# Computational approaches for the systematic detection of cell-cell interactions by spatial transcriptomics - Resubmission - 1

> **NIH NIH R01** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2021 · $360,188

## Abstract

SUMMARY
Many biological processes occur not at the level of a cell but at the level of a system, and cell-cell interactions
are crucial for tissue function. With the introduction of single-cell RNA-Seq, we have robust measures of cell
types and cell states. In this approach however, the tissue under study must be dissociated prior to sequencing
resulting in the loss of spatial context. Spatial transcriptomics is a promising new field, in which several
methods have been developed to profile the transcriptome of cells in their native context. However, the most
widely used implementation of this technology – sequencing-based spatial transcriptomics – has not reached
single-cell resolution. Thus, there is a critical need for novel computational approaches integrating spatial
transcriptomics and single-cell RNA-Seq in order to infer cell-cell relationships in complex tissues. Our lab has
recently developed analyses for multimodal intersection of these two data sources that effectively mitigate the
limitations of each technology. Here, we propose to apply this concept to uncover patterns of cell-cell
interactions in biological systems. In our first Aim, we present the StateMap approach to infer local cell-cell
interactions by spatial transcriptomics-based co-localization and receptor-ligand relationships. A catalog of cell
types and cell states is first delineated using single-cell data, and the spatial transcriptomics data is then
harnessed to map pairs of co-localizing cell states. StateMap then systematically infers the cell-cell interaction
mechanisms among co-localizing cell states by statistically testing for signal/response relationships in the
spatial transcriptomics data. In our second Aim, we propose the ST-motif method to conceptualize the
locations of cell types and states as a network, allowing for systematic analysis by a wealth of available
methods. Our approach thus reframes the problem of finding cell-cell relationships as a network motif problem
in this graph. Throughout our proposal, we develop and test the algorithms on two model systems, the male
germline and the placenta, with which our lab has considerable experience. Conceptually, our proposal
promises to yield novel algorithms for mapping cell-cell interactions that are required for actuating the potential
of two powerful transcriptomic technologies.

## Key facts

- **NIH application ID:** 10299124
- **Project number:** 1R01LM013522-01A1
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** ITAI YANAI
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $360,188
- **Award type:** 1
- **Project period:** 2021-07-01 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10299124

## Citation

> US National Institutes of Health, RePORTER application 10299124, Computational approaches for the systematic detection of cell-cell interactions by spatial transcriptomics - Resubmission - 1 (1R01LM013522-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10299124. Licensed CC0.

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