dataesr/scientific-paragraphs-categorization huggingface dataset:
Here, we describe a novel software called PGFinder for the analysis of MS data. PGFinder is a versatile and straightforward open-source software tool that allows automated identification of muropeptides based on the creation of dynamic databases. Sharing PGFinder as a Jupyter Notebook provides a robust and consistent pipeline with the potential to accelerate discovery in the field of peptidoglycomics. This workflow described here allows a comprehensive description of the analysis strategy for a consistent and reproducible PG structure analysis by users in the community. We applied the PGFinder pipeline to analyse the muropeptides composition of Escherichia coli which has been extensively studied. We demonstrate that PGFinder can capture an unprecedented level of complexity of PG structure, highlighting the limitations of the search strategies reported so far.
PGFinder: a dedicated script for bottom-up identification of PG fragments No pipeline is currently available for the automated analysis of MS PG data. Therefore, we sought to replicate a shotgun proteomics approach to create an analysis pipeline dedicated to PG analysis, referred to as 'peptidoglycomics'  (Wheeler et al. , 2014). To limit misidentifications due to mass coincidences, we established a search strategy relying on an iterative process (Figure  2 ). A first search was carried out using a database made of reduced disaccharide peptides (monomers) and their theoretical monoisotopic masses. MS data were deconvoluted using the Protein Metrics Byos software to generate a list of observed monoisotopic masses alongside other parameters including retention times and signal intensity. Individual theoretical masses contained in the monomer database (Figure  2 , database 1) were compared with observed masses in the experimental dataset.
Any observed mass within 10 ppm tolerance was considered as a match and the corresponding inferred structure and theoretical mass were then added to a list of matched structures (Figure  2 , library 1). As a second step, we used the list of matched monomers to build another database in silico (Figure  2 , database 2), corresponding to dimers and trimers and their theoretical masses. Two types of polymerisation events are included in the original PGFinder version depending on the type of crosslink either through peptide stems or glycan chains. Individual theoretical masses from the in silico database were compared to observed masses to generate a list of matched dimers and trimers (Figure  2 , library 2). As a third step, we combined the lists of matched monomers and multimers to generate a final library of modified muropeptides (Figure  2 , library 3).
The final library contained only modified muropeptides corresponding to matched monomers, dimers, and trimers. The modifications accounted for include the presence of anhydro groups, deacetylated sugars, amidated amino acids, and modifications resulting from N-acetylglucosaminidase or amidase activities (loss of GlcNAc and lack of peptide stems, respectively). In-source decay products (loss of GlcNAc) and Na + /K + salt adducts were also added to library 3. All three libraries corresponding to observed monomers, dimers, trimers, and their modified variants were combined to search the MS data for masses matching theoretical values within a 10 ppm mass accuracy window. This search generated results processed by PGFinder to carry out a 'clean up step'. The intensities of in-source decay products and salt adducts were combined with that from parent ions when found within close crossbridge) and 3-3 bonds are made by D,D-or L,D-transpeptidases, respectively.
The enzymes catalysing 1-3 and 4-2 bonds remain unknown. Acceptors stems are shown in blue and donor stems in red. DAA: diamino acid; m-DAP: meso-diaminopimelic acid; D-Lac: D-lactate; X: cell surface polymer (e. g teichoic acid); Z: lateral chain. Flowchart outlining the algorithm for the matching script. The identification of muropeptides was carried out using four successive steps, indicated by different colours (orange, green, blue, and red, respectively). As a first step, observed masses in the dataset are compared to a list of theoretical masses corresponding to monomers (database 1). Matched masses within the ppm tolerance set (10 ppm for Orbitrap data) are used to build a list of inferred monomeric structures and their corresponding theoretical masses (library 1). This is then used to generate a list of theoretical multimers (dimers and trimers) and their masses (database 2).
A second matching round is carried out to build a list of inferred multimers (library 2). At this stage, matched monomers and multimers are combined to generate a list of modified muropeptides (library 3). Two libraries of matched theoretical masses (monomers and dimers, trimers) and a third library (their modified counterparts) are used to search the dataset. Muropeptide structures are inferred from a match within tolerance between theoretical and observed masses. This data is then 'cleaned up' by combining the intensities of ions corresponding to in-source decay and salt adducts to those of parent ions. The final matched mass spectrometry data is then written to a. csv file. O OH NH O O H O CH 3 C H 3 COOH C CH 3 O O OH NH 3 + O O H OH C H 3 COOH O OH NH O O H O CH 3 C H 3 COOH O P O - O O X O OH NH O O H OH C H 3 COOH CH 2 OH O O OH NH O O H OH C H 3 O O OH NH O O H O CH 3 C H 3 COOH O O OH NH 3 + O H O H OH O OH NH O H O H O CH 3 O P O - O O X O OH NH O H O H O CH 3 C O CH 3 1-6 Anhydro Figure 1 continued retention time (a 0.
Using PGFinder to investigate PG structure and identify low-abundance muropeptides The performance of the matching script was tested using the well-characterised PG from E. coli as a proof of concept. UHPLC-MS/MS data were acquired for three independent PG samples (biological replicates; Appendix 1-figure  1 ). Following MS1 spectral deconvolution (a process calculating masses from observed m/z values), observed masses were matched to theoretical muropeptides masses according to the strategy described above (Figure  2 ). A first search was carried out using a minimal mass database made of 10 simple PG fragments including three glycan chains (di-, tetra-, and hexasaccharides) and seven monomers (Table  1 -source data 1). Due to the. csv format of the database, the diamino acid in position 3 could not be assigned to a symbol or Greek letter and had to be one of the 26 letters already assigned by the IUPAC-IUB Joint Commission on Biochemical Nomenclature.
We used the letter J for mDAP for the initial search and replaced it by the letter m in the final table. The output of the automated search is a. csv file per dataset; all files corresponding to biological replicates were collated into one Excel file (Table  1-source data 2). Each search output contained approximately 3000 rows of masses and corresponding parameters. Depending on the dataset analysed, 41-48% of the total ion intensity was assigned to PG structures. As anticipated, inferred structures were frequently found with multiple retention times, reflecting the existence of stereoisomers, with one species accounting for most of the intensity. In some cases, observed masses matched with more than one inferred structure. The output of the automated search was consolidated as described in Supplementary file 1.
05. This value is higher than an earlier study that reported a predominant chain length of 5-10 disaccharide units  (Harz et al. , 1990 ), but agreed with recent work that reported long glycan chains in E. coli  (Turner et al. , 2018). Overall, the quantification of muropeptides across biological replicates was very consistent, with Pearson's correlation coefficients >0. 96 (Appendix 1-figure  2 ). The most pronounced variations in quantification were observed with low-abundance muropeptides accounting for less than 1% of the species identified. We further explored the structural diversity of E. coli PG, performing a more complex search with a mass database made of glycan chains and all possible monomers containing di-, tri-, tetra-, and pentapeptide stems (Table 1-source data 4; 224 structures in total).
Several monomers with tetra-and pentapeptide stems containing unusual amino acids were identified. Only four structures could be confirmed by MS/MS analysis and corresponding multimers were retained (GM-AEJF, -AEJN, AEJK, -AEJAK, and GM-AEJKD). Collectively, muropeptides containing unusual amino acids accounted for ca. 7. 5% of the 80 structures identified (Table  1 -source data 5) using a complex mass database. Using PGFinder for the comparative analysis of PG structures We showed with E. coli data that PGFinder is suitable to characterise the high-resolution structure of PGs using a 'bottom-up' approach. However, this requires a careful analysis of the search output to confirm the identity of muropeptides identified and discriminate between multiple structures that can be assigned to a unique observed mass.
A more basic application is the use of PGFinder in organisms that have already been studied in detail to either compare PG composition or quantify the abundance of specific structures. This application accounts for most PG analyses described in the Source data 4. E. coli complex mass database. Source data 5. E. coli muropeptide complex table. Table literature, comparing PG structures between different isolates, isogenic mutants, or cells grown in different conditions. We chose the PG of C. difficile as a proof of concept to demonstrate how PGFinder can carry out a straightforward comparative analysis. We prepared PG samples corresponding to biological triplicates from two clinical isolates, R20291 and M7404. To illustrate the versatility of the software, PG samples were digested by mutanolysin, and disaccharide peptides were beta-eliminated to generate lactyl-peptides  (Tipper, 2002)  and analysed by UHPLC-MS (Figure  4 -figure supplement 1).
Since the high-resolution PG structure of C. difficile has been described based on the MS/MS analysis of muropeptides  (Bern et al. , 2017) , we used data previously published to build a PG fragment database for a 'one-off' matching step. The database included monomers identified by MS/MS, containing unusual amino acids and the corresponding dimers resulting from either D,D-or L,D-transpeptidation (with a AEJA or AEJ peptide as donor stem). To limit the complexity of the search output, we limited the list of trimers, tetramers, and pentamers to those containing the most abundant peptide stems found in dimers (AEJA, AEJ, or AEJG). The database of theoretical masses contained 74 PG structures described in Figure  4 -source data 1. MS data were deconvoluted using Byos Feature Finder, and observed monoisotopic masses were matched to theoretical masses using PGFinder.
Benchmarking the automated PG analysis pipeline using available datasets The analysis of E. coli PG established a proof of concept, showing that our matching script is suitable for the automated analysis of PG MS data. We next sought to evaluate the robustness of our peptidoglycomics pipeline using available datasets described in the literature. The most suitable publication was a recent study by Anderson et al. describing a PG analysis of P. aeruginosa planktonic cells  (Anderson et al. , 2020b). Unlike most (if not all) studies published to date, this work provided datasets from biological and technical triplicates. Unlike our E. coli samples, analysed on a Q Exactive Focus Orbitrap (Thermo), P. aeruginosa samples were analysed on an Agilent Q-TOF mass spectrometer. Spectra were deconvoluted using the Byos Feature Finder module, and observed masses were matched using a mass tolerance of 25 ppm as described by Anderson et al.
A PG analysis workflow using freely available tools The automated identification of P. aeruginosa muropeptides using PGFinder and the strategy reported previously  (Anderson et al. , 2020a; Anderson et al. , 2020b)  relied on commercially available deconvolution software (ProteinMetrics Byos or Agilent MassHunter, respectively). We sought to identify a free alternative software for mass deconvolution to make our PG analysis pipeline accessible to everyone. MaxQuant was selected as a tool of choice since it represents a widely used software package to analyse high-resolution mass-spectrometric data for shotgun proteomics  (Cox and Mann, 2008). MaxQuant has native support for Thermo MS data (RAW) and Sciex (WIFF) file formats and supports the open MS data format mzXML. Virtually any proprietary MS data file can be converted to the mzXML open format using freely available tools such as Proteowizard and TOPPAS, making this workflow universally applicable.
As proof of concept, we converted P. aeruginosa data to an mzXML file (Appendix 2-figure  1 ), processed it using MaxQuant for mass deconvolution (Appendix 2figure  2 ), and analysed it using PGFinder for PG structure and composition identification. We were able to identify all the expected muropeptides (Table  2 ). This result confirms that the automated analysis of PG datasets can be carried out using the MaxQuant freeware and our open-source script PGFinder (Table  2  and Table 2-source data 2 ).
Discussion This study describes a workflow for the unbiased and automated analysis of bacterial PG using freely available resources. We analysed high-resolution MS datasets corresponding to PG fragments and demonstrated that this approach is a powerful tool to identify muropeptides and carry out comparative analyses based on the MS1 data. MS analysis of bacterial PG has been carried out since the late 1980s  (Garcia-Bustos et al. , 1988; Martin et al. , 1987). Whilst rp-HPLC-MS is now routinely used to explore PG structure, data analysis remains a manual process. Therefore, this step has become a bottleneck that prevents highthroughput analyses and introduces a series of issues regarding reproducibility. A major issue deals with the definition of the search space used since the list of muropeptides searched is not provided.
The PGFinder algorithm Source data 2. C. difficile 20291 versus M7404, list of muropeptides, abundance, RT. GM-AEJ-GM-AEJK. GM-AEJ-GM-AEJKA (anhydro). GM-AEJ-GM-AEJKA. GM-AEJ-GM-AEJA-GM-AEJKA (anhydro). The online version of this article includes the following source data for table 2: Source data 1. Pseudomonas aeruginosa matched muropeptides not reported previously. Source data 2. Raw output of automated search using MaxQuant and PGFinder. Table  2  continued has been designed to create dynamic databases that are ultimately combined to perform the final matching process. Optimisation of the search space relies on a preliminary identification of masses matching theoretical monoisotopic masses of monomers, limiting misidentifications based on mass coincidence. Another advantage is that the only information provided by the user is a restricted monomer database rather than a comprehensive one.
This avoids a time-consuming operation, prone to human error. PGFinder uses XIC for quantification of PG fragments, providing high resolution, sensitivity, and reproducibility, as indicated by the comparisons across biological replicates (Appendix 1figure  2  and Figure  4a ). It enables the accurate quantification of molecules with overlapping retention times and those present in very low abundance, with a large dynamic range (typically six orders of magnitude). Although a Matlab-based software package (Chromanalysis) has been described to automate the detection and quantification of UV peaks through Gaussian fitting  (Desmarais et al. , 2015) , quantification using XIC is more straightforward. It is worth pointing out that the approach described here does not give absolute quantification of muropeptides.
It is worth pointing out that none of the papers describing PG analysis published to date has reported the list of structures searched in the MS data analysed. One of our objectives was to create an automated PG analysis tool accessible to the broadest audience possible, including people with no prior experience with programming or coding languages. Therefore, we shared PGFinder as a Jupyter Notebook allowing users to customise the search strategy depending on both the question asked and the instrument accuracy. PGFinder is particularly suitable for the characterisation of novel PGs with unknown composition or structural modifications and can be modified by users to add novel functionalities. However, a current limitation of this workflow is that it does not process MS/MS data. Therefore, the fragmentation spectra of individual monomers must be checked using dedicated tools to validate that the inferred structures are correct.
Both reduced disaccharide peptides or lactyl-peptides (generated by beta-elimination) are identified using PGFinder. We anticipate that an open access to PGFinder, in conjunction with freely available deconvolution tools, will allow researchers to carry out comparative MS1 analyses. The pipeline defined in this work enables reproducible and consistent data analysis. This represents the first step towards a standardised approach to PG analysis, opening the possibility to reanalyse datasets in repositories. The Preparation of soluble muropeptides PG (1 mg) was digested overnight with 25 µg of mutanolysin at 37 °C in 150 µl of 20 mM sodium phosphate buffer (pH 5. 5). Soluble disaccharide peptides were recovered in the supernatant following centrifugation (20,000 × g for 20 min at 25 °C). To reduce muropeptides, equal volumes (200 µl) of the solution of disaccharide peptides and of borate buffer (250 mM, pH 9.
UHPLC-MS/MS An Ultimate 3000 Ultra High-Performance Chromatography (UHPLC; Dionex/Thermo Fisher Scientific) system coupled with a high-resolution Q Exactive Focus mass spectrometer (Thermo Fisher Scientific) was used for LC/HRMS analysis. Muropeptides were separated using a C18 analytical column (Hypersil Gold aQ, 1. 9 µm particles, 150 × 2. 1 mm; Thermo Fisher Scientific), column temperature at 50 °C. Muropeptides elution was performed by applying a mixture of solvent A (water, 0. 1 % [v/v] formic acid) and solvent B (acetonitrile, 0. 1 % [v/v] formic acid). After 10 µl sample injection, MS/MS data were acquired during a 40 min step gradient: 0-12. 5% B for 25 min; 12. 5-20% B for 5 min; held at 20% B for 5 min, and the column was re-equilibrated for 10 min under the initial conditions. The Q Exactive Focus was operated under electrospray ionization (H-ESI II)-positive mode.
Figure2. Flowchart outlining the algorithm for the matching script. The identification of muropeptides was carried out using four successive steps, indicated by different colours (orange, green, blue, and red, respectively). As a first step, observed masses in the dataset are compared to a list of theoretical masses corresponding to monomers (database 1). Matched masses within the ppm tolerance set (10 ppm for Orbitrap data) are used to build a list of inferred monomeric structures and their corresponding theoretical masses (library 1). This is then used to generate a list of theoretical multimers (dimers and trimers) and their masses (database 2). A second matching round is carried out to build a list of inferred multimers (library 2). At this stage, matched monomers and multimers are combined to generate a list of modified muropeptides (library 3).
Two libraries of matched theoretical masses (monomers and dimers, trimers) and a third library (their modified counterparts) are used to search the dataset. Muropeptide structures are inferred from a match within tolerance between theoretical and observed masses. This data is then 'cleaned up' by combining the intensities of ions corresponding to in-source decay and salt adducts to those of parent ions. The final matched mass spectrometry data is then written to a. csv file.
Dataset This work used the baseline and follow-up PET/CT images of 60 patients included in the prospective EPICURE seinmeta metastatic breast cancer study (NCT03958136). The EPICURE seinmeta study was approved by the ANSM (2018-A00959-46) and the CPP IDF I, Paris, France (CPPIDF1-2018-ND40-cat. 1). A written informed consent was signed by each participant  [24]. Images were acquired at two sites (A-ICO, N-ICO). At the A-ICO site, imaging was acquired with a Philips Vereos or a GE Discovery PET/CT imaging system; at the N-ICO site, with two different dual-slice Siemens Biograph PET/CT. Each patient had one baseline and one or two follow-up acquisitions: 60 baseline and 104 follow-up PET/CT images were available for this study. Baseline PET/CTs were acquired before initiation of a new treatment.
Metastatic Lesion Segmentation We proposed two networks based on the recently published 3D U-Net implementation called "no new U-Net" (nnU-Net) (Implementation freely available on github: https://github. com/MIC-DKFZ/nnUNet, 15 December 2021) [25]: one network for the segmentation of baseline images called U-Net BL and another one for the segmentation of follow-up acquisitions called U-Net FU. Figure  1  shows the architecture of the two networks and their input images. The nnU-Net achieved state of the art performance in several segmentation challenges such as KiTS2019 and the Medical Segmentation Decathlon  [26, 27]. It allows the automatic configuration of several hyper-parameters depending on given information such as data feature input or memory consumption requirements. The loss was defined as: L Total = L Dice + L CE L Dice is the multi-class Dice loss as in  [28]  and L CE the cross entropy loss as in  [29].
We used a stochastic gradient descent with an Adam optimizer and an initial learning rate of 3 × 10 Both networks were trained and validated using 5-fold cross-validation, with data balanced among folds in terms of acquisition site and number of follow-up images. For each network, we had 5 models trained and validated with different parts of the dataset. The U-Net BL network was trained on baseline acquisitions and validated on baseline and follow-up acquisitions in order to obtain results that the ones from the U-Net FU network could be compared to. For results to be comparable, the same patients' folds were used to train and validate both networks. Validation with the U-Net BL network on a specific patient's follow-up acquisition was done with the model that was not trained with the baseline of this patient during cross-validation to avoid any bias.
Segmentation Evaluation To evaluate the results of each network, segmentation and lesion detection metrics were computed. For each network, validation performances were computed for its 5 models from the cross-validation training and then averaged. To test the performances of our networks on unseen data from both sites, models from the cross-validation training were combined in one single ensemble model. Segmentation metrics: Two metrics based on the Dice score were used: the mean Dice score per acquisition and a global Dice score on all acquisitions combined as one. The Dice score evaluates the degree of overlap between the ground truth and the prediction. The mean Dice score is more affected by predictions errors when there are few lesions, while the global Dice score is more affected by errors when there are large lesions.
The Bone Scan Index (BSI) was found to be a response indicator for patients with castration-resistant metastatic prostate cancer  [34]  and can be used for patients with metastatic breast cancer  [35]. This index is usually applied on Bone Scan with limited results for metastatic breast cancer  [36] , but can be successfully applied on PET  [16]. After the automatic segmentation, bone lesions were labeled using a bone mask generated using a network presented in  [37]. PBI was then calculated as the ratio of the bone lesion volume compared to the total bone volume. PET Bone Index (PLI): In metastatic breast cancer, the liver is the second most frequently affected site and its metastases are known to be of poor prognosis  [38]. PLI is similar to the PBI but for liver lesions. After their automatic segmentation, liver lesions were labeled using a liver mask generated using a CNN trained on the LiTS dataset  [39].