BUSCADOR RECOLECTA

[Introduction]: Monitoring of innate myeloid cells (IMC) is broadly applied in basic and translational research, as well as in diagnostic patient care. Due to their immunophenotypic heterogeneity and biological plasticity, analysis of IMC populations typically requires large panels of markers. Currently, two cytometry-based techniques allow for the simultaneous detection of ≥40 markers: spectral flow cytometry (SFC) and mass cytometry (MC). However, little is known about the comparability of SFC and MC in studying IMC populations., [Methods]: We evaluated the performance of two SFC and MC panels, which contained 21 common markers, for the identification and subsetting of blood IMC populations. Based on unsupervised clustering analysis, we systematically identified 24 leukocyte populations, including 21 IMC subsets, regardless of the cytometry technique., [Results]: Overall, comparable results were observed between the two technologies regarding the relative distribution of these cell populations and the staining resolution of individual markers (Pearson’s ρ=0.99 and 0.55, respectively). However, minor differences were observed between the two techniques regarding intra-measurement variability (median coefficient of variation of 42.5% vs. 68.0% in SFC and MC, respectively; p<0.0001) and reproducibility, which were most likely due to the significantly longer acquisition times (median 16 min vs. 159 min) and lower recovery rates (median 53.1% vs. 26.8%) associated with SFC vs. MC., [Discussion]: Altogether, our results show a good correlation between SFC and MC for the identification, enumeration and characterization of IMC in blood, based on large panels (>20) of antibody reagents., Peer reviewed

[Introduction]: Monitoring of innate myeloid cells (IMC) is broadly applied in basic and translational research, as well as in diagnostic patient care. Due to their immunophenotypic heterogeneity and biological plasticity, analysis of IMC populations typically requires large panels of markers. Currently, two cytometry-based techniques allow for the simultaneous detection of ≥40 markers: spectral flow cytometry (SFC) and mass cytometry (MC). However, little is known about the comparability of SFC and MC in studying IMC populations., [Methods]: We evaluated the performance of two SFC and MC panels, which contained 21 common markers, for the identification and subsetting of blood IMC populations. Based on unsupervised clustering analysis, we systematically identified 24 leukocyte populations, including 21 IMC subsets, regardless of the cytometry technique., [Results]: Overall, comparable results were observed between the two technologies regarding the relative distribution of these cell populations and the staining resolution of individual markers (Pearson’s ρ=0.99 and 0.55, respectively). However, minor differences were observed between the two techniques regarding intra-measurement variability (median coefficient of variation of 42.5% vs. 68.0% in SFC and MC, respectively; p<0.0001) and reproducibility, which were most likely due to the significantly longer acquisition times (median 16 min vs. 159 min) and lower recovery rates (median 53.1% vs. 26.8%) associated with SFC vs. MC., [Discussion]: Altogether, our results show a good correlation between SFC and MC for the identification, enumeration and characterization of IMC in blood, based on large panels (>20) of antibody reagents., Peer reviewed

Data is provided in the recent OBIS-ENV-DATA format, and comprises 8913 georeferenced positions associated with 3195 occurrences of 44 marine taxa.-- 3 files, The CETUS dataset contains effort-based occurrence records collected during a cetacean monitoring programme in the Eastern North Atlantic, since 2012, This research was partially funded by the EU FEDER/FEMP and Portuguese Foundation for Science and Technology (FCT) under the Portugal2020 (Lisboa2020, Algarve2020 and MAR2020) Programme through project OBSERVA.PT (MAR-01.04.02-FEAMP-0002), Peer reviewed

The indicated fungal species were compared by CLUSTAL O multiple sequence alignment. Kinase domain and activation loop are marked. Serines and threonines followed by proline in S. cerevisiae Cbk1 are indicated with an asterisk and the corresponding residue. Those residues were changed to glutamic acid to mimic phosphorylation (cbk1-6E) [42]. This mutant was used in this work., pbio.3002263.s004.pdf, Peer reviewed

[Introduction]: Monitoring of innate myeloid cells (IMC) is broadly applied in basic and translational research, as well as in diagnostic patient care. Due to their immunophenotypic heterogeneity and biological plasticity, analysis of IMC populations typically requires large panels of markers. Currently, two cytometry-based techniques allow for the simultaneous detection of ≥40 markers: spectral flow cytometry (SFC) and mass cytometry (MC). However, little is known about the comparability of SFC and MC in studying IMC populations., [Methods]: We evaluated the performance of two SFC and MC panels, which contained 21 common markers, for the identification and subsetting of blood IMC populations. Based on unsupervised clustering analysis, we systematically identified 24 leukocyte populations, including 21 IMC subsets, regardless of the cytometry technique., [Results]: Overall, comparable results were observed between the two technologies regarding the relative distribution of these cell populations and the staining resolution of individual markers (Pearson’s ρ=0.99 and 0.55, respectively). However, minor differences were observed between the two techniques regarding intra-measurement variability (median coefficient of variation of 42.5% vs. 68.0% in SFC and MC, respectively; p<0.0001) and reproducibility, which were most likely due to the significantly longer acquisition times (median 16 min vs. 159 min) and lower recovery rates (median 53.1% vs. 26.8%) associated with SFC vs. MC., [Discussion]: Altogether, our results show a good correlation between SFC and MC for the identification, enumeration and characterization of IMC in blood, based on large panels (>20) of antibody reagents., Peer reviewed

[Introduction]: Monitoring of innate myeloid cells (IMC) is broadly applied in basic and translational research, as well as in diagnostic patient care. Due to their immunophenotypic heterogeneity and biological plasticity, analysis of IMC populations typically requires large panels of markers. Currently, two cytometry-based techniques allow for the simultaneous detection of ≥40 markers: spectral flow cytometry (SFC) and mass cytometry (MC). However, little is known about the comparability of SFC and MC in studying IMC populations., [Methods]: We evaluated the performance of two SFC and MC panels, which contained 21 common markers, for the identification and subsetting of blood IMC populations. Based on unsupervised clustering analysis, we systematically identified 24 leukocyte populations, including 21 IMC subsets, regardless of the cytometry technique., [Results]: Overall, comparable results were observed between the two technologies regarding the relative distribution of these cell populations and the staining resolution of individual markers (Pearson’s ρ=0.99 and 0.55, respectively). However, minor differences were observed between the two techniques regarding intra-measurement variability (median coefficient of variation of 42.5% vs. 68.0% in SFC and MC, respectively; p<0.0001) and reproducibility, which were most likely due to the significantly longer acquisition times (median 16 min vs. 159 min) and lower recovery rates (median 53.1% vs. 26.8%) associated with SFC vs. MC., [Discussion]: Altogether, our results show a good correlation between SFC and MC for the identification, enumeration and characterization of IMC in blood, based on large panels (>20) of antibody reagents., Peer reviewed

Circulating antibody-secreting cells are present in the peripheral blood of healthy individuals reflecting the continued activity of the humoral immune system. Antibody-secreting cells typically express CD27. Here we describe and characterize a small population of antibody-secreting class switched CD19+CD43+ B cells that lack expression of CD27 in the peripheral blood of healthy subjects. In this study, we characterized CD27-CD43+ cells. We demonstrate that class-switched CD27-CD43+ B cells possess characteristics of conventional plasmablasts as they spontaneously secrete antibodies, are morphologically similar to antibody-secreting cells, show downregulation of B cell differentiation markers, and have a gene expression profile related to conventional plasmablasts. Despite these similarities, we observed differences in IgA and IgG subclass distribution, expression of homing markers, replication history, frequency of somatic hypermutation, immunoglobulin repertoire, gene expression related to Toll-like receptors, cytokines, and cytokine receptors, and antibody response to vaccination. Their frequency is altered in immune-mediated disorders., Peer reviewed

Increased mitotic activity is associated with the genesis and aggressiveness of many cancers. To assess the clinical value of mitotic activity as prognostic biomarker, we performed a pan-cancer study on the mitotic network activity index (MNAI) constructed based on 54-gene mitotic apparatus network. Our pan-cancer assessment on TCGA (33 tumor types, 10,061 patients) and validation on other publicly available cohorts (23 tumor types, 9,209 patients) confirmed the significant association of MNAI with overall survival, progression-free survival, and other prognostic endpoints in multiple cancer types, including lower-grade gliomas (LGG), breast invasive carcinoma (BRCA), as well as many others. We also showed significant association between MNAI and genetic instability, which provides a biological explanation of its prognostic impact at pan-cancer landscape. Our association analysis revealed that patients with high MNAI benefitted more from anti-PD-1 and Anti-CTLA-4 treatment. In addition, we demonstrated that multimodal integration of MNAI and the AI-empowered Cellular Morphometric Subtypes (CMS) significantly improved the predictive power of prognosis compared to using MNAI and CMS alone. Our results suggest that MNAI can be used as a potential prognostic biomarker for different tumor types toward different clinical endpoints, and multimodal integration of MNAI and CMS exceeds individual biomarker for precision prognosis., Peer reviewed

Additional file 1: Table S1. Summary statistics of the Baikal 1600 m long-read sequencing and metagenomic assembly. Fig. S1. A Principal component analysis (PCA) between deep Lake Baikal metagenomes based on a Bray-Curtis similarity k-mer profile frequencies of sequencing reads. Red and blue dots represent summer and winter Illumina metagenomes, respectively, while the green dot is the sample retrieved in this study and sequenced with PacBio Sequel II. B Phylum-level composition based on 16S rRNA gene fragments (Illumina and PacBio CCS5 reads) of the different metagenomes. The single metagenome highlighted in green corresponds to the PacBio sequencing, whilst the rest of datasets belong to previous Illumina sequencing. The phylum Proteobacteria was divided into its class-level classification. Only those groups with abundance values larger than 1% in any of the metagenomes are shown. C Classification of the 1600 m PacBio CCS5 16S rRNA reads at a higher taxonomic resolution. Only sequences larger than 1000 nucleotides were considered. Sequences ascribed to the Ca. Patescibacteria phylum are highlighted in green. Table S2. Genomic parameters of LAGs recovered in this study. Table S3. Genomic parameters of LAGs recovered in this study with ANI > 99.5% to MAGs retrieved from Lake Baikal 1250 and 1350 m deep. Fig. S2. Alignment of two LAGs that are complete in a single contig and the respective MAG from the Illumina assembly. Table S4. Genomic parameters of the resulting bins from the Baikal 1600 m CCS sequences. The four Baikalibacteria bins are highlighted in yellow. Fig. S3. A Maximum likelihood phylogenetic tree of the Baikalibacteria 16S rRNA genes. Sequences outside the deep branch coming from Figure 1 were used as an outgroup for the tree. The reads from the four read bins are colored in the figure. B Diversity of 16S rRNA sequences of Baikalibacteria bins. Linear representation of selected CCS5 reads (indicated with a red circle in the left panel) containing a 16S rRNA gene. A pairwise blastn comparison among reads was performed to detect orthologous genes. Fig. S4. A Average nucleotide identity based on metagenomic reads (ANIr) of LAGs and the four Baikalibacteria Bins. B ANIr of ten randomly selected sequences of each Baikalibacteria bin. Fig. S5. Metagenomic recruitment of the largest fragment of Baikalibacteria RBin09 on Lake Thun 180 m deep. Fig. S6. Maximum likelihood phylogenetic tree of the a phytoene elongase (LyeJ), b carotenoid 3,4-desaturase (CrtD), and c bisanhydrobacterioruberin hydratase (CruF) proteins., Peer reviewed

(A) cbk1-5E-E164S cbk1-aid cdc15-2 (YMF3905), (B) cbk1-5E-E251S cbk1-aid cdc15-2 (YMF3910), (C) cbk1-5E-E409S cbk1-aid cdc15-2 (YMF3995), (D) cbk1-5E-E615T cbk1-aid cdc15-2 (YMF3906), and (E) cbk1-5E-E711S cbk1-aid cdc15-2 (YMF3907) cells were grown in YPD and arrested in late anaphase by shifting the temperature to 37 °C before the addition of rapamycin to half of the culture for 20 min. To allow progression through the cell cycle, cells were released in the absence (i) or presence (ii) of rapamycin. Samples were taken at the indicated times to determine cell-cycle progression by flow cytometry. (F) 3D Cbk1 structure (PDB 4LQS [48]) in which different protein domains are highlighted. Residue T574 in the activation loop, together with residues D475 and S409 in the kinase domain are denoted. (G) cbk1-T574A cbk1-aid cdc15-2 cells (YMF4191) were grown as above. FACS graphs can be found in the supplementary FACS file (S1 File)., pbio.3002263.s005.pdf, Peer reviewed