Large-scale analysis of cell-cell communication reveals angiogenin-dependent tumor progression in clear cell renal cell carcinoma

Cellular crosstalk in the tumor microenvironment (TME) is still largely uncharacterized, while it plays an essential role in shaping immunosuppression or anti-tumor response. Large-scale analyses are needed to better decipher cell-cell communication in cancer. In this work, we used original and publicly available single-cell RNA sequencing (scRNAseq) data to characterize in-depth the communication networks in human clear cell renal cell carcinoma (ccRCC). We identified 50 putative communication channels specifically used by cancer cells to interact with other cells, including two novel angiogenin-mediated interactions. Expression of angiogenin and its receptors was validated at the protein level in primary ccRCC. Mechanistically, angiogenin enhanced ccRCC cell line proliferation and down-regulated secretion of IL-6, IL-8, and MCP-1 proinflammatory molecules. This study provides novel biological insights into molecular mechanisms of ccRCC, and suggests angiogenin and its receptors as potential therapeutic targets in clear cell renal cancer.

Meta-Analysis of Human Cancer Single-Cell RNA-Seq Datasets Using the IMMUcan Database

The development of single-cell RNA sequencing (scRNA-seq) technologies has greatly contributed to deciphering the tumor microenvironment (TME). An enormous amount of independent scRNA-seq studies have been published representing a valuable resource that provides opportunities for meta-analysis studies. However, the massive amount of biological information, the marked heterogeneity and variability between studies, and the technical challenges in processing heterogeneous datasets create major bottlenecks for the full exploitation of scRNA-seq data. We have developed IMMUcan scDB (https://immucanscdb.vital-it.ch), a fully integrated scRNA-seq database exclusively dedicated to human cancer and accessible to nonspecialists. IMMUcan scDB encompasses 144 datasets on 56 different cancer types, annotated in 50 fields containing precise clinical, technological, and biological information. A data processing pipeline was developed and organized in four steps: (i) data collection; (ii) data processing (quality control and sample integration); (iii) supervised cell annotation with a cell ontology classifier of the TME; and (iv) interface to analyze TME in a cancer type-specific or global manner. This framework was used to explore datasets across tumor locations in a gene-centric (CXCL13) and cell-centric (B cells) manner as well as to conduct meta-analysis studies such as ranking immune cell types and genes correlated to malignant transformation. This integrated, freely accessible, and user-friendly resource represents an unprecedented level of detailed annotation, offering vast possibilities for downstream exploitation of human cancer scRNA-seq data for discovery and validation studies.

SIGNIFICANCE

The IMMUcan scDB database is an accessible supportive tool to analyze and decipher tumor-associated single-cell RNA sequencing data, allowing researchers to maximally use this data to provide new insights into cancer biology.

GM-CSF-activated human dendritic cells promote Tfh1 cell polarization in a CD40-dependent manner

T follicular helper (Tfh) cells regulate humoral responses and present a marked phenotypic and functional diversity. Tfh1 cells were recently identified and associated with disease severity in infection and autoimmune diseases. The cellular and molecular requirements to induce human Tfh1 differentiation are not known. Here, using single-cell RNA sequencing and protein validation, we report that GM-CSF-activated human blood CD1c+ dendritic cells drive the differentiation of naïve CD4+ T cells into Tfh1. These Tfh1 cells displayed typical Tfh molecular features, including high levels of PD-1, CXCR5, and ICOS. They co-expressed Bcl6 and T-bet, and secreted large amounts of IL-21 and IFN-γ. Mechanistically, GM-CSF triggered the emergence of two DC sub-populations defined by their expression of CD40 and ICOS-ligand, presenting distinct phenotype, morphology, transcriptomic signature, and function. CD40highICOS-Llow DC efficiently induced Tfh1 differentiation in a CD40-dependent manner. In both patients with mild COVID-19 and latent mycobacterium tuberculosis infection, Tfh1 cells were positively correlated with a CD40highICOS-LLow DC signature in single-cell RNA sequencing of PBMC or blood transcriptomics, respectively. Our study uncovered a novel CD40-dependent Tfh1 axis with potential physiopathological relevance to infection.

Abstract 5011: Meta-analysis of human cancer single cell RNAseq datasets using the fully integrated IMMUcan database

The development of single cell RNA-sequencing (scRNAseq) technologies has greatly contributed to deciphering the tumor microenvironment (TME) landscape, and a wealth of biological data is now publicly accessible. This represents a very valuable resource to researchers in the field, offering a reference for comparison of novel results, as well as opportunities for original meta-analysis studies. However, the massive amount of biological information renders its exploitation difficult in the absence of a well-structured and annotated resource. Marked heterogeneity and variability between studies in terms of cancer type, clinical context, technological platform, data quality, number and type of cells, create additional bottlenecks. We have developed a fully integrated scRNAseq database exclusively dedicated to human cancer. It gathers 134 datasets on 51 different cancer types, annotated in 50 fields containing precise clinical, technological and biological information. We developed an original data processing pipeline organized in 4 steps: first, data collection; second, data processing, which includes quality control, sample integration, cell clustering; third, cell ontology tree of the TME, built and used to annotate the clusters in a supervised and manual manner; and fourth, interface to analyze TME in a cancer type-specific or global manner. This integrated, accessible and user-friendly resource should be of great value to the biomedical community. It represents an unprecedented level of detailed annotation, offering vast possibilities for downstream exploitation of human cancer scRNAseq data for discovery and validation studies. The database is freely accessible at: https://immucanscdb.vital-it.ch.

Citation Format: Jordi Camps, Floriane Noel, Robin Liechti, Lou Goetz, Caroline Hoffmann, Lucile Massenet-Regad, Elise Amblard, Melissa Saichi, Mahmoud M. Ibrahim, Jack Pollard, Jasna Medvedovic, Helge G. Roider, Vassili Soumelis. Meta-analysis of human cancer single cell RNAseq datasets using the fully integrated IMMUcan database [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5011.

Hubness reduction improves clustering and trajectory inference in single-cell transcriptomic data

MOTIVATION

Single-cell RNA-seq (scRNAseq) datasets are characterized by large ambient dimensionality, and their analyses can be affected by various manifestations of the dimensionality curse. One of these manifestations is the hubness phenomenon, i.e. existence of data points with surprisingly large incoming connectivity degree in the datapoint neighbourhood graph. Conventional approach to dampen the unwanted effects of high dimension consists in applying drastic dimensionality reduction. It remains unexplored if this step can be avoided thus retaining more information than contained in the low-dimensional projections, by correcting directly hubness.

RESULTS

We investigated hubness in scRNAseq data. We show that hub cells do not represent any visible technical or biological bias. The effect of various hubness reduction methods is investigated with respect to the clustering, trajectory inference and visualization tasks in scRNAseq datasets. We show that hubness reduction generates neighbourhood graphs with properties more suitable for applying machine learning methods; and that it outperforms other state-of-the-art methods for improving neighbourhood graphs. As a consequence, clustering, trajectory inference and visualization perform better, especially for datasets characterized by large intrinsic dimensionality. Hubness is an important phenomenon characterizing data point neighbourhood graphs computed for various types of sequencing datasets. Reducing hubness can be beneficial for the analysis of scRNAseq data with large intrinsic dimensionality in which case it can be an alternative to drastic dimensionality reduction.

AVAILABILITY AND IMPLEMENTATION

The code used to analyze the datasets and produce the figures of this article is available from https://github.com/sysbio-curie/schubness.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Single-cell RNA sequencing of blood antigen-presenting cells in severe COVID-19 reveals multi-process defects in antiviral immunity

COVID-19 can lead to life-threatening respiratory failure, with increased inflammatory mediators and viral load. Here, we perform single-cell RNA-sequencing to establish a high-resolution map of blood antigen-presenting cells (APCs) in 15 patients with moderate or severe COVID-19 pneumonia, at day 1 and day 4 post admission to intensive care unit or pulmonology department, as well as in 4 healthy donors. We generated a unique dataset of 81,643 APCs, including monocytes and rare dendritic cell (DC) subsets. We uncovered multi-process defects in antiviral immune defence in specific APCs from patients with severe disease: (1) increased pro-apoptotic pathways in plasmacytoid DCs (pDCs, key effectors of antiviral immunity), (2) a decrease of the innate sensors TLR9 and DHX36 in pDCs and CLEC9a⁺ DCs, respectively, (3) downregulation of antiviral interferon-stimulated genes in monocyte subsets and (4) a decrease of major histocompatibility complex (MHC) class II-related genes and MHC class II transactivator activity in cDC1c⁺ DCs, suggesting viral inhibition of antigen presentation. These novel mechanisms may explain patient aggravation and suggest strategies to restore the defective immune defence.