1
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Wu Y, Liu J, Xiao Y, Zhang S, Li L. CoupleVAE: coupled variational autoencoders for predicting perturbational single-cell RNA sequencing data. Brief Bioinform 2025; 26:bbaf126. [PMID: 40178283 PMCID: PMC11966612 DOI: 10.1093/bib/bbaf126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2024] [Revised: 01/21/2025] [Accepted: 03/03/2025] [Indexed: 04/05/2025] Open
Abstract
With the rapid advances in single-cell sequencing technology, it is now feasible to conduct in-depth genetic analysis in individual cells. Study on the dynamics of single cells in response to perturbations is of great significance for understanding the functions and behaviors of living organisms. However, the acquisition of post-perturbation cellular states via biological experiments is frequently cost-prohibitive. Predicting the single-cell perturbation responses poses a critical challenge in the field of computational biology. In this work, we propose a novel deep learning method called coupled variational autoencoders (CoupleVAE), devised to predict the postperturbation single-cell RNA-Seq data. CoupleVAE is composed of two coupled VAEs connected by a coupler, initially extracting latent features for controlled and perturbed cells via two encoders, subsequently engaging in mutual translation within the latent space through two nonlinear mappings via a coupler, and ultimately generating controlled and perturbed data by two separate decoders to process the encoded and translated features. CoupleVAE facilitates a more intricate state transformation of single cells within the latent space. Experiments in three real datasets on infection, stimulation and cross-species prediction show that CoupleVAE surpasses the existing comparative models in effectively predicting single-cell RNA-seq data for perturbed cells, achieving superior accuracy.
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Affiliation(s)
- Yahao Wu
- School of Mathematics and Statistics, Xi’an Jiaotong University, No. 28 Xianning West Road, Xi’an, Shaanxi 710049, China
| | - Jing Liu
- School of Mathematics and Statistics, Xi’an Jiaotong University, No. 28 Xianning West Road, Xi’an, Shaanxi 710049, China
| | - Yanni Xiao
- School of Mathematics and Statistics, Xi’an Jiaotong University, No. 28 Xianning West Road, Xi’an, Shaanxi 710049, China
| | - Shuqin Zhang
- School of Mathematical Sciences, Center for Applied Mathematics, Research Institute of Intelligent Complex Systems, and Shanghai Key Laboratory for Contemporary Applied Mathematics, Fudan University, 220 Handan Road, 200433 Shanghai, China
| | - Limin Li
- School of Mathematics and Statistics, Xi’an Jiaotong University, No. 28 Xianning West Road, Xi’an, Shaanxi 710049, China
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2
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Monfort-Lanzas P, Rungger K, Madersbacher L, Hackl H. Machine learning to dissect perturbations in complex cellular systems. Comput Struct Biotechnol J 2025; 27:832-842. [PMID: 40103613 PMCID: PMC11915099 DOI: 10.1016/j.csbj.2025.02.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2024] [Revised: 02/24/2025] [Accepted: 02/25/2025] [Indexed: 03/20/2025] Open
Abstract
Understanding the responses of biological systems to various perturbations, such as genetic, chemical, or environmental challenges, is essential for reconstructing causal network models. Emerging single-cell technologies have become instrumental in elucidating cell states and phenotypes and they have been used in combination with genetic screening. Recent advances in machine learning and artificial intelligence architectures have stimulated the development of computational tools for modeling perturbations and the response to compounds. This study outlined core principles underpinning perturbation analysis and discussed the methodologies and analytical frameworks used to decode drug and genetic perturbation responses, complex multicellular interactions, and network dynamics. The current tools used for various applications were overviewed. These developments hold great promise for improving drug development and personalized medicine. Foundation models and perturbation cell and tissue atlases offer immense potential for advancing our understanding of cellular behavior and disease mechanisms.
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Affiliation(s)
- Pablo Monfort-Lanzas
- Institute of Bioinformatics, Biocenter, Medical University of Innsbruck, Austria
- Institute of Medical Biochemistry, Biocenter, Medical University of Innsbruck, Austria
| | - Katja Rungger
- Institute of Bioinformatics, Biocenter, Medical University of Innsbruck, Austria
| | - Leonie Madersbacher
- Institute of Bioinformatics, Biocenter, Medical University of Innsbruck, Austria
| | - Hubert Hackl
- Institute of Bioinformatics, Biocenter, Medical University of Innsbruck, Austria
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3
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Gavriilidis GI, Vasileiou V, Orfanou A, Ishaque N, Psomopoulos F. A mini-review on perturbation modelling across single-cell omic modalities. Comput Struct Biotechnol J 2024; 23:1886-1896. [PMID: 38721585 PMCID: PMC11076269 DOI: 10.1016/j.csbj.2024.04.058] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 04/23/2024] [Accepted: 04/23/2024] [Indexed: 01/06/2025] Open
Abstract
Recent advances in single-cell omics technology have transformed the landscape of cellular and molecular research, enriching the scope and intricacy of cellular characterisation. Perturbation modelling seeks to comprehensively grasp the effects of external influences like disease onset or molecular knock-outs or external stimulants on cellular physiology, specifically on transcription factors, signal transducers, biological pathways, and dynamic cell states. Machine and deep learning tools transform complex perturbational phenomena in algorithmically tractable tasks to formulate predictions based on various types of single-cell datasets. However, the recent surge in tools and datasets makes it challenging for experimental biologists and computational scientists to keep track of the recent advances in this rapidly expanding filed of single-cell modelling. Here, we recapitulate the main objectives of perturbation modelling and summarise novel single-cell perturbation technologies based on genetic manipulation like CRISPR or compounds, spanning across omic modalities. We then concisely review a burgeoning group of computational methods extending from classical statistical inference methodologies to various machine and deep learning architectures like shallow models or autoencoders, to biologically informed approaches based on gene regulatory networks, and to combinatorial efforts reminiscent of ensemble learning. We also discuss the rising trend of large foundational models in single-cell perturbation modelling inspired by large language models. Lastly, we critically assess the challenges that underline single-cell perturbation modelling while pointing towards relevant future perspectives like perturbation atlases, multi-omics and spatial datasets, causal machine learning for interpretability, multi-task learning for performance and explainability as well as prospects for solving interoperability and benchmarking pitfalls.
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Affiliation(s)
- George I. Gavriilidis
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece
| | - Vasileios Vasileiou
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece
- Department of Molecular Biology and Genetics, Democritus University of Thrace, Alexandroupolis, Greece
| | - Aspasia Orfanou
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece
| | - Naveed Ishaque
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Center of Digital Health, Berlin, Germany
| | - Fotis Psomopoulos
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece
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4
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Qi X, Zhao L, Tian C, Li Y, Chen ZL, Huo P, Chen R, Liu X, Wan B, Yang S, Zhao Y. Predicting transcriptional responses to novel chemical perturbations using deep generative model for drug discovery. Nat Commun 2024; 15:9256. [PMID: 39462106 PMCID: PMC11513139 DOI: 10.1038/s41467-024-53457-1] [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: 03/11/2024] [Accepted: 10/11/2024] [Indexed: 10/28/2024] Open
Abstract
Understanding transcriptional responses to chemical perturbations is central to drug discovery, but exhaustive experimental screening of disease-compound combinations is unfeasible. To overcome this limitation, here we introduce PRnet, a perturbation-conditioned deep generative model that predicts transcriptional responses to novel chemical perturbations that have never experimentally perturbed at bulk and single-cell levels. Evaluations indicate that PRnet outperforms alternative methods in predicting responses across novel compounds, pathways, and cell lines. PRnet enables gene-level response interpretation and in-silico drug screening for diseases based on gene signatures. PRnet further identifies and experimentally validates novel compound candidates against small cell lung cancer and colorectal cancer. Lastly, PRnet generates a large-scale integration atlas of perturbation profiles, covering 88 cell lines, 52 tissues, and various compound libraries. PRnet provides a robust and scalable candidate recommendation workflow and successfully recommends drug candidates for 233 diseases. Overall, PRnet is an effective and valuable tool for gene-based therapeutics screening.
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Affiliation(s)
- Xiaoning Qi
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Lianhe Zhao
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Chenyu Tian
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yueyue Li
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Zhen-Lin Chen
- University of Chinese Academy of Sciences, Beijing, China
- Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Peipei Huo
- Luoyang Institute of Information Technology Industries, Luoyang, Henan, China
| | - Runsheng Chen
- West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xiaodong Liu
- University of Chinese Academy Sciences, Nanjing, Jiangsu, China
| | - Baoping Wan
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Shengyong Yang
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
| | - Yi Zhao
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
- University of Chinese Academy of Sciences, Beijing, China.
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5
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Gao Y, Wei Z, Dong K, Chen K, Yang J, Chuai G, Liu Q. Toward subtask-decomposition-based learning and benchmarking for predicting genetic perturbation outcomes and beyond. NATURE COMPUTATIONAL SCIENCE 2024; 4:773-785. [PMID: 39333790 DOI: 10.1038/s43588-024-00698-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Accepted: 08/29/2024] [Indexed: 09/30/2024]
Abstract
Deciphering cellular responses to genetic perturbations is fundamental for a wide array of biomedical applications. However, there are three main challenges: predicting single-genetic-perturbation outcomes, predicting multiple-genetic-perturbation outcomes and predicting genetic outcomes across cell lines. Here we introduce Subtask Decomposition Modeling for Genetic Perturbation Prediction (STAMP), a flexible artificial intelligence strategy for genetic perturbation outcome prediction and downstream applications. STAMP formulates genetic perturbation prediction as a subtask decomposition problem by resolving three progressive subtasks in a problem decomposition manner, that is, identifying postperturbation differentially expressed genes, determining the expression change directions of differentially expressed genes and finally estimating the magnitudes of gene expression changes. STAMP exhibits a substantial improvement over the existing approaches on three subtasks and beyond, including the ability to identify key regulatory genes and pathways on small samples and to reveal precise genetic interactions of diverse types.
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Affiliation(s)
- Yicheng Gao
- State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Zhiting Wei
- State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Kejing Dong
- State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Ke Chen
- State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Jingya Yang
- State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, China
| | - Guohui Chuai
- State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, China
| | - Qi Liu
- State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China.
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China.
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, China.
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6
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Deshpande S, Georgaka S, Haley M, Sellers R, Minshull J, Nallala J, Fergie M, Stone N, Rajpoot N, Baker SM, Iqbal M, Couper K, Roncaroli F, Minhas F. Ouroboros: cross-linking protein expression perturbations and cancer histology imaging with generative-predictive modeling. Bioinformatics 2024; 40:ii174-ii181. [PMID: 39230703 PMCID: PMC11373318 DOI: 10.1093/bioinformatics/btae399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/05/2024] Open
Abstract
SUMMARY Imagine if we could simultaneously predict spatial protein expression in tissues from their routine Hematoxylin and Eosin (H&E) stained images, and create tissue images given protein expression profiles thus enabling virtual simulations of how protein expression alterations impact histology in complex diseases like cancer. Such an approach could lead to more informed diagnostic and therapeutic decisions for precision medicine at lower costs and shorter turnaround times, more detailed insights into underlying disease pathology as well as improvement in predictive and generative performance. In this study, we investigate the intricate correlation between protein expressions obtained from Hyperion mass cytometry and histopathological microstructures in conventional H&E stained glioblastoma (GBM) samples, unveiling morphological patterns and cellular-level spatial alterations associated with protein expression changes. To model these complex relationships, we propose a novel generative-predictive framework called Ouroboros for producing H&E images from protein expressions and simultaneously predicting protein expressions from H&E images. Our comprehensive sample-independent validation over 9920 tissue spots from 4 GBM samples encompassing visual image analysis, quantitative analysis, subspace alignment and perturbation experiments shows that the proposed generative-predictive approach offers significant improvements in predicting protein expression from images in comparison to baseline methods as well as accurate generation of virtual GBM sample images. This proof of concept study can contribute to advancing our understanding of histological responses to protein expression perturbations and lays the foundations for further developments in this area. AVAILABILITY AND IMPLEMENTATION Implementation and associated data for the proposed approach are available at the URL: https://github.com/Srijay/Ouroboros.
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Affiliation(s)
- Srijay Deshpande
- Department of Computer Science, Tissue Image Analytics Centre, University of Warwick, Coventry, CV4 7AL, United Kingdom
| | - Sokratia Georgaka
- Manchester Academic Health Science Centre, Faculty of Biology, Medicine and Health, University of Manchester, M13 9PL, Manchester, United Kingdom
| | - Michael Haley
- Manchester Academic Health Science Centre, Faculty of Biology, Medicine and Health, University of Manchester, M13 9PL, Manchester, United Kingdom
| | - Robert Sellers
- Manchester Academic Health Science Centre, Faculty of Biology, Medicine and Health, University of Manchester, M13 9PL, Manchester, United Kingdom
| | - James Minshull
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, M13 9PL, United Kingdom
| | - Jayakrupakar Nallala
- School of Physics and Astronomy, University of Exeter, Exeter, EX4 4QL, United Kingdom
| | - Martin Fergie
- Division of Informatics, Imaging and Data Science, University of Manchester, Manchester, M13 9PT, United Kingdom
| | - Nicholas Stone
- School of Physics and Astronomy, University of Exeter, Exeter, EX4 4QL, United Kingdom
| | - Nasir Rajpoot
- Department of Computer Science, Tissue Image Analytics Centre, University of Warwick, Coventry, CV4 7AL, United Kingdom
| | - Syed Murtuza Baker
- Manchester Academic Health Science Centre, Faculty of Biology, Medicine and Health, University of Manchester, M13 9PL, Manchester, United Kingdom
| | - Mudassar Iqbal
- Manchester Academic Health Science Centre, Faculty of Biology, Medicine and Health, University of Manchester, M13 9PL, Manchester, United Kingdom
| | - Kevin Couper
- Manchester Academic Health Science Centre, Faculty of Biology, Medicine and Health, University of Manchester, M13 9PL, Manchester, United Kingdom
| | - Federico Roncaroli
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, M13 9PL, United Kingdom
| | - Fayyaz Minhas
- Department of Computer Science, Tissue Image Analytics Centre, University of Warwick, Coventry, CV4 7AL, United Kingdom
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7
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Li C, Shao X, Zhang S, Wang Y, Jin K, Yang P, Lu X, Fan X, Wang Y. scRank infers drug-responsive cell types from untreated scRNA-seq data using a target-perturbed gene regulatory network. Cell Rep Med 2024; 5:101568. [PMID: 38754419 PMCID: PMC11228399 DOI: 10.1016/j.xcrm.2024.101568] [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: 05/05/2023] [Revised: 12/27/2023] [Accepted: 04/21/2024] [Indexed: 05/18/2024]
Abstract
Cells respond divergently to drugs due to the heterogeneity among cell populations. Thus, it is crucial to identify drug-responsive cell populations in order to accurately elucidate the mechanism of drug action, which is still a great challenge. Here, we address this problem with scRank, which employs a target-perturbed gene regulatory network to rank drug-responsive cell populations via in silico drug perturbations using untreated single-cell transcriptomic data. We benchmark scRank on simulated and real datasets, which shows the superior performance of scRank over existing methods. When applied to medulloblastoma and major depressive disorder datasets, scRank identifies drug-responsive cell types that are consistent with the literature. Moreover, scRank accurately uncovers the macrophage subpopulation responsive to tanshinone IIA and its potential targets in myocardial infarction, with experimental validation. In conclusion, scRank enables the inference of drug-responsive cell types using untreated single-cell data, thus providing insights into the cellular-level impacts of therapeutic interventions.
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Affiliation(s)
- Chengyu Li
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China
| | - Xin Shao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China.
| | - Shujing Zhang
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University, Hangzhou, China
| | - Yingchao Wang
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University, Hangzhou, China
| | - Kaiyu Jin
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China
| | - Penghui Yang
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China
| | - Xiaoyan Lu
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University, Hangzhou, China
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China; Jinhua Institute of Zhejiang University, Jinhua 321299, China; Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310006, China.
| | - Yi Wang
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China.
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8
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Filipovic D, Kana O, Marri D, Bhattacharya S. Unique challenges and best practices for single cell transcriptomic analysis in toxicology. CURRENT OPINION IN TOXICOLOGY 2024; 38:100475. [PMID: 38645720 PMCID: PMC11027889 DOI: 10.1016/j.cotox.2024.100475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
The application and analysis of single-cell transcriptomics in toxicology presents unique challenges. These include identifying cell sub-populations sensitive to perturbation; interpreting dynamic shifts in cell type proportions in response to chemical exposures; and performing differential expression analysis in dose-response studies spanning multiple treatment conditions. This review examines these challenges while presenting best practices for critical single cell analysis tasks. This covers areas such as cell type identification; analysis of differential cell type abundance; differential gene expression; and cellular trajectories. Towards enhancing the use of single-cell transcriptomics in toxicology, this review aims to address key challenges in this field and offer practical analytical solutions. Overall, applying appropriate bioinformatic techniques to single-cell transcriptomic data can yield valuable insights into cellular responses to toxic exposures.
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Affiliation(s)
- David Filipovic
- Institute for Quantitative Health Science & Engineering, East Lansing, MI, 48824, USA
| | - Omar Kana
- Institute for Quantitative Health Science & Engineering, East Lansing, MI, 48824, USA
- Department of Pharmacology & Toxicology, Michigan State University, East Lansing, MI, 48824, USA
- Institute for Integrative Toxicology, Michigan State University, East Lansing, MI, 48824, USA
| | - Daniel Marri
- Institute for Quantitative Health Science & Engineering, East Lansing, MI, 48824, USA
- Department of Biomedical Engineering, Michigan State University, East Lansing, MI, 48824, USA
| | - Sudin Bhattacharya
- Institute for Quantitative Health Science & Engineering, East Lansing, MI, 48824, USA
- Department of Pharmacology & Toxicology, Michigan State University, East Lansing, MI, 48824, USA
- Institute for Integrative Toxicology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Biomedical Engineering, Michigan State University, East Lansing, MI, 48824, USA
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9
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Jiang Q, Chen S, Chen X, Jiang R. scPRAM accurately predicts single-cell gene expression perturbation response based on attention mechanism. Bioinformatics 2024; 40:btae265. [PMID: 38625746 PMCID: PMC11076148 DOI: 10.1093/bioinformatics/btae265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 04/06/2024] [Accepted: 04/13/2024] [Indexed: 04/17/2024] Open
Abstract
MOTIVATION With the rapid advancement of single-cell sequencing technology, it becomes gradually possible to delve into the cellular responses to various external perturbations at the gene expression level. However, obtaining perturbed samples in certain scenarios may be considerably challenging, and the substantial costs associated with sequencing also curtail the feasibility of large-scale experimentation. A repertoire of methodologies has been employed for forecasting perturbative responses in single-cell gene expression. However, existing methods primarily focus on the average response of a specific cell type to perturbation, overlooking the single-cell specificity of perturbation responses and a more comprehensive prediction of the entire perturbation response distribution. RESULTS Here, we present scPRAM, a method for predicting perturbation responses in single-cell gene expression based on attention mechanisms. Leveraging variational autoencoders and optimal transport, scPRAM aligns cell states before and after perturbation, followed by accurate prediction of gene expression responses to perturbations for unseen cell types through attention mechanisms. Experiments on multiple real perturbation datasets involving drug treatments and bacterial infections demonstrate that scPRAM attains heightened accuracy in perturbation prediction across cell types, species, and individuals, surpassing existing methodologies. Furthermore, scPRAM demonstrates outstanding capability in identifying differentially expressed genes under perturbation, capturing heterogeneity in perturbation responses across species, and maintaining stability in the presence of data noise and sample size variations. AVAILABILITY AND IMPLEMENTATION https://github.com/jiang-q19/scPRAM and https://doi.org/10.5281/zenodo.10935038.
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Affiliation(s)
- Qun Jiang
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division of BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Shengquan Chen
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China
| | - Xiaoyang Chen
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division of BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Rui Jiang
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division of BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
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