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.

Lineage tracing reveals transient phenotypic adaptation of tubular cells during acute kidney injury.

Tubular injury is the hallmark of acute kidney injury (AKI) with a tremendous impact on patients and health-care systems. During injury, any differentiated proximal tubular cell (PT) may transition into a specific injured phenotype, so-called "scattered tubular cell" (STC)-phenotype. To understand the fate of this specific phenotype, we generated transgenic mice allowing inducible, reversible, and irreversible tagging of these cells in a murine AKI model, the unilateral ischemia-reperfusion injury (IRI). For lineage tracing, we analyzed the kidneys using single-cell profiling during disease development at various time points. Labeled cells, which we defined by established endogenous markers, already appeared 8 h after injury and showed a distinct expression set of genes. We show that STCs re-differentiate back into fully differentiated PTs upon the resolution of the injury. In summary, we show the dynamics of the phenotypic transition of PTs during injury, revealing a reversible transcriptional program as an adaptive response during disease.

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.

Proinflammatory phenotype of iPS cell-derived JAK2 V617F megakaryocytes induces fibrosis in 3D in vitro bone marrow niche.

The myeloproliferative disease polycythemia vera (PV) driven by the JAK2 V617F mutation can transform into myelofibrosis (post-PV-MF). It remains an open question how JAK2 V617F in hematopoietic stem cells induces MF. Megakaryocytes are major players in murine PV models but are difficult to study in the human setting. We generated induced pluripotent stem cells (iPSCs) from JAK2 V617F PV patients and differentiated them into megakaryocytes. In differentiation assays, JAK2 V617F iPSCs recapitulated the pathognomonic skewed megakaryocytic and erythroid differentiation. JAK2 V617F iPSCs had a TPO-independent and increased propensity to differentiate into megakaryocytes. RNA sequencing of JAK2 V617F iPSC-derived megakaryocytes reflected a proinflammatory, profibrotic phenotype and decreased ribosome biogenesis. In three-dimensional (3D) coculture, JAK2 V617F megakaryocytes induced a profibrotic phenotype through direct cell contact, which was reversed by the JAK2 inhibitor ruxolitinib. The 3D coculture system opens the perspective for further disease modeling and drug discovery.

Non-canonical Hedgehog signaling mediates profibrotic hematopoiesis-stroma crosstalk in myeloproliferative neoplasms.

The role of hematopoietic Hedgehog signaling in myeloproliferative neoplasms (MPNs) remains incompletely understood despite data suggesting that Hedgehog (Hh) pathway inhibitors have therapeutic activity in patients. We aim to systematically interrogate the role of canonical vs. non-canonical Hh signaling in MPNs. We show that Gli1 protein levels in patient peripheral blood mononuclear cells (PBMCs) mark fibrotic progression and that, in murine MPN models, absence of hematopoietic Gli1, but not Gli2 or Smo, significantly reduces MPN phenotype and fibrosis, indicating that GLI1 in the MPN clone can be activated in a non-canonical fashion. Additionally, we establish that hematopoietic Gli1 has a significant effect on stromal cells, mediated through a druggable MIF-CD74 axis. These data highlight the complex interplay between alterations in the MPN clone and activation of stromal cells and indicate that Gli1 represents a promising therapeutic target in MPNs, particularly that Hh signaling is dispensable for normal hematopoiesis.

Enhanced fatty acid oxidation through metformin and baicalin as therapy for COVID-19 and associated inflammatory states in lung and kidney.

Progressive respiratory failure is the primary cause of death in the coronavirus disease 2019 (COVID-19) pandemic. It is the final outcome of the acute respiratory distress syndrome (ARDS), characterized by an initial exacerbated inflammatory response, metabolic derangement and ultimate tissue scarring. A positive balance of cellular energy may result crucial for the recovery of clinical COVID-19. Hence, we asked if two key pathways involved in cellular energy generation, AMP-activated protein kinase (AMPK)/acetyl-CoA carboxylase (ACC) signaling and fatty acid oxidation (FAO) could be beneficial. We tested the drugs metformin (AMPK activator) and baicalin (CPT1A activator) in different experimental models mimicking COVID-19 associated inflammation in lung and kidney. We also studied two different cohorts of COVID-19 patients that had been previously treated with metformin. These drugs ameliorated lung damage in an ARDS animal model, while activation of AMPK/ACC signaling increased mitochondrial function and decreased TGF-ß-induced fibrosis, apoptosis and inflammation markers in lung epithelial cells. Similar results were observed with two indole derivatives, IND6 and IND8 with AMPK activating capacity. Consistently, a reduced time of hospitalization and need of intensive care was observed in COVID-19 patients previously exposed to metformin. Baicalin also mitigated the activation of pro-inflammatory bone marrow-derived macrophages (BMDMs) and reduced kidney fibrosis in two animal models of kidney injury, another key target of COVID-19. In human epithelial lung and kidney cells, both drugs improved mitochondrial function and prevented TGF-ß-induced renal epithelial cell dedifferentiation. Our results support that favoring cellular energy production through enhanced FAO may prove useful in the prevention of COVID-19-induced lung and renal damage.

EphrinA5 regulates cell motility by modulating Snhg15/DNA triplex-dependent targeting of DNMT1 to the Ncam1 promoter.

Cell-cell communication is mediated by membrane receptors and their ligands, such as the Eph/ephrin system, orchestrating cell migration during development and in diverse cancer types. Epigenetic mechanisms are key for integrating external "signals", e.g., from neighboring cells, into the transcriptome in health and disease. Previously, we reported ephrinA5 to trigger transcriptional changes of lncRNAs and protein-coding genes in cerebellar granule cells, a cell model for medulloblastoma. LncRNAs represent important adaptors for epigenetic writers through which they regulate gene expression. Here, we investigate a lncRNA-mediated targeting of DNMT1 to specific gene loci by the combined power of in silico modeling of RNA/DNA interactions and wet lab approaches, in the context of the clinically relevant use case of ephrinA5-dependent regulation of cellular motility of cerebellar granule cells. We provide evidence that Snhg15, a cancer-related lncRNA, recruits DNMT1 to the Ncam1 promoter through RNA/DNA triplex structure formation and the interaction with DNMT1. This mediates DNA methylation-dependent silencing of Ncam1, being abolished by ephrinA5 stimulation-triggered reduction of Snhg15 expression. Hence, we here propose a triple helix recognition mechanism, underlying cell motility regulation via lncRNA-targeted DNA methylation in a clinically relevant context.

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.

Myelofibrosis at diagnosis is associated with the failure of treatment-free remission in CML patients.

The management of patients with chronic myeloid leukemia (CML) has been revolutionized by the introduction of tyrosine kinase inhibitors (TKIs), which induce deep molecular responses so that treatment can eventually be discontinued, leading to treatment-free remission (TFR) in a subset of patients. Unfortunately, leukemic stem cells (LSCs) often persist and a fraction of these can again expand in about half of patients that attempt TKI discontinuation. In this study, we show that presence of myelofibrosis (MF) at the time of diagnosis is a factor associating with TFR failure. Fibrotic transformation is governed by the action of several cytokines, and interestingly, some of them have also been described to support LSC persistence. At the cellular level, these could be produced by both malignant cells and by components of the bone marrow (BM) niche, including megakaryocytes (MKs) and mesenchymal stromal cells (MSCs). In our cohort of 57 patients, around 40% presented with MF at diagnosis and the number of blasts in the peripheral blood and BM was significantly elevated in patients with higher grade of MF. Employing a CML transgenic mouse model, we could observe higher levels of alpha-smooth muscle actin (α-SMA) in the BM when compared to control mice. Short-term treatment with the TKI nilotinib, efficiently reduced spleen weight and BCR::ABL1 mRNA levels, while α-SMA expression was only partially reduced. Interestingly, the number of MKs was increased in the spleen of CML mice and elevated in both BM and spleen upon nilotinib treatment. Analysis of human CML-vs healthy donor (HD)-derived MSCs showed an altered expression of gene signatures reflecting fibrosis as well as hematopoietic support, thus suggesting MSCs as a potential player in these two processes. Finally, in our cohort, 12 patients qualified for TKI discontinuation, and here we observed that all patients who failed TFR had BM fibrosis at diagnosis, whereas this was only the case in 25% of patients with achieved TFR, further supporting the link between fibrosis and LSC persistence.

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.

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.

A lncRNA identifies Irf8 enhancer element in negative feedback control of dendritic cell differentiation.

Transcription factors play a determining role in lineage commitment and cell differentiation. Interferon regulatory factor 8 (IRF8) is a lineage determining transcription factor in hematopoiesis and master regulator of dendritic cells (DC), an important immune cell for immunity and tolerance. IRF8 is prominently upregulated in DC development by autoactivation and controls both DC differentiation and function. However, it is unclear how Irf8 autoactivation is controlled and eventually limited. Here, we identified a novel long non-coding RNA transcribed from the +32 kb enhancer downstream of Irf8 transcription start site and expressed specifically in mouse plasmacytoid DC (pDC), referred to as lncIrf8. The lncIrf8 locus interacts with the lrf8 promoter and shows differential epigenetic signatures in pDC versus classical DC type 1 (cDC1). Interestingly, a sequence element of the lncIrf8 promoter, but not lncIrf8 itself, is crucial for mouse pDC and cDC1 differentiation, and this sequence element confers feedback inhibition of Irf8 expression. Taken together, in DC development Irf8 autoactivation is first initiated by flanking enhancers and then second controlled by feedback inhibition through the lncIrf8 promoter element in the +32 kb enhancer. Our work reveals a previously unrecognized negative feedback loop of Irf8 that orchestrates its own expression and thereby controls DC differentiation.

scMEGA: single-cell multi-omic enhancer-based gene regulatory network inference.

Summary: The increasing availability of single-cell multi-omics data allows to quantitatively characterize gene regulation. We here describe scMEGA (Single-cell Multiomic Enhancer-based Gene Regulatory Network Inference) that enables an end-to-end analysis of multi-omics data for gene regulatory network inference including modalities integration, trajectory analysis, enhancer-to-promoter association, network analysis and visualization. This enables to study the complex gene regulation mechanisms for dynamic biological processes, such as cellular differentiation and disease-driven cellular remodeling. We provide a case study on gene regulatory networks controlling myofibroblast activation in human myocardial infarction. Availability and implementation: scMEGA is implemented in R, released under the MIT license and available from https://github.com/CostaLab/scMEGA. Tutorials are available from https://costalab.github.io/scMEGA. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Next-Generation Morphometry for pathomics-data mining in histopathology.

Pathology diagnostics relies on the assessment of morphology by trained experts, which remains subjective and qualitative. Here we developed a framework for large-scale histomorphometry (FLASH) performing deep learning-based semantic segmentation and subsequent large-scale extraction of interpretable, quantitative, morphometric features in non-tumour kidney histology. We use two internal and three external, multi-centre cohorts to analyse over 1000 kidney biopsies and nephrectomies. By associating morphometric features with clinical parameters, we confirm previous concepts and reveal unexpected relations. We show that the extracted features are independent predictors of long-term clinical outcomes in IgA-nephropathy. We introduce single-structure morphometric analysis by applying techniques from single-cell transcriptomics, identifying distinct glomerular populations and morphometric phenotypes along a trajectory of disease progression. Our study provides a concept for Next-generation Morphometry (NGM), enabling comprehensive quantitative pathology data mining, i.e., pathomics.

Loss of the Ash2l subunit of histone H3K4 methyltransferase complexes reduces chromatin accessibility at promoters.

Changes in gene expression programs are intimately linked to cell fate decisions. Post-translational modifications of core histones contribute to control gene expression. Methylation of lysine 4 of histone H3 (H3K4) correlates with active promoters and gene transcription. This modification is catalyzed by KMT2 methyltransferases, which require interaction with 4 core subunits, WDR5, RBBP5, ASH2L and DPY30, for catalytic activity. Ash2l is necessary for organismal development and for tissue homeostasis. In mouse embryo fibroblasts (MEFs), Ash2l loss results in gene repression, provoking a senescence phenotype. We now find that upon knockout of Ash2l both H3K4 mono- and tri-methylation (H3K4me1 and me3, respectively) were deregulated. In particular, loss of H3K4me3 at promoters correlated with gene repression, especially at CpG island promoters. Ash2l loss resulted in increased loading of histone H3 and reduced chromatin accessibility at promoters, accompanied by an increase of repressing and a decrease of activating histone marks. Moreover, we observed altered binding of CTCF upon Ash2l loss. Lost and gained binding was noticed at promoter-associated and intergenic sites, respectively. Thus, Ash2l loss and reduction of H3K4me3 correlate with altered chromatin accessibility and transcription factor binding. These findings contribute to a more detailed understanding of mechanistic consequences of H3K4me3 loss and associated repression of gene transcription and thus of the observed cellular consequences.

HIF1α-AS1 is a DNA:DNA:RNA triplex-forming lncRNA interacting with the HUSH complex.

DNA:DNA:RNA triplexes that are formed through Hoogsteen base-pairing of the RNA in the major groove of the DNA duplex have been observed in vitro, but the extent to which these interactions occur in cells and how they impact cellular functions remains elusive. Using a combination of bioinformatic techniques, RNA/DNA pulldown and biophysical studies, we set out to identify functionally important DNA:DNA:RNA triplex-forming long non-coding RNAs (lncRNA) in human endothelial cells. The lncRNA HIF1α-AS1 was retrieved as a top hit. Endogenous HIF1α-AS1 reduces the expression of numerous genes, including EPH Receptor A2 and Adrenomedullin through DNA:DNA:RNA triplex formation by acting as an adapter for the repressive human silencing hub complex (HUSH). Moreover, the oxygen-sensitive HIF1α-AS1 is down-regulated in pulmonary hypertension and loss-of-function approaches not only result in gene de-repression but also enhance angiogenic capacity. As exemplified here with HIF1α-AS1, DNA:DNA:RNA triplex formation is a functionally important mechanism of trans-acting gene expression control.

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.