Search

Abstract γδ T cells provide rapid cellular immunity against pathogens. Here, we conducted matched single-cell RNA-sequencing and γδ-TCR-sequencing to delineate the molecular changes in γδ T cells during a longitudinal study following mRNA SARS-CoV-2 vaccination. While the first dose of vaccine primes Vδ2 T cells, it is the second administration that significantly boosts their immune response. Specifically, the second vaccination uncovers memory features of Vδ2 T cells, shaped by the induction of AP-1 family transcription factors and characterized by a convergent central memory signature, clonal expansion, and an enhanced effector potential. This temporally distinct effector response of Vδ2 T cells was also confirmed in vitro upon stimulation with SARS-CoV-2 spike-peptides. Indeed, the second challenge triggers a significantly higher production of IFNγ by Vδ2 T cells. Collectively, our findings suggest that mRNA SARS-CoV-2 vaccination might benefit from the establishment of long-lasting central memory Vδ2 T cells to confer protection against SARS-CoV-2 infection.

Abstract The study of cellular complexity in the nervous system based on anatomy has shown more practical and objective advantages in morphology than other perspectives on molecular, physiological, and evolutionary aspects. However, morphology-based neuron type classification in the whole rat brain is challenging, given the significant number of neuron types, limited reconstructed neuron samples, and diverse data formats. Here, we report that different types of deep neural network modules may well process different kinds of features and that the integration of these submodules will show power on the representation and classification of neuron types. For SWC-format data, which are compressed but unstructured, we construct a tree-based recurrent neural network (Tree-RNN) module. For 2D or 3D slice-format data, which are structured but with large volumes of pixels, we construct a convolutional neural network (CNN) module. We also generate a virtually simulated dataset with two classes, reconstruct a CASIA rat-neuron dataset with 2.6 million neurons without labels, and select the NeuroMorpho-rat dataset with 35,000 neurons containing hierarchical labels. In the twelve-class classification task, the proposed model achieves state-of-the-art performance compared with other models, e.g., the CNN, RNN, and support vector machine based on hand-designed features.