Cognate recognition of microbial antigens defines constricted CD4+ T cell receptor repertoires in the inflamed colon.

Key aspects of intestinal T cells, including their antigen specificity and their selection by the microbiota and other intestinal antigens, as well as the contribution of individual T cell clones to regulatory and effector functions, remain unresolved. Here we tracked adoptively transferred T cell populations to specify the interrelation of T cell receptor repertoire and the gut antigenic environment. We show that dominant TCRα clonotypes were shared between interferon-γ- and interleukin-17-producing but not regulatory Foxp3+ T cells. Identical TCRα clonotypes accumulated in the colon of different individuals, whereas antibiotics or defined colonization correlated with the expansion of distinct expanded T cell clonotypes. Our results demonstrate key aspects of intestinal CD4+ T cell activation and suggest that few microbial species exert a dominant effect on the intestinal T cell repertoire during colitis. We speculate that dominant proinflammatory T cell clones might provide a therapeutic target in human inflammatory bowel disease.

Spatial multi-omic map of human myocardial infarction.

Myocardial infarction is a leading cause of death worldwide1. Although advances have been made in acute treatment, an incomplete understanding of remodelling processes has limited the effectiveness of therapies to reduce late-stage mortality2. Here we generate an integrative high-resolution map of human cardiac remodelling after myocardial infarction using single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of multiple physiological zones at distinct time points in myocardium from patients with myocardial infarction and controls. Multi-modal data integration enabled us to evaluate cardiac cell-type compositions at increased resolution, yielding insights into changes of the cardiac transcriptome and epigenome through the identification of distinct tissue structures of injury, repair and remodelling. We identified and validated disease-specific cardiac cell states of major cell types and analysed them in their spatial context, evaluating their dependency on other cell types. Our data elucidate the molecular principles of human myocardial tissue organization, recapitulating a gradual cardiomyocyte and myeloid continuum following ischaemic injury. In sum, our study provides an integrative molecular map of human myocardial infarction, represents an essential reference for the field and paves the way for advanced mechanistic and therapeutic studies of cardiac disease.

HMGB2 Loss upon Senescence Entry Disrupts Genomic Organization and Induces CTCF Clustering across Cell Types.

Processes like cellular senescence are characterized by complex events giving rise to heterogeneous cell populations. However, the early molecular events driving this cascade remain elusive. We hypothesized that senescence entry is triggered by an early disruption of the cells' three-dimensional (3D) genome organization. To test this, we combined Hi-C, single-cell and population transcriptomics, imaging, and in silico modeling of three distinct cells types entering senescence. Genes involved in DNA conformation maintenance are suppressed upon senescence entry across all cell types. We show that nuclear depletion of the abundant HMGB2 protein occurs early on the path to senescence and coincides with the dramatic spatial clustering of CTCF. Knocking down HMGB2 suffices for senescence-induced CTCF clustering and for loop reshuffling, while ectopically expressing HMGB2 rescues these effects. Our data suggest that HMGB2-mediated genomic reorganization constitutes a primer for the ensuing senescent program.

Human pluripotent stem cell-derived kidney organoids for personalized congenital and idiopathic nephrotic syndrome modeling.

Nephrotic syndrome (NS) is characterized by severe proteinuria as a consequence of kidney glomerular injury due to podocyte damage. In vitro models mimicking in vivo podocyte characteristics are a prerequisite to resolve NS pathogenesis. The detailed characterization of organoid podocytes resulting from a hybrid culture protocol showed a podocyte population that resembles adult podocytes and was superior compared with 2D counterparts, based on single-cell RNA sequencing, super-resolution imaging and electron microscopy. In this study, these next-generation podocytes in kidney organoids enabled personalized idiopathic nephrotic syndrome modeling, as shown by activated slit diaphragm signaling and podocyte injury following protamine sulfate, puromycin aminonucleoside treatment and exposure to NS plasma containing pathogenic permeability factors. Organoids cultured from cells of a patient with heterozygous NPHS2 mutations showed poor NPHS2 expression and aberrant NPHS1 localization, which was reversible after genetic correction. Repaired organoids displayed increased VEGFA pathway activity and transcription factor activity known to be essential for podocyte physiology, as shown by RNA sequencing. This study shows that organoids are the preferred model of choice to study idiopathic and congenital podocytopathies.

Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT).

Although clinical applications represent the next challenge in single-cell genomics and digital pathology, we still lack computational methods to analyze single-cell or pathomics data to find sample-level trajectories or clusters associated with diseases. This remains challenging as single-cell/pathomics data are multi-scale, i.e., a sample is represented by clusters of cells/structures, and samples cannot be easily compared with each other. Here we propose PatIent Level analysis with Optimal Transport (PILOT). PILOT uses optimal transport to compute the Wasserstein distance between two individual single-cell samples. This allows us to perform unsupervised analysis at the sample level and uncover trajectories or cellular clusters associated with disease progression. We evaluate PILOT and competing approaches in single-cell genomics or pathomics studies involving various human diseases with up to 600 samples/patients and millions of cells or tissue structures. Our results demonstrate that PILOT detects disease-associated samples from large and complex single-cell or pathomics data. Moreover, PILOT provides a statistical approach to find changes in cell populations, gene expression, and tissue structures related to the trajectories or clusters supporting interpretation of predictions.

tRigon: an R package and Shiny App for integrative (path-)omics data analysis.

BACKGROUND: Pathomics facilitates automated, reproducible and precise histopathology analysis and morphological phenotyping. Similar to molecular omics, pathomics datasets are high-dimensional, but also face large outlier variability and inherent data missingness, making quick and comprehensible data analysis challenging. To facilitate pathomics data analysis and interpretation as well as support a broad implementation we developed tRigon (Toolbox foR InteGrative (path-)Omics data aNalysis), a Shiny application for fast, comprehensive and reproducible pathomics analysis. RESULTS: tRigon is available via the CRAN repository ( https://cran.r-project.org/web/packages/tRigon ) with its source code available on GitLab ( https://git-ce.rwth-aachen.de/labooratory-ai/trigon ). The tRigon package can be installed locally and its application can be executed from the R console via the command 'tRigon::run_tRigon()'. Alternatively, the application is hosted online and can be accessed at https://labooratory.shinyapps.io/tRigon . We show fast computation of small, medium and large datasets in a low- and high-performance hardware setting, indicating broad applicability of tRigon. CONCLUSIONS: tRigon allows researchers without coding abilities to perform exploratory feature analyses of pathomics and non-pathomics datasets on their own using a variety of hardware.

Induction of senescence upon loss of the Ash2l core subunit of H3K4 methyltransferase complexes.

Gene expression is controlled in part by post-translational modifications of core histones. Methylation of lysine 4 of histone H3 (H3K4), associated with open chromatin and gene transcription, is catalyzed by type 2 lysine methyltransferase complexes that require WDR5, RBBP5, ASH2L and DPY30 as core subunits. Ash2l is essential during embryogenesis and for maintaining adult tissues. To expand on the mechanistic understanding of Ash2l, we generated mouse embryo fibroblasts (MEFs) with conditional Ash2l alleles. Upon loss of Ash2l, methylation of H3K4 and gene expression were downregulated, which correlated with inhibition of proliferation and cell cycle progression. Moreover, we observed induction of senescence concomitant with a set of downregulated signature genes but independent of SASP. Many of the signature genes are FoxM1 responsive. Indeed, exogenous FOXM1 was sufficient to delay senescence. Thus, although the loss of Ash2l in MEFs has broad and complex consequences, a distinct set of downregulated genes promotes senescence.

RGT: a toolbox for the integrative analysis of high throughput regulatory genomics data.

BACKGROUND: Massive amounts of data are produced by combining next-generation sequencing with complex biochemistry techniques to characterize regulatory genomics profiles, such as protein-DNA interaction and chromatin accessibility. Interpretation of such high-throughput data typically requires different computation methods. However, existing tools are usually developed for a specific task, which makes it challenging to analyze the data in an integrative manner. RESULTS: We here describe the Regulatory Genomics Toolbox (RGT), a computational library for the integrative analysis of regulatory genomics data. RGT provides different functionalities to handle genomic signals and regions. Based on that, we developed several tools to perform distinct downstream analyses, including the prediction of transcription factor binding sites using ATAC-seq data, identification of differential peaks from ChIP-seq data, and detection of triple helix mediated RNA and DNA interactions, visualization, and finding an association between distinct regulatory factors. CONCLUSION: We present here RGT; a framework to facilitate the customization of computational methods to analyze genomic data for specific regulatory genomics problems. RGT is a comprehensive and flexible Python package for analyzing high throughput regulatory genomics data and is available at: https://github.com/CostaLab/reg-gen . The documentation is available at: https://reg-gen.readthedocs.io.

Deep learning-based clustering approaches for bioinformatics.

Clustering is central to many data-driven bioinformatics research and serves a powerful computational method. In particular, clustering helps at analyzing unstructured and high-dimensional data in the form of sequences, expressions, texts and images. Further, clustering is used to gain insights into biological processes in the genomics level, e.g. clustering of gene expressions provides insights on the natural structure inherent in the data, understanding gene functions, cellular processes, subtypes of cells and understanding gene regulations. Subsequently, clustering approaches, including hierarchical, centroid-based, distribution-based, density-based and self-organizing maps, have long been studied and used in classical machine learning settings. In contrast, deep learning (DL)-based representation and feature learning for clustering have not been reviewed and employed extensively. Since the quality of clustering is not only dependent on the distribution of data points but also on the learned representation, deep neural networks can be effective means to transform mappings from a high-dimensional data space into a lower-dimensional feature space, leading to improved clustering results. In this paper, we review state-of-the-art DL-based approaches for cluster analysis that are based on representation learning, which we hope to be useful, particularly for bioinformatics research. Further, we explore in detail the training procedures of DL-based clustering algorithms, point out different clustering quality metrics and evaluate several DL-based approaches on three bioinformatics use cases, including bioimaging, cancer genomics and biomedical text mining. We believe this review and the evaluation results will provide valuable insights and serve a starting point for researchers wanting to apply DL-based unsupervised methods to solve emerging bioinformatics research problems.

MOJITOO: a fast and universal method for integration of multimodal single-cell data.

MOTIVATION: The advent of multi-modal single-cell sequencing techniques have shed new light on molecular mechanisms by simultaneously inspecting transcriptomes, epigenomes and proteomes of the same cell. However, to date, the existing computational approaches for integration of multimodal single-cell data are either computationally expensive, require the delineation of parameters or can only be applied to particular modalities. RESULTS: Here we present a single-cell multi-modal integration method, named Multi-mOdal Joint IntegraTion of cOmpOnents (MOJITOO). MOJITOO uses canonical correlation analysis for a fast and parameter free detection of a shared representation of cells from multimodal single-cell data. Moreover, estimated canonical components can be used for interpretation, i.e. association of modality-specific molecular features with the latent space. We evaluate MOJITOO using bi- and tri-modal single-cell datasets and show that MOJITOO outperforms existing methods regarding computational requirements, preservation of original latent spaces and clustering. AVAILABILITY AND IMPLEMENTATION: The software, code and data for benchmarking are available at https://github.com/CostaLab/MOJITOO and https://doi.org/10.5281/zenodo.6348128. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Targeted DNA Methylation Analysis Facilitates Leukocyte Counts in Dried Blood Samples.

BACKGROUND: Cell-type specific DNA methylation (DNAm) can be employed to determine the numbers of leukocyte subsets in blood. In contrast to conventional methods for leukocyte counts, which are based on cellular morphology or surface marker protein expression, the cellular deconvolution based on DNAm levels is applicable for frozen or dried blood. Here, we further enhanced targeted DNAm assays for leukocyte counts in clinical application. METHODS: DNAm profiles of 40 different studies were compiled to identify CG dinucleotides (CpGs) with cell-type specific DNAm using a computational framework, CimpleG. DNAm levels at these CpGs were then measured with digital droplet PCR in venous blood from 160 healthy donors and 150 patients with various hematological disorders. Deconvolution was further validated with venous blood (n = 75) and capillary blood (n = 31) that was dried on Whatman paper or on Mitra microsampling devices. RESULTS: In venous blood, automated cell counting or flow cytometry correlated well with epigenetic estimates of relative leukocyte counts for granulocytes (r = 0.95), lymphocytes (r = 0.97), monocytes (r = 0.82), CD4 T cells (r = 0.84), CD8 T cells (r = 0.94), B cells (r = 0.96), and NK cells (r = 0.72). Similar correlations and precisions were achieved for dried blood samples. Spike-in with a reference plasmid enabled accurate epigenetic estimation of absolute leukocyte counts from dried blood samples, correlating with conventional venous (r = 0.86) and capillary (r = 0.80) blood measurements. CONCLUSIONS: The advanced selection of cell-type specific CpGs and utilization of digital droplet PCR analysis provided accurate epigenetic blood counts. Analysis of dried blood facilitates self-sampling with a finger prick, thereby enabling easier accessibility to testing.

KIT D816V Mast Cells Derived from Induced Pluripotent Stem Cells Recapitulate Systemic Mastocytosis Transcriptional Profile.

Mast cells (MCs) represent a population of hematopoietic cells with a key role in innate and adaptive immunity and are well known for their detrimental role in allergic responses. Yet, MCs occur in low abundance, which hampers their detailed molecular analysis. Here, we capitalized on the potential of induced pluripotent stem (iPS) cells to give rise to all cells in the body and established a novel and robust protocol for human iPS cell differentiation toward MCs. Relying on a panel of systemic mastocytosis (SM) patient-specific iPS cell lines carrying the KIT D816V mutation, we generated functional MCs that recapitulate SM disease features: increased number of MCs, abnormal maturation kinetics and activated phenotype, CD25 and CD30 surface expression and a transcriptional signature characterized by upregulated expression of innate and inflammatory response genes. Therefore, human iPS cell-derived MCs are a reliable, inexhaustible, and close-to-human tool for disease modeling and pharmacological screening to explore novel MC therapeutics.

Detection of cell markers from single cell RNA-seq with sc2marker.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) allows the detection of rare cell types in complex tissues. The detection of markers for rare cell types is useful for further biological analysis of, for example, flow cytometry and imaging data sets for either physical isolation or spatial characterization of these cells. However, only a few computational approaches consider the problem of selecting specific marker genes from scRNA-seq data. RESULTS: Here, we propose sc2marker, which is based on the maximum margin index and a database of proteins with antibodies, to select markers for flow cytometry or imaging. We evaluated the performances of sc2marker and competing methods in ranking known markers in scRNA-seq data of immune and stromal cells. The results showed that sc2marker performed better than the competing methods in accuracy, while having a competitive running time.

CrossTalkeR: analysis and visualization of ligand-receptorne tworks.

MOTIVATION: Ligand-receptor (LR) network analysis allows the characterization of cellular crosstalk based on single cell RNA-seq data. However, current methods typically provide a list of inferred LR interactions and do not allow the researcher to focus on specific cell types, ligands or receptors. In addition, most of these methods cannot quantify changes in crosstalk between two biological phenotypes. RESULTS: CrossTalkeR is a framework for network analysis and visualization of LR interactions. CrossTalkeR identifies relevant ligands, receptors and cell types contributing to changes in cell communication when contrasting two biological phenotypes, i.e. disease versus homeostasis. A case study on scRNA-seq of human myeloproliferative neoplasms reinforces the strengths of CrossTalkeR for characterization of changes in cellular crosstalk in disease. AVAILABILITY AND IMPLEMENTATION: CrosstalkeR is an R package available at: Github: https://github.com/CostaLab/CrossTalkeR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Isolation and genome-wide characterization of cellular DNA:RNA triplex structures.

RNA can directly bind to purine-rich DNA via Hoogsteen base pairing, forming a DNA:RNA triple helical structure that anchors the RNA to specific sequences and allows guiding of transcription regulators to distinct genomic loci. To unravel the prevalence of DNA:RNA triplexes in living cells, we have established a fast and cost-effective method that allows genome-wide mapping of DNA:RNA triplex interactions. In contrast to previous approaches applied for the identification of chromatin-associated RNAs, this method uses protein-free nucleic acids isolated from chromatin. High-throughput sequencing and computational analysis of DNA-associated RNA revealed a large set of RNAs which originate from non-coding and coding loci, including super-enhancers and repeat elements. Combined analysis of DNA-associated RNA and RNA-associated DNA identified genomic DNA:RNA triplex structures. The results suggest that triplex formation is a general mechanism of RNA-mediated target-site recognition, which has major impact on biological functions.

Unique and assay specific features of NOMe-, ATAC- and DNase I-seq data.

Chromatin accessibility maps are important for the functional interpretation of the genome. Here, we systematically analysed assay specific differences between DNase I-seq, ATAC-seq and NOMe-seq in a side by side experimental and bioinformatic setup. We observe that most prominent nucleosome depleted regions (NDRs, e.g. in promoters) are roboustly called by all three or at least two assays. However, we also find a high proportion of assay specific NDRs that are often 'called' by only one of the assays. We show evidence that these assay specific NDRs are indeed genuine open chromatin sites and contribute important information for accurate gene expression prediction. While technically ATAC-seq and DNase I-seq provide a superb high NDR calling rate for relatively low sequencing costs in comparison to NOMe-seq, NOMe-seq singles out for its genome-wide coverage allowing to not only detect NDRs but also endogenous DNA methylation and as we show here genome wide segmentation into heterochromatic B domains and local phasing of nucleosomes outside of NDRs. In summary, our comparisons strongly suggest to consider assay specific differences for the experimental design and for generalized and comparative functional interpretations.

Detection of RNA-DNA binding sites in long noncoding RNAs.

Long non-coding RNAs (lncRNAs) can act as scaffolds that promote the interaction of proteins, RNA, and DNA. There is increasing evidence of sequence-specific interactions of lncRNAs with DNA via triple-helix (triplex) formation. This process allows lncRNAs to recruit protein complexes to specific genomic regions and regulate gene expression. Here we propose a computational method called Triplex Domain Finder (TDF) to detect triplexes and characterize DNA-binding domains and DNA targets statistically. Case studies showed that this approach can detect the known domains of lncRNAs Fendrr, HOTAIR and MEG3. Moreover, we validated a novel DNA-binding domain in MEG3 by a genome-wide sequencing method. We used TDF to perform a systematic analysis of the triplex-forming potential of lncRNAs relevant to human cardiac differentiation. We demonstrated that the lncRNA with the highest triplex-forming potential, GATA6-AS, forms triple helices in the promoter of genes relevant to cardiac development. Moreover, down-regulation of GATA6-AS impairs GATA6 expression and cardiac development. These data indicate the unique ability of our computational tool to identify novel triplex-forming lncRNAs and their target genes.

Cyclin E1 and cyclin-dependent kinase 2 are critical for initiation, but not for progression of hepatocellular carcinoma.

E-type cyclins E1 (CcnE1) and E2 (CcnE2) are regulatory subunits of cyclin-dependent kinase 2 (Cdk2) and thought to control the transition of quiescent cells into the cell cycle. Initial findings indicated that CcnE1 and CcnE2 have largely overlapping functions for cancer development in several tumor entities including hepatocellular carcinoma (HCC). In the present study, we dissected the differential contributions of CcnE1, CcnE2, and Cdk2 for initiation and progression of HCC in mice and patients. To this end, we tested the HCC susceptibility in mice with constitutive deficiency for CcnE1 or CcnE2 as well as in mice lacking Cdk2 in hepatocytes. Genetic inactivation of CcnE1 largely prevented development of liver cancer in mice in two established HCC models, while ablation of CcnE2 had no effect on hepatocarcinogenesis. Importantly, CcnE1-driven HCC initiation was dependent on Cdk2. However, isolated primary hepatoma cells typically acquired independence on CcnE1 and Cdk2 with increasing progression in vitro, which was associated with a gene signature involving secondary induction of CcnE2 and up-regulation of cell cycle and DNA repair pathways. Importantly, a similar expression profile was also found in HCC patients with elevated CcnE2 expression and poor survival. In general, overall survival in HCC patients was synergistically affected by expression of CcnE1 and CcnE2, but not through Cdk2. Our study suggests that HCC initiation specifically depends on CcnE1 and Cdk2, while HCC progression requires expression of any E-cyclin, but no Cdk2.

Deconvolution of cellular subsets in human tissue based on targeted DNA methylation analysis at individual CpG sites.

BACKGROUND: The complex composition of different cell types within a tissue can be estimated by deconvolution of bulk gene expression profiles or with various single-cell sequencing approaches. Alternatively, DNA methylation (DNAm) profiles have been used to establish an atlas for multiple human tissues and cell types. DNAm is particularly suitable for deconvolution of cell types because each CG dinucleotide (CpG site) has only two states per DNA strand-methylated or non-methylated-and these epigenetic modifications are very consistent during cellular differentiation. So far, deconvolution of DNAm profiles implies complex signatures of many CpGs that are often measured by genome-wide analysis with Illumina BeadChip microarrays. In this study, we investigated if the characterization of cell types in tissue is also feasible with individual cell type-specific CpG sites, which can be addressed by targeted analysis, such as pyrosequencing. RESULTS: We compiled and curated 579 Illumina 450k BeadChip DNAm profiles of 14 different non-malignant human cell types. A training and validation strategy was applied to identify and test for cell type-specific CpGs. We initially focused on estimating the relative amount of fibroblasts using two CpGs that were either hypermethylated or hypomethylated in fibroblasts. The combination of these two DNAm levels into a "FibroScore" correlated with the state of fibrosis and was associated with overall survival in various types of cancer. Furthermore, we identified hypomethylated CpGs for leukocytes, endothelial cells, epithelial cells, hepatocytes, glia, neurons, fibroblasts, and induced pluripotent stem cells. The accuracy of this eight CpG signature was tested in additional BeadChip datasets of defined cell mixtures and the results were comparable to previously published signatures based on several thousand CpGs. Finally, we established and validated pyrosequencing assays for the relevant CpGs that can be utilized for classification and deconvolution of cell types. CONCLUSION: This proof of concept study demonstrates that DNAm analysis at individual CpGs reflects the cellular composition of cellular mixtures and different tissues. Targeted analysis of these genomic regions facilitates robust methods for application in basic research and clinical settings.