CITEgeist: Cellular Indexing of Transcriptomes and Epitopes for Guided Exploration of Intrinsic Spatial Trends

Spatial transcriptomics provides insights into tissue architecture by linking gene expression with spatial localization. Current deconvolution methods rely heavily on single-cell RNA sequencing (scRNA-seq) references, which are costly and often unavailable, mainly if the tissue under evaluation is limited, such as in a core biopsy specimen. We present a novel tool, CITEgeist, that deconvolutes spatial transcriptomics data using antibody capture from the same slide as the reference, directly leveraging cell surface protein measurements from the same tissue section. This approach circumvents the limitations of scRNA-seq as a reference, offering a cost-effective and biologically grounded alternative. Our method employs mathematical optimization to estimate cell type proportions and gene expression profiles, incorporating sparsity constraints for robustness and interpretability. Benchmarks against state-of-the-art deconvolution methods show improved accuracy in cell type resolution, particularly in dense tumor microenvironments, while maintaining computational efficiency. This antibody-based tool advances spatial transcriptomics by providing a scalable, accurate, and reference-independent solution for deconvolution in complex tissues. We validate this tool by using a combined approach of simulated data and clinical samples by applying CITEgeist to translational pre-treatment and post-treatment ER+ breast tumors from an ongoing clinical trial, emphasizing the applicability and robustness of CITEgeist.