scholarly journals Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo

Science ◽  
2018 ◽  
Vol 360 (6392) ◽  
pp. 981-987 ◽  
Author(s):  
Daniel E. Wagner ◽  
Caleb Weinreb ◽  
Zach M. Collins ◽  
James A. Briggs ◽  
Sean G. Megason ◽  
...  
Keyword(s):  
Single Cell ◽  
Zebrafish Embryo ◽  
Cell Lineage ◽  
Vertebrate Development ◽  
Cell Mapping ◽  
Cell Fates ◽  
Web Based ◽  
A Cell ◽  
Cell Data ◽  
Germ Layer Formation

High-throughput mapping of cellular differentiation hierarchies from single-cell data promises to empower systematic interrogations of vertebrate development and disease. Here we applied single-cell RNA sequencing to >92,000 cells from zebrafish embryos during the first day of development. Using a graph-based approach, we mapped a cell-state landscape that describes axis patterning, germ layer formation, and organogenesis. We tested how clonally related cells traverse this landscape by developing a transposon-based barcoding approach (TracerSeq) for reconstructing single-cell lineage histories. Clonally related cells were often restricted by the state landscape, including a case in which two independent lineages converge on similar fates. Cell fates remained restricted to this landscape in embryos lacking the chordin gene. We provide web-based resources for further analysis of the single-cell data.

scholarly journals ESCO: single cell expression simulation incorporating gene co-expression

2020 ◽  
Author(s):  
Jinjin Tian ◽  
Jiebiao Wang ◽  
Kathryn Roeder
Keyword(s):  
Single Cell ◽  
R Package ◽  
Brain Cell ◽  
Gene Interactions ◽  
Cell Type ◽  
Imputation Methods ◽  
Biological Interest ◽  
A Cell ◽  
Cell Expression ◽  
Cell Data

AbstractMotivationGene-gene co-expression networks (GCN) are of biological interest for the useful information they provide for understanding gene-gene interactions. The advent of single cell RNA-sequencing allows us to examine more subtle gene co-expression occurring within a cell type. Many imputation and denoising methods have been developed to deal with the technical challenges observed in single cell data; meanwhile, several simulators have been developed for benchmarking and assessing these methods. Most of these simulators, however, either do not incorporate gene co-expression or generate co-expression in an inconvenient manner.ResultsTherefore, with the focus on gene co-expression, we propose a new simulator, ESCO, which adopts the idea of the copula to impose gene co-expression, while preserving the highlights of available simulators, which perform well for simulation of gene expression marginally. Using ESCO, we assess the performance of imputation methods on GCN recovery and find that imputation generally helps GCN recovery when the data are not too sparse, and the ensemble imputation method works best among leading methods. In contrast, imputation fails to help in the presence of an excessive fraction of zero counts, where simple data aggregating methods are a better choice. These findings are further verified with mouse and human brain cell data.AvailabilityThe ESCO implementation is available as R package SplatterESCO (https://github.com/JINJINT/SplatterESCO)[email protected]

A single cell approach to problems of cell lineage and commitment during embryogenesis of Drosophila melanogaster

Development ◽  
1987 ◽  
Vol 100 (1) ◽  
pp. 1-12 ◽  
Author(s):  
G.M. Technau
Keyword(s):  
Drosophila Melanogaster ◽  
Single Cell ◽  
Cell Lineage ◽  
Cellular Level ◽  
Central Importance ◽  
Cell Level ◽  
Combined Application ◽  
A Cell ◽  
Pole Cells ◽  
Drosophila Embryos

The mechanisms leading to the commitment of a cell to a particular fate or to restrictions in its developmental potencies represent a problem of central importance in developmental biology. Both at the genetic and at the molecular level, studies addressing this topic using the fruitfly Drosophila melanogaster have advanced substantially, whereas, at the cellular level, experimental techniques have been most successfully applied to organisms composed of relatively large and accessible cells. The combined application of the different approaches to one system should improve our understanding of the process of commitment as a whole. Recently, a method has been devised to study cell lineage in Drosophila embryos at the single cell level. This method has been used to analyse the lineages, as well as the state of commitment of single cell progenitors from various ectodermal, mesodermal and endodermal anlagen and of the pole cells. The results obtained from a clonal analysis of wild-type larval structures are discussed in this review.

scholarly journals Single cell network analysis with a mixture of Nested Effects Models

2018 ◽  
Author(s):  
Martin Pirkl ◽  
Niko Beerenwinkel
Keyword(s):  
Single Cell ◽  
New Technologies ◽  
Single Cells ◽  
R Package ◽  
Supplementary Information ◽  
Data Sets ◽  
Cell Network ◽  
A Cell ◽  
Supplementary Material ◽  
Cell Data

AbstractMotivationNew technologies allow for the elaborate measurement of different traits of single cells. These data promise to elucidate intra-cellular networks in unprecedented detail and further help to improve treatment of diseases like cancer. However, cell populations can be very heterogeneous.ResultsWe developed a mixture of Nested Effects Models (M&NEM) for single-cell data to simultaneously identify different cellular sub-populations and their corresponding causal networks to explain the heterogeneity in a cell population. For inference, we assign each cell to a network with a certain probability and iteratively update the optimal networks and cell probabilities in an Expectation Maximization scheme. We validate our method in the controlled setting of a simulation study and apply it to three data sets of pooled CRISPR screens generated previously by two novel experimental techniques, namely Crop-Seq and Perturb-Seq.AvailabilityThe mixture Nested Effects Model (M&NEM) is available as the R-package mnem at https://github.com/cbgethz/mnem/[email protected], [email protected] informationSupplementary data are available.online.

scholarly journals Robust Lineage Reconstruction from High-Dimensional Single-Cell Data

2016 ◽  
Author(s):  
Gregory Giecold ◽  
Eugenio Marco ◽  
Lorenzo Trippa ◽  
Guo-Cheng Yuan
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Gene Expression Data ◽  
Quantitative Estimate ◽  
Cell Lineage ◽  
Computational Method ◽  
Expression Data ◽  
Cell Gene Expression ◽  
Cell Data ◽  
Cell Gene

Single-cell gene expression data provide invaluable resources for systematic characterization of cellular hierarchy in multi-cellular organisms. However, cell lineage reconstruction is still often associated with significant uncertainty due to technological constraints. Such uncertainties have not been taken into account in current methods. We present ECLAIR, a novel computational method for the statistical inference of cell lineage relationships from single-cell gene expression data. ECLAIR uses an ensemble approach to improve the robustness of lineage predictions, and provides a quantitative estimate of the uncertainty of lineage branchings. We show that the application of ECLAIR to published datasets successfully reconstructs known lineage relationships and significantly improves the robustness of predictions. In conclusion, ECLAIR is a powerful bioinformatics tool for single-cell data analysis. It can be used for robust lineage reconstruction with quantitative estimate of prediction accuracy.

Cell death in normal and rough eye mutants of Drosophila

Development ◽  
1991 ◽  
Vol 113 (3) ◽  
pp. 825-839 ◽  
Author(s):  
T. Wolff ◽  
D.F. Ready
Keyword(s):  
Cell Death ◽  
Programmed Cell Death ◽  
Compound Eye ◽  
Cell Lineage ◽  
Cell Fates ◽  
Cone Cells ◽  
A Cell ◽  
Retinal Epithelium ◽  
Fly Eye ◽  
Dying Cells

The regular, reiterated cellular pattern of the Drosophila compound eye makes it a sensitive amplifier of defects in cell death. Quantitative and histological methods reveal a phase of cell death between 35 and 50 h of development which removes between 2 and 3 surplus cells per ommatidium. The timing of this epoch is consistent with cell death as the last fate to be specified in the progressive sequence of cell fates that build the ommatidium. An ultrastructural survey of cell death suggests dying cells in the fly eye have similarities as well as differences with standard descriptions of programmed cell death. A failure of cell death to remove surplus cells disorganizes the retinal lattice. A screen of rough eye mutants identifies two genes, roughest and echinus, required for the normal elimination of cells from the retinal epithelium. The use of an enhancer trap as a cell lineage marker shows that the cone cells, like other retinal cells, are not clonally related to each other or to their neighbors.

scholarly journals Accurate and efficient cell lineage tree inference from noisy single cell data: the maximum likelihood perfect phylogeny approach

2019 ◽  
Author(s):  
Yufeng Wu
Keyword(s):  
Single Cell ◽  
Cell Lineage ◽  
Large Data ◽  
Genomic Variation ◽  
Supplementary Information ◽  
Perfect Phylogeny ◽  
Tree Inference ◽  
Lineage Tree ◽  
Infinite Sites Model ◽  
Cell Data

Abstract Motivation Cells in an organism share a common evolutionary history, called cell lineage tree. Cell lineage tree can be inferred from single cell genotypes at genomic variation sites. Cell lineage tree inference from noisy single cell data is a challenging computational problem. Most existing methods for cell lineage tree inference assume uniform uncertainty in genotypes. A key missing aspect is that real single cell data usually has non-uniform uncertainty in individual genotypes. Moreover, existing methods are often sampling based and can be very slow for large data. Results In this article, we propose a new method called ScisTree, which infers cell lineage tree and calls genotypes from noisy single cell genotype data. Different from most existing approaches, ScisTree works with genotype probabilities of individual genotypes (which can be computed by existing single cell genotype callers). ScisTree assumes the infinite sites model. Given uncertain genotypes with individualized probabilities, ScisTree implements a fast heuristic for inferring cell lineage tree and calling the genotypes that allow the so-called perfect phylogeny and maximize the likelihood of the genotypes. Through simulation, we show that ScisTree performs well on the accuracy of inferred trees, and is much more efficient than existing methods. The efficiency of ScisTree enables new applications including imputation of the so-called doublets. Availability and implementation The program ScisTree is available for download at: https://github.com/yufengwudcs/ScisTree. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Venice: A New Algorithm for Finding Marker Genes in Single-Cell Transcriptomic Data

2020 ◽  
Author(s):  
Hy Vuong ◽  
Thao Truong ◽  
Tan Phan ◽  
Son Pham
Keyword(s):  
Single Cell ◽  
Cell Population ◽  
Cell Types ◽  
Marker Genes ◽  
Data Sets ◽  
Interactive Analysis ◽  
Expression Of Genes ◽  
A Cell ◽  
Definition Of ◽  
Cell Data

AbstractMost widely used tools for finding marker genes in single cell data (SeuratT/NegBinom/Poisson, CellRanger, EdgeR, limmatrend) use a conventional definition of differentially expressed genes: genes with different mean expression values. However, in single-cell data, a cell population can be a mixture of many cell types/cell states, hence the mean expression of genes cannot represent the whole population. In addition, these tools assume that gene expression of a population belongs to a specific family of distribution. This assumption is often violated in single-cell data. In this work, we define marker genes of a cell population as genes that can be used to distinguish cells in the population from cells in other populations. Besides log-fold change, we devise a new metric to classify genes into up-regulated, down-regulated, and transitional states. In a benchmark for finding up-regulated and down-regulated genes, our tool outperforms all compared methods, including Seurat, ROTS, scDD, edgeR, MAST, limma, normal t-test, Wilcoxon and Kolmogorov–Smirnov test. Our method is much faster than all compared methods, therefore, enables interactive analysis for large single-cell data sets in BioTuring Browser. Venice algorithm is available within Signac package: https://github.com/bioturing/signac1).

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