T cells become functionally exhausted in tumors, limiting T cell–based immunotherapies. Although several transcription factors regulating the exhausted T (T ex ) cell differentiation are known, comparatively little is known about the regulators of T ex cell survival. Here, we reported that the regulator of G protein signaling 16 (Rgs-16) suppressed T ex cell survival in tumors. By performing lineage tracing using reporter mice in which mCherry marked Rgs16-expressing cells, we identified that Rgs16 + CD8 + tumor-infiltrating lymphocytes (TILs) were terminally differentiated, expressed low levels of T cell factor 1 (Tcf1), and underwent apoptosis as early as 6 days after the onset of Rgs16 expression. Rgs16 deficiency inhibited CD8 + T cell apoptosis and promoted antitumor effector functions of CD8 + T cells. Furthermore, Rgs16 deficiency synergized with programmed cell death protein 1 (PD-1) blockade to enhance antitumor CD8 + T cell responses. Proteomics revealed that Rgs16 interacted with the scaffold protein IQGAP1, suppressed the recruitment of Ras and B-Raf, and inhibited Erk1 activation. Rgs16 deficiency enhanced antitumor CD8 + TIL survival in an Erk1-dependent manner. Loss of function of Erk1 decreased antitumor functions of Rgs16 -deficient CD8 + T cells. RGS16 mRNA expression levels in CD8 + TILs of patients with melanoma negatively correlated with genes associated with T cell stemness, such as SELL , TCF7 , and IL7R , and predicted low responses to PD-1 blockade. This study uncovers Rgs16 as an inhibitor of T ex cell survival in tumors and has implications for improving T cell–based immunotherapies.
Connectomics is fundamental in propelling our understanding of the nervous system's organization, unearthing cells and wiring diagrams reconstructed from volume electron microscopy (EM) datasets. Such reconstructions, on the one hand, have benefited from ever more precise automatic segmentation methods, which leverage sophisticated deep learning architectures and advanced machine learning algorithms. On the other hand, the field of neuroscience at large, and of image processing in particular, has manifested a need for user-friendly and open source tools which enable the community to carry out advanced analyses. In line with this second vein, here we propose mEMbrain, an interactive MATLAB-based software which wraps algorithms and functions that enable labeling and segmentation of electron microscopy datasets in a user-friendly user interface compatible with Linux and Windows. Through its integration as an API to the volume annotation and segmentation tool VAST, mEMbrain encompasses functions for ground truth generation, image preprocessing, training of deep neural networks, and on-the-fly predictions for proofreading and evaluation. The final goals of our tool are to expedite manual labeling efforts and to harness MATLAB users with an array of semi-automatic approaches for instance segmentation. We tested our tool on a variety of datasets that span different species at various scales, regions of the nervous system and developmental stages. To further expedite research in connectomics, we provide an EM resource of ground truth annotation from 4 different animals and 5 datasets, amounting to around 180 hours of expert annotations, yielding more than 1.2 GB of annotated EM images. In addition, we provide a set of 4 pre-trained networks for said datasets. All tools are available from https://lichtman.rc.fas.harvard.edu/mEMbrain/. With our software, our hope is to provide a solution for lab-based neural reconstructions which does not require coding by the user, thus paving the way to affordable connectomics.
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