dataesr/scientific-paragraphs-categorization huggingface dataset:
Another method called "Smooth 2D manifold extraction from 3D image Stack" has also been recently introduced  [24]. This method provides a very robust approach to extract a 2D layer of signal preserving the local spatial relationship of the stack content. However, this is a parameter-free method, which does not allow the user to precisely specify which layer of signal will be extracted. The three-dimensional image analysis software Morpho-GraphX (MGX)  [25]  is the most versatile and accurate tool available at the moment among image analysis freewares to extract 2D cell contours. Nevertheless, the version currently available online for download requires specific hardware, and it can have a steep learning curve. Here, we introduce a new high-throughput method dedicated to cell contour extraction from 3D stacks (Surf-Cut) and compare it with MGX.
2D cell contour extraction with MGX Confocal stacks were opened with the open source software MorphoGraphX (www. morphographx. org; Fig. 1a ). In order for the process to work properly, the first slice of the stack should be the top of the outer side or the top of the surface of the sample relative to which you want to extract the signal. Then, for each confocal Z-stack, de-noising of the raw signal was performed using the "Gaussian Blur Stack" process with a 0. 3-pixel radius (in MGX, Process > Stack > Filters > Gaussian_Blur_Stack; Fig. 1b ). The edges of the confocal signal were obtained using the "edge detect" process (Process > Stack > Morphology > Edge Detect; threshold 10000, multiplier 2, adaptative factor for threshold 0. 3, fill value 15000; Fig. 1c ). A mesh was created using the "Marching Cubes Surface" process (Process > Mesh > Creation > Marching_Cubes_Surface; cube size 5 μm, threshold 5000; Fig.
2D cell contour extraction with SurfCut We developed a simple ImageJ macro that we named SurfCut (note that this is not related to the image segmentation method with the same name). The scripts as well as a more detailed step by step user guide are available at https://github. com/sverger/SurfCut (Zenodo DOI:https:// doi. org/10. 5281/zenodo. 2635737 [28] ) and in Additional files 1 and 2  [29, 30]. The macro has two modes: (1) "Calibrate," to manually find the proper settings for the cell contour extraction; this mode can also be used to process samples manually one by one (Fig. 2 ); (  2 ) "Batch," to run batch cell contour extraction on series of equivalent Z-stacks, using appropriate parameters as determined with the "Calibrate" mode. In order to run the macro, the script should be opened in Fiji (Plugins > Macros > Run…, and then select the "SurfCut.
Pavement cell analysis with PaCeQuant 2D cell contour images were created with both MGX and SurfCut methods. The PaCeQuant tool  [18]  from in the MiToBo plugin in Fiji was used to segment the output images, generate the ROIs for each cell, and quantify the cell shape parameters. A threshold of 2500 pixels was used to filter out the smaller cells during the segmentation step in order to exclude the guard cells. To compare the accuracy of the surface extraction in both methods, the acquired datasets were further analyzed using the R package PaCe-QuantAna  [18].
Results and discussion 2D cell contour extraction from 3D samples with MGX and SurfCut Here, we report a new method (SurfCut) to extract cell contours or specific thin layers of a signal at a distance from the surface of samples in 3D confocal stacks. The goal is notably to obtain the cell contours of the epidermal layer in a tissue, by extracting the signal from the epidermal anticlinal walls only. We compare these new methods with the 3D image analysis software Morpho-GraphX (MGX)  [25]. In MGX, a 3D triangle mesh is created from a confocal stack, corresponding to the edges of the sample's signal, and notably the surface of the sample (see the "Methods" section and Fig. 1a-d ). This mesh can then be used to crop the raw confocal signal at a chosen distance from the sample's surface to extract a thin layer of signal (Fig.
Therefore, as exemplified above, the associated error can become important for samples with high curvature. In contrast to MGX, the SurfCut-based workflow has the advantage to be much simpler to use and outputs rather similar results as MGX in many cases, the main limitation being the curvature of the sample (see Figs. 3  and 6 ). In addition, it is much easier for a biologist with little or no knowledge of programming to automatize in order to run in batch on multiple samples without manual processing (see Additional file 2). Last, SurfCut can be run with Fiji, a widely-used image processing software that does not require a specific graphics card.
Next, we tested whether these differences in segmented cell number would affect the distribution of pavement cell descriptors. Among the features that can be quantified using the PaCeQuant plugin, circularity indicates how similar a cell shape is to a circle (the maximum value of 1 corresponds to a perfect circle). In our sample set, we found that the circularity of the cell contours extracted with the MGX method was 0. 3868 ± 0. 1233 and for those extracted with the SurfCut script was 0. 3856 ± 0. 1247 (Fig. 5e ), revealing no statistical differences between the two tested populations (Wilcoxon rank-sum test p value = 0. 88). To push the analysis further, we also compared each of the 27 descriptors available with PaCeQuant (Fig. 5f  ). Despite more noticeable differences for some parameters, this comparison could not reveal any statistical differences between the two cell contour extraction methods (Fig.
6 ). This should be carefully considered by the user to determine whether more advanced software such as MGX is more suited for a given analysis. In addition, SurfCut can also be used to extract other types of signals, such as cortical microtubules, allowing a suppression of the background noise coming from the signal below. We could for instance combine a high-throughput cell contour as well as cortical microtubule signal extraction and use the cell contours for automated cell segmentation and generation of ROI within which cortical microtubule arrays were automatically analyzed using an automated version of FibrilTool (Additional file 2,  [33] ), overall yielding a very high-throughput cortical microtubule analysis in many samples. Finally, SurfCut can be a very useful tool for the 2D representation (from image-based screening protocols to publication figures) of 3D confocal data in which overlapping signal from different depths in the stack hinders the visualization of signal or structures of interest.
SurfCut has some similarities with the Python-based MerryProj tool  [21] , although they work in a different way. MerryProj used local transparency masks of various intensities around the confocal signal in order to render an image of the surface signal. In contrast to MGX or MerryProj, SurfCut is an ImageJ plugin and not a standalone software, and it does not require specific hardware. The SurfaceProject and LSM-W 2  [22, 23]  ImageJ plugins also represent very good alternatives. For instance, Surfa-ceProject can be used to extract a layer of signal independently from the surface of the signal, by manually placing points within the 3D stack. These points are then used to define a surface for signal extraction. It is thus more versatile than SurfCut for some cases but requires extensive manual processing of each image.
On the other hand, the "2D virtual cut" tool of LSM-W 2 is more similar to SurfCut. It uses a mask derived from the surface topology to create a virtual cut through the stack. However, it relies on the assumption that the maximum of fluorescence signal intensity is concentrated at the surface of the sample, which is not always the case. In addition, it can only be used for images in the "lsm" Zeiss proprietary confocal microscopy image format. Overall, despite some limitations, SurfCut brings ease of use and high-throughput capacities, while offering complementary advantages with other existing methods and more advanced software such as MGX, for most applications in cell and developmental biology.
Availability of data and materials The datasets generated and analyzed in this study are available in the Zenodo repository (https://zenodo. org/), DOI:https://doi. org/10. 5281/zenodo. 2577053  [34]. The script of the SurfCut macro and a more detailed step-by-step user guide are available at https://github. com/sverger/SurfCut [35], Zenodo DOI:https:// doi. org/10. 5281/zenodo. 2635737 [28].
RNA-Seq Analysis Pre-processing and normalization. The count matrix was derived from FASTQ files of wild-type, CCl 4 plus olive oil and pure olive oil samples using the web application BioJupies  [23]. The samples were normalized using the R package edgeR (version 3. 25. 8) [24]. Differential gene expression analysis. Differential gene expression analysis was performed with the R package limma (version 3. 39. 18) [25]. To extract the effect of chronic CCl 4 intoxication, we computed contrasts comparing the treated samples (CCl 4 plus olive oil) versus matched olive oil samples. While there are matched oil samples for month 2 and 12, there were no oil samples for month 6 available. As the oil effect was relatively constant across time we imputed oil expression values for month 6 by taking the arithmetic mean of month 2 and month 12.
Functional Genomics Analysis of the CCl 4 Signature Pathway analysis with PROGENy. Pathway activity scores were calculated with the functional genomics tools PROGENy  [26, 27]. While classical pathway analysis methods (e. g. , Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis) rely on gene sets containing genes of pathway members PROGENy exploits so called "footprint gene sets" containing not the pathway members but the most responsive genes upon corresponding pathway perturbation. PROGENy was applied on contrast using the moderated t-value as a gene-level statistic. Transcription factor (TF) analysis with DoRothEA. DoRothEA is a high-quality data resource of TF-target interactions (regulons)  [26, 28]. Coupling DoRothEA regulons with a statistical method allows to infer TF activity from the expression of its transcriptional targets.
We used as statistical method the function viper from the R package viper (version: 1. 17. 0) [29] that computes for each TF a normalized enrichment score that we consider as TF activity. DoRothEA in combination with viper was applied on contrasts using moderated t-values as a gene-level statistic. Gene Ontology (GO) term enrichment. We used the R package msigdf (https://github. com/ ToledoEM/msigdf) to query GO terms (biological process and molecular functions) from MsigDB. Gene set enrichment analysis was performed on contrasts using the R package fgsea (version 1. 10. 0) [30]  with 100,000 permutations. The moderated t-value was used as a gene-level statistic. Construction of consensus pericentral and periportal gene sets. Consensus pericentral and periportal gene sets were constructed by integrating pericentral and periportal gene sets from three independent studies  [31] [32] [33].
For each cell and lobule layer combination a probability is given that indicates the likelihood that a cell was originally located in the respective layer. We started our analysis pipeline by removing genes that were expressed in less than 15 cells (out of 1415). Subsequently, we normalized the sub-setted count matrix using the R package scran (version 1. 11. 27) [34]. To assign cells to a specific lobule layer we selected for each cell the layer with the highest probability yielding in a zonation table reporting the spatial distribution of all cells across the lobule. Given this zonation table we identified genes with significant monotonic expression changes across the lobule layer by applying the exact version of the Jonckheere-Terpstra test from the R package clinfun (version 1. 0. 15, https://cran.
r-project. org/web/packages/clinfun/index. html). Monotonically increasing genes (from layer 1-9) were considered as potential periportal and monotonic decreasing as potential pericentral genes. Only genes with an FDR ≤ 0. 001 were considered as pericentral and periportal gene set members. As the three independent studies were published within more than a decade we updated all MGI gene symbols to their current alias using the function alias2SymbolTable from the limma package (version: 3. 39. 18) [25]. The consensus pericentral and periportal gene sets contain only those genes that are reported in at least two studies. Characterization of the overlap of pericentral/periportal genes and the most responsive genes of CCl 4 treatment. In this analysis, the overlap of pericentral and periportal genes with the differentially expressed genes after CCl 4 treatment is characterized with over-representation analysis (ORA).
Image Analyses and 3D Reconstructions In order to provide an unbiased and quantitative description of histological slices, a problemspecific image analysis pipeline was developed. Depending on the utilized staining and after performing necessary preprocessing, various related features were segmented. These segmentations were verified by domain knowledge experts, serving as a gold standard. Subsequent measurements were then performed before producing the final summary statistic. Both ImageJ  [36]  and Matlab R2019a were utilized throughout the quantification process. The former was used for exploration and the later for developing dedicated programs. In the case of bright field scans and after obtaining satisfactory segmentation of a CYP-positive area as well as the associated corresponding background, a ratio was recorded.
Results: The approach was successfully validated in a state-of-the-art dataset from malignant melanoma, and despite multi-omics evidence for somatic mutations, mutated naturally presented HLA ligands remained elusive in HCCs. An analysis of extensive cancer datasets confirmed fundamental differences of tumor mutational burden in HCC and malignant melanoma, challenging the notion that exome-derived mutations contribute relevantly to the expectable neoepitope pool in malignancies with only few mutations. Conclusions: This study suggests that exome-derived mutated HLA ligands appear to be rarely presented in HCCs, inter alia resulting from a low mutational burden as compared to other malignancies such as malignant melanoma. Our results therefore demand widening the target scope for personalized immunotherapy beyond this limited range of mutated neoepitopes, particularly for malignancies with similar or lower mutational burden.
In HCCs, only a small proportion of about 10% of patients showed mutations potentially accessible for drug therapy  [28] , whereas preliminary data for ICP inhibitors showed objective response rates in 15-20% of patients combined with a manageable safety profile  [29] , making neoantigens in principle an interesting case for precision cancer medicine and the use of NGS. Hence, we performed unprecedented in-depth multiomics analyses encompassing whole exome and transcriptome sequencing, combined with proteome and HLA ligandome profiling in selected HCC patients aiming to obtain evidence for the natural presentation of exome-derived mutated HLA ligands, employing various strategies.
HLA ligandomics data analysis MS data obtained from HLA immunoprecipitates was analyzed using tools of the open-source software library for LC/MS OpenMS (2. 3)  [35]. Identification and post-scoring were performed using the OpenMS adapters to Comet 2016. 01 rev. 3  [36]  and Percolator 3. 1. 1 [37]  at a peptide spectrum match (PSM) false discovery rate (FDR) threshold of 5%. Database search was performed against a personalized version of the human reference proteome (Swiss-Prot, reviewed UP000005640), including the patient-specific mutanome without enzymatic restriction and methionine oxidation as the only variable modification.
Database matching HLA ligandome database queries refer to the in-house database (maintained at the Department of Immunology) encompassing > 300,000 unique HLA class I-eluted peptides identified through LC-MS/MS in diverse tissues (non-malignant samples as well as samples with pathologies including malignancies). Database matching was carried out using rSQL, querying for an exact string match of a wild-type ligand (WT lig ) corresponding to the respective predicted mutated neoepitope (PNE). All HLA class I allotypes of the HCC and Mel cohort were covered by respective samples in the database. Each sample containing the WT lig was counted as a separate match (further details are provided in Additional file 2). Besides neoepitopes, we additionally screened our HCC HLA class I ligandome dataset against cancer-testis antigens (CTAs) as deposited in the CTDatabase (http://www.
Proteomic data analysis MS data were processed with MaxQuant software suite v. 1. 5. 2. 8  [39]. Database search was performed using the Andromeda search engine  [40] , integrated into the Max-Quant framework. The human reference database was obtained from UniProt (taxonomy ID 9606, containing 91,646 protein entries and 285 commonly occurring laboratory contaminants) and concatenated with the patient-specific mutanome. Endoprotease trypsin was fixed as enzyme with a maximum of two missed cleavages. Oxidation of methionines and N-terminal acetylation were specified as variable modifications, whereas carbamidomethylation of cysteines was defined as a fixed modification. Initial maximum allowed mass tolerance was set to 6 ppm. Re-quantify was enabled. A FDR of 1% was applied at peptide and protein level.
Variant calling Reads were processed using the megSAP pipeline (https://github. com/imgag/megSAP) and the ngs-bits package (https://github. com/imgag/ngs-bits) by the Department of Medical Genetics and Applied Genomics (Tübingen, Germany). Reads were mapped against the Genome Reference Consortium Human Build 37 (GRCh37) using BWA-mem  [42]. Somatic variant calling was performed using Strelka and Strelka2  [43, 44]  or with a proprietary software (CeGaT GmbH, Tübingen, Germany). Somatic mutations were annotated using SnpEff 4. 1 k  [45]. Further details are provided in Additional file 2.
Peptide prediction To define the sample-specific mutated peptide search space (PSS), peptides of 8-11 amino acid length were constructed by sliding a shifting window of the peptide length over the affected mutated positions. Resulting peptides were filtered against the human proteome (UniProt UP000005640, derived: 02/29/16) and the Ensembl proteome reference (release 84, 04/27/2016) to exclude peptides contained within wild-type proteins. Transcript information was retrieved via BioMart, based on the stable database version of GRCh37 (http://feb2014. archive. ensembl. org). HLA-binding prediction was performed with SYFPEITHI  [47] , netMHC 4. 0  [48, 49] , and netMHCpan 3. 0  [50, 51]. The workflow was implemented using FRED2  [52]  (see Additional file 2 for further details).
Benchmarking HCC and melanoma (Mel) HLA ligandomics datasets To demonstrate the high sensitivity of our neoepitope identification pipeline, we additionally processed a publicly available dataset of somatic variants from five Mel patients as a reference  [24]. The numbers of Var ns and PNE in Mel (Fig. 1d ; red dots) showed remarkable differences from our HCC dataset (blue dots). Whereas in two cases, Mel samples showed comparable properties to the HCCs analyzed with respect to the numbers of Var ns and resulting PNE (Mel8, Mel16), these counts were substantially higher in the majority of Mel samples (Mel5, Mel12, Mel15). This resulted in an average number of 531 Var ns in Mel in comparison to only 66 Var ns in HCC, corresponding to an eightfold increased mutated peptide search space (PSS) in Mel.
Derived predicted neoepitopes amount to an average of 243 PNE in HCC in contrast to 1550 PNE in the Mel data (Fig. 2a ), resulting from a tenfold increased TMB in Mel (on average: 19. 06 ± 13. 97 per megabase; see Additional file 1: Table  S4 ). On a per patient basis (Fig. 2b ), the HCC dataset proved much more homogenous (PSS: ~2500 to 10,000; PNE: 111 to 382) than the Mel data, where the PSS ranged from 4000 to 84,000 (PNE: 169 to 3717). This was corroborated by analyzing datasets from The Cancer Genome Atlas (TCGA; https://cancergenome. nih. gov/) for both entities, showing a mean number (± SD) of Var ns of 90 ± 100 for HCC (n = 363) and 461 ± 761 for Mel (n = 467) (Additional file 3: Figure  S2 ). Assessing only the fraction of tumors with > 100 Var ns as suggested previously  [12] , this amounted to a share of 26% in HCC vs.
77% in Mel. Selecting the share of malignancies that exhibits a high TMB as defined by Goodman et al. , we observed merely 1. 5% of high TMB tumors among HCC vs. 32% among Mel  [62]  (Fig. 3 ). Employing our HLA ligandomics identification pipeline, we were able to reconfirm all of the NE lig that had been discovered previously by Bassani-Sternberg et al. in their MS dataset (Mel5 (n = 2); Mel8 (n = 1); Mel15 (n = 8))  [24, 63]. Furthermore, we discovered one additional NE lig for Mel12 and three additional NE lig for Mel15 that could be validated by matching spectra from synthetic peptides (see Additional file 1: Table  S6 ). Only one of those NE lig was discovered in a sample (Mel8) with properties comparable to our HCC cohort. Importantly, all other NE lig identified on MS level (10/11) were identified on Mel with high TMB (Fig.
2b ). Therefore, it can be stated that our comparatively homogenous HCC cohort, for which no NE lig could be discovered, differs substantially (by at least one order of magnitude concerning TMB) from the properties of Mel patients previously published  [24]. This notion is supported by a thorough comparison of both datasets as shown in Fig. 2 , as well as by comparisons with comprehensive TCGA datasets (Fig. 3  and Additional file 3: Figure  S2 ).
As one way for PNE prioritization, we propose a knowledge-based approach using previously measured wild-type HLA ligands (WT lig ). Hence, we assumed that the more frequently a WT lig was already detected as a natural HLA ligand by MS the more likely its corresponding NE lig counterpart should exist, provided that the mutation does not negatively impact its HLA-binding affinity, or the respective HLA allele was lost. To this end, we compared the number of database matches of all WT lig in HCC and Mel to an in-house database of HLA ligands measured over the last decades (Fig. 5 , Additional file 1: Table  S10  & Table  S11 ). Almost all of the malignancies assessed carry at least one mutation (Var ns ) that could potentially give rise to a PNE whose corresponding WT lig was previously measured multiple times as an HLA-eluted ligand by MS.
Interestingly, four of the 15 MS-detected NE lig in Mel support this approach, since also their corresponding WT lig produced multiple hits in our database, including GA-binding protein alpha chain (GABPA; 20 matches), synaptotagmin like 4 (SYTL4; 8 matches), nucleoporin 153 (NUP153; 2 matches), and outstandingly septin 2 (SEPT2; 298 matches). Moreover, the two pinpointed PNE prot in ALB (59 matches) and RECQL (17 matches) give rise to the two most frequently contained WT lig of the respective patients in our database. In addition, WT lig tensin 1/3 (TENS1/3; 54 matches; HCC027) and SPECC1L-ADORA (33 matches; HCC028) were detectable in the respective tumor HLA immunoprecipitates, proving that at least the wild-type sequence peptide is processed and presented on HLA. Speculatively, this might favor the presentation of their NE lig counterparts on HLA ligandome level, although it could not be detected by us.
The four mentioned WT lig from HCC (ALB; RECQL; TENS1/3; SPECC1L-ADORA) and two from Mel (SEPT2; SYTL4) have also been documented in the immune epitope database (https://www. iedb. org/ access date: September 2018), which might also guide the way. Ultimately, these results may question HLA ligandome level detection depth and call for establishing large community-based HLA peptidomics databases  [67, 68] , since individual attempts do not seem reasonable, given the extent of this task.