Cellular Differentiation of Human Monocytes Is Regulated by Time-Dependent Interleukin-4 Signaling and the Transcriptional Regulator NCOR2

Human in vitro generated monocyte-derived dendritic cells (moDCs) and macrophages are used clinically, e.g., to induce immunity against cancer. However, their physiological counterparts, ontogeny, transcriptional regulation, and heterogeneity remains largely unknown, hampering their clinical use. High-dimensional techniques were used to elucidate transcriptional, phenotypic, and functional differences between human in vivo and in vitro generated mononuclear phagocytes to facilitate their full potential in the clinic. We demonstrate that monocytes differentiated by macrophage colony-stimulating factor (M-CSF) or granulocyte macrophage colony-stimulating factor (GM-CSF) resembled in vivo inflammatory macrophages, while moDCs resembled in vivo inflammatory DCs. Moreover, differentiated monocytes presented with profound transcriptomic, phenotypic, and functional differences. Monocytes integrated GM-CSF and IL-4 stimulation combinatorically and temporally, resulting in a mode- and time-dependent differentiation relying on NCOR2. Finally, moDCs are phenotypically heterogeneous and therefore necessitate the use of high-dimensional phenotyping to open new possibilities for better clinical tailoring of these cellular therapies

Cellular Differentiation of Human Monocytes Is Regulated by Time-Dependent Interleukin-4 Signaling and the Transcriptional Regulator NCOR2.

Human in vitro generated monocyte-derived dendritic cells (moDCs) and macrophages are used clinically, e.g., to induce immunity against cancer. However, their physiological counterparts, ontogeny, transcriptional regulation, and heterogeneity remains largely unknown, hampering their clinical use. High-dimensional techniques were used to elucidate transcriptional, phenotypic, and functional differences between human in vivo and in vitro generated mononuclear phagocytes to facilitate their full potential in the clinic. We demonstrate that monocytes differentiated by macrophage colony-stimulating factor (M-CSF) or granulocyte macrophage colony-stimulating factor (GM-CSF) resembled in vivo inflammatory macrophages, while moDCs resembled in vivo inflammatory DCs. Moreover, differentiated monocytes presented with profound transcriptomic, phenotypic, and functional differences. Monocytes integrated GM-CSF and IL-4 stimulation combinatorically and temporally, resulting in a mode- and time-dependent differentiation relying on NCOR2. Finally, moDCs are phenotypically heterogeneous and therefore necessitate the use of high-dimensional phenotyping to open new possibilities for better clinical tailoring of these cellular therapies

Transcriptome-Based Network Analysis Reveals a Spectrum Model of Human Macrophage Activation

SummaryMacrophage activation is associated with profound transcriptional reprogramming. Although much progress has been made in the understanding of macrophage activation, polarization, and function, the transcriptional programs regulating these processes remain poorly characterized. We stimulated human macrophages with diverse activation signals, acquiring a data set of 299 macrophage transcriptomes. Analysis of this data set revealed a spectrum of macrophage activation states extending the current M1 versus M2-polarization model. Network analyses identified central transcriptional regulators associated with all macrophage activation complemented by regulators related to stimulus-specific programs. Applying these transcriptional programs to human alveolar macrophages from smokers and patients with chronic obstructive pulmonary disease (COPD) revealed an unexpected loss of inflammatory signatures in COPD patients. Finally, by integrating murine data from the ImmGen project we propose a refined, activation-independent core signature for human and murine macrophages. This resource serves as a framework for future research into regulation of macrophage activation in health and disease

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

ImpulseDE: detection of differentially expressed genes in time series data using impulse models.

SummaryPerturbations in the environment lead to distinctive gene expression changes within a cell. Observed over time, those variations can be characterized by single impulse-like progression patterns. ImpulseDE is an R package suited to capture these patterns in high throughput time series datasets. By fitting a representative impulse model to each gene, it reports differentially expressed genes across time points from a single or between two time courses from two experiments. To optimize running time, the code uses clustering and multi-threading. By applying ImpulseDE , we demonstrate its power to represent underlying biology of gene expression in microarray and RNA-Seq data.Availability and implementationImpulseDE is available on Bioconductor ( https://bioconductor.org/packages/ImpulseDE/ )[email protected] informationSupplementary data are available at Bioinformatics online

Prediction Model Based on Easily Available Markers for Aberrant Cardiac Remodeling in Women After Pregnancy

BACKGROUND: Preeclampsia is strongly associated with left ventricular concentric remodeling (LVCR) and left ventricular hypertrophy (LVH) up to 10 years after delivery. This predisposes to heart failure later in life. Adequate detection and prediction of LVCR or LVH is expected to decrease the risk of developing clinical heart failure within this high-risk female population. Therefore, we developed and internally validated a prediction model for aberrant cardiac remodeling in formerly pregnant women. METHODS: This large cohort study included women with a history of preeclampsia or normotensive pregnancy within a postpartum interval of 6 months to 30 years. Cardiovascular assessment was performed, including echocardiography, 30-minute blood pressure measurements, and circulating biomarkers. Aberrant cardiac remodeling based on echocardiographic findings was defined as either LVCR or LVH. Discriminative performance was evaluated by the area under the receiver operating characteristic curve. RESULTS: A total of 1397 women were included, of which 139 (10%) with LVCR or LVH (mean±SD age, 43±9 years) and 1258 (90%) without LVCR or LVH (40±8 years). The final prediction model was established based on the predictors age, waist circumference, systolic blood pressure, glycated hemoglobin, antihypertensive medication use, and early onset preeclampsia (yes/no). After internal validation, the prediction model showed accurate discriminative ability with an area under the receiver operating characteristic curve of 0.702 (95% CI, 0.657-0.756). CONCLUSIONS: Based on the conventional predictors, we developed a prediction model for women who are on average 8 to 12 years postpartum. Internal validation showed accurate discriminative ability. Upon external validation, this model may aid clinicians to initiate further diagnostic testing or clinical follow-up. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT02347540

High-resolution transcriptome of human macrophages.

Macrophages are dynamic cells integrating signals from their microenvironment to develop specific functional responses. Although, microarray-based transcriptional profiling has established transcriptional reprogramming as an important mechanism for signal integration and cell function of macrophages, current knowledge on transcriptional regulation of human macrophages is far from complete. To discover novel marker genes, an area of great need particularly in human macrophage biology but also to generate a much more thorough transcriptome of human M1- and M1-like macrophages, we performed RNA sequencing (RNA-seq) of human macrophages. Using this approach we can now provide a high-resolution transcriptome profile of human macrophages under classical (M1-like) and alternative (M2-like) polarization conditions and demonstrate a dynamic range exceeding observations obtained by previous technologies, resulting in a more comprehensive understanding of the transcriptome of human macrophages. Using this approach, we identify important gene clusters so far not appreciated by standard microarray techniques. In addition, we were able to detect differential promoter usage, alternative transcription start sites, and different coding sequences for 57 gene loci in human macrophages. Moreover, this approach led to the identification of novel M1-associated (CD120b, TLR2, SLAMF7) as well as M2-associated (CD1a, CD1b, CD93, CD226) cell surface markers. Taken together, these data support that high-resolution transcriptome profiling of human macrophages by RNA-seq leads to a better understanding of macrophage function and will form the basis for a better characterization of macrophages in human health and disease