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

Abstract

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.

Cell type prioritization in single-cell data

“…We reasoned that an ideal method for cell type prioritization would make no assumptions about the distributions of features provided as input 29 , and more broadly, would be agnostic to the particular molecular features provided as input: that is, it would readily incorporate single-cell RNA-seq [30][31][32][33] , proteomics 34,35 , epigenomics 11,[36][37][38] , and imaging transcriptomics 15,17,39 datasets, among other modalities. Accordingly, Augur uses a random forest 40 classifier to predict sample labels for each cell type.…”

Cell segmentation-free inference of cell types fromin situtranscriptomics data

“…Currently, the field of in situ transcriptomics is advancing rapidly and more than 10,000 genes can be simultaneously profiled using FISH-based methods Xia et al, 2019). The high number of genes detected in large volumes opens up the potential for in situ transcriptomics methods to at least partially replace single cell RNA sequencing at large scale, placing SSAM as the first generic and segmentation-free pipeline to rapidly and precisely reconstruct tissue structure independent of the underlying imaging technique.…”

GCNG: Graph convolutional networks for inferring cell-cell interactions

“…The second is a very recent dataset from MERFISH. That data consisted of 12,903 genes in 1,368 cells [30]. Unlike the seqFISH+ data that profiled the expression in the mouse cortex, the MERFISH data is from in vitro cultured cells and so does not include a diverse set of cell types.…”

“…We tested our approach on data from the two spatial transcriptomics methods that profile the most number of genes right now, SeqFISH+ [8] and MER-FISH [30]. As we show, GCNG greatly improves upon correlation based methods when trying to infer extracellular interactions.…”