dataesr/scientific-paragraphs-categorization huggingface dataset:
In the crystal structure of the H-PLL/H-fucose complex (PDB: 5C9P), three fucose molecules were observed in three binding sites: site II (between W-motifs 1 and 2), III (between W-motifs 2 and 3), and VII (between W-motifs 6 and 7), respectively (Figure  1 ). Here, we report an additional binding site IV (between repeats W3 and W4) occupied by a fucose molecule in our RT and 100 K X-ray structure, in place of a glycerol molecule in the previously reported structure at 100 K (Figure  3 ). The fucose molecule could be modeled in a mixture of a/b configurations with occupancies of 54/46. Generally, the fucose is bound in site IV in a similar manner to the other sites (Figure  S2 ). The strongest hydrogen bonds are between the O4 oxygen of L-fucose and the backbone ND of Ala190 and O of Thr168.
The fourth binding site described above for the RT X-ray structure is not occupied by the ligand in the neutron structure of the complex. The site is occupied by solvent molecules (Figure  S3B ) that are also observed in the 100 K X-ray structure (PDB: 5C9P). A possible explanation is that this site has a lower affinity for fucose than the other sites, as suggested previously  (Kumar et al. , 2016). In all sites, fucose could be modeled in both a/b configurations with occupancies of 30/70, in agreement with the population of 30/70 observed by NMR in solution  (Ryu et al. , 2004). The neutron scattering density around the deuterated fucose molecule is clearly different from the electron density given that the deuterium atoms contribute strongly to the neutron scattering (Figure  5A ). As a result, neutron peaks of the methyl group together with deuterium atoms on carbon atoms are visible in the density map and the directionality of the fucose hydroxyl groups could be determined.
We observed changes in conformation of short loops in repeats W1 and W5 that were caused by a bound glycerol used as a cryoprotectant in the 100 K structures. Moreover, a fucose molecule in our RT and 100 K structure of D-PLL lectin (PDB: 7B7C and 7BB4, respectively) occupies an additional binding site IV between repeats W3 and W4 of the b propeller. This site has not been described yet as it was occupied by water molecules (PDB: 5C9P) or a glycerol molecule (PDB: 5C9O) in 100 K X-ray crystal structures  (Kumar et al. , 2016). Even though most of the X-ray structures are solved from data collected at cryogenic conditions (100 K), RT structures (293 K) can often provide more relevant information on the protein conformation state and ligand binding since they are closer to physiological conditions and may avoid artifacts caused during cryo-cooling of protein crystals as observed by others  (Maeki et al.
Data and code availability Model coordinates and experimental data have been deposited in the Protein Data Bank under PDB accession codes for jointly refined X-ray/neutron structures PDB: 7BBI for H/D-exchanged apo PLL structure, PDB: 7BBC for the D-PLL/Fuc-d 12 complex. In addition, the following X-ray structures have also been deposited: PDB: 7B7F for H/D-PLL complexed with H-fucose at room temperature, PDB: 7B7E for D-PLL apo at room temperature, PDB: 7B7C for D-PLL complexed with H-fucose at room temperature, and PDB: 7BB4 for D-PLL complexed with H-fucose at 100K.
X-ray data collection and processing Room temperature (RT) X-ray datasets were collected for both crystals from which the neutron data were collected. For the H/Dexchanged apo PLL crystal, the data images were recorded on the BM30A (newly BM07) beamline at the ESRF (Grenoble, France). The same beamline was used to collect RT datasets of H/D-PLL-H-fucose, D-PLL apo and D-PLL-H-fucose crystals (hydrogenated commercial L-fucose was used for the crystals). The collected data were processed using XDS  (Kabsch, 2010) , scaled and merged in AIMLESS  (Evans and Murshudov, 2013)  and converted to structure factors using TRUNCATE from the CCP4 program suite  (Winn et al. , 2011). For the D-PLL-Fuc-d 12 crystal, the RT X-ray datasets were recorded on the GeniX 3D Cu High Flux diffractometer (Xenocs) at the IBS, Grenoble, France.
Data were integrated using iMOSFLM  (Battye et al. , 2011) , scaled and merged using AIMLESS and converted to structure factors using TRUNCATE from the CCP4 program suite. To collect the 100 K X-ray data, crystals were cryo-protected by soaking in 30% (v/v) glycerol in a crystallization solution for a time as short as possible and were subsequently cryo-cooled at 100 K in liquid nitrogen. Datasets from crystals of D-PLL/H-fucose complex were collected on the ID23-1 beamline at the ESRF equipped with the PILATUS 6M detector. The data were processed via XIA2 pipeline. Complete data collection and structure refinement statistics are presented in Table  S2. Phasing and Structure refinement Monomeric structure of the recombinant PLL lectin (PDB: 5C9O) stripped of water molecules was used as the initial model for initial phasing using molecular replacement  (McCoy et al.
, 2007). Crystallographic refinement was carried out with phenix. refine (Afonine et al. , 2012)  from the PHENIX package  (Adams et al. , 2010)  altered with a manual model building and model adjustments using Coot  (Emsley et al. , 2010). Water molecules were introduced automatically using phenix. refine and inspected manually. Ligand molecules were introduced manually in Coot. The joint x-ray/neutron refinement was carried out after the satisfactory R factors were achieved. The final X-ray and neutron R work /R free values for the H/D-exchanged apo PLL were 13. 5%/15. 6% and 21. 5%/24. 0% respectively and for the D-PLL/Fucd 12 complex were 12. 1%/14. 0% and 19. 2%/22. 3% respectively. The ReadySet utility in PHENIX was used to introduce hydrogen and deuterium atoms at appropriate positions in the protein and ligand molecules.
QUANTIFICATION AND STATISTICAL ANALYSIS Processing of X-ray and neutron data was performed as described in the Method Details. Model refinement was carried out in Phenix using phenix. refine (Afonine et al. , 2012). All software used is listed in the Key resources table. Data processing, refinement, and validation statistics are summarized in Tables  1, S1  and S2. Structure validation was performed on the deposited atomic models using Molprobity  (Williams et al. , 2018).
ACKNOWLEDGMENTS The authors wish to acknowledge the  ESRF  and  ILL  for provision of beamtime and technical support. We thank the  ILL  for the provision of studentship funding to L. G. V. T. F. acknowledges the  UK Engineering and Physical Sciences Research Council (EPSRC)  for grants  GR/R99393/01  and  EP/C015452/1  that funded the creation of the  Deuteration Laboratory within ILL's Life Sciences Group. The authors acknowledge support from  Glyco@Alps  ( ANR-15-IDEX02 ),  Labex Arcane/CBH-EUR - GS  ( ANR-17-EURE-0003 ), and the  Czech Ministry of Education , Youth and Sports ( 18-18964S ). This work was supported by access to the  HTX lab facility at EMBL  and the PSB. We wish to acknowledge the  IBS  for access to the X-ray diffractometer and namely  Florine Dupeux  for help. We thank  Nicolas Coquelle  for help with the data collection and processing of X-ray data.
Software and Algorithms XDS  (Kabsch, 2010)  http://www. xds. mpimf-heidelberg. mpg. de/ Phenix 1. 16. 3 (Adams et al. , 2010) https://www. phenix-online. org/ CCP4 (Winn et al. , 2011) http://www. ccp4. ac. uk Coot (Emsley et al. , 2010) https://bernhardcl. github. io/coot Molprobity (Williams et al. , 2018) http://molprobity. biochem. duke. edu/ Pymol 2. 3. 2 Schrodinger, LLC https://pymol. org/2/ iMosflm (Battye et al. , 2011) http://www. ccp4. ac. uk LAUEGEN (Campbell et al. , 1998) https://web. archive. org/web/ 20001024010254/http://www. dl. ac. uk/ SRS/PX/jwc_laue/laue_top. html LSCALE (Arzt et al. , 1999) https://web. archive. org/web/ 20001024010254/http://www. dl. ac. uk/ SRS/PX/jwc_laue/laue_top. html.
γδ T lymphocytes diverge from conventional T CD8 lymphocytes for ontogeny, homing, and antigen specificity, but whether their differentiation in tumors also deviates was unknown. Using innovative analyses of our original and ~150 published single-cell RNA sequencing datasets validated by phenotyping of human tumors and murine models, here we present the first high-resolution view of human γδ T cell differentiation in cancer. While γδ T lymphocytes prominently encompass TCRVγ9 cells more differentiated than T CD8 in healthy donor's blood, a different scenario is unveiled in tumors. Solid tumors and lymphomas are infiltrated by a majority of TCRVγnon9 γδ T cells which are quantitatively correlated and remarkably aligned with T CD8 for differentiation, exhaustion, gene expression profile, and response to immune checkpoint therapy.
2  Unlike T CD8, γδ T cells react to TCR-independent and TCR-dependent stimuli that differ notably from major histocompatibility complex (MHC)-restricted peptides. Peripheral TCRVγ9 γδ T lymphocytes are specific for phosphoantigen-sensing butyrophilins, while tissular TCRVγnon9 γδ T cells recognize more diversified antigens. 8] [9]  So far, however, neither the general landscape of both subsets of cytolytic γδ TILs nor their putative link with the T CD8 had been characterized both at the single cell level and at a cancer-wide scale. To address this issue, the characterization of single-cell transcriptomes from γδ TILs requires to identify exhaustively and selectively these cells in multiple scRNAseq datasets from different human tumors. Although theoretically only the γδ T lymphocytes express a cell surface γδTCR, other cell subsets do actually express some level of TCRγ or TCRδ mRNAs.
Both TCRαβ T CD8 lymphocytes and NK cells also express either of the TRDC, TRGC1, or TRGC2 genes, imposing the use of a multigene signature rather than single genes to reliably identify γδ T lymphocytes only. 10 Here, we incepted a 'score and gate' strategy that significantly improves the digital purification of γδ T lymphocytes from scRNAseq datasets, and developed a cell pseudotime-based visualization of differentiation trajectories. With the γδ T and the T CD8 lymphocytes extracted from ~150 human samples of PBMC and tumor biopsies, the pseudotimed differentiation trajectories of these lineages revealed their strikingly contrasting hallmarks in blood and cancer.
Circulating γδ T are more differentiated than T CD8 lymphocytes in healthy individual's blood From seven scRNAseq datasets of healthy donor's PBMC and γδ T lymphocytes cell-sorted from PBMC,  10  (n = 6,122) γδ T lymphocytes were digitally extracted by 'score and gate' (Supplemental Data Figure  S1 ) (Methods). Then, a differentiation trajectory of these extracted cells was computed, and the trajectory's principal components were projected onto the linearized pseudotime axis to visualize a pseudotimed trajectory (Methods). The pseudotimed differentiation trajectory of peripheral blood γδ T lymphocytes comprised 14 milestones (Figure  1a ) in which TCRVγ9 were prominent over TCRVγnon9 cells, as expected (not shown). This trajectory was anchored by expression of Tn genes including the lymph node homing receptor CCR7  11  and the TCF1 differentiation transcription factor-encoding gene TCF7,  12  which peaked in the M1, M2 milestones.
Furthermore, (n = 137) γδ T and (n = 725) T CD8 lymphocytes were extracted from our CITEseq dataset of healthy donor's PBMC immunostained for differentiation markers,  19  and were mapped onto the above trajectories. Their differentiation stages determined by the cell surface CD45RA and CD62L immunophenotypes were consistent with those assigned by gene signatures (Supplemental Data Figure  S4 ). Reflecting distinct biologies and antigen specificities, however, the proportions of cells at each differentiation stage differed between the lineages and across individuals: γδ T were prominently Tem, while Tn predominated more often among T CD8 (Figures 1i, Supplemental Data Figure  4c ). Therefore, in healthy adults PBMC, the γδ T are generally more advanced than T CD8 lymphocytes on their respective differentiation trajectories.
Convergent differentiations of γδ T and T CD8 lymphocytes infiltrating tumors Then, to compare the differentiation hallmarks of γδ T and T CD8 lymphocytes infiltrating tumors (TILs), we performed 3ʹ chemistry scRNAseq of malignant lesions from (n = 3) splenic marginal zone lymphoma (SMZL) (GSE175785), and (n = 4) ovarian carcinoma (OVCAR) (GSE148162) cancer patients. Additional scRNAseq datasets from (n = 129) additional human tumors of various cancer types were also downloaded (summarized in Table  S2 ), normalized, and integrated for comparison (Methods). Altogether, a total of (n = 4,648) γδ T and (n = 45,174) T CD8 TILs were extracted from a total of 136 tumors (Tables  S3-S6 ), and analyzed as above (Supplemental data). The tumors richest in γδ T TILs corresponded to the richest in T CD8 TILs (Fischer Exact p =.
Both T CD8 and γδ T TILs are impacted by ICB therapy The convergent differentiation of intratumoral γδ T and T CD8 lymphocytes reported above prompted us to investigate whether γδ T TILs are as affected as T CD8 TILs to immune checkpoint blocking (ICB) therapy. We downloaded two published scRNAseq datasets of 21 tumors from (n = 11) BCC  22  and (n = 10) MEL  24  patients-derived tumor biopsies sampled before and after ICB treatment. Their γδ T and T CD8 TILs were extracted as above, mapped on their respective differentiation trajectory, and analyzed with regard to patient's clinical response, tumor infiltration pattern, and infiltrate's reactivity to treatment (post-treatment TIL counts increase) (Methods). In all samples, the T CD8 outnumbered the γδ T cells (Figure  4a ). The pre-treated BCC lesions were more infiltrated than those of MEL (mean: 295 versus 86 TILs, respectively), and were more reactive to treatment (BCC: 8/11 reactive, mean counts: 306 (pre)/785 (post), MEL: 4/10 reactive, mean counts: 73 (pre)/149 (post)) (Figure  4a ).
Nonetheless, the 'public' trajectory paved here by γδ T lymphocytes from ~150 healthy and diseased individuals (Tables  S3, S5 ) constitutes a reference roadmap of γδ T cell differentiation. Upon direct integration  29  of newer γδ T cells from any other scRNAseq dataset, each single cell coordinates on this trajectory will define its differentiation hallmarks, allowing automated characterizations based on this resource. Although well characterized in the mouse,  30  the differentiation of peripheral human γδ T cell has remained less investigated over the last decade. A linear sequence of four successive and non-overlapping Tn to Temra stages initially established for TCRVγ9 cells  1  was assumed to also apply for TCRVγnon9 cells. This model was validated recently at the single cell level,  10  but neither its pseudotime scale nor milestones were known.
Clinical patient sample collection The SMZL samples were collected at the Institut Universitaire du Cancer de Toulouse-Oncopole (IUCT-O) and processed at the CRB Cancer from Hôpitaux de Toulouse, following the standard ethical procedures (Declaration of Helsinki), after obtaining written informed consent from all patients and upon approval by the Institutional Review Board (nDC-2009-989). The carcinoma samples were collected from patients with head and neck squamous cell carcinoma, ovarian cancer, and cervical cancer at the time of surgery at the Institut Universitaire du Cancer de Toulouse-Oncopole (IUCT-O) in accordance with the Declaration of Helsinki, upon approval by the Institutional Review Board (nDC-2016-2656 and DECIdE protocol, NCT03958240) and signed informed consent.
scRNA-seq data pre-processing and QC The sequencing output bcl2 files were converted to FASTQ format by using cellranger-mkfastq™ algorithm (10x Genomics), and cellranger-count was applied to align to the GRCh38 reference transcriptome and build the (cell, UMI) expression matrix for each sample. Preprocessing, normalization of UMI, and QC were assessed using the R package Seurat. 29, 48 First, genes that correspond to human leukocyte antigens (HLA) and genes located in chromosome X and Y were removed, and the quality of cells was then determined by the number of detected genes per cell, and the proportion of mitochondrial genes. Additional datasets downloaded from 10x Genomics website, Gene Expression Omnibus (GEO), European Genome-phenome Archive (EGA), and ArrayExpress followed the same preprocessing and QC analyses.
Integration of PBMC scRNAseq datasets Here, seven different scRNAseq datasets were assembled to digitally extract control γδ T lymphocytes from PBMC of several healthy individuals. This comprised (n = 8,202) pure γδ T lymphocytes cell-sorted by cell surface TCRVδ1 or TCRVδ2 from the PBMC of n = 3 healthy donors  10  and (n = 25,736) PBMC from four other healthy donors (4k +8k from 10XGenomics-3ʹplatform V2, and 10k +1k from 10XGenomics-3ʹplatform V3 downloaded from 10xGenomics resource). After pre-processing and QC discarding of cell doublets and dying cells, the seven datasets were integrated using Seurat 3. 0. 29 Briefly, the datasets were normalized together and variable features from each dataset were identified by FinVariable-Data before identification of anchors correspondences across all the datasets, to yield an integrated PBMC dataset containing 26,313 features across n = 25,252 control cells (n = 17,803 PBMC plus n = 7,449 purified γδ T lymphocytes).
Principal component analysis (PCA) was performed on this integrated dataset and used to calculate uniform manifold approximation and projection (UMAP), as well as t-distributed Stochastic Neighbor Embedding (t-SNE)  49  coordinates. When visualized in UMAP space,  50  its main cell types were annotated by expression of the CD14, CD19, CD3D, and NCAM marker genes (Supplemental Data Figure  1a ).
Single cell signatures and scores Integrated and normalized scRNAseq datasets were then scored for the multigene signatures specified in text (Table  S1 ) which were downloaded from the MSigDB (https:// www. gsea-msigdb. org/gsea/msigdb/collections. jsp) unless specified otherwise. Each single cell score for each geneset was computed as described. 51 Briefly, Single-Cell Signature Scorer computes the score of cell C j for gene set GS x as the sum of all UMI for all the GS x genes expressed by C j divided by the sum of all UMI expressed by Cj: Score of cell C j for geneset GS x = (∑(UMI) GSx )/(∑(UMI) Cj )  1  For further visualization, all cell signatures scores are merged with each cell's UMAP coordinates using Single-Cell Signature Merger. 51.
Score and Gate of γδ T and T CD8 cells from PBMC Based on using single-cell signature scores to identify cells, this procedure allows the selective and exhaustive digital isolation from any scRNAseq dataset of any subset of cells specified by characteristic single-cell scores. The γδ T cell detection, which previously relied on a singlegene signature,  10  was improved here by implementing a sequence of positive and negative cell selections from several gene signatures, called 'Score and Gate. ' The above (n = 25,252) PBMC-extracted cells were scored for the specified gene signatures (Table  S1 ) using Single-Cell Signature Explorer  51  and scores were sequentially visualized on scatterplots by Single-Cell Virtual Cytometer. 19 he Score and Gate procedure comprises five sequential gating steps, here applied starting from the (n = 17,803) PBMC sub-dataset alone: 1.
Validation of the Score and Gate method of digital extraction The method was validated on two different scRNAseq datasets: our formely depicted dataset of purified γδ T lymphocytes from three healthy donors  10  and a CITEseq dataset of PBMC from another healthy donor. 19 When applied to the (n = 7,449) purified γδ T cell sub-dataset, this same score and gate strategy extracted (n = 5,542) γδ T cells (74% sensitivity). Given their already known TCRVδ1/ TCRVδ2 subtype, the TCRVγ9/non9 subdivision delineated (n = 2,328) TCRVγ9Vδ2, (n = 1,993) TCRVγnon9Vδ1, (n = 817) TCRVγnon9Vδ2, and (n = 404) TCRVγ9Vδ1 cells. Notwithstanding the equal rates of annotated TCRVδ1 and TCRVδ2 cells deliberately composing this sub-dataset,  10  such results confirmed the prominence of TCRVγ9Vδ2 cells over all the other TCR subsets of blood γδ T, and the preferential pairings of TCRVγ9 (85%) with TCRVδ2, and of TCRVγnon9 (71%) with TCRVγ1 (Supplemental Data Figure  1c ).
In the UMAP, (n = 5,537 out of the 5,542) extracted γδ T cells are embedded in the T cell clusters (Supplemental Data Figure  1d ) showing the γδ T cell detection specificity by score and gate had improved (99. 9 vs 95%) over earlier method,  10  and successfully extracted those γδ T cells embedded in CD4 and CD8 T cells clusters. Since CITEseq datasets provide both transcriptomes and immune phenotypes of single cells,  52  the same score and gate sequence was also applied to our CITEseq dataset of 3k PBMC from a healthy donor (GSE144434). 19 This extracted (n = 137) γδ T cells (4. 7% of PBMC, encompassing n = 81 TCRVγ9 plus n = 56 TCRVγnon9 cells) of cell surface CD3 + CD4 -CD8 - phenotype, and embedded with T CD8 and NK cells in UMAP (Supplemental Data Figure  7 ), as expected. These results validated the score and gate method, and the lymphocytes extracted from the CITEseq dataset were integrated into the control datasets.
Extraction of TILs from scRNAseq datasets of tumor samples The scRNA-seq of tumor lesions from three patients with splenic marginal zone lymphoma (SMZL) were produced for this study, and additional datasets of tumors from other cancers were downloaded from public resources (GEO, EGA, and AE). These encompassed tumor samples from cutaneous T cell lymphoma (CTCL),  53  Hodgkin lymphoma (HL),  54  lung adenocarcinoma (LUAD)  55, 56  , breast carcinoma (BRCA),  57  triple negative breast cancers (TNBC),  32  head and neck squamous cell carcinoma (HNSCC),  58  hepatocellular carcinoma (HCC),  59  basal cell carcinoma (BCC),  22  and melanoma (MEL)  24  (Table  S2 ). The same score and gate procedure was applied to the pre-processed and quality controlled datasets for digital extraction of (n = 4,648) γδ T and (n = 45,174) T CD8 cells, referred to as TILs.
The TI was calculated following the recommended workflow. 60 riefly, the normalized (log2) of raw expression data, and UMAP coordinates were used as an input. The most suitable and rapid method for our datasets determined by applying 'guidelines' function (dynverse on R) was Minimum Spanning Tree (MST) to infer the maturation trajectories of the γδ T and T CD8, independently ('ti_mst' function, dynverse on R), with default settings. Additional signatures ('Tn' and 'Teff') were scored, as described above, and together with the obtained MST attributes: dimension reduction coordinates 1 and 2 (MST1 and MST2), milestones and pseudotime, were used to define the root of the trajectory ('add_root' function) of the γδ T and T CD8 lymphocytes. The MST attributes allow multiple visualizations, so the data were visualized through pseudotimed trajectories.
Funding This work was supported in part by institutional grants from the  Institut National de la Santé et de la Recherche Médicale (INSERM) , the  Université Toulouse III-Paul Sabatier , the  Centre National de la Recherche Scientifique (CNRS) ;  Laboratoire d'Excellence Toulouse Cancer  ( TOUCAN-2 ) under grant contract  ANR11-LABX , the  Fondation ARC  under grant contract  PGA1-RF2019-0208691 ;  Institut Universitaire du Cancer de Toulouse-Oncopole  under contract CIEL;  Institut Carnot Lymphome  under cointract CALYM. This work was granted access to the HPC resources of CALMIP supercomputing center under allocation  2020-T19001. We are also grateful to the  Genotoul bioinformatics platform  ( Bioinfo Genotoul, Toulouse Midi-Pyrenees ) for providing computing resources; Fondation ARC pour la Recherche sur le Cancer  [ PGA1 RF20190208691 ]; Institut Carnot  CALYM [RAC21009BA];  LabEx Toulouse Cancer  [ ANR11-LABX].