BUSCADOR RECOLECTA

[Description of methods used for collection/generation of data] The long-term monitoring of plant cover of Doñana shrublands is part of a harmonised protocol for the Long-term Ecological Monitoring Program of Natural Resources and Processes targeting Terrestrial Vegetation. The general aim of this protocol is to monitor and assess the dynamics of the main dominant terrestrial and aquatic vegetation types of Doñana. For shrublands, percent cover is recorded annually starting from 2008 to the present (2022) by staff of the Monitoring Team by one sampling campaign per year during the flowering season (between March and May) in 21 permanent square plots (15x15m). Cover is measured using the line intercept method in 3 transects of 15 m length oriented from East to West and located at fixed points of 2.5, 7.5 and 12.5 metres at both sides of the plot. Using the line-intercept method, the coverage of each species is measured with a measuring tape, including the class age (adult or seedling) and the canopy status (green or dead). This method enables to calculate the percent cover for each species across the transect and for the whole plot, including data on class age and percent of dry and green canopy, additionally to the percent of bare soil, plant density, species richness and vascular plant diversity for every plot., [Methods for processing the data] The data was recorded in CyberTracker sequence. The protocol used has been supervised by researchers and the data have been validated by the members who performed the sampling. The raw data was processed with Excel and the percent coverage was calculated and unificated by species, life stage and state., Dataset are structured following well-established data formats. Three files are provided and they are related to each other with the variable eventID. The first file (icts-rbd-shrubPlantCover_event_202300207) contains the information of each event (time of occurrence, geographical coordinates, sampling effort, etc…); the second file (icts-rbd-shrubPlantCover_occ_20230207) contains the percentage of plant cover of shrubland species recorded in each site, numbers of individual recorded and taxonomic classification; the third file (icts-rbd-shrubPlantCover_mof_20230207) contains additional information (measurements or facts) of vegetation recorded in each transect, like average vegetation height., The long-term monitoring on plant cover of Doñana shrublands is part of a harmonised protocol for the Long-term Ecological Monitoring Program of Natural Resources and Processes targeting Terrestrial Vegetation. The general aim of this protocol is to monitor and assess the dynamics of the main dominant terrestrial and aquatic vegetation types of Doñana. For shrublands, percent cover is recorded annually starting from 2008 to the present (2022) by staff of the Monitoring Team by one sampling campaign per year during the flowering season (between March and May) in 21 permanent square plots (15x15m). Cover is measured using the line intercept method in 3 transects of 15 m length oriented from East to West and located at fixed points of 2.5, 7.5 and 12.5 metres at both sides of the plot. Using the line-intercept method, the coverage of each species is measured with a measuring tape, including the class age (adult or seedling) and the canopy status (green or dead). This method enables to calculate the percent cover for each species across the transect and for the whole plot, including data on class age and percent of dry and green canopy, additionally to the percent of bare soil, plant density, species richness and vascular plant diversity for every plot., We acknowledge financial support from National Parks Autonomous Agency (OAPN) between 2004-2007; Singular Scientific and Technical Infrastructures from the Spanish Science and Innovation Ministry (ICTS-MICINN); Ministry of Agriculture, Livestock, Fisheries and Sustainable Development from the Regional Government of Andalusia (CAGPDES-JA) since 2007; and Doñana Biological Station from the Spanish National Research Council (EBD-CSIC) since all the study period (2008-2022)., 1. icts-rbd-shrubPlantCover_event_20230207: eventID, institutionCode, institutionID, datasetName, collectionCode, eventDate, year, month, day, verbatimEventDate, eventTime, country, continent, countryCode, stateProvince, county, municipality, locality, locationRemarks, verbatimLocation, verbatimElevation, minimumElevationInMeters, maximumElevationInMeters, decimalLatitude, decimalLongitude, geodeticDatum, samplingProtocol, sampleSizeValue, sampleSizeUnit, samplingEffort and recordedBy. 2. icts-rbd-shrubPlantCover_occ_20230207: eventID, occurrenceID, collectionCode, occurenceTime, decimalLatitude, decimalLongitude, basisOfRecord, recordedBy, identifiedBy, scientificName, verbatimScientificName, kingdom, phylum, class, order, family, genus, specificEpithet, scientificNameAuthorship, taxonRank, organismQuantity, organismQuantityType, lifeStage and occurrenceRemarks. 3. icts-rbd-shrubPlantCover_mof_20230207: eventID, measurementID, measurementType, measurementValue, measurementUnit, measurementDeterminedBy, measurementDeterminedDate and measurementMethod., Peer reviewed

The long-term monitoring on plant cover of Doñana shrublands is part of a harmonised protocol for the Long-term Ecological Monitoring Program of Natural Resources and Processes targeting Terrestrial Vegetation. The general aim of this protocol is to monitor and assess the dynamics of the main dominant terrestrial and aquatic vegetation types of Doñana. For shrublands, percent cover is recorded annually starting from 2008 to the present (2022) by staff of the Monitoring Team by one sampling campaign per year during the flowering season (between March and May) in 21 permanent square plots (15x15m). Cover is measured using the line intercept method in 3 transects of 15 m length oriented from East to West and located at fixed points of 2.5, 7.5 and 12.5 metres at both sides of the plot. Using the line-intercept method, the coverage of each species is measured with a measuring tape, including the class age (adult or seedling) and the canopy status (green or dead). This method enables to calculate the percent cover for each species across the transect and for the whole plot, including data on class age and percent of dry and green canopy, additionally to the percent of bare soil, plant density, species richness and vascular plant diversity for every plot., The aim of this project is to provide information about the evolution of the conservation status of Doñana. To do that, it has been designed a monitoring program of the dynamic of natural processes and the distribution and abundance of species and communities. This monitoring is generating time series of data which is being used to analyse long-term trends.

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

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

Supplementary Table 5. Univariate Cox proportional hazards regression result of CMB on tumor growth duration 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 6. Univariate Cox proportional hazards regression result of CMB on overall survival 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 7. Defferentially expressed genes between Subtype 2 and Subtype 1 patients., 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