dataesr/scientific-paragraphs-categorization huggingface dataset:
Phenotype analysis of WTL and MBR variants In order to investigate whether WTL and MBR variants associate with different clinical outcomes, we collected clinical data of patients carrying the variants investigated in this study. We were able to retrieve clinical data of 489 ALS patients with A4V, G37R, L38V, G93R, I113T, H46R, H80R, G85R and D125H (see details of individual variants in Table  4 )  [34]. Cox proportional hazard analysis (Figure  10 ) showed that patients with MBR variants presented a longer survival time (approximately 6 years median difference, p < 0. 001) than patients with WTL variants, while no difference (p = 0. 19) was observed for the age of onset (details in Table  4 ). Because A4V and I113T represented the great majority of the SOD1 ALS patients in our sample (312 and 120 respectively), we repeated the survival analysis excluding these two variants from the WTL group.
Using a large clinical dataset (almost 500 SOD1-ALS patients) we were able to show that the survival of patients with a WTL variant was substantially shorter than the survival of patients with an MBR variant (median difference approximately 6 years, p < 0. 001). Experimental data suggests that ALS arises from a toxic gain-of-function mechanism and loss of SOD1 function alone has been linked to a severe phenotype distinct from ALS  [45] [46] [47]. However, loss of function of SOD1 in ALS has been proposed as a potential modifier. A key difference between WTL and MBR variants is their effect on the SOD1 function. MBR variants, but not WTL variants, cause reduction of SOD1 enzymatic function in in vitro experiments of the isolated and engineered SOD1. On such a basis, it follows that our results support the hypothesis of decoupling between the role of gain and loss of SOD1 function in ALS affecting disease onset and duration respectively and potentially independently.
Protein structures The protein structures of wt-SOD1 and 13 variants were obtained from Protein Data Bank (PDB)  [50] ; the detailed structural information is shown in Table  1. SOD1 variant structures were carefully selected; only those variants which had a single point mutation were included. An in-house python script Mutatpipe. py was utilized to select and the filter the variants (https://github. com/Utilon/MutaPipe_Repo). For the variants which had more than one crystal structure, the protein structure with the highest resolution and without missing residues was selected. SOD1 exists in a homodimer form in nature, however, for the scope of this study we focused only on the SOD1 monomer; the additional chains and the copper/zinc ions present in the PDB structures were manually removed.
Variants nomenclature The old nomenclature was used to name SOD1 variants as this reflects the actual position of the amino acids in the chains of the SOD1 structures we have used in our study. The new Human Genome Variation Society (HGVS) v2 nomenclature numbers the amino acids according to the mRNA reference sequence (GenBank: NM_000454. 4) and it includes the start methionine which is cleaved post-translationally. Table  1  reports both nomenclatures for each variant.
MD simulations protocol We employed MD package GROMACS (version 2020. 1)  [52] [53] [54]  to perform all atom MD simulations of wt-SOD1 protein, WTL and MBR variants. All the MD simulations were performed on the GPUs at Rosalind (https://rosalind. kcl. ac. uk), the high-performance computing (HPC) facility at King's College London. AMBER99SB-ILDN forcefield was employed for all the MD simulations performed in the study  [55]. The structure was solvated in the TIP3P water model in a rhombic dodecahedron box; the protein was placed at least 1. 4 nm from the edges in all directions. Na + and Cl -ions were then added to neutralize the system and then the system was energy minimized over 50000 steps using the steepest descent method. The whole system consisted of ~ 30237 atoms; the number slightly varied in different variants.
Principal Component Analysis PCA is a highly used technique to reduce the dimensionality of the data obtained from molecular dynamics simulations and extract the dominant motion in the proteins  [35, 36]. For PCA, the trajectories from three replicates were concatenated into one trajectory and the protein backbone was selected for analysis. gmx covar was then used to calculate and diagonalize the mass weighted covariance matrix. gmx anaeig was used to analyse the eigenvectors. The structures corresponding to PC1 and PC2 were extracted. To make the porcupine plots the structures corresponding to PC1 and PC2 were loaded to PyMOL. The vectors representing domain motion and direction were constructed after uploading the python script Modevectors. py on PyMOL (https://pymolwiki. org/index. php/Modevectors).
Statistical Analysis All the statistical analyses in the study were performed using SciPy. Stats computing library in Python  [61]. Wilcoxon Rank-sum Test was employed to calculate the differences in the statistical significance of the RMSF between the variants and the wt-SOD1. Age of onset and survival time from disease onset analyses were performed using Cox proportional hazards regression. Models were adjusted for site of onset of symptoms and sex. The coxph() function was utilised with tie resolution at the default setting. When modelling survival time from onset, age of onset was also included as a covariate. For all statistical analyses, p < 0. 05 was considered significant.
Clinical dataset Data on people with SOD1-ALS were collected from the ALS Online Database (https://alsod. ac. uk)  [7, 8] , the Project MinE whole-genome sequencing dataset  [65] , and a number of centres. The corresponding authors of the AlSoD database entries with missing data were contacted to fill the gaps. Anonymised records of people with SOD1-ALS were obtained from the following centres: Macquarie University, ANZAC Research Institute, University of Massachusetts, University Hospitals of Montpellier, King's College London, Washington University School of Medicine in St Louis, Peking University Third Hospital, Università degli Studi di Siena, Northwestern Medicine -Feinberg School of Medicine, Istituto Auxologico Italiano IRCCS-University of Milan, University of Belgrade and University of Calabria.
Individuals were eligible to be included if their diagnosis of ALS was made by a consultant neurologist, or their data was reported in the literature with an ALS diagnosis. The clinical and demographic features utilised in this study were country of origin, sex at birth, age of onset (in years) of first motor symptoms, site of onset, survival time (in months) defined as time from diagnosis to death or latest visit. In each analysis, individuals with missing data among the subset of clinical features required were discarded. Individuals with the same SOD1 variant, country of origin, sex, age and site of onset were considered to be duplicates.
Data normalization is fundamental during a mitochondrial stress test since OCR heavily correlates to the number of cells per well, and stimulations or treatments can induce either cell proliferation and/or cell death to a variable extent. Albeit protein amount determination upon Seahorse XF analysis has been widely used for the sake of inter-well normalization; it is inaccurate, time-consuming, and does not allow for downstream applications. Cytation 5 fixes these issues by performing rapid automated cell counting of fluorescently-labeled nuclei followed by straight-forward normalization with the Seahorse Wave software. Our main focus lies on mitochondrial metabolism due to the proven relevance of this organelle for DC differentiation and function  [10]. In brief, during the mitochondrial stress test, cells are sequentially treated with compounds that target the electron transport chain (ETC) or disrupt the mitochondrial membrane potential.
8. Normalize the XF raw data on the Agilent Seahorse XFe96 Analyzer using Wave software. * Note: As the BioTek Cytation 5 can read the bar code of a specific XF cell culture microplate and both instruments are connected, the cell counting provided will be immediately available on the Seahorse XFe96 analyzer. 9. Proceed to export the Excel or Prism files generated by Wave Software.
Pre-processing, quality assessment and control, and analysis of SMARTseq2 single-cell transcriptome data 1. Align paired-end raw reads to the human reference genome (GRCh38 version 25 release; Gencode) using the RSEM program version 1. 3. 0. A step-by-step tutorial for RSEM is available on https://github. com/bli25/RSEM_tutorial [28]. Following the tutorial calculate the "Transcript Per Million read" (TPM) values. These TPM values are then used for downstream analysis. 2. Next, using the Seurat R package (following the "Seurat -Guided Tutorial -2. 700 PBMCs" tutorial) create a Seurat object first, by using the in step 1 generated TPM as input. 3. Run the quality control (QC). QC metrics are then visualized as violin plots. QC has to be assessed individually for each data set. If all cells have passed the QC continue with step 4, otherwise remove cells that failed the QC before moving on.
Dimensionality reduction A Uniform Manifold Approximation and Projection (UMAP)  [29]  of the scRNAseq data will allow us to visualize and explore the datasets in a dimensionally reduced space: 1. To generate such a UMAP use the RunUMAP() function in Seurat using the significant PCs identified previously (in step 6 of 3. 4. 1 Pre-processing, quality assessment, and control and analysis of SMARTseq2 single-cell transcriptome data), based on elbow/scree plots or Jackstraw obtained from the Seurat analysis. 2. Seurat KNN clustering is implemented for scRNAseq data for an unsupervised classification of cells types (Fig. 6A ).
6B ). Note: In some cases, closely related clusters could be grouped together to form mega-clusters with biological relevance. For this, the average expression of each gene obtained from the DEG analysis for each Phenograph cluster is derived, to perform a Spearman correlation. A dendrogram heatmap can then be generated using the Ward's method for hierarchical cluster analysis in the pheatmap Rpackage (https://cran. rproject. org/web/packages/pheatmap/index. html).
Using R: a. Visualize individual gene/protein expressions using the FeaturePlot() or VlnPlot() function. b. Calculate a module score using the AddModuleScore function. Use this to visualize the mean expression of a set of gene signatures using FeaturePlot() or VlnPlot(). 2. Using SeqGeq, a. Similar to standard flow cytometry data analyses in FlowJo, individual protein expression levels can be visualized using the color axis function. b. To plot mean signatures, select <Genes> → <New Static Gene Stat> i. Select genes (e. g. , XCR1, CADM1, WDFY4) and name this list "cDC1 genes" ii. On the workspace under <Operations>, select <Mean> and <Apply operations> iii. The mean signature of the list can now be visualized with the color axis function as "cDC1 genes <Mean>".
Pitfalls Beyond transcriptomic variations in between cells obtained from different patients/donors or different tissues (which correspond to biological variations due to patient-to-patient differences and to different transcriptomic imprinting linked to the variation in tissue microenvironmental signals), a strong bias of scRNAseq data can be due to technical variation in between data obtained in different batches. To circumvent this technical bias, several algorithms (Seurat V4, Harmony, LIGER, and several others) allow to integrated datasets obtained in different batches or using different scRNAseq techniques in a single UMAP or tSNE space. Depending on the technique employed, the number of transcripts sequenced for each cell varies from 30,000 reads (dropletbased methods such as 10× Genomics Chromium or BD Rhapsody) to up to 1 million reads (SMARTseq2).
Data analysis Here, we describe a detailed protocol for the MS-based proteomic characterization of mouse splenic cDC1 and cDC2 subsets isolated by FACS (Fig. 7A ). The detailed gating strategy for the selection of cDC1 (CD11c + MHC-II + XCR1 + SIRPα -), cDC2A (CD11c + MHC-II + SIRPα + ESAM + XCR1 -) as well as cDC2B (CD11c + MHC-II + SIRPα + ESAM -XCR1 -) cells is summarized in Fig. 7B. Cells were directly sorted into SDS-lysis buffer and processed for LC-MS analysis by SP3 to minimize sample loss. The exemplary dataset provided with this protocol contains five biological replicates of each cDC subpopulation. LC-MS raw data processing, peptide identification, and label-free quantification (LFQ) analysis of proteins were performed using DIA-NN (v1. 8)  [46]. To this end, samples were processed using library-free mode with standard parameters including some slight modifications as summarized in Table  14.
The required FASTA protein database containing 17,068 reviewed protein entries of mouse and 172 common contaminant proteins was obtained on April 16, 2021, from uniprot. org. To identify differentially regulated proteins in the different cDC subsets, further statistical analysis was conducted using the protein level LFQ values reported by DIA-NN. A two-sided t-test assuming equal variances was performed considering only proteins that had been identified in at least three biological replicates and were identified by at least two peptides. The resulting p-values were corrected for multiple testing using the Benjamini-Hochberg method. Only proteins with an adjusted pvalue below 0. 01 and a fold change of at least 1. 4 as compared to the other groups were included in the list of cDC subset-specific proteins (Fig.
8 ). The statistical programming language R including the pheatmap  [47] , imputeLCMD  [48]  and pcaMethods  [49]  packages was applied to conduct further analyses and to visualize results (see Fig. 8A  and 8B ). Functional annotation analysis was performed using the DAVID knowledgebase (Database for Annotation, Visualization and Integrated Discovery, version 6. 8) (https://david. ncifcrf. gov/) [50]. Venn diagram data were calculated using the Venny web application (http://bioinfogp. cnb. csic. es/tools/venny/index. html). In total, 4,747 proteins were identified across the whole dataset revealing distinct cDC subset-specific differences that undermine the power of integrated FACS and LC-MS analysis (Fig. 8 ). Besides the cell surface markers used for FACS-sorting (Fig. 8D , left panel) a multitude of additional cell type-specific proteins could be identified by the proteomic analysis, including different transmembrane proteins and cell-surface receptors, cytosolic proteins as well as transcription factors (see Table  15 ).
Data analysis All image analyses were performed using the image processing package Fiji (www. imageJ. net). 1. To quantify podosome formation in DC, open an image with actin staining and the corresponding image with DAPI staining. Count the number of cells forming podosomes on each image of the actin staining and normalize it to the total number of cells by determining the number of nuclei in the DAPI staining. Note that some cells show more than one podosome cluster, while others show none (Fig. 9  and 11). Collected data from all images can be plotted as "percentage of podosome forming cells". 2. The gelatin degradation is quantified by analyzing the proportion of the entire areas that correspond to degradation on a given image using the "area fraction" measurement option in Fiji. Like before, the "area fraction" value will then be normalized to the number of cells in each image as measured from the DAPI channel in the respective field (Fig.
Data analysis DC migrating in the 'under-agarose' assay can be analyzed by multiple open-source-and commercial-image analysis software packages. Below we describe the analysis of basic DC migration parameters using the image processing package Fiji/ImageJ  [104, 105]  (www. imageJ. net). with "OK". The data are reported in a "Summary" window and include parameters describing cell shape, e. g. , area, major and minor axis, and circularity. If it is not possible to generate a representative binary image due to a high background signal, we recommend performing segmentation with Ilastik  [108] , a free open-source software for image classification and segmentation.
cell tracking. 1. For tracking migrating cells use TrackMate v7 plugin from Fiji (ImageJ)  [107, 122]. Open the video created in 4. 1. 1 and start the tracking plugin (Plugins -Tracking -TrackMate). 2. In the first options panel adjust the spatiotemporal calibration of your data. This information is normally provided when reconstituting the movies using. nd files or it is given by the software during image acquisition. Click "next" to move to the following window. 3. In the next section, select a detection algorithm, offering a choice of 3: The Log detector is optimal for examining spot sizes between approximately 5 and 20 pixels in diameter (use this algorithm for DC). Use the DoG detector for small spot sizes less than approximately 5 pixels and the downsample LoG detector for spots larger than around 20 pixels in diameter.
Measuring of secreted IL-1β 1. Data of experiment 9. 3. 2. 1 are analysed using the LEGENDplex TM Data Analysis Software Suite. In a first step, acquired beads are separated into Beads A and B dependent on size and granularity (FSC-A vs. SSC-A) (Fig. 22A ). 2. Then, individual beads are identified by APC signal as the different beads differ in the fluorescence intensity (Fig. 22A ). Thereby, up to seven beads targeting different cytokines can be measured for both Beads A and B. 3. On each gated bead, the fluorescence intensity in the PE channel is measured (Fig. 22B ). By generating a standard curve  using C0-C7, the concentration of the cytokines for each sample is calculated (Fig. 22C ). 4. In this experiment, cell-sorted cDC2 were stimulated as described above with R848 and the NLRP3 inflammasome activated with ATP.
Measuring of inflammasome-induced pyroptosis 5. Data of the experiment 9. 3. 2. 2 are analyzed using Excel. During inflammatory cell death such as pyroptosis, lactate dehydrogenase (LDH), which is usually in the cytosol of a cell, is released into the supernatant due to rupture of the membrane. The LDH in the supernatant then catalyses dehydrogenation of lactate to pyruvate thereby reducing NAD + to NADH. NADH reduces water-soluble tetrazolium salt in the presence of an electron mediator to produce an orange formazan dye. The absorbance (OD) of the orange formazan dye is measured and is proportional to the released LDH. Thereby, cytotoxicity is measured. 6. In order to determine how much cell death occurred, positive (high control) and negative (low control) controls have to be performed and are used for the calculation of induced cell death using this formula: Cytotoxicity (%) = OD (sample) -OD (low control) OD (high control) -OD (low control) × 100 7.
Subsequently, supernatants were harvested and analyzed using the LDH-cytox Assay Kit. Absorbance (OD) was measured using a VersaMax ELISA plate reader (Molecular Devices) and cytotoxicity determined using the formula described in 9. 4. 2. (step 6). Bar graphs show mean + SD of five healthy blood donors. cells of assay 9. 3. 2. 3. are analyzed using FlowJo software for the fluorescence intensity of FLICA and DAPI. After a morphology gate to exclude debris and selection of single cells using FSC-A vs. FSC-H, the remaining cells are analyzed for FLICA and DAPI. Using a quadrant gate, cells are divided into (Fig. 24A ): • FLICA -DAPI -cells, which are alive and did not activate caspase-1, • FLICA + DAPI -cells, which activated caspase-1 but are not dead yet, • FLICA + DAPI + cells, which activated caspase-1 and are already pyroptotic, and • FLICA -DAPI + cells, which died but did not activate caspase-1 before.
3. 2. 1. 2) should be acquired. If the separation of all 13 bead populations is not possible, adjust either FSC-A/SSC-A to better segregate beads A from beads B (Fig. 22A , left panel) or adjust APC to improve the space between the single beads (Fig. 22A , right panel). Problem: The LEGENDplex software does not calculate the standard curve (9. 4. 1) Potential solutions: Different issues can lead to problems with the calculation of the standard curve, such as the reconstitution of the top standard (C7), the serial dilution (C6-C1) as well as the setup of the flow cytometer: Reconstitution of the top standard (C7): Make sure that the lyophilised cytokines are properly reconstituted. Pre-heat the lyophilised cytokines and the Assay buffer to RT. Add the Assay buffer to the lyophilised cytokines without vortexing and do not directly mix by pipetting up and down.
Data analysis For all flow cytometry experiments, a BD LSR II Flow Cytometer was used and the data were analyzed using the FlowJo software. Spectrophotometrical analysis for ELISA was performed using a Microplate reader Infinite 200 PRO (Tecan). There are several ways to quantify proliferation data. Often, the percentage of dividing cells is used for statistical analysis. However, such analysis does not include the amount of cell cycles individual cells went through. Therefore, calculation of  the division index is the scientifically more appropriate way to quantify proliferation. The division index is the average number of cell divisions that a cell in the original population has undergone. It can be calculated by the following formula: Division index = 0 • P0 + 1 • P1 2 + 2 • P2 4 + 3 • P3 8 + 4 • P4 16 +5 • P5 32 + 6 • P6 64 + 7 • P7 128 + • • • P0 + P1 2 + P2 4 + P3 8 + P4 16 + P5 32 + P6 64 + P7 128 + • • • with P0 the percentage of cells undergoing no division, P1 the percentage of cells undergoing one cell division, P2 the percentage of cells undergoing 2 cell divisions,….
The division index can be calculated manually, setting gates for all populations with a specific number of cell cycles. Alterna-tively, it can be calculated with the proliferation tool of the FlowJo software (see also  11. 5. 3. ). Co-culture of CFSE-loaded OT-I T cells with OVA-stimulated BM-DC. A) Exemplary gating of T cells. After elimination of doublets and debris, T cells are identified as CD8 + and CD11b -cells. B) Proliferation profile of gated T cells. C) Division index of CD8 + T cells co-cultured with BM-DC after OVA or SIINFEKL treatment. Data are shown as mean ± SEM.
Calculation of the division index FlowJo includes a function to calculate the division index. Therefore, the software identifies the individual peaks, which are used for its calculation, automatically. To our experience, the gates set by FlowJo do not always represent the correct populations. If you should decide to use this FlowJo feature, it is essential to check whether FlowJo set the gates correctly. If not, calculation of the division index using the formula described above might be the better choice.
6. In the meantime, prepare the antibody staining cocktail. Therefore, use 50 μl of FACS buffer per sample and add the antibodies listed in Table  54  in the indicated dilution; Please note that antibody dilutions are dependent on the    49 ) and analyzed by the commercial software FlowJo v10. 8 (BD). After exclusion of doublets, debry, and dead cells, transferred cells were identified as CD45. 1 + CD45. 2 -(see Fig. 29 ). These cells were analyzed for their CFSE and CTV fluorescence and the number of cells within the 25 different clusters were extracted. The number of not peptide loaded cells served as control for injection efficiency as well as unspecific killing. An exemplary gating is displayed in Fig. 29. Lysis for the different SIINFEKL concentrations is calculated as percentage of non-peptide-loaded cells (example data are presented in Fig.