Multi-omics characterization of type 2 diabetes associated genetic variation.

Discerning the mechanisms driving type 2 diabetes (T2D) pathophysiology from genome-wide association studies (GWAS) remains a challenge. To this end, we integrated omics information from 16 multi-tissue and multi-ancestry expression, protein, and metabolite quantitative trait loci (QTL) studies and 46 multi-ancestry GWAS for T2D-related traits with the largest, most ancestrally diverse T2D GWAS to date. Of the 1,289 T2D GWAS index variants, 716 (56%) demonstrated strong evidence of colocalization with a molecular or T2D-related trait, implicating 657 cis -effector genes, 1,691 distal-effector genes, 731 metabolites, and 43 T2D-related traits. We identified 773 of these cis- and distal-effector genes using either expression QTL data from understudied ancestry groups or inclusion of T2D index variants enriched in underrepresented populations, emphasizing the value of increasing population diversity in functional mapping. Linking these variants, genes, metabolites, and traits into a network, we elucidated mechanisms through which T2D-associated variation may impact disease risk. Finally, we showed that drugs targeting effector proteins were enriched in those approved to treat T2D, highlighting the potential of these results to prioritize drug targets for T2D. These results represent a leap in the molecular characterization of T2D-associated genetic variation and will aid in translating genetic findings into novel therapeutic strategies.

GraphCompass: spatial metrics for differential analyses of cell organization across conditions.

SUMMARY: Spatial omics technologies are increasingly leveraged to characterize how disease disrupts tissue organization and cellular niches. While multiple methods to analyze spatial variation within a sample have been published, statistical and computational approaches to compare cell spatial organization across samples or conditions are mostly lacking. We present GraphCompass, a comprehensive set of omics-adapted graph analysis methods to quantitatively evaluate and compare the spatial arrangement of cells in samples representing diverse biological conditions. GraphCompass builds upon the Squidpy spatial omics toolbox and encompasses various statistical approaches to perform cross-condition analyses at the level of individual cell types, niches, and samples. Additionally, GraphCompass provides custom visualization functions that enable effective communication of results. We demonstrate how GraphCompass can be used to address key biological questions, such as how cellular organization and tissue architecture differ across various disease states and which spatial patterns correlate with a given pathological condition. GraphCompass can be applied to various popular omics techniques, including, but not limited to, spatial proteomics (e.g. MIBI-TOF), spot-based transcriptomics (e.g. 10× Genomics Visium), and single-cell resolved transcriptomics (e.g. Stereo-seq). In this work, we showcase the capabilities of GraphCompass through its application to three different studies that may also serve as benchmark datasets for further method development. With its easy-to-use implementation, extensive documentation, and comprehensive tutorials, GraphCompass is accessible to biologists with varying levels of computational expertise. By facilitating comparative analyses of cell spatial organization, GraphCompass promises to be a valuable asset in advancing our understanding of tissue function in health and disease. .

Integrating single-cell RNA-seq datasets with substantial batch effects.

Integration of single-cell RNA-sequencing (scRNA-seq) datasets has become a standard part of the analysis, with conditional variational autoencoders (cVAE) being among the most popular approaches. Increasingly, researchers are asking to map cells across challenging cases such as cross-organs, species, or organoids and primary tissue, as well as different scRNA-seq protocols, including single-cell and single-nuclei. Current computational methods struggle to harmonize datasets with such substantial differences, driven by technical or biological variation. Here, we propose to address these challenges for the popular cVAE-based approaches by introducing and comparing a series of regularization constraints. The two commonly used strategies for increasing batch correction in cVAEs, that is Kullback-Leibler divergence (KL) regularization strength tuning and adversarial learning, suffer from substantial loss of biological information. Therefore, we adapt, implement, and assess alternative regularization strategies for cVAEs and investigate how they improve batch effect removal or better preserve biological variation, enabling us to propose an optimal cVAE-based integration strategy for complex systems. We show that using a VampPrior instead of the commonly used Gaussian prior not only improves the preservation of biological variation but also unexpectedly batch correction. Moreover, we show that our implementation of cycle-consistency loss leads to significantly better biological preservation than adversarial learning implemented in the previously proposed GLUE model. Additionally, we do not recommend relying only on the KL regularization strength tuning for increasing batch correction, as it removes both biological and batch information without discriminating between the two. Based on our findings, we propose a new model that combines VampPrior and cycle-consistency loss. We show that using it for datasets with substantial batch effects improves downstream interpretation of cell states and biological conditions. To ease the use of the newly proposed model, we make it available in the scvi-tools package as an external model named sysVI. Moreover, in the future, these regularization techniques could be added to other established cVAE-based models to improve the integration of datasets with substantial batch effects.

Delineating mouse ß-cell identity during lifetime and in diabetes with a single cell atlas.

Although multiple pancreatic islet single-cell RNA-sequencing (scRNA-seq) datasets have been generated, a consensus on pancreatic cell states in development, homeostasis and diabetes as well as the value of preclinical animal models is missing. Here, we present an scRNA-seq cross-condition mouse islet atlas (MIA), a curated resource for interactive exploration and computational querying. We integrate over 300,000 cells from nine scRNA-seq datasets consisting of 56 samples, varying in age, sex and diabetes models, including an autoimmune type 1 diabetes model (NOD), a glucotoxicity/lipotoxicity type 2 diabetes model (db/db) and a chemical streptozotocin ß-cell ablation model. The ß-cell landscape of MIA reveals new cell states during disease progression and cross-publication differences between previously suggested marker genes. We show that ß-cells in the streptozotocin model transcriptionally correlate with those in human type 2 diabetes and mouse db/db models, but are less similar to human type 1 diabetes and mouse NOD ß-cells. We also report pathways that are shared between ß-cells in immature, aged and diabetes models. MIA enables a comprehensive analysis of ß-cell responses to different stressors, providing a roadmap for the understanding of ß-cell plasticity, compensation and demise.

Biologically informed deep learning to query gene programs in single-cell atlases.

The increasing availability of large-scale single-cell atlases has enabled the detailed description of cell states. In parallel, advances in deep learning allow rapid analysis of newly generated query datasets by mapping them into reference atlases. However, existing data transformations learned to map query data are not easily explainable using biologically known concepts such as genes or pathways. Here we propose expiMap, a biologically informed deep-learning architecture that enables single-cell reference mapping. ExpiMap learns to map cells into biologically understandable components representing known 'gene programs'. The activity of each cell for a gene program is learned while simultaneously refining them and learning de novo programs. We show that expiMap compares favourably to existing methods while bringing an additional layer of interpretability to integrative single-cell analysis. Furthermore, we demonstrate its applicability to analyse single-cell perturbation responses in different tissues and species and resolve responses of patients who have coronavirus disease 2019 to different treatments across cell types.

Toward modeling metabolic state from single-cell transcriptomics.

BACKGROUND: Single-cell metabolic studies bring new insights into cellular function, which can often not be captured on other omics layers. Metabolic information has wide applicability, such as for the study of cellular heterogeneity or for the understanding of drug mechanisms and biomarker development. However, metabolic measurements on single-cell level are limited by insufficient scalability and sensitivity, as well as resource intensiveness, and are currently not possible in parallel with measuring transcript state, commonly used to identify cell types. Nevertheless, because omics layers are strongly intertwined, it is possible to make metabolic predictions based on measured data of more easily measurable omics layers together with prior metabolic network knowledge. SCOPE OF REVIEW: We summarize the current state of single-cell metabolic measurement and modeling approaches, motivating the use of computational techniques. We review three main classes of computational methods used for prediction of single-cell metabolism: pathway-level analysis, constraint-based modeling, and kinetic modeling. We describe the unique challenges arising when transitioning from bulk to single-cell modeling. Finally, we propose potential model extensions and computational methods that could be leveraged to achieve these goals. MAJOR CONCLUSIONS: Single-cell metabolic modeling is a rising field that provides a new perspective for understanding cellular functions. The presented modeling approaches vary in terms of input requirements and assumptions, scalability, modeled metabolic layers, and newly gained insights. We believe that the use of prior metabolic knowledge will lead to more robust predictions and will pave the way for mechanistic and interpretable machine-learning models.

Transcriptional milestones in Dictyostelium development.

Dictyostelium development begins with single-cell starvation and ends with multicellular fruiting bodies. Developmental morphogenesis is accompanied by sweeping transcriptional changes, encompassing nearly half of the 13,000 genes in the genome. We performed time-series RNA-sequencing analyses of the wild type and 20 mutants to explore the relationships between transcription and morphogenesis. These strains show developmental arrest at different stages, accelerated development, or atypical morphologies. Considering eight major morphological transitions, we identified 1371 milestone genes whose expression changes sharply between consecutive transitions. We also identified 1099 genes as members of 21 regulons, which are groups of genes that remain coordinately regulated despite the genetic, temporal, and developmental perturbations. The gene annotations in these groups validate known transitions and reveal new developmental events. For example, DNA replication genes are tightly coregulated with cell division genes, so they are expressed in mid-development although chromosomal DNA is not replicated. Our data set includes 486 transcriptional profiles that can help identify new relationships between transcription and development and improve gene annotations. We show its utility by showing that cycles of aggregation and disaggregation in allorecognition-defective mutants involve dedifferentiation. We also show sensitivity to genetic and developmental conditions in two commonly used actin genes, act6 and act15, and robustness of the coaA gene. Finally, we propose that gpdA is a better mRNA quantitation standard because it is less sensitive to external conditions than commonly used standards. The data set is available for democratized exploration through the web application dictyExpress and the data mining environment Orange.

Genetic variability of serotonin pathway associated with schizophrenia onset, progression, and treatment.

Schizophrenia (SZ) onset and treatment outcome have important genetic components, however individual genes do not have strong effects on SZ phenotype. Therefore, it is important to use the pathway-based approach and study metabolic and signaling pathways, such as dopaminergic and serotonergic. Serotonin pathway has an important role in brain signaling, nevertheless, its role in SZ is not as thoroughly examined as that of dopamine pathway. In this study, we reviewed serotonin pathway genes and genetic variations associated with SZ, including variations at DNA, RNA, and epigenetic level. We obtained 30 serotonin pathway genes from Kyoto encyclopedia of genes and genomes and used these genes for the literature review. We extracted 20 protein coding serotonin pathway genes with genetic variations associated with SZ onset, development, and treatment from 31 research papers. Genes associated with SZ are present on all levels of serotonin pathway: serotonin synthesis, transport, receptor binding, intracellular signaling, and reuptake; however, regulatory genes are poorly researched. We summarized common challenges of genetic association studies and presented some solutions. The analysis of reported serotonin pathway-SZ associations revealed lack of information about certain serotonin pathway genes potentially associated with SZ. Furthermore, it is becoming clear that interactions among serotonin pathway genes and their regulators may bring further knowledge about their involvement in SZ.

Classification of miRNA-related sequence variations.

miRNA regulome is whole set of regulatory elements that regulate miRNA expression or are under control of miRNAs. Its understanding is vital for comprehension of miRNA functions. Classification of miRNA-related genetic variability is challenging because miRNA interact with different genomic elements and are studied at different omics levels. In the present study, miRNA-associated genetic variability is presented at three levels: miRNA genes and their upstream regulation, miRNA silencing machinery and miRNA targets. Several types of miRNA-associated genetic variations are known, including short and structural polymorphisms and epimutations. Differential expression can also affect miRNA regulome function. Classification of miRNA-associated genetic variability presents a baseline for complementing sequence variant nomenclature, planning of experiments, protocols for multi-omics data integration and development of biomarkers.

Classification of heterogeneous genetic variations of microRNA regulome in cancer.

Genetic variations and differential expression of miRNA regulome components are associated with cancer. Thus miRNA based diagnosis and treatments have been proposed. However, to better explore these options, the molecular changes in miRNA regulome must be understood. MicroRNAs can be involved in regulation of oncogenes and tumour suppressors. As each miRNA targets broad range of genes, minor changes in miRNAs can have great effects, contributing to cell transformation. Many genetic variants of miRNA regulome have been reported to be associated with cancer, but this information needs to be systematized. Therefore, we here classify different types of genetic variations of miRNA regulome in cancer. Genetic variations are comprised of structural and short polymorphisms and changes in epigenetic landscape. Additionally, unexplained differential expression is often reported. These alterations affect miRNA genes and their regulatory elements, processing machinery, degradation machinery, and targets, leading to changes in miRNA silencing. However, miRNA regulome components are not equally explored. A systematic overview over miRNA regulome can contribute to more targeted study design and understanding of miRNA function. We also present treatments and diagnosis based on miRNA regulome genetic variability and expression.

Genetic sex determination assays in 53 mammalian species: Literature analysis and guidelines for reporting standardization.

Erstwhile, sex was determined by observation, which is not always feasible. Nowadays, genetic methods are prevailing due to their accuracy, simplicity, low costs, and time-efficiency. However, there is no comprehensive review enabling overview and development of the field. The studies are heterogeneous, lacking a standardized reporting strategy. Therefore, our aim was to collect genetic sexing assays for mammals and assemble them in a catalogue with unified terminology. Publications were extracted from online databases using key words such as sexing and molecular. The collected data were supplemented with species and gene IDs and the type of sex-specific sequence variant (SSSV). We developed a catalogue and graphic presentation of diagnostic tests for molecular sex determination of mammals, based on 58 papers published from 2/1991 to 10/2016. The catalogue consists of five categories: species, genes, SSSVs, methods, and references. Based on the analysis of published literature, we propose minimal requirements for reporting, consisting of: species scientific name and ID, genetic sequence with name and ID, SSSV, methodology, genomic coordinates (e.g., restriction sites, SSSVs), amplification system, and description of detected amplicon and controls. The present study summarizes vast knowledge that has up to now been scattered across databases, representing the first step toward standardization regarding molecular sexing, enabling a better overview of existing tests and facilitating planned designs of novel tests. The project is ongoing; collecting additional publications, optimizing field development, and standardizing data presentation are needed.