# Molecular Expression Analysis

> **NIH NIH P50** · BAYLOR COLLEGE OF MEDICINE · 2022 · $231,119

## Abstract

The goal of the Molecular and Expression Analysis (MEA) Core is to provide BCM IDDRC investigators with
access to high-throughput methods that can identify and quantify global phenotypic differences between fluids,
cells or tissues at the level of gene expression, protein expression, post-translational modification, and cell
metabolism. Targeted versions of these molecular technologies are valuable for testing and verifying molecular
outcomes in genetic models for disease, but the unbiased nature of many platforms makes them exciting tools
for identifying the mechanistic basis for disease and for direct discovery of biomarkers. The RNA Profiling
sub-core will enable IDDRC investigators to characterize transcriptomes at the single cell level using several
complementary RNA-seq commercial platforms, and to visualize and validate by imaging gene expression
patterns in tissues by RNA in situ hybridization (ISH) and imaging. Differential gene expression inferred from
untargeted transcriptomics can help researchers confirm models, but it can also reveal unanticipated findings
about gene regulatory networks; targeted RNA ISH can validate and visualize these findings in brain tissue.
The Protein and Metabolite Profiling sub-core will provide services and expertise to identify and profile
proteins, protein complexes, post-translational modifications, and metabolites. Proteomics can provide key
insights into the states of protein regulatory networks that control cellular phenotypes, and differences in small
molecule levels revealed by untargeted metabolomics of fluids from animal models or patients can identify
biochemical imbalances and biomarkers for disease. Because processing and interpreting data generated by
these platforms is challenging, the Data Analysis and Integration sub-core will provide computational and
data science expertise to assist IDDRC investigators with analysis of RNA sequencing data, proteomics LC-
MS data, metabolomics LC-HRMS data, and metabolomics NMR data. The Core will also develop new
computational methods to extract information from data derived from the same biological samples but across
different -omics platforms. By providing access to a suite of platforms for the molecular characterization of
phenotype, and the data analysis expertise needed to make sense of these complex systems, the MEA Core
will enable researchers to identify molecular changes that lead to or report on pathogenic mechanism in IDDs.
Tracking differences between healthy and disease states across these different modalities may yield
connections between the genetic, gene regulatory, and biochemical basis for neural dysfunction.

## Key facts

- **NIH application ID:** 10427282
- **Project number:** 5P50HD103555-03
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** Cecilia Ljungberg
- **Activity code:** P50 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $231,119
- **Award type:** 5
- **Project period:** 2020-07-22 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10427282

## Citation

> US National Institutes of Health, RePORTER application 10427282, Molecular Expression Analysis (5P50HD103555-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10427282. Licensed CC0.

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