scholarly journals Understanding the Adult Mammalian Heart at Single-Cell RNA-Seq Resolution

2021 ◽  
Vol 9 ◽  
Author(s):  
Ernesto Marín-Sedeño ◽  
Xabier Martínez de Morentin ◽  
Jose M. Pérez-Pomares ◽  
David Gómez-Cabrero ◽  
Adrián Ruiz-Villalba
Keyword(s):  
Single Cell ◽  
Single Gene ◽  
Cell Lineage ◽  
Cell Types ◽  
Lineage Tracing ◽  
Cardiac Cell ◽  
Rna Seq ◽  
Cardiovascular Research ◽  
Cell Diversity ◽  
Unbiased Manner

During the last decade, extensive efforts have been made to comprehend cardiac cell genetic and functional diversity. Such knowledge allows for the definition of the cardiac cellular interactome as a reasonable strategy to increase our understanding of the normal and pathologic heart. Previous experimental approaches including cell lineage tracing, flow cytometry, and bulk RNA-Seq have often tackled the analysis of cardiac cell diversity as based on the assumption that cell types can be identified by the expression of a single gene. More recently, however, the emergence of single-cell RNA-Seq technology has led us to explore the diversity of individual cells, enabling the cardiovascular research community to redefine cardiac cell subpopulations and identify relevant ones, and even novel cell types, through their cell-specific transcriptomic signatures in an unbiased manner. These findings are changing our understanding of cell composition and in consequence the identification of potential therapeutic targets for different cardiac diseases. In this review, we provide an overview of the continuously changing cardiac cellular landscape, traveling from the pre-single-cell RNA-Seq times to the single cell-RNA-Seq revolution, and discuss the utilities and limitations of this technology.

scholarly journals Single Cell Profiling Reveals Sex, Lineage and Regional Diversity in the Mouse Kidney

2019 ◽  
Author(s):  
Andrew Ransick ◽  
Nils O. Lindström ◽  
Jing Liu ◽  
Zhu Qin ◽  
Jin-Jin Guo ◽  
...  
Keyword(s):  
Single Cell ◽  
Disease Modeling ◽  
Cell Lineage ◽  
Kidney Injury ◽  
Cell Types ◽  
Mouse Kidney ◽  
Lineage Tracing ◽  
Mammalian Kidney ◽  
Organ Systems ◽  
Cell Diversity

SummaryChronic kidney disease affects 10% of the population with notable differences in ethnic and sex-related susceptibility to kidney injury and disease. Kidney dysfunction leads to significant morbidity and mortality, and chronic disease in other organ systems. A mouse organ-centered understanding underlies rapid progress in human disease modeling, and cellular approaches to repair damaged systems. To enhance an understanding of the mammalian kidney, we combined anatomy-guided single cell RNA sequencing of the adult male and female mouse kidney with in situ expression studies and cell lineage tracing. These studies reveal cell diversity and marked sex differences, distinct organization and cell composition of nephrons dependent on the time of nephron specification, and lineage convergence, in which contiguous functionally-related cell types are specified from nephron and collecting system progenitor populations. A searchable database integrating findings to highlight gene-cell relationships in a normal anatomical framework will facilitate study of the mammalian kidney.

scholarly journals Massively parallel single cell lineage tracing using CRISPR/Cas9 induced genetic scars

2017 ◽  
Author(s):  
Bastiaan Spanjaard ◽  
Bo Hu ◽  
Nina Mitic ◽  
Jan Philipp Junker
Keyword(s):  
Single Cell ◽  
Computational Analysis ◽  
Systematic Approach ◽  
Single Cells ◽  
Cell Lineage ◽  
Transcriptome Profiling ◽  
Cell Types ◽  
Lineage Tracing ◽  
Lineage Trees ◽  
Different Cell Types

A key goal of developmental biology is to understand how a single cell transforms into a full-grown organism consisting of many different cell types. Single-cell RNA-sequencing (scRNA-seq) has become a widely-used method due to its ability to identify all cell types in a tissue or organ in a systematic manner 1–3. However, a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here, we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA-seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes, we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses, LINNAEUS (LINeage tracing by Nuclease-Activated Editing of Ubiquitous Sequences) can be used as a systematic approach for identifying the lineage origin of novel cell types, or of known cell types under different conditions.

EPCO-26. INTEGRATIVE MULTI-OMICS IDENTIFIES CONVERGING DEVELOPMENTAL ORIGINS OF DISTINCT MEDULLOBLASTOMA SUBGROUPS

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi7-vi7
Author(s):  
Kyle Smith ◽  
Laure Bihannic ◽  
Brian Gudenas ◽  
Qingsong Gao ◽  
Parthiv Haldipur ◽  
...  
Keyword(s):  
Single Cell ◽  
Developmental Trajectories ◽  
Cell Lineage ◽  
Cell Types ◽  
Lineage Tracing ◽  
Group 4 ◽  
Group 3 ◽  
Mechanistic Basis ◽  
Rhombic Lip

Abstract Understanding the interplay between normal development and tumorigenesis, including the identification and characterization of lineage-specific origins of MB, is a fundamental challenge in the field. Recent studies have highlighted novel associations between biologically distinct MB subgroups and diverse murine cerebellar lineages via cross-species single-cell transcriptomics. Specifically, Group 4-MB correlated with the unipolar brush cell lineage and Group 3-MB resembled Nestin+ stem cells of the early cerebellum. However, these analyses were hampered by low resolution due to the sparsity of pertinent cerebellar cell types and the cross-species nature of the approach. Herein, we profoundly expand the depth of these rare developmental populations in the murine cerebellum using a combination of lineage tracing and integrative multi-omics. Isolation and enrichment of spatially and temporally unique developmental trajectories of key rhombic lip-derived glutamatergic lineages provided an enhanced reference for mapping MB subgroups based on molecular overlap, especially for poorly defined Group 3- and Group 4-MB. Further comparisons to a novel single-cell atlas of the human fetal cerebellum, companioned with laser-capture microdissected transcriptional and epigenetic datasets, reinforced developmental insights extracted from the mouse. Characterization of compartment-specific transcriptional programs and co-expression networks identified in the human upper rhombic lip implicated convergent cellular correlates of Group 3- and Group 4-MB, suggestive of a common developmental link. Together, our results strongly implicate developmental lineages of the upper rhombic lip as the probable origins of poorly defined Group 3- and Group 4-MB. These important findings will shape future efforts to accurately model the biological heterogeneity underlying these subgroups and provide unprecedented opportunities to explore their cellular and mechanistic basis.

scholarly journals From Cell States to Cell Fates: How Cell Proliferation and Neuronal Differentiation Are Coordinated During Embryonic Development

2022 ◽  
Vol 15 ◽  
Author(s):  
Carla Belmonte-Mateos ◽  
Cristina Pujades
Keyword(s):  
Progenitor Cells ◽  
Progenitor Cell ◽  
New Technologies ◽  
Cell Lineage ◽  
Cell Types ◽  
Lineage Tracing ◽  
Vertebrate Brain ◽  
Proliferation And Differentiation ◽  
Cell Diversity ◽  
The Right

The central nervous system (CNS) exhibits an extraordinary diversity of neurons, with the right cell types and proportions at the appropriate sites. Thus, to produce brains with specific size and cell composition, the rates of proliferation and differentiation must be tightly coordinated and balanced during development. Early on, proliferation dominates; later on, the growth rate almost ceases as more cells differentiate and exit the cell cycle. Generation of cell diversity and morphogenesis takes place concomitantly. In the vertebrate brain, this results in dramatic changes in the position of progenitor cells and their neuronal derivatives, whereas in the spinal cord morphogenetic changes are not so important because the structure mainly grows by increasing its volume. Morphogenesis is under control of specific genetic programs that coordinately unfold over time; however, little is known about how they operate and impact in the pools of progenitor cells in the CNS. Thus, the spatiotemporal coordination of these processes is fundamental for generating functional neuronal networks. Some key aims in developmental neurobiology are to determine how cell diversity arises from pluripotent progenitor cells, and how the progenitor potential changes upon time. In this review, we will share our view on how the advance of new technologies provides novel data that challenge some of the current hypothesis. We will cover some of the latest studies on cell lineage tracing and clonal analyses addressing the role of distinct progenitor cell division modes in balancing the rate of proliferation and differentiation during brain morphogenesis. We will discuss different hypothesis proposed to explain how progenitor cell diversity is generated and how they challenged prevailing concepts and raised new questions.

scholarly journals Single-cell lineages reveal the rates, routes, and drivers of metastasis in cancer xenografts

2020 ◽  
Author(s):  
Jeffrey J. Quinn ◽  
Matthew G. Jones ◽  
Ross A. Okimoto ◽  
Shigeki Nanjo ◽  
Michelle M. Chan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Single Cell ◽  
Cancer Progression ◽  
Cell Lineage ◽  
Lineage Tracing ◽  
Rna Seq ◽  
Disease Etiology ◽  
Cell Lineages ◽  
Transient Events

AbstractCancer progression is characterized by rare, transient events which are nonetheless highly consequential to disease etiology and mortality. Detailed cell phylogenies can recount the history and chronology of these critical events – including metastatic seeding. Here, we applied our Cas9-based lineage tracer to study the subclonal dynamics of metastasis in a lung cancer xenograft mouse model, revealing the underlying rates, routes, and drivers of metastasis. We report deeply resolved phylogenies for tens of thousands of metastatically disseminated cancer cells. We observe surprisingly diverse metastatic phenotypes, ranging from metastasis-incompetent to aggressive populations. These phenotypic distinctions result from pre-existing, heritable, and characteristic differences in gene expression, and we demonstrate that these differentially expressed genes can drive invasiveness. Furthermore, metastases transit via diverse, multidirectional tissue routes and seeding topologies. Our work demonstrates the power of tracing cancer progression at unprecedented resolution and scale.One Sentence SummarySingle-cell lineage tracing and RNA-seq capture diverse metastatic behaviors and drivers in lung cancer xenografts in mice.

scholarly journals Quantitative single-cell interactomes in normal and virus-infected mouse lungs

2020 ◽  
Author(s):  
Margo P Cain ◽  
Belinda J Hernandez ◽  
Jichao Chen
Keyword(s):  
Single Cell ◽  
Infected Mouse ◽  
Sendai Virus ◽  
Cell Lineage ◽  
Cell Types ◽  
Rna Seq ◽  
Receptor Interactions ◽  
Cost Efficient ◽  
Intuitive Method ◽  
Mouse Lungs

ABSTRACTMammalian organs consist of diverse, intermixed cell types that signal to each other via ligand-receptor interactions – an interactome – to ensure development, homeostasis, and injury-repair. Dissecting such intercellular interactions is facilitated by rapidly growing single-cell RNA-seq (scRNA-seq) data; however, existing computational methods are often not sufficiently quantitative nor readily adaptable by bench scientists without advanced programming skills. Here we describe a quantitative intuitive algorithm, coupled with an optimized experimental protocol, to construct and compare interactomes in control and Sendai virus-infected mouse lungs. A minimum of 90 cells per cell type compensates for the known gene dropout issue in scRNA-seq and achieves comparable sensitivity to bulk RNA-seq. Cell lineage normalization after cell sorting allows cost-efficient representation of cell types of interest. A numeric representation of ligand-receptor interactions identifies, as outliers, known and potentially new interactions as well as changes upon viral infection. Our experimental and computational approaches can be generalized to other organs and human samples.Summary statementAn intuitive method to construct quantitative ligand-receptor interactomes using single-cell RNA-seq data and its application to normal and Sendai virus-infected mouse lungs.

scholarly journals Single-cell transcriptome analysis of the zebrafish embryonic trunk

2021 ◽  
Author(s):  
Sanjeeva S Metikala ◽  
Satish Casie Chetty ◽  
Saulius Sumanas
Keyword(s):  
Single Cell ◽  
Molecular Mechanisms ◽  
Cell Lineage ◽  
Cell Types ◽  
Rna Seq ◽  
Cell Lineages ◽  
Distinct Cell ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome ◽  
Different Cell Types

During embryonic development, cells differentiate into a variety of distinct cell types and subtypes with diverse transcriptional profiles. To date, transcriptomic signatures of different cell lineages that arise during development have been only partially characterized. Here we used single-cell RNA-seq to perform transcriptomic analysis of over 20,000 cells disaggregated from the trunk region of zebrafish embryos at the 30 hpf stage. Transcriptional signatures of 27 different cell types and subtypes were identified and annotated during this analysis. This dataset will be a useful resource for many researchers in the fields of developmental and cellular biology and facilitate the understanding of molecular mechanisms that regulate cell lineage choices during development.

scholarly journals Approximate distance correlation for selecting highly interrelated genes across datasets

2021 ◽  
Vol 17 (11) ◽  
pp. e1009548
Author(s):  
Qunlun Shen ◽  
Shihua Zhang
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Target Gene ◽  
Mononuclear Cells ◽  
Statistical Significance ◽  
Cell Lineage ◽  
Cell Types ◽  
Rna Seq ◽  
Peripheral Blood Mononuclear ◽  
Distance Correlation

With the rapid accumulation of biological omics datasets, decoding the underlying relationships of cross-dataset genes becomes an important issue. Previous studies have attempted to identify differentially expressed genes across datasets. However, it is hard for them to detect interrelated ones. Moreover, existing correlation-based algorithms can only measure the relationship between genes within a single dataset or two multi-modal datasets from the same samples. It is still unclear how to quantify the strength of association of the same gene across two biological datasets with different samples. To this end, we propose Approximate Distance Correlation (ADC) to select interrelated genes with statistical significance across two different biological datasets. ADC first obtains the k most correlated genes for each target gene as its approximate observations, and then calculates the distance correlation (DC) for the target gene across two datasets. ADC repeats this process for all genes and then performs the Benjamini-Hochberg adjustment to control the false discovery rate. We demonstrate the effectiveness of ADC with simulation data and four real applications to select highly interrelated genes across two datasets. These four applications including 21 cancer RNA-seq datasets of different tissues; six single-cell RNA-seq (scRNA-seq) datasets of mouse hematopoietic cells across six different cell types along the hematopoietic cell lineage; five scRNA-seq datasets of pancreatic islet cells across five different technologies; coupled single-cell ATAC-seq (scATAC-seq) and scRNA-seq data of peripheral blood mononuclear cells (PBMC). Extensive results demonstrate that ADC is a powerful tool to uncover interrelated genes with strong biological implications and is scalable to large-scale datasets. Moreover, the number of such genes can serve as a metric to measure the similarity between two datasets, which could characterize the relative difference of diverse cell types and technologies.

scholarly journals Lineage recording of zebrafish embryogenesis reveals historical and ongoing lineage commitments

2020 ◽  
Author(s):  
Zhuoxin Chen ◽  
Chang Ye ◽  
Zhan Liu ◽  
Shanjun Deng ◽  
Xionglei He ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Cell Lineage ◽  
Cell Types ◽  
Lineage Tracing ◽  
Cell Type ◽  
Single Cell Sequencing ◽  
Sequencing Technologies ◽  
Development And Differentiation ◽  
Zebrafish Embryogenesis

AbstractIt has been challenging to characterize the lineage relationships among cells in vertebrates, which comprise a great number of cells. Fortunately, recent progress has been made by combining the CRISPR barcoding system with single-cell sequencing technologies to provide an unprecedented opportunity to track lineage at single-cell resolution. However, due to errors and/or dropouts introduced by amplification and sequencing, reconstruction of accurate lineage relationships in complex organisms remains a challenge. Thus, improvements in both experimental design and computational analysis are necessary for lineage inference. In this study, we employed single-cell Lineage tracing On Endogenous Scarring Sites (scLOESS), a lineage recording strategy based on the CRISPR-Cas9 system, to trace cell fate commitments for zebrafish larvae. With rigorous quality control, we demonstrated that lineage commitments of complex organisms could be inferred from a limited number of barcoding sites. Together with cell-type characterization, our method could homogenously recover lineage information. In combination with the cell-type and lineage information, we depicted the development histories for germ layers as well as cell types. Furthermore, when combined with trajectory analysis, our methods could capture and resolve the ongoing lineage commitment events to gain further biological insights into later development and differentiation in complex organisms.

scholarly journals Single-cell transcriptome analysis of the zebrafish embryonic trunk

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254024
Author(s):  
Sanjeeva Metikala ◽  
Satish Casie Chetty ◽  
Saulius Sumanas
Keyword(s):  
Single Cell ◽  
Molecular Mechanisms ◽  
Cell Lineage ◽  
Cell Types ◽  
Rna Seq ◽  
Cell Lineages ◽  
Distinct Cell ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome ◽  
Different Cell Types

During embryonic development, cells differentiate into a variety of distinct cell types and subtypes with diverse transcriptional profiles. To date, transcriptomic signatures of different cell lineages that arise during development have been only partially characterized. Here we used single-cell RNA-seq to perform transcriptomic analysis of over 20,000 cells disaggregated from the trunk region of zebrafish embryos at the 30 hpf stage. Transcriptional signatures of 27 different cell types and subtypes were identified and annotated during this analysis. This dataset will be a useful resource for many researchers in the fields of developmental and cellular biology and facilitate the understanding of molecular mechanisms that regulate cell lineage choices during development.

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