dataesr/scientific-paragraphs-categorization huggingface dataset:
However, each peptide concentration is determined from multiple holdup-FP experiment pairs and therefore the variance of individual pairs can be still measured. The difference between the orthogonal pKd values derived from FP and holdup measurements follows a normal distribution with a standard deviation of 0. 227 pKd. If we assume that neither of these methods perform systematically better than the other we can use this standard deviation as a global metrics of the uncertainty of determined pKd values in our database. For example, one can also use it to estimate the confidence of an affinity difference using the following empirical rule: >0. 227 pKd difference indicates ~68. 3% confidence (1 σ), >0. 454 pKd difference indicates ~95. 5% confidence (2 σ),.
collected for the affinities reviewed in Amacher et al. The following data were excluded during collection or from the data reviewed in Amacher et al. : (i) data concerning full-length proteins or PDZ domains in tandem, as opposed to isolated PDZ domains; (ii) data concerning PBM variants (mutations or post-traductional modifications) or artificial PBM sequences (i. e. generated by phage-display); (iii) data for which the length or the sequence of the PBM was not available. The resulting database comprises 1547 K d measurements, extracted from 132 publications, and measured between 142 different PDZ domains, some of them of different origins (human, mouse, rat, and/or vinegar fly), and 280 different PBM motifs from all classes, including 12 internal.
://www. uniprot. org/). Precursor and fragment mass tolerances were set at 7 ppm and 0. 6 Da respectively, and up to 2 missed cleavages were allowed. Oxidation (M, +15. 995Da) was set as variable modification, and Carbamidomethylation (C, + 57. 021 Da) as fixed modification. Peptides and proteins were filtered with a false discovery rate (FDR) at 1%. Label-free quantification was based on the extracted ion chromatography intensity of the peptides. All samples were measured in technical triplicates. The measured extracted ion chromatogram (XIC) intensities were normalized based on median intensities of the entire dataset to correct minor loading differences. For statistical tests and enrichment calculations, not detectable intensity values were treated with an imputation method, where the missing values were replaced by random values similar to the 10% of the lowest intensity values present in the entire dataset.
Unpaired two tailed T-test, assuming equal variance, were performed on obtained log 2 XIC intensities. The detection threshold for enriched proteins in AP-MS experiments were analyzed above 2-fold enrichment with <0. 01 P value. Then, proteins with PDZ-domains or PBMs were extracted from the list of enriched proteins. In case only a specific isoform of the detected protein contains a PBM, the identified peptide fragments were manually checked to check which isoform(s) were most likely detected. All raw LC-MS/MS data (from 165 runs) have been deposited to the ProteomeXchange via the PRIDE database with identifier PXD027743.
Acknowledgements 12 We thank  Celia Caillet-Saguy ,  Nicolas Wolff  and  Goran Bich  for their help in holdup experiments and data curation. We thank  Alastair McEwen  and  Pierre Poussin-Courmontagne  for their help in protein crystallization and data collection. We thank  Guillaume Seith  for his help in establishing the ProfAff server. We thank  Claudine Ebel  and  Muriel Philipps  of flow cytometry platform for their help in cell sorting. We thank  Betty Heller  for her advises and the cell culture platform for their help in cell culturing. We would like to thank  Ylva Ivvarson  for her recommendations with internal motifs. We thank the support of the  Swiss Light Source synchrotron (P. Scherrer Institute, Villigen, Switzerland ) and the help of the beam-scientist at the PXIII beamline, as well as the support at the PX2A beamline at synchrotron SOLEIL (France).
GG was supported by the  Post-doctorants en France program  of the  Fondation ARC. BZ is supported by  Fondation pour la Recherche Médicale  (FRM,  SPF202005011975 ). The work was supported by the Ligue contre le cancer (équipe labellisée 2015 to GT), the  Agence Nationale de la Recherche  (grant  UBE3A ANR-18-CE92-0017  to GT), the  Cancéropôle Grand-Est  (projet émergent to YN) the  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INSB-05-01 , and Instruct-ERIC.
Unpaired two tailed T-test, assuming equal variance, were performed on obtained log 2 XIC intensities. The detection threshold for enriched proteins in AP-MS experiments were analyzed above 2-fold enrichment with <0. 01 P value. Then, proteins with PDZ-domains or PBMs were extracted from the list of enriched proteins. In case only a specific isoform of the detected protein contains a PBM, the identified peptide fragments were manually checked to check which isoform(s) were most likely detected. All raw LC-MS/MS data (from 165 runs) have been deposited to the ProteomeXchange via the PRIDE database with identifier PXD027743. Samples were analyzed using an Ultimate 3000 nano-RSLC (Thermo Scientific, San Jose California) coupled in line with a LTQ-Orbitrap ELITE mass spectrometer via a nano- electrospray ionization source (Thermo Scientific, San Jose California).
(TOP20CID method) Unassigned and single charged states were excluded from fragmentation. The Ion Target Value for the survey scans (in the Orbitrap) and the MS2 mode (in the Linear Ion Trap) were set to 1E6 and 5E3 respectively and the maximum injection time was set to 100 ms for both scan modes. Dynamic exclusion was set to 20 s after one repeat count with mass width at ± 10 ppm. Mass spectrometry data analysis Proteins were identified by database searching using SequestHT (Thermo Fisher Scientific) with Proteome Discoverer 2. 4 software (PD2. 4, Thermo Fisher Scientific) on human FASTA database downloaded from SwissProt (reviewed, release 2020_11_27, 20304 entries, https.
We developed BIODICA, an integrated computational environment for application of Independent Component Analysis (ICA) to bulk and single-cell molecular profiles, interpretation of the results in terms of biological functions and correlation with metadata. The computational core is the novel Python package stabilized-ica which provides interface to several ICA algorithms, a stabilization procedure, meta-analysis and component interpretation tools. BIODICA is equipped with a user-friendly graphical user interface, allowing nonexperienced users to perform the ICA-based omics data analysis. The results are provided in interactive ways, thus facilitating communication with biology experts.
Stabilization procedure for extracting reproducible components The computational core of BIODICA is the Python package stabilized-ica. It implements a stabilization procedure which addresses the variability of the solutions of ICA algorithms when run multiple times  (Himberg and Hyvarinen, 2003). When applied to transcriptomics data, not only did this procedure provide a quantification of the significance of the independent components but it also extracted more reproducible ones than standard ICA  (Cantini et al. , 2019). Besides, it allowed the development of an approach for selecting the optimal number of independent components to extract from omics data  (Kairov et al. , 2017) , which is also available in BIODICA.
Studying inter-data sets reproducibility of extracted components BIODICA provides a tool, based on application of Mutual Nearest Neighbors (MNN), to match the components extracted from several independent omics data sets. Studying the reproducibility of independent components across multiple data sets may help distinguishing biological signals that are specific to a particular disease/data type or technical biases that are specific to particular conditions  (Biton et al. , 2014; Cantini et al. , 2019).
Implementation BIODICA comes with a user-friendly Graphical User Interface called BIODICA Navigator, providing non-experienced users a no-code access to all the BIODICA functionalities. It facilitates the communication with biology experts, producing sortable and interactive HTMLbased reports. The interface has been designed and validated in several studies, including a study of Ewing sarcoma at single-cell level  (Aynaud et al. , 2020). For more advanced users, the stabilized-ica package can be used as a standalone tool to integrate some of the functionalities of BIODICA into Python analysis pipelines.
Future developments For now, ICA-based blind deconvolution has been mainly applied to transcriptomics data. However, other omics technologies are now often incorporated into biological research and could also benefit from the ICA analysis proposed by BIODICA. A few studies recently used ICA to deal with DNA methylation data (e. g. ,  Meunier et al. , 2021). We plan to foster the application of such methodologies to other omics data by adding new tools to BIODICA to interpret and exploit the independent components, taking into account the specificities of each technology. We also plan to integrate multi-omics analysis tools in BIODICA, building on the recent efforts that have been made for the integration of multiple omics layers  (Teschendorff et al. , 2018).
All data remained anonymous. Our regional ethical committee (Comited e E ́tica de la Investigacioń de la Comunidad Autońoma de Aragón, Record No. 20/2018) approved this methodology, detailed in the 18/0336 project. Since 2004, all M. tuberculosis isolates have been genotyped as part of a surveillance protocol, and the DNA remains frozen at -20°C. All MtZ cases were selected based on the identical IS6110-RFLP pattern (12 bands) of their isolates, analyzed by BioNumerics software (v7. 6, Applied Maths, Kortrijk, Belgium). There were some exceptions, previously described by  Millań-Lou et al. in 2015 (Isabel Millan-Lou et al. , 2013)  as evolved isolates with an extra or a lost band. Two cases were identified in Aragon from 1993 to 1995, and 240 cases were identified from January 2001 to December 2020.
Bioinformatics for the Study of the Genomes From the fastQ files obtained after sequencing, the BAM and VCF files were produced. These files were studied using the Integrative Genomics Viewer software [IGV, from the Broad Institute  (Robinson et al. , 2011) ] to search antibiotic resistances and to compare the MtZ strain with other L4. 8 strains we had sequenced for another study. Tuberculist (http://genolist. pasteur. fr/TubercuList/), Mycobrowser (https://mycobrowser. epfl. ch/) and UniProtKB (https://www. uniprot. org/uniprot/) websites were used to find information about the genes and proteins with interesting SNPs. We used GeneWise (https://www. ebi. ac. uk/Tools/psa/genewise/) to determine if the SNP was synonymous or non-synonymous and PROVEAN (http:// provean. jcvi. org/seq_submit.
php) to determine the effect of the non-synonymous SNPs (neutral or deleterious). The fastQ files were also uploaded into BioNumerics software, where the phylogenetic trees were constructed using the UPGMA method. The program mapped the sequences against H37Rv and found the SNPs between the different isolates. SNPs in genes related to resistance were also determined. For greater accuracy, strict SNP filtering that removed positions with at least one ambiguous or unreliable base, gaps (maximum frequency 1%), non-discriminatory positions, and ppe and pgrs genes was applied. The retained SNP positions had a minimum of 5× coverage and the minimum distance between SNPs was at least 12 base pairs (bp). As the program was not able to compare sequences obtained by different sequencing platforms, SNPs were eye reviewed in both different phylogenetic trees constructed for the Illumina and Ion Torrent sequences to detect the shared SNPs.
Geodatabase and Map Design The location (street and number) is available for each MtZ case, and therefore, its assignment to a basic health zone (BHZ) is possible. This allowed the development of a geodatabase that would facilitate cross-referencing with sociodemographic data and its analysis. With this aim, we designed two maps to draw the spatial distribution of TB cases in the city of Zaragoza from 2001 to 2020 together with the two cases identified in the 1993-1995 period. Both maps used the BHZ of the city of Zaragoza as a spatial base, represented by polygons in a vector model of 133 elements. The coordinate system was ETRS 1989 UTM, Zone 30N. Cartography was implemented in a Geographic Information System (ESRI ArcGIS 10. 7 software), where we prepared the base map and built the geodatabase.
Transcriptomic Analysis With the objective of analyzing the effect of the IS6110 in the M. tuberculosis genome, we studied three different MtZ isolates having the same genome background but carrying one different IS copy (case 0, the original strain with 12 IS6110 bands; case 129, with an extra IS6110 located in dnaA:dnaN; and case 241, with one less IS6110 due to recombination around the DR region). They were cultured and sent to the STAB-VIDA Company (Caparica, Portugal, https://www. stabvida. com) for transcriptomic analysis. The library construction of cDNA molecules was carried out using a Ribosomal Depletion Library Preparation Kit. The generated DNA fragments were sequenced in the Illumina NovaSeq platform, using 150-bp paired-end sequencing reads. The analysis of the generated raw sequence data was carried out using CLC Genomics Workbench 12.
RESULTS The MtZ strain was first observed in an epidemiological/genomic study  (Loṕez-Calleja et al. , 2007)  carried out in the Aragon region, which produced the largest outbreak ever recorded there. The RFLP pattern showed 12 bands and a unique spoligotype, defined as unknown in the SITVIT2 database (http://www. pasteur-guadeloupe. fr:8081/SITVIT2/description. jsp). Its IS6110 locations were studied by  Millań-Lou et al. in 2013 (Isabel Millan-Lou et al. , 2013). At that time, WGS was not developed enough for routine application in laboratories; therefore, the molecular characterization was not exhaustively studied. Now, we can observe its molecular properties in much more detail in order to find possible explanations for the high incidence of this strain in the local population of Aragon.
Genomic Study The sequence analysis of the genomes was carried out in BioNumerics software. The data are summarized in Figure  3  for simplicity. Only two isolates could be considered as the original sequence of the strain (case 0, belonging to the period of 1993-1995, and case 7 from 2001). The rest of the isolates had an SNP in position 3702632; therefore, there had to be an intermediate isolate that gained this mutation that was not sequenced (case x). From this intermediate isolate, two branches appeared. Several cases conserved the original base at position 3716874 (CLS-1), while the rest of the isolates had an SNP in that position (CLS-2) (Figure  3 ). In addition, isolates from CLS-1 also shared an SNP at position 1301938, and two of the isolates shared one more SNP at position 2544858, indicating transmission between these two cases (CLS-1.
, 2004)  and also had phenotypic consequences. The specific insertion of the IS6110 in the herein observed case might have a similar function. The study had some limitations. First, just 23. 7% of the isolates belonging to the cluster were sequenced; therefore, several links and new transmission clusters have been lost. Because of this, we cannot know the true extent of CLS-2. 2 and CLS-2. 3, for which transmitted SNPs in genes related to pathogenesis were involved. Moreover, data for 1996-2000, when the strain probably started to spread with more force, are not available. The COVID-19 pandemic required that we perform the sequences with different platforms, making the analysis in BioNumerics software more difficult. This was solved by analyzing the SNPs detected in the isolates singularly.
ACKNOWLEDGMENTS The authors would like to acknowledge  Servicio General de Apoyo a la Investigación-SAI, Universidad de Zaragoza (Servicio de Anaĺisis Microbioloǵico ) and  Servicios Cientıfco Tećnicos ,  IACS (Servicio de Secuenciación y Genómica Funcional and Servicio de Biocomputacioń). We would like to thank the  EPIMOLA group  for supplying the genotyped bacterial DNA used in this work. This work was supported by the  Carlos III Health Institute  in the context of a grant ( FIS18/0336 ) and JC was awarded a scholarship by the  Government of Aragon/ European Social Fund , " Building Europe from Aragon ". JM and RB acknowledge the support by the  Agence Nationale de la Recherche  (grants  ANR-10-LABX-62-IBEID  and  ANR-20-CE15-0013-03 ).
FUNDING This work was supported by the  Carlos III Health Institute  in the context of a grant ( FIS18/0336 ) and JC was awarded a scholarship by the  Government of Aragon/European Social Fund , " Building Europe from Aragon ". JM and RB acknowledge the support by the  Agence Nationale de la Recherche  (grants  ANR-10-LABX-62-IBEID  and  ANR-20-CE15-0013-03 ).
Boutons (b), Mitochondria (m). Scale bar, 200 nm; for uncropped immunoblots see supplementary material or original data files in source data. were not studied further. We thus focused on the enriched proteins. We first compared the 2653 proteins dataset to the broad survey of mouse brain proteins produced by Sharma and colleagues  32. Ninety percent of our dataset is common to the global mouse brain proteome. Among 158 proteins significantly enriched in the bulk dissection of the striatum, 89 are represented in the synaptosome samples which is consistent with the selectivity of our subcellular fractionation (Fig. 3b, c , Supplementary Table  4 ). 403 proteins are common with those previously identified to be specific to a given cell type. A heatmap analysis of these shows the main neuronal origin of our synaptosome samples (Fig.
3d , Supplementary Table  4 , Supplementary Fig. 3 ). We then compared the obtained proteome with the curated database of known synaptic gene ontologies (SynGO)  33. Among 2653 genes from this proteome, 684 genes are documented in SynGO covering all localizations and functions reported in the second level of SynGO terms. This gene set is associated with synapse organization (184 genes over 306 genes in the category), a process in the presynapse (183/269), process in the postsynapse (131/218), synaptic signaling (106/193), metabolism (25/94) and transport (23/36) (Fig. 3e ). Beyond 57 proteins highly enriched during DA-FASS procedure, we identified a strong enrichment of the reporter protein mNeonGreen (12 unique peptides, 5. 12-fold increase, adjusted pvalue = 1. 6 × 10 -16 ; Fig. 3f, g , Supplementary Table  3 ).
mNeon-Green enrichment thus represents the target enrichment value for the most specific dopaminergic proteins. In line with this, the major canonical proteins involved in dopamine metabolism (Th; Ddc: DOPA decarboxylase; Slc18a2/VMAT2: Vesicular Monoamine Transporter type 2) show similar enrichment values. Slc6a3/DAT displays a slightly lower enrichment that may be explained by the loss of DAT proteins present on the axon shaft between varicosities (Fig. 3f  and Supplementary Fig. 2 )  34. Of note, 14 proteins quantified with only 2 peptides display enrichment scores much higher than the five-fold increase of our reporter. The extent of enrichment is possibly distorted by a weak detection in MS/MS. We probed the cell type expression pattern of the 57 enriched proteins with DropViz single-cell RNA sequence database.
For this analysis we focused on afferent and efferent neurons in the mouse striatum  35. This meta-analysis provides hints regarding the identity of neurons expressing the enriched markers. It defines four clusters of gene expression, from ubiquitous expression to expression restricted to Th neurons of the midbrain. This analysis further suggests that some of the DA-FASS enriched proteins belong to various partners of the synaptic hubs (Fig. 3h  and Supplementary Fig. 3 ). Finally, we summarized our proteome data on a model of dopaminergic transmission inspired by the KEGG database (Mmu04728)  36  to represent proteins either enriched, retained or absent from our screen (Fig. 3i  Supplementary Tables  3  and 5 ). Validation of 6 new proteins enriched at dopamine synapses. We identified a set of 12 proteins enriched in our dataset and previously shown to be important for dopamine signaling (Fig.
e Overlap analysis of the DA-FASS proteome with the second level terms of the SynGO database (mostly glutamatergic and GABAergic synapse related genes)  33. f Volcano plot of DA-FASS protein quantification. Values of fold changes versus corrected p-value are plotted for each protein (on logarithmic scales). Thresholds are set at ±1. 5-fold change and p < 0. 05. Proteins are colored by subclass of canonical (green) enriched (red), depleted (cyan) and retained (grey) in the DA-FASS sample. Proteins previously described as playing a role in dopamine signaling (#). Targets selected for further experimental validations (*). Statistical significance was tested using background based two-tailed pairwise t-test adjusted using Benjamini-Hochberg correction for the false discovery rate. g Complete list of depleted and enriched DA-FASS proteins.
Indeed, for all our datasets, random associations occur on less than 0. 5% of all events while we observe at least 10% for synaptic hub-related associations in sorted samples (see Table  1 ). As a final control for the specificity of hub-synaptosome adhesion, we performed an additional VGLUT1-FASS experiment in which we selectively sorted aggregates and large events to analyze them with electron microscopy (Supplementary Fig. 5h-j ). Upon reanalysis, sorted aggregates display a strong increase in the representation of small and large aggregates (Supplementary Fig. 5h, i ). Singlets are still strongly represented in the reanalyzed sample as it is common to break down aggregates into singlets through the shearing forces applied in the nozzle of the sorter (Supplementary Fig. 5h, i ). Electron micrographs display profiles of large particles (3-6 µm in diameter) that are difficult to relate to identifiable features of the tissue and very different from the DA-FASS synaptosomes displayed in Fig.
Cellular organization of dopaminergic projections to the striatum. The nature of dopaminergic synaptic structures is the topic of a long-standing debate. Previous anatomical investigations identified that the distribution of dopamine varicosities in the neuropil is biased toward proximity to glutamatergic or GABAergic synapses, but only a minority were shown to make synapses with a target structure in the striatum  4, 5, 8. However, other authors reported a frequent occurrence of symmetrical synaptic contacts of dopaminergic thin axonal portions with SPNs spines or dendritic shafts  9, [81] [82] [83]. Our current dataset strongly advocates for specific and frequent adhesion of dopaminergic axonal varicosities with target structures (Figs. 1h-l  and 2 ). Indeed, around 50% of our EGFP + varicosities displayed apposed D1R, while ~80% displayed D2R labelling (Fig.
Capillary chemiluminescent images captured through a charge-coupled device camera were analyzed by the manufacturer's Compass software. Briefly, the protein peak area under the curve (AUC) was fitted using a Gaussian distribution. The fitted protein AUC is expressed either as a ratio to the fitted AUC H fraction for each WES (Fig. 1 ) or as fitted protein AUC (Fig. 7 ). Immunofluorescence. Synaptosomes on coverslips were fixed (4% Paraformaldehyde, 4% sucrose, 1× PBS) for 10 min at room temperature, washed three times with PBS for 5 min and then stored at 4 °C until use. Synaptosomes were blocked and permeabilized with PGT buffer (PBS, 2 g/L gelatin, 0. 25% Triton X-100 and when needed 5% normal goat serum) and subsequently incubated with primary antibodies in PGT buffer (1 h at room temperature), washed three times with PGT and incubated with secondary antibodies in PGT (1 h at room temperature).