ggCyto: next generation open-source visualization software for cytometry

Computational flow cytometry of planktonic populations for the evaluation of microbiological-control programs in district cooling plants

Biofouling poses a serious concern for the district cooling (DC) industry. Current industry practises for monitoring biofouling continue to rely on culture-based methods for microbial enumeration, which are ultimately flawed. Computational flow cytometric (cFCM) analyses, which offer enhanced reproducibility and streamlined analytics versus conventional flow cytometry were applied to samples taken from 3 sites in each of 3 plants over a 5-week sampling program. We asked whether the application of cFCM to monitoring planktonic community dynamics in DC plants could be able to provide sufficient information to enhance microbiological-control strategies at site and inform about plant performance impacts. The use of cFCM enabled the evaluation of biocide dosing, deep cleaning treatment efficiencies and routes of microbial ingress into the studied systems. Additionally, inherent risks arising from the reintroduction of microbiological communities into recently cleaned WCT basins from contaminated cooling waters were identified. However, short-term dynamics did not relate with plant performance metrics. In summary, the insights offered by this approach can inform on plant status, enable evaluations of microbial loads during biofouling mitigation programs and, ultimately, enhance industry management of the biofouling process.

Interrogation of the Microenvironmental Landscape in Brain Tumors Reveals Disease-Specific Alterations of Immune Cells

Brain malignancies encompass a range of primary and metastatic cancers, including low-grade and high-grade gliomas and brain metastases (BrMs) originating from diverse extracranial tumors. Our understanding of the brain tumor microenvironment (TME) remains limited, and it is unknown whether it is sculpted differentially by primary versus metastatic disease. We therefore comprehensively analyzed the brain TME landscape via flow cytometry, RNA sequencing, protein arrays, culture assays, and spatial tissue characterization. This revealed disease-specific enrichment of immune cells with pronounced differences in proportional abundance of tissue-resident microglia, infiltrating monocyte-derived macrophages, neutrophils, and T cells. These integrated analyses also uncovered multifaceted immune cell activation within brain malignancies entailing converging transcriptional trajectories while maintaining disease- and cell-type-specific programs. Given the interest in developing TME-targeted therapies for brain malignancies, this comprehensive resource of the immune landscape offers insights into possible strategies to overcome tumor-supporting TME properties and instead harness the TME to fight cancer.

CytoML for cross-platform cytometry data sharing

CytoML is an R/Bioconductor package that enables cross‐platform import, export, and sharing of gated cytometry data. It currently supports Cytobank, FlowJo, Diva, and R, allowing users to import gated cytometry data from commercial platforms into R. Once data are available in R, the data can be further manipulated. For example it can be combined with other computational and analytic approaches, and the results can be exported to FlowJo or Cytobank to be explored by researchers using those platforms. We demonstrate how CytoML and related R packages can be used as a tool to import, modify and export several samples stained with the T cell panel from the FlowCAP IV Lyoplate data set. Once imported, the gating is modified using computational approaches, and exported for visualization in Cytobank and FlowJo. We further show how CytoML can be used to import gated data from a publicly accessible mass cytometry experiment from Cytobank. CytoML is the only tool that allows such sharing of gated cytometry data between researchers working across different platforms, and it will serve as a useful tool for validating and verifying the reproducibility of analyses. © 2018 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Quantification and isolation of Bacillus subtilis spores using cell sorting and automated gating

The Gram-positive bacterium Bacillus subtilis is able to form endospores which have a variety of biotechnological applications. Due to this ability, B. subtilis is as well a model organism for cellular differentiation processes. Sporulating cultures of B. subtilis form sub-populations which include vegetative cells, sporulating cells and spores. In order to readily and rapidly quantify spore formation we employed flow cytometric and fluorescence activated cell sorting techniques in combination with nucleic acid fluorescent staining in order to investigate the distribution of sporulating cultures on a single cell level. Automated gating procedures using Gaussian mixture modeling (GMM) were employed to avoid subjective gating and allow for the simultaneous measurement of controls. We utilized the presented method for monitoring sporulation over time in germination deficient strains harboring different genome modifications. A decrease in the sporulation efficiency of strain Bs02018, utilized for the display of sfGFP on the spores surface was observed. On the contrary, a double knock-out mutant of the phosphatase gene encoding Spo0E and of the spore killing factor SkfA (Bs02025) exhibited the highest sporulation efficiency, as within 24 h of cultivation in sporulation medium, cultures of BS02025 already consisted of 80% spores as opposed to 18% for the control strain. We confirmed the identity of the different subpopulations formed during sporulation by employing sorting and microscopy.

Flow cytometry data analysis: Recent tools and algorithms

Flow cytometry (FCM) allows scientists to rapidly quantify up to 50 parameters for millions of cells per sample. The bottleneck in the application of the technology is data analysis, and the high number of parameters measured by the current generation of instruments requires the use of advanced computational algorithms to make full use of their capabilities. This review summarizes the main steps of FCM data analysis, focusing on the use of the most recent bioinformatic tools developed for an R-based programming environment. In particular, for each stage of the data analysis, libraries and packages currently available are listed, and a brief description of their functioning is included.