CellMAPtracer: A User-Friendly Tracking Tool for Long-Term Migratory and Proliferating Cells Associated with FUCCI Systems

Cell migration is a fundamental biological process of key importance in health and disease. Advances in imaging techniques have paved the way to monitor cell motility. An ever-growing collection of computational tools to track cells has improved our ability to analyze moving cells. One renowned goal in the field is to provide tools that track cell movement as comprehensively and automatically as possible. However, fully automated tracking over long intervals of time is challenged by dividing cells, thus calling for a combination of automated and supervised tracking. Furthermore, after the emergence of various experimental tools to monitor cell-cycle phases, it is of relevance to integrate the monitoring of cell-cycle phases and motility. We developed CellMAPtracer, a multiplatform tracking system that achieves that goal. It can be operated as a conventional, automated tracking tool of single cells in numerous imaging applications. However, CellMAPtracer also allows adjusting tracked cells in a semiautomated supervised fashion, thereby improving the accuracy and facilitating the long-term tracking of migratory and dividing cells. CellMAPtracer is available with a user-friendly graphical interface and does not require any coding or programming skills. CellMAPtracer is compatible with two- and three-color fluorescent ubiquitination-based cell-cycle indicator (FUCCI) systems and allows the user to accurately monitor various migration parameters throughout the cell cycle, thus having great potential to facilitate new discoveries in cell biology.

Download Full-text

CellMAPtracer: A user-friendly tracking tool for long-term migratory and proliferating cells

10.1101/2020.06.15.152462 ◽

2020 ◽

Author(s):

Salim Ghannoum ◽

Kamil Antos ◽

Waldir Leoncio Netto ◽

Alvaro Köhn-Luque ◽

Hesso Farhan

Keyword(s):

Cell Cycle ◽

Cell Migration ◽

Cell Biology ◽

Imaging Techniques ◽

Software Tool ◽

Terminal Phase ◽

Proliferating Cells ◽

Dividing Cells ◽

User Friendly

AbstractBackgroundCell migration is a fundamental cell biological process of key importance in health and disease. Advances in imaging techniques have paved the way to monitor cells motility. An ever-growing collection of computational tools to track cells has improved our ability to analyze moving cells. However, few if any tools let the user supervise and correct cell tracks that are automatically detected. Thus, we developed CellMAPtracer, a tool to track cells in a semi-automated supervised fashion, thereby improving the accuracy and facilitating the long term tracking of migratory and dividing cells. CellMAPtracer is available with a user-friendly graphical user interface and does not require any coding or programming skills.ResultsWe used CellMAPtracer to track fluorescently-labelled BT549 breast cancer cells. It allowed us to track dividing cells and determine the fate of the daughter cells with respect to migration speed or directionality and cell cycle length. Of note, we were able to track multi-daughter divisions, wherein a cell divides and gives rise to more than two cells. We also identified a not previously described speed change in the terminal phase of the cell cycle.ConclusionCellMAPtracer is a software tool for tracking cell migration and proliferation through a user-friendly interface that has a great potential to facilitate new discoveries in cell biology.

Download Full-text

A STRATEGY FOR IDENTIFYING FLUORESCENCE INTENSITY PROFILES OF SINGLE ROD-SHAPED CELLS

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720012500242 ◽

2013 ◽

Vol 11 (02) ◽

pp. 1250024 ◽

Cited By ~ 1

Author(s):

ALEXANDRA HERZOG ◽

BJÖRN VOSS ◽

DANIELA KEILBERG ◽

EDINA HOT ◽

LOTTE SØGAARD-ANDERSEN ◽

...

Keyword(s):

Fluorescence Intensity ◽

Cell Biology ◽

Imaging System ◽

Signal To Noise Ratio ◽

Single Cells ◽

Image Data ◽

Cell Orientation ◽

Cell Axis ◽

Particle Streak Velocimetry ◽

User Friendly

The extraction of fluorescence intensity profiles of single cells from image data is a common challenge in cell biology. The manual segmentation of cells, the extraction of cell orientation and finally the extraction of intensity profiles are time-consuming tasks. This article proposes a routine for the segmentation of single rod-shaped cells (i.e. without neighboring cells in a distance of the cell length) from image data combined with an extraction of intensity distributions along the longitudinal cell axis under the aggravated conditions of (i) a low spatial resolution and (ii) lacking information on the imaging system i.e. the point spread function and signal-to-noise ratio. The algorithm named cipsa transfers a new approach from particle streak velocimetry to cell classification interpreting the rod-shaped as streak-like structures. An automatic reduction of systematic errors such as photobleaching and defocusing is included to guarantee robustness of the proposed approach under the described conditions and to the convenience of end-users unfamiliar with image processing. Performance of the algorithm has been tested on image sequences with high noise level produced by an overlay of different error sources. The developed algorithm provides a user-friendly, stand-alone procedure.

Download Full-text

The effects of proliferation status and cell cycle phase on the responses of single cells to chemotherapy

Molecular Biology of the Cell ◽

10.1091/mbc.e19-09-0515 ◽

2020 ◽

Vol 31 (8) ◽

pp. 845-857 ◽

Cited By ~ 2

Author(s):

Adrián E. Granada ◽

Alba Jiménez ◽

Jacob Stewart-Ornstein ◽

Nils Blüthgen ◽

Simone Reber ◽

...

Keyword(s):

Cell Cycle ◽

Information Theory ◽

Tumor Cells ◽

Single Cells ◽

Residual Tumor ◽

Cell Cycle Phase ◽

Cycle Phase ◽

Proliferating Cells ◽

Probability Of Death

DNA-damaging chemotherapy often leaves residual tumor cells. Combining single-cell long-term live imaging with information theory, we found an unexpected effect: highly proliferative cells were more likely to arrest than to die, whereas more slowly proliferating cells showed a higher probability of death.

Download Full-text

Polyploidy and Mitotic Cell Death Are Two Distinct HIV-1 Vpr-Driven Outcomes in Renal Tubule Epithelial Cells

Journal of Virology ◽

10.1128/jvi.01718-17 ◽

2017 ◽

Vol 92 (2) ◽

Cited By ~ 3

Author(s):

Emily H. Payne ◽

Dhivya Ramalingam ◽

Donald T. Fox ◽

Mary E. Klotman

Keyword(s):

Cell Cycle ◽

Cell Death ◽

Epithelial Cells ◽

Cell Survival ◽

Renal Cell ◽

Cell Biology ◽

Renal Tubule ◽

Mitotic Cell ◽

Hiv Positive

ABSTRACTPrior studies have found that HIV, through the Vpr protein, promotes genome reduplication (polyploidy) in infection-surviving epithelial cells within renal tissue. However, the temporal progression and molecular regulation through which Vpr promotes polyploidy have remained unclear. Here we define a sequential progression to Vpr-mediated polyploidy in human renal tubule epithelial cells (RTECs). We found that as in many cell types, Vpr first initiates G2cell cycle arrest in RTECs. We then identified a previously unreported cascade of Vpr-dependent events that lead to renal cell survival and polyploidy. Specifically, we found that a fraction of G2-arrested RTECs reenter the cell cycle. Following this cell cycle reentry, two distinct outcomes occur. Cells that enter complete mitosis undergo mitotic cell death due to extra centrosomes and aberrant division. Conversely, cells that abort mitosis undergo endoreplication to become polyploid. We further show that multiple small-molecule inhibitors of the phosphatidylinositol 3-kinase-related kinase (PIKK) family, including those that target ATR, ATM, and mTOR, indirectly prevent Vpr-mediated polyploidy by preventing G2arrest. In contrast, an inhibitor that targets DNA-dependent protein kinase (DNA-PK) specifically blocks the Vpr-mediated transition from G2arrest to polyploidy. These findings outline a temporal, molecularly regulated path to polyploidy in HIV-positive renal cells.IMPORTANCECurrent cure-focused efforts in HIV research aim to elucidate the mechanisms of long-term persistence of HIV in compartments. The kidney is recognized as one such compartment, since viral DNA and mRNA persist in the renal tissues of HIV-positive patients. Further, renal disease is a long-term comorbidity in the setting of HIV. Thus, understanding the regulation and impact of HIV infection on renal cell biology will provide important insights into this unique HIV compartment. Our work identifies mechanisms that distinguish between HIV-positive cell survival and death in a known HIV compartment, as well as pharmacological agents that alter these outcomes.

Download Full-text

DOWNSCALING TROPICAL CYCLONES FROM GLOBAL RE-ANALYSIS AND SCENARIOS: STATISTICS OF MULTI-DECADAL VARIABILITY OF TC ACTIVITY IN E ASIA

Coastal Engineering Proceedings ◽

10.9753/icce.v32.management.17 ◽

2011 ◽

Vol 1 (32) ◽

pp. 17 ◽

Cited By ~ 1

Author(s):

Hans Von Storch ◽

Frauke Feser ◽

Monika Barcikowska

Keyword(s):

Regional Climate Model ◽

Tropical Cyclones ◽

Climate Model ◽

Decadal Variability ◽

Tracking System ◽

Regional Climate ◽

Automated Tracking ◽

Stationary Intensity ◽

Environmental Prediction

An atmospheric regional climate model was employed for describing weather of E Asia for the last decades as well as for the coming century. Re-analyses provided by Global National Center for Environmental Prediction - National Center for Atmospheric Research (NCEP-NCAR) for the past six decades, as well a scenario generated by the ECHAM5/MPI-OM model were dynamically downscaled to a 50 km grid using a state-of-the-art regional climate model (CCLM). Using an automated tracking system, all tropical cyclones (TCs) are identified in the multi-decadal simulations. The different analysis products of TC-statistics were found to differ strongly, also in recent times when the data base was good, so that in the long-term statistics 1950-2010 inhomogeneities mask real climatic variations. The 1948-2009 time series of the annual numbers of TCs in the NCEP-driven simulation and in the JMA best track data (BT) correlate favourably. The number is almost constant, even if there is a slight tendency in BT to show less storms, whereas CCLM shows somewhat more storms, which became more intense. The ECHAM5/MPI-OM-driven scenario simulation, subject to 1959-2100 observed and projected greenhouse gas concentrations, shows a reduction of the number of storms, which maintains a stationary intensity in terms of maximum sustained winds and minimum pressure. Thus, BT-trends and downscaled trends were found to be inconsistent, but also the downscaled trends 1948-2009 and the trends derived from the A1B-scenario were different.

Download Full-text

Distinct Regulatory Networks Govern Human Hematopoietic Stem Cell Across Development

Blood ◽

10.1182/blood.v126.23.2375.2375 ◽

2015 ◽

Vol 126 (23) ◽

pp. 2375-2375

Author(s):

Sasan Zandi ◽

John E. Dick ◽

Faiyaz Notta ◽

Naoya Takayama

Keyword(s):

Stem Cell ◽

Cell Biology ◽

Regulatory Networks ◽

Conflicts Of Interest ◽

Fetal Liver ◽

Single Cells ◽

Self Renewal ◽

Hematopoietic Stem ◽

M Phase

Abstract Introduction: Much of our fundamental understanding of stem cell biology comes from studies of hematopoiesis where single cells produce differentiated progeny while still retaining the ability to produce daughter stem cells (self-renewal). The cardinal property of a stem cell, whether normal or malignant, is self-renewal; the key biological process that ensures the ability of the stem cell to maintain long-term clonal growth. However, our understanding of the molecular basis of self-renewal in human hematopoiesis is limited. At the embryonic stage fetal liver is the main source of hematopoiesis; from week 6 of gestation until before birth. At this stage HSCs are in a different microenvironment but capable of self-renewing and differentiation to the full spectrum of blood lineages. While murine studies uncovered several intrinsic differences between fetal and adult HSCs, a comprehensive analysis of human HSC compartment across development is lacking. In this study we have combined HSC purification methods and xenograft quantitative assay in conjunction with low input RNA sequencing and Enhanced Reduced Representation Bisulfite Sequencing (ERRBS) to provide a comprehensive functional and molecular outlook of human stem cell compartment across development. Results: We followed the dynamics of four sub-fractions of CD34+CD38- divided by CD90 and CD49f expression across human blood development: fetal liver (hFL) and adult bone marrow (hBM). Using xenograft model, we identified human long, intermediate and short term HSCs in hFL and hBM. 5 single CD90+CD49f+ hFL cells were capable of sustaining the multilineage graft for over 52 weeks up to tertiary recipient, while BM cells only last for 20 weeks in the primary recipient. The frequency of LT-HSC in the CD90+CD49f+ compartment goes from 1/8 in hFL to 1/50 in hBM. hFL CD90-CD49f+ cells showed an intermediate repopulation capacity up to 44 weeks in secondary recipient. On average 10% of hFL long term HSC (LT-HSC) were in S/G2/M phase, in contrast only 0.4% of BM LT-HSC were in S/G2/M phase indicating that hFL HSCs are 20 times more in cycle compare to BM. We found that 320 genes were expressed differentially between LT-HSC and multipotent progenitors (MPP) in hBM as oppose to only 32 genes found to be differentially expressed in hFL (FDR<0.1). Interestingly, we found only 2 genes in common between these two groups. ERRBS showed an overall increase in methylation of HSC compartment in hBM compare to hFL and gradual demethylation of lineage associated genes in MPP. Conclusion: Our data indicate that there are distinct regulatory networks that govern hFL and hBM HSC self-renewal. We found very little differences in gene expression between all hFL HCS compartments (average 20 genes) compare to hBM (average 224), indicating that by adulthood self-renewal is becoming more restricted to the LT-HSC compartment. Disclosures No relevant conflicts of interest to declare.

Download Full-text

Cell-ACDC: a user-friendly toolset embedding state-of-the-art neural networks for segmentation, tracking and cell cycle annotations of live-cell imaging data

10.1101/2021.09.28.462199 ◽

2021 ◽

Author(s):

Francesco Padovani ◽

Benedikt Mairhoermann ◽

Pascal Falter-Braun ◽

Jette Lengefeld ◽

Kurt M Schmoller

Keyword(s):

Cell Cycle ◽

Image Analysis ◽

Deep Learning ◽

Live Cell Imaging ◽

Cell Imaging ◽

State Of The Art ◽

Single Cells ◽

Live Cell ◽

Cell Segmentation ◽

User Friendly

Live-cell imaging is a powerful tool to study dynamic cellular processes on the level of single cells with quantitative detail. Microfluidics enables parallel high-throughput imaging, creating a downstream bottleneck at the stage of data analysis. Recent progress on deep learning image analysis dramatically improved cell segmentation and tracking. Nevertheless, manual data validation and correction is typically still required and broadly used tools spanning the complete range of live-cell imaging analysis, from cell segmentation to pedigree analysis and signal quantification, are still needed. Here, we present Cell-ACDC, a user-friendly graphical user-interface (GUI)-based framework written in Python, for segmentation, tracking and cell cycle annotation. We included two state-of-the-art and high-accuracy deep learning models for single-cell segmentation of yeast and mammalian cells implemented in the most used deep learning frameworks TensorFlow and PyTorch. Additionally, we developed and implemented a cell tracking method and embedded it into an intuitive, semi-automated workflow for label-free cell cycle annotation of single cells. The open-source and modularized nature of Cell-ACDC will enable simple and fast integration of new deep learning-based and traditional methods for cell segmentation or downstream image analysis. Source code: https://github.com/SchmollerLab/Cell_ACDC

Download Full-text

Regulation of global translation during the cell cycle

10.1101/255729 ◽

2018 ◽

Cited By ~ 1

Author(s):

Vilte Stonyte ◽

Erik Boye ◽

Beáta Grallert

Keyword(s):

Cell Cycle ◽

Stress Responses ◽

Mammalian Cells ◽

Single Cells ◽

Initiation Factor ◽

Temperature Sensitive ◽

Eif2α Phosphorylation ◽

Cell Cycle Phases ◽

General Translation ◽

Global Translation

AbstractIt is generally accepted that global translation varies during the cell cycle and is low in mitosis. However, addressing this issue is challenging because it involves cell synchronization, which evokes stress responses which, in turn, affect translation rates. Here we have used two approaches to measure global translation rates in different cell-cycle phases. First, synchrony in different cell-cycle phases was obtained involving the same stress, by using temperature-sensitive mutants. Second, translation and DNA content were measured by flow cytometry in exponentially growing, single cells. We found no major variation in global translation rates through the cell cycle in either fission-yeast or mammalian cells. We also measured phosphorylation of eukaryotic initiation factor-2α, an event thought to downregulate global translation in mitosis. In contrast with the prevailing view, eIF2α phosphorylation correlated poorly with downregulation of general translation and ectopically induced eIF2α phosphorylation inhibited general translation only at high levels.

Download Full-text

pcnaDeep: A Fast and Robust Single-Cell Tracking Method Using Deep-Learning Mediated Cell Cycle Profiling

10.1101/2021.09.19.460933 ◽

2021 ◽

Author(s):

Yifan Gui ◽

Shuang Shuang Xie ◽

Yanan Wang ◽

Ping Wang ◽

Renzhi Yao ◽

...

Keyword(s):

Cell Cycle ◽

Deep Learning ◽

Single Cell ◽

Cell Tracking ◽

Single Cell Analysis ◽

Single Cells ◽

Time Lapse ◽

Molecular Control ◽

A Cell ◽

User Friendly

Motivation: Computational methods that track single-cells and quantify fluorescent biosensors in time-lapse microscopy images have revolutionised our approach in studying the molecular control of cellular decisions. One barrier that limits the adoption of single-cell analysis in biomedical research is the lack of efficient methods to robustly track single-cells over cell division events. Results: Here, we developed an application that automatically tracks and assigns mother-daughter relationships of single-cells. By incorporating cell cycle information from a well-established fluorescent cell cycle reporter, we associate mitosis relationships enabling high fidelity long-term single-cell tracking. This was achieved by integrating a deep-learning based fluorescent PCNA signal instance segmentation module with a cell tracking and cell cycle resolving pipeline. The application offers a user-friendly interface and extensible APIs for customized cell cycle analysis and manual correction for various imaging configurations. Availability and Implementation: pcnaDeep is an open-source Python application under the Apache 2.0 licence. The source code, documentation and tutorials are available at https://github.com/chan-labsite/PCNAdeep.

Download Full-text

Long-term live cell cycle imaging of single Cyanidioschyzon merolae cells

PROTOPLASMA ◽

10.1007/s00709-020-01592-z ◽

2021 ◽

Author(s):

Takako M. Ichinose ◽

Atsuko H. Iwane

Keyword(s):

Cell Cycle ◽

Environmental Changes ◽

Single Cells ◽

Model Organism ◽

Time Lapse ◽

Live Cell ◽

Observation System ◽

Cyanidioschyzon Merolae ◽

Morphological Studies

AbstractLive cell imaging by fluorescence microscopy is a useful tool for elucidating the localization and function of proteins and organelles in single cells. Especially, time-lapse analysis observing the same field sequentially can be used to observe cells of many organisms and analyze the dynamics of intracellular molecules. By single-cell analysis, it is possible to elucidate the characteristics and fluctuations of individual cells, which cannot be elucidated from the data obtained by averaging the characteristics of an ensemble of cells. The primitive red alga Cyanidioschyzon merolae has a very simple structure and is considered a useful model organism for studying the mechanism of organelle division, since the division is performed synchronously with the cell cycle. However, C. merolae does not have a rigid cell wall, and environmental changes such as low temperature or high pH cause morphological change and disruption easily. Therefore, morphological studies of C. merolae typically use fixed cells. In this study, we constructed a long-term time-lapse observation system to analyze the dynamics of proteins in living C. merolae cells. From the results, we elucidate the cell division process of single living cells, including the function of intracellular components.

Download Full-text