Multi-omics Analysis of Umbilical Cord Hematopoietic Stem Cells from a Multi-ethnic Cohort of Hawaii Reveals the Transgenerational Effect of Maternal Pre-Pregnancy Obesity.

Background: Maternal obesity is a health concern that may predispose newborns to a high risk of medical problems later in life. To understand the transgenerational effect of maternal obesity, we conducted a multi-omics study, using DNA methylation and gene expression in the CD34+/CD38-/Lin- umbilical cord blood hematopoietic stem cells (uHSCs) and metabolomics of the cord blood, all from a multi-ethnic cohort (n=72) from Kapiolani Medical Center for Women and Children in Honolulu, Hawaii (collected between 2016 and 2018). Results: Differential methylation (DM) analysis unveiled a global hypermethylation pattern in the maternal pre-pregnancy obese group (BH adjusted p<0.05), after adjusting for major clinical confounders. Comprehensive functional analysis showed hypermethylation in promoters of genes involved in cell cycle, protein synthesis, immune signaling, and lipid metabolism. Utilizing Shannon entropy on uHSCs methylation, we discerned notably higher quiescence of uHSCs impacted by maternal obesity. Additionally, the integration of multi-omics data-including methylation, gene expression, and metabolomics-provided further evidence of dysfunctions in adipogenesis, erythropoietin production, cell differentiation, and DNA repair, aligning with the findings at the epigenetic level. Conclusions: This study reveals the significant correlation between pre-pregnancy maternal obesity and multi-omics level molecular changes in the uHSCs of offspring, particularly in DNA methylation.

Five-Feature Models to Predict Preeclampsia Onset Time From Electronic Health Record Data: Development and Validation Study.

BACKGROUND:  Preeclampsia is a potentially fatal complication during pregnancy, characterized by high blood pressure and the presence of excessive proteins in the urine. Due to its complexity, the prediction of preeclampsia onset is often difficult and inaccurate. OBJECTIVE:  This study aimed to create quantitative models to predict the onset gestational age of preeclampsia using electronic health records. METHODS:  We retrospectively collected 1178 preeclamptic pregnancy records from the University of Michigan Health System as the discovery cohort, and 881 records from the University of Florida Health System as the validation cohort. We constructed 2 Cox-proportional hazards models: 1 baseline model using maternal and pregnancy characteristics, and the other full model with additional laboratory findings, vitals, and medications. We built the models using 80% of the discovery data, tested the remaining 20% of the discovery data, and validated with the University of Florida data. We further stratified the patients into high- and low-risk groups for preeclampsia onset risk assessment. RESULTS:  The baseline model reached Concordance indices of 0.64 and 0.61 in the 20% testing data and the validation data, respectively, while the full model increased these Concordance indices to 0.69 and 0.61, respectively. For preeclampsia diagnosed at 34 weeks, the baseline and full models had area under the curve (AUC) values of 0.65 and 0.70, and AUC values of 0.69 and 0.70 for preeclampsia diagnosed at 37 weeks, respectively. Both models contain 5 selective features, among which the number of fetuses in the pregnancy, hypertension, and parity are shared between the 2 models with similar hazard ratios and significant P values. In the full model, maximum diastolic blood pressure in early pregnancy was the predominant feature. CONCLUSIONS:  Electronic health records data provide useful information to predict the gestational age of preeclampsia onset. Stratification of the cohorts using 5-predictor Cox-proportional hazards models provides clinicians with convenient tools to assess the onset time of preeclampsia in patients.

Rescue Blood Contamination in scRNA-Seq Data by Originator, a Computational Deciphering Tool using Genetic and Contextual Information.

Single-cell RNA sequencing (scRNA-Seq) data from complex human tissues have prevalent blood cell contamination due to the sample preparation process and may comprise cells of different genetic makeups. To reveal such complexity and annotate cells appropriately, we propose the first-of-its-kind computational framework, Originator, which deciphers single cells by genetic origin and separates blood cells from tissue-resident cells. We show that blood contamination is widely spread in scRNA-Seq data from a variety of tissues. We warn of the significant biases in downstream analysis without considering blood contamination and genetic contexts using pancreatic ductal adenocarcinoma and placenta data, respectively.

Antigen-Clustered Nanovaccine Achieves Long-Term Tumor Remission by Promoting B/CD 4 T Cell Crosstalk.

Current cancer vaccines using T cell epitopes activate antitumor T cell immunity through dendritic cell/macrophage-mediated antigen presentation, but they lack the ability to promote B/CD4 T cell crosstalk, limiting their anticancer efficacy. We developed antigen-clustered nanovaccine (ACNVax) to achieve long-term tumor remission by promoting B/CD4 T cell crosstalk. The topographic features of ACNVax were achieved using an iron nanoparticle core attached with an optimal number of gold nanoparticles, where the clusters of HER2 B/CD4 T cell epitopes were conjugated on the gold surface with an optimal intercluster distance of 5-10 nm. ACNVax effectively trafficked to lymph nodes and cross-linked with BCR, which are essential for stimulating B cell antigen presentation-mediated B/CD4 T cell crosstalk in vitro and in vivo. ACNVax, combined with anti-PD-1, achieved long-term tumor remission (>200 days) with 80% complete response in mice with HER2+ breast cancer. ACNVax not only remodeled the tumor immune microenvironment but also induced a long-term immune memory, as evidenced by complete rejection of tumor rechallenge and a high level of antigen-specific memory B, CD4, and CD8 cells in mice (>200 days). This study provides a cancer vaccine design strategy, using B/CD4 T cell epitopes in an antigen clustered topography, to achieve long-term durable anticancer efficacy through promoting B/CD4 T cell crosstalk.

Discover overlooked complications after preeclampsia using electronic health records.

Background: Preeclampsia (PE) is a severe pregnancy complication characterized by hypertension and end-organ damage such as proteinuria. PE poses a significant threat to women's long-term health, including an increased risk of cardiovascular and renal diseases. Most previous studies have been hypothesis-based, potentially overlooking certain significant complications. This study conducts a comprehensive, non-hypothesis-based analysis of PE-complicated diagnoses after pregnancies using multiple large-scale electronic health records (EHR) datasets. Method: From the University of Michigan (UM) Healthcare System, we collected 4,348 PE patients for the cases and 27,377 patients with pregnancies not complicated by PE or related conditions for the controls. We first conducted a non-hypothesis-based analysis to identify any long-term adverse health conditions associated with PE using logistic regression with adjustments to demographics, social history, and medical history. We confirmed the identified complications with UK Biobank data which contain 443 PE cases and 14,870 non-PE controls. We then conducted a survival analysis on complications that exhibited significance in more than 5 consecutive years post-PE. We further examined the potential racial disparities of identified complications between Caucasian and African American patients. Findings: Uncomplicated hypertension, complicated diabetes, congestive heart failure, renal failure, and obesity exhibited significantly increased risks whereas hypothyroidism showed decreased risks, in 5 consecutive years after PE in the UM discovery data. UK Biobank data confirmed the increased risks of uncomplicated hypertension, complicated diabetes, congestive heart failure, renal failure, and obesity. Further survival analysis using UM data indicated significantly increased risks in uncomplicated hypertension, complicated diabetes, congestive heart failure, renal failure, and obesity, and significantly decreased risks in hypothyroidism. There exist racial differences in the risks of developing hypertension and hypothyroidism after PE. PE protects against hypothyroidism in African American postpartum women but not Cacausians; it also increases the risks of uncomplicated hypertension but less severely in African American postpartum women as compared to Cacausians. Interpretation: This study addresses the lack of a comprehensive examination of PE's long-term effects utilizing large-scale EHR and advanced statistical methods. Our findings underscore the need for long-term monitoring and interventions for women with a history of PE, emphasizing the importance of personalized postpartum care. Notably, the racial disparities observed in the impact of PE on hypertension and hypothyroidism highlight the necessity of tailored aftercare based on race.

Challenges and perspectives in computational deconvolution of genomics data.

Deciphering cell-type heterogeneity is crucial for systematically understanding tissue homeostasis and its dysregulation in diseases. Computational deconvolution is an efficient approach for estimating cell-type abundances from a variety of omics data. Despite substantial methodological progress in computational deconvolution in recent years, challenges are still outstanding. Here we enlist four important challenges related to computational deconvolution: the quality of the reference data, generation of ground truth data, limitations of computational methodologies, and benchmarking design and implementation. Finally, we make recommendations on reference data generation, new directions of computational methodologies, and strategies to promote rigorous benchmarking.

Deep learning and transfer learning identify breast cancer survival subtypes from single-cell imaging data.

BACKGROUND: Single-cell multiplex imaging data have provided new insights into disease subtypes and prognoses recently. However, quantitative models that explicitly capture single-cell resolution cell-cell interaction features to predict patient survival at a population scale are currently missing. METHODS: We quantified hundreds of single-cell resolution cell-cell interaction features through neighborhood calculation, in addition to cellular phenotypes. We applied these features to a neural-network-based Cox-nnet survival model to identify survival-associated features. We used non-negative matrix factorization (NMF) to identify patient survival subtypes. We identified atypical subpopulations of triple-negative breast cancer (TNBC) patients with moderate prognosis and Luminal A patients with poor prognosis and validated these subpopulations by label transferring using the UNION-COM method. RESULTS: The neural-network-based Cox-nnet survival model using all cellular phenotype and cell-cell interaction features is highly predictive of patient survival in the test data (Concordance Index > 0.8). We identify seven survival subtypes using the top survival features, presenting distinct profiles of epithelial, immune, and fibroblast cells and their interactions. We reveal atypical subpopulations of TNBC patients with moderate prognosis (marked by GATA3 over-expression) and Luminal A patients with poor prognosis (marked by KRT6 and ACTA2 over-expression and CDH1 under-expression). These atypical subpopulations are validated in TCGA-BRCA and METABRIC datasets. CONCLUSIONS: This work provides an approach to bridge single-cell level information toward population-level survival prediction.

It may be possible to separate patients with cancer into different groups or subtypes based on the features of their tumor, such as the interactions between different types of cells in the tumor. In this study, we develop a computer-based model to calculate the interactions between cells in breast cancer images. We use these interactions to identify seven subtypes of patients with breast cancer with differences in their survival. We identify some subpopulations of patients with atypical survival outcomes. This work may ultimately help clinicians and researchers to identify patients with breast cancer at increased risk of poorer outcomes and to tailor their treatments accordingly.

Prediction of spontaneous preterm birth using supervised machine learning on metabolomic data: A case-cohort study.

OBJECTIVES: To identify and internally validate metabolites predictive of spontaneous preterm birth (sPTB) using multiple machine learning methods and sequential maternal serum samples, and to predict spontaneous early term birth (sETB) using these metabolites. DESIGN: Case-cohort design within a prospective cohort study. SETTING: Cambridge, UK. POPULATION OR SAMPLE: A total of 399 Pregnancy Outcome Prediction study participants, including 98 cases of sPTB. METHODS: An untargeted metabolomic analysis of maternal serum samples at 12, 20, 28 and 36 weeks of gestation was performed. We applied six supervised machine learning methods and a weighted Cox model to measurements at 28 weeks of gestation and sPTB, followed by feature selection. We used logistic regression with elastic net penalty, followed by best subset selection, to reduce the number of predictive metabolites further. We applied coefficients from the chosen models to measurements from different gestational ages to predict sPTB and sETB. MAIN OUTCOME MEASURES: sPTB and sETB. RESULTS: We identified 47 metabolites, mostly lipids, as important predictors of sPTB by two or more methods and 22 were identified by three or more methods. The best 4-predictor model had an optimism-corrected area under the receiver operating characteristics curve (AUC) of 0.703 at 28 weeks of gestation. The model also predicted sPTB in 12-week samples (0.606, 95% CI 0.544-0.667) and 20-week samples (0.657, 95% CI 0.597-0.717) and it predicted sETB in 36-week samples (0.727, 95% CI 0.606-0.849). A lysolipid, 1-palmitoleoyl-GPE (16:1)*, was the strongest predictor of sPTB at 12 weeks of gestation (0.609, 95% CI 0.548-0.670), 20 weeks (0.630, 95% CI 0.569-0.690) and 28 weeks (0.660, 95% CI 0.599-0.722), and of sETB at 36 weeks (0.739, 95% CI 0.618-0.860). CONCLUSIONS: We identified and internally validated maternal serum metabolites predictive of sPTB. A lysolipid, 1-palmitoleoyl-GPE (16:1)*, is a novel predictor of sPTB and sETB. Further validation in external populations is required.

Deep-learning and transfer learning identify new breast cancer survival subtypes from single-cell imaging data.

Quantitative models that explicitly capture single-cell resolution cell-cell interaction features to predict patient survival at population scale are currently missing. Here, we computationally extracted hundreds of features describing single-cell based cell-cell interactions and cellular phenotypes from a large, published cohort of cyto-images of breast cancer patients. We applied these features to a neural-network based Cox-nnet survival model and obtained high accuracy in predicting patient survival in test data (Concordance Index > 0.8). We identified seven survival subtypes using the top survival features, which present distinct profiles of epithelial, immune, fibroblast cells, and their interactions. We identified atypical subpopulations of TNBC patients with moderate prognosis (marked by GATA3 over-expression) and Luminal A patients with poor prognosis (marked by KRT6 and ACTA2 over-expression and CDH1 under-expression). These atypical subpopulations are validated in TCGA-BRCA and METABRIC datasets. This work provides important guidelines on bridging single-cell level information towards population-level survival prediction. STATEMENT OF TRANSLATIONAL RELEVANCE: Our findings from a breast cancer population cohort demonstrate the clinical utility of using the single-cell level imaging mass cytometry (IMC) data as a new type of patient prognosis prediction marker. Not only did the prognosis prediction achieve high accuracy with a Concordance index score greater than 0.8, it also enabled the discovery of seven survival subtypes that are more distinguishable than the molecular subtypes. These new subtypes present distinct profiles of epithelial, immune, fibroblast cells, and their interactions. Most importantly, this study identified and validated atypical subpopulations of TNBC patients with moderate prognosis (GATA3 over-expression) and Luminal A patients with poor prognosis (KRT6 and ACTA2 over-expression and CDH1 under-expression), using multiple large breast cancer cohorts.

Severe preeclampsia is not associated with significant DNA methylation changes but cell proportion changes in the cord blood - caution on the importance of confounding adjustment.

Epigenome-wide DNA methylation analysis (EWAS) is an important approach to identify biomarkers for early disease detection and prognosis prediction, yet its results could be confounded by other factors such as cell-type heterogeneity and patient characteristics. In this study, we address the importance of confounding adjustment by examining DNA methylation patterns in cord blood exposed to severe preeclampsia (PE), a prevalent and potentially fatal pregnancy complication. Without such adjustment, a misleading global hypomethylation pattern is obtained. However, after adjusting cell type proportions and patient clinical characteristics, most of the so-called significant CpG methylation changes associated with severe PE disappear. Rather, the major effect of PE on cord blood is through the proportion changes in different cell types. These results are validated using a previously published cord blood DNA methylation dataset, where global hypomethylation pattern was also wrongfully obtained without confounding adjustment. Additionally, several cell types significantly change as gestation progress (eg. granulocyte, nRBC, CD4T, and B cells), further confirming the importance of cell type adjustment in EWAS study of cord blood tissues. Our study urges the community to perform confounding adjustments in EWAS studies, based on cell type heterogeneity and other patient characteristics.

ASGARD is A Single-cell Guided Pipeline to Aid Repurposing of Drugs.

Single-cell RNA sequencing technology has enabled in-depth analysis of intercellular heterogeneity in various diseases. However, its full potential for precision medicine has yet to be reached. Towards this, we propose A Single-cell Guided Pipeline to Aid Repurposing of Drugs (ASGARD) that defines a drug score to recommend drugs by considering all cell clusters to address the intercellular heterogeneity within each patient. ASGARD shows significantly better average accuracy on single-drug therapy compared to two bulk-cell-based drug repurposing methods. We also demonstrated that it performs considerably better than other cell cluster-level predicting methods. In addition, we validate ASGARD using the drug response prediction method TRANSACT with Triple-Negative-Breast-Cancer patient samples. We find that many top-ranked drugs are either approved by the Food and Drug Administration or in clinical trials treating corresponding diseases. In conclusion, ASGARD is a promising drug repurposing recommendation tool guided by single-cell RNA-seq for personalized medicine. ASGARD is free for educational use at https://github.com/lanagarmire/ASGARD .

Heterogeneity of neutrophils and inflammatory responses in patients with COVID-19 and healthy controls.

Severe respiratory viral infections, including SARS-CoV-2, have resulted in high mortality rates despite corticosteroids and other immunomodulatory therapies. Despite recognition of the pathogenic role of neutrophils, in-depth analyses of this cell population have been limited, due to technical challenges of working with neutrophils. We undertook an unbiased, detailed analysis of neutrophil responses in adult patients with COVID-19 and healthy controls, to determine whether distinct neutrophil phenotypes could be identified during infections compared to the healthy state. Single-cell RNA sequencing analysis of peripheral blood neutrophils from hospitalized patients with mild or severe COVID-19 disease and healthy controls revealed distinct mature neutrophil subpopulations, with relative proportions linked to disease severity. Disruption of predicted cell-cell interactions, activated oxidative phosphorylation genes, and downregulated antiviral and host defense pathway genes were observed in neutrophils obtained during severe compared to mild infections. Our findings suggest that during severe infections, there is a loss of normal regulatory neutrophil phenotypes seen in healthy subjects, coupled with the dropout of appropriate cellular interactions. Given that neutrophils are the most abundant circulating leukocytes with highly pathogenic potential, current immunotherapies for severe infections may be optimized by determining whether they aid in restoring an appropriate balance of neutrophil subpopulations.

Computational Methods for Single-cell Multi-omics Integration and Alignment.

Recently developed technologies to generate single-cell genomic data have made a revolutionary impact in the field of biology. Multi-omics assays offer even greater opportunities to understand cellular states and biological processes. The problem of integrating different omics data with very different dimensionality and statistical properties remains, however, quite challenging. A growing body of computational tools is being developed for this task, leveraging ideas ranging from machine translation to the theory of networks, and represents another frontier on the interface of biology and data science. Our goal in this review is to provide a comprehensive, up-to-date survey of computational techniques for the integration of single-cell multi-omics data, while making the concepts behind each algorithm approachable to a non-expert audience.

Placental telomere length shortening is not associated with severe preeclampsia but the gestational age.

Variations in telomere length (TL) have been associated with aging, stress, and many diseases. Placenta TL is an essential molecular component influencing the outcome of birth. Previous investigations into the relationship between placenta TL and preeclampsia (PE) have produced conflicting findings. We conducted a retrospective case-control analysis in this study to address the disparity. We used placenta samples from 224 births received from Hawaii Biorepository (HiBR) between 2006 and 2013, comprising 129 healthy full-term controls and 95 severe PE samples. The average absolute placental TL was calculated using the quantitative polymerase chain reaction (qPCR) technique. We utilized multiple linear regressions to associate placental TL with severe PE and other demographic, clinical and physiological data. The outcome demonstrates that the placental TL of severe PE cases did not significantly differ from that of healthy controls. Instead, there is a strong correlation between gestational age and placenta TL shortening. Placental TL also exhibits racial differences: (1) Latino moms' TL is significantly longer than non-Latino mothers' (p=0.009). (2) Caucasian patients with severe PE have shorter TL than non-Caucasian patients (p=0.0037). This work puts the long-standing question of whether severe PE influences placental TL to rest. Placental TL is not related to severe PE but is negatively associated with gestational age and is also affected by race.

Blood-derived lncRNAs as biomarkers for cancer diagnosis: the Good, the Bad and the Beauty.

Cancer ranks as one of the deadliest diseases worldwide. The high mortality rate associated with cancer is partially due to the lack of reliable early detection methods and/or inaccurate diagnostic tools such as certain protein biomarkers. Cell-free nucleic acids (cfNA) such as circulating long noncoding RNAs (lncRNAs) have been proposed as a new class of potential biomarkers for cancer diagnosis. The reported correlation between the presence of tumors and abnormal levels of lncRNAs in the blood of cancer patients has notably triggered a worldwide interest among clinicians and oncologists who have been actively investigating their potentials as reliable cancer biomarkers. In this report, we review the progress achieved ("the Good") and challenges encountered ("the Bad") in the development of circulating lncRNAs as potential biomarkers for early cancer diagnosis. We report and discuss the diagnostic performance of more than 50 different circulating lncRNAs and emphasize their numerous potential clinical applications ("the Beauty") including therapeutic targets and agents, on top of diagnostic and prognostic capabilities. This review also summarizes the best methods of investigation and provides useful guidelines for clinicians and scientists who desire conducting their own clinical studies on circulating lncRNAs in cancer patients via RT-qPCR or Next Generation Sequencing (NGS).

Emerging artificial intelligence applications in Spatial Transcriptomics analysis.

Spatial transcriptomics (ST) has advanced significantly in the last few years. Such advancement comes with the urgent need for novel computational methods to handle the unique challenges of ST data analysis. Many artificial intelligence (AI) methods have been developed to utilize various machine learning and deep learning techniques for computational ST analysis. This review provides a comprehensive and up-to-date survey of current AI methods for ST analysis.

Maternal plasma lipids are involved in the pathogenesis of preterm birth.

BACKGROUND: Preterm birth is defined by the onset of labor at a gestational age shorter than 37 weeks, and it can lead to premature birth and impose a threat to newborns' health. The Puerto Rico PROTECT cohort is a well-characterized prospective birth cohort that was designed to investigate environmental and social contributors to preterm birth in Puerto Rico, where preterm birth rates have been elevated in recent decades. To elucidate possible relationships between metabolites and preterm birth in this cohort, we conducted a nested case-control study to conduct untargeted metabolomic characterization of maternal plasma of 31 women who experienced preterm birth and 69 controls who underwent full-term labor at 24-28 gestational weeks. RESULTS: A total of 333 metabolites were identified and annotated with liquid chromatography/mass spectrometry. Subsequent weighted gene correlation network analysis shows that the fatty acid and carene-enriched module has a significant positive association (P = 8e-04, FDR = 0.006) with preterm birth. After controlling for potential clinical confounders, a total of 38 metabolites demonstrated significant changes uniquely associated with preterm birth, where 17 of them were preterm biomarkers. Among 7 machine-learning classifiers, the application of random forest achieved a highly accurate and specific prediction (AUC = 0.92) for preterm birth in testing data, demonstrating their strong potential as biomarkers for preterm births. The 17 preterm biomarkers are involved in cell signaling, lipid metabolism, and lipid peroxidation functions. Additional modeling using only the 19 spontaneous preterm births (sPTB) and controls identifies 16 sPTB markers, with an AUC of 0.89 in testing data. Half of the sPTB overlap with those markers for preterm births. Further causality analysis infers that suberic acid upregulates several fatty acids to promote preterm birth. CONCLUSIONS: Altogether, this study demonstrates the involvement of lipids, particularly fatty acids, in the pathogenesis of preterm birth.

BML: a versatile web server for bipartite motif discovery.

Motif discovery and characterization are important for gene regulation analysis. The lack of intuitive and integrative web servers impedes the effective use of motifs. Most motif discovery web tools are either not designed for non-expert users or lacking optimization steps when using default settings. Here we describe bipartite motifs learning (BML), a parameter-free web server that provides a user-friendly portal for online discovery and analysis of sequence motifs, using high-throughput sequencing data as the input. BML utilizes both position weight matrix and dinucleotide weight matrix, the latter of which enables the expression of the interdependencies of neighboring bases. With input parameters concerning the motifs are given, the BML achieves significantly higher accuracy than other available tools for motif finding. When no parameters are given by non-expert users, unlike other tools, BML employs a learning method to identify motifs automatically and achieve accuracy comparable to the scenario where the parameters are set. The BML web server is freely available at http://motif.t-ridership.com/ (https://github.com/Mohammad-Vahed/BML).

A Dual-Filtration System for Single-Cell Sequencing of Circulating Tumor Cells and Clusters in HCC.

Hepatocellular carcinoma (HCC) is a leading cause of cancer death worldwide. Identification and sequencing of circulating tumor (CT) cells and clusters may allow for noninvasive molecular characterization of HCC, which is an unmet need, as many patients with HCC do not undergo biopsy. We evaluated CT cells and clusters, collected using a dual-filtration system in patients with HCC. We collected and filtered whole blood from patients with HCC and selected individual CT cells and clusters with a micropipette. Reverse transcription, polymerase chain reaction, and library preparation were performed using a SmartSeq2 protocol, followed by single-cell RNA sequencing (scRNAseq) on an Illumina MiSeq V3 platform. Of the 8 patients recruited, 6 had identifiable CT cells or clusters. Median age was 64 years old; 7 of 8 were male; and 7 of 8 had and Barcelona Clinic Liver Cancer stage C. We performed scRNAseq of 38 CT cells and 33 clusters from these patients. These CT cells and clusters formed two distinct groups. Group 1 had significantly higher expression than group 2 of markers associated with epithelial phenotypes (CDH1 [Cadherin 1], EPCAM [epithelial cell adhesion molecule], ASGR2 [asialoglycoprotein receptor 2], and KRT8 [Keratin 8]), epithelial-mesenchymal transition (VIM [Vimentin]), and stemness (PROM1 [CD133], POU5F1 [POU domain, class 5, transcription factor 1], NOTCH1, STAT3 [signal transducer and activator of transcription 3]) (P < 0.05 for all). Patients with identifiable group 1 cells or clusters had poorer prognosis than those without them (median overall survival 39 vs. 384 days; P = 0.048 by log-rank test). Conclusion: A simple dual-filtration system allows for isolation and sequencing of CT cells and clusters in HCC and may identify cells expressing candidate genes known to be involved in cancer biology. Presence of CT cells/clusters expressing candidate genes is associated with poorer prognosis in advanced-stage HCC.

Early Pregnancy Exposure to Ambient Air Pollution among Late-Onset Preeclamptic Cases Is Associated with Placental DNA Hypomethylation of Specific Genes and Slower Placental Maturation.

Exposure to ambient air pollution during pregnancy has been associated with an increased risk of preeclampsia (PE). Some suggested mechanisms behind this association are changes in placental DNA methylation and gene expression. The objective of this study was to identify how early pregnancy exposure to ambient nitrogen oxides (NOx) among PE cases and normotensive controls influence DNA methylation (EPIC array) and gene expression (RNA-seq). The study included placentas from 111 women (29 PE cases/82 controls) in Scania, Sweden. First-trimester NOx exposure was assessed at the participants' residence using a dispersion model and categorized via median split into high or low NOx. Placental gestational epigenetic age was derived from the DNA methylation data. We identified six differentially methylated positions (DMPs, q < 0.05) comparing controls with low NOx vs. cases with high NOx and 14 DMPs comparing cases and controls with high NOx. Placentas with female fetuses showed more DMPs (N = 309) than male-derived placentas (N = 1). Placentas from PE cases with high NOx demonstrated gestational age deceleration compared to controls with low NOx (p = 0.034). No differentially expressed genes (DEGs, q < 0.05) were found. In conclusion, early pregnancy exposure to NOx affected placental DNA methylation in PE, resulting in placental immaturity and showing sexual dimorphism.