Genetically encoded calcium indicators (GECIs) are mainly represented by two- or one-fluorophore-based sensors. One type of two-fluorophore-based sensor, carrying Opsanus troponin C (TnC) as the Ca2+-binding moiety, has two binding sites for calcium ions, providing a linear response to calcium ions. One-fluorophore-based sensors have four Ca2+-binding sites but are better suited for in vivo experiments. Herein, we describe a novel design for a one-fluorophore-based GECI with two Ca2+-binding sites. The engineered sensor, called NTnC, uses TnC as the Ca2+-binding moiety, inserted in the mNeonGreen fluorescent protein. Monomeric NTnC has higher brightness and pH-stability in vitro compared with the standard GECI GCaMP6s. In addition, NTnC shows an inverted fluorescence response to Ca2+. Using NTnC, we have visualized Ca2+ dynamics during spontaneous activity of neuronal cultures as confirmed by control NTnC and its mutant, in which the affinity to Ca2+ is eliminated. Using whole-cell patch clamp, we have demonstrated that NTnC dynamics in neurons are similar to those of GCaMP6s and allow robust detection of single action potentials. Finally, we have used NTnC to visualize Ca2+ neuronal activity in vivo in the V1 cortical area in awake and freely moving mice using two-photon microscopy or an nVista miniaturized microscope.

Biological age acceleration (BAA) models based on blood tests or DNA methylation emerge as a de facto standard for quantitative characterizations of the aging process. We demonstrate that deep neural networks trained to predict morbidity risk from wearable sensor data can provide a high-quality and cheap alternative for BAA determination. The GeroSense BAA model was trained and validated using steps per minute recordings from 103,830 one-week long and 2,599 of up to 2 years-long longitudinal samples and exhibited a superior association with life-expectancy over the average number of steps per day in, e.g., groups stratified by professional occupations. The association between the BAA and effects of lifestyles, the prevalence of future incidence of diseases was comparable to that of BAA from models based on blood test results. Wearable sensors let sampling of BAA fluctuations at time scales corresponding to days and weeks and revealed the divergence of organism state recovery time (resilience) as a function of chronological age. The number of individuals suffering from the lack of resilience increased exponentially with age at a rate compatible with Gompertz mortality law. We speculate that due to the stochastic character of BAA fluctuations, its mean and auto-correlation properties together comprise the minimum set of biomarkers of aging in humans.

Biological age acceleration (BAA) models based on blood tests or DNA methylation emerge as a de facto standard for quantitative characterizations of the aging process. We demonstrate that deep neural networks trained to predict morbidity risk from wearable sensor data can provide a high-quality and cheap alternative for BAA determination. The GeroSense BAA model presented here was tolerant of gaps in the data, and exhibited a superior association with life-expectancy over the average number of steps per day, e.g., in groups stratified by professional occupations. The association between the BAA and effects of lifestyles, the prevalence or future incidence of diseases was comparable to that of BAA from models based on blood test results. Wearable sensors let sampling of BAA fluctuations at time scales corresponding to days and weeks and revealed the divergence of organism state recovery time (resilience) as a function of chronological age. The number of individuals suffering from the lack of resilience increased exponentially with age at a rate compatible with Gompertz mortality law. We speculate that due to stochastic character of BAA fluctuations, its mean and auto-correlation properties together comprise the minimum set of biomarkers of aging in humans.

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