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A Single-cell Transcriptome Atlas of Cashmere Goat Hair Follicle Morphogenesis.

Wei GE ; Weidong ZHANG ; Yuelang ZHANG ; Yujie ZHENG ; Fang LI ; Shanhe WANG ; Jinwang LIU ; Shaojing TAN ; Zihui YAN ; Lu WANG ; Wei SHEN ; Lei QU ; Xin WANG

Genomics, Proteomics & Bioinformatics.2021;19(3):437-451. doi:10.1016/j.gpb.2021.07.003

Cashmere, also known as soft gold, is produced from the secondary hair follicles (SHFs) of cashmere goats. The number of SHFs determines the yield and quality of cashmere; therefore, it is of interest to investigate the transcriptional profiles present during cashmere goat hair follicle development. However, mechanisms underlying this development process remain largely unexplored, and studies regarding hair follicle development mostly use a murine research model. In this study, to provide a comprehensive understanding of cellular heterogeneity and cell fate decisions, single-cell RNA sequencing was performed on 19,705 single cells of the dorsal skin from cashmere goat fetuses at induction (embryonic day 60; E60), organogenesis (E90), and cytodifferentiation (E120) stages. For the first time, unsupervised clustering analysis identified 16 cell clusters, and their corresponding cell types were also characterized. Based on lineage inference, a detailed molecular landscape was revealed along the dermal and epidermal cell lineage developmental pathways. Notably, our current data also confirmed the heterogeneity of dermal papillae from different hair follicle types, which was further validated by immunofluorescence analysis. The current study identifies different biomarkers during cashmere goat hair follicle development and has implications for cashmere goat breeding in the future.

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Single-cell RNA Sequencing Reveals Sexually Dimorphic Transcriptome and Type 2 Diabetes Genes in Mouse Islet β Cells.

Gang LIU ; Yana LI ; Tengjiao ZHANG ; Mushan LI ; Sheng LI ; Qing HE ; Shuxin LIU ; Minglu XU ; Tinghui XIAO ; Zhen SHAO ; Weiyang SHI ; Weida LI

Genomics, Proteomics & Bioinformatics.2021;19(3):408-422. doi:10.1016/j.gpb.2021.07.004

Type 2 diabetes (T2D) is characterized by the malfunction of pancreatic β cells. Susceptibility and pathogenesis of T2D can be affected by multiple factors, including sex differences. However, the mechanisms underlying sex differences in T2D susceptibility and pathogenesis remain unclear. Using single-cell RNA sequencing (scRNA-seq), we demonstrate the presence of sexually dimorphic transcriptomes in mouse β cells. Using a high-fat diet-induced T2D mouse model, we identified sex-dependent T2D altered genes, suggesting sex-based differences in the pathological mechanisms of T2D. Furthermore, based on islet transplantation experiments, we found that compared to mice with sex-matched islet transplants, sex-mismatched islet transplants in healthy mice showed down-regulation of genes involved in the longevity regulating pathway of β cells. Moreover, the diabetic mice with sex-mismatched islet transplants showed impaired glucose tolerance. These data suggest sexual dimorphism in T2D pathogenicity, indicating that sex should be considered when treating T2D. We hope that our findings could provide new insights for the development of precision medicine in T2D.

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Single-cell Transcriptomes Reveal Characteristics of MicroRNAs in Gene Expression Noise Reduction.

Tao HU ; Lei WEI ; Shuailin LI ; Tianrun CHENG ; Xuegong ZHANG ; Xiaowo WANG

Genomics, Proteomics & Bioinformatics.2021;19(3):394-407. doi:10.1016/j.gpb.2021.05.002

Isogenic cells growing in identical environments show cell-to-cell variations because of the stochasticity in gene expression. High levels of variation or noise can disrupt robust gene expression and result in tremendous consequences for cell behaviors. In this work, we showed evidence from single-cell RNA sequencing data analysis that microRNAs (miRNAs) can reduce gene expression noise at the mRNA level in mouse cells. We identified that the miRNA expression level, number of targets, target pool abundance, and miRNA-target interaction strength are the key features contributing to noise repression. miRNAs tend to work together in cooperative subnetworks to repress target noise synergistically in a cell type-specific manner. By building a physical model of post-transcriptional regulation and observing in synthetic gene circuits, we demonstrated that accelerated degradation with elevated transcriptional activation of the miRNA target provides resistance to extrinsic fluctuations. Together, through the integrated analysis of single-cell RNA and miRNA expression profiles, we demonstrated that miRNAs are important post-transcriptional regulators for reducing gene expression noise and conferring robustness to biological processes.

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Single-cell RNA Sequencing Reveals Thoracolumbar Vertebra Heterogeneity and Rib-genesis in Pigs.

Jianbo LI ; Ligang WANG ; Dawei YU ; Junfeng HAO ; Longchao ZHANG ; Adeniyi C ADEOLA ; Bingyu MAO ; Yun GAO ; Shifang WU ; Chunling ZHU ; Yongqing ZHANG ; Jilong REN ; Changgai MU ; David M IRWIN ; Lixian WANG ; Tang HAI ; Haibing XIE ; Yaping ZHANG

Genomics, Proteomics & Bioinformatics.2021;19(3):423-436. doi:10.1016/j.gpb.2021.09.008

Development of thoracolumbar vertebra (TLV) and rib primordium (RP) is a common evolutionary feature across vertebrates, although whole-organism analysis of the expression dynamics of TLV- and RP-related genes has been lacking. Here, we investigated the single-cell transcriptome landscape of thoracic vertebra (TV), lumbar vertebra (LV), and RP cells from a pig embryo at 27 days post-fertilization (dpf) and identified six cell types with distinct gene expression signatures. In-depth dissection of the gene expression dynamics and RNA velocity revealed a coupled process of osteogenesis and angiogenesis during TLV and RP development. Further analysis of cell type-specific and strand-specific expression uncovered the extremely high level of HOXA10 3'-UTR sequence specific to osteoblasts of LV cells, which may function as anti-HOXA10-antisense by counteracting the HOXA10-antisense effect to determine TLV transition. Thus, this work provides a valuable resource for understanding embryonic osteogenesis and angiogenesis underlying vertebrate TLV and RP development at the cell type-specific resolution, which serves as a comprehensive view on the transcriptional profile of animal embryo development.

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Advanced Single-cell Omics Technologies and Informatics Tools for Genomics, Proteomics, and Bioinformatics Analysis.

Luonan CHEN ; Rong FAN ; Fuchou TANG

Genomics, Proteomics & Bioinformatics.2021;19(3):343-345. doi:10.1016/j.gpb.2021.12.001

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Integration of Droplet Microfluidic Tools for Single-Cell Functional Metagenomics: An Engineering Head Start.

David CONCHOUSO ; Amani AL-MA'ABADI ; Hayedeh BEHZAD ; Mohammed ALARAWI ; Masahito HOSOKAWA ; Yohei NISHIKAWA ; Haruko TAKEYAMA ; Katsuhiko MINETA ; Takashi GOJOBORI

Genomics, Proteomics & Bioinformatics.2021;19(3):504-518. doi:10.1016/j.gpb.2021.03.010

Droplet microfluidic techniques have shown promising outcome to study single cells at high throughput. However, their adoption in laboratories studying "-omics" sciences is still irrelevant due to the complex and multidisciplinary nature of the field. To facilitate their use, here we provide engineering details and organized protocols for integrating three droplet-based microfluidic technologies into the metagenomic pipeline to enable functional screening of bioproducts at high throughput. First, a device encapsulating single cells in droplets at a rate of ∼250 Hz is described considering droplet size and cell growth. Then, we expand on previously reported fluorescence-activated droplet sorting systems to integrate the use of 4 independent fluorescence-exciting lasers (i.e., 405, 488, 561, and 637 nm) in a single platform to make it compatible with different fluorescence-emitting biosensors. For this sorter, both hardware and software are provided and optimized for effortlessly sorting droplets at 60 Hz. Then, a passive droplet merger is also integrated into our pipeline to enable adding new reagents to already-made droplets at a rate of 200 Hz. Finally, we provide an optimized recipe for manufacturing these chips using silicon dry-etching tools. Because of the overall integration and the technical details presented here, our approach allows biologists to quickly use microfluidic technologies and achieve both single-cell resolution and high-throughput capability (>50,000 cells/day) for mining and bioprospecting metagenomic data.

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scGET: Predicting Cell Fate Transition During Early Embryonic Development by Single-cell Graph Entropy.

Jiayuan ZHONG ; Chongyin HAN ; Xuhang ZHANG ; Pei CHEN ; Rui LIU

Genomics, Proteomics & Bioinformatics.2021;19(3):461-474. doi:10.1016/j.gpb.2020.11.008

During early embryonic development, cell fate commitment represents a critical transition or "tipping point" of embryonic differentiation, at which there is a drastic and qualitative shift of the cell populations. In this study, we presented a computational approach, scGET, to explore the gene-gene associations based on single-cell RNA sequencing (scRNA-seq) data for critical transition prediction. Specifically, by transforming the gene expression data to the local network entropy, the single-cell graph entropy (SGE) value quantitatively characterizes the stability and criticality of gene regulatory networks among cell populations and thus can be employed to detect the critical signal of cell fate or lineage commitment at the single-cell level. Being applied to five scRNA-seq datasets of embryonic differentiation, scGET accurately predicts all the impending cell fate transitions. After identifying the "dark genes" that are non-differentially expressed genes but sensitive to the SGE value, the underlying signaling mechanisms were revealed, suggesting that the synergy of dark genes and their downstream targets may play a key role in various cell development processes.The application in all five datasets demonstrates the effectiveness of scGET in analyzing scRNA-seq data from a network perspective and its potential to track the dynamics of cell differentiation. The source code of scGET is accessible at https://github.com/zhongjiayuna/scGET_Project.

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Polar Gini Curve: A Technique to Discover Gene Expression Spatial Patterns from Single-cell RNA-seq Data.

Thanh Minh NGUYEN ; Jacob John JEEVAN ; Nuo XU ; Jake Y CHEN

Genomics, Proteomics & Bioinformatics.2021;19(3):493-503. doi:10.1016/j.gpb.2020.09.006

In this work, we describe the development of Polar Gini Curve, a method for characterizing cluster markers by analyzing single-cell RNA sequencing (scRNA-seq) data. Polar Gini Curve combines the gene expression and the 2D coordinates ("spatial") information to detect patterns of uniformity in any clustered cells from scRNA-seq data. We demonstrate that Polar Gini Curve can help users characterize the shape and density distribution of cells in a particular cluster, which can be generated during routine scRNA-seq data analysis. To quantify the extent to which a gene is uniformly distributed in a cell cluster space, we combine two polar Gini curves (PGCs)-one drawn upon the cell-points expressing the gene (the "foreground curve") and the other drawn upon all cell-points in the cluster (the "background curve"). We show that genes with highly dissimilar foreground and background curves tend not to uniformly distributed in the cell cluster-thus having spatially divergent gene expression patterns within the cluster. Genes with similar foreground and background curves tend to uniformly distributed in the cell cluster-thus having uniform gene expression patterns within the cluster. Such quantitative attributes of PGCs can be applied to sensitively discover biomarkers across clusters from scRNA-seq data. We demonstrate the performance of the Polar Gini Curve framework in several simulation case studies. Using this framework to analyze a real-world neonatal mouse heart cell dataset, the detected biomarkers may characterize novel subtypes of cardiac muscle cells. The source code and data for Polar Gini Curve could be found at http://discovery.informatics.uab.edu/PGC/ or https://figshare.com/projects/Polar_Gini_Curve/76749.

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GranatumX: A Community-engaging, Modularized, and Flexible Webtool for Single-cell Data Analysis.

David G GARMIRE ; Xun ZHU ; Aravind MANTRAVADI ; Qianhui HUANG ; Breck YUNITS ; Yu LIU ; Thomas WOLFGRUBER ; Olivier POIRION ; Tianying ZHAO ; Cédric ARISDAKESSIAN ; Stefan STANOJEVIC ; Lana X GARMIRE

Genomics, Proteomics & Bioinformatics.2021;19(3):452-460. doi:10.1016/j.gpb.2021.07.005

We present GranatumX, a next-generation software environment for single-cell RNA sequencing (scRNA-seq) data analysis. GranatumX is inspired by the interactive webtool Granatum. GranatumX enables biologists to access the latest scRNA-seq bioinformatics methods in a web-based graphical environment. It also offers software developers the opportunity to rapidly promote their own tools with others in customizable pipelines. The architecture of GranatumX allows for easy inclusion of plugin modules, named Gboxes, which wrap around bioinformatics tools written in various programming languages and on various platforms. GranatumX can be run on the cloud or private servers and generate reproducible results. It is a community-engaging, flexible, and evolving software ecosystem for scRNA-seq analysis, connecting developers with bench scientists. GranatumX is freely accessible at http://garmiregroup.org/granatumx/app.

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Quantitative Proteomics Using Isobaric Labeling: A Practical Guide.

Xiulan CHEN ; Yaping SUN ; Tingting ZHANG ; Lian SHU ; Peter ROEPSTORFF ; Fuquan YANG

Genomics, Proteomics & Bioinformatics.2021;19(5):689-706. doi:10.1016/j.gpb.2021.08.012

In the past decade, relative proteomic quantification using isobaric labeling technology has developed into a key tool for comparing the expression of proteins in biological samples. Although its multiplexing capacity and flexibility make this a valuable technology for addressing various biological questions, its quantitative accuracy and precision still pose significant challenges to the reliability of its quantification results. Here, we give a detailed overview of the different kinds of isobaric mass tags and the advantages and disadvantages of the isobaric labeling method. We also discuss which precautions should be taken at each step of the isobaric labeling workflow, to obtain reliable quantification results in large-scale quantitative proteomics experiments. In the last section, we discuss the broad applications of the isobaric labeling technology in biological and clinical studies, with an emphasis on thermal proteome profiling and proteogenomics.
Proteome/metabolism* ; Proteomics/methods* ; Reproducibility of Results ; Tandem Mass Spectrometry/methods*

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