## 3 Results  
### Identification of Informative DHC-Derived Features  
To investigate whether DHC-derived features could be used to distinguish cells undergoing apoptosis, ferprotosis and necroptosis, we generated four replicate datasets (Figure 1). Human melanoma cells (501 mel) were treated with compounds previously established as inducing apoptosis [28; 29; 30; 31] (staurosporine, 0.1 \(\upmu\)m), ferprotosis [8; 25; 32; 33; 34; 35] (erastin, 2 \(\upmu\)m), or necroptosis [36; 37; 38; 39; 40; 41] (shikonin, 1.5 \(\upmu\)m). Cells treated with only DMSO were included as controls (not treated, NT). Cultures were live-imaged and DHC-derived features were generated for each cell. Two of the experiments contained all four conditions, whereas two were performed without erastin. One of the four-conditionexperiments was designated the Training Set and the rest as Test Sets. To first determine whether data generated from independent experiments were comparable, we evaluated the optical thickness (average pixel intensity in object) of all cells within the NT conditions (Figure 2). Optical thickness is a fundamental measurement of DHC, from which other morphological features are derived, so we reasoned that comparable datasets should yield comparable distributions of this feature in NT conditions, where biological variability should be minimal. Indeed, optical thickness distributions were comparable across datasets, indicative of a stable platform and reproducible features. We next compared images from each condition (Figure 3). We observed notable and distinct morphological changes upon treatment with each of the three compounds. The distribution of average optical thickness demonstrated somewhat greater variability in the treated conditions as compared to the NT conditions, indicative of the expected increase in biological variability (Figure 2). We therefore investigated whether changes in morphological features were sufficiently consistent across experiments to classify each form of programed cell death despite biological variability. Many of the morphological features generated by HStudio are highly correlated. In order to avoid over-training, we sought to limit the features considered in classification models. We calculated the correlation coefficient for each combination of features in the Training Set (Figure 4). For features with perfect or near-perfect (r > 0.96) correlations, only one was selected for further analysis. The choice of feature was either arbitrary or, where appropriate, driven by ease of biological interpretation (for example, optical thickness was selected over phaseshift). We compared the distribution of all selected features across the four conditions in the Training Set. Nine features demonstrated a significant difference in distribution (adj. P < 0.0001) between at least two conditions (Figure 5 and Supplemental Tables 1-4). These nine features were selected as the basis for two approaches to classifier generation (Figure 1). Figure 1: Schematic of study design. In four independent experiments, 501 mel cells were treated with DMSO (not treated, N.T. ), staurosporin (Sta. ), erastin (Era.) or shikonin (Shi.). One experiment was designated the Training Set and used to generate two classification models—one to distinguish the form of programmed cell death among dying cells and one to distinguish the form of programmed cell death and healthy cells. The reproducibility of both models was tested with the remaining datasets. Figure 3: Digital holographic cytometry (DHC) images of indicated conditions. Expected form of programmed cell death indicated in parentheses. Figure 2: Distribution of average (ave) optical thickness per cell from indicated conditions across four experiments. Figure 4: Matrix demonstrating correlation coefficients between DHC-derived cellular features. Figure 5: Distributions of indicated DHC-derived single cell features across four conditions. Data represent Training Set only. Ordinary one-way ANOVA with [PERSON]’s multiple comparisons test; Adjusted \(p\) value \(>\) 0.05 (n.s.