Analysis of plasma metabolomes from 11 309 subjects in five population-based cohorts.

Plasma metabolomics holds potential for precision medicine, but limited information is available to compare the performance of such methods across multiple cohorts. We compared plasma metabolite profiles after an overnight fast in 11,309 participants of five population-based Swedish cohorts (50-80 years, 52% women). Metabolite profiles were uniformly generated at a core laboratory (Metabolon Inc.) with untargeted liquid chromatography mass spectrometry and a comprehensive reference library. Analysis of a second sample obtained one year later was conducted in a subset. Of 1629 detected metabolites, 1074 (66%) were detected in all cohorts while only 10% were unique to one cohort, most of which were xenobiotics or uncharacterized. The major classes were lipids (28%), xenobiotics (22%), amino acids (14%), and uncharacterized (19%). The most abundant plasma metabolome components were the major dietary fatty acids and amino acids, glucose, lactate and creatinine. Most metabolites displayed a log-normal distribution. Temporal variability was generally similar to clinical chemistry analytes but more pronounced for xenobiotics. Extensive metabolite-metabolite correlations were observed but mainly restricted to within each class. Metabolites were broadly associated with clinical factors, particularly body mass index, sex and renal function. Collectively, our findings inform the conduct and interpretation of metabolite association and precision medicine studies.

Methods for isolation and transcriptional profiling of individual cells from the human heart.

BACKGROUND: Global transcriptional profiling of individual cells represents a powerful approach to systematically survey contributions from cell-specific molecular phenotypes to human disease states but requires tissue-specific protocols. Here we sought to comprehensively evaluate protocols for single cell isolation and transcriptional profiling from heart tissue, focusing particularly on frozen tissue which is necessary for study of human hearts at scale. METHODS AND RESULTS: Using flow cytometry and high-content screening, we found that enzymatic dissociation of fresh murine heart tissue resulted in a sufficient yield of intact cells while for frozen murine or human heart resulted in low-quality cell suspensions across a range of protocols. These findings were consistent across enzymatic digestion protocols and whether samples were snap-frozen or treated with RNA-stabilizing agents before freezing. In contrast, we show that isolation of cardiac nuclei from frozen hearts results in a high yield of intact nuclei, and leverage expression arrays to show that nuclear transcriptomes reliably represent the cytoplasmic and whole-cell transcriptomes of the major cardiac cell types. Furthermore, coupling of nuclear isolation to PCM1-gated flow cytometry facilitated specific cardiomyocyte depletion, expanding resolution of the cardiac transcriptome beyond bulk tissue transcriptomes which were most strongly correlated with PCM1+ transcriptomes (r = 0.8). We applied these methods to generate a transcriptional catalogue of human cardiac cells by droplet-based RNA-sequencing of 8,460 nuclei from which cellular identities were inferred. Reproducibility of identified clusters was confirmed in an independent biopsy (4,760 additional PCM1- nuclei) from the same human heart. CONCLUSION: Our results confirm the validity of single-nucleus but not single-cell isolation for transcriptional profiling of individual cells from frozen heart tissue, and establishes PCM1-gating as an efficient tool for cardiomyocyte depletion. In addition, our results provide a perspective of cell types inferred from single-nucleus transcriptomes that are present in an adult human heart.

Profiling of the plasma proteome across different stages of human heart failure.

Heart failure (HF) is a major public health problem characterized by inability of the heart to maintain sufficient output of blood. The systematic characterization of circulating proteins across different stages of HF may provide pathophysiological insights and identify therapeutic targets. Here we report application of aptamer-based proteomics to identify proteins associated with prospective HF incidence in a population-based cohort, implicating modulation of immunological, complement, coagulation, natriuretic and matrix remodeling pathways up to two decades prior to overt disease onset. We observe further divergence of these proteins from the general population in advanced HF, and regression after heart transplantation. By leveraging coronary sinus samples and transcriptomic tools, we describe likely cardiac and specific cellular origins for several of the proteins, including Nt-proBNP, thrombospondin-2, interleukin-18 receptor, gelsolin, and activated C5. Our findings provide a broad perspective on both cardiac and systemic factors associated with HF development.

Whole-genome sequencing based on formalin-fixed paraffin-embedded endomyocardial biopsies for genetic studies on outcomes after heart transplantation.

BACKGROUND: Whole-genome sequencing (WGS) of heart transplant recipient- and donor-derived cardiac biopsies may facilitate organ matching, graft failure prediction, and immunotolerance research. The objective of this study was to determine the feasibility of WGS based on formalin-fixed paraffin-embedded endomyocardial biopsies. METHODS AND RESULTS: The study included serial donor- and recipient samples from patients who had undergone heart transplantation at Skane University Hospital, Lund, Sweden, between 1988 and 2009. DNA extraction and WGS were conducted. Additional WGS sequencing quality metrics and coverage were obtained with the Genome Analysis Toolkit (GATK). 455 endomyocardial samples from 37 heart transplant recipients were acquired from routine rejection monitoring and stored as formalin-fixed paraffin-embedded samples. They were analyzed after 3-26 years of storage. DNA was extracted from 114 samples and WGS was run on 85 samples. DNA extraction yielded 313 ng (IQR 96-601) for all samples. A coverage of 11.3x (IQR 9.0-15.9) was recorded for all WGS samples. Three samples stored for > 25 years yielded a coverage of > 25x. Data were generated for 1.7 billion reads per sample (IQR 1.4-2.7). A Transition/Transversion (TiTv) ratio of 2.09 ± 0.05 was calculated for all WGS samples. No associations were found among storage time, DNA yield, or sequencing quality metrics. CONCLUSIONS: The present study demonstrated the feasibility of whole-genome sequencing based on endomyocardial biopsies. This process could enable large-scale retrospective genomic studies using stored histopathological samples.

Accelerating Biomarker Discovery Through Electronic Health Records, Automated Biobanking, and Proteomics.

BACKGROUND: Circulating biomarkers can facilitate diagnosis and risk stratification for complex conditions such as heart failure (HF). Newer molecular platforms can accelerate biomarker discovery, but they require significant resources for data and sample acquisition. OBJECTIVES: The purpose of this study was to test a pragmatic biomarker discovery strategy integrating automated clinical biobanking with proteomics. METHODS: Using the electronic health record, the authors identified patients with and without HF, retrieved their discarded plasma samples, and screened these specimens using a DNA aptamer-based proteomic platform (1,129 proteins). Candidate biomarkers were validated in 3 different prospective cohorts. RESULTS: In an automated manner, plasma samples from 1,315 patients (31% with HF) were collected. Proteomic analysis of a 96-patient subset identified 9 candidate biomarkers (p < 4.42 × 10-5). Two proteins, angiopoietin-2 and thrombospondin-2, were associated with HF in 3 separate validation cohorts. In an emergency department-based registry of 852 dyspneic patients, the 2 biomarkers improved discrimination of acute HF compared with a clinical score (p < 0.0001) or clinical score plus B-type natriuretic peptide (p = 0.02). In a community-based cohort (n = 768), both biomarkers predicted incident HF independent of traditional risk factors and N-terminal pro-B-type natriuretic peptide (hazard ratio per SD increment: 1.35 [95% confidence interval: 1.14 to 1.61; p = 0.0007] for angiopoietin-2, and 1.37 [95% confidence interval: 1.06 to 1.79; p = 0.02] for thrombospondin-2). Among 30 advanced HF patients, concentrations of both biomarkers declined (80% to 84%) following cardiac transplant (p < 0.001 for both). CONCLUSIONS: A novel strategy integrating electronic health records, discarded clinical specimens, and proteomics identified 2 biomarkers that robustly predict HF across diverse clinical settings. This approach could accelerate biomarker discovery for many diseases.