# Measuring multiprotein assemblies that drive biological signals

> **NIH NIH R01** · UNIVERSITY OF MISSOURI-COLUMBIA · 2021 · $410,489

## Abstract

Project Summary/Abstract
Cells perceive and respond to their environment by engaging receptors and transmitting intracellular messages
via signal transduction cascades. This process is largely controlled by networks of proteins that bind,
dissociate, and advance signal progression along biochemical pathways. Signalosomes can be part of this
process, formed when proteins acting as network hubs orchestrate interactions with other protein nodes to
control activation of various signaling pathways simultaneously. It is this modular, conditional
interconnectivity between proteins and pathways that is largely responsible for providing the logic circuits
required for signal transmission, synthesizing instructions for discrete cellular responses from multiple
signaling inputs. But despite its high biological importance, the empirical assessment of signaling protein
complexes at the network level is severely restricted by technological limitations, especially in the case of small
clinical samples that provide low amounts of biomaterial for assessment. We propose to advance a new
strategy, q-PiSCES, to allow molecular quantification of proteins that can be detected in signaling complexes
from physiologic samples, such as those from human clinical patients or pre-clinical mouse models. Q-PiSCES
will initially be developed for a collection of 10 protein targets with 55 unique pairwise associations in the T cell
antigen receptor (TCR) signalosome that is known to exert strong control of immune responses (Specific Aim
1). Biostatistical analysis will feed into a Bioinformatics pipeline to focus on three specific parameters of
protein complexes: protein abundance, clustering of identical proteins, and heterotypic protein co-associations
(Specific Aim 2). We will field-test q-PiSCES by applying it to the analysis of human protein complexes
associated with the autoimmune disease, Alopecia Areata (Specific Aim 3). Together, q-PiSCES stands to
dramatically increase the ability to observe, measure, and study network patterns of physiologic protein
complexes. We propose that the patient-derived q-PiSCES data will exemplify a new strategy for analyzing
these complexes, and illustrate its general applicability to many fields of study and classes of disease.

## Key facts

- **NIH application ID:** 10171863
- **Project number:** 5R01GM103841-08
- **Recipient organization:** UNIVERSITY OF MISSOURI-COLUMBIA
- **Principal Investigator:** Adam G. Schrum
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $410,489
- **Award type:** 5
- **Project period:** 2013-04-01 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10171863

## Citation

> US National Institutes of Health, RePORTER application 10171863, Measuring multiprotein assemblies that drive biological signals (5R01GM103841-08). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10171863. Licensed CC0.

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