Spatial transcriptomics dataset of primary tumours from MDA-MB-231 xenograft model

Identifying functionally important cell states and structure within heterogeneous tumors remains a significant biological and computational challenge. Current clustering or trajectory-based models are ill-equipped to address the notion that cancer cells reside along a phenotypic continuum. We present Archetypal Analysis network (AAnet), a neural network that learns archetypal states within a phenotypic continuum in single-cell data. Unlike traditional archetypal analysis, AAnet learns archetypes in simplex-shaped neural network latent space. Using pre-clinical models and clinical breast cancers, AAnet resolves distinct cell states and processes, including cell proliferation, hypoxia, metabolism and immune interactions. Primary tumor archetypes are recapitulated in matched liver, lung and lymph node metastases. The dataset here comprises of the 10X genomics based spatial transcriptomics (Visium) on MDA-MB-231 xenografts to perform archetypal analysis and understand spatial aspects of tumour hetegeneity. scRNAseq datasets from matched models and metastasis is projected onto this spatial transcriptomics data to understand the spatial dependencies and characteriostics of these archetypes in the manuscript. In an MDA-MB-231 mouse model, single-cell RNA-sequencing and spatial RNA-sequencing data were collected for primary tumors, and single-cell RNA-sequencing was collected for lymph node, liver, and lung metastases. Dataset here presents the spatial transcriptomics profile of the two primary tumours used in this study.