scholarly journals Phototaxis of the dominant marine pico-eukaryote Micromonas sp.: from population to single cell

2019 ◽  
Author(s):  
Richard Henshaw ◽  
Raphaël Jeanneret ◽  
Marco Polin
Keyword(s):  
Single Cell ◽  
Cell Tracking ◽  
Population Level ◽  
Qualitative Description ◽  
Drift Diffusion ◽  
Population Responses ◽  
Wide Range ◽  
Primary Producer ◽  
Run And Tumble ◽  
Micromonas Pusilla

Micromonas commoda (previously Micromonas pusilla, a unicellular photosynthetic picoeukaryote globally dominant in marine ecosystems, has previously been qualified as being strongly phototactic. To date, no detailed quantitative or qualitative description of this behaviour has been reported, nor have thorough studies of its motility been undertaken. This primary producer has only been qualitatively described as utilizing run-and-tumble motion, but such motility strategy is incompatible with its morphology comprising only one propelling flagellum. Moreover, it is still unclear as to how Micromonas sp. detects a light direction due to the lack of a dedicated eyespot; the organism is essentially blind. Here we first perform population-scale phototactic experiments to show that this organism actively responds to a wide range of light wavelengths and intensities. These population responses are well accounted for within a simple drift-diffusion framework. Based on single-cell tracking experiments, we then detail thoroughly Micromonas sp.’s motility which resembles run-and-reverse styles of motion commonly observed in marine prokaryotes and that we name stop-run or reverse. The associated peculiar microscopic changes upon photo-stimulation are finally described and integrating those into jump-diffusion simulations appears to produce phototactic drifts that are quantitatively compatible with those obtained experimentally at the population level.

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

2018 ◽  
Author(s):  
Sachiko Sato ◽  
Ann Rancourt ◽  
Masahiko S. Satoh
Keyword(s):  
Single Cell ◽  
Cell Line ◽  
Cell Population ◽  
Cell Tracking ◽  
Cell Lineage ◽  
Live Cell ◽  
Wide Range ◽  
A Cell ◽  
Analysis System ◽  
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.

scholarly journals Cell region fingerprints enable highly precise single-cell tracking and lineage reconstruction

2021 ◽  
Author(s):  
Andreas P. Cuny ◽  
Aaron Ponti ◽  
Tomas Kuendig ◽  
Fabian Rudolf ◽  
Joerg Stelling
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
Cell Tracking ◽  
Structural Information ◽  
Experimental Studies ◽  
Size Control ◽  
Population Level ◽  
Distance Measures ◽  
Cell Region ◽  
Low Frequencies

Experimental studies of cell growth, inheritance, and their associated processes by microscopy require accurate single-cell observations of sufficient duration to reconstruct the genealogy. However, cell tracking - assigning identical cells on consecutive images to a track - is often challenging due to imperfect segmentation, moving cells, or focus drift, resulting in laborious manual verification. Here, we propose fingerprints to identify problematic assignments rapidly. A fingerprint distance measures the similarity between cells in two consecutive images by comparing the structural information contained in the low frequencies of a Fourier transform. We show that it is broadly applicable across cell types and image modalities, provided the image has sufficient structural information. Our tracker (TracX) uses the concept to reject unlikely assignments, thereby substantially increasing tracking performance on published and newly generated long-term data sets from various species. For S.cerevisiae, we propose a comprehensive model for cell size control at the single-cell and population level centered on the Whi5 regulator. It demonstrates how highly precise tracking can help uncover previously undescribed single-cell biology.

scholarly journals Optical and genetic tools for in vivo single cell tracking

2021 ◽  
Vol 358 ◽  
pp. 109192
Author(s):  
Yajie Liang ◽  
Liset M. de la Prida
Keyword(s):  
Single Cell ◽  
Cell Tracking ◽  
Genetic Tools

scholarly journals Clustering Single-Cell RNA-Seq Data with Regularized Gaussian Graphical Model

Genes ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 311
Author(s):  
Zhenqiu Liu
Keyword(s):  
Single Cell ◽  
Free Parameter ◽  
Graphical Model ◽  
Expression Patterns ◽  
Information Criterion ◽  
Log P ◽  
Rna Seq ◽  
Clustering Methods ◽  
Wide Range ◽  
Free Parameters

Single-cell RNA-seq (scRNA-seq) is a powerful tool to measure the expression patterns of individual cells and discover heterogeneity and functional diversity among cell populations. Due to variability, it is challenging to analyze such data efficiently. Many clustering methods have been developed using at least one free parameter. Different choices for free parameters may lead to substantially different visualizations and clusters. Tuning free parameters is also time consuming. Thus there is need for a simple, robust, and efficient clustering method. In this paper, we propose a new regularized Gaussian graphical clustering (RGGC) method for scRNA-seq data. RGGC is based on high-order (partial) correlations and subspace learning, and is robust over a wide-range of a regularized parameter λ. Therefore, we can simply set λ=2 or λ=log(p) for AIC (Akaike information criterion) or BIC (Bayesian information criterion) without cross-validation. Cell subpopulations are discovered by the Louvain community detection algorithm that determines the number of clusters automatically. There is no free parameter to be tuned with RGGC. When evaluated with simulated and benchmark scRNA-seq data sets against widely used methods, RGGC is computationally efficient and one of the top performers. It can detect inter-sample cell heterogeneity, when applied to glioblastoma scRNA-seq data.

scholarly journals Construction of a mammalian embryo model from stem cells organized by a morphogen signalling centre

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Peng-Fei Xu ◽  
Ricardo Moraes Borges ◽  
Jonathan Fillatre ◽  
Maraysa de Oliveira-Melo ◽  
Tao Cheng ◽  
...  
Keyword(s):  
Stem Cells ◽  
Cell Tracking ◽  
Cardiac Tissue ◽  
Embryonic Stem ◽  
Mammalian Embryo ◽  
Wide Range ◽  
Vitro Model ◽  
Neurula Stage

AbstractGenerating properly differentiated embryonic structures in vitro from pluripotent stem cells remains a challenge. Here we show that instruction of aggregates of mouse embryonic stem cells with an experimentally engineered morphogen signalling centre, that functions as an organizer, results in the development of embryo-like entities (embryoids). In situ hybridization, immunolabelling, cell tracking and transcriptomic analyses show that these embryoids form the three germ layers through a gastrulation process and that they exhibit a wide range of developmental structures, highly similar to neurula-stage mouse embryos. Embryoids are organized around an axial chordamesoderm, with a dorsal neural plate that displays histological properties similar to the murine embryo neuroepithelium and that folds into a neural tube patterned antero-posteriorly from the posterior midbrain to the tip of the tail. Lateral to the chordamesoderm, embryoids display somitic and intermediate mesoderm, with beating cardiac tissue anteriorly and formation of a vasculature network. Ventrally, embryoids differentiate a primitive gut tube, which is patterned both antero-posteriorly and dorso-ventrally. Altogether, embryoids provide an in vitro model of mammalian embryo that displays extensive development of germ layer derivatives and that promises to be a powerful tool for in vitro studies and disease modelling.

scholarly journals Single cell tracking based on Voronoi partition via stable matching

2020 ◽  
Author(s):  
Young Hwan Chang ◽  
Jeremy Linsley ◽  
Josh Lamstein ◽  
Jaslin Kalra ◽  
Irina Epstein ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Tracking ◽  
Stable Matching ◽  
Voronoi Partition

Software tools for single-cell tracking and quantification of cellular and molecular properties

2016 ◽  
Vol 34 (7) ◽  
pp. 703-706 ◽  
Author(s):  
Oliver Hilsenbeck ◽  
Michael Schwarzfischer ◽  
Stavroula Skylaki ◽  
Bernhard Schauberger ◽  
Philipp S Hoppe ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Tracking ◽  
Software Tools ◽  
Molecular Properties

scholarly journals The Development of an Effective Bacterial Single-Cell Lysis Method Suitable for Whole Genome Amplification in Microfluidic Platforms

Micromachines ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 367 ◽  
Author(s):  
Yuguang Liu ◽  
Dirk Schulze-Makuch ◽  
Jean-Pierre de Vera ◽  
Charles Cockell ◽  
Thomas Leya ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Bacterial Species ◽  
Cell Manipulation ◽  
Microfluidic Platforms ◽  
Gram Positive ◽  
Gram Negative ◽  
Genome Amplification ◽  
Single Cell Sequencing ◽  
Wide Range

Single-cell sequencing is a powerful technology that provides the capability of analyzing a single cell within a population. This technology is mostly coupled with microfluidic systems for controlled cell manipulation and precise fluid handling to shed light on the genomes of a wide range of cells. So far, single-cell sequencing has been focused mostly on human cells due to the ease of lysing the cells for genome amplification. The major challenges that bacterial species pose to genome amplification from single cells include the rigid bacterial cell walls and the need for an effective lysis protocol compatible with microfluidic platforms. In this work, we present a lysis protocol that can be used to extract genomic DNA from both gram-positive and gram-negative species without interfering with the amplification chemistry. Corynebacterium glutamicum was chosen as a typical gram-positive model and Nostoc sp. as a gram-negative model due to major challenges reported in previous studies. Our protocol is based on thermal and chemical lysis. We consider 80% of single-cell replicates that lead to >5 ng DNA after amplification as successful attempts. The protocol was directly applied to Gloeocapsa sp. and the single cells of the eukaryotic Sphaerocystis sp. and achieved a 100% success rate.

scholarly journals Mito Hacker: a set of tools to enable high-throughput analysis of mitochondrial network morphology

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Ali Rohani ◽  
Jennifer A. Kashatus ◽  
Dane T. Sessions ◽  
Salma Sharmin ◽  
David F. Kashatus
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Data Science ◽  
Structural Information ◽  
Morphological Changes ◽  
Mitochondrial Morphology ◽  
Single Cell Level ◽  
Mitochondrial Network ◽  
Cell Level ◽  
Wide Range

Abstract Mitochondria are highly dynamic organelles that can exhibit a wide range of morphologies. Mitochondrial morphology can differ significantly across cell types, reflecting different physiological needs, but can also change rapidly in response to stress or the activation of signaling pathways. Understanding both the cause and consequences of these morphological changes is critical to fully understanding how mitochondrial function contributes to both normal and pathological physiology. However, while robust and quantitative analysis of mitochondrial morphology has become increasingly accessible, there is a need for new tools to generate and analyze large data sets of mitochondrial images in high throughput. The generation of such datasets is critical to fully benefit from rapidly evolving methods in data science, such as neural networks, that have shown tremendous value in extracting novel biological insights and generating new hypotheses. Here we describe a set of three computational tools, Cell Catcher, Mito Catcher and MiA, that we have developed to extract extensive mitochondrial network data on a single-cell level from multi-cell fluorescence images. Cell Catcher automatically separates and isolates individual cells from multi-cell images; Mito Catcher uses the statistical distribution of pixel intensities across the mitochondrial network to detect and remove background noise from the cell and segment the mitochondrial network; MiA uses the binarized mitochondrial network to perform more than 100 mitochondria-level and cell-level morphometric measurements. To validate the utility of this set of tools, we generated a database of morphological features for 630 individual cells that encode 0, 1 or 2 alleles of the mitochondrial fission GTPase Drp1 and demonstrate that these mitochondrial data could be used to predict Drp1 genotype with 87% accuracy. Together, this suite of tools enables the high-throughput and automated collection of detailed and quantitative mitochondrial structural information at a single-cell level. Furthermore, the data generated with these tools, when combined with advanced data science approaches, can be used to generate novel biological insights.

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