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  • Organization of the human intestine at single-cell resolution. Nature Hickey, J. W., Becker, W. R., Nevins, S. A., Horning, A., Perez, A. E., Zhu, C., Zhu, B., Wei, B., Chiu, R., Chen, D. C., Cotter, D. L., Esplin, E. D., Weimer, A. K., Caraccio, C., Venkataraaman, V., Schürch, C. M., Black, S., Brbić, M., Cao, K., Chen, S., Zhang, W., Monte, E., Zhang, N. R., Ma, Z., Leskovec, J., Zhang, Z., Lin, S., Longacre, T., Plevritis, S. K., Lin, Y., Nolan, G. P., Greenleaf, W. J., Snyder, M. 2023; 619 (7970): 572-584

    Abstract

    The intestine is a complex organ that promotes digestion, extracts nutrients, participates in immune surveillance, maintains critical symbiotic relationships with microbiota and affects overall health1. The intesting has a length of over nine metres, along which there are differences in structure and function2. The localization of individual cell types, cell type development trajectories and detailed cell transcriptional programs probably drive these differences in function. Here, to better understand these differences, we evaluated the organization of single cells using multiplexed imaging and single-nucleus RNA and open chromatin assays across eight different intestinal sites from nine donors. Through systematic analyses, we find cell compositions that differ substantially across regions of the intestine and demonstrate the complexity of epithelial subtypes, and find that the same cell types are organized into distinct neighbourhoods and communities, highlighting distinct immunological niches that are present in the intestine. We also map gene regulatory differences in these cells that are suggestive of a regulatory differentiation cascade, and associate intestinal disease heritability with specific cell types. These results describe the complexity of the cell composition, regulation and organization for this organ, and serve as an important reference map for understanding human biology and disease.

    View details for DOI 10.1038/s41586-023-05915-x

    View details for PubMedID 37468586

    View details for PubMedCentralID PMC10356619

  • Annotation of spatially resolved single-cell data with STELLAR. Nature methods Brbic, M., Cao, K., Hickey, J. W., Tan, Y., Snyder, M. P., Nolan, G. P., Leskovec, J. 2022

    Abstract

    Accurate cell-type annotation from spatially resolved single cells is crucial to understand functional spatial biology that is the basis of tissue organization. However, current computational methods for annotating spatially resolved single-cell data are typically based on techniques established for dissociated single-cell technologies and thus do not take spatial organization into account. Here we present STELLAR, a geometric deep learning method for cell-type discovery and identification in spatially resolved single-cell datasets. STELLAR automatically assigns cells to cell types present in the annotated reference dataset and discovers novel cell types and cell states. STELLAR transfers annotations across different dissection regions, different tissues and different donors, and learns cell representations that capture higher-order tissue structures. We successfully applied STELLAR to CODEX multiplexed fluorescent microscopy data and multiplexed RNA imaging datasets. Within the Human BioMolecular Atlas Program, STELLAR has annotated 2.6million spatially resolved single cells with dramatic time savings.

    View details for DOI 10.1038/s41592-022-01651-8

    View details for PubMedID 36280720

  • Deep learning identifies robust gender differences in functional brain organization and their dissociable links to clinical symptoms in autism. The British journal of psychiatry : the journal of mental science Supekar, K., de Los Angeles, C., Ryali, S., Cao, K., Ma, T., Menon, V. 2022: 1-8

    Abstract

    BACKGROUND: Autism spectrum disorder (ASD) is a highly heterogeneous disorder that affects nearly 1 in 189 females and 1 in 42 males. However, the neurobiological basis of gender differences in ASD is poorly understood, as most studies have neglected females and used methods ill-suited to capture such differences.AIMS: To identify robust functional brain organisation markers that distinguish between females and males with ASD and predict symptom severity.METHOD: We leveraged multiple neuroimaging cohorts (ASD n = 773) and developed a novel spatiotemporal deep neural network (stDNN), which uses spatiotemporal convolution on functional magnetic resonance imaging data to distinguish between groups.RESULTS: stDNN achieved consistently high classification accuracy in distinguishing between females and males with ASD. Notably, stDNN trained to distinguish between females and males with ASD could not distinguish between neurotypical females and males, suggesting that there are gender differences in the functional brain organisation in ASD that differ from normative gender differences. Brain features associated with motor, language and visuospatial attentional systems reliably distinguished between females and males with ASD. Crucially, these results were observed in a large multisite cohort and replicated in a fully independent cohort. Furthermore, brain features associated with the motor network's primary motor cortex node predicted the severity of restricted/repetitive behaviours in females but not in males with ASD.CONCLUSIONS: Our replicable findings reveal that the brains of females and males with ASD are functionally organised differently, contributing to their clinical symptoms in distinct ways. They inform the development of gender-specific diagnoses and treatment strategies for ASD, and ultimately advance precision psychiatry.

    View details for DOI 10.1192/bjp.2022.13

    View details for PubMedID 35164888

  • Interstellar: Using Halide's Scheduling Language to Analyze DNN Accelerators Yang, X., Gao, M., Liu, Q., Setter, J., Pu, J., Nayak, A., Bell, S., Cao, K., Ha, H., Raina, P., Kozyrakis, C., Horowitz, M., ACM ASSOC COMPUTING MACHINERY. 2020: 369–83

    View details for DOI 10.1145/3373376.3378514

    View details for Web of Science ID 000541369300024

  • Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss Cao, K., Wei, C., Gaidon, A., Arechiga, N., Ma, T., Wallach, H., Larochelle, H., Beygelzimer, A., d'Alche-Buc, F., Fox, E., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2019

    View details for Web of Science ID 000534424301054

  • TransGaGa: Geometry-Aware Unsupervised Image-to-Image Translation Wu, W., Cao, K., Li, C., Qian, C., Loy, C., IEEE Comp Soc IEEE. 2019: 8004–13

    View details for DOI 10.1109/CVPR.2019.00820

    View details for Web of Science ID 000542649301062

  • Learning Temporal Action ProposalsWith Fewer Labels Ji, J., Cao, K., Niebles, J., IEEE IEEE. 2019: 7072–81

    View details for DOI 10.1109/ICCV.2019.00717

    View details for Web of Science ID 000548549202018

  • Delving Deep Into Hybrid Annotations for 3D Human Recovery in the Wild Rong, Y., Liu, Z., Li, C., Cao, K., Loy, C., IEEE IEEE. 2019: 5339–47

    View details for DOI 10.1109/ICCV.2019.00544

    View details for Web of Science ID 000548549200033