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Summary: Cell reprogramming, which guides the conversion between cell states, is a promising technology for tissue repair and regeneration, with the ultimate goal of accelerating recovery from diseases or injuries. To accomplish this, regulators must be identified and manipulated to control cell fate. We propose Fatecode, a computational method that predicts cell fate regulators based only on single-cell RNA sequencing (scRNA-seq) data. Fatecode learns a latent representation of the scRNA-seq data using a deep learning-based classification-supervised autoencoder and then performs in silico perturbation experiments on the latent representation to predict genes that, when perturbed, would alter the original cell type distribution to increase or decrease the population size of a cell type of interest. We assessed Fatecode’s performance using simulations from a mechanistic gene-regulatory network model and scRNA-seq data mapping blood and brain development of different organisms. Our results suggest that Fatecode can detect known cell fate regulators from single-cell transcriptomics datasets. Motivation: How stem and progenitor cells decide which cell types they will generate via cell division is crucial for understanding tissue development and engineering cell types for use in regenerative medicine or cancer therapies. However, the identification of the regulators of these cell fate decisions within the complex and dynamic system of tissues is a major challenge. Experimental high-throughput perturbation screens can help to dissect regulators, but these are not practical or easy to implement in every context of interest. To address this challenge, we developed a computational method, Fatecode, to predict master regulators and key pathways controlling cell fate based on any single-cell transcriptomics dataset.

Abstract Background Glioblastoma (GBM) is an aggressive brain tumor that exhibits resistance to current treatment, making the identification of novel therapeutic targets essential. In this context, cellular prion protein (PrPC) stands out as a potential candidate for new therapies. Encoded by the PRNP gene, PrPC can present increased expression levels in GBM, impacting cell proliferation, growth, migration, invasion and stemness. Nevertheless, the exact molecular mechanisms through which PRNP/PrPC modulates key aspects of GBM biology remain elusive. Methods To elucidate the implications of PRNP/PrPC in the biology of this cancer, we analyzed publicly available RNA sequencing (RNA-seq) data of patient-derived GBMs from four independent studies. First, we ranked samples profiled by bulk RNA-seq as PRNP high and PRNP low and compared their transcriptomic landscape. Then, we analyzed PRNP + and PRNP - GBM cells profiled by single-cell RNA-seq to further understand the molecular context within which PRNP/PrPC might function in this tumor. We explored an additional proteomics dataset, applying similar comparative approaches, to corroborate our findings. Results Functional profiling revealed that vesicular dynamics signatures are strongly correlated with PRNP/PrPC levels in GBM. We found a panel of 73 genes, enriched in vesicle-related pathways, whose expression levels are increased in PRNP high/PRNP + cells across all RNA-seq datasets. Vesicle-associated genes, ANXA1, RAB31, DSTN and SYPL1, were found to be upregulated in vitro in an in-house collection of patient-derived GBM. Moreover, proteome analysis of patient-derived samples reinforces the findings of enhanced vesicle biogenesis, processing and trafficking in PRNP high/PRNP + GBM cells. Conclusions Together, our findings shed light on a novel role for PrPC as a potential modulator of vesicle biology in GBM, which is pivotal for intercellular communication and cancer maintenance. We also introduce GBMdiscovery, a novel user-friendly tool that allows the investigation of specific genes in GBM biology.

Abstract Metastatic breast-cancer is a major cause of death in women worldwide, yet the relationship between oncogenic drivers that promote metastatic versus primary cancer is still contentious. To elucidate this relationship in treatment-naive animals, we hereby describe mammary-specific transposon-mutagenesis screens in female mice together with loss-of-function Rb, which is frequently inactivated in breast-cancer. We report gene-centric common insertion-sites (gCIS) that are enriched in primary-tumors, in metastases or shared by both compartments. Shared-gCIS comprise a major MET-RAS network, whereas metastasis-gCIS form three additional hubs: Rho-signaling, Ubiquitination and RNA-processing. Pathway analysis of four clinical cohorts with paired primary-tumors and metastases reveals similar organization in human breast-cancer with subtype-specific shared-drivers (e.g. RB1-loss, TP53-loss, high MET, RAS, ER), primary-enriched (EGFR, TGFβ and STAT3) and metastasis-enriched (RHO, PI3K) oncogenic signaling. Inhibitors of RB1-deficiency or MET plus RHO-signaling cooperate to block cell migration and drive tumor cell-death. Thus, targeting shared- and metastasis- but not primary-enriched derivers offers a rational avenue to prevent metastatic breast-cancer.

Abstract Malignant peripheral nerve sheath tumor (MPNST) is a highly aggressive sarcoma, and a lethal neurofibromatosis type 1-related malignancy, with little progress made on treatment strategies. Here, we apply a multiplatform integrated molecular analysis on 108 tumors spanning the spectrum of peripheral nerve sheath tumors to identify candidate drivers of MPNST that can serve as therapeutic targets. Unsupervised analyses of methylome and transcriptome profiles identify two distinct subgroups of MPNSTs with unique targetable oncogenic programs. We establish two subgroups of MPNSTs: SHH pathway activation in MPNST-G1 and WNT/ß-catenin/CCND1 pathway activation in MPNST-G2. Single nuclei RNA sequencing characterizes the complex cellular architecture and demonstrate that malignant cells from MPNST-G1 and MPNST-G2 have neural crest-like and Schwann cell precursor-like cell characteristics, respectively. Further, in pre-clinical models of MPNST we confirm that inhibiting SHH pathway in MPNST-G1 prevent growth and malignant progression, providing the rational for investigating these treatments in clinical trials.

Knowledge of the transcriptional programs of human kidney cell populations at homeostasis is limited. Here, the authors show sex-based differences in gene expression of kidney parenchymal cells and examine the complexity of kidney-resident immune cells using single cell RNA sequencing of healthy living kidney donors.

The critical functions of the human liver are coordinated through the interactions of hepatic parenchymal and non‐parenchymal cells. Recent advances in single‐cell transcriptional approaches have enabled an examination of the human liver with unprecedented resolution. However, dissociation‐related cell perturbation can limit the ability to fully capture the human liver’s parenchymal cell fraction, which limits the ability to comprehensively profile this organ. Here, we report the transcriptional landscape of 73,295 cells from the human liver using matched single‐cell RNA sequencing (scRNA‐seq) and single‐nucleus RNA sequencing (snRNA‐seq). The addition of snRNA‐seq enabled the characterization of interzonal hepatocytes at a single‐cell resolution, revealed the presence of rare subtypes of liver mesenchymal cells, and facilitated the detection of cholangiocyte progenitors that had only been observed during in vitro differentiation experiments. However, T and B lymphocytes and natural killer cells were only distinguishable using scRNA‐seq, highlighting the importance of applying both technologies to obtain a complete map of tissue‐resident cell types. We validated the distinct spatial distribution of the hepatocyte, cholangiocyte, and mesenchymal cell populations by an independent spatial transcriptomics data set and immunohistochemistry. Conclusion: Our study provides a systematic comparison of the transcriptomes captured by scRNA‐seq and snRNA‐seq and delivers a high‐resolution map of the parenchymal cell populations in the healthy human liver.

Mild traumatic brain injury (mTBI) is an important public health issue, as it can lead to long-term neurological symptoms and risk of neurodegenerative disease. The pathophysiological mechanisms driving this remain unclear, and currently there are no effective therapies for mTBI. In this study on repeated mTBI (rmTBI), we have induced three mild closed-skull injuries or sham procedures, separated by 24 h, in C57BL/6 mice. We show that rmTBI mice have prolonged righting reflexes and astrogliosis, with neurological impairment in the Morris water maze (MWM) and the light dark test. Cortical and hippocampal tissue analysis revealed DNA damage in the form of double-strand breaks, oxidative damage, and R-loops, markers of cellular senescence including p16 and p21, and signaling mediated by the cGAS-STING pathway. This study identified novel sex differences after rmTBI in mice. Although these markers were all increased by rmTBI in both sexes, females had higher levels of DNA damage, lower levels of the senescence protein p16, and lower levels of cGAS-STING signaling proteins compared to their male counterparts. Single-cell RNA sequencing of the male rmTBI mouse brain revealed activation of the DNA damage response, evidence of cellular senescence, and pro-inflammatory markers reminiscent of the senescence-associated secretory phenotype (SASP) in neurons and glial cells. Cell-type specific changes were also present with evidence of brain immune activation, neurotransmission alterations in both excitatory and inhibitory neurons, and vascular dysfunction. Treatment of injured mice with the senolytic drug ABT263 significantly reduced markers of senescence only in males, but was not therapeutic in females. The reduction of senescence by ABT263 in male mice was accompanied by significantly improved performance in the MWM. This study provides compelling evidence that senescence contributes to brain dysfunction after rmTBI, but may do so in a sex-dependent manner.

Cardiomyocytes of heart ventricles consist of subpopulations of trabecular and compact subtypes. Here the authors describe the generation of structurally, metabolically and functionally mature compact ventricular cardiomyocytes as well as mature atrial cardiomyocytes from human pluripotent stem cells.

Abstract Androgens are a major driver of prostate cancer (PCa) and continue to be a critical treatment target for advanced disease, which includes castration therapy and antiandrogens. However, resistance to these therapies leading to metastatic castration-resistant prostate cancer (mCRPC), and the emergence of treatment-induced neuroendocrine disease (tNEPC) remains an ongoing challenge. Instability of the DNA methylome is well established as a major hallmark of PCa development and progression. Therefore, investigating the dynamics of the methylation changes going from the castration sensitive to the tNEPC state would provide insights into novel mechanisms of resistance. Using an established xenograft model of CRPC, genome-wide methylation analysis was performed on cell lines representing various stages of PCa progression. We confirmed extensive methylation changes with the development of CRPC and tNEPC using this model. This included key genes and pathways associated with cellular differentiation and neurodevelopment. Combined analysis of methylation and gene expression changes further highlighted genes that could potentially serve as therapeutic targets. Furthermore, tNEPC-related methylation signals from this model were detectable in circulating cell free DNA (cfDNA) from mCRPC patients undergoing androgen-targeting therapies and were associated with a faster time to clinical progression. These potential biomarkers could help with identifying patients with aggressive disease.

Sonic Hedgehog medulloblastoma (Shh-MB) comprises four subtypes each with distinct clinical traits. Here the authors characterize the genome, transcriptome, and methylome of Shh-MB subtypes, revealing a complex fusion landscape and the molecular convergence of MYCN and cAMP signaling pathways.

Molecular heterogeneity of acute myeloid leukaemia (AML) across patients is a major challenge for prognosis and therapy. Here, the authors show that NPM1 mutated AML is a heterogeneous class, consisting of two subtypes which exhibit distinct molecular characteristics, differentiation state, patient survival and drug response.

The arginine methyltransferase PRMT5 is over-expressed in cancer and has a role in the maintenance of stem cells. Here, the authors show that PRMT5 inhibitors can block the growth of patient derived glioblastoma stem cell cultures in vitro and in vivo, suggesting that PRMT5 inhibition may be a useful therapeutic strategy

Summary: Gene expression profiling and proteome analysis of normal and malignant hematopoietic stem cells (HSCs) point to shared core stemness properties. However, discordance between mRNA and protein signatures highlights an important role for post-transcriptional regulation by microRNAs (miRNAs) in governing this critical nexus. Here, we identify miR-130a as a regulator of HSC self-renewal and differentiation. Enforced expression of miR-130a impairs B lymphoid differentiation and expands long-term HSCs. Integration of protein mass spectrometry and chimeric AGO2 crosslinking and immunoprecipitation (CLIP) identifies TBL1XR1 as a primary miR-130a target, whose loss of function phenocopies miR-130a overexpression. Moreover, we report that miR-130a is highly expressed in t(8;21) acute myeloid leukemia (AML), where it is critical for maintaining the oncogenic molecular program mediated by the AML1-ETO complex. Our study establishes that identification of the comprehensive miRNA targetome within primary cells enables discovery of genes and molecular networks underpinning stemness properties of normal and leukemic cells.

The Wnt molecular subgroup of medulloblastoma is associated with better prognosis than the other molecular subgroups. Here, the authors show that activating Wnt signaling impairs tumor development and improves survival in Group 3 and Group 4 medulloblastoma preclinical models.

Polycystic ovary syndrome (PCOS) is a complex multigenic disorder and women with PCOS suffer from several comorbidities. Although, obesity is a known risk factor for PCOS, the incidence of lean women with PCOS is on the rise. A systematic and comparative study on lean and obese PCOS with respect to genes, pathways and comorbidity analysis has not been attempted so far. Analysis of differentially expressed genes (DEGs) across tissue types for lean and obese PCOS revealed that the majority of them were downregulated for lean and obese PCOS. Ovarian and endometrial tissues shared several commonly dysregulated genes, suggesting shared PCOS pathophysiology mechanisms exist across tissues. Several pathways for cellular homeostasis, such as inflammation and immune response, insulin signaling, steroidogenesis, hormonal and metabolic signaling, regulation of gonadotrophic hormone secretion, cell structure and signaling that are known to be affected in PCOS were found to be enriched in our gene expression analysis of lean and obese PCOS. The gene-disease network is denser for obese PCOS with a higher comorbidity score as compared to lean PCOS.

In breast cancer the contribution of different genetic variants to disease heritability is complex and not fully understood. Here, the authors present a network-based analysis in 84,567 patients studying ~7.3 million variants, identifying gene modules associated with breast cancer survival.

Single-cell RNA-sequencing (scRNA-seq) has revolutionized molecular biology and medicine by enabling high-throughput studies of cellular heterogeneity in diverse tissues. Applying network biology approaches to scRNA-seq data can provide useful insights into genes driving heterogeneous cell-type compositions of tissues. Here, we present scNetViz — a Cytoscape app to aid biological interpretation of cell clusters in scRNA-seq data using network analysis. scNetViz calculates the differential expression of each gene across clusters and then creates a cluster-specific gene functional interaction network between the significantly differentially expressed genes for further analysis, such as pathway enrichment analysis. To automate a complete data analysis workflow, scNetViz integrates parts of the Scanpy software, which is a popular Python package for scRNA-seq data analysis, with Cytoscape apps such as stringApp, cyPlot, and enhancedGraphics. We describe our implementation of methods for accessing data from public single cell atlas projects, differential expression analysis, visualization, and automation. scNetViz enables users to analyze data from public atlases or their own experiments, which we illustrate with two use cases. Analysis can be performed via the Cytoscape GUI or CyREST programming interface using R (RCy3) or Python (py4cytoscape).

Summary: Mitochondrial protein (MP) assemblies undergo alterations during neurogenesis, a complex process vital in brain homeostasis and disease. Yet which MP assemblies remodel during differentiation remains unclear. Here, using mass spectrometry-based co-fractionation profiles and phosphoproteomics, we generated mitochondrial interaction maps of human pluripotent embryonal carcinoma stem cells and differentiated neuronal-like cells, which presented as two discrete cell populations by single-cell RNA sequencing. The resulting networks, encompassing 6,442 high-quality associations among 600 MPs, revealed widespread changes in mitochondrial interactions and site-specific phosphorylation during neuronal differentiation. By leveraging the networks, we show the orphan C20orf24 as a respirasome assembly factor whose disruption markedly reduces respiratory chain activity in patients deficient in complex IV. We also find that a heme-containing neurotrophic factor, neuron-derived neurotrophic factor [NENF], couples with Parkinson disease-related proteins to promote neurotrophic activity. Our results provide insights into the dynamic reorganization of mitochondrial networks during neuronal differentiation and highlights mechanisms for MPs in respirasome, neuronal function, and mitochondrial diseases. : Biological Sciences; Developmental Neuroscience; Developmental Biology; Proteomics Subject Areas: Biological Sciences, Developmental Neuroscience, Developmental Biology, Proteomics

Abstract Background Global DNA methylation alterations are hallmarks of cancer. The tumor-suppressive TET enzymes, which are involved in DNA demethylation, are decreased in prostate cancer (PCa); in particular, TET2 is specifically targeted by androgen-dependent mechanisms of repression in PCa and may play a central role in carcinogenesis. Thus, the identification of key genes targeted by TET2 dysregulation may provide further insight into cancer biology. Results Using a CRISPR/Cas9-derived TET2-knockout prostate cell line, and through whole-transcriptome and whole-methylome sequencing, we identified seven candidate genes—ASB2, ETNK2, MEIS2, NRG1, NTN1, NUDT10, and SRPX—exhibiting reduced expression and increased promoter methylation, a pattern characteristic of tumor suppressors. Decreased expression of these genes significantly discriminates between recurrent and non-recurrent prostate tumors from the Cancer Genome Atlas (TCGA) cohort (n = 423), and ASB2, NUDT10, and SRPX were significantly correlated with lower recurrence-free survival in patients by Kaplan-Meier analysis. ASB2, MEIS2, and SRPX also showed significantly lower expression in high-risk Gleason score 8 tumors as compared to low or intermediate risk tumors, suggesting that these genes may be particularly useful as indicators of PCa progression. Furthermore, methylation array probes in the TCGA dataset, which were proximal to the highly conserved, differentially methylated sites identified in our TET2-knockout cells, were able to significantly distinguish between matched prostate tumor and normal prostate tissues (n = 50 pairs). Except ASB2, all genes exhibited significantly increased methylation at these probes, and methylation status of at least one probe for each of these genes showed association with measures of PCa progression such as recurrence, stage, or Gleason score. Since ASB2 did not have any probes within the TET2-knockout differentially methylated region, we validated ASB2 methylation in an independent series of matched tumor-normal samples (n = 19) by methylation-specific qPCR, which revealed concordant and significant increases in promoter methylation within the TET2-knockout site. Conclusions Our study identifies seven genes governed by TET2 loss in PCa which exhibit an association between their methylation and expression status and measures of PCa progression. As differential methylation profiles and TET2 expression are associated with advanced PCa, further investigation of these specialized TET2 targets may provide important insights into patterns of carcinogenic gene dysregulation.

During development, multipotent progenitors undergo temporally-restricted differentiation into post-mitotic retinal cells; however, the mechanisms of progenitor division that occurs during retinogenesis remain controversial. Using clonal analyses (lineage tracing and single cell cultures), we identify rod versus cone lineage-specific progenitors derived from both adult retinal stem cells and embryonic neural retinal precursors. Taurine and retinoic acid are shown to act in an instructive and lineage-restricted manner early in the progenitor lineage hierarchy to produce rod-restricted progenitors from stem cell progeny. We also identify an instructive, but lineage-independent, mechanism for the specification of cone-restricted progenitors through the suppression of multiple differentiation signaling pathways. These data indicate that exogenous signals play critical roles in directing lineage decisions and resulting in fate-restricted rod or cone photoreceptor progenitors in culture. Additional factors may be involved in governing photoreceptor fates in vivo. Keywords: Stem cell, Retina, Rod photoreceptor, Cone photoreceptor, Progenitors

Patient classification based on clinical and genomic data will further the goal of precision medicine. Interpretability is of particular relevance for models based on genomic data, where sample sizes are relatively small (in the hundreds), increasing overfitting risk netDx is a machine learning method to integrate multi-modal patient data and build a patient classifier. Patient data are converted into networks of patient similarity, which is intuitive to clinicians who also use patient similarity for medical diagnosis. Features passing selection are integrated, and new patients are assigned to the class with the greatest profile similarity. netDx has excellent performance, outperforming most machine-learning methods in binary cancer survival prediction. It handles missing data – a common problem in real-world data – without requiring imputation. netDx also has excellent interpretability, with native support to group genes into pathways for mechanistic insight into predictive features. The netDx Bioconductor package provides multiple workflows for users to build custom patient classifiers. It provides turnkey functions for one-step predictor generation from multi-modal data, including feature selection over multiple train/test data splits. Workflows offer versatility with custom feature design, choice of similarity metric; speed is improved by parallel execution. Built-in functions and examples allow users to compute model performance metrics such as AUROC, AUPR, and accuracy. netDx uses RCy3 to visualize top-scoring pathways and the final integrated patient network in Cytoscape. Advanced users can build more complex predictor designs with functional building blocks used in the default design. Finally, the netDx Bioconductor package provides a novel workflow for pathway-based patient classification from sparse genetic data.

Summary: The transitions from developing to adult quiescent and activated neural stem cells (NSCs) are not well understood. Here, we use single-cell transcriptional profiling and lineage tracing to characterize these transitions in the murine forebrain. We show that the two forebrain NSC parental populations, embryonic cortex and ganglionic eminence radial precursors (RPs), are highly similar even though they make glutamatergic versus gabaergic neurons. Both RP populations progress linearly to transition from a highly active embryonic to a dormant adult stem cell state that still shares many similarities with embryonic RPs. When adult NSCs of either embryonic origin become reactivated to make gabaergic neurons, they acquire a developing ganglionic eminence RP-like identity. Thus, transitions from embryonic RPs to adult NSCs and back to neuronal progenitors do not involve fundamental changes in cell identity, but rather reflect conversions between activated and dormant NSC states that may be determined by the niche environment.

The development of single cell RNA sequencing technologies has been instrumental in advancing our understanding of tissue biology. Here, MacParland et al. performed single cell RNA sequencing of human liver samples, and identify distinct populations of intrahepatic macrophages that may play specific roles in liver disease.

Summary: CDK4/6 inhibitors are effective against cancer cells expressing the tumor suppressor RB1, but not RB1-deficient cells, posing the challenge of how to target RB1 loss. In triple-negative breast cancer (TNBC), RB1 and PTEN are frequently inactivated together with TP53. We performed kinome/phosphatase inhibitor screens on primary mouse Rb/p53-, Pten/p53-, and human RB1/PTEN/TP53-deficient TNBC cell lines and identified CDC25 phosphatase as a common target. Pharmacological or genetic inhibition of CDC25 suppressed growth of RB1-deficient TNBC cells that are resistant to combined CDK4/6 plus CDK2 inhibition. Minimal cooperation was observed in vitro between CDC25 antagonists and CDK1, CDK2, or CDK4/6 inhibitors, but strong synergy with WEE1 inhibition was apparent. In accordance with increased PI3K signaling following long-term CDC25 inhibition, CDC25 and PI3K inhibitors effectively synergized to suppress TNBC growth both in vitro and in xenotransplantation models. These results provide a rationale for the development of CDC25-based therapies for diverse RB1/PTEN/TP53-deficient and -proficient TNBCs. : Liu et al. report that inhibition of the protein phosphatase CDC25 kills diverse triple-negative breast cancer (TNBC) cells. Moreover, CDC25 antagonists cooperate with other drugs, such as PI3K inhibitors, to efficiently suppress growth of human TNBC engrafted into mice. Keywords: triple negative breast cancer, basal-like breast cancer, therapy, RB1, PTEN, TP53, CDC25, WEE1, CHK1, checkpoint control

Abstract Background Avian influenza A H5N1 virus can cause lethal disease in humans. The virus can trigger severe pneumonia and lead to acute respiratory distress syndrome. Data from clinical, in vitro and in vivo suggest that virus-induced cytokine dysregulation could be a contributory factor to the pathogenesis of human H5N1 disease. However, the precise mechanism of H5N1 infection eliciting the unique host response are still not well understood. Methods To obtain a better understanding of the molecular events at the earliest time points, we used RNA-Seq to quantify and compare the host mRNA and miRNA transcriptomes induced by the highly pathogenic influenza A H5N1 (A/Vietnam/3212/04) or low virulent H1N1 (A/Hong Kong/54/98) viruses in human monocyte-derived macrophages at 1-, 3-, and 6-h post infection. Results Our data reveals that two macrophage populations corresponding to M1 (classically activated) and M2 (alternatively activated) macrophage subtypes respond distinctly to H5N1 virus infection when compared to H1N1 virus or mock infection, a distinction that could not be made from previous microarray studies. When this confounding variable is considered in our statistical model, a clear set of dysregulated genes and pathways emerges specifically in H5N1 virus-infected macrophages at 6-h post infection, whilst was not found with H1N1 virus infection. Furthermore, altered expression of genes in these pathways, which have been previously implicated in viral host response, occurs specifically in the M1 subtype. We observe a significant up-regulation of genes in the RIG-I-like receptor signaling pathway. In particular, interferons, and interferon-stimulated genes are broadly affected. The negative regulators of interferon signaling, the suppressors of cytokine signaling, SOCS-1 and SOCS-3, were found to be markedly up-regulated in the initial round of H5N1 virus replication. Elevated levels of these suppressors could lead to the eventual suppression of cellular antiviral genes, contributing to pathophysiology of H5N1 virus infection. Conclusions Our study provides important mechanistic insights into the understanding of H5N1 viral pathogenesis and the multi-faceted host immune responses. The dysregulated genes could be potential candidates as therapeutic targets for treating H5N1 disease.

Adult neural stem cells (NSCs) derive from embryonic precursors, but little is known about how or when this occurs. We have addressed this issue using single-cell RNA sequencing at multiple developmental time points to analyze the embryonic murine cortex, one source of adult forebrain NSCs. We computationally identify all major cortical cell types, including the embryonic radial precursors (RPs) that generate adult NSCs. We define the initial emergence of RPs from neuroepithelial stem cells at E11.5. We show that, by E13.5, RPs express a transcriptional identity that is maintained and reinforced throughout their transition to a non-proliferative state between E15.5 and E17.5. These slowly proliferating late embryonic RPs share a core transcriptional phenotype with quiescent adult forebrain NSCs. Together, these findings support a model wherein cortical RPs maintain a core transcriptional identity from embryogenesis through to adulthood and wherein the transition to a quiescent adult NSC occurs during late neurogenesis.

Background: Identification of cell type subpopulations from complex cell mixtures using single-cell RNA-sequencing (scRNA-seq) data includes automated steps from normalization to cell clustering. However, assigning cell type labels to cell clusters is often conducted manually, resulting in limited documentation, low reproducibility and uncontrolled vocabularies. This is partially due to the scarcity of reference cell type signatures and because some methods support limited cell type signatures. Methods: In this study, we benchmarked five methods representing first-generation enrichment analysis (ORA), second-generation approaches (GSEA and GSVA), machine learning tools (CIBERSORT) and network-based neighbor voting (METANEIGHBOR), for the task of assigning cell type labels to cell clusters from scRNA-seq data. We used five scRNA-seq datasets: human liver, 11 Tabula Muris mouse tissues, two human peripheral blood mononuclear cell datasets, and mouse retinal neurons, for which reference cell type signatures were available. The datasets span Drop-seq, 10X Chromium and Seq-Well technologies and range in size from ~3,700 to ~68,000 cells. Results: Our results show that, in general, all five methods perform well in the task as evaluated by receiver operating characteristic curve analysis (average area under the curve (AUC) = 0.91, sd = 0.06), whereas precision-recall analyses show a wide variation depending on the method and dataset (average AUC = 0.53, sd = 0.24). We observed an influence of the number of genes in cell type signatures on performance, with smaller signatures leading more frequently to incorrect results. Conclusions: GSVA was the overall top performer and was more robust in cell type signature subsampling simulations, although different methods performed well using different datasets. METANEIGHBOR and GSVA were the fastest methods. CIBERSORT and METANEIGHBOR were more influenced than the other methods by analyses including only expected cell types. We provide an extensible framework that can be used to evaluate other methods and datasets at https://github.com/jdime/scRNAseq_cell_cluster_labeling.

Background: Identification of cell type subpopulations from complex cell mixtures using single-cell RNA-sequencing (scRNA-seq) data includes automated steps from normalization to cell clustering. However, assigning cell type labels to cell clusters is often conducted manually, resulting in limited documentation, low reproducibility and uncontrolled vocabularies. This is partially due to the scarcity of reference cell type signatures and because some methods support limited cell type signatures. Methods: In this study, we benchmarked five methods representing first-generation enrichment analysis (ORA), second-generation approaches (GSEA and GSVA), machine learning tools (CIBERSORT) and network-based neighbor voting (METANEIGHBOR), for the task of assigning cell type labels to cell clusters from scRNA-seq data. We used five scRNA-seq datasets: human liver, 11 Tabula Muris mouse tissues, two human peripheral blood mononuclear cell datasets, and mouse retinal neurons, for which reference cell type signatures were available. The datasets span Drop-seq, 10X Chromium and Seq-Well technologies and range in size from ~3,700 to ~68,000 cells. Results: Our results show that, in general, all five methods perform well in the task as evaluated by receiver operating characteristic curve analysis (average area under the curve (AUC) = 0.91, sd = 0.06), whereas precision-recall analyses show a wide variation depending on the method and dataset (average AUC = 0.53, sd = 0.24). We observed an influence of the number of genes in cell type signatures on performance, with smaller signatures leading more frequently to incorrect results. Conclusions: GSVA was the overall top performer and was more robust in cell type signature subsampling simulations, although different methods performed well using different datasets. METANEIGHBOR and GSVA were the fastest methods. CIBERSORT and METANEIGHBOR were more influenced than the other methods by analyses including only expected cell types. We provide an extensible framework that can be used to evaluate other methods and datasets at https://github.com/jdime/scRNAseq_cell_cluster_labeling.

Single-cell RNA sequencing (scRNAseq) represents a new kind of microscope that can measure the transcriptome profiles of thousands of individual cells from complex cellular mixtures, such as in a tissue, in a single experiment. This technology is particularly valuable for characterization of tissue heterogeneity because it can be used to identify and classify all cell types in a tissue. This is generally done by clustering the data, based on the assumption that cells of a particular type share similar transcriptomes, distinct from other cell types in the tissue. However, nearly all clustering algorithms have tunable parameters which affect the number of clusters they will identify in data. The R Shiny software tool described here, scClustViz, provides a simple interactive graphical user interface for exploring scRNAseq data and assessing the biological relevance of clustering results. Given that cell types are expected to have distinct gene expression patterns, scClustViz uses differential gene expression between clusters as a metric for assessing the fit of a clustering result to the data at multiple cluster resolution levels. This helps select a clustering parameter for further analysis. scClustViz also provides interactive visualisation of: cluster-specific distributions of technical factors, such as predicted cell cycle stage and other metadata; cluster-wise gene expression statistics to simplify annotation of cell types and identification of cell type specific marker genes; and gene expression distributions over all cells and cell types. scClustViz provides an interactive interface for visualisation, assessment, and biological interpretation of cell-type classifications in scRNAseq experiments that can be easily added to existing analysis pipelines, enabling customization by bioinformaticians while enabling biologists to explore their results without the need for computational expertise. It is available at https://baderlab.github.io/scClustViz/.

Abstract Background Freshwater planarians are well known for their regenerative abilities. Less well known is how planarians maintain spatial patterning in long-lived adult animals or how they re-pattern tissues during regeneration. HOX genes are good candidates to regulate planarian spatial patterning, yet the full complement or genomic clustering of planarian HOX genes has not yet been described, primarily because only a few have been detectable by in situ hybridization, and none have given morphological phenotypes when knocked down by RNAi. Results Because the planarian Schmidtea mediterranea (S. mediterranea) is unsegmented, appendage less, and morphologically simple, it has been proposed that it may have a simplified HOX gene complement. Here, we argue against this hypothesis and show that S. mediterranea has a total of 13 HOX genes, which represent homologs to all major axial categories, and can be detected by whole-mount in situ hybridization using a highly sensitive method. In addition, we show that planarian HOX genes do not cluster in the genome, yet 5/13 have retained aspects of axially restricted expression. Finally, we confirm HOX gene axial expression by RNA deep-sequencing 6 anterior–posterior “zones” of the animal, which we provide as a dataset to the community to discover other axially restricted transcripts. Conclusions Freshwater planarians have an unappreciated HOX gene complexity, with all major axial categories represented. However, we conclude based on adult expression patterns that planarians have a derived body plan and their asexual lifestyle may have allowed for large changes in HOX expression from the last common ancestor between arthropods, flatworms, and vertebrates. Using our in situ method and axial zone RNAseq data, it should be possible to further understand the pathways that pattern the anterior–posterior axis of adult planarians.

Progesterone drives mammary stem and progenitor cell dynamics through paracrine mechanisms that are currently not well understood. Here, we demonstrate that CXCR4, the receptor for stromal-derived factor 1 (SDF-1; CXC12), is a crucial instructor of hormone-induced mammary stem and progenitor cell function. Progesterone elicits specific changes in the transcriptome of basal and luminal mammary epithelial populations, where CXCL12 and CXCR4 represent a putative ligand-receptor pair. In situ, CXCL12 localizes to progesterone-receptor-positive luminal cells, whereas CXCR4 is induced in both basal and luminal compartments in a progesterone-dependent manner. Pharmacological inhibition of CXCR4 signaling abrogates progesterone-directed expansion of basal (CD24+CD49fhi) and luminal (CD24+CD49flo) subsets. This is accompanied by a marked reduction in CD49b+SCA-1− luminal progenitors, their functional capacity, and lobuloalveologenesis. These findings uncover CXCL12 and CXCR4 as novel paracrine effectors of hormone signaling in the adult mammary gland, and present a new avenue for potentially targeting progenitor cell growth and malignant transformation in breast cancer.

Networks often contain regions of tightly connected nodes, or clusters, that highlight their shared relationships. An effective way to create a visual summary of a network is to identify clusters and annotate them with an enclosing shape and a summarizing label. Cytoscape provides the ability to annotate a network with shapes and labels, however these annotations must be created manually one at a time, which can be a laborious process. AutoAnnotate is a Cytoscape 3 App that automates the process of identifying clusters and visually annotating them. It greatly reduces the time and effort required to fully annotate clusters in a network, and provides freedom to experiment with different strategies for identifying and labelling clusters. Many customization options are available that enable the user to refine the generated annotations as required. Annotated clusters may be collapsed into single nodes using the Cytoscape groups feature, which helps simplify a network by making its overall structure more visible. AutoAnnotate is applicable to any type of network, including enrichment maps, protein-protein interactions, pathways, or social networks.

Networks that represent connections between individuals can be valuable analytic tools. The Social Network Cytoscape app is capable of creating a visual summary of connected individuals automatically. It does this by representing relationships as networks where each node denotes an individual and an edge linking two individuals represents a connection. The app focuses on creating visual summaries of individuals connected by co-authorship links in academia, created from bibliographic databases like PubMed, Scopus and InCites. The resulting co-authorship networks can be visualized and analyzed to better understand collaborative research networks or to communicate the extent of collaboration and publication productivity among a group of researchers, like in a grant application or departmental review report. It can also be useful as a research tool to identify important research topics, researchers and papers in a subject area.

We present NetMatchStar, a Cytoscape app to find all the occurrences of a query graph in a network and check for its significance as a motif with respect to seven different random models. The query can be uploaded or built from scratch using Cytoscape facilities. The app significantly enhances the previous NetMatch in style, performance and functionality. Notably NetMatchStar allows queries with wildcards.

We present NetMatchStar, a Cytoscape app to find all the occurrences of a query graph in a network and check for its significance as a motif with respect to seven different random models. The query can be uploaded or built from scratch using Cytoscape facilities. The app significantly enhances the previous NetMatch in style, performance and functionality. Notably NetMatchStar allows queries with wildcards.

Haplotypes are often critical for the interpretation of genetic laboratory observations into medically actionable findings. Current massively parallel DNA sequencing technologies produce short sequence reads that are often unable to resolve haplotype information. Phasing short read data typically requires supplemental statistical phasing based on known haplotype structure in the population or parental genotypic data. Here we demonstrate that the MinION nanopore sequencer is capable of producing very long reads to resolve both variants and haplotypes of HLA-A, HLA-B and CYP2D6 genes important in determining patient drug response in sample NA12878 of CEPH/UTAH pedigree 1463, without the need for statistical phasing. Long read data from a single 24-hour nanopore sequencing run was used to reconstruct haplotypes, which were confirmed by HapMap data and statistically phased Complete Genomics and Sequenom genotypes. Our results demonstrate that nanopore sequencing is an emerging standalone technology with potential utility in a clinical environment to aid in medical decision-making.

High-throughput OMICs experiments generate signals for millions of entities (i.e. genes, proteins, metabolites or any measurable biological entity) in the cell. In an effort to summarize and explore these signals, expression results are examined in the context of known pathways and processes, through enrichment analysis to generate a set of pathways and processes that is significantly enriched. Due to the high redundancy in annotation resources this often results in hundreds of sets. To facilitate the analysis of these results, we have developed the Enrichment Map app to visualize enrichments as a network. We have updated Enrichment Map to support Cytoscape 3, and have added additional features including new data formats and command line access.

The GeneMANIA Cytoscape app enables users to construct a composite gene-gene functional interaction network from a gene list. The resulting network includes the genes most related to the original list, and functional annotations from Gene Ontology. The edges are annotated with details about the publication or data source the interactions were derived from. The app leverages GeneMANIA’s database of 1800+ networks, containing over 500 million interactions spanning 8 organisms: A. thaliana, C. elegans, D. melanogaster, D. rerio, H. sapiens, M. musculus, R. norvegicus, and S. cerevisiae. Users may also import their own organisms, networks, and expression profiles. The app is compatible with Cytoscape versions 2 and 3.

Patient classification based on clinical and genomic data will further the goal of precision medicine. Interpretability is of particular relevance for models based on genomic data, where sample sizes are relatively small (in the hundreds), increasing overfitting risk netDx is a machine learning method to integrate multi-modal patient data and build a patient classifier. Patient data are converted into networks of patient similarity, which is intuitive to clinicians who also use patient similarity for medical diagnosis. Features passing selection are integrated, and new patients are assigned to the class with the greatest profile similarity. netDx has excellent performance, outperforming most machine-learning methods in binary cancer survival prediction. It handles missing data – a common problem in real-world data – without requiring imputation. netDx also has excellent interpretability, with native support to group genes into pathways for mechanistic insight into predictive features. The netDx Bioconductor package provides multiple workflows for users to build custom patient classifiers. It provides turnkey functions for one-step predictor generation from multi-modal data, including feature selection over multiple train/test data splits. Workflows offer versatility with custom feature design, choice of similarity metric; speed is improved by parallel execution. Built-in functions and examples allow users to compute model performance metrics such as AUROC, AUPR, and accuracy. netDx uses RCy3 to visualize top-scoring pathways and the final integrated patient network in Cytoscape. Advanced users can build more complex predictor designs with functional building blocks used in the default design. Finally, the netDx Bioconductor package provides a novel workflow for pathway-based patient classification from sparse genetic data.