Imaging the transcriptome - Wiley Online Library

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Nov 26, 2013 - profiling to single cells were demonstrated a decade ago (Klein et al, 2002), and ... measurements inaccessible to cDNA-based transcriptomics.

Molecular Systems Biology 9; Article number 710; doi:10.1038/msb.2013.67 Citation: Molecular Systems Biology 9:710


Imaging the transcriptome Timothe´e Lionnet* Transcription Imaging Consortium, Howard Hughes Medical Institute, Ashburn, VA, USA * Corresponding author. Transcription Imaging Consortium, Howard Hughes Medical Institute, Janelia Farm Research Campus, 19700 Helix Drive, Ashburn, VA 20147, USA. Tel.: þ 1 571 209 4154; Fax: þ 1 571 209 4941; E-mail: [email protected]

Molecular Systems Biology 9: 710; published online 26 November 2013; doi:10.1038/msb.2013.67

It is well known that genetically identical cells can display a high variability in their gene expression profiles. This phenomenon has a profound impact on a myriad of cellular processes, ranging from differentiation to signaling and drug resistance. Large efforts are therefore currently devoted to understand the mechanistic causes and functional consequences of cell-to-cell variability, often called noise (Lionnet and Singer, 2012). There are multiple sources of cell-to-cell variability: cell cycle stage, circadian clock, metastable epigenetic states, fluctuations in the concentration of regulatory factors, inhomogeneous microenvironments, or the stochastic nature of the molecular steps involved in gene expression. These factors are often hard to separate experimentally because they might be unknown a priori and are often challenging to control: they can range from intracellular concentrations of upstream factors to cell shape or extracellular context. As most genetic circuits involve a vast number of genes, it has proven extremely useful to study genome-wide transcriptomes in order to understand the determinants of gene expression variability. The first applications of microarray profiling to single cells were demonstrated a decade ago (Klein et al, 2002), and RNAseq-based methods have recently contributed to increase the assay sensitivity. As a result, these technologies can now map expression data onto the full genome and identify splicing variants. However, scaling the number of cells in this type of assay is both expensive and challenging experimentally; it has so far been limited to less than 20 cells (Shalek et al, 2013). In parallel, multiplex singlecell qPCR techniques have also made great progress and can now interrogate a relatively large number of individual cells, at the expense of the number of genes analyzed (up to 100 genes in 1000 cells; Figure 1A; Wills et al, 2013). Single-molecule mRNA fluorescent in situ hybridization (smFISH) is the main approach complementing sequencing and microarray-based techniques (Itzkovitz and van Oudenaarden, 2011). It consists in hybridizing multiple fluorescent DNA probes to a given mRNA species in a fixed biological sample. Individual mRNA molecules appear as individual spots and can be counted using dedicated algorithms. The technique has the advantage of preserving the integrity of the sample, and thus allows capturing a wealth of parameters (e.g., cell shape and location, mRNA spatial distribution (Chou et al, 2013) or the expression pattern of a protein of interest) that are usually lost in techniques based on & 2013 EMBO and Macmillan Publishers Limited

cDNA libraries. The main limit of smFISH is its modest throughput: the number of genes one can simultaneously image is limited by the number of spectrally separable fluorescent dyes (B5). Barcoding approaches have increased this number to B30 (Lubeck and Cai, 2012), but these numbers remain exceedingly low compared to the tens of thousands genes composing the human genome. Measuring larger number of genes by smFISH has so far only been possible using artificially labeled reporters in bacteria (Taniguchi et al, 2010). In a recent article, Battich et al (2013) have demonstrated an automated pipeline for smFISH that allowed them to interrogate separately B1000 endogenous genes, collecting data from B11000 individual cells for each gene. This experimental tour de force relies on using a variant of smFISH termed ‘branched DNA smFISH’ (bDNA smFISH). Instead of directly labeling the transcripts with fluorescently labeled probes, the technology uses a combination of primary, secondary and tertiary probes that hybridize together in order to label each target site on a given mRNA with tens of fluorescent labels (Figure 1B). As a result of the increased signal, fluorescence images could be acquired faster than with traditional smFISH, using only a low-magnification microscope objective. This allowed scanning a cell population faster, resulting in an increase in the throughput of the technique. The sensitivity of the bDNA smFISH rivals that of RNAseq over most of the expression spectrum (the dynamic range of the FISH technique is slightly lower for highly expressed transcripts). Using their unprecedentedly large data sets, the authors tested the statistical requirements of single-cell mRNA counting. They found that for most genes, at least 1000 individual cells should be analyzed to recapitulate the mRNA copy number distribution in a reproducible fashion. This finding will constitute an important standard for the developing field of single-cell transcriptomics. The main advantage of the approach lies in its image-based nature. Using an integrated image analysis pipeline, the authors were able to extract a battery of spatially resolved measurements inaccessible to cDNA-based transcriptomics techniques. This information is crucial to investigate determinants of cell-to-cell variability; for instance, as biochemical reactions are dependent on factors concentrations rather than numbers, simply knowing the cell volume is important to normalize copy number fluctuations. Furthermore, the authors found that mRNAs sharing statistical and Molecular Systems Biology 2013 1

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variability because they are able to collect single-cell transcriptomes along with information about the respective environment, morphology and eventually function of each cell. As smFISH approaches high throughput, these advantages will make it a major tool for understanding the regulation, function and dysfunction of gene expression heterogeneity.


Physically isolate individual cells Extact mRNA Generate cDNA library

Conflict of interest

Deep sequencing

Multiplex qPCR

Genome wide profiles for ~20 cells

Quantify ~100 genes in ~1000 cells

The author declares that he has no conflict of interest.


B ~1000 parallel samples | 10 000 cells/sample

Hybridize signal amplifying probe to mRNA in situ


Probe gene 1

Probe gene 2

Probe gene 3


Spot counting, localization and extraction of cell features

mRNA copy number/cell for 10 000 cells / 1000 genes Multivariate dataset includes localization patterns, etc

Figure 1 Techniques used for single-cell transcriptomics. (A) cDNA-based approaches require cell extraction using a micropipette or a sorting device, followed by an amplification step where the RNA content from the single-cell is converted into a cDNA library. Finally the library is analyzed using high throughput sequencing, microarrays or Multiplex qPCR. (B) High-throughput FISH. Multiple samples are separately hybridized to a fluorescent probe targeting a given gene. Imaging tens of thousands of cells from each sample yields statistically significant copy number distributions, as well as information about the cell environment, or the localization pattern of the mRNA species (e.g., localization to the edge of the cytosol as in the rightmost sample).

spatiotemporal expression patterns were likely to encode interacting proteins. This finding demonstrates the important role of mRNA (co)localization in gene expression and may suggest a mechanism explaining why functionally related proteins display correlated expression levels, whereas their respective mRNAs levels are essentially uncorrelated (Gandhi et al, 2011). Image-based, multivariate approaches will play a crucial role in understanding the determinants of cell identity and

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Battich N, Stoeger T, Pelkmans L (2013) Image-based transcriptomics in thousands of single human cells at single-molecule resolution. Nat Methods 10: 1127–1133 Chou YY, Heaton NS, Gao Q, Palese P, Singer R, Lionnet T (2013) Colocalization of different influenza viral RNA segments in the cytoplasm before viral budding as shown by single-molecule sensitivity FISH analysis. PLoS Pathog 9: e1003358 Gandhi SJ, Zenklusen D, Lionnet T, Singer RH (2011) Transcription of functionally related constitutive genes is not coordinated. Nat Struct Mol Biol 18: 27–34 Itzkovitz S, van Oudenaarden A (2011) Validating transcripts with probes and imaging technology. Nat Methods 8: S12–S19 Klein CA, Seidl S, Petat-Dutter K, Offner S, Geigl JB, Schmidt-Kittler O, Wendler N, Passlick B, Huber RM, Schlimok G, Baeuerle PA, Riethmu¨ller G (2002) Combined transcriptome and genome analysis of single micrometastatic cells. Nat Biotechnol 20: 387–392 Lionnet T, Singer RH (2012) Transcription goes digital. EMBO Rep 13: 313–321 Lubeck E, Cai L Single-cell systems biology by super-resolution imaging and combinatorial labeling. Nat Methods 9: 743–748 (2012) Shalek AK, Satija R, Adiconis X, Gertner RS, Gaublomme JT, Raychowdhury R, Schwartz S, Yosef N, Malboeuf C, Lu D, Trombetta JJ, Gennert D, Gnirke A, Goren A, Hacohen N, Levin JZ, Park H, Regev A (2013) Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 498: 236–240 Taniguchi Y, Choi PJ, Li GW, Chen H, Babu M, Hearn J, Emili A, Xie XS (2010) Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells. Science 329: 533–538 Wills QF, Livak KJ, Tipping AJ, Enver T, Goldson AJ, Sexton DW, Holmes C (2013) Single-cell gene expression analysis reveals genetic associations masked in whole-tissue experiments. Nat Biotechnol 31: 748–752

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