Cell Proliferation Assay
The HoloMonitor® Cell Proliferation Assay provides label-free growth curves by automatically monitoring cell count and confluence over time. Understand the growth pattern of your cells using the cell proliferation assay.
Automatic Presentation of Kinetic Cell Proliferation
Cell proliferation assays are widely applied in life science to understand the growth pattern of cultured cells and to assess the in vitro safety and efficacy of drugs over time. Traditional methods are end-point assays that often assess cell proliferation indirectly or are based on cell confluence measurements only. HoloMonitor® offers a convenient assay that automatically presents label-free cell proliferation data.
The cell count and confluence growths curve are updated and displayed throughout the experiment.
After the experiment, cell proliferation data is easily exported to Excel (green arrow in the above image).
HoloMonitor video of proliferating cells, showing frequent cell divisions.
Key References Cell Proliferation
General Cytotoxicity and Its Application in Nanomaterial AnalysisIntechOpen (2018)Read more
Review describing and comparing various assays used to study biocompatibility and cytotoxicity of nanomaterials. Holographic phase imaging is pointed out as an excellent tool for cell morphometric characterisation and cell migration studies, and the authors conclude that the interest in the use of DH microscopy in research is constantly increasing.
Label-free High Temporal Resolution Assessment of Cell Proliferation Using Digital Holographic MicroscopyCytometry Part A (2017)Read more
The authors have developed a robust and label-free kinetic cell proliferation assay with high temporal resolution for adherent cells using HoloMonitor M4. Only two image processing settings were adjusted between cell lines, making the assay practical, user friendly, and free of user bias. In the recorded time-lapse image sequences, individual cells were automatically identified to provide detailed growth curves and growth rate data of cell number, confluence, and average cell volume. The results demonstrate how these parameters facilitate a deeper understanding of cell processes than what is achievable with current single-parameter and end-point methods.