Changes in the cellular secretome are implicated in virus infection, malignancy, and anti-tumor immunity. We analyzed the association between transcriptional signatures (TS) from 24 different immune ...and stromal cell types on the prognosis of HPV-infected and HPV-free head and neck squamous carcinoma (HNSCC) patients from The Cancer Genome Atlas (TCGA) cohort. We found that HPV-positive HNSCC patients have tumors with elevated immune cell TS and improved prognosis, which was specifically associated with an increased tumor abundance of memory B and activated natural killer (NK) cell TS, compared to HPV-free HNSCC patients. HPV-infected patients upregulated many transcripts encoding secreted factors, such as growth factors, hormones, chemokines and cytokines, and their cognate receptors. Analysis of secretome transcripts and cognate receptors revealed that tumor expression of
and
are associated with a higher viral load and memory B and activated NK cell TS, as well as improved prognosis in HPV-infected HNSCC patients. The transcriptional parameters that we describe may be optimized to improve prognosis and risk stratification in the clinic and provide insights into gene and cellular targets that may potentially enhance anti-tumor immunity mediated by NK cells and memory B cells in HPV-infected HNSCC patients.
Prostate cancer is caused by genomic aberrations in normal epithelial cells, however clinical translation of findings from analyses of cancer cells alone has been very limited. A deeper understanding ...of the tumour microenvironment is needed to identify the key drivers of disease progression and reveal novel therapeutic opportunities.
In this study, the experimental enrichment of selected cell-types, the development of a Bayesian inference model for continuous differential transcript abundance, and multiplex immunohistochemistry permitted us to define the transcriptional landscape of the prostate cancer microenvironment along the disease progression axis. An important role of monocytes and macrophages in prostate cancer progression and disease recurrence was uncovered, supported by both transcriptional landscape findings and by differential tissue composition analyses. These findings were corroborated and validated by spatial analyses at the single-cell level using multiplex immunohistochemistry.
This study advances our knowledge concerning the role of monocyte-derived recruitment in primary prostate cancer, and supports their key role in disease progression, patient survival and prostate microenvironment immune modulation.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Abstract
Background
Malignant pleural effusions (MPEs) are a common complication of advanced cancers, particularly those adjacent to the pleura, such as lung and breast cancer. The pathophysiology of ...MPE formation remains poorly understood, and although MPEs are routinely used for the diagnosis of breast cancer patients, their composition and biology are poorly understood. It is difficult to distinguish invading malignant cells from resident mesothelial cells and to identify the directionality of interactions between these populations in the pleura. There is a need to characterize the phenotypic diversity of breast cancer cell populations in the pleural microenvironment, and investigate how this varies across patients.
Methods
Here, we used single‐cell RNA‐sequencing to study the heterogeneity of 10 MPEs from seven metastatic breast cancer patients, including three Miltenyi‐enriched samples using a negative selection approach. This dataset of almost 65 000 cells was analysed using integrative approaches to compare heterogeneous cell populations and phenotypes.
Results
We identified substantial inter‐patient heterogeneity in the composition of cell types (including malignant, mesothelial and immune cell populations), in expression of subtype‐specific gene signatures and in copy number aberration patterns, that captured variability across breast cancer cell populations. Within individual MPEs, we distinguished mesothelial cell populations from malignant cells using key markers, the presence of breast cancer subtype expression patterns and copy number aberration patterns. We also identified pleural mesothelial cells expressing a cancer‐associated fibroblast‐like transcriptomic program that may support cancer growth.
Conclusions
Our dataset presents the first unbiased assessment of breast cancer‐associated MPEs at a single cell resolution, providing the community with a valuable resource for the study of MPEs. Our work highlights the molecular and cellular diversity captured in MPEs and motivates the potential use of these clinically relevant biopsies in the development of targeted therapeutics for patients with advanced breast cancer.
Schistosomiasis is a parasitic disease affecting ~200 million people worldwide. Schistosoma haematobium and S. mansoni are two relatively closely related schistosomes (blood flukes), and the ...causative agents of urogenital and hepatointestinal schistosomiasis, respectively. The availability of genomic, transcriptomic and proteomic data sets for these two schistosomes now provides unprecedented opportunities to explore their biology, host interactions and schistosomiasis at the molecular level. A particularly important group of molecules involved in a range of biological and developmental processes in schistosomes and other parasites are the G protein-coupled receptors (GPCRs). Although GPCRs have been studied in schistosomes, there has been no detailed comparison of these receptors between closely related species. Here, using a genomic-bioinformatic approach, we identified and characterised key GPCRs in S. haematobium and S. mansoni (two closely related species of schistosome).
Using a Hidden Markov Model (HMM) and Support Vector Machine (SVM)-based pipeline, we classified and sub-classified GPCRs of S. haematobium and S. mansoni, combined with phylogenetic and transcription analyses.
We identified and classified classes A, B, C and F as well as an unclassified group of GPCRs encoded in the genomes of S. haematobium and S. mansoni. In addition, we characterised ligand-specific subclasses (i.e. amine, peptide, opsin and orphan) within class A (rhodopsin-like).
Most GPCRs shared a high degree of similarity and conservation, except for members of a particular clade (designated SmGPR), which appear to have diverged between S. haematobium and S. mansoni and might explain, to some extent, some of the underlying biological differences between these two schistosomes. The present set of annotated GPCRs provides a basis for future functional genomic studies of cellular GPCR-mediated signal transduction and a resource for future drug discovery efforts in schistosomes.
Prostate cancer is a leading cause of morbidity and cancer-related death worldwide. Androgen deprivation therapy (ADT) is the cornerstone of management for advanced disease. The use of these ...therapies is associated with multiple side effects, including metabolic syndrome and truncal obesity. At the same time, obesity has been associated with both prostate cancer development and disease progression, linked to its effects on chronic inflammation at a tissue level. The connection between ADT, obesity, inflammation and prostate cancer progression is well established in clinical settings; however, an understanding of the changes in adipose tissue at the molecular level induced by castration therapies is missing. Here, we investigated the transcriptional changes in periprostatic fat tissue induced by profound ADT in a group of patients with high-risk tumours compared to a matching untreated cohort. We find that the deprivation of androgen is associated with a pro-inflammatory and obesity-like adipose tissue microenvironment. This study suggests that the beneficial effect of therapies based on androgen deprivation may be partially counteracted by metabolic and inflammatory side effects in the adipose tissue surrounding the prostate.
Abstract
Motivation
Seurat is one of the most popular software suites for the analysis of single-cell RNA sequencing data. Considering the popularity of the tidyverse ecosystem, which offers a large ...set of data display, query, manipulation, integration and visualization utilities, a great opportunity exists to interface the Seurat object with the tidyverse. This interface gives the large data science community of tidyverse users the possibility to operate with familiar grammar.
Results
To provide Seurat with a tidyverse-oriented interface without compromising efficiency, we developed tidyseurat, a lightweight adapter to the tidyverse. Tidyseurat displays cell information as a tibble abstraction, allowing intuitively interfacing Seurat with dplyr, tidyr, ggplot2 and plotly packages powering efficient data manipulation, integration and visualization. Iterative analyses on data subsets are enabled by interfacing with the popular nest-map framework.
Availability and implementation
The software is freely available at cran.r-project.org/web/packages/tidyseurat and github.com/stemangiola/tidyseurat.
Supplementary information
Supplementary data are available at Bioinformatics online.
Abstract
Motivation
The precise characterization of cell-type transcriptomes is pivotal to understanding cellular lineages, deconvolution of bulk transcriptomes, and clinical applications. ...Single-cell RNA sequencing resources like the Human Cell Atlas have revolutionised cell-type profiling. However, challenges persist due to data heterogeneity and discrepancies across different studies. One limitation of prevailing tools such as CIBERSORTx is their inability to address hierarchical data structures and handle nonoverlapping gene sets across samples, relying on filtering or imputation.
Results
Here, we present cellsig, a Bayesian sparse multilevel model designed to improve signature estimation by adjusting data for multilevel effects and modelling for gene-set sparsity. Our model is tailored to large-scale, heterogeneous pseudobulk and bulk RNA sequencing data collections with nonoverlapping gene sets. We tested the performances of cellsig on a novel curated Human Bulk Cell-type Catalogue, which harmonizes 1435 samples across 58 datasets. We show that cellsig significantly enhances cell-type marker gene ranking performance. This approach is valuable for cell-type signature selection, with implications for marker gene validation, single-cell annotation, and deconvolution benchmarks.
Availability and implementation
Codes and the interactive app are available at https://github.com/stemangiola/cellsig; and the database is available at https://doi.org/10.5281/zenodo.7582421.
Cellular omics such as single-cell genomics, proteomics, and microbiomics allow the characterization of tissue and microbial community composition, which can be compared between conditions to ...identify biological drivers. This strategy has been critical to revealing markers of disease progression, such as cancer and pathogen infection. A dedicated statistical method for differential variability analysis is lacking for cellular omics data, and existing methods for differential composition analysis do not model some compositional data properties, suggesting there is room to improve model performance. Here, we introduce sccomp, a method for differential composition and variability analyses that jointly models data count distribution, compositionality, group-specific variability, and proportion mean-variability association, being aware of outliers. sccomp provides a comprehensive analysis framework that offers realistic data simulation and cross-study knowledge transfer. Here, we demonstrate that mean-variability association is ubiquitous across technologies, highlighting the inadequacy of the very popular Dirichlet-multinomial distribution. We show that sccomp accurately fits experimental data, significantly improving performance over state-of-the-art algorithms. Using sccomp, we identified differential constraints and composition in the microenvironment of primary breast cancer.
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