# Integrating single-cell based transcriptomic signatures for identifying therapeutic targets of COPD

> **NIH NIH R21** · YALE UNIVERSITY · 2023 · $125,625

## Abstract

The main goal of this proposal is to apply novel machine learning and network-based methods to facilitate the
discovery of biomarkers in diseases and therapeutic targets of drugs. While single-cell RNA sequencing
(scRNAseq) technology has enabled gene expression profiling at single cell resolution, and has helped detect
perturbation in thousands of genes across many different cell types and potential novel disease mechanisms,
identifying viable therapeutic targets out of thousands of candidates is still a challenging problem. Therefore, it
is essential to identify a high-priority and streamlined set of potential drug targets whose role in the disease
could be experimentally validated in the lab with finite resources. This proposal is motivated by a recent
discovery by our group: We identified both cell-type-specific and disease associated changes by analyzing
single-cell transcriptomics profiles of COPD and healthy lung tissues. One limitation is that these results come
from a subset of most severe COPD patients and may not be generalized to all COPD subtypes. The scRNA
study also has limitation for predicting gene regulatory network (GRN), due to inflated noise level and sparsity
of the data. Therefore, we propose to leverage information from bulk transcriptomic data from large cohorts,
such as the Genotype-Tissue Expression (GTEx) and The Lung Genomics Research Consortium (LGRC). We
believe that using GRN from bulk-level, large cohort, RNAseq data as the baseline GRN for COPD lungs will
lead to more robust identification of disease-associated cell types and pathways, and the results will be more
generalizable to all COPD subtypes. In Aim 1 we will identify a list of transcription factors (TFs) that that are
most active in COPD and most likely to regulate the cell-type-specific transcriptomic signatures identified in our
scRNA study. This will be achieved by applying novel machine learning and network methods to integrate the
GRN from GTEx and LGRC cohorts and scRNA data. In Aim 2 we will apply this method to study the effects of
cigarette smoking (CS). This aim is motivated by our scRNA-seq study which identified distinct gene
expression perturbations in two AT2 subpopulations. As our COPD subjects were advanced patients who had
stopped smoking in anticipation of their lung transplant, these results may reflect persistent pathologic changes
that continue after smoking cessation. Therefore, we will apply the same approach as in Aim 1 to identify cell-
type specific transcriptomic signatures of cigarette smoking in COPD human lung tissues and TFs that are
likely to mediate these signatures. In Aim 3 we will identify a list of potential drugs suitable for targeting the TF
modules based on the results from Aims 1 and 2. We will map the TF modules based on Aims 1 and 2 to
DrugBank, a Drug Target Discovery database, and we will identify the drugs that are most likely to effectively
target these modules based on network proximity to TFs within the mod...

## Key facts

- **NIH application ID:** 10540331
- **Project number:** 5R21HL161723-02
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** NAFTALI KAMINSKI
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $125,625
- **Award type:** 5
- **Project period:** 2022-01-01 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10540331

## Citation

> US National Institutes of Health, RePORTER application 10540331, Integrating single-cell based transcriptomic signatures for identifying therapeutic targets of COPD (5R21HL161723-02). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10540331. Licensed CC0.

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