scholarly journals SAVER: Gene expression recovery for UMI-based single cell RNA sequencing

2017 ◽  
Author(s):  
Mo Huang ◽  
Jingshu Wang ◽  
Eduardo Torre ◽  
Hannah Dueck ◽  
Sydney Shaffer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Single Cell Analysis ◽  
Specific Gene ◽  
Recovery Method ◽  
Single Cell Rna Sequencing ◽  
Cell Gene Expression ◽  
Single Cell Profiling ◽  
Downstream Analysis

AbstractRapid advances in massively parallel single cell RNA sequencing (scRNA-seq) is paving the way for high-resolution single cell profiling of biological samples. In most scRNA-seq studies, only a small fraction of the transcripts present in each cell are sequenced. The efficiency, that is, the proportion of transcripts in the cell that are sequenced, can be especially low in highly parallelized experiments where the number of reads allocated for each cell is small. This leads to unreliable quantification of lowly and moderately expressed genes, resulting in extremely sparse data and hindering downstream analysis. To address this challenge, we introduce SAVER (Single-cell Analysis Via Expression Recovery), an expression recovery method for scRNA-seq that borrows information across genes and cells to impute the zeros as well as to improve the expression estimates for all genes. We show, by comparison to RNA fluorescence in situ hybridization (FISH) and by data down-sampling experiments, that SAVER reliably recovers cell-specific gene expression concentrations, cross-cell gene expression distributions, and gene-to-gene and cell-to-cell correlations. This improves the power and accuracy of any downstream analysis involving genes with low to moderate expression.

scholarly journals Microbial single-cell RNA sequencing by split-pool barcoding

Science ◽  
2020 ◽  
Vol 371 (6531) ◽  
pp. eaba5257 ◽  
Author(s):  
Anna Kuchina ◽  
Leandra M. Brettner ◽  
Luana Paleologu ◽  
Charles M. Roco ◽  
Alexander B. Rosenberg ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
High Throughput ◽  
Single Cell Analysis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Growth Stages ◽  
High Throughput Analysis ◽  
Single Cell Rna Sequencing

Single-cell RNA sequencing (scRNA-seq) has become an essential tool for characterizing gene expression in eukaryotes, but current methods are incompatible with bacteria. Here, we introduce microSPLiT (microbial split-pool ligation transcriptomics), a high-throughput scRNA-seq method for Gram-negative and Gram-positive bacteria that can resolve heterogeneous transcriptional states. We applied microSPLiT to >25,000 Bacillus subtilis cells sampled at different growth stages, creating an atlas of changes in metabolism and lifestyle. We retrieved detailed gene expression profiles associated with known, but rare, states such as competence and prophage induction and also identified unexpected gene expression states, including the heterogeneous activation of a niche metabolic pathway in a subpopulation of cells. MicroSPLiT paves the way to high-throughput analysis of gene expression in bacterial communities that are otherwise not amenable to single-cell analysis, such as natural microbiota.

scholarly journals SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references

2020 ◽  
Author(s):  
Meichen Dong ◽  
Aatish Thennavan ◽  
Eugene Urrutia ◽  
Yun Li ◽  
Charles M Perou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Mixed Cell ◽  
Single Cell Rna Sequencing

Abstract Recent advances in single-cell RNA sequencing (scRNA-seq) enable characterization of transcriptomic profiles with single-cell resolution and circumvent averaging artifacts associated with traditional bulk RNA sequencing (RNA-seq) data. Here, we propose SCDC, a deconvolution method for bulk RNA-seq that leverages cell-type specific gene expression profiles from multiple scRNA-seq reference datasets. SCDC adopts an ENSEMBLE method to integrate deconvolution results from different scRNA-seq datasets that are produced in different laboratories and at different times, implicitly addressing the problem of batch-effect confounding. SCDC is benchmarked against existing methods using both in silico generated pseudo-bulk samples and experimentally mixed cell lines, whose known cell-type compositions serve as ground truths. We show that SCDC outperforms existing methods with improved accuracy of cell-type decomposition under both settings. To illustrate how the ENSEMBLE framework performs in complex tissues under different scenarios, we further apply our method to a human pancreatic islet dataset and a mouse mammary gland dataset. SCDC returns results that are more consistent with experimental designs and that reproduce more significant associations between cell-type proportions and measured phenotypes.

scholarly journals Implication of specific retinal cell-type involvement and gene expression changes in AMD progression using integrative analysis of single-cell and bulk RNA-seq profiling

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yafei Lyu ◽  
Randy Zauhar ◽  
Nicholas Dana ◽  
Christianne E. Strang ◽  
Jian Hu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Age Related Macular Degeneration ◽  
Specific Gene ◽  
Cell Type ◽  
Adult Human ◽  
Single Cell Rna Sequencing ◽  
Cell Type Specific ◽  
Cell Data

AbstractAge‐related macular degeneration (AMD) is a blinding eye disease with no unifying theme for its etiology. We used single-cell RNA sequencing to analyze the transcriptomes of ~ 93,000 cells from the macula and peripheral retina from two adult human donors and bulk RNA sequencing from fifteen adult human donors with and without AMD. Analysis of our single-cell data identified 267 cell-type-specific genes. Comparison of macula and peripheral retinal regions found no cell-type differences but did identify 50 differentially expressed genes (DEGs) with about 1/3 expressed in cones. Integration of our single-cell data with bulk RNA sequencing data from normal and AMD donors showed compositional changes more pronounced in macula in rods, microglia, endothelium, Müller glia, and astrocytes in the transition from normal to advanced AMD. KEGG pathway analysis of our normal vs. advanced AMD eyes identified enrichment in complement and coagulation pathways, antigen presentation, tissue remodeling, and signaling pathways including PI3K-Akt, NOD-like, Toll-like, and Rap1. These results showcase the use of single-cell RNA sequencing to infer cell-type compositional and cell-type-specific gene expression changes in intact bulk tissue and provide a foundation for investigating molecular mechanisms of retinal disease that lead to new therapeutic targets.

scholarly journals The in vivo endothelial cell translatome is highly heterogeneous across vascular beds

2019 ◽  
Author(s):  
Audrey C.A. Cleuren ◽  
Martijn A. van der Ent ◽  
Hui Jiang ◽  
Kristina L. Hunker ◽  
Andrew Yee ◽  
...  
Keyword(s):  
Gene Expression ◽  
Endothelial Cells ◽  
Single Cell ◽  
Blood Vessels ◽  
Rna Sequencing ◽  
Specific Gene ◽  
Sequencing Analysis ◽  
Single Cell Rna Sequencing ◽  
Vascular Beds

AbstractEndothelial cells (ECs) are highly specialized across vascular beds. However, given their interspersed anatomic distribution, comprehensive characterization of the molecular basis for this heterogeneity in vivo has been limited. By applying endothelial-specific translating ribosome affinity purification (EC-TRAP) combined with high-throughput RNA sequencing analysis, we identified pan EC-enriched genes and tissue-specific EC transcripts, which include both established markers and genes previously unappreciated for their presence in ECs. In addition, EC-TRAP limits changes in gene expression following EC isolation and in vitro expansion, as well as rapid vascular bed-specific shifts in EC gene expression profiles as a result of the enzymatic tissue dissociation required to generate single cell suspensions for fluorescence-activated cell sorting (FACS) or single cell RNA sequencing analysis. Comparison of our EC-TRAP to published single cell RNA sequencing data further demonstrates considerably greater sensitivity of EC-TRAP for the detection of low abundant transcripts. Application of EC-TRAP to examine the in vivo host response to lipopolysaccharide (LPS) revealed the induction of gene expression programs associated with a native defense response, with marked differences across vascular beds. Furthermore, comparative analysis of whole tissue and TRAP-selected mRNAs identified LPS-induced differences that would not have been detected by whole tissue analysis alone. Together, these data provide a resource for the analysis of EC-specific gene expression programs across heterogeneous vascular beds under both physiologic and pathologic conditions.SignificanceEndothelial cells (ECs), which line all vertebrate blood vessels, are highly heterogeneous across different tissues. The present study uses a genetic approach to specifically tag mRNAs within ECs of the mouse, thereby allowing recovery and sequence analysis to evaluate the EC-specific gene expression program directly from intact organs. Our findings demonstrate marked heterogeneity in EC gene expression across different vascular beds under both normal and disease conditions, with a more accurate picture than can be achieved using other methods. The data generated in these studies advance our understanding of EC function in different blood vessels and provide a valuable resource for future studies.

scholarly journals SCDC: Bulk Gene Expression Deconvolution by Multiple Single-Cell RNA Sequencing References

2019 ◽  
Author(s):  
Meichen Dong ◽  
Aatish Thennavan ◽  
Eugene Urrutia ◽  
Yun Li ◽  
Charles M. Perou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Mixed Cell ◽  
Single Cell Rna Sequencing

AbstractRecent advances in single-cell RNA sequencing (scRNA-seq) enable characterization of transcriptomic profiles with single-cell resolution and circumvent averaging artifacts associated with traditional bulk RNA sequencing (RNA-seq) data. Here, we propose SCDC, a deconvolution method for bulk RNA-seq that leverages cell-type specific gene expression profiles from multiple scRNA-seq reference datasets. SCDC adopts an ENSEMBLE method to integrate deconvolution results from different scRNA-seq datasets that are produced in different laboratories and at different times, implicitly addressing the problem of batch-effect confounding. SCDC is benchmarked against existing methods using both in silico generated pseudo-bulk samples and experimentally mixed cell lines, whose known cell-type compositions serve as ground truths. We show that SCDC outperforms existing methods with improved accuracy of cell-type decomposition under both settings. To illustrate how the ENSEMBLE framework performs in complex tissues under different scenarios, we further apply our method to a human pancreatic islet dataset and a mouse mammary gland dataset. SCDC returns results that are more consistent with experimental designs and that reproduce more significant associations between cell-type proportions and measured phenotypes.

scholarly journals Single Cell RNA Sequencing of Serial Tumor and Blood Biopsies from Lymphoma Patients on an in Situ Vaccination Clinical Trial

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 4107-4107
Author(s):  
Tanaya Shree ◽  
Anuja Sathe ◽  
Debra K. Czerwinski ◽  
Steven R. Long ◽  
Hanlee Ji ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Peripheral Blood ◽  
Fine Needle ◽  
Fine Needle Aspirates ◽  
Single Cell Rna Sequencing ◽  
Cell Gene Expression ◽  
Cell Gene

Abstract The critical determinants of effective antitumor immune responses, whether native or induced by therapy, remain poorly understood due to the complexity and plasticity of the immune system. To better profile and track these responses, we have employed the novel approach of performing single cell RNA sequencing for paired gene expression and immune repertoire analysis on tumor fine needle aspirates and peripheral blood of lymphoma patients undergoing immunotherapy on a clinical trial (NCT02927964). In this in situ vaccination study, patients with low-grade lymphoma received local low-dose radiation and intratumoral SD-101 (a TLR9 agonist) to one site of disease, with systemic ibrutinib (a BTK inhibitor) added in the second week of treatment. Tumor fine needle aspirates and peripheral blood samples were obtained prior to treatment, at one week (prior to ibrutinib initiation) and at six weeks after treatment start. Single cell preparations were processed using 10X Genomics' single cell RNA transcription and library preparation protocol, followed by sequencing on the Illumina platform. Cells were sequenced to an average depth of 50,000 reads/cell for gene expression libraries and 5000 reads/cell for TCR sequencing. Identification of variable genes, principal component and/or canonical correlation analysis, graph-based clustering and differential expression analysis of single-cell gene expression data was performed using the Seurat algorithm. Single-cell TCR repertoires were analyzed using TCR-specific analysis software. This data is being integrated with data from multiparameter flow cytometry and functional immune assays for these same patients, as well as with their clinical outcomes. Sequencing libraries have been prepared from 37 samples from 4 patients thus far. We have successfully generated single cell gene expression and TCR libraries from 3,000-10,000 cells from tumor fine needle aspirates and peripheral blood, with excellent sequencing quality metrics obtained. From detailed analyses of one patient's samples thus far, we have identified distinct immune populations in blood and tumor (Figure 1), including light-chain restricted B-cells, with good concordance with flow cytometry. Preliminary results show changes occurring in immune cell frequencies and phenotypes at the treated tumor site, at distant tumor sites and in the peripheral blood when samples from before and after treatment are compared. Sample collection, sequencing, and analysis are ongoing. Deep profiling of serial biopsies during immunotherapy using single cell RNA sequencing promises to illuminate underlying cellular dynamics, and paired with clinical outcome data, determinants of response. Ultimately, this may provide a roadmap for successful translation of single-cell genomics into the clinic for treatment monitoring and response prediction. Disclosures No relevant conflicts of interest to declare.

scholarly journals A molecular cell atlas of the human lung from single cell RNA sequencing

2019 ◽  
Author(s):  
Kyle J. Travaglini ◽  
Ahmad N. Nabhan ◽  
Lolita Penland ◽  
Rahul Sinha ◽  
Astrid Gillich ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Immune Cell ◽  
Human Lung ◽  
Expression Profiles ◽  
Cell Types ◽  
Specific Gene ◽  
Cell Trafficking ◽  
Single Cell Rna Sequencing

AbstractAlthough single cell RNA sequencing studies have begun providing compendia of cell expression profiles, it has proven more difficult to systematically identify and localize all molecular cell types in individual organs to create a full molecular cell atlas. Here we describe droplet- and plate-based single cell RNA sequencing applied to ∼75,000 human lung and blood cells, combined with a multi-pronged cell annotation approach, which have allowed us to define the gene expression profiles and anatomical locations of 58 cell populations in the human lung, including 41 of 45 previously known cell types or subtypes and 14 new ones. This comprehensive molecular atlas elucidates the biochemical functions of lung cell types and the cell-selective transcription factors and optimal markers for making and monitoring them; defines the cell targets of circulating hormones and predicts local signaling interactions including sources and targets of chemokines in immune cell trafficking and expression changes on lung homing; and identifies the cell types directly affected by lung disease genes and respiratory viruses. Comparison to mouse identified 17 molecular types that appear to have been gained or lost during lung evolution and others whose expression profiles have been substantially altered, revealing extensive plasticity of cell types and cell-type-specific gene expression during organ evolution including expression switches between cell types. This atlas provides the molecular foundation for investigating how lung cell identities, functions, and interactions are achieved in development and tissue engineering and altered in disease and evolution.

scTSSR: gene expression recovery for single-cell RNA sequencing using two-side sparse self-representation

2020 ◽  
Vol 36 (10) ◽  
pp. 3131-3138
Author(s):  
Ke Jin ◽  
Le Ou-Yang ◽  
Xing-Ming Zhao ◽  
Hong Yan ◽  
Xiao-Fei Zhang
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Differential Expression Analysis ◽  
Supplementary Information ◽  
Expression Levels ◽  
Single Cell Rna Sequencing ◽  
Downstream Analysis ◽  
Gene Expression Levels

Abstract Motivation Single-cell RNA sequencing (scRNA-seq) methods make it possible to reveal gene expression patterns at single-cell resolution. Due to technical defects, dropout events in scRNA-seq will add noise to the gene-cell expression matrix and hinder downstream analysis. Therefore, it is important for recovering the true gene expression levels before carrying out downstream analysis. Results In this article, we develop an imputation method, called scTSSR, to recover gene expression for scRNA-seq. Unlike most existing methods that impute dropout events by borrowing information across only genes or cells, scTSSR simultaneously leverages information from both similar genes and similar cells using a two-side sparse self-representation model. We demonstrate that scTSSR can effectively capture the Gini coefficients of genes and gene-to-gene correlations observed in single-molecule RNA fluorescence in situ hybridization (smRNA FISH). Down-sampling experiments indicate that scTSSR performs better than existing methods in recovering the true gene expression levels. We also show that scTSSR has a competitive performance in differential expression analysis, cell clustering and cell trajectory inference. Availability and implementation The R package is available at https://github.com/Zhangxf-ccnu/scTSSR. Supplementary information Supplementary data are available at Bioinformatics online.

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