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It is a challenge to answer questions like: why some people develop a disease, react to a specific treatment and/or develop severe side-effects while others don’t. In order to explain these occurrences, one has to take a holistic approach and study the body physiology from a systems level perspective. Longitudinal multi-omics measurements together with genetics, on a large population, can serve such a purpose and help in predicting, reasoning, and preventing diseases.
In partnership with Arivale Inc., we have developed infrastructure to collect longitudinal Personalized Dense Dynamic Data clouds (PD3 clouds) on thousands of individuals, which include genetics and longitudinal measurements of clinical labs, microbiome, metabolome, proteome, and self-reported data.
The value of these extremely high-dimensional data clouds is clear; however, it also comprises a challenge in data analysis and interpretation.
One way to reduce data dimensionality is called Pareto Task Inference (PARTI, Hart et al. 2015). We used this method to analyze the clinical labs and found that the data falls on a significant tetrahedron. The 4 vertices are archetypes that specialize in a certain task. Using all other datatypes, we identified enriched traits next to every archetype and revealed the underline tradeoffs that shape the data.
This distinctive analysis uncovers unexpected relationships between datasets such as metabolomics, proteomics and clinical labs, and helps in interconnecting the different datatypes to characterize different states of human health.

Primates live in large and complex social groups. It has been argued that this has led to evolutionary pressure on the brain and that there are networks that have been evolved to play an important role in social cognition and behaviors. Social deficits are widely known in many brain disorders such as autism and anxiety. Here we focused on two brain areas that were shown to play a crucial role in social interactions – the amygdala and the anterior-cingulate-cortex (ACC). Our goal was to understand the neural codes in these two regions and especially under social interactions:
1. Computational techniques that allow the comparison of on-going neural activity across these brain regions and species based on information theory measures was developed. We found that human neurons better utilize information capacity (efficient coding) than macaque neurons in both regions, and that ACC neurons are more efficient than amygdala neurons, in both species. In contrast, we found more overlap in the neural vocabulary and more synchronized activity (robustness coding) in monkeys in both regions, and in the amygdala of both species. Our findings demonstrate a tradeoff between robustness and efficiency across species and regions. We suggest that this tradeoff can contribute to the differential cognitive functions between species, and can underlie the complementary roles of the amygdala and the ACC. It can also contribute to the fragility underlying human psychopathologies. For more, see https://www.sciencedirect.com/science/article/pii/S0092867418316465
2. A novel electrophysiology experiment that induced real and live social interactions between humans and primates was conducted. In each daily session, we recorded neural responses in monkeys to the eye-gaze, direct or averted, of human intruders, and compared it with the responses to valence conditioning, aversive and appetitive. We found that the primate amygdala, but not the ACC, encodes eye-gaze; this coding is shared with valence coding through two mechanisms – “shared-activity” at the expectation epoch (conditioned stimulus, CS) and “shared-intensity” after the outcome (unconditioned stimulus, US). These shared mechanisms can open an indirect window for future therapy. For more, see https://www.biorxiv.org/content/10.1101/736462v1
3. We developed behavioral methods and algorithms to evaluate primates’ emotional states, using the analysis of facial expressions and a number of physiological parameters such as heart rate and respiratory rate. The primates’ emotional state evaluation will be the substrate for future studies that will investigate the neural correlates of these states.

The use of proteomics in the clinical arena has been traditionally hampered by low sample throughput and by difficulties in quantifying proteins in complex proteomes in an absolute manner. The use of ultrasonic energy to speed complex proteomics workflows for protein quantification along with thea advent of High Resolution Mass Spectrometry have made possible to treat and to analyze tens of samples a day, thus making mass spectrometry-based clinical proteomics a modern practical tool to be explored.