# Stanford Tissue Mapping Center

> **NIH NIH U54** · STANFORD UNIVERSITY · 2021 · $100,000

## Abstract

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.

## Key facts

- **NIH application ID:** 10414673
- **Project number:** 3U54HG010426-04S1
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** GARRY P NOLAN
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $100,000
- **Award type:** 3
- **Project period:** 2018-09-19 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10414673

## Citation

> US National Institutes of Health, RePORTER application 10414673, Stanford Tissue Mapping Center (3U54HG010426-04S1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10414673. Licensed CC0.

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