Advances in long-read single-cell transcriptomics.

Long-read single-cell transcriptomics (scRNA-Seq) is revolutionizing the way we profile heterogeneity in disease. Traditional short-read scRNA-Seq methods are limited in their ability to provide complete transcript coverage, resolve isoforms, and identify novel transcripts. The scRNA-Seq protocols developed for long-read sequencing platforms overcome these limitations by enabling the characterization of full-length transcripts. Long-read scRNA-Seq techniques initially suffered from comparatively poor accuracy compared to short read scRNA-Seq. However, with improvements in accuracy, accessibility, and cost efficiency, long-reads are gaining popularity in the field of scRNA-Seq. This review details the advances in long-read scRNA-Seq, with an emphasis on library preparation protocols and downstream bioinformatics analysis tools.

Demystifying emerging bulk RNA-Seq applications: the application and utility of bioinformatic methodology.

Significant innovations in next-generation sequencing techniques and bioinformatics tools have impacted our appreciation and understanding of RNA. Practical RNA sequencing (RNA-Seq) applications have evolved in conjunction with sequence technology and bioinformatic tools advances. In most projects, bulk RNA-Seq data is used to measure gene expression patterns, isoform expression, alternative splicing and single-nucleotide polymorphisms. However, RNA-Seq holds far more hidden biological information including details of copy number alteration, microbial contamination, transposable elements, cell type (deconvolution) and the presence of neoantigens. Recent novel and advanced bioinformatic algorithms developed the capacity to retrieve this information from bulk RNA-Seq data, thus broadening its scope. The focus of this review is to comprehend the emerging bulk RNA-Seq-based analyses, emphasizing less familiar and underused applications. In doing so, we highlight the power of bulk RNA-Seq in providing biological insights.

Using Chaos-Game-Representation for Analysing the SARS-CoV-2 Lineages, Newly Emerging Strains and Recombinants.

Background: Viruses have high mutation rates, facilitating rapid evolution and the emergence of new species, subspecies, strains and recombinant forms. Accurate classification of these forms is crucial for understanding viral evolution and developing therapeutic applications. Phylogenetic classification is typically performed by analyzing molecular differences at the genomic and sub-genomic levels. This involves aligning homologous proteins or genes. However, there is growing interest in developing alignment-free methods for whole-genome comparisons that are computationally efficient. Methods: Here we elaborate on the Chaos Game Representation (CGR) method, based on concepts of statistical physics and free of sequence alignment assumptions. We adopt the CGR method for classification of the closely related clades/lineages A and B of the SARS-Corona virus 2019 (SARS-CoV-2), which is one of the fastest evolving viruses. Results: Our study shows that the CGR approach can easily yield the SARS-CoV-2 phylogeny from the available whole genomes of lineage A and lineage B sequences. It also shows an accurate classification of eight different strains and the newly evolved XBB variant from its parental strains. Compared to alignment-based methods (Neighbour-Joining and Maximum Likelihood), the CGR method requires low computational resources, is fast and accurate for long sequences, and, being a K-mer based approach, allows simultaneous comparison of a large number of closely-related sequences of different sizes. Further, we developed an R pipeline CGRphylo, available on GitHub, which integrates the CGR module with various other R packages to create phylogenetic trees and visualize them. Conclusion: Our findings demonstrate the efficacy of the CGR method for accurate classification and tracking of rapidly evolving viruses, offering valuable insights into the evolution and emergence of new SARS-CoV-2 strains and recombinants.

Computational approaches and challenges for identification and annotation of non-coding RNAs using RNA-Seq.

Significant innovations in next-generation sequencing techniques and bioinformatics tools have impacted our appreciation and understanding of RNA. Practical RNASeq applications have evolved in conjunction with sequence technology and bioinformatic tool advances. In this review, we explained various computational resources, tools, and bioinformatics analyses advancement for small and large non-coding RNAs. These include non-coding RNAs (ncRNAs) such as piwi, micro, circular, and long ncRNAs. In addition, this article discusses future challenges, single-cell level sequencing for non-coding RNAs, and advantages of using long-read sequencing to annotate lncRNAs.

Comprehensive Mutational and Phenotypic Characterization of New Metastatic Cutaneous Squamous Cell Carcinoma Cell Lines Reveal Novel Drug Susceptibilities.

Cutaneous squamous cell carcinoma (cSCC) is a common skin cancer. Most patients who develop metastases (2-5%) present with advanced disease that requires a combination of radical surgery and adjuvant radiation therapy. There are few effective therapies for refractory disease. In this study, we describe novel patient-derived cell lines from cSCC metastases of the head and neck (designated UW-CSCC1 and UW-CSCC2). The cell lines genotypically and phenotypically resembled the original patient tumor and were tumorogenic in mice. Differences in cancer-related gene expression between the tumor and cell lines after various culturing conditions could be largely reversed by xenografting and reculturing. The novel drug susceptibilities of UW-CSCC1 and an irradiated subclone UW-CSCC1-R to drugs targeting cell cycle, PI3K/AKT/mTOR, and DNA damage pathways were observed using high-throughput anti-cancer and kinase-inhibitor compound libraries, which correlate with either copy number variations, targetable mutations and/or the upregulation of gene expression. A secondary screen of top hits in all three cell lines including PIK3CA-targeting drugs supports the utility of targeting the PI3K/AKT/mTOR pathway in this disease. UW-CSCC cell lines are thus useful preclinical models for determining targetable pathways and candidate therapeutics.

From trash to treasure: detecting unexpected contamination in unmapped NGS data.

BACKGROUND: Next Generation Sequencing (NGS) experiments produce millions of short sequences that, mapped to a reference genome, provide biological insights at genomic, transcriptomic and epigenomic level. Typically the amount of reads that correctly maps to the reference genome ranges between 70% and 90%, leaving in some cases a consistent fraction of unmapped sequences. This 'misalignment' can be ascribed to low quality bases or sequence differences between the sample reads and the reference genome. Investigating the source of the unmapped reads is definitely important to better assess the quality of the whole experiment and to check for possible downstream or upstream 'contamination' from exogenous nucleic acids. RESULTS: Here we propose DecontaMiner, a tool to unravel the presence of contaminating sequences among the unmapped reads. It uses a subtraction approach to identify bacteria, fungi and viruses genome contamination. DecontaMiner generates several output files to track all the processed reads, and to provide a complete report of their characteristics. The good quality matches on microorganism genomes are counted and compared among samples. DecontaMiner builds an offline HTML page containing summary statistics and plots. The latter are obtained using the state-of-the-art D3 javascript libraries. DecontaMiner has been mainly used to detect contamination in human RNA-Seq data. The software is freely available at http://www-labgtp.na.icar.cnr.it/decontaminer . CONCLUSIONS: DecontaMiner is a tool designed and developed to investigate the presence of contaminating sequences in unmapped NGS data. It can suggest the presence of contaminating organisms in sequenced samples, that might derive either from laboratory contamination or from their biological source, and in both cases can be considered as worthy of further investigation and experimental validation. The novelty of DecontaMiner is mainly represented by its easy integration with the standard procedures of NGS data analysis, while providing a complete, reliable, and automatic pipeline.

RankerGUI: A Computational Framework to Compare Differential Gene Expression Profiles Using Rank Based Statistics.

The comparison of high throughput gene expression datasets obtained from different experimental conditions is a challenging task. It provides an opportunity to explore the cellular response to various biological events such as disease, environmental conditions, and drugs. There is a need for tools that allow the integration and analysis of such data. We developed the "RankerGUI pipeline", a user-friendly web application for the biological community. It allows users to use various rank based statistical approaches for the comparison of full differential gene expression profiles between the same or different biological states obtained from different sources. The pipeline modules are an integration of various open-source packages, a few of which are modified for extended functionality. The main modules include rank rank hypergeometric overlap, enriched rank rank hypergeometric overlap and distance calculations. Additionally, preprocessing steps such as merging differential expression profiles of multiple independent studies can be added before running the main modules. Output plots show the strength, pattern, and trends among complete differential expression profiles. In this paper, we describe the various modules and functionalities of the developed pipeline. We also present a case study that demonstrates how the pipeline can be used for the comparison of differential expression profiles obtained from multiple platforms' data of the Gene Expression Omnibus. Using these comparisons, we investigate gene expression patterns in kidney and lung cancers.

PIK Your Poison: The Effects of Combining PI3K and CDK Inhibitors against Metastatic Cutaneous Squamous Cell Carcinoma In Vitro.

Cutaneous squamous cell carcinoma (cSCC) is a very common skin malignancy with poor prognosis for patients with locally advanced or metastatic cSCC (mcSCC). PI3K/AKT/mTOR and cell cycle signalling pathways are often dysregulated in mcSCC. A combination drug approach has been theorised to overcome the underwhelming clinical performance of targeted inhibitors as single agents. This study investigates the potential of targeted inhibition of the p110α-subunit of PI3K with PIK-75 or BGT226 (P13Ki), and of CDK1/2/5/9 with dinaciclib (CDKi) as single agents and in combination. The patient-derived mcSCC cell lines, UW-CSCC1 and UW-CSCC2, were used to assess cell viability, migration, cell signalling, cell cycle distribution, and apoptosis. PIK-75, BGT226, and dinaciclib exhibited strong cytotoxic potency as single agents. Notably, the non-malignant HaCaT cell line was unaffected. In 2D cultures, PIK-75 synergistically enhanced the cytotoxic effects of dinaciclib in UW-CSCC2, but not UW-CSCC1. Interestingly, this pattern was reversed in 3D spheroid models. Despite the combination of PIK-75 and dinaciclib resulting in an increase in cell cycle arrest and apoptosis, and reduced cell motility, these differences were largely negligible compared to their single-agent counterpart. The differential responses between the cell lines correlated with driver gene mutation profiles. These findings suggest that personalised medicine approaches targeting PI3K and CDK pathways in combination may yield some benefit for mcSCC, and that more complex 3D models should be considered for drug responsiveness studies in this disease.

Cancer Progression Gene Expression Profiling Identifies the Urokinase Plasminogen Activator Receptor as a Biomarker of Metastasis in Cutaneous Squamous Cell Carcinoma.

Cutaneous squamous cell carcinoma (cSCC) of the head and neck region is the second most prevalent skin cancer, with metastases to regional lymph nodes occurring in 2%-5% of cases. To further our understanding of the molecular events characterizing cSCC invasion and metastasis, we conducted targeted cancer progression gene expression and pathway analysis in non-metastasizing (PRI-) and metastasizing primary (PRI+) cSCC tumors of the head and neck region, cognate lymph node metastases (MET), and matched sun-exposed skin (SES). The highest differentially expressed genes in metastatic (MET and PRI+) versus non-metastatic tumors (PRI-) and SES included PLAU, PLAUR, MMP1, MMP10, MMP13, ITGA5, VEGFA, and various inflammatory cytokine genes. Pathway enrichment analyses implicated these genes in cellular pathways and functions promoting matrix remodeling, cell survival and migration, and epithelial to mesenchymal transition, which were all significantly activated in metastatic compared to non-metastatic tumors (PRI-) and SES. We validated the overexpression of urokinase plasminogen activator receptor (uPAR, encoded by PLAUR) in an extended patient cohort by demonstrating higher uPAR staining intensity in metastasizing tumors. As pathway analyses identified epidermal growth factor (EGF) as a potential upstream regulator of PLAUR, the effect of EGF on uPAR expression levels and cell motility was functionally validated in human metastatic cSCC cells. In conclusion, we propose that uPAR is an important driver of metastasis in cSCC and represents a potential therapeutic target in this disease.

Whole genome analysis reveals the genomic complexity in metastatic cutaneous squamous cell carcinoma.

Metastatic cutaneous squamous cell carcinoma (CSCC) is a highly morbid disease requiring radical surgery and adjuvant therapy, which is associated with a poor prognosis. Yet, compared to other advanced malignancies, relatively little is known of the genomic landscape of metastatic CSCC. We have previously reported the mutational signatures and mutational patterns of CCCTC-binding factor (CTCF) regions in metastatic CSCC. However, many other genomic components (indel signatures, non-coding drivers, and structural variants) of metastatic CSCC have not been reported. To this end, we performed whole genome sequencing on lymph node metastases and blood DNA from 25 CSCC patients with regional metastases of the head and neck. We designed a multifaceted computational analysis at the whole genome level to provide a more comprehensive perspective of the genomic landscape of metastatic CSCC. In the non-coding genome, 3' untranslated region (3'UTR) regions of EVC (48% of specimens), PPP1R1A (48% of specimens), and ABCA4 (20% of specimens) along with the tumor-suppressing long non-coding RNA (lncRNA) LINC01003 (64% of specimens) were significantly functionally altered (Q-value < 0.05) and represent potential non-coding biomarkers of CSCC. Recurrent copy number loss in the tumor suppressor gene PTPRD was observed. Gene amplification was much less frequent, and few genes were recurrently amplified. Single nucleotide variants driver analyses from three tools confirmed TP53 and CDKN2A as recurrently mutated genes but also identified C9 as a potential novel driver in this disease. Furthermore, indel signature analysis highlighted the dominance of ID signature 13 (ID13) followed by ID8 and ID9. ID9 has previously been shown to have no association with skin melanoma, unlike ID13 and ID8, suggesting a novel pattern of indel variation in metastatic CSCC. The enrichment analysis of various genetically altered candidates shows enrichment of "TGF-beta regulation of extracellular matrix" and "cell cycle G1 to S check points." These enriched terms are associated with genetic instability, cell proliferation, and migration as mechanisms of genomic drivers of metastatic CSCC.

What's Genetic Variation Got to Do with It? Starvation-Induced Self-Fertilization Enhances Survival in Paramecium.

The pervasiveness of sex despite its well-known costs is a long-standing puzzle in evolutionary biology. Current explanations for the success of sex in nature largely rely on the adaptive significance of the new or rare genotypes that sex may generate. Less explored is the possibility that sex-underlying molecular mechanisms can enhance fitness and convey benefits to the individuals that bear the immediate costs of sex. Here, we show that the molecular environment associated with self-fertilization can increase stress resistance in the ciliate Paramecium tetraurelia. This advantage is independent of new genetic variation, coupled with a reduced nutritional input, and offers fresh insights into the mechanistic origin of sex. In addition to providing evidence that the molecular underpinnings of sexual reproduction and the stress response are linked in P. tetraurelia, these findings supply an integrative explanation for the persistence of self-fertilization in this ciliate.

Dissection of Factors Affecting the Variability of the Peptide Bond Geometry and Planarity.

Proteins frequently assume complex three-dimensional structures characterized by marginal thermodynamic stabilities. In this scenario, deciphering the folding code of these molecular giants with clay feet is a cumbersome task. Studies performed in last years have shown that the interplay between backbone geometry and local conformation has an important impact on protein structures. Although the variability of several geometrical parameters of protein backbone has been established, the role of the structural context in determining these effects has been hitherto limited to the valence bond angle τ (NC α C). We here investigated the impact of different factors on the observed variability of backbone geometry and peptide bond planarity. These analyses corroborate the notion that the local conformation expressed in terms of (ϕ, ψ) dihedrals plays a predominant role in dictating the variability of these parameters. The impact of secondary structure is limited to bond angles which involve atoms that are usually engaged in H-bonds and, therefore, more susceptible to the structural context. Present data also show that the nature of the side chain has a significant impact on angles such as NC α C ß and C ß C α C. In conclusion, our analyses strongly support the use of variability of protein backbone geometry in structure refinement, validation, and prediction.