Single-Cell and Bulk Transcriptome Data Integration Reveals Dysfunctional Cell Types and Aberrantly Expressed Genes in Hypertrophic Scar

Hypertrophic scar (HS) is a common skin disorder characterized by excessive extracellular matrix (ECM) deposition. However, it is still unclear how the cellular composition, cell-cell communications, and crucial transcriptionally regulatory network were changed in HS. In the present study, we found that FB-1, which was identified a major type of fibroblast and had the characteristics of myofibroblast, was significantly expanded in HS by integrative analysis of the single-cell and bulk RNA sequencing (RNA-seq) data. Moreover, the proportion of KC-2, which might be a differentiated type of keratinocyte (KC), was reduced in HS. To decipher the intercellular signaling, we conducted the cell-cell communication analysis between the cell types, and found the autocrine signaling of HB-1 through COL1A1/2-CD44 and CD99-CD99 and the intercellular contacts between FB-1/FB-5 and KC-2 through COL1A1/COL1A2/COL6A1/COL6A2-SDC4. Almost all the ligands and receptors involved in the autocrine signaling of HB-1 were upregulated in HS by both scRNA-seq and bulk RNA-seq data. In contrast, the receptor of KC-2, SDC4, which could bind to multiple ligands, was downregulated in HS, suggesting that the reduced proportion of KC-2 and apoptotic phenotype of KC-2 might be associated with the downregulation of SDC4. Furthermore, we also investigated the transcriptionally regulatory network involved in HS formation. The integrative analysis of the scRNA-seq and bulk RNA-seq data identified CREB3L1 and TWIST2 as the critical TFs involved in the myofibroblast of HS. In summary, the integrative analysis of the single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data greatly improved our understanding of the biological characteristics during the HS formation.

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Splatter: simulation of single-cell RNA sequencing data

10.1101/133173 ◽

2017 ◽

Cited By ~ 8

Author(s):

Luke Zappia ◽

Belinda Phipson ◽

Alicia Oshlack

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Real Data ◽

Cell Types ◽

Rna Seq ◽

Sequencing Data ◽

Sequencing Technologies ◽

Simulation Based ◽

Single Cell Rna Sequencing ◽

Multiple Cell

AbstractAs single-cell RNA sequencing technologies have rapidly developed, so have analysis methods. Many methods have been tested, developed and validated using simulated datasets. Unfortunately, current simulations are often poorly documented, their similarity to real data is not demonstrated, or reproducible code is not available.Here we present the Splatter Bioconductor package for simple, reproducible and well-documented simulation of single-cell RNA-seq data. Splatter provides an interface to multiple simulation methods including Splat, our own simulation, based on a gamma-Poisson distribution. Splat can simulate single populations of cells, populations with multiple cell types or differentiation paths.

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Equivalent high-resolution identification of neuronal cell types with single-nucleus and single-cell RNA-sequencing

10.1101/239749 ◽

2017 ◽

Cited By ~ 6

Author(s):

Trygve E. Bakken ◽

Rebecca D. Hodge ◽

Jeremy M. Miller ◽

Zizhen Yao ◽

Thuc N. Nguyen ◽

...

Keyword(s):

High Resolution ◽

Single Cell ◽

Rna Sequencing ◽

Transcriptional Profiling ◽

Neuronal Cell ◽

Cell Types ◽

Matched Pair ◽

Rna Seq ◽

Nuclear Rna ◽

Single Nucleus

AbstractTranscriptional profiling of complex tissues by RNA-sequencing of single nuclei presents some advantages over whole cell analysis. It enables unbiased cellular coverage, lack of cell isolation-based transcriptional effects, and application to archived frozen specimens. Using a well-matched pair of single-nucleus RNA-seq (snRNA-seq) and single-cell RNA-seq (scRNA-seq) SMART-Seq v4 datasets from mouse visual cortex, we demonstrate that similarly high-resolution clustering of closely related neuronal types can be achieved with both methods if intronic sequences are included in nuclear RNA-seq analysis. More transcripts are detected in individual whole cells (∼11,000 genes) than nuclei (∼7,000 genes), but the majority of genes have similar detection across cells and nuclei. We estimate that the nuclear proportion of total cellular mRNA varies from 20% to over 50% for large and small pyramidal neurons, respectively. Together, these results illustrate the high information content of nuclear RNA for characterization of cellular diversity in brain tissues.

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Detecting cell-type-specific allelic expression imbalance by integrative analysis of bulk and single-cell RNA sequencing data

10.1101/2020.08.26.267815 ◽

2020 ◽

Author(s):

Jiaxin Fan ◽

Xuran Wang ◽

Rui Xiao ◽

Mingyao Li

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Cell Types ◽

Allelic Expression ◽

Rna Seq ◽

Allelic Expression Imbalance ◽

Cell Type ◽

Single Cell Rna Sequencing ◽

Cell Type Specific ◽

Different Cell Types

AbstractAllelic expression imbalance (AEI), quantified by the relative expression of two alleles of a gene in a diploid organism, can help explain phenotypic variations among individuals. Traditional methods detect AEI using bulk RNA sequencing (RNA-seq) data, a data type that averages out cell-to-cell heterogeneity in gene expression across cell types. Since the patterns of AEI may vary across different cell types, it is desirable to study AEI in a cell-type-specific manner. Although this can be achieved by single-cell RNA sequencing (scRNA-seq), it requires full-length transcript to be sequenced in single cells of a large number of individuals, which are still cost prohibitive to generate. To overcome this limitation and utilize the vast amount of existing disease relevant bulk tissue RNA-seq data, we developed BSCET, which enables the characterization of cell-type-specific AEI in bulk RNA-seq data by integrating cell type composition information inferred from a small set of scRNA-seq samples, possibly obtained from an external dataset. By modeling covariate effect, BSCET can also detect genes whose cell-type-specific AEI are associated with clinical factors. Through extensive benchmark evaluations, we show that BSCET correctly detected genes with cell-type-specific AEI and differential AEI between healthy and diseased samples using bulk RNA-seq data. BSCET also uncovered cell-type-specific AEIs that were missed in bulk data analysis when the directions of AEI are opposite in different cell types. We further applied BSCET to two pancreatic islet bulk RNA-seq datasets, and detected genes showing cell-type-specific AEI that are related to the progression of type 2 diabetes. Since bulk RNA-seq data are easily accessible, BSCET provided a convenient tool to integrate information from scRNA-seq data to gain insight on AEI with cell type resolution. Results from such analysis will advance our understanding of cell type contributions in human diseases.Author SummaryDetection of allelic expression imbalance (AEI), a phenomenon where the two alleles of a gene differ in their expression magnitude, is a key step towards the understanding of phenotypic variations among individuals. Existing methods detect AEI use bulk RNA sequencing (RNA-seq) data and ignore AEI variations among different cell types. Although single-cell RNA sequencing (scRNA-seq) has enabled the characterization of cell-to-cell heterogeneity in gene expression, the high costs have limited its application in AEI analysis. To overcome this limitation, we developed BSCET to characterize cell-type-specific AEI using the widely available bulk RNA-seq data by integrating cell-type composition information inferred from scRNA-seq samples. Since the degree of AEI may vary with disease phenotypes, we further extended BSCET to detect genes whose cell-type-specific AEIs are associated with clinical factors. Through extensive benchmark evaluations and analyses of two pancreatic islet bulk RNA-seq datasets, we demonstrated BSCET’s ability to refine bulk-level AEI to cell-type resolution, and to identify genes whose cell-type-specific AEIs are associated with the progression of type 2 diabetes. With the vast amount of easily accessible bulk RNA-seq data, we believe BSCET will be a valuable tool for elucidating cell type contributions in human diseases.

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Detecting cell-type-specific allelic expression imbalance by integrative analysis of bulk and single-cell RNA sequencing data

PLoS Genetics ◽

10.1371/journal.pgen.1009080 ◽

2021 ◽

Vol 17 (3) ◽

pp. e1009080

Author(s):

Jiaxin Fan ◽

Xuran Wang ◽

Rui Xiao ◽

Mingyao Li

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Cell Types ◽

Allelic Expression ◽

Rna Seq ◽

Allelic Expression Imbalance ◽

Cell Type ◽

Single Cell Rna Sequencing ◽

Cell Type Specific ◽

Different Cell Types

Allelic expression imbalance (AEI), quantified by the relative expression of two alleles of a gene in a diploid organism, can help explain phenotypic variations among individuals. Traditional methods detect AEI using bulk RNA sequencing (RNA-seq) data, a data type that averages out cell-to-cell heterogeneity in gene expression across cell types. Since the patterns of AEI may vary across different cell types, it is desirable to study AEI in a cell-type-specific manner. Although this can be achieved by single-cell RNA sequencing (scRNA-seq), it requires full-length transcript to be sequenced in single cells of a large number of individuals, which are still cost prohibitive to generate. To overcome this limitation and utilize the vast amount of existing disease relevant bulk tissue RNA-seq data, we developed BSCET, which enables the characterization of cell-type-specific AEI in bulk RNA-seq data by integrating cell type composition information inferred from a small set of scRNA-seq samples, possibly obtained from an external dataset. By modeling covariate effect, BSCET can also detect genes whose cell-type-specific AEI are associated with clinical factors. Through extensive benchmark evaluations, we show that BSCET correctly detected genes with cell-type-specific AEI and differential AEI between healthy and diseased samples using bulk RNA-seq data. BSCET also uncovered cell-type-specific AEIs that were missed in bulk data analysis when the directions of AEI are opposite in different cell types. We further applied BSCET to two pancreatic islet bulk RNA-seq datasets, and detected genes showing cell-type-specific AEI that are related to the progression of type 2 diabetes. Since bulk RNA-seq data are easily accessible, BSCET provided a convenient tool to integrate information from scRNA-seq data to gain insight on AEI with cell type resolution. Results from such analysis will advance our understanding of cell type contributions in human diseases.

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Uncovering hypergraphs of cell-cell interaction from single cell RNA-sequencing data

10.1101/566182 ◽

2019 ◽

Cited By ~ 10

Author(s):

Koki Tsuyuzaki ◽

Manabu Ishii ◽

Itoshi Nikaido

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Cell Types ◽

Cell Interaction ◽

Receptor Expression ◽

Sequencing Data ◽

Sequencing Technologies ◽

Single Cell Rna Sequencing ◽

Ligand Expression ◽

Cell Cell

AbstractComplex biological systems can be described as a multitude of cell-cell interactions (CCIs). Recent single-cell RNA-sequencing technologies have enabled the detection of CCIs and related ligand-receptor (L-R) gene expression simultaneously. However, previous data analysis methods have focused on only one-to-one CCIs between two cell types. To also detect many-to-many CCIs, we proposescTensor, a novel method for extracting representative triadic relationships (hypergraphs), which include (i) ligand-expression, (ii) receptor-expression, and (iii) L-R pairs. When applied to simulated and empirical datasets,scTensorwas able to detect some hypergraphs including paracrine/autocrine CCI patterns, which cannot be detected by previous methods.

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Inferring cell-cell interactions from pseudotime ordering of scRNA-Seq data

10.1101/2021.07.28.454054 ◽

2021 ◽

Author(s):

Dongshunyi Li ◽

Jeremy J. Velazquez ◽

Jun Ding ◽

Joshua Hislop ◽

Mo R. Ebrahimkhani ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Vascular Development ◽

Cell Types ◽

Cell Interactions ◽

Computational Method ◽

Dynamic Information ◽

Single Cell Rna Sequencing ◽

Signaling Interactions ◽

Cell Cell

A major advantage of single cell RNA-Sequencing (scRNA-Seq) data is the ability to reconstruct continuous ordering and trajectories for cells. To date, such ordering was mainly used to group cells and to infer interactions within cells. Here we present TraSig, a computational method for improving the inference of cell-cell interactions in scRNA-Seq studies. Unlike prior methods that only focus on the average expression levels of genes in clusters or cell types, TraSig fully utilizes the dynamic information to identify significant ligand-receptor pairs with similar trajectories, which in turn are used to score interacting cell clusters. We applied TraSig to several scRNA-Seq datasets. As we show, using the ordering information allows TraSig to obtain unique predictions that improve upon those identified by prior methods. Functional experiments validate the ability of TraSig to identify novel signaling interactions that impact vascular development in liver organoid.

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Cell Dissociation from Butterfly Pupal Wing Tissues for Single-Cell RNA Sequencing

Methods and Protocols ◽

10.3390/mps3040072 ◽

2020 ◽

Vol 3 (4) ◽

pp. 72

Author(s):

Anupama Prakash ◽

Antónia Monteiro

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Single Cells ◽

Expression Patterns ◽

Cell Types ◽

Rna Seq ◽

Bicyclus Anynana ◽

Single Cell Sequencing ◽

Pupal Wing

Butterflies are well known for their beautiful wings and have been great systems to understand the ecology, evolution, genetics, and development of patterning and coloration. These color patterns are mosaics on the wing created by the tiling of individual units called scales, which develop from single cells. Traditionally, bulk RNA sequencing (RNA-seq) has been used extensively to identify the loci involved in wing color development and pattern formation. RNA-seq provides an averaged gene expression landscape of the entire wing tissue or of small dissected wing regions under consideration. However, to understand the gene expression patterns of the units of color, which are the scales, and to identify different scale cell types within a wing that produce different colors and scale structures, it is necessary to study single cells. This has recently been facilitated by the advent of single-cell sequencing. Here, we provide a detailed protocol for the dissociation of cells from Bicyclus anynana pupal wings to obtain a viable single-cell suspension for downstream single-cell sequencing. We outline our experimental design and the use of fluorescence-activated cell sorting (FACS) to obtain putative scale-building and socket cells based on size. Finally, we discuss some of the current challenges of this technique in studying single-cell scale development and suggest future avenues to address these challenges.

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Polled Digital Cell Sorter (p-DCS): Automatic identification of hematological cell types from single cell RNA-sequencing clusters

10.1101/539833 ◽

2019 ◽

Cited By ~ 3

Author(s):

Sergii Domanskyi ◽

Anthony Szedlak ◽

Nathaniel T Hawkins ◽

Jiayin Wang ◽

Giovanni Paternostro ◽

...

Keyword(s):

Bone Marrow ◽

Single Cell ◽

Rna Sequencing ◽

Statistical Significance ◽

Cell Types ◽

Automatic Identification ◽

Molecular Signatures ◽

Rna Seq ◽

Cell Type ◽

Single Cell Rna Sequencing

AbstractBackgroundSingle cell RNA sequencing (scRNA-seq) brings unprecedented opportunities for mapping the heterogeneity of complex cellular environments such as bone marrow, and provides insight into many cellular processes. Single cell RNA-seq, however, has a far larger fraction of missing data reported as zeros (dropouts) than traditional bulk RNA-seq. This makes difficult not only the clustering of cells, but also the assignment of the resulting clusters into predefined cell types based on known molecular signatures, such as the expression of characteristic cell surface markers.ResultsWe present a computational tool for processing single cell RNA-seq data that uses a voting algorithm to identify cells based on approval votes received by known molecular markers. Using a stochastic procedure that accounts for biases due to dropout errors and imbalances in the number of known molecular signatures for different cell types, the method computes the statistical significance of the final approval score and automatically assigns a cell type to clusters without an expert curator. We demonstrate the utility of the tool in the analysis of eight samples of bone marrow from the Human Cell Atlas. The tool provides a systematic identification of cell types in bone marrow based on a recently-published manually-curated cell marker database [1], and incorporates a suite of visualization tools that can be overlaid on a t-SNE representation. The software is freely available as a python package at https://github.com/sdomanskyi/DigitalCellSorterConclusionsThis methodology assures that extensive marker to cell type matching information is taken into account in a systematic way when assigning cell clusters to cell types. Moreover, the method allows for a high throughput processing of multiple scRNA-seq datasets, since it does not involve an expert curator, and it can be applied recursively to obtain cell sub-types. The software is designed to allow the user to substitute the marker to cell type matching information and apply the methodology to different cellular environments.

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SC2MeNetDrug: A computational tool to uncover inter-cell signaling targets and identify relevant drugs based on single cell RNA-seq data

10.1101/2021.11.15.468755 ◽

2021 ◽

Author(s):

Jiarui Feng ◽

Simon Peter Goedegebuure ◽

Amanda Zeng ◽

Ye Bi ◽

Ting Wang ◽

...

Keyword(s):

Data Analysis ◽

Cell Signaling ◽

Single Cell ◽

Communication Networks ◽

Cell Types ◽

Rna Seq ◽

Computational Tool ◽

User Friendly ◽

Effective Drugs ◽

Cell Cell

Single cell RNA sequencing (scRNA-seq) is a powerful technology to investigate the transcriptional programs in stromal, immune and tumor cells or neuron cells within the tumor or Alzheimer's Disease (AD) brain microenvironment (ME) or niche. Cell-cell communications within ME play important roles in disease progression and immunotherapy response, and are novel and critical therapeutic targets. Though many tools of scRNA-seq analysis have been developed to investigate the heterogeneity and sub-populations of cells, few were designed for uncovering cell-cell communications of ME and predict the potentially effective drugs to inhibit the communications. Moreover, the data analysis processes of discovering signaling communication networks and effective drugs using scRNA-seq data are complex and involving a set of critical analysis processes and external supportive data resources, which are difficult for researchers who have no strong computational background and training in scRNA-seq data analysis. To address these challenges, in this study, we developed a novel computational tool, SC2MeNetDrug (https://fuhaililab.github.io/sc2MeNetDrug/). It was specifically designed using scRNA-seq data to identify cell types within MEs, uncover the dysfunctional signaling pathways within individual cell types, inter-cell signaling communications, and predict effective drugs that can potentially disrupt cell-cell signaling communications. SC2MeNetDrug provided a user-friendly graphical user interface to encapsulate the data analysis modules, which can facilitate the scRNA-seq data based-discovery of novel inter-cell signaling communications and novel therapeutic regimens.

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XCVATR: Detection and Characterization of Variant Impact on the Embeddings of Single -Cell and Bulk RNA-Sequencing Samples

10.1101/2021.06.01.446668 ◽

2021 ◽

Author(s):

Arif Ozgun Harmanci ◽

Akdes Serin Harmanci ◽

Tiemo Klisch ◽

Akash J Patel

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Large Scale ◽

Joint Analysis ◽

Rna Seq ◽

Single Nucleotide Variants ◽

Cell Counts ◽

A Cell ◽

Local Enrichment ◽

Cell Cell

Gene expression profiling via RNA-sequencing has become standard for measuring and analyzing the gene activity in bulk and at single cell level. Increasing sample sizes and cell counts provides substantial information about transcriptional architecture of samples. In addition to quantification of expression at cellular level, RNA-seq can be used for detecting of variants, including single nucleotide variants and small insertions/deletions and also large variants such as copy number variants. The joint analysis of variants with transcriptional state of cells or samples can provide insight about impact of mutations. To provide a comprehensive method to jointly analyze the genetic variants and cellular states, we introduce XCVATR, a method that can identify variants, detect local enrichment of expressed variants within embedding of samples and cells. The embeddings provide information about cellular states among cells by defining a cell-cell distance metric. Unlike clustering algorithms, which depend on a cell-cell distance and use it to define clusters that explain cells globally, XCVATR detects the local enrichment of expressed variants in the embedding space such that embedding can be computed using any type of measurement or method, for example by PCA or tSNE of the expression levels. In other words, XCVATR searches patterns of association of each variant with the positions of cells in an embedding of the cells. XCVATR also visualizes the local clumps of small and large-scale variant calls in single cell and bulk RNA-sequencing datasets. We perform simulations and demonstrate that XCVATR can identify the enrichments of expressed variants and demonstrate its application on several single cell and bulk RNA-seq datasets.

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