scholarly journals CellSIUS provides sensitive and specific detection of rare cell populations from complex single cell RNA-seq data

2019 ◽  
Author(s):  
Rebekka Wegmann ◽  
Marilisa Neri ◽  
Sven Schuierer ◽  
Bilada Bilican ◽  
Huyen Hartkopf ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Populations ◽  
Rna Seq ◽  
Differentiation Protocol ◽  
Layer V ◽  
Retzius Cells ◽  
Rare Cells ◽  
Cell Cycle Status

AbstractComprehensive benchmarking of computational methods for single-cell RNA sequencing (scRNA-seq) analysis is scarce. Using a modular workflow and a large dataset with known cell composition, we benchmarked feature selection and clustering methodologies for scRNA-seq data. Results highlighted a methodology gap for rare cell population identification for which we developed CellSIUS (Cell Subtype Identification from Upregulated gene Sets). CellSIUS outperformed existing approaches, enabled the identification of rare cell populations and, in contrast to other methods, simultaneously revealed transcriptomic signatures indicative of the rare cells’ function. We exemplified the use of our workflow and CellSIUS for the characterization of a human pluripotent cell 3D spheroid differentiation protocol recapitulating deep-layer corticogenesis in vitro. Results revealed lineage bifurcation between Cajal-Retzius cells and layer V/VI neurons as well as rare cell populations that differ by migratory, metabolic, or cell cycle status, including a choroid plexus neuroepithelial subgroup, revealing previously unrecognized complexity in human stem cell-derived cellular populations.

scholarly journals A single cell transcriptional atlas of early synovial joint development

Development ◽  
2020 ◽  
Vol 147 (14) ◽  
pp. dev185777 ◽  
Author(s):  
Qin Bian ◽  
Yu-Hao Cheng ◽  
Jordan P. Wilson ◽  
Emily Y. Su ◽  
Dong Won Kim ◽  
...  
Keyword(s):  
Knee Joint ◽  
Single Cell ◽  
Lineage Tracing ◽  
Rna Seq ◽  
Synovial Joint ◽  
Isolated Populations ◽  
Joint Development

ABSTRACTSynovial joint development begins with the formation of the interzone, a region of condensed mesenchymal cells at the site of the prospective joint. Recently, lineage-tracing strategies have revealed that Gdf5-lineage cells native to and from outside the interzone contribute to most, if not all, of the major joint components. However, there is limited knowledge of the specific transcriptional and signaling programs that regulate interzone formation and fate diversification of synovial joint constituents. To address this, we have performed single cell RNA-Seq analysis of 7329 synovial joint progenitor cells from the developing murine knee joint from E12.5 to E15.5. By using a combination of computational analytics, in situ hybridization and in vitro characterization of prospectively isolated populations, we have identified the transcriptional profiles of the major developmental paths for joint progenitors. Our freely available single cell transcriptional atlas will serve as a resource for the community to uncover transcriptional programs and cell interactions that regulate synovial joint development.

In Vitro Assay for Single-cell Characterization of Impaired Deformability in Red Blood Cells under Recurrent Episodes of Hypoxia

Lab on a Chip ◽  
2021 ◽  
Author(s):  
YUHAO QIANG ◽  
Jia Liu ◽  
Ming Dao ◽  
E Du
Keyword(s):  
Shear Stress ◽  
Red Blood Cells ◽  
Single Cell ◽  
Blood Cells ◽  
Blood Circulation ◽  
In Vitro Assay ◽  
Vitro Assay ◽  
Cyclic Shear

Red blood cells (RBCs) are subjected to recurrent changes in shear stress and oxygen tension during blood circulation. The cyclic shear stress has been identified as an important factor that...

scholarly journals Identification of Potential Key lncRNAs in the Context of Mouse Myeloid Differentiation by Systematic Transcriptomics Analysis

Genes ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 630
Author(s):  
Yongqing Lan ◽  
Meng Li ◽  
Shuangli Mi
Keyword(s):  
Transcription Factor ◽  
Cell Model ◽  
Myeloid Differentiation ◽  
Rna Seq ◽  
Hematopoietic Differentiation ◽  
Granulocytic Differentiation ◽  
Expression Of Genes ◽  
Non Coding Rnas

Hematopoietic differentiation is a well-orchestrated process by many regulators such as transcription factor and long non-coding RNAs (lncRNAs). However, due to the large number of lncRNAs and the difficulty in determining their roles, the study of lncRNAs is a considerable challenge in hematopoietic differentiation. Here, through gene co-expression network analysis over RNA-seq data generated from representative types of mouse myeloid cells, we obtained a catalog of potential key lncRNAs in the context of mouse myeloid differentiation. Then, employing a widely used in vitro cell model, we screened a novel lncRNA, named Gdal1 (Granulocytic differentiation associated lncRNA 1), from this list and demonstrated that Gdal1 was required for granulocytic differentiation. Furthermore, knockdown of Cebpe, a principal transcription factor of granulocytic differentiation regulation, led to down-regulation of Gdal1, but not vice versa. In addition, expression of genes involved in myeloid differentiation and its regulation, such as Cebpa, were influenced in Gdal1 knockdown cells with differentiation blockage. We thus systematically identified myeloid differentiation associated lncRNAs and substantiated the identification by investigation of one of these lncRNAs on cellular phenotype and gene regulation levels. This study promotes our understanding of the regulation of myeloid differentiation and the characterization of roles of lncRNAs in hematopoietic system.

scholarly journals Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Tracy M. Yamawaki ◽  
Daniel R. Lu ◽  
Daniel C. Ellwanger ◽  
Dev Bhatt ◽  
Paolo Manzanillo ◽  
...  
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Immune Cell ◽  
Cell Types ◽  
Data Interpretation ◽  
Detection Sensitivity ◽  
Rna Seq ◽  
Cell Recovery

Abstract Background Elucidation of immune populations with single-cell RNA-seq has greatly benefited the field of immunology by deepening the characterization of immune heterogeneity and leading to the discovery of new subtypes. However, single-cell methods inherently suffer from limitations in the recovery of complete transcriptomes due to the prevalence of cellular and transcriptional dropout events. This issue is often compounded by limited sample availability and limited prior knowledge of heterogeneity, which can confound data interpretation. Results Here, we systematically benchmarked seven high-throughput single-cell RNA-seq methods. We prepared 21 libraries under identical conditions of a defined mixture of two human and two murine lymphocyte cell lines, simulating heterogeneity across immune-cell types and cell sizes. We evaluated methods by their cell recovery rate, library efficiency, sensitivity, and ability to recover expression signatures for each cell type. We observed higher mRNA detection sensitivity with the 10x Genomics 5′ v1 and 3′ v3 methods. We demonstrate that these methods have fewer dropout events, which facilitates the identification of differentially-expressed genes and improves the concordance of single-cell profiles to immune bulk RNA-seq signatures. Conclusion Overall, our characterization of immune cell mixtures provides useful metrics, which can guide selection of a high-throughput single-cell RNA-seq method for profiling more complex immune-cell heterogeneity usually found in vivo.

scholarly journals Single-Cell Transcriptomics Reveals the Expression of Aging- and Senescence-Associated Genes in Distinct Cancer Cell Populations

Cells ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 3126
Author(s):  
Dominik Saul ◽  
Robyn Laura Kosinsky
Keyword(s):  
Single Cell ◽  
Myelogenous Leukemia ◽  
Ductal Adenocarcinoma ◽  
Cell Populations ◽  
Rna Seq ◽  
Healthy Control ◽  
Transcriptional Changes ◽  
The Relationship ◽  
Senescence Associated Genes

The human aging process is associated with molecular changes and cellular degeneration, resulting in a significant increase in cancer incidence with age. Despite their potential correlation, the relationship between cancer- and ageing-related transcriptional changes is largely unknown. In this study, we aimed to analyze aging-associated transcriptional patterns in publicly available bulk mRNA-seq and single-cell RNA-seq (scRNA-seq) datasets for chronic myelogenous leukemia (CML), colorectal cancer (CRC), hepatocellular carcinoma (HCC), lung cancer (LC), and pancreatic ductal adenocarcinoma (PDAC). Indeed, we detected that various aging/senescence-induced genes (ASIGs) were upregulated in malignant diseases compared to healthy control samples. To elucidate the importance of ASIGs during cell development, pseudotime analyses were performed, which revealed a late enrichment of distinct cancer-specific ASIG signatures. Notably, we were able to demonstrate that all cancer entities analyzed in this study comprised cell populations expressing ASIGs. While only minor correlations were detected between ASIGs and transcriptome-wide changes in PDAC, a high proportion of ASIGs was induced in CML, CRC, HCC, and LC samples. These unique cellular subpopulations could serve as a basis for future studies on the role of aging and senescence in human malignancies.

scholarly journals Single cell analysis reveals the impact of age and maturation stage on the human oocyte transcriptome

2020 ◽  
Author(s):  
Silvia Llonch ◽  
Montserrat Barragán ◽  
Paula Nieto ◽  
Anna Mallol ◽  
Marc Elosua-Bayes ◽  
...  
Keyword(s):  
Single Cell ◽  
Chromosome Segregation ◽  
Maternal Age ◽  
Maturation Stage ◽  
P Value ◽  
Rna Seq ◽  
Transcript Levels ◽  
Human Oocytes ◽  
The Impact

AbstractStudy questionTo which degree does maternal age affect the transcriptome of human oocytes at the germinal vesicle (GV) stage or at metaphase II after maturation in vitro (IVM-MII)?Summary answerWhile the oocytes’ transcriptome is predominantly determined by maturation stage, transcript levels of genes related to chromosome segregation, mitochondria and RNA processing are affected by age after in vitro maturation of denuded oocytes.What is known alreadyFemale fertility is inversely correlated with maternal age due to both a depletion of the oocyte pool and a reduction in oocyte developmental competence. Few studies have addressed the effect of maternal age on the human mature oocyte (MII) transcriptome, which is established during oocyte growth and maturation, and the pathways involved remain unclear. Here, we characterize and compare the transcriptomes of a large cohort of fully grown GV and IVM-MII oocytes from women of varying reproductive age.Study design, size, durationIn this prospective molecular study, 37 women were recruited from May 2018 to June 2019. The mean age was 28.8 years (SD=7.7, range 18-43). A total of 72 oocytes were included in the study at GV stage after ovarian stimulation, and analyzed as GV (n=40) and in vitro matured oocytes (IVM-MII; n=32).Participants/materials, setting, methodsDenuded oocytes were included either as GV at the time of ovum pick-up or as IVM-MII after in vitro maturation for 30 hours in G2™ medium, and processed for transcriptomic analysis by single-cell RNA-seq using the Smart-seq2 technology. Cluster and maturation stage marker analysis were performed using the Seurat R package. Genes with an average fold change greater than 2 and a p-value < 0.01 were considered maturation stage markers. A Pearson correlation test was used to identify genes whose expression levels changed progressively with age. Those genes presenting a correlation value (R) >= |0.3| and a p-value < 0.05 were considered significant.Main results and the role of chanceFirst, by exploration of the RNA-seq data using tSNE dimensionality reduction, we identified two clusters of cells reflecting the oocyte maturation stage (GV and IVM-MII) with 4,445 and 324 putative marker genes, respectively. Next we identified genes, for which RNA levels either progressively increased or decreased with age. This analysis was performed independently for GV and IVM-MII oocytes. Our results indicate that the transcriptome is more affected by age in IVM-MII oocytes (1,219 genes) than in GV oocytes (596 genes). In particular, we found that genes involved in chromosome segregation and RNA splicing significantly increase in transcript levels with age, while genes related to mitochondrial activity present lower transcript levels with age. Gene regulatory network analysis revealed potential upstream master regulator functions for genes whose transcript levels present positive (GPBP1, RLF, SON, TTF1) or negative (BNC1, THRB) correlation with age.Limitations, reasons for cautionIVM-MII oocytes used in this study were obtained after in vitro maturation of denuded GV oocytes, therefore, their transcriptome might not be fully representative of in vivo matured MII oocytes.The Smart-seq2 methodology used in this study detects polyadenylated transcripts only and we could therefore not assess non-polyadenylated transcripts.Wider implications of the findingsOur analysis suggests that advanced maternal age does not globally affect the oocyte transcriptome at GV or IVM-MII stages. Nonetheless, hundreds of genes displayed altered transcript levels with age, particularly in IVM-MII oocytes. Especially affected by age were genes related to chromosome segregation and mitochondrial function, pathways known to be involved in oocyte ageing. Our study thereby suggests that misregulation of chromosome segregation and mitochondrial pathways also at the RNA-level might contribute to the age-related quality decline in human oocytes.Study funding/competing interest(s)This study was funded by the AXA research fund, the European commission, intramural funding of Clinica EUGIN, the Spanish Ministry of Science, Innovation and Universities, the Catalan Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR) and by contributions of the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) to the EMBL partnership and to the “Centro de Excelencia Severo Ochoa”.The authors have no conflict of interest to declare.

scholarly journals Single cell RNA sequencing of calvarial and long bone endocortical cells

2019 ◽  
Author(s):  
Ugur M. Ayturk ◽  
Joseph P. Scollan ◽  
Alexander Vesprey ◽  
Christina M. Jacobsen ◽  
Paola Divieti Pajevic ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cultured Cells ◽  
Neutralizing Antibody ◽  
Long Bone ◽  
Appendicular Skeleton ◽  
Rna Seq ◽  
Isolated Cells

ABSTRACTSingle cell RNA-seq (scRNA-seq) is emerging as a powerful technology to examine transcriptomes of individual cells. We determined whether scRNA-seq could be used to detect the effect of environmental and pharmacologic perturbations on osteoblasts. We began with a commonly used in vitro system in which freshly isolated neonatal mouse calvarial cells are expanded and induced to produce a mineralized matrix. We used scRNA-seq to compare the relative cell type abundances and the transcriptomes of freshly isolated cells to those that had been cultured for 12 days in vitro. We observed that the percentage of macrophage-like cells increased from 6% in freshly isolated calvarial cells to 34% in cultured cells. We also found that Bglap transcripts were abundant in freshly isolated osteoblasts but nearly undetectable in the cultured calvarial cells. Thus, scRNA-seq revealed significant differences between heterogeneity of cells in vivo and in vitro. We next performed scRNA-seq on freshly recovered long bone endocortical cells from mice that received either vehicle or Sclerostin-neutralizing antibody for 1 week. Bone anabolism-associated transcripts were also not significantly increased in immature and mature osteoblasts recovered from Sclerostin-neutralizing antibody treated mice; this is likely a consequence of being underpowered to detect modest changes in gene expression, since only 7% of the sequenced endocortical cells were osteoblasts, and a limited portion of their transcriptomes were sampled. We conclude that scRNA-seq can detect changes in cell abundance, identity, and gene expression in skeletally derived cells. In order to detect modest changes in osteoblast gene expression at the single cell level in the appendicular skeleton, larger numbers of osteoblasts from endocortical bone are required.

scholarly journals Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM

2019 ◽  
Author(s):  
Marcus Alvarez ◽  
Elior Rahmani ◽  
Brandon Jew ◽  
Kristina M. Garske ◽  
Zong Miao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Types ◽  
Supervised Machine Learning ◽  
Data Sets ◽  
Rna Seq ◽  
Novel Approach ◽  
Single Nucleus ◽  
Downstream Analysis

AbstractSingle-nucleus RNA sequencing (snRNA-seq) measures gene expression in individual nuclei instead of cells, allowing for unbiased cell type characterization in solid tissues. Contrary to single-cell RNA seq (scRNA-seq), we observe that snRNA-seq is commonly subject to contamination by high amounts of extranuclear background RNA, which can lead to identification of spurious cell types in downstream clustering analyses if overlooked. We present a novel approach to remove debris-contaminated droplets in snRNA-seq experiments, called Debris Identification using Expectation Maximization (DIEM). Our likelihood-based approach models the gene expression distribution of debris and cell types, which are estimated using EM. We evaluated DIEM using three snRNA-seq data sets: 1) human differentiating preadipocytes in vitro, 2) fresh mouse brain tissue, and 3) human frozen adipose tissue (AT) from six individuals. All three data sets showed various degrees of extranuclear RNA contamination. We observed that existing methods fail to account for contaminated droplets and led to spurious cell types. When compared to filtering using these state of the art methods, DIEM better removed droplets containing high levels of extranuclear RNA and led to higher quality clusters. Although DIEM was designed for snRNA-seq data, we also successfully applied DIEM to single-cell data. To conclude, our novel method DIEM removes debris-contaminated droplets from single-cell-based data fast and effectively, leading to cleaner downstream analysis. Our code is freely available for use at https://github.com/marcalva/diem.

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