Multi-omic analyses in immune cell development with lessons learned from T cell development

Traditionally, flow cytometry has been the preferred method to characterize immune cells at the single-cell level. Flow cytometry is used in immunology mostly to measure the expression of identifying markers on the cell surface, but—with good antibodies—can also be used to assess the expression of intracellular proteins. The advent of single-cell RNA-sequencing has paved the road to study immune development at an unprecedented resolution. Single-cell RNA-sequencing studies have not only allowed us to efficiently chart the make-up of heterogeneous tissues, including their most rare cell populations, it also increasingly contributes to our understanding how different omics modalities interplay at a single cell resolution. Particularly for investigating the immune system, this means that these single-cell techniques can be integrated to combine and correlate RNA and protein data at the single-cell level. While RNA data usually reveals a large heterogeneity of a given population identified solely by a combination of surface protein markers, the integration of different omics modalities at a single cell resolution is expected to greatly contribute to our understanding of the immune system

Single-cell immune profiling reveals thymus-seeding populations, T cell commitment, and multilineage development in the human thymus

T cell development in the mouse thymus has been studied extensively, but less is known regarding T cell development in the human thymus. We used a combination of single-cell techniques and functional assays to perform deep immune profiling of human T cell development, focusing on the initial stages of prelineage commitment. We identified three thymus-seeding progenitor populations that also have counterparts in the bone marrow. In addition, we found that the human thymus physiologically supports the development of monocytes, dendritic cells, and NK cells, as well as limited development of B cells. These results are an important step toward monitoring and guiding regenerative therapies in patients after hematopoietic stem cell transplantation

Transcriptional Profiling of Human Familial Longevity Indicates a Role for ASF1A and IL7R

The Leiden Longevity Study consists of families that express extended survival across generations, decreased morbidity in middle-age, and beneficial metabolic profiles. To identify which pathways drive this complex phenotype of familial longevity and healthy aging, we performed a genome-wide gene expression study within this cohort to screen for mRNAs whose expression changes with age and associates with longevity. We first compared gene expression profiles from whole blood samples between 50 nonagenarians and 50 middle-aged controls, resulting in identification of 2,953 probes that associated with age. Next, we determined which of these probes associated with longevity by comparing the offspring of the nonagenarians (50 subjects) and the middle-aged controls. The expression of 360 probes was found to change differentially with age in members of the long-lived families. In a RT-qPCR replication experiment utilizing 312 controls, 332 offspring and 79 nonagenarians, we confirmed a nonagenarian specific expression profile for 21 genes out of 25 tested. Since only some of the offspring will have inherited the beneficial longevity profile from their long-lived parents, the contrast between offspring and controls is expected to be weak. Despite this dilution of the longevity effects, reduced expression levels of two genes, ASF1A and IL7R, involved in maintenance of chromatin structure and the immune system, associated with familial longevity already in middle-age. The size of this association increased when controls were compared to a subfraction of the offspring that had the highest probability to age healthily and become long-lived according to beneficial metabolic parameters. In conclusion, an “aging-signature” formed of 21 genes was identified, of which reduced expression of ASF1A and IL7R marked familial longevity already in middle-age. This indicates that expression changes of genes involved in metabolism, epigenetic control and immune function occur as a function of age, and some of these, like ASF1A and IL7R, represent early features of familial longevity and healthy ageing

Comprehensive diagnostics of acute myeloid leukemia by whole transcriptome RNA sequencing

Acute myeloid leukemia (AML) is caused by genetic aberrations that also govern the prognosis of patients and guide risk-adapted and targeted therapy. Genetic aberrations in AML are structurally diverse and currently detected by different diagnostic assays. This study sought to establish whole transcriptome RNA sequencing as single, comprehensive, and flexible platform for AML diagnostics. We developed HAMLET (Human AML Expedited Transcriptomics) as bioinformatics pipeline for simultaneous detection of fusion genes, small variants, tandem duplications, and gene expression with all information assembled in an annotated, user-friendly output file. Whole transcriptome RNA sequencing was performed on 100 AML cases and HAMLET results were validated by reference assays and targeted resequencing. The data showed that HAMLET accurately detected all fusion genes and overexpression of EVI1 irrespective of 3q26 aberrations. In addition, small variants in 13 genes that are often mutated in AML were called with 99.2% sensitivity and 100% specificity, and tandem duplications in FLT3 and KMT2A were detected by a novel algorithm based on soft-clipped reads with 100% sensitivity and 97.1% specificity. In conclusion, HAMLET has the potential to provide accurate comprehensive diagnostic information relevant for AML classification, risk assessment and targeted therapy on a single technology platform

Metabolomic profiles predict individual multidisease outcomes

Publisher Copyright: © 2022, The Author(s).Risk stratification is critical for the early identification of high-risk individuals and disease prevention. Here we explored the potential of nuclear magnetic resonance (NMR) spectroscopy-derived metabolomic profiles to inform on multidisease risk beyond conventional clinical predictors for the onset of 24 common conditions, including metabolic, vascular, respiratory, musculoskeletal and neurological diseases and cancers. Specifically, we trained a neural network to learn disease-specific metabolomic states from 168 circulating metabolic markers measured in 117,981 participants with ~1.4 million person-years of follow-up from the UK Biobank and validated the model in four independent cohorts. We found metabolomic states to be associated with incident event rates in all the investigated conditions, except breast cancer. For 10-year outcome prediction for 15 endpoints, with and without established metabolic contribution, a combination of age and sex and the metabolomic state equaled or outperformed established predictors. Moreover, metabolomic state added predictive information over comprehensive clinical variables for eight common diseases, including type 2 diabetes, dementia and heart failure. Decision curve analyses showed that predictive improvements translated into clinical utility for a wide range of potential decision thresholds. Taken together, our study demonstrates both the potential and limitations of NMR-derived metabolomic profiles as a multidisease assay to inform on the risk of many common diseases simultaneously.Peer reviewe

Hypertension Is Associated with Marked Alterations in Sphingolipid Biology: A Potential Role for Ceramide

Background Hypertension is, amongst others, characterized by endothelial dysfunction and vascular remodeling. As sphingolipids have been implicated in both the regulation of vascular contractility and growth, we investigated whether sphingolipid biology is altered in hypertension and whether this is reflected in altered vascular function. Methods and Findings In isolated carotid arteries from spontaneously hypertensive rats (SHR) and normotensive Wistar-Kyoto (WKY) rats, shifting the ceramide/S1P ratio towards ceramide dominance by administration of a sphingosine kinase inhibitor (dimethylsphingosine) or exogenous application of sphingomyelinase, induced marked endothelium-dependent contractions in SHR vessels (DMS: 1.4±0.4 and SMase: 2.1±0.1 mN/mm; n = 10), that were virtually absent in WKY vessels (DMS: 0.0±0.0 and SMase: 0.6±0.1 mN/mm; n = 9, p Conclusions Hypertension is associated with marked alterations in vascular sphingolipid biology such as elevated ceramide levels and signaling, that contribute to increased vascular tone

Genome-wide characterization of circulating metabolic biomarkers

Genome-wide association analyses using high-throughput metabolomics platforms have led to novel insights into the biology of human metabolism1,2,3,4,5,6,7. This detailed knowledge of the genetic determinants of systemic metabolism has been pivotal for uncovering how genetic pathways influence biological mechanisms and complex diseases8,9,10,11. Here we present a genome-wide association study for 233 circulating metabolic traits quantified by nuclear magnetic resonance spectroscopy in up to 136,016 participants from 33 cohorts. We identify more than 400 independent loci and assign probable causal genes at two-thirds of these using manual curation of plausible biological candidates. We highlight the importance of sample and participant characteristics that can have significant effects on genetic associations. We use detailed metabolic profiling of lipoprotein- and lipid-associated variants to better characterize how known lipid loci and novel loci affect lipoprotein metabolism at a granular level. We demonstrate the translational utility of comprehensively phenotyped molecular data, characterizing the metabolic associations of intrahepatic cholestasis of pregnancy. Finally, we observe substantial genetic pleiotropy for multiple metabolic pathways and illustrate the importance of careful instrument selection in Mendelian randomization analysis, revealing a putative causal relationship between acetone and hypertension. Our publicly available results provide a foundational resource for the community to examine the role of metabolism across diverse diseases

Multi-trait analysis characterizes the genetics of thyroid function and identifies causal associations with clinical implications

To date only a fraction of the genetic footprint of thyroid function has been clarified. We report a genome-wide association study meta-analysis of thyroid function in up to 271,040 individuals of European ancestry, including reference range thyrotropin (TSH), free thyroxine (FT4), free and total triiodothyronine (T3), proxies for metabolism (T3/FT4 ratio) as well as dichotomized high and low TSH levels. We revealed 259 independent significant associations for TSH (61% novel), 85 for FT4 (67% novel), and 62 novel signals for the T3 related traits. The loci explained 14.1%, 6.0%, 9.5% and 1.1% of the total variation in TSH, FT4, total T3 and free T3 concentrations, respectively. Genetic correlations indicate that TSH associated loci reflect the thyroid function determined by free T3, whereas the FT4 associations represent the thyroid hormone metabolism. Polygenic risk score and Mendelian randomization analyses showed the effects of genetically determined variation in thyroid function on various clinical outcomes, including cardiovascular risk factors and diseases, autoimmune diseases, and cancer. In conclusion, our results improve the understanding of thyroid hormone physiology and highlight the pleiotropic effects of thyroid function on various diseases