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Su, X.-D.

Publications and source records attributed to Su, X.-D..

3 recordsLinked to original sources

Plant-on-Chip: core morphogenesis processes in the tiny plant Wolffia australiana

A plant can be thought of as a colony comprising numerous growth buds, each developing to its own rhythm. Such lack of synchrony impedes efforts to describe core principles of plant morphogenesis, dissect the underlying mechanisms, and identify regulators. Here, we use the tiniest known angiosperm to overcome this challenge and provide an ideal model system for plant morphogenesis. We present a detailed morphological description of the monocot Wolffia australiana, as well as high-quality genome information. Further, we developed the Plant-on-Chip culture system and demonstrate the application of advanced technologies such as snRNA-seq, protein structure prediction, and gene editing. We provide proof-of-concept examples that illustrate how W. australiana can open a new horizon for deciphering the core regulatory mechanisms of plant morphogenesis. SignificanceWhat is the core morphogenetic process in angiosperms, a plant like a tree indeterminately growing, or a bud sequentially generating limited types of organs? Wolffia australiana, one of the smallest angiosperms in the world may help to make a distinction. Wolffia plantlet constitutes of only three organs that are indispensable to complete life cycle: one leaf, one stamen and one gynoecium. Before the growth tip is induced to flower, it keeps branching from the leaf axil and the branches separate from the main plantlet. Here we present a high-quality genome of W. australiana, detailed morphological description, a Plant-on-Chip cultural system, and some principle-proof experiments, demonstrating that W. australiana is a promising model system for deciphering core developmental program in angiosperms.

plant biology↗

Surveying Brain Tumor Heterogeneity by Single-Cell RNA Sequencing of Multi-sector Biopsies

Brain tumors are among the most challenging human tumors for which the mechanisms driving progression and heterogeneity remain poorly understood. We combined single-cell RNA-seq with multisector biopsies to sample and analyze single-cell expression profiles of gliomas from 13 Chinese patients. After classifying individual cells, we generated a spatial and temporal landscape of glioma that revealed the patterns of invasion between the different sub-regions of gliomas. We also used single-cell inferred CNVs and pseudotime trajectories to inform on the crucial branches that dominate tumor progression. The dynamic cell components of the multi-region biopsy analysis allowed us to spatially deconvolute with unprecedented accuracy the transcriptomic features of the core and those of the periphery of glioma at single cell level. Through this rich and geographically detailed dataset, we were also able to characterize and construct the chemokine and chemokine receptor interactions that exist among different tumor and non-tumor cells. This study provides the first spatial-level analysis of the cellular states that characterize human gliomas. It also presents an initial molecular map of the crosstalks between glioma cells and the surrounding microenvironment with single cell resolution.

genomics↗

A MAP of tumor-host interactions in glioma at single cell resolution

Single-cell RNA sequencing is the reference technique to characterize the heterogeneity of tumor microenvironment and can be efficiently used to discover cross-talk mechanisms between immune cells and cancer cells. We present a novel method, single cell Tumor-Host Interaction tool (scTHI), to identify significantly activated ligand-receptor interactions across clusters of cells from single-cell RNA sequencing data. We apply our approach to uncover the ligand-receptor interactions in glioma using six publicly available human glioma datasets encompassing 71 patients. We provide a comprehensive map of the signalling mechanisms between malignant cells and non-malignant cells in glioma uncovering potential novel therapeutic targets.

bioinformatics↗