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.

scholarly journals Computational approaches towards reducing contamination in single-cell RNA-seq data

2020 ◽  
Author(s):  
Siamak Yousefi ◽  
Hao Chen ◽  
Jesse F. Ingels ◽  
Melinda S. McCarty ◽  
Arthur G. Centeno ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Real Life ◽  
Cell Types ◽  
Cell Capture ◽  
Rna Seq ◽  
Sequence Analyses ◽  
Cell Functions ◽  
Biological Interpretation ◽  
Different Cell Types

SUMMARYSingle cell RNA sequencing has enabled quantification of single cells and identification of different cell types and subtypes as well as cell functions in different tissues. Single cell RNA sequence analyses assume acquired RNAs correspond to cells, however, RNAs from contamination within the input data are also captured by these assays. The sequencing of background contamination as well as unwanted cells making their way to the final assay Potentially confound the correct biological interpretation of single cell transcriptomic data. Here we demonstrate two approaches to deal with background contamination as well as profiling of unwanted cells in the assays. We use three real-life datasets of whole-cell capture and nucleotide single-cell captures generated by Fluidigm and 10x technologies and show that these methods reduce the effect of contamination, strengthen clustering of cells and improves biological interpretation.

scholarly journals Mapping multicellular programs from single-cell profiles

2020 ◽  
Author(s):  
Livnat Jerby-Arnon ◽  
Aviv Regev
Keyword(s):  
Single Cell ◽  
Spatial Data ◽  
Spatial Information ◽  
Single Cells ◽  
Cell Types ◽  
Risk Genes ◽  
Health And Disease ◽  
Different Cell Types ◽  
Cellular Components ◽  
Acting In Concert

ABSTRACTTissue homeostasis relies on orchestrated multicellular circuits, where interactions between different cell types dynamically balance tissue function. While single-cell genomics identifies tissues’ cellular components, deciphering their coordinated action remains a major challenge. Here, we tackle this problem through a new framework of multicellular programs: combinations of distinct cellular programs in different cell types that are coordinated together in the tissue, thus forming a higher order functional unit at the tissue, rather than only cell, level. We develop the open-access DIALOGUE algorithm to systematically uncover such multi-cellular programs not only from spatial data, but even from tissue dissociated and profiled as single cells, e.g., by single-cell RNA-Seq. Tested on spatial transcriptomes from the mouse hypothalamus, DIALOGUE recovered spatial information, predicted the properties of a cell’s environment only based on its transcriptome, and identified multicellular programs that mark animal behavior. Applied to brain samples and colon biopsies profiled by scRNA-Seq, DIALOGUE identified multicellular configurations that mark Alzheimer’s disease and ulcerative colitis (UC), including a program spanning five cell types that is predictive of response to anti-TNF therapy in UC patients and enriched for UC risk genes from GWAS, each acting in different cell types, but all cells acting in concert. Taken together, our study provides a novel conceptual and methodological framework to unravel multicellular regulation in health and disease.

scholarly journals Massively multiplex single-cell Hi-C

2016 ◽  
Author(s):  
Vijay Ramani ◽  
Xinxian Deng ◽  
Kevin L Gunderson ◽  
Frank J Steemers ◽  
Christine M Disteche ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Cell Types ◽  
Cell Heterogeneity ◽  
Proof Of Concept ◽  
Chromosome Conformation ◽  
Large Numbers ◽  
Conformational Properties ◽  
Novel Method ◽  
Different Cell Types

AbstractWe present combinatorial single cell Hi-C, a novel method that leverages combinatorial cellular indexing to measure chromosome conformation in large numbers of single cells. In this proof-of-concept, we generate and sequence combinatorial single cell Hi-C libraries for two mouse and four human cell types, comprising a total of 9,316 single cells across 5 experiments. We demonstrate the utility of single-cell Hi-C data in separating different cell types, identify previously uncharacterized cell-to-cell heterogeneity in the conformational properties of mammalian chromosomes, and demonstrate that combinatorial indexing is a generalizable molecular strategy for single-cell genomics.

Machine learning and statistical methods for clustering single-cell RNA-sequencing data

2019 ◽  
Vol 21 (4) ◽  
pp. 1209-1223 ◽  
Author(s):  
Raphael Petegrosso ◽  
Zhuliu Li ◽  
Rui Kuang
Keyword(s):  
Machine Learning ◽  
Single Cell ◽  
Statistical Methods ◽  
Large Scale ◽  
Time Series Data ◽  
Single Cells ◽  
Transcriptome Profiling ◽  
Cell Types ◽  
Series Data ◽  
Sequencing Data

Abstract   Single-cell RNAsequencing (scRNA-seq) technologies have enabled the large-scale whole-transcriptome profiling of each individual single cell in a cell population. A core analysis of the scRNA-seq transcriptome profiles is to cluster the single cells to reveal cell subtypes and infer cell lineages based on the relations among the cells. This article reviews the machine learning and statistical methods for clustering scRNA-seq transcriptomes developed in the past few years. The review focuses on how conventional clustering techniques such as hierarchical clustering, graph-based clustering, mixture models, $k$-means, ensemble learning, neural networks and density-based clustering are modified or customized to tackle the unique challenges in scRNA-seq data analysis, such as the dropout of low-expression genes, low and uneven read coverage of transcripts, highly variable total mRNAs from single cells and ambiguous cell markers in the presence of technical biases and irrelevant confounding biological variations. We review how cell-specific normalization, the imputation of dropouts and dimension reduction methods can be applied with new statistical or optimization strategies to improve the clustering of single cells. We will also introduce those more advanced approaches to cluster scRNA-seq transcriptomes in time series data and multiple cell populations and to detect rare cell types. Several software packages developed to support the cluster analysis of scRNA-seq data are also reviewed and experimentally compared to evaluate their performance and efficiency. Finally, we conclude with useful observations and possible future directions in scRNA-seq data analytics. Availability All the source code and data are available at https://github.com/kuanglab/single-cell-review.

scholarly journals ProtAnno, an Automated Cell Type Annotation Tool for Single Cell Proteomics Data that Integrates Information from Multiple Reference Sources

2021 ◽  
Author(s):  
Wenxuan Deng ◽  
Biqing Zhu ◽  
Seyoung Park ◽  
Tomokazu S. Sumida ◽  
Avraham Unterman ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Earth Metal ◽  
Cell Types ◽  
Specific Cell ◽  
Cell Type ◽  
Proteomics Data ◽  
Data Annotation ◽  
Different Cell Types ◽  
Unique Molecular Identifier

Compared with sequencing-based global genomic profiling, cytometry labels targeted surface markers on millions of cells in parallel either by conjugated rare earth metal particles or Unique Molecular Identifier (UMI) barcodes. Correct annotation of these cells to specific cell types is a key step in the analysis of these data. However, there is no computational tool that automatically annotates single cell proteomics data for cell type inference. In this manuscript, we propose an automated single cell proteomics data annotation approach called ProtAnno to facilitate cell type assignments without laborious manual gating. ProtAnno is designed to incorporate information from annotated single cell RNA-seq (scRNA-seq), CITE-seq, and prior data knowledge (which can be imprecise) on biomarkers for different cell types. We have performed extensive simulations to demonstrate the accuracy and robustness of ProtAnno. For several single cell proteomics datasets that have been manually labeled, ProtAnno was able to correctly label most single cells. In summary, ProtAnno offers an accurate and robust tool to automate cell type annotations for large single cell proteomics datasets, and the analysis of such annotated cell types can offer valuable biological insights.

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 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 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 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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