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Defining the ontogeny of the human adaptive immune system during embryogenesis has implications for understanding childhood diseases including leukaemias and autoimmune conditions. Using RAG1:GFP human pluripotent stem cell reporter lines, we examined human T-cell genesis from pluripotent-stem-cell-derived haematopoietic organoids. Under conditions favouring T-cell development, RAG1+ cells progressively upregulated a cohort of recognized T-cell-associated genes, arresting development at the CD4+CD8+ stage. Sort and re-culture experiments showed that early RAG1+ cells also possessed B-cell, myeloid and erythroid potential. Flow cytometry and single-cell-RNA-sequencing data showed that early RAG1+ cells co-expressed the endothelial/haematopoietic progenitor markers CD34, VECAD and CD90, whereas imaging studies identified RAG1+ cells within CD31+ endothelial structures that co-expressed SOX17+ or the endothelial marker CAV1. Collectively, these observations provide evidence for a wave of human T-cell development that originates directly from haemogenic endothelium via a RAG1+ intermediate with multilineage potential.

Despite the volume of experiments performed and data available, the complex biology of coronavirus SARS-COV-2 is not yet fully understood. Existing molecular profiling studies have focused on analysing functional omics data of a single type, which captures changes in a small subset of the molecular perturbations caused by the virus. As the logical next step, results from multiple such omics analysis may be aggregated to comprehensively interpret the molecular mechanisms of SARS-CoV-2. An alternative approach is to integrate data simultaneously in a parallel fashion to highlight the inter-relationships of disease-driving biomolecules, in contrast to comparing processed information from each omics level separately. We demonstrate that valuable information may be masked by using the former fragmented views in analysis, and biomarkers resulting from such an approach cannot provide a systematic understanding of the disease aetiology. Hence, we present a generic, reproducible and flexible open-access data harmonisation framework that can be scaled out to future multi-omics analysis to study a phenotype in a holistic manner. The pipeline source code, detailed documentation and automated version as a R package are accessible. To demonstrate the effectiveness of our pipeline, we applied it to a drug screening task. We integrated multi-omics data to find the lowest level of statistical associations between data features in two case studies. Strongly correlated features within each of these two datasets were used for drug–target analysis, resulting in a list of 84 drug–target candidates. Further computational docking and toxicity analyses revealed seven high-confidence targets, amsacrine, bosutinib, ceritinib, crizotinib, nintedanib and sunitinib as potential starting points for drug therapy and development.

A major challenge for the estimation of crop traits (biophysical variables) from canopy reflectance is the creation of a high-quality training dataset. This can be addressed by using radiative transfer models (RTMs) to generate training dataset representing "real-world" data in situations with varying crop types and growth status as well as various observation configurations. However, this approach can lead to "ill-posed" problems related to assumptions in the sampling strategy and due to uncertainty in the model, resulting in unsatisfactory inversion results for retrieval of target variables. In order to address this problem, this research investigates a practical way to generate higher quality "synthetic" training data by integrating a crop growth model (CGM, in this case APSIM) with an RTM (in this case PROSAIL). This allows control of uncertainties of the RTM by imposing biological constraints on distribution and co-distribution of related variables. Subsequently, the method was theoretically validated on two types of synthetic dataset generated by PROSAIL or the coupling of APSIM and PROSAIL through comparing estimation precision for leaf area index (LAI), leaf chlorophyll content (Cab), leaf dry matter (Cm) and leaf water content (Cw). Additionally, the capabilities of current deep learning techniques using high spectral resolution hyperspectral data were investigated. The main findings include: (1) Feedforward neural network (FFNN) provided with appropriate configuration is a promising technique to retrieve crop traits from input features consisting of 1 nm-wide hyperspectral bands across 400-2500 nm range and observation configuration (solar and viewing angles), leading to a precise joint estimation for LAI (RMSE=0.061 m2 m-2), Cab (RMSE=1.42 μg cm-2), Cm (RMSE=0.000176 g cm-2) and Cw (RMSE=0.000319 g cm-2); (2) For the aim of model simplification, a narrower range in 400-1100 nm without observation configuration in input of FFNN model provided less precise estimation for LAI (RMSE=0.087 m2 m-2), Cab (RMSE=1.92 μg cm-2), Cm (RMSE=0.000299 g cm-2) and Cw (RMSE=0.001271 g cm-2); (3) The introduction of biological constraints in training datasets improved FFNN model performance in both average precision and stability, resulting in a much accurate estimation for LAI (RMSE=0.006 m2 m-2), Cab (RMSE=0.45 μg cm-2), Cm (RMSE=0.000039 g cm-2) and Cw (RMSE=0.000072 g cm-2), and this improvement could be further increased by enriching sample diversity in training dataset.

Gene expression atlases have transformed our understanding of the development, composition and function of human tissues. New technologies promise improved cellular or molecular resolution, and have led to the identification of new cell types, or better defined cell states. But as new technologies emerge, information derived on old platforms becomes obsolete. We demonstrate that it is possible to combine a large number of different profiling experiments summarised from dozens of laboratories and representing hundreds of donors, to create an integrated molecular map of human tissue. As an example, we combine 850 samples from 38 platforms to build an integrated atlas of human blood cells. We achieve robust and unbiased cell type clustering using a variance partitioning method, selecting genes with low platform bias relative to biological variation. Other than an initial rescaling, no other transformation to the primary data is applied through batch correction or renormalisation. Additional data, including single-cell datasets, can be projected for comparison, classification and annotation. The resulting atlas provides a multi-scaled approach to visualise and analyse the relationships between sets of genes and blood cell lineages, including the maturation and activation of leukocytes in vivo and in vitro. In allowing for data integration across hundreds of studies, we address a key reproduciblity challenge which is faced by any new technology. This allows us to draw on the deep phenotypes and functional annotations that accompany traditional profiling methods, and provide important context to the high cellular resolution of single cell profiling. Here, we have implemented the blood atlas in the open access Stemformatics.org platform, drawing on its extensive collection of curated transcriptome data. The method is simple, scalable and amenable for rapid deployment in other biological systems or computational workflows., Author summary Combining data from many different studies is an attractive way of capturing new aspects of the biology being studied. Biological variance attributable to cell type, cellular niche, origin, disease status or environmental stimuli is the basis of most small-n transcriptome studies. In aggregation, these promise to capture emergent dimensions of a biology that is not possible to view from any individual study. However biological signal is easily swamped by technical artifact, especially when data is generated on platforms with profoundly different data structures. This is the case when comparing microarray data to RNAseq, or RNAseq to single cell profiling. Consequently, transcriptome atlases are generally comprised from a small number of donors/conditions surveyed using one technology platform. In this paper we present a simple and scalable data integration method that is platform agnostic. We provide a proof-of-principle by constructing an atlas of blood cells that combines many data sets measured on different platforms, and that in combination, recapitulates the known blood hierarchy. The atlas provides a reference to compare external samples to, allowing users to benchmark new derivation or isolation methods. It also provides a reference point for new data types, such as the classification of single cells. The approach allows for FAIR data reuse and robust identification of molecular signatures across multiple studies and experimental conditions.

Stemformatics is an established gene expression data portal containing over 420 public gene expression datasets derived from microarray, RNA sequencing and single cell profiling technologies. Developed for the stem cell community, it has a major focus on pluripotency, tissue stem cells, and staged differentiation. Stemformatics includes curated ‘collections’ of data relevant to cell reprogramming, as well as hematopoiesis and leukaemia. Rather than simply rehosting datasets as they appear in public repositories, Stemformatics uses a stringent set of quality control metrics and its own pipelines to process handpicked datasets from raw files. This means that about 30% of datasets processed by Stemformatics fail the quality control metrics and never make it to the portal, ensuring that Stemformatics data are of high quality and have been processed in a consistent manner. Stemformatics provides easy-to-use and intuitive tools for biologists to visually explore the data, including interactive gene expression profiles, principal component analysis plots and hierarchical clusters, among others. The addition of tools that facilitate cross-dataset comparisons provides users with snapshots of gene expression in multiple cell and tissues, assisting the identification of cell-type restricted genes, or potential housekeeping genes. Stemformatics is freely available at stemformatics.org.

Data from multiple omics layers of a biological system is growing in quantity, heterogeneity and dimensionality. Simultaneous multi-omics data integration is a growing field of research as it has strong potential to unlock information on previously hidden biological relationships leading to early diagnosis, prognosis and expedited treatments. Many tools for multi-omics data integration are being developed. However, these tools are often restricted to highly specific experimental designs, and types of omics data. While some general methods do exist, they require specific data formats and experimental conditions. A major limitation in the field is a lack of a single or multi-omics pipeline which can accept data in an unrefined, information-rich form pre-integration and subsequently generate output for further investigation. There is an increasing demand for a generic multi-omics pipeline to facilitate general-purpose data exploration and analysis of heterogeneous data. Therefore, we present our R multiomics pipeline as an easy to use and flexible pipeline that takes unrefined multi-omics data as input, sample information and user-specified parameters to generate a list of output plots and data tables for quality control and downstream analysis. We have demonstrated application of the pipeline on two separate COVID-19 case studies. We enabled limited checkpointing where intermediate output is staged to allow continuation after errors or interruptions in the pipeline and generate a script for reproducing the analysis to improve reproducibility. A seamless integration with the mixOmics R package is achieved, as the R data object can be loaded and manipulated with mixOmics functions. Our pipeline can be installed as an R package or from the git repository, and is accompanied by detailed documentation with walkthroughs on two case studies. The pipeline is also available as Docker and Singularity containers.

Defining the ontogeny of the human adaptive immune system during embryogenesis has implications for understanding childhood diseases including leukaemias and autoimmune conditions. Using RAG1:GFP human pluripotent stem cell reporter lines, we examined human T-cell genesis from pluripotent-stem-cell-derived haematopoietic organoids. Under conditions favouring T-cell development, RAG1+ cells progressively upregulated a cohort of recognized T-cell-associated genes, arresting development at the CD4+CD8+ stage. Sort and re-culture experiments showed that early RAG1+ cells also possessed B-cell, myeloid and erythroid potential. Flow cytometry and single-cell-RNA-sequencing data showed that early RAG1+ cells co-expressed the endothelial/haematopoietic progenitor markers CD34, VECAD and CD90, whereas imaging studies identified RAG1+ cells within CD31+ endothelial structures that co-expressed SOX17+ or the endothelial marker CAV1. Collectively, these observations provide evidence for a wave of human T-cell development that originates directly from haemogenic endothelium via a RAG1+ intermediate with multilineage potential.

http://dx.doi.org/10.6084/m9.figshare.10099412Stemformatics [1] (www.stemformatics.org) is primarily a web-based pocket dictionary for stem cells biologists running on the NeCTAR cloud [2]. Part of the stem cell community for over 6 years, it allows biologists to quickly and easily visualise their private datasets. They can also benchmark their datasets against 330+ high quality, preprocessed public datasets.In this presentation we will show that there is more to Stemformatics than just visualisation and how this is changing what end users expect from their online tools.Stemformatics is also a collaboration tool. The first major collaboration was when the multi-omics “Project Grandiose” was published with Stemformatics in December 2014, resulting in in 2 Nature papers [3,4] and 3 Nature Communications papers [5,6,7]. Recently, LEUKomics, a blood cancer atlas, was created using the Stemformatics ecosystem [8]. Stemformatics is also part of the Bioplatforms Australia Stem Cell project.Stemformatics curates public and proprietary data. To ensure community confidence we only allow high quality experiments to be made public. The failure rate of datasets processed by Stemformatics is currently 29.21%. The main reason to fail a dataset in Stemformatics is poor experimental design. The remaining datasets are high quality datasets that are valuable to the community for benchmarking and data mining.Stemformatics has also started to mine these public datasets. The Rohart Mesenchymal Stromal Cell (MSC) Test [9] was the first publication that used Stemformatics public datasets to use machine learning to create a classification algorithm for MSCs.Stemformatics is funded by a grant from the Australian Research Council Special Initiative in Stem Cell Science through Stem Cells Australia [10].

http://dx.doi.org/10.6084/m9.figshare.10099412Stemformatics [1] (www.stemformatics.org) is primarily a web-based pocket dictionary for stem cells biologists running on the NeCTAR cloud [2]. Part of the stem cell community for over 6 years, it allows biologists to quickly and easily visualise their private datasets. They can also benchmark their datasets against 330+ high quality, preprocessed public datasets.In this presentation we will show that there is more to Stemformatics than just visualisation and how this is changing what end users expect from their online tools.Stemformatics is also a collaboration tool. The first major collaboration was when the multi-omics “Project Grandiose” was published with Stemformatics in December 2014, resulting in in 2 Nature papers [3,4] and 3 Nature Communications papers [5,6,7]. Recently, LEUKomics, a blood cancer atlas, was created using the Stemformatics ecosystem [8]. Stemformatics is also part of the Bioplatforms Australia Stem Cell project.Stemformatics curates public and proprietary data. To ensure community confidence we only allow high quality experiments to be made public. The failure rate of datasets processed by Stemformatics is currently 29.21%. The main reason to fail a dataset in Stemformatics is poor experimental design. The remaining datasets are high quality datasets that are valuable to the community for benchmarking and data mining.Stemformatics has also started to mine these public datasets. The Rohart Mesenchymal Stromal Cell (MSC) Test [9] was the first publication that used Stemformatics public datasets to use machine learning to create a classification algorithm for MSCs.Stemformatics is funded by a grant from the Australian Research Council Special Initiative in Stem Cell Science through Stem Cells Australia [10].

Defining the ontogeny of the human adaptive immune system during early embryogenesis has implications for understanding childhood diseases including leukemias and autoimmune diseases. Using RAG1:GFP human pluripotent stem cell reporter lines, we examined human T-cell genesis from pluripotent stem cell derived hematopoietic organoids. Under conditions favouring T-cell development, RAG1+ cells progressively up regulated a cohort of recognised T-cell associated genes, arresting development at the CD4+CD8+ stage. However, sort and re-culture experiments showed that early RAG1+ cells also possessed B-cell, myeloid and erythroid potential. Moreover, RNAseq analysis of the early RAG1+ cells indicated that, in addition to T-lineage genes, this population expressed genes associated with erythroid and myeloid lineages, as well as genes marking endothelium and hematopoietic progenitors. Consistent with this, imaging studies, flow cytometry and single cell RNAseq data showed that the first RAG1+ cells emerged directly from SOX17+ endothelial structures and co-expressed CD90, CDH5 and the endothelial marker, CAV1. These observations provide evidence for a wave of human T-cell development that originates directly from hemogenic endothelium via a RAG1+ intermediate with multilineage potential.

Mesenchymal stromal cells (MSC) are widely used for the study of mesenchymal tissue repair, and increasingly adopted for cell therapy, despite the lack of consensus on the identity of these cells. In part this is due to the lack of specificity of MSC markers. Distinguishing MSC from other stromal cells such as fibroblasts is particularly difficult using standard analysis of surface proteins, and there is an urgent need for improved classification approaches. Transcriptome profiling is commonly used to describe and compare different cell types; however, efforts to identify specific markers of rare cellular subsets may be confounded by the small sample sizes of most studies. Consequently, it is difficult to derive reproducible, and therefore useful markers. We addressed the question of MSC classification with a large integrative analysis of many public MSC datasets. We derived a sparse classifier (The Rohart MSC test) that accurately distinguished MSC from non-MSC samples with >97% accuracy on an internal training set of 635 samples from 41 studies derived on 10 different microarray platforms. The classifier was validated on an external test set of 1,291 samples from 65 studies derived on 15 different platforms, with >95% accuracy. The genes that contribute to the MSC classifier formed a protein-interaction network that included known MSC markers. Further evidence of the relevance of this new MSC panel came from the high number of Mendelian disorders associated with mutations in more than 65% of the network. These result in mesenchymal defects, particularly impacting on skeletal growth and function. The Rohart MSC test is a simplein silicotest that accurately discriminates MSC from fibroblasts, other adult stem/progenitor cell types or differentiated stromal cells. It has been implemented in thewww.stemformatics.orgresource, to assist researchers wishing to benchmark their own MSC datasets or data from the public domain. The code is available from the CRAN repository and all data used to generate the MSC test is available to download via the Gene Expression Omnibus or the Stemformatics resource.

Stromal support is critical for lung homeostasis and the maintenance of an effective epithelial barrier. Despite this, previous studies have found a positive association between the number of mesenchymal stromal cells (MSCs) isolated from the alveolar compartment and human lung diseases associated with epithelial dysfunction. We hypothesised that bronchoalveolar lavage derived MSCs (BAL-MSCs) are dysfunctional and distinct from resident lung tissue MSCs (LT-MSCs). In this study, we comprehensively interrogated the phenotype and transcriptome of human BAL-MSCs and LT-MSCs. We found that MSCs were rarely recoverable from the alveolar space in healthy humans, but could be readily isolated from lung transplant recipients by bronchoalveolar lavage. BAL-MSCs exhibited a CD90Hi, CD73Hi, CD45Neg, CD105Lo immunophenotype and were bipotent, lacking adipogenic potential. In contrast, MSCs were readily recoverable from healthy human lung tissue and were CD90Hi or Lo, CD73Hi, CD45Neg, CD105Int and had full tri-lineage potential. Transcriptional profiling of the two populations confirmed their status as bona fide MSCs and revealed a high degree of similarity between each other and the archetypal bone-marrow MSC. 105 genes were differentially expressed; 76 of which were increased in BAL-MSCs including genes involved in fibroblast activation, extracellular matrix deposition and tissue remodelling. Finally, we found the fibroblast markers collagen 1A1 and α-smooth muscle actin were increased in BAL-MSCs. Our data suggests that in healthy humans, lung MSCs reside within the tissue, but in disease can differentiate to acquire a profibrotic phenotype and migrate from their in-tissue niche into the alveolar space.