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

Science ◽  
2021 ◽  
Vol 371 (6532) ◽  
pp. eabc1944 ◽  
Author(s):  
Jeffrey J. Quinn ◽  
Matthew G. Jones ◽  
Ross A. Okimoto ◽  
Shigeki Nanjo ◽  
Michelle M. Chan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Mouse Model ◽  
Single Cell ◽  
Cancer Cells ◽  
Cancer Progression ◽  
Cell Lineage ◽  
Metastatic Dissemination ◽  
Cell Lineages ◽  
Metastatic Capacity

Detailed phylogenies of tumor populations can recount the history and chronology of critical events during cancer progression, such as metastatic dissemination. We applied a Cas9-based, single-cell lineage tracer to study the rates, routes, and drivers of metastasis in a lung cancer xenograft mouse model. We report deeply resolved phylogenies for tens of thousands of cancer cells traced over months of growth and dissemination. This revealed stark heterogeneity in metastatic capacity, arising from preexisting and heritable differences in gene expression. We demonstrate that these identified genes can drive invasiveness and uncovered an unanticipated suppressive role for KRT17. We also show that metastases disseminated via multidirectional tissue routes and complex seeding topologies. Overall, we demonstrate the power of tracing cancer progression at subclonal resolution and vast scale.

scholarly journals Comprehensive RNA analysis of CSF reveals a role for CEACAM6 in lung cancer leptomeningeal metastases

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Yingmei Li ◽  
Dina Polyak ◽  
Layton Lamsam ◽  
Ian David Connolly ◽  
Eli Johnson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Single Cell ◽  
Cell Lines ◽  
Expression Profiles ◽  
Leptomeningeal Metastases ◽  
Detection Sensitivity ◽  
Nsclc Cell ◽  
Rna Seq ◽  
The Brain

AbstractNon-small cell lung cancer (NSCLC) metastatic to the brain leptomeninges is rapidly fatal, cannot be biopsied, and cancer cells in the cerebrospinal fluid (CSF) are few; therefore, available tissue samples to develop effective treatments are severely limited. This study aimed to converge single-cell RNA-seq and cell-free RNA (cfRNA) analyses to both diagnose NSCLC leptomeningeal metastases (LM), and to use gene expression profiles to understand progression mechanisms of NSCLC in the brain leptomeninges. NSCLC patients with suspected LM underwent withdrawal of CSF via lumbar puncture. Four cytology-positive CSF samples underwent single-cell capture (n = 197 cells) by microfluidic chip. Using robust principal component analyses, NSCLC LM cell gene expression was compared to immune cells. Massively parallel qPCR (9216 simultaneous reactions) on human CSF cfRNA samples compared the relative gene expression of patients with NSCLC LM (n = 14) to non-tumor controls (n = 7). The NSCLC-associated gene, CEACAM6, underwent in vitro validation in NSCLC cell lines for involvement in pathologic behaviors characteristic of LM. NSCLC LM gene expression revealed by single-cell RNA-seq was also reflected in CSF cfRNA of cytology-positive patients. Tumor-associated cfRNA (e.g., CEACAM6, MUC1) was present in NSCLC LM patients’ CSF, but not in controls (CEACAM6 detection sensitivity 88.24% and specificity 100%). Cell migration in NSCLC cell lines was directly proportional to CEACAM6 expression, suggesting a role in disease progression. NSCLC-associated cfRNA is detectable in the CSF of patients with LM, and corresponds to the gene expression profile of NSCLC LM cells. CEACAM6 contributes significantly to NSCLC migration, a hallmark of LM pathophysiology.

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 An Efficient and Flexible Method for Deconvoluting Bulk RNA-Seq Data with Single-Cell RNA-Seq Data

Cells ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1161 ◽  
Author(s):  
Xifang Sun ◽  
Shiquan Sun ◽  
Sheng Yang
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Disease Etiology ◽  
Expression Levels ◽  
Cell Type Specific ◽  
Gene Expression Levels

Estimating cell type compositions for complex diseases is an important step to investigate the cellular heterogeneity for understanding disease etiology and potentially facilitate early disease diagnosis and prevention. Here, we developed a computationally statistical method, referring to Multi-Omics Matrix Factorization (MOMF), to estimate the cell-type compositions of bulk RNA sequencing (RNA-seq) data by leveraging cell type-specific gene expression levels from single-cell RNA sequencing (scRNA-seq) data. MOMF not only directly models the count nature of gene expression data, but also effectively accounts for the uncertainty of cell type-specific mean gene expression levels. We demonstrate the benefits of MOMF through three real data applications, i.e., Glioblastomas (GBM), colorectal cancer (CRC) and type II diabetes (T2D) studies. MOMF is able to accurately estimate disease-related cell type proportions, i.e., oligodendrocyte progenitor cells and macrophage cells, which are strongly associated with the survival of GBM and CRC, respectively.

scholarly journals Single-cell lineage tracing by integrating CRISPR-Cas9 mutations with transcriptomic data

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Hamim Zafar ◽  
Chieh Lin ◽  
Ziv Bar-Joseph
Keyword(s):  
Single Cell ◽  
Cell Lineage ◽  
Lineage Tracing ◽  
Expression Data ◽  
Random Mutation ◽  
Novel Technologies ◽  
Cell Lineages ◽  
Gene Sets ◽  
Lineage Tree ◽  
Differentiation Pathways

Abstract Recent studies combine two novel technologies, single-cell RNA-sequencing and CRISPR-Cas9 barcode editing for elucidating developmental lineages at the whole organism level. While these studies provided several insights, they face several computational challenges. First, lineages are reconstructed based on noisy and often saturated random mutation data. Additionally, due to the randomness of the mutations, lineages from multiple experiments cannot be combined to reconstruct a species-invariant lineage tree. To address these issues we developed a statistical method, LinTIMaT, which reconstructs cell lineages using a maximum-likelihood framework by integrating mutation and expression data. Our analysis shows that expression data helps resolve the ambiguities arising in when lineages are inferred based on mutations alone, while also enabling the integration of different individual lineages for the reconstruction of an invariant lineage tree. LinTIMaT lineages have better cell type coherence, improve the functional significance of gene sets and provide new insights on progenitors and differentiation pathways.

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 SOMSC: Self-Organization-Map for High-Dimensional Single-Cell Data of Cellular States and Their Transitions

2017 ◽  
Author(s):  
Tao Peng ◽  
Qing Nie
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Embryo Development ◽  
Single Cells ◽  
High Dimensional ◽  
Data Sets ◽  
Rna Seq ◽  
Expression Levels ◽  
Cell Lineages ◽  
Cell Data

Measurements of gene expression levels for multiple genes in single cells provide a powerful approach to study heterogeneity of cell populations and cellular plasticity. While the expression levels of multiple genes in each cell are available in such data, the potential connections among the cells (e.g. the lineage relationship) are not directly evident from the measurement. Classifying cellular states and identifying transitions among those states are challenging due to many factors, including the small number of cells versus the large number of genes collected in the data. In this paper we adapt a classical self-organizing-map approach to single-cell gene expression data, such as those based on qPCR and RNA-seq. In this method (SOMSC), a cellular state map (CSM) is derived and employed to identify cellular states inherited in a population of measured single cells. Cells located in the same basin of the CSM are considered as in one cellular state while barriers between the basins provide information on transitions among the cellular states. Consequently, paths of cellular state transitions (e.g. differentiation) and a temporal ordering of the measured single cells are obtained. Applied to a set of synthetic data, two single-cell qPCR data sets and two single-cell RNA-seq data sets for a simulated model of cell differentiation, and systems on the early embryo development, haematopoietic cell lineages, human preimplanation embryo development, and human skeletal muscle myoblasts differentiation, the SOMSC shows good capabilities in identifying cellular states and their transitions in the high-dimensional single-cell data. This approach will have broad applications in studying cell lineages and cellular fate specification.

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.

scholarly journals Deciphering neural heterogeneity through cell lineage tracing

2020 ◽  
Author(s):  
María Figueres-Oñate ◽  
Rebeca Sánchez-González ◽  
Laura López-Mascaraque
Keyword(s):  
Single Cell ◽  
Viral Vectors ◽  
Genetically Modified Organisms ◽  
Cell Lineage ◽  
Lineage Tracing ◽  
Adult Brain ◽  
Neural Cells ◽  
Final Size ◽  
Single Cell Sequencing ◽  
Cell Lineages

Abstract Understanding how an adult brain reaches an appropriate size and cell composition from a pool of progenitors that proliferates and differentiates is a key question in Developmental Neurobiology. Not only the control of final size but also, the proper arrangement of cells of different embryonic origins is fundamental in this process. Each neural progenitor has to produce a precise number of sibling cells that establish clones, and all these clones will come together to form the functional adult nervous system. Lineage cell tracing is a complex and challenging process that aims to reconstruct the offspring that arise from a single progenitor cell. This tracing can be achieved through strategies based on genetically modified organisms, using either genetic tracers, transfected viral vectors or DNA constructs, and even single-cell sequencing. Combining different reporter proteins and the use of transgenic mice revolutionized clonal analysis more than a decade ago and now, the availability of novel genome editing tools and single-cell sequencing techniques has vastly improved the capacity of lineage tracing to decipher progenitor potential. This review brings together the strategies used to study cell lineages in the brain and the role they have played in our understanding of the functional clonal relationships among neural cells. In addition, future perspectives regarding the study of cell heterogeneity and the ontogeny of different cell lineages will also be addressed.

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