Tumor-associated macrophages (TAMs) constitute up to 50% of tumor bulk in glioblastoma (GBM) and play an important role in tumor maintenance and progression. The recently discovered differences between invading tumour periphery and hypoxic tumor core implies that macrophage biology is also distinct by location. This may provide further insight into the observed treatment resistance to immune modulation. We hypothesize that macrophage activation occurs through processes that are distinct in tumor periphery versus core. We therefore investigated regional differences in TAM recruitment and evolution in GBM by combining open source single cell and bulk gene expression data. We used single cell gene expression data from 4 glioblastomas (total of 3589 cells) and 122 total bulk samples obtained from 10 different patients. Cell identity, ontogeny (bone-marrow derived macrophages-BMDM vs microglia), and macrophage activation state were inferred using verified gene expression signatures. We captured the spectrum of immune states using cell trajectory analysis with pseudotime ordering. In keeping with previous studies, TAMs carrying BMDM identity were more abundant in tumor bulk while microglia-derived TAMs dominated the tumor periphery across all macrophage activation states including pre-activation. We note that core TAMs evolve towards a pro-inflammatory state and identify a subpopulation of cells based on a gene program exhibiting strong, opposing correlation with Programmed cell Death-1 (PD-1) signaling, which may correlate to their response to PD-1 inhibition. By contrast, peripheral TAMs evolve towards anti-inflammatory phenotype and contains a population of cells strongly associated with NFkB signaling. Our preliminary analysis suggests important regional differences in TAMs with regard to recruitment and evolution. We identify regionally distinct and potentially actionable cell subpopulations and advocate the need for a multi-targeted approach to GBM therapeutics.

Objective. Recording and stimulating from the peripheral nervous system are becoming important components in a new generation of bioelectronics systems. Although neurostimulation has seen a history of successful chronic applications in humans, peripheral nerve recording in humans chronically remains a challenge. Multi-contact nerve cuff electrode configurations have the potential to improve recording selectivity. We introduce the idea of using a convolutional neural network (CNN) to associate recordings of individual naturally evoked compound action potentials (CAPs) with neural pathways of interest, by exploiting the spatiotemporal patterns in multi-contact nerve cuff recordings. Approach. Nine Long-Evan rats were implanted with a 56-channel nerve cuff electrode on the sciatic nerve and afferent activity was selectively evoked in different fascicles (tibial, peroneal, sural) using mechanical stimuli. A recurrent neural network was then used to predict joint angles based on the predicted firing patterns from the CNN. Performance was measured based on the classification accuracy, F1-score and the ability to track the ankle joint angle. Main results. Classification accuracy and F1-score of the best CNN configuration were and 0.747  ±  0.114, respectively. The mean Pearson correlation coefficient between the manually measured ankle angle and the angle predicted from the estimated firing rate was Significance. The proposed method demonstrates that CAP-based classification can be achieved with high accuracy and can be used to track a physiological meaningful measure (e.g. joint angle). These results provide a promising direction for realizing more effective and intuitive neuroprosthetic systems.

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