Cell type-specific aging clocks to quantify aging and rejuvenation in regenerative regions of the brain

Aging manifests as progressive dysfunction culminating in death. The diversity of cell types is a challenge to the precise quantification of aging and its reversal. Here we develop a suite of 'aging clocks' based on single cell transcriptomic data to characterize cell type-specific aging and rejuvenation strategies. The subventricular zone (SVZ) neurogenic region contains many cell types and provides an excellent system to study cell-level tissue aging and regeneration. We generated 21,458 single-cell transcriptomes from the neurogenic regions of 28 mice, tiling ages from young to old. With these data, we trained a suite of single cell-based regression models (aging clocks) to predict both chronological age (passage of time) and biological age (fitness, in this case the proliferative capacity of the neurogenic region). Both types of clocks perform well on independent cohorts of mice. Genes underlying the single cell-based aging clocks are mostly cell-type specific, but also include a few shared genes in the interferon and lipid metabolism pathways. We used these single cell-based aging clocks to measure transcriptomic rejuvenation, by generating single cell RNA-seq datasets of SVZ neurogenic regions for two interventions - heterochronic parabiosis (young blood) and exercise. Interestingly, the use of aging clocks reveals that both heterochronic parabiosis and exercise reverse transcriptomic aging in the niche, but in different ways across cell types and genes. This study represents the first development of high-resolution aging clocks from single cell transcriptomic data and demonstrates their application to quantify transcriptomic rejuvenation.

Enhanced competitive protein exchange at the nano-bio interface enables ultra-deep coverage of the human plasma proteome

We have developed a scalable system that leverages protein nano interactions to overcome current limitations of deep plasma proteomics in large cohorts. Introducing proprietary engineered nanoparticles (NPs) into a biofluid such as blood plasma leads to the formation of a selective and reproducible protein corona at the particle protein interface, driven by the relationship between protein-NP affinity and protein abundance. Here we demonstrate the importance of tuning the protein to NP surface ratio (P/NP), which determines the competition between proteins for binding. We demonstrate how optimized P/NP ratio affects protein corona composition, ultimately enhancing performance of a fully automated NP based deep proteomic workflow (Proteograph). By limiting the available binding surface of NPs and increasing the binding competition, we identify 1.2 to 1.7x more proteins with only 1% false discovery rate on the surface of each NP, and up to 3x compared to a standard neat plasma proteomics workflow. Moreover, increased competition means proteins are more consistently identified and quantified across replicates, yielding precise quantification and improved coverage of the plasma proteome when using multiple physicochemically distinct NPs. In summary, by optimizing NPs and assay conditions, we capture a larger and more diverse set of proteins, enabling deep proteomic studies at scale.

Precise quantification of behavioral individuality from 80 million decisions across 183,000 flies

Individual animals behave differently from each other. This variability is a component of personality and arises even when genetics and environment are held constant. Discovering the biological mechanisms underlying behavioral variability depends on efficiently measuring individual behavioral bias, a requirement that is facilitated by automated, high-throughput experiments. We compiled a large data set of individual locomotor behavior measures, acquired from over 183,000 fruit flies walking in Y-shaped mazes. With this data set we first conducted a "computational ethology natural history" study to quantify the distribution of individual behavioral biases with unprecedented precision and examine correlations between behavioral measures with high power. We discovered a slight, but highly significant, left-bias in spontaneous locomotor decision-making. We then used the data to evaluate standing hypotheses about biological mechanisms affecting behavioral variability, specifically: the neuromodulator serotonin and its precursor transporter, heterogametic sex, and temperature. We found a variety of significant effects associated with each of these mechanisms that were behavior-dependent. This indicates that the relationship between biological mechanisms and behavioral variability may be highly context dependent. Going forward, automation of behavioral experiments will likely be essential in teasing out the complex causality of individuality.

Deep Link Entropy for Quantifying Edge Significance in Social Networks

Through online political communications, fragmented groups appear around ideological lines, which might form echo chambers if the communications within like-minded groups are dominant over the communications among different-minded groups, potentially contributing to political polarization and extremism. The antidote is the interactions between individuals who constitute social bridges between different minded groups. Hence, exploring the significance of connections between the individuals of a network is a center of attraction especially for the global connectivity and diffusion in networks. Based on the divergence of probability distributions of pairs of nodes, Link Entropy (LE) is a recently proposed method outperforming the others in quantifying edge significance. In this work, considering that the adjacent nodes of the two nodes of an edge are also in charge in determining its significance, we propose the Deep Link Entropy (DLE) method for a more precise quantification through taking into account the uncertainty distributions of the adjacent nodes as well. We show experimentally that DLE significantly outperforms LE especially in large-scale complex network with several groups or communities. We believe our method contributes to not only online political communications but a wide range of fields from biology to quantum networks, where edge significance has an operational meaning.

Deconvolution of Gas Diffusion Polarization in Ni/Gadolinium-Doped Ceria Fuel Electrodes

The deconvolution of physicochemical processes in impedance spectra of SOCs with nickel/ceria fuel electrodes is challenging as gas diffusion strongly overlaps with the electrochemical processes at fuel and air electrode. To overcome this issue, symmetrical cells were applied and the gas diffusion process at the fuel electrode was quantified by altering the inert component (nitrogen / helium) in a ternary fuel gas mixture. An effective gas transport parameter considering microstructural and geometrical features was derived, enabling a precise quantification of polarization resistances related to gas diffusion and hydrogen electrooxidation. The obtained values were applied to parameterize a dc cell model. The model validation in fuel cell and electrolyzer mode showed an excellent agreement between measured and simulated current/voltage characteristics over a wide range of technically meaningful gas compositions and operating temperatures.

Optimized extraction of polyphenolic antioxidants from the leaves of Himalayan Oak species

In this study heat-assisted extraction conditions were optimized to enhance extraction yield of antioxidant polyphenols from leaves of Himalayan Quercus species. In initial experiments, a five-factor Plackett-Burman design including 12 experimental runs was tested against the total polyphenolic content (TP). Amongst, XA: extraction temperature, XC: solvent concentration and XE: sample-to-solvent ratio had shown significant influence on yield. These influential factors were further subject to a three-factor-three-level Box-Wilson Central Composite Design; including 20 experimental runs and 3D response surface methodology plots were used to determine optimum conditions [i.e. XA: (80°C), XC:(87%), XE: (1g/40ml)].This optimized condition was further used in other Quercus species of western Himalaya, India. The High-Performance Liquid Chromatography (HPLC) revealed occurrence of 12 polyphenols in six screened Quercus species with the highest concentration of catechin followed by gallic acid. Amongest, Q. franchetii and Q. serrata shared maximum numbers of polyphenolic antioxidants (8 in each). This optimized extraction condition of Quercus species can be utilized for precise quantification of polyphenols and their use in pharmaceutical industries as a potential substitute of synthetic polyphenols.

Making sense of gas measurements: quantification of multicomponent gas mixtures in biological and chemical laboratory experiments

Textbooks in physical chemistry start from the treatise of the ideal gas. Gaseous compounds are important reactants and products of (bio)chemical reactions, and thus their absolute amounts are needed to establish mass balances. However, in contrast to solid, for dissolved and liquid compounds, their qualitative and especially quantitative analysis is less widely established in biological and chemical laboratories. This can be mainly ascribed to the seemingly simple chemical nature of gaseous compounds that is in contrast to the effort needed for their precise quantification. In this article, we will guide the reader through the considerations and steps needed to perform a meaningful analysis of multicomponent gas mixtures, which are reactants for or products of (bio)chemical reactions in aqueous solutions in the laboratory environment and scale. After a brief introduction, special focus is set on the methods for quantification and calculations needed to derive absolute amounts of gases in a mixture. The overall concept will be exemplified by biogas production as well as by an electroorganic reaction (Kolbe electrolysis of n-hexanoic acid), and general pitfalls will be highlighted.

A Bioluminescent 3CLPro Activity Assay to Monitor SARS-CoV-2 Replication and Identify Inhibitors

Our therapeutic arsenal against viruses is very limited and the current pandemic of SARS-CoV-2 highlights the critical need for effective antivirals against emerging coronaviruses. Cellular assays allowing a precise quantification of viral replication in high-throughput experimental settings are essential to the screening of chemical libraries and the selection of best antiviral chemical structures. To develop a reporting system for SARS-CoV-2 infection, we generated cell lines expressing a firefly luciferase maintained in an inactive form by a consensus cleavage site for the viral protease 3CLPro of coronaviruses, so that the luminescent biosensor is turned on upon 3CLPro expression or SARS-CoV-2 infection. This cellular assay was used to screen a metabolism-oriented library of 492 compounds to identify metabolic vulnerabilities of coronaviruses for developing innovative therapeutic strategies. In agreement with recent reports, inhibitors of pyrimidine biosynthesis were found to prevent SARS-CoV-2 replication. Among the top hits, we also identified the NADPH oxidase (NOX) inhibitor Setanaxib. The anti-SARS-CoV-2 activity of Setanaxib was further confirmed using ACE2-expressing human pulmonary cells Beas2B as well as human primary nasal epithelial cells. Altogether, these results validate our cell-based functional assay and the interest of screening libraries of different origins to identify inhibitors of SARS-CoV-2 for drug repurposing or development.