PD-L1 regulates inflammatory programs of macrophages from human pluripotent stem cells.

Programmed death ligand 1 (PD-L1) serves as a pivotal immune checkpoint in both the innate and adaptive immune systems. PD-L1 is expressed in macrophages in response to IFNγ. We examined whether PD-L1 might regulate macrophage development. We established PD-L1 KO (CD274 -/- ) human pluripotent stem cells and differentiated them into macrophages and observed a 60% reduction in CD11B+CD45+ macrophages in CD274 -/- ; this was orthogonally verified, with the PD-L1 inhibitor BMS-1166 reducing macrophages to the same fold. Single-cell RNA sequencing further confirmed the down-regulation of the macrophage-defining transcription factors SPI1 and MAFB Furthermore, CD274 -/- macrophages reduced the level of inflammatory signals such as NF-κB and TNF, and chemokine secretion of the CXCL and CCL families. Anti-inflammatory TGF-ß was up-regulated. Finally, we identified that CD274 -/- macrophages significantly down-regulated interferon-stimulated genes despite the presence of IFNγ in the differentiation media. These data suggest that PD-L1 regulates inflammatory programs of macrophages from human pluripotent stem cells.

Lineage tracing and single-cell analysis reveal proliferative Prom1+ tumour-propagating cells and their dynamic cellular transition during liver cancer progression.

OBJECTIVE: Hepatocellular carcinoma (HCC) has high intratumoral heterogeneity, which contributes to therapeutic resistance and tumour recurrence. We previously identified Prominin-1 (PROM1)/CD133 as an important liver cancer stem cell (CSC) marker in human HCC. The aim of this study was to investigate the heterogeneity and properties of Prom1+ cells in HCC in intact mouse models. DESIGN: We established two mouse models representing chronic fibrotic HCC and rapid steatosis-related HCC. We performed lineage tracing post-HCC induction using Prom1C-L/+; Rosa26tdTomato/+ mice, and targeted depletion using Prom1C-L/+; Rosa26DTA/+ mice. Single-cell RNA sequencing (scRNA-seq) was carried out to analyse the transcriptomic profile of traced Prom1+ cells. RESULTS: Prom1 in HCC tumours marks proliferative tumour-propagating cells with CSC-like properties. Lineage tracing demonstrated that these cells display clonal expansion in situ in primary tumours. Labelled Prom1+ cells exhibit increasing tumourigenicity in 3D culture and allotransplantation, as well as potential to form cancers of differential lineages on transplantation. Depletion of Prom1+ cells impedes tumour growth and reduces malignant cancer hallmarks in both HCC models. scRNA-seq analysis highlighted the heterogeneity of Prom1+ HCC cells, which follow a trajectory to the dedifferentiated status with high proliferation and stem cells traits. Conserved gene signature of Prom1 linage predicts poor prognosis in human HCC. The activated oxidant detoxification underlies the protective mechanism of dedifferentiated transition and lineage propagation. CONCLUSION: Our study combines in vivo lineage tracing and scRNA-seq to reveal the heterogeneity and dynamics of Prom1+ HCC cells, providing insights into the mechanistic role of malignant CSC-like cells in HCC progression.

Effect of machine learning re-sampling techniques for imbalanced datasets in 18F-FDG PET-based radiomics model on prognostication performance in cohorts of head and neck cancer patients.

PURPOSE: Biomedical data frequently contain imbalance characteristics which make achieving good predictive performance with data-driven machine learning approaches a challenging task. In this study, we investigated the impact of re-sampling techniques for imbalanced datasets in PET radiomics-based prognostication model in head and neck (HNC) cancer patients. METHODS: Radiomics analysis was performed in two cohorts of patients, including 166 patients newly diagnosed with nasopharyngeal carcinoma (NPC) in our centre and 182 HNC patients from open database. Conventional PET parameters and robust radiomics features were extracted for correlation analysis of the overall survival (OS) and disease progression-free survival (DFS). We investigated a cross-combination of 10 re-sampling methods (oversampling, undersampling, and hybrid sampling) with 4 machine learning classifiers for survival prediction. Diagnostic performance was assessed in hold-out test sets. Statistical differences were analysed using Monte Carlo cross-validations by post hoc Nemenyi analysis. RESULTS: Oversampling techniques like ADASYN and SMOTE could improve prediction performance in terms of G-mean and F-measures in minority class, without significant loss of F-measures in majority class. We identified optimal PET radiomics-based prediction model of OS (AUC of 0.82, G-mean of 0.77) for our NPC cohort. Similar findings that oversampling techniques improved the prediction performance were seen when this was tested on an external dataset indicating generalisability. CONCLUSION: Our study showed a significant positive impact on the prediction performance in imbalanced datasets by applying re-sampling techniques. We have created an open-source solution for automated calculations and comparisons of multiple re-sampling techniques and machine learning classifiers for easy replication in future studies.

Single-cell expression profiling reveals dynamic flux of cardiac stromal, vascular and immune cells in health and injury.

Besides cardiomyocytes (CM), the heart contains numerous interstitial cell types which play key roles in heart repair, regeneration and disease, including fibroblast, vascular and immune cells. However, a comprehensive understanding of this interactive cell community is lacking. We performed single-cell RNA-sequencing of the total non-CM fraction and enriched (Pdgfra-GFP+) fibroblast lineage cells from murine hearts at days 3 and 7 post-sham or myocardial infarction (MI) surgery. Clustering of >30,000 single cells identified >30 populations representing nine cell lineages, including a previously undescribed fibroblast lineage trajectory present in both sham and MI hearts leading to a uniquely activated cell state defined in part by a strong anti-WNT transcriptome signature. We also uncovered novel myofibroblast subtypes expressing either pro-fibrotic or anti-fibrotic signatures. Our data highlight non-linear dynamics in myeloid and fibroblast lineages after cardiac injury, and provide an entry point for deeper analysis of cardiac homeostasis, inflammation, fibrosis, repair and regeneration.

Gene-gene interaction filtering with ensemble of filters.

BACKGROUND: Complex diseases are commonly caused by multiple genes and their interactions with each other. Genome-wide association (GWA) studies provide us the opportunity to capture those disease associated genes and gene-gene interactions through panels of SNP markers. However, a proper filtering procedure is critical to reduce the search space prior to the computationally intensive gene-gene interaction identification step. In this study, we show that two commonly used SNP-SNP interaction filtering algorithms, ReliefF and tuned ReliefF (TuRF), are sensitive to the order of the samples in the dataset, giving rise to unstable and suboptimal results. However, we observe that the 'unstable' results from multiple runs of these algorithms can provide valuable information about the dataset. We therefore hypothesize that aggregating results from multiple runs of the algorithm may improve the filtering performance. RESULTS: We propose a simple and effective ensemble approach in which the results from multiple runs of an unstable filter are aggregated based on the general theory of ensemble learning. The ensemble versions of the ReliefF and TuRF algorithms, referred to as ReliefF-E and TuRF-E, are robust to sample order dependency and enable a more informative investigation of data characteristics. Using simulated and real datasets, we demonstrate that both the ensemble of ReliefF and the ensemble of TuRF can generate a much more stable SNP ranking than the original algorithms. Furthermore, the ensemble of TuRF achieved the highest success rate in comparison to many state-of-the-art algorithms as well as traditional χ2-test and odds ratio methods in terms of retaining gene-gene interactions.