Office of Science and Technology Resources
HiTIF supports commercial high-content analysis, imaging bioinformatics, spatial bioinformatics, image storage, and computational infrastructure for high-throughput imaging projects.
HiTIF uses a commercial Revvity Signals Imaging Artist (ImA) image-analysis server for arrayed HCI assays that do not require advanced image analysis. Imaging Artist provides interactive, web-based image browsing and analysis setup for high-content datasets and can queue batch analysis jobs for multiple users.
It runs on a dedicated multi-core server maintained by NCI IT in the NCI One Cloud (AWS) environment.
Learn MoreFor arrayed and pooled HCI assays that require advanced analysis, HiTIF develops and maintains in-house, open-source software for deep learning-based segmentation, live-cell tracking, single-cell statistics, and functional genomics screen analysis.
HiTIF-developed
HiTIPS was developed in house at HiTIF to provide an open-source platform for advanced high-throughput image analysis in both live and fixed cells. For more details and instructions, refer to the original HiTIPS publication. https://www.nature.com/articles/s41598-024-66600-1
HiTIF-developed
MetaChrome is an open-source, user-friendly tool developed in house at HiTIF for automated metaphase chromosome analysis. For more details, refer to the MetaChrome publication in Methods (2026)
For pooled HCI screens, HiTIF has developed custom Python pipelines for in situ sequencing image analysis, base calling, genotype-to-phenotype linking, and screening statistics, extracting single-cell phenotypic measurements and gene-level statistics for up to millions of cells per experiment.
Exploratory and statistical analysis of single biological objects, such as nuclei or FISH spots, uses custom R and/or Python pipelines that HiTIF provides to collaborators. Reproducibility is ensured through Quarto and Jupyter, and analysis code is deposited publicly through resources such as GitHub and Figshare.
HiTIF develops and tests custom advanced spatial-statistics pipelines for the analysis of spatial transcriptomics and proteomics datasets acquired on tissue samples.
We integrate and modify robust open-source spatial frameworks, including SpatialData and bin2Cell, and develop custom Bayesian models to quantify spatial statistics, account for technical variation, and estimate the effect size of experimental treatments on measured biological phenotypes.
Collaborators are provided with open-source, point-and-click data-visualization interfaces built on the Napari Python framework.
Example project (Nguyen lab, IL15/IL21 spatial analysis):
The NCI Data Management Environment (DME) offers open-ended storage and management of scientific research datasets. If you have an NIH account, the NCI Data Vault team can give you access to DME.
For access requests or any other questions, contact NCIDataVault@mail.nih.gov.
Learn MoreHiTIF’s computational pipelines run on the NIH High-Performance Computing (HPC) cluster, using latest-generation GPUs for deep learning-based image analysis.