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Article ; Online: Spatial transcriptomics deconvolution at single-cell resolution using Redeconve.

Zhou, Zixiang / Zhong, Yunshan / Zhang, Zemin / Ren, Xianwen

2023 Volume 14, Issue 1, Page(s) 7930

Abstract: Computational deconvolution with single-cell RNA sequencing data as reference is pivotal to interpreting spatial transcriptomics data, but the current methods are limited to cell-type resolution. Here we present Redeconve, an algorithm to deconvolute ... ...

Abstract	Computational deconvolution with single-cell RNA sequencing data as reference is pivotal to interpreting spatial transcriptomics data, but the current methods are limited to cell-type resolution. Here we present Redeconve, an algorithm to deconvolute spatial transcriptomics data at single-cell resolution, enabling interpretation of spatial transcriptomics data with thousands of nuanced cell states. We benchmark Redeconve with the state-of-the-art algorithms on diverse spatial transcriptomics platforms and datasets and demonstrate the superiority of Redeconve in terms of accuracy, resolution, robustness, and speed. Application to a human pancreatic cancer dataset reveals cancer-clone-specific T cell infiltration, and application to lymph node samples identifies differential cytotoxic T cells between IgA+ and IgG+ spots, providing novel insights into tumor immunology and the regulatory mechanisms underlying antibody class switch.
MeSH term(s)	Humans ; Transcriptome/genetics ; Gene Expression Profiling ; Algorithms ; Benchmarking ; Immunoglobulin Isotypes ; Single-Cell Analysis
Chemical Substances	Immunoglobulin Isotypes
Language	English
Publishing date	2023-12-01
Publishing country	England
Document type	Journal Article
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-023-43600-9
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Article: Editorial: The Genetic Causes Underlying Immune Mediated Disease: A Focus on Autoimmunity and Cancer.

Jiménez-Morales, Silvia / Ren, Xianwen / Dean, Michael

Frontiers in genetics

2022 Volume 13, Page(s) 889160

Language	English
Publishing date	2022-03-25
Publishing country	Switzerland
Document type	Editorial
ZDB-ID	2606823-0
ISSN	1664-8021
ISSN	1664-8021
DOI	10.3389/fgene.2022.889160
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Article ; Online: Human Genetic Variants Associated with COVID-19 Severity are Enriched in Immune and Epithelium Regulatory Networks.

Feng, Zhanying / Ren, Xianwen / Duren, Zhana / Wang, Yong

Phenomics (Cham, Switzerland)

2022 Volume 2, Issue 6, Page(s) 389–403

Abstract: Human genetic variants can influence the severity of symptoms infected with SARS-COV-2. Several genome-wide association studies have identified human genomic risk single nucleotide polymorphisms (SNPs) associated with coronavirus disease 2019 (COVID-19) ... ...

Abstract	Human genetic variants can influence the severity of symptoms infected with SARS-COV-2. Several genome-wide association studies have identified human genomic risk single nucleotide polymorphisms (SNPs) associated with coronavirus disease 2019 (COVID-19) severity. However, the causal tissues or cell types underlying COVID-19 severity are uncertain. In addition, candidate genes associated with these risk SNPs were investigated based on genomic proximity instead of their functional cellular contexts. Here, we compiled regulatory networks of 77 human contexts and revealed those risk SNPs' enriched cellular contexts and associated risk SNPs with transcription factors, regulatory elements, and target genes. Twenty-one human contexts were identified and grouped into two categories: immune cells and epithelium cells. We further aggregated the regulatory networks of immune cells and epithelium cells. These two aggregated regulatory networks were investigated to reveal their association with risk SNPs' regulation. Two genomic clusters, the chemokine receptors cluster and the oligoadenylate synthetase (OAS) cluster, showed the strongest association with COVID-19 severity, and they had different regulatory programs in immune and epithelium contexts. Our findings were supported by analysis of both SNP array and whole genome sequencing-based genome wide association study (GWAS) summary statistics. Supplementary information: The online version contains supplementary material available at 10.1007/s43657-022-00066-x.
Language	English
Publishing date	2022-08-13
Publishing country	Switzerland
Document type	Journal Article
ISSN	2730-5848
ISSN (online)	2730-5848
DOI	10.1007/s43657-022-00066-x
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Article ; Online: Toward a more systematic understanding of bacterial virulence factors and establishing Koch postulates in silico.

Ren, Xianwen

Virulence

2013 Volume 4, Issue 6, Page(s) 437–438

MeSH term(s)	Bacteria/pathogenicity ; Bacterial Proteins/chemistry ; Bacterial Proteins/metabolism ; Humans ; Virulence Factors/chemistry ; Virulence Factors/metabolism
Chemical Substances	Bacterial Proteins ; Virulence Factors
Language	English
Publishing date	2013-08-17
Publishing country	United States
Document type	Editorial ; Research Support, Non-U.S. Gov't ; Comment
ZDB-ID	2657572-3
ISSN	2150-5608 ; 2150-5594
ISSN (online)	2150-5608
ISSN	2150-5594
DOI	10.4161/viru.26211
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Article ; Online: Understanding tumor-infiltrating lymphocytes by single cell RNA sequencing.

Ren, Xianwen / Zhang, Zemin

Advances in immunology

2019 Volume 144, Page(s) 217–245

Abstract: The clinical success of immune checkpoint blockade provides great hope for curing cancers. However, the patient responses are not even. Precise understanding of tumor immunity is necessary to improving the current cancer immunotherapies and to developing ...

Abstract	The clinical success of immune checkpoint blockade provides great hope for curing cancers. However, the patient responses are not even. Precise understanding of tumor immunity is necessary to improving the current cancer immunotherapies and to developing new treatment options. Here we applied full-length single cell RNA-seq (scRNA-seq) to three cancer types and provide a comprehensive single T cell data resource for understanding various characteristics of tumor-infiltrating T cells. We also developed an analytical framework named as STARTRAC to quantitatively characterize the dynamic properties of various T cell subsets including tissue preference, clonal expansion, migration, and state transitions from the scRNA-seq snapshots of tumor immune microenvironments. Conserved and cancer type-specific T cell subsets and developmental patterns were revealed, and detailed molecular portrait of the tumor immunity-relevant T cell clusters were provided, shedding lights into the cellular and molecular mechanisms underlying the composition, heterogeneity, and formation of tumor immune microenvironments. Important genes such as LAYN and IGFLR1 also provided new options for future development of cancer therapeutics.
MeSH term(s)	CD8-Positive T-Lymphocytes/immunology ; Carcinoma, Hepatocellular/genetics ; Carcinoma, Hepatocellular/immunology ; Carcinoma, Non-Small-Cell Lung/genetics ; Carcinoma, Non-Small-Cell Lung/immunology ; Colorectal Neoplasms/genetics ; Colorectal Neoplasms/immunology ; Humans ; Lectins, C-Type/genetics ; Lectins, C-Type/metabolism ; Liver Neoplasms/genetics ; Liver Neoplasms/immunology ; Lung Neoplasms/genetics ; Lung Neoplasms/immunology ; Lymphocytes, Tumor-Infiltrating/immunology ; RNA-Seq ; Single-Cell Analysis ; T-Lymphocyte Subsets/immunology ; T-Lymphocytes/immunology ; Tumor Microenvironment/immunology
Chemical Substances	LAYN protein, human ; Lectins, C-Type
Language	English
Publishing date	2019-09-12
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	80226-8
ISSN	1557-8445 ; 0065-2776
ISSN (online)	1557-8445
ISSN	0065-2776
DOI	10.1016/bs.ai.2019.08.004
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Article: Distinctive Network Topology of Phase-Separated Proteins in Human Interactome

Yu, Chunyu / Lang, Yunzhi / Hou, Chao / Yang, Ence / Ren, Xianwen / Li, Tingting

Journal of molecular biology. 2022 Jan. 15, v. 434, no. 1

2022

Abstract: Liquid-liquid phase separation (LLPS) is an important mechanism that mediates the formation of biomolecular condensates. Despite the immense interest in LLPS, phase-separated proteins verified by experiments are still limited, and identification of phase- ...

Abstract	Liquid-liquid phase separation (LLPS) is an important mechanism that mediates the formation of biomolecular condensates. Despite the immense interest in LLPS, phase-separated proteins verified by experiments are still limited, and identification of phase-separated proteins at proteome-scale is a challenging task. Multivalent interaction among macromolecules is the driving force of LLPS, which suggests that phase-separated proteins may harbor distinct biological characteristics in protein–protein interactions (PPIs). In this study, we constructed an integrated human PPI network (HPIN) and mapped phase-separated proteins into it. Analysis of the network parameters revealed differences of network topology between phase-separated proteins and others. The results further suggested the efficiency when applying topological similarities in distinguishing components of MLOs. Furthermore, we found that affinity purification mass spectrometry (AP-MS) detects PPIs more effectively than yeast-two hybrid system (Y2H) in phase separation-driven condensates. Our work provides the first global view of the distinct network topology of phase-separated proteins in human interactome, suggesting incorporation of PPI network for LLPS prediction in further studies.
Keywords	humans ; mass spectrometry ; molecular biology ; prediction ; separation ; topology
Language	English
Dates of publication	2022-0115
Publishing place	Elsevier Ltd
Document type	Article
ZDB-ID	80229-3
ISSN	1089-8638 ; 0022-2836
ISSN (online)	1089-8638
ISSN	0022-2836
DOI	10.1016/j.jmb.2021.167292
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Article ; Online: Direct Comparative Analyses of 10X Genomics Chromium and Smart-seq2.

Wang, Xiliang / He, Yao / Zhang, Qiming / Ren, Xianwen / Zhang, Zemin

Genomics, proteomics & bioinformatics

2021 Volume 19, Issue 2, Page(s) 253–266

Abstract: Single-cell RNA sequencing (scRNA-seq) is generally used for profiling transcriptome of individual cells. The droplet-based 10X Genomics Chromium (10X) approach and the plate-based Smart-seq2 full-length method are two frequently used scRNA-seq platforms, ...

Abstract	Single-cell RNA sequencing (scRNA-seq) is generally used for profiling transcriptome of individual cells. The droplet-based 10X Genomics Chromium (10X) approach and the plate-based Smart-seq2 full-length method are two frequently used scRNA-seq platforms, yet there are only a few thorough and systematic comparisons of their advantages and limitations. Here, by directly comparing the scRNA-seq data generated by these two platforms from the same samples of CD45
MeSH term(s)	Chromium ; Gene Expression Profiling/methods ; Genomics ; RNA-Seq ; Sequence Analysis, RNA/methods ; Single-Cell Analysis/methods
Chemical Substances	Chromium (0R0008Q3JB)
Language	English
Publishing date	2021-03-02
Publishing country	China
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2240213-5
ISSN	2210-3244 ; 1672-0229
ISSN (online)	2210-3244
ISSN	1672-0229
DOI	10.1016/j.gpb.2020.02.005
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Article: Identification of transcriptional isoforms associated with survival in cancer patient.

Tang, Zefang / Chen, Tianxiang / Ren, Xianwen / Zhang, Zemin

Journal of genetics and genomics = Yi chuan xue bao

2019 Volume 46, Issue 9, Page(s) 413–421

Abstract: The Cancer Genome Atlas (TCGA) project produced RNA-Seq data for tens of thousands of cancer and non-cancer samples with clinical survival information, providing an unprecedented opportunity for analyzing prognostic genes and their isoforms. In this ... ...

Abstract	The Cancer Genome Atlas (TCGA) project produced RNA-Seq data for tens of thousands of cancer and non-cancer samples with clinical survival information, providing an unprecedented opportunity for analyzing prognostic genes and their isoforms. In this study, we performed the first large-scale identification of transcriptional isoforms that are specifically associated with patient prognosis, even without gene-level association. These specific isoforms are defined as Transcripts Associated with Patient Prognosis (TAPPs). Although a group of TAPPs are the principal isoforms of their genes with intact functional protein domains, another group of TAPPs lack important protein domains found in their canonical gene isoforms. This dichotomy in the distribution of protein domains may indicate different patterns of TAPPs association with cancer. TAPPs in protein-coding genes, especially those with altered protein domains, are rich in known cancer driver genes. We further identified multiple types of cancer recurrent TAPPs, such as DCAF17-201, providing a new approach for the detection of cancer-associated events. In order to make the wide research community to study prognostic isoforms, we developed a portal named GESUR (http://gesur.cancer-pku.cn/), which illustrates the detailed prognostic characteristics of TAPPs and other isoforms. Overall, our integrated analysis of gene expression and clinical parameters provides a new perspective for understanding the applications of different gene isoforms in tumor progression.
MeSH term(s)	Alternative Splicing/genetics ; Gene Expression Profiling ; Gene Expression Regulation, Neoplastic/genetics ; Humans ; Neoplasm Recurrence, Local/genetics ; Neoplasms/genetics ; Neoplasms/pathology ; Prognosis ; Protein Isoforms/genetics ; Protein Isoforms/metabolism ; Sequence Analysis, RNA
Chemical Substances	Protein Isoforms
Language	English
Publishing date	2019-09-25
Publishing country	China
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2374568-X
ISSN	1873-5533 ; 1673-8527
ISSN (online)	1873-5533
ISSN	1673-8527
DOI	10.1016/j.jgg.2019.08.003
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Article ; Online: scRNAss: a single-cell RNA-seq assembler via imputing dropouts and combing junctions.

Liu, Juntao / Liu, Xiangyu / Ren, Xianwen / Li, Guojun

Bioinformatics (Oxford, England)

2019 Volume 35, Issue 21, Page(s) 4264–4271

Abstract: Motivation: Full-length transcript reconstruction is essential for single-cell RNA-seq data analysis, but dropout events, which can cause transcripts discarded completely or broken into pieces, pose great challenges for transcript assembly. Currently ... ...

Abstract	Motivation: Full-length transcript reconstruction is essential for single-cell RNA-seq data analysis, but dropout events, which can cause transcripts discarded completely or broken into pieces, pose great challenges for transcript assembly. Currently available RNA-seq assemblers are generally designed for bulk RNA sequencing. To fill the gap, we introduce single-cell RNA-seq assembler, a method that applies explicit strategies to impute lost information caused by dropout events and a combing strategy to infer transcripts using scRNA-seq. Results: Extensive evaluations on both simulated and biological datasets demonstrated its superiority over the state-of-the-art RNA-seq assemblers including StringTie, Cufflinks and CLASS2. In particular, it showed a remarkable capability of recovering unknown 'novel' isoforms and highly computational efficiency compared to other tools. Availability and implementation: scRNAss is free, open-source software available from https://sourceforge.net/projects/single-cell-rna-seq-assembly/files/. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	RNA-Seq ; Sequence Analysis, RNA ; Single-Cell Analysis ; Software ; Whole Exome Sequencing
Language	English
Publishing date	2019-04-05
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btz240
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Article ; Online: SSCC: A Novel Computational Framework for Rapid and Accurate Clustering Large-scale Single Cell RNA-seq Data.

Ren, Xianwen / Zheng, Liangtao / Zhang, Zemin

Genomics, proteomics & bioinformatics

2019 Volume 17, Issue 2, Page(s) 201–210

Abstract: Clustering is a prevalent analytical means to analyze single cell RNA sequencing (scRNA-seq) data but the rapidly expanding data volume can make this process computationally challenging. New methods for both accurate and efficient clustering are of ... ...

Abstract	Clustering is a prevalent analytical means to analyze single cell RNA sequencing (scRNA-seq) data but the rapidly expanding data volume can make this process computationally challenging. New methods for both accurate and efficient clustering are of pressing need. Here we proposed Spearman subsampling-clustering-classification (SSCC), a new clustering framework based on random projection and feature construction, for large-scale scRNA-seq data. SSCC greatly improves clustering accuracy, robustness, and computational efficacy for various state-of-the-art algorithms benchmarked on multiple real datasets. On a dataset with 68,578 human blood cells, SSCC achieved 20% improvement for clustering accuracy and 50-fold acceleration, but only consumed 66% memory usage, compared to the widelyused software package SC3. Compared to k-means, the accuracy improvement of SSCC can reach 3-fold. An R implementation of SSCC is available at https://github.com/Japrin/sscClust.
MeSH term(s)	Algorithms ; Animals ; Cluster Analysis ; Computational Biology/methods ; Databases as Topic ; Gene Expression Profiling/methods ; Humans ; Mice ; Sequence Analysis, RNA ; Single-Cell Analysis ; Software ; Statistics, Nonparametric
Language	English
Publishing date	2019-06-13
Publishing country	China
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2240213-5
ISSN	2210-3244 ; 1672-0229
ISSN (online)	2210-3244
ISSN	1672-0229
DOI	10.1016/j.gpb.2018.10.003
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