Spatial mapping of Ethiopian cutaneous leishmaniasis lesions reveals distinct tissue level immune programs

Data Files Description Study Overview Ethiopian cutaneous leishmaniasis shows heterogeneous clinical presentations. This study used 10x Visium spatial transcriptomics on paired lesional and non-lesional skin biopsies from five patients to characterize local immune responses. Using reference-free deconvolution and morphology-guided analyses, we identified five spatially distinct immunopathological response patterns that can co-exist within individual lesions. Raw sequencing data: NCBI GEO accession GSE317987 Analysis code: https://github.com/NidhiSDey/SpatialCL.git Files Uploaded Seurat Objects (.rds) 1. integrated_seurat_obj.Rds - Updated Seurat 5.4.0 obj compatible with Seurat_5.5.0- Initial integrated Seurat object containing all 10 samples (5 patients × 2 conditions)- Quality-filtered, normalized, and integrated SCTtransform- Does not include LDA topic deconvolution- Used as input for downstream LDA analyses 3. integrated_seurat_obj_23_LDA_topics_added_updated.rds -Updated Seurat 5.4.0 obj compatible with Seurat_5.5.0- Integrated object with 23 LDA topics added to metadata- Contains all 10 samples (lesional + non-lesional)- Used for lesional vs non-lesional comparisons- Includes topic proportions and dominant topic assignments 4. lesion_only_integrated_obj_35_LDA_topics_added.rds -Updated Seurat 5.4.0 obj compatible with Seurat_5.5.0- Lesion-only samples (5 patients) with 35 LDA topics- Primary analysis object for patient-specific studies- Contains spatial domain annotations and cell type assignments- Used for all patient-specific analyses and main figures LDA Models (.rds/.Rdata) 5. combined_lda_model_k8_60.Rdata- LDA topic models for all samples (lesional + non-lesional)- Models computed for k = 8, 10, 12, ..., 60 topics- Used to select optimal topic number (k=23) for combined analysis 6. lesion_LDA_model_k_26_52.rds- LDA topic models for lesion-only samples- Models computed for k = 26, 28, 30, ..., 52 topics- Used to select optimal topic number (k=35) for lesion analysis