dataesr/scientific-paragraphs-categorization huggingface dataset:
Data and code availability RNA-seq, ChiP-seq and Hi-C data have been deposited at GEO and are publicly available as of the date of publication. Accession numbers are listed in the key resources table. Code availability: This paper does not report original code. Scripts used on this paper are available on https://github. com/PayreLab/manchenoferris_immarigeon_etal and https://github. com/ CuvierLab/Hic. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.
RNA extraction, sequencing on S2 cells RNAseq experiments were made on control, 1B and FS cells (GSE199511). Cells were harvested and RNA extraction was made with RNeasy Kit (Qiagen). Bank of reads and sequencing were done by HiSeq 200 (Illumina) at BGI to obtain 29-34M of reads (single-end 50nt) per replicates.
RNAseq, sequencing and quality check on intestinal cells (ISC, EB, eEC and mEC) RNAs were purified from each sample using MasterPure RNA purification kit, following manufacturer's recommendation (including the DNAse treatment). Final RNAs were resuspended in 10mL TE and added 1mL RiboGuard (RNases inhibitor). Construction of RNA banks and sequencing was made on the Montpellier GenomiX Platform. RNA banks were done with the Ovation SoLo RNAseq System kit (Nugen). Bank validation was done with the quantification of the complementary DNA with the Standard Sensitivity NGS kit on Fragment Analyzer and with qPCR (ROCHE Light Cycler 480). The sequencing was done on NovaSeq 6000 (Illumina) with NovaSeq Reagent Kits (100cycles). Singlereads of 100nt were sequenced. ISC= GSE199510, EB=GSE220323, eEC=GSE220558, mEC=GSE220560.
Hi-C Hi-C data pertaining to this study (GSE221863) were generated using the genome-wide Hi-C kit from Arima Genomic S2 cells. Hi-C analysis at high resolution (1kb) was performed for cells expressing either Svb-REP or Svb-ACT and performed as previously described  87  using pipelines available through Github https://github. com/CuvierLab/Hi-C data for analysis of 3D genomic interactions in Drosophila S2 cells. 2D plots (Figure  5A ) were obtained using Juicebox (https://www. aidenlab. org/juicebox/) at 5kb resolution, using the sqrt format coverage (sqrt).
ChIPseq analysis Data of ChIPseq for H3K4me1 (GSE63518), H3K4me3, H3K27ac (GSE36374), H3K27me3 (GSE93100), H3K9me2(GSE47229), H4K16ac (GSE94115) were used to analyze the chromatin landscape of Svb binding sites. ChIP sequencing of control, FS (REP) and 1B (ACT) S2 cells (single-end 50 nt) were performed with HiSeq 2000 (Illumina) at BGI (GSE199513). Histones ChIP sequencing (paired-end 50 nt) was performed at Montpellier GenomiX platform on NovaSeq 6000 (Illumina) with NovaSeq Reagent Kits (300 cycles). The H3K27ac ChIPseq bank construction was also done by Montpellier Genomix platform with TruSeqâ ChIP Sample Preparation (Illumina) (GSE199512). For all conditions, reads were mapped on dm6 genome (r6. 13 flybase) with bwa (v0. 7. 17-r1188)  70  with default parameters, unique reads were filtered and peak calling was done on replicates merge files with MACS2 (v2.
2. 7. 1)  71  for histone marks of databases (-nomodel -broad -f BAM -g dm -B -q 0. 00001) or for histone marks made in this study ( -nomodel -f BAMPE -g dm -B -q 1 e-05 -broad) and (-f BAM -B -g dm -to-large -nomodel -q 0. 0001 or -q 0. 1) for Svb forms. To obtain the merge replicates files, we used samtools merge. 85  Metrics and quality of ChIPseq peaks were calculated with FastQC (v0. 11. 8)  59  and MultiQC (v1. 14). 88  To standardize and make quicker the analysis Snakemake (v 5. 4. 5)  75  pipelines were done (available on Github: https://github. com/PayreLab/manchenoferris_immarigeon_etal). To compare the set of peaks, ChIPpeakAnno (v3. 24. 2)  76  packages and BEDtools 77 fisher (v2. 29. 2) were used. The ratio of Svb-Act and Svb-Rep ChIP-seq were calculated with a homemade python (v 3.
For both sets of bound regions, no significant difference was observed, p value = 0. 12. (G) DNA motifs being the most enriched in Svb-ACT peaks as defined by RSAT suite, the correlation score reflects result of a Pearson test between the motif detected and logos present in the RSAT database. 32 (H) Genome browser view of the Rhr locus showing respective intensity of Svb-ACT and Svb-REP ChIP-seq signal.
Because of poorer signal/noise ratio, data for H3K27me3 were not kept for analysis. (C) Intersection between Svb-ACT peaks and active enhancers identified by STARR-seq. 44  The Venn diagram shows the overlap between Svb-ACT-bound regions (green, n = 6939) and active enhancers (brown, n = 5,499). Metaprofiles of 6 kb centered on Svb peak midpoint show the average enrichment of Svb-ACT (green), STARR signal (brown), and H3K27ac (blue) for all active enhancers, or Svb-bound and Svb-unbound enhancers. (D) Genome browser view of a characteristic genomic region, showing the behavior of H3K27ac (GSM890121, Kc cells), H3K27ac (this study, S2 cells), and ATACseq (GSE103177, S2 cells) around STARR-seq identified enhancers (S2 cells), bound or not by Svb-ACT.
Acknowledgements We gratefully acknowledge the support received for this work from the  European Joint Programme on Rare Diseases  ( EJP RD ) under the project " UPS-NDDiag ," as well as the  Agence Nationale de la Recherche (ANR)  under the project  ANR-21-CE17-0005. Additionally, we extend our appreciation to  Mutuelles AXA  for their generous support. FE is a recipient of an I-site  NExT Junior Talent.
Spatial transcriptomics involves capturing the transcriptomic profiles of millions of cells within their spatial contexts, enabling the analysis of cell crosstalk in healthy and diseased organs. However, spatial transcriptomics also raises new computational challenges for analyzing multidimensional data associated with spatial coordinates. Depending on the technology used, the capture of transcript is either at a near single-cell level or at a subcellular level. This introduces corresponding challenges, such as inferring cell composition in regions covering multiple cells or segmenting z-stacked cells from images to assign transcripts to specific cells. In this context, we introduce a novel framework called CellsFromSpace. This framework allows users to analyze various commercially available technologies without relying on a single-cell reference dataset.
Based on the independent component analysis, CellsFromSpace decomposes spatial transcriptomic data into components that represent distinct cell types or activities. The framework provides a user-friendly graphical interface that enables non-bioinformaticians to perform a full analysis and to annotate the components based on marker genes and spatial distributions. The direct annotation of components, allows users to identify and isolate cell populations in the latent space, even when they overlap. Then, they can focus their studies on specific populations or analyze the proximity between different populations. Additionally, CellsFromSpace offers the capability to reduce noise or artifacts by component selection and supports analyses on multiple datasets simultaneously. Here, we demonstrate the efficiency of CellsFromSpace to successfully identifies spatially distributed cells as well as rare diffuse cells on datasets from the Visium, Slide-seq, MERSCOPE, and COSMX technologies.
10X Genomic's Visium technology, currently the most widespread commercial solution by publication metrics, outputs manageable data sizes at the cost of lower resolution compared to other techniques, with 55µm diameter spots typically encompassing 1-20 cells. Due to the "mini-bulk" nature of the technology, classical methods used in single cell analysis are less efficient and will cluster spots according to the cell mixture. In the worst-case scenario, such as the complex and unevenly spatially distributed tumor tissues, numerous clusters will be generated obfuscating the cell composition of each spot. Therefore, a deconvolution/decomposition step is often required to gain insight into the mixture of cells populating each spot. These deconvolution methods, at the exception of few  8  , are mostly scRNA-seq reference based  9, [10] [11] [12] [13] [14] [15] [16] [17] [18] [19]  , with known drawbacks such as the necessity for high-quality reference datasets and the loss of information.
Recently, Miller et al  8  , with STdeconvolve, proposed a reference free method to decompose the signal with Latent Dirichlet Allocation (LDA), a text processing technique, approximating genes to words and spot to documents. Their work presents several interesting developments but the known drawbacks of LDA (Sensitivity to noisy and sparse data -Difficulty to detect rare topics -Fixed numbed of topics) might limits its performance when applied to ST dataset. Here, we propose a new reference free decomposition framework for ST dataset, named CellsFromSpace (CFS) that overcomes some of the limitation of LDA. CFS is based on the independent component analysis (ICA), a blind source separation technique that attempts to extract sources from a mixture of these sources  20. ICA has been successfully applied to bulk transcriptomic data in hundreds of publications and shows the best performance over other methods to detect gene module from bulk RNA-seq  21.
Finally, the permissiveness of ICA regarding over-decomposition  24  is a significant advantage that allows to fix an arbitrary large number k with a minimum drawback of generating near identical components, easily corrected by component annotations and subsequent merging. Altogether, the characteristics of ICA in combination to an expert-supervised annotation and component selection, implemented in the CFS framework, enables: i) to overcome the identification of optimal number of components, ii) to select only biologically relevant components and remove the noise, iii) to study specific cell subtypes in the IC latent space, iv) to identify conserved signal though multi-sample ST datasets. When applied to ST analysis, CFS allows a biologically relevant decomposition of the spot mixture into its subcomponents responsible for common expression patterns observed within the tissue, such as cell type signatures, biological processes and tissue organisation.
CFS consists of a preprocessing pipeline and an easy-to-use Shiny user interface (UI) to analyze, annotate, visualize, and subset ST data. CFS was used to fully process sample datasets from multiple major sequencing-based ST technologies and was also applied to ISH ST as an intermediary tool for sample screening and identification of regions of interest. This highlights the flexibility and effectiveness of a semi-supervised ICA-based signal decomposition for the analysis of ST in healthy and diseased samples. The companion shiny interface enables non-bioinformaticians to quickly perform, without programming, a complete analysis of ST data from Visium, Slide-seq, MERSCOPE or CosMX dataset; and to generate Seurat  25  objects compatible with subsequent analyses.
CFS overview and use case The package is split into two main components: A) the package itself which includes functions to preprocess data loaded in Seurat by i) prepare_data which normalizes the count matrix, ii) RunICA which runs the fastica algorithm to separate the signal into independent components (IC), corrects the signs of ICs (by convention we consistently flip the sign to define the max weight as positive), and filters out ICs with a kurtosis below a user-defined threshold (default: 3), iii) Enrich_ICA which queries EnrichR databases to run and curate functional enrichment analyzes for individual ICs, and iv) in addition, functions to convert ISH ST data into a format compatible with CFS, and B) a Shiny UI to run the previously mentioned preprocessing steps and downstream annotation and analysis of ICA results.
Samples used The package efficiency was assessed using Visium reference sample datasets of Mouse Brain Coronal Section, Human Breast Cancer and Human Prostate Cancer. Slide-seqV2 mouse hippocampus sample dataset was obtained through the SeuratData package. MERSCOPE sample dataset MERFISH Mouse Brain Receptor Map was obtained from Vizgen (https://info. vizgen. com/mouse-brain-data). CosMX sample datasets and annotations of formalin-fixed paraffin-embedded (FFPE) human nonsmall cell lung cancer were obtained from nanostring (https://nanostring. com/products/cosmxspatial-molecular-imager/ffpe-dataset/).
Preprocessing pipeline All samples were pre-processed using the CFS package and the following pipeline. Samples were normalized using Seurat's sctransform function. Using the ICASpatial function, 100 ICs were calculated for the ICA analysis with 600 iterations using the Icafast method. Only leptokurtic ICs (kurtosis > 3) were retained for downstream processing and analysis. By convention, IC sign correction was then applied by flipping signs to define max IC weights as positive. The sign doesn't alter the interpretation of ICs, but we have empirically observed that this correction improves the interpretation of ICs by experts and better fit with the biology. Functional enrichment analysis of ICcontributing genes (defined as genes with feature loading absolute z-score ≥ 3) was done using the enrichR package's Show_Enrich function for the desired databases.
ISH data pseudospot binning To process ISH technologies' datasets within CFS, samples first needed to be converted into a pseudospot format. To do so, a count matrix was recreated using the reported detected transcript table from standard output using CFS's Create_vizgen_seurat or Create_CosMX_seurat functions (detected_transcript. csv for MERSCOPE and tx_file. csv for CosMX) with transcripts placed in grids of variable bin sizes (40x40 µm for MERSCOPE, 200x200 px or 24x24 µm for CosMX). Each pseudo-spot of this grid was then treated similarly to a Visium spot with the corresponding associated transcript pseudo-counts. A Seurat object was then created using this matrix as count matrix input and the pre-processing pipeline was run as described previously. For the MERSCOPE sample, pseudospots with less than five total transcripts detected were filtered out.
Visium reference data analysis The efficiency of the CFS workflow was first assessed by analyzing Spatial Gene Expression Visium datasets available from 10x Genomics. Visium fresh frozen and FFPE samples of adult mouse brain and human tumors were thus analyzed using our standard coding-free methodology directly in the Shiny application. For the FFPE mouse brain sample, after standard preprocessing with CFS (see Methods), 92 ICs passed the kurtosis threshold for downstream analysis. Taking advantage of the annotation tools included in the Shiny app, in half a day, 75 ICs were manually annotated by a biologist as relevant based on their distribution pattern or gene signature (Table  S1 ). Most ICs were directly associated with well defined brain structures, demonstrating the direct capture by ICs of spatially distributed gene co-expression.
02, Figs  s2d, e ) and revealed a subpopulation characterized by high IGFBP5, GSTP1 and GNAT3 and low SERF2 expression, a second with high TGM2, SERPINA3, IL32, UBD and ICAM1 and low SCGB1D2, SCGB2A2, AZGP1, MUCL1 and DBI expression, and a third with high SOD2 and low SCGB1D2 AZGP1 and DBI expression. For the prostate cancer sample, the same trajectory inference methodology (Figs  1u, s3a ) also identified 3 branches (Figs  s3b, c, d ), although less defined, and a glmnet analysis revealed the genes associated with these phenotypic paths and distinguished by their expression of ODC1, SPON2, ADGRF1, TSPAN8, CLDN3 and KRT8 among others (Figs  s3e, f ). With ICA, CFS efficiently identified distinct cancerous cell phenotypes within a tumor. Furthermore, it identified and distinguished stromal signatures such as immune infiltrates without the use of prior knowledge about the sample or reliance on external reference single cell datasets, which is particularly relevant for highly heterogeneous tissues such as cancer where reference atlases might not faithfully recapitulate the patient's cancer phenotype or tumor composition.
ISH-based ST data analysis ISH-based ST approaches are emerging as powerful technologies for detailed characterization of transcript expression. Commercial solutions are now available for the detection of hundreds to thousands of gene transcripts via multiplexed panels at subcellular resolutions with centimeter-scale capture areas, leading to the generation of enormous datasets with considerable computational and analytical challenges. One such challenge is the identifications of regions of interest (ROI) with a more manageable size for detailed analysis. We thus propose the use of CFS for FISH-based ST screening and ROI identification by signal binning. To that end, we tested the approach on both Vizgen MERSCOPE Mouse Brain Receptor Map and Nanostring CosMX FFPE human non-small cell lung cancer reference datasets.
ISH-based ST analysis with CFS requires a user-defined pseudo-spot generation step to ensure compatibility with the standard CFS workflow. This step was conducted as described in the Methods section and illustrated in Figure  3a. The choice of the pseudo-spot area size aims at striking a balance between signal resolution and computational cost. As show in Figure  3b , IC kurtosis distribution, used as a descriptor of IC super Gaussianity, decreased with the increases in pseudo-spot size, and their number. We found that for the MERSCOPE mouse brain dataset 40 µm bin sizes (1600 µm 2 bins) stroke the right balance between resolution and computational resources use for pre-screening application (Fig. 3b ). Interestingly, the total number of IC-defining genes was observed to increase with decreasing pseudo-spot sizes, ranging from 219 to 302 unique contributing genes, with 10 and 450 µm bin sizes respectively, out of the 649 probed genes for this dataset (Fig.
3c ). Without surprise, a higher resolution increased the number and resolution of clusters (Fig. s4 ). Most bin sizes between 20 and 90 µm appeared usable while, interestingly, a 10 µm resolution appeared unstable and overclustered in addition to being computationally impractical. Despite the relatively low number of genes probed in FISH-based approach compared to whole transcriptome sequencing approaches, CFS was able to isolate structure and cell type-specific signal in the analyzed samples. For instance, the MERSCOPE mouse brain dataset at a 40 µm bin size yielded 100 leptokurtic ICs (min 12,78, max 588. 60, Fig. 3d , Table  S9-   S9 ). The use of a limited set of targeted genes in ISH-based methods as opposed to whole transcriptome thus does not appear to impair the ability of ICA to identify cell-and ontology-specific signal (Table  S10 ).
The interpretation of the ICs can however be more imprecise based on the limited number of contributive genes (Fig. 3f ). (IC 25, bottom) showing their distinct localization on the UMAP space, suggesting this level of resolution is sufficient to limit cell mixtures within pseudospots and capture specific cell populations. (f) Heatmap of contributive genes associated to the ICs in (e) from the 649 genes probed in the MERSCOPE experiment. The distribution of publicly available cancer datasets from the CosMX SMI platform by Nanostring also allowed for the evaluation of the modified CFS pipeline on FISH-based non-structured tumor tissue to assess the ability of ICA to deconvolute spatial signals of a more restricted dimensional nature (980 probed genes). The CosMX NSCLC dataset is comprised of multiple samples from distinct presentations of non-small cell lung cancer from different donors (5 donors, 8 samples) allowing for integrated sample analysis using the CFS workflow.
Manual IC annotation enables the rapid and reference-free identification of cell types and states within samples and, among other things, the identification of genes with similar spatial distributions, defined as IC contributive genes. Manual curation also allows for the elimination of non-specific, overspecific, redundant, or uninterpretable ICs, thus denoising the dataset with a minimal loss of relevant signals thanks to the independent property of ICA. While we believe in remaining in the latent ICA space for data interpretation and analysis, CFS also implements the traditional workflow used in single cell transcriptomics and ST, which collapses the latent dimensions into clusters and twodimensional projections for easy data visualization (UMAP, t-SNE, etc. ) and downstream differential gene expression analysis.
Conclusion In this work, we presented a new framework and tool for the analysis of spatial transcriptomics data. CellsFromSpace is versatile with its support for all commercially available ST technologies, independent of high-quality reference datasets, easy to use with its Shiny UI, compatible with other single cell and ST analysis packages, and easily allows for the integrated analysis of multiple samples. We hope CFS will increase the accessibility and ease of ST data analysis to researchers and improve data interpretation. Y  for full list of contributive genes). Spatial (f) and UMAP (g) projections of spot clustering using louvain algorithm at a 1. 2 resolution with all filtered and annotated ICs as input with cancerassociated clusters highlighted in the UMAP projection. (h) Kernel density projection on the spatial embedding of Cancer-associated signal (based on sum of cancer IC weights, see fig.
Acknowledgments We warmly thank  Mrs. Marie-José Ghoris  her technical support. We the laboratories of  Dr. Erin L. Davies  and  Dr. Alexandr Blinov  for independently testing the novel methods of dsRNA production in bacteria and single soak RNAi in M. lignano, and providing their feedback. We thank  Dr. Elizabeth Werkmeister  and  Sophie Salome-Desnoulez  from the  BiCel imaging facility in Lille  for their precious support in confocal microscopy and image reconstitutions. This work has been funded by the  Dutch Research Council  grant to EB (grant number  OCENW. KLEIN. 054 ) and by  CNRS ,  INSERM and University of Lille  recurring endowment ( UMR9017-U1019 ) to JV.
Comparison of ADAE GUI and pHusion. We were motivated to develop this more robust analysis tool because we found that ADAE GUI failed frequently for new users, in different cell types and/or for data sets acquired by distinct imaging paradigms. For example, when we changed our camera from an EMCCD to a high-speed sCMOS, the analysis failed completely for all movies over 4. 29GB, as well as for unknown reasons on smaller datasets. For those cells for which results were returned, the set of frequencies calculated was far more varied with several clearly spurious results (Figure  2 , sCMOS GUI). Using pHusion we identified consistent and comparable exocytic frequencies in murine cortical neurons expressing VAMP2-sepHluorin that resulted in no failures regardless of camera type (Figure  2 , pHusion).
Discussion Due to the complexity and volume of image-based datasets generated by modern microscopy systems, computer-vision based approaches for analysis are warranted and, in some cases, essential. Though valuable, designing automated methods for quantitation of visual data is challenging and often results in brittle methods that work only for specific data sets. This is the problem we faced with automated analysis of exocytosis using ADAE GUI. Our initial processing scheme worked very well for the data on which it was developed, but failed frequently when used to analyze even slightly divergent datasets. To develop a more versatile and robust application we used ImageTank, software that seamlessly merges numerical methods and visualization of all data types from 3D image stacks to 1D data tables.