Academic Appointments

  • Basic Life Science Research Associate, Biology

All Publications

  • Reciprocal repulsions instruct the precise assembly of parallel hippocampal networks. Science (New York, N.Y.) Pederick, D. T., Lui, J. H., Gingrich, E. C., Xu, C., Wagner, M. J., Liu, Y., He, Z., Quake, S. R., Luo, L. 2021; 372 (6546): 1068-1073


    Mammalian medial and lateral hippocampal networks preferentially process spatial- and object-related information, respectively. However, the mechanisms underlying the assembly of such parallel networks during development remain largely unknown. Our study shows that, in mice, complementary expression of cell surface molecules teneurin-3 (Ten3) and latrophilin-2 (Lphn2) in the medial and lateral hippocampal networks, respectively, guides the precise assembly of CA1-to-subiculum connections in both networks. In the medial network, Ten3-expressing (Ten3+) CA1 axons are repelled by target-derived Lphn2, revealing that Lphn2- and Ten3-mediated heterophilic repulsion and Ten3-mediated homophilic attraction cooperate to control precise target selection of CA1 axons. In the lateral network, Lphn2-expressing (Lphn2+) CA1 axons are confined to Lphn2+ targets via repulsion from Ten3+ targets. Our findings demonstrate that assembly of parallel hippocampal networks follows a "Ten3Ten3, Lphn2Lphn2" rule instructed by reciprocal repulsions.

    View details for DOI 10.1126/science.abg1774

    View details for PubMedID 34083484

  • A community-based transcriptomics classification and nomenclature of neocortical cell types. Nature neuroscience Yuste, R., Hawrylycz, M., Aalling, N., Aguilar-Valles, A., Arendt, D., Arnedillo, R. A., Ascoli, G. A., Bielza, C., Bokharaie, V., Bergmann, T. B., Bystron, I., Capogna, M., Chang, Y., Clemens, A., de Kock, C. P., DeFelipe, J., Dos Santos, S. E., Dunville, K., Feldmeyer, D., Fiath, R., Fishell, G. J., Foggetti, A., Gao, X., Ghaderi, P., Goriounova, N. A., Gunturkun, O., Hagihara, K., Hall, V. J., Helmstaedter, M., Herculano, S., Hilscher, M. M., Hirase, H., Hjerling-Leffler, J., Hodge, R., Huang, J., Huda, R., Khodosevich, K., Kiehn, O., Koch, H., Kuebler, E. S., Kuhnemund, M., Larranaga, P., Lelieveldt, B., Louth, E. L., Lui, J. H., Mansvelder, H. D., Marin, O., Martinez-Trujillo, J., Moradi Chameh, H., Nath, A., Nedergaard, M., Nemec, P., Ofer, N., Pfisterer, U. G., Pontes, S., Redmond, W., Rossier, J., Sanes, J. R., Scheuermann, R., Serrano-Saiz, E., Steiger, J. F., Somogyi, P., Tamas, G., Tolias, A. S., Tosches, M. A., Garcia, M. T., Vieira, H. M., Wozny, C., Wuttke, T. V., Yong, L., Yuan, J., Zeng, H., Lein, E. 2020


    To understand the function of cortical circuits, it is necessary to catalog their cellular diversity. Past attempts to do so using anatomical, physiological or molecular features of cortical cells have not resulted in a unified taxonomy of neuronal or glial cell types, partly due to limited data. Single-cell transcriptomics is enabling, for the first time, systematic high-throughput measurements of cortical cells and generation of datasets that hold the promise of being complete, accurate and permanent. Statistical analyses of these data reveal clusters that often correspond to cell types previously defined by morphological or physiological criteria and that appear conserved across cortical areas and species. To capitalize on these new methods, we propose the adoption of a transcriptome-based taxonomy of cell types for mammalian neocortex. This classification should be hierarchical and use a standardized nomenclature. It should be based on a probabilistic definition of a cell type and incorporate data from different approaches, developmental stages and species. A community-based classification and data aggregation model, such as a knowledge graph, could provide a common foundation for the study of cortical circuits. This community-based classification, nomenclature and data aggregation could serve as an example for cell type atlases in other parts of the body.

    View details for DOI 10.1038/s41593-020-0685-8

    View details for PubMedID 32839617

  • Mapping mesoscale axonal projections in the mouse brain using a 3D convolutional network. Proceedings of the National Academy of Sciences of the United States of America Friedmann, D., Pun, A., Adams, E. L., Lui, J. H., Kebschull, J. M., Grutzner, S. M., Castagnola, C., Tessier-Lavigne, M., Luo, L. 2020


    The projection targets of a neuronal population are a key feature of its anatomical characteristics. Historically, tissue sectioning, confocal microscopy, and manual scoring of specific regions of interest have been used to generate coarse summaries of mesoscale projectomes. We present here TrailMap, a three-dimensional (3D) convolutional network for extracting axonal projections from intact cleared mouse brains imaged by light-sheet microscopy. TrailMap allows region-based quantification of total axon content in large and complex 3D structures after registration to a standard reference atlas. The identification of axonal structures as thin as one voxel benefits from data augmentation but also requires a loss function that tolerates errors in annotation. A network trained with volumes of serotonergic axons in all major brain regions can be generalized to map and quantify axons from thalamocortical, deep cerebellar, and cortical projection neurons, validating transfer learning as a tool to adapt the model to novel categories of axonal morphology. Speed of training, ease of use, and accuracy improve over existing tools without a need for specialized computing hardware. Given the recent emphasis on genetically and functionally defining cell types in neural circuit analysis, TrailMap will facilitate automated extraction and quantification of axons from these specific cell types at the scale of the entire mouse brain, an essential component of deciphering their connectivity.

    View details for DOI 10.1073/pnas.1918465117

    View details for PubMedID 32358193

  • Differential encoding in prefrontal cortex projection neuron classes across cognitive tasks. Cell Lui, J. H., Nguyen, N. D., Grutzner, S. M., Darmanis, S. n., Peixoto, D. n., Wagner, M. J., Allen, W. E., Kebschull, J. M., Richman, E. B., Ren, J. n., Newsome, W. T., Quake, S. R., Luo, L. n. 2020


    Single-cell transcriptomics has been widely applied to classify neurons in the mammalian brain, while systems neuroscience has historically analyzed the encoding properties of cortical neurons without considering cell types. Here we examine how specific transcriptomic types of mouse prefrontal cortex (PFC) projection neurons relate to axonal projections and encoding properties across multiple cognitive tasks. We found that most types projected to multiple targets, and most targets received projections from multiple types, except PFC→PAG (periaqueductal gray). By comparing Ca2+ activity of the molecularly homogeneous PFC→PAG type against two heterogeneous classes in several two-alternative choice tasks in freely moving mice, we found that all task-related signals assayed were qualitatively present in all examined classes. However, PAG-projecting neurons most potently encoded choice in cued tasks, whereas contralateral PFC-projecting neurons most potently encoded reward context in an uncued task. Thus, task signals are organized redundantly, but with clear quantitative biases across cells of specific molecular-anatomical characteristics.

    View details for DOI 10.1016/j.cell.2020.11.046

    View details for PubMedID 33338423

  • Secretagogin is Expressed by Developing Neocortical GABAergic Neurons in Humans but not Mice and Increases Neurite Arbor Size and Complexity. Cerebral cortex (New York, N.Y. : 1991) Raju, C. S., Spatazza, J., Stanco, A., Larimer, P., Sorrells, S. F., Kelley, K. W., Nicholas, C. R., Paredes, M. F., Lui, J. H., Hasenstaub, A. R., Kriegstein, A. R., Alvarez-Buylla, A., Rubenstein, J. L., Oldham, M. C. 2017: 1-13


    The neocortex of primates, including humans, contains more abundant and diverse inhibitory neurons compared with rodents, but the molecular foundations of these observations are unknown. Through integrative gene coexpression analysis, we determined a consensus transcriptional profile of GABAergic neurons in mid-gestation human neocortex. By comparing this profile to genes expressed in GABAergic neurons purified from neonatal mouse neocortex, we identified conserved and distinct aspects of gene expression in these cells between the species. We show here that the calcium-binding protein secretagogin (SCGN) is robustly expressed by neocortical GABAergic neurons derived from caudal ganglionic eminences (CGE) and lateral ganglionic eminences during human but not mouse brain development. Through electrophysiological and morphometric analyses, we examined the effects of SCGN expression on GABAergic neuron function and form. Forced expression of SCGN in CGE-derived mouse GABAergic neurons significantly increased total neurite length and arbor complexity following transplantation into mouse neocortex, revealing a molecular pathway that contributes to morphological differences in these cells between rodents and primates.

    View details for DOI 10.1093/cercor/bhx101

    View details for PubMedID 28449024

  • Single-cell analysis of long non-coding RNAs in the developing human neocortex. Genome biology Liu, S. J., Nowakowski, T. J., Pollen, A. A., Lui, J. H., Horlbeck, M. A., Attenello, F. J., He, D., Weissman, J. S., Kriegstein, A. R., Diaz, A. A., Lim, D. A. 2016; 17 (1): 67-?


    Long non-coding RNAs (lncRNAs) comprise a diverse class of transcripts that can regulate molecular and cellular processes in brain development and disease. LncRNAs exhibit cell type- and tissue-specific expression, but little is known about the expression and function of lncRNAs in the developing human brain. Furthermore, it has been unclear whether lncRNAs are highly expressed in subsets of cells within tissues, despite appearing lowly expressed in bulk populations.We use strand-specific RNA-seq to deeply profile lncRNAs from polyadenylated and total RNA obtained from human neocortex at different stages of development, and we apply this reference to analyze the transcriptomes of single cells. While lncRNAs are generally detected at low levels in bulk tissues, single-cell transcriptomics of hundreds of neocortex cells reveal that many lncRNAs are abundantly expressed in individual cells and are cell type-specific. Notably, LOC646329 is a lncRNA enriched in single radial glia cells but is detected at low abundance in tissues. CRISPRi knockdown of LOC646329 indicates that this lncRNA regulates cell proliferation.The discrete and abundant expression of lncRNAs among individual cells has important implications for both their biological function and utility for distinguishing neural cell types.

    View details for DOI 10.1186/s13059-016-0932-1

    View details for PubMedID 27081004

    View details for PubMedCentralID PMC4831157