Recently, we’ve had a pretty good bump in the usage of our ImageStream X (Amnis, now a part of EMD-Millipore), but many of the new users are using the technology to confirm things they’re seeing on the conventional flow cytometers.  So, needless to say, I’ve been doing a bit more phenotyping on the ISX instead of the usual nuclear translocation or apoptosis assays that we typically do.  In doing so, I was reminded of some comments thrown out by Amnis at the 2011 CYTO meeting saying (and I’ll paraphrase) the ISX, and by extension the FlowSight, is the most sensitive cytometer available.  The evidence of such a claim was a screen grab of good ole 8-peak beads (Please don’t get me started).  So, I had some data that I recently collected and thought I’d try and validate those statements with some data that makes sense to me.

It’s a really simple example, but in short it involves a surface marker (coupled to PE), a Live/Dead dye (Green) and a Nuclear dye (Violet).  By conventional flow cytometry, the PE signal was pretty weak and the user was skeptical that the staining was “real.”  So, the idea was to make sure the cells were live (Green low/neg), were actually cells (Violet pos) and had surface staining of PE.  After going through the normal groups of gating, it came time to look at the PE signal.  Surprisingly, it wasn’t bad at all (especially with the 561nm laser cranked up to 200mW), however there were some dimmer PE+ cells that were hanging out a bit too close to the negative.

I remember having a discussion with other people in the lab about using carefully calculated masks to pull out the membrane staining and completely removing the cytoplasmic and/or nuclear background which should bring the negative population pretty much down to zero while retaining the specific PE positive fluorescence.  This procedure is actually pretty simple so I’ll briefly explain it, and if this whole concept of image masking is foreign to you, just think of masks as parts of a cell defined by morphology within which you’re going to measure fluorescence.  This is very different from flow cytometry where you can only measure fluorescence from the entire cell regardless of where that fluorescence is coming from.  With this data, I’m creating a membrane mask, which basically looks like a ring encompassing the outside of the cell.  This should retain most of the specific PE fluorescence and remove both background from intracellular autofluorescence, but also background from the nuclear dye and live/dead dye.  The figures below demonstrate these findings.

The figure to the left is the originally analyzed data.  On the far left is just a gallery of images that show the different fluorescence (the green live/dead wasn’t show since dead cells were gated out).  The top dot plot is a simple SSC/PE scatter plot to show the distribution of the negative and positives.  The image just below is showing the mask used (bluish semitransparent shape overlaying the PE image).  Below that is the ungated population showing the Live/Dead Green fluorescence spilling into the PE channel using the whole cell mask.  And lastly, a histogram showing the PE Fluorescence.  Altogether, a pretty straightforward analysis.  However, I wanted to see what would happen if I restricted the PE mask to only the membrane area, so that is what is shown in the figure below.  Now, it’s important to note that this is the exact same data file, analyzing the exact same group of cells.  The only thing changed here is the mask on PE, which is now shown as a ring overlaying the membrane of the cell.  If you now look at the SSC/PE scatter plot at the top, you can see the dramatic tightening of the negative population, which implies a reduction of the high autofluorescence cells that were trailing to the right of the negative population in the total cell mask.  Another benefit of this restrictive masking strategy was the reduction in the spillover of the green dye into the PE channel as shown in the ungated  Live/Dead Green versus PE plot.  And lastly, when you look at the histogram, you can see unequivocally the increase in separation between the negatives and positives.  To drive the point home a bit more, we can overlay the two histograms so you can see exactly how they match up.  Notice that there is a reduction in the intensity of the positive population as well, but this is likely a similar reduction in background fluorescence as is seen in the negative population.  The key here and really in all of flow cytometry is RESOLUTION.  This is, in fact, what most people are really thinking about when they say ‘sensitivity.’

So, can we confirm the original statement here about these imaging cytometers being the most sensitive cytometers available?  Well, I’m not sure I’m ready to crown this instrument as the winner just yet, but at least in some circumstances, the ability to only analyze the part of the cell that is actually stained or not stained could prove to be an extremely vital tool especially if you need to resolve dimly stained cells from unstained cells.