scholarly journals How a cell decides its own fate: a single-cell view of molecular mechanisms and dynamics of cell-type specification

2021 ◽  
Author(s):  
Maria Mircea ◽  
Stefan Semrau
Keyword(s):  
Single Cell ◽  
Molecular Mechanisms ◽  
Cell Types ◽  
Lineage Tracing ◽  
Molecular Profile ◽  
Cell Type ◽  
Molecular Changes ◽  
Starting Point ◽  
A Cell ◽  
Type Specification

On its path from a fertilized egg to one of the many cell types in a multicellular organism, a cell turns the blank canvas of its early embryonic state into a molecular profile fine-tuned to achieve a vital organismal function. This remarkable transformation emerges from the interplay between dynamically changing external signals, the cell's internal, variable state, and tremendously complex molecular machinery; we are only beginning to understand. Recently developed single-cell omics techniques have started to provide an unprecedented, comprehensive view of the molecular changes during cell-type specification and promise to reveal the underlying gene regulatory mechanism. The exponentially increasing amount of quantitative molecular data being created at the moment is slated to inform predictive, mathematical models. Such models can suggest novel ways to manipulate cell types experimentally, which has important biomedical applications. This review is meant to give the reader a starting point to participate in this exciting phase of molecular developmental biology. We first introduce some of the principal molecular players involved in cell-type specification and discuss the important organizing ability of biomolecular condensates, which has been discovered recently. We then review some of the most important single-cell omics methods and relevant findings they produced. We devote special attention to the dynamics of the molecular changes and discuss methods to measure them, most importantly lineage tracing. Finally, we introduce a conceptual framework that connects all molecular agents in a mathematical model and helps us make sense of the experimental data.

scholarly journals Single-cell transcriptomics and cell-specific proteomics reveals molecular signatures of sleep

2020 ◽  
Author(s):  
Pawan K. Jha ◽  
Utham K. Valekunja ◽  
Sandipan Ray ◽  
Mathieu Nollet ◽  
Akhilesh B. Reddy
Keyword(s):  
Single Cell ◽  
Molecular Mechanisms ◽  
Cell Types ◽  
Brain Regions ◽  
Specific Protein ◽  
Cell Type ◽  
Transcriptional Responses ◽  
A Cell ◽  
Cell Type Specific ◽  
Different Cell Types

Every day, we sleep for a third of the day. Sleep is important for cognition, brain waste clearance, metabolism, and immune responses. The molecular mechanisms governing sleep are largely unknown. Here, we used a combination of single cell RNA sequencing and cell-type specific proteomics to interrogate the molecular underpinnings of sleep. Different cell types in three important brain regions for sleep (brainstem, cortex, and hypothalamus) exhibited diverse transcriptional responses to sleep need. Sleep restriction modulates astrocyte-neuron crosstalk and sleep need enhances expression of specific sets of transcription factors in different brain regions. In cortex, we also interrogated the proteome of two major cell types: astrocytes and neurons. Sleep deprivation differentially alters the expression of proteins in astrocytes and neurons. Similarly, phosphoproteomics revealed large shifts in cell-type specific protein phosphorylation. Our results indicate that sleep need regulates transcriptional, translational, and post-translational responses in a cell-specific manner.

scholarly journals SCSA: a cell type annotation tool for single-cell RNA-seq data

2019 ◽  
Author(s):  
Yinghao Cao ◽  
Xiaoyue Wang ◽  
Gongxin Peng
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Rna Seq ◽  
Cell Type ◽  
Confidence Levels ◽  
User Expertise ◽  
Model Combining ◽  
A Cell ◽  
Automatic Tool ◽  
Different Sources

AbstractCurrently most methods take manual strategies to annotate cell types after clustering the single-cell RNA sequencing (scRNA-seq) data. Such methods are labor-intensive and heavily rely on user expertise, which may lead to inconsistent results. We present SCSA, an automatic tool to annotate cell types from scRNA-seq data, based on a score annotation model combining differentially expressed genes (DEGs) and confidence levels of cell markers from both known and user-defined information. Evaluation on real scRNA-seq datasets from different sources with other methods shows that SCSA is able to assign the cells into the correct types at a fully automated mode with a desirable precision.

scholarly journals Profiling of Primary and Mature miRNA Expression in Atherosclerosis Associated Cell Types

2021 ◽  
Author(s):  
Pierre R. Moreau ◽  
Vanesa Tomas Bosch ◽  
Maria Bouvy-Liivrand ◽  
Kadri Õunap ◽  
Tiit Örd ◽  
...  
Keyword(s):  
Endothelial Cells ◽  
Smooth Muscle ◽  
Molecular Mechanisms ◽  
Umbilical Vein ◽  
Cell Types ◽  
Integrative Approach ◽  
Dependent Manner ◽  
Cell Type ◽  
A Cell ◽  
Cell Type Specific

Objective: Atherosclerosis is the underlying cause of most cardiovascular diseases. The main cell types associated with disease progression in the vascular wall are endothelial cells, smooth muscle cells, and macrophages. Although their role in atherogenesis has been extensively described, molecular mechanisms underlying gene expression changes remain unknown. The objective of this study was to characterize microRNA (miRNA)-related regulatory mechanisms taking place in the aorta during atherosclerosis: Approach and Results: We analyzed the changes in primary human aortic endothelial cells and human umbilical vein endothelial cell, human aortic smooth muscle cell, and macrophages (CD14+) under various proatherogenic stimuli by integrating GRO-seq, miRNA-seq, and RNA-seq data. Despite the highly cell-type-specific expression of multi-variant pri-miRNAs, the majority of mature miRNAs were found to be common to all cell types and dominated by 2 to 5 abundant miRNA species. We demonstrate that transcription contributes significantly to the mature miRNA levels although this is dependent on miRNA stability. An analysis of miRNA effects in relation to target mRNA pools highlighted pathways and targets through which miRNAs could affect atherogenesis in a cell-type-dependent manner. Finally, we validate miR-100-5p as a cell-type specific regulator of inflammatory and HIPPO-YAP/TAZ-pathways. Conclusions: This integrative approach allowed us to characterize miRNA dynamics in response to a proatherogenic stimulus and identify potential mechanisms by which miRNAs affect atherogenesis in a cell-type-specific manner.

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 CellMixS: quantifying and visualizing batch effects in single cell RNA-seq data

2020 ◽  
Author(s):  
Almut Luetge ◽  
Joanna Zyprych-Walczak ◽  
Urszula Brykczynska Kunzmann ◽  
Helena L Crowell ◽  
Daniela Calini ◽  
...  
Keyword(s):  
Single Cell ◽  
Dimensional Space ◽  
Synthetic Data ◽  
Real Data ◽  
Cell Types ◽  
Batch Effects ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Distance Distributions ◽  
A Cell

A key challenge in single cell RNA-sequencing (scRNA-seq) data analysis are dataset- and batch-specific differences that can obscure the biological signal of interest. While there are various tools and methods to perform data integration and correct for batch effects, their performance can vary between datasets and according to the nature of the bias. Therefore, it is important to understand how batch effects manifest in order to adjust for them in a reliable way. Here, we systematically explore batch effects in a variety of scRNA-seq datasets according to magnitude, cell type specificity and complexity. We developed a cell-specific mixing score (cms) that quantifies how well cells from multiple batches are mixed. By considering distance distributions (in a lower dimensional space), the score is able to detect local batch bias and differentiate between unbalanced batches (i.e., when one cell type is more abundant in a batch) and systematic differences between cells of the same cell type. We implemented cms and related metrics to detect batch effects or measure structure preservation in the CellMixS R/Bioconductor package. We systematically compare different metrics that have been proposed to quantify batch effects or bias in scRNA-seq data using real datasets with known batch effects and synthetic data that mimic various real data scenarios. While these metrics target the same question and are used interchangeably, we find differences in inter- and intra-dataset scalability, sensitivity and in a metric's ability to handle batch effects with differentially abundant cell types. We find that cell-specific metrics outperform cell type-specific and global metrics and recommend them for both method benchmarks and batch exploration.

scholarly journals Alignment of single-cell RNA-seq samples without over-correction using kernel density matching

2020 ◽  
Author(s):  
Mengjie Chen ◽  
Qi Zhan ◽  
Zepeng Mu ◽  
Lili Wang ◽  
Zhaohui Zheng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Kernel Density ◽  
Cell Types ◽  
Clinical Samples ◽  
Cell Type ◽  
Inflammatory Monocytes ◽  
A Cell ◽  
Cell Type Specific

AbstractSingle-cell RNA sequencing (scRNA-seq) technology is poised to replace bulk cell RNA sequencing for most biological and medical applications as it allows users to measure gene expression levels in a cell-type-specific manner. However, data produced by scRNA-seq often exhibit batch effects that can be specific to a cell-type, to a sample, or to an experiment, which prevent integration or comparisons across multiple experiments. Here, we present Dmatch, a method that leverages an external expression atlas of human primary cells and kernel density matching to align multiple scRNA-seq experiments for downstream biological analysis. Dmatch facilitates alignment of scRNA-seq datasets with cell-types that may overlap only partially, and thus allows integration of multiple distinct scRNA-seq experiments to extract biological insights. In simulation, Dmatch compares favorably to other alignment methods, both in terms of reducing sample-specific clustering, and in terms of avoiding over-correction. When applied to scRNA-seq data collected from clinical samples in a healthy individual and five autoimmune disease patients, Dmatch enabled cell-type-specific differential gene expression comparisons across biopsy sites and disease conditions, and uncovered a shared population of pro-inflammatory monocytes across biopsy sites in RA patients. We further show that Dmatch increases the number of eQTLs mapped from population scRNA-seq data. Dmatch is fast, scalable, and improves the utility of scRNA-seq for several important applications. Dmatch is freely available online (https://qzhan321.github.io/dmatch/).

scholarly journals Discovering Novel Cell Types across Heterogeneous Single-cell Experiments

2020 ◽  
Author(s):  
Maria Brbić ◽  
Marinka Zitnik ◽  
Sheng Wang ◽  
Angela O. Pisco ◽  
Russ B. Altman ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Rna Seq ◽  
Cell Type ◽  
Learning To Learn ◽  
Multiple Datasets ◽  
Temporal Relationships ◽  
Meta Learning ◽  
A Cell ◽  
Cell Type Specific

Although tremendous effort has been put into cell type annotation and classification, identification of previously uncharacterized cell types in heterogeneous single-cell RNA-seq data remains a challenge. Here we present MARS, a meta-learning approach for identifying and annotating known as well as novel cell types. MARS overcomes the heterogeneity of cell types by transferring latent cell representations across multiple datasets. MARS uses deep learning to learn a cell embedding function as well as a set of landmarks in the cell embedding space. The method annotates cells by probabilistically defining a cell type based on nearest landmarks in the embedding space. MARS has a unique ability to discover cell types that have never been seen before and annotate experiments that are yet unannotated. We apply MARS to a large aging cell atlas of 23 tissues covering the life span of a mouse. MARS accurately identifies cell types, even when it has never seen them before. Further, the method automatically generates interpretable names for novel cell types. Remarkably, MARS estimates meaningful cell-type-specific signatures of aging and visualizes them as trajectories reflecting temporal relationships of cells in a tissue.

scholarly journals A single cell atlas of the developing Drosophila ovary identifies follicle stem cell progenitors

2019 ◽  
Author(s):  
Maija Slaidina ◽  
Torsten U. Banisch ◽  
Selena Gupta ◽  
Ruth Lehmann
Keyword(s):  
Stem Cell ◽  
Single Cell ◽  
Cell Types ◽  
Lineage Tracing ◽  
Cell Type ◽  
Systematic Analysis ◽  
Adult Cell ◽  
Cell Type Specific ◽  
Follicle Stem Cells ◽  
Drosophila Ovary

AbstractAddressing the complexity of organogenesis at a system-wide level requires a complete understanding of adult cell types, their origin and precursor relationships. The Drosophila ovary has been a model to study how coordinated stem cell units, germline and somatic follicle stem cells, maintain and renew an organ. However, lack of cell-type specific tools have limited our ability to study the origin of individual cell types and stem cell units. Here, we use a single cell RNA sequencing approach to uncover all known cell types of the developing ovary, reveal transcriptional signatures, and identify cell type specific markers for lineage tracing. Our study identifies a novel cell type corresponding to the elusive follicle stem cell precursors and predicts sub-types of known cell types. Altogether, we reveal a previously unanticipated complexity of the developing ovary, and provide a comprehensive resource for the systematic analysis of ovary morphogenesis.

Abstract 136: Distinct Temporal Requirements for Scl to Repress Cardiomyogenesis in Endothelial-derived Cells in Hemogenic Tissues and the Heart

2013 ◽  
Vol 113 (suppl_1) ◽  
Author(s):  
Ben Van Handel ◽  
Tonis Org ◽  
Amelie Montel-Hagen ◽  
Haruko Nakano ◽  
Atsushi Nakano ◽  
...  
Keyword(s):  
Endothelial Cells ◽  
Time Window ◽  
Ectopic Expression ◽  
Cell Types ◽  
Yolk Sac ◽  
Lineage Tracing ◽  
Cell Type ◽  
Specific Manner ◽  
A Cell ◽  
Cell Type Specific

Identification of precursors with the capacity to generate cardiomyocytes is critical for advancing cardiac regenerative medicine. By analyzing knockout embryos for the bHLH factor Scl, we demonstrated that endothelial cells in hematopoietic tissues and the heart possess latent cardiomyogenic capacity. Furthermore, analysis of tamoxifen-inducible Rosa26-Cre ERT2 Scl fl/fl embryos suggested that the time window during which Scl is required for cardiac repression extends later in the heart versus the yolk sac. However, the cell types in which Scl acts remained elusive. We then deleted Scl in a cell-type specific manner in early mesoderm using Mesp1-Cre and in endothelial cells using Tie2-Cre. Lineage tracing in Mesp1-Cre Rosa26-YFP embryos demonstrated that at E9.5, a large majority of hematopoietic and endothelial cells in the yolk sac and heart were labeled. Moreover, deletion of Scl in Mesp1-Cre Scl fl/fl embryos phenocopied the germline knockout, essentially abrogating hematopoiesis and promoting the emergence of CD31 + PDGFRα + cardiomyogenic precursors and ectopic expression of the cardiomyocyte genes Myl7 and Tnnt2 in yolk sac vasculature. In contrast, deletion of Scl after endothelium had been specified in Tie2-Cre Scl fl/fl embryos did not grossly affect yolk sac hematopoiesis, nor did it induce ectopic cardiomyogenesis in hemogenic tissues. However, endothelial-derived cells in the hearts of Tie2-Cre Scl fl/fl embryos evidenced profound expansion of CD31 + PDGFRα + cardiogenic precursors at E11.5 and E13.5, as well as displayed dramatic upregulation of Myl7 and Tnnt2 , showing that the requirement for Scl to repress the cardiomyogenic program extends longer in endothelial derivatives in the heart than in the yolk sac. These data demonstrate that endocardial-derived cells in the heart retain latent cardiomyogenic potential until mid-gestation and nominate Scl as a critical regulator of endocardial fate.

scholarly journals A Single-cell Transcriptomic Atlas of the Developing Chicken Limb

2019 ◽  
Author(s):  
Christian Feregrino ◽  
Fabio Sacher ◽  
Oren Parnas ◽  
Patrick Tschopp
Keyword(s):  
Pattern Formation ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Cell Populations ◽  
Cell Type ◽  
Data Set ◽  
Cellular Resolution ◽  
Single Cell Rna Sequencing ◽  
Type Specification

AbstractBackgroundThrough precise implementation of distinct cell type specification programs, differentially regulated in both space and time, complex patterns emerge during organogenesis. Thanks to its easy experimental accessibility, the developing chicken limb has long served as a paradigm to study vertebrate pattern formation. Through decades’ worth of research, we now have a firm grasp on the molecular mechanisms driving limb formation at the tissue-level. However, to elucidate the dynamic interplay between transcriptional cell type specification programs and pattern formation at its relevant cellular scale, we lack appropriately resolved molecular data at the genome-wide level. Here, making use of droplet-based single-cell RNA-sequencing, we catalogue the developmental emergence of distinct tissue types and their transcriptome dynamics in the distal chicken limb, the so-called autopod, at cellular resolution.ResultsUsing single-cell RNA-sequencing technology, we sequenced a total of 17,628 cells coming from three key developmental stages of chicken autopod patterning. Overall, we identified 23 cell populations with distinct transcriptional profiles. Amongst them were small, albeit essential populations like the apical ectodermal ridge, demonstrating the ability to detect even rare cell types. Moreover, we uncovered the existence of molecularly distinct sub-populations within previously defined compartments of the developing limb, some of which have important signaling functions during autopod pattern formation. Finally, we inferred gene co-expression modules that coincide with distinct tissue types across developmental time, and used them to track patterning-relevant cell populations of the forming digits.ConclusionsWe provide a comprehensive functional genomics resource to study the molecular effectors of chicken limb patterning at cellular resolution. Our single-cell transcriptomic atlas captures all major cell populations of the developing autopod, and highlights the transcriptional complexity in many of its components. Finally, integrating our data-set with other single-cell transcriptomics resources will enable researchers to assess molecular similarities in orthologous cell types across the major tetrapod clades, and provide an extensive candidate gene list to functionally test cell-type-specific drivers of limb morphological diversification.

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