Broadband Electrical Spectroscopy to Distinguish Single-Cell Ca2+ Changes Due to Ionomycin Treatment in a Skeletal Muscle Cell Line.

Many skeletal muscle diseases such as muscular dystrophy, myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), and sarcopenia share the dysregulation of calcium (Ca2+) as a key mechanism of disease at a cellular level. Cytosolic concentrations of Ca2+ can signal dysregulation in organelles including the mitochondria, nucleus, and sarcoplasmic reticulum in skeletal muscle. In this work, a treatment is applied to mimic the Ca2+ increase associated with these atrophy-related disease states, and broadband impedance measurements are taken for single cells with and without this treatment using a microfluidic device. The resulting impedance measurements are fitted using a single-shell circuit simulation to show calculated electrical dielectric property contributions based on these Ca2+ changes. From this, similar distributions were seen in the Ca2+ from fluorescence measurements and the distribution of the S-parameter at a single frequency, identifying Ca2+ as the main contributor to the electrical differences being identified. Extracted dielectric parameters also showed different distribution patterns between the untreated and ionomycin-treated groups; however, the overall electrical parameters suggest the impact of Ca2+-induced changes at a wider range of frequencies.

Single-Cell Classification Based on Population Nucleus Size Combining Microwave Impedance Spectroscopy and Machine Learning.

Many recent efforts in the diagnostic field address the accessibility of cancer diagnosis. Typical histological staining methods identify cancer cells visually by a larger nucleus with more condensed chromatin. Machine learning (ML) has been incorporated into image analysis for improving this process. Recently, impedance spectrometers have been shown to generate all-inclusive lab-on-a-chip platforms to detect nucleus abnormities. In this paper, a wideband electrical sensor and data analysis paradigm that can identify nuclear changes shows the realization of a single-cell microfluidic device to detect nuclei of altered sizes. To model cells of altered nucleus, Jurkat cells were treated to enlarge or shrink their nucleus followed by broadband sensing to obtain the S-parameters of single cells. The ability to deduce important frequencies associated with nucleus size is demonstrated and used to improve classification models in both binary and multiclass scenarios, despite a heterogeneous and overlapping cell population. The important frequency features match those predicted in a double-shell circuit model published in prior work, demonstrating a coherent new analytical technique for electrical data analysis. The electrical sensing platform assisted by ML with impressive accuracy of cell classification looks forward to a label-free and flexible approach to cancer diagnosis.