Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Quantification of collective signalling in time-lapse microscopy images.

ABSTRACT: Live-cell imaging of fluorescent biosensors has demonstrated that space-time correlations in signalling of cell collectives play an important organisational role in morphogenesis, wound healing, regeneration, and maintaining epithelial homeostasis. Here, we demonstrate how to quantify one such phenomenon, namely apoptosis-induced ERK activity waves in the MCF10A epithelium. We present a protocol that starts from raw time-lapse fluorescence microscopy images and, through a sequence of image manipulations, ends with ARCOS, our computational method to detect and quantify collective signalling. We also describe the same workflow in the interactive napari image viewer to quantify collective phenomena for users without prior programming experience. Our approach can be applied to space-time correlations in cells, cell collectives, or communities of multicellular organisms, in 2D and 3D geometries.

SUBMITTER: Dobrzynski M

PROVIDER: S-EPMC11308913 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Quantification of collective signalling in time-lapse microscopy images.

Dobrzyński Maciej M Grädel Benjamin B Gagliardi Paolo Armando PA Pertz Olivier O

Methods in microscopy 20240401 1

Live-cell imaging of fluorescent biosensors has demonstrated that space-time correlations in signalling of cell collectives play an important organisational role in morphogenesis, wound healing, regeneration, and maintaining epithelial homeostasis. Here, we demonstrate how to quantify one such phenomenon, namely apoptosis-induced ERK activity waves in the MCF10A epithelium. We present a protocol that starts from raw time-lapse fluorescence microscopy images and, through a sequence of image manip ...[more]

PMID: 39119253

Similar Datasets

STrack: A Tool to Simply Track Bacterial Cells in Microscopy Time-Lapse Images.

Project description:Bacterial growth can be studied at the single cell level through time-lapse microscopy imaging. Technical advances in microscopy lead to increasing image quality, which in turn allows to visualize larger areas of growth, containing more and more cells. In this context, the use of automated computational tools becomes essential. In this paper, we present STrack, a tool that allows to track cells in time-lapse images in a fast and efficient way. We compared it to 3 recently published tracking tools on images ranging over 6 different bacterial strains with various morphologies. STrack showed to be the most consistent tracking tool, returning more than 80% of correct cell lineages on average, in comparison to manually annotated ground-truth. The python implementation of STrack, a docker structure, and a tutorial on how to download and use the tool can be found on the following github page: https://github.com/Helena-todd/STrack. IMPORTANCE Automated image analysis of growing prokaryotic cell populations becomes indispensable with larger data sets, such as derived by time-lapse microscopy. The tracking of the same individual cells and their daughter lineages is cumbersome and prone to errors in image alignment or poor resolution. Here, we present a simplified but highly effective tool for non-specialists to engage in cell tracking. The tool can be downloaded and run as a contained script-structure requiring minimal user input. Run times are fast, in comparison to other equivalent tools, and outputs consist of cell tables that can be subsequently used for lineage analysis, for which we offer examples. By providing open code, training data sets, as well as simplified script execution, we aimed to facilitate wide usage and further tool development for image analysis.

| S-EPMC10117057 | biostudies-literature

Quantification of Mouse Hematopoietic Progenitors' Formation Using Time-lapse Microscopy and Image Analysis.

Project description:In vitro differentiation of mouse embryonic stem cells (mESCs) towards blood cells constitutes a well-established system to study the endothelial-to-hematopoietic transition (EHT) at the onset of blood development. Assessing the emergence of small non-adherent round blood cells in the culture without disturbing it is essential to evaluate the progression of EHT and also to test conditions potentially enhancing or repressing this process. Here, we describe how to quantify the formation of mouse hematopoietic progenitors during EHT in normal conditions or following over-expression of eight essential transcription factors using time-lapse microscopy and image analysis.

| S-EPMC6331043 | biostudies-literature

High-resolution quantification of discrete phagocytic events by live cell time-lapse high-content microscopy imaging.

Project description:Phagocytosis is a dynamic process central to immunity and tissue homeostasis. Current methods for quantification of phagocytosis largely rely on indirect or static measurements, such as target clearance or dye uptake, and thus provide limited information about engulfment rates or target processing. Improved kinetic measurements of phagocytosis could provide useful, basic insights in many areas. We present a live-cell, time-lapse and high-content microscopy imaging method based on the detection and quantification of fluorescent dye 'voids' within phagocytes that result from target internalization to quantify phagocytic events with high temporal resolution. Using this method, we measure target cell densities and antibody concentrations needed for optimal antibody-dependent cellular phagocytosis. We compare void formation and dye uptake methods for phagocytosis detection, and examine the connection between target cell engulfment and phagolysosomal processing. We demonstrate how this approach can be used to measure distinct forms of phagocytosis, and changes in macrophage morphology during phagocytosis related to both engulfment and target degradation. Our results provide a high-resolution method for quantifying phagocytosis that provides opportunities to better understand the cellular and molecular regulation of this fundamental biological process.

| S-EPMC7075070 | biostudies-literature

Network analysis of time-lapse microscopy recordings.

Project description:Multicellular organisms rely on intercellular communication to regulate important cellular processes critical to life. To further our understanding of those processes there is a need to scrutinize dynamical signaling events and their functions in both cells and organisms. Here, we report a method and provide MATLAB code that analyzes time-lapse microscopy recordings to identify and characterize network structures within large cell populations, such as interconnected neurons. The approach is demonstrated using intracellular calcium (Ca(2+)) recordings in neural progenitors and cardiac myocytes, but could be applied to a wide variety of biosensors employed in diverse cell types and organisms. In this method, network structures are analyzed by applying cross-correlation signal processing and graph theory to single-cell recordings. The goal of the analysis is to determine if the single cell activity constitutes a network of interconnected cells and to decipher the properties of this network. The method can be applied in many fields of biology in which biosensors are used to monitor signaling events in living cells. Analyzing intercellular communication in cell ensembles can reveal essential network structures that provide important biological insights.

| S-EPMC4166320 | biostudies-literature

dNEMO: a tool for quantification of mRNA and punctate structures in time-lapse images of single cells.

Project description:MotivationMany biological processes are regulated by single molecules and molecular assemblies within cells that are visible by microscopy as punctate features, often diffraction limited. Here, we present detecting-NEMO (dNEMO), a computational tool optimized for accurate and rapid measurement of fluorescent puncta in fixed-cell and time-lapse images.ResultsThe spot detection algorithm uses the à trous wavelet transform, a computationally inexpensive method that is robust to imaging noise. By combining automated with manual spot curation in the user interface, fluorescent puncta can be carefully selected and measured against their local background to extract high-quality single-cell data. Integrated into the workflow are segmentation and spot-inspection tools that enable almost real-time interaction with images without time consuming pre-processing steps. Although the software is agnostic to the type of puncta imaged, we demonstrate dNEMO using smFISH to measure transcript numbers in single cells in addition to the transient formation of IKK/NEMO puncta from time-lapse images of cells exposed to inflammatory stimuli. We conclude that dNEMO is an ideal user interface for rapid and accurate measurement of fluorescent molecular assemblies in biological imaging data.Availability and implementationThe data and software are freely available online at https://github.com/recleelab.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC8354270 | biostudies-literature

Quantitative time-lapse fluorescence microscopy in single cells.

Project description:The cloning of green fluorescent protein (GFP) 15 years ago revolutionized cell biology by permitting visualization of a wide range of molecular mechanisms within living cells. Though initially used to make largely qualitative assessments of protein levels and localizations, fluorescence microscopy has since evolved to become highly quantitative and high-throughput. Computational image analysis has catalyzed this evolution, enabling rapid and automated processing of large datasets. Here, we review studies that combine time-lapse fluorescence microscopy and automated image analysis to investigate dynamic events at the single-cell level. We highlight examples where single-cell analysis provides unique mechanistic insights into cellular processes that cannot be otherwise resolved in bulk assays. Additionally, we discuss studies where quantitative microscopy facilitates the assembly of detailed 4D lineages in developing organisms. Finally, we describe recent advances in imaging technology, focusing especially on platforms that allow the simultaneous perturbation and quantitative monitoring of biological systems.

| S-EPMC3137897 | biostudies-literature

Time-lapse microscopy of Streptomyces coelicolor growth and sporulation.

Project description:Bacteria from the genus Streptomyces are among the most complex of all prokaryotes; not only do they grow as a complex mycelium, they also differentiate to form aerial hyphae before developing further to form spore chains. This developmental heterogeneity of streptomycete microcolonies makes studying the dynamic processes that contribute to growth and development a challenging procedure. As a result, in order to study the mechanisms that underpin streptomycete growth, we have developed a system for studying hyphal extension, protein trafficking, and sporulation by time-lapse microscopy. Through the use of time-lapse microscopy we have demonstrated that Streptomyces coelicolor germ tubes undergo a temporary arrest in their growth when in close proximity to sibling extension sites. Following germination, in this system, hyphae extended at a rate of approximately 20 microm h(-1), which was not significantly different from the rate at which the apical ring of the cytokinetic protein FtsZ progressed along extending hyphae through a spiraling movement. Although we were able to generate movies for streptomycete sporulation, we were unable to do so for either the erection of aerial hyphae or the early stages of sporulation. Despite this, it was possible to demonstrate an arrest of aerial hyphal development that we suggest is through the depolymerization of FtsZ-enhanced green fluorescent protein (GFP). Consequently, the imaging system reported here provides a system that allows the dynamic movement of GFP-tagged proteins involved in growth and development of S. coelicolor to be tracked and their role in cytokinesis to be characterized during the streptomycete life cycle.

| S-EPMC2576688 | biostudies-literature

3D Time-lapse Imaging and Quantification of Mitochondrial Dynamics.

Project description:We present a 3D time-lapse imaging method for monitoring mitochondrial dynamics in living HeLa cells based on photothermal optical coherence microscopy and using novel surface functionalization of gold nanoparticles. The biocompatible protein-based biopolymer coating contains multiple functional groups which impart better cellular uptake and mitochondria targeting efficiency. The high stability of the gold nanoparticles allows continuous imaging over an extended time up to 3000 seconds without significant cell damage. By combining temporal autocorrelation analysis with a classical diffusion model, we quantify mitochondrial dynamics and cast these results into 3D maps showing the heterogeneity of diffusion parameters across the whole cell volume.

| S-EPMC5322395 | biostudies-literature

Traxtile: Interactive editing of cell tracks in time-lapse images.

Project description:Time-lapse imaging can be used to quantify how cells move, divide, and die over time and under defined culture conditions. Open source software packages such as CellProfiler, Icy, and Fiji provide robust and convenient interfaces for performing such analyses. However, object tracking algorithms are imperfect, and validation of significant events is often required. This is challenging, as CellProfiler produces only tabular data for object tracking, and the graphical tools in Icy and Fiji are not optimal for manual review of these events. Here we describe Traxtile, a program that allows interactive graphical review and revision of object tracking assignments. Traxtile imports initial assignments and automatically identifies events needing review (i.e., apparent creation of new objects, splits, merges, and losses). For each such event, the object track is displayed on a montage of images centered on the event and spanning the preceding and subsequent frames. Links between cells in successive frames can be reviewed and edited, yielding validated tracks for the image series. Reports summarize events from the validated tracks. Traxtile is implemented in Python version 2.7 using standard distribution libraries (available at www.python.org) and is freely available at https://github.com/braunb/traxtile-public.

| S-EPMC4560953 | biostudies-literature

Time-lapse live-cell imaging of pyroptosis by confocal microscopy.

Project description:Pyroptosis is a type of regulated necrosis executed by gasdermin. Osmotic cell swelling and membrane perforation are the key features of pyroptosis. This protocol presents time-lapse imaging of morphological changes during pyroptosis using a confocal microscope. We describe the step-by-step ectopic expression of gasdermin, cell staining with nuclear and membrane probes, and visualization of pyroptosis by time-lapse imaging. This protocol is applicable to monitoring pyroptosis in various situations. For complete details on the use and execution of this protocol, please refer to Qin et al. (2023).1.

| S-EPMC10679943 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data