# Transformative Computational Infrastructures for Cell-Based Biomarker Diagnostics

> **NIH NIH U01** · J. CRAIG VENTER INSTITUTE, INC. · 2020 · $801,165

## Abstract

Project Summary
The presence of abnormal cell populations in patient samples is diagnostic for a variety of human diseases,
especially leukemias and lymphomas. One of the main technologies used for cell-based diagnostic evaluation
is flow cytometry, which employs fluorescent reagents to measure molecular characteristics of cell populations
in complex mixtures. While cytometry evaluation is routinely used for the diagnosis of blood-borne
malignancies, it could be more widely applied to the diagnosis of other diseases (e.g. asthma, allergy and
autoimmunity) if it could be reproducibly used to interpret higher complexity staining panels and recognize
more subtle cell population differences. Flow cytometry analysis is also widely used for single cell phenotyping
in translational research studies to explore the mechanisms of normal and abnormal biological processes.
More recently, the development of mass cytometry promises to further increase the application of single cell
cytometry evaluation to understand a wide range of physiological, pathological and therapeutic processes.
The current practice for cytometry data analysis relies on “manual gating” of two-dimensional data plots to
identify cell subsets in complex mixtures. However, this process is subjective, labor intensive, and
irreproducible making it difficult to deploy in multicenter translational research studies or clinical trials where
protocol standardization and harmonization are essential. The goal of this project is to develop, validate and
disseminate a user-friendly infrastructure for the computational analysis of cytometry data for both diagnostic
and discovery applications that could help overcome the current limitations of manual analysis and provide for
more efficient, objective and accurate analysis, through the following aims: Specific Aim 1 – Implement a novel
computational infrastructure – FlowGate – for cytometry data analysis that includes visual analytics and
machine learning; Specific Aim 2 – Assess the utility of FlowGate for cell population characterization in
mechanistic translational research studies (T1); Specific Aim 3 – Assess the robustness and accuracy of
FlowGate for clinical diagnostics in comparison with the current standard-of-care analysis of diagnostic
cytometry data (T2); Specific Aim 4 – Develop training and educational resources and conduct directed
outreach activities to stimulate adoption and use of the resulting FlowGate cyberinfrastructure.
The project will have a major impact in advancing translational science by overcoming key hurdles for adoption
of these computational methods by facilitating analysis pipeline optimization, providing intuitive user
interfacing, and delivering directed training activities. The application of the developed computational
infrastructure for improved diagnostics of AML and CLL will contribute to the new emphasis on precision
medicine by more precisely quantifying the patient-specific characteristics of neoplastic and nor...

## Key facts

- **NIH application ID:** 9975252
- **Project number:** 5U01TR001801-05
- **Recipient organization:** J. CRAIG VENTER INSTITUTE, INC.
- **Principal Investigator:** Yu Qian
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $801,165
- **Award type:** 5
- **Project period:** 2016-09-15 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9975252

## Citation

> US National Institutes of Health, RePORTER application 9975252, Transformative Computational Infrastructures for Cell-Based Biomarker Diagnostics (5U01TR001801-05). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9975252. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*