Building a FAIR data ecosystem for incorporating single-cell transcriptomics data into agricultural genome to phenome research.

Introduction: The 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.

Single-cell RNA sequencing offers opportunities to explore the depth of physiology, adaptation, and biochemistry in non-model organisms exposed to pollution.

Single-cell Sequencing technology (scSeq) has revolutionized our understanding of individual cells, uncovering unprecedented heterogeneity within tissues and cell populations, principality through single-cell RNA Sequencing (scRNA-Seq). This short review highlights the pivotal role of scRNA-Seq in elucidating genotype-phenotype relationships, particularly in biological systems. Based on published articles, our analysis involved manual curation and automated Scopus tools to illustrate recent advances in the application of scRNA-Seq.

Integrated Proteomics Analysis of Baseline Protein Expression in Pig Tissues.

The availability of an increasingly large amount of public proteomics data sets presents an opportunity for performing combined analyses to generate comprehensive organism-wide protein expression maps across different organisms and biological conditions. , a domestic pig, is a model organism relevant for food production and for human biomedical research. Here, we reanalyzed 14 public proteomics data sets from the PRIDE database coming from pig tissues to assess baseline (without any biological perturbation) protein abundance in 14 organs, encompassing a total of 20 healthy tissues from 128 samples.

CATD: a reproducible pipeline for selecting cell-type deconvolution methods across tissues.

Motivation: Cell-type deconvolution methods aim to infer cell composition from bulk transcriptomic data. The proliferation of developed methods coupled with inconsistent results obtained in many cases, highlights the pressing need for guidance in the selection of appropriate methods. Additionally, the growing accessibility of single-cell RNA sequencing datasets, often accompanied by bulk expression from related samples enable the benchmark of existing methods.

Canonical Wnt and TGF-β/BMP signaling enhance melanocyte regeneration but suppress invasiveness, migration, and proliferation of melanoma cells.

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.

Expression Atlas update: insights from sequencing data at both bulk and single cell level.

Expression Atlas (www.ebi.ac.

Benchmarking strategies for cross-species integration of single-cell RNA sequencing data.

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.

The Comparative Pathology Workbench: Interactive visual analytics for biomedical data.

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 Promise of Single-Cell RNA Sequencing to Redefine the Understanding of Crohn's Disease Fibrosis Mechanisms.

Crohn's disease (CD) is a chronic inflammatory bowel disease with a high prevalence throughout the world. The development of Crohn's-related fibrosis, which leads to strictures in the gastrointestinal tract, presents a particular challenge and is associated with significant morbidity. There are currently no specific anti-fibrotic therapies available, and so treatment is aimed at managing the stricturing complications of fibrosis once it is established.

A Roadmap for the Human Gut Cell Atlas.

The number of studies investigating the human gastrointestinal tract using various single-cell profiling methods has increased substantially in the past few years. Although this increase provides a unique opportunity for the generation of the first comprehensive Human Gut Cell Atlas (HGCA), there remains a range of major challenges ahead. Above all, the ultimate success will largely depend on a structured and coordinated approach that aligns global efforts undertaken by a large number of research groups.

Diagnosis of Multisystem Inflammatory Syndrome in Children by a Whole-Blood Transcriptional Signature.

Background: To identify a diagnostic blood transcriptomic signature that distinguishes multisystem inflammatory syndrome in children (MIS-C) from Kawasaki disease (KD), bacterial infections, and viral infections.

Methods: Children presenting with MIS-C to participating hospitals in the United Kingdom and the European Union between April 2020 and April 2021 were prospectively recruited. Whole-blood RNA Sequencing was performed, contrasting the transcriptomes of children with MIS-C (n = 38) to those from children with KD (n = 136), definite bacterial (DB; n = 188) and viral infections (DV; n = 138).

SelectBCM tool: a batch evaluation framework to select the most appropriate batch-correction methods for bulk transcriptome analysis.

Bulk transcriptomes are an essential data resource for understanding basic and disease biology. However, integrating information from different experiments remains challenging because of the batch effect generated by various technological and biological variations in the transcriptome. Numerous batch-correction methods to deal with this batch effect have been developed in the past.

Towards a clinically-based common coordinate framework for the human gut cell atlas: the gut models.

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.

Integrated View of Baseline Protein Expression in Human Tissues.

The availability of proteomics datasets in the public domain, and in the PRIDE database, in particular, has increased dramatically in recent years. This unprecedented large-scale availability of data provides an opportunity for combined analyses of datasets to get organism-wide protein abundance data in a consistent manner. We have reanalyzed 24 public proteomics datasets from healthy human individuals to assess baseline protein abundance in 31 organs.

EMBL's European Bioinformatics Institute (EMBL-EBI) in 2022.

The European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI) is one of the world's leading sources of public biomolecular data. Based at the Wellcome Genome Campus in Hinxton, UK, EMBL-EBI is one of six sites of the European Molecular Biology Laboratory (EMBL), Europe's only intergovernmental life sciences organisation. This overview summarises the status of services that EMBL-EBI data resources provide to scientific communities globally.

Toward a data infrastructure for the Plant Cell Atlas.

We review how a data infrastructure for the Plant Cell Atlas might be built using existing infrastructure and platforms. The Human Cell Atlas has developed an extensive infrastructure for human and mouse single cell data, while the European Bioinformatics Institute has developed a Single Cell Expression Atlas, that currently houses several plant data sets. We discuss issues related to appropriate ontologies for describing a plant single cell experiment.

Integrated view and comparative analysis of baseline protein expression in mouse and rat tissues.

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.

Implementing the reuse of public DIA proteomics datasets: from the PRIDE database to Expression Atlas.

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.

Fly Cell Atlas: A single-nucleus transcriptomic atlas of the adult fruit fly.

For more than 100 years, the fruit fly has been one of the most studied model organisms. Here, we present a single-cell atlas of the adult fly, Tabula , that includes 580,000 nuclei from 15 individually dissected sexed tissues as well as the entire head and body, annotated to >250 distinct cell types. We provide an in-depth analysis of cell type-related gene signatures and transcription factor markers, as well as sexual dimorphism, across the whole animal.

Brain Regeneration Resembles Brain Cancer at Its Early Wound Healing Stage and Diverges From Cancer Later at Its Proliferation and Differentiation Stages.

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.

The discovAIR project: a roadmap towards the Human Lung Cell Atlas.

The Human Cell Atlas (HCA) consortium aims to establish an atlas of all organs in the healthy human body at single-cell resolution to increase our understanding of basic biological processes that govern development, physiology and anatomy, and to accelerate diagnosis and treatment of disease. The Lung Biological Network of the HCA aims to generate the Human Lung Cell Atlas as a reference for the cellular repertoire, molecular cell states and phenotypes, and cell-cell interactions that characterise normal lung homeostasis in healthy lung tissue. Such a reference atlas of the healthy human lung will facilitate mapping the changes in the cellular landscape in disease.

Single-Cell Analysis Reveals the Immune Characteristics of Myeloid Cells and Memory T Cells in Recovered COVID-19 Patients With Different Severities.

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.

Expression Atlas update: gene and protein expression in multiple species.

The EMBL-EBI Expression Atlas is an added value knowledge base that enables researchers to answer the question of where (tissue, organism part, developmental stage, cell type) and under which conditions (disease, treatment, gender, etc) a gene or protein of interest is expressed. Expression Atlas brings together data from >4500 expression studies from >65 different species, across different conditions and tissues. It makes these data freely available in an easy to visualise form, after expert curation to accurately represent the intended experimental design, re-analysed via standardised pipelines that rely on open-source community developed tools.

Meta-analysis of COVID-19 single-cell studies confirms eight key immune responses.

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.

A proteomics sample metadata representation for multiomics integration and big data analysis.

The amount of public proteomics data is rapidly increasing but there is no standardized format to describe the sample metadata and their relationship with the dataset files in a way that fully supports their understanding or reanalysis. Here we propose to develop the transcriptomics data format MAGE-TAB into a standard representation for proteomics sample metadata. We implement MAGE-TAB-Proteomics in a crowdsourcing project to manually curate over 200 public datasets.