## 4 Discussion  
Here we present the application of DHC to the classification of human melanoma cells undergoing three active forms of programmed cell death--apoptosis, ferroptosis, and necroptosis. This approach to monitoring and distinguishing forms of programmed cell death has several advantages over other methods. First, digital holographic imaging is label-free and therefore does not require the manipulation of the cells through addition of dyes or introduction of exogenous genetic material. Second, the assay is compatible with live-imaging, permitting real-time monitoring of cell viability and assessment of the kinetics of each form of programmed cell death. Third, the approach has single-cell resolution, and can therefore provide information on the percentages of a population that undergo different forms  
Figure 8: Four-node decision tree for classifying form of programmed cell death when healthy cells are included. Graphs depict all datapoints from Training Set not yet classified at each node. Dotted lines and “x” indicate threshold used to classify. Tables provide raw results and calculated metrics of accuracy for Training Set and combined Test Sets. of programmed cell death. Finally, DHC is a non-invasive and non-endpoint assay, such that once the analysis is complete, the cells remain unadulterated and can be used for further molecular analyses. We expect this assay will be a valuable addition to the toolbox for investigating programed cell death, and that it will be particularly useful for assessing the dynamics and interplay of different forms of cell death in the same culture. The strengths of this assay make it particularly well suited for real-time assessment of the therapeutic response of cells directly isolated from human tumors. The potential clinical utility of assessing tumor-specific sensitivity to a collection of potential single agent or combination therapies using _in vitro_ approaches has long been appreciated [42]. However, several demanding requirements impede the successful implementation of this approach as a widespread clinically valuable tool for personalized medicine. First, in order to provide efficient turnaround time and to most accurately capture the _in situ_ cell state, tumor-derived cells must be minimally expanded in vitro. As a label-free approach that requires few cells per condition, the assay presented here requires little _in vitro_ manipulation and selection. Second, an ideal assay would capture the phenotypic heterogeneity of both the naive tumor cells and their response to therapeutic agents. Capturing heterogeneity of response is crucial for assessing the adoption of \"persister states\"--cells that survive an initial toxic treatment, often leading to therapeutic resistance, but also increasing susceptibility to other therapeutic agents [2]. The assay presented here could, for example, simultaneously compare not only the efficacy of two different agents, but also determine whether phenotypic subsets of tumor cells were predominately affected, and whether different forms of programmed cell death were triggered. Such information would be instructive when selecting candidate combination therapies. For these reasons, DHC platforms have already been implemented in the high-throughput real-time screening of tumor-derived cells [43; 44]. The approach presented here offers additional granularity to the information regarding cell death provided by these systems. We present here the classification of three forms of programmed cell death. However, other major forms of programmed cell death, most notably, pyproptosis, have also been described [2]. We limited our analyses to the forms of cell death that we could confidently and uniformly induce based upon prior work. It is possible that similar approaches could yield models that also identify pyproptosis. One limitation of this study is the inclusion of only single agents to induce each form of cell death.