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Julien-Laferriere, A.

Publications and source records attributed to Julien-Laferriere, A..

3 recordsLinked to original sources

Charting the Heterogeneity of Colorectal Cancer Consensus Molecular Subtypes using Spatial Transcriptomics

The heterogeneity of colorectal cancer (CRC) contributes to substantial differences in patient response to standard therapies. The consensus molecular subtypes (CMS) of CRC is the most widely-used gene expression-based classification and has contributed to a better understanding of disease heterogeneity and prognosis. Nevertheless, CMS intratumoral heterogeneity restricts its clinical application, stressing the necessity of further characterizing the composition and architecture of CRC. Here, we used Spatial Transcriptomics (ST) in combination with single-cell RNA sequencing (scRNA-seq) to decipher the spatially resolved cellular and molecular composition of CRC. In addition to mapping the intratumoral heterogeneity of CMS and their microenvironment, we identified cell communication events in the tumor-stroma interface of CMS2 carcinomas. This includes tumor growth-inhibiting as well as -activating signatures, such as the potential regulation of the ETV4 transcriptional activity by DCN or the PLAU-PLAUR ligand-receptor interaction. Our data show the power of ST to bring the CMS-based classification of CRC to another level and thereby gain useful molecular insights for personalized therapy.

cancer biology↗

Besca, a single-cell transcriptomics analysis toolkit to accelerate translational research

Single-cell RNA sequencing (scRNA-seq) revolutionised our understanding of disease biology and presented the promise of transforming translational research. We developed Besca, a toolkit that streamlines scRNA-seq analyses according to current best practices. A standard workflow covers quality control, filtering, and clustering. Two complementary Besca modules, utilizing hierarchical cell signatures or supervised machine learning, automate cell annotation and provide harmonised nomenclatures across studies. Subsequently, Besca enables estimation of cell type proportions in bulk transcriptomics studies. Using multiple heterogeneous scRNA-seq datasets we show how Besca aids acceleration, interoperability, reusability, and interpretability of scRNA-seq data analysis, crucial aspects in translational research and beyond.

bioinformatics↗

MassBlast: A workflow to accelerate RNA-seq and DNA database analysis

SummaryCurrent workflows for sequence analysis heavily depend on user input and manual curation. New specialized tools and methods are appearing all the time, but the actions required for a full analysis are disconnected and very time-consuming. The software we propose, MassBlast, combines BLAST+ and an automated workflow analysis to filter the results and significantly improve the annotation of multiple sequencing databases for exploring new biosynthetic pathways and new protein families, among other applications. MassBlast is fully configurable and reproducible.\n\nAvailability and ImplementationThe MassBlast package is written in Ruby. Source code and releases are freely available from Github (https://github.com/averissimo/mass-blast) for all major platforms (Linux, MS Windows and OS X) under the GPLv3 license.\n\nContactandre.verissimo@tecnico.ulisboa.pt

bioinformatics↗