Biology subjects

Shuai, J.

Publications and source records attributed to Shuai, J..

4 recordsLinked to original sources

bioRxiv2023

Topological design principle for the robustness of necroptosis biphasic, emergent, and coexistent (BEC) dynamics

Biphasic dynamics, the variable-dependent ability to enhance or restrain biological function, is prevalent in natural systems. Accompanied by biphasic dynamics, necroptosis signaling dominated by RIP1 also appears emergent and coexistent dynamics. Here, we identify the RIP1-RIP3-C8 incoherent feedforward loop embedded with positive feedback of RIP3 to RIP1 is the core topology, and the scale-free feature of RIP3 peak value dictates necroptosis BEC dynamics. Entropy production is introduced to quantify the uncertainty of coexistent dynamics. RIP3 auto-phosphorylation is further determined as a complementary process for robustly attaining necroptosis BEC dynamics. Through screening all possible two- and three-node circuit topologies, a complete atlas of three-node circuit BEC dynamics is generated and only three minimal circuits emerge as robust solutions, proving incoherent feedforward loop is the core topology. Overall, through highlighting a finite set of circuits, this study yields guiding principles for mapping, modulating, and designing circuits for BEC dynamics in biological systems.

systems biology↗

bioRxiv2022

SCTC: inference of developmental potential from single-cell transcriptional complexity

Inference of single-cell developmental potential from scRNA-Seq data enables us to reconstruct the pseudo-temporal path of cell development, which is an important and challenging task for single-cell analysis. Single-cell transcriptional diversity (SCTD), measured by the number of expressed genes per cell, has been found to be negatively correlated with the development time, and thus can be considered as a hallmark of developmental potential. However, in some cases, the gene expression level of the cells in the early stages of development may be lower than that of the later stages, which may lead to incorrect estimation of differentiation states by gene diversity-based inference. Here we refer to the economic complexity theory and propose single-cell transcriptional complexity (SCTC) metrics as a measure of single-cell developmental potential, given the intrinsic similarities between biological and economic complex systems. We take into account not only the number of genes expressed by cells, but also the more sophisticated structure information of gene expression by treating the scRNA-seq count matrix as a bipartite network. We show that complexity metrics characterize the developmental potential more accurately than the diversity metrics. Especially, in the early stages of development, cells typically have lower gene expression level than that in the later stages, while their complexity in the early stages is significantly higher than that in the later stages. Based on the measurement of SCTC, we provide an unsupervised method for accurate, robust, and transferable inference of single-cell pseudotime. Our findings suggest that the complexity emerging from the interaction between cells and genes determines the developmental potential, which may bring new insights into the understanding of biological development from the perspective of the complexity theory.

bioinformatics↗

bioRxiv2022

Dear-DIAXMBD: deep autoencoder for data-independent acquisition proteomics

Data-independent acquisition (DIA) technology for protein identification from mass spectrometry and related algorithms is developing rapidly. The spectrum-centric analysis of DIA data without the use of spectra library from data-dependent acquisition (DDA) data represents a promising direction. In this paper, we proposed an untargeted analysis method, Dear-DIAXMBD, for direct analysis of DIA data. Dear-DIAXMBD first integrates the deep variational autoencoder and triplet loss to learn the representations of the extracted fragment ion chromatograms, then uses the k-means clustering algorithm to aggregate fragments with similar representations into the same classes, and finally establishes the inverted index tables to determine the precursors of fragment clusters between precursors and peptides, and between fragments and peptides. We show that Dear-DIAXMBD performs superiorly with the highly complicated DIA data of different species obtained by different instrument platforms. Dear-DIAXMBD is publicly available at https://github.com/jianweishuai/Dear-DIA-XMBD.

bioinformatics↗

bioRxiv2021

DreamDIA-XMBD: deep representation features improve the analysis of data-independent acquisition proteomics

We developed DreamDIA-XMBD, a software suite for data-independent acquisition (DIA) data analysis. DreamDIA-XMBD adopts a data-driven strategy to capture comprehensive information from elution patterns of target peptides in DIA data and achieves considerable improvements on both identification and quantification performance compared with other state-of-the-art methods such as OpenSWATH, Skyline and DIA-NN. More specifically, in contrast to existing methods which use only 6 to 10 selected transitions from spectral library, DreamDIA-XMBD extracts additional features from dozens of theoretical elution profiles originated from different ions of each precursor using a deep representation network. To achieve higher coverage of target peptides without sacrificing specificity, the extracted features are further processed by non-linear discriminative models under the framework of positive-unlabeled learning with decoy peptides as affirmative negative controls. DreamDIA-XMBD is written in Python, and is publicly available at https://github.com/xmuyulab/Dream-DIA-XMBD for high coverage and precision DIA data analysis.

bioinformatics↗