Abstract

BACKGROUND:

   The immune system’s role in ST-segment–elevated myocardial infarction
   (STEMI) remains poorly characterized but is an important driver of
   recurrent cardiovascular events. While anti-inflammatory drugs show
   promise in reducing recurrence risk, their broad immune system
   impairment may induce severe side effects. To overcome these
   challenges, a nuanced understanding of the immune response to STEMI is
   needed.

METHODS:

   For this, we compared peripheral blood mononuclear single-cell
   RNA-sequencing (scRNA-seq) and plasma protein expression over time
   (hospital admission, 24 hours, and 6–8 weeks post-STEMI) in 38 patients
   and 38 controls (95 995 diseased and 33 878 control peripheral blood
   mononuclear cells).

RESULTS:

   Compared with controls, classical monocytes were increased and CD56^dim
   natural killer cells were decreased in patients with STEMI at admission
   and persisted until 24 hours post-STEMI. The largest gene expression
   changes were observed in monocytes, associating with changes in
   toll-like receptor, interferon, and interleukin signaling activity.
   Finally, a targeted cardiovascular biomarker panel revealed expression
   changes in 33/92 plasma proteins post-STEMI. Interestingly,
   interleukin-6R, MMP9 (matrix metalloproteinase-9), and LDLR
   (low-density lipoprotein receptor) were affected by coronary artery
   disease–associated genetic risk variation, disease status, and time
   post-STEMI, indicating the importance of considering these aspects when
   defining potential future therapies.

CONCLUSIONS:

   Our analyses revealed the immunologic pathways disturbed by STEMI,
   specifying affected cell types and disease stages. Additionally, we
   provide insights into patients expected to benefit most from
   anti-inflammatory treatments by identifying the genetic variants and
   disease stage at which these variants affect the outcome of these
   (drug-targeted) pathways. These findings advance our knowledge of the
   immune response post-STEMI and provide guidance for future therapeutic
   studies.

   Keywords: coronary artery disease, immunity, single-cell gene
   expression analysis, ST-segment elevation myocardial infarction
     __________________________________________________________________

   See Editorial by [48]Yarahmadi & Nguyen

   ST-segment–elevated myocardial infarction (STEMI) is a major cause of
   mortality and morbidity. The immune system is crucial during both
   atherosclerotic plaque formation and rupture and in the consequent
   inflammatory response.^[49]1,[50]2 Targeting this response has remained
   challenging due to its complexity and severe side effects, as seen in
   the CANTOS (Canakinumab Anti-inflammatory Thrombosis Outcome Study;
   anti-IL-1ß) and ASSAIL-MI (ASSessing the effect of Anti-IL-6 treatment
   in MI; anti-IL6R) trials.^[51]3,[52]4 To overcome these challenges, the
   pathophysiological mechanisms post-STEMI should be studied in greater
   molecular and cellular detail.^[53]5,[54]6

   Previously, studying cell types relied on bulk analyses using
   predefined marker genes. Single-cell RNA-sequencing (scRNA-seq) now
   facilitates unbiased transcriptome-wide analysis of 100 000s of
   individual cells simultaneously.^[55]7,[56]8 This technology reveals
   new insights into the inflammatory response post-STEMI by concurrently
   mapping changes in cell type composition, gene expression level, and
   downstream pathways at various cellular resolutions.

   Both normal physiological processes in the heart^[57]9 and blood,^[58]8
   and pathophysiological processes during atherosclerosis,^[59]10,[60]11
   and neovascularization,^[61]12 have been studied at the single-cell
   level. A spatial multi-omics map of the heart upon STEMI revealed an
   increased dependency between lymphoid and myeloid cells in ischemic
   samples compared with healthy controls, indicating their communication
   during cardiac repair.^[62]13 While many aspects of STEMI have been
   explored, a detailed single-cell view of how circulating immune cells
   are affected during acute and chronic phases remains lacking.

   Here, we compared scRNA-seq data of 95 995 peripheral blood mononuclear
   cells (PBMCs) from 38 patients with STEMI (during hospital admission
   [t0]), 24 hours [t24h], and 6–8 weeks [t8w] post-STEMI) to 33 878 PBMCs
   from 38 age- and sex-balanced general population controls. This
   revealed large changes in cell type composition, gene and plasma
   protein expression levels, and cell-cell communication, both compared
   with controls and post-STEMI ([63]Figure S1). Additionally, we found
   key plasma proteins affected by a combination of genetics, disease
   status, or disease phase. Altogether, this study presents the first
   single-cell view of the circulating immune system during STEMI and
   emphasizes the importance of considering person- and disease-related
   characteristics to fully grasp the underlying molecular changes.

METHODS

   Methods are available in the [64]Supplemental Material. The ethics
   committee of the University Medical Center Groningen (Medical Ethical
   Review Committee-UMCG-2012/296) approved this study. All patients
   provided written, informed consent.

RESULTS

Patient Characteristics

   To dissect the immune response post-STEMI, PBMCs and plasma from 38
   patients with a first STEMI were collected at t0, t24h, and t8w (Figure
   [65]1). Patients with STEMI were mostly male (84%), 60±11 years old,
   with a median body mass index of 26.4±3.5 kg/m^2. Most patients
   presented with 1 stenosed vessel (55%), with 39% showing complete
   vessel occlusion (Table; [66]Table S1). These patients were compared to
   previously published scRNA-seq data from 38 age- and sex-balanced
   controls in the LifeLines DEEP cohort.^[67]7

Figure 1.

   [68]Figure 1.
   [69]Open in a new tab

   Study overview. A, Selection of patients with STEMI (CardioLines
   biobank) and controls (LifeLines DEEP biobank). B, Experimental setup.
   CABG indicates coronary artery bypass graft surgery; CVD,
   cardiovascular disease; HC, healthy control; PBMC, peripheral blood
   mononuclear cell; SCAD, coronary artery bypass graft surgery;
   scRNA-seq, single-cell RNA-sequencing; and STEMI, ST-segment–elevated
   myocardial infarction.

Table.

   Baseline Characteristics

   graphic file with name hcg-17-e004374-g001.jpg
   [70]Open in a new tab

Single-Cell Profiling of Immune Cells in Patients With STEMI and Controls

   Collected PBMCs were used for 10X Genomics scRNA-seq analysis using v2
   and v3 chemistries. We captured an average of 842
   cells/individual/condition after quality control in patients with STEMI
   (v2: 831 genes/cell, v3: 1548 genes/cell) and 891 cells/individual in
   controls (v2: 1012 genes/cell, v3: 1931 genes/cell; [71]Table S2). To
   annotate donors to cells and identify doublets, genotype-dependent
   demultiplexing was performed, revealing 1 incorrectly omitted
   donor-time point combination (t8w) and 10.0% doublets on average.
   Quality control was performed separately per chemistry due to technical
   differences. After quality control, 129 873 cells (95 995 diseased and
   33 878 control) remained in the final data set. K-nearest neighbors
   clustering was performed on the normalized, integrated count data,
   allowing the identification of cell types.

Monocytes and Natural Killer Cells Show Compositional Changes Post-STEMI

   We identified 10 major cell types in the PBMCs of patients with STEMI
   and controls, including B, CD4T, CD8T, dendritic cell, hematopoietic
   stem and progenitor cell, monocyte, natural killer cell (NK),
   plasmablast, platelet, and other T cells (Figure [72]2A). These major
   cell types could be split into 29 minor cell populations (Figure
   [73]2B; [74]Supplemental Methods).

Figure 2.

   [75]Figure 2.
   [76]Open in a new tab

   Cell type composition changes. Peripheral blood mononuclear cells
   (PBMCs) of patients with ST segment–elevated myocardial infarction
   (STEMI) (t0, t24h, and t6–8w) and controls presented in uniform
   manifold approximation and projections (UMAPs): A, showing the 10
   major; B, 29 minor cell types. C, Proportions of monocytes (major class
   and its 2 subtypes) and (D) natural killer (NK) cells (major class and
   its 2 subtypes) in controls and post-STEMI. ASDC indicates AXL SIGLEC6
   dendritic cell; C, control; cMono, classical monocytes; CTL, cytotoxic
   T cell; cDC, conventional dendritic cell; HSPC, hematopoietic stem and
   progenitor cell; ILC, innate lymphoid cell; MAIT, mucosal associated
   invariant T cell; ncMono, nonclassical monocytes; pDC, plasmacytoid
   dendritic cell; TCM, T-central memory; and TEM, T-effector memory.
   Significant Holm’s adjusted P value *P<0.05, **P<0.01, ***P<0.001. Each
   donor’s proportion depicts a cell count-weighted data point, each
   boxplot the weighted median, 25th and 75th percentile.

   To conduct a robust cell type composition analysis, we initially
   focused our analyses on the 6 most abundant cell types ([77]Table S3),
   after which we zoomed in on the subtypes for the significant changes
   (Figure [78]2C and [79]2D). Compared with controls, monocytes from
   patients with STEMI during admission increased (Holm’s adjusted
   P=6.0×10^−5) and NK cells decreased their relative abundance (Holm’s
   adjusted P=0.029; Figure [80]2C and [81]2D; [82]Table S4). These
   changes primarily stemmed from compositional shifts within the
   classical monocytes (Holm’s adjusted P=6.0×10^−5) and the NKdim (Holm’s
   adjusted P=0.040) subtypes (Figure [83]2C and [84]2D; [85]Table S4).
   Similarly, during the disease course, we observed composition changes
   in monocytes (decrease t0–t8w: Holm’s adjusted P=0) and NK cells
   (decrease t0–t24h: Holm’s adjusted P=3.6×10^−2) that stemmed from
   changes in classical monocytes (Holm’s adjusted P=8.0×10^−5) and NKdim
   (Holm’s adjusted P=0.047; Figure [86]2C and [87]2D). Additionally, CD4T
   cells exhibited an increase (t0–t8w: Holm’s adjusted P=2.2×10^−2;
   [88]Figure S4) that could not be attributed to a specific cell subtype
   ([89]Table S4). These analyses indicate the importance of analyzing
   compositional changes at higher resolution.

Largest Gene Expression Changes Occur in Monocytes

   To assign gene expression differences (DE) at STEMI t0 versus controls,
   we conducted pseudobulk DE analysis on the 6 major cell types using
   Limma Dream ([90]Table S5). CD4T cells (690 genes) and monocytes (681
   genes) showed the largest and B cells the least amount of change (62
   genes; Figure [91]3A). Overall, more genes were downregulated (Figure
   [92]3A), and the majority were uniquely identified in monocytes (Figure
   [93]3B). We then split the DE genes into an up- or downregulated set
   and conducted pathway enrichment analysis separately ([94]Table S6).
   The largest enriched pathway category was the immune response
   ([95]Figure S6). Therefore, we focused on this category and observed
   that the strongest enrichment in monocytes was found among the
   upregulated DE genes; both pro- (IL-1) and anti-inflammatory (IL-4,
   IL-10, and IL-13) interleukins, chemokines, and the CLEC7A inflammasome
   pathway (acting upstream of IL-1) were enriched (Figure [96]3E and
   [97]3F).

Figure 3.

   [98]Figure 3.
   [99]Open in a new tab

   Enriched differential expression (DE) genes and pathways. A, The number
   of up- and downregulated DE genes/cell type at ST-segment–elevated
   myocardial infarction (STEMI) t0 (n=37) vs controls (n=38) or (C) in
   patients with STEMI over time (n=37 t24h, n=38 t8w). B, Overlap of DE
   genes in cell types at t0 vs controls or (D) in patients with STEMI
   over time (taking all DE genes significant in at least one of the
   comparisons [t0–t24h or t0–t8w]). E, Top enriched inflammatory pathways
   in monocytes between conditions. F, Heatmap showing the LFC of the
   maximum 10 most significant up and down DE genes in monocytes that are
   involved in the immune system pathway. Genes are hierarchically
   clustered and the LFC is only provided for significant differences. C
   indicates control; DC, dendritic cells; logFC, log fold change; and NK,
   natural killer cells.

   Subsequent comparisons of DE post-STEMI revealed a general decrease in
   the number of DE genes from t24h to t8w (Figure [100]3C). Again,
   monocytes, but not CD4T cells, had the largest amount of total and
   unique DE genes post-STEMI (Figure [101]3C and [102]3D; [103]Table S5).
   Similarly, these upregulated genes in monocytes were mostly enriched
   for immune response pathways (Figure [104]6S). During the disease
   course, the enrichment for anti-inflammatory pathways remained stable
   (IL-4 and IL-13) or further increased (IL-10), whereas genes involved
   in monocyte chemoattraction peaked at t24h (CCL7, CX3CR1; Figure
   [105]3E and [106]3F). Together with, but independently of, the observed
   cell type composition changes (Figure [107]2C and [108]2D), these
   results indicate that monocytes contribute significantly to the
   immunologic changes observed post-STEMI.

Figure 6.

   [109]Figure 6.
   [110]Open in a new tab

   Plasma expression/protein quantitative trait loci (pQTLs) in patients
   with ST-segment–elevated myocardial infarction (STEMI) vs controls show
   both condition-dependent and condition-independent changes. Plasma
   pQTLs in 38 patients with STEMI and 1142 general population
   controls.^[111]7 In A, a pQTL is highlighted that is universal (i.e.
   found in both STEMI patients and the general population), whereas in B
   a pQTL is highlighted that is disease-specific (i.e. only in STEMI
   patients) or in C disease and condition-specific (i.e. only in STEMI
   patients 24h after hospital admission). The number of samples for each
   genotype group: [112]Table S11. *FDR-corrected significant P values; r,
   Spearman correlation. IL indicates interleukin; LDLR, low-density
   lipoprotein receptor; and MMP9, matrix metalloproteinase 9.

Monocytes Show Most Potential Differential Cell-to-Cell Communication
Post-STEMI

   As many of the enriched pathways were related to cell-to-cell
   communication (Figure [113]6S), we next defined how interactions among
   immune cell types could underlie these observed gene expression changes
   post-STEMI. For this, we used a cell-to-cell communication tool that
   identifies potential interactions by assuming that ligand-receptor
   interactions can be predicted based on ligand expression in 1 cell type
   and downstream gene expression changes of a known ligand-receptor
   interaction in another cell type ([114]Table S7).^[115]14 In the acute
   phase (t0-t24h) of STEMI, the outcoming communication was largely
   balanced among the cell types, whereas the incoming communication was
   largest in the CD8T cells and monocytes (Figure [116]4A). In the
   chronic phase (t24h–t8w), the dendritic cells and B cells were the
   largest senders, and the NK cells and monocytes were the largest
   receivers (Figure [117]4B). Most of these ligand-receptor pairs were
   uniquely involved in communication by just 1 cell type; similar to the
   DE analyses, the monocytes showed the most unique interactions (Figure
   [118]4C and [119]4D). While this bioinformatically informed
   communication is solely a prediction and additional experimental
   follow-up would be needed for confirmation, the analyses have been
   conducted in a comparative fashion. Therefore, any method-derived
   biases will be comparable in each comparison and are not expected to
   affect the identified results.

Figure 4.

   [120]Figure 4.
   [121]Open in a new tab

   Differential putative cell-to-cell communication
   post-ST-segment–elevated myocardial infarction (STEMI). Differential
   incoming (receiver) and outgoing (sender) putative cell-to-cell
   communication at t0-t24h (A) and t24h-t8w (B). An active cell-to-cell
   communication link is counted as being a ligand-receptor (L-R) link
   that has resulted in differential downstream gene expression. For each
   active L-R link in A (t0–t24h) and B (t24h–t8w), the sharedness of
   ligands among the major cell types is depicted in C and D,
   respectively. The number of participants in each condition is 38, 37,
   and 38 for t0, t24h, and t8w, respectively. DC indicates dendritic
   cells; and NK, natural killer cells.

Cardiovascular Disease–Associated Proteins Change Post-STEMI

   As changes in plasma proteins provide additional insights on the
   systemic consequences and are easily monitored in clinical follow-up,
   we then analyzed during the disease course the plasma levels of 92
   proteins that are known or exploratory human cardiovascular and
   inflammatory markers. This revealed changes in 14 of 92 proteins during
   the first 24 hours (8 up and 6 down) and 28 of 92 proteins during the
   first 8 weeks (22 up and 6 down) post-STEMI. The top 3 upregulated
   proteins within the first 24 hours were NT-proBNP (N-terminal
   pro-B-type natriuretic peptide), IL1RL1, and CHI3L1 (Figure [122]5;
   [123]Table S8). Both NT-proBNP and the soluble form of IL1RL1 are
   well-known proteins upregulated in response to increased wall stress
   during STEMI.^[124]15 Moreover, both are independent predictors of
   heart failure and cardiovascular death.^[125]16,[126]17 CHI3L1 is an
   extracellular matrix protein involved in atherosclerosis and plaque
   rupture.^[127]18 This indicates that CHI3L1 upregulation could
   contribute to STEMI, whereas NT-proBNP and IL1RL1 reflect STEMI-induced
   mechanical stress on cardiomyocytes. The top 3 downregulated proteins
   during the acute phase were SPON1 (spondin-1), AZU1 (azurocidin 1), and
   IGFBP1 (insulin-like growth factor-binding protein 1). Each of these
   has been previously associated with processes preceding STEMI (eg,
   atherosclerosis for AZU1^[128]16 and IGFBP1^[129]19) or resulted from
   unsuccessful STEMI treatment (eg, worsened systolic heart function for
   SPON1^[130]20). Therefore, their sustained downregulation may indicate
   effective treatment, leading to restored blood flow and eventually
   preserving heart function.

Figure 5.

   [131]Figure 5.
   [132]Open in a new tab

   Differentially expressed proteins post-ST-segment–elevated myocardial
   infarction (STEMI). Top 3 up- and downregulated proteins at t24h (n=38)
   and t8w (n=37) post-STEMI vs t0 (n=38). *Significant
   Bonferroni-adjusted P<0.05. AZU1 indicates azurocidin1; CHI3L1,
   chitinase-3-like protein 1; IGFBP1, insulin-like growth factor-binding
   protein 1; IL1RL1, interleukin-1 receptor ligand 1; MMP2, matrix
   metallopeptidase 2; MPO, myeloperoxidase; NT-proBMP, N-terminal
   pro-B-type natriuretic peptide; PCSK9, proprotein convertase
   subtilisin/kexin type 9; and SPON1, spondin 1.

   During the chronic phase, the top 3 upregulated proteins were
   NT-proBNP, PCSK9 (proprotein convertase subtilisin/kexin type 9), and
   MMP2. The increased NT-proBNP levels may reflect further increased wall
   stress.^[133]15 PCSK9 is involved in degradation of the LDLR
   (low-density lipoprotein receptor)^[134]21 and might therefore reflect
   hyperlipidemia or patients’ statin treatment.^[135]22 MMP2 acts in the
   fibrotic pathway^[136]23 and is involved in cardiac remodeling.^[137]24
   Both SPON1 and AZU1 continued to be downregulated at t8w. Additionally,
   MPO (myeoloperoxidase) was found to be downregulated, a protein
   contributing to plaque destabilization through local oxidative tissue
   injury.^[138]25 Together, these changes suggest a sustained restoration
   of heart function and blood flow.

   When trying to assign the potential cell type responsible for these
   differential protein changes using scRNA-seq data, we could only
   replicate 4 of 34 DE proteins (75% concordant) in at least 1 cell type
   within the same condition comparison ([139]Table S8). This low
   mRNA-protein replicability may reflect the inherent differences in what
   is captured: bulk plasma proteins (reflecting the secretome of PBMCS
   but also other blood cells or even other tissues) versus single-cell
   PBMC-derived mRNAs.

   In a comparable cohort of 48 patients with STEMI, the same plasma
   proteins were compared at hospital admission and 3 months post-STEMI,
   showing 29 of 92 DE proteins.^[140]26 Twelve replicated in our study
   (100% concordant) and were mainly involved in STEMI-associated
   processes, like the immune response and tissue remodeling, indicating
   robustness ([141]Table S8).

Gene and Protein Expression Profiles Were Not Associated to
Biochemical-Defined Infarct Size

   Patients with STEMI exhibit variable symptoms upon presentation.
   Despite our study’s criteria narrowing clinical variation (Table),
   donor-to-donor variation persists, influenced by factors like age, sex,
   infarct size, and genetics. To assess the correlation between infarct
   size and molecular measurements, we used plasma peak CK-MB (creatine
   kinase myocardial band) levels as proxy.^[142]27 This biomarker reaches
   a peak within 24 hours and predicts left ventricle dysfunction. Neither
   cell type proportions nor monocyte gene expression showed a significant
   association with peak CK-MB at any time point. However, NT-proBNP was
   positively associated with peak CK-MB (t24h: r^2-adjusted=0.49, Holm’s
   adjusted P=2.8×10^−3; [143]Table S9). This marker for myocardial wall
   stress is commonly measured to assess left ventricular
   dysfunction,^[144]15 and therefore, both are expected to be correlated
   during the time CK-MB peaks.^[145]15,[146]28

Coronary Artery Disease–Associated Genetic Risk Variants Show Disease- and
Condition-Dependent Effects on Plasma Proteins of Known Drug Targets

   Beyond infarct size, genetic variation contributes to a patients’
   molecular response. Recognizing this is crucial when assessing a
   patient’s disease course and defining patient-tailored treatment. As
   plasma proteins provide a pool of potential therapeutic targets, we
   assessed the effect of genetic variation on these (pQTL [protein
   quantitative trait locus]). To provide a direct clinical link, we
   focused on coronary artery disease–associated variants.^[147]29 For 3
   of 92 proteins, we detected a pQTL in at least 1 time point post-STEMI
   ([148]Table S10). For these 3, we assessed their disease-specificity by
   comparing pQTL effect sizes with a control cohort of 1142 individuals
   from the general population. One pQTL was significant in all conditions
   and showed similar effect size in patients and controls:
   single-nucleotide polymorphism (SNP) rs6686750 affecting IL6R (patients
   with STEMI: genotype β=0.42, FDR-corrected P=1.6×10^−5; Figure
   [149]6A). The 2 other pQTLs were disease- or even condition-specific:
   SNP rs8124182 affecting MMP9 was found specifically in patients with
   STEMI (genotype β=−0.68, P=0.016; Figure [150]6B) and SNP rs10422256
   affecting LDLR was found only in patients at t24h (genotype*t24h
   interaction β=−0.29, FDR-corrected P=0.022; Figure [151]6C). These
   results indicate the importance of considering genetics when studying
   the relationship between STEMI and molecular phenotype.

DISCUSSION

   Here, we provided an unbiased, longitudinal overview of the single-cell
   immune response in patients with STEMI versus controls. We observed an
   important role for the classical monocytes, CD4T cells, and NKdim cells
   post-STEMI, both in terms of cell type compositional and gene
   expression changes. In circulation, we observed increases in the
   monocyte and decreases in the NK fraction that peak or dip,
   respectively, at 24 hours post-STEMI. These changes agree with previous
   studies in STEMI^[152]30,[153]31 and angina pectoris.^[154]30,[155]32
   Furthermore, previous studies revealed biphasic changes in monocyte
   composition post-STEMI^[156]30; in the early ischemic phase (3 days
   post-STEMI), classical monocytes peaked, whereas later (5 days
   post-STEMI), the nonclassical fraction peaked. These changes in
   circulation co-occur with specific changes in the heart, suggesting an
   initial recruitment of classical monocytes to facilitate dead cardiac
   tissue removal, followed by nonclassical monocytes concluding the
   inflammatory response and promoting tissue repair.^[157]33 Altogether,
   the observed changes are not solely the consequence of the STEMI but
   may also contribute to it. Therefore, studying circulating immune cell
   and blood protein level changes during a STEMI may provide leads for
   novel therapies.

   Our study indicated the importance of considering both genetic
   variation and disease phase when assessing the molecular consequences
   of a STEMI, as several of the plasma proteins were affected by a
   combination of these parameters. For all identified pQTLs, the A allele
   is the coronary artery disease–associated risk allele.^[158]29
   Indicating that lower plasma protein levels of IL-6R (Figure [159]6A),
   and higher levels of MMP9 (Figure [160]6B) and LDLR (Figure [161]6C)
   increase coronary artery disease risk. This agrees with previous
   phenome-wide association studies showing that the risk allele of these
   pQTL is associated with lower IL6 and IL6R plasma levels and higher LDL
   cholesterol levels.^[162]34

   We observed a disease-specific pQTL of rs8124182 on MMP9 (Figure
   [163]6B), aligning with a previously described whole blood MMP9
   eQTL.^[164]35 We hypothesize this disease-specificity is due to this
   SNP’s direct effect on phospholipid transfer protein (PLTP) gene
   expression,^[165]35 regulating MMP9 levels. Evidence from Pltp/ApoE
   double knock-out mice supports this, showing reduced MMP9 protein
   levels could be reversed by Pltp overexpression.^[166]36 Notably, this
   PLTP effect on MMP9 protein levels was observed solely in
   atherosclerotic conditions induced by ApoE knock out, explaining why
   the MMP9 pQTL was evident only in patients with STEMI, not in controls
   (Figure [167]6B).

   We observed a disease-state- and disease-stage-specific (t24h) pQTL for
   LDLR. Normally, membrane-bound LDLR is mostly present in the liver,
   enabling LDL uptake from circulation. When ADAM17 sheds LDLR from the
   membrane into circulation, reduced availability of membrane-bound LDLR
   is expected, leading to decreased LDL uptake. ADAM17’s activity is
   stimulated by inflammation,^[168]37 which may explain why this pQTL on
   soluble LDLR manifests only during the acute inflammatory stage
   post-STEMI (Figure [169]6C).

   Finally, our analyses shed new light on the IL-6 signaling pathway,
   which is currently being targeted by various drugs in clinical
   trials.^[170]38,[171]39 IL-6 signaling can be activated through 3
   signaling modes, each having its own consequences: classical signaling
   (anti-inflammatory), trans-signaling (proinflammatory), and
   trans-presentation (Th17-cell promoting).^[172]38 Through cell-cell
   communication analyses, we found differential activation of the
   classical and trans-presentation pathways post-STEMI. While our pQTL
   analysis indicated that trans-signaling was affected by SNP rs6689206
   regulating soluble IL6R levels both in patients with STEMI and controls
   (Figure [173]6A). Together, this indicates that classical and
   trans-presentation signaling may be suitable targets (eg,
   anti-IL-6^[174]38,[175]39 or anti-IL-6R) post-STEMI, while patients
   with the GG genotype at SNP rs6689206 may benefit from treatments
   targeting specifically the trans-signaling pathway (eg, soluble gp130).

   Unfortunately, when focusing on how each of the molecular layers could
   be contributing to or being the consequence of biochemically measured
   infarct size, no such relationship was found except for plasma
   NT-proBNP levels ([176]Table S9)—a known marker for heart
   failure.^[177]15 This lack of association might have resulted from
   assessing each molecular parameter separately or not considering all
   potentially relevant clinical and donor variables in the model.^[178]40
   Such a model that considers all these parameters requires a larger
   sample size. We foresee future feasibility through meta-analyses across
   uniformly processed (disease) population-based single-cell data sets in
   efforts such as the single-cell eQTLGen consortium.^[179]41

   In conclusion, this study highlights the importance of studying STEMI
   at a cell type–specific resolution while taking genetic variation,
   disease status, and disease phase into consideration. We expect that
   such an integrative approach will help to better grasp the molecular
   processes underlying STEMI and will be essential for the development of
   effective future therapies with reduced side effects.

ARTICLE INFORMATION

Acknowledgments