dataesr/scientific-paragraphs-categorization huggingface dataset:
This is done to reflect new (i. e. improved) localization precision and to avoid appearance of very bright pixels in the reconstructed images, which would happen if all localizations of the group were set to have exactly the same coordinates  21. The center of the refined distribution lies at the weighted mean position of the initial localizations, the weights of the initial localizations being proportional to their photon counts. This method improves the resolution of the reconstructed images, both visually and according to the FRC criterion (Fig. 2E ). After the refinement, the localizations with low photon numbers can be additionally removed, which further improves resolution (Fig. 2E ). This method of processing of consecutive localizations is now implemented in the software suite SharpViSu  33.
SplitViSu, a software for demixing of spectrally close fluorophores To integrate different aspects of fluorophore colocalization analysis into a convenient tool, we developed SplitViSu, a software for demixing SMLM data acquired with an image splitter. The software can be used as a stand-alone application for Windows, or can be run under Matlab or from SharpViSu  33. It allows for visualization of input data, flexible selection of the regions for each channel, automatic or manual alignment of localizations between the channels, pairing of localizations within a given distance, visualization of the paired localizations as a bivariate histogram of intensities, flexible assignment of the localizations to fluorophores based on the photon counts and their ratios, and estimation of cross-talk between unmixed localizations.
Discussion Simultaneous multi-color SMLM with spectral image splitting was demonstrated in the early days of super-resolution microscopy  18, 19  , but it did not spread much beyond experimental setups. In this work, we describe splitSMLM, an image splitting method for multi-color SMLM using a spectral image splitter, a dedicated software for spectral demixing (SplitViSu), a selected fluorophore triplet with 3 close wavelengths and an optimized buffer system that keeps all 3 fluorophores active in an equivalent manner. Thanks to simultaneous imaging and improved signal-to-noise ratios as essentially all photons are used, splitSMLM makes drift correction much easier, which is crucial for precise colocalization studies. The splitSMLM implementation is simple and robust and results in much improved localization precision, drift correction, compensation of chromatic aberrations at the nanometer scale and performance of fluorophores in optimized buffers.
Figs. S8  & S9 ). The optimized imaging buffer composition hence improves the photon budget and the amount of reactivation of far-red fluorophores, which is beneficial for splitSMLM. To analyse splitSMLM data conveniently, we developed a new demixing software, SplitViSu. This tool allows visualization of input data, flexible selection of the regions for each channel, automatic or manual alignment of localizations between the channels, pairing of localizations within a given distance, visualization of the paired localizations as a bivariate histogram of intensities, flexible assignment of the localizations to fluorophores based on the photon counts and their ratios, estimation of (unwanted) cross-talk between unmixed localizations (Suppl. Fig. S10 ). The software can be used as a stand-alone application under Windows, or can be run under Matlab or from SharpViSu 33 in which it was added as a plugin.
The demixing algorithm uses all detectable photons both for demixing and localization, improves the localization precision and corrects chromatic aberrations. The usage of similar wavelengths has the specific advantage here of reducing chromatic aberration thanks to the capability of the software to demix spectrally close fluorophores, which is improved even further by additional processing in SplitViSu. Moreover, we introduced the refinement of re-localization events, which further increases image resolution. Additionally, we show the benefits of an image splitting setup and fluorophore demixing also for single-labelled samples (Fig. 1B , Suppl. Fig. S6 ) thanks to filtering out localizations that originate from background noise or from autofluorescence. Unlike other software with some similar functionalities  22, 23  , the SplitViSu tool provides flexibility in calculation of output localizations, with the possibility of freely selecting options to use only coordinates from a selected channel or use their mean or weighted mean values, thus improving the localization precision and compensating chromatic aberrations.
Here we show with precise three-color imaging that it localizes very closely to the nuclear membrane and next to the NPC component Nup96 as visible in the "side views" (Fig. 4B ). Taken together, the analysis of NPC components Nup96, Nup62, Pom121 and lamin B1 illustrates the strength of our spectral demixing method for high-precision multi-color localization microscopy of challenging, multi-component cellular complexes. The splitSMLM method will be widely applicable, e. g. for a multitude of colocalization studies requiring multi-color SMLM and super-resolution imaging in biology. The SplitViSu software is made available under https://github. com/andronovl/SharpViSu.
Data processing The localization and fitting of single-molecule events were performed in the Leica LAS X software with the "direct fit" fitting method, using an appropriate detection threshold. The localization tables were then exported for further processing in the SharpViSu software workflow  33  and with customized Matlab procedures as implemented in SplitViSu. If imaged with the image splitter, the localizations were first demixed with the SplitViSu plugin and then processed in the main SharpViSu software for drift correction and relocalization on consecutive frames. Since the drift is the same for all unmixed channels, for drift correction in SharpViSu, we added possibilities to calculate the drift based on data in one of the channels or on the sum of data in all channels. The calculated by cross-correlation drift is then subtracted from localizations of each channel.
For the data in this paper, we calculated the drift based on the sum of localizations in all channels. For comparison between the imaging buffers (Suppl. Fig. S8-S9 ), all localizations were detected using a threshold of 25 photons/pixel in the LAS X wizard. After drift correction, the datasets were cropped to include only the surface of a nucleus with labelled nuclear pores (one nucleus per dataset). The photon counts and other localization statistics were calculated within these cropped regions, and the occurrence of localizations were normalized on the area of the corresponding regions, in order to get densities comparable between various datasets. All detected localizations within the region were assessed, without correction for relocalizations in consecutive frames and without any filtering.
For all other datasets, we used a search radius of 50 nm for finding the localizations in the consecutive frames with their subsequent refinement. The resulting super-resolution images were built as 2D histograms of the single-molecule coordinates. The emission spectra of fluorophores, observed in the λ S or the λ L channels, were calculated FRC resolution (Fig. 2E ) was calculated in SharpViSu 33 according to the FRC 1/7th criterion  36  , using the histogram image reconstruction method with a pixel size of 5 nm and 90 frequency steps. The calculation was repeated 10 times for each dataset and the resolution was represented as a mean ± standard deviation of the obtained values for the corresponding image. Scale bar, 100 nm. Figure legends.
Acknowledgements We thank  Yongrong Liao  and  Izabela Sumara  for anti-Pom121 antibodies;  Amelie Zachayus  and  Arnaud Poterszman  for the U2OS-NUP96-mEGFP cell line;  Yvez Lutz  ( IGBMC Imaging Platform ) for optimization of the labelling protocols & training. This work was supported by the  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INSB-05-01 , the  Alsace Region , the infrastructures Instruct-ERIC and iNEXT-Discovery, the  CNRS ,  Association pour la Recherche sur le Cancer (ARC) ,  Institut National du Cancer (INCa) , the  Fondation pour la Recherche Médicale (FRM) , and  USIAS of the University of Strasbourg  ( USIAS-2018-012 ).
6 kDa, pI = 12. 5) (Fig. 1 , A and B and S1A). A3Alt-L and A3Alt-S only differ by 18 N-terminal residues. The observation was further confirmed by in silico analysis using the OpenProt tool, designed to unravel cryptic proteins encoded within transcripts  (40) , predicting A3Alt proteins translation from A3A mRNAs (Fig. S1, B  and C ). To experimentally evaluate the differential translation from those four initiation codons, reporter constructs were generated fusing Firefly luciferase cDNA downstream of A3A 5 0 sequence, either in A3A or in A3Alt + 1-shifted reading frames (Fig. 1C ). AUG initiation codons were substituted by ACG by site-directed mutagenesis to estimate their relative contribution to protein expression (Fig. 1C ). Reporter constructs were then transfected in HeLa cells along with a Renilla luciferase coding plasmid used as transfection control.
A3Alt and A3Alt-S are mitochondrial proteins To help determine the functionality of those proteins, in silico analysis was conducted. Topology prediction was first performed using PSIPRED program  (41)  and both sequences were predicted to contain a central 21 amino acid transmembrane domain (Fig. 1 , A and B in blue and Fig. S3A  in pink). Subcellular localization prediction was next conducted using DeepLoc deep learning algorithms  (42) , locating A3Alt proteins to the mitochondria (Fig. S3B ). Mitochondrial addressing was further explored by motif search revealing that the A3Alt N-terminus may act as a mitochondrial targeting peptide, potentially cleaved by Mitochondrial Processing Peptidase (MPP) through an R3a/R3b mitochondrial cleavage site motif (RIFS) identified using TPpred2 software (Fig.
FACS analysis ROS production At 24 h post transfection, HeLa cells were incubated with 20 μM of the cell permeant 2 0 ,7 0 -dichlorofluorescine diacetate (DCFDA) reagent for 1 h at 37 C, following manufacturer's recommendations (DCFDA Cellular ROS Detection Assay Kit, Abcam). Cells were the washed twice in PBS, harvested, and analyzed by flowcytometry. Oxidative stress inducer tertiary-butyl hydroperoxide (TBHP) treatment was performed at a concentration of 100 μM for 30 min before DCFDA staining as positive control. Samples were analyzed on a MACSQuant Analyzer (Miltenyi Biotec) and data were processed with FlowJo software (Tree Star Inc, version 8. 7. 1).
FACS analysis of double-strand breaks At 48 h after transfection, cells were washed with PBS, fixed in 2 to 4% ice-cold paraformaldehyde (Electron Microscopy Sciences) for 10 min, and permeabilized in 90% ice-cold methanol (Sigma) for 30 min. After washing with PBS, cells were incubated with 1:100 diluted mouse Alexa Fluor 647-conjugated anti-V5 antibody (clone SV5-Pk1, Biorad) and 1:50 Alexa Fluor 488-conjugated rabbit monoclonal anti-γH2AX (Ser139, 20E3) antibody (#9719, Cell Signaling Technology) in PBS-BSA 0. 5% for 2 h. After PBS washings, DNA double-strand breaks were analyzed on a MACSQuant Analyzer (Miltenyi Biotec) and data were processed with FlowJo software (Tree Star Inc, version 8. 7. 1).
In addition, an infrared plasmonic biosensor was described that allows the detection of supported lipid bilayers and their real-time formation kinetics in an aqueous solution  [10]. The lipid bilayer formation on this plasmonic surface was monitored by considering the time evolution of the absorbance signal of specific vibrational bands of the lipid, CH 2. To best of our knowledge no literature data concerning the modification of the infrared signature of phospholipids integrated with nanoantennas were reported. However, organic model molecules were used to investigate the behavior of the line-shape of the vibrational signals of molecular layers adsorbed on Au nanoantenna, such as octadecanthiol  [13] , poly(methyl methacrylate)  [14] , 4,4 -bis(N-carbazolyl)-1,1 -biphenyl  [15, 16]. The vibrational signals of the molecules exhibit asymmetric Fano profile like shapes and spectral shifts in the absorption peaks, resulting from the strong interaction between the plasmonic resonances of nanoantennas and the molecular dipoles of the molecules.
AFM Characterization of the DPPTE Bilayers The immobilization of the DPPTE lipid via its thiol group on the thin film of gold and on the gold nanoantennas was controlled by AFM (Figure  2 ). 2D topography and cross-sectional view of these two gold substrates were performed before and after lipid immobilization followed by the rinsing step with chloroform. It is noted that the immobilization protocol of the lipid (see Materials and Methods section) used for the AFM measurements corresponds to the one used to obtain the IR data in Figures  3  and 4. surface roughness decreases from approximately 4. 09 to 2. 13 nm after heating the sample. Thus, the structure of gold nanoantenna was shown to be stable upon exposure to high temperatures. This is in line with previous studies  [20] , where for example for the 200 nm thick single and polycrystalline gold films, the dielectric function remains almost intact upon heating the sample from 23 to 100 °C in the wavelength range from 370 to 2000 nm.
AFM Characterization of the DPPTE Bilayers The immobilization of the DPPTE lipid via its thiol group on the thin film of gold and on the gold nanoantennas was controlled by AFM (Figure  2 ). 2D topography and crosssectional view of these two gold substrates were performed before and after lipid immobilization followed by the rinsing step with chloroform. It is noted that the immobilization protocol of the lipid (see Materials and Methods section) used for the AFM measurements corresponds to the one used to obtain the IR data in Figures  3  and 4. The AFM analysis of 7 nm thick gold substrate confirmed significant changes in root mean square roughness (RMS) values before and after lipid immobilization followed by the chloroform rinsing step (Figure  2A, B ). The RMS roughness increases from approximately 0.
AFM Imaging of the DPPTE Lipid Bilayers on Gold Substrates The Atomic Force Microscopy Multimode Nanoscope IV from Bruker (Palaiseau, France) was used to characterize the nanostructured gold surfaces (plain gold layer and nanoantennas substrates, both, with and without lipid bilayers). The plain gold layer and Au nanoantennas substrates were prepared using the immobilization procedure described above. The lipid immobilization on the substrates was observed before chloroform rinsing for control purposes (data not shown) and after chloroform rinsing. To probe the samples topography, a Peak Force Tapping mode (ScanAsyst) in dry state was selected using silicon nitride tips (SCANASYST-AIR from Bruker, having a spring constant "k" of around 0. 4 Nm -1 ). The images were obtained preferably along the fast scan axis at a scan rate of 1 Hz with a resolution of 512 × 512 pixels.
Organoid Derivation, Molecular Characterization and Correlation with Patient Tumor Data A total of 62 samples were collected from 38 patients with stage IV CRC, including 49 CRLM. Thirteen matched primary tumor samples were also collected from 9 patients (Details in Suppl. Figure  1 ). Of the 38 patients from which samples were obtained, 20 (53%) had received neoadjuvant chemotherapy (neoCT) at the time of surgical resection, while 16/38 (47%) were chemotherapy naïve. Details of patient demographics, clinico-pathological details and specific chemotherapy regimens are provided in Table  1  and  Suppl. Table  1. To allow investigation of resistance mechanisms ex-vivo, we generated PDTOs from chemo-naïve and post-neoadjuvant CRLM, as well as from primary tumors that matched a subset of the chemo-naïve CRLM.
Total RNA was then extracted from PDTOs and their parent tumors (N Pairs = 18) and analyzed using RNAseq. Within matched samples, the RNA abundance log-ratios were mostly centered around zero and as expected abundance data were highly correlated (r Spearman : 0. 68 to 0. 86), demonstrating good consistency between PDTOs and their parent tumors (Figure  1b , left and right panels respectively). Genes that were highly expressed in tumor samples (logRPKM > 5) and lowly expressed in PDTOs (logRPKM < 0. 05), were mostly genes associated with ECM/stroma (e. g. Collagens, VIM, ZEB1/2, FN1), and infiltrating immune cells (PTPRC, CD8A, CD3D/E/G, TIGIT, and LAG3). Similar to previous studies, a high level of genomic consistency was demonstrated between PDTOs and their parent tumor using WGS/WES  [14] [15] [16]  , in regards to the number, type and consequence of mutations (Figure  1c , left panel), the mutation patterns across highly mutated genes (HMG) (Figure  1c , right panel) and the copy number variation (CNV) patterns (Figure  1d ; Suppl.
641A>G, according to the IARC TP53 Database https://p53. iarc. fr/). In contrast, 60% (3/5) sensitive PDTO displayed loss of function variants within the DNA damage repair gene ATM (Figure  4a ). ATM loss of function mutations have been reported as common in CRC  25. ATM appears to be required for tumor cells to repair oxaliplatin-induced double-stranded DNA damage  26  , and IHC-based detection of ATM loss is associated with better overall survival following oxaliplatin-based treatment in CRC patients  27. Overall, TP53 and ATM mutations were mutually exclusive within our PDTO cohort , consistent with the TCGA data 17. Interestingly, 80% (4/5) FOLFOX-sensitive PDTOs were found to carry a missense variant in the FGD3 gene, which encodes an inhibitor of cell motility. High FGD3 expression has been found in oxaliplatin and CapeOX (capecitabine/oxaliplatin) resistant gastric cancer cells  28  and was recently shown to correlate with favorable prognosis in breast and other cancers  29.
The adaptor was ligated and the molecular barcode/index incorporated through amplification. The library was checked to ensure quality (Qubit, Bioanalyzer and KAPA Illumina library quantification kit using qPCR) prior to normalization and pooling before loading onto the Illumina NovaSeq 6000 for sequencing following Illumina's Sequencing by Synthesis technology. Each image was converted to a BCL file by Illumina software Sequencing Analysis Viewer. Upon completion of sequencing, each BCL file was converted to a FASTQ file using Illumina default pipeline for further analysis including alignment and annotation.
COMPUTATIONAL & STATISTICAL ANALYSES We used R version 3. 6. 1 to perform data analysis and visualizations. The versions and citations for all statistical methods or packages used in this paper are provided in Suppl. Table  11. Details of applying different method are given in the corresponding sections. For general data wrangling and visualization, we used the base R functions as we all as several core packages from the tidyverse R package, such as tidyr (v 1. 0. 0), dplyr (v 1. 0. 0), and readr (v 1. 3. 1) for reading and manipulating the data, and ggplot2 (v 3. 2. 1) for data visualization. Heatmaps were generated using the custom functions generated based on the complexHeatmap R package (Suppl. Table  11 ).
RNA-seq data analysis and DEG derivation in PDTO samples The strand specific (forward) 3' RNA-seq reads were aligned using HISAT2 against human genome GRCh37 release 75 version of Ensembl. We used featureCounts from the RSubread package to quantify the number of gene-level reads for each gene per individual samples. For the RNA-seq data from both PDTO and tumor samples, we examined the consistency between replicates using correlation and mean-average plots, and further examined the quality of the data using relative log expression (RLE) plots (Suppl. Figure  16 ) and principal component analysis (PCA) plots (Suppl. Figure  17 ). We used the filterByExpr function from the edgeR package to filter lowly expressed genes and calculated count-per-million (CPM) values. Differential expression analysis was performed using the voom-limma pipeline.
RNA-seq data analysis and DEGs derivation in tumor samples For the tumor data, all samples were sequenced together and therefore we did not have known batch effects in the data; however, as we had one outlier sample, we used voomWithQualityWeights function, whose results were used in duplicateCorrelation function when calculating the correlation between samples from the same patient by considering patient variable as the blocking variable. We then ran voomWithQualityWeights again, this time by providing the consensus correlation between samples from the same patient as well as the blocking argument (i. e. patients), followed by fitting of linear models and empirical bayes provided in the limma package. We considered genes with absolute log2FC > 1 and adjusted p-value < 0. 05 to define DEGs between PD vs CR, PR vs CR, and PD + PR vs CR.
Single-sample scoring We used the singscore R package to score samples against established molecular signatures and quantify concordance between the transcriptome of each sample with the given signatures  21. This approach is an unsupervized, rank-based method that does not need a list of DEGs or group labels, but instead calculates the normalized averaged mean-ranks of signatures genes within individual samples. This results in a signature score for each sample with a higher score associated with higher concordance of a sample to a given signature. Depending on gene set directionality, we used different settings of the simpleScore function as specified within the package documentation. Signatures used with singscore were collected from different sources, with the majority of the signatures were taken from the Hallmark, C2 and C6 categories in MSigDB  20.
All the cell cycle, spindle, and E2F related signatures were taken from MSigDB by searching for associated key words. Other signatures were extracted from the literature: DNA damage repair genes from Wood et al  30  , the TP53-negative signature from Markert et al  56  , pan-cancer stromal and immune signatures from Aran et al  57  , an NK signature from Cursons et al  58  , a T cell signature form Jerby-Arnon et al  59  , and an interferon signature from the imsig R package  60.
Variant calling and copy number variation analysis in WGS data For WGS data (N WGS = 36), trimming of sequence reads was carried out using Cutadapt, they were aligned to the reference GRCh37 assembly using BWA-MEM, coordinate-sorted with Samtools and duplicate-marked with Picard. The average depth of read in WGS data for the tumors was 58. 9X, organoids 36. 2X and normal 31. 8X. severity from the Ensemble database (https://asia. ensembl. org/info/genome/variation/prediction/predicted_data. html). We then only focused on the mutations on the canonical transcript for further visualizations, and for a small subset of genes with more than one canonical transcript, we annotated their variants as multi-hits (MHT). For CNV analyses, as FACETS does not give information for chromosome Y, we separately examined genes on the X chromosome for male samples to make sure that we do not annotate them as deletions.
Acknowledgements The authors are very grateful to staff from the  University of Melbourne  imaging ( Paul McMillan  and  Ellie Cho ) and Flow cytometry ( Vanta Jameson ) platforms, the  Peter MacCallum Cancer CAHM  ( Centre for Advanced Histology and Microscopy ) and the  Molecular Genomics Core  for their technical help during completion of this work. We also wish to acknowledge funding from the  CSSANZ Foundation Grant  (AGH and CB), the  PMCC Foundation Grant  (AGH and CB),  Covidien Grant  (CB), the  Tour de Cure Foundation  ( Senior Research Grant , FH) and the  NHMRC of Australia  (Project grants # GNT1164081 , FH). We also thank  Mr. Jamie Keck ,  Mr. Michael Johnston ,  Mr. Adrian Fox ,  Mr. Simon Banting ,  Mr. Jacob McCormick ,  Mr. Satish Warrier ,  Mr David Chan ,  Mr. Glen Guerra , and  Mr.