scholarly journals PtdIns(4,5)P2 turnover is required for multiple stages during clathrin- and actin-dependent endocytic internalization

2007 ◽  
Vol 177 (2) ◽  
pp. 355-367 ◽  
Author(s):  
Yidi Sun ◽  
Susheela Carroll ◽  
Marko Kaksonen ◽  
Junko Y. Toshima ◽  
David G. Drubin
Keyword(s):  
Fusion Proteins ◽  
Double Mutant ◽  
Actin Assembly ◽  
Endocytic Vesicle ◽  
Cell Image ◽  
Endocytic Protein ◽  
Cell Image Analysis ◽  
Endocytic Machinery ◽  
Live Cell Image ◽  
Multiple Stages

The lipid phosphatidylinositol-4,5-bisphosphate (PtdIns[4,5]P2) appears to play an important role in endocytosis. However, the timing of its formation and turnover, and its specific functions at different stages during endocytic internalization, have not been established. In this study, Sla2 ANTH-GFP and Sjl2-3GFP were expressed as functional fusion proteins at endogenous levels to quantitatively explore PtdIns(4,5)P2 dynamics during endocytosis in yeast. Our results indicate that PtdIns(4,5)P2 levels increase and decline in conjunction with coat and actin assembly and disassembly, respectively. Live-cell image analysis of endocytic protein dynamics in an sjl1Δ sjl2Δ mutant, which has elevated PtdIns(4,5)P2 levels, revealed that the endocytic machinery is still able to assemble and disassemble dynamically, albeit nonproductively. The defects in the dynamic behavior of the various endocytic proteins in this double mutant suggest that PtdIns(4,5)P2 turnover is required for multiple stages during endocytic vesicle formation. Furthermore, our results indicate that PtdIns(4,5)P2 turnover may act in coordination with the Ark1/Prk1 protein kinases in stimulating disassembly of the endocytic machinery.

When a single lineage is not enough: Uncertainty-Aware Tracking for spatio-temporal live-cell image analysis

2018 ◽  
Vol 35 (7) ◽  
pp. 1221-1228 ◽  
Author(s):  
Axel Theorell ◽  
Johannes Seiffarth ◽  
Alexander Grünberger ◽  
Katharina Nöh
Keyword(s):  
Image Analysis ◽  
Live Cell ◽  
Cell Image ◽  
Cell Image Analysis ◽  
Spatio Temporal ◽  
Live Cell Image

Live cell, image-based high-throughput screen to quantitate p53 stabilization and viability in human papillomavirus positive cancer cells

Virology ◽  
2021 ◽  
Author(s):  
Gustavo Martínez-Noël ◽  
Valdimara Corrêa Vieira ◽  
Patricia Szajner ◽  
Erin M. Lilienthal ◽  
Rebecca E. Kramer ◽  
...  
Keyword(s):  
Human Papillomavirus ◽  
Cancer Cells ◽  
High Throughput ◽  
Live Cell ◽  
High Throughput Screen ◽  
Cell Image ◽  
Live Cell Image

The use of the digital cell image analysis of Feulgen-stained nuclei to detect apoptosis

1995 ◽  
Vol 104 (5) ◽  
pp. 407-414 ◽  
Author(s):  
Isabelle Camby ◽  
Isabelle Salmon ◽  
Andr� Danguy ◽  
Jean-Lambert Pasteels ◽  
Robert Kiss
Keyword(s):  
Image Analysis ◽  
Cell Image ◽  
Cell Image Analysis

scholarly journals ME-VAE: Multi-Encoder Variational AutoEncoder for Controlling Multiple Transformational Features in Single Cell Image Analysis

2021 ◽  
Author(s):  
Luke Ternes ◽  
Mark Dane ◽  
Marilyne Labrie ◽  
Gordon Mills ◽  
Joe Gray ◽  
...  
Keyword(s):  
Image Analysis ◽  
Single Cell ◽  
Imaging Features ◽  
Phenotypic Differences ◽  
Cell Image ◽  
Intensity Measurements ◽  
Quantitative Measurements ◽  
Variational Autoencoder ◽  
Cell Image Analysis ◽  
Organizational Features

AbstractImage-based cell phenotyping relies on quantitative measurements as encoded representations of cells; however, defining suitable representations that capture complex imaging features is challenging since there are many obstacles, including segmentation and identifying subcellular compartments for feature extraction. Variational autoencoder (VAE) approaches produce encouraging results by mapping from an image to a representative descriptor, and outperform classical hand-crafted features for morphology, intensity, and texture at differentiating data. Although VAEs show promising results for capturing morphological and organizational features in tissue, single cell image analyses based on VAEs often fail to identify biologically informative features due to the intrinsic amount of uninformative variability. Herein, we propose a multi-encoder VAE (ME-VAE) in single cell image analysis using transformed images as a self-supervised signal to extract transform-invariant biologically meaningful features. We show that the proposed architecture improves analysis by making distinct populations more separable compared to traditional VAEs and intensity measurements by enhancing phenotypic differences between cells and by improving correlations to other modalities.

Measurement of RBC agglutination with microscopic cell image analysis in a microchannel chip

2014 ◽  
Vol 56 (1) ◽  
pp. 67-74 ◽  
Author(s):  
Chi Hyun Cho ◽  
Ju Yeon Kim ◽  
Agnes E. Nyeck ◽  
Chae Seung Lim ◽  
Dae Sung Hur ◽  
...  
Keyword(s):  
Image Analysis ◽  
Cell Image ◽  
Cell Image Analysis

Cancer Cell Image Analysis and Visualization

2012 ◽  
pp. 233-243
Author(s):  
Tae-Yun Kim ◽  
Hae-Gil Hwang ◽  
Heung-Kook Choi
Keyword(s):  
Image Analysis ◽  
Feature Extraction ◽  
Case Studies ◽  
Cancer Cell ◽  
Three Dimensional ◽  
Two Dimensional ◽  
Dimensional Image ◽  
The Past ◽  
Cell Image ◽  
Cell Image Analysis

We review computerized cancer cell image analysis and visualization research over the past 30 years. Image acquisition, feature extraction, classification, and visualization from two-dimensional to three-dimensional image algorithms are introduced with case studies of bladder, prostate, breast, and renal carcinomas.

Cell image analysis for malaria detection using deep convolutional network

2020 ◽  
Vol 14 (1) ◽  
pp. 55-65
Author(s):  
Nikita Jain ◽  
Ayush Chauhan ◽  
Prakhar Tripathi ◽  
Saad Bin Moosa ◽  
Prateek Aggarwal ◽  
...  
Keyword(s):  
Image Analysis ◽  
Convolutional Network ◽  
Cell Image ◽  
Cell Image Analysis ◽  
Deep Convolutional Network
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