NCI Data Jamboree (Project Abstract Submission): Submission #67
Submission information
Submission Number: 67
Submission ID: 189304
Submission UUID: e1fb5d41-0ecf-4b6a-9d99-8db107d6642c
Submission URI: /nci/datajamboree/abstractsubmission
Submission Update: /nci/datajamboree/abstractsubmission?token=uRqMy63gulx3Tv33n4Lnxz3Vbek4-bpWK2cjvK7OQ1c
Created: Tue, 07/28/2026 - 12:20
Completed: Tue, 07/28/2026 - 12:22
Changed: Tue, 07/28/2026 - 14:15
Remote IP address: 10.208.24.192
Submitted by: bojae
Language: English
Is draft: No
Webform: NCI Data Jamboree (Abstracts)
Submitted to: NCI Data Jamboree (Project Abstract Submission)
Locked: Yes
Presenter Information
Yuanwei Bay
{Empty}
Xu
PhD, MSE
Postdoc Researcher
Johns Hopkins University
Baltimore, Maryland
Additional Authors
Abstract Information
Developing, refining, or validating tools, methods, algorithms, and pipelines
Spatial proteomics, CRDC, imaging
An Automated Computational Workflow for Co-Registering Spatially-resolved Proteomics with Public Reference Atlases
Spatial proteomics technologies generate rich microenvironmental data, yet critical barriers prevent their integration into public multi-omic repositories. This project will develop an open-source computational workflow to harmonize custom grid-based spatial proteomic datasets with standardized reference atlases and multi-modal imaging archives, enabling seamless data sharing within the NCI Cancer Research Data Commons (CRDC) ecosystem.
Specific Aim 1: Implement Image Registration Algorithms for Spatial Coordinate Harmonization. We will develop computational methods to transform SPOTTER-derived spatial proteomic data from horizontal mouse brain sections into standardized coordinate systems, specifically the Allen Mouse Brain Atlas Common Coordinate Framework (CCFv3) and H&E-stained histology references. Using image registration libraries (Valis, SimpleITK), we will generate validated transformation matrices, establish annotation transfer protocols from atlas regions to proteomic measurements, and define quality control metrics for alignment accuracy. This aim addresses the fundamental technical challenge of bridging experimental coordinate spaces with canonical anatomical frameworks.
Specific Aim 2: We will package the registration tools into a cloud-based, containerized workflow that merges spatial proteomics with complementary data modalities and prepares CRDC-compatible submissions. The pipeline will accept custom proteomic datasets, execute automated coordinate alignment, overlay spatial transcriptomics from 10x Visium/HTAN repositories, incorporate whole-slide DICOM pathology from NCI Imaging Data Commons, and output standardized data objects using community frameworks (such as SpatialExperiment, Scanpy, Seurat v5). Comprehensive documentation will enable plug-and-play adoption across platforms.
Expected Outcomes & Impact: This collaborative project will deliver an end-to-end workflow democratizing spatial proteomics data sharing, accelerating multi-omic cancer research through standardized harmonization procedures. All code and example datasets will be released as open source.
Specific Aim 1: Implement Image Registration Algorithms for Spatial Coordinate Harmonization. We will develop computational methods to transform SPOTTER-derived spatial proteomic data from horizontal mouse brain sections into standardized coordinate systems, specifically the Allen Mouse Brain Atlas Common Coordinate Framework (CCFv3) and H&E-stained histology references. Using image registration libraries (Valis, SimpleITK), we will generate validated transformation matrices, establish annotation transfer protocols from atlas regions to proteomic measurements, and define quality control metrics for alignment accuracy. This aim addresses the fundamental technical challenge of bridging experimental coordinate spaces with canonical anatomical frameworks.
Specific Aim 2: We will package the registration tools into a cloud-based, containerized workflow that merges spatial proteomics with complementary data modalities and prepares CRDC-compatible submissions. The pipeline will accept custom proteomic datasets, execute automated coordinate alignment, overlay spatial transcriptomics from 10x Visium/HTAN repositories, incorporate whole-slide DICOM pathology from NCI Imaging Data Commons, and output standardized data objects using community frameworks (such as SpatialExperiment, Scanpy, Seurat v5). Comprehensive documentation will enable plug-and-play adoption across platforms.
Expected Outcomes & Impact: This collaborative project will deliver an end-to-end workflow democratizing spatial proteomics data sharing, accelerating multi-omic cancer research through standardized harmonization procedures. All code and example datasets will be released as open source.