dataesr/scientific-paragraphs-categorization huggingface dataset:
Probing membrane-associated protein networks. Proteins from each of the three selected membrane networks; ATPase subunits (pink), BAM complex (orange), and pil proteins (green), are highlighted for both the membrane pellet (Figure  3A ) and the whole cell (Figure  3B ). To see whether their overall increase in abundance resulted in better cross-linking data, we analyzed the networks we were able to obtain in each dataset. All interactions between proteins in the same group (same color) were kept, but a filtering of at least 2 different cross-links or higher was done for proteins not in the same group to ensure the removal of any false positive interactions. In the whole cell data, as the ATPase proteins were mostly above the cut-off, we were unsurprisingly able to obtain sufficient cross-link information to establish a small network.
Searching for BAM proteins in the whole cell dataset, a small network was found but no interaction between the BAM proteins was detected. This is correlated to the abundance of BAM proteins teetering the threshold cut-off. A search of pil proteins resulted in mostly intra-XLs resulting and an absence of a network. In the membrane pellet data set, a more complete network between the proteins from the ATPase and BAM complex was established along with reinforcement of most of the cross-links found in the whole cell experiment. For the ATPase, the interactions between AtpA with AtpF and AtpH were reinforced with more cross-links found between the three proteins, and a more thorough network established by capturing interaction with AtpD and AtpG. Furthermore, the protein folding chaperone DnaK has a high ATP affinity which can explain its association with the ATPase 36.
Out of the 7 ATPase protein subunits, 6 were already in the top 20 % as well as 2 out of the 5 BAM complex proteins. In the membrane pellet all ATPase and BAM complex proteins were amongst the top 20 %, resulting in an increase of only 1 protein for ATP and 3 for BAM. A much larger difference in the pil cross-link percentage is seen as the total pil protein count in the top 20 % from the whole cell to the membrane pellet conditions went from 8 to 15. Additionally, from some of our optimization experiments, we had a duplicate dataset of membrane pellet cross-linking acquired at 60 K MS 2 resolution where we obtained about half the cross-links compared to the presented membrane pellet data, yet the percent contribution from the three membrane assembly proteins was equivalent (Table  S4 ). The data obtained from our whole cell and membrane pellet cross-linking experiments confirm the rule establishing a protein abundance cut off at 20 % for valuable cross-link data to be achieved.
To assess whether this could act as a quick quality control test it has to hold true for other datasets that ours, and thus we decided to evaluate large-scale publicly available crosslinking datasets for which we already had standard bottom-up proteomics data. Evaluating our quality control test in other datasets. Three datasets were selected on organisms with various complexities: Escherichia coli  39  (Reference Proteome: 4,403), Table  1. Number of cross-links in the whole cell and membrane pellet conditions with the total and percentage crosslink contribution from the ATPase, BAM complex and pil proteins. Saccharomyces cerevisiae  40  (Reference Proteome: 6,060), and HeLa 39 (Reference Proteome: 82,678). Each dataset was conducted with at least two different cross-linker concentrations which allowed us to evaluate whether this could influence the abundance cut-off.
Count The E. coli dataset did have slightly different behavior for the 1 mM DSSO concentration as the drastic decrease in cross-link occurred after the top 10 % of abundant proteins, and with a steady decrease until the top 30 %. This was the first dataset that passed a 25 % abundance cut off, but only by an extra 5 %. A spike is seen at the 45-50 % interval (*) however, this was due to many cross-links from a single protein rather than a combined increase in cross-links of proteins in that range. One possibility for the slightly higher abundance cut off is that E. coli was the least complex organism from these studies which could have led to being able to go slightly deeper into the proteome. Furthermore, the cross-link data from all three organisms were collected by fractionation, with 6 fractions per DSSO concentration for the yeast (in duplicate) while both HeLa and E.
coli data sets contained 18-22 fractions. Despite simplification of the cross-link samples with fractionation, and increasing the number of MS runs, this did not aid in pushing the abundance threshold further than the first top 20 %. Overall, comparing to NNP9, the total average of cross-links even in the first 5 % abundance range is lower in all three datasets. These data indicate that should a system be studied with a non-enrichable cross-linker, we recommend that proteins of interest should be closer to the top 10 -15 % of abundance to achieve well rounded XL-MS data as overall cross-link hits will be lower on the whole data set. CONCLUSION Although it is known that cross-linking results are typically dominated by higher abundance proteins, here we have established an abundance threshold below which no valuable XL data can be expected.
This leads to capturing a more complete protein interaction network data and intra protein cross-links for structural information. We have demonstrated that this threshold lies in the first top 20 % of abundant proteins and even within this rather narrow abundance range, there is a drastic decrease of cross-links that can be expected of proteins in the first and second half. Testing this method on several datasets, we show that despite the complexity of the organism or the amount of cross-linker used, it is still the top 20 % abundant proteins that show the most cross-links. Furthermore, cross-links of proteins below this cut-off are not only scarcer but less reproducible, resulting in a more random chance that proteins of interest in the lower abundance ranges will get cross-linked. We hope this general rule based on the use of a simple iBAQ quantification to follow the abundance of proteins of interest will serve as guideline for those interested in studying a complex of interest with XL-MS, especially in complex samples, to help ensure conditions for a successful cross-linking experiment from the get-go.
ACKNOWLEDGMENT This work was supported by the  European Union 's  Horizon 2020 Research and Innovation Program  under the grant numbers  823839  (EPIC-XS), and the  French Research Agency  ( ANR-18-CE11-0022 ,  T4PNanoAction  project). J. C. R, L. N. and M. R. aknowledge the  Region Ile de France (DIM 1HEALTH)  and the  LABEX IBEID  (grant  n°ANR-10-LABX-62-IBEID  from the  Programme d'Investissements d'Avenir)  for funding the Eclipse Orbitrap. Graphical abstract and Figure 1 were created with BioRender. com.
| Participants Twenty patients who underwent surgery for drug-resistant epilepsy and 38 healthy control participants (CON) were tested. Of the 20 patients, 8 had RTLE, whereas 12 had LTLE. They were tested on average 25. 76 months after the surgery and presented no other medical or psychiatric conditions. All patients underwent anterior temporal lobectomy according to the standardized approach  (Boling, 2018 , Spencer, et al. , 1984 ; Figure  1 ). Anatomical magnetic resonance imaging data were available for 16 patients. Segmentation using the Vol2brain procedure  (Manjon et al. , 2022)  confirmed that surgery led to vast unilateral anterior temporal lesions in all patients (these data and statistical comparisons between patient groups are presented in Figure  2 ). At the time of the experiment, 14 patients took antiepileptic drugs (5 RTLE and 9 LTLE), and 6 patients still experienced seizures (2 RTLE and 4 LTLE).
| Analyses Statistical analyses were performed with R. Accuracy (i. e. , percentages of correct responses per experimental condition) and response times (RT) were analyzed. Groups were analyzed as a between-subject variable (i. e. , CON, RTLE, and LTLE); material type (i. e. , scenes, buildings, and objects) and viewpoint (i. e. , same vs. different) were analyzed as withinsubject variables. Because the data and sample size violated the general linear model assumptions, non-parametric statistical tests were used.
We determined the complete mitogenome sequence of the bioluminescent fish Malacosteus niger using long-read sequencing technologies. The 21,263 bp mitogenome features a complex structure with two copies of a 1198-bp inverted-repeat and a region of 2616-bp containing alternating copies of 16 and 26 bp repeat elements. Whole mitogenome phylogenies inferred from both nucleotide and amino-acid datasets place M. niger among Melanostomiinae. The need for additional complete mitogenome sequences from the subfamily Malacosteinae is discussed.
Assembly and annotation All bioinformatics analyses were performed on the THOT superdome flex server at "Université Laval". MiSeq reads were first processed with AfterQC  (Chen et al. 2017)  to remove adapters and low-quality reads. They were assembled using SPAdes v. 3. 15. 5  (Bankevich et al. 2012) , with a k-mer parameter of 125. Data mining in the pool of contigs was done with blastn command line  (Camacho et al. 2009 ). Boundaries of the contigs were extended using the addSolexaReads. pl script of Consed (Gordon and Green 2013). Basic statistics of the Nanopore reads were obtained with NanoStat  (De Coster et al. 2018). Nanopore reads were first filtered using Filtlong v. 0. 2. 1 (https:// github. com/rrwick/Filtlong), with the two contigs obtained from short reads as a reference. The filtered reads were assembled using Canu v.
Maximum likelihood phylogeny Protein-coding genes and the corresponding amino-acid sequences were extracted from the mitochondrial genomes of 15 taxa of Stomiiformes, including M. niger. Xiphias gladius Linnaeus, 1758 was used as an outgroup. Nucleotide and inferred amino-acid sequences of all conserved mitochondrial genes (ATP6, ATP8, cox1, cox2, cox3, cytB, ND1, ND2, ND3, ND4, ND4L, ND5, ND6) were first concatenated for each species/ dataset and then aligned using MAFFT 7 (Katoh and Standley 2013) with the "-auto" option. Poorly aligned regions were filtered out with trimAl (Capella-Gutiérrez et al. 2009) using the "-automated1" option, and the trimmed version of each data set was used to determine the best model of nucleotide and amino-acid evolution with Model-Test-NG  (Darriba et al. 2020).
Structure and gene content of the M. niger mitogenome The 21 263 bp mitogenome of M. niger (GenBank: OP326280) contains 46% G+C and encodes 13 proteins, 22 tRNAs and 2 rRNAs (Fig. 3 ). It features a 1198 bp inverted-repeat structure with 44% G+C (Fig. 4 ); one copy of this repeat is located between the genes encoding tRNA-Thr and tRNA-Ile, while the second copy is located between the genes encoding tRNA-Met and tRNA-Pro. Another region rich in repeated elements is found between the genes encoding tRNA-Phe and tRNA-Met. This region of 2616 bp contains 36% G+C and features repeat elements of 41 and 26 bp (Table  3 ): The 41-bp and 26-bp elements are repeated 16 and 26 times, respectively, at positions that alternate between the two elements. There is an overlap between ATP6 and ATP8 and also between ND4L and ND4.
Finally, it should be noted that the cox2, ND4 and cob genes feature stop codons that are carried by flanking sequences of tRNA genes. Genes are coded on both strands. Our long-read supported assembly proves that ND1, rrnL and rrnS are on the opposite strand in contrast to most other genes. Phylogenic position of M. niger among the Stomiiformes Phylogenetic analyses of the 11 411 nucleotides and 3795 amino-acid datasets were conducted using the GTR+I+G and mtMAM+I+G+F evolutionary models, respectively. Separation between the Stomiidae and Gonostomatidae was weakly supported in trees inferred from both datasets, but several nodes within the Stomiidae clade proved to be more robust, especially in the amino acid inferred phylogeny. Both the nucleotide and amino-acid trees revealed that the Malacosteinae M.
Acknowledgements Dr Tracy Sutton  ( Oceanic Ecology lab, Nova South-Eastern University, FL, USA ) is thanked for determination and provision of several M. niger specimens. We thank  Stéphane Larose  of the bioinformatics platform from  Laval Université  for his constant and patient help with IT. We would like to thank  Esben Horn  (https://www. 10tons. dk/) for his marvellous model of Malacosteus niger and for letting us use a photo of it in this publication. This work was supported by the  2017-2022  research funds granted for implementation of a co-financed international research project from the  Polish Ministry of Science and Higher Education. CL and MT are supported by grant  RGPIN-2017-04506  from the  Natural Sciences and Engineering Research Council of Canada. BB, JSC and CO are supported by the " Programme d'appui aux plateformes technologiques stratégiques" from  the  Ministère de l'Économie, de l'Innovation et de l'Énergie Québec.
The rRNA-depleted RNA was then used to build the Illumina library using the TruSeq RNA library preparation kit, followed by sequencing on a owcell S' de Novaseq a sequencer on Paired-End 25O. The RNAseq data of each sample were analyzed using both Rockhopper v2. 0. 3 and SPARTA with the default parameters to calculate the FPKM and TPM values for each coding sequence. The sequencing performed gave 646 million reads generated (323 M fragments) and 645 million reads (322. 5 M fragments) for astrocytes from rat cortex and rat spinal cord respectively.
CRISPR-CAS9 sgRNAs were designed using the Biology software Benchling (https://benchling. com). Optimization of DNA target speci city and minimization of the off-target effects were obtained as described in  14  and in  15  , respectively. sgRNAs were cloned into the plasmid LentiCRISPRv2 16. The corresponding lentiviruses were generated in HEK293T cells by co-transfection of LentiCRISPRv2-sgRNA with the packaging plasmids pVSVg 17 and psPAX2 18. Lentiviral particles were puri ed from the HEK293T culture supernatant and utilized for infection of the target cells. Stable knock-out of IgH6 was obtained by selection in puromycin at 3 µg/ml. The sequences of the sgRNAs targeting IgH6 were as follows: IgH6 CRISPR #1: 5'-TGTGACATGTAGGGCATGTA-3' (strand antisense); IgH6 CRISPR #2: 5'-TGGTGATACAACCAGCTCCA-3' (strand sense).
Shot-gun proteomics data analysis RAW data obtained from the nLC-MS/MS run were treated using MaxQuant v1. 6. 1. 0 using the LFQ annotation of the protein identi ed. Proteins were identi ed by searching MS and MS/MS data against the Decoy version of the complete proteome for Rattus norvegicus of the UniProt database (Release June 2014, 33,675 entries) combined with 262 commonly detected contaminants. Statistical analyses were carried out using Perseus software after ltering for "reverse", and "contaminant" proteins. For the comparison between control and treated groups, a t-test was performed with a permutation-based FDR of 0. 05, and p-values less than 0. 05 were considered statistically signi cant. A heat-map of differentially expressed proteins across the two different groups was generated.
Proteogenomic analyses The AltProt database of rat is a prediction of the possible start codon around the classical Open Reading Frame (ORF), permitting the prevision of proteins on UTR, overlapping between UTR and coding sequence (CDS, and shift of ORF in +2 and +3 and conserving an initiator AUG codon. This database was combined with the reference Uniprot database on the same FASTA le. Label-Free Quanti cation (LFQ) was performed by MaxQuant 1. 5. 6. 5. During this analysis, principal parameters were assigned as follows: Trypsin digestion with maximum missed cleavage of 2, carbamidomethylation as a xed modi cation, and oxidation as a variable modi cation. The rst search peptide tolerance was adjusted at 20 ppm and the main search peptide at 6 ppm. Finally, the minimum peptide length was restricted to 6 amino acids.
The length of this kind of protein, a mean of 50 to 100 amino acids, obliges to decrease the number of unique peptides identi ed at 1. Statistical analysis was performed with Perseus 1. 5. 5. 3, log2(x) was then realized and results were ltrated to eliminate identi cation by site as well as reverse and potential contaminants. Signi cant variations between samples were assessed by t-test. Filtration for AltProt was applied to keep only the AltProt identi ed with a unique peptide and no classical protein redundancy on Majority ID. Variation of quanti cation was revealed by hierarchical clustering with Euclidian distance measurement. Identi cation of AltProt was performed using BlastP and non-redundant protein sequences to nd their sequence homology with canonical and unknown proteins. The gene accession numbers were used to retrieve mRNA or ncRNA sequences from the Ensembl database.
Sub-network Enrichment Pathway Analysis. Using Elsevier's Pathway Studio (version 11. 0/ /Elsevier), all relationships between the differentially expressed proteins among all conditions were depicted based on the Ariadne ResNet  22. For proteins identi ed in the shotgun analysis, the Subnetwork Enrichment Analysis (SNEA) algorithm was used to detect the statistically signi cant altered biological pathways in which the identi ed proteins are involved. This algorithm uses Fisher's statistical test to detect any non-random associations between two categorical variables organized by a speci c relationship. Also, this algorithm starts by creating a central "seed" from all the relevant identities in the database and builds connections with associated entities based on their relationship with the seed.
SNEA compares the sub-network distribution to the background distribution using a one-sided Mann-Whitney U-Test and calculates a p-value; thus, representing a statistical signi cance between different distributions. In all analyses that we performed, the GenBank ID was used to form experimental groups based on the different conditions present for analysis. The pathway networks were reconstructed based on biological processes and molecular functions for every single protein, along with its associated targets. Overexpression of rat astrocyte IgG2B-Flag in DI TNC1 cells DI TNC1 cells were plated in 6 well plates and grown in a complete medium until they reached about 80% con uence. One hour before the transfection, the medium was renewed. One microgram of the construct was mixed with 50 µL of DMEM-free medium and 3 µL of PolyJet were mixed with 50 µL of DMEM-free medium in another tube.
BioID data acquisition The samples were separated by online reversed-phase chromatography using a Thermo Scienti c Easy-nLC1000 system equipped with a Proxeon trap column (75 μm ID x 2 cm, 3 μm, Thermo Scienti c,) and a C18 packed-tip column (Acclaim PepMap, 75 μm ID x 50 cm, 2 μm, Thermo Scienti c). The digested peptides were separated using an increasing amount of acetonitrile in 0. 1% formic acid from 2 to 30% for 2 hours at a ow rate of 300 nL/min. A voltage of 2. 4 kV was applied by the liquid junction to electrospray the eluent using the nanospray source. A high-resolution mass spectrometer Q-Exactive TM Thermo Scienti c TM was coupled to the chromatography system to acquire the 10 most intense ions of MS1 analysis (Top 10) in a data-dependent mode. The MS analyses were performed in positive mode at a resolving power of 70,000 FWHM, using an automatic gain control target of 3e6, the default charge state was set at 2 and a maximum injection time at 120 ms.
BioID data analysis The proteins were identi ed by comparing all MS/MS data with the Homo sapiens proteome database (Uniprot, release March 2020, Canonical+ Isoforms, comprising 42,360 entries), using the MaxQuant software version 1. 5. 8. 3. The digestion parameters were de ned using trypsin with 2 maximum missed cleavages. The oxidation of methionine and N-terminal protein acetylation were de ned as variable modi cations. Label-free quanti cation (LFQ) was done while keeping the default parameters of the software. As for initial mass tolerance, 6 ppm was selected for MS mode, and 20 ppm was set for fragmentation data to match MS/MS tolerance. The identi cation parameters of the proteins and peptides were performed with a false discovery rate (FDR) at 1%, and a minimum of 2 unique peptides per protein.
The LFQ values from the 20 control runs (regrouping FlagBirA* and BirA*Flag alone samples), were collapsed to the three highest values for each given ID. These three values were de ned as the control group for comparison. The statistical analysis was done using Perseus software (version 1. 6. 2. 3). Brie y, the LFQ intensity of each sample was downloaded in Perseus and the data matrix was ltered by removing the potential contaminants, reverse, and only identi ed by site. The data were then transformed using the log2(x) function. Only preys with detected values in all three replicates of a given the bait protein were kept for further analysis. Missing values (not detected) were then replaced by the minimal valid LFQ value separately for each column. Two-sample Student's T-test was then performed comparing all three biological replicates the bait against the three control runs.
High con dence proximal labeling interactors were de ned with a cut-off of p<0. 05. Detailed experimental values are reported in Supplemental Data 7. dataset identi er PXD040562 (User name: reviewer_pxd040562@ebi. ac. uk; password: ptWWnlXS). Genbank acessory numbers : OQ559557 (truncated IgG2b) and OQ559558 (Jexon IgKc). BioSample accessions for RNA-Seq data : SAMN33645519, SAMN33645520.
However, triple staining con rmed that these cells were GFAP positive (Figure  1C ). Additional RNAseq analyses allowed the comparison between CD markers of the different B cell populations and astrocyte markers. This revealed that astrocytes are CD19, CD23, CD27, and CD52 negative but express some plasmocyte markers such as CD38 and CD138 (Figure  1D ). Altogether, this points out the existence of a subpopulation of astrocytes sharing some common features with plasmocytes. To verify the astrocytic nature of the cells, the detection of GFAP by western blot was carried out and con rmed the presence of its band at 55 kDa (Supp. Figure  1B ). Moreover, additional bands corresponding to cleaved forms of GFAP were also detected which is in line with previous reports from literature  28. Supporting the fact that a speci c sub-population of astrocytes exists, recent data reveal the presence of an astrocyte-like population in the spleen  29, 30.
To con rm the ability of astrocytes to produce IgG, we performed a pan-transcriptomic and proteomic analyses of the primary cultures and the cell line during normal and after LPS stimulation. After RNAseq analyses, MagicBlast alignment was performed against a speci c bank containing all sequences coding for each V (Variable), D (Diversity), J (Junction) exons and heavy and light chain referenced in IMGT immunoglobulin database  31  for the Rattus norvegicus. It con rmed that reads corresponded to Ig constant part of heavy and light chains. Among them, 63 reads and 3 trinity corresponding to Igg2b; 30 reads for Ighm and 9 reads and 1 trinity for IgK were identi ed in spinal astrocytes (Supp. Data 1). Only 2 reads for Igg2b, 26 reads and 1 trinity of Ighm, 6 reads and 1 trinity for Igg2a and 4 reads and 1 trinity for IgK were found in astrocytes from the cortex (Supp.