scholarly journals Single cell RNAseq provides a molecular and cellular cartography of changes to the human endometrium through the menstrual cycle

2018 ◽  
Author(s):  
Wanxin Wang ◽  
Felipe Vilella ◽  
Pilar Alama ◽  
Inmaculada Moreno ◽  
Marco Mignardi ◽  
...  
Keyword(s):  
Menstrual Cycle ◽  
Single Cell ◽  
Cell Types ◽  
Human Endometrium ◽  
Cellular Heterogeneity ◽  
Cell Type ◽  
Stromal Fibroblasts ◽  
Unique Type ◽  
Ciliated Epithelial Cell ◽  
Human Menstrual Cycle

SummaryIn a human menstrual cycle, the endometrium undergoes remodeling, shedding, and regeneration, all of which are driven by substantial gene expression changes in the underlying cellular hierarchy. Despite its importance in human fertility and regenerative biology, mechanistic understanding of this unique type of tissue homeostasis remains rudimentary. We characterized the transcriptomic transformation of human endometrium at single cell resolution, dissecting the multidimensional cellular heterogeneity of this tissue across the entire natural menstrual cycle. We profiled the behavior of 6 endometrial cell types, including a previously uncharacterized ciliated epithelial cell type, during four major phases of endometrial transformation, and found characteristic signatures for each cell type and phase. We discovered that human window of implantation opens with an abrupt and discontinuous transcriptomic activation in the epithelia, accompanied with widespread decidualized feature in the stromal fibroblasts. These data reveal signatures in the luminal and glandular epithelia during epithelial gland reconstruction, and suggest a mechanism for adult gland formation.

scholarly journals Bulk Tissue Cell Type Deconvolution with Multi-Subject Single-Cell Expression Reference

2018 ◽  
Author(s):  
Xuran Wang ◽  
Jihwan Park ◽  
Katalin Susztak ◽  
Nancy R. Zhang ◽  
Mingyao Li
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Tissue Cell ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Cell Type Specific ◽  
Cell Expression

AbstractWe present MuSiC, a method that utilizes cell-type specific gene expression from single-cell RNA sequencing (RNA-seq) data to characterize cell type compositions from bulk RNA-seq data in complex tissues. When applied to pancreatic islet and whole kidney expression data in human, mouse, and rats, MuSiC outperformed existing methods, especially for tissues with closely related cell types. MuSiC enables characterization of cellular heterogeneity of complex tissues for identification of disease mechanisms.

scholarly journals The single-cell epigenetic regulatory landscape in mammalian perinatal testis development

2021 ◽  
Author(s):  
Jinyue Liao ◽  
Hoi Ching Suen ◽  
Shitao Rao ◽  
Alfred Chun Shui Luk ◽  
Ruoyu Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Fate ◽  
Germ Cells ◽  
Somatic Cells ◽  
Cell Types ◽  
Regulatory Elements ◽  
Cellular Heterogeneity ◽  
Cell Populations ◽  
Cell Type

AbstractSpermatogenesis depends on an orchestrated series of developing events in germ cells and full maturation of the somatic microenvironment. To date, the majority of efforts to study cellular heterogeneity in testis has been focused on single-cell gene expression rather than the chromatin landscape shaping gene expression. To advance our understanding of the regulatory programs underlying testicular cell types, we analyzed single-cell chromatin accessibility profiles in more than 25,000 cells from mouse developing testis. We showed that scATAC-Seq allowed us to deconvolve distinct cell populations and identify cis-regulatory elements (CREs) underlying cell type specification. We identified sets of transcription factors associated with cell type-specific accessibility, revealing novel regulators of cell fate specification and maintenance. Pseudotime reconstruction revealed detailed regulatory dynamics coordinating the sequential developmental progressions of germ cells and somatic cells. This high-resolution data also revealed putative stem cells within the Sertoli and Leydig cell populations. Further, we defined candidate target cell types and genes of several GWAS signals, including those associated with testosterone levels and coronary artery disease. Collectively, our data provide a blueprint of the ‘regulon’ of the mouse male germline and supporting somatic cells.

scholarly journals RNA-seq library preparation from single pancreatic acinar cells

2016 ◽  
Author(s):  
Damian Wollny ◽  
Sheng Zhao ◽  
Ana Martin-Villalba
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Acinar Cells ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Pancreatic Acinar Cells ◽  
Library Preparation ◽  
Cell Type ◽  
Promising Tool ◽  
Single Cell Rna Sequencing

Single cell RNA sequencing technology has emerged as a promising tool to uncover previously neglected cellular heterogeneity. Multiple methods and protocols have been developed to apply single cell sequencing to different cell types from various organs. However, library preparation for RNA sequencing remains challenging for cell types with high RNAse content due to rapid degradation of endogenous RNA molecules upon cell lysis. To this end, we developed a protocol based on the SMART-seq2 technology for single cell RNA sequencing of pancreatic acinar cells, the cell type with one of the highest ribonuclease concentration measured to date. This protocol reliably produces high quality libraries from single acinar cells reaching a total of 5x106 reads / cell and ∼ 80% transcript mapping rate with no detectable 3´end bias. Thus, our protocol makes single cell transcriptomics accessible to cell type with very high RNAse content.

scholarly journals A cis-regulatory atlas in maize at single-cell resolution

2020 ◽  
Author(s):  
Alexandre P. Marand ◽  
Zongliang Chen ◽  
Andrea Gallavotti ◽  
Robert J. Schmitz
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Regulatory Elements ◽  
Cellular Heterogeneity ◽  
Cell Type ◽  
Crop Species ◽  
Cell Functions ◽  
Cell Type Specific ◽  
Type Specification ◽  
Accessible Chromatin

ABSTRACTCis-regulatory elements (CREs) encode the genomic blueprints for coordinating spatiotemporal gene expression programs underlying highly specialized cell functions. To identify CREs underlying cell-type specification and developmental transitions, we implemented single-cell sequencing of Assay for Transposase Accessible Chromatin in an atlas of Zea mays organs. We describe 92 distinct states of chromatin accessibility across more than 165,913 putative CREs, 56,575 cells, and 52 known cell-types in maize using a novel implementation of regularized quasibinomial logistic regression. Cell states were largely determined by combinatorial accessibility of transcription factors (TFs) and their binding sites. A neural network revealed that cell identity could be accurately predicted (>0.94) solely based on TF binding site accessibility. Co-accessible chromatin recapitulated higher-order chromatin interactions, with distinct sets of TFs coordinating cell type-specific regulatory dynamics. Pseudotime reconstruction and alignment with Arabidopsis thaliana trajectories identified conserved TFs, associated motifs, and cis-regulatory regions specifying sequential developmental progressions. Cell-type specific accessible chromatin regions were enriched with phenotype-associated genetic variants and signatures of selection, revealing the major cell-types and putative CREs targeted by modern maize breeding. Collectively, our analysis affords a comprehensive framework for understanding cellular heterogeneity, evolution, and cis-regulatory grammar of cell-type specification in a major crop species.

scholarly journals Sensei: How many samples to tell evolution in single-cell studies?

2020 ◽  
Author(s):  
Shaoheng Liang ◽  
Jason Willis ◽  
Jinzhuang Dou ◽  
Vakul Mohanty ◽  
Yuefan Huang ◽  
...  
Keyword(s):  
Single Cell ◽  
Web Application ◽  
Expression Profiles ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Controlled Study ◽  
Cancer Evolution ◽  
Cell Type ◽  
Specific Expression ◽  
Mathematical Accuracy

1AbstractCellular heterogeneity underlies cancer evolution and metastasis. Advances in single-cell technologies such as single-cell RNA sequencing and mass cytometry have enabled interrogation of cell type-specific expression profiles and abundance across heterogeneous cancer samples obtained from clinical trials and preclinical studies. However, challenges remain in determining sample sizes needed for ascertaining changes in cell type abundances in a controlled study. To address this statistical challenge, we have developed a new approach, named Sensei, to determine the number of samples and the number of cells that are required to ascertain such changes between two groups of samples in single-cell studies. Sensei expands the t-test and models the cell abundances using a beta-binomial distribution. We evaluate the mathematical accuracy of Sensei and provide practical guidelines on over 20 cell types in over 30 cancer types based on knowledge acquired from the cancer cell atlas (TCGA) and prior single-cell studies. We provide a web application to enable user-friendly study design via https://kchen-lab.github.io/sensei/table_beta.html.

scholarly journals Sensei: how many samples to tell a change in cell type abundance?

2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Shaoheng Liang ◽  
Jason Willis ◽  
Jinzhuang Dou ◽  
Vakul Mohanty ◽  
Yuefan Huang ◽  
...  
Keyword(s):  
Single Cell ◽  
Web Application ◽  
Expression Profiles ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Controlled Study ◽  
Cancer Evolution ◽  
Cell Type ◽  
Specific Expression ◽  
Mathematical Accuracy

AbstractCellular heterogeneity underlies cancer evolution and metastasis. Advances in single-cell technologies such as single-cell RNA sequencing and mass cytometry have enabled interrogation of cell type-specific expression profiles and abundance across heterogeneous cancer samples obtained from clinical trials and preclinical studies. However, challenges remain in determining sample sizes needed for ascertaining changes in cell type abundances in a controlled study. To address this statistical challenge, we have developed a new approach, named Sensei, to determine the number of samples and the number of cells that are required to ascertain such changes between two groups of samples in single-cell studies. Sensei expands the t-test and models the cell abundances using a beta-binomial distribution. We evaluate the mathematical accuracy of Sensei and provide practical guidelines on over 20 cell types in over 30 cancer types based on knowledge acquired from the cancer cell atlas (TCGA) and prior single-cell studies. We provide a web application to enable user-friendly study design via https://kchen-lab.github.io/sensei/table_beta.html.

scMRA: A robust deep learning method to annotate scRNA-seq data with multiple reference datasets

2021 ◽  
Author(s):  
Musu Yuan ◽  
Liang Chen ◽  
Minghua Deng
Keyword(s):  
Deep Learning ◽  
Single Cell ◽  
Reference Data ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Supplementary Information ◽  
Batch Effects ◽  
Cell Type ◽  
Convolutional Network ◽  
Multiple Reference

Abstract Motivation Single-cell RNA-seq (scRNA-seq) has been widely used to resolve cellular heterogeneity. After collecting scRNA-seq data, the natural next step is to integrate the accumulated data to achieve a common ontology of cell types and states. Thus, an effective and efficient cell-type identification method is urgently needed. Meanwhile, high quality reference data remain a necessity for precise annotation. However, such tailored reference data are always lacking in practice. To address this, we aggregated multiple datasets into a meta-dataset on which annotation is conducted. Existing supervised or semi-supervised annotation methods suffer from batch effects caused by different sequencing platforms, the effect of which increases in severity with multiple reference datasets. Results Herein, a robust deep learning based single-cell Multiple Reference Annotator (scMRA) is introduced. In scMRA, a knowledge graph is constructed to represent the characteristics of cell types in different datasets, and a graphic convolutional network (GCN) serves as a discriminator based on this graph. scMRA keeps intra-cell-type closeness and the relative position of cell types across datasets. scMRA is remarkably powerful at transferring knowledge from multiple reference datasets, to the unlabeled target domain, thereby gaining an advantage over other state-of-the-art annotation methods in multi-reference data experiments. Furthermore, scMRA can remove batch effects. To the best of our knowledge, this is the first attempt to use multiple insufficient reference datasets to annotate target data, and it is, comparatively, the best annotation method for multiple scRNA-seq datasets. Availability An implementation of scMRA is available from https://github.com/ddb-qiwang/scMRA-torch Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Virtual methylome dissection facilitated by single-cell analyses

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Liduo Yin ◽  
Yanting Luo ◽  
Xiguang Xu ◽  
Shiyu Wen ◽  
Xiaowei Wu ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Single Cell ◽  
Nonnegative Matrix ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Cell Type ◽  
Mixed Cell ◽  
Numerous Cell ◽  
Cell Type Specific ◽  
Methylation Patterns

Abstract Background Numerous cell types can be identified within plant tissues and animal organs, and the epigenetic modifications underlying such enormous cellular heterogeneity are just beginning to be understood. It remains a challenge to infer cellular composition using DNA methylomes generated for mixed cell populations. Here, we propose a semi-reference-free procedure to perform virtual methylome dissection using the nonnegative matrix factorization (NMF) algorithm. Results In the pipeline that we implemented to predict cell-subtype percentages, putative cell-type-specific methylated (pCSM) loci were first determined according to their DNA methylation patterns in bulk methylomes and clustered into groups based on their correlations in methylation profiles. A representative set of pCSM loci was then chosen to decompose target methylomes into multiple latent DNA methylation components (LMCs). To test the performance of this pipeline, we made use of single-cell brain methylomes to create synthetic methylomes of known cell composition. Compared with highly variable CpG sites, pCSM loci achieved a higher prediction accuracy in the virtual methylome dissection of synthetic methylomes. In addition, pCSM loci were shown to be good predictors of the cell type of the sorted brain cells. The software package developed in this study is available in the GitHub repository (https://github.com/Gavin-Yinld). Conclusions We anticipate that the pipeline implemented in this study will be an innovative and valuable tool for the decoding of cellular heterogeneity.

scholarly journals Sensei: How Many Samples to Tell Evolution in Single-Cell Studies?

2021 ◽  
Author(s):  
Shaoheng Liang ◽  
Jason Willis ◽  
Jinzhuang Dou ◽  
Vakul Mohanty ◽  
Yuefan Huang ◽  
...  
Keyword(s):  
Single Cell ◽  
Web Application ◽  
Expression Profiles ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Controlled Study ◽  
Cancer Evolution ◽  
Cell Type ◽  
Specific Expression ◽  
Mathematical Accuracy

Abstract Cellular heterogeneity underlies cancer evolution and metastasis. Advances in single-cell technologies such as single-cell RNA sequencing and mass cytometry have enabled interrogation of cell type-specific expression profiles and abundance across heterogeneous cancer samples obtained from clinical trials and preclinical studies. However, challenges remain in determining sample sizes needed for ascertaining changes in cell type abundances in a controlled study. To address this statistical challenge, we have developed a new approach, named Sensei, to determine the number of samples and the number of cells that are required to ascertain such changes between two groups of samples in single-cell studies. Sensei expands the t-test and models the cell abundances using a beta-binomial distribution. We evaluate the mathematical accuracy of Sensei and provide practical guidelines on over 20 cell types in over 30 cancer types based on knowledge acquired from the cancer cell atlas (TCGA) and prior single-cell studies. We provide a web application to enable user-friendly study design via https://kchen-lab.github.io/sensei/table_beta.html.

scholarly journals STRIDE: accurately decomposing and integrating spatial transcriptomics using single-cell RNA sequencing

2021 ◽  
Author(s):  
Dongqing Sun ◽  
Yihan Xiao ◽  
Zhaoyang Liu ◽  
Taiwen Li ◽  
Qiu Wu ◽  
...  
Keyword(s):  
Single Cell ◽  
Three Dimensional ◽  
Cell Types ◽  
Computational Method ◽  
Cellular Heterogeneity ◽  
Integrated Analysis ◽  
Cell Type ◽  
Specificity And Sensitivity ◽  
Cell Type Specific ◽  
Spatial Locations

AbstractThe recent advances in spatial transcriptomics have brought unprecedented opportunities to understand the cellular heterogeneity in the spatial context. However, the current limitations of spatial technologies hamper the exploration of cellular localizations and interactions at single-cell level. Here, we present spatial transcriptomics deconvolution by topic modeling (STRIDE), a computational method to decompose cell-types from spatial mixtures by leveraging topic profiles trained from single-cell transcriptomics. STRIDE accurately estimated the cell-type proportions and showed balanced specificity and sensitivity compared to existing methods. We demonstrate STRIDE’s utility by applying it to different spatial platforms and biological systems. Deconvolution by STRIDE not only mapped rare cell-types to spatial locations but also improved the identification of spatial localized genes and domains. Moreover, topics discovered by STRIDE were associated with cell-type-specific functions, and could be further used to integrate successive sections and reconstruct the three-dimensional architecture of tissues. Taken together, STRIDE is a versatile and extensible tool for integrated analysis of spatial and single-cell transcriptomics and is publicly available at https://github.com/DongqingSun96/STRIDE.

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