Whole-Tissue Deconvolution and scRNAseq Analysis Identify Altered Endometrial Cellular Compositions and Functionality Associated With Endometriosis

The uterine lining (endometrium) exhibits a pro-inflammatory phenotype in women with endometriosis, resulting in pain, infertility, and poor pregnancy outcomes. The full complement of cell types contributing to this phenotype has yet to be identified, as most studies have focused on bulk tissue or select cell populations. Herein, through integrating whole-tissue deconvolution and single-cell RNAseq, we comprehensively characterized immune and nonimmune cell types in the endometrium of women with or without disease and their dynamic changes across the menstrual cycle. We designed metrics to evaluate specificity of deconvolution signatures that resulted in single-cell identification of 13 novel signatures for immune cell subtypes in healthy endometrium. Guided by statistical metrics, we identified contributions of endometrial epithelial, endothelial, plasmacytoid dendritic cells, classical dendritic cells, monocytes, macrophages, and granulocytes to the endometrial pro-inflammatory phenotype, underscoring roles for nonimmune as well as immune cells to the dysfunctionality of this tissue.

Sample Preparation Approach Influences PAM50 Risk of Recurrence Score in Early Breast Cancer

The PAM50 gene expression subtypes and the associated risk of recurrence (ROR) score are used to predict the risk of recurrence and the benefits of adjuvant therapy in early-stage breast cancer. The Prosigna assay includes the PAM50 subtypes along with their clinicopathological features, and is approved for treatment recommendations for adjuvant hormonal therapy and chemotherapy in hormone-receptor-positive early breast cancer. The Prosigna test utilizes RNA extracted from macrodissected tumor cells obtained from formalin-fixed, paraffin-embedded (FFPE) tissue sections. However, RNA extracted from fresh-frozen (FF) bulk tissue without macrodissection is widely used for research purposes, and yields high-quality RNA for downstream analyses. To investigate the impact of the sample preparation approach on ROR scores, we analyzed 94 breast carcinomas included in an observational study that had available gene expression data from macrodissected FFPE tissue and FF bulk tumor tissue, along with the clinically approved Prosigna scores for the node-negative, hormone-receptor-positive, HER2-negative cases (n = 54). ROR scores were calculated in R; the resulting two sets of scores from FFPE and FF samples were compared, and treatment recommendations were evaluated. Overall, ROR scores calculated based on the macrodissected FFPE tissue were consistent with the Prosigna scores. However, analyses from bulk tissue yielded a higher proportion of cases classified as normal-like; these were samples with relatively low tumor cellularity, leading to lower ROR scores. When comparing ROR scores (low, intermediate, and high), discordant cases between the two preparation approaches were revealed among the luminal tumors; the recommended treatment would have changed in a minority of cases.

The Application of Nanomaterials in Angiogenesis

Induction of angiogenesis has enormous potential in the treatment of ischemic diseases and the promotion of bulk tissue regeneration. However, the poor activity of angiogenic cells and proangiogenic factors after transplantation is the main problem that imposes its wide applications. Recent studies have found that the development of nanomaterials has solved this problem to some extent. Nanomaterials can be mainly classified into inorganic nanomaterials represented by metals, metal oxides and metal hydroxides, and organic nanomaterials including DNA tetrahedrons, graphene, graphene oxide, and carbon nanotubes. These nanomaterials can induce the release of angiogenic factors either directly or indirectly, thereby initiating a series of signaling pathways to induce angiogenesis. Moreover, appropriate surface modifications of nanomaterial facilitate a variety of functions, such as enhancing its biocompatibility and biostability. In clinical applications, nanomaterials can promote the proliferation and differentiation of endothelial cells or mesenchymal stem cells, thereby promoting the migration of hemangioblast cells to form new blood vessels. This review outlines the role of nanomaterials in angiogenesis and is intended to provide new insights into the clinical treatment of systemic and ischemic diseases.

EPCO-33. CELL-TYPE PROPORTION DECONVOLUTION OF PEDIATRIC CENTRAL NERVOUS SYSTEM TUMORS FROM SINGLE NUCLEI RNA-seq UNCOVERS UNDERLYING TRANSCRIPTOMIC CHANGES FROM BULK TUMOR RNA-seq COMPARED TO NORMAL BRAIN

Identifying transcriptomic alterations in pediatric central nervous system (pCNS) tumors often relies on transcriptomic profiles from bulk tissue RNA-sequencing that can be confounded by varying cell type proportions across tumor and normal brain tissues. We utilized single nuclei RNA-sequencing (snRNA-seq) and bulk RNA-seq in 33 pCNS tumors and 3 non-diseased pediatric brain tissue samples collected from the Norris Cotton Cancer Center to identify variation in gene expression in bulk tissue attributed to overrepresentation of specific cell-type populations when determining differentially expressed genes comparing pCNS tumors to normal pediatric brain tissues. snRNA-seq of 43,515 nuclei (mean = 1,209 nuclei/sample) revealed large proportions of astrocytes (median = 0.45, range = 0.24–0.93) and oligodendrocytes (median = 0.37, range = 0.00–0.66) in pCNS tumors. Compared to normal pediatric brain, proportions of astrocytes were significantly higher (P = 9.2E-03) and neurons were significantly lower (P = 9.4E-03) in pCNS tumors. Differential expression analyses comparing bulk RNA-sequencing data from pCNS tumors to normal pediatric brain identified 902 additional differentially expressed genes (# DE genes = 1,802) when adjusting for astrocyte and neuron proportions compared with unadjusted analysis (# DE genes = 900). In cell-type proportion unadjusted analysis, top DE genes included astrocyte-specific markers, GFAP and CIITA, both of which were found to be not significantly differentially expressed in cell-type proportion adjusted analysis. Indeed, pathways enrichment analysis revealed DE genes in unadjusted models were associated with processes of the neurons and astrocytes such as interferon signaling and postsynaptic signal transmission. After adjustment for astrocyte and neuron proportions, DE genes were associated with defensins and DNA replication-related processes. Our results highlight new potential biological pathways essential in pCNS tumors and indicate the significance of the distribution of varying cell types in tissue samples when conducting studies to investigate transcriptomic alterations in bulk tissue of pCNS tumors.

Physiology Drives Reworking of Amino Acid δ2H and δ13C in Butterfly Tissues

Studies of animal movement and migration over large geospatial scales have long relied on natural continental-scale hydrogen isotope (δ2H) gradients in precipitation, yet the physiological processes that govern incorporation of δ2H from precipitation into plant and then herbivore tissues remain poorly understood, especially at the molecular level. Establishing a biochemical framework for the propagation of δ2H through food webs would enable us to resolve more complicated regional-scale animal movements and potentially unlock new applications for δ2H data in animal ecology and eco-physiology. Amino acid δ2H analysis offers a promising new avenue by which to establish this framework. We report bulk tissue δ2H, δ13C, and δ15N data as well as amino acid δ2H and δ13C data from three Pipevine swallowtail (Battus philenor) tissues—caterpillars, butterfly bodies, and wings—as well as their obligate plant source: pipevine leaves (Aristolochia macrophylla). Insects are often dominant herbivores in terrestrial food webs and a major food source for many higher-level consumers, so it is particularly important to understand the mechanisms that influence insect tissue δ2H values. Our data reveal extensive δ2H variation within and among individuals of a relatively simple plant-herbivore system that cannot be explained by temporal or geospatial gradients of precipitation δ2H or dietary differences. Variations in essential amino acid δ2H and δ13C indicate that B. philenor acquire these compounds from an additional source that is isotopically distinct from pipevine leaves, potentially gut microbes. We also found multiple isotopic carryover effects associated with metamorphosis. This study emphasizes the strong influence of physiology on consumer-diet δ2H discrimination in a local population of pipevines and swallowtails and provides a template that can be broadly applied to Lepidoptera—the second most diverse insect order—and other holometabolous insects. Understanding these physiological mechanisms is critical to interpreting the large degree of δ2H variation in consumer tissues often observed at a single collection site, which has implications for using δ2H isoscapes to study animal movement. Further investigation into amino acid δ2H holds promise to elucidate how subsets of amino acids may be best utilized to address specific ecological and physiological questions for which bulk tissue δ2H is insufficient.

Compound-Specific Stable Isotope Analysis of Amino Acids in Pelagic Shark Vertebrae Reveals Baseline, Trophic, and Physiological Effects on Bulk Protein Isotope Records

Variations in stable carbon and nitrogen isotope compositions in incremental tissues of pelagic sharks can be used to infer aspects of their spatial and trophic ecology across life-histories. Interpretations from bulk tissue isotopic compositions are complicated, however, because multiple processes influence these values, including variations in primary producer isotope ratios and consumer diets and physiological processing of metabolites. Here we challenge inferences about shark tropho-spatial ecology drawn from bulk tissue isotope data using data for amino acids. Stable isotope compositions of individual amino acids can partition the isotopic variance in bulk tissue into components associated with primary production on the one hand, and diet and physiology on the other. The carbon framework of essential amino acids (EAAs) can be synthesised de novo only by plants, fungi and bacteria and must be acquired by consumers through the diet. Consequently, the carbon isotopic composition of EAAs in consumers reflects that of primary producers in the location of feeding, whereas that of non-essential amino acids (non-EAAs) is additionally influenced by trophic fractionation and isotope dynamics of metabolic processing. We determined isotope chronologies from vertebrae of individual blue sharks and porbeagles from the North Atlantic. We measured carbon and nitrogen isotope compositions in bulk collagen and carbon isotope compositions of amino acids. Despite variability among individuals, common ontogenetic patterns in bulk isotope compositions were seen in both species. However, while life-history movement inferences from bulk analyses for blue sharks were supported by carbon isotope data from essential amino acids, inferences for porbeagles were not, implying that the observed trends in bulk protein isotope compositions in porbeagles have a trophic or physiological explanation, or are suprious effects. We explored variations in carbon isotope compositions of non-essential amino acids, searching for systematic variations that might imply ontogenetic changes in physiological processing, but patterns were highly variable and did not explain variance in bulk protein δ13C values. Isotopic effects associated with metabolite processing may overwhelm spatial influences that are weak or inconsistently developed in bulk tissue isotope values, but interpreting mechanisms underpinning isotopic variation in patterns in non-essential amino acids remains challenging.

Whole-tissue deconvolution and scRNAseq analysis identify altered endometrial cellular compositions and functionality associated with endometriosis

The uterine lining (endometrium) exhibits a pro-inflammatory phenotype in women with endometriosis, resulting in pain, infertility, and poor pregnancy outcomes. The full complement of cell types contributing to this phenotype has yet to be identified, as most studies have focused on bulk tissue or select cell populations. Herein, through integrating whole-tissue deconvolution and single cell RNAseq, we comprehensively characterized immune and nonimmune cell types in endometrium of women with or without disease and their dynamic changes across the menstrual cycle. We designed metrics to evaluate specificity of deconvolution signatures that resulted in single cell identification of 13 novel signatures for immune cell subtypes in healthy endometrium. Guided by statistical metrics, we identified contributions of endometrial epithelial, endothelial, plasmacytoid dendritic cells, classical dendritic cells, monocytes, macrophages, and granulocytes to the endometrial pro-inflammatory phenotype, underscoring roles for nonimmune as well as immune cells to the dysfunctionality of this tissue.Teaser SentenceCell type deconvolution and single cell RNAseq analysis identify altered endometrial cellular compositions in women with endometriosis

Scientific and clinical implications of genetic and cellular heterogeneity in uveal melanoma

Here, we discuss the presence and roles of heterogeneity in the development of uveal melanoma. Both genetic and cellular heterogeneity are considered, as their presence became undeniable due to single cell approaches that have recently been used in uveal melanoma analysis. However, the presence of precursor clones and immune infiltrate in uveal melanoma have been described as being part of the tumour already decades ago. Since uveal melanoma grow in the corpus vitreous, they present a unique tumour model because every cell present in the tumour tissue is actually part of the tumour and possibly plays a role. For an effective treatment of uveal melanoma metastasis, it should be clear whether precursor clones and normal cells play an active role in progression and metastasis. We propagate analysis of bulk tissue that allows analysis of tumour heterogeneity in a clinical setting.

Low variability in the underlying cellular landscape adversely affects the performance of interaction-based approaches for conducting cell-specific analyses of DNA methylation in bulk samples

Statistical methods that allow for cell type specific DNA methylation (DNAm) analyses based on bulk-tissue methylation data have great potential to improve our understanding of human disease and have created unprecedented opportunities for new insights using the wealth of publicly available bulk-tissue methylation data. These methodologies involve incorporating interaction terms formed between the phenotypes/exposures of interest and proportions of the cell types underlying the bulk-tissue sample used for DNAm profiling. Despite growing interest in such “interaction-based” methods, there has been no comprehensive assessment how variability in the cellular landscape across study samples affects their performance. To answer this question, we used numerous publicly available whole-blood DNAm data sets along with extensive simulation studies and evaluated the performance of interaction-based approaches in detecting cell-specific methylation effects. Our results show that low cell proportion variability results in large estimation error and low statistical power for detecting cell-specific effects of DNAm. Further, we identified that many studies targeting methylation profiling in whole-blood may be at risk to be underpowered due to low variability in the cellular landscape across study samples. Finally, we discuss guidelines for researchers seeking to conduct studies utilizing interaction-based approaches to help ensure that their studies are adequately powered.

Partitioning gene-mediated disease heritability without eQTLs

Unknown SNP-to-gene regulatory architecture complicates efforts to link noncoding GWAS associations with genes implicated by sequencing or functional studies. eQTLs are used to link SNPs to genes, but expression in bulk tissue explains a small fraction of disease heritability. A simple but successful approach has been to link SNPs with nearby genes, but the fraction of heritability mediated by these genes is unclear, and gene-proximal (vs. gene-mediated) heritability enrichments are attenuated accordingly. We propose the Abstract Mediation Model (AMM) to estimate (1) the fraction of heritability mediated by the closest or kth-closest gene to each SNP and (2) the mediated heritability enrichment of a gene set (e.g. genes with rare-variant associations). AMM jointly estimates these quantities by matching the decay in SNP enrichment with distance from genes in the gene set. Across 47 complex traits and diseases, we estimate that the closest gene to each SNP mediates 27% (SE: 6%) of heritability, and that a substantial fraction is mediated by genes outside the ten closest. Mendelian disease genes are strongly enriched for common-variant heritability; for example, just 21 dyslipidemia genes mediate 25% of LDL heritability (211x enrichment, P = 0.01). Among brain-related traits, genes involved in neurodevelopmental disorders are only about 4x enriched, but gene expression patterns are highly informative, with detectable differences in per-gene heritability even among weakly brain-expressed genes.