Abstract Metastasis from the primary tumor site to the brain is the most lethal complication of advanced breast cancer and is experienced by approximately 20% of breast cancers worldwide. There is at present no approach to detect if a tumor has brain metastatic potential, no markers that predict successful future metastasis, and thus no therapies to target any of the processes involved. These gaps are difficult to bridge due to a lack of technology that can elucidate the underlying mechanisms by classifying a cancer cell based on its brain metastatic potential. Current in vivo murine models are slow to manifest metastasis and do not have the capability of capturing single cell morphology and dynamics; and current in vitro models are short lived and lack software support therefore, we propose an in vitro blood brain niche (µm-BBN) on-a-chip to measure the phenotypic differences between cancer cells and normal cells and amongst cancer cells as they transit through the model niche and to assign them a brain metastatic potential. Moreover, we propose capturing the cells that transit through the BBN for further analysis. This system is composed of the µm-BBN, an integrated piezo pump and controller, automated phenotyping software and a classification algorithm. The µm-BBN has two chambers which form a vessel (human brain endothelial cells and brain stroma (ECM and Normal Human Astrocytes (NHA), Microglia and Pericytes) separated by a 5µm porous membrane coated with Matrigel. The goal is to culture cancer cells in the device for up to 20 days using the integrated pump. Expanding on previous work, the cellular phenotype and migratory behavior of a library of patient cells will be recorded. Generation of phenotypic measures for individual cancer cells, micro-metastasis and the tumor micro-environment will enable automated profiling of the metastatic signature of tumor cells. After culture the cells will be recovered and fixed or analyzed to create a multi-omic readout enabling a complimentary cellular and molecular signature for single cells and sub- populations of metastatic cancer cells. This work will enable improved understanding of the underlying mechanisms of brain metastasis and, downstream from it, in a more robust set of targetable pathways for prevention of brain metastasis.