Single-cell atlas of the human brain vasculature across development, adulthood and disease.

A broad range of brain pathologies critically relies on the vasculature, and cerebrovascular disease is a leading cause of death worldwide. However, the cellular and molecular architecture of the human brain vasculature remains incompletely understood1. Here we performed single-cell RNA sequencing analysis of 606,380 freshly isolated endothelial cells, perivascular cells and other tissue-derived cells from 117 samples, from 68 human fetuses and adult patients to construct a molecular atlas of the developing fetal, adult control and diseased human brain vasculature. We identify extensive molecular heterogeneity of the vasculature of healthy fetal and adult human brains and across five vascular-dependent central nervous system (CNS) pathologies, including brain tumours and brain vascular malformations. We identify alteration of arteriovenous differentiation and reactivated fetal as well as conserved dysregulated genes and pathways in the diseased vasculature. Pathological endothelial cells display a loss of CNS-specific properties and reveal an upregulation of MHC class II molecules, indicating atypical features of CNS endothelial cells. Cell-cell interaction analyses predict substantial endothelial-to-perivascular cell ligand-receptor cross-talk, including immune-related and angiogenic pathways, thereby revealing a central role for the endothelium within brain neurovascular unit signalling networks. Our single-cell brain atlas provides insights into the molecular architecture and heterogeneity of the developing, adult/control and diseased human brain vasculature and serves as a powerful reference for future studies.

Fatecode enables cell fate regulator prediction using classification-supervised autoencoder perturbation.

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.

Drugst.One - a plug-and-play solution for online systems medicine and network-based drug repurposing.

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce Drugst.One, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, Drugst.One turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, Drugst.One has been successfully integrated with 21 computational systems medicine tools. Available at https://drugst.one, Drugst.One has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research.

The multimodality cell segmentation challenge: toward universal solutions.

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multimodality cell segmentation benchmark, comprising more than 1,500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging.

Integrated transcriptomics uncovers an enhanced association between the prion protein gene expression and vesicle dynamics signatures in glioblastomas.

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 PRNPhigh and PRNPlow 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 PRNPhigh/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 PRNPhigh/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.

Metformin reduces the clonal fitness of Dnmt3aR878H hematopoietic stem and progenitor cells by reversing their aberrant metabolic and epigenetic state.

Clonal hematopoiesis (CH) arises when a hematopoietic stem cell (HSC) acquires a mutation that confers a competitive advantage over wild-type (WT) HSCs, resulting in its clonal expansion. Individuals with CH are at an increased risk of developing hematologic neoplasms and a range of age-related inflammatory illnesses1-3. Therapeutic interventions that suppress the expansion of mutant HSCs have the potential to prevent these CH-related illnesses; however, such interventions have not yet been identified. The most common CH driver mutations are in the DNA methyltransferase 3 alpha (DNMT3A) gene with arginine 882 (R882) being a mutation hotspot. Here we show that murine hematopoietic stem and progenitor cells (HSPCs) carrying the Dnmt3aR878H/+ mutation, which is equivalent to human DNMT3AR882H/+, have increased mitochondrial respiration compared with WT cells and are dependent on this metabolic reprogramming for their competitive advantage. Importantly, treatment with metformin, an oral anti-diabetic drug with inhibitory activity against complex I in the electron transport chain (ETC), reduced the fitness of Dnmt3aR878H/+ HSCs. Through a multi-omics approach, we discovered that metformin acts by enhancing the methylation potential in Dnmt3aR878H/+ HSPCs and reversing their aberrant DNA CpG methylation and histone H3K27 trimethylation (H3K27me3) profiles. Metformin also reduced the fitness of human DNMT3AR882H HSPCs generated by prime editing. Our findings provide preclinical rationale for investigating metformin as a preventive intervention against illnesses associated with DNMT3AR882 mutation-driven CH in humans.

Single-cell, single-nucleus, and spatial transcriptomics characterization of the immunological landscape in the healthy and PSC human liver.

BACKGROUND & AIMS: Primary sclerosing cholangitis (PSC) is an immune-mediated cholestatic liver disease for which there is an unmet need to understand the cellular composition of the affected liver and how it underlies disease pathogenesis. We aimed to generate a comprehensive atlas of the PSC liver using multi-omic modalities and protein-based functional validation. METHODS: We employed single-cell and single-nucleus RNA sequencing (47,156 cells and 23,000 nuclei) and spatial transcriptomics (one sample by 10x Visium and five samples with Nanostring GeoMx DSP) to profile the cellular ecosystem in 10 PSC livers. Transcriptomic profiles were compared to 24 neurologically deceased donor livers (107,542 cells) and spatial transcriptomics controls, as well as 18,240 cells and 20,202 nuclei from three PBC livers. Flow cytometry was performed to validate PSC-specific differences in immune cell phenotype and function. RESULTS: PSC explants with parenchymal cirrhosis and prominent periductal fibrosis contained a population of cholangiocyte-like hepatocytes that were surrounded by diverse immune cell populations. PSC-associated biliary, mesenchymal, and endothelial populations expressed chemokine and cytokine transcripts involved in immune cell recruitment. Additionally, expanded CD4+ T cells and recruited myeloid populations in the PSC liver expressed the corresponding receptors to these chemokines and cytokines, suggesting potential recruitment. Tissue-resident macrophages, by contrast, were reduced in number and exhibited a dysfunctional and downregulated inflammatory response to lipopolysaccharide and interferon-γ stimulation. CONCLUSIONS: We present a comprehensive atlas of the PSC liver and demonstrate an exhaustion-like phenotype of myeloid cells and markers of chronic cytokine expression in late-stage PSC lesions. This atlas expands our understanding of the cellular complexity of PSC and has potential to guide the development of novel treatments. IMPACT AND IMPLICATIONS: Primary sclerosing cholangitis (PSC) is a rare liver disease characterized by chronic inflammation and irreparable damage to the bile ducts, which eventually results in liver failure. Due to a limited understanding of the underlying pathogenesis of disease, treatment options are limited. To address this, we sequenced healthy and diseased livers to compare the activity, interactions, and localization of immune and non-immune cells. This revealed that hepatocytes lining PSC scar regions co-express cholangiocyte markers, whereas immune cells infiltrate the scar lesions. Of these cells, macrophages, which typically contribute to tissue repair, were enriched in immunoregulatory genes and demonstrated a lack of responsiveness to stimulation. These cells may be involved in maintaining hepatic inflammation and could be a target for novel therapies.

Adversarial training improves model interpretability in single-cell RNA-seq analysis.

Motivation: Predictive computational models must be accurate, robust, and interpretable to be considered reliable in important areas such as biology and medicine. A sufficiently robust model should not have its output affected significantly by a slight change in the input. Also, these models should be able to explain how a decision is made to support user trust in the results. Efforts have been made to improve the robustness and interpretability of predictive computational models independently; however, the interaction of robustness and interpretability is poorly understood. Results: As an example task, we explore the computational prediction of cell type based on single-cell RNA-seq data and show that it can be made more robust by adversarially training a deep learning model. Surprisingly, we find this also leads to improved model interpretability, as measured by identifying genes important for classification using a range of standard interpretability methods. Our results suggest that adversarial training may be generally useful to improve deep learning robustness and interpretability and that it should be evaluated on a range of tasks. Availability and implementation: Our Python implementation of all analysis in this publication can be found at: https://github.com/MehrshadSD/robustness-interpretability. The analysis was conducted using numPy 0.2.5, pandas 2.0.3, scanpy 1.9.3, tensorflow 2.10.0, matplotlib 3.7.1, seaborn 0.12.2, sklearn 1.1.1, shap 0.42.0, lime 0.2.0.1, matplotlib_venn 0.11.9.

A rat liver cell atlas reveals intrahepatic myeloid heterogeneity.

The large size and vascular accessibility of the laboratory rat (Rattus norvegicus) make it an ideal hepatic animal model for diseases that require surgical manipulation. Often, the disease susceptibility and outcomes of inflammatory pathologies vary significantly between strains. This study uses single-cell transcriptomics to better understand the complex cellular network of the rat liver, as well as to unravel the cellular and molecular sources of inter-strain hepatic variation. We generated single-cell and single-nucleus transcriptomic maps of the livers of healthy Dark Agouti and Lewis rat strains and developed a factor analysis-based bioinformatics analysis pipeline to study data covariates, such as strain and batch. Using this approach, we discovered transcriptomic variation within the hepatocyte and myeloid populations that underlie distinct cell states between rat strains. This finding will help provide a reference for future investigations on strain-dependent outcomes of surgical experiment models.

Fate Specification of GFAP-Negative Primitive Neural Stem Cells and Their Progeny at Clonal Resolution.

The mature brain contains an incredible number and diversity of cells that are produced and maintained by heterogeneous pools of neural stem cells (NSCs). Two distinct types of NSCs exist in the developing and adult mouse brain: Glial Fibrillary Acidic Protein (GFAP)-negative primitive (p)NSCs and downstream GFAP-positive definitive (d)NSCs. To better understand the embryonic functions of NSCs, we performed clonal lineage tracing within neurospheres grown from either pNSCs or dNSCs to enrich for their most immediate downstream neural progenitor cells (NPCs). These clonal progenitor lineage tracing data allowed us to construct a hierarchy of progenitor subtypes downstream of pNSCs and dNSCs that were then validated using single-cell transcriptomics. Further, we identify Nexn as required for neuronal specification from neuron/astrocyte progenitor cells downstream of rare pNSCs. Combined, these data provide single-cell resolution of NPC lineages downstream of rare pNSCs that likely would be missed from population-level analyses in vivo.

Cell networks in the mouse liver during partial hepatectomy.

In solid tissues homeostasis and regeneration after injury involve a complex interplay between many different cell types. The mammalian liver harbors numerous epithelial and non-epithelial cells and little is known about the global signaling networks that govern their interactions. To better understand the hepatic cell network, we isolated and purified 10 different cell populations from normal and regenerative mouse livers. Their transcriptomes were analyzed by bulk RNA-seq and a computational platform was used to analyze the cell-cell and ligand-receptor interactions among the 10 populations. Over 50,000 potential cell-cell interactions were found in both the ground state and after partial hepatectomy. Importantly, about half of these differed between the two states, indicating massive changes in the cell network during regeneration. Our study provides the first comprehensive database of potential cell-cell interactions in mammalian liver cell homeostasis and regeneration. With the help of this prediction model, we identified and validated two previously unknown signaling interactions involved in accelerating and delaying liver regeneration. Overall, we provide a novel platform for investigating autocrine/paracrine pathways in tissue regeneration, which can be adapted to other complex multicellular systems.

Distinct shared and compartment-enriched oncogenic networks drive primary versus metastatic breast cancer.

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, TGFß 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.

Drugst.One - A plug-and-play solution for online systems medicine and network-based drug repurposing.

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce Drugst.One, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, Drugst.One turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, Drugst.One has been successfully integrated with 21 computational systems medicine tools. Available at https://drugst.one, Drugst.One has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research.

Multiplatform molecular profiling uncovers two subgroups of malignant peripheral nerve sheath tumors with distinct therapeutic vulnerabilities.

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.

Multi-scale systems genomics analysis predicts pathways, cell types, and drug targets involved in normative variation in peri-adolescent human cognition.

An open challenge in human genetics is to better understand the systems-level impact of genotype variation on developmental cognition. To characterize the genetic underpinnings of peri-adolescent cognition, we performed genotype-phenotype and systems analysis for binarized accuracy in nine cognitive tasks from the Philadelphia Neurodevelopmental Cohort (~2,200 individuals of European continental ancestry aged 8-21 years). We report a region of genome-wide significance within the 3' end of the Fibulin-1 gene (P = 4.6 × 10-8), associated with accuracy in nonverbal reasoning, a heritable form of complex reasoning ability. Diffusion tensor imaging data from a subset of these participants identified a significant association of white matter fractional anisotropy with FBLN1 genotypes (P < 0.025); poor performers show an increase in the C and A allele for rs77601382 and rs5765534, respectively, which is associated with increased fractional anisotropy. Integration of published human brain-specific 'omic maps, including single-cell transcriptomes of the developing human brain, shows that FBLN1 demonstrates greatest expression in the fetal brain, as a marker of intermediate progenitor cells, demonstrates negligible expression in the adolescent and adult human brain, and demonstrates increased expression in the brain in schizophrenia. Collectively these findings warrant further study of this gene and genetic locus in cognition, neurodevelopment, and disease. Separately, genotype-pathway analysis identified an enrichment of variants associated with working memory accuracy in pathways related to development and to autonomic nervous system dysfunction. Top-ranking pathway genes include those genetically associated with diseases with working memory deficits, such as schizophrenia and Parkinson's disease. This work advances the "molecules-to-behavior" view of cognition and provides a framework for using systems-level organization of data for other biomedical domains.

Genotyping SARS-CoV-2 Variants Using Ratiometric Nucleic Acid Barcode Panels.

Designing diagnostic assays to genotype rapidly mutating viruses remains a challenge despite the overall improvements in nucleic acid detection technologies. RT-PCR and next-generation sequencing are unsuitable for genotyping during outbreaks or in point-of-care detection due to their infrastructure requirements and longer turnaround times. We developed a quantum dot barcode multiplexing system to genotype mutated viruses. We designed multiple quantum dot barcodes to target conserved, wildtype, and mutated regions of SARS-CoV-2. We calculated ratios of the signal output from different barcodes that enabled SARS-CoV-2 detection and identified SARS-CoV-2 variant strains from a sample. We detected different sequence types, including conserved genes, nucleotide deletions, and single nucleotide substitutions. Our system detected SARS-CoV-2 patient specimens with 98% sensitivity and 94% specificity across 91 patient samples. Further, we leveraged our barcoding and ratio system to track the emergence of the N501Y SARS-CoV-2 mutation from December 2020 to May 2021 and demonstrated that the more transmissible N501Y mutation started to dominate infections by April 2021. Our barcoding and signal ratio approach can genotype viruses and track the emergence of viral mutations in a single diagnostic test. This technology can be extended to tracking other viruses. Combined with smartphone detection technologies, this assay can be adapted for point-of-care tracking of viral mutations in real time.

Cytoscape.js 2023 update: a graph theory library for visualization and analysis.

SUMMARY: Cytoscape.js is an open-source JavaScript-based graph library. Its most common use case is as a visualization software component, so it can be used to render interactive graphs in a web browser. It also can be used in a headless manner, useful for graph operations on a server, such as Node.js. This update describes new features and enhancements introduced over many new versions from 2015 to 2022. AVAILABILITY AND IMPLEMENTATION: Cytoscape.js is implemented in JavaScript. Documentation, downloads and source code are available at http://js.cytoscape.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Single-nucleus multiomic atlas of frontal cortex in amyotrophic lateral sclerosis with a deep learning-based decoding of alternative polyadenylation mechanisms.

The understanding of how different cell types contribute to amyotrophic lateral sclerosis (ALS) pathogenesis is limited. Here we generated a single-nucleus transcriptomic and epigenomic atlas of the frontal cortex of ALS cases with C9orf72 (C9) hexanucleotide repeat expansions and sporadic ALS (sALS). Our findings reveal shared pathways in C9-ALS and sALS, characterized by synaptic dysfunction in excitatory neurons and a disease-associated state in microglia. The disease subtypes diverge with loss of astrocyte homeostasis in C9-ALS, and a more substantial disturbance of inhibitory neurons in sALS. Leveraging high depth 3'-end sequencing, we found a widespread switch towards distal polyadenylation (PA) site usage across ALS subtypes relative to controls. To explore this differential alternative PA (APA), we developed APA-Net, a deep neural network model that uses transcript sequence and expression levels of RNA-binding proteins (RBPs) to predict cell-type specific APA usage and RBP interactions likely to regulate APA across disease subtypes.

Robust segregation of donor and recipient cells from single-cell RNA-sequencing of transplant samples.

Background: Single-cell RNA-sequencing (scRNA-seq) technology has revealed novel cell populations in organs, uncovered regulatory relationships between genes, and allowed for tracking of cell lineage trajectory during development. It demonstrates promise as a method to better understand transplant biology; however, fundamental bioinformatic tools for its use in the context of transplantation have not been developed. One major need has been a robust method to identify cells as being either donor or recipient genotype origin, and ideally without the need to separately sequence the donor and recipient. Methods: We implemented a novel two-stage genotype discovery method (scTx) optimized for transplant samples by being robust to disparities in cell number and cell type. Using both in silico and real-world scRNA-seq transplant data, we benchmarked our method against existing demultiplexing methods to profile their limitations in terms of sequencing depth, donor and recipient cell imbalance, and single nucleotide variant input selection. Results: Using in silico data, scTx could more accurately separate donor from recipient cells and at much lower genotype ratios than existing methods. This was further validated using solid-organ scRNA-seq data where scTx could more reliably identify when a second genotype was present and at lower numbers of cells from a second genotype. Conclusion: scTx introduces the capability to accurately segregate donor and recipient gene expression at the single-cell level from scRNA-seq data without the need to separately genotype the donor and recipient. This will facilitate the use of scRNA-seq in the context of transplantation.