Molecular characterization of chronic cutaneous wounds reveals subregion- and wound type-specific differential gene expression.

A limited understanding of the pathology underlying chronic wounds has hindered the development of effective diagnostic markers and pharmaceutical interventions. This study aimed to elucidate the molecular composition of various common chronic ulcer types to facilitate drug discovery strategies. We conducted a comprehensive analysis of leg ulcers (LUs), encompassing venous and arterial ulcers, foot ulcers (FUs), pressure ulcers (PUs), and compared them with surgical wound healing complications (WHCs). To explore the pathophysiological mechanisms and identify similarities or differences within wounds, we dissected wounds into distinct subregions, including the wound bed, border, and peri-wound areas, and compared them against intact skin. By correlating histopathology, RNA sequencing (RNA-Seq), and immunohistochemistry (IHC), we identified unique genes, pathways, and cell type abundance patterns in each wound type and subregion. These correlations aim to aid clinicians in selecting targeted treatment options and informing the design of future preclinical and clinical studies in wound healing. Notably, specific genes, such as PITX1 and UPP1, exhibited exclusive upregulation in LUs and FUs, potentially offering significant benefits to specialists in limb preservation and clinical treatment decisions. In contrast, comparisons between different wound subregions, regardless of wound type, revealed distinct expression profiles. The pleiotropic chemokine-like ligand GPR15L (C10orf99) and transmembrane serine proteases TMPRSS11A/D were significantly upregulated in wound border subregions. Interestingly, WHCs exhibited a nearly identical transcriptome to PUs, indicating clinical relevance. Histological examination revealed blood vessel occlusions with impaired angiogenesis in chronic wounds, alongside elevated expression of genes and immunoreactive markers related to blood vessel and lymphatic epithelial cells in wound bed subregions. Additionally, inflammatory and epithelial markers indicated heightened inflammatory responses in wound bed and border subregions and reduced wound bed epithelialization. In summary, chronic wounds from diverse anatomical sites share common aspects of wound pathophysiology but also exhibit distinct molecular differences. These unique molecular characteristics present promising opportunities for drug discovery and treatment, particularly for patients suffering from chronic wounds. The identified diagnostic markers hold the potential to enhance preclinical and clinical trials in the field of wound healing.

DRUG-seq Provides Unbiased Biological Activity Readouts for Neuroscience Drug Discovery.

Unbiased transcriptomic RNA-seq data has provided deep insights into biological processes. However, its impact in drug discovery has been narrow given high costs and low throughput. Proof-of-concept studies with Digital RNA with pertUrbation of Genes (DRUG)-seq demonstrated the potential to address this gap. We extended the DRUG-seq platform by subjecting it to rigorous testing and by adding an open-source analysis pipeline. The results demonstrate high reproducibility and ability to resolve the mechanism(s) of action for a diverse set of compounds. Furthermore, we demonstrate how this data can be incorporated into a drug discovery project aiming to develop therapeutics for schizophrenia using human stem cell-derived neurons. We identified both an on-target activation signature, induced by a set of chemically distinct positive allosteric modulators of the N-methyl-d-aspartate (NMDA) receptor, and independent off-target effects. Overall, the protocol and open-source analysis pipeline are a step toward industrializing RNA-seq for high-complexity transcriptomics studies performed at a saturating scale.

Biomarker-Based Classification and Localization of Renal Lesions Using Learned Representations of Histology-A Machine Learning Approach to Histopathology.

Several deep learning approaches have been proposed to address the challenges in computational pathology by learning structural details in an unbiased way. Transfer learning allows starting from a learned representation of a pretrained model to be directly used or fine-tuned for a new domain. However, in histopathology, the problem domain is tissue-specific and putting together a labelled data set is challenging. On the other hand, whole slide-level annotations, such as biomarker levels, are much easier to obtain. We compare two pretrained models, one histology-specific and one from ImageNet on various computational pathology tasks. We show that a domain-specific model (HistoNet) contains richer information for biomarker classification, localization of biomarker-relevant morphology within a slide, and the prediction of expert-graded features. We use a weakly supervised approach to discriminate slides based on biomarker level and simultaneously predict which regions contribute to that prediction. We employ multitask learning to show that learned representations correlate with morphological features graded by expert pathologists. All of these results are demonstrated in the context of renal toxicity in a mechanistic study of compound toxicity in rat models. Our results emphasize the importance of histology-specific models and their knowledge representations for solving a wide range of computational pathology tasks.

IRF2 is a master regulator of human keratinocyte stem cell fate.

Resident adult epithelial stem cells maintain tissue homeostasis by balancing self-renewal and differentiation. The stem cell potential of human epidermal keratinocytes is retained in vitro but lost over time suggesting extrinsic and intrinsic regulation. Transcription factor-controlled regulatory circuitries govern cell identity, are sufficient to induce pluripotency and transdifferentiate cells. We investigate whether transcriptional circuitry also governs phenotypic changes within a given cell type by comparing human primary keratinocytes with intrinsically high versus low stem cell potential. Using integrated chromatin and transcriptional profiling, we implicate IRF2 as antagonistic to stemness and show that it binds and regulates active cis-regulatory elements at interferon response and antigen presentation genes. CRISPR-KD of IRF2 in keratinocytes with low stem cell potential increases self-renewal, migration and epidermis formation. These data demonstrate that transcription factor regulatory circuitries, in addition to maintaining cell identity, control plasticity within cell types and offer potential for therapeutic modulation of cell function.

UTS2B Defines a Novel Enteroendocrine Cell Population and Regulates GLP-1 Secretion Through SSTR5 in Male Mice.

The gut-pancreas axis plays a key role in the regulation of glucose homeostasis and may be therapeutically exploited to treat not only type 2 diabetes but also hypoglycemia and hyperinsulinemia. We identify a novel enteroendocrine cell type expressing the peptide hormone urotensin 2B (UTS2B). UTS2B inhibits glucagon-like peptide-1 (GLP-1) secretion in mouse intestinal crypts and organoids, not by signaling through its cognate receptor UTS2R but through the activation of the somatostatin receptor (SSTR) 5. Circulating UTS2B concentrations in mice are physiologically regulated during starvation, further linking this peptide hormone to metabolism. Furthermore, administration of UTS2B to starved mice demonstrates that it is capable of regulating blood glucose and plasma concentrations of GLP-1 and insulin in vivo. Altogether, our results identify a novel cellular source of UTS2B in the gut, which acts in a paracrine manner to regulate GLP-1 secretion through SSTR5. These findings uncover a fine-tuning mechanism mediated by a ligand-receptor pair in the regulation of gut hormone secretion, which can potentially be exploited to correct metabolic unbalance caused by overactivation of the gut-pancreas axis.

YAP, but Not RSPO-LGR4/5, Signaling in Biliary Epithelial Cells Promotes a Ductular Reaction in Response to Liver Injury.

Biliary epithelial cells (BECs) form bile ducts in the liver and are facultative liver stem cells that establish a ductular reaction (DR) to support liver regeneration following injury. Liver damage induces periportal LGR5+ putative liver stem cells that can form BEC-like organoids, suggesting that RSPO-LGR4/5-mediated WNT/ß-catenin activity is important for a DR. We addressed the roles of this and other signaling pathways in a DR by performing a focused CRISPR-based loss-of-function screen in BEC-like organoids, followed by in vivo validation and single-cell RNA sequencing. We found that BECs lack and do not require LGR4/5-mediated WNT/ß-catenin signaling during a DR, whereas YAP and mTORC1 signaling are required for this process. Upregulation of AXIN2 and LGR5 is required in hepatocytes to enable their regenerative capacity in response to injury. Together, these data highlight heterogeneity within the BEC pool, delineate signaling pathways involved in a DR, and clarify the identity and roles of injury-induced periportal LGR5+ cells.

Screening of Intestinal Crypt Organoids: A Simple Readout for Complex Biology.

Oral and intestinal mucositis is a debilitating side effect of radiation treatment. A mouse model of radiation-induced mucositis leads to weight loss and tissue damage, reflecting the human ailment as it responds to keratinocyte growth factor (KGF), the standard-of-care treatment. Cultured intestinal crypt organoids allowed the development of an assay monitoring the effect of treatments of intestinal epithelium to radiation-induced damage. This in vitro assay resembles the mouse model as KGF and roof plate-specific spondin-1 (RSPO1) enhanced crypt organoid recovery following radiation. Screening identified compounds that increased the survival of organoids postradiation. Testing of these compounds revealed that the organoids changed their responses over time. Unbiased transcriptome analysis was performed on crypt organoid cultures at various time points in culture to investigate this adaptive behavior. A number of genes and pathways were found to be modulated over time, providing a rationale for the altered sensitivity of the organoid cultures. This report describes an in vitro assay that reflects aspects of human disease. The assay was used to identify bioactive compounds, which served as probes to interrogate the biology of crypt organoids over prolonged culture. The pathways that are changing over time may offer potential targets for treatment of mucositis.

Exploring Glucocorticoid Receptor Agonists Mechanism of Action Through Mass Cytometry and Radial Visualizations.

Recent advances in combining flow cytometry and mass spectrometry have led to the development of mass cytometry, allowing for the interrogation of complex cell populations on an unprecedented scale. The volumes and high dimensionality of mass cytometry data pose significant challenges in terms of analysis and visualization. We implement a method called Radviz, where multidimensional single cell data can be visualized as a projection that maintains the original dimensions and data complexity whilst facilitating analysis and visualization. This enables identification of changes in populations, focusing the analysis on the most relevant aspect of large multidimensional datasets. To highlight the potential of Radviz, we profiled peripheral mononuclear blood cells (PBMCs) from three healthy donors and showed donor-specific differences in the number and composition of cell populations. In a second study, we explored the anti-inflammatory effects of two glucocorticoid receptor (GR) ligands (cpd6 and cpd11) compared to dexamethasone (Dex) on human primary macrophages. Standard analysis at the population level showed that cpd6 and cpd11 have an overall anti-inflammatory profile similar to that of Dex. CyTOF profiling and Radviz-driven analysis at the single cell level confirmed this observation, and identified a concentration-dependent effect of cpd6 that was not detected at the population level. Altogether, Radviz combines the strengths of a projection method, reducing the dimensionality of datasets, with that of a scatter plot, where the identity of each point can be inferred from the distance to the axis. This enables the visual exploration, analysis, and interpretation of complex, high dimensional data. © 2016 International Clinical Cytometry Society.