Live Cell Morphology Assay
The HoloMonitor® Cell Morphology Assay enables non-invasive live cell quantification of a wide range of morphological properties, including individual cell volume, area and thickness.
Individual Cell Morphology
Morphological changes caused by treatment or cellular transformation occur early, making kinetic cell morphology studies a significantly more sensitive tool than indirect biochemical assays to observe cellular response.
In the past, in vitro cell morphology has been difficult and time-consuming to quantify as it involves inconsistent staining and handling of complicated imaging software. Contrary to conventional live cell imagers, HoloMonitor acquires a time-lapse sequence of label-free quantitative phase images.
Since no phototoxic or fading fluorescent labels are required, an unlimited number of label-free and cell-friendly time-lapse images can be acquired just minutes apart. This allows HoloMonitor to non-invasively quantify treatment response of adherent cell populations, by automatically quantifying alterations in individual cell morphology.
Morphological Scatter Plots & Histograms
HoloMonitor measures a wide range of morphological properties, including cell volume, area and thickness. Scatter plots help visualize differences between control and treated cells or to compare cellular characteristics at different time-points of the experiment.
Gating regions enable classification in subpopulations and identification of (rare) cells of interest, for further studies using HoloMonitor Cell Tracking. Histograms show cell population distribution of selected morphological parameter at selected time-points.
Screenshot of the HoloMonitor Cell Morphology Assay
showing cells classified in subpopulations using gating regions.
Label-free Live Cell Assays
HoloMonitor offers several label-free live cell assays, ranging from cell culture quality control to single-cell tracking. After time-lapse image sequences have been recorded at selected sample positions, each of the assays may be used to provide additional results, without requiring further experiments or lab work.
For example, cell motility may be quantified together with cell morphology by tracking cells individually or by applying the HoloMonitor Cell Motility Assay, on the acquired time-lapse image sequence. As the saved image sequence is reused, no further samples are necessary. Additionally, cell proliferation data may also be generated by applying the HoloMonitor Cell Proliferation Assay, again using the previously acquired and saved image sequence.
Some of the morphological properties that can be analyzed using HoloMonitor.
Links to further applications based on cell morphology analysis
- Total mitosis time for each individual cell.
- Graphs and data showing when each cell was in mitosis.
- Assess cell cycle duration time.
- Detect drug effects on cell cycle phase distribution.
- Detect early signs of drug responses before the actual cell death occurs.
- Identify dying cells.
Key References Cell Morphology
Quantitative evaluation of morphological changes in activated platelets in vitro using digital holographic microscopyMicron (2018)Read more
The feasibility of DHM (Digital Holographic Microscopy) in the quantitative evaluation of morphological changes in activated platelets was evaluated. Data shown implies that DHM using HoloMonitor could be a promissing method for quantitative examination of morphological changes in platelets in vitro. The publication also includes a comparision of the performance of different techniques for this purpose.
A Time-lapse, Label-free, Quantitative Phase Imaging Study of Dormant and Active Human Cancer CellsJoVe (video journal) (2018)Read more
The video and publication demonstrate how HoloMonitor is used to conduct time-lapse and labeling-free characterizations of angiogenic and non-angiogenic human osteosarcoma KHOS cells. A panel of cellular parameters, including cell morphology, proliferation, and motility, were quantitatively measured and analyzed using HoloMonitor.
High accuracy label-free classification of single-cell kinetic states from holographic cytometry of human melanoma cellsScientific Reports (2017)Read more
The authors used singel-cell tracking and machine learning to develop a robust method for label-free classification of adherent cells.