Intratumoral T cells that might otherwise control tumors are often identified in an "exhausted" state, defined by specific epigenetic modifications and upregulation of genes such as CD38, cytotoxic T-lymphocyte-associated protein 4 (CTLA4), and programmed cell death 1 (PD1). Although the term might imply inactivity, there has been little study of this state at the phenotypic level in tumors to understand the extent of their incapacitation. Starting with the observation that T cells move more quickly through mouse tumors the longer they reside there and progress toward exhaustion, we developed a nonstimulatory, live-biopsy method for the real-time study of T cell behavior within individual patient tumors. Using 2-photon microscopy, we studied native CD8+ T cell interaction with antigen-presenting cells (APCs) and cancer cells in different microniches of human tumors and found that T cell speed was variable by region and by patient and was inversely correlated with local tumor density. Across a range of tumor types, we found a strong relationship between CD8+ T cell motility and the exhausted T cell state that corresponded with our observations made in mouse models in which exhausted T cells moved faster. Our study demonstrates T cell dynamic states in individual human tumors and supports the existence of an active program in "exhausted" T cells that extends beyond incapacitating them.

Assigning behavioral functions to neural structures has long been a central goal in neuroscience and is a necessary first step toward a circuit-level understanding of how the brain generates behavior. Here, we map the neural substrates of locomotion and social behaviors for Drosophila melanogaster using automated machine-vision and machine-learning techniques. From videos of 400,000 flies, we quantified the behavioral effects of activating 2,204 genetically targeted populations of neurons. We combined a novel quantification of anatomy with our behavioral analysis to create brain-behavior correlation maps, which are shared as browsable web pages and interactive software. Based on these maps, we generated hypotheses of regions of the brain causally related to sensory processing, locomotor control, courtship, aggression, and sleep. Our maps directly specify genetic tools to target these regions, which we used to identify a small population of neurons with a role in the control of walking.

In the past decade, high-dimensional single-cell technologies have revolutionized basic and translational immunology research and are now a key element of the toolbox used by scientists to study the immune system. However, analysis of the data generated by these approaches often requires clustering algorithms and dimensionality reduction representation, which are computationally intense and difficult to evaluate and optimize. Here, we present Cytometry Clustering Optimization and Evaluation (Cyclone), an analysis pipeline integrating dimensionality reduction, clustering, evaluation, and optimization of clustering resolution, and downstream visualization tools facilitating the analysis of a wide range of cytometry data. We benchmarked and validated Cyclone on mass cytometry (CyTOF), full-spectrum fluorescence-based cytometry, and multiplexed immunofluorescence (IF) in a variety of biological contexts, including infectious diseases and cancer. In each instance, Cyclone not only recapitulates gold standard immune cell identification but also enables the unsupervised identification of lymphocytes and mononuclear phagocyte subsets that are associated with distinct biological features. Altogether, the Cyclone pipeline is a versatile and accessible pipeline for performing, optimizing, and evaluating clustering on a variety of cytometry datasets, which will further power immunology research and provide a scaffold for biological discovery.