# Multidimensional cell recording with single-cell genomics

> **NIH NIH R00** · DARTMOUTH COLLEGE · 2021 · $237,065

## Abstract

Abstract. A zygote - a single cell - successively divides to ultimately give rise to a highly organized mass of 40
trillion cells that constitutes an adult human. This complex cell lineage tree is shaped by genetic, molecular and
environmental cues. Despite enormous progress over the past one hundred and fifty years – how, when and
where the decisions are made that determine the developmental lineage tree remain poorly understood for not
only humans but nearly all multicellular organisms. Together with colleagues, I pioneered GESTALT (genome
editing of synthetic target array for lineage tracing), a new technology based on in vivo genome editing during
development that is capable of tracing cell lineage at the scale of whole animals. In our experiments to date, we
have successfully captured the interrelated fates of hundreds of thousands of cells within a single organism, a
critical step towards our eventual goal of globally mapping cell lineage in key model organisms. In this K99/R01
proposal, I describe how my lab will expand our initial proof-of-concept of GESTALT into a rich, flexible platform
for biological recording, including molecular signals and cell lineage history in conjunction with transcriptomes,
regulatory landscapes, and other measurements of single cell state. In the K99 phase of my award, I will enhance
the information capacity of GESTALT system and further develop the requisite computational methods (Aim 1),
and also integrate lineage recording with single cell transcriptional profiling (Aim 2). In the R00 phase of my
award, I will expand GESTALT into a fully-fledged information recording platform, capable of recording key
signaling events over the span of organismal development (Aim 3), and then apply this integrated platform to
produce an annotated tree of brain development in Drosophila. The methods that I develop here will empower
my lab, and the field at large, to answer long-standing questions about normal development as well as about the
origins of diseases with complex etiologies rooted in cell lineage (e.g. cancer, developmental disorders).

## Key facts

- **NIH application ID:** 10112941
- **Project number:** 5R00HG010152-04
- **Recipient organization:** DARTMOUTH COLLEGE
- **Principal Investigator:** Aaron H McKenna
- **Activity code:** R00 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $237,065
- **Award type:** 5
- **Project period:** 2019-05-01 → 2022-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10112941

## Citation

> US National Institutes of Health, RePORTER application 10112941, Multidimensional cell recording with single-cell genomics (5R00HG010152-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10112941. Licensed CC0.

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