Bulk and single-cell RNA-seq reveal dmrtb1 gene expression profiles during sex change in zig-zag eel (Mastacembelus armatus)

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.

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

10.1101/743591 ◽

2019 ◽

Cited By ~ 1

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.

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Sources of Variation in Cell-Type RNA-Seq Profiles

10.21203/rs.2.23415/v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Johan Gustafsson ◽

Felix Held ◽

Jonathan Robinson ◽

Elias Björnson ◽

Rebecka Jörnsten ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Specific Gene ◽

Rna Seq ◽

Cell Type ◽

Specific Gene Expression ◽

Cell Type Specific ◽

Technical Factors

Abstract Background Cell-type specific gene expression profiles are needed for many computational methods operating on bulk RNA-Seq samples, such as deconvolution of cell-type fractions and digital cytometry. However, the gene expression profile of a cell type can vary substantially due to both technical factors and biological differences in cell state and surroundings, reducing the efficacy of such methods. Here, we investigated which factors contribute most to this variation. Results We evaluated different normalization methods, quantified the magnitude of variation introduced by different sources, and examined the differences between UMI-based single-cell RNA-Seq and bulk RNA-Seq. We applied methods such as random forest regression to a collection of publicly available bulk and single-cell RNA-Seq datasets containing B and T cells, and found that the technical variation across laboratories is of the same magnitude as the biological variation across cell types. Tissue of origin and cell subtype are less important but still substantial factors, while the difference between individuals is relatively small. We also show that much of the differences between UMI-based single-cell and bulk RNA-Seq methods can be explained by the number of read duplicates per mRNA molecule in the single-cell sample.Conclusions Our work shows the importance of either matching or correcting for technical factors when creating cell-type specific gene expression profiles that are to be used together with bulk samples.

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Genome-Wide Characterization of Human Hematopoietic Progenitor Cell Heterogeneity by Expression Profiling of Single Cells: A Pilot Study

Blood ◽

10.1182/blood.v120.21.1231.1231 ◽

2012 ◽

Vol 120 (21) ◽

pp. 1231-1231

Author(s):

Chih Long Liu ◽

Bo Dai ◽

Aaron M. Newman ◽

Ravi Majeti ◽

Ash A Alizadeh

Keyword(s):

Gene Expression ◽

Single Cell ◽

Progenitor Cells ◽

Expression Profiles ◽

Single Cells ◽

Gene Expression Profiles ◽

Rna Seq ◽

Hematopoietic Stem ◽

Single Donor ◽

Normal Human

Abstract Abstract 1231 Background: Current methods for defining and isolating human hematopoietic stem and progenitor cells using surface markers enrich for unique functional properties of these populations. However, significant functional heterogeneity in these compartments remains with important implications for understanding normal and altered hematopoiesis. Using flow sorting to enrich >10,000 cells as progenitor subpopulations, we previously characterized the gene expression signature of normal human HSC (Majetiet al 2009 PNAS 106(9):3396–3401). We hypothesized that interrogation of the transcriptomes of single cells from this compartment could resolve remaining heterogeneity and help identify and better define features of progenitor cells and hematopoietic stem cells (HSCs). Methods: Using normal human bone marrow aspirates and a FACS Aria II instrument equipped with a specialized single-cell sorting apparatus, we sorted cells enriched for HSCs based on expression of Lin-CD34+CD38-CD90+CD45RA− into 1-cell, 10-cell, 100-cell, and 40000-cell (bulk) representations. We used at least 5 replicates per group and verified single cell deposition by direct visualization. We amplified cDNA from these corresponding inputs using an exponential whole transcriptome amplification (WTA) scheme (Miltenyi SuperAmp), and evaluated gene expression profiles by two microarray platforms (Agilent/GE Healthcare 60K, and Affymetrix U133 plus 2.0), and by RNA-Seq (Illumina). We used gene expression correlation between replicates within and between microarrays as means of assessing methodological reproducibility and estimating population heterogeneity. Results: Whole transcriptome amplification yielded cDNA ranging from 0.2–1 kb for 10 and 100 cells, with significantly lower size distribution of amplified cDNA observed for single cells. Gene expression profiles had significantly better replicate reproducibility and array coverage with the Agilent microarray platform when compared with the Affymetrix U133 Plus 2.0 platform (gene coverage of 84 % for 100 cells, 73 % for 10 cells and 50% for 1 cell for Agilent vs 24 % for 100 cells, 11 % for 10 cells and 5.7% for 1 cell for Affymetrix). RNA-Seq profiling of the same populations is ongoing with major technical optimizations focused on reducing amplification of non-human templates while maintaining library complexity and representation. Using biological replicates for each input size, we observed high inter-replicate correlation levels for expression profiles obtained for bulk sorted HSCs from 8 healthy donors (∼40000-cells, average r=0.97) and for 100-cell and 10-cell inputs from a single donor (r=0.96–0.99, respectively). While intra-array concordance of replicate measurements (n=14642) was high (r>0.91) within each of 5 single cells from a single donor, comparison of 5-single cells from the same donor identified significant heterogeneity, when compared to the 10-cell and 100-cell sub-clusters (Figure 1). Individual genes characteristically expressed by these heterogeneous single cell populations are currently being investigated by FACS and Fluidigm arrays. A larger experiment characterizing 192 single progenitor cells, employing Agilent microarrays and RNA-Seq is currently in progress. Conclusions: Single cell transcriptome profiling is feasible, with best performance on 60-mer microarrays. Single cell transcriptomes exhibit lower, but reasonable levels of reproducibility (r>0.7) and precision as compared with higher cell numbers. Gene expression profiles of single cells capture gene expression heterogeneity in HSCs. Disclosures: No relevant conflicts of interest to declare.

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Estimation of immune cell content in tumour tissue using single-cell RNA-seq data

10.1101/127001 ◽

2017 ◽

Cited By ~ 2

Author(s):

Max Schelker ◽

Sonia Feau ◽

Jinyan Du ◽

Nav Ranu ◽

Edda Klipp ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Solid Tumour ◽

Immune Cell ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Specific Reference ◽

Malignant Cells ◽

Cellular Composition ◽

Rna Seq

AbstractAs interactions between the immune system and tumour cells are governed by a complex network of cell-cell interactions, knowing the specific immune cell composition of a solid tumour may be essential to predict a patient’s response to immunotherapy. Here, we analyse in depth how to derive the cellular composition of a solid tumour from bulk gene expression data by mathematical deconvolution, using indication- and cell type-specific reference gene expression profiles (RGEPs) from tumour-derived single-cell RNA sequencing data. We demonstrate that tumour-derived RGEPs are essential for the successful deconvolution and that RGEPs from peripheral blood are insufficient. We distinguish nine major cell types as well as three T cell subtypes. As the ratios of CD4+, CD8+ and regulatory T cells have been shown to predict overall survival, we extended our analysis to include the estimation of prognostic ratios that may enable the application in a clinical setting. Using the tumour derived RGEPs, we can estimate, for the first time, the content of cancer associated fibroblasts, endothelial cells and the malignant cells in a patient sample by a deconvolution approach. In addition, improved tumour cell gene expression profiles can be obtained by this method by computationally removing contamination from non-malignant cells. Given the difficulty around sample preparation and storage to obtain high quality single-cell RNA-seq data in the clinical context, the presented method represents a computational solution to derive the cellular composition of a tissue sample.

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Sources of Variation in Cell-Type RNA-Seq Profiles

10.21203/rs.2.23415/v2 ◽

2020 ◽

Author(s):

Johan Gustafsson ◽

Felix Held ◽

Jonathan Robinson ◽

Elias Björnson ◽

Rebecka Jörnsten ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Specific Gene ◽

Rna Seq ◽

Cell Type ◽

Specific Gene Expression ◽

Cell Type Specific ◽

Technical Factors

Abstract Cell-type specific gene expression profiles are needed for many computational methods operating on bulk RNA-Seq samples, such as deconvolution of cell-type fractions and digital cytometry. However, the gene expression profile of a cell type can vary substantially due to both technical factors and biological differences in cell state and surroundings, reducing the efficacy of such methods. Here, we investigated which factors contribute most to this variation. We evaluated different normalization methods, quantified the variance explained by different factors, evaluated the effect on deconvolution of cell type fractions, and examined the differences between UMI-based single-cell RNA-Seq and bulk RNA-Seq. We investigated a collection of publicly available bulk and single-cell RNA-Seq datasets containing B and T cells, and found that the technical variation across laboratories is substantial, even for genes specifically selected for deconvolution, and has a confounding effect on deconvolution. Tissue of origin is also a substantial factor, highlighting the challenge of applying cell type profiles derived from blood on mixtures from other tissues. We also show that much of the differences between UMI-based single-cell and bulk RNA-Seq methods can be explained by the number of read duplicates per mRNA molecule in the single-cell sample. Our work shows the importance of either matching or correcting for technical factors when creating cell-type specific gene expression profiles that are to be used together with bulk samples.

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A robust method to isolate Drosophila fat body nuclei for transcriptomic analysis

10.1101/2021.01.27.428429 ◽

2021 ◽

Cited By ~ 1

Author(s):

Vanika Gupta ◽

Brian P. Lazzaro

Keyword(s):

Gene Expression ◽

Single Cell ◽

Fat Body ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Rna Seq ◽

Cell Nuclei ◽

Preparation Methods ◽

Physiological Processes ◽

The Individual

ABSTRACTGene expression profiles are typically described at the level of the tissue or, often in Drosophila, at the level of the whole organism. Collapsing the gene expression of entire tissues into single measures averages over potentially important heterogeneity among the cells that make up that tissue. The advent of single-cell RNA-sequencing technology (sc-RNAseq) allows transcriptomic evaluation of the individual cells that make up a tissue. However, sc-RNAseq requires a high-quality suspension of viable cells or nuclei, and cell dissociation methods that yield healthy cells and nuclei are still lacking for many important tissues. The insect fat body is a polyfunctional tissue responsible for diverse physiological processes and therefore is an important target for sc-RNAseq. The Drosophila adult fat body consists of fragile cells that are difficult to dissociate while maintaining cell viability. As an alternative, we developed a method to isolate single fat body nuclei for RNA-seq. Our isolation method is largely free of mitochondrial contamination and yields higher capture of transcripts per nucleus compared to other nuclei preparation methods. Our method works well for single cell nuclei sequencing and potentially can be implemented for bulk RNA-seq.

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GeneSwitches : Ordering gene-expression and functional events in single-cell experiments

10.1101/832626 ◽

2019 ◽

Author(s):

Elaine Y. Cao ◽

John F. Ouyang ◽

Owen J.L. Rackham

Keyword(s):

Gene Expression ◽

Single Cell ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Supplementary Information ◽

Rna Seq ◽

Link Type ◽

Statistical Framework ◽

Changes Over Time ◽

Over Time

AbstractSummaryEmerging single-cell RNA-seq technologies has made it possible to capture and assess the gene expression of individual cells. Based on the similarity of gene expression profiles, many tools have been developed to generate an in silico ordering of cells in the form of pseudo-time trajectories. However, these tools do not provide a means to find the ordering of critical gene expression changes over pseudo-time. We present GeneSwitches, a tool that takes any single-cell pseudo-time trajectory and determines the precise order of gene-expression and functional-event changes over time. GeneSwitches uses a statistical framework based on logistic regression to identify the order in which genes are either switched on or off along pseudo-time. With this information, users can identify the order in which surface markers appear, investigate how functional ontologies are gained or lost over time, and compare the ordering of switching genes from two related pseudo-temporal processes.AvailabilityGeneSwitches is available at https://geneswitches.ddnetbio.comContactowen.rackham@duke-nus.edu.sgSupplementary Informationis available at http://www.ddnetbio.com/files/GeneSwitches_SI.pdf

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4 Molecularly guided multiplexed digital spatial analysis reveals differential gene expression profiles in the WNT-β-catenin pathway between melanoma and prostate tumors

Journal for ImmunoTherapy of Cancer ◽

10.1136/jitc-2020-sitc2020.0004 ◽

2020 ◽

Vol 8 (Suppl 3) ◽

pp. A4-A4

Author(s):

Anushka Dikshit ◽

Dan Zollinger ◽

Karen Nguyen ◽

Jill McKay-Fleisch ◽

Kit Fuhrman ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Single Molecule ◽

Spatial Information ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Fluorescence Assay ◽

Differentially Expressed ◽

Tumor Type ◽

Prostate Tumors

BackgroundThe canonical WNT-β-catenin signaling pathway is vital for development and tissue homeostasis but becomes strongly tumorigenic when dysregulated. and alter the transcriptional signature of a cell to promote malignant transformation. However, thorough characterization of these transcriptomic signatures has been challenging because traditional methods lack either spatial information, multiplexing, or sensitivity/specificity. To overcome these challenges, we developed a novel workflow combining the single molecule and single cell visualization capabilities of the RNAscope in situ hybridization (ISH) assay with the highly multiplexed spatial profiling capabilities of the GeoMx™ Digital Spatial Profiler (DSP) RNA assays. Using these methods, we sought to spatially profile and compare gene expression signatures of tumor niches with high and low CTNNB1 expression.MethodsAfter screening 120 tumor cores from multiple tumors for CTNNB1 expression by the RNAscope assay, we identified melanoma as the tumor type with the highest CTNNB1 expression while prostate tumors had the lowest expression. Using the RNAscope Multiplex Fluorescence assay we selected regions of high CTNNB1 expression within 3 melanoma tumors as well as regions with low CTNNB1 expression within 3 prostate tumors. These selected regions of interest (ROIs) were then transcriptionally profiled using the GeoMx DSP RNA assay for a set of 78 genes relevant in immuno-oncology. Target genes that were differentially expressed were further visualized and spatially assessed using the RNAscope Multiplex Fluorescence assay to confirm GeoMx DSP data with single cell resolution.ResultsThe GeoMx DSP analysis comparing the melanoma and prostate tumors revealed that they had significantly different gene expression profiles and many of these genes showed concordance with CTNNB1 expression. Furthermore, immunoregulatory targets such as ICOSLG, CTLA4, PDCD1 and ARG1, also demonstrated significant correlation with CTNNB1 expression. On validating selected targets using the RNAscope assay, we could distinctly visualize that they were not only highly expressed in melanoma compared to the prostate tumor, but their expression levels changed proportionally to that of CTNNB1 within the same tumors suggesting that these differentially expressed genes may be regulated by the WNT-β-catenin pathway.ConclusionsIn summary, by combining the RNAscope ISH assay and the GeoMx DSP RNA assay into one joint workflow we transcriptionally profiled regions of high and low CTNNB1 expression within melanoma and prostate tumors and identified genes potentially regulated by the WNT- β-catenin pathway. This novel workflow can be fully automated and is well suited for interrogating the tumor and stroma and their interactions.GeoMx Assays are for RESEARCH ONLY, not for diagnostics.

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P02.10 FocuSCOPE: a single cell, multi-omics solution to simultaneously analyze tumor variants and microenvironment

Journal for ImmunoTherapy of Cancer ◽

10.1136/jitc-2021-itoc8.22 ◽

2021 ◽

Vol 9 (Suppl 1) ◽

pp. A12.1-A12

Author(s):

Y Arjmand Abbassi ◽

N Fang ◽

W Zhu ◽

Y Zhou ◽

Y Chen ◽

...

Keyword(s):

Gene Expression ◽

Tumor Microenvironment ◽

Single Cell ◽

High Throughput ◽

Immune Cells ◽

Genetic Variants ◽

Expression Profiles ◽

Single Cells ◽

Gene Expression Profiles ◽

Single Cell Sequencing

Recent advances of high-throughput single cell sequencing technologies have greatly improved our understanding of the complex biological systems. Heterogeneous samples such as tumor tissues commonly harbor cancer cell-specific genetic variants and gene expression profiles, both of which have been shown to be related to the mechanisms of disease development, progression, and responses to treatment. Furthermore, stromal and immune cells within tumor microenvironment interact with cancer cells to play important roles in tumor responses to systematic therapy such as immunotherapy or cell therapy. However, most current high-throughput single cell sequencing methods detect only gene expression levels or epigenetics events such as chromatin conformation. The information on important genetic variants including mutation or fusion is not captured. To better understand the mechanisms of tumor responses to systematic therapy, it is essential to decipher the connection between genotype and gene expression patterns of both tumor cells and cells in the tumor microenvironment. We developed FocuSCOPE, a high-throughput multi-omics sequencing solution that can detect both genetic variants and transcriptome from same single cells. FocuSCOPE has been used to successfully perform single cell analysis of both gene expression profiles and point mutations, fusion genes, or intracellular viral sequences from thousands of cells simultaneously, delivering comprehensive insights of tumor and immune cells in tumor microenvironment at single cell resolution.Disclosure InformationY. Arjmand Abbassi: None. N. Fang: None. W. Zhu: None. Y. Zhou: None. Y. Chen: None. U. Deutsch: None.

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