Time-lapse cytometry allows non-invasive visualization and analysis of live cell populations by tracking and quantifying individual cells.
Understanding Cellular Dynamics
An advantage of holographic microscopy is that the created quantitative phase images are focused when viewed, not when recorded. This makes the HoloMonitor time-lapse cytometer ideal for long-term imaging and analysis of living cells by means of time-lapse microscopy, which acquires a series of cell images at regular time intervals to analyze the dynamics of various cellular events. Unfocused images, caused by focus drift, are simply refocused by letting the computer software recreate the phase image from the recorded hologram.
An example time-lapse image sequence of a monster HeLa cell, imaged by HoloMonitor. HeLa cells are cancer cells named after Henrietta Lacks, who in the early 1950s donated the first cells that was successfully kept alive and cultured in a laboratory environment. HeLa cells and other immortal cells are today routinely cultured by scientists to study the complex behavior of cells and their response to drug treatments.
In addition to identifying each individual cell, HoloMonitor provides data for analysis of more than 30 morphological parameters. However, the true power of time-lapse cytometry first emerges when the same cells are monitored over time. The HoloMonitor design utilizes recent technological advances to allow time-lapse image sequences of cultured cells to be effortlessly recorded over long time periods.
With HoloMonitor installed in a cell incubator, the cells are kept in a cell friendly environment during the entire experiment. Long-term live cell kinetic data can easily be obtained using time-lapse imaging. Images are recorded at selected intervals, down to 1 image/sec. Depending on the application, cell images are played back as a video recording to aid analysis of dynamic cell behavior.
From recorded time-lapse sequences, the HoloMonitor App Suite software helps the user to automatically extract and kinetically analyze live cell population data based on individual cell data. When preferred, individual cell data – such as cell count, cell morphology, cell velocity, and cell division rate – can all be measured from the same experimental time-lapse data, without requiring additional experiments.
HoloMonitor App Suite
Time-lapse Cytometry References
High accuracy label-free classification of single-cell kinetic states from holographic cytometry of human melanoma cellsScientific Reports (2017)Read more
The authors used machine learning to develop a method for robust and kinetic label-free classification of single adherent cells info functional states.
HoloMonitor M4: holographic imaging cytometer for real-time kinetic label-free live-cell analysis of adherent cellsProceedings, Quantitative Phase Imaging II (2016)Read more
Live-cell imaging enables studying dynamic cellular processes that cannot be visualized in fixed-cell assays. An increasing number of scientists in academia and the pharmaceutical industry are choosing live-cell analysis over or in addition to traditional fixed-cell assays. We have developed a time-lapse label-free imaging cytometer HoloMonitor M4. HoloMonitor M4 assists researchers to overcome inherent disadvantages of fluorescent analysis, specifically effects of chemical labels or genetic modifications which can alter cellular behavior. Additionally, label-free analysis is simple and eliminates the costs associated with staining procedures.
Moving into a New Dimension: Tracking Migrating Cells with Digital Holographic Cytometry in 3DCytometry Part A (2018)Read moreCommentary article discussing the fundamental role of cell movement studies in cancer research. Advantages of the HoloMonitor Track Cells and Wound Healing modules over the transwell migration and invasion assays are highlighted, and includes the possibility to use the cells in experiments for other purposes after completing the imaging. In addition to single cell tracking, the HoloMonitor technology also benefits from the fact that morphology analysis can be performed of each cell. The author concludes that this indeed opens up for almost unlimited possibilities to perform cell morphology analysis using this methodology, since each image is very rich in cellular information.