Identifying the relationships between chromosome structures, chromatin states, and gene expression is an overarching goal of nuclear organization studies. Because individual cells are highly variable at all three levels, it is essential to map all three modalities in the same single cell, a task that has been difficult to accomplish with existing tools. Here, we report the direct super-resolution imaging of over 3,660 chromosomal loci in single mouse embryonic stem cells (mESCs) by DNA seqFISH+, along with 17 chromatin marks by sequential immunofluorescence (IF) and the expression profile of 70 RNAs, in the same cells. We discovered that the nucleus is separated into zones defined by distinct combinatorial chromatin marks. DNA loci and nascent transcripts are enriched at the interfaces between specific nuclear zones, and the level of gene expression correlates with an association between active or nuclear speckle zones. Our analysis also uncovered several distinct mESCs subpopulations with characteristic combinatorial chromatin states that extend beyond known transcriptional states, suggesting that the metastable states of mESCs are more complex than previously appreciated. Using clonal analysis, we show that the global levels of some chromatin marks, such as H3K27me3 and macroH2A1 (mH2A1), are heritable over at least 3-4 generations, whereas other marks fluctuate on a faster time scale. The longlived chromatin states may represent "hidden variables" that explain the observed functional heterogeneity in differentiation decisions in single mESCs. Our integrated spatial genomics approach can be used to further explore the existence and biological relevance of molecular heterogeneity within cell populations in diverse biological systems. MainCurrently, the main approaches to examine nuclear organization are 1) sequencing-based genomics, which measures contacts between DNA loci [1][2][3][4] and between DNA and nuclear bodies 5-10 , and 2) microscopy-based imaging of chromosomes in single cells, conventionally by multicolor DNA fluorescence in situ hybridization (DNA FISH) 11,12 . Genomics approaches have been powerful in mapping global contacts between chromosomes and have been scaled down to the single cell level [13][14][15][16][17][18][19] . However, reconstructing .

Single molecule FISH (smFISH) has been the gold standard in quantifying individual transcripts abundances. Here, we demonstrate the scaling up of smFISH to the transcriptome level by profiling of 10,212 different mRNAs from mouse fibroblast and embryonic stem cells. This methods, called RNA SPOTs (Sequential Probing of Targets), provides an accurate and low-cost alternative to sequencing in profiling transcriptomes.RNA sequencing (RNAseq) 1,2 has been a powerful method to quantify RNAs in a diverse range of biological samples. While RNAseq has replaced microarrays as the de-rigueur method for genomics studies because of higher sensitivities and dynamic range, reverse transcription and other steps needed to convert RNA to cDNA to sequencing libraries can introduce biases in the quantitation of mRNAs. Moreover, sequencing the RNAs at nucleotide level is not necessary for counting the abundances of transcripts. Single molecule fluorescence in situ hybridization (smFISH) 3,4 , which directly hybridize DNA oligonucleotide probes to transcripts in cells, is highly sensitive and accurate in quantitating mRNA abundances. Here, we demonstrate transcriptome level profiling of mRNAs with single molecule sensitivity and high accuracy using a method based on sequential FISH (seqFISH) 5 . We had shown that seqFISH can be applied to image hundreds of transcripts in cells and tissues 6 , image dynamics of chromosomes 7 and allow lineage tracking with single cell resolution 8 . However, the major limitation of seqFISH is that optical diffraction limit prevents many mRNAs from being resolved simultaneously in single cells. In principle, super-resolution microscopy 9 and expansion microscopy 10 can resolve the optical density issue in situ.

Identifying the relationships between chromosome structures, chromatin states, and gene expression is an overarching goal of nuclear organization studies. Because individual cells are highly variable at all three levels, it is essential to map all three modalities in the same single cell, a task that has been difficult to accomplish with existing tools. Here, we report the direct super-resolution imaging of over 3,660 chromosomal loci in single mouse embryonic stem cells (mESCs) by DNA seqFISH+, along with 17 chromatin marks by sequential immunofluorescence (IF) and the expression profile of 70 RNAs, in the same cells. We discovered that the nucleus is separated into zones defined by distinct combinatorial chromatin marks. DNA loci and nascent transcripts are enriched at the interfaces between specific nuclear zones, and the level of gene expression correlates with an association between active or nuclear speckle zones. Our analysis also uncovered several distinct mESCs subpopulations with characteristic combinatorial chromatin states that extend beyond known transcriptional states, suggesting that the metastable states of mESCs are more complex than previously appreciated. Using clonal analysis, we show that the global levels of some chromatin marks, such as H3K27me3 and macroH2A1 (mH2A1), are heritable over at least 3-4 generations, whereas other marks fluctuate on a faster time scale. The long-lived chromatin states may represent “hidden variables” that explain the observed functional heterogeneity in differentiation decisions in single mESCs. Our integrated spatial genomics approach can be used to further explore the existence and biological relevance of molecular heterogeneity within cell populations in diverse biological systems.

Single molecule FISH (smFISH) has been the gold standard in quantifying individual transcripts abundances. We recently demonstrate the scaling up of smFISH to the transcriptome level by profiling of 10,212 different mRNAs from mouse fibroblast and embryonic stem cells. This method, called RNA SPOTs (Sequential Probing of Targets), provides an accurate and low-cost alternative to sequencing in profiling transcriptomes. This protocol contains the steps to carry out RNA SPOTs. It accompanies Eng et al, Nature Methods