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Label-free measurement of cell death using ptychographic imaging


Numerous human diseases are caused or treated by dysregulating cell death pathways. The study of cell death is, therefore, of fundamental importance to research and drug discovery within the life sciences. Assays for cell death have a fundamental requirement to be non-invasive and as ‘close to real life’ as possible, making the field conducive to label-free imaging technologies such as ptychographic microscopy.

Here, we investigate the potential of time-lapse ptychographic imaging, using the Phasefocus VL21 imaging system, to assess cell death in untreated cultures of B104 neuronal cells and in NIH-3T3 cells treated with different concentrations of a broad-spectrum protein kinase inhibitor (Staurosporine). The high contrast images generated were amenable to automated segmentation and subsequent extraction of the phase delay introduced by each cell, which was used to derive metrics to assess the dynamics of cell death in populations and single cells.

NIH-3T3 cell populations treated with high concentrations (mM-M) of Staurosporine demonstrated a rapid loss in total phase volume, which reached a baseline level after 8 h. In contrast, the phase volume of cells treated with lower concentrations (nM-µM) of Staurosporine took far longer (~40 h), or did not succeed, in reaching the same baseline level within 72 h. This response indicated a slower progression to cell death and, in certain cases, a level of drug tolerance or clearance. Distinct changes in phase-derived metrics during the process of cell death were also apparent at the single cell level, as highlighted by single cell analysis of a dying cell within a B104 neuronal culture. The dying cell underwent a series of stepwise increases in mean nuclear thickness, remaining at a plateau thickness value between each increment, before finally undergoing an abrupt decrease in thickness during plasma membrane rupture.

In combination, these results show that phase metrics derived from ptychographic images can be used to assess the dynamics of cell death in a population and can potentially be utilised to generate a label-free fingerprint associated with different stages of cell death.
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