scholarly journals PROSSTT: probabilistic simulation of single-cell RNA-seq data for complex differentiation processes

2018 ◽  
Author(s):  
Nikolaos Papadopoulos ◽  
R. Gonzalo Parra ◽  
Johannes Söding
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Noise Model ◽  
Rna Seq ◽  
Enabling Technology ◽  
Differentiated Cells ◽  
Probabilistic Simulation ◽  
Lineage Tree ◽  
Lineage Trees

BackgroundSingle-cell RNA sequencing (scRNA-seq) is an enabling technology for the study of cellular differentiation and heterogeneity. From snapshots of the transcriptomic profiles of differentiating single cells, the cellular lineage tree that leads from a progenitor population to multiple types of differentiated cells can be derived. The underlying lineage trees of most published datasets are linear or have a single branchpoint, but many studies with more complex lineage trees will soon become available. To test and further develop tools for lineage tree reconstruction, we need test datasets with known trees.ResultsPROSSTT can simulate scRNA-seq datasets for differentiation processes with lineage trees of any desired complexity, noise level, noise model, and size. PROSSTT also provides scripts to quantify the quality of predicted lineage trees.Availabilityhttps://github.com/soedinglab/[email protected]

scholarly journals PROSSTT: probabilistic simulation of single-cell RNA-seq data for complex differentiation processes

2019 ◽  
Vol 35 (18) ◽  
pp. 3517-3519 ◽  
Author(s):  
Nikolaos Papadopoulos ◽  
Parra R Gonzalo ◽  
Johannes Söding
Keyword(s):  
Single Cell ◽  
Noise Model ◽  
Supplementary Information ◽  
Rna Seq ◽  
Tree Reconstruction ◽  
Probabilistic Simulation ◽  
Single Cell Rna Sequencing ◽  
Lineage Tree ◽  
Lineage Trees

Abstract Summary Cellular lineage trees can be derived from single-cell RNA sequencing snapshots of differentiating cells. Currently, only datasets with simple topologies are available. To test and further develop tools for lineage tree reconstruction, we need test datasets with known complex topologies. PROSSTT can simulate scRNA-seq datasets for differentiation processes with lineage trees of any desired complexity, noise level, noise model and size. PROSSTT also provides scripts to quantify the quality of predicted lineage trees. Availability and implementation https://github.com/soedinglab/prosstt. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Fast, scalable and accurate differential expression analysis for single cells

2016 ◽  
Author(s):  
Debarka Sengupta ◽  
Nirmala Arul Rayan ◽  
Michelle Lim ◽  
Bing Lim ◽  
Shyam Prabhakar
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Differential Expression Analysis ◽  
Nonparametric Method ◽  
Rna Seq ◽  
Noise Levels ◽  
High Noise ◽  
Technical Variability ◽  
Massive Sample

ABSTRACTAnalysis of single-cell RNA-seq data is challenging due to technical variability, high noise levels and massive sample sizes. Here, we describe a normalization technique that substantially reduces technical variability and improves the quality of downstream analyses. We also introduce a nonparametric method for detecting differentially expressed genes that scales to > 1,000 cells and is both more accurate and ~10 times faster than existing parametric approaches.

scholarly journals SUGAR-seq enables simultaneous detection of glycans, epitopes, and the transcriptome in single cells

2021 ◽  
Vol 7 (8) ◽  
pp. eabe3610
Author(s):  
Conor J. Kearney ◽  
Stephin J. Vervoort ◽  
Kelly M. Ramsbottom ◽  
Izabela Todorovski ◽  
Emily J. Lelliott ◽  
...  
Keyword(s):  
Single Cell ◽  
Posttranslational Modification ◽  
Cellular Differentiation ◽  
Single Cells ◽  
Simultaneous Detection ◽  
Surface Protein ◽  
Rna Seq ◽  
Cell Level ◽  
Simultaneous Integration ◽  
Unique Surface

Multimodal single-cell RNA sequencing enables the precise mapping of transcriptional and phenotypic features of cellular differentiation states but does not allow for simultaneous integration of critical posttranslational modification data. Here, we describe SUrface-protein Glycan And RNA-seq (SUGAR-seq), a method that enables detection and analysis of N-linked glycosylation, extracellular epitopes, and the transcriptome at the single-cell level. Integrated SUGAR-seq and glycoproteome analysis identified tumor-infiltrating T cells with unique surface glycan properties that report their epigenetic and functional state.

scholarly journals Leveraging high-powered RNA-Seq datasets to improve inference of regulatory activity in single-cell RNA-Seq data

2019 ◽  
Author(s):  
Ning Wang ◽  
Andrew E. Teschendorff
Keyword(s):  
Transcription Factors ◽  
Single Cell ◽  
Cell Fate ◽  
Regulatory Networks ◽  
Large Scale ◽  
Single Cells ◽  
Differential Expression Analysis ◽  
Dropout Rate ◽  
Rna Seq ◽  
Regulatory Activity

AbstractInferring the activity of transcription factors in single cells is a key task to improve our understanding of development and complex genetic diseases. This task is, however, challenging due to the relatively large dropout rate and noisy nature of single-cell RNA-Seq data. Here we present a novel statistical inference framework called SCIRA (Single Cell Inference of Regulatory Activity), which leverages the power of large-scale bulk RNA-Seq datasets to infer high-quality tissue-specific regulatory networks, from which regulatory activity estimates in single cells can be subsequently obtained. We show that SCIRA can correctly infer regulatory activity of transcription factors affected by high technical dropouts. In particular, SCIRA can improve sensitivity by as much as 70% compared to differential expression analysis and current state-of-the-art methods. Importantly, SCIRA can reveal novel regulators of cell-fate in tissue-development, even for cell-types that only make up 5% of the tissue, and can identify key novel tumor suppressor genes in cancer at single cell resolution. In summary, SCIRA will be an invaluable tool for single-cell studies aiming to accurately map activity patterns of key transcription factors during development, and how these are altered in disease.

scholarly journals Characterizing Intercellular Communication of Pan-Cancer Reveals SPP1+ Tumor-Associated Macrophage Expanded in Hypoxia and Promoting Cancer Malignancy Through Single-Cell RNA-Seq Data

2021 ◽  
Vol 9 ◽  
Author(s):  
Jinfen Wei ◽  
Zixi Chen ◽  
Meiling Hu ◽  
Ziqing He ◽  
Dawei Jiang ◽  
...  
Keyword(s):  
Single Cell ◽  
Cancer Cells ◽  
Cancer Progression ◽  
Epithelial Mesenchymal Transition ◽  
Single Cells ◽  
Matrix Remodeling ◽  
Rna Seq ◽  
Mesenchymal Transition ◽  
Tumor Associated Macrophage ◽  
Cancer Types

Hypoxia is a characteristic of tumor microenvironment (TME) and is a major contributor to tumor progression. Yet, subtype identification of tumor-associated non-malignant cells at single-cell resolution and how they influence cancer progression under hypoxia TME remain largely unexplored. Here, we used RNA-seq data of 424,194 single cells from 108 patients to identify the subtypes of cancer cells, stromal cells, and immune cells; to evaluate their hypoxia score; and also to uncover potential interaction signals between these cells in vivo across six cancer types. We identified SPP1+ tumor-associated macrophage (TAM) subpopulation potentially enhanced epithelial–mesenchymal transition (EMT) by interaction with cancer cells through paracrine pattern. We prioritized SPP1 as a TAM-secreted factor to act on cancer cells and found a significant enhanced migration phenotype and invasion ability in A549 lung cancer cells induced by recombinant protein SPP1. Besides, prognostic analysis indicated that a higher expression of SPP1 was found to be related to worse clinical outcome in six cancer types. SPP1 expression was higher in hypoxia-high macrophages based on single-cell data, which was further validated by an in vitro experiment that SPP1 was upregulated in macrophages under hypoxia-cultured compared with normoxic conditions. Additionally, a differential analysis demonstrated that hypoxia potentially influences extracellular matrix remodeling, glycolysis, and interleukin-10 signal activation in various cancer types. Our work illuminates the clearer underlying mechanism in the intricate interaction between different cell subtypes within hypoxia TME and proposes the guidelines for the development of therapeutic targets specifically for patients with high proportion of SPP1+ TAMs in hypoxic lesions.

scholarly journals DEsingle for detecting three types of differential expression in single-cell RNA-seq data

2017 ◽  
Author(s):  
Zhun Miao ◽  
Ke Deng ◽  
Xiaowo Wang ◽  
Xuegong Zhang
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Negative Binomial ◽  
Single Cells ◽  
R Package ◽  
Supplementary Information ◽  
Binomial Model ◽  
Supplementary Data ◽  
Rna Seq ◽  
Real Zeros

AbstractSummaryThe excessive amount of zeros in single-cell RNA-seq data include “real” zeros due to the on-off nature of gene transcription in single cells and “dropout” zeros due to technical reasons. Existing differential expression (DE) analysis methods cannot distinguish these two types of zeros. We developed an R package DEsingle which employed Zero-Inflated Negative Binomial model to estimate the proportion of real and dropout zeros and to define and detect 3 types of DE genes in single-cell RNA-seq data with higher accuracy.Availability and ImplementationThe R package DEsingle is freely available at https://github.com/miaozhun/DEsingle and is under Bioconductor’s consideration [email protected] informationSupplementary data are available at bioRxiv online.

scholarly journals RNA velocity in single cells

2017 ◽  
Author(s):  
Gioele La Manno ◽  
Ruslan Soldatov ◽  
Hannah Hochgerner ◽  
Amit Zeisel ◽  
Viktor Petukhov ◽  
...  
Keyword(s):  
Single Cell ◽  
Cellular Differentiation ◽  
Single Cells ◽  
Time Derivative ◽  
Dynamic Processes ◽  
Embryonic Brain ◽  
Future State ◽  
Single Cell Rna Sequencing ◽  
Mouse Hippocampus ◽  
Lineage Tree

AbstractRNA abundance is a powerful indicator of the state of individual cells, but does not directly reveal dynamic processes such as cellular differentiation. Here we show that RNA velocity—the time derivative of RNA abundance—can be estimated by distinguishing unspliced and spliced mRNAs in standard single-cell RNA sequencing protocols. We show that RNA velocity is a vector that predicts the future state of individual cells on a timescale of hours. We validate the accuracy of RNA velocity in the neural crest lineage, demonstrate its use on multiple technical platforms, reconstruct the branching lineage tree of the mouse hippocampus, and measure RNA kinetics in human embryonic brain. We expect RNA velocity to greatly aid the analysis of developmental lineages and cellular dynamics, particularly in humans.

scholarly journals Single cell census of human kidney organoids shows reproducibility and diminished off-target cells after transplantation

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Ayshwarya Subramanian ◽  
Eriene-Heidi Sidhom ◽  
Maheswarareddy Emani ◽  
Katherine Vernon ◽  
Nareh Sahakian ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Human Kidney ◽  
Mouse Kidney ◽  
Target Cells ◽  
Rna Seq ◽  
Kidney Capsule ◽  
Time Points ◽  
Human Ipsc ◽  
Kidney Organoids

AbstractHuman iPSC-derived kidney organoids have the potential to revolutionize discovery, but assessing their consistency and reproducibility across iPSC lines, and reducing the generation of off-target cells remain an open challenge. Here, we profile four human iPSC lines for a total of 450,118 single cells to show how organoid composition and development are comparable to human fetal and adult kidneys. Although cell classes are largely reproducible across time points, protocols, and replicates, we detect variability in cell proportions between different iPSC lines, largely due to off-target cells. To address this, we analyze organoids transplanted under the mouse kidney capsule and find diminished off-target cells. Our work shows how single cell RNA-seq (scRNA-seq) can score organoids for reproducibility, faithfulness and quality, that kidney organoids derived from different iPSC lines are comparable surrogates for human kidney, and that transplantation enhances their formation by diminishing off-target cells.

scholarly journals Computational approaches towards reducing contamination in single-cell RNA-seq data

2020 ◽  
Author(s):  
Siamak Yousefi ◽  
Hao Chen ◽  
Jesse F. Ingels ◽  
Melinda S. McCarty ◽  
Arthur G. Centeno ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Real Life ◽  
Cell Types ◽  
Cell Capture ◽  
Rna Seq ◽  
Sequence Analyses ◽  
Cell Functions ◽  
Biological Interpretation ◽  
Different Cell Types

SUMMARYSingle cell RNA sequencing has enabled quantification of single cells and identification of different cell types and subtypes as well as cell functions in different tissues. Single cell RNA sequence analyses assume acquired RNAs correspond to cells, however, RNAs from contamination within the input data are also captured by these assays. The sequencing of background contamination as well as unwanted cells making their way to the final assay Potentially confound the correct biological interpretation of single cell transcriptomic data. Here we demonstrate two approaches to deal with background contamination as well as profiling of unwanted cells in the assays. We use three real-life datasets of whole-cell capture and nucleotide single-cell captures generated by Fluidigm and 10x technologies and show that these methods reduce the effect of contamination, strengthen clustering of cells and improves biological interpretation.

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