Single-cell Samples Research Articles

A Photoelectrochemical Nanoreactor for Single-Cell Sampling and Near Zero-Background Faradaic Detection of Intracellular microRNA.

Rational utilization of the rich light-bio-matter interplay taking place in single-cell analysis represents a new technological direction in the field. The light-fueled operation is expected to achieve advanced photoelectrochemical (PEC) single-cell analysis with unknown possibilities. Here, a PEC nanoreactor capable of single-cell sampling and near zero-background Faradaic detection of intracellular microRNA (miR) is devised by the construction of a small reaction chamber accommodating the target-triggered hybridization chain reaction for binding the metallointercalator of [Ru(bpy)2 (dppz)]2+ as the signal reporter. Light stimulation of the dsDNA/metallointercalator adduct will induce the generation of photocurrents, underpinning a zero-biased and near zero-background PEC method toward Faradaic detection of non-electrogenic miR at the single-cell level. Using this nanotool, lower miR concentration in the near-nucleus region than that in the main cytosol was revealed.

Variant calling enhances the identification of cancer cells in single-cell RNA sequencing data.

Single-cell RNA-sequencing is an invaluable research tool that allows for the investigation of gene expression in heterogeneous cancer cell populations in ways that bulk RNA-seq cannot. However, normal (i.e., non tumor) cells in cancer samples have the potential to confound the downstream analysis of single-cell RNA-seq data. Existing methods for identifying cancer and normal cells include copy number variation inference, marker-gene expression analysis, and expression-based clustering. This work aims to extend the existing approaches for identifying cancer cells in single-cell RNA-seq samples by incorporating variant calling and the identification of putative driver alterations. We found that putative driver alterations can be detected in single-cell RNA-seq data obtained with full-length transcript technologies and noticed that a subset of cells in tumor samples are enriched for putative driver alterations as compared to normal cells. Furthermore, we show that the number of putative driver alterations and inferred copy number variation are not correlated in all samples. Taken together, our findings suggest that augmenting existing cancer-cell filtering methods with variant calling and analysis can increase the number of tumor cells that can be confidently included in downstream analyses of single-cell full-length transcript RNA-seq datasets.

Quercetin targets VCAM1 to prevent diabetic cerebrovascular endothelial cell injury.

IntroductionEndothelial cells play important roles in neurodegenerative diseases caused by diabetes, therefore, we aimed at investigating the mechanisms through which endothelial cells are involved in diabetes development.MethodsSingle cell analysis was performed to identify the major endothelial cell subtypes in cardiovascular tissues that are involved in diabetes development. A cell-cell communication approach was then used to identify ligand-receptor interaction pairs between these cell types. Differential expression analysis between the two experimental groups [standard chow diet group and diabetogenic diet with cholesterol (DDC) group] was used to identify diabetes-related differentially expressed genes (DEGs). The upregulated genes were used to identify candidate ligands or receptors, as well as the corresponding cell types. Cell trajectory inference was performed to identify the stage of cell development and changes in expression of candidate ligands or receptors during cell development. Gene set enrichment analysis (GSEA) was conducted to investigate the biological functions of genes of purpose. Finally, molecular dynamics simulations (MDSs) were used to predict potential drugs with the ability to target the proteins of purpose.ResultsSeven cell types, including five endothelial cell subtypes (EC_1, EC_2, EC_3, EC_4, and EC_EndMT), were identified from endothelial cell-enriched single cell samples from the heart and aorta of mice. Cell-cell communication analysis revealed the potential ligand-receptor interactions between these cell types while five important ligand-receptor-associated genes, including Fn1, Vcam1, Fbn1, Col4a1, and Col4a2, were established by differential expression analysis. Among them, Vcam1 is mainly expressed in EC_EndMT and is involved in interactions between EC_EndMT and other cells. Cell trajectory extrapolation analysis revealed a shift from EC_2/EC_4 to EC_EndMT and a shift from EC_EndMT to EC_3/EC_1 during the progression of diabetes. GSEA analysis revealed that upregulation of VCAM1 may have inhibitory effects on cell growth and energy metabolism.ConclusionEC_EndMT subtypes have a complex role in neurodegenerative diseases caused by diabetes. Through mechanisms involved in cell-cell communication, Vcam1 may play an important role in dysregulation of biological functions of EC_ EndMT. Molecular docking results of the quercetin-VCAM1 complex suggest that quercetin may be an effective drug for targeting this protein.

Single-cell transcriptome and translatome dual-omics reveals potential mechanisms of human oocyte maturation

The combined use of transcriptome and translatome as indicators of gene expression profiles is usually more accurate than the use of transcriptomes alone, especially in cell types governed by translational regulation, such as mammalian oocytes. Here, we developed a dual-omics methodology that includes both transcriptome and translatome sequencing (T&T-seq) of single-cell oocyte samples, and we used it to characterize the transcriptomes and translatomes during mouse and human oocyte maturation. T&T-seq analysis revealed distinct translational expression patterns between mouse and human oocytes and delineated a sequential gene expression regulation from the cytoplasm to the nucleus during human oocyte maturation. By these means, we also identified a functional role of OOSP2 inducing factor in human oocyte maturation, as human recombinant OOSP2 induced in vitro maturation of human oocytes, which was blocked by anti-OOSP2. Single-oocyte T&T-seq analyses further elucidated that OOSP2 induces specific signaling pathways, including small GTPases, through translational regulation.

Simultaneous single-cell genome and transcriptome sequencing in nanoliter droplet with digital microfluidics identifying essential driving genes

Single-cell genome and transcriptome sequencing investigates how genotype influences the phenotype of single cells, so as to comprehensively interpret biological inheritance and explain functional heterogeneity at the single-cell level. Current sample preparation technologies for simultaneous DNA and RNA sequencing of the same cell are cumbersome, expensive, and suffer from cross-contamination and limited sensitivity. Herein we describe DMF-DR-seq, a nanoliter-scale single-cell multi-omics sample preparation platform based on digital microfluidics. DMF-DR-seq integrates the major steps of single-cell isolation, DNA/RNA separation, and nucleic acid amplification in situ. The results confirm the enhanced ability of DMF-DR-seq relative to current state-of-the-art technology, with lower amplification bias, higher genome-wide coverage in DNA sequencing and better gene detection ability in RNA sequencing results. By using DMF-DR-seq, we identified the genome variation-induced abnormal transcriptome expression of single circulating tumor cells (CTCs) and cancer cells from multiple myeloma patients. The results identified potentially essential genes, known as transporters associated with antigen presentation (TAP1 and TAP2), that participate in the pathologic progress. The unique flexibility, sensitivity, and accuracy of DMF-DR-seq suggest its potential utility in deeper multi-omics analysis for inheritance mechanism study in single-cell biology.

Separation methods in single-cell proteomics: RPLC or CE?

Cellular heterogeneity is commonly investigated using single-cell genomics and transcriptomics to investigate biological questions such as disease mechanism, therapeutic screening, and genomic and transcriptomic diversity between cellular populations and subpopulations at the single-cell level. Single-cell mass spectrometry (MS)-based proteomics enables the high-throughput examination of protein expression at the single-cell level with wide applicability, and with spatial and temporal resolution, applicable to the study of cellular development, disease, effect of treatment, etc. The study of single-cell proteomics has lagged behind genomics and transcriptomics largely because proteins from single-cell samples cannot be amplified as DNA and RNA can using well established techniques such as PCR. Therefore, analytical methods must be robust, reproducible, and sensitive enough to detect the very small amount of protein within a single cell. To this end, nearly every step of the proteomics process has been extensively altered and improved to facilitate the proteomics analysis of single cells including cell counting and sorting, lysis, protein digestion, sample cleanup, separation, MS data acquisition, and data analysis. Here, we have reviewed recent advances in single-cell protein separation using nano reversed phase liquid chromatography (nRPLC) and capillary electrophoresis (CE) to inform application driven selection of separation techniques in the laboratory setting.

Scelestial: Fast and accurate single-cell lineage tree inference based on a Steiner tree approximation algorithm.

Single-cell genome sequencing provides a highly granular view of biological systems but is affected by high error rates, allelic amplification bias, and uneven genome coverage. This creates a need for data-specific computational methods, for purposes such as for cell lineage tree inference. The objective of cell lineage tree reconstruction is to infer the evolutionary process that generated a set of observed cell genomes. Lineage trees may enable a better understanding of tumor formation and growth, as well as of organ development for healthy body cells. We describe a method, Scelestial, for lineage tree reconstruction from single-cell data, which is based on an approximation algorithm for the Steiner tree problem and is a generalization of the neighbor-joining method. We adapt the algorithm to efficiently select a limited subset of potential sequences as internal nodes, in the presence of missing values, and to minimize cost by lineage tree-based missing value imputation. In a comparison against seven state-of-the-art single-cell lineage tree reconstruction algorithms—BitPhylogeny, OncoNEM, SCITE, SiFit, SASC, SCIPhI, and SiCloneFit—on simulated and real single-cell tumor samples, Scelestial performed best at reconstructing trees in terms of accuracy and run time. Scelestial has been implemented in C++. It is also available as an R package named RScelestial.

The scINSIGHT Package for Integrating Single-Cell RNA-Seq Data from Different Biological Conditions.

Data integration is a critical step in the analysis of multiple single-cell RNA sequencing samples to account for heterogeneity due to both biological and technical variability. scINSIGHT is a new integration method for single-cell gene expression data, and can effectively use the information of biological condition to improve the integration of multiple single-cell samples. scINSIGHT is based on a novel non-negative matrix factorization model that learns common and condition-specific gene modules in samples from different biological or experimental conditions. Using these gene modules, scINSIGHT can further identify cellular identities and active biological processes in different cell types or conditions. Here we introduce the installation and main functionality of the scINSIGHT R package, including how to preprocess the data, apply the scINSIGHT algorithm, and analyze the output.

Life sciences discovery and technology highlights

Life sciences discovery and technology highlights

Structural signatures: a web server for exploring a database of and generating protein structural features from human cell lines and tissues.

Structural features of proteins provide powerful insights into biological function and similarity. Specifically, previous work has demonstrated that structural features of tissue and drug-treated cell line samples can be used to predict tissue type and characterize drug relationships, respectively. We have developed structural signatures, a web server for annotating and analyzing protein features from gene sets that are often found in transcriptomic and proteomic data. This platform provides access to a structural feature database derived from normal and disease human tissue samples. We show how analysis using this database can shed light on the relationship between states of single-cell RNA-sequencing lung cancer samples. These various structural feature signatures can be visualized on the server itself or downloaded for additional analysis. The structural signatures server tool is freely available at https://structural-server.kinametrix.com/.

P-533 Single-cell long-read nanopore sequencing as a fast and cost-efficient method for aneuploidy detection and potentially PGT-A, a pre-clinical study

Abstract Study question Is long-read nanopore sequencing feasible and reproducible as routine technique for aneuploidy detection, potentially allowing fresh embryo transfer PGT-A cycles? Summary answer Pre-clinical analysis of euploid and aneuploid DNA and single cells using long-read nanopore sequencing resulted in high concordance and reproducible results for aneuploidy detection. What is known already PGT-A is mostly performed using short-read next-generation sequencing, which requires high initial investment costs and high running expenses. Third-generation sequencing is a novel sequencing technology with the potential of fast, easy, and cost-effective sequencing analysis, possible even for small and less well-financed clinics. Long-read nanopore sequencing for PGT was mainly shown for structural variants and monogenetic disease, which comes with high costs and is far from clinical routine. PGT-A from trophectoderm biopsy samples using nanopore sequencing was so far demonstrated in a small pilot study and further pre-clinical and clinical studies are needed to transfer the technology into clinical routine. Study design, size, duration In this pre-clinical study, euploid and aneuploid DNA and single cells, as well as three to 20 cells were analyzed for aneuploidy using two different whole genome amplification (WGA) kits and long-read nanopore sequencing. In total, 44 different samples were analyzed after multiple displacement amplification (MDA) using REPLIg WGA kit (QIAGEN) and so far, 15 samples were analyzed using PicoPlex WGA kit (Takara) from April 2021. To confirm reproducibility, certain WGA samples were sequenced repetitively. Participants/materials, setting, methods Different euploid and aneuploid gDNA samples were diluted to 4.5 pg DNA per sample. Four different human fibroblast cell lines were diluted to approx. 20 cells, or single cells/three cells were picked using micromanipulation technique. DNA and cells were amplified, prepared for sequencing, and sequenced on MinION sequencer from Oxford Nanopore Technology. Data were analyzed using a custom pipeline consisting of pre-processing, alignment and copy-number calling. QC values and whole chromosome aneuploidies were determined automatically. Main results and the role of chance From 44 different single cells (n = 14), few cells (n = 9) or diluted gDNA (n = 21) samples amplified using MDA, 42 samples showed good quality sequencing results. Two samples result in QC failure, yielding a sample-success-rate (SSR) of 95.5%. Overall, sample sensitivity was 100%, specificity 95.2% with positive predictive value (PPV) of 95.5% and negative predictive value (NPV) of 100%. Per chromosome sensitivity was 100%, specificity 99.8% with PPV of 93.3% and NPV of 100%. Using PicoPlex WGA, 15 samples were analyzed so far: 7 gDNA and 8 single cell samples. The study is ongoing. All samples analyzed resulted in high quality sequencing data with the correct karyotype, leading to 100% SSR, 100% sensitivity, specificity, PPV, NPV per samples as well as per chromosome. Repetitive sequencing (n = 3) of four MDA amplified single cells showed identical sequencing results, indicating high reproducibility of library preparation, and sequencing. A 5p deletion in one cell line was correctly identified in all single cell analyses from either MDA or PicoPlex amplification, indicating sufficient resolution even for segmental aneuploidies. The whole workflow is feasible in under 24 hours. These results indicate high accuracy and high reproducibility of nanopore sequencing technology for single cell aneuploidy detection, possibly transferable for PGT-A. Limitations, reasons for caution The results of this pre-clinical study look very promising and workflow for sample preparation, sequencing and data analysis is fast, cost-efficient, and feasible for PGT-A applications. Nevertheless, no polar body, blastomere or trophectoderm biopsy sample was used and a clinical trial using real clinical samples is needed to confirm applicability. Wider implications of the findings Implementing novel technologies into clinical routine requires extensive systematic pre-clinical and clinical testing. To our knowledge, this is the first study that systematically analyzes sensitivity, specificity, PPV and NPV for aneuploidy detection using long-read sequencing technology. The fast workflow principally allows day 3/5 fresh embryo transfer after polar body biopsy. Trial registration number Not applicable

Abstract 6089: Novel workflow enables enhanced tissue dissociation and gentle clean-up for single cell applications

Abstract With genomic instability being a hallmark of cancer, heterogeneity within solid tumors is prevalent. Intra-tumoral heterogeneity contributes strongly to clinical outcomes. In fact, heterogeneity is often cited as a driver of therapeutic failure, chemo-resistance, and recurrence of disease. Current bulk profiling analysis of tumors is limited in its ability to provide the level of granularity necessary for uncovering aspects of heterogeneity that contribute to poor outcomes. Thus, it is imperative to gain a deeper understanding of tumors at the single-cell level. To properly analyze tumor heterogeneity at the single-cell level it is critical that quality methods for tissue dissociation and cell purification be utilized. While the most frequently used workflows rely on a combination of enzymatic and mechanical dissociation of the tissue followed by RBC lysis to remove contaminating red blood cells, these approaches tend to be harsh on cells leading to activation of cell stress pathways or even the loss of target cells due to their death during processing. The analysis of tumor heterogeneity has long been hampered by issues with low yield and sample preparation-induced alterations in cell physiology. To alleviate these issues, we present a workflow that couples Cellsonics’ SimpleFlow™ with Akadeum’s Buoyancy Activated Cell Sorting (BACS™) Microbubbles for gentle and effective tumor dissociation and clean-up that does not compromise precious samples. Cellsonics’ SimpleFlow™ system features the proprietary Bulk Lateral Ultrasonic™ (BLU™) energy to generate single cells from solid tissue with unparalleled speed, quality, and sample integrity. SimpleFlow™ is engineered to enable any lab to obtain the full spectrum of heterogeneous cell populations while maintaining the native gene expression patterns across all cell types. Single cell samples can be generated from solid tissues in 4-10 minutes under cold temperature without the use of enzymes. Akadeum’s BACS™ Microbubble technology features low-density microbubbles that are functionalized to capture an array of analytes including particular cell types. Akadeum’s BACS™ Microbubbles enable the purification of cell types of interest quickly, easily, and without sacrificing cell health and physiology. In this study, The SimpleFlow™ system was used to dissociate fresh tissues from mice to obtain a heterogeneous suspension of single cells. Following the dissociation, up to 99% of RBCs were removed from samples using Akadeum’s Mouse RBC Depletion. In subsequent studies, cancer cells were spiked into tissue samples prior to processing to assess their recovery and physiology. The novel workflow presented here combing SimpleFlow™ and microbubbles demonstrates the recovery of the full spectrum of cells and did not harm tumor cell recovery or health. Citation Format: Chananat Klomsiri, Edward Grimley, Jon Roussey, Carolina Livi, Casey Wegner, Brandon H. McNaughton, Steve Levers, Vibhu Vivek, Dan Snyder, Doug Summers, Ma Phiengsai. Novel workflow enables enhanced tissue dissociation and gentle clean-up for single cell applications [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6089.

FAST-FNA molecular diagnostic assay facilitates rapid diagnosis and ntrk biomarker testing of salivary gland tumors.

e18089 Background: Primary epithelial salivary gland tumors (SGTs)are classified into 31 tumor types. Diagnosing SGTs through fine needle aspiration (FNA) biopsies is challenging due to the overlapping cytomorphologic features between benign and malignant tumors. In this study, we developed an innovative, multiplexed cycling technology which allows for the rapid cellular analyses of single cells obtained from FNA which can facilitate the rapid molecular analyses and diagnosis of SGTs. Methods: Antibodies against 29 cell surface markers associated with 7 SGT subtypes were validated and chemically modified via custom linker–bio-orthogonal quencher probes (FAST). Single cell homogenates and a FNA sample set representing 7 SGT subtypes were profiled by FAST cyclic staining, imaging, and automated computational analysis. A prediction model was generated utilizing a training set of 151,926 single cells obtained from primary SGTs (N = 26) and validated on a separate cohort of SGTs (N = 30). Companion biomarker testing, such as NTRK, was also assessed with the FAST technology. Results: The customized FAST-FNA SGT molecular diagnostic and biomarker panel was validated on human cell lines and specificity confirmed by both flow cytometry and clinical immunohistochemical staining on primary SGTs. The FAST molecular diagnostic assay was able to distinguish between benign and malignant SGTs with an accuracy of 0.86 for single-cell suspension samples and 0.88 for FNA samples. Profiling of multiple markers as compared to a single tumor marker increased the diagnostic accuracy of the SGTs, 0.82 as compared to 0.65-0.74, respectively. The results of the assay were highly reproducible and independent of the cell number sampled. NTRK expression in SGTs was also assessed by the FAST assay within hours, highlighting the potential therapeutic application of this technology. Conclusions: High dimensional single-cell profiling and molecular diagnostic testing on FNA samples are feasible at relatively low cost and as a rapid test. Application of the novel multiplexed single cell technology improves the diagnostic accuracy of SGT biopsy and facilitates multiplex biomarker testing from FNA samples. The customizable and modular FAST-FNA approach has relevance to multiple pathologies and organ systems where cytologic samples are often scarce and/or indeterminate resulting in improved diagnostic workflows and timely therapeutic clinical decision making.

HIVE Single-Cell TCR Sequencing: Assay Principle and Applications

Abstract Biological resolution at the level of individual cells is powering the next phase of precision health. Honeycomb Biotechnologies currently offers an assay for single-cell RNA sequencing with the HIVE™ scRNAseq Solution. The HIVE is a portable, handheld, single-use device that enables gentle capture, robust storage, and easy processing for the analysis of single-cell samples. The HIVE scRNAseq assay integrates sample storage and single-cell profiling into a single workflow, facilitating decentralized collection and centralized processing of samples. The design of the HIVE is ideal for the recovery of fragile cells, loading of large sample volumes, and efficient processing of numerous samples in parallel, particularly valuable for clinical applications. Here, we extend the application of the HIVE by offering a single-cell T cell receptor enrichment and sequencing (scTCRseq) assay. Using the stable whole transcriptome amplification (WTA) product obtained from the scRNAseq assay as the sample input, the HIVE scTCRseq assay enriches TCR in a streamlined target-specific amplification workflow. The assay is compatible with all human tissues, and can accommodate a wide range of cell numbers. We typically achieve over 75% TCR molecule recovery from the WTA, and over 60% sequencing reads are mapped to TCR. Here we also present TCR profiling of several specimens, demonstrating its value in clinical applications. Honeycomb aims to expand single-cell opportunities across basic, translational, and clinical research throughout the world.

DualGCN: a dual graph convolutional network model to predict cancer drug response

BackgroundDrug resistance is a critical obstacle in cancer therapy. Discovering cancer drug response is important to improve anti-cancer drug treatment and guide anti-cancer drug design. Abundant genomic and drug response resources of cancer cell lines provide unprecedented opportunities for such study. However, cancer cell lines cannot fully reflect heterogeneous tumor microenvironments. Transferring knowledge studied from in vitro cell lines to single-cell and clinical data will be a promising direction to better understand drug resistance. Most current studies include single nucleotide variants (SNV) as features and focus on improving predictive ability of cancer drug response on cell lines. However, obtaining accurate SNVs from clinical tumor samples and single-cell data is not reliable. This makes it difficult to generalize such SNV-based models to clinical tumor data or single-cell level studies in the future.ResultsWe present a new method, DualGCN, a unified Dual Graph Convolutional Network model to predict cancer drug response. DualGCN encodes both chemical structures of drugs and omics data of biological samples using graph convolutional networks. Then the two embeddings are fed into a multilayer perceptron to predict drug response. DualGCN incorporates prior knowledge on cancer-related genes and protein–protein interactions, and outperforms most state-of-the-art methods while avoiding using large-scale SNV data.ConclusionsThe proposed method outperforms most state-of-the-art methods in predicting cancer drug response without the use of large-scale SNV data. These favorable results indicate its potential to be extended to clinical and single-cell tumor samples and advancements in precision medicine.

Seed or soil: Tracing the immune subsets in metastatic tumors.

Mapping the tumor-infiltrating immune subsets to an origin of malignancy or tissue niche is important for designing effective immunotherapies, yet it remains a challenging task. In this issue of Cancer Cell, Liu etal. pieced together this puzzle through a comprehensive single-cell analysis of colorectal cancer patients with liver metastases.

ScINSIGHT for interpreting single-cell gene expression from biologically heterogeneous data

The increasing number of scRNA-seq data emphasizes the need for integrative analysis to interpret similarities and differences between single-cell samples. Although different batch effect removal methods have been developed, none are suitable for heterogeneous single-cell samples coming from multiple biological conditions. We propose a method, scINSIGHT, to learn coordinated gene expression patterns that are common among, or specific to, different biological conditions, and identify cellular identities and processes across single-cell samples. We compare scINSIGHT with state-of-the-art methods using simulated and real data, which demonstrate its improved performance. Our results show the applicability of scINSIGHT in diverse biomedical and clinical problems.

StemSC: a cross-dataset human stemness index for single-cell samples

BackgroundStemness is defined as the potential of cells for self-renewal and differentiation. Many transcriptome-based methods for stemness evaluation have been proposed. However, all these methods showed low negative correlations with differentiation time and can’t leverage the existing experimentally validated stem cells to recognize the stem-like cells.MethodsHere, we constructed a stemness index for single-cell samples (StemSC) based on relative expression orderings (REO) of gene pairs. Firstly, we identified the stemness-related genes by selecting the genes significantly related to differentiation time. Then, we used 13 RNA-seq datasets from both the bulk and single-cell embryonic stem cell (ESC) samples to construct the reference REOs. Finally, the StemSC value of a given sample was calculated as the percentage of gene pairs with the same REOs as the ESC samples.ResultsWe validated the StemSC by its higher negative correlations with differentiation time in eight normal datasets and its higher positive correlations with tumor dedifferentiation in three colorectal cancer datasets and four glioma datasets. Besides, the robust of StemSC to batch effect enabled us to leverage the existing experimentally validated cancer stem cells to recognize the stem-like cells in other independent tumor datasets. And the recognized stem-like tumor cells had fewer interactions with anti-tumor immune cells. Further survival analysis showed the immunotherapy-treated patients with high stemness had worse survival than those with low stemness.ConclusionsStemSC is a better stemness index to calculate the stemness across datasets, which can help researchers explore the effect of stemness on other biological processes.

Rapid and Multiplexed Detection of Single Cells of Salmonella, Escherichia coli O157, and Shigella flexneri in Ground Beef by Flow Cytometry.

Salmonella, Escherichia coli O157, and Shigella flexneri are typical foodborne pathogens in ground beef, which can cause severe infection even when present as a single cell. Flow cytometry (FCM) methods are widely applied in the rapid detection of pathogens in food products. In this study, we report an FCM-based method for detecting single cells of Salmonella, E. coli O157, and S. flexneri in 25 g ground beef samples. We fluorescently labeled specific antibodies that could effectively identify bacterial cells, prepared single-cell samples by serial dilution, and optimized the pre-enrichment time. The results showed that 7 h of pre-enrichment is appropriate for sensitive single-cell detection by FCM. Finally, we evaluated this method in artificially contaminated and retail beef samples. This study outlines a novel highly sensitive FCM-based method to detect Salmonella, E. coli O157, and S. flexneri in beef samples within 8 h that can be applied to the rapid and multiplexed detection of foodborne pathogens.

Discordant Genome Assemblies Drastically Alter the Interpretation of Single-Cell RNA Sequencing Data Which Can Be Mitigated by a Novel Integration Method.

Advances in sequencing and assembly technology have led to the creation of genome assemblies for a wide variety of non-model organisms. The rapid production and proliferation of updated, novel assembly versions can create vexing problems for researchers when multiple-genome assembly versions are available at once, requiring researchers to work with more than one reference genome. Multiple-genome assemblies are especially problematic for researchers studying the genetic makeup of individual cells, as single-cell RNA sequencing (scRNAseq) requires sequenced reads to be mapped and aligned to a single reference genome. Using the Astyanax mexicanus, this study highlights how the interpretation of a single-cell dataset from the same sample changes when aligned to its two different available genome assemblies. We found that the number of cells and expressed genes detected were drastically different when aligning to the different assemblies. When the genome assemblies were used in isolation with their respective annotations, cell-type identification was confounded, as some classic cell-type markers were assembly-specific, whilst other genes showed differential patterns of expression between the two assemblies. To overcome the problems posed by multiple-genome assemblies, we propose that researchers align to each available assembly and then integrate the resultant datasets to produce a final dataset in which all genome alignments can be used simultaneously. We found that this approach increased the accuracy of cell-type identification and maximised the amount of data that could be extracted from our single-cell sample by capturing all possible cells and transcripts. As scRNAseq becomes more widely available, it is imperative that the single-cell community is aware of how genome assembly alignment can alter single-cell data and their interpretation, especially when reviewing studies on non-model organisms.