# IMAT-ITCR Collaboration: Multiplexed Spatial Data calibration and analysis using micro-capsules

> **NIH NIH U01** · HARVARD MEDICAL SCHOOL · 2024 · $84,750

## Abstract

PROJECT SUMMARY
Highly multiplexed imaging methods such as CycIF, CODEX, mxIF, MIBI, and IMC are providing
remarkable insight into tumor micro-environments, but they suffer from substantial variability between
runs and substantial differences from one technology to the next. The reasons for changes in staining
intensity with cycle number are not fully known, but it appears that it is absolute intensity rather than
morphology that is variable. Variability in staining intensity severely limits the ability to create large
multi-specimen datasets within a single lab (due to batch effects) and it precludes comparison across
laboratories and institutions. A central goal of this proposal is to leverage micro-capsule technology to
develop reliable calibration standards and software algorithms that use these standards for data
normalization. A set of standards would allow systematic comparison between experimental methods,
which has been a persistent problem in the field. Quality control and calibration are also essential
elements in the implementation of CLIA-compatible workflows for clinical use. Thus, we propose that
developing calibration standards is urgently needed to accelerate development of both antibody and
nucleic-acid based spatial profiling methods for studying tumors.
To achieve this goal, this administrative supplement will build a new collaboration between the labs of
Drs. Klein, Pfister and Santagata who are supported by an R33 IMAT grant (Klein) and a U01 ITCR
grant (Pfister and Santagata). The proposal brings together two efforts: micro-capsule technology,
and spatial multi-omic computational image analysis. The project proposes to use micro-capsule
technology to develop these much-needed imaging calibration standards.
The goal is realized through two aims. In the first aim, we adapt computational image analysis tools
from Pfister and Santagata labs to register, detect, segment, and normalize data from sequential
imaging of capsules. This aim is stand-alone, as it directly advances the original goals of Klein’s IMAT
grant. In the second aim, we generate imaging calibration standards using cell lysates in micro-
particles, then evaluate them as standards for multiplexed imaging, and building on Aim 1 we
establish computational tools for building calibration into imaging tasks.

## Key facts

- **NIH application ID:** 11136707
- **Project number:** 3U01CA284207-02S1
- **Recipient organization:** HARVARD MEDICAL SCHOOL
- **Principal Investigator:** Hanspeter Pfister
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $84,750
- **Award type:** 3
- **Project period:** 2023-09-12 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11136707

## Citation

> US National Institutes of Health, RePORTER application 11136707, IMAT-ITCR Collaboration: Multiplexed Spatial Data calibration and analysis using micro-capsules (3U01CA284207-02S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/11136707. Licensed CC0.

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