Excessive extracellular matrix (ECM) deposition leading to organ dysfunction, represents the concluding pathological outcome of many chronic fibrotic conditions. Single-cell studies have identified disease-associated fibroblast populations, including a CTHRC1⁺ subpopulation with high collagen gene expression. While TGF-β1-driven fibrogenesis involves cooperation between Smad and mTORC1/4E-BP1 signaling, the role of mTORC1 in regulating ECM transcriptional programs and fibroblast heterogeneity remains poorly understood. To address this, we analyzed RNA-seq datasets from primary human lung fibroblasts (pHLFs) to examine the effects of mTOR inhibition on the TGF-β1-induced transcriptome. Differential expression analysis compared dual mTOR inhibition (AZD8055) with allosteric mTORC1 inhibition (rapamycin) (GSE102674), followed by CRISPR-Cas9-mediated disruption of either RPTOR or RICTOR to disrupt mTORC1 and mTORC2 (RICTOR) signaling, respectively and subsequent RNA-seq (GSE272972). These analyses identified a broad set of TGF-β1-responsive genes dependent on mTORC1 activity. To investigate how mTOR signaling influences fibroblast subpopulation identity in the setting of the setting of idiopathic pulmonary fibrosis (IPF), we integrated bulk RNA-seq data with publicly available human IPF single-cell RNA-seq datasets (GSE136831, GSE135893) using the Scissor framework. Scissor integrates bulk phenotype information with single-cell transcriptomic data to identify phenotype-associated cell subpopulations. TGF-β1-stimulated pHLFs displayed transcriptional similarity to a CTHRC1⁺ fibroblast subpopulation enriched in IPF, whereas disruption of mTORC1 signaling abolished this overlap, indicating a critical role for the TGF-β1/mTORC1 axis in establishing this pathogenic fibroblast state. Additional analyses characterized baseline fibroblast populations and their transcriptional signatures. Functional validation using a selective mTORC1 inhibitor, RMC-5552, demonstrated suppression of TGF-β1-induced COL1A1 and CTHRC1 expression across multiple fibroblast lines. This repository provides the code used for differential expression and pathway analysis, bulk–single-cell integration (Scissor), and statistical modeling, including a linear mixed-effects model to account for donor and passage variability.