Peer review of: Ion mobility collision cross-section atlas for known and unknown metabolite annotation in (2020)

REVIEWER COMMENTS 

 

Reviewer #1 (Remarks to the Author): 

 

This paper described the creation and validation of an atlas of CCS values. The atlas is created using a 
combination of experimental CCS (merged from multiple databases) and in-silico predicted CCS. The 
authors show that they are able to predict CCS better than current systems, and that combining 
predicted CCS with other data (m/z, predicted MS/MS) improved candidate ranking. The atlas looks 
to be a resource that has practical utility and the authors do a good job showing that it performs 
well. 

 

1. Overall I enjoyed reading this paper. I think it does a good job in setting out the system, evaluating 
it, and describing the benefits it brings. There are quite a few places in the text though in which 
more thorough proof-reading is required (too numerous to list here). 

 

2. The improvement in CCS prediction is good, and the ML methodology looks sound. I would 
welcome a little more discussion on where the authors think this improvement comes from. Is it 
simply the larger unified database? 

 

3. The SVM regression for CCS prediction: the gamma parameters (note: radial basis, not radio basis) 
seem extraordinarily high (2^8 and 2^13). Depending on the way the RBF function is parameterised, 
this effectively becomes either a linear regression, or something akin to a nearest neighbours 
scheme. Not necessarily a problem, but something that if I saw it in an analysis, would ring some 
alarm bells for me. Have the authors looked into this? 

 

4. When combining with MS/MS, the authors use only in-silico MS/MS on their side. Why is this? in-
silico MS/MS prediction is still not great. For the "unknown" experiments, predicted MS/MS is 
obviously the only route. However, could the authors not have done some experiments with real 
MS/MS library spectra? Playing devils advocate: perhaps the improvement CCS gives about in-silico 
MS/MS is because in-silico MS/MS is not reliable. Would we see the same improvements with real 
MS/MS libraries? There are plenty in the public domain (Massbank, GNPS, HMDB, etc). 

 

5. Reproducibility: the authors do a good job of providing the supplementary material to allow 
another to produce their results. One thing that I cannot find are the in-silico predicted spectra that 
were used in the experimental section. Recreating these would be challenging (e.g. CFM-ID takes a 

long time to run) and could prohibit reproduction. I think they should be included as, e.g., one or 
more .mgf files. 

 

 

Reviewer #2 (Remarks to the Author): 

 

The manuscript by Zhou and colleagues describes a software tool for the large scale deployment of 
ion mobility collision cross-section data for metabolite identification of both known and unknown 
metabolites. While a number of tools have been created previously, including by the senior author, 
to search databases of known standards and also predict CCS values for metabolites without CCS 
measures, AllCCS dramatically extends the use of CCS values and ion mobility for metabolite 
identification and annotation by unifying a number of experimental CCS values to produce 
consensus values and predicting CCS values for an impressive database of putative metabolites. The 
authors demonstrate the use of AllCCS for a number of biological applications and I was impressed 
with the identification of novel metabolites. 

My major comment is that if I have understood AllCCS it is available through a webserver maintained 
by the group and people need to register for access. While the authors make some data available a 
lot of what is generated is behind the webserver interface. What are the plans to making this 
available for the wider community? The database of putative metabolites looks particularly useful. 
Also, if the tools are made freely available to the community they can be greatly improved in terms 
of versatility – as an example I think about the developments of xcms by the community. Have the 
authors considered this? Also, how does the server cope with large datasets as can be generated by 
ion mobility data? 

As a follow up comment while its impressive how well CCS values can be estimated – in many cases 
its less than 2% - can the authors discuss examples where this is not enough. Some discussion of 
what can be separated in terms of structural isomers would help. I am thinking of metabolites such 
as glucose-6-P, glucose-1-P and fructose-6-P which are poorly separated by both chromatography 
and ion mobility. Some examples of where an annotation can be made but not an identification 
would be useful. 

In addition, I do have some specific comments. 

Abstract, line 17. The first sentence should be “the metabolome” rather than just metabolome. I 
think the paper would benefit from a careful read to fix some of the grammar errors that creep in. 

Introduction, line 32. This first sentence is also odd and should be re-written. Similarly for the 
sentences on lines 54, 58 and 66. 

Results. Line 94 – should be laboratories. 

Line 98 “As a result, a total of 3,539 unified CCS values were calculated for 2,193 compounds…” I 
think from what the authors say elsewhere the discrepancy in numbers is down to ionisation mode 

rather than different conformers – its probably worth stating this for the reader and the authors are 
largely annotating one conformer. 

Lines 138-141 also need re-writing. 

Line 283. Is KEGG the best choice for metabolic reconstructions? Is RECON a better alternative for 
human metabolism? 

A minor point – Student’s t-test and student’s t-test are written. I think the correct form is with a 
capital S but needs to be consistent one way or the other depending on whether you believe Student 
was a genuine author or not! 

“All other data supporting the findings of this study are available from the corresponding author on 
reasonable 602 request.” Should the data be made openly available? 

Something is wrong with reference 9. 

 

 

 

Reviewer #3 (Remarks to the Author): 

 

The manuscript by Zhu et al. describes a curated atlas of ion mobility collision cross sections (CCS). 
These collision cross sections are being increasingly used for the purpose of metabolite annotation 
as a complementary parameter by which unknown molecules, as particularly metabolites in 
untargeted metabolomics studies, can be better annotated. Although the number of databases with 
collision cross sections is growing steadily both in number and in size, the dearth of comprehensive 
CCS databases has prompted many scientists to also look into predicting such CCS values using 
machine learning approaches. The authors of this manuscript have, in the past, presented several 
aspects of the work covered in this new manuscript in a number of articles. These include the 
following: Anal. Chem. 2016, 88, 11084−11091 (MetCCS); Anal. Chem. 2017, 89, 9559−9566 
(LipidCCS); Bioinformatics, 33(14), 2017, 2235–2237 (MetCCS) etc. Similar work has also been 
published by other teams, including: Anal. Chem. 2020, 92, 1720−1729; Anal. Chem. 2019, 91, 
5191−5199; Anal. Chem. 2017, 89, 6583−6589; Chem. Commun., 2017,53, 7624-7627; Journal of 
Chromatography A, 1542 (2018) 82–88; Analytica Chimica Acta 924 (2016) 68e76. In that sense, and 
despite the interesting nature of what is being described in the manuscript, the work is seen as 
incremental, but not transformative, so publication in Nature Communications is not seen as a good 
option, and a more specialized Journal such as Analytical Chemistry or The Analyst is recommended.