Deciphering maternal-fetal cross-talk in the human placenta during parturition using single-cell RNA sequencing.

Labor is a complex physiological process requiring a well-orchestrated dialogue between the mother and fetus. However, the cellular contributions and communications that facilitate maternal-fetal cross-talk in labor have not been fully elucidated. Here, single-cell RNA sequencing (scRNA-seq) was applied to decipher maternal-fetal signaling in the human placenta during term labor. First, a single-cell atlas of the human placenta was established, demonstrating that maternal and fetal cell types underwent changes in transcriptomic activity during labor. Cell types most affected by labor were fetal stromal and maternal decidual cells in the chorioamniotic membranes (CAMs) and maternal and fetal myeloid cells in the placenta. Cell-cell interaction analyses showed that CAM and placental cell types participated in labor-driven maternal and fetal signaling, including the collagen, C-X-C motif ligand (CXCL), tumor necrosis factor (TNF), galectin, and interleukin-6 (IL-6) pathways. Integration of scRNA-seq data with publicly available bulk transcriptomic data showed that placenta-derived scRNA-seq signatures could be monitored in the maternal circulation throughout gestation and in labor. Moreover, comparative analysis revealed that placenta-derived signatures in term labor were mirrored by those in spontaneous preterm labor and birth. Furthermore, we demonstrated that early in gestation, labor-specific, placenta-derived signatures could be detected in the circulation of women destined to undergo spontaneous preterm birth, with either intact or prelabor ruptured membranes. Collectively, our findings provide insight into the maternal-fetal cross-talk of human parturition and suggest that placenta-derived single-cell signatures can aid in the development of noninvasive biomarkers for the prediction of preterm birth.

A single-cell atlas of murine reproductive tissues during preterm labor.

Preterm birth, the leading cause of perinatal morbidity and mortality worldwide, frequently results from the syndrome of preterm labor. The best-established causal link to preterm labor is intra-amniotic infection, which involves premature activation of the parturition cascade in the reproductive tissues. Herein, we utilize single-cell RNA sequencing (scRNA-seq) to generate a single-cell atlas of the murine uterus, decidua, and cervix in a model of infection-induced preterm labor. We show that preterm labor affects the transcriptomic profiles of specific immune and non-immune cell subsets. Shared and tissue-specific gene expression signatures are identified among affected cells. Determination of intercellular communications implicates specific cell types in preterm labor-associated signaling pathways across tissues. In silico comparison of murine and human uterine cell-cell interactions reveals conserved signaling pathways implicated in labor. Thus, our scRNA-seq data provide insights into the preterm labor-driven cellular landscape and communications in reproductive tissues.

A single-cell atlas of the myometrium in human parturition.

Parturition is a well-orchestrated process characterized by increased uterine contractility, cervical ripening, and activation of the chorioamniotic membranes; yet, the transition from a quiescent to a contractile myometrium heralds the onset of labor. However, the cellular underpinnings of human parturition in the uterine tissues are still poorly understood. Herein, we performed a comprehensive study of the human myometrium during spontaneous term labor using single-cell RNA sequencing (scRNA-Seq). First, we established a single-cell atlas of the human myometrium and unraveled the cell type-specific transcriptomic activity modulated during labor. Major cell types included distinct subsets of smooth muscle cells, monocytes/macrophages, stromal cells, and endothelial cells, all of which communicated and participated in immune (e.g., inflammation) and nonimmune (e.g., contraction) processes associated with labor. Furthermore, integrating scRNA-Seq and microarray data with deconvolution of bulk gene expression highlighted the contribution of smooth muscle cells to labor-associated contractility and inflammatory processes. Last, myometrium-derived single-cell signatures can be quantified in the maternal whole-blood transcriptome throughout pregnancy and are enriched in women in labor, providing a potential means of noninvasively monitoring pregnancy and its complications. Together, our findings provide insights into the contributions of specific myometrial cell types to the biological processes that take place during term parturition.

Maternal-fetal immune responses in pregnant women infected with SARS-CoV-2.

Pregnant women represent a high-risk population for severe/critical COVID-19 and mortality. However, the maternal-fetal immune responses initiated by SARS-CoV-2 infection, and whether this virus is detectable in the placenta, are still under investigation. Here we show that SARS-CoV-2 infection during pregnancy primarily induces unique inflammatory responses at the maternal-fetal interface, which are largely governed by maternal T cells and fetal stromal cells. SARS-CoV-2 infection during pregnancy is also associated with humoral and cellular immune responses in the maternal blood, as well as with a mild cytokine response in the neonatal circulation (i.e., umbilical cord blood), without compromising the T-cell repertoire or initiating IgM responses. Importantly, SARS-CoV-2 is not detected in the placental tissues, nor is the sterility of the placenta compromised by maternal viral infection. This study provides insight into the maternal-fetal immune responses triggered by SARS-CoV-2 and emphasizes the rarity of placental infection.

The Distinct Immune Nature of the Fetal Inflammatory Response Syndrome Type I and Type II.

Fetal inflammatory response syndrome (FIRS) is strongly associated with neonatal morbidity and mortality and can be classified as type I or type II. Clinically, FIRS type I and type II are considered as distinct syndromes, yet the molecular underpinnings of these fetal inflammatory responses are not well understood because of their low prevalence and the difficulty of postdelivery diagnosis. In this study, we performed RNA sequencing of human cord blood samples from preterm neonates diagnosed with FIRS type I or FIRS type II. We found that FIRS type I was characterized by an upregulation of host immune responses, including neutrophil and monocyte functions, together with a proinflammatory cytokine storm and a downregulation of T cell processes. In contrast, FIRS type II comprised a mild chronic inflammatory response involving perturbation of HLA transcripts, suggestive of fetal semiallograft rejection. Integrating single-cell RNA sequencing-derived signatures with bulk transcriptomic data confirmed that FIRS type I immune responses were mainly driven by monocytes, macrophages, and neutrophils. Last, tissue- and cell-specific signatures derived from the BioGPS Gene Atlas further corroborated the role of myeloid cells originating from the bone marrow in FIRS type I. Collectively, these data provide evidence that FIRS type I and FIRS type II are driven by distinct immune mechanisms; whereas the former involves the innate limb of immunity consistent with host defense, the latter resembles a process of semiallograft rejection. These findings shed light on the fetal immune responses caused by infection or alloreactivity that can lead to deleterious consequences in neonatal life.

Maternal-Fetal Immune Responses in Pregnant Women Infected with SARS-CoV-2.

Pregnant women are a high-risk population for severe/critical COVID-19 and mortality. However, the maternal-fetal immune responses initiated by SARS-CoV-2 infection, and whether this virus is detectable in the placenta, are still under investigation. Herein, we report that SARS-CoV-2 infection during pregnancy primarily induced specific maternal inflammatory responses in the circulation and at the maternal-fetal interface, the latter being governed by T cells and macrophages. SARS-CoV-2 infection during pregnancy was also associated with a cytokine response in the fetal circulation (i.e. umbilical cord blood) without compromising the cellular immune repertoire. Moreover, SARS-CoV-2 infection neither altered fetal cellular immune responses in the placenta nor induced elevated cord blood levels of IgM. Importantly, SARS-CoV-2 was not detected in the placental tissues, nor was the sterility of the placenta compromised by maternal viral infection. This study provides insight into the maternal-fetal immune responses triggered by SARS-CoV-2 and further emphasizes the rarity of placental infection.

Identification of cell types from single cell data using stable clustering.

Single-cell RNA-seq (scRNASeq) has become a powerful technique for measuring the transcriptome of individual cells. Unlike the bulk measurements that average the gene expressions over the individual cells, gene measurements at individual cells can be used to study several different tissues and organs at different stages. Identifying the cell types present in the sample from the single cell transcriptome data is a common goal in many single-cell experiments. Several methods have been developed to do this. However, correctly identifying the true cell types remains a challenge. We present a framework that addresses this problem. Our hypothesis is that the meaningful characteristics of the data will remain despite small perturbations of data. We validate the performance of the proposed method on eight publicly available scRNA-seq datasets with known cell types as well as five simulation datasets with different degrees of the cluster separability. We compare the proposed method with five other existing methods: RaceID, SNN-Cliq, SINCERA, SEURAT, and SC3. The results show that the proposed method performs better than the existing methods.

MAGPEL: an autoMated pipeline for inferring vAriant-driven Gene PanEls from the full-length biomedical literature.

In spite of the efforts in developing and maintaining accurate variant databases, a large number of disease-associated variants are still hidden in the biomedical literature. Curation of the biomedical literature in an effort to extract this information is a challenging task due to: (i) the complexity of natural language processing, (ii) inconsistent use of standard recommendations for variant description, and (iii) the lack of clarity and consistency in describing the variant-genotype-phenotype associations in the biomedical literature. In this article, we employ text mining and word cloud analysis techniques to address these challenges. The proposed framework extracts the variant-gene-disease associations from the full-length biomedical literature and designs an evidence-based variant-driven gene panel for a given condition. We validate the identified genes by showing their diagnostic abilities to predict the patients' clinical outcome on several independent validation cohorts. As representative examples, we present our results for acute myeloid leukemia (AML), breast cancer and prostate cancer. We compare these panels with other variant-driven gene panels obtained from Clinvar, Mastermind and others from literature, as well as with a panel identified with a classical differentially expressed genes (DEGs) approach. The results show that the panels obtained by the proposed framework yield better results than the other gene panels currently available in the literature.

GSMA: an approach to identify robust global and test Gene Signatures using Meta-Analysis.

MOTIVATION: Recent advances in biomedical research have made massive amount of transcriptomic data available in public repositories from different sources. Due to the heterogeneity present in the individual experiments, identifying reproducible biomarkers for a given disease from multiple independent studies has become a major challenge. The widely used meta-analysis approaches, such as Fisher's method, Stouffer's method, minP and maxP, have at least two major limitations: (i) they are sensitive to outliers, and (ii) they perform only one statistical test for each individual study, and hence do not fully utilize the potential sample size to gain statistical power. RESULTS: Here, we propose a gene-level meta-analysis framework that overcomes these limitations and identifies a gene signature that is reliable and reproducible across multiple independent studies of a given disease. The approach provides a comprehensive global signature that can be used to understand the underlying biological phenomena, and a smaller test signature that can be used to classify future samples of a given disease. We demonstrate the utility of the framework by constructing disease signatures for influenza and Alzheimer's disease using nine datasets including 1108 individuals. These signatures are then validated on 12 independent datasets including 912 individuals. The results indicate that the proposed approach performs better than the majority of the existing meta-analysis approaches in terms of both sensitivity as well as specificity. The proposed signatures could be further used in diagnosis, prognosis and identification of therapeutic targets. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A Multi-Cohort and Multi-Omics Meta-Analysis Framework to Identify Network-Based Gene Signatures.

Although massive amounts of condition-specific molecular profiles are being accumulated in public repositories every day, meaningful interpretation of these data remains a major challenge. In an effort to identify the biomarkers that describe the key biological phenomena for a given condition, several approaches have been developed over the past few years. However, the majority of these approaches either (i) do not consider the known intermolecular interactions, or (ii) do not integrate molecular data of multiple types (e.g., genomics, transcriptomics, proteomics, epigenomics, etc.), and thus potentially fail to capture the true biological changes responsible for complex diseases (e.g., cancer). In addition, these approaches often ignore the heterogeneity and study bias present in independent molecular cohorts. In this manuscript, we propose a novel multi-cohort and multi-omics meta-analysis framework that overcomes all three limitations mentioned above in order to identify robust molecular subnetworks that capture the key dynamic nature of a given biological condition. Our framework integrates multiple independent gene expression studies, unmatched DNA methylation studies, and protein-protein interactions to identify methylation-driven subnetworks. We demonstrate the proposed framework by constructing subnetworks related to two complex diseases: glioblastoma and low-grade gliomas. We validate the identified subnetworks by showing their ability to predict patients' clinical outcome on multiple independent validation cohorts.

A new computational drug repurposing method using established disease-drug pair knowledge.

MOTIVATION: Drug repurposing is a potential alternative to the classical drug discovery pipeline. Repurposing involves finding novel indications for already approved drugs. In this work, we present a novel machine learning-based method for drug repurposing. This method explores the anti-similarity between drugs and a disease to uncover new uses for the drugs. More specifically, our proposed method takes into account three sources of information: (i) large-scale gene expression profiles corresponding to human cell lines treated with small molecules, (ii) gene expression profile of a human disease and (iii) the known relationship between Food and Drug Administration (FDA)-approved drugs and diseases. Using these data, our proposed method learns a similarity metric through a supervised machine learning-based algorithm such that a disease and its associated FDA-approved drugs have smaller distance than the other disease-drug pairs. RESULTS: We validated our framework by showing that the proposed method incorporating distance metric learning technique can retrieve FDA-approved drugs for their approved indications. Once validated, we used our approach to identify a few strong candidates for repurposing. AVAILABILITY AND IMPLEMENTATION: The R scripts are available on demand from the authors. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A novel computational approach for drug repurposing using systems biology.

Motivation: Identification of novel therapeutic effects for existing US Food and Drug Administration (FDA)-approved drugs, drug repurposing, is an approach aimed to dramatically shorten the drug discovery process, which is costly, slow and risky. Several computational approaches use transcriptional data to find potential repurposing candidates. The main hypothesis of such approaches is that if gene expression signature of a particular drug is opposite to the gene expression signature of a disease, that drug may have a potential therapeutic effect on the disease. However, this may not be optimal since it fails to consider the different roles of genes and their dependencies at the system level. Results: We propose a systems biology approach to discover novel therapeutic roles for established drugs that addresses some of the issues in the current approaches. To do so, we use publicly available drug and disease data to build a drug-disease network by considering all interactions between drug targets and disease-related genes in the context of all known signaling pathways. This network is integrated with gene-expression measurements to identify drugs with new desired therapeutic effects based on a system-level analysis method. We compare the proposed approach with the drug repurposing approach proposed by Sirota et al. on four human diseases: idiopathic pulmonary fibrosis, non-small cell lung cancer, prostate cancer and breast cancer. We evaluate the proposed approach based on its ability to re-discover drugs that are already FDA-approved for a given disease. Availability and implementation: The R package DrugDiseaseNet is under review for publication in Bioconductor and is available at https://github.com/azampvd/DrugDiseaseNet. Supplementary information: Supplementary data are available at Bioinformatics online.