Discovering sparse transcription factor codes for cell states and state transitions during development

Computational analysis of gene expression to determine both the sequence of lineage choices made by multipotent cells and to identify the genes influencing these decisions is challenging. Here we discover a pattern in the expression levels of a sparse subset of genes among cell types in B- and T-cell developmental lineages that correlates with developmental topologies. We develop a statistical framework using this pattern to simultaneously infer lineage transitions and the genes that determine these relationships. We use this technique to reconstruct the early hematopoietic and intestinal developmental trees. We extend this framework to analyze single-cell RNA-seq data from early human cortical development, inferring a neocortical-hindbrain split in early progenitor cells and the key genes that could control this lineage decision. Our work allows us to simultaneously infer both the identity and lineage of cell types as well as a small set of key genes whose expression patterns reflect these relationships.

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Bayesian Framework for Detecting Gene Expression Outliers in Individual Samples

JCO Clinical Cancer Informatics ◽

10.1200/cci.19.00095 ◽

2020 ◽

pp. 160-170

Author(s):

John Vivian ◽

Jordan M. Eizenga ◽

Holly C. Beale ◽

Olena M. Vaske ◽

Benedict Paten

Keyword(s):

Gene Expression ◽

Expression Patterns ◽

Rna Seq ◽

Single Patient ◽

Tissue Samples ◽

Composite Tissue ◽

Patient Sample ◽

Statistical Framework ◽

Therapeutic Leads ◽

Upregulated Genes

PURPOSE Many antineoplastics are designed to target upregulated genes, but quantifying upregulation in a single patient sample requires an appropriate set of samples for comparison. In cancer, the most natural comparison set is unaffected samples from the matching tissue, but there are often too few available unaffected samples to overcome high intersample variance. Moreover, some cancer samples have misidentified tissues of origin or even composite-tissue phenotypes. Even if an appropriate comparison set can be identified, most differential expression tools are not designed to accommodate comparisons to a single patient sample. METHODS We propose a Bayesian statistical framework for gene expression outlier detection in single samples. Our method uses all available data to produce a consensus background distribution for each gene of interest without requiring the researcher to manually select a comparison set. The consensus distribution can then be used to quantify over- and underexpression. RESULTS We demonstrate this method on both simulated and real gene expression data. We show that it can robustly quantify overexpression, even when the set of comparison samples lacks ideally matched tissue samples. Furthermore, our results show that the method can identify appropriate comparison sets from samples of mixed lineage and rediscover numerous known gene-cancer expression patterns. CONCLUSION This exploratory method is suitable for identifying expression outliers from comparative RNA sequencing (RNA-seq) analysis for individual samples, and Treehouse, a pediatric precision medicine group that leverages RNA-seq to identify potential therapeutic leads for patients, plans to explore this method for processing its pediatric cohort.

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Comprehensive characterization of tissue-specific chromatin accessibility in L2 Caenorhabditis elegans nematodes

10.1101/2020.09.15.299123 ◽

2020 ◽

Author(s):

Timothy J. Durham ◽

Riza M. Daza ◽

Louis Gevirtzman ◽

Darren A. Cusanovich ◽

William Stafford Noble ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Expression Patterns ◽

Cell Types ◽

Chromatin Accessibility ◽

Gene Expression Patterns ◽

Rna Seq ◽

Cell Type ◽

Tissue Specific ◽

C Elegans

AbstractRecently developed single cell technologies allow researchers to characterize cell states at ever greater resolution and scale. C. elegans is a particularly tractable system for studying development, and recent single cell RNA-seq studies characterized the gene expression patterns for nearly every cell type in the embryo and at the second larval stage (L2). Gene expression patterns are useful for learning about gene function and give insight into the biochemical state of different cell types; however, in order to understand these cell types, we must also determine how these gene expression levels are regulated. We present the first single cell ATAC-seq study in C. elegans. We collected data in L2 larvae to match the available single cell RNA-seq data set, and we identify tissue-specific chromatin accessibility patterns that align well with existing data, including the L2 single cell RNA-seq results. Using a novel implementation of the latent Dirichlet allocation algorithm, we leverage the single-cell resolution of the sci-ATAC-seq data to identify accessible loci at the level of individual cell types, providing new maps of putative cell type-specific gene regulatory sites, with promise for better understanding of cellular differentiation and gene regulation in the worm.

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Inference of cell state transitions and cell fate plasticity from single-cell with MARGARET

10.1101/2021.10.22.465455 ◽

2021 ◽

Author(s):

Kushagra Pandey ◽

Hamim Zafar

Keyword(s):

Single Cell ◽

Cell Fate ◽

Markov Chain Model ◽

Metric Learning ◽

Transitional Cell ◽

Cell Types ◽

State Transitions ◽

Chain Model ◽

Rna Seq ◽

Cell State

Despite recent advances in inferring cellular dynamics using single-cell RNA-seq data, existing trajectory inference (TI) methods face difficulty in accurately reconstructing cell-state manifold and inferring trajectory and cell fate plasticity for complex topologies. We present MARGARET, a novel TI method that utilizes a deep unsupervised metric learning-based approach for inferring the cellular embeddings and employs a novel measure of connectivity between cell clusters and a graph-partitioning approach to reconstruct complex trajectory topologies. MARGARET utilizes the inferred trajectory for determining terminal states and inferring cell-fate plasticity using a scalable absorbing Markov Chain model. On a diverse simulated benchmark, MARGARET outperformed state-of-the-art methods in recovering global topology and cell pseudotime ordering. When applied to experimental datasets from hematopoiesis, embryogenesis, and colon differentiation, MARGARET reconstructed major lineages and associated gene expression trends, better characterized key branching events and transitional cell types, and identified novel cell types, and branching events that were previously uncharacterized.

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A Bayesian Framework for Detecting Gene Expression Outliers in Individual Samples

10.1101/662338 ◽

2019 ◽

Author(s):

John Vivian ◽

Jordan Eizenga ◽

Holly C. Beale ◽

Olena Morozova-Vaske ◽

Benedict Paten

Keyword(s):

Gene Expression ◽

Expression Patterns ◽

Expression Data ◽

Rna Seq ◽

Single Patient ◽

Composite Tissue ◽

Patient Sample ◽

Statistical Framework ◽

Therapeutic Leads ◽

Upregulated Genes

ABSTRACTObjectiveMany antineoplastics are designed to target upregulated genes, but quantifying upregulation in a single patient sample requires an appropriate set of samples for comparison. In cancer, the most natural comparison set is unaffected samples from the matching tissue, but there are often too few available unaffected samples to overcome high inter-sample variance. Moreover, some cancer samples have misidentified tissues or origin, or even composite-tissue phenotypes. Even if an appropriate comparison set can be identified, most differential expression tools are not designed to accommodate comparing to a single patient sample.Materials and MethodsWe propose a Bayesian statistical framework for gene expression outlier detection in single samples. Our method uses all available data to produce a consensus background distribution for each gene of interest without requiring the researcher to manually select a comparison set. The consensus distribution can then be used to quantify over- and under-expression.ResultsWe demonstrate this method on both simulated and real gene expression data. We show that it can robustly quantify overexpression, even when the set of comparison samples lacks ideally matched tissues samples. Further, our results show that the method can identify appropriate comparison sets from samples of mixed lineage and rediscover numerous known gene-cancer expression patterns.ConclusionsThis exploratory method is suitable for identifying expression outliers from comparative RNA-seq analysis for individual samples and Treehouse, a pediatric precision medicine group that leverages RNA-seq to identify potential therapeutic leads for patients, plans to explore this method for processing their pediatric cohort.

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Mining TCGA Data for Key Biomarkers Related to Immune Microenvironment in Endometrial Carcinoma by Immune Score and Weighted Correlation Network Analysis

10.21203/rs.3.rs-126793/v1 ◽

2020 ◽

Author(s):

Chengbin Guo ◽

Yuqin Tang ◽

Yongqiang Zhang ◽

Gen Li

Keyword(s):

Network Analysis ◽

Immune Cell ◽

Function Analysis ◽

Expression Patterns ◽

Cell Types ◽

Strong Correlations ◽

Immune Microenvironment ◽

Black Module ◽

Key Genes ◽

Immune Score

Abstract Background: Endometrial cancer (EC) is one of the most lethal gynecological cancer in women. It is imperative to identify the potential immune microenvironment-related biomarkers associated with the prognosis for EC. Methods: RNA-seq data and related clinical information of EC patients were derived from The Cancer Genome Atlas (TCGA). The immune score of each EC sample was obtained by ESTIMATE algorithm. Weighted gene co-expression network analysis (WCGNA) was used to identify the interesting module and potential key genes concerning the immune score. Further, the expression patterns of the key genes were verified via the GEPIA database. Last, CIBERSORT was used to evaluate the relative abundances of 22 immune cell types in EC. Results: Immune scores were significantly associated with tumor grade and histology of EC, and high immune scores may exert a protective influence on the survival outcome for EC. WCGNA indicated that the black module was significantly correlated with the immune score in EC. Function analysis revealed it mainly involved in those terms related to immune regulation and inflammatory response. Moreover, 11 key genes were identified from the black module, validated by the GEPIA database, and revealed strong correlations with infiltration levels of multiple immune cell types, as was the prognosis of EC. Conclusion: In our study, 11 key genes showed abnormal expressions and strong correlations with immune cell infiltration in EC, most of which were significantly associated with the prognosis of EC. These findings made them promising therapeutic targets for the treatment of EC.

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Evidence Supporting the Regulatory Relationships through a Paracrine Pathway between the Sternum and Pectoral Muscles in Ducks

Genes ◽

10.3390/genes12040463 ◽

2021 ◽

Vol 12 (4) ◽

pp. 463

Author(s):

Yanying Li ◽

Hehe Liu ◽

Lei Wang ◽

Yang Xi ◽

Jiwen Wang ◽

...

Keyword(s):

Muscle Development ◽

Regulatory Mechanism ◽

Expression Patterns ◽

Pectoral Muscle ◽

Scientific Data ◽

Secreted Proteins ◽

Rna Seq ◽

Genes Encoding ◽

Key Genes ◽

Bone Calcification

Muscles and bones are anatomically closely linked, and they can conduct communication by mechanical and chemical signals. However, the specific regulatory mechanism between the pectoral muscle and sternum in birds was largely unknown. The present study explored the potential relationship between them in ducks. The result of the sections showed that more nuclei in proliferate states were observed in the pectoral muscle fibers attached to the calcified sternum, than those attached to the un-calcified sternum. The RNA-seq identified 328 differentially expressed genes (DEGs) in the sternum between the calcified and un-calcified groups. Gene ontology (GO) showed that the DEGs were mainly enriched in pathways associated with calcification. In addition, DEGs in the muscles between the calcified and un-calcified sternum groups were mainly annotated to signal transduction receptor pathways. The expression patterns of genes encoding for secreted proteins, in bone (CXCL12, BMP7 and CTSK) and muscle (LGI1), were clustered with muscle development (MB) and bone calcification (KCNA1, OSTN, COL9A3, and DCN) related genes, respectively, indicating the regulatory relationships through a paracrine pathway existing between the sternum and pectoral muscles in ducks. Together, we demonstrated that the pectoral muscle development was affected by the sternal ossification states in ducks. The VEGFA, CXCL12, SPP1, NOG, and BMP7 were possibly the key genes to participate in the ossification of the duck sternum. We firstly listed evidence supporting the regulatory relationships through a paracrine pathway between the sternum and pectoral muscles in ducks, which provided scientific data for the study of the synergistic development of bone and skeletal muscle.

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scQuery: a web server for comparative analysis of single-cell RNA-seq data

10.1101/323238 ◽

2018 ◽

Author(s):

Amir Alavi ◽

Matthew Ruffalo ◽

Aiyappa Parvangada ◽

Zhilin Huang ◽

Ziv Bar-Joseph

Keyword(s):

Single Cell ◽

Large Scale ◽

Web Server ◽

Cell Types ◽

Marker Genes ◽

Heterogeneous Environments ◽

Rna Seq ◽

Cell Type ◽

Small Set ◽

Unique Cell

SummarySingle cell RNA-Seq (scRNA-seq) studies often profile upward of thousands of cells in heterogeneous environments. Current methods for characterizing cells perform unsupervised analysis followed by assignment using a small set of known marker genes. Such approaches are limited to a few, well characterized cell types. To enable large scale supervised characterization we developed an automated pipeline to download, process, and annotate publicly available scRNA-seq datasets. We extended supervised neural networks to obtain efficient and accurate representations for scRNA-seq data. We applied our pipeline to analyze data from over 500 different studies with over 300 unique cell types and show that supervised methods greatly outperform unsupervised methods for cell type identification. A case study of neural degeneration data highlights the ability of these methods to identify differences between cell type distributions in healthy and diseased mice. We implemented a web server that compares new datasets to collected data employing fast matching methods in order to determine cell types, key genes, similar prior studies, and more.

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Finding cell-specific expression patterns in the early Ciona embryo with single-cell RNA-seq

10.1101/197699 ◽

2017 ◽

Author(s):

Garth R. Ilsley ◽

Ritsuko Suyama ◽

Takeshi Noda ◽

Nori Satoh ◽

Nicholas M. Luscombe

Keyword(s):

Gene Expression ◽

Single Cell ◽

Expression Patterns ◽

Cell Types ◽

Specific Gene ◽

Rna Seq ◽

Cell Stage ◽

Specific Expression ◽

Temporal Gene Expression

AbstractSingle-cell RNA-seq has been established as a reliable and accessible technique enabling new types of analyses, such as identifying cell types and studying spatial and temporal gene expression variation and change at single-cell resolution. Recently, single-cell RNA-seq has been applied to developing embryos, which offers great potential for finding and characterising genes controlling the course of development along with their expression patterns. In this study, we applied single-cell RNA-seq to the 16-cell stage of the Ciona embryo, a marine chordate and performed a computational search for cell-specific gene expression patterns. We recovered many known expression patterns from our single-cell RNA-seq data and despite extensive previous screens, we succeeded in finding new cell-specific patterns, which we validated by in situ and single-cell qPCR.

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Single cell RNA-seq study of wild type and Hox9,10,11 mutant developing uterus

10.1101/395574 ◽

2018 ◽

Cited By ~ 1

Author(s):

Michael L. Mucenski ◽

Robert Mahoney ◽

Mike Adam ◽

Andrew S. Potter ◽

S. Steven Potter

Keyword(s):

Gene Expression ◽

Single Cell ◽

Hox Genes ◽

Expression Patterns ◽

Wild Type Mouse ◽

Cell Types ◽

Specific Gene ◽

Rna Seq ◽

Wild Type ◽

Mouse Uterus

AbstractThe uterus is a remarkable organ that must guard against infections while maintaining the ability to support growth of a fetus without rejection. The Hoxa10 and Hoxa11 genes have previously been shown to play essential roles in uterus development and function. In this report we show that the Hoxc9,10,11 genes play a redundant role in the formation of uterine glands. In addition, we use single cell RNA-seq to create a high resolution gene expression atlas of the developing wild type mouse uterus. Cell types and subtypes are defined, for example dividing endothelial cells into arterial, venous, capillary, and lymphatic, while epithelial cells separate into luminal and glandular subtypes. Further, a surprising heterogeneity of stromal and myocyte cell types are identified. Transcription factor codes and ligand/receptor interactions are characterized. We also used single cell RNA-seq to globally define the altered gene expression patterns in all developing uterus cell types for two Hox mutants, with 8 or 9 mutant Hox genes. The mutants show a striking disruption of Wnt signaling as well as the Cxcl12/Cxcr4 ligand/receptor axis.Summary statementA single cell RNA-seq study of the developing mouse uterus defines cellular heterogeneities, lineage specific gene expression programs and perturbed pathways in Hox9,10,11 mutants.

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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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