Resultados de investigación

List of Supplementary Tables 1. Supplementary Table 1: Metadata and WGS/WES specifications a. Supplementary Table 1a: Metadata b. Supplementary Table 1b: WGS/WES specifications c. Supplementary Table 1c: Sex and age at CLL diagnosis 2. Supplementary Table 2: Immunoglobulin gene rearrangements and oncogenic translocations determined by IgCaller 3. Supplementary Table 3: Mutations (SNV and indels) 4. Supplementary Table 4: Copy number alterations a. Supplementary Table 4a: List of copy number alterations b. Supplementary Table 4b: Candidate driver genes affected by copy number alterations 5. Supplementary Table 5: Structural variants 6. Supplementary Table 6: DNA methylation analyses a. Supplementary Table 6a: Metadata for samples with DNA methylation data b. Supplementary Table 6b: Differentially methylated CpGs between CLL and RT 7. Supplementary Table 7: Bulk ChIP-seq of H3K27ac and transcription factor analysis a. Supplementary Table 7a: Samples and metadata b. Supplementary Table 7b: Number of changes c. Supplementary Table 7c: Richter-specific common changes d. Supplementary Table 7d: Annotated differential expression genes in Richter-specific common regions e. Supplementary Table 7e: Transcription factors (expressed in RT) f. Supplementary Table 7f: Transcription factors (differentially expressed between RT and CLL) 8. Supplementary Table 8: Bulk ATAC-seq analyses a. Supplementary Table 8a: Samples and metadata b. Supplementary Table 8b: Number of changes c. Supplementary Table 8c: Richter-specific common changes d. Supplementary Table 8d: Annotated differential expression genes in Richter-specific common regions 9. Supplementary Table 9: Coding mutations in CLL and RT 10. Supplementary Table 10: CLL and lymphoma driver genes according to previous literature a. Supplementary Table 10a: Driver gene list b. Supplementary Table 10b: Regions considered for driver genes 11. Supplementary Table 11: Bulk RNA-seq analyses a. Supplementary Table 11a: Samples and metadata b. Supplementary Table 11b: Differentially expressed genes between RT and CLL c. Supplementary Table 11c: GSEA using hallmark gene sets d. Supplementary Table 11d: GSEA using curated C2 canonical pathways e. Supplementary Table 11e: Gene Ontology (GO) analysis 12. Supplementary Table 12: SNV identified in 147 CLL samples from the ICGC cohort and in 27 CLL posttreatment samples 13. Supplementary Table 13: Extraction and assignment of genome-wide mutational signatures a. Supplementary Table 13a: Single base substitution signatures extracted by HDP b. Supplementary Table 13b: Assignment of signatures extracted by HDP c. Supplementary Table 13c: Single base substitution signatures extracted by SignatureAnalyzer d. Supplementary Table 13d: Assignment of signatures extracted by SignatureAnalyzer e. Supplementary Table 13e: Single base substitution signatures extracted by SigProfiler f. Supplementary Table 13f: Assignment of signatures extracted by SigProfiler g. Supplementary Table 13g: Single base substitution signatures extracted by sigfit h. Supplementary Table 13h: Assignment of signatures extracted by sigfit i. Supplementary Table 13i. Comparison of SBS-RT with known signatures 14. Supplementary Table 14: Extraction and assignment of mutational signatures leading to clustered mutations a. Supplementary Table 14a: Single base substitution signatures extracted by HDP b. Supplementary Table 14b: Assignment of signatures extracted by HDP c. Supplementary Table 14c: Single base substitution signatures extracted by SignatureAnalyzer d. Supplementary Table 14d: Assignment of signatures extracted by SignatureAnalyzer e. Supplementary Table 14e: Single base substitution signatures extracted by SigProfiler f. Supplementary Table 14f: Assignment of signatures extracted by SigProfiler g. Supplementary Table 14g: Single base substitution signatures extracted by sigfit h. Supplementary Table 14h: Assignment of signatures extracted by sigfit 15. Supplementary Table 15: Fitting of genome-wide mutational signatures a. Supplementary Table 15a: Fitting of mutational signatures per sample (CLL/RT cohort) b. Supplementary Table 15b: Fitting of mutational signatures per sample (147 cases from the ICGCCLL cohort) c. Supplementary Table 15c: Fitting of mutational signatures per sample (27 CLL post-treatment) d. Supplementary Table 15d: Presence of SBS-melphalan in CLL/RT samples (mSigAct) e. Supplementary Table 15e: Fitting of mutational signatures per clone 16. Supplementary Table 16: Characterization of SBS-RT a. Supplementary Table 16a: SBS-RT in coding gene mutations b. Supplementary Table 16b: Activity of mutational processes on specific chromatin states (RTprivate mutations) c. Supplementary Table 16c: Enrichment of SBS-RT on specific chromatin states (RT-specific mutations) d. Supplementary Table 16d: Activity of mutational processes on early/late replication regions (RTprivate mutations) e. Supplementary Table 16e: Enrichment of SBS-RT on early-late replication (RT-private mutations) f. Supplementary Table 16f: Replication strand bias analysis in RT- private mutations g. Supplementary Table 16g: Transcriptional strand bias analysis in RT- private mutations 17. Supplementary Table 17: Subclonal reconstruction from WGS a. Supplementary Table 17a: MCMC sampler details and tolerated errors b. Supplementary Table 17b: Clusters identified c. Supplementary Table 17c: Abundance of clusters in each time point 18. Supplementary Table 18: High-coverage, UMI-based NGS analysis a. Supplementary Table 18a: Metadata b. Supplementary Table 18b: Targeted mutations c. Supplementary Table 18c: Design of the amplicon-based NGS panel d. Supplementary Table 18d: Results 19. Supplementary Table 19: Fitting of clustered mutational signatures a. Supplementary Table 19a: Kataegis identified in the ICGC-CLL cohort b. Supplementary Table 19b: Kataegis identified in the initial CLL (#1) and RT subclones c. Supplementary Table 19c: Fitting of mutational signatures in kataegis 20. Supplementary Table 20: Single-cell DNA-seq a. Supplementary Table 20a: Samples and metadata b. Supplementary Table 20b: Studied genes c. Supplementary Table 20c: Mutations identified by scDNA-seq (from Tapestri Insights) d. Supplementary Table 20d: Allele dropout and doublet rates e. Supplementary Table 20e: Count matrices (based on infSCITE) 21. Supplementary Table 21: Characterization of immunoglobulin heavy chain gene rearrangements using high-coverage NGS a. Supplementary Table 21a: Samples and summary (Lymphotrack, DNA-based) b. Supplementary Table 21b: IGH subclones identified in case 3495 at time point 1 (Lymphotrack) c. Supplementary Table 21c: IGH subclones identified in case 3495 at time point 2 (Lymphotrack) d. Supplementary Table 21d: IGH subclones identified in case 12 at time point 1 (Lymphotrack) e. Supplementary Table 21e: Samples and results (RNA-based) 22. Supplementary Table 22: Single-cell RNA-seq: metadata, QC, clusters, and marker genes a. Supplementary Table 22a: Samples and metadata b. Supplementary Table 22b: Marker genes for clusters of case 12 c. Supplementary Table 22c: Marker genes for clusters of case 19 d. Supplementary Table 22d: Marker genes for clusters of case 63 e. Supplementary Table 22e: Marker genes for clusters of case 365 f. Supplementary Table 22f: Marker genes for clusters of case 3299 g. Supplementary Table 22g: Number of cells per time point and cluster 23. Supplementary Table 23: Single-cell RNA-seq: patient-specific DEA and GSEA a. Supplementary Table 23a: DEA for case 12 (RT vs CLL) b. Supplementary Table 23b: DEA for case 19 (RT vs CLL) c. Supplementary Table 23c: DEA for case 63 (RT vs CLL) d. Supplementary Table 23d: DEA for case 365 (RT vs CLL) e. Supplementary Table 23e: DEA for case 3299 (RT vs CLL) f. Supplementary Table 23f: GSEA for case 12 (RT vs CLL) g. Supplementary Table 23g: GSEA for case 19 (RT vs CLL) h. Supplementary Table 23h: GSEA for case 63 (RT vs CLL) i. Supplementary Table 23i: GSEA for case 365 (RT vs CLL) j. Supplementary Table 23j: GSEA for case 3299 (RT vs CLL) 24. Supplementary Table 24: Respirometry assays in intact CLL and RT cells a. Supplementary Table 24a: Samples and metadata b. Supplementary Table 24b: Measurements c. Supplementary Table 24c: Summary 25. Supplementary Table 25: BCR signaling and cell growth assays in CLL and RT cells a. Supplementary Table 25a: Samples and metadata b. Supplementary Table 25b: Mean fluorescent ratio Indo-1(violet)/Indo-1(blue) c. Supplementary Table 25c: Flow cytometry gating strategy and proliferation results, Peer reviewed

Innate myeloid cell (IMC) populations form an essential part of innate immunity. Flow cytometric (FCM) monitoring of IMCs in peripheral blood (PB) has great clinical potential for disease monitoring due to their role in maintenance of tissue homeostasis and ability to sense micro-environmental changes, such as inflammatory processes and tissue damage. However, the lack of standardized and validated approaches has hampered broad clinical implementation. For accurate identification and separation of IMC populations, 62 antibodies against 44 different proteins were evaluated. In multiple rounds of EuroFlow-based design-testing-evaluation-redesign, finally 16 antibodies were selected for their non-redundancy and separation power. Accordingly, two antibody combinations were designed for fast, sensitive, and reproducible FCM monitoring of IMC populations in PB in clinical settings (11-color; 13 antibodies) and translational research (14-color; 16 antibodies). Performance of pre-analytical and analytical variables among different instruments, together with optimized post-analytical data analysis and reference values were assessed. Overall, 265 blood samples were used for design and validation of the antibody combinations and in vitro functional assays, as well as for assessing the impact of sample preparation procedures and conditions. The two (11- and 14-color) antibody combinations allowed for robust and sensitive detection of 19 and 23 IMC populations, respectively. Highly reproducible identification and enumeration of IMC populations was achieved, independently of anticoagulant, type of FCM instrument and center, particularly when database/software-guided automated (vs. manual “expert-based”) gating was used. Whereas no significant changes were observed in identification of IMC populations for up to 24h delayed sample processing, a significant impact was observed in their absolute counts after >12h delay. Therefore, accurate identification and quantitation of IMC populations requires sample processing on the same day. Significantly different counts were observed in PB for multiple IMC populations according to age and sex. Consequently, PB samples from 116 healthy donors (8-69 years) were used for collecting age and sex related reference values for all IMC populations. In summary, the two antibody combinations and FCM approach allow for rapid, standardized, automated and reproducible identification of 19 and 23 IMC populations in PB, suited for monitoring of innate immune responses in clinical and translational research settings., Peer reviewed

Data on soil organic carbon fractions in global drylands, European Research Council (ERC Grant agreement 647038 1004, BIODESERT).- Spanish Ministry of Science and Innovation (PID2020-116578RB-I00).- Generalitat Valenciana (CIDEGENT/2018/041), No

The file includes the data used in all the figures and supplementary figures, as well as the codes used for simulations and escape rate estimation. The data files are in .mat format. The codes are Matlab codes with the exception of the analytical estimate of the escape rate which is a Mathematica worksheet., This file contains the data and codes used in the paper “Confinement-induced accumulation accumulation and de-mixing of microscopic active-passive mixtures”, S. Williams et al, 2022., Grants: Agence Nationale de la Recherche: PSL - Paris Sciences et Lettres (ANR-10-IDEX-0001); ENS-ICFP - ENS- International Center for Fundamental Physics and its interfaces (ANR-10-LABX-0010), Peer reviewed

Supplementary Figure 1. Isolated EVs present typical size and topology. Supplementary Figure 2. Protein profiling from total cell lysates and their derived EVs from WT and KO-HDAC6 BMDCs. Supplementary Figure 3. Enrichment in acetylated and ubiquitinated DC proteins upon Lm infection. Supplementary Figure 4. Ubiquitination in K-48 and K-63 state in T lymphoblast total cell lysates and their derived EVs. Supplementary Figure 5. Pore filtration methods restrain Lm and do not induce strong antipathogenic responses. Supplementary Figure 6. IFN-β is detected following Lm infection. Table S1. List of antibodies used for Western-blot and Flow Cytometry and the used dilution. Table S2. List of primers, with their corresponding sequence, used for qPCR. Table S3: Protein quantification in total cell lysates Table S4: IPA analysis of total cell lysates: canonical pathways and diseases and functions category Table S5: Protein quantification in EVs Table S6: IPA analysis of EVs: diseases and functions category Table S7: Ubiquitinated and acetylated peptides in total cell lysates and EVs Table S8: Enrichment analysis of ubiquitinated and acetylated proteins, Peer reviewed

Table S3: Protein quantification in total cell lysates Table S4: IPA analysis of total cell lysates: canonical pathways and diseases and functions category Table S5: Protein quantification in EVs Table S6: IPA analysis of EVs: diseases and functions category Table S7: Ubiquitinated and acetylated peptides in total cell lysates and EVs Table S8: Enrichment analysis of ubiquitinated and acetylated proteins, Peer reviewed

Supplementary Table 1. Characteristics of samples in Trp53-null mammary tumor cohort., Mouse models of cancer provide a powerful tool for investigating all aspects of cancer biology. In this study, we used our recently developed machine learning approach to identify the cellular morphometric biomarkers (CMB) from digital images of hematoxylin and eosin (H&E) micrographs of orthotopic Trp53-null mammary tumors (n = 154) and to discover the corresponding cellular morphometric subtypes (CMS). Of the two CMS identified, CMS-2 was significantly associated with shorter survival (p = 0.0084). We then evaluated the learned CMB and corresponding CMS model in MMTV-Erbb2 transgenic mouse mammary tumors (n = 53) in which CMS-2 was significantly correlated with the presence of metastasis (p = 0.004). We next evaluated the mouse CMB and CMS model on The Cancer Genome Atlas breast cancer (TCGA-BRCA) cohort (n = 1017). Kaplan–Meier analysis showed significantly shorter overall survival (OS) of CMS-2 patients compared to CMS-1 patients (p = 0.024) and added significant prognostic value in multi-variable analysis of clinical and molecular factors, namely, age, pathological stage, and PAM50 molecular subtype. Thus, application of CMS to digital images of routine workflow H&E preparations can provide unbiased biological stratification to inform patient care., Peer reviewed

Supplementary Table 2. Characteristics of samples in MMTV-Erbb2 mammary tumor cohort., Mouse models of cancer provide a powerful tool for investigating all aspects of cancer biology. In this study, we used our recently developed machine learning approach to identify the cellular morphometric biomarkers (CMB) from digital images of hematoxylin and eosin (H&E) micrographs of orthotopic Trp53-null mammary tumors (n = 154) and to discover the corresponding cellular morphometric subtypes (CMS). Of the two CMS identified, CMS-2 was significantly associated with shorter survival (p = 0.0084). We then evaluated the learned CMB and corresponding CMS model in MMTV-Erbb2 transgenic mouse mammary tumors (n = 53) in which CMS-2 was significantly correlated with the presence of metastasis (p = 0.004). We next evaluated the mouse CMB and CMS model on The Cancer Genome Atlas breast cancer (TCGA-BRCA) cohort (n = 1017). Kaplan–Meier analysis showed significantly shorter overall survival (OS) of CMS-2 patients compared to CMS-1 patients (p = 0.024) and added significant prognostic value in multi-variable analysis of clinical and molecular factors, namely, age, pathological stage, and PAM50 molecular subtype. Thus, application of CMS to digital images of routine workflow H&E preparations can provide unbiased biological stratification to inform patient care., Peer reviewed

Supplementary Table 3. Characteristics of patients in the TCGA-BRCA cohort., Mouse models of cancer provide a powerful tool for investigating all aspects of cancer biology. In this study, we used our recently developed machine learning approach to identify the cellular morphometric biomarkers (CMB) from digital images of hematoxylin and eosin (H&E) micrographs of orthotopic Trp53-null mammary tumors (n = 154) and to discover the corresponding cellular morphometric subtypes (CMS). Of the two CMS identified, CMS-2 was significantly associated with shorter survival (p = 0.0084). We then evaluated the learned CMB and corresponding CMS model in MMTV-Erbb2 transgenic mouse mammary tumors (n = 53) in which CMS-2 was significantly correlated with the presence of metastasis (p = 0.004). We next evaluated the mouse CMB and CMS model on The Cancer Genome Atlas breast cancer (TCGA-BRCA) cohort (n = 1017). Kaplan–Meier analysis showed significantly shorter overall survival (OS) of CMS-2 patients compared to CMS-1 patients (p = 0.024) and added significant prognostic value in multi-variable analysis of clinical and molecular factors, namely, age, pathological stage, and PAM50 molecular subtype. Thus, application of CMS to digital images of routine workflow H&E preparations can provide unbiased biological stratification to inform patient care., Peer reviewed

Supplementary Table 4. Clinical characteristics of patients in TCGA-BRCA cohort, Mouse models of cancer provide a powerful tool for investigating all aspects of cancer biology. In this study, we used our recently developed machine learning approach to identify the cellular morphometric biomarkers (CMB) from digital images of hematoxylin and eosin (H&E) micrographs of orthotopic Trp53-null mammary tumors (n = 154) and to discover the corresponding cellular morphometric subtypes (CMS). Of the two CMS identified, CMS-2 was significantly associated with shorter survival (p = 0.0084). We then evaluated the learned CMB and corresponding CMS model in MMTV-Erbb2 transgenic mouse mammary tumors (n = 53) in which CMS-2 was significantly correlated with the presence of metastasis (p = 0.004). We next evaluated the mouse CMB and CMS model on The Cancer Genome Atlas breast cancer (TCGA-BRCA) cohort (n = 1017). Kaplan–Meier analysis showed significantly shorter overall survival (OS) of CMS-2 patients compared to CMS-1 patients (p = 0.024) and added significant prognostic value in multi-variable analysis of clinical and molecular factors, namely, age, pathological stage, and PAM50 molecular subtype. Thus, application of CMS to digital images of routine workflow H&E preparations can provide unbiased biological stratification to inform patient care., Peer reviewed