Protocol for induction and characterization of microglia extracellular traps in murine and human microglia cells.

Extracellular traps (ETs) are composed of decondensed chromatin and are embedded with various antimicrobial proteins like myeloperoxidase and histones. Recently, we reported that dopamine (DA) induces ETs in BV2 microglia cell line and primary adult human microglia in a manner independent of cell death, reactive oxygen species, and actin polymerization. This protocol details how to characterize DA-induced ETs in BV2 microglia and human microglia. The protocols for characterization of ETs may also be used for other adherent cell lines. For complete details on the use and execution of this protocol, please refer to Agrawal et al. (2021).

Dopamine induces functional extracellular traps in microglia.

Dopamine (DA) plays many roles in the brain, especially in movement, motivation, and reinforcement of behavior; however, its role in regulating innate immunity is not clear. Here, we show that DA can induce DNA-based extracellular traps in primary, adult, human microglia and BV2 microglia cell line. These DNA-based extracellular traps are formed independent of reactive oxygen species, actin polymerization, and cell death. These traps are functional and capture fluorescein (FITC)-tagged Escherichia coli even when reactive oxygen species production or actin polymerization is inhibited. We show that microglial extracellular traps are present in Glioblastoma multiforme. This is crucial because Glioblastoma multiforme cells are known to secrete DA. Our findings demonstrate that DA plays a significant role in sterile neuro-inflammation by inducing microglia extracellular traps.

Granular Flow Graph, Adaptive Rule Generation and Tracking.

A new method of adaptive rule generation in granular computing framework is described based on rough rule base and granular flow graph, and applied for video tracking. In the process, several new concepts and operations are introduced, and methodologies formulated with superior performance. The flow graph enables in defining an intelligent technique for rule base adaptation where its characteristics in mapping the relevance of attributes and rules in decision-making system are exploited. Two new features, namely, expected flow graph and mutual dependency between flow graphs are defined to make the flow graph applicable in the tasks of both training and validation. All these techniques are performed in neighborhood granular level. A way of forming spatio-temporal 3-D granules of arbitrary shape and size is introduced. The rough flow graph-based adaptive granular rule-based system, thus produced for unsupervised video tracking, is capable of handling the uncertainties and incompleteness in frames, able to overcome the incompleteness in information that arises without initial manual interactions and in providing superior performance and gaining in computation time. The cases of partial overlapping and detecting the unpredictable changes are handled efficiently. It is shown that the neighborhood granulation provides a balanced tradeoff between speed and accuracy as compared to pixel level computation. The quantitative indices used for evaluating the performance of tracking do not require any information on ground truth as in the other methods. Superiority of the algorithm to nonadaptive and other recent ones is demonstrated extensively.