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Abstract Cell type deconvolution methods can impute cell proportions from bulk transcriptomics data, revealing changes in disease progression or organ development. But benchmarking studies often use simulated bulk data from the same source as the reference, which limits its application scenarios. This study examines batch effects in deconvolution and introduces SCCAF-D, a computational workflow that ensures a Pearson Correlation Coefficient above 0.75 across simulated and real bulk data for various tissue types. Applied to non-alcoholic fatty liver disease, SCCAF-D unveils meaningful insights into changes in cell proportions during disease progression.

IntroductionThe agriculture genomics community has numerous data submission standards available, but the standards for describing and storing single-cell (SC, e.g., scRNA- seq) data are comparatively underdeveloped.MethodsTo bridge this gap, we leveraged recent advancements in human genomics infrastructure, such as the integration of the Human Cell Atlas Data Portal with Terra, a secure, scalable, open-source platform for biomedical researchers to access data, run analysis tools, and collaborate. In parallel, the Single Cell Expression Atlas at EMBL-EBI offers a comprehensive data ingestion portal for high-throughput sequencing datasets, including plants, protists, and animals (including humans). Developing data tools connecting these resources would offer significant advantages to the agricultural genomics community. The FAANG data portal at EMBL-EBI emphasizes delivering rich metadata and highly accurate and reliable annotation of farmed animals but is not computationally linked to either of these resources.ResultsHerein, we describe a pilot-scale project that determines whether the current FAANG metadata standards for livestock can be used to ingest scRNA-seq datasets into Terra in a manner consistent with HCA Data Portal standards. Importantly, rich scRNA-seq metadata can now be brokered through the FAANG data portal using a semi-automated process, thereby avoiding the need for substantial expert curation. We have further extended the functionality of this tool so that validated and ingested SC files within the HCA Data Portal are transferred to Terra for further analysis. In addition, we verified data ingestion into Terra, hosted on Azure, and demonstrated the use of a workflow to analyze the first ingested porcine scRNA-seq dataset. Additionally, we have also developed prototype tools to visualize the output of scRNA-seq analyses on genome browsers to compare gene expression patterns across tissues and cell populations. This JBrowse tool now features distinct tracks, showcasing PBMC scRNA-seq alongside two bulk RNA-seq experiments.DiscussionWe intend to further build upon these existing tools to construct a scientist-friendly data resource and analytical ecosystem based on Findable, Accessible, Interoperable, and Reusable (FAIR) SC principles to facilitate SC-level genomic analysis through data ingestion, storage, retrieval, re-use, visualization, and comparative annotation across agricultural species.

Abstract The growing number of available single-cell gene expression datasets from different species creates opportunities to explore evolutionary relationships between cell types across species. Cross-species integration of single-cell RNA-sequencing data has been particularly informative in this context. However, in order to do so robustly it is essential to have rigorous benchmarking and appropriate guidelines to ensure that integration results truly reflect biology. Here, we benchmark 28 combinations of gene homology mapping methods and data integration algorithms in a variety of biological settings. We examine the capability of each strategy to perform species-mixing of known homologous cell types and to preserve biological heterogeneity using 9 established metrics. We also develop a new biology conservation metric to address the maintenance of cell type distinguishability. Overall, scANVI, scVI and SeuratV4 methods achieve a balance between species-mixing and biology conservation. For evolutionarily distant species, including in-paralogs is beneficial. SAMap outperforms when integrating whole-body atlases between species with challenging gene homology annotation. We provide our freely available cross-species integration and assessment pipeline to help analyse new data and develop new algorithms.

Abstract Background The Human Cell Atlas resource will deliver single cell transcriptome data spatially organised in terms of gross anatomy, tissue location and with images of cellular histology. This will enable the application of bioinformatics analysis, machine learning and data mining revealing an atlas of cell types, sub-types, varying states and ultimately cellular changes related to disease conditions. To further develop the understanding of specific pathological and histopathological phenotypes with their spatial relationships and dependencies, a more sophisticated spatial descriptive framework is required to enable integration and analysis in spatial terms. Methods We describe a conceptual coordinate model for the Gut Cell Atlas (small and large intestines). Here, we focus on a Gut Linear Model (1-dimensional representation based on the centreline of the gut) that represents the location semantics as typically used by clinicians and pathologists when describing location in the gut. This knowledge representation is based on a set of standardised gut anatomy ontology terms describing regions in situ, such as ileum or transverse colon, and landmarks, such as ileo-caecal valve or hepatic flexure, together with relative or absolute distance measures. We show how locations in the 1D model can be mapped to and from points and regions in both a 2D model and 3D models, such as a patient's CT scan where the gut has been segmented. Results The outputs of this work include 1D, 2D and 3D models of the human gut, delivered through publicly accessible Json and image files. We also illustrate the mappings between models using a demonstrator tool that allows the user to explore the anatomical space of the gut. All data and software is fully open-source and available online. Conclusions Small and large intestines have a natural “gut coordinate” system best represented as a 1D centreline through the gut tube, reflecting functional differences. Such a 1D centreline model with landmarks, visualised using viewer software allows interoperable translation to both a 2D anatomogram model and multiple 3D models of the intestines. This permits users to accurately locate samples for data comparison.

Melanoma is the deadliest form of skin cancer and develops from the melanocytes that are responsible for the pigmentation of the skin. The skin is also a highly regenerative organ, harboring a pool of undifferentiated melanocyte stem cells that proliferate and differentiate into mature melanocytes during regenerative processes in the adult. Melanoma and melanocyte regeneration share remarkable cellular features, including activation of cell proliferation and migration. Yet, melanoma considerably differs from the regenerating melanocytes with respect to abnormal proliferation, invasive growth, and metastasis. Thus, it is likely that at the cellular level, melanoma resembles early stages of melanocyte regeneration with increased proliferation but separates from the later melanocyte regeneration stages due to reduced proliferation and enhanced differentiation. Here, by exploiting the zebrafish melanocytes that can efficiently regenerate and be induced to undergo malignant melanoma, we unravel the transcriptome profiles of the regenerating melanocytes during early and late regeneration and the melanocytic nevi and malignant melanoma. Our global comparison of the gene expression profiles of melanocyte regeneration and nevi/melanoma uncovers the opposite regulation of a substantial number of genes related to Wnt signaling and transforming growth factor beta (TGF-β)/(bone morphogenetic protein) BMP signaling pathways between regeneration and cancer. Functional activation of canonical Wnt or TGF-β/BMP pathways during melanocyte regeneration promoted melanocyte regeneration but potently suppressed the invasiveness, migration, and proliferation of human melanoma cells in vitro and in vivo. Therefore, the opposite regulation of signaling mechanisms between melanocyte regeneration and melanoma can be exploited to stop tumor growth and develop new anti-cancer therapies.

Abstract The number of mass spectrometry (MS)-based proteomics datasets in the public domain keeps increasing, particularly those generated by Data Independent Acquisition (DIA) approaches such as SWATH-MS. Unlike Data Dependent Acquisition datasets, the re-use of DIA datasets has been rather limited to date, despite its high potential, due to the technical challenges involved. We introduce a (re-)analysis pipeline for public SWATH-MS datasets which includes a combination of metadata annotation protocols, automated workflows for MS data analysis, statistical analysis, and the integration of the results into the Expression Atlas resource. Automation is orchestrated with Nextflow, using containerised open analysis software tools, rendering the pipeline readily available and reproducible. To demonstrate its utility, we reanalysed 10 public DIA datasets from the PRIDE database, comprising 1,278 SWATH-MS runs. The robustness of the analysis was evaluated, and the results compared to those obtained in the original publications. The final expression values were integrated into Expression Atlas, making SWATH-MS experiments more widely available and combining them with expression data originating from other proteomics and transcriptomics datasets.

Pathologists need to compare histopathological images of normal and diseased tissues between different samples, cases, and species. We have designed an interactive system, termed Comparative Pathology Workbench (CPW), which allows direct and dynamic comparison of images at a variety of magnifications, selected regions of interest, as well as the results of image analysis or other data analyses such as scRNA-seq. This allows pathologists to indicate key diagnostic features, with a mechanism to allow discussion threads amongst expert groups of pathologists and other disciplines. The data and associated discussions can be accessed online from anywhere in the world. The Comparative Pathology Workbench (CPW) is a web-browser-based visual analytics platform providing shared access to an interactive “spreadsheet” style presentation of image and associated analysis data. The CPW provides a grid layout of rows and columns so that images that correspond to matching data can be organised in the form of an image-enabled “spreadsheet”. An individual workbench can be shared with other users with read-only or full edit access as required. In addition, each workbench element or the whole bench itself has an associated discussion thread to allow collaborative analysis and consensual interpretation of the data.The CPW is a Django-based web-application that hosts the workbench data, manages users, and user-preferences. All image data are hosted by other resource applications such as OMERO or the Digital Slide Archive. Further resources can be added as required. The discussion threads are managed using WordPress and include additional graphical and image data. The CPW has been developed to allow integration of image analysis outputs from systems such as QuPath or ImageJ. All software is open-source and available from a GitHub repository.

Abstract Several single-cell RNA sequencing (scRNA-seq) studies analyzing immune response to COVID-19 infection have been recently published. Most of these studies have small sample sizes, which limits the conclusions that can be made with high confidence. By re-analyzing these data in a standardized manner, we validated 8 of the 20 published results across multiple datasets. In particular, we found a consistent decrease in T-cells with increasing COVID-19 infection severity, upregulation of type I Interferon signal pathways, presence of expanded B-cell clones in COVID-19 patients but no consistent trend in T-cell clonal expansion. Overall, our results show that the conclusions drawn from scRNA-seq data analysis of small cohorts of COVID-19 patients need to be treated with some caution.

The number of publicly available proteomics datasets is growing rapidly, but a standardized approach for describing the associated metadata is lacking. Here, the authors propose a format and a software pipeline to present and validate metadata, and integrate them into ProteomeXchange repositories.

Abstract Using 11 proteomics datasets, mostly available through the PRIDE database, we assembled a reference expression map for 191 cancer cell lines and 246 clinical tumour samples, across 13 lineages. We found unique peptides identified only in tumour samples despite a much higher coverage in cell lines. These were mainly mapped to proteins related to regulation of signalling receptor activity. Correlations between baseline expression in cell lines and tumours were calculated. We found these to be highly similar across all samples with most similarity found within a given sample type. Integration of proteomics and transcriptomics data showed median correlation across cell lines to be 0.58 (range between 0.43 and 0.66). Additionally, in agreement with previous studies, variation in mRNA levels was often a poor predictor of changes in protein abundance. To our knowledge, this work constitutes the first meta-analysis focusing on cancer-related public proteomics datasets. We therefore also highlight shortcomings and limitations of such studies. All data is available through PRIDE dataset identifier PXD013455 and in Expression Atlas.

The increasingly large amount of proteomics data in the public domain enables, among other applications, the combined analyses of datasets to create comparative protein expression maps covering different organisms and different biological conditions. Here we have reanalysed public proteomics datasets from mouse and rat tissues (14 and 9 datasets, respectively), to assess baseline protein abundance. Overall, the aggregated dataset contained 23 individual datasets, including a total of 211 samples coming from 34 different tissues across 14 organs, comprising 9 mouse and 3 rat strains, respectively. In all cases, we studied the distribution of canonical proteins between the different organs. The number of canonical proteins per dataset ranged from 273 (tendon) and 9,715 (liver) in mouse, and from 101 (tendon) and 6,130 (kidney) in rat. Then, we studied how protein abundances compared across different datasets and organs for both species. As a key point we carried out a comparative analysis of protein expression between mouse, rat and human tissues. We observed a high level of correlation of protein expression among orthologs between all three species in brain, kidney, heart and liver samples, whereas the correlation of protein expression was generally slightly lower between organs within the same species. Protein expression results have been integrated into the resource Expression Atlas for widespread dissemination.

With the advent of single-cell RNA sequencing (scRNA-seq) technologies, there has been a spike in studies involving scRNA-seq of several tissues across diverse species including Drosophila. Although a few databases exist for users to query genes of interest within the scRNA-seq studies, search tools that enable users to find orthologous genes and their cell type-specific expression patterns across species are limited. Here, we built a new search database, DRscDB (https://www.flyrnai.org/tools/single_cell/web/), to address this need. DRscDB serves as a comprehensive repository for published scRNA-seq datasets for Drosophila and relevant datasets from human and other model organisms. DRscDB is based on manual curation of Drosophila scRNA-seq studies of various tissue types and their corresponding analogous tissues in vertebrates including zebrafish, mouse, and human. Of note, our search database provides most of the literature-derived marker genes, thus preserving the original analysis of the published scRNA-seq datasets. Finally, DRscDB serves as a web-based user interface that allows users to mine gene expression data from scRNA-seq studies and perform cell cluster enrichment analyses pertaining to various scRNA-seq studies, both within and across species.

A fundamental issue related to the understanding of the molecular mechanisms, is the way in which common pathways act across different biological experiments related to complex diseases. Using network-based approaches, this work aims to provide a numeric characterization of pathways across different biological experiments, in the prospect to create unique footprints that may characterise a specific disease under study at a pathway network level. In this line we propose PathExNET, a web service that allows the creation of pathway-to-pathway expression networks that hold the over- and under expression information obtained from differential gene expression analyses. The unique numeric characterization of pathway expression status related to a specific biological experiment (or disease), as well as the creation of diverse combination of pathway networks generated by PathExNET, is expected to provide a concrete contribution towards the individualization of disease, and further lead to a more precise personalised medicine and management of treatment.PathExNET is available at: https://bioinformatics.cing.ac.cy/PathExNET and at https://pathexnet.cing-big.hpcf.cyi.ac.cy/

The Pan-Cancer Analysis of Whole Genomes project generated a vast array of data. In this article, the authors describe five different online resources to enable readers to explore and visualize the data.

Gliomas are the most frequent type of brain cancers and characterized by continuous proliferation, inflammation, angiogenesis, invasion and dedifferentiation, which are also among the initiator and sustaining factors of brain regeneration during restoration of tissue integrity and function. Thus, brain regeneration and brain cancer should share more molecular mechanisms at early stages of regeneration where cell proliferation dominates. However, the mechanisms could diverge later when the regenerative response terminates, while cancer cells sustain proliferation. To test this hypothesis, we exploited the adult zebrafish that, in contrast to the mammals, can efficiently regenerate the brain in response to injury. By comparing transcriptome profiles of the regenerating zebrafish telencephalon at its three different stages, i.e., 1 day post-lesion (dpl)-early wound healing stage, 3 dpl-early proliferative stage and 14 dpl-differentiation stage, to those of two brain cancers, i.e., low-grade glioma (LGG) and glioblastoma (GBM), we reveal the common and distinct molecular mechanisms of brain regeneration and brain cancer. While the transcriptomes of 1 dpl and 3 dpl harbor unique gene modules and gene expression profiles that are more divergent from the control, the transcriptome of 14 dpl converges to that of the control. Next, by functional analysis of the transcriptomes of brain regeneration stages to LGG and GBM, we reveal the common and distinct molecular pathways in regeneration and cancer. 1 dpl and LGG and GBM resemble with regard to signaling pathways related to metabolism and neurogenesis, while 3 dpl and LGG and GBM share pathways that control cell proliferation and differentiation. On the other hand, 14 dpl and LGG and GBM converge with respect to developmental and morphogenetic processes. Finally, our global comparison of gene expression profiles of three brain regeneration stages, LGG and GBM exhibit that 1 dpl is the most similar stage to LGG and GBM while 14 dpl is the most distant stage to both brain cancers. Therefore, early convergence and later divergence of brain regeneration and brain cancer constitutes a key starting point in comparative understanding of cellular and molecular events between the two phenomena and development of relevant targeted therapies for brain cancers.

Despite many studies on the immune characteristics of Coronavirus disease 2019 (COVID-19) patients in the progression stage, a detailed understanding of pertinent immune cells in recovered patients is lacking. We performed single-cell RNA sequencing on samples from recovered COVID-19 patients and healthy controls. We created a comprehensive immune landscape with more than 260,000 peripheral blood mononuclear cells (PBMCs) from 41 samples by integrating our dataset with previously reported datasets, which included samples collected between 27 and 47 days after symptom onset. According to our large-scale single-cell analysis, recovered patients, who had severe symptoms (severe/critical recovered), still exhibited peripheral immune disorders 1–2 months after symptom onset. Specifically, in these severe/critical recovered patients, human leukocyte antigen (HLA) class II and antigen processing pathways were downregulated in both CD14 monocytes and dendritic cells compared to healthy controls, while the proportion of CD14 monocytes increased. These may lead to the downregulation of T-cell differentiation pathways in memory T cells. However, in the mild/moderate recovered patients, the proportion of plasmacytoid dendritic cells increased compared to healthy controls, accompanied by the upregulation of HLA-DRA and HLA-DRB1 in both CD14 monocytes and dendritic cells. In addition, T-cell differentiation regulation and memory T cell–related genes FOS, JUN, CD69, CXCR4, and CD83 were upregulated in the mild/moderate recovered patients. Further, the immunoglobulin heavy chain V3-21 (IGHV3-21) gene segment was preferred in B-cell immune repertoires in severe/critical recovered patients. Collectively, we provide a large-scale single-cell atlas of the peripheral immune response in recovered COVID-19 patients.

Owing to its pronounced regenerative capacity in many tissues and organs, the zebrafish brain represents an ideal platform to understand the endogenous regeneration mechanisms that restore tissue integrity and function upon injury or disease. Although radial glial and neuronal cell populations have been characterized with respect to specific marker genes, comprehensive transcriptomic profiling of the regenerating telencephalon has not been conducted so far. Here, by processing the lesioned and unlesioned hemispheres of the telencephalon separately, we reveal the differentially expressed genes (DEGs) at the early wound healing and early proliferative stages of regeneration, i.e., 20 h post-lesion (hpl) and 3 days post-lesion (dpl), respectively. At 20 hpl, we detect a far higher number of DEGs in the lesioned hemisphere than in the unlesioned half and only 7% of all DEGs in both halves. However, this difference disappears at 3 dpl, where the lesioned and unlesioned hemispheres share 40% of all DEGs. By performing an extensive comparison of the gene expression profiles in these stages, we unravel that the lesioned hemispheres at 20 hpl and 3 dpl exhibit distinct transcriptional profiles. We further unveil a prominent activation of Wnt/β-catenin signaling at 20 hpl, returning to control level in the lesioned site at 3 dpl. Wnt/β-catenin signaling indeed appears to control a large number of genes associated primarily with the p53, apoptosis, forkhead box O (FoxO), mitogen-activated protein kinase (MAPK), and mammalian target of rapamycin (mTOR) signaling pathways specifically at 20 hpl. Based on these results, we propose that the lesioned and unlesioned hemispheres react to injury dynamically during telencephalon regeneration and that the activation of Wnt/β-catenin signaling at the early wound healing stage plays a key role in the regulation of cellular and molecular events.

Abstract FoxO transcription factors, inhibited by insulin/insulin‐like growth factor signalling (IIS), are crucial players in numerous organismal processes including lifespan. Using genomic tools, we uncover over 700 direct dFOXO targets in adult female Drosophila. dFOXO is directly required for transcription of several IIS components and interacting pathways, such as TOR, in the wild‐type fly. The genomic locations occupied by dFOXO in adults are different from those observed in larvae or cultured cells. These locations remain unchanged upon activation by stresses or reduced IIS, but the binding is increased and additional targets activated upon genetic reduction in IIS. We identify the part of the IIS transcriptional response directly controlled by dFOXO and the indirect effects and show that parts of the transcriptional response to IIS reduction do not require dfoxo. Promoter analyses revealed GATA and other forkhead factors as candidate mediators of the indirect and dfoxo‐independent effects. We demonstrate genome‐wide evolutionary conservation of dFOXO targets between the fly and the worm Caenorhabditis elegans, enriched for a second tier of regulators including the dHR96/daf‐12 nuclear hormone receptor.

Forkhead box O (FoxO) transcription factors (TFs) are key drivers of complex transcriptional programmes that determine animal lifespan. FoxOs regulate a number of other TFs, but how these TFs in turn might mediate the anti-ageing programmes orchestrated by FoxOs in vivo is unclear. Here, we identify an E-twenty six (ETS)-family transcriptional repressor, Anterior open (Aop), as regulated by the single Drosophila melanogaster FoxO (dFOXO) in the adult gut. AOP, the functional orthologue of the human Etv6/Tel protein, binds numerous genomic sites also occupied by dFOXO and counteracts the activity of an ETS activator, Pointed (Pnt), to prevent the lifespan-shortening effects of co-activation of dFOXO and PNT. This detrimental synergistic effect of dFOXO and PNT appears to stem from a mis-regulation of lipid metabolism. At the same time, AOP activity in another fly organ, the fat body, has further beneficial roles, regulating genes in common with dfoxo, such as the secreted, non-sensory, odorant binding protein (Obp99b), and robustly extending lifespan. Our study reveals a complex interplay between evolutionarily conserved ETS factors and dFOXO, the functional significance of which may extend well beyond animal lifespan.

The DAF-16/FoxO transcription factor controls growth, metabolism and aging in Caenorhabditis elegans. The large number of genes that it regulates has been an obstacle to understanding its function. However, recent analysis of transcript and chromatin profiling implies that DAF-16 regulates relatively few genes directly, and that many of these encode other regulatory proteins. We have investigated the regulation by DAF-16 of genes encoding the AMP-activated protein kinase (AMPK), which has α, β and γ subunits. C. elegans has 5 genes encoding putative AMP-binding regulatory γ subunits, aakg-1-5. aakg-4 and aakg-5 are closely related, atypical isoforms, with orthologs throughout the Chromadorea class of nematodes. We report that ∼75% of total γ subunit mRNA encodes these 2 divergent isoforms, which lack consensus AMP-binding residues, suggesting AMP-independent kinase activity. DAF-16 directly activates expression of aakg-4, reduction of which suppresses longevity in daf-2 insulin/IGF-1 receptor mutants. This implies that an increase in the activity of AMPK containing the AAKG-4 γ subunit caused by direct activation by DAF-16 slows aging in daf-2 mutants. Knock down of aakg-4 expression caused a transient decrease in activation of expression in multiple DAF-16 target genes. This, taken together with previous evidence that AMPK promotes DAF-16 activity, implies the action of these two metabolic regulators in a positive feedback loop that accelerates the induction of DAF-16 target gene expression. The AMPK β subunit, aakb-1, also proved to be up-regulated by DAF-16, but had no effect on lifespan. These findings reveal key features of the architecture of the gene-regulatory network centered on DAF-16, and raise the possibility that activation of AMP-independent AMPK in nutritionally replete daf-2 mutant adults slows aging in C. elegans. Evidence of activation of AMPK subunits in mammals suggests that such FoxO-AMPK interactions may be evolutionarily conserved.

A challenge of systems biology is to integrate incomplete knowledge on pathways with existing experimental data sets and relate these to measured phenotypes. Research on ageing often generates such incomplete data, creating difficulties in integrating RNA expression with information about biological processes and the phenotypes of ageing, including longevity. Here, we develop a logic-based method that employs Answer Set Programming, and use it to infer signalling effects of genetic perturbations, based on a model of the insulin signalling pathway. We apply our method to RNA expression data from Drosophila mutants in the insulin pathway that alter lifespan, in a foxo dependent fashion. We use this information to deduce how the pathway influences lifespan in the mutant animals. We also develop a method for inferring the largest common sub-paths within each of our signalling predictions. Our comparisons reveal consistent homeostatic mechanisms across both long- and short-lived mutants. The transcriptional changes observed in each mutation usually provide negative feedback to signalling predicted for that mutation. We also identify an S6K-mediated feedback in two long-lived mutants that suggests a crosstalk between these pathways in mutants of the insulin pathway, in vivo. By formulating the problem as a logic-based theory in a qualitative fashion, we are able to use the efficient search facilities of Answer Set Programming, allowing us to explore larger pathways, combine molecular changes with pathways and phenotype and infer effects on signalling in in vivo, whole-organism, mutants, where direct signalling stimulation assays are difficult to perform. Our methods are available in the web-service NetEffects: http://www.ebi.ac.uk/thornton-srv/software/NetEffects.

Single-cell omics (SCO) has revolutionized the way and the level of resolution by which life science research is conducted, not only impacting our understanding of fundamental cell biology but also providing novel solutions in cutting-edge medical research. The rapid development of single-cell technologies has been accompanied by the active development of data analysis methods, resulting in a plethora of new analysis tools and strategies every year. Such a rapid development of SCO methods and tools poses several challenges in standardization, benchmarking, computational resources and training. These challenges are in line with the activities of ELIXIR, the European coordinated infrastructure for life science data. Here, we describe the current landscape of and the main challenges in SCO data, and propose the creation of the ELIXIR SCO Community to coordinate the efforts in order to best serve SCO researchers in Europe and beyond. The Community will build on top of national experiences and pave the way towards integrated long-term solutions for SCO research.