue-specific expression and to a regulated expression program of lncRNAs in S. mansoni. This will be the basis for further exploration of lncRNA genes as potential therapeutic targets.

Single-cell RNA-seq analyses show that long non-coding RNAs are conspicuously expressed in Schistosoma mansoni gamete and tegument progenitor cell populations

Schistosoma mansoni is a flatworm that causes schistosomiasis, a neglected tropical disease that affects over 200 million people worldwide. New therapeutic targets are needed with only one drug available for treatment and no vaccine. Long non-coding RNAs (lncRNAs) are transcripts longer than 200 nucleotides with low or no protein-coding potential. In other organisms, they have been shown as involved with reproduction, stem cell maintenance and drug resistance, and they tend to exhibit tissue-specific expression patterns. S. mansoni expresses thousands of lncRNA genes; however, the cell type expression patterns of lncRNAs in the parasite remain uncharacterized. Here, we have re-analyzed publicly available single-cell RNA-sequencing (scRNA-seq) data obtained from adult S. mansoni to identify the lncRNAs signature of adult schistosome cell types. A total of 8023 lncRNAs (79% of all lncRNAs) were detected. Analyses of the lncRNAs expression profiles in the cells using statistically stringent criteria were performed to identify 74 lncRNA gene markers of cell clusters. Male gamete and tegument progenitor lineages clusters contained most of the cluster-specific lncRNA markers. We also identified lncRNA markers of specific neural clusters. Whole-mount in situ hybridization (WISH) and double fluorescence in situ hybridization were used to validate the cluster-specific expression of 13 out of 16 selected lncRNA genes (81%) in the male and female adult parasite tissues; for one of these 16 gene loci, probes for two different lncRNA isoforms were used, which showed differential isoform expression in testis and ovary. An atlas of the expression profiles across the cell clusters of all lncRNAs detected in our analysis is available as a public website resource (http://verjolab.usp.br:8081). The results presented here give strong support to a tissue-specific expression and to a regulated expression program of lncRNAs in S. mansoni. This will be the basis for further exploration of lncRNA genes as potential therapeutic targets.

Personalized Medicine Using Cutting Edge Technologies for Genetic Epilepsies.

Epilepsy is the most common chronic neurologic disorder in the world, affecting 1-2% of the population. Besides, 30% of epilepsy patients are drug-resistant. Genomic mutations seem to play a key role in its etiology and knowledge of strong effect mutations in protein structures might improve prediction and the development of efficacious drugs to treat epilepsy. Several genetic association studies have been undertaken to examine the effect of a range of candidate genes for resistance. Although, few studies have explored the effect of the mutations into protein structure and biophysics in the epilepsy field. Much work remains to be done, but the plans made for exciting developments will hold therapeutic potential for patients with drug-resistance. In summary, we provide a critical review of the perspectives for the development of individualized medicine for epilepsy based on genetic polymorphisms/mutations in light of core elements such as transcriptomics, structural biology, disease model, pharmacogenomics and pharmacokinetics in a manner to improve the success of trial designs of antiepileptic drugs.

Weighted Gene Co-Expression Analyses Point to Long Non-Coding RNA Hub Genes at Different Schistosoma mansoni Life-Cycle Stages.

Long non-coding RNAs (lncRNAs) (>200 nt) are expressed at levels lower than those of the protein-coding mRNAs, and in all eukaryotic model species where they have been characterized, they are transcribed from thousands of different genomic loci. In humans, some four dozen lncRNAs have been studied in detail, and they have been shown to play important roles in transcriptional regulation, acting in conjunction with transcription factors and epigenetic marks to modulate the tissue-type specific programs of transcriptional gene activation and repression. In Schistosoma mansoni, around 10,000 lncRNAs have been identified in previous works. However, the limited number of RNA-sequencing (RNA-seq) libraries that had been previously assessed, together with the use of old and incomplete versions of the S. mansoni genome and protein-coding transcriptome annotations, have hampered the identification of all lncRNAs expressed in the parasite. Here we have used 633 publicly available S. mansoni RNA-seq libraries from whole worms at different stages (n = 121), from isolated tissues (n = 24), from cell-populations (n = 81), and from single-cells (n = 407). We have assembled a set of 16,583 lncRNA transcripts originated from 10,024 genes, of which 11,022 are novel S. mansoni lncRNA transcripts, whereas the remaining 5,561 transcripts comprise 120 lncRNAs that are identical to and 5,441 lncRNAs that have gene overlap with S. mansoni lncRNAs already reported in previous works. Most importantly, our more stringent assembly and filtering pipeline has identified and removed a set of 4,293 lncRNA transcripts from previous publications that were in fact derived from partially processed mRNAs with intron retention. We have used weighted gene co-expression network analyses and identified 15 different gene co-expression modules. Each parasite life-cycle stage has at least one highly correlated gene co-expression module, and each module is comprised of hundreds to thousands lncRNAs and mRNAs having correlated co-expression patterns at different stages. Inspection of the top most highly connected genes within the modules' networks has shown that different lncRNAs are hub genes at different life-cycle stages, being among the most promising candidate lncRNAs to be further explored for functional characterization.

SSU-rRNA Gene Sequencing Survey of Benthic Microbial Eukaryotes from Guaymas Basin Hydrothermal Vent.

Microbial eukaryotes have important roles in marine food webs, but their diversity and activities in hydrothermal vent ecosystems are poorly characterized. In this study, we analyzed microbial eukaryotic communities associated with bacterial (Beggiatoa) mats in the 2,000 m deep-sea Guaymas Basin hydrothermal vent system using 18S rRNA gene high-throughput sequencing of the V4 region. We detected 6,954 distinct Operational Taxonomic Units (OTUs) across various mat systems. Of the sequences that aligned with known protistan phylotypes, most were affiliated with alveolates (especially dinoflagellates and ciliates) and cercozoans. OTU richness and community structure differed among sediment habitats (e.g. different mat types and cold sediments away from mats). Additionally, full-length 18S rRNA genes amplified and cloned from single cells revealed the identities of some of the most commonly encountered, active ciliates in this hydrothermal vent ecosystem. Observations and experiments were also conducted to demonstrate that ciliates were trophically active and ingesting fluorescent bacteria or Beggiatoa trichomes. Our work suggests that the active and diverse protistan community at the Guaymas Basin hydrothermal vent ecosystem likely consumes substantial amounts of bacterial biomass, and that the different habitats, often defined by distances of just a few 10s of cm, select for particular assemblages and levels of diversity.

Weighted gene co-expression analyses point to long non-coding RNA hub genes at different Schistosoma mansoni life-cycle stages

Long non-coding RNAs (lncRNAs) (>200 nt) are expressed at levels lower than those of the protein-coding mRNAs, and in all eukaryotic model species where they have been characterized, they are transcribed from thousands of different genomic loci. In humans, some four dozen lncRNAs have been studied in detail, and they have been shown to play important roles in transcriptional regulation, acting in conjunction with transcription factors and epigenetic marks to modulate the tissue-type specific programs of transcriptional gene activation and repression. In Schistosoma mansoni, around 10,000 lncRNAs have been identified in previous works. However, the limited number of RNA-sequencing (RNA-seq) libraries that had been previously assessed, together with the use of old and incomplete versions of the S. mansoni genome and protein-coding transcriptome annotations, have hampered the identification of all lncRNAs expressed in the parasite. Here we have used 633 publicly available S. mansoni RNA-seq libraries from whole worms at different stages (n = 121), from isolated tissues (n = 24), from cell-populations (n = 81), and from single-cells (n = 407). We have assembled a set of 16,583 lncRNA transcripts originated from 10,024 genes, of which 11,022 are novel S. mansoni lncRNA transcripts, whereas the remaining 5,561 transcripts comprise 120 lncRNAs that are identical to and 5,441 lncRNAs that have gene overlap with S. mansoni lncRNAs already reported in previous works. Most importantly, our more stringent assembly and filtering pipeline has identified and removed a set of 4,293 lncRNA transcripts from previous publications that were in fact derived from partially processed mRNAs with intron retention. We have used weighted gene co-expression network analyses and identified 15 different gene co-expression modules. Each parasite life-cycle stage has at least one highly correlated gene co-expression module, and each module is comprised of hundreds to thousands lncRNAs and mRNAs having correlated co-expression patterns at different stages. Inspection of the top most highly connected genes within the modules’ networks has shown that different lncRNAs are hub genes at different life-cycle stages, being among the most promising candidate lncRNAs to be further explored for functional characterization.