dataesr/scientific-paragraphs-categorization huggingface dataset:
LLT1 is Detected on Infiltrating Immune Cells and is a Marker of Tertiary Lymphoid Structures Besides expression on tumor cells, CLEC2D mRNA and LLT1 protein have been detected in immune cells within the TME. This is not surprising considering that LLT1 is expressed on activated hematopoietic cells. CLEC2D mRNA was found in myeloid cells from GBM and IDH-G diffuse gliomas scRNAseq datasets  (65). LLT1 protein was detected on B and T cells infiltrating HPV + OPSCC  (62)  and on B and T lymphocytes within the stroma of non-small cell lung cancer (NSCLC) but not in adjacent lung tissue  (54). In HPV -OPSCC, LLT1 was detected on tumors and on TILs  (90). Based on the IHC stainings, the study showed that the HPV -OPSCC patients with LLT1 + tumors and low density of LLT1 + TILs had the worst prognostic while patients with LLT1 -tumors and high density of LLT1 + TILs had the highest survival rate.
Besides, the functional analysis of CD161 + CD8 + T cells compared to CD161 -CD8 + T cells showed that CD161 + CD8 + T cells were less cytotoxic and displayed an exhausted phenotype with higher frequencies of PD-1 and Tim-3-positive cells. Nearly all of them also expressed the tissue resident CD103 integrin which is thought to promote retention of T cells in epithelial tumor islets  (106). Altogether, the analysis of CD161 + CD4 + and CD161 + CD8 + T cells within NSLCL tumors, and the detection of LLT1 expression primarily within TLS, strongly suggests that LLT1/CD161 interaction plays a critical role in immune surveillance of NSCLC. Corroborating this, processing of public databases identified CLEC2D and KLRB1 association with favorable clinical outcome in NSCLC  (54). The study of TILs from HPV + and HPV -OPSCC also identified high frequencies of CD161 + effector memory CD4 + and CD8 + T cells which were associated with better overall survival  (107).
FUNDING This research was supported by  Centre National de la Recherche Scientifique ;  Institut national de la santéet de la recherche medicale; UniversitéCote d'Azur; Canceŕopole PACA; Reǵion Provence-Alpes-Cote d'Azur ;  Fondation ARC pour la recherche sur le Cancer; Ligue Nationale contre le Cancer; French Government (National Research Agency, ANR)  through the  "Investments for the Future" programs LABEX SIGNALIFE   ANR-11-LABX-0028  and  IDEX  UCAJedi  ANR-15-IDEX-01.
To explore the mechanisms of this early progression, here we digitally extracted the γδ T lymphocytes from a recently published scRNAseq dataset of γδ CTCL  [10]  with patch and plaque annotations (MF318 from GSE173205), and analyzed these cells by 'trajectory mapping'. The findings we report from this new type of data exploration provide novel insight into the cellular mechanisms underlying the early stages of this T cell malignancy.
Materials and methods scRNAseq datasets, pre-processing, and reference trajectory A series of scRNAseq datasets from ~150 healthy individuals and cancer patients' samples were downloaded from Gene Expression Omnibus (GEO, https://www. ncbi. nlm. nih. gov/geo) integrated and preprocessed as one single reference dataset as depicted  [2]. Briefly, the sequencing output bcl2 files were converted to FASTQ format by using cellranger-mkfastq TM algorithm (10× 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 3. 0  [11]. First, genes that correspond to human leukocyte antigens (HLA) and genes located in chromosomes 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.
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 dataset. Principal components analysis (PCA) was then performed on this integrated dataset before dimensionality reduction by uniform manifold approximation and projection (UMAP)  [12]. All its single cells were then scored for various multigene signatures, to further perform the digital extraction of γδ T and of CD8 T lymphocytes by score and gate  [2]. An unbiased differentiation trajectory of these γδ T lymphocytes was computed without prior information by a minimum spanning tree (MST) algorithm to determine each γδ T cell's position, pseudotime, and milestone. The reference γδ T cells matrix (cell, gene, pseudotime, and other hallmarks) is published elsewhere  [2].
This reference γδ T cell trajectory was visualized as a pseudotimed plot featuring every single cell by its pseudotime (x-axis) vs. its MST1 and MST2 dimensions projection (y-axis)  [2]. For clarity, this reference trajectory is shown as a grey cells background onto which additional γδ T cells, here from the CTCL patient, were mapped. The same pipeline was applied for CD8 T cells whenever mentioned in the text. In parallel, the γδ T and CD8 T cells were extracted likewise from the CTCL patient MF318 scRNAseq dataset, downloaded from the publicly available GEO GSE173205  [10]. This dataset was separately pre-processed by using the same Seurat 3. 0 pipeline, and its single cells were scored for multigene signatures to further digitally extract its γδ T and CD8 T lymphocytes by score and gate as above.
Injection of ab extra γδ T cells on the public γδ T cell differentiation trajectory Since the differentiation trajectory of N cells is computed from their N transcriptomes, the resulting trajectory varies with N. As a consequence, adding any newer dataset of ab extra cells (e. g. , 'M cells') to a previous 'N cells' dataset creates a newer ('N + M cells') dataset which trajectory differs from the former one. Here, we aimed at freezing the 'N cells' trajectory such as to keep it as a stable reference when adding ab extra cells and identify their hallmarks by their position on this reference. We incepted and applied 'single-cell injector', a two-step procedure based on Seurat's anchoring integration function  [11]  (Figure  S1 ). The single-cell injector integrates as described  [11]  a new dataset (named 'Query') into a reference dataset (named 'reference') in which cells coordinates are already annotated (cells, cluster, t-SNE, UMAP, trajectory, milestone, pseudotime).
The resulting dataset ('Query 2') is then used by Seurat's function MapQuery to anchor Query 2 in the reference as already published  [11]. Because pseudotime and milestones are already known in the reference, the N 'auto' anchors of the reference's N cells to themselves allow to precisely determine the pseudotime and milestone attributes of each Query's single cell, without change to the reference trajectory. The injector procedure was validated using the γδ T cell reference dataset since it comprises cells whose MST1, MST2, milestones, and pseudotime are already known. So, n = 7,449 γδ T cells randomly selected from this reference were used as a Query, and this Query was injected into the reference by the depicted procedure. For each Query cell, the injected position and initial position were compared, using as a metric the Euclidian distance (△) between the injected and the initial (MST1, MST2) coordinates.
Differential pathway/signature analysis Pathway enrichment analysis was performed using Single-Cell Signature Explorer function scorer  [13]  to score all the signatures from the MSIgDB (http://www. gsea-msigdb. org) collections Kyoto Encyclopedia of Genes and Genomes (KEGG), GO-MF, GO-BP, Reactome, hallmarks, C6, and C7. These genesets were scored in each single γδ T cell of the specified subset from the reference's healthy donors  [2]  and from the patch or plaque samples. Each score was compared between the specified CTCL and healthy cell groups by using unpaired Student's t and Bonferroni correction false discovery rate (FDR). Plots of representative differential pathways were produced by using the R package pathfindR.
Extraction and integration of skin γδ T lymphocytes from the γδ CTCL lymphoma patient MF318 The γδ T lymphoma cells of CTCL type were sourced from the public scRNAseq dataset of CTCL patient MF318 (GEO GSE173205, Methods), a 55 years old Caucasian female patient diagnosed with stage IIB CD30 + γδ MF without large cell transformation  [10]. Its γδ T lymphocytes were digitally purified using score and gate by successively discarding the keratinocytes, melanocytes, myeloid, B, natural killer (NK), CD4 T, and finally CD8 T cells from the dataset. A total of 1,220 γδ T cells were obtained. We previously produced a so-called 'public' differentiation trajectory of γδ T cells from > 150 individuals' blood and tissue samples  [2]. Using the single-cell injector procedure (Methods, Figure  S1 ), the (n = 1,220) γδ T cells from the CTCL patient MF318 were injected into this reference.
With ~20,000 cells distributed in 14 milestones from Tn to Temra cells, this trajectory represents the most exhaustive and resolutive map of γδ T differentiation so far (Figure  1a ). Together, these milestones still form a consistent and coherent continuum when the integrated dataset is represented by an alternative computational method such as UMAP (Figure  1b ). Also, visualization in this UMAP of the trajectory pseudotime validated the previous sequence of differentiation milestones (Figure  1b ). In UMAP, the γδ T cells from the CTCL patient MF318 were embedded in all areas of the dataset, confirming their transcriptomic similarity to the reference γδ T cells. Also, these cells encompassed both TCRVγ9 and TCRVγnon9 γδ T cell subsets (Figure  1d ). The expression profile of a series of published marker genes  [2, 21]  in each milestone showed smooth profile transitions across the trajectory (Figure  1e ).
Based on expression intensity and percentage of expressing cells, the Tn marker genes typically encompass CCR7, TCF7, LTB, and SELL; Tcm genes include ANXA1, PABPC1, IL7R, H3F3B, CD69, LYAR, CXCR4, KLRB1, NR4A2 and FOS, Tem genes comprise S100A6, KLRD1, NKG7, CST7, PFN1, GZMM, GZMB, DUSP2, FTH1, CCL4, and IL2RG, and Temra genes include FCGR3A, S100A4, PRF1, GZMA, FGFBP2, GZMH, and KLRK1. These profiles confirmed earlier reports  [2, 21]  depicting a progressive differentiation of type-1 cytotoxic functions along Tem and Temra stages, with the Temra cells detected here in both M13 and M14 instead of M14 only  [2]. Few innate-like γδ T cells were present in this integrated reference dataset, as KLRB1 (encoding for CD161) was expressed by most of the late Tcm (M7) cells but KLRC1 (encoding for NKG2A) and TBX1 (encoding for PLZF1) by very few Tem (M13) cells.
Fetal differentiation-derived type-3 effector γδ T cells revealed by their CCR6, RORC, IL23R, and DPP4 marker genes  [21]  were not abundant in the integrated reference, though clearly represented in most milestones (not shown). Given the correspondence of the differentiation trajectory and UMAP visualizations of the integrated dataset, we investigated further the γδ T cells from the CTCL patient MF318 with the higher resolution of pseudotimed trajectory maps.
Differentiation trajectory of the γδ T and CD8 T cells from the patient MF318 Among the variants of CTCL, MF is depicted as a malignancy of skin-resident Tem cells, in contrast to leukemic CTCL which is composed of skin-recirculating malignant Tcm cells  [9]. So to determine precisely the differentiation of the skin γδ T cells from patient MF318, the (n = 1,220) γδ T cells were analyzed by injection in the reference γδ T pseudotimed trajectory. In parallel controls, (n = 238) CD8 T lymphocytes extracted from the same CTCL dataset were injected into their respective CD8 T reference differentiation trajectory  [2]. These CD8 T cells encompassed a few Tn, Tcm, and early Tem cells, but no Temra (Figure  2 ). In contrast, the γδ T cells displayed all stages of differentiation. When cells were stratified by patch or plaque origin, the CD8 T cells differentiation was similar in both cases.
This progress is not achieved by harnessing both the profiles of genes transcribed in each cell, but also by determining precisely each cell's relative position in a pseudotime-ordered collection of the same cell type ('reference dataset'). We had previously depicted the pseudotimed differentiation trajectory of human γδ T lymphocytes from either TCRVγ9TCRVδ2 and TCRVγnon9TCRVδ1 subset, and their subdivision into 14 discrete milestones  [2]. Consistent with earlier reports  [1, 21] , the naive γδ T lymphocytes encompass two stages M1 and M2 sharing graded expression levels of LEF1 and TCF7, two genes encoding for Tn-typifying transcription factors  [24, 25]  together with those of the homing factors CCR7 and SELL  [26]. This finer subdivision of Tn γδ T lymphocytes was not reported in earlier scRNAseq studies analyzing fewer cells  [27].
The prototype of Th1 gene encoding for interferon gamma (IFNG) peaked (M10-M11) before the cytotoxicity genes whose expression increased continuously along M8-M14. Both the perforin-encoding PRF1 gene and the high-affinity receptor for antibody-dependent cell cytotoxicity FCGR3A peak at the Temra stage (M13-M14)  [1, 31]  constituting the endpoint of this type-1 cytotoxic differentiation. Presumably, the scarcity of innate-like γδ T and fetal wave-derived type 3 effector cells found in the integrated reference dataset reflects its lack of neonatal or cord blood cells recently depicted elsewhere  [21]. Overall, this trajectory clarified how γδ T lymphocytes blend their innate programming and functional polarization  [32]  through temporal tiling. This trajectory also constitutes a reference roadmap onto which ab extra γδ T cells from any additional scRNAseq datasets can now be positioned such as to identify their hallmarks.
The γδ T cells infiltrating human tumors, and those from blood or lung lesions of COVID-19 patients were characterized accordingly  [3]. Likewise, mapping of γδ T cells extracted from a human γδ T cell lymphoma scRNAseq dataset had never been attempted, however, and the present study reports its successful use for characterizing CTCL of γδ T lineage. Our report shows that trajectory mappings can help to explore the cell-of-origin of a cancer cell type and its molecular transitions, beyond the more classic characterization of normal cell differentiation as currently reported  [33]. Specifically, CTCL is a heterogeneous group of non-Hodgkin T cell lymphoma whose etiology remains ill-defined  [34]. Among these, the γδ CTCL constitutes an uncommon type of aggressive disease in which cell-of-origin depends on the tissue from which the lymphoma derives.
On the other hand, TCRVγnon9 γδ T cells are physiological skin-resident and the cells under study were from a thigh skin patch and plaque diagnosed as stage IIB CD30 + γδ MF without large cell transformation  [10]. Although next-generation sequencing of a single cell's whole exomes would be necessary to formally detect whether-/which-plaque's TCRVγnon9 γδ T cells carry some of the known CTCL driver mutation(s)  [7] , this can not be done with the currently available scRNAseq dataset. This study evidenced a TCRVγnon9-selective differentiation of a recirculating subset of skin γδ T lymphocytes associated with MF disease progression. The non-random TCR chain usage of γδ CTCL suggests common triggering antigens for TCRVγnon9TCRVδ1 and TCRVγ9TCRVδ2 γδ lymphomas  [7]. Indeed, skin-colonizing Staphylococcus aureus can contribute to the pathogenesis of MF and Sezary syndrome (SS), notably through superantigens driving tumor cells proliferation  [36].
Figure1. Differentiation trajectory, UMAP, and marker genes of reference and CTCL patient γδ T cells. a). Differentiation trajectory of all γδ T cells digitally extracted from the reference and dataset of MF318 patient skin is shown with the corresponding reference trajectory milestones. Differentiation pseudotime scale: 0-5 for Tn, 5-20 for Tcm, 20-48 for Tem; 48-55 for Temra. UMAP of the same cells featuring b). the above milestones (same color key as a); or c). their pseudotime; d). UMAP of γδ T cells extracted from the CTCL patient specifying the TCRVγ subtype overlaid to the reference cells (grey); e). mean and percentage of marker gene expression level per milestones.
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.
However, many children are still susceptible to VPDs and death. Poliomyelitis, yellow fever, and measles outbreaks have been reported across the country over the last ten years  [11] [12] [13] [14] [15]. In 2018, the DRC launched an emergency plan to scale up routine immunization to vaccinate an additional 220. 000 children within 18 months. This plan targeted 9 provinces: Ituri, Kasaï, Haut-Katanga, Mongala, Kwilu, Tanganyika, Tshuapa, Haut-Lomami, and Kinshasa (the capital of the DRC). Of the 35 health districts in Kinshasa, Mont Ngafula II was the only one with high vaccination dropout rates among children aged <12 months for nearly five consecutive years. Routine EPI reports showed dropout rates for the DTP3 dose of 15%, 19%, 10% and 11% in 2014, 2016, 2017 and 2018, respectively (Dataset V1).
Study design and sampling procedure A cross-sectional household survey was conducted. All mothers/caregivers with children aged 12-23 months, between the 1st of June and the 1st of July 2019, were eligible. Since the exhaustive number of households and children aged 12-23 months was not available, we referred to the WHO vaccination coverage cluster surveys to perform a two-stage sampling design  [16]. As recommended to select an appropriate sample size by ensuring a maximum absolute confidence interval width of ± 10% at fairly low vaccination coverage, 30 clusters were first sampled using probability proportional to their size, based on estimated population data per village/neighborhood provided by the health district authority. The sample size was determined using the following formula: N= Z 2 ×p(1-p)/d 2 , where N is the sample size, Z (1.
Data collection A structured questionnaire translated into the local language was used to collect data on sociodemographic characteristics (children, respondents and household) and respondents' perceptions. Interviewers were previously recruited and trained on the study protocol and the questionnaire use. After the training, a pre-test was conducted in the field to check the reliability and validity of our study tools. The questionnaire included information on socio-demographic characteristics, child immunization status, and perception of respondents (mothers/caregivers) on the organization of vaccination in health facilities. Door-to-door visits were conducted and only consenting respondents of selected children participated in the survey. In case where two or more children aged 12-23 months were found in the same household, the youngest child was selected  [18].
Information on children's immunization, such as dates and doses of each vaccine received, was obtained from vaccination cards or respondents' recall if the card was not available. The data collected was then recorded on Microsoft Excel® 2013 files. Annual routine immunization coverage and dropout rate data were extracted from the 2017-2018 database collected at the health area scale provided by the local health authority. They were also recorded in Microsoft Excel® 2013 (Dataset V2). According to the Congolese routine immunization schedule (Table  1 ), the dropout rate was considered the percentage of children aged 12-23 months who received the 1st but not the 3rd dose of DTP: [(first doselast dose)/first dose] ×100  [4].
Data analysis We used data obtained from the Mont Ngafula II health authority to map the annual geographic distribution of routine immunization coverage and dropout rates at the health area scale using QGIS 2. 14. 4 (Open-Source Geospatial Foundation). The Mont Ngafula II health district shapefile was provided by the Directorate of Disease Control of the DRC Ministry of Public Health (validated by WHO DRC) and used to generate the map. The latter allowed to assess the dynamic system of vaccination services at the health area level. Data collected through the cross-sectional household survey were used to identify factors predicting high vaccination dropout rates among children aged 12-23 months in the Mont Ngafula II health district. Descriptive statistics such as frequencies and percentages were calculated and presented into tables.
Differences in children, respondents, household characteristics, and respondents' perceptions were tested by the Chi-square test. Since our data were clustered at the health area scale, we performed logistic random effects regression models using the R package glmmML to measure the association between independent predictors and immunization dropout. The dependent variable "dropout" was coded as 0 for children who received the threedose DTP series and 1 for children who received the DTP1 but not the DTP3. The explanatory variables were also summarized into two categories: sex of child (0 = male; 1 = female), birth order (0 = ≤ 2nd; 1 = ≥ 3rd), card possession (0 = yes; 1 = no), age of respondent (0 = ≤ 19 years; 1 = > 19 years), education level (0 = ≥ secondary; 1 = ≤ primary), occupation (0 = no; 1 = yes), place of residence (0 = urban; 1 = rural), type of family (0 = 2-parents; 1 = single parent), number of children (0 = ≤ 2 children; 1 = ≥ 3 children), attitude of health staff (0 = positive; 1 = negative), availability of seats (0 = yes; 1 = no), respect of the order of arrival (0 = yes; 1 = no), waiting time (0 = < 1 h; 1 = ≥1 h), reminder system (0 = yes; 1 = no).
Acknowledgements We are grateful to all staff of the Mont Ngafula II health district. We also thank the researchers of the  Ecology and Control of Infectious Diseases Unit (Service d'Écologie et Contrôle des Maladies Infectieuses)  and the students of the  Master of Ecology of Infectious Diseases, Natural Hazards and Risk Management (Écologie des maladies infectieuses, aléas naturels et gestion des risques ),  Faculty of Medicine, University of Kinshasa. We also thank  Muriel Matunga  and  Herve Christian Ntumba Matunga  for reviewing the manuscript.
Availability of data and materials The datasets generated and/or analysed during this study are available in the: [Vaccination_dropout_rates_Mont_Ngafula_II_Dataset]  repository, [https:// www. editorialmanager. com/aoph/download. aspx?id=33405&guid=289f34cc-a2e1-45de-b862-f013c6f0bcf9&scheme=1] [Dataset V1] repository, [https://www. editorialmanager. com/aoph/download. aspx?id=33406&guid=453a4c34-238a-4d2c-ab4a-7548220d5a91&scheme=1] [Dataset V2] repository, [https://www. editorialmanager. com/aoph/download. aspx?id=33407&guid=8a0acba5-461b-4eb0-ad95-86eac6486ba2&scheme=1].
Results Four clinically defined prognostic outcomes of ABCA4 disease. Demographic, clinical, and genetic characteristics of all 112 patients in the study are summarized in Supplemental Table  1 ; supplemental material available online with this article; https://doi. org/10. 1172/jci. insight. 156154DS1. Clinical data from the most recent visit for each patient were used in the study. Each patient was categorized into 1 of 4 prognosis categories based on the observable spatial progression of ABCA4-associated disease features in the fundus (Figure  1A ) by age 50 years or older. Patients categorized as having prognosis 1 (n = 28) had the mildest disease outcome (in the cohort), manifesting early retinal pigment epithelium atrophy within the central macula without any apparent pisciform flecks.