Biology subjects

Culhane, A.

Publications and source records attributed to Culhane, A..

5 recordsLinked to original sources

bioRxiv2025

Enabling reproducible and reusable genetic demultiplexing benchmarking with Nextflow and Apptainer

Reproducibility, reusability, and portability were identified as key requirements for analysis pipelines in genomics. A previous study identified that less than 4% of notebooks used for biomedical research were fully reproducible. Reusable and portable software are required to ensure compatibility with federated and cloud computing. The field requires computational workflows that meet high standards for reproducibility, reusability, and portability to allow benchmarking to be repeated and updated periodically. This is of particular importance in areas where new methods are frequently released, such as demultiplexing scRNA-seq, a critical early step in most scRNA-seq analysis pipelines. To address this, we developed demux_bench for benchmarking genetic demultiplexing methods in single-cell RNA sequencing, which meets the gold standard for reproducibility of computational workflows, and incorporates best practices for reusability and portability. We used workflow manager Nextflow to enable the simulation and testing of various benchmarking scenarios and methods in parallel from a single pipeline execution. Different experimental configurations can be simulated, including % doublets and class size imbalance. The pipeline includes genotype-free methods, Vireo and souporcell, and additional existing or novel methods can be added modularly. Demux_bench is configurable to reproduce specific analyses and generalisable to address new research questions. Software dependencies are handled through containers via Apptainer, allowing portability to different compute environments and avoiding the need for manual installation of software. Demux_bench is available on WorkflowHub (https://workflowhub.eu/workflows/1769), and can also be run on Galaxy and other platforms through RO-Crates. Demux_bench facilitates gold standard benchmarking for genetic demultiplexing through reproducibility, reusability, scalability, and portability.

bioinformatics↗

bioRxiv2023

The Minimal Dataset for Cancer of the 1+Million Genomes Initiative

For a real impact on healthcare, precision cancer medicine requires accessibility and interoperability of clinical and genomic data across centres and countries. Due to the heterogeneous digitization in Europe and worldwide, the definition of models for standardised data collection and usability becomes mandatory if countries want to work together on this mission. The European Union 1+Million Genomes (1+MG) initiative, supported by the Horizon 2020 Beyond 1 Million Genomes project, aims at outlining data models, guidance, best practices, and technical infrastructures for transnational access to sequenced genomes, including cancer genomes. Within the framework of the cancer-focused Working Group 9, we developed the 1+MG-Minimal Dataset for Cancer (1+MG-MDC)-a data model encompassing 140 items and organized in eight conceptual domains for the collection of cancer-related clinical information and genomics metadata. The 1+MG-MDC, which results from a multidisciplinary effort, leverages pre-existing models and emphasizes the annotation and traceability of multiple aspects relevant to the complex longitudinal path of the cancer disease and its treatment. We strived to make the 1+MG-MDC easy to adopt, yet comprehensive, addressing the needs of both clinicians and researchers. We will periodically revise and update it to ensure it remains fit for purpose. We propose the 1+MG-MDC as a model to create homogeneous databases, which would, in turn, guide discussions on clinical and genomic features with prognostic or therapeutic value and foster real-world data research.

cancer biology↗

bioRxiv2022

Multi-omic spatial profiling reveals the unique virus-driven immune landscape of COVID-19 placentitis

COVID-19 placentitis, a rare complication of maternal SARS-CoV-2 infection, only shows detectable virus in the placenta of a subset of cases. We provide a deep multi-omic spatial characterisation of placentitis from obstetrically complicated maternal COVID-19 infection. We found that SARS-CoV-2 infected placentas have a distinct transcriptional and immunopathological signature. This signature overlaps with virus-negative cases supporting a common viral aetiology. An inverse correlation between viral load and disease duration suggests viral clearance over time. Quantitative spatial analyses revealed a unique microenvironment surrounding virus-infected trophoblasts characterised by PDL1-expressing macrophages, T-cell exclusion, and interferon blunting. In contrast to uninfected mothers, ACE2 was localised to the maternal side of the placental trophoblast layer of almost all mothers with placental SARS-CoV-2 infection, which may explain variable susceptibility to placental infection. Our results demonstrate a pivotal role for direct placental SARS-CoV-2 infection in driving the unique immunopathology of COVID-19 placentitis. Graphical Abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

immunology↗

bioRxiv2021

Curated Single Cell Multimodal Landmark Datasets for R/Bioconductor

BackgroundThe majority of high-throughput single-cell molecular profiling methods quantify RNA expression; however, recent multimodal profiling methods add simultaneous measurement of genomic, proteomic, epigenetic, and/or spatial information on the same cells. The development of new statistical and computational methods in Bioconductor for such data will be facilitated by easy availability of landmark datasets using standard data classes. ResultsWe collected, processed, and packaged publicly available landmark datasets from important single-cell multimodal protocols, including CITE-Seq, ECCITE-Seq, SCoPE2, scNMT, 10X Multiome, seqFISH, and G&T. We integrate data modalities via the MultiAssayExperiment Bioconductor class, document and re-distribute datasets as the SingleCellMultiModal package in Bioconductors Cloud-based ExperimentHub. The result is single-command actualization of landmark datasets from seven single-cell multimodal data generation technologies, without need for further data processing or wrangling in order to analyze and develop methods within Bioconductors ecosystem of hundreds of packages for single-cell and multimodal data. ConclusionsWe provide two examples of integrative analyses that are greatly simplified by SingleCellMultiModal. The package will facilitate development of bioinformatic and statistical methods in Bioconductor to meet the challenges of integrating molecular layers and analyzing phenotypic outputs including cell differentiation, activity, and disease. Author SummaryExperimental data packages that provide landmark datasets have historically played an important role in the development of new statistical methods in Bioconductor by lowering the barrier of access to relevant data, providing a common testing ground for software development and benchmarking, and encouraging interoperability around common data structures. In this manuscript, we review major classes of technologies for collecting multimodal data including genomics, transcriptomics, epigenetics, proteomics, and spatial information at the level of single cells. We present the SingleCellMultiModal R/Bioconductor package that provides single-command access to landmark datasets from seven different technologies, storing datasets using HDF5 and sparse arrays for memory efficiency and integrating data modalities via the MultiAssayExperiment class. We demonstrate two integrative analyses that are greatly simplified by SingleCellMultiModal. The package facilitates development and benchmarking of bioinformatic and statistical methods to integrate molecular layers at the level of single cells with phenotypic outputs including cell differentiation, activity, and disease, within Bioconductors ecosystem of hundreds of packages for single-cell and multimodal data.

bioinformatics↗

bioRxiv2018

The impact of stroma on the discovery of molecular subtypes and prognostic gene signatures in serous ovarian cancer

PurposeRecent efforts to improve outcomes for high-grade serous ovarian cancer, a leading cause of cancer death in women, have focused on identifying molecular subtypes and prognostic gene signatures, but existing subtypes have poor cross-study robustness. We tested the contribution of cell admixture in published ovarian cancer molecular subtypes and prognostic gene signatures.\n\nExperimental DesignPublic gene expression data, two molecular subtype classifications, and 61 published gene signatures of ovarian cancer were examined. Using microdissected data, we developed gene signatures of ovarian tumor and stroma. Computational simulations of increasing stromal cell proportion were performed by mixing gene expression profiles of paired microdissected ovarian tumor and stroma.\n\nResultsEstablished ovarian cancer molecular subtypes are strongly associated with the cell admixture. Tumors were classified as different molecular subtypes in simulations, when the percentage of stromal cells increased. Stromal gene expression in bulk tumor was weakly prognostic, and in one dataset, increased stroma was associated with anatomic sampling location. Five published prognostic gene signatures were no longer prognostic in a multivariate model that adjusted for stromal content alone.\n\nConclusionsThe discovery that molecular subtypes of high grade serous ovarian cancer is influenced by cell admixture, and stromal cell gene expression is crucial for interpretation and reproduction of ovarian cancer molecular subtypes and gene signatures derived from bulk tissue. Single cell analysis may be required to refine the molecular subtypes of high grade serous ovarian cancer. Because stroma proportion was weakly prognostic, elucidating the role of the tumor microenvironments components will be important.\n\nTranslational relevanceOvarian cancer is a leading cause of cancer death in women in the United States. Although the tumor responds to standard therapy for the majority of patients, it frequently recurs and becomes drug-resistant. Recent efforts have focused on identifying molecular subtypes and prognostic gene signatures of ovarian cancer in order to tailor therapy and improve outcomes. This study demonstrates that molecular subtype identification depends on the ratio of tumor to stroma within the specimen. We show that the specific anatomic location of the biopsy may influence the proportion of stromal involvement and potentially the resulting gene expression pattern. It will be crucial for these factors to be taken into consideration when interpreting and reproducing ovarian cancer molecular subtypes and gene signatures derived using bulk tissue and single cells. Furthermore, it will be important to define the relative proportions of stromal cells and model their prognostic importance in the tumor microenvironment.

bioinformatics↗