A cloud-based platform for the analysis of single cell RNA sequencing data

AbstractMotivationSingle-cell RNA sequencing (scRNA-seq) is a recent technology that has provided many valuable biological insights. Notable uses include identifying novel cell-types, measuring the cellular response to treatment, and tracking trajectories of distinct cell lineages in time. The raw data generated in this process typically amounts to hundreds of millions of sequencing reads and requires substantial computational infrastructure for downstream analysis, a major hurdle for a biological research lab. Fortunately, the preprocessing step that converts this huge sequence data into manageable cell-specific expression profiles is standardized and can be performed in the cloud. We demonstrate how a cloud-based computational framework can be used to transform the raw data into biologically interpretable cell-type-specific information, using either 3’ or 5’ transcriptome libraries from 10x Genomics. The processed data which is an order of magnitude smaller in size can be easily downloaded to a laptop for customized analysis to gain deeper biological insights.ResultsWe produced an automated and easily extensible pipeline in the cloud for the analysis of single-cell RNA-seq data which provides a convenient method to handle post-processing of scRNA sequencing using next generation sequencing platforms. The basic step provides the transformation of the scRNA-seq data to cell-type-specific expression profiles and computes the quality control metrics for the dataset. The extensibility of the platform is demonstrated by adding a doublet-removal algorithm and recomputing the clustering of the cells. Any additional computational steps that take a cell-type expression counts matrix as input can be easily added to this framework with minimal effort.AvailabilityThe framework and its documentation for installation is available at the Github repository http://github.com/nj3252/CB-Source/[email protected] informationSupplementary data available at Bioinformatics online.

O4-10-01: CHARACTERIZING CELL TYPE-SPECIFIC EXPRESSION SIGNATURES IN BRAIN USING SINGLE CELL RNA SEQUENCING

Alzheimer s & Dementia ◽

10.1016/j.jalz.2019.06.4794 ◽

2019 ◽

Vol 15 ◽

pp. P1258-P1258 ◽

Cited By ~ 1

Author(s):

Tulsi Patel ◽

Troy Carnwath ◽

Laura Lewis-Tuffin ◽

Mariet Allen ◽

Sarah J. Lincoln ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Cell Type ◽

Specific Expression ◽

Cell Type Specific Expression ◽

Optimized design of single-cell RNA sequencing experiments for cell-type-specific eQTL analysis

Nature Communications ◽

10.1038/s41467-020-19365-w ◽

2020 ◽

Vol 11 (1) ◽

Cited By ~ 1

Author(s):

Igor Mandric ◽

Tommer Schwarz ◽

Arunabha Majumdar ◽

Kangcheng Hou ◽

Leah Briscoe ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Statistical Power ◽

Eqtl Analysis ◽

Optimized Design ◽

Rna Seq ◽

Cell Type ◽

Specific Expression ◽

Abstract Single-cell RNA-sequencing (scRNA-Seq) is a compelling approach to directly and simultaneously measure cellular composition and state, which can otherwise only be estimated by applying deconvolution methods to bulk RNA-Seq estimates. However, it has not yet become a widely used tool in population-scale analyses, due to its prohibitively high cost. Here we show that given the same budget, the statistical power of cell-type-specific expression quantitative trait loci (eQTL) mapping can be increased through low-coverage per-cell sequencing of more samples rather than high-coverage sequencing of fewer samples. We use simulations starting from one of the largest available real single-cell RNA-Seq data from 120 individuals to also show that multiple experimental designs with different numbers of samples, cells per sample and reads per cell could have similar statistical power, and choosing an appropriate design can yield large cost savings especially when multiplexed workflows are considered. Finally, we provide a practical approach on selecting cost-effective designs for maximizing cell-type-specific eQTL power which is available in the form of a web tool.

Optimal design of single-cell RNA sequencing experiments for cell-type-specific eQTL analysis

10.1101/766972 ◽

2019 ◽

Cited By ~ 2

Author(s):

Igor Mandric ◽

Tommer Schwarz ◽

Arunabha Majumdar ◽

Richard Perez ◽

Meena Subramaniam ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Statistical Power ◽

Cost Savings ◽

Eqtl Analysis ◽

Cell Type ◽

Specific Expression ◽

High Coverage ◽

AbstractSingle-cell RNA-sequencing (scRNA-Seq) is a compelling approach to simultaneously measure cellular composition and state which is impossible with bulk profiling approaches. However, it has not yet become a widely used tool in population-scale analyses, due to its prohibitively high cost. Here we show that given the same budget, the statistical power of cell-type-specific expression quantitative trait loci (eQTL) mapping can be increased through low-coverage per-cell sequencing of more samples rather than high-coverage sequencing of fewer samples. We also show that multiple experimental designs with different numbers of samples, cells per sample and reads per cell could have similar statistical power, and choosing an appropriate design can yield large cost savings especially when multiplexed workflows are considered. Finally, we provide a practical approach on selecting cost-effective designs for maximizing cell-type-specific eQTL power.

Single-cell RNA sequencing of the mammalian pineal gland identifies two pinealocyte subtypes and cell type-specific daily patterns of gene expression

PLoS ONE ◽

10.1371/journal.pone.0205883 ◽

2018 ◽

Vol 13 (10) ◽

pp. e0205883 ◽

Cited By ~ 9

Author(s):

Joseph C. Mays ◽

Michael C. Kelly ◽

Steven L. Coon ◽

Lynne Holtzclaw ◽

Martin F. Rath ◽

...

Keyword(s):

Gene Expression ◽

Pineal Gland ◽

Single Cell ◽

Rna Sequencing ◽

Cell Type ◽

Cell Type Specific ◽

Mammalian Pineal Gland ◽

Daily Patterns

Localization of migraine susceptibility genes in human brain by single-cell RNA sequencing

Cephalalgia ◽

10.1177/0333102418762476 ◽

2018 ◽

Vol 38 (13) ◽

pp. 1976-1983 ◽

Cited By ~ 5

Author(s):

William Renthal

Keyword(s):

Human Brain ◽

Single Cell ◽

Rna Sequencing ◽

Expression Profiles ◽

Cell Types ◽

Susceptibility Genes ◽

Brain Cell ◽

Cell Type ◽

Brain Cell Types

Background Migraine is a debilitating disorder characterized by severe headaches and associated neurological symptoms. A key challenge to understanding migraine has been the cellular complexity of the human brain and the multiple cell types implicated in its pathophysiology. The present study leverages recent advances in single-cell transcriptomics to localize the specific human brain cell types in which putative migraine susceptibility genes are expressed. Methods The cell-type specific expression of both familial and common migraine-associated genes was determined bioinformatically using data from 2,039 individual human brain cells across two published single-cell RNA sequencing datasets. Enrichment of migraine-associated genes was determined for each brain cell type. Results Analysis of single-brain cell RNA sequencing data from five major subtypes of cells in the human cortex (neurons, oligodendrocytes, astrocytes, microglia, and endothelial cells) indicates that over 40% of known migraine-associated genes are enriched in the expression profiles of a specific brain cell type. Further analysis of neuronal migraine-associated genes demonstrated that approximately 70% were significantly enriched in inhibitory neurons and 30% in excitatory neurons. Conclusions This study takes the next step in understanding the human brain cell types in which putative migraine susceptibility genes are expressed. Both familial and common migraine may arise from dysfunction of discrete cell types within the neurovascular unit, and localization of the affected cell type(s) in an individual patient may provide insight into to their susceptibility to migraine.

Single-cell RNA sequencing reveals cell type- and artery type-specific vascular remodelling in male spontaneously hypertensive rats

Cardiovascular Research ◽

10.1093/cvr/cvaa164 ◽

2020 ◽

Cited By ~ 1

Author(s):

Jun Cheng ◽

Wenduo Gu ◽

Ting Lan ◽

Jiacheng Deng ◽

Zhichao Ni ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Spontaneously Hypertensive Rats ◽

Cell Types ◽

Vascular Remodelling ◽

Cell Type ◽

Hypertensive Rats ◽

Spontaneously Hypertensive ◽

Abstract Aims Hypertension is a major risk factor for cardiovascular diseases. However, vascular remodelling, a hallmark of hypertension, has not been systematically characterized yet. We described systematic vascular remodelling, especially the artery type- and cell type-specific changes, in hypertension using spontaneously hypertensive rats (SHRs). Methods and results Single-cell RNA sequencing was used to depict the cell atlas of mesenteric artery (MA) and aortic artery (AA) from SHRs. More than 20 000 cells were included in the analysis. The number of immune cells more than doubled in aortic aorta in SHRs compared to Wistar Kyoto controls, whereas an expansion of MA mesenchymal stromal cells (MSCs) was observed in SHRs. Comparison of corresponding artery types and cell types identified in integrated datasets unravels dysregulated genes specific for artery types and cell types. Intersection of dysregulated genes with curated gene sets including cytokines, growth factors, extracellular matrix (ECM), receptors, etc. revealed vascular remodelling events involving cell–cell interaction and ECM re-organization. Particularly, AA remodelling encompasses upregulated cytokine genes in smooth muscle cells, endothelial cells, and especially MSCs, whereas in MA, change of genes involving the contractile machinery and downregulation of ECM-related genes were more prominent. Macrophages and T cells within the aorta demonstrated significant dysregulation of cellular interaction with vascular cells. Conclusion Our findings provide the first cell landscape of resistant and conductive arteries in hypertensive animal models. Moreover, it also offers a systematic characterization of the dysregulated gene profiles with unbiased, artery type-specific and cell type-specific manners during hypertensive vascular remodelling.

PRIME: a probabilistic imputation method to reduce dropout effects in single-cell RNA sequencing

Bioinformatics ◽

10.1093/bioinformatics/btaa278 ◽

2020 ◽

Vol 36 (13) ◽

pp. 4021-4029

Author(s):

Hyundoo Jeong ◽

Zhandong Liu

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Expression Profiles ◽

Expression Patterns ◽

Imputation Method ◽

Supplementary Information ◽

Single Cell Sequencing ◽

Depth Analysis ◽

Single Cell Rna Sequencing

Abstract Summary Single-cell RNA sequencing technology provides a novel means to analyze the transcriptomic profiles of individual cells. The technique is vulnerable, however, to a type of noise called dropout effects, which lead to zero-inflated distributions in the transcriptome profile and reduce the reliability of the results. Single-cell RNA sequencing data, therefore, need to be carefully processed before in-depth analysis. Here, we describe a novel imputation method that reduces dropout effects in single-cell sequencing. We construct a cell correspondence network and adjust gene expression estimates based on transcriptome profiles for the local subnetwork of cells of the same type. We comprehensively evaluated this method, called PRIME (PRobabilistic IMputation to reduce dropout effects in Expression profiles of single-cell sequencing), on synthetic and eight real single-cell sequencing datasets and verified that it improves the quality of visualization and accuracy of clustering analysis and can discover gene expression patterns hidden by noise. Availability and implementation The source code for the proposed method is freely available at https://github.com/hyundoo/PRIME. Supplementary information Supplementary data are available at Bioinformatics online.

161 Single-cell RNA sequencing combined with interstitial fluid proteomics defines cell-type-specific immune gene regulation in atopic dermatitis

Journal of Investigative Dermatology ◽

10.1016/j.jid.2020.03.165 ◽

2020 ◽

Vol 140 (7) ◽

pp. S19

Author(s):

T.B. Rojahn ◽

V. Vorstandlechner ◽

T. Krausgruber ◽

W. Bauer ◽

N. Alkon ◽

...

Keyword(s):

Gene Regulation ◽

Atopic Dermatitis ◽

Single Cell ◽

Rna Sequencing ◽

Interstitial Fluid ◽

Immune Gene ◽

Cell Type ◽

Diverse nephron cell type‐specific adaptation to furosemide by morphometry and single cell RNA sequencing

The FASEB Journal ◽

10.1096/fasebj.2019.33.1_supplement.862.29 ◽

2019 ◽

Vol 33 (S1) ◽

Author(s):

Steven Schaffert ◽

Aram Krauson ◽

Elisabeth Walczak ◽

Jonathan Nizar ◽

Gwendolyn Holdgate ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Cell Type ◽

Specific Adaptation ◽

COMP-16. COMPREHENSIVE TRANSCRIPTOMIC ANALYSIS OF SINGLE CELLS FROM RECURRENT AND PRIMARY GLIOBLASTOMA TO PREDICT CELL-TYPE SPECIFIC THERAPEUTICS

Neuro-Oncology ◽

10.1093/neuonc/noz175.259 ◽

2019 ◽

Vol 21 (Supplement_6) ◽

pp. vi64-vi64

Author(s):

Robert Suter ◽

Vasileios Stathias ◽

Anna Jermakowicz ◽

Alexa Semonche ◽

Michael Ivan ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Tumor Heterogeneity ◽

Drug Response ◽

Adult Brain ◽

Cell Populations ◽

Cell Type ◽

Cell Type Specific ◽

Recurrent Gbm

Abstract Glioblastoma (GBM) remains the most common adult brain tumor, with poor survival expectations, and no new therapeutic modalities approved in the last decade. Our laboratories have recently demonstrated that the integration of a transcriptional disease signature obtained from The Cancer Genome Atlas’ GBM dataset with transcriptional cell drug-response signatures in the LINCS L1000 dataset yields possible combinatorial therapeutics. Considering the extreme intra-tumor heterogeneity associated with the disease, we hypothesize that the utilization of single-cell RNA-sequencing (scRNA-seq) of patient tumors will further strengthen our predictive model by providing insight on the unique transcriptomes of the cellular niches present within these tumors, and into the transcriptional dynamics of these same cellular niches. By sequencing single-cell transcriptomes from recurrent GBM tumors resected from patients at the University of Miami, and integrating our datasets with previously published scRNA-seq data from primary GBM tumors, we are able to gain additional insight into the differences between these clinical distinctions. We have analyzed the differential expression of kinases both across and within distinct cell populations of primary and recurrent GBM tumors. This transcriptional map of kinase expression represents the heterogeneity of potential targets within individual tumors and between recurrent and primary GBM. Additionally, by generating disease signatures unique to each cellular population, and integrating these with transcriptional drug-response signatures from LINCS, we are able to predict compounds to target specific cell populations within GMB tumors. Additional computational techniques such as RNA velocity analysis and cell cycle scoring elucidate temporal insights to further prioritize these cell-type specific therapeutics, and reveal the intra-cellular dynamics present within these tumors. Collectively, our studies suggest that we have developed a novel omics pipeline based on the single cell RNA-sequencing of individual GBM cells that addresses intra-tumor heterogeneity, and may lead to novel therapeutic combinations for the treatment of this incurable disease.