dataesr/scientific-paragraphs-categorization huggingface dataset:
The adaptive immune response relies on T cells that combine phenotypic specialization with diversity of T cell receptors (TCRs) to recognize a wide range of pathogens. TCRs are acquired and selected during T cell maturation in the thymus. Characterizing TCR repertoires across individuals and T cell maturation stages is important for better understanding adaptive immune responses and for developing new diagnostics and therapies. Analyzing a dataset of human TCR repertoires from thymocyte subsets, we find that the variability between individuals generated during the TCR V(D)J recombination is maintained through all stages of T cell maturation and differentiation. The inter-individual variability of repertoires of the same cell type is of comparable magnitude to the variability across cell types within the same individual.
A. Generative models of individual T-cell subsets We analyse high-throughput TCRβ repertoire sequence data of purified T cell subsets from different stages of maturation (see Fig. 1A  for a schematic of the different stages). We analysed purified samples of CD4+CD8+CD3+ (double positive: DP), CD3+CD8+ (CD8+), CD3+CD4+CD25+ (Treg), CD3+CD4+CD25-(Tconv) from thymic samples from n = 9 individuals. Details on sorting strategies and highthroughput sequencing steps are described in  [27]  where part of this dataset was presented. DP cells represent an early stage of development after successful recombination and selection of a receptor, prior to commitment to any functional fate. The statistics of their repertoire should closely follow that of the recombination process. During selection, cells with higher affinity to one of the two major classes of MHC differentiate into either CD8+ or CD4+ cells (Treg and Tconv).
We observe a progression of thymic differentiation along dimensions 1 and 2, with clusters corresponding to each stage, while dimension 3 delineates individuals. Notably, the VJ gene features alone cannot separate the intra-individual cell subsets (Fig. S6B ). This result suggests that individual heterogeneity imprinted by initial differences in VDJ recombination remains frozen throughout the maturation process, in agreement with previous analyses on unsorted repertoires  [17]. An alternative way to represent similarities between repertoires is to find a common encoding space for sequences (by using neural networks, as discussed in the Methods) instead of whole repertoires, and then directly compare the location of different receptor sequences from different datasets (Fig. S2 ). While sequence variability is large and no clear separation of subsets is visible at the individual sequence level, we find the average location of receptor sequences in the encoding space to follow the expectations from the differentiation process (Fig.
In other words, selection during thymic development amplifies neighboring sequences within certain regions of the sequence space, while depleting others, which results in repertoires with reduced overall diversity but with local agglomeration of sequences. An alternative way to measure repertoire concentration is to look at the distribution of distances between any two receptor sequences (Fig. 2C ). Here, we consider the Hamming distance (number of unmatched amino acids in the junction) between sequences with the same V and J gene and junction length. Receptors tend to have on average lower Hamming distances following selection, for both CD4+ and CD8+ cells (Fig. 2B ). Together, these observations are consistent with the results of  [27]  on the CD8+ T cells obtained from the same datasets, and extend them to CD4+ T cells.
The performance of the soNNia model reaches the Spearman correlation of ρ ∼ 0. 75 for CD8+ and Tconv subsets. The Treg and DP datasets have smaller sizes and contain fewer nearest neighbours on average (Fig. S7 ), resulting in a nosier comparison to the model, which reduces the absolute correlation. The smooth landscape approximation only moderately degrades predictability (Fig. 2F ), while showing much faster computation times (by a factor of 19 times the mean junction length), thanks to the fact that it does not require computing the probability of each neighboring sequence. These observations carry over to the prediction of the number of second neighbors (i. e. , sequences with at most 2 amino acid differences) and beyond (Fig. S4 ). Overall, these results show that the local properties of individual repertoires are well captured by the model and that the probability landscape of finding receptors sequences is relatively smooth as a function of sequence distance.
E. Local differences between repertoires Next, we asked how the neighborhood structures differed between cell types and individuals at the level of single sequences. For a given sequence x, we want to measure differences in the local network structure of nearest neighbors across different repertoires. We quantify these differences by defining a distance based on the spearman correlation between the number of neighbors of each sequence in the two datasets: D L (D 1 , D 2 ) = 1 -ρ(n D1 (x), n D2 (x)) ( 2 ) where n D1 (x) and n D2 (x) are the number of neighbors of sequence x in repertoires D 1 and D 2. Compared to the Jensen-Shannon Divergence D JS , this distance is based on the local sequence information only, and in principle is model-independent. However, its accuracy strongly depends on sequencing depth, because it relies on counting the number of neighbors for each sequence, see Fig.
III. DISCUSSION Previous work  [1] [2] [3] [4]  has characterized global statistical differences between the TRB repertoires of fully mature CD4+ and CD8+ T cells. Here we tracked how these differences emerge during thymic development. We find that the diversity of the repertoire shrinks as a result of thymic selection. This reduction occurs through the concentration of the repertoire around particular regions of the sequence space, consistent with the findings of  [27]  on CD8+ repertoires from the same dataset. These favored sequences are typically likely to be generated by VDJ recombination (high P gen ) even before thymic selection, suggesting that the recombination process has evolved to produce sequences that are likely to survive thymic selection. This idea was first proposed in  [29]  based on the analysis of peripheral repertoires.
B. Multidimensional Scaling The objective of Multidimensional scaling is to find a lower dimensional representation of data that preserves the similarity between samples in the dataset. Given D data points and precomputed distances d ij between points it infers data coordinates (x 1 ,. , x D ) with x i ∈ R N and N small by minimizing an objective function called stress: Stress(x 1 ,. , x D ) = i =j (d ij -||x i -x j || 2 ) 2 (3) C. Inference of a representation space for TCR sequences In order to better visualize the differences between repertoire subsets from different stages of maturation in the thymus, we develop a method to map the receptor sequences in a representation space that carries information about selection. We build a feed-forward neural network that outputs the selection factor for each subset and has a hidden layer of dimension 2; see Fig  S2A for  a sketch of the network architecture.
We infer selection factors by maximizing the joint objective L(θ) = t∈T E t [-E t θ ] -log E G [e -E t θ ] ( 4 ) where G is the set of sequences generated by the P gen model, θ the model parameters, E θ the energy that the model assigns to the sequence, and, with abuse of notation, we identify with T the cell types of the thymic samples and the corresponding dataset. It is important to clarify that this architecture is not an autoencoder because we are not trying to reconstruct the distribution of sequences, but we are only interested in characterizing the selection factor. For this reason, the representation space will carry information only about selection. We implement the model using the Keras software  [36]  and infer its parameters using the RMSprop stochastic gradient descent algorithm  [37].
F. Local Distance between repertoires DL The local distance D L between repertoires, as defined in Eq. 2, can be directly evaluated on sequences shared between repertoires by computing the correlation between the observed number of neighbors in the respective datasets. In Fig. S5A , we evaluate the D L between independent subsamples from the same dataset. We show that for typical repertoire sizes that are found in the literature (10 4 -10 5 unique receptor sequences), the estimator is too noisy to resolve the observed differences between repertoires (Figs. 3B, C ), which can be as small as D L ∼ 0. 1 bits. In order to overcome this difficulty we evaluate the estimator using the expected number of neighbors n pred computed by the models. We find that D L converges quickly as a function of the number of sequences evaluated; see Fig.
S5B. In the main analysis we evaluate D L with 200 sequences sampled from each dataset. As sequencing technologies improve, we expect the model-free estimator to successfully be used to compare the local structure of different repertoires. As a proof of concept, we compare the model-free version of the D L estimator to the global distance D JS for the biggest datasets present in our data (6 datasets with at least 80,000 unique amino acid sequences) and we are able to reproduce the results of Fig. 3A , see Fig. S5C.
V. ACKNOWLEDGEMENT The study was supported by the European Research Council COG 724208 (AMW, TM, GI), the ANR-19-CE45-0018 "RESP-REP" (AMW, TM) from the Agence Nationale de la Recherche, the DFG grant CRC 1310 "Predictability in Evolution" (AMW, AN, TM, GI), the National Institutes of Health MIRA award (R35 GM142795 to A. N. ), the CAREER award from the National Science Foundation (grant No: 2045054 to A. N. ), the MPRG funding through the Max Planck Society (AN, GI), the ERC-Advanced TRiPoD (322856 to D. K. ), LabEx Transimmunom (ANR-11-IDEX-0004-02 to D. K. ), RHU iMAP (ANR-16-RHUS-0001 to D. K. ), the European Research Area Network-cardiovascular diseases (ANR-18-ECVD-0001 to E. M. F. ), and iReceptor-Plus (H2020 Research and Innovation Programme 825821 to D. K. and E. M. F). This work benefited from equipment and services from the iGenSeq core facility at Institut du Cerveau (ICM, Paris, France) for all the data production.
Acknowledgements We thank  Olivier Jaen  and  Flora Guillot  ( Cytek Biosciences );  Bénédicte Hoareau  ( UMS 37 PASS );  Gwendolyn Marguerit ,  Encarnita Mariotti-Ferrandiz ,  Gabriel Pires  and  Thomas Vasquez  ( i3 lab );  Doriane Foret ,  Flora Issert ,  Kim Nguyen ,  Olivier Bregerie  ( UMS 28 ). Funding: AB was supported by the  Fondation pour la Recherche Médicale  and PE by a grant from the  Swiss National Science Foundation  and the  Novartis Research Foundation.
For the construct designed to obtain the Tg(kdrl:mKate2-Podxl2) fish line 2 overlapping fragments were amplified for cloning into a pG1-flk1 vector after digestion with EcoR1, with the following couples of primers: pG1-flk1_CD8_mKate2_fw and delPodxL2-mKate2_rev; mKate2-delPodxL2_fw and Podxl2_PG1-flk1_rev. For the ArhGEF11(PDZ-PRD-RGS-eGFP) construct, the cDNA encoding for the peptide ending upstream of the DH domain at Valine 709 of the amino-acid sequence (GenBank: AY295347. 1; PALDEDV; fragment 1-709) was fused upstream of the sequence encoding for eGFP, for expression using the pG1-4xNR vector. The cDNA was amplified using the GB_4xUAS-gef11_fw; GB_eGFP-RGS_rev couple of primers. For the construct designed to obtain the Tg(kdrl:Gal4;UAS:RFP;4xNR:dt-runx1-eGFP) fish line, the Runx1 sequence amplified omits the ATG (replaced by an nls) and ends downstream of the Runt domain (ending at amino-acid sequence PAHSQIP).
Whole mount in situ hybridization For the sequence of all the primers used to amplify probes, see the Key Resources Table. The probes for WISH concern Pard3ab, Pard3bb (Figure  3 ) and PDZ-RhoGEFs (Figure  7  -figure supplement 2). Whole-mount chromogenic in situ hybridization was performed as described in Lancino et al 2018. Probes were synthetized using the manufacturer recommendation (T7 RNA polymerase, Promega, Cat# P2075, DIG-nucleotides, Jena Bioscence, Cat# NU-803-DIGX). For Pard3ab (GenBank: AY524776. 2), a probe of 717bp (nucleotides  encompassing the nucleotide sequence starting in the 3-prime of the sequence encoding for PDZ1 and ending between PDZ2 and PDZ3 was amplified with the couple of primers pard3ab_WISH_Nter_fw and pard3ab_WISH_Nter_T7_rev. For Pard3bb (NCBI Reference sequence: XM_021478865.
Signal quantification and recovery analysis File were converted from. nd2 to. tiff using FIJI batch converter, scale factor 1. 00, bilinear interpolation, no average when downsizing, images read using bioformat. To collect fluorescence intensity, ROIs for each stimulation point was manually drawn using Fiji and named with an individual label using Fiji ROI manager. A metadata table was manually curated with all information for each ROI (junction type, condition, date of experiment, embryo number). An additional ROI was created to collect background intensity for subsequent corrections. Acquisitions with clear embryo movement or drifting were either completely removed from the dataset (~5% of the acquisitions) or only kept for early recovery analysis (see below) if the drift only occurred in the last step (fifth) of the acquisition.
Acknowledgments We wish to warmly thank  Philippe Herbomel  for his encouragements and for ensuring the funding of this study. We thank our fish facility members  Y. Rolin  and  K. Sebastien  for their daily help,  R. Patient  ( University of Oxford ) for the Hsp70 promoter. This work was supported by  Institut Pasteur, CNRS , grants to  P. Herbomel  from the  Fondation pour la Recherche Médicale  (# DEQ20160334881 ), the  Fondation ARC pour la Recherche sur le Cancer , and the  Agence Nationale de la Recherche Laboratoire d'Excellence Revive (Investissement d'Avenir ,  ANR-10-LABX-73 ), and a grant to AS from the  Institut Pasteur  ( PTR 439-2021 ). LT was recipient of a  PhD fellowship  from the  Collège Doctoral, Sorbonne Université , and from the  Labex Revive  ( ANR-10-LABX-73 ).
The therapeutic manipulation of NK cells is much more advanced for oncological indications than for liver diseases. To date, over 200 clinical trials (clinicaltrials. gov) have been conducted to exploit their anti-tumoural properties. NK cellbased therapeutic approaches leading to promising results include infusion of activated and/or engineered NK cells, NK cell stimulation with cytokines and the use of agonistic and blocking antibodies that target NK cells. 120  Regarding the treatment of HCC, it has been shown that injecting allogenic NK cells into patients increased progression-free survival. 121, 122  The use of chimeric antigen receptor (CAR)-modified NK cells also represents a promising approach in HCC treatment. CAR-NK cells have been engineered against glypican-3, c-MET oncoproteins, CD147 glycoprotein (immune cell activator) and TGF-b (inhibitor of NK cell cytotoxicity).
Financial support This work is supported by grants from  Institut National de la santé et de la Recherche Médicale (INSERM) ,  Université Côte d'Azur  and various charities ( Association Française pour l'Etude du Foie (AFEF)  to CL,  Société Nationale Française de Gastro-Entérologie (SNFGE)  to RA. This work was also funded by the  French Government (National Research Agency, ANR) : # ANR-18-CE14-0019-02 , # ANR-19-CE14-0044-01 , # ANR-21-CE14-0015-03 , #  ANR-22-CE14-0027-01  and through the " Investments for the Future" LABEX SIGNALIFE  (# ANR-11-LABX-0028-01 ) and the  UCAJEDI Investments in the Future project  (# ANR-15-IDEX-01 ). We would also like to thank  Catherine Buchanan  for her assistance with the English syntax and grammar in this manuscript. MB was successively supported by the  ANR  (# ANR-18-CE14-0019-02 ), and by doctoral grants from the  French Ministry of Research  and the  Fondation pour la Recherche Médicale (FRM).
The principle of the classification is based on matching of query MS profiles with a MS reference spectra database (RSDB). The correct classification requires however that MS spectra are intra-species reproducible and inter-species specific. As for most phenotypic approaches, MS protein profiles could vary according to body part, developmental stages or sample preparation mode  [24]. Then, to compare and to share MS results, a standardization of the protocols were previously established for mosquitoes  [25, 26]. We also showed that the independent submission of legs and thorax from the same specimen to MALDI-TOF MS  [27]  can improve the identification rate and confidence level, that may be decisive for discriminating similar phenotypes, such as cryptic species  [26, 28]. In this context, the aim of the present work was to determine whether resistance to the pyrethroid deltamethrin, could be detected in Ae.
Database creation and blind tests The reference MS spectra were created using spectra from two specimens per line and per body parts (legs and thorax) using MALDI-Biotyper software v3. 0. (Bruker Daltonics). MS spectra were created with an unbiased algorithm using information on the peak position, intensity and frequency. The remaining MS spectra per line and body part were queried against these reference MS spectra and a classification as deltamethrin-resistant or -susceptible was done according to the result of spectral matching with DB. The reliability of sample classification was determined using the Log Score Values (LSVs) given by the MALDI-Biotyper software v. 3. 0, corresponding to a matched degree of mass spectra between the query and the reference spectra from the DB. LSVs ranging from 0 to 3 were obtained for each spectrum of the samples tested.
3-fold between susceptible and resistant lines  [40]. Results Low MS spectra diversity between Ae. aegypti lines. Legs and thoraxes from 20 specimens per line (BORA, IR13, IR03 and IRF) were submitted independently to MALDI-TOF MS analysis (Figure  1 ). At the exception of the legs from one specimen from the IR03 line, MS profiles of high intensity (>2000 a. u. ) were obtained for all samples from both body parts. To control MS spectra "quality", they were queried against our MS spectra database (DB) which includes reference MS spectra of legs and thoraxes from 16 distinct mosquito species  [27, 28]  and notably Ae. aegypti. One hundred percent of the legs and thoraxes MS spectra matched with reference MS spectra from Ae. aegypti with respective body parts. Respectively, 97. 5% (78/80) and 100% (80/80) of the MS spectra from legs and thoraxes reached the threshold LSV of 1.
The remaining MS spectra from legs (n=144, 18 samples per lines (x4) loaded in duplicate (x2)) and from thoraxes (n=144) were queried against these reference MS spectra. Overall, 98. 9% (n=285/288) of the MS spectra queried against the database, obtained LSVs over 2. 0, and all (100%) reached the threshold established for relevant identification (LSVs>1. 8) (Additional Figure  S1 ). The assessment of concordance of classification results (resistant or susceptible) between blind tests and lines revealed an agreement of 77. 1% with a Cohen's κ coefficient of 0. 542 corresponding to a moderate agreement of the data for legs. Similarly, a moderate agreement (76. 4% with a Cohen's κ coefficient of 0. 528) was obtained for thoraxes. The sensitivity and specificity of blind test strategy were, respectively, 72.
2% and 81. 9% for legs and 72. 2% and 80. 6% for thoraxes using the four Ae. aegypti lines as reference (Table  3 ). 81. 9% 80. 6% *Results of spectra classification queried against the reference MS spectra included in the DB. # Coefficient of agreement, the agreement level is indicated into brackets, as previously defined  [38]. DB, database; MS, mass spectrometry; Resistant, deltamethrin-resistant lines; Susceptible, deltamethrin-susceptible lines. Potential association between the 4870 m/z MS peak and kdr mutations For each specimen tested in the present study, the V410L, V1016G/I, and F1534C kdr mutations were genotyped in an attempt to identify their potential association with the 4870 m/z MS resistance discriminating peak. These SNPs were selected because they are known to be strongly associated to pyrethroid resistance in Ae.
aegypti specimens from distinct origins (reproducibility of spectra). The presence of reference spectra from specimens of the same species was then sufficient for correct and reliable identification of species identification, confirming the compliance of the MS spectra dataset from legs and thoraxes for next analyses  [24, 27]. The significant higher LSVs obtained for thoraxes compared to legs confirmed the better MS spectra reproducibility of the thoraxes for species identification, as recently demonstrated  [26]. Nevertheless, for both body parts, comparisons of CCI, cluster analysis and PCAs indicated that MS spectra were more reproducible among strains coming from the same geographical origin (the three lines from French Guiana) than from BORA line, which is originating from French Polynesia and has been maintained in the laboratory for more than 30 years.
The same color code was used for all the panels. Supporting information Additional Figure S1. LSVs following upgrading homemade reference database with MS spectra from legs (A) and thoraxes (B) of the four Ae. aegypti lines. Eighteen specimens per line were tested. Horizontal dashed lines represent the threshold value for reliable identification (LSV>1. 8). Red, green, blue and yellow dots correspond to BORA, IR13, IR03 and IRF Ae. aegypti lines, respectively. LSVs, log score values; a. u. , arbitrary units. Additional Figure S2. Variation of the MS peak at about 4870 m/z among the IR03 Ae. aegypti line according to their 1534 kdr genotyping. Overlay mean profile view of leg (A) and thorax (D) body parts according to 1534 genotype. Line color code of each genotype is indicated in the top right part.
Gel view of leg (B) and thorax (E) MS spectra from the IR03 specimens per genotype. The two replicates loaded on the MS plate for each specimen per body part are presented. The discriminant MS peak (m/z: 4870 Da) is indicated by an asterisk (*). Graphical representation of the intensity of the 4870 m/z MS peak from legs (C) and thoraxes (F) according to 1534 genotype of IR03 Ae. aegypti line. Standard deviations of intensities are represented by vertical lines. A. U. : arbitrary units; m/z: mass to charge ratio; Red: haplotype homozygotes without mutation (VV/VV/FF); Green: mutant haplotype heterozygotes (VV/VV/FC) and homozygotes (VV/VV/CC). The same color code was used for all the panels. Additional file 1. Raw leg and thorax MS spectra from the four Ae. aegypti lines included in the MS reference database.
Using the Immuno-Navigator database  16  , a batch-corrected collection of RNA quantification across numerous studies, samples and cell types, we confirm that Penk is highly correlated to Foxp3 in mice (R=0. 871, Figure  1B ). Enriched expression of Penk in Treg was previously reported in several specific contexts including TCR-transgenic mice  17  , UVBexposure  6  or in the brain of mice recovering from stroke  8. Using publicly available datasets, we also observe that Penk is enriched in Treg in the thymus, the visceral adipose tissue and in muscles (Figure  1C ). Thus, Penk mRNA expression in Treg is consubstantial of their generation and is independent of their tissue localization at steady state.