Nianping Liu

All Publications

• Subcellular transcriptome sequencing with single cell APEX-seq identifies regulators of cell-cell interactions. bioRxiv : the preprint server for biology Xue, A., Cai, B., Xue, Q., Liu, N., Qiu, X., Hernandez-Lopez, R. A., Ting, A. Y. 2026

  Abstract

  Single-cell RNA sequencing has transformed our understanding of tissue complexity and heterogeneous cell states, yet provides little information about the subcellular organization of transcriptomes - despite the central role of RNA localization in splicing, translation, and function. Here we introduce single-cell APEX-seq (scAPEX-seq), a proximity labeling-based method for mapping subcellular transcriptomes at single-cell resolution. Improvements in probe design and RNA recovery enable APEX integration with droplet-based RNA-seq to capture endoplasmic reticulum-associated transcripts from thousands of individual cells. Applied to tumor-macrophage co-cultures, ER-targeted scAPEX-seq revealed interaction-dependent cell states and transcriptomic signatures by enriching for cell surface and secretory transcripts that are poorly resolved by conventional scRNA-seq. We further applied scAPEX-seq to short- and long-term co-cultures of HER2+ tumor cells with human chimeric antigen receptor (CAR) T cells, resolving distinct activated CAR T cell states, including populations characterized by upregulated NT5E or CTSW expression. We showed that overexpression of CTSW, a cathepsin protease, in CAR T cells promotes stem-like phenotypes, long-term proliferation, and sustained tumor cell killing. scAPEX-seq provides a powerful and scalable approach for profiling subcellular RNA populations, enabling the discovery of cell-cell interaction regulators missed by conventional approaches.

  View details for DOI 10.64898/2026.03.17.712496

  View details for PubMedID 41889815

• What insights can spatiotemporal esophageal atlases and deep learning bring to engineering the esophageal mucosa? Developmental cell Wang, S., Liu, N., Qu, K. 2025; 60 (9): 1279-1280

  Abstract

  In this issue of Developmental Cell, Yang et al. present an integrated experimental and computational platform that maps the spatiotemporal development of the human esophagus and predicts key signaling pathways governing epithelial differentiation. Their findings enable a xeno-free, scalable strategy for generating esophageal mucosa from human pluripotent stem cells (hPSCs), demonstrating the power of combining spatial developmental data with deep learning.

  View details for DOI 10.1016/j.devcel.2025.04.009

  View details for PubMedID 40328228

• MorPhiC Consortium: towards functional characterization of all human genes. Nature Adli, M., Przybyla, L., Burdett, T., Burridge, P. W., Cacheiro, P., Chang, H. Y., Engreitz, J. M., Gilbert, L. A., Greenleaf, W. J., Hsu, L., Huangfu, D., Hung, L. H., Kundaje, A., Li, S., Parkinson, H., Qiu, X., Robson, P., Schürer, S. C., Shojaie, A., Skarnes, W. C., Smedley, D., Studer, L., Sun, W., Vidović, D., Vierbuchen, T., White, B. S., Yeung, K. Y., Yue, F., Zhou, T. 2025; 638 (8050): 351-359

  Abstract

  Recent advances in functional genomics and human cellular models have substantially enhanced our understanding of the structure and regulation of the human genome. However, our grasp of the molecular functions of human genes remains incomplete and biased towards specific gene classes. The Molecular Phenotypes of Null Alleles in Cells (MorPhiC) Consortium aims to address this gap by creating a comprehensive catalogue of the molecular and cellular phenotypes associated with null alleles of all human genes using in vitro multicellular systems. In this Perspective, we present the strategic vision of the MorPhiC Consortium and discuss various strategies for generating null alleles, as well as the challenges involved. We describe the cellular models and scalable phenotypic readouts that will be used in the consortium's initial phase, focusing on 1,000 protein-coding genes. The resulting molecular and cellular data will be compiled into a catalogue of null-allele phenotypes. The methodologies developed in this phase will establish best practices for extending these approaches to all human protein-coding genes. The resources generated-including engineered cell lines, plasmids, phenotypic data, genomic information and computational tools-will be made available to the broader research community to facilitate deeper insights into human gene functions.

  View details for DOI 10.1038/s41586-024-08243-w

  View details for PubMedID 39939790

  View details for PubMedCentralID 9903716