Clinical proteomics mostly relies on the absolute quantification of targeted proteins or on global proteome quantification. Although highly successful, this type of analysis does not reveal the turnover (i.e. synthesis and clearance rates) behind the observed abundance, which detailed and tissue-specific knowledge constitutes a complementary perspective that provides a unique insight into protein regulation. Turnover data are commonly obtained by mass spectrometry and hence isotopic tracers are employed to label newly synthesized proteins. Different tracing protocols exist and a common choice consists in delivering the tracer continuously over a rather long time (weeks). Limited in vivo protein dynamics data are available in human and the protocol called Stable Isotope Labeling Kinetics (SILK) using C13 Leucine allows obtaining a quantitative picture of a single protein dynamics. In a recent work, we introduced large-scale mapping of protein turnover in human cerebrospinal (CSF) using a novel proteomics framework based on unbiased SILK experiments. We demonstrated the potential of this approach on ventricular CSF by obtaining the turnover parameters of ~200 proteins in vivo in human patients (5 known before, a 40-fold increase). Those preliminary data thus showed the ability of wpSILK to map human protein turnover data in vivo on a large scale, including the generation of a repertoire of proteotypic peptides amenable to subsequent reuse in other turnover experiments by the community. Here, we propose to establish a first resource (SILK_road) covering the landscape of CSF and blood plasma proteins dynamics in vivo in human. We will first focus on healthy (amyloid-negative) individuals to be used as a reference. Additional analysis of amyloid-positive cases will allow us to support the Alzheimer’s community and to perform a first survey of deregulated dynamics in this disease. Mathematical modeling of the data will allow us to find significant differences in dynamics but also in the blood/CSF or CNS/CSF exchanges, areas of investigation that were never covered so far at this scale. This could reveal alterations of the choroid plexus or blood-brain barrier as well as the relative peripheral vs. brain protein production. Moreover, our data will be useful to researchers studying target engagement of a new treatment by detecting alterations in the production rate of a specific protein in CSF or blood in response to a putative disease-modifying therapy. Namely, SILK_road will provide a compendium of peptides amenable to dynamics studies for many proteins, which other researchers will be able to use to design their own experiments. All the data processing and modeling software codes will be made available. Data will be deposited in public repositories (e.g., PRIDE) and made available and navigable from a dedicated web site. In addition, we will share our processed data with interested proteomics databases, e.g., neXtProt with which we already had contacts. Taken together, our objectives are 1. To establish the first human in vivo blood/CSF open access, protein dynamics atlas, providing the synthesis, degradation and estimates of blood-CSF exchange rates of healthy individuals. 2. To develop a complete, robust software implementation of the necessary bioinformatics pipeline and mathematical modeling, thus making multi-biological compartment SILK available to the community. To make SILK_road data available and navigable from a dedicated web site. 3. To validate the relevance of this atlas through a pilot study on Amyloid positive patients, prodromal for Alzheimer’s disease.

The spatial embedding of the cortical connectome reflects the fact that the probability of connectivity is determined by interareal distance, which determines the spatial embedding of the cerebral cortex thereby having a profound impact on the cortical connectome across primates and rodents. Further exploring the spatial embedding of the cortex in the non-human primate will allow improving our understanding of inter-areal connectivity. To further this aim we are developing technology allowing combining MRI imaging and tract tracing in macaque in order to create template surface connectivity maps that will facilitate major advances with respect to existing connectomes. Further, we are developing a machine leaning approaches to predict connections, that will enable us to propose complete graphs of the interareal cortical graph. With our CONNECTOMICS platform in SBRI Lyon and its mirror platform at ION Shanghai we have considerably expanded our inter-areal data base. This will enable us to pursue the following Objectives: (i) Generating a novel area-based connectivity data base. We shall use surface mapping and template to propose flexible data base that can adapt to changing criteria of cortical areas. With outside funding we are developing a comprehensive multimodal macaque atlas that will incorporate spatial transcriptomics. We shall incorporate the connectivity data into this atlas and examine the network properties of the data; (ii) To make progress in cortical connectomics we need to go beyond connectomes based on cortical areas. We will developing super dense inter-areal connectivity maps. A triangulated surface template mesh will used to create 350-500µm2 regions of interest (ROIs) on surface mapped connectivity. Theoretically, these so-defined ROIs will have strength-distance relations that are considerably more sharply defined, and predictability greatly enhanced. ROI clustering analysis will allow us to examine the contribution of quantitative connectivity measures along with spatial transcriptomics to areal identity and establish an edge-complete graph of the cortex; (ii) Explore visuospatial patterning of the cortical connectome. Numerous connectomes assume the fovea as a proxy for the early visual areas. However, because connectivity in the cortex obeys a strict distance rule, peripheral representations of the cortex connect very differently from that of the fovea. Multiple injections of retrograde tracers in retinal subdivisions of early visual areas show eccentricity-specific feedback connectivity profiles from extensive regions of the cortex. We shall complement this connectivity with the connectivity from (i) above to address the visuospatial patterning exploiting graph theoretic measures to better understand information flow in the macaque connectome.