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Supplementary Table S10. Comparative analysis of -omics platforms Supplementary Table S10. Comparative analysis of -omics platforms Supplementary Table S7.xlsx Supplementary Table S8.xlsx Supplementary Table S9.xlsx, Mass spectrometry (MS)-based proteomics profiling has undoubtedly increased the knowledge about cellular processes and functions. However, its applicability for paucicellular sample analyses is currently limited. Although new approaches have been developed for single-cell studies, most of them have not (yet) been standardized and/or require highly specific (often home-built) devices, thereby limiting their broad implementation, particularly in non-specialized settings. To select an optimal MS-oriented proteomics approach applicable in translational research and clinical settings, we assessed 10 different sample preparation procedures in paucicellular samples of closely-related cell types. Particularly, five cell lysis protocols using different chemistries and mechanical forces were combined with two sample clean-up techniques (C18 filter- and SP3-based), followed by tandem mass tag (TMT)-based protein quantification. The evaluation was structured in three phases: first, cell lines from hematopoietic (THP-1) and non-hematopoietic (HT-29) origins were used to test the approaches showing the combination of a urea-based lysis buffer with the SP3 bead-based clean-up system as the best performer. Parameters such as reproducibility, accessibility, spatial distribution, ease of use, processing time and cost were considered. In the second phase, the performance of the method was tested on maturation-related cell populations: three different monocyte subsets from peripheral blood and, for the first time, macrophages/microglia (MAC) from glioblastoma samples, together with T cells from both tissues. The analysis of 50,000 cells down to only 2,500 cells revealed different protein expression profiles associated with the distinct cell populations. Accordingly, a closer relationship was observed between non-classical monocytes and MAC, with the latter showing the co-expression of M1 and M2 macrophage markers, although pro-tumoral and anti-inflammatory proteins were more represented. In the third phase, the results were validated by high-end spectral flow cytometry on paired monocyte/MAC samples to further determine the sensitivity of the MS approach selected. Finally, the feasibility of the method was proven in 194 additional samples corresponding to 38 different cell types, including cells from different tissue origins, cellular lineages, maturation stages and stimuli. In summary, we selected a reproducible, easy-to-implement sample preparation method for MS-based proteomic characterization of paucicellular samples, also applicable in the setting of functionally closely-related cell populations., Peer reviewed

Additional file 1: Table S1. List of primer sequences used for RT-PCR analysis. F: Forward primer. R: Reverse primer., Instituto de Salud Carlos III Gerencia Regional de Salud, Junta de Castilla y León Asociación Española Contra el Cancer (AECC) Consejería de Educación, Junta de Castilla y León, Peer reviewed

Additional file 2: Fig. S1. Efficiency of cleavage reaction. (A) XBP1 and (B) BLOC1S1 mRNA levels measured by qRT-PCR using the cDNAs synthesized with oligo (dT), and primers mapping the cleavage site of IRE1 from mock and IRE1-treated samples., Instituto de Salud Carlos III Gerencia Regional de Salud, Junta de Castilla y León Asociación Española Contra el Cancer (AECC) Consejería de Educación, Junta de Castilla y León, Peer reviewed

Additional file 4: Fig. S2. Reactome pathway analysis using RNA-seq data. Bar chart representing the most significantly enriched pathways. FDR ≤ 0.05., Instituto de Salud Carlos III Gerencia Regional de Salud, Junta de Castilla y León Asociación Española Contra el Cancer (AECC) Consejería de Educación, Junta de Castilla y León, Peer reviewed

Additional file 5: Fig. S3. Validation of putative IRE1 substrates. Exon-usage plots of the 28 remaining putative mRNAs, showing the number of reads in mock (red) and IRE1-treated (blue) samples. The black arrows represent the site of primers used in the 5´ region of the putative IRE1-substrates. Red arrows represent the site of primers mapping the predicted cleavage site. Right panel of each exon-usage plot shows the abundance of mRNA in the corresponding target. All results are presented as the means ± SD of three experiments. (*p < 0.05, **p < 0.01, ***p < 0.001)., Instituto de Salud Carlos III Gerencia Regional de Salud, Junta de Castilla y León Asociación Española Contra el Cancer (AECC) Consejería de Educación, Junta de Castilla y León, Peer reviewed

Additional file 7: Fig. S5. Synergistic effect of ER-stress inducers and IMiDs treatment in MMCLs. (A) H929 and (B) MM1S cells were exposed for 48 h to the indicated concentrations of ER- stress inducers and IMiDs, and cell viability assay was assessed by MTT. CI values less than 1 indicated a synergistic effect. These values were calculated using Compusyn Software. C: control (untreated cells). IMiDs; poma (pomalidomide) or lena (lenalidomide). ER inducers; Tm (tunicamycin) or Tg (thapsigargin)., Instituto de Salud Carlos III Gerencia Regional de Salud, Junta de Castilla y León Asociación Española Contra el Cancer (AECC) Consejería de Educación, Junta de Castilla y León, Peer reviewed

5 Pág., Plant recruitment interactions (i.e., what recruits under what) shape the composition, diversity, and structure of plant communities. Despite the huge body of knowledge on the mechanisms underlying recruitment interactions among species, we still know little about the structure of the recruitment networks emerging in ecological communities. Modeling and analyzing the community-level structure of plant recruitment interactions as a complex network can provide relevant information on ecological and evolutionary processes acting both at the species and ecosystem levels. We report a data set containing 143 plant recruitment networks in 23 countries across five continents, including temperate and tropical ecosystems. Each network identifies the species under which another species recruits. All networks report the number of recruits (i.e., individuals) per species. The data set includes >850,000 recruiting individuals involved in 118,411 paired interactions among 3318 vascular plant species across the globe. The cover of canopy species and open ground is also provided. Three sampling protocols were used: (1) The Recruitment Network (RN) protocol (106 networks) focuses on interactions among established plants ("canopy species") and plants in their early stages of recruitment ("recruit species"). A series of plots was delimited within a locality, and all the individuals recruiting and their canopy species were identified; (2) The paired Canopy-Open (pCO) protocol (26 networks) consists in locating a potential canopy plant and identifying recruiting individuals under the canopy and in a nearby open space of the same area; (3) The Georeferenced plot (GP) protocol (11 networks) consists in using information from georeferenced individual plants in large plots to infer canopy-recruit interactions. Some networks incorporate data for both herbs and woody species, whereas others focus exclusively on woody species. The location of each study site, geographical coordinates, country, locality, responsible author, sampling dates, sampling method, and life habits of both canopy and recruit species are provided. This database will allow researchers to test ecological, biogeographical, and evolutionary hypotheses related to plant recruitment interactions. There are no copyright restrictions on the data set; please cite this data paper when using these data in publications., Fondo Europeo de Desarrollo Regional, Grant/Award Number: ICTS-2017-08-CSIC-4; SUMHAL, Grant/Award Numbers: 418RT0555, 501100011033, LIFEWATCH-2019-09-CSIC-13, MCIN/AEI/10.13039, PGC2018-100966-B-100, PID2020-113157GB-I00, POPE 2014-2020, Peer reviewed

Additional file 1: Supplementary Figure 1. Schematic flow chart representation of the genomic and proteomic approaches used to identify cell surface proteins in TNBC. Supplementary Figure 2. A) The table shows the data generated form the microarray analyses to identify cell surface proteins upregulated in TNBC. B) Venn diagram showing the number of genes specifically identified in each array and those that are common among them. Supplementary Figure 3. A) Procedure used to obtain enriched plasma membrane microsomal fraction, used to identify plasma membrane proteins in TNBC cell lines. B) The table shows the total number of proteins identified, as well as those that correspond to plasma membrane proteins. C) Venn diagram showing the number of proteins specifically identified in each cell line and those that are common among them. Supplementary Figure 4. A) Schematic representation of the protocol used in cell surface biotinylation experiments. B) The table shows the proteins identified and those that correspond to plasma membrane proteins. C) Venn diagram showing the number of proteins identified in each cell line and those that are common among them. Supplementary Figure 5. BT549 (A and B) and MDA-MB231 (C and D) cells were infected with lentivirus containing the shRNA control (sh-Control) or the shRNA sequences targeting GLUT1 or LAT1. Knockdown efficiency was verified by western (A and C), and the effect of the knockdowns on cell proliferation was analyzed by MTT metabolization (B and D). GAPDH was used as a loading control. Supplementary Figure 6. BT549 and HCC3153 cells were seeded on coverslips and treated with 10 nM of anti-CD98hc for the indicated times. Cells were fixed and stained for CD98hc (red), LAMP1 (green) and DNA (blue). Scale bar = 25 μm. Magnification of one cell at 24 hours of treatment is shown. Scale bar = 10 and 7.5 μm. Supplementary Figure 7. A) Dose-response analyses of the anti-proliferative effect of anti-CD98hc-DM1 in MDA-MB231 CD98hc CRISPR #B3, #G3 and parental MDA-MB231 cells. Cells were treated with anti-CD98hc-DM1 for four days at the indicated doses. Results are shown as the mean ± SD of quadruplicates of an experiment repeated three times. B and D) BT549 (B) and MDA-MB231 (D) cells were infected with lentivirus containing the shRNA control (sh-Control) or two shRNA sequences targeting CD98hc (sh-CD98hc #3 and #7). To verify the knockdown efficiency, levels of CD98hc were analyzed by Western blot. Calnexin was used as a loading control. C and E) BT549 (C) and MDA-MB231 (E) cells infected with lentivirus containing the shRNA control (sh-Control) or two shRNA sequences targeting CD98hc were plated and the MTT metabolization was measured at the times indicated. Supplementary Figure 8. Cell cycle profiles of TNBC cells treated with CD98hc-DM1. Cells were treated for one day with CD98hc-DM1 (10 nM), and then harvested and stained with propidium iodide for cell cycle analysis, following the procedure described in the materials and methods section. Supplementary Figure 9. Graphical representation of the surfaceome and the strategy to develop ADCs against differentially expressed proteins. Genomic as well as proteomic strategies allow the identification of proteins overexpressed or newly expressed by tumors with respect to normal tissue. That information may be used to develop an antibody that targets the differentially expressed protein, and that may be used as a backbone for the preparation of an ADC. Once prepared, in vitro and in vivo models can be used to define the antitumoral activity of the ADC as well as its mechanism of action., Instituto de Salud Carlos III Consejo Superior de Investigaciones Científicas Consejería de Educación, Junta de Castilla y León CRIS Cancer Foundation ACMUMA UCCTA ALMOM, Peer reviewed

Instituto de Salud Carlos III Consejo Superior de Investigaciones Científicas Consejería de Educación, Junta de Castilla y León CRIS Cancer Foundation ACMUMA UCCTA ALMOM, Peer reviewed

Instituto de Salud Carlos III Consejo Superior de Investigaciones Científicas Consejería de Educación, Junta de Castilla y León CRIS Cancer Foundation ACMUMA UCCTA ALMOM, Peer reviewed