Genetic dissection of the pluripotent proteome through multi-omics data integration.

Genetic background drives phenotypic variability in pluripotent stem cells (PSCs). Most studies to date have used transcript abundance as the primary molecular readout of cell state in PSCs. We performed a comprehensive proteogenomics analysis of 190 genetically diverse mouse embryonic stem cell (mESC) lines. The quantitative proteome is highly variable across lines, and we identified pluripotency-associated pathways that were differentially activated in the proteomics data that were not evident in transcriptome data from the same lines. Integration of protein abundance to transcript levels and chromatin accessibility revealed broad co-variation across molecular layers as well as shared and unique drivers of quantitative variation in pluripotency-associated pathways. Quantitative trait locus (QTL) mapping localized the drivers of these multi-omic signatures to genomic hotspots. This study reveals post-transcriptional mechanisms and genetic interactions that underlie quantitative variability in the pluripotent proteome and provides a regulatory map for mESCs that can provide a basis for future mechanistic studies.

Naive Pluripotent Stem Cells Exhibit Phenotypic Variability that Is Driven by Genetic Variation.

Variability among pluripotent stem cell (PSC) lines is a prevailing issue that hampers not only experimental reproducibility but also large-scale applications and personalized cell-based therapy. This variability could result from epigenetic and genetic factors that influence stem cell behavior. Naive culture conditions minimize epigenetic fluctuation, potentially overcoming differences in PSC line differentiation potential. Here we derived PSCs from distinct mouse strains under naive conditions and show that lines from distinct genetic backgrounds have divergent differentiation capacity, confirming a major role for genetics in PSC phenotypic variability. This is explained in part through inconsistent activity of extra-cellular signaling, including the Wnt pathway, which is modulated by specific genetic variants. Overall, this study shows that genetic background plays a dominant role in driving phenotypic variability of PSCs.

Mapping the Effects of Genetic Variation on Chromatin State and Gene Expression Reveals Loci That Control Ground State Pluripotency.

Mouse embryonic stem cells (mESCs) cultured in the presence of LIF occupy a ground state with highly active pluripotency-associated transcriptional and epigenetic circuitry. However, ground state pluripotency in some inbred strain backgrounds is unstable in the absence of ERK1/2 and GSK3 inhibition. Using an unbiased genetic approach, we dissect the basis of this divergent response to extracellular cues by profiling gene expression and chromatin accessibility in 170 genetically heterogeneous mESCs. We map thousands of loci affecting chromatin accessibility and/or transcript abundance, including 10 QTL hotspots where genetic variation at a single locus coordinates the regulation of genes throughout the genome. For one hotspot, we identify a single enhancer variant ∼10 kb upstream of Lifr associated with chromatin accessibility and mediating a cascade of molecular events affecting pluripotency. We validate causation through reciprocal allele swaps, demonstrating the functional consequences of noncoding variation in gene regulatory networks that stabilize pluripotent states in vitro.

High throughput bioassay for beta1-adrenoceptor autoantibody detection.

BACKGROUND: While the involvement of adrenergic beta1-autoantibodies (beta1-AABs) in pathogenesis of cardiomyopathies is well established as are the benefits associated with autoantibody removal by immunoapheresis, the development of drugs neutralizing beta1-AABs in-vivo has been slowed due to a lack of high throughput autoantibody analytics. Highly scalable routine diagnostics involving immobilized binding partners have mostly failed in comparison to the laborious bioassays, which are difficult to scale up, but present the most reliable and sensitive tools for detecting the beta1-autoantibodies. METHODS: A high throughput, image-based assay to measure cardiomyocyte beat rate and contractility was developed and tested for its applicability for detecting adrenergic beta1-autoantibodies. The classical bioassay of spontaneously beating neonatal rat cardiomyocytes was used for comparison. RESULTS: The high throughout assay using human iPSC-derived cardiomyocytes was able to detect beta1-AAB activity of biological sample material. The results from the high throughput assay were very similar to the data obtained from the original bioassay of spontaneously beating neonatal cardiomyocytes, with one exception, where a control antibody targeting the N-terminal end of the human beta1-receptor induced a response when tested with the high throughput imager, while none was observed by the classical bioassay. This discrepancy may be explained by the differences in host species of cardiomyocytes tested by the two methods. CONCLUSION: The high throughput system using iPSC-derived cardiomyocytes for the detection of beta1-AAB provides a realistic option to overcome the sample-size limitations of the bioassay-based diagnostics.

Effectively identifying eQTLs from multiple tissues by combining mixed model and meta-analytic approaches.

Gene expression data, in conjunction with information on genetic variants, have enabled studies to identify expression quantitative trait loci (eQTLs) or polymorphic locations in the genome that are associated with expression levels. Moreover, recent technological developments and cost decreases have further enabled studies to collect expression data in multiple tissues. One advantage of multiple tissue datasets is that studies can combine results from different tissues to identify eQTLs more accurately than examining each tissue separately. The idea of aggregating results of multiple tissues is closely related to the idea of meta-analysis which aggregates results of multiple genome-wide association studies to improve the power to detect associations. In principle, meta-analysis methods can be used to combine results from multiple tissues. However, eQTLs may have effects in only a single tissue, in all tissues, or in a subset of tissues with possibly different effect sizes. This heterogeneity in terms of effects across multiple tissues presents a key challenge to detect eQTLs. In this paper, we develop a framework that leverages two popular meta-analysis methods that address effect size heterogeneity to detect eQTLs across multiple tissues. We show by using simulations and multiple tissue data from mouse that our approach detects many eQTLs undetected by traditional eQTL methods. Additionally, our method provides an interpretation framework that accurately predicts whether an eQTL has an effect in a particular tissue.