It has been more than two months since we got our XT instrument installed at the CRUK Scotland Institute, thanks to generous funding from the UKRI FLF programme. Since then, we have been busy QC’ing existing and new panels. Why? Because with the privilege of having our own instrument, and having access to it all the time, comes responsibility: responsibility for developing internal standards and robust expectations for experimental design and result interpretation.
Having now finished the analyses of our initial QC, there are a few lessons that I thought would be useful to share with the wider community. These are not universal rules, but QC principles that emerged from our first XT workflows. I share them in the spirit of collaboration. They are for people generating data, and for those evaluating datasets as reviewers, editors, collaborators or future data re-users.
I know that time is limited for everyone these days, so I shall keep this in a bite-sized format, supported by actual data from a recent presentation I gave at UMons, thanks to Standard BioTools. There are six key bites, with the take-home messages also captured visually in the following scribble. Thanks ChatGPT!
1) Do not validate signalling antibodies without signalling biology
Understand the biology of what you are measuring, and let that biology shape your experimental validation.
If you are validating a signalling antibody, you need more than a positive-looking signal. You need a signal-inducing perturbation, measurements that cover the relevant time scale of the signalling event, targeted inhibitors or genetic controls to assess antibody specificity, and knowledge of the cell states or cell types that are actually permissive to the signalling event in question.
In other words: a signalling antibody should be tested in a context where the signal is expected to change, at a time point when the pathway should respond, and in cells where the pathway is competent to respond.
2) Do not mistake abundance for antibody performance
The ability to measure large differences in antigen abundance may be confounded by suboptimal antibody performance.
A low-sensitivity antibody can appear to perform well when the target is abundant, but fail when the target in a sample is close to the limit of detection. This is especially important because an antibody can be specific and still not be sensitive enough for a particular biological context, fixation condition, dissociation workflow or sample preparation.
In the example below, two different pERK T202/Y204 antibodies give similar distributions in an iPSC sample that had not suffered antigen leakage. At first glance, both antibodies look acceptable. However, in the more challenging setting of fixed venous-like endothelial spheroids subjected to dissociation, the picture is very different. We now clearly unmask a substantially lower sensitivity for one of the two antibodies.
The lesson is simple: do not assume that antibody performance observed in an easy sample will hold in a harder one. Test antibodies under conditions that resemble the actual samples you care about.
3) Do not overinterpret distributions from n < 500 cells
The previous example was not the only plot twist.
The more sensitive pERK antibody gave rise to a very different single-cell distribution in an otherwise identical sample, with one important exception: the number of target cells was lower, with fewer than 500 endothelial cells after debarcoding and gating.
This got us thinking. How many target cells do we need after debarcoding and gating to recover a stable approximation of the underlying single-cell distribution?
The simulation below holds the answer. Based on this analysis, we now treat n > 500 as a practical lower bound, and n > 750 as preferable, for robust distribution-level inferences in this type of analysis. Anything lower can result in substantial sampling variability, especially if the biological question depends on the shape of the distribution rather than just a simple average.
Of course, this is not a universal threshold for every marker, cell type or biological question. Rare subpopulations, skewed distributions, subtle treatment effects and clustering-based analyses may require more cells. But the general principle is important: if the target population is too small, the distribution you see may be as much a sampling artefact as a biological feature.
4) Do not ignore isotope impurity
Mass cytometry relies on rare earth metal-conjugated antibodies. These metals are not 100% pure. Each metal has characteristic impurity percentages into neighbouring mass channels of the same element, and occasionally into adjacent elements. These values are available from the published isotope purity matrix.
This matters because isotope impurities can become significant confounders when a high-signal antigen is detected through a metal with substantial impurity into another channel.
In the example below, 168Er constitutes approximately 3.2% of 167Er. In practical terms, this means that approximately 3.2% of one of my pERK antibodies is actually tagged with 168Er rather than 167Er. If the pERK signal is very high, then this 3.2% contribution becomes significant in absolute terms. It can then contribute to the apparent detection of an unrelated marker, if that marker is detected with an 168Er-conjugated antibody.
In the example, pSMAD2/3 is detected with 168Er in two different panels: one that contains a 167Er-conjugated pERK antibody, and one that does not. There is clear evidence of spillover. In this case, the issue was both predictable from the isotope purity matrix and visible in the data, so our decision was simple: avoid this combination altogether in the final panel.
The lesson is that predictable impurity risks should be considered during panel design, especially when high-signal donor channels sit next to lower-signal recipient channels.
5) Do not rescue bad panel design with computational correction if you can avoid the problem upfront
Beyond isotope impurities, you also need to be careful of oxides. Some elements are more oxide-prone than others. 150Nd is one example. It can give rise to a derivative that is +16 Da heavier, meaning that it is detectable in the 166 mass channel.
In one of our panels, we had 150Nd tagged onto an antibody used for SOX2 detection. SOX2 is highly abundant in iPSCs, so we worried that it might confound our measurements of total AKT through a 166Er-conjugated antibody. This would be especially concerning if the total AKT signal were low, either because of intrinsic protein abundance or because of low antibody sensitivity.
How can we check this? Easily. Plot the total AKT signal against the expected contaminating counts contributed by the 150Nd donor channel. This can be calculated for different oxide fractions, up to the established maximum of 2.1%.
As you can see in the following plot, anything that falls close to, or on, the red lines may be significantly confounded by oxide formation.
To sum up: be aware of oxide formation, and avoid placing low-abundance or low-signal targets in channels that may suffer from oxide spillover. Rather than attempting computational correction after the fact, avoid the problematic combination entirely where possible.
6) Do not trust correlations before estimating the technical floor
We all know that correlation is not causation. But when is a correlation worth noticing in the first place?
It is well known from proteomics that cell size and total protein content can drive variation across many measured proteins. Bigger cells, or cells with higher total protein content, tend to produce higher signal across many channels, including background. This means that correlations between measured markers are often expected to be positive, even when there is no meaningful biological relationship between those markers.
The same applies to mass cytometry if you have no way of correcting for cell size or total protein content. In other words, a positive correlation may be worthless.
How would you know?
Our tip is to include at least one negative marker in all panels. By negative marker, I mean one or more antibodies that should only give rise to background staining, either because they do not work in that context or because their antigen is not present in your sample. You can then calculate the correlation between these negative markers and all other markers to establish a technical correlation floor.
As shown below, the 95th percentile of this technical correlation floor can vary depending on the stickiness of your samples. This means it is important to estimate it empirically for each sample preparation. Otherwise, you risk mistaking generic sample-wide variation for coordinated pathway activity, signalling rewiring or meaningful cell-state biology.
Bottom line
In highly multiplexed single-cell signalling assays, rigour is not demonstrated by the number of markers measured. It is demonstrated by showing that each signal is biologically plausible, technically detectable, sufficiently sampled and protected from predictable channel-level artefacts.
This is not a nice-to-have. It is a must-have, especially as AI-driven models increasingly ingest datasets without access to the experimental execution details, tacit domain knowledge or unpublished QC standards needed to interpret them properly.
Acknowledgement: Initial draft written by Dr Ralitsa Madsen. ChatGPT 5.5 / Claude Opus 4.7 were used for type-setting, typo corrections and textual clarifications. The final content was reviewed and edited by the author.
]]>
