# High Throughput Screening

> **NIH NIH P30** · UNIVERSITY OF IOWA · 2021 · $2

## Abstract

The High Throughput Screening Core (HTS) supports basic and translational biomedical research by
designing, optimizing, and running high throughput assays that deliver potential therapeutics for cancer. This is
done by integrating robotics, detection systems, chemical /biologics libraries and data management with
expertise in optimizing technologies and outcomes along each step in the process. HTS provides Holden
Comprehensive Cancer Center (HCCC) members with scalable early, pre-clinical development of therapeutics,
including small molecule therapeutics, antibodies, siRNAs, antisense oligonucleotides and other biologics
including patient-derived cell therapeutics. It also supports high throughput screening for studies exploring the
biology of cancer. The HTS is equipped to perform high-throughput screening in 96-, 384- and 1536- well
formats, with plate reader detection (Perkin-Elmer EnVision) using absorbance, fluorescence and
luminescence, including advanced FRET and BRET techniques. HTS can also perform high content screening
(HCS, Perkin-Elmer Operetta Confocal Imaging System) to detect and quantify phenotypic changes, i.e., cell
differentiation, cell migration, neurite outgrowth, and target trafficking or by fluorescence intensities for target
protein expression, transcription factor or signaling pathway analysis.

## Key facts

- **NIH application ID:** 10169608
- **Project number:** 2P30CA086862-21
- **Recipient organization:** UNIVERSITY OF IOWA
- **Principal Investigator:** Meng Wu
- **Activity code:** P30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $2
- **Award type:** 2
- **Project period:** 2000-07-14 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10169608

## Citation

> US National Institutes of Health, RePORTER application 10169608, High Throughput Screening (2P30CA086862-21). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10169608. Licensed CC0.

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