Comment: Thank you for reading this. Keep in mind that this is ALL about FIA. It’s an extreme case and a special niche. Don’t extrapolate any of this to LC-MS, because it’s mixing apples and oranges. I’ll post later on “Why do we prefer Orbitraps over TOFs for LC-MSMS”, and stress critical flaws of TOF detectors and TOF data analysis. For now, happy reading – possibly to the very end and including the many constructive comments.
As the first post ever, I wanted to tackle a question we are frequently asked.
For >10 years, we analysed all kinds of samples by flow injection analysis – HRMS. I will not discuss the pros and cons of including a column (LC-MS) or omitting it (FIA-MS). In brief, FIA-MS is great to profile endogenous metabolites from cellular systems, less for the more exotic samples that require in-depth MS2. Believe it or not, we used FIA to profile>1.4 million samples and generating data that used in hundreds of publications by collaborators or us. In less than 1% of the cases, we backed up the MS1-only, no-LC FIA data with targeted or MS2 experiments.
We described the original method using a TOF-MS system in a paper published in 2011 (https://doi.org/10.1021/ac201267k). The relevant details are that the flow rate optimum is about 250 microL/min, chromatographic peaks are ca. 7 seconds wide, and the MS is operated at about 1.5-2 Hz MS1 full scan mode to collect sufficient points over the elution peak (this is relevant for quantification, adduct detection, etc.). Ten years later, the field of high-res MS instruments has evolved massively. We witnessed 2-3 new generations of TOF instruments from most vendors. Most importantly, Orbitrap instruments improved massively in speed, to the point that at the necessary scan speed of 1.5 Hz and above, their resolution is better than virtually any TOF system for the whole mass range from 50 to 1000 m/z.
Given that in FIA the only mechanism to discriminate and separate metabolites is by m/z, one would expect that (higher) resolution is the critical factor to maximize coverage. Thus, intuitively, Orbitraps should be better than TOFs.
We are in the lucky position to own both TOF and Orbitrap instruments. Since the original publication, we tested and compared all types of instruments with real-life samples. Ten years later, we still come to the opposite conclusion: TOF-MS is better suited for flow injection analysis of metabolite extracts. Let’s go through real data to explain this unintuitive result.
A representative test
We used a very representative sample for metabolomics: a polar bacterial extract obtained from Escherichia coli happily growing on glucose at an OD600 of about 1, extracted with ethanol, and injecting about 1:100 of the extract (1 microL) without concentrating the sample.
We used a QExactive HF-X at a resolution of 240’000 (AGC 3e6) and an Agilent 6546 QTOF. Both were operated in full scan mode (75-1000) and at a similar MS1 acquisition rate. Data were acquired in profile mode, extracted to mzML, and analyzed with in-house software with optimised settings for the two instruments (you need to trust me on this). At first glance, the spectra look comparable, if not for the very different range of intensities that are obvious because the two instruments “count” differently.
At the relevant scanning rate, the HF-X eclipses TOF’s resolution over the full mass range.
At first, we measured the resolution on profile data. This was done by picking peaks with continuous wavelet transformation, retrieving FWHM, and calculating the resolution (all in Matlab). The results meet expectations: the Orbitrap resolution is 5-6x higher in the low mass range and about 2x better around m/z 1000. The curves are likely to intersect for higher mass ranges, but this is not of interest for small molecule analysis.
… and yet, the TOF detects many more compounds!
So far, so good. Does higher resolution translate into more detected peaks and more detected metabolites? Surprisingly no, it doesn’t. On the Orbitrap, we detect fewer peaks (3566 instead of 5109 on the TOF). If we adopt a simple annotation scheme matching m/z against an E. coli metabolome database (tolerance 0.5 mDa), with the Orbitrap we obtain 122 putative matches (level 4). On the TOF, the number of metabolites is 3x higher (362). In central carbon metabolism, which covers the most relevant and abundant metabolites, the difference in coverage is striking.
How comes that the Orbitrap has 3x higher resolution, but the TOF detects 3x more metabolites? The reason is dynamic range, in particular in the low end. I go first through the explanation before showing how metabolite detection is ultimately affected.
The role of intrascan dynamic range
TOFs have superior intrascan dynamic range. The ones we use adopt ADCs that provide 5+ orders of magnitude of dynamic range. It is not totally linear, but this is not relevant here. The key aspect is that it allows quantifying peaks over 5 decades or so. The intrascan dynamic range of Orbitraps is limited to 3-4 decades. The AGC does a great job to expand the dynamic range between scan and across an entire LC-MS run, but the intrascan dynamic range is less than 4. In addition, there is a limit “imposed” by the Fourier Transform: the ratio between the largest and the smallest centroids in a spectrum is specified to be 5000, and we observed 7000-8000.
In addition, one should keep in mind that the Orbitrap is a trap and needs to confine ions in a finite space to measure their m/z. As the space is limited, capacity is limited. To my knowledge, the max number of charges is 5E6; at least, it is what the AGC allows to select. Working with that many ions is far from ideal because many problems become visible: drops in mass accuracy, peak coalescence, and so on (good examples are https://doi.org/10.1021/acs.analchem.7b05372, https://doi.org/10.1021/ac500140s, and many more). The vendor recommends operating with lower values (around 5E5 or less).
The issue is that even the max number of 5E6 is meagre. As soon as 2-3 very abundant ions coelute, or maybe 20 moderately abundant ions, they will take most of the free seats in the trap before the AGC closes the gate. Less abundant ions are outcompeted. Their presence in the trap becomes highly stochastic. In the most extreme case, they become invisible. It is a form of ion suppression in the trap, which is exacerbated with complex samples. In practice, limited trapping can further restrict the intrascan dynamic range to less than the specified 1:5000. A simple way of experimenting with this is by playing with the AGC.
The problem is given by the “natural” abundance of metabolites in biological samples
In the case of flow injection analysis of e.g. cellular extract, the dynamic range has dramatic consequences. On the right, we compare the profile data directly around m/z 147. We show the profiles of triplicates measurements. Note that the scale of the y-axis is logarithmic. The striking difference is the baseline quality with the TOF: even the smallest peaks are detected very reproducibly.
On the Orbitrap, however, peaks are thinner but only exist for the most abundant features. Thus, the vast majority of peaks visible on the TOF spectrum do not appear in the Orbitrap spectra. This is because of the reduced dynamic range that sacrifices low abundant ions. The limited dynamic range has a drastic effect on metabolite detection because – in a spectrum – metabolites tend to be the lower quantiles. In summary, the limited trapping capacity (and interscan dynamic range) of Orbitraps prevents detecting the majority of metabolites in cellular/natural extracts. With these premises, there is no benefit from higher resolving power.
This problem always occurs when complex spectra with a wide dynamic range have to be fully characterized. The problem also exists in LC-MS (e.g. with lipids) but is particularly acute with flow injection analysis.
Are there workarounds? In theory, anything that reduces the dynamic range of a sample would help. However, all we could think of compromises speed and throughput.
One example is to slice the mass range in smaller chunks like it was done with BoxCar. We tried with 3, 4, and 5 slices with different strategies. The benefits in terms of detection were marginal and far from what we obtain on TOFs.
Our take is that the differences in dynamic range are simply too extreme to be compensated (a linear fix can’t solve a logarithmic problem). Notably, every additional slice requires an independent scan event in the orbitrap. Hence, to keep the cycle time of 1-2 Hz, the resolution must be reduced to 120k and 60k, thereby losing all putative benefits of the Orbitraps.
Consequences for peak integration
Stochastic trapping of rare ions has one more underappreciated consequence: noisy XICs. The lower is the intensity of an ion, the higher is the risk that it will not be detected. This results in either missing values or underestimated counts (depending on how data are processed). The consequence is that the coefficient of variation (or relative standard deviation, RSD) of low abundant ions increases dramatically. On TOFs, the increase in RSD is marginal. Exemplary XICs are shown on the left of the figure.
Workarounds exist. One is to smooth data but only works if sufficient points have been collected during peak elution. An alternative approach is to assume a given chromatographic peak shape and fit it to the measured points (this is what Compound Discoverer seems to do, with some funny effects). Unfortunately, both approaches only mitigate the problem and fail to work in severe cases such as the one of glutamine (GLN) shown below.
We still haven’t found a workaround to operate FIA-like analyses on Orbitraps at full resolution without suffering from its poor dynamic range. We are all ears for novel ideas. For the time being, we stick to TOFs that deliver many more metabolites more robustly.