dataesr/scientific-paragraphs-categorization huggingface dataset:
1a , b and Supplementary Fig. 6 ). Notably, as intermediate generated images are also validated by the conditional GAN discriminator as possible images from the dataset, cells on the border do not gradually disappear, but tend to gently shift out of the field of view. This last example demonstrates that this type of morphing doesn't match any sort of real dynamic. However, it does certainly help to visualize the differences between phenotypes, especially when these are subtle.
Some recent work has used generative models to explain what a classifier learnt in order to display already perceptible or known features (that were actually used to annotate the pictures), yet to our knowledge, these were never evaluated on invisible cell phenotypes in the context of various assays  12, 13. Furthermore, our approach does not focus on explaining a trained classifier and thus does not require one. On the contrary, these methods attempt to explain what a classifier can discriminate against, but do not directly explain the differences between datasets. While these goals are different, using a trained classifier toward our end goal would add a non neutral intermediate proxy to assess the difference between phenotypes. As expected, any hyper parameter of the classifier, such as its capacity or even the way it is trained, would add its form of bias between the images and our actual understanding of what the difference between conditions might be.
Datasets do include differences and a conditional GAN will just display them without favoring relevant biological features over unwanted biases. Using such an approach, we could then reverse the classifier explanation paradigm, and rather than explaining a posteriori a classifier trained blindly, we could envision first extracting relevant and irrelevant differences between two conditions from a dataset using this approach, and then building an efficient guided system that would consider only the features of interest, disregarding the identified biases or irrelevant features. The staining artifacts in the red blood cell example is typically such an irrelevant feature that we would rather not want a classifier upon which to base its discrimination. An extension of this work could consider disentangling the image transformation into interpretable factors of variation so that the weight of quantitative features may be better established and used subsequently.
Boxes represent the q1-q3 interval (25-75% of the distribution). The central bar is the median. The lower whisker is the first datum greater than q1 -1. 5 × IQR and the upper whisker is the last datum lower than q3 + 1. 5 × IQR. Source data are provided as a Source Data file. information. Although it would necessitate datasets including time lapse or cell correspondence between conditions, an interesting extension of this method could then be used to enforce learning of single-cell trajectories during training. This would make it possible to infer whether or not a perturbation is contingent on initial cell state. This method is straightforward to use, can be applied to any microscopy image modality and is accessible to the community. Beyond displaying invisible phenotypic variations, this system proposes a straightforward way to discover yet unknown biomarkers.
Conditional GAN training and inference We developed scripts to prepare any image dataset with two or more conditions for a conditional StyleGAN2 training and for performing image translation through latent interpolation (https://github. com/ biocompibens/phenexplain). Once the dataset was prepared with each image conditionally annotated and packed into a single zip file, training for each dataset was performed on a Computing server equipped with 4 NVidia A100 GPU which took between 6 h and 48 h depending on the dataset. BBBC021 took the longest time because it is made of 73 conditions (45 million images were seen in 48 h). We stopped training when FID plateaued long enough (~5 million images). Our translation script generates latent traversal videos of a grid of any number of samples (in row) from any condition to one or several other conditions (columns).
Frechet inception distance The Frechet Inception Distance measures the difference between the Inception representations of two image sample distributions. While the use of FID is debated in the literature, there is no ideal metric for assessing synthetic image quality and FID is still the most widely accepted reference to evaluate the capability of a generative model to reproduce an image distribution: a low FID value means the distribution of real image is close to the distribution of generated images in the inception representation (in the Frechet distance sense). We provide FID values for the conditional generative models we trained on each dataset and FID values in comparison to real natural images for comparison (Supplementary Table  2 ). We also performed an experiment where we gradually corrupted cell images to provide a scale and a threshold at which FID can still be considered as good in this context (Supplementary Fig.
Convolutional Neural Networks classification Convolutional Neural Networks (CNNs) were employed to provide evidence that the various visually indiscernible conditions used in this paper were discernible by a deep network (Supplementary Table  1 ). In this case, as we had large datasets with annotated conditions, the network was kept as simple as possible, made up of only four convolutional layers and trained for 10 epochs on each dataset using a 4-fold cross-validation. The mean and standard deviation of the accuracy over these 4 runs were then reported. CNNs were also used to quantitatively validate on real images the intuitions provided by conditionally generated images (Supplementary Figs. 12  and 14 ). In this case, as we had a large dataset but a limited amount of manually annotated image phenotypes, we took advantage of transfer learning and used a ResNet50V2 model pre-trained on ImageNet.
Phenexplain web interface For ease of understanding and visualization, we created an interactive web interface with data produced from all the datasets we used in this study, making it possible to explore and manipulate more examples than the one presented in the figures (https://www. phenexplain. bio. ens. psl. eu/). For all datasets, latent traversal can be explored interactively for a selection of examples. For the BBBC021 dataset, which provides more conditions, a more detailed exploration is possible. First, a linear discriminant analysis (LDA) was used to provide a projection of the latent space that properly separates three conditions (DMSO, and nocodazole or cytochalasin B at a high concentration). Their respective regions in the latent space can be explored to show that a variety of images can be generated for each condition.
Golgi assay HeLa cells obtained from the commercial provider American Type Tissue Collection (ATCC Cat# CCL-2, RRID:CVCL_0030) stably expressing EGFP-CCR5 (CC chemokine receptor 5) were kindly provided by F. Perez's team, UMR144, Institut Curie) and cultured as described in ref. 28. Cells were briefly grown in Dulbecco's modified Eagle's medium (DMEM) (Thermo Fisher Scientific) supplemented with 10% fetal calf serum (FCS), 1 mM sodium pyruvate, and penicillin and streptomycin (100 μg/ml) (Thermo Fisher Scientific). For the Golgi assay, 5. 0 × 10 3 cells were seeded on black clear-bottom 384-well plates (ViewPlate-384 Black, 784201, PerkinElmer) in 40 μl of complete medium. Twenty-four hours after cell seeding, DMSO (control solvent) and nocodazole were transferred robotically to plates containing cells to a final concentration of 10 μM and 0.
NF-κB nuclear translocation assay HCC1143 cancer cells were obtained from the commercial provider American Type Tissue Collection (ATCC Cat# CRL-2321, RRID:CVCL_1245) and cultured in RPMI 1640 medium (11875085, Gibco Thermofisher) supplemented with 10% bovine fetal serum (A5209502, Gibco Thermofisher), 100 U/mL penicillin and 100 µg/mL streptomycin in a humidified environment consisting of 95% air and 5% CO 2 at 37 °C. Cells were seeded on black clear-bottom 384-well plates (View-Plate-384 Black, 784201, PerkinElmer) in 40 μl of complete medium at density of 1000 cells/well for 24 h before exposure to 20 ng/ml TNFα for 30 min. After the incubation period, cells were fixed with 4% (vol/vol) buffered paraformaldehyde solution (PFA, Sigma-Aldrich) for 15 min, quenched with 50 nM NH 4 Cl solution, and permeabilized with 0.
Statistics and reproducibility The total amount of images per dataset is available in Supplementary Table  2  and the number of distinct samples and images for each condition is available in details in Supplementary Table  3. Distinct samples are wells from well plates except for the Malaria dataset where each considered sample is a patient thin blood smear slide. 600 images were acquired from each of these slides. A distinct sample size of 3 wells was provided with the BBBC021 dataset and is a common choice for high throughput screening assays with many conditions. For the datasets we generated we did not use a statistical method to predetermined the number of replicates or the image sample size but we chose an unusually high number of replicates (30 or above) to ensure reproducibility. Except for the Malaria and the LRRK2 datasets, image crops of 128 × 128 or 256 × 256 pixels were collected around each nucleus from all the samples.
For the LRRK2 dataset, images acquired on the border of the wells were discarded. For the training of StyleGAN2, no data were excluded from the datasets prepared as described above. Image analysis tasks for evaluation were performed on full datasets, except in the following cases. The classification tasks described in Supplementary Table  1  were performed while randomly excluding a third of the images for validation. FID computations in Supplementary Table  2  were performed on random subsamples, as further described in the supplementary table caption. All microscopy images displayed in the figures were linearly scaled to 8 bits using the first and ninety-ninth percentiles of the distribution of raw image pixel values.
Acknowledgements This work has received support under the program  Investissements d'Avenir launched  by the  French Government  and implemented by the  ANR , with the references:  ANR-10-LABX-54 MEMO LIFE ANR-11-IDEX-0001-02 PSL * Research University,  ANR PSEUDOTIME , AL was funded by  Inserm ITMO Cancer 2021 -PHENEXPLAIN. TC was co-funded by  ANRT  and  Ksilink  under the  Cifre program. This work was granted access to the HPC resources of IDRIS under the allocation 2020- AD011011495  made by  GENCI. We thank  Franck Perez  ( Institut Curie ) for kindly providing cell lines, the computing service of IBENS,  Nikita Menezes  for her help in assembling the figures and  Mary-Ann Letellier  for editing the manuscript.
Data availability Synthetic images of all of our datasets can easily be manipulated here: https://www. phenexplain. bio. ens. psl. eu/. As raw data we used four original datasets we generated and a subset of the BBBC021v1 image set available from the Broad Bioimage Benchmark Collection  14, 30. All datasets used in this study are available in a Zenodo repository  31. Source data of the figure plots are provided with this paper. Source data are provided with this paper.
In this setting, Faessler et al. , very recently reported how murine and rat ArpC5 subunit isoforms (i. e. , Arpc5 and Arpc5L) can alternatively and differently regulate particular Arp2/3-dependant functions. Interestingly, some differences became evident between human vs. rodent species as ARPC5 deficiency in our work does not seem to affect ARPC5L accumulation, while ArpC5 deficiency consistently increased the ArpC5L levels likely affecting the functions dependent on each type of Arp2/3 complexes  16. Not surprisingly, the data presented here showed that depriving cells of the ARPC5 subunit resulted in dysregulation of several but not all Arp2/3-dependent functions. Altogether, these features help to explain why signaling through IL-6Rα but not all surfaceexpressed cytokine receptors are affected by ARPC5 deficiency.
Patients and samples The patients' legal representatives provided informed consent at their respective institutions, in accordance with the Declaration of Helsinki, under protocols approved by local institutional boards at the Rockefeller University, and/or National Institutes of Health (NIH)/ National Institute of Allergy and Infectious Diseases (NIAID) protocol 10-I-0216; ClinicalTrials. gov Identifier: NCT01222741. The patients' legal representatives consented to the publication of medical information and images related to the cases. Samples from Patient 1's family were collected at several time points between 2019 and 2021 at Dalhousie University, Halifax, Nova Scotia, Canada and sent to NIH for genetic and functional studies; Patient 2's family samples were collected at different timepoints between 2012 and 2013 at the Children's Medical Center, Tehran, Iran and sent to the Rockefeller University for genetic testing.
Blood samples and biopsies from patients, family members, and healthy donors were obtained following standard of care under approved protocols by the National Institutes of Health institutional review board. Results are shown as mean + SEM. Data generated from two independent experiments, performed in duplicate each time. b IL-6 trans-signaling modulation in P1's CD4+ T cells. P1's cells were pretreated with soluble gp130Fc (1 µg/mL) or tocilizumab (10 µg/mL) for 90 min. A subset of cells was left untreated to serve as control. Then, cells were stimulated with hyper IL-6 (100 ng/mL) for 20 min. A subset of cells was not stimulated to serve as baseline. Sequentially, phosphorylation of STAT3 was measured by flow cytometry (upper panel). The bar graph shows soluble gp130Fc and tocilizumab pretreatment effect on STAT3 phosphorylation in response to hyper IL-6 stimulation compared to untreated (-), stimulated cells (lower panel).
This experiment was conducted once. Source data are provided as a Source Data file. Whole exome sequencing (WES) and filtering of candidate variants WES was performed using genomic DNA extracted from PBMCs using the Illumina-IDT Exome Enrichment Kit and HiSeq 2500 instrument according to the manufacturer's protocols. To prioritize genetic variants, we filtered the results to include only novel or rare and potentially functional non-synonymous, nonsense, indel, intronic and splicing variants, using tools in the Ensembl Variant Effect Predictor (VEP; https://useast. ensembl. org/info/docs/tools/vep/index. html) and ANNOVAR package (annovar. openbioinformatics. org), including gene/amino acid annotation (refGene), allele frequencies (gnomAD, NCBI dbSNP), functional prediction scores (SIFT, PolyPhen2, LRT, MutationAssessor, MutationTaster,PROVEAN, MetaSVM, MetaLR,RE-VEL, CADD, DANN, FATHMM, fathmm-MKL, GenoCanyon), conservation scores (GERP++, SiPhy), and genotype/phenotype correlation databases (InterVar, ClinVar, COSMIC).
Sanger sequencing Selected next-generation sequencing results were confirmed and carrier testing was performed by Sanger sequencing. Genomic DNA was PCR-amplified using M13-tagged specific variant-flanking primers, and the PCR products were subjected to Sanger sequencing using M13primers and the BigDye Terminator v1. 1 Cycle Sequencing Kit (Applied Biosystems, Cat. 4337452) according to the manufacturer's protocol. The Sanger sequencing data was analyzed using DNAStar Lasergene 16 SeqMan.
Fastq files were processed using BD's Rhapsody analysis pipeline on the Seven Bridges Platform using default parameters, the GRCh38-PhiX-gencodev29 reference genome, and the gencodev29-20181205 transcriptome annotation. Processed data (molecules per gene per cell based on DBEC error correction) were analyzed using BD SeqGeq v1. 8 and the Seurat plug-in to perform dimensionality reduction (UMAP) and differential analysis output for selected cell subsets (CD4+ T cells, CD8+ T cells, NK cells, NKT cells, classical monocytes [CD14+, CD16-], intermediate monocytes [CD14+, CD16+], and non-classical monocytes [CD14 dim , CD16+]). The differential analysis expression ratios were uploaded to the Ingenuity Pathway Analysis server (QIAGEN, IPA) and converted to expression fold changes. To identify clusters of diseases or biological functions that are predicted to increase or decrease similarly across different datasets, IPA core analyses of the fold changes for the subsets were compared using the Diseases and Functions Heat Map (filtered by z-score >2).
111035003 or 115035003) diluted in 5% (wt/vol) of nonfat dry milk in 1X TBS-T. Protein bands were detected by chemiluminescence (Thermo Scientific, Cat. 32209, 34075, or 34095). Images were acquired using the C-Digit Blot scanner (LI-COR) and visualized in the Image Studio software (LI-COR). The following primary antibodies were used: anti-Arp2 (Abcam, Cat. ab128934); anti-Arp3 (Abcam, Cat. ab151729); anti-ARPC1A (Sigma-Aldrich, Cat. HPA004334); anti-ARPC1B (Sigma-Aldrich, Cat. HPA004832); anti-ARPC2 (Sigma-Aldrich, Cat. HPA008352); anti-ARPC3 (Sigma-Aldrich, Cat. HPA006550); anti-ARPC4 (Sigma-Aldrich, Cat. SAB2106287); anti-ARPC5 (Synaptic Systems, Cat. 305011); anti-ARPC5L (Abcam, Cat. Fluorescence microscopy of fibroblasts P1's or HC's fibroblasts were seeded in 6-well plates pre-coated with RetroNectin (Takara, Cat.
Real-time cell analysis of adhesion and spreading The rate and magnitude of adherence of fibroblasts was measured with the xCELLigence RTCA MP (Agilent, Santa Clara, CA). Briefly, E-plates 96 (Agilent, Santa Clara, CA) were background normalized after equilibrating the wells with 100 µL of complete media for 30 min. Following this step, 1 × 10 4 HC's or P1's fibroblasts were seeded and immediately placed onto the xCELLigence RTCA MP machine at 37 °C with 5% CO 2 to begin recording impedance every 1-2 min for 5 h and every 15 min thereafter. Impedance values were converted into the cell index by the RTCA software (Agilent, Santa Clara, CA) and plotted over time using the GraphPad Prism software version 8. 3. 0 (GraphPad, LLC). To measure spreading of neutrophils, isolated polymorphonuclear neutrophils (1 × 10 6 cells/ml in 20 µL HBSS with Ca 2+ and Mg 2+ buffer (Gibco, Cat.
Cell migration assays Fibroblasts migration was assessed with a wound healing (scratch) assay. Fibroblasts were seeded in a 12-well plate and serum-starved overnight. The next day, a scratch in the cell monolayer was generated with a pipette tip (20 μl tip, tip width 0. 94 mm, Thermo Scientific Art tips). Cells were washed 2x in PBS and from then on cultured in DMEM supplemented with 2% FBS and 2 mM L-glutamine. Cells were imaged right after scratching (t = 0), at 8 h, and 24 h after the scratch was made. Images were captured with a Lionheart FX Automated Live Cell Imager (Agilent BioTeK) using a 4× objective with the high contrast brightfield accessory. To improve visualization, digital phase contrast processing was applied to entire images using Gen5 Image+ software (Agilent BioTeK). Neutrophil chemotaxis was monitored across a 260 µm platform separating the "cell" well from the "chemoattractant" well using the EZ-TAXIScan (Effector Cell Institute, Tokyo, Japan).
Isolated neutrophils (5 × 10 3 cells in 1. 0 µl) were added to the "cell" well of the EZ-TAXIScan and 1. 0 µl of either buffer (0. 1% BSA in RPMI, 20 mM HEPES) or 5 × 10 -8 M fMLF (N-formylmethionyl-leucyl-phenylalanine, Sigma-Aldrich, Cat. F3506) was added to the opposing "chemoattractant" well. Digital images of the migrating PMNs were captured every 30 s for 1 h. Images were converted to stacks using the ImageJ software (version 1. 53t; NIH). Ten randomly selected cells were electronically traced using the ImageJ plug-in, MTrackJ and the sequential positional coordinates of individual migrating cells were determined as a function of time. The tracks of individual migrating cells were reconstructed and plotted with the position of each cell anchored at the origin at t = 0. Since data were collected with time and position, multiple parameters could be derivedoverall distance, directed distance (parallel to the chemoattractant, random distance (orthogonal to the chemoattractant), overall velocity, directed and random velocity vectors, and time-to-event analysis (number of cells completing migration and elapsed time).
20004), IL-6 (10 ng/mL, Peprotech, Cat. 20006), IL-7 (10 ng/mL, Peprotech, Cat. 20007), IL-15 (100 ng/mL, Peprotech, Cat. 20015), IL-21 (50 ng/mL, Peprotech, Cat. 20021), IFNα (10 ng/mL, Cell Signaling Technology, Cat. 8927), IFNγ (10 ng/mL, Peprotech, Cat. 30002), or IL-6/IL-6R alpha protein chimera (Hyper IL-6, 100 ng/mL, R&D Systems, Cat. 8954SR). Next, cells were washed, fixed with BD Cytofix fixation buffer (BD Biosciences, Cat. BD554655), washed again, then permeabilized with BD Phosflow Perm Buffer III (BD Biosciences, Cat. 558050). After washing, cells were stained with phospho-specific antibodies (see list below) for 1 h on ice. After two additional washes, cells were ready for acquisition. For STAT phosphorylation assays, monocytes were gated by FSC/SSC. All samples were acquired with BD FACSCanto II flow cytometer (BD Biosciences) using BD FACSDiva software.
Acknowledgements This work was supported by the  Intramural Research Program, National Institutes of Health Clinical Center and National Institute of Allergy and Infectious Diseases  (to S. D. R. ) and in part by federal funds from the  National Cancer Institute ,  NIH , under Contract No. 75N91019D00024  (to D. B. K. ). The content of this publication does not necessarily reflect the views or policies of the  Department of Health and Human Services , nor does mention of trade names, commercial products, or organizations imply endorsement by the  U. S. Government. The Laboratory of Human Genetics of Infectious Diseases  is supported by the  Howard Hughes Medical Institute , the  Rockefeller University , the  St. Giles Foundation , the  National Institutes of Health (NIH)  ( R01 AI127564-06  to J.
L. C. and A. P. ,  R21AI159728  to  B. Boi. & P01AI061093  to J. L. C. ), the  National Center for Advancing Translational Sciences (NCATS) ,  NIH Clinical and Translational Science Award (CTSA) program  ( UL1TR001866 ), the  French National Research Agency (ANR)  under the  "Investments for the Future" program  ( ANR-10-IAHU-01 ), the  Integrative Biology of Emerging Infectious Diseases Laboratory of Excellence  ( ANR-10-LABX-62-IBEID  to J. L. C. ), the  French Foundation for Medical Research (FRM)  ( EQU201903007798  to J. L. C. ), the  Square Foundation, Grandir -Fonds de solidarité pour l'enfance, Institut National de la Santé et de la Recherche Médicale (INSERM)  and the  University of Paris Cité. We acknowledge  Drs. Andrew Issekutz  and  Dimas Mateos-Corral  for their involvement in the care of P1.
Data availability Whole exome sequencing datasets generated and analyzed during the current study are available in NCBI's Sequence Read Archive with accession no. PRJNA889418. Single-cell RNA sequencing raw and processed data have been deposited in NCBI's GEO data repository with accession no. GSE215451. T-cell receptor β sequencing data have been deposited in the ImmuneACCESS database [https://doi. org/10. 21417/ CJNS2023NC]. Source data are provided with this paper.