# A Platform for Scalable Spatial Somatic Variant Profiling

> **NIH NIH UG3** · BROAD INSTITUTE, INC. · 2024 · $382,724

## Abstract

SUMMARY
Recent studies have begun to characterize the accumulation of somatic mutations over the
lifetime of an individual. A variety of mutational processes, both cell-intrinsic and -extrinsic,
underpin these mutations, which if occurring in key driver genes, may alter the fitness of the cell
and lead to adverse outcomes. However, much work is needed to fully understand the functional
effect of clonal somatic mutations across human tissues. In particular, tissues emerge from
coordinated migration, differentiation and expansion of progenitor cells. For many tissues, such
as most epithelial tissue types, spatially cohesive clonal fields emerge as common tissue-resident
progenitors expand. Measuring the spatial arrangement of clones offers two critical insights for
studying the effects of somatic mutations: 1) clone-specific genetic variants spatially aggregate,
creating a local dominance in allele frequency, facilitating the discovery of somatic mutations, and
2) tissues require the proper spatial organization of cell types for function, with clonal mosaicism
extrinsic cues may drive the expansion of clonal fields and/or genetically altered clones may
remodel their surrounding tissue to drive tissue dysfunction. As such, there is an immense
opportunity and need for methods that spatially localize clonal somatic variants. We have
developed an approach to capture DNA onto high resolution (10 micron) spatially barcoded
arrays. This approach, Slide-DNA-seq, is unbiased, modular, and allows for paired measurements
with other modalities such as the transcriptome and epigenome. Here, we seek to develop a
technology platform, built on Slide-DNA-seq, to 1) perform spatial variant detection at scale in
human tissues, 2) to associate those variants with functional changes in cell-types and states,
and 3) to disseminate these technologies within the SMaHT consortium.

## Key facts

- **NIH application ID:** 10830484
- **Project number:** 5UG3NS132135-02
- **Recipient organization:** BROAD INSTITUTE, INC.
- **Principal Investigator:** Jason Daniel Buenrostro
- **Activity code:** UG3 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $382,724
- **Award type:** 5
- **Project period:** 2023-04-19 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10830484

## Citation

> US National Institutes of Health, RePORTER application 10830484, A Platform for Scalable Spatial Somatic Variant Profiling (5UG3NS132135-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10830484. Licensed CC0.

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