dataesr/scientific-paragraphs-categorization huggingface dataset:
There have been no previous studies investigating GzmA expression in CRC patients. In agreement with the results in the mouse model, we have found a clear correlation between GZMA and inflammation in two independent datasets comprising human tumors with different clinical and molecular characteristics. No such correlation exists between GZMB and inflammation. Although this does not necessarily imply a causal role of GZMA expression in inflammation, the combined results in mouse and human models strongly suggest a role for GzmA in regulating inflammation during CRC. We also analyzed whether GzmA could have a prognostic value in CRC patients but did not find correlation between GZMA expression and prognosis (measured as time to recurrence or overall survival) in our Colonomics database or in larger public databases (GEO: GSE39582 or the Human Protein Atlas).
These results suggest that GzmA would have a prominent role in the first stages of CR carcinogenesis, but once macroscopic tumors have been developed, other molecules would be more relevant for cancer progression. Supporting this hypothesis, other factors known to critically contribute to CRC development, such IL-6 or Cox-2, did not show prognostic value in the databases indicated earlier, suggesting that like GzmA, these molecules might contribute to cancer initiation, rather than progression. Indeed, recent data indicate that Th1-related inflammation in human cancer, including CRC, is a protective factor once tumors are established, suggesting that the detrimental effects of inflammation during tumor initiation might have opposing role once tumors are established  (Ponzetta et al. , 2019).
METHOD DETAILS Transcriptomics analyses in human samples Transcriptomic data from human CRC tissues were used to explore genes whose expression was associated with GZMA and GZMB expression. Spearman's correlation was calculated to generate a ranked gene list sorted by their level of co-expression with both genes. Then, the Gene Set Enrichment analysis (GSEA) algorithm  (Subramanian et al. , 2005)  was used on these pre-ranked lists to identify enrichment in specific cellular functions and pathways. Specifically, the Hallmarks gene sets were interrogated. The Gene Set Variation Analysis from GSVA R package was used to calculate an inflammatory score using the Positive Regulation of Inflammatory Response gene set from Gene Ontology (GO). This function performs a non-parametric, unsupervised analysis for estimating variation of the given gene sets through the samples in the expression matrix, returning an enrichment score for each sample.
Also, a heat-map was plotted with all genes included in the gene set for visualization purposes (Figure  S1A ). Spearman's correlation was calculated to assess if there is a relationship between the inflammatory score and the GZMA/GZMB expression. The same analysis was done stratifying samples into CMS subtyping. Linear regression models were also adjusted to calculate R-squared and p values in each case. To validate these results, an extended gene expression dataset was downloaded from GEO repository (GSE39582). This comprises 566 CRC human samples with distinct clinicopathological characteristics, which underwent surgery between 1987 and 2007. It includes patients from 0 to IV stage, both MSI and MSS and receiving different treatments  (Marisa et al. , 2013). Finally, tumors from both datasets were classify into the four consensus molecular subtypes CMS1 to CMS4 using the CMS classifier R package  (Guinney et al.
1 % Tween-20. For blocking, cells were incubated 2 hours at room temperature with NGBS (10 % Normal goat serum, 1 % BSA, 0. 1 % Cold fish gelatin, 0. 1 % Triton X-100, 0. 05 % Tween-20, 0. 05 % sodium azide)  (Francı ´et al. , 2006). Primary antibodies were diluted in PBS containing 1 % BSA (except in the case of SNAIL for which 1% of NGBS was added to the tissue culture supernatant of the hybridoma) and were incubated for 90 min at 37 C or o/n at 4 C. As secondary antibody, the corresponding HRP-labeled secondary antibody or anti-mouse or rabbit PowerVision TM (Leica Biosystems) were used. The HRP colorimetric substrate TMB (Vector) was used for visualization. Sections were counterstained with hematoxylin, dehydrated, and mounted. Positive cells were enumerated on 5 randomly chosen visual fields for each tissue sample with the aid of ImageJ software.
For Ki-67, the percentage of positively stained nuclei was determinates using the ImmunoRatio image analysis software, as an open source of ImageJ plugin or as a freely accessed online program for image analysis from immunohistochemistry  (Tuominen et al. , 2010). Immunohistochemistry stained slides were visualized and scanned on a ZEISS Axio Scan. Z1 at 20 X magnification. Quantitative real-time RT-PCR RNA extractions were carried out with the RNeasy mini kit (QIAGEN, Hilden, Germany) following the manufacturer's instructions, as previously described  (Matheus et al. , 2009). Total RNA was extracted from distal and proximal colonic tissue. cDNA was subsequently generated with SuperScript II reverse transcriptase (Invitrogen Life Technologies). Quantitative real-time PCR was performed with StepOne Plus PCR system (Invitrogen Life Technologies) in combination with specific primers for GZMA and COX-2 on the iQiCycler (Bio-Rad, Hercules, CA) according to a standard protocol (40 cycles, annealing temperature 60 C).
Gut microbiota composition Faces from both Wt and GzmA KO were collected gently perianal stimulation. After a complete mixing of the sample by vortex, total DNA was obtained using the QIAamp DNA Mini Kit (QIAGEN, Germany), adequate controls of quality and concentration, DNA samples were sent to the FISABIO institution (http://fisabio. san. gva. es) for massive sequencing of 16S rDNA amplicons of the V3 and V4 regions were amplified with the appropriate primers  (Quast et al. , 2013). The primers sequences are listed in the Key Resources Table. Taxonomic affiliations were assigned using SILVA_release_119 database  (Quast et al. , 2013). The statistical analysis was performed using R statistical software and open source libraries. The quantitative data of the reads were homogenized using their relative percentage from the total reads of each sample to facilitate the comparison between samples.
QUANTIFICATION AND STATISTICAL ANALYSIS Statistical analysis was performed using GraphPad Prism 5 software using the test indicated in the corresponding figure legends. Data are presented as mean ± SEM. The difference between means of unpaired samples was performed using the two-way ANOVA or the one-way ANOVA with Bonferroni's post-test or using un-paired t test. Survival curves were compared using both the log rank test and the Gehan-Wilcoxon test. The tumor incidence was analyzed using a Fisher's exact test. The results are given as the confidence interval (p), and are considered significant when p < 0. 05. Biological replicates are considered as the number of individual mice. In human tumor samples Spearman's correlation was calculated to assess the relationship between the inflammatory score and the GZMA / GZMB expression.
ACKNOWLEDGMENTS This work was supported in part by  FEDER/Gobierno de Aragon  (group  B29 ),  Ministerio de Economia y Competitividad  ( SAF2014-54763-C2-1  and  SAF2017-83120-C2-1-R  to J. P. and  SAF2014-54763-C2-2-R  to E. M. G. ), and  Instituto de Salud Carlos III  ( PI13/00864  to L. M. -L. ). Predoctoral grants/ contracts from  Fundacion Santander/Universidad de Zaragoza  (to L. S. and M. A. A. ),  Gobierno de Aragon  (to I. U. -M. , L. C. , L. S. , and P. J. -S. ), and  FPU/Ministerio de Educacion, Cultura y Deportes  (to P. M. L. ). I. U. -M. was supported by  Fondo Garantı ´a Empleo Juvenil/INAEM. M. A. A. has a  Juan de la Cierva  contract ( Ministerio de Ciencia, Innovacion y Universidades ). J. P. was supported by  Fundacion Aragon I+D (ARAID). The authors acknowledge the use of  Servicios Cientı ´fico Tecnicos del CIBA (IACS-Universidad de Zaragoza ) and  Servicios Apoyo Investigacion de la Universidad de Zaragoza.
Human samples Gene expression data from a set of 98 tumor tissues from CRC patients was used (Colonomics project: https://www. colonomics. org, NCBI BioProject PRJNA188519). Briefly, RNA extracted from each sample was hybridized in Affymetrix chips Human Genome U219 and analyzed under standard protocol  (Sanz-Pamplona et al. , 2014). Both raw and normalized data are available in the Gene Expression Omnibus (GEO) database through accession number GSE44076. All patients were stage II, MSS, treated with radical surgery and did not received adjuvant chemotherapy. They were recruited at the Bellvitge University Hospital (Spain) between 1998 and.
Immunofluorescence studies further revealed that S824-phosphorylated KAP1 accumulated in the cell nucleus upon heat shock (electronic supplementary material, figure  S4C ), as previously reported  [19]. Another set of inducible genes, including early growth response, immediate early response protein genes and the transcription factor JunB, were not deregulated in our datasets upon KAP1 removal at basal level, although several of them displayed royalsocietypublishing. org/journal/rstb Phil. Trans. R. Soc. B 375: 20190334 mild shifts in PolII distribution (figure  4c  and electronic supplementary material, figure  S4D ).
A limit of this work is the use of a qualitative description of sexual dimorphism and not a quantitative one, which we could have compared to the number of strata. Future work could explore strata formation in more species and gain sufficient statistical power to compare the number of strata to phenotypic data on sexual dimorphism in primates using trait-evolution phylogenetic methods, which requires large datasets. Evidence for the sexually antagonistic mutations hypothesis has been found in other organisms. In guppies, while the Y chromosome exhibits low levels of divergence from the X  (Wright et al. , 2017; Bergero et al. , 2019; Darolti et al. , 2019) , populations exhibiting stronger sexual dimorphism seem to have a larger non-recombining region  (Wright et al. , 2017; Wright et al. , 2019; Almeida et al.
Sampling We selected seven species covering as much phylogenetic diversity of Strepsirrhini as possible (see Supplementary file 1A). Both infra-orders (Lemuriformes and Lorisiformes) are equally represented. A male and a female individual were sampled for all species (except O. garnettii, the northern greater galago, for which sequence data from a female individual were retrieved from NCBI, see Supplementary file 1A). Blood samples of E. rubriventer (red-bellied lemur) and P. simus (greater bamboo lemur) were collected from living animals at Zoo de Lyon in EDTA blood collection tubes to avoid coagulation. Hair samples (with follicles and roots) of the female Daubentonia madagascarensis (aye-aye) were collected from a living animal at Zoo Frankfurt. Samples of M. murinus belong to the Brunoy laboratory (UMR7179, France; agreement E91-114-1 from the Direction Departementale de la Protection des Populations de l'Essonne): the biopsies were obtained from muscle tissues after the animals' natural death.
Coverage analysis Read depth was calculated for each sex at each position from the mapped reads on the de novo X using Samtools. The coverage for each sex was then normalised by dividing the depth at each position by the mean coverage depth for that species and sex on an autosome (chromosome four). The ratio of normalised male to female coverage was then calculated at each position and the data was summarised as a sliding window average using a window size of 150 kb sliding at increments of 10 kb or larger windows and increments depending on the species. This data manipulation was performed using AWK version 4. 1. 3. Analysis of the regions of the strepsirrhine X chromosomes with unusual male:female coverage ratio In Figure  1 , both lemur and loris X chromosomes exhibit regions with male:female coverage ratio close to 1 (shown in grey) in their X-specific parts, where a ratio of 0.
SNP density analysis To detect potential regions that may have stopped recombining between strepsirrhine X and Y chromosomes relatively recently, the difference in male to female SNP density was examined for all species. For each sex of each species, SNPs were called from the mapped reads using Samtools mpileup and then converted to profiles using sam2pro version 0. 8 from the mlRho package  (Haubold et al. , 2010). Specifically, sites with coverage <5 were excluded from the analysis and SNPs were called when a site had a minor allele frequency of 0. 3 times the site coverage. The ratio of male to female SNP density was calculated for 600 kb sliding windows at increments of 10 kb. 0. 001 was added to allow for a Log transformation and male to female SNP density was calculated at each window as Log(sum male SNPs) -Log(sum female SNPs).
Acknowledgements We thank the  Museum National d'Histoire Naturelle de Paris  for access to their collections of tissues and cryopreserved cells,  Fabienne Aujard  ( UMR7179   MECADEV -CNRS ,  MNHN, Brunoy, France ) for providing the grey mouse lemur samples and  Christina Geiger  from  Zoo Frankfurt  for providing the female aye-ayes samples. This work was performed using the computing facilities of the  CC LBBE/PRABI. We thank  Bruno Spataro  and  Stephane Delmotte  for the CC LBBE/PRABI management. We thank  Peter Kappeler ,  Qi Zhou ,  Mathieu Joron  and three anonymous referees for suggestions to improve this manuscript and  Alice Baniel ,  Lounes Chikhi ,  Laurent Duret  and HervePhilippe for discussions. RS, EL,  BCR , and GABM acknowledge the financial support by  ANR  (grant number  ANR-12-BSV7-0002-04 ).
Data and code accessibility All the data generated in this study is available at NCBI (project # PRJNA482296). Scripts for the entire coverage analysis pipeline (suitable for compute clusters using Torque job scheduling) are available on GitHub (https://github. com/rylanshearn/sex-read-depth; Shearn, 2018; copy archived at swh:1:rev:0e33f6b8158f4e1385af58117afeb762576cc0fb).
Image processing and data quantification Calibration of the cytosolic pH probe. To calibrate the probe, 2 mL of exponentially growing culture (0. 5 OD600) were centrifuged and resuspended in 200 μL calibration buffer (50 mM MES, 50 mM HEPES, 50 mM KCl, 50 mM NaCl, 200 mM NH4CH3CO2) at various pH from 5 to 8, and supplemented with 75 μM monensin, 10 μM nigericin, 10 mM 2-deoxyglucose, and 10 mM NaN3, as described previously  (Mouton et al. 2020 ). The cells were incubated in this buffer for 30min and then imaged in the microfluidic device to perform a ratiometric fluorescence measurement (Fig. S1I ). Image processing. The raw images were processed using Matlab-based software (PhyloCell) and custom additional routines  (Fehrmann et al. 2013; Paoletti et al. 2016). This software has a complete graphical interface for cell segmentation (based on a watershed algorithm), tracking (assignment cost minimization), and fluorescence signal quantification.
Data replicates. All measurements reported in this study are based on at least 3 replicates. Quantification of growth rate. The growth rate of the cells was computed using the evolution of the area of segmented cells (and buds) over time. Identification of growth phases. To determine the limit between successive growth phases, we used a piecewise linear fit to the evolution of total number of cells with time (D'Errico 2017). The time point of the intersection between two pieces of the piecewise linear fit was defined as the time of transition between the two corresponding phases (See Fig. S1G ). Globularization score. The Ilv3-mCherry globularization score was measured in each cell by calculating the mean intensity of the 5 brightest pixels of the cell minus that of the other pixels, as previously described  (Cai, Dalal, and Elowitz 2008).
Acknowledgments We thank  Sara Mouton  and  Liesbeth Veenhoff  for discussions and for sharing the pHluorin strain ahead of their publication,  Audrey Matifas  for constant technical support throughout this work,  Titus Franzmann  for fruitful discussions,  Sophie Quintin  and  Sandrine Morlot  for careful reading of the manuscript. This work was supported by the  Fondation pour la Recherche Médicale  (FRM, B. J, and G. C. ), the  Agence Nationale pour la Recherche  (T. A. and G. C. ), the grant  ANR-10-LABX-0030-INRT , a  French State  fund managed by the  Agence Nationale de la Recherche  under the  frame program Investissements d'Avenir   ANR-10-IDEX-0002-02.
Despite advances in single-cell and molecular techniques, it is still unclear how to best quantify phenotypic heterogeneity in cancer cells that evolved beyond normal, known classifications. We present an approach to phenotypically characterize cells based on their activities rather than static classifications. We validated the detectability of specific activities (epithelial-mesenchymal transition, glycolysis) in single cells, using targeted RT-qPCR analyses and in vitro inductions. We analyzed 50 established activity signatures as a basis for phenotypic description in public data and computed cell-cell distances in 28,513 cells from 85 patients and 8 public datasets. Despite not relying on any classification, our measure correlated with standard diversity indices in populations of known structure.
, 2019)  and uneven repartition of expressed transcripts per gene  (Hinohara et al. , 2018 ). Yet, there is no golden standard approach to quantify phenotypic diversity in cancer. Here we investigated the feasibility of predicting the activities that a single cell partakes in and the relevance of considering them as traits to describe the cell's overall phenotypic profile. We performed targeted single-cell experiments on three cellular activities induced in vitro (epithelial-mesenchymal transition, DNA repair, glycolysis), which suggested that targeted panels can reliably identify the presence of a given activity from single cell RNA expression data. To expand on this limited data, we then analyzed 50 hallmark activity signatures from the Molecular Signature database (MSigDB) in eight publicly available single-cell tumor datasets.
Whole Transcriptome Cell Activity Scores Following these targeted in vitro results supporting the feasibility of predicting the activities of single cells, we investigated the relevance of an activity-centered approach to quantify phenotypic diversity in high-throughput patient datasets. In the absence of single-cell inference methods tailored to each of the 50 hallmark cell activities, we used standard tools to investigate the behavior of the related signatures in patient data. We used the AUCell  (Aibar et al. , 2017)  software to score the enrichment of all MSigDB hallmark gene sets in all cells from eight datasets  (Fan et al. 2018; Filbin et al. 2018; Li et al. 2017; Neftel et al. 2019; Patel et al. 2014; Tirosh, Izar et al. 2016; Tirosh, Venteicher et al. 2016; Venteicher et al. 2017 ).
We normalized these data per set and merged them into a meta-dataset of 50 activity scores per cell in 28,513 cells from different cancer types (see Methods). No major batch effect could be observed as samples did not specifically cluster according to their sets of origin, whereas similar cell types appear to cluster together (Figure  2 ). However, the most common cell types (T cells, macrophages, and malignant cells) segregated into more than one cluster each. This suggests that cells with similar identity tend to behave similarly across batches and tissues but that different subset of activity profiles could also be observed among cells of identical classification. prior to single-cell analysis and RNA quantification. Targeted marker genes expression is used to assess the likelihood that an activity, considered as a phenotypic trait, is present in a cell.
Redundancy Reduction to Obtain Phenotypic Profiles We designed two methods to tackle redundancy, based on Principal Component (PC) and clustering analyses (see Methods). The first three PCs of the entire meta-dataset, respectively, explained 25. 9%, 12. 8%, and 7. 7% of the variance in the data, whereas 11 PCs explained more than 2% of the variance (Figure  3B ). For the clustering analyses, we investigated the relevance of splitting the data into 2-15 clusters. Using the consensus indices from bootstrapping experiments, we defined an optimal range between 6 and 10 clusters, after which increasing the number of clusters would not improve consensus (Figures  3C  and 3D ). We defined phenotypic profiles for each cell based on either the PC scores or the average activity scores per cluster. We analyzed the six sets that provided metadata describing the predicted (sub)type of each cell (see Methods), using leave-one-out procedures to prevent overfitting.
We investigated the pan-cancer relevance of our activity-based phenotypic divergence measure, using the six datasets for which cell type metadata were available. We report differences in cell-cell divergence distributions, according to whether two cells are of the same type or not and what that cell type is (Figures  4  and S11 ). In agreement with our pan-cancer observations that cells clustered by type more than dataset, the divergence between cells of different cell types was always the highest distribution (compared with sametype distributions) in all six datasets. This suggests that our metric will assign smaller divergence scores to cells from the same cell type. Using bootstrapped clustering analyses, we also investigated if different recurrent activity profiles could be observed among cancer cells only, in each set (Figures S12-S16, see Methods).
Clusters related to proliferation and immune response could be observed in most analyses, whereas the most discriminant activities, and PC scores derived from them, varied between datasets. In the Venteicher astrocytoma dataset, a discernible sub-population tied to immune activities can be distinguished on the left, with marked differences in interferon alpha and gamma signatures (Figure  5 ). A separate sub-population with strong proliferation signaling can be observed in the center, whereas cells on the right side do not display particularly strong proliferation or immune-related signal. This suggests that activity-based distances can separate distinct subpopulations of malignant cells presenting different phenotypic characteristics.
Phenotypic Diversity Quantification We further analyzed the relevance of activity-based approaches on two subsets with extended characterization in a large number of patients: 7 non-malignant cell types (T cell, B cell, Macrophage, Endothelial, Fibroblast, NK, Undefined) in 19 patients from the Tirosh melanoma dataset; 6 malignant subtypes (AC-like, OPC-like, MES1-like, MES2-like, NPC1-like, NPC2-like) in 28 patients from the Neftel glioma dataset. The average divergence in a group of cells was used as a surrogate for the group's phenotypic heterogeneity. We observed differences across the average profiles calculated for the distinct cell types, suggesting they are each characterized by specific activity patterns. The differences between the most divergent cells in each category, however, exemplify that individual cells can strongly deviate from these overall profiles (Figures 6A, 6B, and S17).
Such variability, possibly due to the stochastic nature of gene expression, would be absent from standard classifying methods. We proceeded to reclassify all cells according to the smallest Euclidean distance between their PCA-based profiles and the average profiles of each classification in both datasets. We observed a stronger concordance (p = 0. 022, Wilcoxon test) when reclassifying cells from established normal cell types in melanoma samples according to their activities (Figure  6C , 82% G 14 correctly reclassified samples), compared with subtypes of malignant glioma cells (Figure  6D , 54% G 23). This confirmed that cells of similar type tend to partake in similar activities. However, in the glioma samples we analyzed, the differences between marker-based malignant subtypes were not as closely reflected by activity profiles as was observed in normal cell types.