ARIC: accurate and robust inference of cell type proportions from bulk gene expression or DNA methylation data

The antifibrotic potential of transglutaminase 2 inhibition beyond TGFβ1 release in human kidney tissue and isolated cell cultures

AIMS

The open conformation of the enzyme transglutaminase 2 (TG2) contributes to kidney fibrosis through transamidase activity by cross-linking extracellular matrix fibres and releasing transforming growth factor β1 (TGFβ1), a key driver of fibrogenesis. We investigated the antifibrotic potential of TG2 inhibition downstream of TGFβ1 using two TG2 inhibitors, LDN27219 and Z-DON, which modulate TG2 into the closed and open state, respectively.

MATERIALS AND METHODS

The TG2 inhibitors were studied in human precision-cut kidney slices (PCKS) and in cell cultures of primary renal cell types: endothelial and epithelial cells, and fibroblasts. PCKS and cell cultures were stimulated with TGFβ1 (10 ng/ml) for 48 h with or without LDN27219 (10 μmol/l) or Z-DON (40 μmol/l). We evaluated mRNA and protein expression of TG2 and fibrosis markers (fibronectin, α-smooth muscle actin and collagens), and TG2 transamidase activity.

KEY FINDINGS

In PCKS, TG2 was unaffected by TGFβ1, but mRNA levels of fibrosis markers increased with the stimulation and decreased in most LDN27219-treated PCKS compared to the control. No changes in protein expression of fibrosis markers were achieved with TGFβ1. In endothelial and epithelial cells, but not fibroblasts, fibronectin expression was increased with TG2 inhibition. Conversely, collagen 3α1 decreased by TG2 inhibition, further amplified by the closed conformation.

SIGNIFICANCE

The antifibrotic effects of TG2 inhibition extend beyond the release of TGFβ1, specifically in the closed conformation, although this varies among cell types. Our results indicate that the closed conformation of TG2 has an active antifibrotic potential in humans, in addition to blocking transamidase activity.

Computational analysis of DNA methylation from long-read sequencing

DNA methylation is a critical epigenetic mechanism in numerous biological processes, including gene regulation, development, ageing and the onset of various diseases such as cancer. Studies of methylation are increasingly using single-molecule long-read sequencing technologies to simultaneously measure epigenetic states such as DNA methylation with genomic variation. These long-read data sets have spurred the continuous development of advanced computational methods to gain insights into the roles of methylation in regulating chromatin structure and gene regulation. In this Review, we discuss the computational methods for calling methylation signals, contrasting methylation between samples, analysing cell-type diversity and gaining additional genomic insights, and then further discuss the challenges and future perspectives of tool development for DNA methylation research.

Brain metastases lung adenocarcinoma patients with BRG1 loss have a grim prognosis, featuring unique morphological and methylation characteristics

BRG1 deficiency in patients with lung adenocarcinoma that has metastasized to the brain, termed BRG1-deficient brain metastasis lung adenocarcinoma, is an uncommon event. Prior to this study, these patients had not undergone extensive molecular and (epi)genetic analysis. We report a comprehensive clinical, histopathologic, and molecular assessment of 9 BRG1-deficient brain metastasis lung adenocarcinoma cohort (BRG1-deficient BM cohort) in comparison with a 16 BRG1-retained brain metastasis lung adenocarcinoma cohort (BRG1-retained BM cohort). Patients with BRG1-deficient BM exhibited a significantly increased risk of mortality. Molecular analysis revealed a high prevalence of mutations in SMARCA4 and TP53 genes within this group. DNA methylation molecular diagnostics showed a high rate of genomic instability and a markedly lower DNA methylation age in these patients. Functional enrichment analysis of differentially methylated genes suggested that hypomethylation genes were primarily associated with the negative regulation of neuron differentiation, G protein-coupled receptor signaling pathways, and cell differentiation. Conversely, hypermethylation was linked to the regulation of small GTPase mediated signal transduction, Rho protein signal transduction, DNA damage response, and apoptotic processes. This study investigated a rare subgroup of lung adenocarcinoma patients with brain metastasis characterized by BRG1 deficiency and a poor prognosis. Our study not only provides a comprehensive multi-omic data resource but also provides valuable biological insights into patients. The findings may serve as a valuable reference for the future pathological diagnosis of BRG1-deficient brain metastasis in lung adenocarcinoma patients.

Spall: accurate and robust unveiling cellular landscapes from spatially resolved transcriptomics data using a decomposition network

Recent developments in spatially resolved transcriptomics (SRT) enable the characterization of spatial structures for different tissues. Many decomposition methods have been proposed to depict the cellular distribution within tissues. However, existing computational methods struggle to balance spatial continuity in cell distribution with the preservation of cell-specific characteristics. To address this, we propose Spall, a novel decomposition network that integrates scRNA-seq data with SRT data to accurately infer cell type proportions. Spall introduced the GATv2 module, featuring a flexible dynamic attention mechanism to capture relationships between spots. This improves the identification of cellular distribution patterns in spatial analysis. Additionally, Spall incorporates skip connections to address the loss of cell-specific information, thereby enhancing the prediction capability for rare cell types. Experimental results show that Spall outperforms the state-of-the-art methods in reconstructing cell distribution patterns on multiple datasets. Notably, Spall reveals tumor heterogeneity in human pancreatic ductal adenocarcinoma samples and delineates complex tissue structures, such as the laminar organization of the mouse cerebral cortex and the mouse cerebellum. These findings highlight the ability of Spall to provide reliable low-dimensional embeddings for downstream analyses, offering new opportunities for deciphering tissue structures.

DNA Methylation-Derived Immune Cell Proportions and Cancer Risk in Black Participants

SIGNIFICANCE

This study describes associations between immune cell types and cancer risk in a Black population; elevated regulatory T-cell proportions that were associated with increased overall cancer and lung cancer risk, and elevated memory B-cell proportions that were associated with increased prostate and all cancer risk.

Neural-net-based cell deconvolution from DNA methylation reveals tumor microenvironment associated with cancer prognosis

DNA methylation is a pivotal epigenetic modification that defines cellular identity. While cell deconvolution utilizing this information is considered useful for clinical practice, current methods for deconvolution are limited in their accuracy and resolution. In this study, we collected DNA methylation data from 945 human samples derived from various tissues and tumor-infiltrating immune cells and trained a neural network model with them. The model, termed MEnet, predicted abundance of cell population together with the detailed immune cell status from bulk DNA methylation data, and showed consistency to those of flow cytometry and histochemistry. MEnet was superior to the existing methods in the accuracy, speed, and detectable cell diversity, and could be applicable for peripheral blood, tumors, cell-free DNA, and formalin-fixed paraffin-embedded sections. Furthermore, by applying MEnet to 72 intrahepatic cholangiocarcinoma samples, we identified immune cell profiles associated with cancer prognosis. We believe that cell deconvolution by MEnet has the potential for use in clinical settings.

Fourteen years of cellular deconvolution: methodology, applications, technical evaluation and outstanding challenges

Single-cell RNA sequencing (scRNA-Seq) is a recent technology that allows for the measurement of the expression of all genes in each individual cell contained in a sample. Information at the single-cell level has been shown to be extremely useful in many areas. However, performing single-cell experiments is expensive. Although cellular deconvolution cannot provide the same comprehensive information as single-cell experiments, it can extract cell-type information from bulk RNA data, and therefore it allows researchers to conduct studies at cell-type resolution from existing bulk datasets. For these reasons, a great effort has been made to develop such methods for cellular deconvolution. The large number of methods available, the requirement of coding skills, inadequate documentation, and lack of performance assessment all make it extremely difficult for life scientists to choose a suitable method for their experiment. This paper aims to fill this gap by providing a comprehensive review of 53 deconvolution methods regarding their methodology, applications, performance, and outstanding challenges. More importantly, the article presents a benchmarking of all these 53 methods using 283 cell types from 30 tissues of 63 individuals. We also provide an R package named DeconBenchmark that allows readers to execute and benchmark the reviewed methods (https://github.com/tinnlab/DeconBenchmark).

DNA Methylation-Derived Immune Cell Proportions and Cancer Risk, Including Lung Cancer, in Black Participants

Prior cohort studies assessing cancer risk based on immune cell subtype profiles have predominantly focused on White populations. This limitation obscures vital insights into how cancer risk varies across race. Immune cell subtype proportions were estimated using deconvolution based on leukocyte DNA methylation markers from blood samples collected at baseline on participants without cancer in the Atherosclerosis Risk in Communities (ARIC) Study. Over a mean of 17.5 years of follow-up, 668 incident cancers were diagnosed in 2,467 Black participants. Cox proportional hazards regression was used to examine immune cell subtype proportions and overall cancer incidence and site-specific incidence (lung, breast, and prostate cancers). Higher T regulatory cell proportions were associated with statistically significantly higher lung cancer risk (hazard ratio = 1.22, 95% confidence interval = 1.06-1.41 per percent increase). Increased memory B cell proportions were associated with significantly higher risk of prostate cancer (1.17, 1.04-1.33) and all cancers (1.13, 1.05-1.22). Increased CD8+ naïve cell proportions were associated with significantly lower risk of all cancers in participants ≥55 years (0.91, 0.83-0.98). Other immune cell subtypes did not display statistically significant associations with cancer risk. These results in Black participants align closely with prior findings in largely White populations. Findings from this study could help identify those at high cancer risk and outline risk stratifying to target patients for cancer screening, prevention, and other interventions. Further studies should assess these relationships in other cancer types, better elucidate the interplay of B cells in cancer risk, and identify biomarkers for personalized risk stratification.

Computational deconvolution of DNA methylation data from mixed DNA samples

In this review, we provide a comprehensive overview of the different computational tools that have been published for the deconvolution of bulk DNA methylation (DNAm) data. Here, deconvolution refers to the estimation of cell-type proportions that constitute a mixed sample. The paper reviews and compares 25 deconvolution methods (supervised, unsupervised or hybrid) developed between 2012 and 2023 and compares the strengths and limitations of each approach. Moreover, in this study, we describe the impact of the platform used for the generation of methylation data (including microarrays and sequencing), the applied data pre-processing steps and the used reference dataset on the deconvolution performance. Next to reference-based methods, we also examine methods that require only partial reference datasets or require no reference set at all. In this review, we provide guidelines for the use of specific methods dependent on the DNA methylation data type and data availability.

Challenges and perspectives in computational deconvolution of genomics data

Deciphering cell-type heterogeneity is crucial for systematically understanding tissue homeostasis and its dysregulation in diseases. Computational deconvolution is an efficient approach for estimating cell-type abundances from a variety of omics data. Despite substantial methodological progress in computational deconvolution in recent years, challenges are still outstanding. Here we enlist four important challenges related to computational deconvolution: the quality of the reference data, generation of ground truth data, limitations of computational methodologies, and benchmarking design and implementation. Finally, we make recommendations on reference data generation, new directions of computational methodologies, and strategies to promote rigorous benchmarking.

Integrated transcriptome study of the tumor microenvironment for treatment response prediction in male predominant hypopharyngeal carcinoma

The efficacy of the first-line treatment for hypopharyngeal carcinoma (HPC), a predominantly male cancer, at advanced stage is only about 50% without reliable molecular indicators for its prognosis. In this study, HPC biopsy samples collected before and after the first-line treatment are classified into different groups according to treatment responses. We analyze the changes of HPC tumor microenvironment (TME) at the single-cell level in response to the treatment and identify three gene modules associated with advanced HPC prognosis. We estimate cell constitutions based on bulk RNA-seq of our HPC samples and build a binary classifier model based on non-malignant cell subtype abundance in TME, which can be used to accurately identify treatment-resistant advanced HPC patients in time and enlarge the possibility to preserve their laryngeal function. In summary, we provide a useful approach to identify gene modules and a classifier model as reliable indicators to predict treatment responses in HPC.