Ajit J. Nirmal

The paper identifies crater-shaped structures in melanoma that enhance immune response to checkpoint blockade therapy in humans and zebrafish.

The paper maps spatial patterns of tumor cell dedifferentiation and plasticity in melanoma by analyzing RNA and protein expression across 300 histological domains.

This study shows chemotherapy causes myeloid-driven spatial T-cell exhaustion in ovarian cancer by altering immune cell states and interactions, suggesting new immunotherapy approaches.

SpatialCells automates the analysis of spatially resolved multiplexed single-cell data to characterize tumor microenvironments, enabling efficient feature extraction and downstream machine learning applications.

Cell Spotter (CSPOT) uses supervised machine learning to automate cell spotting and quantification in highly multiplexed tissue images, improving speed, accuracy, and reproducibility.

The paper introduces a method called Dye Drop for high-throughput single-cell assays in 384-well plates, enabling detailed dose-response analysis of small molecules in breast cancer cell lines.

Galaxy-ME is a web-based software hub for analyzing and visualizing multiplex tissue imaging datasets, demonstrating its utility on normal and cancerous tissue data.

MCMICRO processes multiplexed tissue images into single-cell data, demonstrating its versatility across different tissues and imaging platforms.

The paper introduces survivALL, an R package for continuous biomarker assessment through survival analysis, enabling reliable evaluation of gene expression cut-points across datasets.