dataesr/scientific-paragraphs-categorization huggingface dataset:
Results We aimed to overcome these shortcomings thanks to a lymphoblastic derived JURKAT cell line seeding in each experiment to standardize the Seahorse process. We used an adapted XF Cell MitoStress Kit protocol, consisting in the evaluation of basal, stressed and maximal glycolysis and oxidative phosphorylation related parameters, through sequential addition of oligomycin and FCCP to a glucose containing medium. Data were acquired and analyzed through Agilent Seahorse XFe96 analyzer. Indeed, we validated this method in the light of ICH Q2 (R1) guidelines. We were able to con rm the speci city and accuracy of the method. We also demonstrated the precision, linearity and range of the method in our experimental conditions.
Culture medium composition, in term of nutrients content and pH, was monitored extemporaneously before performing experiment and should not be likely to interfere with cell metabolic activity detection. Poly-D-lysine residues potentially released in the medium could also interfere with OCR and ECAR evaluation. However, it is highly unlikely because data were automatically corrected by the Seahorse system using four-edge calibration wells and were equal to 0. 00 for every performed experiment (n = 4, data not shown). For all three sequential steps, a dose-response between cell concentration and OCR and ECAR measurement was observed. The higher the cell concentration was, the higher the OCR and ECAR values were. This proportionality relationship would not be prone to arise in case of impurities interference.
Altogether, these elements contributed to speci city assessment. Basal OCR/ECAR ratio obtained for 50 000, 100 000 and 150 000 JURKAT cells/well are 2. 12, 2. 66 and 3. 19, respectively. All these three values are inferior to 4, allowing us to conclude that contribution of OXPHOS to ECAR is negligible. Further, it is important to note that FCCP did not have consequent impact on ECAR in this experimental setting, underlying a non-substantial contribution of OXPHOS to medium acidi cation. For the following experiments, a concentration of 100 000 JURKAT cells/well was chosen in accordance with Agilent's database.
Accuracy of the method Seahorse technology implies a systematic calibration of the equipment by measuring OCR and ECAR of Calibrant buffer containing wells, using the utility plate. Raw data concerning this calibration step are provided for every experiment by Wave software. This calibration operation is able to provide data related to accuracy of the method, even if it is not possible to con rm this validation criteria in the setting of the use of JURKAT cell line. Nevertheless, method bias obtained in the setting of Seahorse calibration assessment of O 2 (Fig 3A ) and pH (Fig  3B ) emission values are 0. 04 % and -0. 33 %, respectively , for 96 replicates (Fig  3C ). These bias values are largely less than 5 %, con rming the excellent accuracy of the detection of O 2 consumption and medium acidi cation.
Discussion & Conclusion: Metabolic pathways analysis gains considerable interest in the eld of immunology because of its impact on immune cell effector function. Seahorse technology makes these analysis easy to implement in every lab, as soon as it is provided with the equipment. However, many critical environmental parameters can affect data reliability and including appropriate experimental controls is critical to be con dent in scienti c results. In this article, we described a simple procedure to standardize metabolic analysis using XFe96 Seahorse analyzer. We sought to validate a method involving JURKAT cell line as an internal control for metabolic potential assessment according to ICHQ2 (R1) guidelines. In this setting, we con rmed that the optimal concentration of JURKAT, in our setting, is 100 000 cells/well, corresponding to an about 90 % cell con uence, as recommended by Agilent Technologies and Plitzko et al.
OCR and ECAR pro les obtained for JURKAT cell line at basal state and after oligomycin and FCCP exposure are in accordance with expected pro les, given the mechanism of action described for these two compounds. These pro les are also in accordance with those described in other models  [26]. It is well known that phenotypically less differentiated and less activated T cells showed a more OXPHOS-dependent basal metabolism and a minority resort to glycolysis than more differentiated and activated T cells  [28]. Concomitantly, our team realized a study about cultured T cells differentiation status (Marton et al. , unpublished data). Thus, authors demonstrated that metabolic potential of T cells is correlated with their differentiation pro le evaluated by ow cytometry. Taken together, these data con rmed the appropriate capacity of the evaluated method to measure relevant analytes.
[21] , by using a mathematical modeling of the inter-assay variability of the internal control, then integrated in experimentally determined data values. In our assay, we took into account for analysis all three sequential measured values obtained for each basal and post compounds injections. According to Divakaruni et al. [24] , it could be appropriate to analyze only the minimum or last measurement in the presence of oligomycin because its bioavailability can often result in a time-dependent effect. Similarly, only the maximum measurement after FCCP exposure, due to potentially occurring cell consumption of the molecule, should be considered. This kind of analysis could improve the method precision even more. In our study, we performed all assays using a standard concentration of FCCP, i.
5. 10 6 cells/mL. At D 0 , JURKAT are enumerated and resuspended in unbuffered prewarmed XF base medium supplemented with glutamine 2 mM, glucose 10 mM and pyruvate 1 mM (103680-100, Agilent Technologies). A minimal acceptable viability of cells of 90 % is de ned. XF base medium pH is monitored (target value of 7. 4). JURKAT cells are seeded in 180 µL/well, according to manufacturer's instructions and incubated for 1 h in a 37 °C non-CO2 incubator, in a poly-D-lysine (100 µg/mL, 30 µL/well, overnight, 4 °C) (A300-E, Sigma-Aldrich) pre-coated culture plate (Seahorse mini Fluxpak XFe96, 102601-100, Agilent Technologies). Cell density varied from 200 000 to 25 000 cells/well in a half dilution manner. The cell monolayer is monitored by optic microscopy (Olympus, DP71). The cell area measurement is performed through ImageJ tool, 10 measurement/culture condition are realized; data are presented as the ratio of the mean cell area measured in every seeding conditions on the mean cell area measured in the subcon uent condition (50 000 cells/well).
5 µM, respectively. The utility plate lled with XF Calibrant and capped with the cartridge is positioned in the Seahorse XFe96 analyzer's tray (Agilent Technologies), and the calibration of the signals generated by all 96 wells is performed. The culture plate is then introduced in the tray and the acquisition program is runned. This program includes a step of equilibration followed by basal, post-oligomycin injection and post-FCCP injection measurements. Each of these three steps comprises three loops of three minutes mixing, two minutes waiting and three minutes measuring. Data are analysed through the Wave 2. 6. 1 Software (Agilent Technologies).
Funding: This work is supported by the  Agence Nationale de la Recherche (ANR)  under the  program 'Investissements d'Avenir'  with reference  ANR-11-LABX-0021-LipSTIC , by the  Region Bourgogne Franche-Comté  (Seahorse XFe96 analyzer [Agilent]. This work is also founded by the  Ligue Nationale contre le Cancer  (grant  UFC/904/2000/19V11 ). CM have bene ted from a fellowship from  Nancy Regional University Hospital.
Quantifying the proportion of the different cell types present in tumor biopsies remains a priority in cancer research. So far, a number of deconvolution methods have emerged for estimating cell composition using reference signatures, either based on gene expression or on DNA methylation from purified cells. These two deconvolution approaches could be complementary to each other, leading to even more performant signatures, in cases where both data types are available. However, the potential relationship between signatures based on gene expression and those based on DNA methylation remains underexplored. Here we present five new deconvolution signature matrices, based on DNA methylation or RNAseq data, which can estimate the proportion of immune cells and cancer cells in a tumour sample. We test these signature matrices on available datasets for in-silico and in-vitro mixtures, peripheral blood, cancer samples from TCGA, bone marrow from multiple myeloma patients and a single-cell melanoma dataset.
Macrophages acquire different phenotypes that can be indicatively distributed along a spectrum of polarization going from M1 polarization, in which they promote inflammation, to M2 polarization state, which involves a tumor protective behaviour generated by their role in tissue reconstruction, promoting cancer cell survival  [9]. However, these cells are extremely plastic and our knowledge on their behaviour has been mostly generated through in-vitro experiments that probably remain quite distant from the situations they encounter inside tumors  [9, 10]. Identifying the presence of different macrophage types in bulk samples has proven particularly challenging due to two main reasons: first, macrophages are not found in the circulating blood, and obtaining them in-vitro requires their stimulation with cytokines that can either produce an M1 or an M2 polarization state in an artificial way, which leaves no guarantee that their phenotype will be comparable to that of TAMs found inside tumors; second, the two macrophage states are very plastic and unstable, while also being quite similar, such that distinguishing between the two states in a bulk dataset is particularly difficult  [9, 10].
Collection of WGBS data To build the Blueprint signature matrix, we collected 52 samples generated from whole-genome bisulfite sequencing (WGBS) of 10 purified blood-derived immune cells on GRCh38 (Homo sapiens genome) (Additional file 1: Table  S1 ). Bigwig files including methylation signal and coverage of methylation signal were downloaded from Blueprint epigenome level 3 data (http://www. blueprint-epigenome. eu). In addition, we also downloaded 7 cancer datasets, 4 WB and 11 normal tissues (Additional file 1: Table  S2 ) via Gene Expression Omnibus (GEO, https://www. ncbi. nlm. nih. gov/geo/) and Brinkman et al. [22].
WGBS data processing The files were parsed into R data structures, we then discarded bases that have coverage below 10X  [23]  and also have more than 99. 9th percentile of coverage in each sample. Methylation signal (WGBS 𝛽 ) was calculated with the following formula: WGBS 𝛽 = methylatedCounts / (methylatedCounts + unmethylatedCounts) The hg19 coordinates of WGBS from GEO were converted to GRCh38 with the liftOver R package. To map the common methylated cytosines from GEO and Blueprint datasets by genomic position to the Illumina 850K Methylation Array CpG sites, we used the Infinium MethylationEPIC v1. 0 B5 Manifest (https://support. illumina. com/downloads). After the coordinate transformation, the missing values on the beta matrix were imputed with impute R package  [24]  using default parameters to generate the final beta matrix without missing values.
Deconvolution Blueprint (BP) signature matrices generation procedure The signature matrices were established through the following steps: 1. The limma  [25]  R package was used to determine differentially methylated CpGs (DM-CpGs) or differentially expressed genes (DEGs) for all pairwise comparisons between cell types: for GE signature matrices, the voom function was used to remove heteroscedasticity for RNA-seq data using log2(TPM + 1). 2. We preselected significant candidate DM-CpGs/DEGs for the signature matrices using Benjamini-Hochberg corrected p-values. For GE signature matrices, a false discovery rate (FDR) cutoff of 0. 05 was selected, while for the DM-CpGs, 2 different FDR values were selected (Additional file 2: Method S1) to generate their signature matrices: a. FDR < 0. 05 was used to generate BPmet in blood.
Generation of the DNA methylation signature matrix (BPmet) We started from WGBS public datasets from the Blueprint project to generate a methylation based signature matrix (BPmet), which we used for immune cell decomposition in blood from healthy donors. Datasets for purified immune cells of 6 types were considered: CD4 (N=8), CD8 (N=8), B cells (N=9), Monocytes (N=4), NK (N=2) and Neutrophils (N=7)  [20]. The signature matrix includes only CpGs with false discovery rate (FDR) < 0. 05, absolute log fold change (abs(lfc)) > 0. 2 and the top 100 CpGs (Additional file 3: Table  S1 ).
RNAseq data processing The TCGA expression data normalized as Fragments Per Kilobase of transcript per Million (FPKM) were taken from public datasets  [28]. The transcripts per millions (TPM) expression datasets for WB were downloaded from GTEx portal (https://gtexportal. org/home/). 9 purified blood-derived immune cells TPM expression datasets on GRCh38 (Additional file 1: Table  S3 ) were collected from the Blueprint project portal  [20]. The expression value FPKM was first converted to TPM using the following formula: TPM i = (FPKM i / ∑FPKM j ) * 10 6  (1) The final expression value for each gene i in the sample j was normalized from TPM with the following formula: TPM ij = TPM ij *10 6 / ∑ t TPM tj (2).
Briefly, we used a bootstrap approach by which 50 samples were randomly taken from each of the three datasets to construct the complete dataset (150 samples in total) for the analysis of differential expression by limma. The mean-variance relationship was modeled with the voom function and the Benjamini-Hochberg method was used for multiple hypothesis testing. lfc > 2. 5 and FDR < 0. 005 were used to select differentially expressed genes. Only genes which are highly expressed in cancer cells compared to normal tissues and compared to blood samples were selected as "UP" genes. This procedure was repeated 30 times and only the UP genes which are present in each iteration are selected as cancer cell specific genes (Fig. 2b ). The expression profile of cancer cells was computed as the median of the expression values over all samples for all UP genes in the CCLE dataset, resulting in the CCLE signature matrix (138 genes).
Whole blood methylation datasets For validating and assessing the performance of the BPmet signature matrix, we used two independent public datasets (Additional file 2: Table  S1 ): 100 WB samples from the Grady Trauma Project (GSE132203) profiled using IlluminaHumanMethylationEPIC and another 6 WB samples using the 450k methylation array from Koestler et al. (GSE77797)  [33]. Flow-cytometry estimates of the proportion of blood cell types were available for the two WB datasets. The estimated fraction of cells obtained by deconvolution using the EpiDISH (RPC: robust partial correlation) method  [34]  was compared to the flow-cytometry estimated proportions using Pearson Correlation.
Cancer samples with methylation and gene expression profiles To test the BPmetCan and BPRNACan signature matrices, 495 methylation profiles from LUAD samples in TCGA were downloaded in level 3 (beta-value, 450k Illumina array) as well as their corresponding gene expression profiles measured by RNAseq (RNASeq2GeneNorm) using the RTCGAToolbox  [38]  R package. Finally, we also considered 59 RNA-seq datasets from multiple myeloma bone marrow samples after removal of cancer cells  [39]  (Additional file 2: Table  S1 ).
Regression models to predict response to immunotherapy Three public melanoma datasets and one bladder cancer dataset with response to anti-PD1  [40] [41] [42] [43]  were considered. ElasticNet  [44]  penalized logistic regression models were run using the results from different deconvolution methods and signature matrices as features. For each combination of signature matrix and deconvolution method, 5 models were trained, including 4 models trained by leave-one-dataset-out (lodo) and one model trained by 5-fold cross-validation (standard CV). The training includes a hyperparameter search for the l1 ratio and penalty strength. For the lodo training this search is performed by 5-fold CV on training datasets, and models are evaluated on the remaining test dataset. For the standard CV, one fourth of samples is kept as a hold-out test set, and the hyperparameter search is performed by 5-fold CV on the remaining samples, the model is then evaluated on the hold-out test set.
BPmet: A Novel DNA methylation-based signature matrix for immune cell deconvolution We exploited the WGBS methylation datasets that were produced for bulk samples of purified cells as part of the Blueprint project  [47]  to generate a signature matrix of 502 CpGs which allows us to identify 6 major immune cell types in blood (see methods, Fig. 2a  and Additional File 1: Table  S1 ). We named this signature matrix BPmet (Additional file 3: Table  S1 ). To test this newly generated signature matrix, we performed deconvolution with the EpiDISH R package  [34] , using our BPmet signature matrix, and choosing the RPC method as well as other available methods (Additional file 2: Table  S2 ). We then proceeded to test our BPmet new signature matrix on various datasets (Additional file 2: Table  S1 ).
Testing the BPmet signature matrix on peripheral blood samples To evaluate the performance of the BPmet signature matrix using the EpiDISH method, we first applied it to an independent publicly available Illumina EPIC array (850k CpGs) dataset choosing 100 whole blood samples from healthy donors with the true cell composition verified by FACS (gold standard) (GSE132203, Grady Trauma Project). We observed extremely high Pearson correlations between the estimated cell compositions and FACS fractions for all included samples (Pearson's R = 0. 993, p < 2. 2e-16) and each cell subtype (Fig. 3a  and Additional file 2: Figure  S1a ). We then compared the results from using the BPmet signature matrix in these 100 whole blood samples from the previously mentioned gold standard dataset (GSE132203) to the results using other signature matrices.
The signature matrices we compared were the MethylCIBERSORT signature matrix  [48] , and the default signature matrix for the EpiDISH method  [34] , based on DNAse hypersensitivity sites (DHS), which are highly cell-type specific regions of open chromatin  [49]. Altogether, correlations between the different signature matrix estimates and the FACS fractions were similar for EpiDISH-DHS and BPmet, while MethylCIBERSORT had a worse performance. Only NK cells estimate was worse correlated with BPmet compared to EpiDISH-DHS and MethylCIBERSORT, probably due to the few samples included for this cell type in creating our signature matrix (NK: n = 2) (Additional file 2: Figure  S1a-c ) Since the MethylCIBERSORT and EpiDISH-DHS signature matrices were generated starting from the Illumina 450k platform, we tested them on another set of 6 whole blood samples analysed by the same technology and compared results to the flow cytometry measurements provided for that dataset  [33].
To test the performance of BPmetCan in a realistic scenario, we used it to estimate the proportion of cancer cells in 495 Lung Adenocarcinoma (LUAD) DNA methylation datasets from TCGA. Firstly, we retrieved publicly available estimates of the proportion of cancer cells in these samples (purity) produced using the ABSOLUTE method  [50] , which involves analysing somatic DNA alterations to estimate ploidy and hence tumor purity. When comparing purity estimates using BPMetCan with ABSOLUTE results, we obtained a Pearson correlation of R = 0. 79 and P < 2. 2e-16, proving that our signature matrix is able to identify the proportion of cancer cells in a tumor sample (Fig. 4a ). We then compared the results obtained with BPmetCan to other previously published purity estimation methods from ref  [12, 48].
We observe lower accuracy of estimated total cell fractions for this dataset, especially for NK and CD8 cells (Fig  5a ). This is probably due to the limited number of samples available for NK (N=2) and to a possible discrepancy between the CD8 activation state in these PBMC samples and the ones used in the signature matrix (which exclude naive CD8 cells). Multiple gene expression reference-based methods have demonstrated a high accuracy in estimating cell proportions in blood and immune infiltrates from bulk RNA-seq data, amongst which we chose two for comparison to estimates produced by BPRNA: we first compared to two methods that come with their own signature: MCP-counter  [54] , which is a scoring method based on marker genes, and quanTIseq  [29] , which is based on constrained least squares regression and can estimate immune cell fractions and fractions of unknown cells with high accuracy.
Aware of the difference between cancer cell lines and cancer cells, we also developed a signature matrix for detecting cancer and specific immune cells starting from expression data in cancer, adjacent normal tissues and immune cells. To this end, similarly to what was done in the case of DNA methylation, we looked for samples with RNAseq data for tumor and non-tumor tissues and whole blood, which are readily available through the TCGA and GTEx, and integrated it with the BPRNA immune cell signature matrix, which was based on 9 immune cell types  [20]. This new signature matrix, which we called BPRNACan consists of 1403 genes (see Methods, Fig. 2a  and Additional file3: Table  S5 ). We used the CCLE_TIL10 and BPRNACan signature matrices with the EpiDISH method to estimate cell composition in different samples with gene expression datasets (Additional file 2: Table  S1 ).
Validation of the CCLE_TIL10 and BPRNACan signature matrices on in-silico and in-vivo tumor samples As a first step to validate our method on samples containing cancer cells, we considered an in-silico simulated cancer RNAseq dataset, composed of reads from purified samples from 10 immune cells (B-cells, NK-cells, CD4+ T-cells, CD8+ T-cells, Monocytes, NK, Neutrophils, M1, M2 and Dendritic cells), which were added to reads from a sample of MCF10 cancer cell lines in different proportions  [29]. The results of using the CCLE_TIL10 signature matrix on this in-silico mixture are in excellent agreement with true mRNA proportions (Pearson R > 0. 9), for 11 cell types (Additional file 2: Figure  S4a ), as expected, since the TIL10 signature matrix was derived from this data and CCLE_TIL10 signature matrix is an extension of it.
We therefore analysed the results obtained with the 2 signature matrices on TCGA samples (Additional File 2, Figure  S5 ). We first compared the estimation of cancer cell purity in the samples to ABSOLUTE, ESTIMATE, LUMP and IHC results, as was done for the methylation analysis. We found that the tumor purity estimates derived using BPRNACan were better than those derived from CCLE_TIL10 on the TCGA-LUAD dataset, with correlations with the ESTIMATE method reaching Pearson's R = 0. 72 (Additional File 2, Figure  S5b ). As far as different cell types are concerned, we again compared our deconvolved proportions to estimations based on H&E images  [52]  (Additional file 2: Table  S1 ). Interestingly, once again, the BPRNACan signature matrix performed better than CCLE_TIL10 on biological samples (Fig.