MCMICRO for MACSima on HPC | Alejandro Gil Gomez

Scripts and configuration files to process MACSima spatial proteomics data with the MCMICRO pipeline on the NVwulf high-performance computing cluster at Stony Brook University.

The workflow was optimized for tissue microarray (TMA) analysis of Pancreatic Ductal Adenocarcinoma (PDAC), using samples from the Stony Brook University Department of Pathology, to quantify the spatial dynamics of the Epithelial-Mesenchymal Transition (EMT).

What it does

Converts raw cyclic imaging data (the RawData folder from the MACSima instrument, using the high-exposure images) into OME-TIFF files with macsima2mc, with optional illumination correction.
Runs MCMICRO for image registration, Cellpose-based nuclei and cell segmentation, and single-cell quantification.
Runs ROIs in parallel with Slurm, with GPU and Singularity settings for NVwulf.
Can merge several experimental runs of the same sample into a single RawData directory.

Code and documentation: github.com/StonyBrookRCI/MACSIMA_MCMICRO

Spatial Proteomics Nextflow MCMICRO HPC

Authors

Alejandro Gil Gómez

Computational Scientist

Alejandro Gil Gómez, Ph.D., is a Computational Scientist at Stony Brook Medicine and Research Computing focused on clinical and translational research. His expertise spans end-to-end bioinformatics, AI/ML workflows, and high-performance computing (HPC). He specializes in developing scalable, reproducible pipelines for a wide array of data types, including RNA-seq differential expression, genomic variant analysis, proteomics, and spatial omics. Experienced in processing HIPAA-regulated clinical datasets, he bridges advanced computational workflows with practical biomedical discovery. He holds a Ph.D. in Ecology and Evolution from Stony Brook University and has also taught bioinformatics and mentored students in data-intensive research.