Characterizing spatial gene expression heterogeneity in spatially resolved single-cell transcriptomic data with nonuniform cellular densities
- Brendan F. Miller1,2,
- Dhananjay Bambah-Mukku3,4,
- Catherine Dulac3,4,
- Xiaowei Zhuang3,5,6 and
- Jean Fan3,5,6,7
- 1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA;
- 2Center for Computational Biology, Johns Hopkins University, Baltimore, Maryland 21211, USA;
- 3Howard Hughes Medical Institute, Cambridge, Massachusetts 02138, USA;
- 4Department of Molecular and Cellular Biology, Cambridge, Massachusetts 02138, USA;
- 5Department of Chemistry and Chemical Biology, Cambridge, Massachusetts 02138, USA;
- 6Department of Physics, Harvard University, Cambridge, Massachusetts 02138, USA
Abstract
Recent technological advances have enabled spatially resolved measurements of expression profiles for hundreds to thousands of genes in fixed tissues at single-cell resolution. However, scalable computational analysis methods able to take into consideration the inherent 3D spatial organization of cell types and nonuniform cellular densities within tissues are still lacking. To address this, we developed MERINGUE, a computational framework based on spatial autocorrelation and cross-correlation analysis to identify genes with spatially heterogeneous expression patterns, infer putative cell–cell communication, and perform spatially informed cell clustering in 2D and 3D in a density-agnostic manner using spatially resolved transcriptomic data. We applied MERINGUE to a variety of spatially resolved transcriptomic data sets including multiplexed error-robust fluorescence in situ hybridization (MERFISH), spatial transcriptomics, Slide-seq, and aligned in situ hybridization (ISH) data. We anticipate that such statistical analysis of spatially resolved transcriptomic data will facilitate our understanding of the interplay between cell state and spatial organization in tissue development and disease.
Footnotes
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at https://www.genome.org/cgi/doi/10.1101/gr.271288.120.
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Freely available online through the Genome Research Open Access option.
- Received September 2, 2020.
- Accepted May 13, 2021.
This article, published in Genome Research, is available under a Creative Commons License (Attribution 4.0 International), as described at http://creativecommons.org/licenses/by/4.0/.
