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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)., Competing Interests: The authors declare no competing interests. S.L. is currently an employee of Boehringer-Ingelheim., (© 2021 The Author(s).)

Human Papillomavirus infection is highly prevalent worldwide. While most types of HPV cause benign warts, some high-risk types are known to cause cervical cancer, as well as cancer of the oral cavity and head and neck. Persistent cutaneous HPV infection can be particularly problematic in patients with chronic immunosuppression, for example following organ transplantation. Due to unknown mechanisms, these patients may develop numerous warts, as well as present with a dramatically increased skin cancer prevalence. Despite an association between HPV persistence in the epidermis and excessive wart or squamous cancer development, the molecular mechanisms linking immunosuppression, HPV expression and excessive epidermal proliferation have not been determined, largely due to low-sensitivity methodology to capture rare viral transcription events. Here, we use single-cell RNA sequencing to profile HPV-positive skin lesions from an immunosuppressed patient that were found to express the alphapapillomavirus HPV78 in basal keratinocytes, suprabasal keratinocytes and hair follicle stem cells. This method can be applied to detect and investigate HPV transcripts in cutaneous lesions, allowing mechanistic links between immunosuppression-induced HPV life cycle and epidermal hyperproliferation to be uncovered., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Background: Giant cell arteritis (GCA) is the most common form of vasculitis affecting elderly people. It is one of the few true ophthalmic emergencies but symptoms and signs are variable thereby making it a challenging disease to diagnose. A temporal artery biopsy is the gold standard to confirm GCA, but there are currently no specific biochemical markers to aid diagnosis. We aimed to identify a less invasive method to confirm the diagnosis of GCA, as well as to ascertain clinically relevant predictive biomarkers by studying the transcriptome of purified peripheral CD4+ and CD8+ T lymphocytes in patients with GCA., Methods: We recruited 16 patients with histological evidence of GCA at the Royal Victorian Eye and Ear Hospital, Melbourne, Australia, and aimed to collect blood samples at six time points: acute phase, 2-3 weeks, 6-8 weeks, 3 months, 6 months and 12 months after clinical diagnosis. CD4+ and CD8+ T-cells were positively selected at each time point through magnetic-assisted cell sorting. RNA was extracted from all 195 collected samples for subsequent RNA sequencing. The expression profiles of patients were compared to those of 16 age-matched controls., Results: Over the 12-month study period, polynomial modelling analyses identified 179 and 4 statistically significant transcripts with altered expression profiles (FDR < 0.05) between cases and controls in CD4+ and CD8+ populations, respectively. In CD8+ cells, two transcripts remained differentially expressed after 12 months; SGTB, associated with neuronal apoptosis, and FCGR3A, associatied with Takayasu arteritis. We detected genes that correlate with both symptoms and biochemical markers used for predicting long-term prognosis. 15 genes were shared across 3 phenotypes in CD4 and 16 across CD8 cells. In CD8, IL32 was common to 5 phenotypes including Polymyalgia Rheumatica, bilateral blindness and death within 12 months., Conclusions: This is the first longitudinal gene expression study undertaken to identify robust transcriptomic biomarkers of GCA. Our results show cell type-specific transcript expression profiles, novel gene-phenotype associations, and uncover important biological pathways for this disease. In the acute phase, the gene-phenotype relationships we have identified could provide insight to potential disease severity and as such guide in initiating appropriate patient management.

Diabetic foot ulcers (DFUs) are a serious chronic complication of diabetes mellitus and a leading cause of disability and death in diabetic patients. However, current treatments remain unsatisfactory. Although macrophages are associated with DFU, their exact role in this disease remains uncertain. This study sought to detect macrophage‐related genes in DFU and identify possible therapeutic targets. Single‐cell datasets (GSE223964) and RNA‐seq datasets (GSM68183, GSE80178, GSE134431 and GSE147890) associated with DFU were retrieved from the gene expression omnibus (GEO) database for this study. Analysis of the provided single‐cell data revealed the distribution of macrophage subpopulations in the DFU. Four independent RNA‐seq datasets were merged into a single DFU cohort and further analysed using bioinformatics. This included differential expression (DEG) analysis, multiple machine learning algorithms to identify biomarkers and enrichment analysis. Finally, key results were validated using reverse transcription‐quantitative polymerase chain reaction (RT‐qPCR) and Western bolt. Finally, the findings were validated using RT‐qPCR and western blot. We obtained 802 macrophage‐related genes in single‐cell analysis. Differential expression analysis yielded 743 DEGs. Thirty‐seven macrophage‐associated DEGs were identified by cross‐analysis of marker genes with macrophage‐associated DEGs. Thirty‐seven intersections were screened and cross‐analysed using four machine learning algorithms. Finally, HMOX1 was identified as a potentially valuable biomarker. HMOX1 was significantly associated with biological pathways such as the insulin signalling pathway. The results showed that HMOX1 was significantly overexpressed in DFU samples. In conclusion, the analytical results of this study identified HMOX1 as a potentially valuable biomarker associated with macrophages in DFU. The results of our analysis improve our understanding of the mechanism of macrophage action in this disease and may be useful in developing targeted therapies for DFU. [ABSTRACT FROM AUTHOR]

Background: Endometriosis is a common, gynaecological disease characterised by the presence of endometrial-like cells growing outside the uterus. Lesions appear at multiple locations, present with variation in appearance, size and depth of invasion. Despite hormones being the recommended first-line treatment, their efficacy, success and side effects vary widely amongst study populations. Current, hormonal medication for endometriosis is designed to suppress systemic oestrogen. Whether these hormones can influence the lesions themselves is not yet clear. Evidence of hormone receptor expression in endometriotic lesions and their ability to respond is conflicting. A variation in their expression, activation of transcriptional co-regulators and the potential to respond may contribute to their variation in patient outcomes. Identifying patients who would benefit from hormonal treatments remain an important goal in endometriosis research. Methods: Using gene expression data from endometriosis lesions including endometrioma (OMA, n = 28), superficial peritoneal lesions (SUP, n = 72) and deeply infiltrating lesions (DIE, n = 78), we performed principal component analysis, differential gene expression and gene correlation analyses to assess the impact of menstrual stage, lesion subtype and hormonal treatment on the gene expression. Results: The gene expression profiles did not vary based on menstrual stage, but could distinguish lesion subtypes with OMA significantly differentiating from both SUP and DIE. Additionally, the effect of oestrogen suppression medication altered the gene expression profile in OMA, while such effect was not observed in SUP or DIE. Analysis of the target receptors for hormonal medication indicated ESR2 was differentially expressed in OMA and that genes that correlated with ESR2 varied significantly between medicated and non-medicated OMA samples. Conclusions: Our results demonstrate of the different lesion types OMA present with strongest response to hormonal treatment directly through ESR2. The data suggests that there may be the potential to target treatment options to individual patients based on pre-surgical diagnoses. [ABSTRACT FROM AUTHOR]

Background: Systematic description of library quality and sequencing performance of single-cell RNA sequencing (scRNA-seq) data is imperative for subsequent downstream modules, including re-pooling libraries. While several packages have been developed to visualise quality control (QC) metrics for scRNA-seq data, they do not include expression-based QC to discriminate between true variation and background noise. Results: We present scQCEA (acronym of the single-cell RNA sequencing Quality Control and Enrichment Analysis), an R package to generate reports of process optimisation metrics for comparing sets of samples and visual evaluation of quality scores. scQCEA can import data from 10X or other single-cell platforms and includes functions for generating an interactive report of QC metrics for multi-omics data. In addition, scQCEA provides automated cell type annotation on scRNA-seq data using differential gene expression patterns for expression-based quality control. We provide a repository of reference gene sets, including 2348 marker genes, which are exclusively expressed in 95 human and mouse cell types. Using scRNA-seq data from 56 gene expressions and V(D)J T cell replicates, we show how scQCEA can be applied for the visual evaluation of quality scores for sets of samples. In addition, we use the summary of QC measures from 342 human and mouse shallow-sequenced gene expression profiles to specify optimal sequencing requirements to run a cell-type enrichment analysis function. Conclusions: The open-source R tool will allow examining biases and outliers over biological and technical measures, and objective selection of optimal cluster numbers before downstream analysis. scQCEA is available at https://isarnassiri.github.io/scQCEA/ as an R package. Full documentation, including an example, is provided on the package website. [ABSTRACT FROM AUTHOR]

To expedite gene discovery in eye development and its associated defects, we previously developed a bioinformatics resource-tool iSyTE (integrated Systems Tool for Eye gene discovery). However, iSyTE is presently limited to lens tissue and is predominantly based on transcriptomics datasets. Therefore, to extend iSyTE to other eye tissues on the proteome level, we performed high-throughput tandem mass spectrometry (MS/MS) on mouse embryonic day (E)14.5 retina and retinal pigment epithelium combined tissue and identified an average of 3300 proteins per sample (n = 5). High-throughput expression profiling-based gene discovery approaches–involving either transcriptomics or proteomics—pose a key challenge of prioritizing candidates from thousands of RNA/proteins expressed. To address this, we used MS/MS proteome data from mouse whole embryonic body (WB) as a reference dataset and performed comparative analysis–termed "in silico WB-subtraction"—with the retina proteome dataset. In silico WB-subtraction identified 90 high-priority proteins with retina-enriched expression at stringency criteria of ≥ 2.5 average spectral counts, ≥ 2.0 fold-enrichment, false discovery rate < 0.01. These top candidates represent a pool of retina-enriched proteins, several of which are associated with retinal biology and/or defects (e.g., Aldh1a1, Ank2, Ank3, Dcn, Dync2h1, Egfr, Ephb2, Fbln5, Fbn2, Hras, Igf2bp1, Msi1, Rbp1, Rlbp1, Tenm3, Yap1, etc.), indicating the effectiveness of this approach. Importantly, in silico WB-subtraction also identified several new high-priority candidates with potential regulatory function in retina development. Finally, proteins exhibiting expression or enriched-expression in the retina are made accessible in a user-friendly manner at iSyTE (https://research.bioinformatics.udel.edu/iSyTE/), to allow effective visualization of this information and facilitate eye gene discovery. [ABSTRACT FROM AUTHOR]

A single population of interferon-regulatory factor 8 (Irf8)-dependent conventional dendritic cell (cDC type1) is considered to be responsible for both immunogenic and tolerogenic responses depending on the surrounding cytokine milieu. Here, we challenge this concept of an omnipotent single Irf8-dependent cDC1 cluster through analysis of pulmonary cDCs at single cell resolution. We report existence of a pulmonary cDC1 cluster lacking Xcr1 with an immunogenic signature that clearly differs from the Xcr1 positive cDC1 cluster. The Irf8+Batf3+Xcr1- cluster expresses high levels of pro-inflammatory genes associated with antigen presentation, migration and co-stimulation such as Ccr7, Cd74, MHC-II, Ccl5, Il12b and Relb while, the Xcr1+ cDC1 cluster expresses genes corresponding to immune tolerance mechanisms like Clec9a, Pbx1, Cadm1, Btla and Clec12a. In concordance with their pro-inflammatory gene expression profile, the ratio of Xcr1- cDC1s but not Xcr1+cDC1 is increased in the lungs of allergen-treated mice compared to the control group, in which both cDC1 clusters are present in comparable ratios. The existence of two distinct Xcr1+ and Xcr1- cDC1 clusters is furthermore supported by velocity analysis showing markedly different temporal patterns of Xcr1- and Xcr1+cDC1s. In summary, we present evidence for the existence of two different cDC1 clusters with distinct immunogenic profiles in vivo. Our findings have important implications for DC-targeting immunomodulatory therapies. [ABSTRACT FROM AUTHOR]

Single-cell RNA-sequencing (scRNA-seq) has been widely used to depict gene expression profiles at the single-cell resolution. However, its relatively high dropout rate often results in artificial zero expressions of genes and therefore compromised reliability of results. To overcome such unwanted sparsity of scRNA-seq data, several imputation algorithms have been developed to recover the single-cell expression profiles. Here, we propose a novel approach, GE-Impute, to impute the dropout zeros in scRNA-seq data with graph embedding-based neural network model. GE-Impute learns the neural graph representation for each cell and reconstructs the cell–cell similarity network accordingly, which enables better imputation of dropout zeros based on the more accurately allocated neighbors in the similarity network. Gene expression correlation analysis between true expression data and simulated dropout data suggests significantly better performance of GE-Impute on recovering dropout zeros for both droplet- and plated-based scRNA-seq data. GE-Impute also outperforms other imputation methods in identifying differentially expressed genes and improving the unsupervised clustering on datasets from various scRNA-seq techniques. Moreover, GE-Impute enhances the identification of marker genes, facilitating the cell type assignment of clusters. In trajectory analysis, GE-Impute improves time-course scRNA-seq data analysis and reconstructing differentiation trajectory. The above results together demonstrate that GE-Impute could be a useful method to recover the single-cell expression profiles, thus enabling better biological interpretation of scRNA-seq data. GE-Impute is implemented in Python and is freely available at https://github.com/wxbCaterpillar/GE-Impute. [ABSTRACT FROM AUTHOR]

The complex heterogeneity of head and neck squamous cell carcinoma (HNSCC) reflects a diverse underlying etiology. This heterogeneity is also apparent within Human Papillomavirus-positive (HPV+) HNSCC subtypes, which have distinct gene expression profiles and patient outcomes. One aggressive HPV+ HNSCC subtype is characterized by elevated expression of genes involved in keratinization, a process regulated by the oncogenic transcription factor ΔNp63. Furthermore, the human TP63 gene locus is a frequent HPV integration site and HPV oncoproteins drive ΔNp63 expression, suggesting an unexplored functional link between ΔNp63 and HPV+ HNSCC. Here we show that HPV+ HNSCCs can be molecularly stratified according to ΔNp63 expression levels and derive a ΔNp63-associated gene signature profile for such tumors. We leveraged RNA-seq data from p63 knockdown cells and ChIP-seq data for p63 and histone marks from two ΔNp63high HPV+ HNSCC cell lines to identify an epigenetically refined ΔNp63 cistrome. Our integrated analyses reveal crucial ΔNp63-bound super-enhancers likely to mediate HPV+ HNSCC subtype-specific gene expression that is anchored, in part, by the PI3K-mTOR pathway. These findings implicate ΔNp63 as a key regulator of essential oncogenic pathways in a subtype of HPV+ HNSCC that can be exploited as a biomarker for patient stratification and treatment choices. [ABSTRACT FROM AUTHOR]

The aorta, with ascending, arch, thoracic and abdominal segments, responds to the heartbeat, senses metabolites and distributes blood to all parts of the body. However, the heterogeneity across aortic segments and how metabolic pathologies change it are not known. Here, a total of 216 612 individual cells from the ascending aorta, aortic arch, and thoracic and abdominal segments of mouse aortas under normal conditions or with high blood glucose levels, high dietary salt, or high fat intake were profiled using single-cell RNA sequencing. We generated a compendium of 10 distinct cell types, mainly endothelial (EC), smooth muscle (SMC), stromal and immune cells. The distributions of the different cells and their intercommunication were influenced by the hemodynamic microenvironment across anatomical segments, and the spatial heterogeneity of ECs and SMCs may contribute to differential vascular dilation and constriction that were measured by wire myography. Importantly, the composition of aortic cells, their gene expression profiles and their regulatory intercellular networks broadly changed in response to high fat/salt/glucose conditions. Notably, the abdominal aorta showed the most dramatic changes in cellular composition, particularly involving ECs, fibroblasts and myeloid cells with cardiovascular risk factor-related regulons and gene expression networks. Our study elucidates the nature and range of aortic cell diversity, with implications for the treatment of metabolic pathologies. [ABSTRACT FROM AUTHOR]