Bulk Tissue Research Articles

Profiling Epigenetic Age in Single Cells

Abstract DNA methylation of a defined set of CpG dinucleotides emerged as a critical and precise biomarker of the aging process. Multi-variate machine learning models, known as epigenetic clocks, can exploit quantitative changes in the methylome to predict the age of bulk tissue with remarkable accuracy. However, intrinsic sparsity and digitized methylation in individual cells have so far precluded the assessment of aging in single cell data. We developed scAge, a probabilistic approach to determine the epigenetic age of single cells, and validated our results in mice. scAge tissue-specific and multi-cell type single cell clocks correctly recapitulated the chronological age of the original tissue, while uncovering the inherent heterogeneity that exists at the single-cell level. The data suggested that while cells in a tissue age in a coordinated fashion, some cells age more or less rapidly than others. We showed that individual embryonic stem cells exhibit an age close to zero, that certain stem cells in a tissue showed a reduced age compared to their chronological age, and that early embryogenesis is associated with the reduction of epigenetic age in individual cells, the latter supporting a natural rejuvenation event during gastrulation. scAge is both robust against the low coverage that is characteristic of single cell sequencing techniques and is flexible for studying any cell type and mammalian organism of interest. We demonstrate the potential for accurate epigenetic age profiling at single-cell resolution.

Mass Cytometry for the Characterization of Individual Cell Types in Ovarian Solid Tumors.

Mass cytometry aka Cytometry by Time-Of-Flight (CyTOF) is one of several recently developed multiparametric single-cell technologies designed to address cellular heterogeneity within healthy and diseased tissue. Mass cytometry is an adaptation of flow cytometry in which antibodies are labeled with stable heavy metal isotopes and the readout is by time-of-flight mass spectrometry. With minimal spillover between channels, mass cytometry enables readouts of up to 60 parameters per single cell. Critically, mass cytometry can identify minority cell populations that are lost in bulk tissue analysis. Mass cytometry has been used to great effect for the study of immune cells. We have extended its use to examine single cells within disaggregated solid tissues, specifically freshly resected tubo-ovarian high-grade serous tumors. Here we detail our protocols designed to ensure the production of high-quality single-cell datasets. The methodology can be modified to accommodate the study of other solid tissues.

SwCAM: estimation of subtype-specific expressions in individual samples with unsupervised sample-wise deconvolution.

Complex biological tissues are often a heterogeneous mixture of several molecularly distinct cell subtypes. Both subtype compositions and subtype-specific (STS) expressions can vary across biological conditions. Computational deconvolution aims to dissect patterns of bulk tissue data into subtype compositions and STS expressions. Existing deconvolution methods can only estimate averaged STS expressions in a population, while many downstream analyses such as inferring co-expression networks in particular subtypes require subtype expression estimates in individual samples. However, individual-level deconvolution is a mathematically underdetermined problem because there are more variables than observations. We report a sample-wise Convex Analysis of Mixtures (swCAM) method that can estimate subtype proportions and STS expressions in individual samples from bulk tissue transcriptomes. We extend our previous CAM framework to include a new term accounting for between-sample variations and formulate swCAM as a nuclear-norm and ℓ2,1-norm regularized matrix factorization problem. We determine hyperparameter values using cross-validation with random entry exclusion and obtain a swCAM solution using an efficient alternating direction method of multipliers. Experimental results on realistic simulation data show that swCAM can accurately estimate STS expressions in individual samples and successfully extract co-expression networks in particular subtypes that are otherwise unobtainable using bulk data. In two real-world applications, swCAM analysis of bulk RNASeq data from brain tissue of cases and controls with bipolar disorder or Alzheimer's disease identified significant changes in cell proportion, expression pattern and co-expression module in patient neurons. Comparative evaluation of swCAM versus peer methods is also provided. The R Scripts of swCAM are freely available at https://github.com/Lululuella/swCAM. A user's guide and a vignette are provided. Supplementary data are available at Bioinformatics online.

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

Simple SummaryThe PAM50 risk of recurrence (ROR) score is predictive of the risk of distant recurrence and the benefits of adjuvant therapy in early breast cancer. The Prosigna assay utilizes RNA from tumor cells selected via macrodissection of formalin-fixed, paraffin-embedded (FFPE) tissue sections to measure the activity of the PAM50 genes. An alternative and widely used extraction method is RNA purification from fresh-frozen (FF) bulk tissue without enriching the tumor cellularity. However, the impact of the RNA preparation approach on ROR scores and subsequent treatment selection has not been systematically evaluated. We compared the different approaches and found high correlation between risk of recurrence scores estimated from macrodissected FFPE tissue and scores estimated from bulk FF tumor tissue. However, important discrepancies were revealed for luminal tumors, which may have consequences for treatment recommendations for these patients.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.

A role for epigenetic mechanisms in Lewy body dementias.

The Lewy body diseases, Dementia with Lewy bodies (DLB), Parkinson's disease (PD) and Parkinson's disease dementia (PDD) are all neurodegenerative diseases classified by the accumulation of alpha-synuclein in neurons, forming Lewy bodies (LB). We hypothesise that these LBs cause epigenetic changes within neurons and surrounding cells and that these changes can be used to distinguish the different LB diseases from one another. Bulk tissue from the cingulate gyrus and prefrontal cortex will be as analysed for DNA methylation levels using the Illumina Infinium Methylation EPIC array to generate quantitative methylation data for over 850,000 CpG sites across the genome (n=∼100/disease group). Linear regression and pathway analyses will be used to identify loci that are significantly different or specific to each disease. Following this we will validate loci and determine their cellular specificity using a subset of samples (15 DLB, 15 PDD, 15 PD only, 15 controls) using fluorescence activated cell sorting (FACS). In each sample we will isolate various different cellular populations, including neurons, microglia, oligodendrocytes and astrocytes before profiling these using the EPIC array. Study groups have been sourced consisting of cases with PD, PDD and DLB based on LB deposition and clinical symptom staging. Control cases have been selected for matched age and levels of concomitant AD pathology. Cases for FACS (n=15/group) have been selected to allow where possible a high base RIN, pH and minimal post-mortem interval. We are collating a well powered study cohort to interrogate the epigenetic basis of neuropathological progression and clinical staging of LB disease, controlling for levels of concomitant AD pathology. Follow up FACS sorting and analysis will allow for the cell specific methylation changes occurring in each of the LB diseases.

Profiling epigenetic age in single cells.

DNA methylation dynamics emerged as a promising biomarker of mammalian aging, with multivariate machine learning models ('epigenetic clocks') enabling measurement of biological age in bulk tissue samples. However, intrinsically sparse and binarized methylation profiles of individual cells have so far precluded the assessment of aging in single-cell data. Here, we introduce scAge, a statistical framework for epigenetic age profiling at single-cell resolution, and validate our approach in mice. Our method recapitulates the chronological age of tissues, while uncovering heterogeneity among cells. We show accurate tracking of the aging process in hepatocytes, demonstrate attenuated epigenetic aging in muscle stem cells, and track age dynamics in embryonic stem cells. We also use scAge to reveal, at the single-cell level, a natural and stratified rejuvenation event occurring during early embryogenesis. We provide our framework as a resource to enable exploration of epigenetic aging trajectories at single-cell resolution.

Pathology-related alterations in gene expression in Alzheimer's disease: Single cell resolution.

Alzheimer's disease is pathologically defined by the presence of extracellular amyloid beta plaques and intracellular tau tangles, resulting in neurodegeneration and reactive gliosis in affected brain regions such as temporal cortex (TCX). The degree and distribution of AD pathology can be defined by Braak stage (tau) and Thal phase (amyloid). Brain tissue is comprised of multiple cell types that have different molecular profiles, the proportions of which can vary between individuals and in response to disease; creating challenges for the interpretation of bulk tissue transcriptome profiling study. We aimed to use single nucleus RNA sequencing (snRNAseq) to explore cell proportion and cell-type specific transcriptome changes associated with AD and neuropathology. We selected age and sex matched post mortem TCX from 12 AD and 12 control patients. We obtained snRNAseq profiles using 10x Genomics platform. After quality control 79,751 nuclei remained for analysis and were annotated for their type according to commonly used cellular markers. Nuclei proportions were tested for association with AD, Braak stage and Thal phase using Wilcoxon rank sum test. Differentially expressed genes (DEGs) and pathways were identified for each cell cluster using MAST and validated through in situ hybridization via RNAscope and qPCR. Annotation according to common cellular markers distinguished major and rare cell types of the brain in 35 clusters. We detected a unique excitatory neuron cluster, predominantly from control individuals that show less pathology and APOE ε4 genotype. DEG and pathway analysis revealed alterations in myelination pathways in oligodendrocytes, and synaptic signaling pathways in excitatory neurons. Analysis of the effects of sex, age, and APOE ε4 genotype on selected DEGs and pathways are conducted. In this study, we developed and optimized an snRNAseq approach for frozen human temporal cortex that captures the major brain cell types. We identified robust DEGs and pathways for these between AD vs control and demonstrated shared and unique cell-specific pathways and DEGs amongst different pathologies in AD.

Elucidating cellular contributions to tau-mediated neurodegeneration using drosophila and single-cell transcriptomics.

Alzheimer's Dementia (AD) is neuropathologically defined by aggregations of amyloid-beta plaques and tau neurofibrillary tangles in the brain, with tau pathology being most strongly correlated with cognitive decline. Systems biology approaches profiling post-mortem human AD brain tissue have highlighted involvement of key processes such as innate immunity and translational regulation. However, interpretation of cross-sectional post-mortem gene expression data is confounded by co-morbidities and mixed brain pathologies at autopsy. Moreover, characterizing longitudinal, age-related changes in Alzheimer's dementia (AD) is of particular importance because clinical manifestation of AD is a decades-long process, and age is the strongest known risk factor. We previously completed RNA-sequencing and proteomics of several highly tractable, longitudinal, Drosophila models of tauopathy to characterize tau- and age-specific perturbations. However, cellular heterogeneity confounds interpretation of bulk tissue data. We thus leverage single cell RNA sequencing (scRNA-seq) to explore cell-specific contributions to tau- and age-mediated gene expression. Whole brains of adult Drosophila pan-neuronally expressing a mutant variant of human Tau (R406W) and controls were dissociated for scRNA-seq (10x Genomics Chromium) at 1-, 10-, and 20- days of age. Perturbations in cellular composition and gene expression were quantified. To address cross-species conservation of tau-mediated scRNAseq signatures, we leverage existing human AD brain scRNA-seq data. As expected, we highlight evidence for tau-mediated degeneration of Kenyon cells, a neuronal population in the Drosophila "mushroom body" that is required for learning and memory, along with a concomitant increase in glial cell numbers (e.g. gliosis). Gene set enrichment analysis reveal overrepresentation of acetylcholine receptor regulation pathways in vulnerable mushroom body neurons. Interestingly, regularized regression analyses highlight innate immune activity as predictive of tau-related neuronal reduction. Cluster-specific transcriptional signatures between flies and humans demonstrate strong correlations among inferred cell identities. Our results comprise a powerful single cell transcriptomic resource for studying tau-mediated disruption of gene expression and dynamic age-dependent changes at cellular resolution in a tractable model system that is amenable for high throughput functional validation.

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

Abstract 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.

Transcriptomic profiling of whole blood in 22q11.2 reciprocal copy number variants reveals that cell proportion highly impacts gene expression

22q11.2 reciprocal copy number variants (CNVs) offer a powerful quasi-experimental “reverse-genetics” paradigm to elucidate how gene dosage (i.e., deletions and duplications) disrupts the transcriptome to cause further downstream effects. Clinical profiles of 22q11.2 CNV carriers indicate that disrupted gene expression causes alterations in neuroanatomy, cognitive function, and psychiatric disease risk. However, interpreting transcriptomic signal in bulk tissue requires careful consideration of potential changes in cell composition. We first characterized transcriptomic dysregulation in peripheral blood from reciprocal 22q11.2 CNV carriers using differential expression analysis and weighted gene co-expression network analysis (WGCNA) to identify modules of co-expressed genes. We also assessed for group differences in cell composition and re-characterized transcriptomic differences after accounting for cell type proportions and medication usage. Finally, to explore whether CNV-related transcriptomic changes relate to downstream phenotypes associated with 22q11.2 CNVs, we tested for associations of gene expression with neuroimaging measures and behavioral traits, including IQ and psychosis or ASD diagnosis. 22q11.2 deletion carriers (22qDel) showed widespread expression changes at the individual gene as well as module eigengene level compared to 22q11.2 duplication carriers (22qDup) and controls. 22qDup showed increased expression of 5 genes within the 22q11.2 locus, and CDH6 located outside of the locus. Downregulated modules in 22qDel implicated altered immune and inflammatory processes. Celltype deconvolution analyses revealed significant differences between CNV and control groups in T-cell, mast cell, and macrophage proportions; differential expression of individual genes between groups was substantially attenuated after adjusting for cell composition. Individual gene, module eigengene, and cell proportions were not significantly associated with psychiatric or neuroanatomic traits. Our findings suggest broad immune-related dysfunction in 22qDel and highlight the importance of understanding differences in cell composition when interpreting transcriptomic changes in clinical populations. Results also suggest novel directions for future investigation to test whether 22q11.2 CNV effects on macrophages have implications for brain-related microglial function that may contribute to psychiatric phenotypes in 22q11.2 CNV carriers.

Comparative tribology II–Measurable biphasic tissue properties have predictable impacts on cartilage rehydration and lubricity

Healthy articular cartilage supports load bearing and frictional properties unmatched among biological tissues and man-made bearing materials. Balancing fluid exudation and recovery under loaded and articulated conditions is essential to the tissue's biological and mechanical longevity. Our prior tribological investigations, which leveraged the convergent stationary contact area (cSCA) configuration, revealed that sliding alone can modulate cartilage interstitial fluid pressurization and the recovery and maintenance of lubrication under load through a mechanism termed ‘tribological rehydration.’ Our recent comparative assessment of tribological rehydration revealed remarkably consistent sliding speed-dependent fluid recovery and lubrication behaviors across femoral condyle cartilage from five mammalian species (equine/horse, bovine/cow, porcine/pig, ovine/sheep, and caprine/goat). In the present study, we identified and characterized key predictive relationships among tissue properties, sliding-induced tribological rehydration, and the modulation/recovery of lubrication within healthy articular cartilage. Using correlational analysis, we linked observed speed-dependent tribological rehydration behaviors to cartilage's geometry and biphasic properties (tensile and compressive moduli, and permeability). Together, these findings demonstrate that easily measurable biphasic tissue characteristics (e.g., bulk tissue material properties, compressive strain magnitude, and strain rates) can be used to predict cartilage's rehydration and lubricating abilities, and ultimately its function in vivo. Statement of significanceIn healthy cartilage, articulation recovers fluid lost to static loading thereby sustaining tissue lubricity. Osteoarthritis causes changes to cartilage composition, stiffness, and permeability associated with faster fluid exudation and presumably poorer frictional outcomes. Yet, the relationship between mechanical properties and fluid recovery during articulation/sliding remains unclear. Through innovative, high-speed benchtop sliding and indentation experiments, we found that cartilage's tissue properties regulate its exudation/hydration under slow sliding speeds but have minimal effect at high sliding speeds. In fact, cartilage rehydration appears insensitive to permeability and stiffness under high fluid load support conditions. This new understanding of the balance of cartilage exudation and rehydration during activity, based upon comparative tribology studies, may improve prevention and rehabilitation strategies for joint injuries and osteoarthritis.

The Epithelial and Stromal Immune Microenvironment in Gastric Cancer: A Comprehensive Analysis Reveals Prognostic Factors with Digital Cytometry.

Simple SummaryWe investigated the tumor microenvironment of gastric cancer (GC) by combining single cell and bulk transcriptomic profiles. We built a novel signature matrix to dissect epithelium and stroma signals from tissue samples using a scRNA-seq data set for GC and then applied cell mixture deconvolution to estimate diverse epithelial, stromal, and immune cell proportions from bulk transcriptome data in four independent GC cohorts. Using a robust computational pipeline, we identified an early malignant epithelial cell (EMEC) population whose proportions were significantly higher in patients with stage I cancer than other stages, and it was predominantly present in tumor samples but not typically found in normal samples. By using univariate and multivariate analyses in the training cohort, we identified that the ratio of EMECs to stromal cells and the ratio of adaptive T cells to monocytes were the most significant prognostic factors within the non-immune and immune factors, respectively. The STEM score, which unifies these two prognostic factors, was an independent prognostic factor of overall survival for GC.Gastric cancer (GC) is the third leading cause of cancer-related deaths worldwide. Tumor heterogeneity continues to confound researchers’ understanding of tumor growth and the development of an effective therapy. Digital cytometry allows interpretation of heterogeneous bulk tissue transcriptomes at the cellular level. We built a novel signature matrix to dissect epithelium and stroma signals using a scRNA-seq data set (GSE134520) for GC and then applied cell mixture deconvolution to estimate diverse epithelial, stromal, and immune cell proportions from bulk transcriptome data in four independent GC cohorts (GSE62254, GSE15459, GSE84437, and TCGA-STAD) from the GEO and TCGA databases. Robust computational methods were applied to identify strong prognostic factors for GC. We identified an EMEC population whose proportions were significantly higher in patients with stage I cancer than other stages, and it was predominantly present in tumor samples but not typically found in normal samples. We found that the ratio of EMECs to stromal cells and the ratio of adaptive T cells to monocytes were the most significant prognostic factors within the non-immune and immune factors, respectively. The STEM score, which unifies these two prognostic factors, was an independent prognostic factor of overall survival (HR = 0.92, CI = 0.89–0.94, ). The entire GC cohort was stratified into three risk groups (high-, moderate-, and low-risk), which yielded incremental survival times (). For stage III disease, patients in the moderate- and low-risk groups experienced better survival benefits from radiation therapy ((HR = 0.16, 95% CI = 0.06–0.4, ), whereas those in the high-risk group did not (HR = 0.49, 95% CI = 0.14–1.72, ). We concluded that the STEM score is a promising prognostic factor for gastric cancer.

Single-Neuron RNA Modification Analysis by Mass Spectrometry: Characterizing RNA Modification Patterns and Dynamics with Single-Cell Resolution.

The entire collection of post-transcriptional modifications to RNA, known as the epitranscriptome, has been increasingly recognized as a critical regulatory layer in the cellular translation machinery. However, contemporary methods for the analysis of RNA modifications are limited to the detection of highly abundant modifications in bulk tissue samples, potentially obscuring unique epitranscriptomes of individual cells with population averages. We developed an approach, single-neuron RNA modification analysis by mass spectrometry (SNRMA-MS), that enables the detection and quantification of numerous post-transcriptionally modified nucleosides in single cells. When compared to a conventional RNA extraction approach that does not allow detection of RNA modifications in single cells, SNRMA-MS leverages an optimized sample preparation approach to detect up to 16 RNA modifications in individual neurons from the central nervous system of Aplysia californica. SNRMA-MS revealed that the RNA modification profiles of identified A. californica neurons with different physiological functions were mostly cell specific. However, functionally homologous neurons tended to demonstrate similar modification patterns. Stable isotope labeling with CD3-Met showed significant differences in RNA methylation rates that were dependent on the identity of the modification and the cell, with metacerebral cells (MCCs) displaying the fastest incorporation of CD3 groups into endogenous RNAs. Quantitative SNRMA-MS showed higher intracellular concentrations for 2'-O-methyladenosine and 2'-O-methylcytidine in homologous R2/LPl1 cell pairs than in MCCs. Overall, SNRMA-MS is the first analytical approach capable of simultaneously quantifying numerous RNA modifications in single neurons and revealing cell-specific modification profiles.

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 thatB. philenoracquire 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.

Integrated laser ablation-dropletProbe-mass spectrometry for absolute drug quantitation, metabolite detection, and distribution in tissue.

Spatially resolved and accurate quantitation of drug-related compounds in tissue is a much-needed capability in drug discovery research. Here, application of an integrated laser ablation-dropletProbe-mass spectrometry surface sampling system (LADP-MS) is reported, which achieved absolute quantitation of propranolol measured from <500 × 500 μm thin tissue samples. Mouse liver and kidney thin tissue sections were coated with parylene C and analyzed for propranolol by a laser ablation/liquid extraction workflow. Non-coated adjacent sections were microdissected for validation and processed using standard bulk tissue extraction protocols. High-performance liquid chromatography with positive ion mode electrospray ionization tandem mass spectrometry was applied to detect the drug and its metabolites. Absolute propranolol concentration in ~500 × 500 μm tissue regions measured by the two methods agreed within ±8% and had a relative standard deviation within ±17%. Quantitation down to ~400 × 400 μm tissue regions was shown, and this resolution was also used for automated mapping of propranolol and phase II hydroxypropranolol glucuronide metabolites in kidney tissue. This study exemplifies the capabilities of integrated laser ablation-dropletProbe-mass spectrometry (LADP-MS) for high resolution absolute drug quantitation analysis of thin tissue sections. This capability will be valuable for applications needing to quantitatively understand the spatial distribution of small molecules in tissue.

Applications of single-cell genomics and computational strategies to study common disease and population-level variation.

The advent and rapid development of single-cell technologies have made it possible to study cellular heterogeneity at an unprecedented resolution and scale. Cellular heterogeneity underlies phenotypic differences among individuals, and studying cellular heterogeneity is an important step toward our understanding of the disease molecular mechanism. Single-cell technologies offer opportunities to characterize cellular heterogeneity from different angles, but how to link cellular heterogeneity with disease phenotypes requires careful computational analysis. In this article, we will review the current applications of single-cell methods in human disease studies and describe what we have learned so far from existing studies about human genetic variation. As single-cell technologies are becoming widely applicable in human disease studies, population-level studies have become a reality. We will describe how we should go about pursuing and designing these studies, particularly how to select study subjects, how to determine the number of cells to sequence per subject, and the needed sequencing depth per cell. We also discuss computational strategies for the analysis of single-cell data and describe how single-cell data can be integrated with bulk tissue data and data generated from genome-wide association studies. Finally, we point out open problems and future research directions.

Sites of active gene regulation in the prenatal frontal cortex and their role in neuropsychiatric disorders.

Common genetic variation appears to largely influence risk for neuropsychiatric disorders through effects on gene regulation. It is therefore possible to shed light on the biology of these conditions by testing for enrichment of associated genetic variation within regulatory genomic regions operating in specific tissues or cell types. Here, we have used the assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-Seq) to map open chromatin (an index of active regulatory genomic regions) in bulk tissue, NeuN+ and NeuN- nuclei from the prenatal human frontal cortex, and tested enrichment of single-nucleotide polymorphism (SNP) heritability for five neuropsychiatric disorders (autism spectrum disorder, attention deficit hyperactivity disorder [ADHD], bipolar disorder, major depressive disorder, and schizophrenia) within these regions. We observed significant enrichment of SNP heritability for ADHD, major depressive disorder, and schizophrenia within open chromatin regions (OCRs) mapped in bulk fetal frontal cortex, and for all five tested neuropsychiatric conditions when we restricted these sites to those overlapping histone modifications indicative of enhancers (H3K4me1) or promoters (H3K4me3) in fetal brain. SNP heritability for neuropsychiatric disorders was significantly enriched in OCRs identified in fetal frontal cortex NeuN- as well as NeuN+ nuclei overlapping fetal brain H3K4me1 or H3K4me3 sites. We additionally demonstrate the utility of our mapped OCRs for prioritizing potentially functional SNPs at genome-wide significant risk loci for neuropsychiatric disorders. Our data provide evidence for an early neurodevelopmental component to a range of neuropsychiatric conditions and highlight an important role for regulatory genomic regions active within both NeuN+ and NeuN- cells of the prenatal brain.

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.

A compendium of uniformly processed human gene expression and splicing quantitative trait loci

Many gene expression quantitative trait locus (eQTL) studies have published their summary statistics, which can be used to gain insight into complex human traits by downstream analyses, such as fine mapping and co-localization. However, technical differences between these datasets are a barrier to their widespread use. Consequently, target genes for most genome-wide association study (GWAS) signals have still not been identified. In the present study, we present the eQTL Catalogue (https://www.ebi.ac.uk/eqtl), a resource of quality-controlled, uniformly re-computed gene expression and splicing QTLs from 21 studies. We find that, for matching cell types and tissues, the eQTL effect sizes are highly reproducible between studies. Although most QTLs were shared between most bulk tissues, we identified a greater diversity of cell-type-specific QTLs from purified cell types, a subset of which also manifested as new disease co-localizations. Our summary statistics are freely available to enable the systematic interpretation of human GWAS associations across many cell types and tissues.

Quantification of Oxygen Depletion During FLASH Irradiation In Vitro and In Vivo.

Delivery of radiation at ultrahigh dose rates (UHDRs), known as FLASH, has recently been shown to preferentially spare normal tissues from radiation damage compared with tumor tissues. However, the underlying mechanism of this phenomenon remains unknown, with one of the most widely considered hypotheses being that the effect is related to substantial oxygen depletion upon FLASH, thereby altering the radiochemical damage during irradiation, leading to different radiation responses of normal and tumor cells. Testing of this hypothesis would be advanced by direct measurement of tissue oxygen in vivo during and after FLASH irradiation. Oxygen measurements were performed in vitro and in vivo using the phosphorescence quenching method and a water-soluble molecular probe Oxyphor 2P. The changes in oxygen per unit dose (G-values) were quantified in response to irradiation by 10 MeV electron beam at either UHDR reaching 300 Gy/s or conventional radiation therapy dose rates of 0.1 Gy/s. In vitro experiments with 5% bovine serum albumin solutions at 23°C resulted in G-values for oxygen consumption of 0.19 to 0.21 mm Hg/Gy (0.34-0.37 μM/Gy) for conventional irradiation and 0.16 to 0.17 mm Hg/Gy (0.28-0.30 μM/Gy) for UHDR irradiation. In vivo, the total decrease in oxygen after a single fraction of 20 Gy FLASH irradiation was 2.3 ± 0.3 mm Hg in normal tissue and 1.0 ± 0.2 mm Hg in tumor tissue (P < .00001), whereas no decrease in oxygen was observed from a single fraction of 20 Gy applied in conventional mode. Our observations suggest that oxygen depletion to radiologically relevant levels of hypoxia is unlikely to occur in bulk tissue under FLASH irradiation. For the same dose, FLASH irradiation induces less oxygen consumption than conventional irradiation in vitro, which may be related to the FLASH sparing effect. However, the difference in oxygen depletion between FLASH and conventional irradiation could not be quantified in vivo because measurements of oxygen depletion under conventional irradiation are hampered by resupply of oxygen from the blood.