Endothelial reprogramming by disturbed flow revealed by single-cell RNA and chromatin accessibility study

2020 ◽  
Author(s):  
Aitor Andueza ◽  
Sandeep Kumar ◽  
Juyoung Kim ◽  
Dong-Won Kang ◽  
Hope L Mumme ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cell ◽  
Single Cells ◽  
Chromatin Accessibility ◽  
Marker Genes ◽  
Hematopoietic Stem ◽  
Disturbed Flow ◽  
Single Cell Rna Sequencing ◽  
Left And Right ◽  
Stable Flow

SUMMARYDisturbed flow (d-flow) induces atherosclerosis by regulating gene expression in endothelial cells (ECs). For further mechanistic understanding, we carried out a single-cell RNA sequencing (scRNAseq) and scATACseq study using endothelial-enriched single-cells from the left- and right carotid artery exposed to d-flow (LCA) and stable-flow (s-flow in RCA) using the mouse partial carotid ligation (PCL) model. We found 8 EC clusters along with immune cells, fibroblasts, and smooth muscle cells. Analyses of marker genes, pathways, and pseudo-time revealed that ECs are highly heterogeneous and plastic. D-flow induced a dramatic transition of ECs from atheroprotective phenotypes to pro-inflammatory, mesenchymal (EndMT), hematopoietic stem cells, endothelial stem/progenitor cells, and an unexpected immune cell-like (EndICLT) phenotypes. While confirming KLF4/KLF2 as s-flow-sensitive transcription factor binding site, we also found those sensitive to d-flow (RELA, AP1, STAT1, and TEAD1). D-flow reprograms ECs from atheroprotective to pro-atherogenic phenotypes including EndMT and potentially EndICLT.

scholarly journals Comprehensive analysis of single-cell RNA sequencing data from healthy human marrow hematopoietic cells

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Xin Zhao ◽  
Shouguo Gao ◽  
Sachiko Kajigaya ◽  
Qingguo Liu ◽  
Zhijie Wu ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Fate ◽  
Noncoding Rna ◽  
Single Cells ◽  
Regulation Of Gene Expression ◽  
Sequencing Data ◽  
Healthy Human ◽  
Hematopoietic Stem ◽  
Single Cell Rna Sequencing

Abstract Objective Single cell methodology enables detection and quantification of transcriptional changes and unravelling dynamic aspects of the transcriptional heterogeneity not accessible using bulk sequencing approaches. We have applied single-cell RNA-sequencing (scRNA-seq) to fresh human bone marrow CD34+ cells and profiled 391 single hematopoietic stem/progenitor cells (HSPCs) from healthy donors to characterize lineage- and stage-specific transcription during hematopoiesis. Results Cells clustered into six distinct groups, which could be assigned to known HSPC subpopulations based on lineage specific genes. Reconstruction of differentiation trajectories in single cells revealed four committed lineages derived from HSCs, as well as dynamic expression changes underlying cell fate during early erythroid-megakaryocytic, lymphoid, and granulocyte-monocyte differentiation. A similar non-hierarchical pattern of hematopoiesis could be derived from analysis of published single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq), consistent with a sequential relationship between chromatin dynamics and regulation of gene expression during lineage commitment (first, altered chromatin conformation, then mRNA transcription). Computationally, we have reconstructed molecular trajectories connecting HSCs directly to four hematopoietic lineages. Integration of long noncoding RNA (lncRNA) expression from the same cells demonstrated mRNA transcriptome, lncRNA, and the epigenome were highly homologous in their pattern of gene activation and suppression during hematopoietic cell differentiation.

scholarly journals Automated identification of Cell Types in Single Cell RNA Sequencing

2019 ◽  
Author(s):  
Feiyang Ma ◽  
Matteo Pellegrini
Keyword(s):  
Neural Network ◽  
Single Cell ◽  
Rna Sequencing ◽  
Immune Cell ◽  
Cell Types ◽  
Marker Genes ◽  
Complex Data ◽  
Cell Type ◽  
Human T Cell ◽  
Single Cell Rna Sequencing

AbstractCell type identification is one of the major goals in single cell RNA sequencing (scRNA-seq). Current methods for assigning cell types typically involve the use of unsupervised clustering, the identification of signature genes in each cluster, followed by a manual lookup of these genes in the literature and databases to assign cell types. However, there are several limitations associated with these approaches, such as unwanted sources of variation that influence clustering and a lack of canonical markers for certain cell types. Here, we present ACTINN (Automated Cell Type Identification using Neural Networks), which employs a neural network with 3 hidden layers, trains on datasets with predefined cell types, and predicts cell types for other datasets based on the trained parameters. We trained the neural network on a mouse cell type atlas (Tabula Muris Atlas) and a human immune cell dataset, and used it to predict cell types for mouse leukocytes, human PBMCs and human T cell sub types. The results showed that our neural network is fast and accurate, and should therefore be a useful tool to complement existing scRNA-seq pipelines.Author SummarySingle cell RNA sequencing (scRNA-seq) provides high resolution profiling of the transcriptomes of individual cells, which inevitably results in high volumes of data that require complex data processing pipelines. Usually, one of the first steps in the analysis of scRNA-seq is to assign individual cells to known cell types. To accomplish this, traditional methods first group the cells into different clusters, then find marker genes, and finally use these to manually assign cell types for each cluster. Thus these methods require prior knowledge of cell type canonical markers, and some level of subjectivity to make the cell type assignments. As a result, the process is often laborious and requires domain specific expertise, which is a barrier for inexperienced users. By contrast, our neural network ACTINN automatically learns the features for each predefined cell type and uses these features to predict cell types for individual cells. This approach is computationally efficient and requires no domain expertise of the tissues being studied. We believe ACTINN allows users to rapidly identify cell types in their datasets, thus rendering the analysis of their scRNA-seq datasets more efficient.

scholarly journals Single-cell sequencing techniques from individual to multiomics analyses

2020 ◽  
Vol 52 (9) ◽  
pp. 1419-1427
Author(s):  
Yukie Kashima ◽  
Yoshitaka Sakamoto ◽  
Keiya Kaneko ◽  
Masahide Seki ◽  
Yutaka Suzuki ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Experimental Methods ◽  
Chromatin Accessibility ◽  
Data Sets ◽  
Transcriptome Data ◽  
Detailed Understanding ◽  
Single Cell Sequencing ◽  
Single Cell Rna Sequencing ◽  
Molecular Profiles

Abstract Here, we review single-cell sequencing techniques for individual and multiomics profiling in single cells. We mainly describe single-cell genomic, epigenomic, and transcriptomic methods, and examples of their applications. For the integration of multilayered data sets, such as the transcriptome data derived from single-cell RNA sequencing and chromatin accessibility data derived from single-cell ATAC-seq, there are several computational integration methods. We also describe single-cell experimental methods for the simultaneous measurement of two or more omics layers. We can achieve a detailed understanding of the basic molecular profiles and those associated with disease in each cell by utilizing a large number of single-cell sequencing techniques and the accumulated data sets.

scholarly journals Development of a Prognostic Signature Based on Single-Cell RNA Sequencing Data of Immune Cells in Intrahepatic Cholangiocarcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Miao Su ◽  
Kuang-Yuan Qiao ◽  
Xiao-Li Xie ◽  
Xin-Ying Zhu ◽  
Fu-Lai Gao ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Intrahepatic Cholangiocarcinoma ◽  
Immune Cells ◽  
Immune Cell ◽  
Functional Enrichment ◽  
Marker Genes ◽  
Tissue Cell ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

Analysis of single-cell RNA sequencing (scRNA-seq) data of immune cells from the tumor microenvironment (TME) may identify tumor progression biomarkers. This study was designed to investigate the prognostic value of differentially expressed genes (DEGs) in intrahepatic cholangiocarcinoma (ICC) using scRNA-seq. We downloaded the scRNA-seq data of 33,991 cell samples, including 17,090 ICC cell samples and 16,901 ICC adjacent tissue cell samples regarded as normal cells. scRNA-seq data were processed and classified into 20 clusters. The immune cell clusters were extracted and processed again in the same way, and each type of immune cells was divided into several subclusters. In total, 337 marker genes of macrophages and 427 marker genes of B cells were identified by comparing ICC subclusters with normal subclusters. Finally, 659 DEGs were obtained by merging B cell and macrophage marker genes. ICC sample clinical information and gene expression data were downloaded. A nine-prognosis-related-gene (PRG) signature was established by analyzing the correlation between DEGs and overall survival in ICC. The robustness and validity of the signature were verified. Functional enrichment analysis revealed that the nine PRGs were mainly involved in tumor immune mechanisms. In conclusion, we established a PRG signature based on scRNA-seq data from immune cells of patients with ICC. This PRG signature not only reflects the TME immune status but also provides new biomarkers for ICC prognosis.

scholarly journals Minnow: a principled framework for rapid simulation of dscRNA-seq data at the read level

2019 ◽  
Vol 35 (14) ◽  
pp. i136-i144 ◽  
Author(s):  
Hirak Sarkar ◽  
Avi Srivastava ◽  
Rob Patro
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Polymerase Chain Reaction Amplification ◽  
Single Cells ◽  
Simulated Data ◽  
Supplementary Information ◽  
Marker Genes ◽  
Gene Level ◽  
Single Cell Rna Sequencing ◽  
The Impact

Abstract Summary With the advancements of high-throughput single-cell RNA-sequencing protocols, there has been a rapid increase in the tools available to perform an array of analyses on the gene expression data that results from such studies. For example, there exist methods for pseudo-time series analysis, differential cell usage, cell-type detection RNA-velocity in single cells, etc. Most analysis pipelines validate their results using known marker genes (which are not widely available for all types of analysis) and by using simulated data from gene-count-level simulators. Typically, the impact of using different read-alignment or unique molecular identifier (UMI) deduplication methods has not been widely explored. Assessments based on simulation tend to start at the level of assuming a simulated count matrix, ignoring the effect that different approaches for resolving UMI counts from the raw read data may produce. Here, we present minnow, a comprehensive sequence-level droplet-based single-cell RNA-sequencing (dscRNA-seq) experiment simulation framework. Minnow accounts for important sequence-level characteristics of experimental scRNA-seq datasets and models effects such as polymerase chain reaction amplification, cellular barcodes (CB) and UMI selection and sequence fragmentation and sequencing. It also closely matches the gene-level ambiguity characteristics that are observed in real scRNA-seq experiments. Using minnow, we explore the performance of some common processing pipelines to produce gene-by-cell count matrices from droplet-bases scRNA-seq data, demonstrate the effect that realistic levels of gene-level sequence ambiguity can have on accurate quantification and show a typical use-case of minnow in assessing the output generated by different quantification pipelines on the simulated experiment. Supplementary information Supplementary data are available at Bioinformatics online.

Single cell RNA sequencing of upper tract urothelial carcinoma to reveal significant heterogeneity of the tumor and immune microenvironment.

2021 ◽  
Vol 39 (6_suppl) ◽  
pp. 484-484
Author(s):  
Peter Reisz ◽  
Andrew Tracey ◽  
Fengshen Kuo ◽  
Jasmine Thomas ◽  
Timothy Nguyen Clinton ◽  
...  
Keyword(s):  
Urothelial Carcinoma ◽  
Single Cell ◽  
Rna Sequencing ◽  
Immune Cell ◽  
Marker Genes ◽  
Low Grade ◽  
Significant Heterogeneity ◽  
High Grade ◽  
Single Cell Rna Sequencing ◽  
Tissue Specimens

484 Background: Upper tract urothelial carcinoma (UTUC) comprises 5-10% of urothelial malignancies but demonstrates unique clinical and molecular characteristics compared to urothelial carcinoma of the bladder. Prior investigations have used bulk profiling of tumor tissue to identify molecular subtypes, classifying the majority of UTUC as luminal and T-cell depleted. However, bulk sequencing does not allow for analysis of the significant heterogeneity known to be present in urothelial tumors. Single-cell RNA sequencing (scRNA-seq) allows examination of intra-tumoral heterogeneity, clonality, and the complex interactions of the immune tumor microenvironment (TME). We sought to apply this technology to better characterize UTUC and the TME. Methods: Single cell RNA sequencing (scRNA-seq) was performed on nine UTUC tissue specimens from six different patients collected fresh via ureteroscopic biopsy using an established institutional process and the 10X Genomics platform. Sequencing reads were normalized and analyzed using R/Seurat package. We assessed the composition of each tumor specimen with known marker genes for molecular subtypes (luminal, basal, squamous, EMT, and claudin-low). We then assessed the composition of immune cells in each specimen using known marker genes. We compared high- and low-grade specimens by subtype composition and immune cell infiltrates. Results: Lineage density analyses demonstrate the intra- and inter-tumoral heterogeneity of the nine endoscopic samples analyzed by molecular subtype composition. There is higher expression of luminal and claudin-low subtypes across all samples. The high-grade samples have higher expression of squamous markers. There is significant heterogeneity of immune cell infiltrates in seven specimens (two specimens were excluded due to low CD45+ cell counts). There is higher macrophage infiltration in high-grade samples, which was the only significant difference (Wilcoxon two-sided p-value = 0.05). Conclusions: This is the first known study using scRNA-seq expression analysis to characterize the notable heterogeneity of high and low-grade UTUC and the associated TME. Lineage density analysis demonstrates high luminal gene expression across samples, which has been demonstrated on prior bulk sequencing studies. The immune TME is also heterogeneous, with notable increased infiltration of macrophages in high-grade disease. There are unique limitations to performing and analyzing scRNA-seq of fresh UTUC tissue specimens, thus data should be interpreted cautiously. However, this study demonstrates the marked heterogeneity of UTUC tumors and frames our current approaches to bulk molecular subtyping of urothelial cancers and immune deconvolution. Further high-resolution studies are needed to characterize UTUC and inform bulk-sequencing efforts.

scholarly journals Insights Into Human Intrahepatic NK Cell Function From Single Cell RNA Sequencing Datasets

2021 ◽  
Vol 12 ◽  
Author(s):  
Gráinne Jameson ◽  
Mark W. Robinson
Keyword(s):  
Nk Cells ◽  
Single Cell ◽  
Rna Sequencing ◽  
Human Liver ◽  
Immune Cell ◽  
Nk Cell ◽  
Single Cells ◽  
Cell Populations ◽  
Cellular Interactions ◽  
Single Cell Rna Sequencing

Diverse populations of natural killer (NK) cells have been identified in circulating peripheral blood and a wide variety of different tissues and organs. These tissue-resident NK cell populations are phenotypically distinct from circulating NK cells, however, functional descriptions of their roles within tissues are lacking. Recent advances in single cell RNA sequencing (scRNA-seq) have enabled detailed transcriptional profiling of tissues at the level of single cells and provide the opportunity to explore NK cell diversity within tissues. This review explores potential novel functions of human liver-resident (lr)NK cells identified in human liver scRNA-seq studies. By comparing these datasets we identified up-regulated and down-regulated genes associated with lrNK cells clusters. These genes encode a number of activating and inhibiting receptors, as well as signal transduction molecules, which highlight potential unique pathways that lrNK cells utilize to respond to stimuli within the human liver. This unique receptor repertoire of lrNK cells may confer the ability to regulate a number of immune cell populations, such as circulating monocytes and T cells, while avoiding activation by liver hepatocytes and Kupffer cells. Validating the expression of these receptors on lrNK cells and the proposed cellular interactions within the human liver will expand our understanding of the liver-specific homeostatic roles of this tissue-resident immune cell population.

scholarly journals Single cell transcriptome atlas of mouse mammary epithelial cells across development

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Bhupinder Pal ◽  
Yunshun Chen ◽  
Michael J. G. Milevskiy ◽  
François Vaillant ◽  
Lexie Prokopuk ◽  
...  
Keyword(s):  
Epithelial Cells ◽  
Single Cell ◽  
Developmental Stages ◽  
Mammary Epithelial Cells ◽  
Expression Profiles ◽  
Single Cells ◽  
Chromatin Accessibility ◽  
Mammary Epithelial ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

Abstract Background Heterogeneity within the mouse mammary epithelium and potential lineage relationships have been recently explored by single-cell RNA profiling. To further understand how cellular diversity changes during mammary ontogeny, we profiled single cells from nine different developmental stages spanning late embryogenesis, early postnatal, prepuberty, adult, mid-pregnancy, late-pregnancy, and post-involution, as well as the transcriptomes of micro-dissected terminal end buds (TEBs) and subtending ducts during puberty. Methods The single cell transcriptomes of 132,599 mammary epithelial cells from 9 different developmental stages were determined on the 10x Genomics Chromium platform, and integrative analyses were performed to compare specific time points. Results The mammary rudiment at E18.5 closely aligned with the basal lineage, while prepubertal epithelial cells exhibited lineage segregation but to a less differentiated state than their adult counterparts. Comparison of micro-dissected TEBs versus ducts showed that luminal cells within TEBs harbored intermediate expression profiles. Ductal basal cells exhibited increased chromatin accessibility of luminal genes compared to their TEB counterparts suggesting that lineage-specific chromatin is established within the subtending ducts during puberty. An integrative analysis of five stages spanning the pregnancy cycle revealed distinct stage-specific profiles and the presence of cycling basal, mixed-lineage, and 'late' alveolar intermediates in pregnancy. Moreover, a number of intermediates were uncovered along the basal-luminal progenitor cell axis, suggesting a continuum of alveolar-restricted progenitor states. Conclusions This extended single cell transcriptome atlas of mouse mammary epithelial cells provides the most complete coverage for mammary epithelial cells during morphogenesis to date. Together with chromatin accessibility analysis of TEB structures, it represents a valuable framework for understanding developmental decisions within the mouse mammary gland.

scholarly journals Cryopreservation of human cancers conserves tumour heterogeneity for single-cell multi-omics analysis

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Sunny Z. Wu ◽  
Daniel L. Roden ◽  
Ghamdan Al-Eryani ◽  
Nenad Bartonicek ◽  
Kate Harvey ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
High Throughput ◽  
Single Cells ◽  
Cellular Heterogeneity ◽  
Tumour Heterogeneity ◽  
Fresh Tissue ◽  
Human Cancers ◽  
Cryopreserved Cell ◽  
Single Cell Rna Sequencing

Abstract Background High throughput single-cell RNA sequencing (scRNA-Seq) has emerged as a powerful tool for exploring cellular heterogeneity among complex human cancers. scRNA-Seq studies using fresh human surgical tissue are logistically difficult, preclude histopathological triage of samples, and limit the ability to perform batch processing. This hindrance can often introduce technical biases when integrating patient datasets and increase experimental costs. Although tissue preservation methods have been previously explored to address such issues, it is yet to be examined on complex human tissues, such as solid cancers and on high throughput scRNA-Seq platforms. Methods Using the Chromium 10X platform, we sequenced a total of ~ 120,000 cells from fresh and cryopreserved replicates across three primary breast cancers, two primary prostate cancers and a cutaneous melanoma. We performed detailed analyses between cells from each condition to assess the effects of cryopreservation on cellular heterogeneity, cell quality, clustering and the identification of gene ontologies. In addition, we performed single-cell immunophenotyping using CITE-Seq on a single breast cancer sample cryopreserved as solid tissue fragments. Results Tumour heterogeneity identified from fresh tissues was largely conserved in cryopreserved replicates. We show that sequencing of single cells prepared from cryopreserved tissue fragments or from cryopreserved cell suspensions is comparable to sequenced cells prepared from fresh tissue, with cryopreserved cell suspensions displaying higher correlations with fresh tissue in gene expression. We showed that cryopreservation had minimal impacts on the results of downstream analyses such as biological pathway enrichment. For some tumours, cryopreservation modestly increased cell stress signatures compared to freshly analysed tissue. Further, we demonstrate the advantage of cryopreserving whole-cells for detecting cell-surface proteins using CITE-Seq, which is impossible using other preservation methods such as single nuclei-sequencing. Conclusions We show that the viable cryopreservation of human cancers provides high-quality single-cells for multi-omics analysis. Our study guides new experimental designs for tissue biobanking for future clinical single-cell RNA sequencing studies.

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.

Sign in / Sign up

Export Citation Format

Share Document