Benchmarking of methods for DNA methylome deconvolution.

Defining the number and abundance of different cell types in tissues is important for understanding disease mechanisms as well as for diagnostic and prognostic purposes. Typically, this is achieved by immunohistological analyses, cell sorting, or single-cell RNA-sequencing. Alternatively, cell-specific DNA methylome information can be leveraged to deconvolve cell fractions from a bulk DNA mixture. However, comprehensive benchmarking of deconvolution methods and modalities was not yet performed. Here we evaluate 16 deconvolution algorithms, developed either specifically for DNA methylome data or more generically. We assess the performance of these algorithms, and the effect of normalization methods, while modeling variables that impact deconvolution performance, including cell abundance, cell type similarity, reference panel size, method for methylome profiling (array or sequencing), and technical variation. We observe differences in algorithm performance depending on each these variables, emphasizing the need for tailoring deconvolution analyses. The complexity of the reference, marker selection method, number of marker loci and, for sequencing-based assays, sequencing depth have a marked influence on performance. By developing handles to select the optimal analysis configuration, we provide a valuable source of information for studies aiming to deconvolve array- or sequencing-based methylation data.

The serine synthesis pathway drives osteoclast differentiation through epigenetic regulation of NFATc1 expression.

Bone-resorbing osteoclasts are vital for postnatal bone health, as increased differentiation or activity results in skeletal pathologies such as osteoporosis. The metabolism of mature osteoclasts differs from their progenitor cells, but whether the observed metabolic changes are secondary to the altered cell state or actively drive the process of cell differentiation is unknown. Here, we show that transient activation of the serine synthesis pathway (SSP) is essential for osteoclastogenesis, as deletion of the rate-limiting enzyme phosphoglycerate dehydrogenase in osteoclast progenitors impairs their differentiation and results in increased bone mass. In addition, pharmacological phosphoglycerate dehydrogenase inhibition abrogated bone loss in a mouse model of postmenopausal osteoporosis by blocking bone resorption. Mechanistically, SSP-derived α-ketoglutarate is necessary for histone demethylases that remove repressive histone methylation marks at the nuclear factor of activated T cells, cytoplasmic 1 (Nfatc1) gene locus, thereby inducing NFATc1 expression and consequent osteoclast maturation. Taken together, this study reveals a metabolic-epigenetic coupling mechanism that directs osteoclast differentiation and suggests that the SSP can be therapeutically targeted to prevent osteoporotic bone loss.

Evaluating offspring Genomic and Epigenomic alterations after prenatal exposure to Cancer treatment In Pregnancy (GE-CIP): a multicentric observational study.

INTRODUCTION: Around 1 in 1000-2000 pregnancies are affected by a cancer diagnosis. Previous studies have shown that chemotherapy during pregnancy has reassuring cognitive and cardiac neonatal outcomes, and hence has been proposed as standard of care. However, although these children perform within normal ranges for their age, subtle differences have been identified. Given that chemotherapeutic compounds can cross the placenta, the possibility that prenatal chemotherapy exposure mutates the offspring's genome and/or epigenome, with potential deleterious effects later in life, urges to be investigated. METHODS AND ANALYSES: This multicentric observational study aims to collect cord blood, meconium and neonatal buccal cells at birth, as well as peripheral blood, buccal cells and urine from infants when 6, 18 and/or 36 months of age. Using bulk and single-cell approaches, we will compare samples from chemotherapy-treated pregnant patients with cancer, pregnant patients with cancer not treated with chemotherapy and healthy pregnant women. Potential chemotherapy-related newborn genomic and/or epigenomic alterations, such as single nucleotide variants, copy number variants and DNA-methylation alterations, will be identified in mononuclear and epithelial cells, isolated from blood, buccal swabs and urine. DNA from maternal peripheral blood and paternal buccal cells will be used to determine de novo somatic mutations in the neonatal blood and epithelial cells. Additionally, the accumulated exposure of the fetus, and biological effective dose of alkylating agents, will be assessed in meconium and cord blood via mass spectrometry approaches. ETHICS AND DISSEMINATION: The Ethics Committee Research of UZ/KU Leuven (EC Research) and the Medical Ethical Review Committee of University Medical Center Amsterdam have approved the study. Results of this study will be disseminated via presentations at (inter)national conferences, through peer-reviewed, open-access publications, via social media platforms aimed to inform patients and healthcare workers, and through the website of the International Network on Cancer, Infertility and Pregnancy (www.cancerinpregnancy.org).

Cell type signatures in cell-free DNA fragmentation profiles reveal disease biology.

Circulating cell-free DNA (cfDNA) fragments have characteristics that are specific to the cell types that release them. Current methods for cfDNA deconvolution typically use disease tailored marker selection in a limited number of bulk tissues or cell lines. Here, we utilize single cell transcriptome data as a comprehensive cellular reference set for disease-agnostic cfDNA cell-of-origin analysis. We correlate cfDNA-inferred nucleosome spacing with gene expression to rank the relative contribution of over 490 cell types to plasma cfDNA. In 744 healthy individuals and patients, we uncover cell type signatures in support of emerging disease paradigms in oncology and prenatal care. We train predictive models that can differentiate patients with colorectal cancer (84.7%), early-stage breast cancer (90.1%), multiple myeloma (AUC 95.0%), and preeclampsia (88.3%) from matched controls. Importantly, our approach performs well in ultra-low coverage cfDNA datasets and can be readily transferred to diverse clinical settings for the expansion of liquid biopsy.