SPICEMIX: Integrative single-cell spatial modeling of cell identity

Spatial transcriptomics technologies promise to reveal spatial relationships of cell-type composition in complex tissues. However, the development of computational methods that capture the unique properties of single-cell spatial transcriptome data to unveil cell identities remains a challenge. Here, we report SpiceMix, a new probabilistic model that enables effective joint analysis of spatial information and gene expression of single cells based on spatial transcriptome data. Both simulation and real data evaluations demonstrate that SpiceMix consistently improves upon the inference of the intrinsic cell types compared with existing approaches. As a proof-of-principle, we use SpiceMix to analyze single-cell spatial transcriptome data of the mouse primary visual cortex acquired by seqFISH+ and STARmap. We find that SpiceMix can improve cell identity assignments and uncover potentially new cell subtypes. SpiceMix is a generalizable framework for analyzing spatial transcriptome data that may provide critical insights into the cell-type composition and spatial organization of cells in complex tissues.

[1]  Carlo Colantuoni,et al.  Decomposing cell identity for transfer learning across cellular measurements, platforms, tissues, and species , 2018, bioRxiv.

[2]  Guocheng Yuan,et al.  Identification of spatially associated subpopulations by combining scRNA-seq and sequential fluorescence in situ hybridization data , 2018, Nature Biotechnology.

[3]  S. Teichmann,et al.  From Tissues to Cell Types and Back: Single-Cell Gene Expression Analysis of Tissue Architecture , 2018, Annual Review of Biomedical Data Science.

[4]  Staci A. Sorensen,et al.  Adult Mouse Cortical Cell Taxonomy Revealed by Single Cell Transcriptomics , 2016 .

[5]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[6]  A. Regev,et al.  Revealing the vectors of cellular identity with single-cell genomics , 2016, Nature Biotechnology.

[7]  Sarah A. Teichmann,et al.  Faculty Opinions recommendation of histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data. , 2017 .

[8]  Cole Trapnell,et al.  Defining cell types and states with single-cell genomics , 2015, Genome research.

[9]  George M. Church,et al.  Highly Multiplexed Subcellular RNA Sequencing in Situ , 2014, Science.

[10]  Kevin P. Murphy,et al.  Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.

[11]  Evan Z. Macosko,et al.  Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity , 2019, Cell.

[12]  R. Satija,et al.  Integrative single-cell analysis , 2019, Nature Reviews Genetics.

[13]  Pardis C. Sabeti,et al.  Identifying Gene Expression Programs of Cell-type Identity and Cellular Activity with Single-Cell RNA-Seq , 2018 .

[14]  H. Sebastian Seung,et al.  Algorithms for Non-negative Matrix Factorization , 2000, NIPS.

[15]  S. Goldman,et al.  Dual regulatory switch through interactions of Tcf7l2/Tcf4 with stage-specific partners propels oligodendroglial maturation , 2016, Nature Communications.

[16]  Richard Bonneau,et al.  High-definition spatial transcriptomics for in situ tissue profiling , 2019, Nature Methods.

[17]  L. Cai,et al.  In Situ Transcription Profiling of Single Cells Reveals Spatial Organization of Cells in the Mouse Hippocampus , 2016, Neuron.

[18]  Stephen M. Smith,et al.  Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.

[19]  Srinivas Malladi,et al.  The Apaf-1 apoptosome induces formation of caspase-9 homo- and heterodimers with distinct activities , 2016, Nature Communications.

[20]  X. Zhuang,et al.  Spatially resolved, highly multiplexed RNA profiling in single cells , 2015, Science.

[21]  Julio Saez-Rodriguez,et al.  Modeling Cell-Cell Interactions from Spatial Molecular Data with Spatial Variance Component Analysis , 2019, Cell reports.

[22]  J. Besag.  On the Statistical Analysis of Dirty Pictures , 1986 .

[23]  Patrik L. Ståhl,et al.  Visualization and analysis of gene expression in tissue sections by spatial transcriptomics , 2016, Science.

[24]  Pablo Tamayo,et al.  Metagenes and molecular pattern discovery using matrix factorization , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[25]  Cindy C. Guo,et al.  High-Spatial-Resolution Multi-Omics Sequencing via Deterministic Barcoding in Tissue , 2020, Cell.

[26]  Holger Heyn,et al.  Seeded NMF regression to Deconvolute Spatial Transcriptomics Spots with Single-Cell Transcriptomes , 2020 .

[27]  Christopher C. Overall,et al.  Evidence for oligodendrocyte progenitor cell heterogeneity in the adult mouse brain , 2020, Scientific Reports.

[28]  Nir Friedman,et al.  Gene expression cartography , 2019, Nature.

[29]  Åsa K. Björklund,et al.  Transcriptional Convergence of Oligodendrocyte Lineage Progenitors during Development , 2018, Developmental cell.

[30]  H. Lassmann,et al.  Augmentation of demyelination in rat acute allergic encephalomyelitis by circulating mouse monoclonal antibodies directed against a myelin/oligodendrocyte glycoprotein. , 1988, The American journal of pathology.

[31]  Allan R. Jones,et al.  Genome-wide atlas of gene expression in the adult mouse brain , 2007, Nature.

[32]  A. Lieberman,et al.  A qualitative and quantitative ultrastructural study of glial cells in the developing visual cortex of the rat. , 1983, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[33]  Fan Zhang,et al.  Fast, sensitive, and accurate integration of single cell data with Harmony , 2018, bioRxiv.

[34]  Shila Ghazanfar,et al.  The human body at cellular resolution: the NIH Human Biomolecular Atlas Program , 2019, Nature.

[35]  Nimrod D. Rubinstein,et al.  Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region , 2018, Science.

[36]  G. Wagner,et al.  The origin and evolution of cell types , 2016, Nature Reviews Genetics.

[37]  William E. Allen,et al.  Three-dimensional intact-tissue sequencing of single-cell transcriptional states , 2018, Science.

[38]  Christoph Hafemeister,et al.  Comprehensive integration of single cell data , 2018, bioRxiv.

[39]  Guo-Cheng Yuan,et al.  Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+ , 2019, Nature.

[40]  S. Teichmann,et al.  SpatialDE: identification of spatially variable genes , 2018, Nature Methods.

[41]  Shiquan Sun,et al.  Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies , 2020, Nature Methods.

[42]  Lars E. Borm,et al.  The promise of spatial transcriptomics for neuroscience in the era of molecular cell typing , 2017, Science.

[43]  O. Harismendy,et al.  Deciphering cell–cell interactions and communication from gene expression , 2020, Nature reviews. Genetics.

[44]  Evan Z. Macosko,et al.  Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution , 2019, Science.