scholarly journals Novel Molecular Subgroups of Intrahepatic Cholangiocarcinoma Discovered by Single-Cell RNA Sequencing-Assisted Multi-Omics Analysis

2021 ◽  
Author(s):  
Xuanwen Bao ◽  
Qiong Li ◽  
Jinzhang Chen ◽  
Diyu Chen ◽  
Chanqi Ye ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Intrahepatic Cholangiocarcinoma ◽  
Poor Prognosis ◽  
Molecular Subtypes ◽  
Omics Analysis ◽  
Single Cell Rna Sequencing ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

Abstract Intrahepatic cholangiocarcinoma (ICC) is a relatively rare but highly aggressive tumour type that responds poorly to chemotherapy and immunotherapy. Comprehensive molecular characterization of ICC is essential for the development of novel therapeutics. We performed a comprehensive multi-omics analysis of ICC via proteomic, whole-exon sequencing (WES) and single-cell RNA sequencing (scRNA-seq). We identified three molecular subtypes with deteriorating prognosis in ICC: chromatin remodelling, metabolism, and inflammation. The inflammation subtype was associated with a poor prognosis. Our random forest algorithm revealed that the mutation of KMT2D frequently occurred in the metabolism subtype and were associated with lower inflammatory activity. scRNA-seq further identified a novel APOE+C1QB+ macrophage subtype, which showed the capacity of promoting the inflammation subtype and contributing to a poor prognosis in ICC. Taking together, with a single-cell transcriptome-assisted multi-omics analysis, we identified novel molecular subtypes of ICC and valided APOE+C1QB+ TAMs as potential novel immunotherapy targets against ICC.

scholarly journals Super-cells untangle large and complex single-cell transcriptome networks

2021 ◽  
Author(s):  
Mariia Bilous ◽  
Loc Tran ◽  
Chiara Cianciaruso ◽  
Santiago J Carmona ◽  
Mikael J Pittet ◽  
...  
Keyword(s):  
Single Cell ◽  
Coarse Graining ◽  
Cell Populations ◽  
Cell Type ◽  
Large Numbers ◽  
Single Cell Rna Sequencing ◽  
Gene Correlation ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

Single-cell RNA sequencing (scRNA-seq) technologies offer unique opportunities for exploring heterogeneous cell populations. However, in-depth single-cell transcriptomic characterization of complex tissues often requires profiling tens to hundreds of thousands of cells. Such large numbers of cells represent an important hurdle for downstream analyses, interpretation and visualization. Here we develop a network-based coarse-graining framework where highly similar cells are merged into super-cells. We demonstrate that super-cells not only preserve but often improve the results of downstream analyses including visualization, clustering, differential expression, cell type annotation, gene correlation, imputation, RNA velocity and data integration. By capitalizing on the redundancy inherent to scRNA-seq data, super-cells significantly facilitate and accelerate the construction and interpretation of single-cell atlases, as demonstrated by the integration of 1.46 million cells from COVID-19 patients in less than two hours on a standard desktop.

scholarly journals Single-cell transcriptome sequencing: recent advances and remaining challenges

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 182 ◽  
Author(s):  
Serena Liu ◽  
Cole Trapnell
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Developmental Trajectories ◽  
Population Data ◽  
Transcriptional Dynamics ◽  
Single Cell Rna Sequencing ◽  
Cell Assays ◽  
Potential Applications ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

Single-cell RNA-sequencing methods are now robust and economically practical and are becoming a powerful tool for high-throughput, high-resolution transcriptomic analysis of cell states and dynamics. Single-cell approaches circumvent the averaging artifacts associated with traditional bulk population data, yielding new insights into the cellular diversity underlying superficially homogeneous populations. Thus far, single-cell RNA-sequencing has already shown great effectiveness in unraveling complex cell populations, reconstructing developmental trajectories, and modeling transcriptional dynamics. Ongoing technical improvements to single-cell RNA-sequencing throughput and sensitivity, the development of more sophisticated analytical frameworks for single-cell data, and an increasing array of complementary single-cell assays all promise to expand the usefulness and potential applications of single-cell transcriptomic profiling.

scholarly journals MBRS-46. CHARTING NEOPLASTIC AND IMMUNE CELL HETEROGENEITY IN HUMAN AND GEM MODELS OF MEDULLOBLASTOMA USING scRNAseq

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii406-iii406
Author(s):  
Andrew Donson ◽  
Kent Riemondy ◽  
Sujatha Venkataraman ◽  
Ahmed Gilani ◽  
Bridget Sanford ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Immune Cell ◽  
Genetically Engineered ◽  
Cellular Heterogeneity ◽  
Cell Heterogeneity ◽  
Transcriptomic Data ◽  
Single Cell Rna Sequencing ◽  
Transcript Profiles

Abstract We explored cellular heterogeneity in medulloblastoma using single-cell RNA sequencing (scRNAseq), immunohistochemistry and deconvolution of bulk transcriptomic data. Over 45,000 cells from 31 patients from all main subgroups of medulloblastoma (2 WNT, 10 SHH, 9 GP3, 11 GP4 and 1 GP3/4) were clustered using Harmony alignment to identify conserved subpopulations. Each subgroup contained subpopulations exhibiting mitotic, undifferentiated and neuronal differentiated transcript profiles, corroborating other recent medulloblastoma scRNAseq studies. The magnitude of our present study builds on the findings of existing studies, providing further characterization of conserved neoplastic subpopulations, including identification of a photoreceptor-differentiated subpopulation that was predominantly, but not exclusively, found in GP3 medulloblastoma. Deconvolution of MAGIC transcriptomic cohort data showed that neoplastic subpopulations are associated with major and minor subgroup subdivisions, for example, photoreceptor subpopulation cells are more abundant in GP3-alpha. In both GP3 and GP4, higher proportions of undifferentiated subpopulations is associated with shorter survival and conversely, differentiated subpopulation is associated with longer survival. This scRNAseq dataset also afforded unique insights into the immune landscape of medulloblastoma, and revealed an M2-polarized myeloid subpopulation that was restricted to SHH medulloblastoma. Additionally, we performed scRNAseq on 16,000 cells from genetically engineered mouse (GEM) models of GP3 and SHH medulloblastoma. These models showed a level of fidelity with corresponding human subgroup-specific neoplastic and immune subpopulations. Collectively, our findings advance our understanding of the neoplastic and immune landscape of the main medulloblastoma subgroups in both humans and GEM models.

scholarly journals Integrated Single-Cell RNA-Sequencing Analysis of Aquaporin 5-Expressing Mouse Lung Epithelial Cells Identifies GPRC5A as a Novel Validated Type I Cell Surface Marker

Cells ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 2460 ◽  
Author(s):  
Masafumi Horie ◽  
Alessandra Castaldi ◽  
Mitsuhiro Sunohara ◽  
Hongjun Wang ◽  
Yanbin Ji ◽  
...  
Keyword(s):  
Cell Surface ◽  
Single Cell ◽  
Rna Sequencing ◽  
Surface Marker ◽  
Mouse Lung ◽  
Type I ◽  
Cell Markers ◽  
Lung Epithelial ◽  
Single Cell Rna Sequencing

Molecular and functional characterization of alveolar epithelial type I (AT1) cells has been challenging due to difficulty in isolating sufficient numbers of viable cells. Here we performed single-cell RNA-sequencing (scRNA-seq) of tdTomato+ cells from lungs of AT1 cell-specific Aqp5-Cre-IRES-DsRed (ACID);R26tdTomato reporter mice. Following enzymatic digestion, CD31-CD45-E-cadherin+tdTomato+ cells were subjected to fluorescence-activated cell sorting (FACS) followed by scRNA-seq. Cell identity was confirmed by immunofluorescence using cell type-specific antibodies. After quality control, 92 cells were analyzed. Most cells expressed ‘conventional’ AT1 cell markers (Aqp5, Pdpn, Hopx, Ager), with heterogeneous expression within this population. The remaining cells expressed AT2, club, basal or ciliated cell markers. Integration with public datasets identified three robust AT1 cell- and lung-enriched genes, Ager, Rtkn2 and Gprc5a, that were conserved across species. GPRC5A co-localized with HOPX and was not expressed in AT2 or airway cells in mouse, rat and human lung. GPRC5A co-localized with AQP5 but not pro-SPC or CC10 in mouse lung epithelial cell cytospins. We enriched mouse AT1 cells to perform molecular phenotyping using scRNA-seq. Further characterization of putative AT1 cell-enriched genes revealed GPRC5A as a conserved AT1 cell surface marker that may be useful for AT1 cell isolation.

scholarly journals Expressed barcodes enable clonal characterization of chemotherapeutic responses in chronic lymphocytic leukemia

2019 ◽  
Author(s):  
Aziz Al’Khafaji ◽  
Catherine Gutierrez ◽  
Eric Brenner ◽  
Russell Durrett ◽  
Kaitlyn E. Johnson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Chronic Lymphocytic Leukemia ◽  
Single Cell ◽  
Rna Sequencing ◽  
Genetic Alterations ◽  
Response To Therapy ◽  
Lymphocytic Leukemia ◽  
Single Cell Rna Sequencing ◽  
Cancer Cell Population

AbstractThe remarkable evolutionary capacity of cancer is a major challenge to current therapeutic efforts. Fueling this evolution is its vast clonal heterogeneity and ability to adapt to diverse selective pressures. Although the genetic and transcriptional mechanisms underlying these responses have been independently evaluated, the ability to couple genetic alterations present within individual clones to their respective transcriptional or functional outputs has been lacking in the field. To this end, we developed a high-complexity expressed barcode library that integrates DNA barcoding with single-cell RNA sequencing through use of the CROP-seq sgRNA expression/capture system, and which is compatible with the COLBERT clonal isolation workflow for subsequent genomic and epigenomic characterization of specific clones of interest. We applied this approach to study chronic lymphocytic leukemia (CLL), a mature B cell malignancy notable for its genetic and transcriptomic heterogeneity and variable disease course. Here, we demonstrate the clonal composition and gene expression states of HG3, a CLL cell line harboring the common alteration del(13q), in response to front-line cytotoxic therapy of fludarabine and mafosfamide (an analog of the clinically used cyclophosphamide). Analysis of clonal abundance and clonally-resolved single-cell RNA sequencing revealed that only a small fraction of clones consistently survived therapy. These rare highly drug tolerant clones comprise 94% of the post-treatment population and share a stable, pre-existing gene expression state characterized by upregulation of CXCR4 and WNT signaling and a number of DNA damage and cell survival genes. Taken together, these data demonstrate at unprecedented resolution the diverse clonal characteristics and therapeutic responses of a heterogeneous cancer cell population. Further, this approach provides a template for the high-resolution study of thousands of clones and the respective gene expression states underlying their response to therapy.

scholarly journals A single cell transcriptome atlas of the developing zebrafish hindbrain

2019 ◽  
Author(s):  
Monica Tambalo ◽  
Richard Mitter ◽  
David G. Wilkinson
Keyword(s):  
Single Cell ◽  
Neuronal Differentiation ◽  
Cell Types ◽  
Spatial Patterning ◽  
Rna Seq ◽  
Valuable Resource ◽  
Dorsoventral Axis ◽  
Single Cell Rna Sequencing ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

AbstractSegmentation of the vertebrate hindbrain leads to the formation of rhombomeres, each with a distinct anteroposterior identity. Specialised boundary cells form at segment borders that act as a source or regulator of neuronal differentiation. In zebrafish, there is spatial patterning of neurogenesis in which non-neurogenic zones form at bounderies and segment centres, in part mediated by Fgf20 signaling. To further understand the control of neurogenesis, we have carried out single cell RNA sequencing of the zebrafish hindbrain at three different stages of patterning. Analyses of the data reveal known and novel markers of distinct hindbrain segments, of cell types along the dorsoventral axis, and of the transition of progenitors to neuronal differentiation. We find major shifts in the transcriptome of progenitors and of differentiating cells between the different stages analysed. Supervised clustering with markers of boundary cells and segment centres, together with RNA-seq analysis of Fgf-regulated genes, has revealed new candidate regulators of cell differentiation in the hindbrain. These data provide a valuable resource for functional investigations of the patterning of neurogenesis and the transition of progenitors to neuronal differentiation.

scholarly journals Deep Parallel Characterization of AAV Tropism and AAV-Mediated Transcriptional Changes via Single-Cell RNA Sequencing

2021 ◽  
Vol 12 ◽  
Author(s):  
David Brown ◽  
Michael Altermatt ◽  
Tatyana Dobreva ◽  
Sisi Chen ◽  
Alexander Wang ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Type ◽  
Gene Therapies ◽  
P53 Signaling ◽  
Single Cell Rna Sequencing ◽  
Delivery Vehicles ◽  
Tissue Tropisms

Engineered variants of recombinant adeno-associated viruses (rAAVs) are being developed rapidly to meet the need for gene-therapy delivery vehicles with particular cell-type and tissue tropisms. While high-throughput AAV engineering and selection methods have generated numerous variants, subsequent tropism and response characterization have remained low throughput and lack resolution across the many relevant cell and tissue types. To fully leverage the output of these large screening paradigms across multiple targets, we have developed an experimental and computational single-cell RNA sequencing (scRNA-seq) pipeline for in vivo characterization of barcoded rAAV pools at high resolution. Using this platform, we have both corroborated previously reported viral tropisms and discovered unidentified AAV capsid targeting biases. As expected, we observed that the tropism profile of AAV.CAP-B10 in mice was shifted toward neurons and away from astrocytes when compared with AAV-PHP.eB. Transcriptomic analysis revealed that this neuronal bias is due mainly to increased targeting efficiency for glutamatergic neurons, which we confirmed by RNA fluorescence in situ hybridization. We further uncovered cell subtype tropisms of AAV variants in vascular and glial cells, such as low transduction of pericytes and Myoc+ astrocytes. Additionally, we have observed cell-type-specific transitory responses to systemic AAV-PHP.eB administration, such as upregulation of genes involved in p53 signaling in endothelial cells three days post-injection, which return to control levels by day twenty-five. The presented experimental and computational approaches for parallel characterization of AAV tropism will facilitate the advancement of safe and precise gene delivery vehicles, and showcase the power of understanding responses to gene therapies at the single-cell level.

scholarly journals DSAVE: Detection of misclassified cells in single-cell RNA-Seq data

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243360
Author(s):  
Johan Gustafsson ◽  
Jonathan Robinson ◽  
Juan S. Inda-Díaz ◽  
Elias Björnson ◽  
Rebecka Jörnsten ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Types ◽  
Rna Seq ◽  
Cell Type ◽  
Log Likelihood ◽  
Single Cell Rna Sequencing ◽  
Cell Transcriptome ◽  
Average Gene ◽  
Single Cell Transcriptome

Single-cell RNA sequencing has become a valuable tool for investigating cell types in complex tissues, where clustering of cells enables the identification and comparison of cell populations. Although many studies have sought to develop and compare different clustering approaches, a deeper investigation into the properties of the resulting populations is lacking. Specifically, the presence of misclassified cells can influence downstream analyses, highlighting the need to assess subpopulation purity and to detect such cells. We developed DSAVE (Down-SAmpling based Variation Estimation), a method to evaluate the purity of single-cell transcriptome clusters and to identify misclassified cells. The method utilizes down-sampling to eliminate differences in sampling noise and uses a log-likelihood based metric to help identify misclassified cells. In addition, DSAVE estimates the number of cells needed in a population to achieve a stable average gene expression profile within a certain gene expression range. We show that DSAVE can be used to find potentially misclassified cells that are not detectable by similar tools and reveal the cause of their divergence from the other cells, such as differing cell state or cell type. With the growing use of single-cell RNA-seq, we foresee that DSAVE will be an increasingly useful tool for comparing and purifying subpopulations in single-cell RNA-Seq datasets.

scholarly journals High-throughput single-cell transcriptome profiling of plant cell types

2018 ◽  
Author(s):  
Christine N. Shulse ◽  
Benjamin J. Cole ◽  
Gina M. Turco ◽  
Yiwen Zhu ◽  
Siobhan M. Brady ◽  
...  
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Transcriptome Profiling ◽  
Cell Types ◽  
Marker Genes ◽  
Single Cell Rna Sequencing ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome ◽  
Heterogeneous Tissues ◽  
Genomic Basis

AbstractSingle-cell transcriptome analysis of heterogeneous tissues can provide high-resolution windows into the genomic basis and spatiotemporal dynamics of developmental processes. Here we demonstrate the feasibility of high-throughput single-cell RNA sequencing of plant tissue using the Drop-seq approach. Profiling of >4,000 individual cells from the Arabidopsis root provides transcriptomes and marker genes for a diversity of cell types and illuminates the gene expression changes that occur across endodermis development.

Sign in / Sign up

Export Citation Format

Share Document