dataesr/scientific-paragraphs-categorization huggingface dataset:
Although most of them can be adapted to treat classical MFC data, these different solutions are not well adapted to MRD analyses: the number of events to process needs to be quite high to obtain a good sensitivity and software-based analysis time can be very long, up to several hours and thus incompatible with daily routine. Among these methods, the FlowSOM unsupervised solution, included in the open-access R software (Bioconductor), appears to be the best adapted for routine work and especially for MRD analysis in AML. It combines a short analysis time, a high sensitivity  [11]  and complies to ELN recommendations by combining "DfN" and "LAIP" approaches. We have previously explored and published  [13, 15]  how FlowSOM can be integrated with such classical analysis software as Kaluza ®. In this work, we aimed at appreciating the performance of the FlowSOM solution together with Kaluza ® in real-life for MRD assessment.
Patients and Samples From the laboratory database of Bordeaux University Hospital (Bordeaux, France), diagnosis data were selected from untreated AML (excluding promyelocytic AML) patients who had been tested between April 2015 and March 2020 and carried a molecular marker allowing for molecular FU. Diagnosis had been performed by morphologic examination according to the WHO classification  [20]. All patients were treated by an intensive classical chemotherapy regimen based on a 3 + 7 backbone combining 3 days of anthracyclin and 7 days of cytarabine  [21]. Among these patients, only those who benefited from FU by both molecular methods and MFC, at least for the post-induction time-point, were retained. Whenever further matched FU points were available, they were also collected and analyzed.
Overall, 40 patients were included (24 men and 16 women, mean age 54 years old, Table  1  and Table  S1 ), with 96 MRD points of FU assayed between 1-and 28-months post diagnosis. Written informed consent was obtained from all patients in accordance with the Declaration of Helsinki, allowing the collection of bioclinical data in the anonymized Bordeaux DATAML registry (authorization n • 915285). MFC analysis at diagnosis and all time points was performed on whole BM samples collected on EDTA-K in a stain-lysis-no wash fashion. Two 10 color antibody combinations were used, according to recommendations of the GEIL (Groupe d'Etude Immunologique des Leucémies) as published before  [22, 23]. A common backbone of CD33, CD34 and CD45 was present in both tubes. One combination (tube 1) then associated CD65, CD14, CD13, CD117, CD7, CD11b and CD16 and the other one (tube 2) comprised CD64, CD10, CD4, CD123, CD56, CD19 and CD38.
FlowSOM Process For each antibody combination, all listmode (lmd) files were processed using the FlowSOM module (Bioconductor version 3. 3. 2 with flowsom and flocore packages) integrated to the analysis software Kaluza (Beckman Coulter) as reported  [13]. In a first step, compensations were checked and the 12 parameters of each lmd file were normalized. Normalized files of the 19 NBM were then merged and processed to obtain a reference minimal spanning tree (MST) of 100 nodes, called "FROZEN"  [13]. The lmd file of merged NBM was then processed together with the normalized diagnosis (AML Dg) and, one by one, each normalized FU (AML FU) lmd file for each patient, in the FlowSOM module. Merge of these three files generated two types of MST. Three (respectively for NBM, Dg and FU) were based on the reference NBM FROZEN MST and assigned Dg and FU populations to the 100 nodes of this NBM MST.
MFC MRD Analyses Data were available for all samples after MFC analysis with both Tube 1 and Tube 2 panels. Therefore, 192 samples and about 1000 potential MRD nodes were examined. The global strategy adopted to investigate for MFC MRD was, as described above, to set a tracking gate allowing for node-by-node exploration. Nodes of interest were provided by the NOI strategy, completed in some instances by the exploration of a few nearby nodes in the FREE display. Each retained node had to belong to the area of the Dg blast population, confirmed by backgating on the CD45/SSC histogram displayed together with the Dg MST (Figure  1 ). Cell numbers and percentages of each node in NBM, Dg and FU displays were collected. In no instance did the number of NOI exceed 10 (median and (range) for tubes 1 and 2 respectively 4 (1-9) and 5 (1-10)).
Discussion This study demonstrates that an unsupervised analysis of MFC data allows to explore MRD in AML patients with a high confidence in the populations observed. The setting of this work was challenging, since it was decided to include only patients with a possible concomitant molecular FU, known to represent only a proportion of AML patients. Moreover, NPM1 mutations are notably associated with myelomonocytic differentiation  [28]. This is a real challenge as there is a continuum between monocytic blasts and normal monocytes at diagnosis and normal reconstitution at FU is difficult to dissociate from remaining tumor cells. No specific strategy has been published in the literature regarding these specific populations  [29]. Here, the position of MRD nodes was clearly related to monocytic differentiation but the concomitant comparison with NBM greatly facilitated their discrimination.
This has also been a strategy retained for example by Kern et al. [33]. Of note, this imposes to use highly harmonized set-ups as well as exactly the same panels of antibodies for the NBM and patient's samples. This has been demonstrated to be feasible, especially with the very stable current instruments and validated strategies  [24, 34]. However, by definition, LAIP cannot be compared to NBM in such separated analyses in the majority of cases. Another advantage of the FlowSOM strategy is of course that the software identifies populations that would have been difficult to select through sequential gating yet can be perfectly characterized during the supervised part of the analysis. To our great satisfaction, FlowSOM allowed to straightforwardly demonstrate concepts taken into account as important in the ELN proposal  [6].
FlowSOM concomitant analysis of NBM, Dg and FU further allowed to approach the critical issue of significant threshold. As established in the definition of the FlowSOM scripts  [15] , we chose to consider nodes displaying at least twice the percentage of cells as the corresponding NBM node. This was confirmed here by the statistical analysis comparing MFC and molecular final data, which yielded a 1. 9 ratio between FU and NBM as the most pertinent. In our series, the lowest number of FU cells complying with this criterion was 17 cells. This confirms the global opinion of considering clusters of 10-20 cells for limit of detection (LOD)  [6]. Of course, this is limited by the number of acquired cells. As we used retrospective data, but also because cell number limitations are inherent to the samples, some results would certainly have been consolidated by the acquisition of more events.
Conclusions Overall, this study demonstrates the power of the combined unsupervised FlowSOM and Kaluza ® software, to evaluate AML MRD. In the challenging set-up of this study, that only considered patients with molecular MRD targets, MFC MRD assessment stood out as a useful monitoring tool. It is thus likely that FlowSOM-assisted MFC MRD assessment, using unsupervised treatment of. fcs files, will be applicable to all AML patients and provide clinicians with useful hints for treatment options.
Data We use the ID™ SARS-CoV-2/N501Y/E484K Quadruplex assay from ID Solution to test N=53,687 SARS-CoV-2 positive samples collected between Monday April 12 and Friday May 7 2021 in 13 French regions, mainly from the Ile-de-France region (47. 9%). Some of the samples (7. 95%) originate from hospitals (mostly hospitalized patients). We only analyzed data from individuals aged from 5 to 80 years old to minimize the weight of preschool children and aged care facilities in the analysis. 17. 3% of the tests could not be interpreted, because of Ct value too high to keep an equal sensitivity for the N501Y and E484K targets, and were removed from the analysis. Therefore, to avoid biasing the variant screening, all tests with cycle thresholds (Ct) values above 30 were ignored (28%). Overall, we analyze 36,590 tests, i.
Similar results were obtained when immunoprecipitation was performed with human eotaxin-1/CLL11 instead of sG protein and G. CCR3 extract proteins (Figure  2B ). This interaction between CCR3 and sG protein was specific, as no association was seen in immunoprecipitation performed with control IgG isotype Ab (Figure  2B ), in the absence of sG or eotaxin-1/ CLL11 proteins. Similarly, when membranes were blotted with control Abs, no interaction can be detected (data not shown). Because epithelial cells produce chemokines, the eluate was also examined by western blotting with anti-eotaxin-1/CLL11 antibodies. No band was detected using these antibodies (not shown). A549 and G. CCR3 cells were then used to determine whether sG and RSV F proteins were able to bind CCR3 on these cells (Figure  2 ).
These data were confirmed in Ghost cells using siRNA for CCR3, CCR5, CXCR4, and CX3CR1 receptors. As shown in Figure  5B , significant protection from infection with RSV was seen in G. CCR3 + and G. CX3CR1 + cells transduced with CCR3-siRNA and CX3CR1-siRNA, respectively. We also studied the potential interaction/synergy of binding to CCR3 and CX3CR1 and subsequent infection with RSV. Therefore, we deleted CCR3 and CX3CR1 receptors in airway epithelial cells using CRISPR-Cas-9, a specific and efficient gene-editing technology (see STAR Methods). CCR3 and CX3CR1 expression was measured in A549 wild-type (WT) and CRISPR-Cas9 edited (A549) cell lines. Using confocal microscopy and FACS analysis, CCR3 and CX3CR1 expression proved to be reduced efficiency in the edited cell line (Figures  S2A  and S2B ).
Lung fixation and histology The right lung was removed, weighed, resuspended in DMEM and serial dilutions of clarified lung homogenates were plated for viral titer. The right ventricle was flushed with 5 ml clean PBS to flush the left lung vasculature. The trachea was cannulated and the lungs were inflated with phosphate-buffered formalin (10% formalin) at 20 cm H 2 O pressure. We then removed the lung and submerged it in formalin for fixation and subsequent histological sectioning. Thin sections were cut from paraffin-embedded lungs and stained with hematoxylin and eosin (H&E). As a semiquantitative analysis of mucus induction, mucus-producing goblet cells were identified and quantified in lung sections stained with Periodic-acid Schiff (PAS) reagent. The mucus index was calculated using ImageJ software (NIH) on entire lung sections encompassing large and small bronchial airways.
ACKNOWLEDGMENTS The authors thank  C. Terryn  and  S. Audonnet  for their assistance with the confocal microscopy and flow cytometry. Additionally, we thank  N. Bouland  and  Pr M. D. Diebold  ( Laboratoire Central d'Anatomie et de Cytologie Pathologiques, CHU de Reims, France ) for facilitating sample processing and preparation of lung sections. Special thanks are addressed to all volunteers who, by their consent, allowed this study to be performed. This work was supported by the  Canadian Institutes of Health Research (CIHR  grant  MOP38011 ),  Fonds de Recherche en Santedu Quebec (FRSQ) Canada, Fondation pour la Recherche Medicale (FRM) France  and the  University of Reims, France. The agencies had no role in study design, data collection and analysis, decision to publish, or manuscript preparation.
Single-cell somy estimation Details about the bioinformatic analysis for somy values determination are provided in the supplementary material. In summary, sequence reads were associated to each sequenced cell based on their barcodes and mapped to a customized version of the reference L. donovani genome LdBPKv2  (19)  using the Cell Ranger DNA TM software (10× Genomics). The matrix generated by the software with the number of mapped reads per 20 kb bins was used as input to a custom script written in R  (30). In this script, bins with outlier values were excluded, and the mean normalized read depth of each chromosome was calculated for each cell. Cells displaying a high intra-chromosomal variation were removed from downstream analysis. In order to establish the baseline ploidy of each cell and define the somies of a cell, the mean normalized depth values of the cell's chromosomes were multiplied by the cell scale factor, defined for each cell as the lowest number between 1.
8 and 5 that leads to the shortest distances to integers when the chromosomes' mean normalized depth values are multiplied by it. The scaled mean normalized depth values are referred here as 'raw somies'. To convert the raw somies (continuous) into integer copy numbers (discrete), a univariate gaussian mixture-model was built for each chromosome by an expectation-maximization algorithm based on the distribution of the raw somy values between all cells of the same sample using the Mixtools package  (31). For each possible integer somy, a gaussian mixture-model was generated and each raw somy value was assigned to the rounded mean of the gaussian to which it has higher probability of belonging to.
Karyotype identification and network analysis A karyotype was defined as the combination of integer somies of all chromosomes in a cell. Karyotypes were numerically named according to their frequency in the sequenced population. To generate the network representing the dissimilarities between the karyotypes, a pairwise distance matrix was built based on the number of different chromosomes between all karyotypes in a sample, and used to create a randomized minimum spanning tree with 100 randomizations, using the Pegas R package  (32, 33). The network visualization was made with the visNetwork package  (34).
Bulk genome sequencing Genomic DNA from the BPK282 cl4 and BPK081 cl8 clones was extracted in bulk using the QIAmp™ DNA Mini kit (Qiagen) following manufacturer's recommendations. PCR-free whole genome sequencing was performed on the Illumina NovaSeq platform using 2 × 150 bp paired reads. Reads were mapped to the reference genome L. donovani LdBPKv2 (available at ftp://ftp. sanger. ac. uk/pub/project/pathogens/Leishmania/ donovani/LdBPKPAC2016beta/) using BWA (version 0. 7. 17) with seed length set to 100  (36). Only properly paired reads with a mapping quality higher than 30 were selected using SAMtools  (37). Duplicates reads were removed using the RemoveDuplicates command in the Picard software (http://broadinstitute. github. io/picard/). The average somy values were calculated as described previously  (1) , by dividing the median sequencing depth of a chromosome by the overall median sequencing depth over all chromosomes, and multiplying this ratio by 2.
These values were used to define an average karyotype for the sequenced population of cells. Gene Ontology analysis and in silico screening for small RNA Gene Ontology (GO) classes were obtained from TriT-rypDB release 49  (38). As the genome sequence stored on TriTrypDB does not correspond to the reference genome used in this work, the GO annotation was obtained by mapping back all genes to our reference genome using BlastP  (39). Clustering of the different chromosomes based on their assigned GO classes was performed using the prcomp command in R. Two different data sources were used for non-coding RNA screening. First, the Rfam (40) database version 14. 4 was used to screen the L. donovani LdBPKv2 reference genome using the cmscan algorithm as implemented in Infernal (41) using default parameters and setting the search space parameter to 64.
Second, the non-coding RNA nucleotide sequences as annotated in the L. donovani BPK282A1 genome on TriTrypDB version 54 were extracted, and aligned versus the reference genome used in this work (L. donovani LdBPKv2). Next, both data sources were compared with each other using BLAST, and redundant copies were removed (i. e. , only one copy retained to avoid duplicates).
High throughput single-cell genome sequencing as a reliable tool to explore karyotype heterogeneity in Leishmania populations We applied high throughput single-cell genome sequencing (SCGS) to address mosaic aneuploidy in promastigotes of two Leishmania clones differing substantially in average aneuploidy as revealed by bulk genome sequencing: (i) BPK282 cl4, an aneuploid clone showing seven chromosomes with an average trisomy apart from the usual average tetrasomy in chr31 and (ii) BPK081 cl8, showing an average disomy for all chromosomes except chr31 (average tetrasomic); for simplicity, we will call BPK081 cl8 the 'diploid' clone. Details about sequencing statistics are provided in Supplementary Table  S1. First analyses of the SCGS data were made with the Cell Ranger DNA™ pipeline. Although the software was developed for mammalian genomes, which are up to 2 orders of magnitude larger than Leishmania's nuclear genome, it allowed detecting (i) aneuploidy, (ii) mosaicism and (iii) large intrachromosomal CNVs, as, for instance, the H-and M-amplicons (1) in chr23 and chr36 respectively (Supplementary Figure  S2 ).
A total of 1900 promastigotes were individually sequenced, of which, 1636 remained after data filtering. This 'super mosaic' population displayed a high aneuploidy diversity: 388 identified karyotypes in total. As expected, the 1636 promastigotes formed four distinct clusters based on their integer somy values, with discrete differences in the aneuploidy patterns between each cluster (Figure  1D ). Since one of the strains (HU3) used in this super mosaic is phylogenetically distant from the other three strains (BPK475, BPK498 and BPK506), we could distinguish HU3 promastigotes from the others based on their SNP profiles. Interestingly, all HU3 cells were grouped together in cluster C (Figure  1D --orange lines in the annotation bar), suggesting that the discrete karyotypic differences between the major clusters reflect differences among the aneuploidy profiles of the four strains, so that each cluster likely represents one of the strains.
As this karyotype is found only in one cell, it is considered a rare karyotype. (D) Graph indicating the number of somy change events for each chromosome among the common karyotypes (found in two or more cells -top panel) or the rare karyotypes (found in only one cell -bottom) in the 3 samples submitted to SCGS. Chromosomes are divided in three groups: mainly disomic (found as disomic in more than 95% of the cells), intermediate (found as polysomic in more than 5% of the cells but less than 50%), and the mainly polysomic (found as polysomic in more than 50% of the cells). (E) Distribution of non-coding RNAs across L. donovani genome. Ribosomal RNAs (rRNA), small nucleolar RNAs (snoRNAs) and transporter RNAs (tRNAs) were identified based on the Rfam and TrypDB database. kar 35 in Figure  2B , Supplementary Figure  S7 ).
Our data indicate that the analysis of transcriptional profiles of blood cells with the support of appropriate deconvolution approaches represents a valid strategy for monitoring immune cell behavior at a high throughput and reliable level. To achieve the results reported here, one of the major challenges was the identification of gene signatures appropriate to capture the activity of circulating MDSCs. Indeed, monocytic MDSCs are defined in flow cytometry only by the lack/low expression of HLA-DR in cells expressing the monocytic marker CD14, alone or in combination with CD11b and CD33, but their genomic features have been poorly defined. To this aim, we used the dataset selected by Angelova  51  and Fridlender  52  as reference transcriptional data for myeloid cells and the data set obtained from human MDSCs generated in vitro according to a model developed in our laboratory.
Transcriptomic analysis Suitable material for transcriptional analysis was available from eight patients. RNA was extracted using miRNeasy kit (Qiagen). After quality check and quantification by 2100 Bioanalyzer system (Agilent) and Nanodrop ND-1000 spectrophotometer (ThermoFisher), respectively, RNA expression was assessed using Illumina HT12v4 BeadChip. Illumina's BeadStudio version 1. 9. 0 software was used to generate signal intensity values from the scans. Data were further processed using the Bioconductor "Lumi" package. Following background correction and quantile normalization, expressions were log2-transformed for further analysis. Raw expression and normalized data matrix have been deposited at NCBI's Gene Expression Omnibus database (http://www. ncbi. nlm. nih. gov/geo/), with accession numbers GSE146163.
Leucocyte subset estimations To estimate the enrichment of various cell types, gene expression deconvolution analyses were performed with ssGSEA  81, 82  implemented in the "GSVA package" using cell-specific signatures (Supporting information Table  S3 ): T cells, CD8 T cells, cytotoxic T cells, T helper 1 cells (Th1 cells), central memory T cells (Tcm), Tem, T-helper cells, Tfh, Th2 cells, Th17 cells, gamma delta T cells (Tgd), natural killer cells (NK cells), NK CD56 dim , NK CD56 bright cells, B cells,  83  Treg, NKT cells, DCs, immature DCs (iDC), plasmacytoid DCs, myeloid DCs (mDC). 51 For MDSCs, we constructed a specific signature based on 25 genes highly correlated in the present dataset, selected from the top 100 genes upregulated in extracellular vesicle-MDSCs versus monocytes (MDSC_INT, Supporting information Figure  S4 ) in our recent work.
Modular repertoire analysis A set of 260 modules (coexpressed genes) was used for the analysis of this data set. This fixed modular repertoire was a priori determined, being constructed based on coexpression measured across nine reference datasets encompassing a wide range of diseases (infectious, autoimmune, inflammatory)  15, 16, 37  (https://github. com/Drinchai/ DC_Module_Generation2). This data-driven approach allowed the capture of a broad repertoire of immune perturbations, which were subsequently subjected to functional interpretation. This collection of annotated modules was then used as a framework for analysis and interpretation of our blood transcriptome dataset. The approach used for the construction, annotation, and reuse of modular blood transcriptome repertoires was previously reported.
TCGA transcriptomic analysis RNA-seq data from TCGA clear cell RCC (KIRC) cohort were downloaded using TCGA Assembler (version 2. 0. 3). Data normalization was performed within lanes, and between lanes using R package EDAS Equation (version 2. 12. 0) and quantile normalized using preprocessCore (version 1. 36. 0). A single primary tumor sample was included per patient using the TCGA Assembler "ExtractTissue-SpecificSamples" function. Previously flagged samples that did not pass assay-specific QCs were excluded. 84, 85 ata were log2 transformed with an (+1) offset. ES were calculated by ssGSEA on the log2 transformed, normalized gene-level data. Gene sets to define ES of tumor-associated pathways (n = 51) were used as described previously. 43 or immune-reated signatures, we used the same gene sets used for the PBMC analysis.
Survival analysis Clinical data from the TCGA RCC cohort (KIRC) were obtained from the TCGA Pan-Cancer Clinical Data Resource. 86 Patients were divided in tertiles based on enrichment scores of MDSC gene signatures (MDSC_INT, G-MDSC, and MDSC_Angel). OS was used to generate Kaplan-Meier curves using a modified version of the ggkm function. 87 Survival data were censored after a follow-up period of 10 years. HR between groups, corresponding pvalues, and confidence intervals were calculated using cox proportional hazard regression with R package survival (version 2. 41-3). For each variable, the proportional hazard assumption (PHA) was checked by computing the Pearson product-moment correlation (rho) between the Schoenfeld residuals and the transformed (log) survival time was computed using the cox.