Systems biology is a field that aims to understand the behavior and interactions between multiple biological components, in a quantitative manner. Thus, systems biology routinely uses “-omics” analyses, on whole cell populations, yielding an average behavior of heterogenic populations. Little is known about the interaction of multiple components in a single cell.
Fluorescent microscopy is a great tool to study events in whole populations at the single cell level: i.e. determine the exact behavior of 100 or 1000 single cells in the population and extrapolate from that on the heterogeneity of the population, and which different parameters may affect one cell and not another in the same population (which could be isogenic or hetrogenic). However, fluorescent microscopy is limited by the number of spectrally distinct fluorophores. Therefore, most experiments make use of 1-3 colors to study 1-3 different components (proteins or RNA). Thus, systems biology on a single cell level is a challenge not easily met. Here, Eric Lubeck and Long Cai describe a novel method that allows visualizing multiple RNA species in single cells.
Before I go on to their paper, I want to briefly discuss mRNA detection by optical means. There are two major methods to optically detect mRNAs in cells. The first method, suitable for live-cell imaging, is by tagging an RNA binding protein with a fluorescent protein. Thus, by following the protein we are following the RNA it binds to. However, RNA binding proteins typically associate with multiple mRNA species in the cells. Therefore, an artificial system was developed (commonly called the MS2 system). In this method, a specific sequence, MS2 binding sequence (MBS) is genetically inserted into the RNA we wish to study. This sequence is recognized specifically by the bacteriophage MS2 coat protein (MCP). Thus, MCP, fused to a fluorescent protein, specifically recognizes only the one MBS-tagged RNA. Similar systems, based on the PP7 coat protein and the U1A protein were developed, allowing visualization of two-three mRNAs in the same cell.
The second method, termed Fluorescent in situ hybridization (FISH), uses the inherent specificity of nucleic acid binding – complementing single-strand sequences can hybridize to form a double stranded (ds)DNA, dsRNA of DNA:RNA hybrid. Thus, with FISH, we use short, fluorescently labels, DNA oligonucleotides (typically 20-50 bases long) that are complimentary to the RNA species we wish to study. The disadvantage of this method is that you must fix the cells (thus, it is not used for live imaging).
Up until now, FISH probes for a single mRNA were labeled with the same color (in some cases, people use two colors to distinguish the 5’ and 3’ of an mRNA). Lubeck & Cai took the FISH method a huge leap forward, devising a nanoscale colored “barcode” system which allowed them to visualize 32 different mRNA species simultaneously in the same yeast cell.
Their initial strategy was to spatially order probes in different colors along the same mRNA. The length of each probe was 20 bases, which is equal to about 7nm. Super resolution microscopy can get to 1-20nm resolution, so theoretically, if different mRNAs have probes in different orders along its length (see figure), the order of the probes on each mRNA can be resolved, thus identifying that mRNA. For the first attempt, they hybridized 12 probes, four of each color, in a spatially order way (four green, four red, four blue or four red, four green four blue) to the PUN1 or GFP mRNAs. They calculated that four probes for each color increase the probability of at least one probe hybridizing to 99%, and probes from all three colors to 96%, which is what they detected (i.e. 96% of the PUN1 mRNAs showed discrete spots that showed co-localization in all three colors. Using Gaussian fitting, they were able to correctly determine the barcode of 74% of the mRNAs. That is pretty good!
This method is good enough if you have a few labeled mRNAs in the cytoplasm. However, when the transcript density increases spots may overlap thus making it impossible to read the barcode. The authors then used a different strategy to label and visualize the mRNAs. This time they used photoswitchable dye pairs. What it means is that you have probed that are labeled with activator dyes and probes that are labeled with emitter dyes. Only when an activator dye is close to the emitter dye you will get fluorescence of the emitter. This method reduces background, since you must have both probes hybridizing one next to the other. They then turned to an imaging method called stochastic optical reconstruction microscopy (STORM) which is used for super-resolution microscopy. Again, the dye pairs were spatially distributed along the mRNA so that the spatial order will create a barcode. This method also allowed for proper barcode identification of ~72% of the mRNAs.
A major problem with both methods is that to get better barcode identification, the RNAs need to be linearized. They do that by compressing the cells between the coverglass. However, they also destroy the spatial organization of the cell. Another drawback is the obvious need for high-resolution microscopy that also requires high photon emission by the fluorophores.
So they turned to a third strategy: spectral barcoding. In spectral barcoding, the order in which the fluorophores are distributed along the mRNA molecule is not important, only the presence of the correct colors is relevant. This reduces the need for super-resolution (since 100nm is sufficient for single mRNAs), no need for linearization of the mRNA, and less photons are required. With this technique, they got 100% positive identifications (compared to 72-74% with the spatial barcode).
As proof of principle, they profiled the transcripts from 32 stress-responsive genes in single yeast cells, in response to calcium stress: 5 general stress response genes, 14 regulated by Crz1 transcription factor and 13 which are related to ageing and other stress markers. All in all they use barcodes of 3 colors combinations out of 7 possible colors (which gives a total of 35 possibilities).
First, they validated their results by comparing their spectral barcoding to single mRNA FISH, to qPCR and by comparing to different sets of barcodes. Their results seem pretty convincing. The main concern was “cross-talk” among the barcodes due to background fluorescence, blinking of non-specifically bound probes, cross-talk among fluoreophores and computational errors.
To overcome these concerns, they only accepted emission values 3 standard deviations above background “cross-talk”; in their imaging scheme the inserted frames with no activation of the activators (to reduce auto fluorescence); and they intentionally left 3 barcodes unassigned so they could measure the false-positive rate. This appeared to be 0.67±0.84 copies per cell. twelve genes were within that range of ~1 copy per cell, so they left them out of future calculations.
They then compared the entire expression profiles of the 32 genes in ~60 cells. From their results it is evident that there is a great deal of heterogeneity. Actually, it looks like each cell responded differently to the same stress!
They then go on to try and find correlations between genes that are differentially regulated, transcription bursts in wild type & mutant strains and so on.
So, here they have proof of principle of “only” 32 genes (a great achievement on it own). In their discussion, they mention that the total area covered by the mRNAs that they imaged was only 2% of the cell area. They claim that addition of another color to the barcode would enable to profile 792 different genes. Scaling up will require 3D super-resolution microscopy, an additional color (which gives 18,532 possibilities) but also more sophisticated algorithms to identify single mRNAs. If true ( and I believe we can get there within a few years) that would be amazing, and will open an entire new area of research for single-cell systems biology.
We could then see how single cancer cells react to a drug, and maybe determine why the cells who didn’t die survived, and find a compatible treatment.
We could determine how single neurons in a neural circuit in the brain differ from one another, and maybe get more clues as to how memories are formed.
Lubeck E, & Cai L (2012). Single-cell systems biology by super-resolution imaging and combinatorial labeling. Nature methods, 9 (7), 743-748 PMID: 22660740