CellActivision Applications

Application:  CellActivision Applications


Colony Formation

Colony growth is easily quantified using the area measurement and cell counting tools providing creative and flexible ways to analyse samples.

 

 

Cells area(Pixels)

 

 

 

 

Cell type 1|Cells

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Scratch Wound

The threshold tool facilitates the automatic kinetic analysis of the scratch wound area, wound width and % closure rate.

 

 

 

 

Wound width(Pixels)

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Cytotoxicity

Cell classification and counting tools allow morphological differences such as dying cells to be easily distinguished from healthy cells and subsequently quantified. The kinetic data generated allows important information such as the LD50 value to be obtained with ease.

Cytotoxicity Time(Hour)

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Neurite Growth

In the absence of any labels, neurite formation and cell number can be automatically quantified. Information on cell number, cell phase and movement statistics can also be generated.

 

 

Time(Hour)

 

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Co-culture Analysis

Through using the cell counting and classification tool, machine leaning technology allows different cell types to be classified and quantified over time.

Co-culture Analysis Time(Hour)

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Cell Tracking

Single cells can be automatically tracked providing information on cell movement, and directionality. Additionally; Total trajectories, or centered trajectories can be generated.

Cell Tracking X Movement(um)

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Related Products & Solutions

CellActivision

CellActivision uses Machine Learning Technology and a unique digital filter to recognise cells or colonies directly from label free images. CellActivision can also classify and quantify these cells by the use of sample libraries which are easily prepared by the user.

Life Science

Yokogawa’s high content analysis systems and dual spinning disk confocal technologies provide high-speed and high-resolution live cell imaging, enabling leading-edge research around the world.

Software

With powerful machine learning functions, our analysis software suite provides researchers with advanced insights into their HCA experiments such as label-free analysis.

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