Single-Cell Sequencing Applications in the Inner Ear

Genomics studies face specific challenges in the inner ear due to the multiple types and limited amounts of inner ear cells that are arranged in a very delicate structure. However, advances in single-cell sequencing (SCS) technology have made it possible to analyze gene expression variations across different cell types as well as within specific cell groups that were previously considered to be homogeneous. In this review, we summarize recent advances in inner ear research brought about by the use of SCS that have delineated tissue heterogeneity, identified unknown cell subtypes, discovered novel cell markers, and revealed dynamic signaling pathways during development. SCS opens up new avenues for inner ear research, and the potential of the technology is only beginning to be explored.

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MO262THE SINGLE-CELL TRANSCRIPTOMICS OF HUMAN IGA NEPHROPATHY

Nephrology Dialysis Transplantation ◽

10.1093/ndt/gfab104.0020 ◽

2021 ◽

Vol 36 (Supplement_1) ◽

Author(s):

Yong Zhong ◽

Xiangcheng Xiao

Keyword(s):

Gene Expression ◽

Single Cell ◽

Signaling Pathways ◽

Iga Nephropathy ◽

Molecular Mechanisms ◽

Mesangial Cells ◽

Therapeutic Targets ◽

Cell Types ◽

Receptor Crosstalk ◽

Overt Proteinuria

Abstract Background and Aims The exact molecular mechanisms underlying IgA nephropathy (IgAN) remains incompletely defined. Therefore, it is necessary to further elucidate the mechanism of IgA nephropathy and find novel therapeutic targets. Method Single-cell RNA sequencing (scRNA-seq) was applied to kidney biopsies from 4 IgAN and 1 control subjects to define the transcriptomic landscape at the single-cell resolution. Unsupervised clustering analysis of kidney specimens was used to identify distinct cell clusters. Differentially expressed genes and potential signaling pathways involved in IgAN were also identified. Results Our analysis identified 14 cell subsets in kidney biopsies from IgAN patients, and analyzed changing gene expression in distinct renal cell types. We found increased mesangial expression of several novel genes including MALAT1, GADD45B, SOX4 and EDIL3, which were related to proliferation and matrix accumulation and have not been reported in IgAN previously. The overexpressed genes in tubule cells of IgAN were mainly enriched in inflammatory pathways including TNF signaling, IL-17 signaling and NOD-like receptor signaling. Moreover, the receptor-ligand crosstalk analysis revealed potential interactions between mesangial cells and other cells in IgAN. Specifically, IgAN with overt proteinuria displayed elevated genes participating in several signaling pathways which may be involved in pathogenesis of progression of IgAN. Conclusion The comprehensive analysis of kidney biopsy specimen demonstrated different gene expression profile, potential pathologic ligand-receptor crosstalk, signaling pathways in human IgAN. These results offer new insight into pathogenesis and identify new therapeutic targets for patients with IgA nephropathy.

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Self-reporting transposons enable simultaneous readout of gene expression and transcription factor binding in single cells

10.1101/538553 ◽

2019 ◽

Cited By ~ 3

Author(s):

Arnav Moudgil ◽

Michael N. Wilkinson ◽

Xuhua Chen ◽

June He ◽

Alex J. Cammack ◽

...

Keyword(s):

Gene Expression ◽

Transcription Factor ◽

Single Cell ◽

Binding Sites ◽

Expression Profiles ◽

Single Cells ◽

Gene Expression Profiles ◽

Cell Types ◽

Specific Cell

AbstractIn situ measurements of transcription factor (TF) binding are confounded by cellular heterogeneity and represent averaged profiles in complex tissues. Single cell RNA-seq (scRNA-seq) is capable of resolving different cell types based on gene expression profiles, but no technology exists to directly link specific cell types to the binding pattern of TFs in those cell types. Here, we present self-reporting transposons (SRTs) and their use in single cell calling cards (scCC), a novel assay for simultaneously capturing gene expression profiles and mapping TF binding sites in single cells. First, we show how the genomic locations of SRTs can be recovered from mRNA. Next, we demonstrate that SRTs deposited by the piggyBac transposase can be used to map the genome-wide localization of the TFs SP1, through a direct fusion of the two proteins, and BRD4, through its native affinity for piggyBac. We then present the scCC method, which maps SRTs from scRNA-seq libraries, thus enabling concomitant identification of cell types and TF binding sites in those same cells. As a proof-of-concept, we show recovery of cell type-specific BRD4 and SP1 binding sites from cultured cells. Finally, we map Brd4 binding sites in the mouse cortex at single cell resolution, thus establishing a new technique for studying TF biology in situ.

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Bulk and Single-Cell Transcriptomics Identify Tobacco-Use Disparity in Lung Gene Expression of ACE2, the Receptor of 2019-nCov

10.20944/preprints202002.0051.v2 ◽

2020 ◽

Cited By ~ 6

Author(s):

Guoshuai Cai

Keyword(s):

Gene Expression ◽

Single Cell ◽

Large Scale ◽

Cell Types ◽

Smoking History ◽

Normal Lung ◽

Specific Cell ◽

Susceptible Population ◽

Former Smokers ◽

Ace2 Gene

In current severe global emergency situation of 2019-nCov outbreak, it is imperative to identify vulnerable and susceptible groups for effective protection and care. Recently, studies found that 2019-nCov and SARS-nCov share the same receptor, ACE2. In this study, we analyzed four large-scale bulk transcriptomic datasets of normal lung tissue and two single-cell transcriptomic datasets to investigate the disparities related to race, age, gender and smoking status in ACE2 gene expression and its distribution among cell types. We didn’t find significant disparities in ACE2 gene expression between racial groups (Asian vs Caucasian), age groups (>60 vs <60) or gender groups (male vs female). However, we observed significantly higher ACE2 gene expression in former smoker’s lung compared to non-smoker’s lung. Also, we found higher ACE2 gene expression in Asian current smokers compared to non-smokers but not in Caucasian current smokers, which may indicate an existence of gene-smoking interaction. In addition, we found that ACE2 gene is expressed in specific cell types related to smoking history and location. In bronchial epithelium, ACE2 is actively expressed in goblet cells of current smokers and club cells of non-smokers. In alveoli, ACE2 is actively expressed in remodelled AT2 cells of former smokers. Together, this study indicates that smokers especially former smokers may be more susceptible to 2019-nCov and have infection paths different with non-smokers. Thus, smoking history may provide valuable information in identifying susceptible population and standardizing treatment regimen.

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Integrated analysis of bulk multi omic and single-cell sequencing data confirms the molecular origin of hemodynamic changes in Covid-19 infection explaining coagulopathy and higher geriatric mortality

10.1101/2020.04.26.20081182 ◽

2020 ◽

Author(s):

Shreya Johri ◽

Deepali Jain ◽

Ishaan Gupta

Keyword(s):

Gene Expression ◽

Single Cell ◽

Older Patients ◽

Cell Types ◽

Integrated Analysis ◽

Molecular Evidence ◽

Sequencing Data ◽

Phagocytic Cells ◽

Hemodynamic Changes ◽

Single Cell Sequencing

AbstractBesides severe respiratory distress, recent reports in Covid-19 patients have found a strong association between platelet counts and patient survival. Along with hemodynamic changes such as prolonged clotting time, high fibrin degradation products and D-dimers, increased levels of monocytes with disturbed morphology have also been identified. In this study, through an integrated analysis of bulk RNA-sequencing data from Covid-19 patients with data from single-cell sequencing studies on lung tissues, we found that most of the cell-types that contributed to the altered gene expression were of hematopoietic origin. We also found that differentially expressed genes in Covid-19 patients formed a significant pool of the expressing genes in phagocytic cells such as Monocytes and platelets. Interestingly, while we observed a general enrichment for Monocytes in Covid-19 patients, we found that the signal for FCGRA3+ Monocytes was depleted. Further, we found evidence that age-associated gene expression changes in Monocytes and platelets, associated with inflammation, mirror gene expression changes in Covid-19 patients suggesting that pro-inflammatory signalling during aging may worsen the infection in older patients. We identified more than 20 genes that change in the same direction between Covid-19 infection and aging cells that may act as potential therapeutic targets. Of particular interest were IL2RG, GNLY and GMZA expressed in platelets, which facilitates cytokine signalling in Monocytes through an interaction with platelets. To understand whether infection can directly manipulate the biology of Monocytes and platelets, we hypothesize that these non-ACE2 expressing cells may be infected by the virus through the phagocytic route. We observed that phagocytic cells such as Monocytes, T-cells, and platelets have a significantly higher expression of genes that are a part of the Covid-19 viral interactome. Hence these cell-types may have an active rather than a reactive role in viral pathogenesis to manifest clinical symptoms such as coagulopathy. Therefore, our results present molecular evidence for pursuing both anti-inflammatory and anticoagulation therapy for better patient management especially in older patients.

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Single-Cell Sequencing Highlights Why Cancers Form in Specific Cell Types

Oncology Times ◽

10.1097/01.cot.0000550784.92046.35 ◽

2018 ◽

Vol 40 (24) ◽

pp. 20

Keyword(s):

Single Cell ◽

Cell Types ◽

Specific Cell ◽

Single Cell Sequencing

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Bayesian estimation of cell-type-specific gene expression per bulk sample with prior derived from single-cell data

10.1101/2020.08.05.238949 ◽

2020 ◽

Cited By ~ 1

Author(s):

Jiebiao Wang ◽

Kathryn Roeder ◽

Bernie Devlin

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Cell Types ◽

Autism Spectrum ◽

Specific Cell ◽

Expression Data ◽

Cell Type ◽

Disease Etiology ◽

Cell Type Specific

AbstractWhen assessed over a large number of samples, bulk RNA sequencing provides reliable data for gene expression at the tissue level. Single-cell RNA sequencing (scRNA-seq) deepens those analyses by evaluating gene expression at the cellular level. Both data types lend insights into disease etiology. With current technologies, however, scRNA-seq data are known to be noisy. Moreover, constrained by costs, scRNA-seq data are typically generated from a relatively small number of subjects, which limits their utility for some analyses, such as identification of gene expression quantitative trait loci (eQTLs). To address these issues while maintaining the unique advantages of each data type, we develop a Bayesian method (bMIND) to integrate bulk and scRNA-seq data. With a prior derived from scRNA-seq data, we propose to estimate sample-level cell-type-specific (CTS) expression from bulk expression data. The CTS expression enables large-scale sample-level downstream analyses, such as detecting CTS differentially expressed genes (DEGs) and eQTLs. Through simulations, we demonstrate that bMIND improves the accuracy of sample-level CTS expression estimates and power to discover CTS-DEGs when compared to existing methods. To further our understanding of two complex phenotypes, autism spectrum disorder and Alzheimer’s disease, we apply bMIND to gene expression data of relevant brain tissue to identify CTS-DEGs. Our results complement findings for CTS-DEGs obtained from snRNA-seq studies, replicating certain DEGs in specific cell types while nominating other novel genes in those cell types. Finally, we calculate CTS-eQTLs for eleven brain regions by analyzing GTEx V8 data, creating a new resource for biological insights.

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ProtAnno, an Automated Cell Type Annotation Tool for Single Cell Proteomics Data that Integrates Information from Multiple Reference Sources

10.1101/2021.09.13.460162 ◽

2021 ◽

Author(s):

Wenxuan Deng ◽

Biqing Zhu ◽

Seyoung Park ◽

Tomokazu S. Sumida ◽

Avraham Unterman ◽

...

Keyword(s):

Single Cell ◽

Single Cells ◽

Earth Metal ◽

Cell Types ◽

Specific Cell ◽

Cell Type ◽

Proteomics Data ◽

Data Annotation ◽

Different Cell Types ◽

Unique Molecular Identifier

Compared with sequencing-based global genomic profiling, cytometry labels targeted surface markers on millions of cells in parallel either by conjugated rare earth metal particles or Unique Molecular Identifier (UMI) barcodes. Correct annotation of these cells to specific cell types is a key step in the analysis of these data. However, there is no computational tool that automatically annotates single cell proteomics data for cell type inference. In this manuscript, we propose an automated single cell proteomics data annotation approach called ProtAnno to facilitate cell type assignments without laborious manual gating. ProtAnno is designed to incorporate information from annotated single cell RNA-seq (scRNA-seq), CITE-seq, and prior data knowledge (which can be imprecise) on biomarkers for different cell types. We have performed extensive simulations to demonstrate the accuracy and robustness of ProtAnno. For several single cell proteomics datasets that have been manually labeled, ProtAnno was able to correctly label most single cells. In summary, ProtAnno offers an accurate and robust tool to automate cell type annotations for large single cell proteomics datasets, and the analysis of such annotated cell types can offer valuable biological insights.

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SC2disease: a manually curated database of single-cell transcriptome for human diseases

Nucleic Acids Research ◽

10.1093/nar/gkaa838 ◽

2020 ◽

Vol 49 (D1) ◽

pp. D1413-D1419 ◽

Cited By ~ 1

Author(s):

Tianyi Zhao ◽

Shuxuan Lyu ◽

Guilin Lu ◽

Liran Juan ◽

Xi Zeng ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Cell Types ◽

Cellular Level ◽

Human Diseases ◽

Cell Type ◽

Cell Type Specific ◽

Different Cell Types

Abstract SC2disease (http://easybioai.com/sc2disease/) is a manually curated database that aims to provide a comprehensive and accurate resource of gene expression profiles in various cell types for different diseases. With the development of single-cell RNA sequencing (scRNA-seq) technologies, uncovering cellular heterogeneity of different tissues for different diseases has become feasible by profiling transcriptomes across cell types at the cellular level. In particular, comparing gene expression profiles between different cell types and identifying cell-type-specific genes in various diseases offers new possibilities to address biological and medical questions. However, systematic, hierarchical and vast databases of gene expression profiles in human diseases at the cellular level are lacking. Thus, we reviewed the literature prior to March 2020 for studies which used scRNA-seq to study diseases with human samples, and developed the SC2disease database to summarize all the data by different diseases, tissues and cell types. SC2disease documents 946 481 entries, corresponding to 341 cell types, 29 tissues and 25 diseases. Each entry in the SC2disease database contains comparisons of differentially expressed genes between different cell types, tissues and disease-related health status. Furthermore, we reanalyzed gene expression matrix by unified pipeline to improve the comparability between different studies. For each disease, we also compare cell-type-specific genes with the corresponding genes of lead single nucleotide polymorphisms (SNPs) identified in genome-wide association studies (GWAS) to implicate cell type specificity of the traits.

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Neuronal differentiation strategies: insights from single-cell sequencing and machine learning

Development ◽

10.1242/dev.193631 ◽

2020 ◽

Vol 147 (23) ◽

pp. dev193631

Author(s):

Nikolaos Konstantinides ◽

Claude Desplan

Keyword(s):

Machine Learning ◽

Single Cell ◽

Neuronal Differentiation ◽

Cell Types ◽

Specific Cell ◽

Single Cell Sequencing ◽

Differentiated Cells ◽

History Of ◽

Induced Pluripotent

ABSTRACTNeuronal replacement therapies rely on the in vitro differentiation of specific cell types from embryonic or induced pluripotent stem cells, or on the direct reprogramming of differentiated adult cells via the expression of transcription factors or signaling molecules. The factors used to induce differentiation or reprogramming are often identified by informed guesses based on differential gene expression or known roles for these factors during development. Moreover, differentiation protocols usually result in partly differentiated cells or the production of a mix of cell types. In this Hypothesis article, we suggest that, to overcome these inefficiencies and improve neuronal differentiation protocols, we need to take into account the developmental history of the desired cell types. Specifically, we present a strategy that uses single-cell sequencing techniques combined with machine learning as a principled method to select a sequence of programming factors that are important not only in adult neurons but also during differentiation.

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STEM-05. SINGLE CELL SEQUENCING CHARACTERIZES SPATIAL AND TEMPORAL RELATIONSHIPS OF ENHANCING AND NON-ENHANCING REGIONS IN HIGH-GRADE GLIOMA

Neuro-Oncology ◽

10.1093/neuonc/noaa215.822 ◽

2020 ◽

Vol 22 (Supplement_2) ◽

pp. ii197-ii197

Author(s):

Kunal Patel ◽

Riki Kawaguchi ◽

Alvaro Alvarado ◽

Deepthi Muthukrishnan ◽

Richard Everson ◽

...

Keyword(s):

Gene Expression ◽

Tumor Cell ◽

Single Cell ◽

Tumor Cells ◽

High Grade Glioma ◽

Cell Types ◽

Magnetic Resonance Images ◽

Stem Cell Markers ◽

High Grade ◽

Single Cell Sequencing

Abstract INTRODUCTION Treatments of high grade glioma focus on the contrast enhancing (CE) portion of the tumor. However, given the invasive nature of glioma, residual tumor cells responsible for recurrence likely exist in the non-enhancing (NE) region. METHODS In 3 patients undergoing surgery for malignant glioma, we used pre-operative magnetic resonance images to prospectively identify biopsy targets in the CE region and locations 0.5-2.0cm beyond the CE edge. We carried out single cell sequencing on image guided biopsy specimens to generate 12,528 RNA profiles. RESULTS In all samples, tumor cells clustered into three predominant groups: tumor cells with astrocyte markers (ASC-like), tumor cells with oligodendrocyte markers (ODC-like), and tumor cells with neither marker. This last group consisted of a small proportion of tumor cells and expressed putative stem cell markers (PROM1, NES, SLC1A3, A2B5, ID1). A trajectory analysis consistently positioned this group as branching off into either ASC-like or ODC-like cells. CE regions had different cellular compositions than NE regions, with higher proportions of tumor, endothelial cells, T-cells, and NK cells. Using location and cell density data, we modeled expected tumor burden, predicting tumor cells up to 1.5cm beyond the CE region of the tumor. There were significant differences in gene expression between CE and NE tumor cells, with increased inflammation and hypoxia in the CE region versus increased proliferative markers in the NE region. Tumor cells in the NE region were characterized by increased proliferation, markers of invasion, and markers of self-renewal. CONCLUSIONS Single cell sequencing illustrates multiple glioma cell types and suggests a hierarchical relationship between tumor cell types. CE and NE regions exhibit different tumor and non-tumor cell populations as well as different gene expression profiles within individual cell types. There remains a significant tumor cell burden in the NE portion of tumor, including actively proliferating cells.

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