dataesr/scientific-paragraphs-categorization huggingface dataset:
Monitoring of anti-cyclic citrullinated peptide (anti-CCP) antibodies and anti-mutated citrullinated vimentin (anti-MCV) antibodies titers in RA, brought the conclusion that anti-CCP antibodies have a better diagnostic power than anti-MCV antibodies. However, anti-MCV antibodies have a higher sensitivity as compared to anti-CCP antibodies. A significant correlation has been found between anti-MCV antibodies titers and both the severity of RA and the disease-activity score (DAS28). Anti-MCV-positive patients presented lower reduction in disease activity and an increased number of swollen joints. This finding allowed concluding that anti-MCV antibodies titers may work as an indicator correlating the disease activity and patient outcome  [170]. Also, an attempt was made to characterize the etiology of rheumatoid arthritis from lung level (NCT03832374), as it was found that diffuse interstitial pneumopathy and bronchial dysfunctions occur in one-third of RA patients, and that some subtypes of ACPAs are preferentially present in the lungs  [171].
This study (NCT03832374), still ongoing, addresses a very important question regarding the subtype(s) of ACPAs with a preferential tropism for the lung, that might be responsible for the infection within parenchyma and pulmonary airways, and the overall increased morbidity and mortality among RA patients with lung co-morbidities. Likewise, an observational study was performed to assess the impact of ACPA in RA participants who were treated with the immunosuppressive drug Abatacept or the tumur necrosis factor inhibitors (TNFis): adalimumab, certolizumab, etanercept or golimumab (NCT03663829). A total of 2052 patients were included of which 1415 were in the TNFis cohort (n = 1053 ACPA positive) and 637 in the Abatacept cohort (n = 445 ACPA positive). The baseline ACPA score was associated with an improvement in clinical response using a measure of clinical disease-activity index at 12 months for Abatacept therapy, but not for TNFis therapy  [172].
As of today, most of the interventional trials are at an early clinical testing phase (Table  6 ). The most advanced clinical studies concern investigation of the effect of combination therapy of Abatacept and methotrexate on rheumatoid arthritis (NCT03492658). Abatacept (CTLA4-Ig) is a fusion protein, combining cytotoxic T cell antigen 4 (CTLA4) and IgG 1 Fc 1 , dedicated to modulate the T cell co-stimulatory signal. Abatacept works as an immune checkpoint and downregulates immune responses mediated through the CD28-CD80/86 pathway. The dose ranging pilot study of patients with active RA, treated with both Abatacept and LEA29Y (Belatacept, a CTLA4-Ig variant), showed dose dependent reductions in the clinical manifestations of disease  [173]. Following the pilot studies, an investigation of combined therapy with Abatacept and methotrexate, an immune system suppressant, is now ongoing for the determination of its effectiveness on RA phenotype, transcriptional profile of the disease, B cell receptor usage and functional parameters of circulating B cells expressing ACPA (NCT03492658).
Patients will be randomized to treatment with (i) methotrexate monotherapy (10-25 mg once weekly) reference group or a (ii) combination therapy of methotrexate (10-25 mg once weekly) and Abatacept (125 mg subcutaneously once weekly) for 6 months, followed by methotrexate monotherapy (10-25 mg once weekly) in both groups for another 6 months. Phase II (NCT00254293)  [174]  and III (NCT00048568) of the trial gave promising results and showed that long-term treatment with Abatacept provided sustained efficacy with high RA patient retention  [175]. Other ongoing clinical trials addressing the study of citrullination process are aiming to evaluate the effect of Enbrel treatment with regards to anti-cyclic citrullinated peptide in psoriatic arthritis activity (NCT04428502) and the impact of rheumatoid factor and anti-cyclic citrullinated peptide in rheumatoid arthritis (NCT04428424).
Sensory organ innervation and function Immunostainings of acetylated -tubulin and a transgenic marker labeling cell outlines revealed that regenerated setae are reinnervated (Fig. 3 , A and B, and fig. S2 ). This is consistent with restoration of their sensory function. To explicitly test this, we assayed mechanosensory function by touching comb and ventral array setae (Fig. 3A ) with a fine probe. This elicited a characteristic escape response (movie S1). We assessed the sensitivity of Parhyale to touch, before and after regeneration, by probing each individual several times and recording the frequency of response (from 0 to 100%) per individual. Tests were conducted separately on three groups of mechanosensory setae that elicit a response rate of 83 to 87% (Fig. 3, A  and C , and data file S2).
S7, C  and D ). The remaining unidentified clusters may correspond to known (e. g. , tendons) or previously undescribed cell types in crustacean legs (see data file S3 for cluster-defining transcripts). To probe cellular composition after regeneration, we performed snRNAseq on regenerated adult legs approximately 2 and 6 months following amputation (see fig. S9 ). To account for genetic, environmental and physiological (e. g. , nutritional, circadian, molt-, or agerelated) effects on the nuclear transcriptomes, we compared snRNAseq data from regenerated legs (8925 and 6668 nuclei from 2 and 6 months after amputation, respectively) with data from contralateral uninjured legs collected simultaneously from the same individuals (8539 and 7370 nuclei) (Fig. 5A ). The analysis was performed de novo on the integrated datasets, projecting the data on a new uniform manifold approximate and projection (UMAP) (Fig.
5 , B to D, and figs. S13 and S14). Methods for integrating data from separate experiments might fail to distinguish experimental batch effects from real biological variation. To address whether data integration may influence our conclusions, we also analyzed all the datasets by simply aggregating them, without an explicit integration step. Again, we found no consistent differences in the cell clusters recovered from control and regenerated legs (fig. S15 ). Within the large epidermal cell cluster, we find that cell abundance among different epidermal subclusters varies by sample rather than by experimental condition (fig. S16 ). These results show that, even without data integration, regenerated legs are indistinguishable from controls. In the 6-month experiment, some of our analyses revealed the existence of additional cell clusters in both uninjured and regenerated legs (arrowheads in Fig.
Transcriptional profiles of cell types In addition to cell composition, we also compared the transcriptional profile of each cell type between uninjured and regenerated legs by performing differential expression analyses per cell cluster, across all the single-nuclei datasets. We found limited differences, and the great majority represented variation across experiments rather than between regenerated and control legs (fig. S18, B to D ). For increased sensitivity, we also performed pseudo-bulk differential expression analysis using DESeq2, with our control and regenerated samples considered as replicates. We found few genes with cluster-specific differential expression in control versus regenerated legs (109 genes with a P value of <0. 05), but the great majority of these genes were expressed in a single replicate or in just a handful of cells (fig.
Testing the spatial distribution of setae To examine the distribution of ventral array elements in the propodus, we measured the distances between successive elements (as shown in fig. S3C ) in control and regenerated legs one molt and 3 months after amputation. The effect of regeneration on this distribution was assessed using a linear mixed-effects model (R function lme from the package nlme v3. 1-157). The individual of origin was tested as a random effect, while the type of leg (T4 or T5), the type of interval measured (proximal, distal, or middle; see fig. S3C ), the experimental condition (control, one molt after amputation or 3 months after amputation), or the interaction between the experimental conditions and the type of interval were considered as fixed effects. All factors had an effect.
Testing mechanosensory function Large adult males were immobilized on standard 60-mm petri dishes from their body using surgical glue [as described in  (11) ]. The setae on T4 and T5 legs were stimulated using a minutien pin (Fine Science Tools, #26002-10), and the animals' whole-body response was recorded (movie S1 and data file S2). Two rounds of experiments were performed under dim light; in the first round, the posterior comb setae of the carpus were stimulated twice (n = 22 individuals). After each stimulus, the response was recorded, and the animals were left to relax for 15 min. Following this, either one of the T4 (n = 10) or T5 (n = 12) legs of each animal was amputated. Six days after amputation, the same setae were stimulated again on both legs, and the animals were left to molt.
After molting, they were immobilized again, and the same setae were simulated on both legs. In the second round, the animals were glued the same way, and the posterior comb (n = 25) and middle ventral array setae (n = 28) of the propodus of T4 and T5 legs were stimulated twice with 15-min intervals. After either of the legs was amputated and following the molting, the animals were glued, and the same setae were stimulated. The effect of amputation and subsequent regeneration on the response to mechanical stimulus was assessed using a generalized linear mixed-effects model (R function glmer from the package lme4 v1. 1-27. 1) with a binomial distribution of errors. The sample of origin was tested as a random effect, while the leg of origin (T4 or T5), the nature of the leg (amputated or control), and the progress of regeneration (unamputated, 6 days post amputation, regenerated) were considered as fixed effects.
Nuclear isolation and sequencing For each snRNAseq dataset, we collected a pool of 12 to 16 thoracic T4 and T5 legs from large adult males. The smallest specimens had a Downloaded from https://www. science. org on August 24, 2022 body length of ~9 mm (estimated age, >12 months), and the average had a body length of ~11 mm (estimated age, >18 months). The legs were amputated at the proximal end of the basis (excluding the gills and the coxal plates) using a microsurgical knife (Electron Microscopy Sciences, #72047-30) under anesthesia in 0. 02% clove oil. To compare regenerated and intact legs, T4 and T5 thoracic legs from one side of the animal were amputated and allowed to regenerate and to grow until ~2 months (8 weeks) after amputation in one pool of individuals or ~ 6 months (24 weeks) after amputation in a separate pool.
Nuclei suspensions, including 20,000 to 25,000 nuclei, were loaded on the 10x Genomics Chromium Controller following the manufacturer's instructions, within 1 hour of preparation. Single-nucleus complementary DNA (cDNA) libraries were prepared using the Chromium Single Cell 3' Reagent Kit according to the manufacturer's instructions (chemistries v3 and v3. 1). Libraries were sequenced on an Illumina NextSeq500 (28 bp for R1 and 122 bp for R2), and 248 to 315 million reads were obtained per library. Reads were mapped to the P. hawaiensis genome version Phaw_5. 0 (see below) using the Cell Ranger Software Suite 6. 0. 1 [10x Genomics;  (32) ]. The Cell Ranger output included lists of features, barcodes, and matrices (corresponding to putative genes, cells, and read counts, respectively). Subsequent analyses were performed using the R package Seurat v4  (21).
Genome annotation The snRNAseq reads were mapped on the P. hawaiensis genome assembly Phaw_5. 0 (https://ncbi. nlm. nih. gov/assembly/GCA_001587735. 2/) in which 19 poorly assembled mitochondrial scaffolds were removed and replaced by the publicly available complete mitochondrial genome (RefSeq NC_039402. 1). To assign reads to genes, we used the gene annotation described in Sinigaglia et al. (12). Many genes in this annotation were of undefined or incorrect strandedness. We defined the correct gene strandedness using information from our snRNAseq data (stranded reads from all five snRNAseq experiments) or by identifying putative coding sequences through sequence similarity. First, we compared the total number of snRNAseq read mapping to opposite strands per gene model. In gene models where at least 80% of reads mapped to one strand (with >20 unambiguously mapped reads in total), we considered that strand as the correct one.
In gene models where >20% mapped to opposite strands (or when fewer than 20 reads were recovered), we identified the longest open reading frame on each strand, translated this to a putative protein sequence, and used BLAST (basic local alignment search tool) to search the Drosophila and human UniProt databases (UP000000803 and UP000005640, respectively) for similar sequences. Strandedness was then assigned on the basis of the best BLAST score (with a minimum threshold of 50). Using this approach, we were able to assign strandedness to 40,047 of 54,699 gene models (the strandedness of 19,334 genes was modified from the original annotation, 13,990 of which using the snRNAseq data and 5344 of which using coding sequence similarity). For the remaining 14,652 genes, both snRNAseq mapping statistics and BLAST scores were inconclusive, and therefore, both strands were included in the gene models (none of those genes had introns).
Analysis of snRNAseq data The analysis was performed using the Seurat v4. 0. 3 scRNA-seq R package, following the associated documentation  (21). Genes transcribed in fewer than five cells were removed from the analysis. Cells with fewer than 100 transcribed genes or more than 10% of the genes mapped to the mitochondrial genes were also excluded from the analysis. Datasets were normalized (Seurat function LogNormalize), and variable genes were found using the variance-stabilizing transformation Downloaded from https://www. science. org on August 24, 2022 (vst) method with a maximum of 2000 variable features. Next, the datasets were scaled, and principal components analysis (PCA) was performed. A shared nearest neighbor (SNN) graph was computed with 20 dimensions (resolution 1) to identify the clusters.
S6 ). The top five DE markers per cluster were selected to generate a dot plot color coded by dataset using the package ggplot2 version3. 3 (fig. S10 ). We functionally annotated our cell clusters by GO term enrichment analysis using the topGO v2. 40. 0 package (A. Alexa and J. Rahnenfuhrer, 2021, topGO: Enrichment Analysis for Gene Ontology. R package version 2. 48. 0. DOI: 10. 18129/B9. bioc. topGO) and the database org. Dm. eg. db v3. 11. 4 (M. Carlson, 2019. org. Mm. eg. db: Genome wide annotation for mouse. R package version 3. 8. 2. DOI: 10. 18129/B9. bioc. org. Mm. eg. db) on the list of DE genes. To compare the Parhyale cell clusters with cell clusters identified in Drosophila legs, we first identified the sets of genes that are differentially in each Drosophila leg cluster (18) using default parameters in Seurat.
Then, we used the putative orthologs of those genes in Parhyale (as described above) to score our nuclei using the Seurat function AddModuleScore. The results were displayed using the Seurat function DotPlot (fig. S8 ). To integrate and compare the five snRNAseq datasets, we used the anchoring approach provided by Seurat. First, we extracted the raw counts from each Seurat object, performed data normalization (function LogNormalize), and found variable features independently for each dataset. Then, we performed the identification and integration of the anchors with 20 dimensions, followed by scaling, PCA, and the construction of an SNN graph with 20 dimensions (resolution 1). A UMAP was used to display clustering and dimensionality reduction. Closely related cell clusters were merged and labeled on the basis of the cell-type markers shown in Fig.
4C. The relative numbers of cells assigned to each cluster were extracted, calculated from the Seurat object, and visualized using the function barplot of the ggplot2 v3. 3. 6 R package. We also repeated this analysis on Seurat using nonintegrated data to test the effect of the anchoring method on the clustering and the recovery of the putative cell types in each dataset (we obtained similar results, shown in fig. S15 ). To explore potential differences in cell distribution within a complex cluster, we examined more specifically cell abundance and differential expression of top markers across the subclusters that comprise the epidermal cluster (fig. S16 ). To examine the impact of the gene annotation in the analysis, we also performed the same analysis as described above using the entire genome, including parts of the genome that lack gene annotations (see the "Genome annotation" section), for read mapping.
The two integrated datasets were compared using Clustree v0. 4. 3 (33)  to visualize the overlap of clusters. The datasets were also integrated using Liger v1. 0. 0 package  (22)  and SeuratWrappers v0. 3. 0 to explore the effect of the integration method. The datasets were first preprocessed separately (normalization, finding most variable features, and scaling the data without centering) according to the Liger tutorial. We then calculated the integrative nonnegative matrix factorization with 20 matrix factors to identify the joint clusters and perform quantile normalization by dataset, factor, and cluster to integrate the datasets. In addition, we ran Louvain community detection (resolution 1) and UMAP on Liger to visualize the clustering of cells graphically. The approach described above was used to merge related clusters.
To probe the cell clusters at higher resolution, we investigated whether the scRNAseq data might allow us to distinguish differences in the abundance of specific neuronal subtypes. Subclustering analysis was performed by selecting the neural cells (clusters 12 and 18 in Fig. 4C ) and performing a similar analysis as described above. We extracted the raw counts from the Seurat integrated object, performed data normalization (function LogNormalize), and found variable features independently for each snRNAseq dataset. Then, we performed the identification and integration of the anchors with 30 dimensions, followed by scaling, PCA, and the construction of an SNN graph with 30 dimensions (resolution 1). To counteract the oversplitting of clusters, we applied an approach to merge cluster pairs that differed by less than 20 DE genes with twofold change  (34).
science. org on  August 24, 2022  of the transcription unit) and the presence of these fortuitous priming sites. Despite these limitations, a given transcript should be detected with similar efficiency across all cell types. Therefore, droplet-based snRNAseq allows us to identify cell types/states based on the differential expression of the subset of mRNAs that are efficiently captured. We focus here on marker transcripts that could be found in our datasets. Orthology assignments are based on reciprocal best BLAST hits with Drosophila [for gene names, see FlyBase (http://flybase. org/)]. The numbering of cell clusters below refers to the analysis (UMAP and dot plots) shown in Fig. 4.
Genes and cell clusters associated with molting Using an existing dataset (12), we identified 1294 genes that are strongly up-regulated in Parhyale legs 3 to 4 days before molting [DESeq2; P adj < 0. 001;  (74) ]. We used the Seurat function AddModuleScore to measure the average expression of these genes in our nuclei, and the results were displayed using the Seurat function FeaturePlot (Fig. 5E ). We used the Seurat function Dotplot to compare the expression of top 57 up-regulated genes between cell clusters (fig. S17 ).
Comparing the distribution of cells across cell clusters We tested whether the proportion of cells recovered in each cell cluster is similar in controls and regenerated legs. We used ANOVA to test the effect of each of three factors on cell count: which condition each cell is associated with (control/regenerated), which cell cluster it belongs to, and which experiment it was sampled from (0/2/6 months; all experiments shown in Fig. 5A ). For the regression, we used a negative binomial distribution because it captured best the dispersion of our cell count dataset. We used the Anova function from the R package car with an F-test and deviance as error estimate. We found that there was no significant effect of the control/ regeneration condition (either as a simple explanatory variable or in interaction with other variables).
However, the experiment and the cell type of origin had a strong impact on cell count distribution (P values of <0. 001 and <2. 2 x 10 -16 , respectively). To further identify differences between experiments and cell clusters, we used the R function emmeans. In the 6-month dataset, there were significantly more cells in one of the cell clusters associated with molting (cluster 18 in Fig. 5D  and cluster 12 in fig. S13B ). In addition, there were significantly more epidermal cells in the 0-month dataset (compared to the 6-month dataset) and significantly more cells in the muscle and neuronal clusters in the 2-month Downloaded from https://www. science. org on August 24, 2022 dataset compared to 0-or 6-month dataset. However, no statistically significant difference was observed between cells from regenerated legs and controls in any cluster.
Power of detection in comparisons of cell abundance To quantify our power to detect differences in cell abundance between control and regenerated samples, we simulated datasets with the same structure as the original dataset, except that we lowered the number of cells of a given cluster in the regenerated samples by 1. 2-to 10-fold compared to the corresponding control sample. We then used an ANOVA test (same as described above) to determine whether we would detect the change as significant. We reported the minimum fold change for which a difference in cell abundance was significantly detected (P value of <0. 05; fig. S11 ). This analysis was repeated for each cluster.
Differential expression analysis Differential expression analysis of uninjured versus regenerated legs was performed separately on nonnormalized and nonintegrated counts of the 2-and 6-month datasets using the R package Seurat v4. 0. 3 function FindMarkers and the bimod method. We chose this method because it gave the most exhaustive list of DE genes compared to other methods (Wilcoxon test, t test, logistic regression test, and the MAST package), which gave very similar but smaller lists of DE genes. Visualization of gene abundance for the DE genes (in fig. S18D ) was done using the Seurat function DotPlot. For differential expression on pseudo-bulk data, counts were aggregated for each cell type using the Seurat function Aggregate-Expression, and further analyses were performed using DESeq2 v1.
Prediction of control versus regenerated condition based on transcriptome To test the presence of a transcriptional signature of regeneration, we tried to predict the regeneration state (regenerated versus control) for each cell based on our snRNAseq data using the R package scPred v1. 9. 2  (75). scPred was designed to predict cluster of origin, but we reasoned that it could be used to predict any given status of a cell. As a control, we also used the same procedure to predict the cluster of origin of each cell (epidermis, muscle, etc. ). The 2-and 6-month datasets were subsampled, such that the number of uninjured and regenerated cells was identical within each cluster for each experiment: We sampled 7291 cells for each of the uninjured and regenerated 2-month experiments (a total of 14,582 cells) and 5694 cells for each of the uninjured and regenerated 6-month experiments (a total of 11,388 cells).