Altered Immune Profiles in Non‐Failing Heart Donors With Induced Long QT Syndrome: A Potential Risk Factor for Cardiac Allograft Vasculopathy

ABSTRACT

Introduction

Cardiac allograft vasculopathy (CAV) develops more aggressively in recipients of hearts from brain‐dead (BD) donors with induced long QT syndrome (iLQTS), yet the underlying mechanisms remain poorly understood. In this study, we employ a multi‐omics and experimental framework to explore the role of neuro‐immune interactions in non‐failing donor hearts affected by iLQTS.

Methods

Single‐nuclei RNA sequencing (snRNA‐seq) compared four iLQTS and four non‐arrhythmic non‐failing donor hearts. Pathway enrichment and cell‐cell communication were assessed. Plasma proteome data from BD donors and neuronal differentially expressed (DE) genes were integrated via Omicsnet. Human BD donor transcriptomes data were analyzed for immune correlation.

Results

ILQTS hearts showed elevated T/mast cells and upregulated leukocyte migration/focal adhesion pathways. Neuronal signaling (NGF, HSPG) and adhesion molecules (ITGB1, LAMININ) drove immune trafficking. Integrative proteomics identified ITGB1 as a central hub linking neuronal DE genes to BD‐associated plasma proteins. Human validation linked activated CD4⁺ T cells/Th2 enrichment to prolonged QT intervals.

Conclusions

Neuro‐immune dysregulation and integrin signaling related T cell activation underlie iLQTS‐related heart donors. Targeting neuronal‐integrin crosstalk may reduce CAV progression and improve transplant outcomes.

Abbreviations

AMR

antibody‐mediated rejection

APC

antigen presenting cells

BD

brain dead

BMI

body mass index

CAV

cardiac allograft vasculopathy

CD

cluster of Differentiation

CNTN

contactin

DEG

differential expression genes

DSA

donor‐specific antibody

ECM

extracellular matrix

GEO

gene expression omnibus

GLM

generalized linear model

GRCh38

Genome Research Consortium human build 38

GSEA

gene set enrichment analysis

GSVA

gene set variation analysis

HSPG

heparan sulfate proteoglycan

ICAM

Intercellular adhesion molecule

iLQTS

induced long QT syndrome

ITGB1

integrin subunit beta 1

JAM

junctional adhesion molecule

KEGG

Kyoto Encyclopedia of Genes and Genomes

KNN

k‐nearest neighbor

LM22

Leukocyte matrix 22

LQTS

long QT syndrome

MSigDB

the molecular signature database

NGF

neuronal growth factor

PECAM1

plate endothelial cell adhesion molecule

PPI

protein‐protein interaction

PSM

propensity score matching

SMD

standard mean differences

snRNAseq

single‐nuclei RNA sequencing

ssGSEA

single‐sample gene set enrichment analysis

T3

triiodothyronine

T4

thyroxine

TBI

traumatic brain injury

TBST

tris‐buffered saline with 0.05% Tween‐20

UMAP

uniform manifold approximation and projection

1. Introduction

Cardiac allograft vasculopathy (CAV) remains a significant challenge for heart transplantation, affecting long‐term graft survival and patient outcomes [1]. A growing body of evidence suggests that donor‐related factors play a crucial role in the development and progression of CAV. Among these, brain death (BD) has been linked to several physiological disturbances that may predispose the transplanted heart to early dysfunction and accelerated vascular disease [2]. One notable cardiac manifestation associated with BD is the development of Long QT syndrome (LQTS), a condition characterized by prolonged ventricular repolarization on the electrocardiogram (ECG). Although LQTS is often genetic, in the context of BD, it arises as a secondary response to neurological injury. Importantly, hearts from donors with BD induced LQTS (iLQTS) have been associated with an increased risk of CAV in recipients, though the underlying mechanisms remain unclear [3].

The brain‐heart axis has emerged as a critical area of research, highlighting the bidirectional communication between the central nervous system and the cardiovascular system [4, 5]. Neurological insults during BD can lead to profound cardiovascular consequences, including arrhythmias, myocardial dysfunction, and even ischemic changes [6]. These effects are largely mediated through sympathetic overactivation (“catecholamine storm”), hormonal dysregulation, and systemic inflammation [7, 8]. In heart transplantation, BD donors represent the majority of available heart organs [9]. The cascade of events following BD includes initial hemodynamic instability, followed by immune activation within peripheral organs including the heart can exacerbate ischemia‐reperfusion injury and promote acute rejection [2, 10, 11, 12]. Despite these insights, reliable biomarkers predicting transplant outcomes based on donor status remain scarce [13, 14].

Recent studies have highlighted the importance of neuro‐immune signaling in modulating post‐transplant responses [15, 16]. Brain death can alter immune cell trafficking and function in peripheral organs, suggesting that immune changes within the donor heart—often referred to as “passenger leukocytes”—may influence post‐transplant outcomes [17, 18, 19]. For example, antigen‐presenting cells within the donor graft may activate recipient T cells via direct recognition pathways, contributing to acute cellular rejection [17]. Additionally, donor CD4⁺ T cells can recognize recipient B cells, a process known as “inverted recognition”, can trigger the production of donor‐specific antibodies and accelerate antibody‐mediated rejection (AMR), further contributing to CAV [20]. However, the specific molecular and cellular changes in donor hearts affected by BD iLQTS remain poorly understood. Although prolonged QT in BD donors is not currently a criterion for donor risk stratification in guidelines, identifying these alterations could offer new opportunities for improving donor management, refining allocation systems, and developing targeted therapies to prolong graft survival.

To address this gap, we performed single‐nuclei RNA sequencing (snRNA‐seq) on left ventricular tissue from unused BD donor hearts—comparing those with iLQTS to non‐arrhythmic controls [21]. Our analysis revealed distinct immune profiles in iLQTS donor hearts, including elevated levels of activated T cells. Integration with plasma proteomics data identified key neuronal‐immune interactions involving integrins, particularly ITGB1. Transcriptomic analysis of immune correlation in 166 human donor hearts supported our findings. Together, these results provide novel insights into how neuro‐immune crosstalk in iLQTS donor hearts may contribute to post‐transplant complications such as CAV and AMR, opening new avenues for diagnostic and therapeutic strategies.

2. Methods

2.1. Identification of Non‐Arrhythmic and iLQTS Left Ventricular BD Donor Sequencing Samples and Propensity Score Matching (PSM)

We searched for single cell/nuclei RNA sequencing (scRNA‐seq/snRNA‐seq) data of unused heart donors on the Gene Expression Omnibus (GEO) database with information on arrhythmia events and baseline clinical characteristics. One dataset, GSE183852, met our criteria. Given the differences in baseline characteristics among the snRNA‐seq samples, a 1:1 ratio propensity score matching (PSM) was applied before comparing the transcriptome between arrhythmic and non‐arrhythmic donor hearts, resulting in four healthy and four iLQTS samples for further bioinformatic analyses. Detailed sample filtering processes are available in the Supporting Information. No executed criminal donor organs were utilized in this study.

2.2. Single‐Nuclei RNA Sequencing Data Pre‐Processing and Analysis

Seurat object for snRNAseq dataset GSE183852 were downloaded for analysis of iLQTS and non‐arrhythmic donor heart (Figure 1A). Detailed description of snRNA‐seq analyses is provided in Supporting Information.

FIGURE 1.

Myocardial single‐nuclei profiling reveals increased T cell composition in BD donor hearts with iLQTS. (A) Entire workflow of current study. (B) Manually annotated cell type clusters according to previous knowledge (C) After PSM, unsupervised UMAP clustering of 13651 iLQTS in four samples and 16901 normal nuclei in four samples. (D) Highly expressed specific genes according to previously reported literature, Endothelial (VWF), myeloid (C1QC), T (CD3E), Pericyte (KCNJ8), Fibroblast (DCN), Smooth Muscle (MYH11), Cardiomyocyte (RYR2), Lymphatic Endothelial (CCL21), Neuronal (NRXN1), Mast (KIT). (E). Neighborhood graph illustrates the outcomes of the differential abundance testing using the Milo package. Each node in the graph signifies a neighborhood, with the color indicating the logarithmic fold change between iLQTS and Normal. Neighborhoods that are not significantly differentially abundant are depicted in white, and the size of each node is proportional to the count of cells within that neighborhood. The edges in the graph denote the presence of shared cells between different neighborhoods, and the arrangement of the nodes mirrors the UMAP positioning of the cells. (F) Beeswarm plot showing the distribution of log fold changes across neighborhoods that comprise cells from various cell type clusters. This comparison is made between iLQTS and Normal. Neighborhoods identified as differentially abundant are highlighted with color‐coding, and the cell types that exhibit significant differential abundance are explicitly labeled. (G) Per‐sample ratios of each cell types. (H) Statistical testing result of cellular ratios between both conditions. Wilcoxon rank sum test for continuous variables. p < 0.05 as significant.

2.3. Unused Heart Donors RNA Sequencing Data Acquisition and Processing

Human BD non‐failing left ventricles normalized and log‐transformed microarray data, which were sequenced by the Affymetrix Human Gene 1.1 ST array, were downloaded from the NCBI Gene Expression Omnibus (GEO) database using R package GEOquery through accession GSE57338. To investigate compositional changes underlying neuro‐inflammation in iLQTS BD heart donors, we employed several analytical approaches: single‐sample gene set enrichment analysis (ssGSEA) and gene set variation analysis (GSVA) were conducted to obtain pathway enrichment and leukocyte composition scores for correlation analysis; CIBERSORT digital sorting algorithm, utilizing the gene signature of Leukocyte matrix 22 (LM22) [22, 23], was used to obtain leukocyte composition scores for correlation analysis; cell enrichment analysis was performed using XCell [24], a computational method designed to estimate the abundance of various cell types within complex tissue samples using gene expression data. Permutations were run 1000 times in both ssGSEA and GSVA analyses.

2.4. Statistical Analysis

All data in Table S1 are presented as n (%) or mean ± SD. Between‐group differences in baseline characteristics were analyzed using a Pearson's Chi‐square for categorical variables and a Wilcoxon rank sum test for continuous variables. Analysis was performed using R statistical software, Version 4.1. A two‐sided 0.05 significance level was used for hypothesis testing. Correlations between pathways enrichment score and immune cell compositions were analyzed using Pearson correlation method.

3. Results

3.1. Baseline Characteristics of All and Propensity Score Matched BD Donor snRNAseq Heart Samples

We identified 20 BD donor left ventricular snRNAseq samples (healthy, N = 16 and iLQTS, N = 4) in a study which fulfilled our criteria for further downstream analyses. Baseline characteristic of the samples was shown in Table S1. Before adjusting the variables, we found that age of iLQTS were significantly higher compared to the other non‐arrhythmic healthy hearts (average 64 vs. 46 years, p = 0.037), whereas other baseline parameters including sex, BMI, hypertension, diabetes, chronic kidney disease, and smoking history were similar. This indicates that aged donors itself may represent an increased risk for BD related iLQTS, but require further validating. Owing to the discrepancy of baseline characteristics between the group, we performed PSM with all the parameters to remove potential confounding factors prior to subsequent transcriptome analyses (Table S1). We applied PSM with a 1:1 sampling ratio, leaving four normal samples without arrhythmia and four iLQTS samples with balanced baseline characteristics (SMD < 0.1 for all variables). Before and after PSM baseline characteristics comparing both groups were shown in Table S1.

3.2. Myocardial Single‐Nuclei Profiling Reveals Increased T Cell Composition in BD Donor Hearts With iLQTS

Here we present single nuclei study of propensity score matched non‐arrhythmic and iLQTS BD donor left ventricular samples. Brief workflow of the current study as shown in Figure 1A. A total of 30,552 number of nuclei (N _nuclei) from four normal (N _nuclei = 16,901) and four iLQTS (13,651) samples passed the quality control threshold and were included in our subsequent analysis (Figure 1B,C). Following this, we conducted UMAP dimensionality reduction to simplify the high‐dimensional data. Subsequently, we applied KNN‐clustering to identify distinct cellular populations. We manually annotated each cluster based on their highly expressed specific genes, as previously reported in the literature [25]. Specifically, we identified clusters as Endothelial (VWF), myeloid (C1QC), T cells (CD3E), Pericyte (KCNJ8), Fibroblast (DCN), Smooth Muscle (MYH11), Cardiomyocyte (RYR2), Lymphatic Endothelial (CCL21), Neuronal (NRXN1), and Mast cells (KIT) (Figure 1D). Absolute count of nuclei across all annotated cell types in each sample were shown in Table S2. We then compared cellular compositions between both conditions by using Milo differential abundance testing (Figure 1E,F). Interestingly, we found that the abundance of mast cells and T cells were increased in iLQTS compared to healthy donors. Furthermore, both cell types showed statistically significant differences in their ratios (Figure 1G illustrates the per‐sample ratios, while Figure 1H presents the statistical testing results).

3.3. Differential Pathway Analysis in iLQTS‐Affected Cell Types Show Immune Activation in iLQTS

To evaluate the transcriptional changes associated with BD iLQTS, we conducted a differential pathway analysis using the SCPA package with KEGG pathways. Significant alterations in pathway enrichment were observed across various cell types under different conditions (Figure 2A). Notably, T cells exhibited the most pronounced changes in pathways related to focal adhesion, tight junctions, natural killer cell‐mediated cytotoxicity, leukocyte transendothelial migration, adherens junctions, JAK‐STAT signaling, and chemokine signaling (Figure 2B). These findings support the hypothesis that the increased presence of T cells in iLQTS may be linked to these pathway alterations. To further elucidate the cellular and sample heterogeneity, we performed gene set enrichment analysis on all cells using the ESCape UCell method. Consistent with our expectations, the focal adhesion and leukocyte transendothelial migration pathways were significantly upregulated in iLQTS compared to normal samples, as depicted in Figure 2C–F. To ascertain the pathways associated with increased allograft rejection due to heightened T cell infiltration, we performed correlation tests between various pathways under both conditions. Notably, the focal adhesion pathway exhibited a stronger correlation with the transendothelial leukocyte migration pathway in both normal (R = 0.26) and iLQTS (R = 0.32) states (Figure 2G). Similarly, a significant correlation was observed between the focal adhesion pathway and allograft rejection pathways in normal (R = 0.35) and iLQTS (R = 0.41) conditions (Figure 2H). Consistent with our expectations, the enhanced transendothelial leukocyte migration pathway was predominantly driven by the increased chemokine signaling pathway, with correlation coefficients of 0.33 in normal and 0.43 in iLQTS samples (Figure 2I).

FIGURE 2.

Differential pathway analysis in iLQTS‐Affected Cell types (A) Heatmap clusters of Qvals generated by SCPA after comparing iLQTS with Normal samples in each cell types. (B) Heatmap of Qvals generated by SCPA comparing LQTS with Normal T cells, ranked by decreasing Qvals. (C and D) Ridgeplot of KEGG focal adhesion and leukocyte transendothelial migration pathway in all cell types of both conditions. (E and F) Density enrichment of KEGG focal adhesion and leukocyte transendothelial migration pathway using escape method in all samples. (G–I) Scatterplot correlation between several interested pathways normalized enriched score using escape method in LQTS and Normal. Each scatterplot compares the enrichment scores between the two conditions, allowing for the fassessment of significant differences in pathway activity. The correlation coefficients and p values are displayed to indicate the strength and significance of the relationships observed. Correlation was determined using the Pearson correlation test. p < 0.05 as significant.

3.4. Cell Receptor‐Ligand Comparison Analysis Reveal Significant Increase in Neuronal Signaling Associated With Inflammation and Immune Cell Trafficking Through Adhesion Molecules in iLQTS

To determine whether cellular receptor‐ligand communication differs between iLQTS and healthy BD donors, we implemented CellChat multiple condition comparison analysis. In Figure 3A, it was shown that overall number of pathway interactions (1146 vs. 1494) and their strength (0.092 vs. 0.115) substantially increases in LQTS compared to normal donors. Cell‐type specific differential interactions showed that non‐immune cells including Pericyte, Myeloid and VSMC emerged as top differential communicating senders (Figure 3B) while Neuronal and Cardiomyocyte acts as top differential receiver in number of interactions. Evidently, Neuronal showed the highest received differentially increased in communication number and strength (Heatmap of Figure 3B, red indicates increased communication while blue indicates decreased in iLQTS dataset), indicating the iLQTS abnormality was indeed driven by neuronal changes. At the same time, significant differences were also observed in Cardiomyocyte to Lymphatic Endothelial signaling (Figure 3B), Next, we ranked all the significant pathways based on differences in the overall information flow within the inferred networks between iLQTS and healthy donor hearts (Figure 3C). Interestingly, pathways associated with neuronal regulation of immunity such as Heparan Sulfate Proteoglycan (HSPG), Epidermal Growth Factor (EGF), Adrenaline, neuronal growth factor (NGF), Contactin (CNTN), adhesion molecules including Junctional Adhesion Molecules (JAM), Intercellular Adhesion Molecule (ICAM), Platelet Endothelial Cell Adhesion Molecule (PECAM1), as well as immune activation signaling such as C–C motif ligand 2 (CCL), MHC‐II and CD99 were also specific to iLQTS samples. The overall ingoing and outgoing signaling were shown in Figure 3D.

FIGURE 3.

Cell receptor‐ligand comparison analysis reveals significant increase in Neuronal signaling associated with immune modulation in LQTS. (A) Comparison of the total number of pathway interactions and their strength between iLQTS and normal donor hearts, indicating a significant increase in iLQTS. The metrics were generated using the CellChat package, where ‘N’ denotes the number of interactions. (B) Heatmaps display cell‐type specific differences in the number and strength of interactions. Notably, Neuronal cell (receiver, X axis) communication increased (red) substantially in LQTS compared to normal donors. (C) Ranking of significant pathways based on differences in information flow within inferred networks. Pathways implicated in LQTS specific networks were related to growth factor signaling (NGF, FGF), ECM and Integrin signaling (LAMININ, HSPG, WNT, etc), adhesion molecule (LAMININ, JAM, CNTN, ICAM, PECAM1, NCAM), chemokine (CD30, CXCL, etc), immune signaling (LIGHT, SEMA4) as well as antigen presenting (MHC‐II) attributed to possible neuro‐modulation of immune system and increased lymphocytes migration in iLQTS samples. (D) Overall incoming and outgoing signaling changes associated with iLQTS hearts. Number of interactions represents significant ligand‐receptor pairs identified between each pair of cell populations. Interaction Strength (Weight) is calculated as the product of the expression levels for each ligand‐receptor pair, indicative of their role in cell communication. Role of Fibroblast, Neuronal, Lymphatic Endothelial and Endothelial cell in modulating immune cell trafficking through adhesion molecules in iLQTS. (A) Major signaling changes of Fibroblast, Endothelial, Neuronal and Lymphatic Endothelial revealed ECM (COLLAGEN, FN1) and integrin signaling (LAMININ), adhesion molecules (PECAM1, NCAM), and neurogenesis (NEGR) as well as cellular proliferation (IGF, EGF) associated with LQTS. (B) Shows an increase in specific ligand‐receptor interactions where Neurons act as receivers in iLQTS. This highlights the potential role of neuronal cells in receiving signals that may influence immune cell trafficking. (C) Demonstrates an increase in specific ligand‐receptor interactions with T cells as receivers in iLQTS. This finding underscores the potential role of T cells in iLQTS‐related immune responses. The analysis was conducted using the CellChat package, which quantifies cell‐cell communication based on the expression levels of ligand‐receptor pairs. The strength of interactions is represented by the product of the expression levels, and statistical significance was determined using the Benjamini‐Hochberg procedure for multiple hypothesis testing correction.

A detailed examination of the signaling changes in Fibroblasts, Neurons, Endothelial cells, and Lymphatic Endothelial cells between normal and iLQTS samples, as depicted in Figure S1A, reveals a notable increase in the signaling strength and interaction of key molecules. Specifically, there is enhanced signaling associated with LAMININ, a component of the extracellular matrix, and adhesion molecules such as PECAM1 and NCAM. These molecules are crucial for cell‐cell interactions and immune cell trafficking.

Figure S1B presents a closer look at the increased ligand‐receptor interactions where Neuronal cells are the receiving cells. This suggests that Neuronal cells may be actively involved in modulating immune responses in iLQTS. Figure S1C further illustrates the heightened interactions with T cells as receivers, indicating a potential direct role of neuronal signaling in the recruitment and activation of T cells. Altogether, our findings lead us to hypothesize that Neuronal cells in iLQTS may play a pivotal role in regulating immune cell trafficking and inflammatory responses. This regulation appears to be mediated, at least in part, by the expression of neuronal and endothelial adhesion molecules. By influencing the adhesive interactions between immune cells and the vascular endothelium, these neuronal cells may modulate the infiltration of immune cells into tissues, thereby impacting the overall immune response in iLQTS. This hypothesis is supported by the observed changes in adhesion molecule signaling and suggests that targeting these pathways could potentially offer a novel therapeutic strategy for modulating immune responses in iLQTS.

3.5. Integrated Analysis of Differentially Expressed BD Donor Plasma Proteome and Neuronal DE Genes Revealed Integrin Cell Surface Interactions as Important Regulatory Pathway Implicated in iLQTS

To further by examining how BD impacts these pathways at the proteome level. A total of 463 proteins were identified in their study using label‐free protein quantification using high‐definition mass spectrometry. Using adjusted p value < 0.05 as significant, we identified 119 up‐regulated and 124 down‐regulated plasma proteins (Figure S2A). Through our single nuclei analysis, we hypothesized a major role of Neuronal in driving the molecular changes implicated in BD related iLQTS. Thus, we sought to explore the relationship between BD plasma proteins and Neuronal in iLQTS in order to determine potential cause and therapeutic targets. To accomplish this, we used an online multi‐omics analysis platform, Omicsnet 2.0 [26], which gave clear visualization of biological network visualization using DE BD donor plasma proteome and Neuronal gene expression with protein‐protein interaction (PPI) network framework as shown in Figure S2B (Pink nodes as plasma protein and Blue nodes as Neuronal DE genes). ITGB1 (Integrin Subunit Beta 1) emerged as gene with highest connectivity (Figure S2B; increasing node size implicate higher connectivity). Through Reactome and KEGG database enrichment, relevant pathways were enriched. Integrin cell surface interactions, axon guidance, PDGF and NGF signaling, gap junction, apoptosis, as well as innate immune system and cytokine‐cytokine receptor interaction pathways (Figure S2C).

3.6. Enhanced Activation of T Cells in iLQTS Non‐Failing Heart Donors and Validation Using 166 BD Non‐failing Heart Donors From MAGNET Consortium

To determine whether T cell states were altered in iLQTS, we conducted a subcluster analysis of T lymphocyte clusters and identified five primary subclusters: CD8⁺, Activated CD4⁺, NK, Naïve CD4⁺, and a minor proportion of B cells (Figure 4A). Specifically, Naïve CD4⁺ cells were distinguished by their higher expression levels of LEF1 and CCR7, while Activated CD4⁺ cells exhibited increased expression of CD44, IL7R, and CD40LG. CD8⁺ and NK cells were identified by the expression of CD8A and NCAM1, respectively (Figure 4B). Proportion analysis of cell types within the T cell compartment revealed a trend of increased CD8⁺ and Activated CD4⁺ cells in iLQTS compared to normal (Figure 4C). However, due to the limited sample size in the snRNA‐seq data, we sought to validate these findings using bulk RNA sequencing data from the MAGNET consortium, which includes 166 samples from non‐failing heart donors. First, we utilized validated prolonged QT‐related gene sets, “HP_PROLONGED_QT_INTERVAL” and “HP_ABNORMAL_QT_INTERVAL”, from the Human Phenotype Ontology (HP) database, available in MSigDB, to delineate the possibility of iLQTS in the donor hearts. As expected, iLQTS exhibited increased enrichment scores for both gene sets in the snRNA‐seq samples, thus confirming the practicality for utilizing the genesets (Figure 4D). Subsequently, we identified pathways associated with leukocyte trafficking, including integrin cell surface interaction, focal adhesion, and gap junction, which were activated in iLQTS donor hearts. We tested the correlations between their enrichment scores and CIBERSORT immune cell proportion scores for the 22 leukocyte groups with both prolonged QT‐associated gene sets in the donor heart RNA sequencing samples (Figure 4E). Notably, “Integrin cell surface interactions” showed a high correlation with “T cells CD4 memory resting” (R = 0.57; p < 0.001), “B cells naïve” (R = 0.27; p < 0.001), “Macrophages M2” (R = 0.35; p < 0.001), “Allograft rejection” (R = 0.52; p < 0.001), and “Focal adhesion” (R = 0.33; p < 0.001) (Figure 4E). Additionally, we observed a significant mild to moderate correlation between HP_PROLONGED_QT_INTERVAL and T cells CD4 memory activation (R = 0.19; p < 0.013) and FOCAL_ADHESION_ASSEMBLY (R = 0.29; p < 0.001) (Figure 4F).

FIGURE 4.

Enhanced activation of CD4+ T cells in LQTS^high non‐failing heart donors. (A) UMAP sub‐clustering of nuclei from T cell clusters, with distinct colors representing five major cell types. Each dot corresponds to an individual nucleus. (B) Dot plot displaying the expression of marker genes across different cell types. (C) Boxplot illustrating the proportion of T cell clusters under LQTS and Normal conditions. (D) Dotplot showing enrichment of HP_PROLONGED_ QT_INTERVAL and ABNORMAL_QT_INTERVAL across condition._(E) Validation of bulk RNA‐sequencing data using MAGNET Consortium non‐failing donors through ssGSEA and Cibersort correlation analysis. (F) Scatterplot depicting the correlation between the HP_PROLONGED_QT_INTERVAL enrichment score with activated CD4+ memory T cells and GOBP_FOCAL_ADHESION_ASSEMBLY. Distinct colors represent the LQT^high and LQT^low groups, delineated by the median expression of HP_PROLONGED_QT_INTERVAL. (G) Boxplot comparing the scores of LM22 T lymphocytes between LQT^high and LQT^low groups, as determined by the CIBERSORT digital sorting algorithm. (H) Boxplot comparing the scores of Th2 and NKT lymphocytes between LQT^high and LQT^low groups, as assessed by XCell cell type enrichment analysis. Correlations were calculated using Pearson's correlation coefficient, with p < 0.05 considered statistically significant. Pathway enrichment scores were derived using single‐sample gene set enrichment analysis (ssGSEA) in the gene set variation analysis (GSVA) software. Statistical comparisons between groups were performed using the T test, with p < 0.05 indicating significance.

To further explore these differences, we stratified the RNA‐seq samples based on the median expression of HP_PROLONGED_QT_INTERVAL into LQT^high and LQT^low groups. Consistently, T cells CD4 memory activated were significantly increased in the LQT^high group, with a trend for increased T cells follicular helper, although this was not statistically significant (Figure 4G). Due to the limited proportion of T cell subsets in the heart, we employed XCell cell enrichment analysis, which calculates enrichment scores rather than percentages, to further investigate differences in T cell subsets. Using this approach, we found a significant increase in Th2 enrichment scores in the LQT^high group (Figure 4H). In summary, our findings demonstrate that the functional states of T cells are altered in snRNA‐seq samples, and these observations were validated using bulk RNA sequencing data, indicating an association between increased an activated CD4⁺ T cells and the occurrence of iLQTS in human heart donors.

4. Discussion

Although BD donors were known to induce a poor prognosis in heart transplantation when certain conditions such as inflammation or arrhythmia were met, the underlying mechanisms have been understudied [3, 6, 27, 28]. In the present study, we analyzed the cellular composition, transcriptional changes, as well as dissected the cellular interaction of BD donor hearts presenting with iLQTS and without arrhythmia. Our results revealed that iLQTS donor hearts exhibit a distinct immune profile, characterized by increased infiltration of T cells—particularly activated CD4⁺ and CD8⁺ subsets. This aligns with previous evidence suggesting that donor‐derived CD4⁺ T cells may accelerate graft failure via a process called inverted recognition, where donor T cells recognize recipient B cells, triggering the production of donor‐specific antibodies and promoting chronic antibody‐mediated rejection (AMR), which contributes to CAV [19, 20, 29]. Bioinformatic analyses further demonstrated that the myocardial microenvironment in iLQTS is associated with heightened immune activation. This appears to be driven by neuronal signaling pathways that regulate inflammation and leukocyte trafficking through integrins and adhesion molecules. These findings are consistent with established neuro‐immunological mechanisms, where the brain communicates with peripheral immune systems through multiple pathways—including sympathetic and parasympathetic nerves, endocrine signals, and meningeal lymphatics [15, 30]. In models of cerebral injury such as stroke, microglia rapidly adopt an activated phenotype, releasing inflammatory mediators that recruit neutrophils and macrophages, leading to sterile inflammation and tissue swelling. Subsequently, T cells infiltrate the affected area through endothelial activation markers like P‐selectin and VCAM1. Our data suggest a similar cascade occurs in the heart following BD: we observed upregulation of inflammatory pathways, increased expression of integrins (notably ITGB1), and enhanced immune cell infiltration in iLQTS donor hearts. Prior studies have also shown that the severity of BD particularly in cases involving traumatic injury is linked to earlier development of CAV compared to other causes of BD [3]. Importantly, many of these severe BD cases progress to develop LQTS [31, 32].

4.1. Immune Activation in LQTS Donor Hearts

Despite their small overall proportion in the myocardium, T cells showed notable differences between iLQTS and non‐LQTS donor hearts. Specifically, subcluster analysis revealed a significant increase in both CD8+ T cells and activated CD4⁺ T cells in iLQTS samples. This suggests a state of heightened immune activation within the iLQTS myocardium, which may contribute to the pathophysiology of the condition. We validated these findings using bulk RNA‐seq data from the MAGNET consortium and leveraged gene sets related to prolonged QT intervals from the Human Phenotype (HP) database. This allowed us to confirm the presence of iLQTS in donor hearts and provided a robust foundation for downstream analyses. Pathway enrichment identified key processes involved in leukocyte trafficking, including integrin signaling and focal adhesion. These pathways may facilitate immune cell migration and retention in the heart, potentially priming the graft for adverse immune responses post‐transplant. Further stratification of samples based on expression levels of HP_PROLONGED_QT_INTERVAL genes (into LQThigh and LQTlow groups) revealed a significant enrichment of Th2‐related gene signatures in the LQThigh group. This shift toward a Th2‐biased immune environment may have important implications for post‐transplant immunity, as Th2 cells can promote B cell activation and antibody production—potentially accelerating AMR and CAV development via inverted recognition.

4.2. Clinical Implications and Future Directions

Our findings suggest that neuro‐immune interactions in BD donor hearts particularly those with iLQTS may play a previously underappreciated role in shaping post‐transplant outcomes. Although current clinical guidelines do not consider QT prolongation alone as a criterion for donor risk stratification, our data indicate the increased presence of activated T cells (especially CD4⁺ subsets) in these hearts may predispose recipients to early‐onset CAV through AMR. This suggests the need for reevaluation of donor selection criteria for BD donors with marked QT prolongation, and exploration of targeted immunomodulatory therapies in high‐risk cases. However, several limitations must be acknowledged. First, despite propensity score matching (PSM) to enhance group comparability, our sample size remains relatively small—particularly when analyzing rare cell populations. This limitation stems from the scarcity of high‐quality donor heart tissues and ethical constraints surrounding their use. Future studies should aim to replicate our findings in larger cohorts or complement snRNA‐seq with flow cytometry or immunohistochemical validation. Second, while our data support a plausible link between donor LQTS and post‐transplant AMR/CAV, prospective clinical studies or murine transplant models will be necessary to establish causality. Such investigations would provide critical evidence to guide donor risk stratification and inform the development of novel interventions aimed at improving long‐term graft survival.

5. Conclusion

In summary, our study identifies a unique immune signature in iLQTS donor hearts, characterized by increased T cell infiltration and activation, particularly of CD4⁺ subsets. These changes appear to be influenced by neuronal‐integrin signaling pathways, linking neurological injury to immune dysregulation in the heart. Our findings offer new mechanistic insights into how BD‐induced LQTS may drive post‐transplant complications, particularly CAV mediated by antibody responses. Targeting these pathways may open avenues for improving donor heart viability and recipient outcomes.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Supporting File 1: ctr70286‐sup‐0001‐SuppMat.docx

Supporting File 2: ctr70286‐sup‐0002‐FigureS1.pdf

Supporting File 3: ctr70286‐sup‐0003‐FigureS2.pdf

Wen Z., Shao S., Feng Y., et al. “Altered Immune Profiles in Non‐Failing Heart Donors With Induced Long QT Syndrome: A Potential Risk Factor for Cardiac Allograft Vasculopathy.” Clinical Transplantation 39, no. 8 (2025): 39, e70286. 10.1111/ctr.70286

Data Availability Statement

The datasets presented in this study can be found in online GEO repositories with accession and GSE183852 for single‐nuclei RNA sequencing data and GSE57338 for unused heart donor microarray gene expression data. BD donor plasma proteome data can be found on previously published literature [14].