Spatial transcriptome profiling by MERFISH reveals subcellular RNA compartmentalization and cell cycle-dependent gene expression

By C. Xia, Jean Fan, George Emanuel, Junjie Hao, X. Zhuang. Proceedings of the National Academy of Sciences of the United States of America, 2019

637 citations recorded by the source corpus.

Significance The spatial organization of RNAs within cells and spatial patterning of cells within tissues play crucial roles in many biological processes. Here, we demonstrate that multiplexed error-robust FISH (MERFISH) can achieve near-genome-wide, spatially resolved RNA profiling of individual cells with high accuracy and high detection efficiency. Using this approach, we identified RNA species enriched in different subcellular compartments, observed transcriptionally distinct cell states corresponding to different cell-cycle phases, and revealed spatial patterning of transcriptionally distinct cells. Spatially resolved transcriptome quantification within cells further enabled RNA velocity and pseudotime analysis, which revealed numerous genes with cell cycle-dependent expression. We anticipate that spatially resolved transcriptome analysis will advance our understanding of the interplay between gene regulation and spatial context in biological systems. The expression profiles and spatial distributions of RNAs regulate many cellular functions. Image-based transcriptomic approaches provide powerful means to measure both expression and spatial information of RNAs in individual cells within their native environment. Among these approaches, multiplexed error-robust fluorescence in situ hybridization (MERFISH) has achieved spatially resolved RNA quantification at transcriptome scale by massively multiplexing single-molecule FISH measurements. Here, we increased the gene throughput of MERFISH and demonstrated simultaneous measurements of RNA transcripts from ∼10,000 genes in individual cells with ∼80% detection efficiency and ∼4% misidentification rate. We combined MERFISH with cellular structure imaging to determine subcellular compartmentalization of RNAs. We validated this approach by showing enrichment of secretome transcripts at the endoplasmic reticulum, and further revealed enrichment of long noncoding RNAs, RNAs with retained introns, and a subgroup of protein-coding mRNAs in the cell nucleus. Leveraging spatially resolved RNA profiling, we developed an approach to determine RNA velocity in situ using the balance of nuclear versus cytoplasmic RNA counts. We applied this approach to infer pseudotime ordering of cells and identified cells at different cell-cycle states, revealing ∼1,600 genes with putative cell cycle-dependent expression and a gradual transcription profile change as cells progress through cell-cycle stages. Our analysis further revealed cell cycle-dependent and cell cycle-independent spatial heterogeneity of transcriptionally distinct cells. We envision that the ability to perform spatially resolved, genome-wide RNA profiling with high detection efficiency and accuracy by MERFISH could help address a wide array of questions ranging from the regulation of gene expression in cells to the development of cell fate and organization in tissues.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a19ade0cf8927bac06f2889c9c8661d4e7de4410

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Citations in the corpus

Spatial components of molecular tissue biology (2022, positive): “Methods developed for dissociation-based data can be directly applied to spatial data, thus providing trajectory visualization in a spatial context 2,67,68 .”
Museum of spatial transcriptomics (2020, background): “The length of barcodes can be increased to encode 10,000 genes [76].”
Cell segmentation in imaging-based spatial transcriptomics (2021, background): “The other challenge is posed by intracellular heterogeneity, which is particularly noticeable in highly multiplexed measurements possible with MERFISH 9,17 or sequential FISH + (seqFISH + ) 7 .”
Spatially Informed Cell Type Deconvolution for Spatial Transcriptomics (2022, background): “In single-cell resolution spatial transcriptomics 37,38 , we also observed that similar cell types tend to colocalize, with colocalization patterns decaying with distance (Supplementary Figs.”
Methods and applications for single-cell and spatial multi-omics (2023, methodology): “5c ) 107,111,113,123–125 .”