dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2022). Our results support this idea and extend this classification to lymphoid tissues. We identified in human tonsils three subsets of macrophages equivalents to the ones previously evidenced in human heart, liver, lung, and kidney  (Dick et al. , 2022). Of note, we had previously shown that CD14 + cells in tonsils do not contain any dendritic cell population  (Durand et al. , 2019) , including the recently described DC3 subset  (Villani et al. , 2017). CD36 lo macrophages display self-renewing capacity and selective expression of FOLR2; however, we could not detect the expression of TIMD4 or LYVE1 in our datasets. CD36 hi macrophages express high levels of CCR2 and show no sign of proliferation. Similar to mouse Lyve1 lo macrophages  (Chakarov et al. , 2019) , CD36 hi macrophages also express high levels of surface CX3CR1.
Each symbol represents an individual donor (n = 8 in eight independent experiments). Friedman test; *, P < 0. 05; **, P < 0. 01. (F) Linear regression between the number of germinal center Tfh cells and of macrophage subsets in tonsils (n = 13 in 10 independent experiments). (G) GSEA for gene signatures of CD36 hi and CD36 lo macrophages, and Tfh cells in human lymph nodes from control donors or patients with sarcoidosis or tuberculosis (GSE157671). NES, normalized enrichment score; FDR, false discovery rate; ns, not significant.
Flow cytometry Cells were stained in FACS buffer: PBS containing 0. 5% human AB serum and 2 mM EDTA. Unspecific staining was prevented using TruStain blocking solution (BioLegend). Unless otherwise indicated, DAPI (100 ng/ml; Thermo Fisher Scientific) was added immediately prior to acquisition. For intracellular flow cytometry, cells were first stained for surface markers, then washed and stained with Live/dead eFluor450 (Thermo Fisher Scientific) for 10 min at 4°C. Cells were fixed and permeabilized (Intracellular Fixation & Permeabilization Buffer Set, eBioscience) and stained for intracellular molecules at room temperature for 60 min in a buffer containing 2% of normal mouse serum and 10 µg/ml human Fc block (BD). Samples were acquired on a ZE5 Cell Analyzer (Bio-Rad). Data were analyzed with FlowJo (FlowJo LLC).
Tissue sections were cover-slipped with Prolong Diamond Antifade Mountant (Thermo Fisher Scientific) and stored at 4°C. Slides were scanned using the Vectra 3 automated quantitative pathology imaging system (Vectra 3. 0. 7; Akoya Biosciences). Multispectral images were unmixed using the inForm Advanced Image Analysis Software (inForm 2. 6. 0; Akoya Biosciences) and analyzed with the HALO software (HALO 3. 3; IndicaLabs).
Imaging mass cytometry Tonsil tissue sections were pre-treated with heat using a target retrieval solution for 30 min at 95°C (K8000, EnVision flex target retrieval solution, pH 9; Agilent Technologies). Slides were cooled down for 20 min at room temperature before rinsing with water and PBS, and then incubated for 45 min with SuperBlock blocking buffer (Thermo Fisher Scientific). Tissue sections were then incubated overnight with the antibody cocktail in PBS BSA 0. 4% in a humidified chamber. learning Stardist model was trained with TissueNet data. Macrophages were identified using the cell detection command in QuPath based on expression of CD68. CSV files were exported to OMIQ software (Dotmatics) for further analysis. Unsupervised analysis on phenotype and location was performed with Flow-Som  (Van Gassen et al.
, 2015). scRNA-seq library preparation Cellular suspension (3,000 cells) was loaded on a 10X Chromium instrument (10X Genomics) according to manufacturer's protocol. scRNA-seq libraries were prepared using Chromium Single Cell 39 v3. 1 Reagent Kit (10X Genomics) according to the manufacturer's protocol. Library quantification and quality assessment were performed using Qubit fluorometric assay (Invitrogen) with dsDNA High Sensitivity Assay Kit and Bioanalyzer Agilent 2100 using a High Sensitivity DNA chip (Agilent Genomics). Indexed libraries were equimolarly pooled and sequenced on an Illumina NovaSeq 6000 using paired-end 26 × 91 bp as sequencing mode. scRNA-seq data analysis For analysis of human tonsil CD14 + cells (n = 1), we reanalyzed our published dataset (GSE119506). Single-cell expression was analyzed using the Cell Ranger Single Cell Software Suite (v6.
0) to perform quality control, sample de-multiplexing, barcode processing, and single-cell 39 gene counting. For macaque samples (n = 1 per condition), sequencing reads were aligned to the UCSC M. fascicularis (v1. 0) transcriptome using the Cell Ranger suite with default parameters. Further analysis was performed in R (v4. 1. 0) using the Seurat package (v4. 0. 3). The gene-cellbarcode matrix of the samples was log-transformed and filtered based on the number of genes detected per cell (any cell with <200 genes or >2,500 genes per cell was filtered out). Regression in gene expression was performed based on the number of unique molecular identifiers and the percentage of mitochondrial genes. Only genes detected in at least three cells were included. Cells were then scaled to a total of 1 e4 molecules.
Any cell with >5% of mitochondrial unique molecular identifier counts was filtered out. PCA was run on the normalized gene-barcode matrix. The first 15 principal components were used for the UMAP projection and clustering analysis using the Elbow Plot approach. Clusters were identified using the FindClusters function in Seurat with a resolution parameter of 0. 4 for the human macrophage dataset. Unique cluster-specific genes were identified by running the Seurat FindAllMarkers function. Differentially expressed genes were identified based on adjusted P value < 0. 05 and Log 2 FoldChange > 0. 25. The number of differentially expressed genes was 568 for C1QC + macrophages, 145 for S100A9 + macrophages, and 136 for intermediate macrophages. Enrichment for gene signatures was computed with the AddModuleScore function.
For the macaque tonsils dataset, each sample was first analyzed separately. Clusters showing expression of either FLT3, CSF1R, or CD68 were selected for further analysis and integrated for the three samples using the Integration function of Seurat. Clusters containing cells other than mononuclear phagocytes were removed from the analysis. These clusters were plasmacytoid DCs (top genes SLC23A1, DERL3, JCHAIN), B cells (top genes PAX5, MS4A1, CD79B or RGS13, SYNE2, AICDA), EC (top genes CPE, ACKR1, CLEC14A), and stromal cells (top genes COL1A1, COL1A2, MFAP4 or KRT19, S100P, KRT4). In the integrated dataset, clusters were identified with a resolution parameter of 1. 6. For label transfer analysis, aligned and integrated macaque scRNA-seq data were subsetted to retain monocytes and macrophages clusters.
Then, we used a referencemapping approach to annotate cell labels of the macaque dataset (query) by projecting each query cell onto the previously computed UMAP visualization of the human tonsil dataset using the MapQuery function of Seurat v4. For RNA velocity analysis, the three macrophage clusters and the monocyte cluster were subsetted and re-projected in UMAP, maintaining the original annotations. Annotations of unspliced/spliced reads were obtained using velocyto CLI with default parameters. Next, unspliced counts were merged with the preprocessed, normalized, integrated, annotated, and subset spliced count matrix (Seurat), and RNA velocity analysis was performed using the scVelo (v0. 2. 4) workflow  (Bergen et al. , 2020). Connectivity graph was used to visualize all velocity-inferred cell-to-cell connections/transitions using a threshold of 0.
RNA-seq data analysis Genome assembly was based on the Genome Reference Consortium (hg38). Quality of RNA-seq data was assessed using FastQC. Reads were aligned to the transcriptome using Salmon  (Patro et al. , 2017). Differential gene expression analysis was performed using DESeq2 (v1. 22. 2;  Love et al. , 2014). Genes with low number of counts (<10) were filtered out. Differentially expressed genes between populations were calculated using the design "donor + group. " Differentially expressed genes were identified based on adjusted P value < 0. 01 and Log 2 Fold-Change > 0. 5. Heatmaps of log 2 -scaled expression were generated with ComplexHeatmap. Pathway enrichment was analyzed using EnrichR  (Kuleshov et al. , 2016). Potential ligand-receptor interactions between tonsil macrophages and stromal cells were computed using NicheNetR  (Browaeys et al.
, 2020)  using RNA-seq data from tonsil stromal cells generated previously (GSE148656;  Mourcin et al. , 2021). Transcription factor activity was calculated using DoRothea regulons and VIPER  (Garcia-Alonso et al. , 2019). Data from human tonsil macrophages are available at GEO (GSE207260). Visualization of ATAC-seq peaks ATAC-seq data (big wig files) were retrieved from GEO (GSE147314;  Song et al. , 2021). Specific data sets used were GSM4425562 (WT THP1-derived macrophages, unstimulated), GSM4425563 (WT THP1-derived macrophages, stimulated with LPS for 1 h), GSM4425566 (IRF1 KO THP1-derived macrophages, unstimulated), and GSM4425567 (IRF1 KO THP1-derived macrophages, stimulated with LPS for 1 h). Gene tracks were visualized using pyGenomeTracks  (Lopez-Delisle et al. , 2021). GSEA GSEA  (Subramanian et al.
, 2005)  was performed using the GSEA software (version 4. 0. 3) with the default parameters, except for the number of permutations that we fixed at n = 1,000. Gene signatures were obtained from published studies for human blood CD14 + monocytes  (Goudot et al. , 2017) , human yolk sac macrophages, and yolk sac progenitors  (Bian et al. , 2020)  and cell cycle (1640170; Reactome;  Gillespie et al. , 2022). For analysis of human lymph nodes, the count matrix was downloaded from GEO (GSE157671;  Casanova et al. , 2020)  and normalized using DESeq2. shRNA interference shRNA (all from Sigma-Aldrich) against IRF1 (NM_002198or nontargeting control shRNA (MISSION shRNA SHC002) were in the LKO. 1-puro vector (MISSION, Sigma-Aldrich). Viral particles were produced by transfection of 293FT cells in 6-well plates with 3 mg DNA and 8 μl TransIT-293 (Mirus Bio) per well: for VSV-G pseudotyped SIVmac VLPs, 0.
Acknowledgments The authors wish to thank  C. Hérate  and members of the  "Sciences de l'animal et bien-être animal " team at  IDMIT (Fontenayaux-Roses, France)  for technical assistance with the macaque experiments,  V. Enouf  ( Institut Pasteur, Paris, France ) for providing the H3N2 influenza virus strain,  N. Bonnefoy  and  Y. Glasson  ( Institut de Recherche en Cancérologie de Montpellier ) for providing access to Hyperion, the Next Generation Sequencing platform, the  Flow Cytometry Core , and the  Experimental Pathology platform of Institut Curie. This work was funded by  Institut National de la Santé et de la Recherche Médicale ,  Institut Curie  ( CIC IGR-Curie 1428 ), the  Agence Nationale de la Recherche  ( ANR-10-LABX-0043 ,  ANR-10-IDEX-0001-02 PSL ),  Institut National du Cancer  ( 2018-1-PL BIO-01-1 ), and  Fondation ARC pour la Recherche sur le Cancer  ( PJA 20171206249 ) and  TRANSVAC2 H2020  (grant # 730964 ).
Department of IDMIT (Fontenay-aux-Roses, France)  infrastructure is supported by fundings of the " Programme Investissements d'Avenir " managed by the  ANR  under reference  ANR-11-INBS-0008. J. Villar  is a fellow of the  IC-3i PhD program  supported by the  ERC   Horizon 2020 -Marie Sklodowska-Curie Actions  (grant # 666003 ) and a fellow of the  Fondation pour la Recherche Médicale  ( FDT202106013025 ). Author contributions: Investigation:  L. Alaoui ,  J. Villar ,  F. Relouzat , and  H. -A. Michaud. Conceptualization:  L. Alaoui ,  M. Roussel ,  K. Tarte , and  E. Segura. Methodology and formal analysis:  L. Alaoui ,  J. Villar ,  R. Leclere ,  S. L. Gallou ,  M. Roussel , and  E. Segura. Provision of essential material:  F. Relouzat ,  N. Teissier ,  K. Tarte , and  B. Favier. Supervision:  B.
Several relevant automatic classification techniques have been recently proposed in the literature, which can discriminate different shapes of blood cells to overcome laborious techniques and reduce workload in medical imaging analysis and diagnosis. Among these approaches, we can distinguish two types of methods depending on the availability of prior information relating to the cells to be recognized: supervised and unsupervised classification approaches. For supervised classification, it is imperative to have a database of already labeled samples (examples of cells of each class), called a training database. This thus ensures the learning of the classifier. Then, a new database called the test database can be processed once the classifier parameters are determined. Among these algorithms, we can cite, for example, naïve Bayesian classifier (NB), Support Vector Machine (SVM), k-Nearest Neighbors (k-NNs), and Multilayer Perceptron (MLP).
It is a very powerful method that combines ANN and fuzzy logic. Although its algorithmic complexity is higher than that of other classifiers, ANFIS contributes to better classification performance used for the early diagnosis of several diseases  [59]. In  [60] , ANFIS was used to classify immature blood cells in leukemia patients from blood microscopic images, where it gives satisfactory results. In contrast to supervised classification approaches, unsupervised classification methods do not require a database of labeled examples. Thus, these approaches learn by themselves and alternatively, class characterization is done from the available dataset. Among these algorithms, we can cite the K-means algorithm, the fuzzy C-means algorithm (FCM), the Expectation-Maximization (EM) algorithm, and the Mean-Shift algorithm (MS).
Then, we propose other methods using fuzzy logic. We demonstrate that fuzzy logic is best suited for this problem. We show also that the features of the different classes of DCs often overlap and are difficult to distinguish. Consequently, the choice of descriptors is very crucial for classification, even when using automatic feature selection methods. Finally, we proposed to use fuzzy logic to propose an interpretation tool to let the biologist or the physician say the last word in the classification of doubtful cells. The remaining sections of the paper are structured as follows: Section 2 introduces our proposed classification approaches. In Section 3, we show experimental results, and we discuss the obtained results on a collected dataset from microscopic DC images. Finally, we present conclusions and future perspectives in Section 4.
Material and Methods In this work, we utilize the dataset described in previous work  [61]. It consists of 421 microscopic DC images containing a total of 515 DCs with different cell types of which 248 are immature, 70 are mature, and 197 are inhibited. It is obvious that this database is unbalanced, particularly the mature class is considerably infrequent compared to the other two classes (minority class). To overcome the problem of unbalanced classes, we applied the classical data augmentation methods. In the proposed study, we use horizontal and vertical flipping techniques, which aim to increase the number of instances of the mature class by generating new images based on geometric transformation. Finally, the new database consists of 631 cells of which 248 are immature, 186 are mature, and 197 are inhibited.
FLD aims to minimize the within-class (intra-class) measure while maximizing the between-class (inter-class) measure by maximizing the following ratio: J(w) = w T S b w w T S w w (3) The linear transformation w is obtained by the eigenvectors of the matrix S -1 w S b. The dimension of the training data set is reduced to two variables in our case (F1 and F2). The new attributes of the training database F in = (F1, F2) T are calculated using the following transformation of the original data X in : F in = X in w (4) The originally considered attributes (Table  2 ) may be correlated, and the cells of the different classes in the database sometimes present a substantial similarity. We propose to use FLD to obtain new independent variables representing the cells and maximize the difference between the classes in the learning database.
Classification Results Using SVM and FLD-SVM Classifiers This section presents a comparative study of the classification results using the SVM method with those obtained by the FLD-SVM classifier. FLD-SVM is a combination of the FLD feature extraction method and the SVM classifier. FLD is used to generate new uncorrelated and relevant features from the input dataset, and then, SVM is used to classify these newly obtained components. SVM is the classical support vector machine trained with the original DC features extracted from a set of training images. In our experimental study, three binary SVMs, with Gaussian kernel function are designed to estimate the cell class. To evaluate the performance of the used recognition method, we use a cross-validation technique  [73]. This latter was also used to choose the Gaussian kernel giving our dataset's best performance compared to the other kernels mentioned before.
For experiments, the leave-one-out cross-validation (LOOCV) approach is applied. At each step, one cell is used for the test and the remaining examples are for training. This procedure is performed N times, where N is the number of cells in the dataset. Table  4  reports the obtained correct classification rates according to the DC class assigned and the classifier used. The SVM and FLD-SVM reach 75. 75% and 78. 13% of the correct classification rate, respectively. In other words, the SVM method correctly classifies (195/248) immature cells, (160/186) mature cells, and (123/197) inhibited cells, while the FLD-SVM method correctly classifies (198/248) immature cells (166/186) mature cells and (129/197) inhibited cells. To better illustrate the results obtained by the FLD-SVM classifier, the confusion matrix for the three different classes of DCs is presented in Figure  7.
Classification Results Using Fuzzy Inference System (FIS) We used Matlab's Fuzzy Toolbox to generate the fuzzy inference systems. In the fuzzification step, we estimated the membership functions from the feature histograms. We implemented Mamdani FIS. To automatically generate the fuzzy rules of the system, we also used the FCM method. The defuzzification method used is the maxima method for the FIS1 system and the center of gravity method for the FIS2 system, with Gaussian membership functions for the output (Figure  8 ). The cross-validation method is used to find the optimal training data for the construction of the FISs. For the input features, we experimented with three scenarios. For the first scenario, we used all original features at the classifier's input. Then, in the second one, we used the FLD technique to select the relevant features.
4  and 6 ) and labeled the cells with AF647-NT-Lys or AF647-D4 after 24 h of transfection. We then recorded fluorescence images followed by the analysis of 4-6 cell images per sample in each of three independent experiments. PALM/dSTORM visualized domains of different sizes labeled with the two fluorophores (Fig. 2b , g, Supplementary Figs. 7  and 8 ). Although Gag WT-mEos4b did not completely overlap with AF647-NT-Lys or AF647-D4, it was often associated with AF647-NT-Lys or AF647-D4 domains (yellow dotted lines in Fig. 2c , h), in line with the confocal microscopy data. Gag-WM-mEos4b was either associated with AF647-NT-Lys or localized in areas where AF647-NT-Lys or AF647-D4 signal was sparse (Fig. 2d , i, yellow arrows). To analyze the colocalization between Gag-mEos4b and AF647-NT-Lys or AF647-D4, we calculated the Mander's overlap coefficient (MOC) using Coloc-Tesseler  72  , which is based on overlapped Voronoi diagrams of the two molecular species and also implemented in PoCA program  73  A Voronoi diagram is a tessellation where a tile corresponding to a given data point is a locus of all points in space closest to this data point  74.
Gag association enlarges AF647-NT-Lys-positive lipid domains We next examined the effect of Gag expression on the organization of SM-rich and Chol-rich lipid domains. To compare the size of these domains in the absence and presence of Gag expression, we estimated the sizes of these domains and the localization numbers within these domains by SR-Tesseler  75  , a program based on Voronoi tessellation of individual localizations and also implemented in the PoCA program (see "Methods"). Gag WT-mEos4b formed a round shape of domains isolated from other domains and large domains including several round domains as shown by arrows and arrowheads, respectively in Supplementary Fig. 9. We analyzed all AF647-NT-Lys and Gag-mEos4b domains including 10 or more localizations that were detected in a whole cell area of 4-6 cell images per sample in three independent experiments (Fig.
Gag multimerization induces SM-rich and Chol-rich domains to be in close proximity To better understand the possible effect of Gag on the lipid distribution in the PM, we investigated by FLIM-FRET the interaction of Cholrich lipid domains with SM-rich lipid domains and its dependence on Gag multimerization. When cells were doubly labeled with EGFP-NT-Lys (FRET donor) and mCherry-D4 (FRET acceptor) in the absence of Gag, a single component analysis showed a reduction of the lifetime from 2. 2 ± 4. 0 × 10 -4 (mean ± SEM) ns in cells labeled with EGFP-NT-Lys alone to 2. 1 ± 1. 0 × 10 -2 ns, indicating FRET between the donor and acceptor (Fig. 6a ). Next, we analyzed data in cells doubly labeled with donor and acceptor with the double exponential model. Means of lifetime τ 1 and amplitude α 1 were 1.
6e, f , and "Methods"). Consistent with the observation in Fig. 6d , there were negative or positive correlations between mTagBFP2 intensities and τ 1 or α 1 values, respectively, in Gag WT-expressing cells but not in Gag-WM-expressing cells (Fig. 6e, f ). This indicates that the reorganization of SM-rich and Chol-rich domains is related to Gag multimerization. We confirmed the above FLIM-FRET results with super-resolution imaging of NT-Lys and D4 in the absence or presence of Gag expression. To this end, we carried out two-color STORM imaging of HeLa cells labeled with JF549-D4 and AF647-NT-Lys 24 h after transfection with a vector or Gag/Gag-EGFP (Supplementary Fig. 15 ). We calculated the MOC values of the two fluorophores by Coloc-Tesseler implemented in the PoCA program to assess the extent of colocalization between JF549-D4 and AF647-NT-Lys.
The value in each cell, the mean in each experiment, and the mean ± SEM in the three independent experiments are shown as dots and bars in three different blue colors, and a black bar and error bar, respectively (n = 30 for all except for EE (n = 29) (a-f), and n = 18 and 15 for vec and WT (g and h), over three experiments). The two-tailed paired t-test is applied on (a), (g), and (h), and the one-way ANOVA post-hoc Tukey test on (b) and (c). * and ** indicate significant differences at the significance levels of 0. 05 and 0. 01, respectively. ns indicates not significant. Source data and detailed statistics were provided as a Source Data file. replacing mTagBFP2 with mEos4b sequence amplified by PCR using mEos4b-C1 as a template and primers (5'-ACGGATCCATGGT-GAGTGCGATTAAGCCAGAC-3' and 5'-TTGAATTCTCGTCTGG-CATTGTCAGGCAATC-3').