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Herein, we implement and access machine learning architectures to ascertain models that differentiate healthy from apoptotic cells using exclusively forward (FSC) and side (SSC) scatter flow cytometry information. To generate training data, colorectal cancer HCT116 cells were subjected to miR-34a treatment and then classified using a conventional Annexin V/propidium iodide (PI)-staining assay. The apoptotic cells were defined as Annexin V-positive cells, which include early and late apoptotic cells, necrotic cells, as well as other dying or dead cells. In addition to fluorescent signal, we collected cell size and granularity information from the FSC and SSC parameters. Both parameters are subdivided into area, height, and width, thus providing a total of six numerical features that informed and trained our models. A collection of logistical regression, random forest, k-nearest neighbor, multilayer perceptron, and support vector machine was trained and tested for classification performance in predicting cell states using only the six aforementioned numerical features. Out of 1046 candidate models, a multilayer perceptron was chosen with 0.91 live precision, 0.93 live recall, 0.92 livefvalue and 0.97 live area under the ROC curve when applied on standardized data. We discuss and highlight differences in classifier performance and compare the results to the standard practice of forward and side scatter gating, typically performed to select cells based on size and/or complexity. We demonstrate that our model, a ready-to-use module for any flow cytometry-based analysis, can provide automated, reliable, and stain-free classification of healthy and apoptotic cells using exclusively size and granularity information.

The ability to detect pathogens specifically and sensitively is critical to combat infectious diseases outbreaks and pandemics. Colorimetric assays involving loop‐mediated isothermal amplification (LAMP) provide simple readouts yet suffer from the intrinsic non‐template amplification. Herein, a highly specific and sensitive assay relying on plasmonic sensing of LAMP amplicons via DNA hybridization, termed as plasmonic LAMP, is developed for the severe acute respiratory syndrome‐related coronavirus 2 (SARS‐CoV‐2) RNA detection. This work has two important advances. First, gold and silver (Au–Ag) alloy nanoshells are developed as plasmonic sensors that have 4‐times stronger extinction in the visible wavelengths and give a 20‐times lower detection limit for oligonucleotides over Au counterparts. Second, the integrated method allows cutting the complex LAMP amplicons into short repeats that are amendable for hybridization with oligonucleotide‐functionalized Au–Ag nanoshells. In the SARS‐CoV‐2 RNA detection, plasmonic LAMP takes ≈75 min assay time, achieves a detection limit of 10 copies per reaction, and eliminates the contamination from non‐template amplification. It also shows better detection specificity and sensitivity over commercially available LAMP kits due to the additional sequence identification. This work opens a new route for LAMP amplicon detection and provides a method for virus testing at its early representation.