Facilitating Imaging Mass Spectrometry of Microbial Specialized Metabolites with METASPACE.

Metabolite annotation from imaging mass spectrometry (imaging MS) data is a difficult undertaking that is extremely resource intensive. Here, we adapted METASPACE, cloud software for imaging MS metabolite annotation and data interpretation, to quickly annotate microbial specialized metabolites from high-resolution and high-mass accuracy imaging MS data. Compared with manual ion image and MS1 annotation, METASPACE is faster and, with the appropriate database, more accurate. We applied it to data from microbial colonies grown on agar containing 10 diverse bacterial species and showed that METASPACE was able to annotate 53 ions corresponding to 32 different microbial metabolites. This demonstrates METASPACE to be a useful tool to annotate the chemistry and metabolic exchange factors found in microbial interactions, thereby elucidating the functions of these molecules.

OffsampleAI: artificial intelligence approach to recognize off-sample mass spectrometry images.

BACKGROUND: Imaging mass spectrometry (imaging MS) is an enabling technology for spatial metabolomics of tissue sections with rapidly growing areas of applications in biology and medicine. However, imaging MS data is polluted with off-sample ions caused by sample preparation, particularly by the MALDI (matrix-assisted laser desorption/ionization) matrix application. Off-sample ion images confound and hinder statistical analysis, metabolite identification and downstream analysis with no automated solutions available. RESULTS: We developed an artificial intelligence approach to recognize off-sample ion images. First, we created a high-quality gold standard of 23,238 expert-tagged ion images from 87 public datasets from the METASPACE knowledge base. Next, we developed several machine and deep learning methods for recognizing off-sample ion images. The following methods were able to reproduce expert judgements with a high agreement: residual deep learning (F1-score 0.97), semi-automated spatio-molecular biclustering (F1-score 0.96), and molecular co-localization (F1-score 0.90). In a test-case study, we investigated off-sample images corresponding to the most common MALDI matrix (2,5-dihydroxybenzoic acid, DHB) and characterized properties of matrix clusters. CONCLUSIONS: Overall, our work illustrates how artificial intelligence approaches enabled by open-access data, web technologies, and machine and deep learning open novel avenues to address long-standing challenges in imaging MS.

Data-Driven Rescoring of Metabolite Annotations Significantly Improves Sensitivity.

When analyzing mass spectrometry imaging data sets, assigning a molecule to each of the thousands of generated images is a very complex task. Recent efforts have taken lessons from (tandem) mass spectrometry proteomics and applied them to imaging mass spectrometry metabolomics, with good results. Our goal is to go a step further in this direction and apply a well established, data-driven method to improve the results obtained from an annotation engine. By using a data-driven rescoring strategy, we are able to consistently improve the sensitivity of the annotation engine while maintaining control of statistics like estimated rate of false discoveries. All the code necessary to run a search and extract the additional features can be found at https://github.com/anasilviacs/sm-engine and to rescore the results from a search in https://github.com/anasilviacs/rescore-metabolites .

FDR-controlled metabolite annotation for high-resolution imaging mass spectrometry.

High-mass-resolution imaging mass spectrometry promises to localize hundreds of metabolites in tissues, cell cultures, and agar plates with cellular resolution, but it is hampered by the lack of bioinformatics tools for automated metabolite identification. We report pySM, a framework for false discovery rate (FDR)-controlled metabolite annotation at the level of the molecular sum formula, for high-mass-resolution imaging mass spectrometry (https://github.com/alexandrovteam/pySM). We introduce a metabolite-signal match score and a target-decoy FDR estimate for spatial metabolomics.

SETT--subcutaneous endoscopic transaxillary tenotomy for congenital muscular torticollis.

BACKGROUND: Surgical correction of the congenital muscular torticollis (CMT) is recommended for patients with unsuccessful conservative treatment. Open operative techniques all leave noticeable scars. Tenotomy can be performed endoscopically. We proposed the modified endoscopic two-trocar transaxillary approach for the treatment of CMT. MATERIALS AND METHODS: We have applied a technique of endoscopic release of the sternocleidomastoid (SCM) muscle in 5 pediatric patients. We performed the tunnelization of the space over the clavicular and sternal heads of the SCM muscle applied balloon inflation of the Foley catheter (Fr. 16). The sternal and clavicular attachments were dissected and divided by electrocautery hook. RESULTS: An excellent result was found in all 5 patients. There were no complications to be seen. The neurovascular structures were preserved in all cases. CONCLUSIONS: We believe that the subcutaneous endoscopic transaxcillary tenotomy procedure is a good method for the treatment of congenital muscular torticollis. This endoscopic technique avoids injury to the neurovascular structures and does not leave visible scars.