Identification of SNA-I-positive cells as stem-like cells in an established cell line using computerized single-cell lineage tracking

AbstractSingle-cell tracking analysis is a potential research technique for the accurate investigation of cellular behaviors and events occurring within a cell population. However, this analysis is challenging because of a lack of microscope hardware and software suitable for single-cell tracking analysis of a wide range of cell types and densities. We therefore developed a computerized single-cell lineage tracking analysis system based on a microscope optimized for differential interference contrast-based long-term live cell imaging, with software designed to automatically generate live cell videos, perform image segmentation, carry out single-cell tracking, and create and analyze a cell lineage database. We previously reported that minor cell sub-populations (3%–7%) within a cultured cancer cell line could play a critical role in maintaining the cell population. Given that sub-population characterization requires large-scale single-cell tracking analysis, we tracked single cells using the above computerized system and identified a minor cell population (1.5%) composed ofSambucus nigraagglutinin-I-positive cells, which acted as stem-like cells for the established culture. These results demonstrate the potential value of this computerized single-cell lineage tracking analysis system as a routine tool in cell biology, opening new avenues for research aimed at identifying previously unknown characteristics of individual cultured cells with high accuracy.

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Single-cell lineage tracking analysis reveals that an established cell line comprises putative cancer stem cells and their heterogeneous progeny

Scientific Reports ◽

10.1038/srep23328 ◽

2016 ◽

Vol 6 (1) ◽

Cited By ~ 26

Author(s):

Sachiko Sato ◽

Ann Rancourt ◽

Yukiko Sato ◽

Masahiko S. Satoh

Keyword(s):

Stem Cells ◽

Cancer Stem Cells ◽

Single Cell ◽

Cell Line ◽

Cell Lineage ◽

Established Cell Line ◽

Established Cell ◽

Lineage Tracking

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Dose-dependent spatiotemporal responses of mammalian cells to an alkylating agent

10.1101/215764 ◽

2017 ◽

Cited By ~ 1

Author(s):

Ann Rancourt ◽

Sachiko Sato ◽

Masahiko S. Satoh

Keyword(s):

Cell Death ◽

Single Cell ◽

Cell Lines ◽

Hela Cells ◽

Cell Tracking ◽

Cell Lineage ◽

Cellular Responses ◽

Spatiotemporal Data ◽

Significant Heterogeneity ◽

Lineage Tracking

AbstractCultured cell populations are composed of heterogeneous cells, and previous single-cell lineage tracking analysis of individual HeLa cells provided empirical evidence for significant heterogeneity of cell fates. Nevertheless, such cell lines have been used for investigations of cellular responses to various substances, resulting in incomplete characterizations. This problem caused by heterogeneity within cell lines could be overcome by analyzing the spatiotemporal responses of individual cells to a substance. However, no approach to investigate the responses using spatiotemporal data is currently available. Thus, the current study aimed to analyze the spatiotemporal responses of individual HeLa cells to cytotoxic, sub-cytotoxic, and non-cytotoxic doses of the well-characterized carcinogen, N-methyl-N'-nitro-N-nitrosoguanidine (MNNG). Although cytotoxic doses of MNNG are known to induce cell death, the single-cell tracking approach revealed that cell death occurred following at least four different cellular events, suggesting that cell death is induced via multiple processes. We also found that HeLa cells exposed to sub-cytotoxic doses of MNNG were in a state of equilibrium between cell proliferation and cell death, with cell death again induced through different processes. However, exposure of cells to non-cytotoxic doses of MNNG promoted growth by reducing the cell doubling time, thus promoting the growth of a sub-population of cells previously recognized as putative cancer stem cells. These results demonstrate that the responses of cells to MNNG can be analyzed precisely using spatiotemporal data, regardless of the presence of heterogeneity among cultured cells, suggesting that single-cell lineage tracking analysis can be used as a novel and accurate analytical method to investigate cellular responses to various substances.

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Live-cell time-lapse imaging and single-cell tracking of in vitro cultured neural stem cells – Tools for analyzing dynamics of cell cycle, migration, and lineage selection

Methods ◽

10.1016/j.ymeth.2017.10.003 ◽

2018 ◽

Vol 133 ◽

pp. 81-90 ◽

Cited By ~ 11

Author(s):

Katja M. Piltti ◽

Brian J. Cummings ◽

Krystal Carta ◽

Ayla Manughian-Peter ◽

Colleen L. Worne ◽

...

Keyword(s):

Cell Cycle ◽

Stem Cells ◽

Neural Stem Cells ◽

Single Cell ◽

Cell Tracking ◽

Time Lapse ◽

Live Cell ◽

Time Lapse Imaging

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

Development ◽

10.1242/dev.100.1.1 ◽

1987 ◽

Vol 100 (1) ◽

pp. 1-12 ◽

Cited By ~ 6

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.

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

Science ◽

10.1126/science.aar4362 ◽

2018 ◽

Vol 360 (6392) ◽

pp. 981-987 ◽

Cited By ~ 278

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.

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Cell-Line Annotation on Europe PubMed Central

10.1101/011700 ◽

2014 ◽

Author(s):

Jee-Hyub Kim

Keyword(s):

Cell Culture ◽

Single Cell ◽

Cell Line ◽

Cell Lines ◽

Biomedical Literature ◽

Pubmed Central ◽

Preliminary Results ◽

Data Citation ◽

A Cell

A cell line is a cell culture developed from a single cell and therefore consisting of cells with a uniform genetic make-up. A cell line has an important role as a research resource such as organisms, antibodies, constructs, knockdown reagents, etc. Unique identification of cell lines in the biomedical literature is important for the reproducibility of science. As data citation, resource citation is also important for resource re-use. In this paper, we mention the challenges of identifying cell lines and describe a system for cell line annotation with preliminary results.

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Cell size distribution of lineage data: analytic results and parameter inference

10.1101/2020.12.24.424287 ◽

2020 ◽

Author(s):

Chen Jia ◽

Abhyudai Singh ◽

Ramon Grima

Keyword(s):

Size Distribution ◽

Cell Size ◽

Cell Tracking ◽

Cell Lineage ◽

Growth Conditions ◽

Cell Size Distribution ◽

Time Resolved ◽

Cell Cycles ◽

A Cell ◽

Non Gaussian

AbstractRecent advances in single-cell technologies have enabled time-resolved measurements of the cell size over several cell cycles. This data encodes information on how cells correct size aberrations so that they do not grow abnormally large or small. Here we formulate a piecewise deterministic Markov model describing the evolution of the cell size over many generations, for all three cell size homeostasis strategies (timer, sizer, and adder). The model is solved to obtain an analytical expression for the non-Gaussian cell size distribution in a cell lineage; the theory is used to understand how the shape of the distribution is influenced by the parameters controlling the dynamics of the cell cycle and by the choice of cell tracking protocol. The theoretical cell size distribution is found to provide an excellent match to the experimental cell size distribution of E. coli lineage data collected under various growth conditions.

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

10.1101/2020.11.16.384479 ◽

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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Interaction between the type 4 pili machinery and a diguanylate cyclase fine-tune c-di-GMP levels during early biofilm formation

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2105566118 ◽

2021 ◽

Vol 118 (26) ◽

pp. e2105566118

Author(s):

Shanice S. Webster ◽

Calvin K. Lee ◽

William C. Schmidt ◽

Gerard C. L. Wong ◽

George A. O’Toole

Keyword(s):

Single Cell ◽

Biofilm Formation ◽

Cell Tracking ◽

Bimolecular Fluorescence Complementation ◽

Initial Surface ◽

Diguanylate Cyclase ◽

Complex Protein ◽

A Cell ◽

The Mean ◽

Fine Tune

To initiate biofilm formation, it is critical for bacteria to sense a surface and respond precisely to activate downstream components of the biofilm program. Type 4 pili (T4P) and increasing levels of c-di-GMP have been shown to be important for surface sensing and biofilm formation, respectively; however, mechanisms important in modulating the levels of this dinucleotide molecule to define a precise output response are unknown. Here, using macroscopic bulk assays and single-cell tracking analyses of Pseudomonas aeruginosa, we uncover a role of the T4P alignment complex protein, PilO, in modulating the activity of the diguanylate cyclase (DGC) SadC. Two-hybrid and bimolecular fluorescence complementation assays, combined with genetic studies, are consistent with a model whereby PilO interacts with SadC and that the PilO–SadC interaction inhibits SadC’s activity, resulting in decreased biofilm formation and increased motility. Using single-cell tracking, we monitor both the mean c-di-GMP and the variance of this dinucleotide in individual cells. Mutations that increase PilO–SadC interaction modestly, but significantly, decrease both the average and variance in c-di-GMP levels on a cell-by-cell basis, while mutants that disrupt PilO–SadC interaction increase the mean and variance of c-di-GMP levels. This work is consistent with a model wherein P. aeruginosa uses a component of the T4P scaffold to fine-tune the levels of this dinucleotide signal during surface commitment. Finally, given our previous findings linking SadC to the flagellar machinery, we propose that this DGC acts as a bridge to integrate T4P and flagellar-derived input signals during initial surface engagement.

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