Biology subjects

Mattar, M.

Publications and source records attributed to Mattar, M..

2 recordsLinked to original sources

bioRxiv2020

Single cell profiling reveals novel tumor and myeloid subpopulations in small cell lung cancer

Small cell lung cancer (SCLC) is an aggressive malignancy that includes subtypes defined by differential expression of ASCL1, NEUROD1, and POU2F3 (SCLC-A, -N, and -P, respectively), which are associated with distinct therapeutic vulnerabilities. To define the heterogeneity of tumors and their associated microenvironments across subtypes, we sequenced 54,523 cellular transcriptomes from 21 human biospecimens. Our single-cell SCLC atlas reveals tumor diversity exceeding lung adenocarcinoma, driven by canonical, intermediate, and admixed subtypes. We discovered a PLCG2-high tumor cell population with stem-like, pro-metastatic features that recurs across subtypes and predicts worse overall survival, and manipulation of PLCG2 expression in cells confirms correlation with key metastatic markers. Treatment and subtype are associated with substantial phenotypic changes in the SCLC immune microenvironment, with greater T-cell dysfunction in SCLC-N than SCLC-A. Moreover, the recurrent, PLCG2-high subclone is associated with exhausted CD8+ T-cells and a pro-fibrotic, immunosuppressive monocyte/macrophage population, suggesting possible tumor-immune coordination to promote metastasis.

cancer biology↗

bioRxiv2020

Experience replay supports non-local learning

To make effective decisions we need to consider the relationship between actions and outcomes. They are, however, often separated by time and space. The biological mechanism capable of spanning those gaps remains unknown. One promising, albeit hypothetical, mechanism involves neural replay of non-local experience. Using a novel task, that segregates direct from indirect learning, combined with magnetoencephalography (MEG), we tested the role of neural replay in non-local learning in humans. Following reward receipt, we found significant backward replay of non-local experience, with a 160 msec state-to-state time lag, and this replay facilitated learning of action values. This backward replay, combined with behavioural evidence of non-local learning, was more pronounced in experiences that were of greater benefit for future behavior, as predicted by theories of prioritization. These findings establish rationally targeted non-local replay as a neural mechanism for solving complex credit assignment problems during learning. One Sentence SummaryReverse sequential replay is found, for the first time, to support non-local reinforcement learning in humans and is prioritized according to utility.

neuroscience↗