1
|
Liarou M, Matthes T, Marchand-Maillet S. TimeFlow: A Density-Driven Pseudotime Method for Flow Cytometry Data Analysis. Cytometry A 2025; 107:233-247. [PMID: 40111028 DOI: 10.1002/cyto.a.24928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2024] [Revised: 01/15/2025] [Accepted: 02/27/2025] [Indexed: 03/22/2025]
Abstract
Pseudotime methods order cells undergoing differentiation from the least to the most differentiated. We developed TimeFlow, a new method for computing pseudotime in multi-dimensional flow cytometry datasets. TimeFlow tracks the differentiation path of each cell on a graph by following smooth changes in the cell population density. To compute the probability density function of the cells, it uses a normalizing flow model. We profiled bone marrow samples from three healthy patients using a 20-color antibody panel for flow cytometry and prepared datasets that ranged from 5,000 to 600,000 cells and included monocytes, neutrophils, erythrocytes, and B-cells at various maturation stages. TimeFlow computed fine-grained pseudotime for all the datasets, and the cell orderings were consistent with prior knowledge of human hematopoiesis. Experiments showed its potential in generalizing across patients and unseen cell states. We compared our method to 11 other pseudotime methods using in-house and public datasets and found very good performance for both linear and branching trajectories. TimeFlow's pseudotemporal orderings are useful for modeling the dynamics of cell surface proteins along linear trajectories. The biologically meaningful results in branching trajectories suggest the possibility of future applications with automated cell lineage detection. Code is available at https://github.com/MargaritaLiarou1/TimeFlow and data at https://osf.io/ykue7/.
Collapse
Affiliation(s)
- Margarita Liarou
- Department of Computer Science, Viper Group, University of Geneva, Carouge, Switzerland
| | - Thomas Matthes
- Hematology Service, Oncology Department, University Hospital Geneva, Geneva, Switzerland
- Clinical Pathology Service, Diagnostics Department, University Hospital Geneva, Geneva, Switzerland
| | - Stéphane Marchand-Maillet
- Department of Computer Science, Viper Group, University of Geneva, Carouge, Switzerland
- Centre Universitaire d'Informatique, University of Geneva, Carouge, Switzerland
| |
Collapse
|
2
|
Chen X, Ma Y, Shi Y, Zhang B, Wu H, Gao J. Fuzzy-Based Identification of Transition Cells to Infer Cell Trajectory for Single-Cell Transcriptomics. J Comput Biol 2025; 32:253-273. [PMID: 39670822 DOI: 10.1089/cmb.2023.0432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2024] Open
Abstract
With the continuous evolution of single-cell RNA sequencing technology, it has become feasible to reconstruct cell development processes using computational methods. Trajectory inference is a crucial downstream analytical task that provides valuable insights into understanding cell cycle and differentiation. During cell development, cells exhibit both stable and transition states, which makes it challenging to accurately identify these cells. To address this challenge, we propose a novel single-cell trajectory inference method using fuzzy clustering, named scFCTI. By introducing fuzzy clustering and quantifying cell uncertainty, scFCTI can identify transition cells within unstable cell states. Moreover, scFCTI can obtain refined cell classification by characterizing different cell stages, which gain more accurate single-cell trajectory reconstruction containing transition paths. To validate the effectiveness of scFCTI, we conduct experiments on five real datasets and four different structure simulation datasets, comparing them with several state-of-the-art trajectory inference methods. The results demonstrate that scFCTI outperforms these methods by successfully identifying unstable cell clusters and obtaining more accurate cell paths with transition states. Especially the experimental results demonstrate that scFCTI can reconstruct the cell trajectory more precisely.
Collapse
Affiliation(s)
- Xiang Chen
- School of Science, Jiangnan University, Wuxi, China
| | - Yibing Ma
- School of Science, Jiangnan University, Wuxi, China
| | - Yongle Shi
- School of Science, Jiangnan University, Wuxi, China
| | - Bai Zhang
- School of Science, Jiangnan University, Wuxi, China
| | - Hanwen Wu
- School of Science, Jiangnan University, Wuxi, China
| | - Jie Gao
- School of Science, Jiangnan University, Wuxi, China
| |
Collapse
|
3
|
Ren J, Zhou Y, Hu Y, Yang J, Fang H, Lyu X, Guo J, Shi X, Li Q. A model-based factorization method for scRNA data unveils bifurcating transcriptional modules underlying cell fate determination. eLife 2025; 13:RP97424. [PMID: 39907554 PMCID: PMC11798574 DOI: 10.7554/elife.97424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2025] Open
Abstract
Manifold-learning is particularly useful to resolve the complex cellular state space from single-cell RNA sequences. While current manifold-learning methods provide insights into cell fate by inferring graph-based trajectory at cell level, challenges remain to retrieve interpretable biology underlying the diverse cellular states. Here, we described MGPfactXMBD, a model-based manifold-learning framework and capable to factorize complex development trajectories into independent bifurcation processes of gene sets, and thus enables trajectory inference based on relevant features. MGPfactXMBD offers a more nuanced understanding of the biological processes underlying cellular trajectories with potential determinants. When bench-tested across 239 datasets, MGPfactXMBD showed advantages in major quantity-control metrics, such as branch division accuracy and trajectory topology, outperforming most established methods. In real datasets, MGPfactXMBD recovered the critical pathways and cell types in microglia development with experimentally valid regulons and markers. Furthermore, MGPfactXMBD discovered evolutionary trajectories of tumor-associated CD8+ T cells and yielded new subtypes of CD8+ T cells with gene expression signatures significantly predictive of the responses to immune checkpoint inhibitor in independent cohorts. In summary, MGPfactXMBD offers a manifold-learning framework in scRNA-seq data which enables feature selection for specific biological processes and contributing to advance our understanding of biological determination of cell fate.
Collapse
Affiliation(s)
- Jun Ren
- National Institute for Data Science in Health and Medicine, School of Medicine, Xiamen UniversityXiamenChina
- Department of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen UniversityXiamenChina
- School of Informatics, Xiamen University, XiamenXiamenChina
| | - Ying Zhou
- National Institute for Data Science in Health and Medicine, School of Medicine, Xiamen UniversityXiamenChina
- Department of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen UniversityXiamenChina
| | - Yudi Hu
- National Institute for Data Science in Health and Medicine, School of Medicine, Xiamen UniversityXiamenChina
| | - Jing Yang
- National Institute for Data Science in Health and Medicine, School of Medicine, Xiamen UniversityXiamenChina
| | - Hongkun Fang
- Department of Scientific Research Management, Weifang People’s Hospital, Shandong Second Medical UniversityWeifangChina
| | - Xuejing Lyu
- National Institute for Data Science in Health and Medicine, School of Medicine, Xiamen UniversityXiamenChina
| | - Jintao Guo
- Department of Scientific Research Management, Weifang People’s Hospital, Shandong Second Medical UniversityWeifangChina
| | - Xiaodong Shi
- School of Informatics, Xiamen University, XiamenXiamenChina
| | - Qiyuan Li
- National Institute for Data Science in Health and Medicine, School of Medicine, Xiamen UniversityXiamenChina
- Department of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen UniversityXiamenChina
| |
Collapse
|
4
|
Yin QZ, Liu YJ, Zhang Q, Xi SY, Yang TB, Li JP, Gao J. Overexpression of Basonuclin Zinc Finger Protein 2 in stromal cell is related to mesenchymal phenotype and immunosuppression of mucinous colorectal adenocarcinoma. Int Immunopharmacol 2024; 142:113184. [PMID: 39306894 DOI: 10.1016/j.intimp.2024.113184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2024] [Revised: 09/02/2024] [Accepted: 09/13/2024] [Indexed: 10/12/2024]
Abstract
BACKGROUND Mucinous carcinoma (MC) is a distinct histologic subtype of colorectal cancer (CRC) that is less studied and associated with poor prognosis. This study aimed to identify MC-specific therapeutic targets and biomarkers to improve the prognosis of this aggressive disease. METHODS CRC samples from The Cancer Genome Atlas (TCGA) were categorized into MC and non-MC (NMC) groups based on histologic type. A multi-scale embedded gene co-expression network analysis (MEGENA) was constructed to identify gene modules associated with the MC group. The potential functions of Basonuclin Zinc Finger Protein 2 (BNC2) were further analyzed using the Biomarker Exploration for Solid Tumors (BEST) database. In vivo and in vitro experiments were conducted to validate the predicted results. RESULTS We identified the stromal component-related gene, BNC2, in the MC population. This gene is associated with a shorter progression-free interval (PFI) in CRC patients. BNC2 promotes FAP (encoding Fibroblast Activation Protein Alpha) transcription in cancer-associated fibroblasts (CAFs) and is involved in angiogenesis through two pathways. Additionally, BNC2 enhances tumor cell invasiveness in a CAF-dependent manner. Patients with high BNC2 expression benefited less from immunotherapy compared to those with low BNC2 expression. CONCLUSIONS Our study highlights the clinical importance of BNC2 in MC, and targeting BNC2 on stromal cells (fibroblasts and endothelial cells) may be an effective strategy for treating MC.
Collapse
Affiliation(s)
- Qing-Zhong Yin
- Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Yuan-Jie Liu
- Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, Jiangsu, 210029, China
| | - Qian Zhang
- Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, Jiangsu, 210029, China
| | - Song-Yang Xi
- Zhenjiang Hospital of Chinese Traditional and Western Medicine, Zhenjiang, Jiangsu 212000, China
| | - Tian-Bao Yang
- Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Jie-Pin Li
- Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, Jiangsu, 210029, China.
| | - Ju Gao
- The Yangzhou Clinical Medical College of Xuzhou Medical University, Yangzhou, Jiangsu 225009, China; Northern Jiangsu People's Hospital, Yangzhou, Jiangsu 225009, China.
| |
Collapse
|
5
|
Shi Y, Wan J, Zhang X, Liang T, Yin Y. scCRT: a contrastive-based dimensionality reduction model for scRNA-seq trajectory inference. Brief Bioinform 2024; 25:bbae204. [PMID: 38701412 PMCID: PMC11066919 DOI: 10.1093/bib/bbae204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 03/28/2024] [Accepted: 04/15/2024] [Indexed: 05/05/2024] Open
Abstract
Trajectory inference is a crucial task in single-cell RNA-sequencing downstream analysis, which can reveal the dynamic processes of biological development, including cell differentiation. Dimensionality reduction is an important step in the trajectory inference process. However, most existing trajectory methods rely on cell features derived from traditional dimensionality reduction methods, such as principal component analysis and uniform manifold approximation and projection. These methods are not specifically designed for trajectory inference and fail to fully leverage prior information from upstream analysis, limiting their performance. Here, we introduce scCRT, a novel dimensionality reduction model for trajectory inference. In order to utilize prior information to learn accurate cells representation, scCRT integrates two feature learning components: a cell-level pairwise module and a cluster-level contrastive module. The cell-level module focuses on learning accurate cell representations in a reduced-dimensionality space while maintaining the cell-cell positional relationships in the original space. The cluster-level contrastive module uses prior cell state information to aggregate similar cells, preventing excessive dispersion in the low-dimensional space. Experimental findings from 54 real and 81 synthetic datasets, totaling 135 datasets, highlighted the superior performance of scCRT compared with commonly used trajectory inference methods. Additionally, an ablation study revealed that both cell-level and cluster-level modules enhance the model's ability to learn accurate cell features, facilitating cell lineage inference. The source code of scCRT is available at https://github.com/yuchen21-web/scCRT-for-scRNA-seq.
Collapse
Affiliation(s)
- Yuchen Shi
- Hangzhou Dianzi University, Hangzhou City, Zhejiang Province, China
| | - Jian Wan
- Hangzhou Dianzi University, the Key Laboratory of Biomedical Intelligent Computing Technology of Zhejiang Province, and Zhejiang University of Science and Technology, Hangzhou City, Zhejiang Province, China
| | - Xin Zhang
- Hangzhou Dianzi University, Hangzhou City, Zhejiang Province, China
| | - Tingting Liang
- Hangzhou Dianzi University, Hangzhou City, Zhejiang Province, China
| | - Yuyu Yin
- Hangzhou Dianzi University, Hangzhou City, Zhejiang Province, China
| |
Collapse
|
6
|
Pan X, Zhang X. Studying temporal dynamics of single cells: expression, lineage and regulatory networks. Biophys Rev 2024; 16:57-67. [PMID: 38495440 PMCID: PMC10937865 DOI: 10.1007/s12551-023-01090-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 06/27/2023] [Indexed: 03/19/2024] Open
Abstract
Learning how multicellular organs are developed from single cells to different cell types is a fundamental problem in biology. With the high-throughput scRNA-seq technology, computational methods have been developed to reveal the temporal dynamics of single cells from transcriptomic data, from phenomena on cell trajectories to the underlying mechanism that formed the trajectory. There are several distinct families of computational methods including Trajectory Inference (TI), Lineage Tracing (LT), and Gene Regulatory Network (GRN) Inference which are involved in such studies. This review summarizes these computational approaches which use scRNA-seq data to study cell differentiation and cell fate specification as well as the advantages and limitations of different methods. We further discuss how GRNs can potentially affect cell fate decisions and trajectory structures. Supplementary Information The online version contains supplementary material available at 10.1007/s12551-023-01090-5.
Collapse
Affiliation(s)
- Xinhai Pan
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA
| | - Xiuwei Zhang
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA
| |
Collapse
|
7
|
Islam MT, Xing L. Leveraging cell-cell similarity for high-performance spatial and temporal cellular mappings from gene expression data. PATTERNS (NEW YORK, N.Y.) 2023; 4:100840. [PMID: 37876896 PMCID: PMC10591141 DOI: 10.1016/j.patter.2023.100840] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 06/29/2023] [Accepted: 08/10/2023] [Indexed: 10/26/2023]
Abstract
Single-cell trajectory mapping and spatial reconstruction are two important developments in life science and provide a unique means to decode heterogeneous tissue formation, cellular dynamics, and tissue developmental processes. The success of these techniques depends critically on the performance of analytical tools used for high-dimensional (HD) gene expression data processing. Existing methods discern the patterns of the data without explicitly considering the underlying biological characteristics of the system, often leading to suboptimal solutions. Here, we present a cell-cell similarity-driven framework of genomic data analysis for high-fidelity spatial and temporal cellular mappings. The approach exploits the similarity features of the cells to discover discriminative patterns of the data. We show that for a wide variety of datasets, the proposed approach drastically improves the accuracies of spatial and temporal mapping analyses compared with state-of-the-art techniques.
Collapse
Affiliation(s)
- Md Tauhidul Islam
- Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA
| | - Lei Xing
- Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA
| |
Collapse
|
8
|
Smolander J, Junttila S, Elo LL. Cell-connectivity-guided trajectory inference from single-cell data. Bioinformatics 2023; 39:btad515. [PMID: 37624916 PMCID: PMC10474950 DOI: 10.1093/bioinformatics/btad515] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 07/24/2023] [Accepted: 08/23/2023] [Indexed: 08/27/2023] Open
Abstract
MOTIVATION Single-cell RNA-sequencing enables cell-level investigation of cell differentiation, which can be modelled using trajectory inference methods. While tremendous effort has been put into designing these methods, inferring accurate trajectories automatically remains difficult. Therefore, the standard approach involves testing different trajectory inference methods and picking the trajectory giving the most biologically sensible model. As the default parameters are often suboptimal, their tuning requires methodological expertise. RESULTS We introduce Totem, an open-source, easy-to-use R package designed to facilitate inference of tree-shaped trajectories from single-cell data. Totem generates a large number of clustering results, estimates their topologies as minimum spanning trees, and uses them to measure the connectivity of the cells. Besides automatic selection of an appropriate trajectory, cell connectivity enables to visually pinpoint branching points and milestones relevant to the trajectory. Furthermore, testing different trajectories with Totem is fast, easy, and does not require in-depth methodological knowledge. AVAILABILITY AND IMPLEMENTATION Totem is available as an R package at https://github.com/elolab/Totem.
Collapse
Affiliation(s)
- Johannes Smolander
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland
| | - Sini Junttila
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland
- Institute of Biomedicine, University of Turku, 20520 Turku, Finland
| |
Collapse
|
9
|
Nie X, Qin D, Zhou X, Duo H, Hao Y, Li B, Liang G. Clustering ensemble in scRNA-seq data analysis: Methods, applications and challenges. Comput Biol Med 2023; 159:106939. [PMID: 37075602 DOI: 10.1016/j.compbiomed.2023.106939] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 03/31/2023] [Accepted: 04/14/2023] [Indexed: 04/21/2023]
Abstract
With the rapid development of single-cell RNA-sequencing techniques, various computational methods and tools were proposed to analyze these high-throughput data, which led to an accelerated reveal of potential biological information. As one of the core steps of single-cell transcriptome data analysis, clustering plays a crucial role in identifying cell types and interpreting cellular heterogeneity. However, the results generated by different clustering methods showed distinguishing, and those unstable partitions can affect the accuracy of the analysis to a certain extent. To overcome this challenge and obtain more accurate results, currently clustering ensemble is frequently applied to cluster analysis of single-cell transcriptome datasets, and the results generated by all clustering ensembles are nearly more reliable than those from most of the single clustering partitions. In this review, we summarize applications and challenges of the clustering ensemble method in single-cell transcriptome data analysis, and provide constructive thoughts and references for researchers in this field.
Collapse
Affiliation(s)
- Xiner Nie
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing, 400044, China; College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Dan Qin
- Department of Biology, College of Science, Northeastern University, Boston, MA, 02115, USA
| | - Xinyi Zhou
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Hongrui Duo
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Youjin Hao
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Bo Li
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China.
| | - Guizhao Liang
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing, 400044, China.
| |
Collapse
|