Biology subjects

Hillje, R.

Publications and source records attributed to Hillje, R..

4 recordsLinked to original sources

bioRxiv2022

The breast cancer pro-metastatic phenotype requires concomitant hyper-activation of ECM remodeling and dsRNA-IFN1 signaling in rare clone cells

The molecular determinants of breast cancer (BC) pro-metastatic phenotype are largely unknown. Here, we leveraged lentiviral barcoding coupled to single-cell RNA sequencing to trace clonal and transcriptional evolution during BC metastatization. We showed that metastases derive from rare pro-metastatic clones that are under-represented in primary tumors. Both low clonal-fitness and high metastatic-potential are independent of clonal origin. Differential expression and classification analyses revealed that the pro-metastatic phenotype is acquired in rare cells by concomitant hyper-activation of extracellular-matrix remodeling, dsRNA-interferon signaling, and stress-response pathways. Notably, genetic silencing of single pro-metastatic genes from different pathways significantly impairs migration in vitro and metastatization in vivo, with negligible effects on cell proliferation and tumor growth. In addition, gene-expression signatures from identified pro-metastatic genes predicts metastatic progression in BC patients, independently of known prognostic factors. This study elucidates previously unknown mechanisms of BC metastatization, and provides novel prognosis predictors and therapeutic targets for metastasis prevention.

cancer biology↗

bioRxiv2021

A single-cell transcriptomic landscape of innate and adaptive intratumoral immunity in triple negative breast cancer during chemo- and immunotherapies

Breast cancer (BC) constitutes a major health problem worldwide, making it the most common malignancy in women. Current treatment options for BC depend primarily on histological type, molecular markers, clinical aggressiveness and stage of disease. Immunotherapy, such as anti-PD-1, have shown combinatorial clinical activity with chemotherapy in triple negative breast cancer (TNBC) delineating some therapeutic combinations as more effective than others. However, a clear overview of the main immune cell populations involved in these treatments has never been provided. Here, an assessment of the immune landscape in the tumour microenvironment (TME) of two TNBC mouse models (4T1 and EMT6 cell lines) has been performed using single-cell RNA sequencing (scRNA-seq) technology. Specifically, immune cells were evaluated in untreated conditions and after being treated with chemotherapy or immunotherapy used as single agents or in combination. A decrease of regulatory T cells, compared to the untreated TME, was found in treatments with in vivo efficacy as well as {gamma}{delta} T cells, which have a pro-tumoral activity in mice. Focusing on Cd8 T cells, across all the conditions, a general increase of exhausted-like Cd8 T cells was confirmed in pre-clinical treatments with low efficacy; on the other hand, an opposite trend was found for the proliferative Cd8 T cells. Regarding macrophages, M2-like cells were found enriched in treatments with low efficacy while opposite behaviour was associated with M1-like macrophages. For both cell lines, similar proportions of B cells were detected with an increase of proliferative B cells in treatments that involved cisplatin in combination with anti-PD-1. The fine-scale characterization of the immune TME in this work can lead to new insights on the diagnosis and treatment of TNBC for a possible application at the clinical level.

cancer biology↗

bioRxiv2019

XenoCell: classification of cellular barcodes in single cell experiments from xenograft samples

Single-cell sequencing technologies provide unprecedented opportunities to deconvolve the genomic, transcriptomic or epigenomic heterogeneity of complex biological systems. Its application in samples from xenografts of patient-derived biopsies (PDX), however, is limited by the presence in the analysed samples of a mixture of cells arising from the host and the graft.\n\nWe have developed XenoCell, the first stand-alone pre-processing tool that performs fast and reliable classification of host and graft cellular barcodes. We show its application on a single cell dataset composed by human and mouse cells.\n\nAvailability and implementationXenoCell is available for non-commercial use on GitLab: https://gitlab.com/XenoCell/XenoCell

bioinformatics↗

bioRxiv2019

Cerebro: Interactive visualization of scRNA-seq data

SummaryDespite the growing availability of sophisticated bioinformatic methods for the analysis of single-cell RNA-seq data, few tools exist that allow biologists without bioinformatic expertise to directly visualize and interact with their own data and results. Here, we present Cerebro (cell report browser), a Shiny- and Electron-based standalone desktop application for macOS and Windows, which allows investigation and inspection of pre-processed single-cell transcriptomics data without requiring bioinformatic experience of the user.\n\nThrough an interactive and intuitive graphical interface, users can i) explore similarities and heterogeneity between samples and cells clusters in 2D or 3D projections such as t-SNE or UMAP, ii) display the expression level of single genes or genes sets of interest, iii) browse tables of most expressed genes and marker genes for each sample and cluster.\n\nWe provide a simple example to show how Cerebro can be used and which are its capabilities. Through a focus on flexibility and direct access to data and results, we think Cerebro offers a collaborative framework for bioinformaticians and experimental biologists which facilitates effective interaction to shorten the gap between analysis and interpretation of the data.\n\nAvailabilityCerebro and example data sets are available at https://github.com/romanhaa/Cerebro. Similarly, the R packages cerebroApp and cerebroPrepare R packages are available at https://github.com/romanhaa/cerebroApp and https://github.com/romanhaa/cerebroPrepare, respectively. All components are released under the MIT License.

bioinformatics↗