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

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

[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

Document S1. Figures S1–S12, Table S1, and Note S1. Document S2. Article plus supplemental information., 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

Table of Contents: Table S1. Metadata for the samples included in this study, the partitioning of Aglais urticae samples for gene flow analyses (Pages 2-5).-- Table S2. “Pure” and putative hybrid specimens for the lineage pairs of Europe – Sierra Nevada and Europe – Sicily/Calabria, for hybrid simulation analyses (Pages 6-7).-- Table S3. Dataset and results of genetic diversity analyses of the European lineage of A. urticae (Page 8).-- Table S4. Values of Akaike information criterion (AIC) for the demographic models tested in dadi (Pages 9-10).-- Table S5. Parameters from model anc_asym_mig and respective conversions to real time, population size and migration rates for population pairs of Europe – Sierra Nevada and Europe – Sicily/Calabria (Page 11).-- Figure S1. Response curves of Aglais urticae (top) and Urtica dioica (bottom) to the bioclimatic variables used for the Ecological Niche Modeling analyses (Page 12)., Table S1. Metadata for the samples included in this study, the partitioning of Aglais urticae samples for gene flow analyses (Pages 2-5).-- Table S2. “Pure” and putative hybrid specimens for the lineage pairs of Europe – Sierra Nevada and Europe – Sicily/Calabria, for hybrid simulation analyses (Pages 6-7).-- Table S3. Dataset and results of genetic diversity analyses of the European lineage of A. urticae (Page 8).-- Table S4. Values of Akaike information criterion (AIC) for the demographic models tested in dadi (Pages 9-10).-- Table S5. Parameters from model anc_asym_mig and respective conversions to real time, population size and migration rates for population pairs of Europe – Sierra Nevada and Europe – Sicily/Calabria (Page 11).-- Figure S1. Response curves of Aglais urticae (top) and Urtica dioica (bottom) to the bioclimatic variables used for the Ecological Niche Modeling analyses (Page 12)., Peer reviewed

Au_filtered_PCA.vcf - used as input for PCA, Au_STRUCTURE.str - used as input for STRUCTURE analysis, Au_IQTREE.phy - used as input for phylogenetic inference in IQTREE, Au_PhyloNetworks.loci - used as input to create gene trees for subsequent use in PhyloNetworks analysis, Au_filtered_TreeMix.vcf - used as input for TreeMix analysis, Au_TASSEL_western.vcf; Au_TASSEL_eastern.vcf; Au_TASSEL_central.vcf - used as input for genetic diversity analysis in TASSEL, Au_filtered_dadi.vcf - used to produce SFS file for dadi analysis, Au_EuSicily_hybridsim; Au_EuSierra_hybridsim - used to simulate hybrids with HYBRIDLAB, Peer reviewed