{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T11:27:19Z","timestamp":1768217239606,"version":"3.49.0"},"reference-count":47,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T00:00:00Z","timestamp":1768176000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neural Circuits"],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>\n                      Sparse and bright labeling of retinal ganglion cell (RGC) is essential for correlating single-cell morphology with brain-wide visual circuitry. This study aimed to develop a cell-type-specific, sparse labeling strategy for parvalbumin-expressing RGCs (PV\n                      <jats:sup>+<\/jats:sup>\n                      RGCs) in the transgenic mouse retina using recombinant adeno-associated virus (rAAV) and to map the whole-brain projection patterns of single PV\n                      <jats:sup>+<\/jats:sup>\n                      RGCs via fluorescence micro-optical sectioning tomography (fMOST).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      A cell-type-specific dual AAV system was employed, co-packaging a Cre-dependent Flpo plasmid and an Flpo-dependent enhanced yellow fluorescent protein (EYFP) plasmid. Key parameters-including the mixing ratio of core plasmids (ranging from 1\/100 to 1\/1000), gene copy number of Flpo and EYFP (single versus double), and AAV serotype (AAV2.2 versus engineered AAV2.NN)-were systematically optimized. Transduction efficiency and labeling sparsity under each condition were compared. Whole-retina-to-brain imaging was performed using fMOST on samples injected with the optimal condition (AAV2.2-double-1\/1000), enabling the reconstruction of complete axonal trajectories of individual PV\n                      <jats:sup>+<\/jats:sup>\n                      RGCs from the retina to the brain.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The sparsity and signal intensity of labeled RGCs varied significantly with the core plasmid ratio, AAV serotype, and gene copy number. The engineered AAV2.NN serotype increased transduction efficiency and labeling density under equivalent conditions, which facilitated the morphological subclassification of PV\n                      <jats:sup>+<\/jats:sup>\n                      RGCs into ON, ON-OFF, and OFF types based on their stratification relative to ChAT bands. Axonal projections of single PV\n                      <jats:sup>+<\/jats:sup>\n                      RGCs were successfully traced to the superior colliculus (SC), dorsal and ventral lateral geniculate nuclei (dLGN\/vLGN).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>This viral labeling platform effectively resolves the classical trade-off between sparsity and signal intensity, providing a robust methodology for whole-brain mapping of individual RGC projections. The approach establishes a practical foundation for future mechanistic and therapeutic studies investigating subtype-selective vulnerability in RGCs.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fncir.2025.1740624","type":"journal-article","created":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T08:14:39Z","timestamp":1768205679000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Tunable dual-AAV sparse labeling of PV+ retinal ganglion cells enables single-neuron projection by fMOST"],"prefix":"10.3389","volume":"19","author":[{"given":"Lingbo","family":"Zhou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gao","family":"Tan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Man","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sen","family":"Jin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenhui","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qitian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yin","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,1,12]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"i574","DOI":"10.1093\/bioinformatics\/bty231","article-title":"NeuroMorphoVis: A collaborative framework for analysis and visualization of neuronal morphology skeletons reconstructed from microscopy stacks.","volume":"34","author":"Abdellah","year":"2018","journal-title":"Bioinformatics"},{"key":"B2","doi-asserted-by":"publisher","first-page":"5310","DOI":"10.3390\/s21165310","article-title":"Residential water meters as edge computing nodes: Disaggregating end uses and creating actionable information at the edge.","volume":"21","author":"Attallah","year":"2021","journal-title":"Sensors"},{"key":"B3","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1038\/nature16468","article-title":"The functional diversity of retinal ganglion cells in the mouse.","volume":"529","author":"Baden","year":"2016","journal-title":"Nature"},{"key":"B4","doi-asserted-by":"publisher","first-page":"5893","DOI":"10.3390\/jcm13195893","article-title":"Fully Automated detection of the appendix using U-net deep learning architecture in CT scans.","volume":"13","author":"Bastug","year":"2024","journal-title":"J. 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