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Gonadal development is a complex process that involves sex determination followed by divergent maturation into either testes or ovaries 1 . Historically, limited tissue accessibility, a lack of reliable in vitro models and critical differences between humans and mice have hampered our knowledge of human gonadogenesis, despite its importance in gonadal conditions and infertility. Here, we generated a comprehensive map of first- and second-trimester human gonads using a combination of single-cell and spatial transcriptomics, chromatin accessibility assays and fluorescent microscopy. We extracted human-specific regulatory programmes that control the development of germline and somatic cell lineages by profiling equivalent developmental stages in mice. In both species, we define the somatic cell states present at the time of sex specification, including the bipotent early supporting population that, in males, upregulates the testis-determining factor SRY and sPAX8s, a gonadal lineage located at the gonadal–mesonephric interface. In females, we resolve the cellular and molecular events that give rise to the first and second waves of granulosa cells that compartmentalize the developing ovary to modulate germ cell differentiation. In males, we identify human SIGLEC15 + and TREM2 + fetal testicular macrophages, which signal to somatic cells outside and inside the developing testis cords, respectively. This study provides a comprehensive spatiotemporal map of human and mouse gonadal differentiation, which can guide in vitro gonadogenesis. This study provides a comprehensive spatiotemporal map of human and mouse gonadal differentiation, using a combination of single-cell and spatial transcriptomics, chromatin accessibility assays and fluorescent microscopy, which can guide in vitro gonadogenesis.

Common variable immunodeficiency (CVID), the most prevalent symptomatic primary immunodeficiency, displays impaired terminal B-cell differentiation and defective antibody responses. Incomplete genetic penetrance and ample phenotypic expressivity in CVID suggest the participation of additional pathogenic mechanisms. Monozygotic (MZ) twins discordant for CVID are uniquely valuable for studying the contribution of epigenetics to the disease. Here, we generate a single-cell epigenomics and transcriptomics census of naïve-to-memory B cell differentiation in a CVID-discordant MZ twin pair. Our analysis identifies DNA methylation, chromatin accessibility and transcriptional defects in memory B-cells mirroring defective cell-cell communication upon activation. These findings are validated in a cohort of CVID patients and healthy donors. Our findings provide a comprehensive multi-omics map of alterations in naïve-to-memory B-cell transition in CVID and indicate links between the epigenome and immune cell cross-talk. Our resource, publicly available at the Human Cell Atlas, gives insight into future diagnosis and treatments of CVID patients. Common variable immunodeficiency (CVID) is the most prevalent primary immunodeficiency. Here the authors perform single-cell omics analyses in CVID-discordant monozygotic twins and show epigenetic and transcriptional alterations associated with activation in memory B cells.

In the last decade, single cell RNAseq (scRNAseq) datasets have grown in size from a single cell to millions of cells. Due to its high dimensionality, it is not always feasible to visualize scRNAseq data and share it in a scientific report or an article publication format. Recently, many interactive analysis and visualization tools have been developed to address this issue and facilitate knowledge transfer in the scientific community. In this study, we review several of the currently available scRNAseq visualization tools and benchmark the subset that allows to visualize the data on the web and share it with others. We consider the memory and time required to prepare datasets for sharing as the number of cells increases, and additionally review the user experience and features available in the web interface. To address the problem of format compatibility we have also developed a user-friendly R package, sceasy, which allows users to convert their own scRNAseq datasets into a specific data format for visualization.

In processive catalysis, a catalyst binds to a substrate and remains bound as it performs several consecutive reactions, as exemplified by DNA polymerases. Processivity is essential in nature and is often mediated by a clamp-like structure that physically tethers the catalyst to its (polymeric) template. In the case of the bacteriophage T4 replisome, a dedicated clamp protein acts as a processivity mediator by encircling DNA and subsequently recruiting its polymerase. Here we use this DNA-binding protein to construct a biohybrid catalyst. Conjugation of the clamp protein to a chemical catalyst with sequence-specific oxidation behaviour formed a catalytic clamp that can be loaded onto a DNA plasmid. The catalytic activity of the biohybrid catalyst was visualized using a procedure based on an atomic force microscopy method that detects and spatially locates oxidized sites in DNA. Varying the experimental conditions enabled switching between processive and distributive catalysis and influencing the sliding direction of this rotaxane-like catalyst. Clamp proteins that encircle DNA and then recruit enzymes are one of nature's ways of making catalysis on DNA processive. Here, a clamp protein is equipped with a synthetic catalyst that sequence-specifically oxidizes DNA. The resulting biohybrid catalyst shows processive behaviour, which is visualized by atomic force microscopy.

A discrete uncoupling process for finite spaces is introduced, called the Markov Cluster Process or the MCL process. The process is the engine for the graph clustering algorithm called the MCL algorithm. The MCL process takes a stochastic matrix as input, and then alternates expansion and inflation, each step defining a stochastic matrix in terms of the previous one. Expansion corresponds with taking the $k$th power of a stochastic matrix, where $k\\in\\N$. Inflation corresponds with a parametrized operator $\\Gamma_r$, $r\\geq 0$, that maps the set of (column) stochastic matrices onto itself. The image $\\Gamma_r M$ is obtained by raising each entry in $M$ to the $r$th power and rescaling each column to have sum 1 again. In practice the process converges very fast towards a limit that is invariant under both matrix multiplication and inflation, with quadratic convergence around the limit points. The heuristic behind the process is its expected behavior for (Markov) graphs possessing cluster structure. The process is typically applied to the matrix of random walks on a given graph $G$, and the connected components of (the graph associated with) the process limit generically allow a clustering interpretation of $G$. The limit is in general extremely sparse and iterands are sparse in a weighted sense, implying that the MCL algorithm is very fast and highly scalable. Several mathematical properties of the MCL process are established. Most notably, the process (and algorithm) iterands posses structural properties generalizing the mapping from process limits onto clusterings. The inflation operator $\\Gamma_r$ maps the class of matrices that are diagonally similar to a symmetric matrix onto itself. The phrase diagonally positive semi-definite (dpsd) is used for matrices that are diagonally similar to a positive semi-definite matrix. For $r\\in\\N$ and for $M$ a stochastic dpsd matrix, the image $\\Gamma_r M$ is again dpsd. Determinantal inequalities satisfied by a dpsd matrix $M$ imply a natural ordering among the diagonal elements of $M$, generalizing the mapping of process limits onto clusterings. The spectrum of $\\Gamma_{\\infty} M$ is of the form $\\{0^{n-k}, 1^k\\}$, where $k$ is the number of endclasses of the ordering associated with $M$, and $n$ is the dimension of $M$. This attests to the uncoupling effect of the inflation operator.

Network analysis transcends conventional pairwise approaches to data analysis as the context of components in a network graph can be taken into account. Such approaches are increasingly being applied to genomics data, where functional linkages are used to connect genes or proteins. However, while microarray gene expression datasets are now abundant and of high quality, few approaches have been developed for analysis of such data in a network context. We present a novel approach for 3-D visualisation and analysis of transcriptional networks generated from microarray data. These networks consist of nodes representing transcripts connected by virtue of their expression profile similarity across multiple conditions. Analysing genome-wide gene transcription across 61 mouse tissues, we describe the unusual topography of the large and highly structured networks produced, and demonstrate how they can be used to visualise, cluster, and mine large datasets. This approach is fast, intuitive, and versatile, and allows the identification of biological relationships that may be missed by conventional analysis techniques. This work has been implemented in a freely available open-source application named BioLayout Express(3D).

Dengue is an important arboviral infectious disease for which there is currently no specific cure. We report gemini-like (geminoid) alkylated amphiphilic peptides containing lysines in combination with glycines or alanines (C15H31C(O)-Lys-(Gly or Ala)nLys-NHC16H33, shorthand notation C16-KXnK-C16 with X = A or G, and n = 0–2). The representatives with 1 or 2 Ala inhibit dengue protease and human furin, two serine proteases involved in dengue virus infection that have peptides with cationic amino acids as their preferred substrates, with IC50 values in the lower µM range. The geminoid C16-KAK-C16 combined inhibition of DENV2 protease (IC50 2.3 µM) with efficacy against replication of wildtype DENV2 in LLC-MK2 cells (EC50 4.1 µM) and an absence of toxicity. We conclude that the lysine-based geminoids have activity against dengue virus infection, which is based on their inhibition of the proteases involved in viral replication and are therefore promising leads to further developing antiviral therapeutics, not limited to dengue.

Over 80% of multiple-tested siRNAs and shRNAs targeting CD95 or CD95 ligand (CD95L) induce a form of cell death characterized by simultaneous activation of multiple cell death pathways preferentially killing transformed and cancer stem cells. We now show these si/shRNAs kill cancer cells through canonical RNAi by targeting the 3’UTR of critical survival genes in a unique form of off-target effect we call DISE (death induced by survival gene elimination). Drosha and Dicer-deficient cells, devoid of most miRNAs, are hypersensitive to DISE, suggesting cellular miRNAs protect cells from this form of cell death. By testing 4666 shRNAs derived from the CD95 and CD95L mRNA sequences and an unrelated control gene, Venus, we have identified many toxic sequences - most of them located in the open reading frame of CD95L. We propose that specific toxic RNAi-active sequences present in the genome can kill cancer cells. Cells store their genetic code within molecules of DNA. Some of this information will be copied into chemically similar molecules called RNAs, from which the sequence of letters in the genetic code can be translated to build proteins. However, these messenger RNAs are not the only RNA molecules that cells can make. MicroRNAs are other short pieces of RNA that closely match sequences in parts of certain messenger RNAs. The messenger RNAs targeted by microRNAs are broken down inside the cell, which reduces how much protein can be produced from them. Since its discovery, scientists have exploited this process – called RNA interference (or RNAi for short) – and designed microRNA-like small interfering RNAs (siRNAs) to target particular messenger RNAs and decrease the levels of the corresponding proteins in countless experiments. Two proteins that have been studied in RNAi experiments are CD95 and its interaction partner CD95L. Both of these proteins are important in human cancer cells, and targeting them via RNAi killed cancer cells in an unknown mechanism that the cancer cells were unable to resist. RNAi experiments are designed to be specific, but sometimes they can accidently target other non-target messenger RNAs. Putzbach, Gao, Patel et al. have now analyzed all of the siRNAs that can be made from the messenger RNAs for CD95 and CD95L to mediate RNAi in cancer cells. This revealed that several messenger RNAs, other than those for CD95 and CD95L, were unintentionally being targeted, including many that code for proteins that cells need to survive. Further examination of the messenger RNA for CD95 and CD95L showed that they contain short sequences that are similar to those in the messenger RNAs of the genes that encode these survival proteins. Putzbach et al. were able to study and then predict which siRNA sequences would be toxic to cancer cells. These findings indicate that an RNAi off-target effect may actually be used to kill cancer cells. Future studies will determine whether this effect could be exploited to shrink tumors in animal models of cancer. If successful, this in turn could lead to new treatments for cancer patients.

[This corrects the article DOI: 10.1371/journal.pntd.0005559.].

The endometrium, the mucosal lining of the uterus, undergoes dynamic changes throughout the menstrual cycle in response to ovarian hormones. We have generated dense single-cell and spatial reference maps of the human uterus and three-dimensional endometrial organoid cultures. We dissect the signaling pathways that determine cell fate of the epithelial lineages in the lumenal and glandular microenvironments. Our benchmark of the endometrial organoids reveals the pathways and cell states regulating differentiation of the secretory and ciliated lineages both in vivo and in vitro. In vitro downregulation of WNT or NOTCH pathways increases the differentiation efficiency along the secretory and ciliated lineages, respectively. We utilize our cellular maps to deconvolute bulk data from endometrial cancers and endometriotic lesions, illuminating the cell types dominating in each of these disorders. These mechanistic insights provide a platform for future development of treatments for common conditions including endometriosis and endometrial carcinoma. Single-cell and spatial transcriptomic profiling of the human endometrium highlights pathways governing the proliferative and secretory phases of the menstrual cycle. Analyses of endometrial organoids show that WNT and NOTCH signaling modulate differentiation into the secretory and ciliated epithelial lineages, respectively.