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.

Transcriptome-based identification of novel endotypes in adult atopic dermatitis

BACKGROUND

Atopic dermatitis (AD) is a frequent and heterogeneous inflammatory skin disease, for which personalized medicine remains a challenge. High-throughput approaches have improved understanding of the complex pathophysiology of AD. However, a purely data-driven AD classification is still lacking.

METHODS

To address this question, we applied an original unsupervised approach on the largest available transcriptome dataset of AD lesional (n = 82) and healthy (n = 213) skin biopsies.

RESULTS

Taking into account pathological and physiological state, a variance-based filtering revealed 222 AD-specific hyper-variable genes that efficiently classified the AD samples into 4 clusters that turned out to be clinically and biologically distinct. Comparison of gene expressions between clusters identified 3 sets of upregulated genes used to derive metagenes (MGs): MG-I (19 genes) was associated with IL-1 family signaling (including IL-36A and 36G) and skin remodeling, MG-II (23 genes) with negative immune regulation (including IL-34 and 37) and skin architecture, and MG-III (17 genes) with B lymphocyte immunity. Sample clusters differed in terms of disease severity (p = .02) and S. aureus (SA) colonization (p = .02). Cluster 1 contained the most severe AD, highest SA colonization, and overexpressed MG-I. Cluster 2 was characterized by less severe AD, low SA colonization, and high MG-II expression. Cluster 3 included mild AD, mild SA colonization, and mild expression of all MGs. Cluster 4 had the same clinical features as cluster 3 but had hyper-expression of MG-III. Last, we successfully validated our method and results in an independent cohort.

CONCLUSION

Our study revealed unrecognized AD endotypes with specific underlying biological pathways, highlighting novel pathophysiological mechanisms. These data could provide new insights into personalized treatment strategies.

Pre-COVID-19 Immunity to Common Cold Human Coronaviruses Induces a Recall-Type IgG Response to SARS-CoV-2 Antigens Without Cross-Neutralisation

The capacity of pre-existing immunity to human common coronaviruses (HCoV) to cross-protect against de novo COVID-19is yet unknown. In this work, we studied the sera of 175 COVID-19 patients, 76 healthy donors and 3 intravenous immunoglobulins (IVIG) batches. We found that most COVID-19 patients developed anti-SARS-CoV-2 IgG antibodies before IgM. Moreover, the capacity of their IgGs to react to beta-HCoV, was present in the early sera of most patients before the appearance of anti-SARS-CoV-2 IgG. This implied that a recall-type antibody response was generated. In comparison, the patients that mounted an anti-SARS-COV2 IgM response, prior to IgG responses had lower titres of anti-beta-HCoV IgG antibodies. This indicated that pre-existing immunity to beta-HCoV was conducive to the generation of memory type responses to SARS-COV-2. Finally, we also found that pre-COVID-19-era sera and IVIG cross-reacted with SARS-CoV-2 antigens without neutralising SARS-CoV-2 infectivity in vitro. Put together, these results indicate that whilst pre-existing immunity to HCoV is responsible for recall-type IgG responses to SARS-CoV-2, it does not lead to cross-protection against COVID-19.

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.