Central nervous system immune interactome is a function of cancer lineage, tumor microenvironment, and STAT3 expression.

BACKGROUNDImmune cell profiling of primary and metastatic CNS tumors has been focused on the tumor, not the tumor microenvironment (TME), or has been analyzed via biopsies.METHODSEn bloc resections of gliomas (n = 10) and lung metastases (n = 10) were analyzed via tissue segmentation and high-dimension Opal 7-color multiplex imaging. Single-cell RNA analyses were used to infer immune cell functionality.RESULTSWithin gliomas, T cells were localized in the infiltrating edge and perivascular space of tumors, while residing mostly in the stroma of metastatic tumors. CD163+ macrophages were evident throughout the TME of metastatic tumors, whereas in gliomas, CD68+, CD11c+CD68+, and CD11c+CD68+CD163+ cell subtypes were commonly observed. In lung metastases, T cells interacted with CD163+ macrophages as dyads and clusters at the brain-tumor interface and within the tumor itself and as clusters within the necrotic core. In contrast, gliomas typically lacked dyad and cluster interactions, except for T cell CD68+ cell dyads within the tumor. Analysis of transcriptomic data in glioblastomas revealed that innate immune cells expressed both proinflammatory and immunosuppressive gene signatures.CONCLUSIONOur results show that immunosuppressive macrophages are abundant within the TME and that the immune cell interactome between cancer lineages is distinct. Further, these data provide information for evaluating the role of different immune cell populations in brain tumor growth and therapeutic responses.FUNDINGThis study was supported by the NIH (NS120547), a Developmental research project award (P50CA221747), ReMission Alliance, institutional funding from Northwestern University and the Lurie Comprehensive Cancer Center, and gifts from the Mosky family and Perry McKay. Performed in the Flow Cytometry & Cellular Imaging Core Facility at MD Anderson Cancer Center, this study received support in part from the NIH (CA016672) and the National Cancer Institute (NCI) Research Specialist award 1 (R50 CA243707). Additional support was provided by CCSG Bioinformatics Shared Resource 5 (P30 CA046592), a gift from Agilent Technologies, a Research Scholar Grant from the American Cancer Society (RSG-16-005-01), a Precision Health Investigator Award from University of Michigan (U-M) Precision Health, the NCI (R37-CA214955), startup institutional research funds from U-M, and a Biomedical Informatics & Data Science Training Grant (T32GM141746)

Competing Sound Sources Reveal Spatial Effects in Cortical Processing

Neurons in the avian auditory forebrain show strong sensitivity to the spatial configuration of two competing sources, even though there is only weak spatial dependence for any single source

Divisive Gain Modulation with Dynamic Stimuli in Integrate-and-Fire Neurons

The modulation of the sensitivity, or gain, of neural responses to input is an important component of neural computation. It has been shown that divisive gain modulation of neural responses can result from a stochastic shunting from balanced (mixed excitation and inhibition) background activity. This gain control scheme was developed and explored with static inputs, where the membrane and spike train statistics were stationary in time. However, input statistics, such as the firing rates of pre-synaptic neurons, are often dynamic, varying on timescales comparable to typical membrane time constants. Using a population density approach for integrate-and-fire neurons with dynamic and temporally rich inputs, we find that the same fluctuation-induced divisive gain modulation is operative for dynamic inputs driving nonequilibrium responses. Moreover, the degree of divisive scaling of the dynamic response is quantitatively the same as the steady-state responses—thus, gain modulation via balanced conductance fluctuations generalizes in a straight-forward way to a dynamic setting

Chronic Monocular Deprivation Reveals MMP9-Dependent and -Independent Aspects of Murine Visual System Plasticity

The deletion of matrix metalloproteinase MMP9 is combined here with chronic monocular deprivation (cMD) to identify the contributions of this proteinase to plasticity in the visual system. Calcium imaging of supragranular neurons of the binocular region of primary visual cortex (V1b) of wild-type mice revealed that cMD initiated at eye opening significantly decreased the strength of deprived-eye visual responses to all stimulus contrasts and spatial frequencies. cMD did not change the selectivity of V1b neurons for the spatial frequency, but orientation selectivity was higher in low spatial frequency-tuned neurons, and orientation and direction selectivity were lower in high spatial frequency-tuned neurons. Constitutive deletion of MMP9 did not impact the stimulus selectivity of V1b neurons, including ocular preference and tuning for spatial frequency, orientation, and direction. However, MMP9−/− mice were completely insensitive to plasticity engaged by cMD, such that the strength of the visual responses evoked by deprived-eye stimulation was maintained across all stimulus contrasts, orientations, directions, and spatial frequencies. Other forms of experience-dependent plasticity, including stimulus selective response potentiation, were normal in MMP9−/− mice. Thus, MMP9 activity is dispensable for many forms of activity-dependent plasticity in the mouse visual system, but is obligatory for the plasticity engaged by cMD.https://doi.org/10.3390/ijms2305243

On the semantics of concurrency: Partial orders and transition systems

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McMillan’s Complete Prefix for Contextual Nets

In a seminal paper, McMillan proposed a technique for constructing a finite complete prefix of the unfolding of bounded (i.e., finitestate) Petri nets, which can be used for verification purposes. Contextual nets are a generalisation of Petri nets suited to model systems with readonly access to resources. When working with contextual nets, a finite complete prefix can be obtained by applying McMillan’s construction to a suitable encoding of the contextual net into an ordinary net. However, it has been observed that if the unfolding is itself a contextual net, then the complete prefix can be significantly smaller than the one obtained with the above technique. A construction for generating such a contextual complete prefix has been proposed for a special class of nets, called read-persistent. In this paper we propose a new algorithm that works for arbitrary semi-weighted, bounded contextual nets. The construction explicitly takes into account the fact that, unlike ordinary or read-persistent nets, an event can have several different histories in contextual net computations