scholarly journals Automated detection and tracking of many cells by using 4D live-cell imaging data

2014 ◽  
Vol 30 (12) ◽  
pp. i43-i51 ◽  
Author(s):  
Terumasa Tokunaga ◽  
Osamu Hirose ◽  
Shotaro Kawaguchi ◽  
Yu Toyoshima ◽  
Takayuki Teramoto ◽  
...  
Keyword(s):  
Live Cell Imaging ◽  
Cell Imaging ◽  
Automated Detection ◽  
Live Cell ◽  
Imaging Data ◽  
Detection And Tracking

scholarly journals mycelyso – high-throughput analysis of Streptomyces mycelium live cell imaging data

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Christian Carsten Sachs ◽  
Joachim Koepff ◽  
Wolfgang Wiechert ◽  
Alexander Grünberger ◽  
Katharina Nöh
Keyword(s):  
High Throughput ◽  
Live Cell Imaging ◽  
Cell Imaging ◽  
Live Cell ◽  
Imaging Data ◽  
High Throughput Analysis ◽  
Throughput Analysis

scholarly journals Deconvolution of subcellular protrusion heterogeneity and the underlying actin regulator dynamics from live cell imaging

2017 ◽  
Author(s):  
Chuangqi Wang ◽  
Hee June Choi ◽  
Sung-Jin Kim ◽  
Aesha Desai ◽  
Namgyu Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Live Cell Imaging ◽  
Cell Imaging ◽  
Leading Edge ◽  
Live Cell ◽  
Imaging Data ◽  
Computational Framework ◽  
Cell Protrusion ◽  
Learning Analysis ◽  
Subcellular Level

AbstractCell protrusion is morphodynamically heterogeneous at the subcellular level. However, the mechanistic understanding of protrusion activities is usually based on the ensemble average of actin regulator dynamics at the cellular or population levels. Here, we establish a machine learning-based computational framework called HACKS (deconvolution of Heterogeneous Activity Coordination in cytosKeleton at a Subcellular level) to deconvolve the subcellular heterogeneity of lamellipodial protrusion in migrating cells. HACKS quantitatively identifies distinct subcellular protrusion phenotypes from highly heterogeneous protrusion activities and reveals their underlying actin regulator dynamics at the leading edge. Furthermore, it can identify specific subcellular protrusion phenotypes susceptible to pharmacological perturbation and reveal how actin regulator dynamics are changed by the perturbation. Using our method, we discovered ‘accelerating’ protrusion phenotype in addition to ‘fluctuating’ and ‘periodic’ protrusions. Intriguingly, the accelerating protrusion was driven by the temporally coordinated actions between Arp2/3 and VASP: initiated by Arp2/3-mediated actin nucleation, and then accelerated by VASP-mediated actin elongation. We were able to confirm it by pharmacological perturbations using CK666 and Cytochalasin D, which specifically reduced ‘strong accelerating protrusion’ activities. Taken together, we have demonstrated that HACKS allows us to discover the fine differential coordination of molecular dynamics underlying subcellular protrusion heterogeneity via a machine learning analysis of live cell imaging data.

scholarly journals Factor graph analysis of live cell–imaging data reveals mechanisms of cell fate decisions

2015 ◽  
Vol 31 (11) ◽  
pp. 1816-1823 ◽  
Author(s):  
Theresa Niederberger ◽  
Henrik Failmezger ◽  
Diana Uskat ◽  
Don Poron ◽  
Ingmar Glauche ◽  
...  
Keyword(s):  
Cell Fate ◽  
Live Cell Imaging ◽  
Cell Imaging ◽  
Live Cell ◽  
Factor Graph ◽  
Graph Analysis ◽  
Imaging Data ◽  
Cell Fate Decisions

scholarly journals Accurate cell tracking and lineage construction in live-cell imaging experiments with deep learning

2019 ◽  
Author(s):  
Erick Moen ◽  
Enrico Borba ◽  
Geneva Miller ◽  
Morgan Schwartz ◽  
Dylan Bannon ◽  
...  
Keyword(s):  
Deep Learning ◽  
Single Cell ◽  
Live Cell Imaging ◽  
Cell Tracking ◽  
Cell Imaging ◽  
Single Cells ◽  
Live Cell ◽  
Imaging Data ◽  
Cell Nuclei ◽  
Data Types

AbstractLive-cell imaging experiments have opened an exciting window into the behavior of living systems. While these experiments can produce rich data, the computational analysis of these datasets is challenging. Single-cell analysis requires that cells be accurately identified in each image and subsequently tracked over time. Increasingly, deep learning is being used to interpret microscopy image with single cell resolution. In this work, we apply deep learning to the problem of tracking single cells in live-cell imaging data. Using crowdsourcing and a human-in-the-loop approach to data annotation, we constructed a dataset of over 11,000 trajectories of cell nuclei that includes lineage information. Using this dataset, we successfully trained a deep learning model to perform cell tracking within a linear programming framework. Benchmarking tests demonstrate that our method achieves state-of-the-art performance on the task of cell tracking with respect to multiple accuracy metrics. Further, we show that our deep learning-based method generalizes to perform cell tracking for both fluorescent and brightfield images of the cell cytoplasm, despite having never been trained on those data types. This enables analysis of live-cell imaging data collected across imaging modalities. A persistent cloud deployment of our cell tracker is available at http://www.deepcell.org.

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):  
Live Cell Imaging ◽  
Cell Tracking ◽  
Cell Imaging ◽  
Stable Matching ◽  
Live Cell ◽  
High Temporal Resolution ◽  
Imaging Data ◽  
Matching Problem ◽  
Voronoi Partition ◽  
Over Time

AbstractLive-cell imaging is an important technique to study cell migration and proliferation as well as image-based profiling of drug perturbations over time. To gain biological insights from live-cell imaging data, it is necessary to identify individual cells, follow them over time and extract quantitative information. However, since often biological experiment does not allow the high temporal resolution to reduce excessive levels of illumination or minimize unnecessary oversampling to monitor long-term dynamics, it is still a challenging task to obtain good tracking results with coarsely sampled imaging data. To address this problem, we consider cell tracking problem as “stable matching problem” and propose a robust tracking method based on Voronoi partition which adapts parameters that need to be set according to the spatio-temporal characteristics of live cell imaging data such as cell population and migration. We demonstrate the performance improvement provided by the proposed method using numerical simulations and compare its performance with proximity-based tracking and nearest neighbor-based tracking.

scholarly journals eDetect: A Fast Error Detection and Correction Tool for Live Cell Imaging Data Analysis

iScience ◽  
2019 ◽  
Vol 13 ◽  
pp. 1-8 ◽  
Author(s):  
Hongqing Han ◽  
Guoyu Wu ◽  
Yuchao Li ◽  
Zhike Zi
Keyword(s):  
Data Analysis ◽  
Error Detection ◽  
Live Cell Imaging ◽  
Cell Imaging ◽  
Live Cell ◽  
Imaging Data ◽  
Error Detection And Correction
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