Dopamine is essential for striatal function and learning. Striatal dopamine release can be triggered by dopamine cell firing, but also by coordinated cholinergic interneuron activity, which stimulates dopamine release via presynaptic nicotinic acetylcholine receptors on dopamine axons. While acetylcholine-dependent dopamine release is well-documented ex vivo and under artificial optogenetic stimulation in vivo, its role during natural behavior has remained unclear. One possible endogenous driver of acetylcholine-dependent dopamine release is thalamic input, which provides strong excitatory drive to cholinergic interneurons. To examine whether thalamic input provokes acetylcholine-dependent dopamine release during behavior, we performed simultaneous fiber photometry recordings of striatal dopamine (GRAB-rDA3m) and thalamic axon activity (gCaMP8m) in the dorsomedial (DMS) and dorsolateral striatum (DLS) of mice learning the accelerating rotarod, a striatal-dependent task that demands precise and effortful motor control. Recordings were obtained on- and off-task and across days of training to capture the full arc of learning. Dopamine transients in DMS, but not DLS, were frequently coupled to peaks in thalamic axon activity via an acetylcholine-dependent mechanism. The occurrence of these thalamic-evoked DMS dopamine transients depended on learning, task engagement, and the recent history of dopamine activity, but did not contribute to motor error signals. Together, these findings establish thalamic input as a physiological driver of acetylcholine-dependent dopamine release in DMS. Moreover, they reveal that striatal sensitivity to this local release mechanism is dynamically gated by dopaminergic history, providing a compelling framework for understanding how local and soma-triggered dopamine signals are coordinated to support learning.

High-resolution extracellular electrophysiology is the gold standard for recording spikes from distributed neural populations and is especially powerful when combined with optogenetics for manipulation of specific cell types with high temporal resolution. We integrated these approaches into prototype Neuropixels Opto probes, which combine electronic and photonic circuits. These devices pack 960 electrical recording sites and two sets of 14 light emitters onto a 70-μm-wide, 1-cm-long shank, allowing spatially addressable optogenetic stimulation with blue and red light. In mouse cortex, Neuropixels Opto probes delivered high-quality recordings together with spatially addressable optogenetics, differentially activating or silencing neurons at distinct cortical depths. In the mouse striatum and other deep structures, Neuropixels Opto probes delivered efficient optotagging, facilitating the identification of two cell types in parallel. Neuropixels Opto probes represent a promising tool for recording, identifying and manipulating neuronal populations.

Although learning in response to extrinsic reinforcement is theorized to be driven by dopamine signals that encode the difference between expected and experienced rewards, skills that enable verbal or musical expression can be learned without extrinsic reinforcement. Instead, spontaneous execution of these skills is thought to be intrinsically reinforcing. Whether dopamine signals similarly guide learning of these intrinsically reinforced behaviours is unknown. In juvenile zebra finches learning from an adult tutor, dopamine signalling in a song-specialized basal ganglia region is required for successful song copying, a spontaneous, intrinsically reinforced process. Here we show that dopamine dynamics in the song basal ganglia faithfully track the learned quality of juvenile song performance on a rendition-by-rendition basis. Furthermore, dopamine release in the basal ganglia is driven not only by inputs from midbrain dopamine neurons classically associated with reinforcement learning but also by song premotor inputs, which act by means of local cholinergic signalling to elevate dopamine during singing. Although both cholinergic and dopaminergic signalling are necessary for juvenile song learning, only dopamine tracks the learned quality of song performance. Therefore, dopamine dynamics in the basal ganglia encode performance quality during self-directed, long-term learning of natural behaviours.

Basal Ganglia Advances is a collection highlighting research on the structure, function, and disorders of the basal ganglia. It features studies spanning neuroscience, clinical insights, and computational models, serving as a hub for advances in movement, cognition, and behavior.

Recent advances in the field of Voltage Imaging, with a special focus on new constructs and novel implementations.

Work related to place tuning, spatial navigation, orientation and direction. Mainly includes articles on connectivity in the hippocampus, retrosplenial cortex, and related areas.

Semantic segmentation was performed on 177 large histological images of re-endothelialized mouse lung vasculatures. Specifically, patch-based semantic segmentation algorithms were used to classify pixels corresponding to two classes: organ tissue, which includes lung and heart tissue; and ruptured and/or dilated vessels, which are abnormal vessels formed during re-endothelialization. Semantic segmentation is a potential means to automate the end-to-end analysis of these images, circumventing denoising and enhancement operations to visualize tissue and bypassing the manual diagnosis of ruptured and/or dilated vessels. To increase data quantity, images were compressed to sizes 1024 × 1024, 768 × 768, and 512 × 512 and then divided into nonoverlapping 256 × 256 patches. To benchmark the performance of the patch-based models, a vanilla model trained on complete images compressed to size 256 × 256 was also evaluated. The U-Net and LinkNet architectures were used to train and test each model using a data augmentation and transfer learning approach, and their results were ensembled. The loss of image context in the 3 × 3 and 4 × 4 patch-based models negatively impacted performance, generating many false positive predictions for target classes, whereas the low-image quantity of the vanilla model hindered performance. The 2 × 2 ensemble patch-based model returned the best performance, classifying organ tissue with a precision, recall, and intersection over union (IOU) of 88.0% ± 5.7%, 84.7% ± 9.2%, and 76.3% ± 10.9%, respectively, and classifying ruptured/dilated vessels with a precision, recall, and IOU of 78.4% ± 5.2%, 60.2% ± 11.4%, and 51.0% ± 8.4%, respectively.

Cancer immunotherapy has fundamentally transformed the management of solid tumors, ranging from immune checkpoint blockade to a broader spectrum of immune-modulating strategies. While inhibitors of CTLA-4 and the PD-1/PD-L1 axis remain central to clinical practice, heterogeneous clinical responses, immune-related toxicities, and different resistance mechanisms underscore the need for next-generation approaches. This review integrates recent advances in cancer immunotherapy for solid tumors, with an emphasis on emerging biological concepts and therapeutic platforms that extend beyond classical checkpoint inhibition. We discuss novel immune checkpoints, biomarker-driven approvals, and the expanding role of immunotherapy in different disease settings. Antibody-based platforms are highlighted as strategies that integrate direct tumor targeting with immune activation, which have reshaped standards of care in several malignancies. We further review advances in adoptive cellular therapies as well as next-generation cytokine therapies and cancer vaccines aimed at enhancing tumor-specific immune responses while mitigating systemic toxicity. Finally, we address key unresolved challenges, including mechanisms of resistance, optimization of sequencing and dosing strategies, and clinical trial design considerations. Together, these developments reflect a rapidly evolving field focused on broadening efficacy, improving safety, and personalizing treatment in solid tumors.

Cells produce numerous types of RNAs. Among these, transcripts produced by RNA polymerase II include protein-coding mRNAs as well as a variety of long noncoding RNAs. In this latter group, enhancer (e) RNAs constitute a class of RNAs transcribed from enhancer sites. Although eRNAs are typically unstable and degraded rapidly, multiple roles related to enhancer function have been suggested. But eRNAs also share similarities with mRNAs, such as in a limited number the presence of translated open reading frames. Indeed, other "noncoding" RNAs have also been found to contain coding sequences, and together these transcripts blur the line between coding and noncoding. Here, we review current models of eRNA function, the discoveries that led to them, and additional functions, specifically the potential for translation. We also review the characteristics of proteins encoded by such "noncoding" transcripts, and their possible implications regarding the function and evolution of both eRNAs and mRNAs.