Stanford Tissue Mapping Center

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HuBMAP Supplemental Research Proposal Overview We are requesting supplemental funds for two items: 1. Development of a new computational tool based on a recently developed deep-learning network called ORCA to use spatial features with single-cell expression data from CODEX to more accurately transfer cell type annotations to unlabeled CODEX datasets (50% funds are requested to fund a member from Prof. Jure Leskovec from Computer Science at Stanford University to join our efforts at the Stanford TMC). This tool has already been used to transfer cell type annotations to unlabeled HuBMAP donor small intestine and large intestine single cell CODEX data, already saving nearly 100 hours of annotations required for annotating 2 donors datasets, yet does not incorporate spatial annotations to help with cell type annotations. 2. An EvoSep Liquid Chromatography system (50% funds are requested) for scProteomics. It is one of the few systems which can run a true low nano flow rate gradient ( <100 nl/min). Low nano flow can dramatically improve sensitivity which is the key for the success of scProteomics using mass spectrometry.