Fig 1.
Public single cell RNA-seq data of pulmonary artery hypertension lung and its reanalysis.
(A) Schematic of the PAH-former development pipeline. We created a new AI tool that understands the gene expression network of pulmonary arterial hypertension (PAH) by fine-tuning Geneformer, a transfer learning tool that has been trained on 1 billion single-cell analysis data, with publicly available PAH single-cell analysis data. The aim is to extract the genes that are involved in PAH. scRNA-seq, single-cell RNA sequencing; t-SNE, t-distributed stochastic neighbor embedding; PAH, pulmonary arterial hypertension. (B) t-SNE plot showing cell type prediction. Cell type annotation of the GSE169471 data was performed using CellTypist v2.0. t-SNE, t-distributed stochastic neighbor embedding. (C) t-SNE plot showing sample type distribution. CON, control; IPAH, idiopathic pulmonary artery hypertension; t-SNE, t-distributed stochastic neighbor embedding. (D) t-SNE plot visualizing SOX18 expression levels. t-SNE, t-distributed stochastic neighbor embedding.
Fig 2.
Dataset selection for the fine-tuning of Geneformer.
(A) Datasets used for Geneformer model training. This table outlines the composition of training data for each model. HLCA, human Lung Cell Atlas. (B, C) Prediction likelihood heatmap for cell classification and confusion matrix of Model A. (D, E) Prediction likelihood heatmap for cell classification and confusion matrix of Model B. (F, G) Prediction likelihood heatmap for cell classification and confusion matrix of Model C. CON, control; IPAH, idiopathic pulmonary artery hypertension.
Fig 3.
In silico perturbation using PAH-former and extraction of disease-related genes.
(A) The workflow for in silico perturbation analysis using the fine-tuned Geneformer model (PAH-former). In silico manipulation (deletion or overexpression) results in shifts in cell embedding (representing cell state). IPAH, idiopathic pulmonary artery hypertension. (B-E) Gene Ontology (GO) analysis of candidate genes identified by in silico perturbation analysis by PAH-former and top 40 genes that shift cell embedding most for each of the four directions. (B) Gene Ontology (GO) analysis for candidate genes whose in silico deletion shifts the cell state towards IPAH. (C) Gene Ontology (GO) analysis for candidate genes whose in silico overexpression shifts the cell state towards control. (D) Gene Ontology (GO) analysis for candidate genes whose in silico overexpression shifts the cell state towards IPAH. (E) Gene Ontology (GO) analysis for candidate genes whose in silico deletion shifts the cell state towards control.
Fig 4.
In Silico perturbation in pulmonary endothelial cells using PAH-former and in vitro validation.
(A) Table showing partial lists of genes identified in endothelial cells by Differential Gene Expression (DEG) analysis (FDR < 10%) in the original article and genes enriched in the GO (gene ontology) pathways of blood vessel development pathway and cardiovascular development pathway. (B) Venn diagram comparing the number of candidate genes identified by Differential Gene Expression (DEG) analysis in the original article and the number of disease-associated candidate genes identified by PAH-former. PAH, pulmonary arterial hypertension. (C) Knockdown experiments of selected candidate genes (S100A6, HSP90AA1, TXNIP, and MT2A) using siRNA in Human Pulmonary Artery Endothelial Cells (HPAECs). Top panels show the knockdown efficiency of each target gene relative to control siRNA, displaying relative mRNA expression levels at 48 hours and indicating successful knockdown (data normalized to GAPDH expression, n = 9). Bottom panels show the relative mRNA expression levels of SOX18 at 48 hours after knockdown of each candidate gene compared to control siRNA (data normalized to GAPDH expression, n = 9). Graphs are presented as median with interquartile range (IQR). Statistical significance is indicated as: * p < 0.05, ** p < 0.01, **** p < 0.0001, ns: not significant.