Biology subjects

Xi, N. M.

Publications and source records attributed to Xi, N. M..

3 recordsLinked to original sources

bioRxiv2023

Regenerative Signatures in Bronchioalveolar Lavage of Acute Respiratory Distress Syndrome

BackgroundIn patients with severe acute respiratory distress syndrome (ARDS) associated with sepsis, lung recovery is considerably delayed, and mortality is much high. More insight into the process of lung regeneration in ARDS patients is needed. Exosomes are important cargos for intercellular communication by serving as autocrine and/or paracrine. Cutting-edge exomics (exosomal proteomics) makes it possible to study the mechanisms of re-alveolarization in ARDS lungs. AimsThis study aimed to identify potential regenerative niches by characterizing differentially expressed proteins in the exosomes of bronchioalveolar lavage (BAL) in ARDS patients. MethodsWe purified exosomes from BAL samples collected from ARDS patients by NIH-supported ALTA and SPIROMICS trials. The abundance of exosomal proteins/peptides was quantified using liquid chromatography-mass spectrometry (LC-MS). Differentially expressed exosomal proteins between healthy controls and ARDS patients were profiled for functional annotations, cell origins, signaling pathways, networks, and clinical correlations. ResultsOur results show that more exosomal proteins were identified in the lungs of late-stage ARDS patients. Immune cells and lung epithelial stem cells were major contributors to BAL exosomes in addition to those from other organs. We enriched a wide range of functions, stem cell signals, growth factors, and immune niches in both mild and severe patients. The differentially expressed proteins that we identified were associated with key clinical variables. The severity-associated differences in protein-protein interaction, RNA crosstalk, and epigenetic network were observed between mild and severe groups. Moreover, alveolar type 2 epithelial cells could serve as both exosome donors and recipients via autocrine and paracrine mechanisms. ConclusionsThis study identifies novel exosomal proteins associated with diverse functions, signaling pathways, and cell origins in ARDS lavage samples. These differentiated proteins may serve as regenerative niches for re-alveolarization in injured lungs.

pathology↗

bioRxiv2023

Benchmarking the Autoencoder Design for Imputing Single-Cell RNA Sequencing Data

Autoencoders are the backbones of many imputation methods that aim to relieve the sparsity issue in single-cell RNA sequencing (scRNA-seq) data. The imputation performance of an autoencoder relies on both the neural network architecture and the hyperparameter choice. So far, literature in the single-cell field lacks a formal discussion on how to design the neural network and choose the hyperparameters. Here, we conducted an empirical study to answer this question. Our study used many real and simulated scRNA-seq datasets to examine the impacts of the neural network architecture, the activation function, and the regularization strategy on imputation accuracy and downstream analyses. Our results show that (i) deeper and narrower autoencoders generally lead to better imputation performance; (ii) the sigmoid and tanh activation functions consistently outperform other commonly used functions including ReLU; (iii) regularization improves the accuracy of imputation and downstream cell clustering and DE gene analyses. Notably, our results differ from common practices in the computer vision field regarding the activation function and the regularization strategy. Overall, our study offers practical guidance on how to optimize the autoencoder design for scRNA-seq data imputation.

bioinformatics↗

bioRxiv2022

scSampler: fast diversity-preserving subsampling of large-scale single-cell transcriptomic data

SummaryThe number of cells measured in single-cell transcriptomic data has grown fast in recent years. For such large-scale data, subsampling is a powerful and often necessary tool for exploratory data analysis. However, the easiest random subsampling is not ideal from the perspective of preserving rare cell types. Therefore, diversity-preserving subsampling is required for fast exploration of cell types in a large-scale dataset. Here we propose scSampler, an algorithm for fast diversity-preserving subsampling of single-cell transcriptomic data. Using simulated and real data, we show that scSampler consistently outperforms existing subsam-pling methods in terms of both the computational time and the Hausdorff distance between the full and subsampled datasets. AvailabilityscSampler is implemented in Python and is published under the MIT source license. It can be installed by pip install scsampler and used with the Scanpy pipline. The code is available on GitHub: https://github.com/SONGDONGYUAN1994/scsampler. Contactlinwang@gwu.edu; jli@stat.ucla.edu

bioinformatics↗