Biology subjects

Nie, R.

Publications and source records attributed to Nie, R..

6 recordsLinked to original sources

bioRxiv2025

TUPAD: A Tn5-Mediated, Pre-amplification/Adapter-Dimer-Free Platform for Ultra-Uniform, One-Step PGT Profiling with High Stability and Broad Compatibility

High-throughput sequencing stands as a cornerstone of clinical molecular diagnostics, particularly in preimplantation genetic testing (PGT)--a key application where the selection of chromosomally normal embryos is critical to enhancing in vitro fertilization (IVF) success rates. A fundamental challenge inherent to PGT, however, lies in the ultra-low gDNA input (typically <100 pg) from embryo biopsies. To mitigate this limitation, traditional workflows rely on whole genome amplification (WGA) technologies, such as MDA, PicoPlex, and MALBAC. Yet these WGA-based protocols inherently entail pre-amplification of gDNA prior to standard library construction---a step that introduces significant coverage bias and compromised uniformity. These are intrinsic flaws irreparable by downstream library preparation or bioinformatics analysis, ultimately leading to clinical uncertainty in the interpretation of copy number variations (CNVs). In this study, we developed TUPAD (Tn5-Mediated, Uniform Pre-amplification-free and Adapter-Dimer-Free Library preparation), a transformative technology that bypasses the limitations of traditional WGA. By engineering a novel adapter-dimer-free transposon system, TUPAD enables high-efficiency library construction from as little as 1 picogram (pg) of gDNA. This breakthrough allows for a pre-amplification-free workflow, effectively eliminating the primary source of amplification bias. Compared to existing WGA standards, these advantages are fully retained in embryo biopsy and positive sample testing, TUPAD consistently generates highly uniform library data, significantly enhancing the detection limit and accuracy of CNVs and other genomic variants. Beyond these performance advantages, TUPAD also cuts wet-lab processing time by 50%. Practical validation confirms TUPAD results are stable, reliable, with proven practical utility in PGT testing.

molecular biology↗

bioRxiv2024

Pathway enrichment-based unsupervised learning identifies novel subtypes of cancer-associated fibroblasts in pancreatic ductal carcinoma

Existing single-cell clustering methods are based on gene expressions that are susceptible to dropout events in single-cell RNA sequencing (scRNA-seq) data. To overcome this limitation, we proposed a pathway-based clustering method for single cells (scPathClus). scPathClus first transforms single-cell gene expression matrix into pathway enrichment matrix and generates its latent feature matrix. Based on the latent feature matrix, scPathClus clusters single cells using the method of community detection. Applying scPathClus to PDAC scRNA-seq datasets, we identified two types of cancer-associated fibroblasts (CAFs), termed csCAFs and gapCAFs, which highly expressed complement system and gap junction-related pathways, respectively. Spatial transcriptome analysis revealed that gapCAFs and csCAFs are located at cancer and non-cancer regions, respectively. Pseudotime analysis suggest a potential differentiation trajectory from csCAFs to gapCAFs. Bulk transcriptome analysis showed that gapCAFs-enriched tumors are more endowed with tumor-promoting characteristics and worse clinical outcomes, while csCAFs-enriched tumors confront stronger antitumor immune responses. Compared to established CAF subtyping methods, this method displays better prognostic relevance.

bioinformatics↗

bioRxiv2023

InClust+: the multimodal version of inClust for multimodal data integration, imputation, and cross modal generation

With the development of single-cell technology, many cell traits (e.g. gene expression, chromatin accessibility, DNA methylation) can be measured. Furthermore, the multi-omic profiling technology could jointly measure two or more traits in a single cell simultaneously. In order to process the various data accumulated rapidly, computational methods for multimodal data integration are needed. Previously, we developed inClust, a flexible all-in deep generative framework for transcriptome data. Here, we extend the applicability of inClust into the realm of multimodal data by adding two mask modules: an input-mask module in front of the encoder and an output-mask module behind the decoder. We call this augmented model inClust+, and apply it to various multimodal data. InClust+ was first used to integrate scRNA and MERFISH data from similar cell populations and to impute MERFISH data based on scRNA data. Then, inClust+ is shown to have the capability to integrate a multimodal data contain scRNA and scATAC or two multimodal CITE datasets with batch effect. Finally, inClust+ is used to integrate a monomodal scRNA dataset and two multimodal CITE datasets, and generate the missing modality of surface protein in monomodal scRNA data. In the above examples, the performance of inClust+ is better than or comparable to the most recent tools to the corresponding task, which prove inClust+ is a suitable framework for handling multimodal data. Meanwhile, the successful implementation of mask in inClust+ means that it can be applied to other deep learning methods with similar encoder-decoder architecture to broaden the application scope of these models.

bioinformatics↗

bioRxiv2022

inClust: a general framework for clustering that integrates data from multiple sources

Clustering is one of the most commonly used methods in single-cell RNA sequencing (scRNA-seq) data analysis and other fields of biology. Traditional clustering methods usually use data from a single source as the input (e.g. scRNA-seq data). However, as the data become more and more complex and contain information from multiple sources, a clustering method that could integrate multiple data is required. Here, we present inClust (integrated clustering), a clustering method that integrates information from multiple sources based on variational autoencoder and vector arithmetic in latent space. inClust perform information integration and clustering jointly, meanwhile it could utilize the labeling information from data as regulation information. It is a flexible framework that can accomplish different tasks under different modes, ranging from supervised to unsupervised. We demonstrate the capability of inClust in the tasks of conditional out-of-distribution generation under supervised mode; label transfer under semi-supervised mode and guided clustering mode; spatial domain identification under unsupervised mode. inClust performs well in all tasks, indicating that it is an excellent general framework for clustering and task-related clustering in the era of multi-omics.

bioinformatics↗

bioRxiv2018

scCapsNet: a deep learning classifier with the capability of interpretable feature extraction, applicable for single cell RNA data analysis

Recently deep learning methods have been applied to process biological data and greatly pushed the development of the biological research forward. However, the interpretability of the deep learning methods still needs to improve. Here for the first time, we present scCapsNet, a totally interpretable deep learning model adapted from CapsNet. The scCapsNet model retains the capsule parts of CapsNet but replaces the part of convolutional neural networks with several parallel fully connected neural networks. We apply scCapsNet to scRNA-seq data. The results show that scCapsNet performs well as a classifier and also that the parallel fully connected neural networks function like feature extractors as we supposed. The scCapsNet model provides contribution of each extracted feature to the cell type recognition. Evidences show that some extracted features are nearly orthogonal to each other. After training, through analysis of the internal weights of each neural network connected inputs and primary capsule, and with the information about the contribution of each extracted feature to the cell type recognition, the scCapsNet model could relate gene sets from inputs to cell types. The specific gene set is responsible for the identification of its corresponding cell types but does not affect the recognition of other cell types by the model. Many well-studied cell type markers are in the gene set with corresponding cell type. The internal weights of neural network for those well-studied cell type markers are different for different primary capsules. The internal weights of neural network connected to a primary capsule could be viewed as an embedding for genes, convert genes to real value low dimensional vectors. Furthermore, we mix the RNA expression data of two cells with different cell types and then use the scCapsNet model trained with non-mixed data to predict the cell types in the mixed data. Our scCapsNet model could predict cell types in a cell mixture with high accuracy.

bioinformatics↗

bioRxiv2018

The contribution of mitochondrial metagenomics to largescale data mining and phylogenetic analysis of Coleoptera

A phylogenetic tree at the species level is still far off for highly diverse insect orders, including the Coleoptera, but the taxonomic breadth of public sequence databases is growing. In addition, new types of data may contribute to increasing taxon coverage, such as metagenomic shotgun sequencing for assembly of mitogenomes from bulk specimen samples. The current study explores the application of these techniques for large-scale efforts to build the tree of Coleoptera. We used shotgun data from 17 different ecological and taxonomic datasets (5 unpublished) to assemble a total of 1942 mitogenome contigs of >3000 bp. These sequences were combined into a single dataset together with all mitochondrial data available at GenBank, in addition to nuclear markers widely used in molecular phylogenetics. The resulting matrix of nearly 16000 species with two or more loci produced trees (RAxML) showing overall congruence with the Linnaean taxonomy at hierarchical levels from suborders to genera. We tested the role of full-length mitogenomes in stabilizing the tree from GenBank data, as mitogenomes might link terminals with non-overlapping gene representation. However, the mitogenome data were only partly useful in this respect, presumably because of the purely automated approach to assembly and gene delimitation, but improvements in future may be possible by using multiple assemblers and manual curation. In conclusion, the combination of data mining and metagenomic sequencing of bulk samples provided the largest phylogenetic tree of Coleoptera to date, which represents a summary of existing phylogenetic knowledge and a defensible tree of great utility, in particular for studies at the intra-familial level, despite some shortcomings for resolving basal nodes.

evolutionary biology↗