The copy number and intracellular localization of RNA are important regulators of gene expression. Measurement of these properties at the transcriptome scale in single cells will give answers to many questions related to gene expression and regulation. Single-molecule RNA imaging approaches, such as single-molecule fluorescence in situ hybridization (smFISH), are powerful tools for counting and mapping RNA; however, the number of RNA species that can be simultaneously imaged in individual cells has been limited. This makes it challenging to perform transcriptomic analysis of single cells in a spatially resolved manner. Here, we report multiplexed error-robust FISH (MERFISH), a single-molecule imaging method that allows thousands of RNA species to be imaged in single cells by using combinatorial FISH labeling with encoding schemes capable of detecting and/or correcting errors.

We labeled each cellular RNA with a set of encoding probes, which contain targeting sequences that bind the RNA and readout sequences that bind fluorescently labeled readout probes. Each RNA species is encoded with a particular combination of readout sequences. We used successive rounds of hybridization and imaging, each with a different readout probe, to identify the readout sequences bound to each RNA and to decode the RNA. In principle, combinatorial labeling allows the number of detectable RNA species to grow exponentially with the number of imaging rounds, but the detection errors also increase exponentially. To combat such accumulating errors, we exploited error-robust encoding schemes used in digital electronics, such as the extended Hamming code, in the design of our encoding probes but modified these schemes in order to account for the error properties in FISH measurements. We assigned each RNA a binary word in our modified Hamming code and encoded the RNA with a combination of readout sequences according to this binary word.

We first imaged 140 RNA species in human fibroblast cells using MERFISH with 16 rounds of hybridization and a modified Hamming code capable of both error detection and correction. We obtained ~80% detection efficiency and observed excellent correlation of RNA copy numbers determined with MERFISH with both bulk RNA sequencing data and conventional smFISH measurements of individual genes.

Next, we used an alternative MERFISH encoding scheme, which is capable of detecting but not correcting errors, to image 1001 RNA species in individual cells using only 14 rounds of hybridization. The observed RNA copy numbers again correlate well with bulk sequencing data. However, the detection efficiency is only one-third that of the error-correcting encoding scheme.

We performed correlation analysis of the 10⁴ to 10⁶ pairs of measured genes and identified many covarying gene groups that share common regulatory elements. Such grouping allowed us to hypothesize potential functions of ~100 unannotated or partially annotated genes of unknown functions. We further analyzed correlations in the spatial distributions of different RNA species and identified groups of RNAs with different distribution patterns in the cell.

This highly multiplexed imaging approach enables analyses based on the variation and correlation of copy numbers and spatial distributions of a large number of RNA species within single cells. Such analyses should facilitate the delineation of regulatory networks and in situ identification of cell types. We envision that this approach will allow spatially resolved transcriptomes to be determined for single cells.