# Validation and Kinetic Characterization of Blood-Cerebrospinal Fluid Barrier-Traversing Proteins in Aging With Library-Free Multiplexed Targeted Proteomics > **NIH NIH K99** · HARVARD MEDICAL SCHOOL · 2024 · $125,172 ## Abstract PROJECT SUMMARY/ABSTRACT The choroid plexus (CP) is a critical component of the blood-brain barrier (BBB), whose function is known to degrade with aging. A better understanding of CP biology and how this biology shifts with age is a prerequisite for utilizing the CP for blood-to-brain delivery of anti-aging drugs and may reveal new therapeutic targets in the CP for age-associated diseases. One long-term goal of this project is to reveal how CP behavior, and transcytotic protein transport from the blood to the cerebrospinal fluid (CSF) specifically, changes with aging. Technical challenges inherent in this goal require new proteomic methods. Therefore, a second long-term goal of this project is to bring proteomic technology, and targeted proteomics in particular, to a level where any set of peptides can be detected and quantified with high sensitivity and dynamic range, even in challenging samples such as CP and cerebrospinal fluid (CSF). In my current work, I develop targeted proteomic methods based on the recently published GoDig targeted proteomic technology, which enables simultaneous detection and quantification of hundreds of targets in up to 18 biological samples in a single run. I have submitted a manuscript to Journal of Proteome Research as a first author describing a next-generation version of GoDig with much higher success rates, quantifying over 95% of 400 peptides. However, this method is still limited by the required pre-assembly of a data library, which is infeasible to do with synthetic chemical labels and low-protein-amount samples such as mouse CP and CSF. I have developed a new method using deep learning-based prediction to eliminate this requirement, resulting in a method with superior flexibility and ease of use. In Aim 1, I propose to complete this method, using automatic statistical scoring to ensure reproducibility (Subaim 1.1) and using theoretical spectra in place of deep-learning- based spectrum predictions where necessary to enable library-free targeted proteomics of peptides with any modification (Subaim 1.2), including those derived from the synthetic probes used in Aims 2 and 3. Aims 2 and 3 describe using targeted proteomics to study blood-to-brain transport in aging. In Aim 2, I propose to characterize the kinetics of blood-to-CSF translocation by several known and suspected CP-crossing blood plasma proteins in young and aged mice. I will chemically label recombinant proteins, inject them into the bloodstream, and then track their passage from blood to CSF by collecting CSF at various time points, enriching the labeled peptides from the digested CSF, and performing targeted detection and quantification. In Aim 3, I will study proteins on the CP surface, measuring the kinetics of movement from the blood-facing surface to the CSF- facing surface, which occurs in transcytosis. I will use in vivo bioconjugation and click chemistry to create a chemical “double label” that conclusively signifies this movement; this d... ## Key facts - **NIH application ID:** 10948161 - **Project number:** 1K99AG088297-01 - **Recipient organization:** HARVARD MEDICAL SCHOOL - **Principal Investigator:** Steven Robert Shuken - **Activity code:** K99 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $125,172 - **Award type:** 1 - **Project period:** 2024-09-03 → 2026-08-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10948161 ## Citation > US National Institutes of Health, RePORTER application 10948161, Validation and Kinetic Characterization of Blood-Cerebrospinal Fluid Barrier-Traversing Proteins in Aging With Library-Free Multiplexed Targeted Proteomics (1K99AG088297-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10948161. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*