Many variants associated with complex traits are in noncoding regions and contribute to phenotypes by disrupting regulatory sequences. To characterize these variants, we developed a streamlined protocol for a high-throughput reporter assay, Biallelic Targeted STARR-seq (BiT-STARR-seq), that identifies allele-specific expression (ASE) while accounting for PCR duplicates through unique molecular identifiers. We tested 75,501 oligos (43,500 SNPs) and identified 2720 SNPs with significant ASE (FDR < 10%). To validate disruption of binding as one of the mechanisms underlying ASE, we developed a new high-throughput allele-specific binding assay for NFKB1. We identified 2684 SNPs with allele-specific binding (ASB) (FDR < 10%); 256 of these SNPs also had ASE (OR = 1.97, P-value = 0.0006). Of variants associated with complex traits, 1531 resulted in ASE, and 1662 showed ASB. For example, we characterized that the Crohn's disease risk variant for rs3810936 increases NFKB1 binding and results in altered gene expression. Supplemental http://genome.cshlp.org/content/suppl/2018/10/17/gr.237354.118.DC1 References http://genome.cshlp.org/content/28/11/1701.full.html#ref-list-1

Cardiovascular disease encompasses a wide range of conditions, resulting in the highest number of deaths worldwide. The underlying pathologies surrounding cardiovascular disease include a vast and complicated network of both cellular and molecular mechanisms. Unique phenotypic alterations in specific cell types, visualized as varying RNA expression-levels (both coding and non-coding), have been identified as crucial factors in the pathology underlying conditions such as heart failure and atherosclerosis. Recent advances in single-cell RNA sequencing (scRNA-seq) have elucidated a new realm of cell subpopulations and transcriptional variations that are associated with normal and pathological physiology in a wide variety of diseases. This breakthrough in the phenotypical understanding of our cells has brought novel insight into cardiovascular basic science. scRNA-seq allows for separation of widely distinct cell subpopulations which were, until recently, simply averaged together with bulk-tissue RNA-seq. scRNA-seq has been used to identify novel cell types in the heart and vasculature that could be implicated in a variety of disease pathologies. Furthermore, scRNA-seq has been able to identify significant heterogeneity of phenotypes within individual cell subtype populations. The ability to characterize single cells based on transcriptional phenotypes allows researchers the ability to map development of cells and identify changes in specific subpopulations due to diseases at a very high throughput. This review looks at recent scRNA-seq studies of various aspects of the cardiovascular system and discusses their potential value to our understanding of the cardiovascular system and pathology.

Many variants associated with complex traits are in non-coding regions, and contribute to phenotypes by disrupting regulatory sequences. To characterize these variants, we developed a streamlined protocol for a high-throughput reporter assay, BiT-STARR-seq (Biallelic Targeted STARR-seq), that identifies allele-specific expression (ASE) while accounting for PCR duplicates through unique molecular identifiers. We tested 75,501 oligos (43,500 SNPs) and identified 2,720 SNPs with significant ASE (FDR 10%). To validate disruption of binding as one of the mechanisms underlying ASE, we developed a new high throughput allele specific binding assay for NFKB-p50. We identified 2,951 SNPs with allele-specific binding (ASB) (FDR 10%); 173 of these SNPs also had ASE (OR=1.97, p-value=0.0006). Of variants associated with complex traits, 1,531 resulted in ASE and 1,662 showed ASB. For example, we characterized that the Crohn's disease risk variant for rs3810936 increases NFKB binding and results in altered gene expression.

Fetal inflammatory response syndrome (FIRS) is strongly associated with neonatal morbidity and mortality and can be classified as type I or type II. Clinically, FIRS type I and type II are considered as distinct syndromes, yet the molecular underpinnings of these fetal inflammatory responses are not well understood because of their low prevalence and the difficulty of postdelivery diagnosis. In this study, we performed RNA sequencing of human cord blood samples from preterm neonates diagnosed with FIRS type I or FIRS type II. We found that FIRS type I was characterized by an upregulation of host immune responses, including neutrophil and monocyte functions, together with a proinflammatory cytokine storm and a downregulation of T cell processes. In contrast, FIRS type II comprised a mild chronic inflammatory response involving perturbation of HLA transcripts, suggestive of fetal semiallograft rejection. Integrating singlecell RNA sequencing-derived signatures with bulk transcriptomic data confirmed that FIRS type I immune responses were mainly driven by monocytes, macrophages, and neutrophils. Last, tissue-and cell-specific signatures derived from the BioGPS Gene Atlas further corroborated the role of myeloid cells originating from the bone marrow in FIRS type I. Collectively, these data provide evidence that FIRS type I and FIRS type II are driven by distinct immune mechanisms; whereas the former involves the innate limb of immunity consistent with host defense, the latter resembles a process of semiallograft rejection. These findings shed light on the fetal immune responses caused by infection or alloreactivity that can lead to deleterious consequences in neonatal life. ImmunoHorizons, 2021, 5: 735-751.

Introduction B-cells have been strongly implicated in cardiac allograft rejection (CAR). Recently, however, the CTOT-11 trial showed that depleting mature CD20+ B-cells did not reduce rates of rejection in cardiac allograft recipients and unexpectedly increased the severity of allograft vasculopathy. Therefore, it can be hypothesized that differing phenotypic subtypes of B-cells correspond with different biological mechanisms relating to CAR. Though, current applications to quantify these subtypes of immune cells, i.e with immunohistochemistry or flow cytometry, are often restricted by limited cell markers and cost-burden; therefore, we demonstrate a novel deconvolution method, FARDEEP, that has been validated to accurately enumerate peripheral blood mononuclear cell-subtypes (PBMCs) in a quicker and more cost-effective manner. Purpose To better understand the association of different B-cell subtypes in CAR by identifying the B-cell subtype most predictive for pathologically defined rejection. Methods The machine learning tool, FARDEEP, was trained with the transcriptomic signatures of 29 PBMC subtypes, characterized by previous single-cell RNA experiments. FARDEEP then was used to deconvolute data-mined RNA from 259 blood samples from 98 cardiac allograft recipients enrolled in the CARGO study (GSE2445). Random forest tree (RF) was then used to analyze the levels of deconvoluted subtypes to predict the severity of rejection assessed by endomyocardial biopsy. Finally, RF was used to identify the subtypes of PBMCs most valuable in predicting rejection. Results Out of the 259 samples with consensus pathological readings, 140 had a consensus International Society of Heart and Lung Transplant grade of 0, 63 with grade 1a, 31 with grade 1b, and 25 with grade 3a or higher. We grouped biopsy samples with grade 0, 1a, and 1b as “low-risk” rejection (n=234). 3a or higher samples were grouped as “high-risk” (n=25). There were no grade 2s in the dataset. According to the dataset, blood was extracted from patients on average 72.5 days post-transplant. The RF had good performance in predicting rejection severity. (Figure 1a) CD20- plasmablast cells were stronger predictors for differentiating high-risk from low-risk compared to CD20+ B-cell populations (i.e B Naive and B Memory cells). (Figure 1b) Overall, however, dendritic cells (DCs), neutrophils, monocytes, and basophils were the strongest predictors for rejection. Conclusion Our findings support the results from the CTOT-11 trial showing that CD20+ B-cells may not contribute to CAR as significantly as seen with other PBMC subtypes. Instead, we showed that among B-cells, CD20- plasmablasts were more likely associated with CAR, possibly explaining why targeting CD20 was ineffective in preventing rejection. Thus, targeting plasmablast-associated markers could potentially be more useful to prevent CAR. Model Performance with Variables Funding Acknowledgement Type of funding source: Private grant(s) and/or Sponsorship. Main funding source(s): 1) Society of Academic Emergency Medicine Foundation; 2) The Jewish Fund