Biology subjects

Mellert, D. J.

Publications and source records attributed to Mellert, D. J..

3 recordsLinked to original sources

bioRxiv2023

Easing OMERO adoption with ezomero

Many research laboratories need to manage, process, and analyze the increasingly large volumes and complexity of data being produced by state-of-the-art bioimaging platforms. OMERO is a popular open-source client-server application that provides a unified interface for managing and working with bioimages and their associated measurements and metadata. Integrating OMERO into analysis pipelines, such as those developed around the scientific Python ecosystem, will thus be a common pattern across the field of bioimaging. While OMERO has a powerful Python API, it provides minimal abstraction from the underlying OMERO object model and associated methods, which represent more complexity than most users are interested in for the context of an analysis script. We introduce ezomero, which was designed to provide a convenience layer on top of existing OMERO APIs and return data types that are either Python primitive or commonly used in scientific Python. Ezomero has minimal dependencies in addition to the OMERO Python library itself and is installable directly from PyPI. Here, we provide an overview of ezomero as well as several vignettes to illustrate how it can be used to accelerate discovery.

bioinformatics↗

bioRxiv2023

Cellos: High-throughput deconvolution of 3D organoid dynamics at cellular resolution for cancer pharmacology

Three-dimensional (3D) culture models, such as organoids, are flexible systems to interrogate cellular growth and morphology, multicellular spatial architecture, and cell interactions in response to drug treatment. However, new computational methods to segment and analyze 3D models at cellular resolution with sufficiently high throughput are needed to realize these possibilities. Here we report Cellos (Cell and Organoid Segmentation), an accurate, high throughput image analysis pipeline for 3D organoid and nuclear segmentation analysis. Cellos segments organoids in 3D using classical algorithms and segments nuclei using a Stardist-3D convolutional neural network which we trained on a manually annotated dataset of 3,862 cells from 36 organoids confocally imaged at 5 m z-resolution. To evaluate the capabilities of Cellos we then analyzed 74,450 organoids with 1.65 million cells, from multiple experiments on triple negative breast cancer organoids containing clonal mixtures with complex cisplatin sensitivities. Cellos was able to accurately distinguish ratios of distinct fluorescently labelled cell populations in organoids, with <3% deviation from the seeding ratios in each well and was effective for both fluorescently labelled nuclei and independent DAPI stained datasets. Cellos was able to recapitulate traditional luminescence-based drug response quantifications by analyzing 3D images, including parallel analysis of multiple cancer clones in the same well. Moreover, Cellos was able to identify organoid and nuclear morphology feature changes associated with treatment. Finally, Cellos enables 3D analysis of cell spatial relationships, which we used to detect ecological affinity between cancer cells beyond what arises from local cell division or organoid composition. Cellos provides powerful tools to perform high throughput analysis for pharmacological testing and biological investigation of organoids based on 3D imaging.

bioinformatics↗

bioRxiv2019

Sexual-dimorphism in human immune system aging

Differences in immune function and responses contribute to health- and life-span disparities between sexes. However, the role of sex in immune system aging is not well understood. Here, we characterize peripheral blood mononuclear cells from 172 healthy adults 22-93 years of age using ATAC-seq, RNA-seq, and flow-cytometry. These data reveal a shared epigenomic signature of aging including declining naive T cell and increasing monocyte/cytotoxic cell functions. These changes were greater in magnitude in men and accompanied by a male-specific genomic decline in B-cell specific loci. Age-related epigenomic changes first spike around late-thirties with similar timing and magnitude between sexes, whereas the second spike is earlier and stronger in men. Unexpectedly, genomic differences between sexes increase after age 65, with men having higher innate and pro-inflammatory activity and lower adaptive activity. Impact of age and sex on immune cell genomes can be visualized at https://immune-aging.jax.org to provide insights into future studies.

immunology↗