Single-cell transcriptomics offers unprecedented opportunities to infer the ligand–receptor (LR) interactions underlying cellular networks. We introduce a new, curated LR database and a novel regularized score to perform such inferences. For the first time, we try to assess the confidence in predicted LR interactions and show that our regularized score outperforms other scoring schemes while controlling false positives. SingleCellSignalR is implemented as an open-access R package accessible to entry-level users and available from https://github.com/SCA-IRCM. Analysis results come in a variety of tabular and graphical formats. For instance, we provide a unique network view integrating all the intercellular interactions, and a function relating receptors to expressed intracellular pathways. A detailed comparison of related tools is conducted. Among various examples, we demonstrate SingleCellSignalR on mouse epidermis data and discover an oriented communication structure from external to basal layers.

Next-generation sequencing (NGS) of circulating tumor DNA (ctDNA) represents a minimally invasive alternative to conventional tissue biopsies, providing real-time genomic snapshots of heterogeneous tumors from blood draws. This liquid biopsy approach has demonstrated significant utility for early detection, molecular profiling, and monitoring treatment response in cancer patients. However, significant barriers to widespread clinical implementation still remain, such as a lack of standardized methods for ctDNA content quantification and limited variant detection sensitivity at ultra-low frequencies. Herein, we discuss three key improvements: (i) reducing the limit of detection (LoD) from 0.5% to 0.1%, which would increase alteration detection from 50% to approximately 80%; (ii) developing a dynamic LoD approach calibrated to sequencing depth, thereby enhancing result reliability and confidence in clinical interpretation; and (iii) utilizing strategic bioinformatics pipelines with “allowed” and “blocked” lists to enhance accuracy while minimizing false positives. While ctDNA analysis remains approximately 30% less sensitive than tissue-based testing, addressing these limitations through technological advancement and standardization protocols could accelerate integration into routine clinical practice, potentially transforming cancer management while reducing healthcare costs.

Recent advances in molecular biology have been successfully applied to the exploration of microbiota from various fluids. However, the urinary microbiota remains poorly explored, as its analysis requires specific technical considerations. Indeed, urine is a low microbial biomass environment, in which the representativity of each bacterium must be respected to obtain accurate data. Thus, sensitive extraction methods must be used to obtain good quality DNA while preserving the proportions between species. To address this, we compared the efficiency of five extraction methods on artificial urine samples spiked with low amounts of four bacteria species. The quality of the DNA obtained was further evaluated by different molecular biology approaches, including quantitative PCR and amplicon-based next-generation sequencing (NGS). Although two extraction methods allowed DNA of sufficient quality for NGS analysis to be obtained, one kit extracted a larger amount of DNA, which is more suitable for the detection of low-abundant bacteria. Results from the subsequent assessment of this kit on 29 human clinical samples correlated well with results obtained using conventional bacterial urine culture. We hope that our work will make investigators aware of the importance of challenging and adapting their practice in terms of the molecular biology approaches used for the exploration of microbiota.

Next-generation sequencing (NGS) has taken on major importance in clinical oncology practice. With the advent of targeted therapies capable of effectively targeting specific genomic alterations in cancer patients, the development of bioinformatics processes has become crucial. Thus, bioinformatics pipelines play an essential role not only in the detection and in identification of molecular alterations obtained from NGS data but also in the analysis and interpretation of variants, making it possible to transform raw sequencing data into meaningful and clinically useful information. In this review, we aim to examine the multiple steps of a bioinformatics pipeline as used in current clinical practice, and we also provide an updated list of the necessary bioinformatics tools. This resource is intended to assist researchers and clinicians in their genetic data analyses, improving the precision and efficiency of these processes in clinical research and patient care.

Recent interest in noninvasive diagnostic approaches has highlighted the potential of urinary microbiota as a novel biomarker for bladder cancer. This study investigated the urinary microbiota of 30 bladder cancer patients and 32 healthy controls using a specific NGS protocol that sequences eight hypervariable regions of the 16S rRNA gene, providing detailed insights into urinary microbiota composition. The relative abundance of microbial compositions in urine samples from cancer patients and healthy controls was analyzed across various taxonomic levels. No notable differences were highlighted at the phylum, class, order, and family levels. At the genus level, 53% of detected genera were represented in either cancer patients or healthy controls. Microbial diversity was significantly lower in cancer patients. The differential analysis identified five genera, Rhodanobacter, Cutibacterium, Alloscardovia, Moryella, and Anaeroglobus, that were significantly more abundant in cancer patients. Notably, Rhodanobacter was present in 20 cancer samples but absent in healthy controls. Conversely, 40 genera, including Lactobacillus, Propionibacterium, and Bifidobacterium, exhibited reduced abundance in cancer patients. These findings suggest that some genera may serve as potential biomarkers for bladder cancer, highlighting the need for further research to explore their roles in disease pathogenesis and their potential applications in diagnostics and therapeutics.

Copy-number variations (CNVs) are an essential component of genetic variation distributed across large parts of the human genome. CNV detection from next-generation sequencing data and artificial intelligence algorithms have progressed in recent years. However, only a few tools have taken advantage of machine-learning algorithms for CNV detection, and none propose using artificial intelligence to automatically detect probable CNV-positive samples. The most developed approach is to use a reference or normal dataset to compare with the samples of interest, and it is well known that selecting appropriate normal samples represents a challenging task that dramatically influences the precision of results in all CNV-detecting tools. With careful consideration of these issues, we propose here ifCNV, a new software based on isolation forests that creates its own reference, available in R and python with customizable parameters. ifCNV combines artificial intelligence using two isolation forests and a comprehensive scoring method to faithfully detect CNVs among various samples. It was validated using targeted next-generation sequencing (NGS) datasets from diverse origins (capture and amplicon, germline and somatic), and it exhibits high sensitivity, specificity, and accuracy. ifCNV is a publicly available open-source software (https://github.com/SimCab-CHU/ifCNV) that allows the detection of CNVs in many clinical situations.