High-risk Escherichia coli clones that cause neonatal meningitis and association with recrudescent infection.

Neonatal meningitis is a devastating disease associated with high mortality and neurological sequelae. Escherichia coli is the second most common cause of neonatal meningitis in full-term infants (herein NMEC) and the most common cause of meningitis in preterm neonates. Here, we investigated the genomic relatedness of a collection of 58 NMEC isolates spanning 1974-2020 and isolated from seven different geographic regions. We show NMEC are comprised of diverse sequence types (STs), with ST95 (34.5%) and ST1193 (15.5%) the most common. No single virulence gene profile was conserved in all isolates; however, genes encoding fimbrial adhesins, iron acquisition systems, the K1 capsule, and O antigen types O18, O75, and O2 were most prevalent. Antibiotic resistance genes occurred infrequently in our collection. We also monitored the infection dynamics in three patients that suffered recrudescent invasive infection caused by the original infecting isolate despite appropriate antibiotic treatment based on antibiogram profile and resistance genotype. These patients exhibited severe gut dysbiosis. In one patient, the causative NMEC isolate was also detected in the fecal flora at the time of the second infection episode and after treatment. Thus, although antibiotics are the standard of care for NMEC treatment, our data suggest that failure to eliminate the causative NMEC that resides intestinally can lead to the existence of a refractory reservoir that may seed recrudescent infection.

HOPX-associated molecular programs control cardiomyocyte cell states underpinning cardiac structure and function.

Genomic regulation of cardiomyocyte differentiation is central to heart development and function. This study uses genetic loss-of-function human-induced pluripotent stem cell-derived cardiomyocytes to evaluate the genomic regulatory basis of the non-DNA-binding homeodomain protein HOPX. We show that HOPX interacts with and controls cardiac genes and enhancer networks associated with diverse aspects of heart development. Using perturbation studies in vitro, we define how upstream cell growth and proliferation control HOPX transcription to regulate cardiac gene programs. We then use cell, organoid, and zebrafish regeneration models to demonstrate that HOPX-regulated gene programs control cardiomyocyte function in development and disease. Collectively, this study mechanistically links cell signaling pathways as upstream regulators of HOPX transcription to control gene programs underpinning cardiomyocyte identity and function.

Spatial Transcriptomics in Kidney Tissue.

Unlike bulk and single-cell/single-nuclei RNA sequencing methods, spatial transcriptome sequencing (ST-seq) resolves transcriptome expression within the spatial context of intact tissue. This is achieved by integrating histology with RNA sequencing. These methodologies are completed sequentially on the same tissue section placed on a glass slide with printed oligo-dT spots, termed ST-spots. Transcriptomes within the tissue section are captured by the underlying ST-spots and receive a spatial barcode in the process. The sequenced ST-spot transcriptomes are subsequently aligned with the hematoxylin and eosin (H&E) image, giving morphological context to the gene expression signatures within intact tissue. We have successfully employed ST-seq to characterize mouse and human kidney tissue. Here, we describe in detail the application of Visium Spatial Tissue Optimization (TO) and Visium Spatial Gene Expression (GEx) protocols for ST-seq in fresh frozen kidney tissue.

Global Analysis of Transcription Start Sites and Enhancers in Endometrial Stromal Cells and Differences Associated with Endometriosis.

Identifying tissue-specific molecular signatures of active regulatory elements is critical to understanding gene regulatory mechanisms. In this study, transcription start sites (TSS) and enhancers were identified using Cap analysis of gene expression (CAGE) across endometrial stromal cell (ESC) samples obtained from women with (n = 4) and without endometriosis (n = 4). ESC TSSs and enhancers were compared to those reported in other tissue and cell types in FANTOM5 and were integrated with RNA-seq and ATAC-seq data from the same samples for regulatory activity and network analyses. CAGE tag count differences between women with and without endometriosis were statistically tested and tags within close proximity to genetic variants associated with endometriosis risk were identified. Over 90% of tag clusters mapping to promoters were observed in cells and tissues in FANTOM5. However, some potential cell-type-specific promoters and enhancers were also observed. Regions of open chromatin identified using ATAC-seq provided further evidence of the active transcriptional regions identified by CAGE. Despite the small sample number, there was evidence of differences associated with endometriosis at 210 consensus clusters, including IGFBP5, CALD1 and OXTR. ESC TSSs were also located within loci associated with endometriosis risk from genome-wide association studies. This study provides novel evidence of transcriptional differences in endometrial stromal cells associated with endometriosis and provides a valuable cell-type specific resource of active TSSs and enhancers in endometrial stromal cells.

Spatially Resolved Transcriptomes of Mammalian Kidneys Illustrate the Molecular Complexity and Interactions of Functional Nephron Segments.

Available transcriptomes of the mammalian kidney provide limited information on the spatial interplay between different functional nephron structures due to the required dissociation of tissue with traditional transcriptome-based methodologies. A deeper understanding of the complexity of functional nephron structures requires a non-dissociative transcriptomics approach, such as spatial transcriptomics sequencing (ST-seq). We hypothesize that the application of ST-seq in normal mammalian kidneys will give transcriptomic insights within and across species of physiology at the functional structure level and cellular communication at the cell level. Here, we applied ST-seq in six mice and four human kidneys that were histologically absent of any overt pathology. We defined the location of specific nephron structures in the captured ST-seq datasets using three lines of evidence: pathologist's annotation, marker gene expression, and integration with public single-cell and/or single-nucleus RNA-sequencing datasets. We compared the mouse and human cortical kidney regions. In the human ST-seq datasets, we further investigated the cellular communication within glomeruli and regions of proximal tubules-peritubular capillaries by screening for co-expression of ligand-receptor gene pairs. Gene expression signatures of distinct nephron structures and microvascular regions were spatially resolved within the mouse and human ST-seq datasets. We identified 7,370 differentially expressed genes (p adj < 0.05) distinguishing species, suggesting changes in energy production and metabolism in mouse cortical regions relative to human kidneys. Hundreds of potential ligand-receptor interactions were identified within glomeruli and regions of proximal tubules-peritubular capillaries, including known and novel interactions relevant to kidney physiology. Our application of ST-seq to normal human and murine kidneys confirms current knowledge and localization of transcripts within the kidney. Furthermore, the generated ST-seq datasets provide a valuable resource for the kidney community that can be used to inform future research into this complex organ.

Single-cell eQTL mapping identifies cell type-specific genetic control of autoimmune disease.

The human immune system displays substantial variation between individuals, leading to differences in susceptibility to autoimmune disease. We present single-cell RNA sequencing (scRNA-seq) data from 1,267,758 peripheral blood mononuclear cells from 982 healthy human subjects. For 14 cell types, we identified 26,597 independent cis-expression quantitative trait loci (eQTLs) and 990 trans-eQTLs, with most showing cell type-specific effects on gene expression. We subsequently show how eQTLs have dynamic allelic effects in B cells that are transitioning from naïve to memory states and demonstrate how commonly segregating alleles lead to interindividual variation in immune function. Finally, using a Mendelian randomization approach, we identify the causal route by which 305 risk loci contribute to autoimmune disease at the cellular level. This work brings together genetic epidemiology with scRNA-seq to uncover drivers of interindividual variation in the immune system.

Acquisition of murine splenic myeloid cells for protein and gene expression profiling by advanced flow cytometry and CITE-seq.

Here, we outline detailed protocols to isolate and profile murine splenic dendritic cells (DCs) through advanced flow cytometry of the myeloid compartment and single-cell transcriptomic profiling with integrated cell surface protein expression through CITE-seq. This protocol provides a general transferrable road map for different tissues and species. For complete details on the use and execution of this protocol, please refer to Lukowski et al. (2021).

Absence of Batf3 reveals a new dimension of cell state heterogeneity within conventional dendritic cells.

Conventional dendritic cells (cDCs) are traditionally subdivided into cDC1 and cDC2 lineages. Batf3 is a cDC1-required transcription factor, and we observed that Batf3-/- mice harbor a population of cDC1-like cells co-expressing cDC2-associated surface molecules. Using single-cell RNA sequencing with integrated cell surface protein expression (CITE-seq), we found that Batf3-/- mitotic immature cDC1-like cells showed reduced expression of cDC1 features and increased levels of cDC2 features. In wild type, we also observed a proportion of mature cDC1 cells expressing surface features characteristic to cDC2 and found that overall cDC cell state heterogeneity was mainly driven by developmental stage, proliferation, and maturity. We detected population diversity within Sirpa+ cDC2 cells, including a Cd33+ cell state expressing high levels of Sox4 and lineage-mixed features characteristic to cDC1, cDC2, pDCs, and monocytes. In conclusion, these data suggest that multiple cDC cell states can co-express lineage-overlapping features, revealing a level of previously unappreciated cDC plasticity.

Network mapping of primary CD34+ cells by Ampliseq based whole transcriptome targeted resequencing identifies unexplored differentiation regulatory relationships.

With the exception of a few master transcription factors, regulators of neutrophil maturation are poorly annotated in the intermediate phenotypes between the granulocyte-macrophage progenitor (GMP) and the mature neutrophil phenotype. Additional challenges in identifying gene expression regulators in differentiation pathways relate to challenges wherein starting cell populations are heterogeneous in lineage potential and development, are spread across various states of quiescence, as well as sample quality and input limitations. These factors contribute to data variability make it difficult to draw simple regulatory inferences. In response we have applied a multi-omics approach using primary blood progenitor cells primed for homogeneous proliferation and granulocyte differentiation states which combines whole transcriptome resequencing (Ampliseq RNA) supported by droplet digital PCR (ddPCR) validation and mass spectrometry-based proteomics in a hypothesis-generation study of neutrophil differentiation pathways. Primary CD34+ cells isolated from human cord blood were first precultured in non-lineage driving medium to achieve an active, proliferating phenotype from which a neutrophil primed progenitor was isolated and cultured in neutrophil lineage supportive medium. Samples were then taken at 24-hour intervals over 9 days and analysed by Ampliseq RNA and mass spectrometry. The Ampliseq dataset depth, breadth and quality allowed for several unexplored transcriptional regulators and ncRNAs to be identified using a combinatorial approach of hierarchical clustering, enriched transcription factor binding motifs, and network mapping. Network mapping in particular increased comprehension of neutrophil differentiation regulatory relationships by implicating ARNT, NHLH1, PLAG1, and 6 non-coding RNAs associated with PU.1 regulation as cell-engineering targets with the potential to increase total neutrophil culture output. Overall, this study develops and demonstrates an effective new hypothesis generation methodology for transcriptome profiling during differentiation, thereby enabling identification of novel gene targets for editing interventions.

Single-cell transcriptomics of alloreactive CD4+ T cells over time reveals divergent fates during gut graft-versus-host disease.

Acute gastrointestinal (GI) graft-versus-host disease (GVHD) is a primary determinant of mortality after allogeneic hematopoietic stem cell transplantation (alloSCT). The condition is mediated by alloreactive donor CD4+ T cells that differentiate into pathogenic subsets expressing IFN-γ, IL-17A, or GM-CSF and is regulated by subsets expressing IL-10 and/or Foxp3. Developmental relationships between Th cell states during priming in mesenteric lymph nodes (mLNs) and effector function in the GI tract remain undefined at genome scale. We applied scRNA-Seq and computational modeling to a mouse model of donor DC-mediated GVHD exacerbation, creating an atlas of putative CD4+ T cell differentiation pathways in vivo. Computational trajectory inference suggested emergence of pathogenic and regulatory states along a single developmental trajectory in mLNs. Importantly, we inferred an unexpected second trajectory, categorized by little proliferation or cytokine expression, reduced glycolysis, and high tcf7 expression. TCF1hi cells upregulated α4ß7 before gut migration and failed to express cytokines. These cells exhibited recall potential and plasticity following secondary transplantation, including cytokine or Foxp3 expression, but reduced T cell factor 1 (TCF1). Thus, scRNA-Seq suggested divergence of alloreactive CD4+ T cells into quiescent and effector states during gut GVHD exacerbation by donor DC, reflecting putative heterogeneous priming in vivo. These findings, which are potentially the first at a single-cell level during GVHD over time, may assist in examination of T cell differentiation in patients undergoing alloSCT.

Comparative performance of the BGI and Illumina sequencing technology for single-cell RNA-sequencing.

The libraries generated by high-throughput single cell RNA-sequencing (scRNA-seq) platforms such as the Chromium from 10× Genomics require considerable amounts of sequencing, typically due to the large number of cells. The ability to use these data to address biological questions is directly impacted by the quality of the sequence data. Here we have compared the performance of the Illumina NextSeq 500 and NovaSeq 6000 against the BGI MGISEQ-2000 platform using identical Single Cell 3' libraries consisting of over 70 000 cells generated on the 10× Genomics Chromium platform. Our results demonstrate a highly comparable performance between the NovaSeq 6000 and MGISEQ-2000 in sequencing quality, and the detection of genes, cell barcodes, Unique Molecular Identifiers. The performance of the NextSeq 500 was also similarly comparable to the MGISEQ-2000 based on the same metrics. Data generated by both sequencing platforms yielded similar analytical outcomes for general single-cell analysis. The performance of the NextSeq 500 and MGISEQ-2000 were also comparable for the deconvolution of multiplexed cell pools via variant calling, and detection of guide RNA (gRNA) from a pooled CRISPR single-cell screen. Our study provides a benchmark for high-capacity sequencing platforms applied to high-throughput scRNA-seq libraries.

Genotype-free demultiplexing of pooled single-cell RNA-seq.

A variety of methods have been developed to demultiplex pooled samples in a single cell RNA sequencing (scRNA-seq) experiment which either require hashtag barcodes or sample genotypes prior to pooling. We introduce scSplit which utilizes genetic differences inferred from scRNA-seq data alone to demultiplex pooled samples. scSplit also enables mapping clusters to original samples. Using simulated, merged, and pooled multi-individual datasets, we show that scSplit prediction is highly concordant with demuxlet predictions and is highly consistent with the known truth in cell-hashing dataset. scSplit is ideally suited to samples without external genotype information and is available at: https://github.com/jon-xu/scSplit.

ascend: R package for analysis of single-cell RNA-seq data.

BACKGROUND: Recent developments in single-cell RNA sequencing (scRNA-seq) platforms have vastly increased the number of cells typically assayed in an experiment. Analysis of scRNA-seq data is multidisciplinary in nature, requiring careful consideration of the application of statistical methods with respect to the underlying biology. Few analysis packages exist that are at once robust, are computationally fast, and allow flexible integration with other bioinformatics tools and methods. FINDINGS: ascend is an R package comprising tools designed to simplify and streamline the preliminary analysis of scRNA-seq data, while addressing the statistical challenges of scRNA-seq analysis and enabling flexible integration with genomics packages and native R functions, including fast parallel computation and efficient memory management. The package incorporates both novel and established methods to provide a framework to perform cell and gene filtering, quality control, normalization, dimension reduction, clustering, differential expression, and a wide range of visualization functions. CONCLUSIONS: ascend is designed to work with scRNA-seq data generated by any high-throughput platform and includes functions to convert data objects between software packages. The ascend workflow is simple and interactive, as well as suitable for implementation by a broad range of users, including those with little programming experience.

Single-Cell Transcriptional Profiling of Aortic Endothelium Identifies a Hierarchy from Endovascular Progenitors to Differentiated Cells.

The cellular and molecular profiles that govern the endothelial heterogeneity of the circulatory system have yet to be elucidated. Using a data-driven approach to study the endothelial compartment via single-cell RNA sequencing, we characterized cell subpopulations within and assigned them to a defined endothelial hierarchy. We show that two transcriptionally distinct endothelial populations exist within the aorta and, using two independent trajectory analysis methods, confirm that they represent transitioning cells rather than discrete cell types. Gene co-expression analysis revealed crucial regulatory networks underlying each population, including significant metabolic gene networks in progenitor cells. Using mitochondrial activity assays and phenotyping, we confirm that endovascular progenitors display higher mitochondrial content compared to differentiated endothelial cells. The identities of these populations were further validated against bulk RNA sequencing (RNA-seq) data obtained from normal and tumor-derived vasculature. Our findings validate the heterogeneity of the aortic endothelium and previously suggested hierarchy between progenitor and differentiated cells.

Shedding light: The importance of reverse transcription efficiency standards in data interpretation.

The RNA-to-cDNA conversion step in transcriptomics experiments is widely recognised as inefficient and variable, casting doubt on the ability to do quantitative transcriptomics analyses. Multiple studies have focused on ways to optimise this process, resulting in contradictory recommendations. Here we explore the problem of reverse transcription efficiency using digital PCR and the RT method's impact on subsequent data analysis. Using synthetic RNA standards, an example experiment is presented, outlining a method to (1) determine relevant efficiency and variability values and then to (2) incorporate this information into downstream analyses as a way to improve the accuracy of quantitative transcriptomics experiments.

Single-Cell Profiling Identifies Key Pathways Expressed by iPSCs Cultured in Different Commercial Media.

We assessed the pluripotency of human induced pluripotent stem cells (iPSCs) maintained on an automated platform using StemFlex and TeSR-E8 media. Analysis of transcriptome of single cells revealed similar expression of core pluripotency genes, as well as genes associated with naive and primed states of pluripotency. Analysis of individual cells from four samples consisting of two different iPSC lines each grown in the two culture media revealed a shared subpopulation structure with three main subpopulations different in pluripotency states. By implementing a machine learning approach, we estimated that most cells within each subpopulation are very similar between all four samples. The single-cell RNA sequencing analysis of iPSC lines grown in both media reports the molecular signature in StemFlex medium and how it compares to that observed in the TeSR-E8 medium.

Detection of HPV E7 Transcription at Single-Cell Resolution in Epidermis.

Persistent human papillomavirus (HPV) infection is responsible for at least 5% of human malignancies. Most HPV-associated cancers are initiated by the HPV16 genotype, as confirmed by detection of integrated HPV DNA in cells of oral and anogenital epithelial cancers. However, single-cell RNA sequencing may enable prediction of HPV involvement in carcinogenesis at other sites. We conducted single-cell RNA sequencing on keratinocytes from a mouse transgenic for the E7 gene of HPV16 and showed sensitive and specific detection of HPV16-E7 mRNA, predominantly in basal keratinocytes. We showed that increased E7 mRNA copy number per cell was associated with increased expression of E7 induced genes. This technique enhances detection of active viral transcription in solid tissue and may clarify possible linkage of HPV infection to development of squamous cell carcinoma.

Representing genetic variation with synthetic DNA standards.

The identification of genetic variation with next-generation sequencing is confounded by the complexity of the human genome sequence and by biases that arise during library preparation, sequencing and analysis. We have developed a set of synthetic DNA standards, termed 'sequins', that emulate human genetic features and constitute qualitative and quantitative spike-in controls for genome sequencing. Sequencing reads derived from sequins align exclusively to an artificial in silico reference chromosome, rather than the human reference genome, which allows them them to be partitioned for parallel analysis. Here we use this approach to represent common and clinically relevant genetic variation, ranging from single nucleotide variants to large structural rearrangements and copy-number variation. We validate the design and performance of sequin standards by comparison to examples in the NA12878 reference genome, and we demonstrate their utility during the detection and quantification of variants. We provide sequins as a standardized, quantitative resource against which human genetic variation can be measured and diagnostic performance assessed.

Spliced synthetic genes as internal controls in RNA sequencing experiments.

RNA sequencing (RNA-seq) can be used to assemble spliced isoforms, quantify expressed genes and provide a global profile of the transcriptome. However, the size and diversity of the transcriptome, the wide dynamic range in gene expression and inherent technical biases confound RNA-seq analysis. We have developed a set of spike-in RNA standards, termed 'sequins' (sequencing spike-ins), that represent full-length spliced mRNA isoforms. Sequins have an entirely artificial sequence with no homology to natural reference genomes, but they align to gene loci encoded on an artificial in silico chromosome. The combination of multiple sequins across a range of concentrations emulates alternative splicing and differential gene expression, and it provides scaling factors for normalization between samples. We demonstrate the use of sequins in RNA-seq experiments to measure sample-specific biases and determine the limits of reliable transcript assembly and quantification in accompanying human RNA samples. In addition, we have designed a complementary set of sequins that represent fusion genes arising from rearrangements of the in silico chromosome to aid in cancer diagnosis. RNA sequins provide a qualitative and quantitative reference with which to navigate the complexity of the human transcriptome.

Development of a novel cell sorting method that samples population diversity in flow cytometry.

Flow cytometry based electrostatic cell sorting is an important tool in the separation of cell populations. Existing instruments can sort single cells into multi-well collection plates, and keep track of cell of origin and sorted well location. However currently single sorted cell results reflect the population distribution and fail to capture the population diversity. Software was designed that implements a novel sorting approach, "Slice and Dice Sorting," that links a graphical representation of a multi-well plate to logic that ensures that single cells are sampled and sorted from all areas defined by the sort region/s. Therefore the diversity of the total population is captured, and the more frequently occurring or rarer cell types are all sampled. The sorting approach was tested computationally, and using functional cell based assays. Computationally we demonstrate that conventional single cell sorting can sample as little as 50% of the population diversity dependant on the population distribution, and that Slice and Dice sorting samples much more of the variety present within a cell population. We then show by sorting single cells into wells using the Slice and Dice sorting method that there are cells sorted using this method that would be either rarely sorted, or not sorted at all using conventional single cell sorting approaches. The present study demonstrates a novel single cell sorting method that samples much more of the population diversity than current methods. It has implications in clonal selection, stem cell sorting, single cell sequencing and any areas where population heterogeneity is of importance.