SUMMARY: Gene signature scoring provides a simple yet powerful approach for quantifying biological signals within single-cell omics datasets. UCell and pyUCell offer fast and robust implementations of rank-based signature scoring for R and Python, respectively, integrating seamlessly with leading single-cell analysis ecosystems such as Seurat, Bioconductor, and scanpy/scverse. AVAILABILITY AND IMPLEMENTATION: UCell v2 is distributed as an R package by BioConductor (https://bioconductor.org/packages/UCell/) and as a Python package by pyPI (https://pypi.org/project/pyucell/).

SUMMARY: A common bioinformatics task in single-cell data analysis is to purify a cell type or cell population of interest from heterogeneous datasets. Here, we present scGate, an algorithm that automatizes marker-based purification of specific cell populations, without requiring training data or reference gene expression profiles. scGate purifies a cell population of interest using a set of markers organized in a hierarchical structure, akin to gating strategies employed in flow cytometry. scGate outperforms state-of-the-art single-cell classifiers and it can be applied to multiple modalities of single-cell data (e.g. RNA-seq, ATAC-seq, CITE-seq). scGate is implemented as an R package and integrated with the Seurat framework, providing an intuitive tool to isolate cell populations of interest from heterogeneous single-cell datasets. AVAILABILITY AND IMPLEMENTATION: scGate is available as an R package at https://github.com/carmonalab/scGate (https://doi.org/10.5281/zenodo.6202614). Several reproducible workflows describing the main functions and usage of the package on different single-cell modalities, as well as the code to reproduce the benchmark, can be found at https://github.com/carmonalab/scGate.demo (https://doi.org/10.5281/zenodo.6202585) and https://github.com/carmonalab/scGate.benchmark. Test data are available at https://doi.org/10.6084/m9.figshare.16826071. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) technologies offer unique opportunities for exploring heterogeneous cell populations. However, in-depth single-cell transcriptomic characterization of complex tissues often requires profiling tens to hundreds of thousands of cells. Such large numbers of cells represent an important hurdle for downstream analyses, interpretation and visualization. RESULTS: We develop a framework called SuperCell to merge highly similar cells into metacells and perform standard scRNA-seq data analyses at the metacell level. Our systematic benchmarking demonstrates that metacells not only preserve but often improve the results of downstream analyses including visualization, clustering, differential expression, cell type annotation, gene correlation, imputation, RNA velocity and data integration. By capitalizing on the redundancy inherent to scRNA-seq data, metacells significantly facilitate and accelerate the construction and interpretation of single-cell atlases, as demonstrated by the integration of 1.46 million cells from COVID-19 patients in less than two hours on a standard desktop. CONCLUSIONS: SuperCell is a framework to build and analyze metacells in a way that efficiently preserves the results of scRNA-seq data analyses while significantly accelerating and facilitating them.

SUMMARY: STACAS is a computational method for the identification of integration anchors in the Seurat environment, optimized for the integration of single-cell (sc) RNA-seq datasets that share only a subset of cell types. We demonstrate that by (i) correcting batch effects while preserving relevant biological variability across datasets, (ii) filtering aberrant integration anchors with a quantitative distance measure and (iii) constructing optimal guide trees for integration, STACAS can accurately align scRNA-seq datasets composed of only partially overlapping cell populations. AVAILABILITY AND IMPLEMENTATION: Source code and R package available at https://github.com/carmonalab/STACAS; Docker image available at https://hub.docker.com/repository/docker/mandrea1/stacas_demo.

The protozoan parasite Trypanosoma cruzi is the causative agent of Chagas' disease, endemic in 18 countries in Central and South America. Transmission also occurs in non-endemic countries by way of blood transfusion and organ transplantation. Diagnosis of American trypanosomiasis is based on the detection of antibodies directed against T. cruzi antigens. In this work we mined the T. cruzi genome sequence to identify new peptidic diagnostic biomarkers. An integrative bioinformatic strategy was adopted to prioritize peptidic antigens with low cross-reactivity in the genome of T. cruzi . A computational pipeline was developed to assess a set of molecular properties on each protein from the reference T. cruzi genome, such as subcellular localization or expression level (by mass spec. evidence, number of gene copies and synonymous codon usage bias). At a higher resolution, a set of local properties were evaluated, such as repetitive motifs, disorder (structured vs natively unstructured regions), trans-membrane spans, glycosylation sites, polymorphisms (conserved vs. divergent regions), predicted B-cell epitopes, sequence similarity against human proteins and Leishmania (potential cross-reacting species) (Figure 1 ). A scoring function based on these properties was used to rank each of the ~10 million 12-residue overlapping peptides in which the ~ 22,000 T. cruzi proteins can be virtually fragmented. Experimental validation of predicted epitopes was performed with peptide microarrays, screened using pooled sera from human chagasic patients and controls. Example sequence profile generated by the pipeline using BioPerl. Vertical boxes in the plot represent overlapping 12-residue peptides, and its height and colour, the resulting score based on the mapped features shown below. We show that our integrative method outperforms alternative antigen prioritizations based on individual properties (such as B-cell epitope predictors alone). Our genome-wide prioritization uncovered more than 300 promising biomarker candidates. 200 high-scoring peptides corresponding mostly to hypothetical proteins were selected for immunological validation, along with 40 peptides derived from previously validated B-cell epitopes and an additional set of 40 low-scoring peptides as controls. Preliminary results based on microarray images revealed that ~25% (49/200) of the candidate peptides reacted specifically against the positive sera pools assayed. The developed bioinformatic approach proved to be successful, leading from a genome-wide prioritization to the identification of novel peptidic antigens with diagnostic potential. Moreover, the algorithm may be used to prioritize biomarkers in other pathogen species.

Background Information about chemical compounds and their activity against whole organisms or specific molecular targets is available from the literature or from specialized databases. However, there are few resources that effectively integrate such large chemical datasets with genome data and provide a mechanism to link active compounds to potential target genes. Here, we showcase the integration of chemoinformatic tools for querying chemical datasets and linking chemicals to genes in TDR Targets database (tdrtargets.org), a web accessible resource that integrates a wide range of functional genomic datasets from tropical disease pathogens and provides a ranking mechanism for identifying and prioritising novel therapeutic targets [1].