Deep models of integrated multiscale molecular data decipher the endothelial cell response to ionizing radiation

Novel dimensionality reduction method, Taelcore, enhances lung transplantation risk prediction

In this work, we present a new approach to predict the risk of acute cellular rejection (ACR) after lung transplantation by using machine learning algorithms, such as Multilayer Perceptron (MLP) or Autoencoder (AE), and combining them with topological data analysis (TDA) tools. Our proposed method, named topological autoencoder with best linear combination for optimal reduction of embeddings (Taelcore), effectively reduces the dimensionality of high-dimensional datasets and yields better results compared to other models. We validate the effectiveness of Taelcore in reducing the prediction error rate on four datasets. Furthermore, we demonstrate that Taelcore's topological improvements have a positive effect on the majority of the machine learning algorithms used. By providing a new way to diagnose patients and detect complications early, this work contributes to improved clinical outcomes in lung transplantation.

Protocol for in vitro assessment of human monocyte transendothelial migration using a high-throughput live cell imaging system

In vitro modeling of the different steps of immune cell recruitment is essential to decipher the role of endothelial cells in this process. Here, we present a protocol for the assessment of human monocyte transendothelial migration using a live cell imaging system. We describe steps for culture of fluorescent monocytic THP-1 cells and chemotaxis plate preparation with HUVEC monolayers. We then detail real-time analysis using the IncuCyte® S3 live-cell imaging system, image analysis, and assessment of transendothelial migration rates. For complete details on the use and execution of this protocol, please refer to Ladaigue et al.1.

Plasma proteome dynamics of COVID-19 severity learnt by a graph convolutional network of multi-scale topology

Efforts to understand the molecular mechanisms of COVID-19 have led to the identification of ACE2 as the main receptor for the SARS-CoV-2 spike protein on cell surfaces. However, there are still important questions about the role of other proteins in disease progression. To address these questions, we modelled the plasma proteome of 384 COVID-19 patients using protein level measurements taken at three different times and incorporating comprehensive clinical evaluation data collected 28 d after hospitalisation. Our analysis can accurately assess the severity of the illness using a metric based on WHO scores. By using topological vectorisation, we identified proteins that vary most in expression based on disease severity, and then utilised these findings to construct a graph convolutional network. This dynamic model allows us to learn the molecular interactions between these proteins, providing a tool to determine the severity of a COVID-19 infection at an early stage and identify potential pharmacological treatments by studying the dynamic interactions between the most relevant proteins.

Profiling mRNA, miRNA and lncRNA expression changes in endothelial cells in response to increasing doses of ionizing radiation

Recent and past research have highlighted the importance of the endothelium in the manifestation of radiation injury. Our primary focus is on medical triage and management following whole body or partial-body irradiation. Here we investigated the usability of endothelial cells' radiation response for biodosimetry applications. We profiled the transcriptome in cultured human endothelial cells treated with increasing doses of X-rays. mRNA expression changes were useful 24 h and 72 h post-radiation, microRNA and lncRNA expression changes were useful 72 h after radiation. More mRNA expressions were repressed than induced while more miRNA and lncRNA expressions were induced than repressed. These novel observations imply distinct radiation responsive regulatory mechanisms for coding and non-coding transcripts. It also follows how different RNA species should be explored as biomarkers for different time-points. Radiation-responsive markers which could classify no radiation (i.e., '0 Gy') and dose-differentiating markers were also predicted. IPA analysis showed growth arrest-related processes at 24 h but immune response coordination at the 72 h post-radiation. Collectively, these observations suggest that endothelial cells have a precise dose and time-dependent response to radiation. Further studies in the laboratory are examining if these differences could be captured in the extracellular vesicles released by irradiated endothelial cells.

Treatment-associated remodeling of the pancreatic cancer endothelium at single-cell resolution

Pancreatic ductal adenocarcinoma (PDAC) is one of the most treatment refractory and lethal malignancies. The diversity of endothelial cell (EC) lineages in the tumor microenvironment (TME) impacts the efficacy of antineoplastic therapies, which in turn remodel EC states and distributions. Here, we present a single-cell resolution framework of diverse EC lineages in the PDAC TME in the context of neoadjuvant chemotherapy, radiotherapy, and losartan. We analyzed a custom single-nucleus RNA-seq dataset derived from 37 primary PDAC specimens (18 untreated, 14 neoadjuvant FOLFIRINOX + chemoradiotherapy, 5 neoadjuvant FOLFIRINOX + chemoradiotherapy + losartan). A single-nucleus transcriptome analysis of 15,185 EC profiles revealed two state programs (ribosomal, cycling), four lineage programs (capillary, arterial, venous, lymphatic), and one program that did not overlap significantly with prior signatures but was enriched in pathways involved in vasculogenesis, stem-like state, response to wounding and hypoxia, and endothelial-to-mesenchymal transition (reactive EndMT). A bulk transcriptome analysis of two independent cohorts (n = 269 patients) revealed that the lymphatic and reactive EndMT lineage programs were significantly associated with poor clinical outcomes. While losartan and proton therapy were associated with reduced lymphatic ECs, these therapies also correlated with an increase in reactive EndMT. Thus, the development and inclusion of EndMT-inhibiting drugs (e.g., nintedanib) to a neoadjuvant chemoradiotherapy regimen featuring losartan and/or proton therapy may be most effective in depleting both lymphatic and reactive EndMT populations and potentially improving patient outcomes.