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Foodborne illness is exacerbated by novel and emerging pathotypes, persistent contamination, antimicrobial resistance, an ever-changing environment, and the complexity of food production systems. Sporadic and outbreak events of common foodborne pathogens like Shiga toxigenic E. coli (STEC), Salmonella, Campylobacter, and Listeria monocytogenes are increasingly identified. Methods of controlling human infections linked with food products are essential to improve food safety and public health and to avoid economic losses associated with contaminated food product recalls and litigations. Bacteriophages (phages) are an attractive additional weapon in the ongoing search for preventative measures to improve food safety and public health. However, like all other antimicrobial interventions that are being employed in food production systems, phages are not a panacea to all food safety challenges. Therefore, while phage-based biocontrol can be promising in combating foodborne pathogens, their antibacterial spectrum is generally narrower than most antibiotics. The emergence of phage-insensitive single-cell variants and the formulation of effective cocktails are some of the challenges faced by phage-based biocontrol methods. This review examines phage-based applications at critical control points in food production systems with an emphasis on when and where they can be successfully applied at production and processing levels. Shortcomings associated with phage-based control measures are outlined together with strategies that can be applied to improve phage utility for current and future applications in food safety.

Accurate variant calling and genotyping represent major limiting factors for downstream applications of single-cell genomics. Here, we report Conbase for the identification of somatic mutations in single-cell DNA sequencing data. Conbase leverages phased read data from multiple samples in a dataset to achieve increased confidence in somatic variant calls and genotype predictions. Comparing the performance of Conbase to three other methods, we find that Conbase performs best in terms of false discovery rate and specificity and provides superior robustness on simulated data, in vitro expanded fibroblasts and clonal lymphocyte populations isolated directly from a healthy human donor.

There is no effective way to detect structure variations (SVs) and extra-chromosomal circular DNAs (ecDNAs) at single-cell whole-genome level. Here, we develop a novel third-generation sequencing platform-based single-cell whole-genome sequencing (scWGS) method named SMOOTH-seq (single-molecule real-time sequencing of long fragments amplified through transposon insertion). We evaluate the method for detecting CNVs, SVs, and SNVs in human cancer cell lines and a colorectal cancer sample and show that SMOOTH-seq reliably and effectively detects SVs and ecDNAs in individual cells, but shows relatively limited accuracy in detection of CNVs and SNVs. SMOOTH-seq opens a new chapter in scWGS as it generates high fidelity reads of kilobases long.

Respiratory failure associated with COVID-19 has placed focus on the lungs. Here, we present single-nucleus accessible chromatin profiles of 90,980 nuclei and matched single-nucleus transcriptomes of 46,500 nuclei in non-diseased lungs from donors of ~30 weeks gestation,~3 years and ~30 years. We mapped candidate cis -regulatory elements (cCREs) and linked them to putative target genes. We identified distal cCREs with age-increased activity linked to SARS-CoV-2 host entry gene TMPRSS2 in alveolar type 2 cells, which had immune regulatory signatures and harbored variants associated with respiratory traits. At the 3p21.31 COVID-19 risk locus, a candidate variant overlapped a distal cCRE linked to SLC6A20 , a gene expressed in alveolar cells and with known functional association with the SARS-CoV-2 receptor ACE2. Our findings provide insight into regulatory logic underlying genes implicated in COVID-19 in individual lung cell types across age. More broadly, these datasets will facilitate interpretation of risk loci for lung diseases.

DEAD-box helicase 41 ( DDX41 ) is implicated in germline (GL)-predisposed myeloid neoplasms, where pathogenic GL variants often lead to disease following the acquisition of a somatic variant in trans, most commonly p.R525H. However, the precise molecular mechanisms by which DDX41 variants contribute to the pathogenesis of myeloid neoplasms remain poorly understood, partly due to challenges in establishing cellular and animal models that faithfully recapitulate the human disease phenotype. This limitation highlights the necessity of directly analyzing primary human disease cells. In this case report, conducted to pursue this objective, we implemented single-cell RNA sequencing integrated with genotyping at the p.R525 locus in a myelodysplastic neoplasm (MDS) harboring both germline and somatic DDX41 variants, leveraging highly efficient Terminator-Assisted Solid-phase cDNA amplification and sequencing. We found that acquiring p.R525H induced G2/M cell cycle arrest selectively in colony-forming unit-erythroid cells, accompanied by R-loop accumulation, which impaired erythropoiesis through DNA damage. In hematopoietic stem and myeloid progenitor populations, gene expression profiles were largely similar between p.R525H-positive and -negative cells. However, ligand-receptor interaction and transcriptional regulation analyses suggested a non-cell-autonomous influence from p.R525H-expressing cells on GL variant-only cells. This interaction drove convergence toward a shared expression profile, highlighting an intricate interplay shaping the patient’s MDS phenotype.

Background Systematic interrogation of single-nucleotide variants (SNVs) is one of the most promising approaches to delineate the cellular heterogeneity and phylogenetic relationships at the single-cell level. While SNV detection from abundant single-cell RNA sequencing (scRNA-seq) data is applicable and cost-effective in identifying expressed variants, inferring sub-clones, and deciphering genotype-phenotype linkages, there is a lack of computational methods specifically developed for SNV calling in scRNA-seq. Although variant callers for bulk RNA-seq have been sporadically used in scRNA-seq, the performances of different tools have not been assessed. Results Here, we perform a systematic comparison of seven tools including SAMtools, the GATK pipeline, CTAT, FreeBayes, MuTect2, Strelka2, and VarScan2, using both simulation and scRNA-seq datasets, and identify multiple elements influencing their performance. While the specificities are generally high, with sensitivities exceeding 90% for most tools when calling homozygous SNVs in high-confident coding regions with sufficient read depths, such sensitivities dramatically decrease when calling SNVs with low read depths, low variant allele frequencies, or in specific genomic contexts. SAMtools shows the highest sensitivity in most cases especially with low supporting reads, despite the relatively low specificity in introns or high-identity regions. Strelka2 shows consistently good performance when sufficient supporting reads are provided, while FreeBayes shows good performance in the cases of high variant allele frequencies. Conclusions We recommend SAMtools, Strelka2, FreeBayes, or CTAT, depending on the specific conditions of usage. Our study provides the first benchmarking to evaluate the performances of different SNV detection tools for scRNA-seq data.

Background Deletion variants in mitochondrial DNA (mtDNA) are associated with various diseases, such as mitochondrial disorders and neurodegenerative diseases. Traditionally, mtDNA deletions have been studied using bulk DNA sequencing, but bulk methods average signals across cells, thereby masking the cell-type-specific mutational landscapes. Resolving mtDNA deletions at single-cell resolution is beneficial for understanding how these mutations affect distinct cell populations. To date, no specialized method exists for detecting cell-type-specific mtDNA deletions from single-cell RNA sequencing data. Notably, mtDNA possesses unique molecular features: a high copy number, stable transcription, and compact structure of the mitochondrial genome. This results in a relatively high abundance of mtDNA-derived reads even in single-cell RNA sequencing data, suggesting the possibility of detecting mtDNA deletion variants directly from transcriptomic data. Results Here, we present MitoDelta, a computational pipeline that enables the detection of mtDNA deletions at cell-type resolution solely from single-cell RNA sequencing data. MitoDelta combines a sensitive alignment strategy with robust statistical filtering based on a beta-binomial distribution model, allowing accurate identification of deletion events even from noisy single-cell transcriptomes. To capture cell-type-specific deletion patterns, MitoDelta analyzes reads pooled by annotated cell types, enabling quantification of deletion burden across distinct cellular populations. We benchmarked MitoDelta against existing mtDNA deletion detection tools and demonstrated superior overall performance. As a practical application, we applied MitoDelta to a published single-nucleus RNA sequencing dataset for Parkinson’s disease and revealed distinct mtDNA deletion burdens across neuronal subtypes. Conclusions MitoDelta enables the transcriptome-integrated, cell-type-specific detection of mtDNA deletions from single-cell RNA sequencing data alone, offering a valuable framework for reanalyzing public datasets and studying mitochondrial genome alterations at cell-type resolution. This integrated approach enables insights into how mtDNA deletions are distributed across specific cell types and cellular states, providing new opportunities to investigate the role of mtDNA deletions in cell-type-specific disease mechanisms. The tool is available at https://github.com/NikaidoLaboratory/mitodelta .

Background Mitochondrial DNA (mtDNA) variants hold promise as endogenous barcodes for tracking human cell lineages, but their efficacy as reliable lineage markers are hindered by the complex dynamics of mtDNA in somatic tissues. Results Here, we use computational modeling and single-cell genomics to thoroughly interrogate the origin and clonal dynamics of mtDNA variants across various biological settings. Our findings reveal that the majority of mtDNA variants which are specifically present in a cell subpopulation, termed subpopulation-specific variants, are pre-existing heteroplasmies in the first cell instead of de novo somatic mutations during divisions. Moreover, subpopulation-specific variants demonstrate limited discriminatory power among different genuine lineages under weak clonal expansion; however, certain subpopulation-specific variants with consistently high frequencies among a subpopulation are capable of faithfully labeling cell lineages in scenarios of stringent clonal expansion, such as strongly expanded T cell populations in diseased conditions and clonal hematopoiesis in aged individuals. Inspired by our simulations, we introduce a lineage informative score, facilitating the identification of reliable mitochondrial lineage tracing markers across different modalities of single-cell genomic data. Conclusions Combining computational modeling and single-cell sequencing, our study reveals that the performance of mitochondrial lineage tracing is highly dependent on the extent of clonal expansion, which thus should be considered when applying mitochondrial lineage tracing.

With rapid advancements in single-cell DNA sequencing (scDNA-seq), various computational methods have been developed to study evolution and call variants on single-cell level. However, modeling deletions remains challenging because they affect total coverage in ways that are difficult to distinguish from technical artifacts. We present DelSIEVE, a statistical method that infers cell phylogeny and single-nucleotide variants, accounting for deletions, from scDNA-seq data. DelSIEVE distinguishes deletions from mutations and artifacts, detecting more evolutionary events than previous methods. Simulations show high performance, and application to cancer samples reveals varying amounts of deletions and double mutants in different tumors.

Blood malignancies provide unique opportunities for longitudinal tracking of disease evolution following therapeutic bottlenecks and for the monitoring of changes in anti-tumor immunity. The expanding development of multi-modal single-cell sequencing technologies affords newer platforms to elucidate the mechanisms underlying these processes at unprecedented resolution. Furthermore, the identification of molecular events that can serve as in-vivo barcodes now facilitate the tracking of the trajectories of malignant and of immune cell populations over time within primary human samples, as these permit unambiguous identification of the clonal lineage of cell populations within heterogeneous phenotypes. Here, we provide an overview of the potential for chromosomal copy number changes, somatic nuclear and mitochondrial DNA mutations, single nucleotide polymorphisms, and T and B cell receptor sequences to serve as personal natural barcodes and review technical implementations in single-cell analysis workflows. Applications of these methodologies include the study of acquired therapeutic resistance and the dissection of donor- and host cellular interactions in the context of allogeneic hematopoietic stem cell transplantation.