# Inferring cell state tumor microenvironment maps by integrating single-cell and spatial transcriptomics

> **NIH NIH R21** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2021 · $237,724

## Abstract

SUMMARY
Single-cell RNA-Sequencing (scRNA-Seq) has proved to be a transformative technology for cancer biology,
enabling the unbiased transcriptomic profiling of individual tumor cells and revealing a striking amount of
transcriptional heterogeneity in malignant cells. Many reports in recent years have identified a range of cancer
cell states in diverse cancer types suggesting that these are stable and functional tumor units, with roles in
tumor maintenance and progression. However, a major shortcoming of scRNA-Seq analysis is the loss of
spatial information which follows from the dissociation of the tumor prior to sequencing. Lacking knowledge of
the general location of each cell within the tissue, as well as its local neighborhood, scRNA-Seq cannot alone
inform us about the complex set of relationships among cancer cell states, together with their interactions with
the elements of the tumor microenvironment. Spatial transcriptomics is a disruptive new technology that for the
first time is able to measure whole transcriptomes in a robust fashion throughout a tissue. While spatial
transcriptomics maps the expression of all genes simultaneously – enabling systematic and unbiased
transcriptome analysis – it is not itself a single-cell technology and thus also cannot alone inform us on the
patterning of cancer cell states together with states of the tumor microenvironment. Sensitive and robust
algorithms are thus required to harness the full power implicit in an integration of these technologies. Here we
propose to develop a new computational method called SNAP (Single-cell Neighborhood Map) which uses
matched scRNA-Seq and spatial transcriptomics data from the same tumor to infer the spatial location of each
scRNA-Seq-identified cell by reference to the spatial transcriptomics data, and produces a neighborhood
transcriptome for each scRNA-Seq cell. To analyze these novel neighborhood transcriptomes we propose an
approach to cluster cells with common patterns of neighbors, thereby identifying sets of colocalizing cell states.
SNAP promises to exploit the complementary aspects of single-cell and spatial transcriptomics to link co-
localizing cancer cell states and states of the tumor microenvironment. The methodology presented here
includes several novel algorithms, all of which will be made freely available to the community, where we expect
them to be broadly applicable across cancer biology.

## Key facts

- **NIH application ID:** 10305360
- **Project number:** 1R21CA264361-01
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** ITAI YANAI
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $237,724
- **Award type:** 1
- **Project period:** 2021-09-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10305360

## Citation

> US National Institutes of Health, RePORTER application 10305360, Inferring cell state tumor microenvironment maps by integrating single-cell and spatial transcriptomics (1R21CA264361-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10305360. Licensed CC0.

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