# Implementation of Slide-seq for high-resolution, whole-transcriptome human tissue maps.

> **NIH NIH UH3** · BROAD INSTITUTE, INC. · 2021 · $443,661

## Abstract

Abstract:
Cells are the unit building blocks of tissues and organs, thus, to understand the organization of tissues and
organs, it is important to understand where different types of cells reside in the tissue. Here, we implement a
newly invented technology called Slide-seq to characterize gene expression relationships in large tissue
volumes at 10-micron resolution. First, we will develop tissue quality metrics, and modifications to the Slide-
seq protocol, that optimize Slide-seq data quality across a range of tissues that are the focus of the HuBMAP
consortium. Next, we will generate large-scale Slide-seq datasets from human colon and kidney, and compare
and contrast the resulting data from spatial technologies being deployed by existing HuBMAP TMCs. Finally,
we will scale the production of the Slide-seq arrays, and host training workshops, to enable the technology to
be successfully adopted across the HuBMAP consortium. Together, we aim to make Slide-seq a routine and
valuable measurement tool for the construction of comprehensive molecular maps of human tissues.

## Key facts

- **NIH application ID:** 10249988
- **Project number:** 5UH3CA246632-03
- **Recipient organization:** BROAD INSTITUTE, INC.
- **Principal Investigator:** Evan Z Macosko
- **Activity code:** UH3 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $443,661
- **Award type:** 5
- **Project period:** 2019-09-20 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10249988

## Citation

> US National Institutes of Health, RePORTER application 10249988, Implementation of Slide-seq for high-resolution, whole-transcriptome human tissue maps. (5UH3CA246632-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10249988. Licensed CC0.

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