dataesr/scientific-paragraphs-categorization huggingface dataset:
Author Contributions: Conceptualization, A. Z. and O. G. ; methodology, A. Z. and O. G. ; formal analysis, A. Z. ; resources, D. D. and the UK HIV Drug Resistance Database & the Collaborative HIV, Anti-HIV Drug Resistance Network; writing-original draft preparation, A. Z. and O. G. ; writingreview and editing, A. Z. , O. G. , and D. D. ; visualization, A. Z. ; supervision, O. G. All authors have read and agreed to the published version of the manuscript. Funding: O. G. was supported by PRAIRIE (ANR-19-P3IA-0001).
IHC evaluation After staining, two experienced pathologists (O. A and A. G) blinded to patient data independently evaluated the slides and counted the number of CD3+, CD4+, CD8+, FOXP3+, CD20+, and CD68 + positive cells by microscopic examination (x40 magni cation) in ve randomly-chosen intense immune cells representative staining foci without necrosis or brosis. A brown membranous staining for CD3+, CD4+, CD8+, and CD20 + markers, membranous and cytoplasmic staining for CD68+, and nuclear staining for FOXP3 + were designated as a positive expression. The proportion of immunostained cells was calculated by dividing the number of positive cells by the total number of in ltrating immune cells for each marker. A specimen was considered EBV positive when typical cytoplasmic and membranous staining for LMP1 was observed in HRS cells.
Statistical analysis Data were analyzed using the Statistical Package Software R (Version 3. 4. 1, www. r-project. org) and GraphPad Prism 5 software (GraphPad Software, Inc. , USA). Correlations between variables were analyzed using the chi-square (χ2), Fisher's exact, and Mann-Whitney's tests, when appropriate. Overall survival (OS) or event-free survival (EFS) was estimated from the date of diagnosis to death from any cause, tumor relapse/refractoriness or death from a CHL-related cause or the last follow-up contact if the subject did not die. Survival was estimated by the Kaplan-Meier method and compared by the log-rank test. Univariate and multivariate analyses were performed using Cox regression modeling with hazard ratios (HR) and 95% con dence intervals (CI). All tests were two-sided, and a p-value < 0.
Assessment of Systemic Pharmacokinetics in Rats An overview of the pharmacokinetic studies is presented in Figure  1a. Since previous pharmacokinetic data were extremely sparse, a pilot study was performed in rats (n = 2), and simulations were performed in Berkeley Madonna (version 8. 3. 18 for Windows, Berkeley, CA, USA), defining suitable dose and sampling intervals from the parameters obtained in the pilot study. The surgical implantation of catheters made of polyethylene (PE50) in the femoral vein and artery for dosing of peptide and sampling of blood, respectively, was performed the day before experiments under isoflurane anaesthesia. The animals were then housed in a CMA120 system (CMA, Solna, Sweden) for freely moving animals with ad libitum access to food and water. To determine the systemic pharmacokinetic parameters of the two peptides, a 10 min infusion was administered at a dose of 0.
(b) Total plasma concentration-time profiles of SFTI-1 (turquoise, n = 5) and kalata B1 (blue, n = 7) after a 10 min infusion of Sprague Dawley male rats with 0. 5 mg/kg of the respective peptide. Data for individual rats are shown. All data points for SFTI-1 post 120 min time point were below the quantification limit, i. e. , 5 ng/mL. (c) The extent of blood-brain barrier (BBB) transport of SFTI-1 (turquoise) and kalata B1 (blue), assessed using the unbound brain-to-plasma concentration ratio, Kp,uu,brain. Data are Figure  1. Pharmacokinetic studies with a focus on the fate of cCPPs in blood and the extent of BBB transport in rats. (a) Overview of pharmacokinetic studies focused on the assessment of systemic parameters after a 10 min intravenous infusion of peptides and brain distribution after a 4 h constant-rate intravenous infusion with the achievement of steady-state conditions.
(b) Total plasma concentration-time profiles of SFTI-1 (turquoise, n = 5) and kalata B1 (blue, n = 7) after a 10 min infusion of Sprague Dawley male rats with 0. 5 mg/kg of the respective peptide. Data for individual rats are shown. All data points for SFTI-1 post 120 min time point were below the quantification limit, i. e. , 5 ng/mL. (c) The extent of blood-brain barrier (BBB) transport of SFTI-1 (turquoise) and kalata B1 (blue), assessed using the unbound brain-to-plasma concentration ratio, K p,uu,brain. Data are presented as the mean + SD (n = 6 rats for each cyclotide) and assessed using the propagation of uncertainty method (see, Section 2. 12. (d) Key compartments involved in brain disposition of peptides with the essential pharmacokinetic parameters describing the extent of BBB transport, K p,uu,brain , and the extent of cellular barrier (CB) transport, K p,uu,cell , indicated.
Evaluation of Tissue and Cell Binding Properties In Vitro Peptide binding to plasma proteins, brain tissue homogenate, and lung tissue homogenate as well as to neuronal and astrocyte cell culture homogenates was investigated using equilibrium dialysis  [47]. Undiluted plasma and brain from three peptide-naïve rats (b) Unbound intracellular-to-extracellular concentration ratio, K p,uu,cell , of SFTI-1 and kalata B1, determined in brain and lung slices. Data were analysed using one-way ANOVA and Dunnett's multiple comparisons test. Note the differences in scale between SFTI-1 and kalata B1 data. (c) Unbound intracellular-to-extracellular concentration ratio, K p,uu,cell , of SFTI-1 and kalata B1, determined in neurons and astrocytes that had been differentiated from human neural stem cells. Data are presented as the mean ± SD, assessed using the propagation of uncertainty method (see Section 2.
Statistical Analysis Statistical analysis was performed using GraphPad Prism v6. 07 (GraphPad Software, San Diego, CA, USA). Permeability data were analysed with one-way ANOVA with Tukey's multiple comparisons test, normality was tested using the D'Agostino-Pearson test. Differences were considered to be significant when p-values were <0. 05. Brain slice and K p,uu,cell data were analysed with one-way ANOVA with Dunnett's multiple comparisons test. Differences in cellular uptake were analysed with an unpaired t-test. Illustrations are prepared using BioRender (Toronto, ON, Canada). Composite parameters (K p,uu,brain , K p,uu,cell ) were calculated using the propagation of error method in order to obtain standard deviations  [54, 55]. As the K p,uu,brain and K p,uu,cell pharmacokinetic parameters were not a result of a direct single measurement but obtained in two or three steps, the assessment of uncertainty also involved those steps  [54].
) of the respective peptides. Because most SFTI-1 is unbound in plasma (f u,plasma is 0. 936), the unbound and total plasma concentrations of SFTI-1 are overlapping. The initial total plasma concentrations were selected based on the achieved in vivo concentration in rats in the current study, i. e. , 400 nM for SFTI-1 and 1000 nM for kalata B1. NB: for direct comparison between two cCPPs the concentrations are presented in nM; Figure  S3. HPLC-UV chromatogram showing purity of SFTI-1, recorded at wavelength 215 nm. Gradient (in cyan) is the percentage of solvent B (0. 045% TFA in ACN). Author Contributions: E. M. : investigation, formal analysis, methodology, validation, writing-original draft preparation, and data visualization. C. E. : methodology, formal analysis, manuscript review, and editing.
3' RNA sequencing. Pipeline for raw data processing Raw paired reads generated by high-throughput sequencing were first processed using a bioinformatic pipeline developed with the tool Snakemake. 82  Briefly, 3 extra G added during library preparation were removed from read 2 using trimmomatic. 83  Then, the Unique Molecular Identifier (UMI) sequence was extracted from read 1 and appended to the name of the paired read 2 (extract function from the UMI-tools package. 84  Read 2 from each pair were then mapped against the reference genome (GRCh37) with STAR. 85  Reads mapped at a given position of the genome with identical or near identical UMI sequence were deduplicated with the program umi_tools dedup, using the method ''directional. '' Readcount per cellular gene was then determined with the program htseq-count, mode ''union''.
ACKNOWLEDGMENTS HL is a recipient of a  CIFRE grant  from  Agence Nationale de la Recherche et de l'Innovation (ANRT, France)  to support her PhD work. The authors thank  Dr. Joseph Seitlinger , now at  CHU   Nancy , for initiating the project,  Dr. MP Chenard , and the  Center de Ressources Biologiques (CRB) department of Strasbourg University Hospital  for contribution in sample collection, and management. The study was conducted in accordance with the  Declaration of Helsinki , and approved by  Ethics Committee of Grand Est, France  ( CNRIPH N 20. 11. 12. 42058). Transgene also acknowledges the  European Commission  for its support to IMI/imSAVAR ( Immune Safety Avatar ) consortium (grant agreement n  853988 ). The various funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Currently, a phase I clinical trial is open to determine the maximum tolerated dose of early administration of increasing doses of IL-2 in combination with a fixed dose of zoledronate, in order to expand Vd2 cells in patients with a hematological disease eligible for a haplo-stem cell transplantation (NCT03862833). Some antibodies have also been shown as activating gd T cell anti-tumor functions. A first-in-class humanized anti-BTN3A1 antibody was designed to harness and enhance Vd2 cell-driven anti-tumor activity against multiple tumor cell lines and primary tumor cells  (67) , opening promising perspectives. A phase I/IIa trial in patients with advanced-stage relapsed and/or refractory cancers including DLBCL and FL (NCT04243499) are currently opened to assess the safety, tolerability and efficacy alone or in combination with the anti-PD-1 mAb pembrolizumab.
Recently, a novel bispecific molecule was developed by linking the extracellular domains of tumor-reactive Vg9Vd2 TCR to a CD3-binding moiety, creating gdTCR-anti-CD3 bispecific molecules (GABs). The high affinity of Vd2 for PAg enriched in tumor cells favored the recruitment of other CD3 + T cells in the TME enhancing the in vivo targeting of MM cells and leading to tumor regression  (76). Altogether, these bsAbs represent promising candidates for the development of novel treatments for B-cell malignancies and for now, only one phase I/ II clinical trial is opened (NCT04887259) to assess the efficacy of LAVA-051 (vg9TCRxCD1d bAb) in patients with relapsed/refractory CLL and MM in whom it appears to be well tolerated (77).
So far, this approach has only been described for solid tumors  (78). In MM patients, high-dose administration of ex vivo zoledronate-activated Vd2 T cells resulted in a measurable increase of Vd2 cell number in PB and BM, which was correlated with an anti-tumoral effect in 4 of 6 patients (79). A phase I clinical trial is currently opened in CLL (NCT03533816) in order to extract, concentrate, and activate gd T cells from the PB to provide an innate anti-tumor effect.
These observations were also obtained by other groups  (87). Due to their long persistence in vivo, Vd1 T cells represent attractive candidates for ACT. Based on the "DOT protocol" (84), a clinically translatable protocol for Vd1 T cell expansion allowed the development of CAR-d1 T cells that exhibited highly consistent innate cytotoxicity against different leukemic cell lines  (88). Of note, CD20 directed CAR-d1 T cells (under the name ADI-001) exhibited a potent anti-cancer activity both in vitro and in vivo, in B-cell lymphoma xenografts in NSG mice bringing strong evidence to propose the assessment of its efficacy in phase I clinical trial (NCT04735471) in patients with B-cell malignancies  (89). The first results showed that ADI-001 maintained a favorable safety profile, and preliminary efficacy showed very encouraging CR rate (4/5) and sustained durability in patients, including those previously exposed to conventional CAR-T therapy  (90).
Currently, a phase I clinical trial (NCT04688853) is testing the TEG002, an autologous T cell transduced with a specific gd TCR, in relapsed/refractory MM patients. Another strategy is based on the combination of the Fab domain of an antibody with the g and d chains of the TCR (AbTCR) as the effector domain  (95). This CD19-CAR (Ab)TCR (ET190L1 ARTEMIS ™ ) triggered Ag-specific cytokine production, degranulation and killing of CD19 + cancer cells in vitro and in a xenograft mouse model. Whether these pre-clinical findings for AbTCR translate into clinical settings has been assessed in two clinical trials in relapsed and refractory CD19 + NHL (NCT03379493, NCT03415399). Very recently, a novel anti-CD19 CAR-T cell system was obtained by fusing the anti-CD19 antibody Fab domain with the transmembrane and intracellular domains from the gd TCR with addition of an ET190L1-scFv/CD28 co-stimulatory molecule (ET019003 T cells)  (96).
ET019003 T cells were tested in preclinical studies followed by a phase I clinical trial in relapsed/ refractory CD19 + B-cell leukemia and lymphoma (NCT04014894). So far, it was shown that these CAR-T cells presented a good safety profile and could induce rapid responses and durable CR in patients with relapsed or refractory DLBCL. Although, these results are preliminary and are limited to a small sample size, they offer new promising therapeutic strategies for patients with high-risk profiles. The spectrum of gd T cells based IT in B-cell malignancies is summarized in the Figure  3.
Adoptive cell transfer using allogenic gd T cells expanded ex vivo Zoledronate/IL-2 activated allogeneic gd T cells from healthy Advanced (83) donors refractory MM 2 cycles of 2 dosage escalated manner infusions at 14 days R/R NHL Institute of Hematology & Blood NCT04696705 intervals Diseases Hospital (Continued) Cell-Based Immunotherapy approaches in B-cell malignancies.
Online content Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi. org/10. 1038/s41590-023-01493-2. analysis of ear skin tissue of transgenic Kitl-TdTomato mice reveals close proximity and interaction of MCPT6 high cells with periarteriolar KITLG-expressing fibroblasts. Representative images are volume projection (left) and focal z plane (right). j, Immunofluorescence staining for MCPT6 in ear skin whole-mount tissue of an adult Tln1 ΔMC mouse. The data presentation is the same as for WT in Fig. 4k. k, Comparative analysis of MCPT6 expression in periarteriolar MCs was performed for WT and Tln1 ΔMC mice.
Final data analysis was done with FlowJo software (BD Bioscience). Antibodies and working dilutions are listed in the Reporting Summary. To measure integrin cell surface expression of endogenous skin MCs, flow cytometric analysis was performed on MCs isolated from mouse ear skin. Ears were separated into dorsal and ventral parts, finely minced on ice using sharp scissors and incubated with Opti-MEM (Gibco) containing 1 mg ml -1 Liberase DL (Roche, 05401160001) and 2 mg ml -1 DNase I (Roche, 10104159001) for 60 min at 37 °C while shaking at 1,500 r. p. m. in a thermomixer. Samples were collected on ice in a 50-ml reaction tube containing 10 ml of Opti-MEM. The suspension was filtered through a 30-µm cell strainer into a fresh 15-ml reaction tube and centrifuged for 5 min at 400g at room temperature, and the resulting cell pellet was washed once using 10 ml of Opti-MEM before the samples were centrifuged once more, and the supernatant was discarded.
Technical details on imaging are provided in Supplementary Note 1. MC adhesion to native FN matrices was investigated. Briefly, eight-well Lab-Tek imaging chambers containing rhodamine-labeled FN matrices were washed twice with PBS and filled with 30 µl of imaging medium. BMMCs were resuspended in imaging medium at a concentration of 10 6 cells per ml, and 70 µl of cell suspension was added to the matrices for 1 h at 37 °C. To stimulate adhesion, 100 µl of SCF (final concentration of 100 ng ml -1 ) was added to the well. Technical details on imaging are provided in Supplementary Note 2. Raw imaging data were processed with Imaris (V9. 5. 1, Bitplane) using a Gaussian filter for noise reduction and displayed as 2D maximum intensity projections.
After excluding dead cells (DAPI) and doublets, MCs were identified as CD45 + , Lin -(CD3e -, CD19 -, CD4 -, CD8 -, CD11c -and NK1. 1 -) and YFP + cells. Cells were sorted into 384-well plates using an Aria Fusion II (BD). Antibodies and working dilutions are listed in the Reporting Summary. All sorted cells expressed high transcript levels of the MC marker Cpa3 (Extended Data Fig. 5b ). For scRNA-seq analysis of VSMCs and pericytes, CD45 -GFP + cells were sorted from ear skin digests of Myh11 GFP/+ mice.
scRNA-seq data analysis Clustering and visualization were performed using the RaceID3 (v0. 2. 3) algorithm  23. Cells expressing >2% of Kcnq1ot1, a potential marker for low-quality cells  57  , were not considered for the analysis. For normalization, the total transcript counts in each cell were normalized to 1 and multiplied by the minimum total transcript count across all cells that passed the quality control threshold of >1,000 transcripts per cell (Poisson-corrected UMIs  57  ). For the MC data, 1,895 cells passed the quality control threshold. For the smooth muscle cell data, 330 cells passed the quality control threshold. RaceID3 was run with the following parameters: mintotal = 1,000, minexpr = 5 and minnumber = 5. Mitochondrial and ribosomal genes as well as genes with Gm-identifiers were used as input for CGenes.
Differential gene expression analysis Differential gene expression analysis between cells and clusters was performed using the diffexpnb function from the RaceID3 package. First, negative binomial distributions reflecting the gene expression variability within each subgroup were inferred on the basis of the background model for the expected transcript count variability computed by RaceID3. Using these distributions, a P value for the observed difference in transcript counts between the two subgroups was calculated and corrected for multiple testing using the Benjamini-Hochberg method, as previously described  58. UMAP representation was done as previously published  59.
Analysis of Ubow mice To quantify MC cluster formation in Ubow mice, ear skin whole mounts were imaged using a LSM780 (Zeiss) confocal microscope equipped with a Plan-Apochromat ×20 M27 objective (Zeiss). Z stacks of 25 to 42 µm with a 2-µm step size and multiple tiles were acquired using ZEN black software, and 2 × 2 tiled images (1. 2 × 1. 2 mm; covering a minimum of 600 MCs) were stitched using ZEN blue software. Statistical analysis and quantification of spatial MC distribution were performed using ClusterQuant2D software  21, 60. Detailed information of the analysis is provided in Supplementary Note 5.
Statistics and reproducibility Analyses were performed using Prism software (GraphPad Software,  version 9. 3. 1). No statistical methods were used to predetermine sample size and were determined based on previous publications  61, 62  , prior experience or pilot experiments. Sample sizes for biological replicates in cell culture experiments (that is, cultures generated from Letter https://doi. org/10. 1038/s41590-023-01493-2 different individual mice) aimed for a minimum of mouse donors to reduce the number of laboratory animals. Data exclusion methods were not applied for most experiments. Exceptions are listed in the Reporting Summary. If not indicated otherwise, comparisons for two groups were evaluated using a two-sided unpaired t-test after confirming that samples fulfilled the criteria of normal distribution and equal variance.
Reporting summary Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. (c, d ) Two experimental strategies to identify dermal vessel types were applied for the analysis of MC proximity to different blood vessels. (c) Workflow for the post-imaging analysis of MC proximity to arterioles, venules and capillaries with Imaris software. This analysis was based on immunofluorescence stainings of ear skin whole mount for MCs (avidin), α-smooth muscle actin (ACTA2) and endomucin (EMCN). ACTA2 + / EMCN -vessels were classified as arterioles, ACTA2 + /EMCN + vessels as venules and ACTA2 -/EMCN + vessels as capillaries. This workflow was used for the data analysis in Fig. 2c. (d) Workflow for the post-imaging analysis of MC proximity to arterioles, venules and capillaries with Imaris software.
Software and code Policy information about availability of computer code Data collection For imaging data collection: Zen Blue (v 2012) and Zen Black (v8. 1)  (Carl Zeiss Microscopy)  For FACS data collection: FACSDiva (BD, v8. 0. 1). For Western Blot data collection: Image Lab TM Touch Software (Bio-Rad, v6)   Data analysis Kymographs were analyzed using a Matlab program which is available at GitHub (https://github. com/KMGlaser/Bambach-et-al. git). Mast cell clusters were analyzed using ClusterQuant2D. ClusterQuant2D code is provided at: https://zenodo. org/record/7684553. Single-cell RNA sequencing analysis was done with the RaceID3 (v0. 2. 3) algorithm (Herman et al, 2018 Nat Methods 15, 379) and is available on CRAN (https://cran. r-project. org/) and RStudio (v4. 0. 2 GUI 1. 72, source code as described previously, DOI: https://doi.
org/10. 1038/ nature12175). Paired-end reads were aligned to the transcriptome using bwa (version 0. 6. 2-r126; https://bioweb. pasteur. fr/packages/ pack@bwa@0. 6. 2). For Imaging data analysis: Imaris (Bitplane, v9. 1. 2 and v9. 5. 1), Fiji (v2. 1. 0/1. 53) and Huygens (SVI; v20. 04) For FACS data analysis: FlowJo (BD, v10. 6. 1) For qPCR data analysis: Excel (Microsoft, v16)   For statistical data analysis and generation of graphs: Graphpad Prism (v9. 3. 1)   For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors and reviewers. We strongly encourage code deposition in a community repository (e. g. GitHub). See the Nature Portfolio guidelines for submitting code & software for further information.
Acknowledgements We thank  R. Fässler  ( Max Planck Institute of Biochemistry ),  C. Brakebusch  ( BRIC, University of Copenhagen ),  R. Wedlich-Söldner  ( University of Münster ),  D. Critchley  and  S. Monkley  (both  University of Leicester ),  G. Häcker  and  S. Minguet  (both  University of Freiburg ) and  R. Grosschedl  ( Max Planck Institute of Immunobiology and Epigenetics ) for kindly providing mice and reagents for this study,  J. Friehs  for technical help,  W. Römer  for initial help in this project, members of the  MPI Imaging Facility  for assistance with imaging and  B. Wehrle-Haller  for helpful discussions. This work was supported by the  Max Planck Society  (T. L. and D. G. ), the  Deutsche Forschungsgemeinschaft (German Research Foundation ; project  IDs 248768892  (T. L.
), 431347925 (T. L. ) and 322359157 (A. R. )), the  Else Kröner-Fresenius-Stiftung  ( 2015_A237 , M. S. -S. ), the  Agence nationale de la recherche  ( ANR-19-CE15-0030 , M. B. ) and the  European Research Council  ( ERC-2018-COG , ImmuNiche,  818846 , D. G. ).