MAGNETO: Cell type marker panel generator from single-cell transcriptomic data.

Single-cell RNA sequencing experiments produce data useful to identify different cell types, including uncharacterized and rare ones. This enables us to study the specific functional roles of these cells in different microenvironments and contexts. After identifying a (novel) cell type of interest, it is essential to build succinct marker panels, composed of a few genes referring to cell surface proteins and clusters of differentiation molecules, able to discriminate the desired cells from the other cell populations. In this work, we propose a fully-automatic framework called MAGNETO, which can help construct optimal marker panels starting from a single-cell gene expression matrix and a cell type identity for each cell. MAGNETO builds effective marker panels solving a tailored bi-objective optimization problem, where the first objective regards the identification of the genes able to isolate a specific cell type, while the second conflicting objective concerns the minimization of the total number of genes included in the panel. Our results on three public datasets show that MAGNETO can identify marker panels that identify the cell populations of interest better than state-of-the-art approaches. Finally, by fine-tuning MAGNETO, our results demonstrate that it is possible to obtain marker panels with different specificity levels.

CATCH-UP: A High-Throughput Upstream-Pipeline for Bulk ATAC-Seq and ChIP-Seq Data.

Assay for transposase-accessible chromatin (ATAC) and chromatin immunoprecipitation (ChIP), coupled with next-generation sequencing (NGS), have revolutionized the study of gene regulation. A lack of standardization in the analysis of the highly dimensional datasets generated by these techniques has made reproducibility difficult to achieve, leading to discrepancies in the published, processed data. Part of this problem is due to the diverse range of bioinformatic tools available for the analysis of these types of data. Secondly, a number of different bioinformatic tools are required sequentially to convert raw data into a fully processed and interpretable output, and these tools require varying levels of computational skills. Furthermore, there are many options for quality control that are not uniformly employed during data processing. We address these issues with a complete assay for transposase-accessible chromatin sequencing (ATAC-seq) and chromatin immunoprecipitation sequencing (ChIP-seq) upstream pipeline (CATCH-UP), an easy-to-use, Python-based pipeline for the analysis of bulk ChIP-seq and ATAC-seq datasets from raw fastq files to visualizable bigwig tracks and peaks calls. This pipeline is simple to install and run, requiring minimal computational knowledge. The pipeline is modular, scalable, and parallelizable on various computing infrastructures, allowing for easy reporting of methodology to enable reproducible analysis of novel or published datasets.

GTAC enables parallel genotyping of multiple genomic loci with chromatin accessibility profiling in single cells.

Understanding clonal evolution and cancer development requires experimental approaches for characterizing the consequences of somatic mutations on gene regulation. However, no methods currently exist that efficiently link high-content chromatin accessibility with high-confidence genotyping in single cells. To address this, we developed Genotyping with the Assay for Transposase-Accessible Chromatin (GTAC), enabling accurate mutation detection at multiple amplified loci, coupled with robust chromatin accessibility readout. We applied GTAC to primary acute myeloid leukemia, obtaining high-quality chromatin accessibility profiles and clonal identities for multiple mutations in 88% of cells. We traced chromatin variation throughout clonal evolution, showing the restriction of different clones to distinct differentiation stages. Furthermore, we identified switches in transcription factor motif accessibility associated with a specific combination of driver mutations, which biased transformed progenitors toward a leukemia stem cell-like chromatin state. GTAC is a powerful tool to study clonal heterogeneity across a wide spectrum of pre-malignant and neoplastic conditions.