Zeitschriftenartikel: „Single-Cell omics“

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Choi, Joung Min, Chaelin Park und Heejoon Chae. „moSCminer: a cell subtype classification framework based on the attention neural network integrating the single-cell multi-omics dataset on the cloud“. PeerJ 12 (26.02.2024): e17006. http://dx.doi.org/10.7717/peerj.17006.

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Single-cell omics sequencing has rapidly advanced, enabling the quantification of diverse omics profiles at a single-cell resolution. To facilitate comprehensive biological insights, such as cellular differentiation trajectories, precise annotation of cell subtypes is essential. Conventional methods involve clustering cells and manually assigning subtypes based on canonical markers, a labor-intensive and expert-dependent process. Hence, an automated computational prediction framework is crucial. While several classification frameworks for predicting cell subtypes from single-cell RNA sequencing datasets exist, these methods solely rely on single-omics data, offering insights at a single molecular level. They often miss inter-omic correlations and a holistic understanding of cellular processes. To address this, the integration of multi-omics datasets from individual cells is essential for accurate subtype annotation. This article introduces moSCminer, a novel framework for classifying cell subtypes that harnesses the power of single-cell multi-omics sequencing datasets through an attention-based neural network operating at the omics level. By integrating three distinct omics datasets—gene expression, DNA methylation, and DNA accessibility—while accounting for their biological relationships, moSCminer excels at learning the relative significance of each omics feature. It then transforms this knowledge into a novel representation for cell subtype classification. Comparative evaluations against standard machine learning-based classifiers demonstrate moSCminer’s superior performance, consistently achieving the highest average performance on real datasets. The efficacy of multi-omics integration is further corroborated through an in-depth analysis of the omics-level attention module, which identifies potential markers for cell subtype annotation. To enhance accessibility and scalability, moSCminer is accessible as a user-friendly web-based platform seamlessly connected to a cloud system, publicly accessible at http://203.252.206.118:5568. Notably, this study marks the pioneering integration of three single-cell multi-omics datasets for cell subtype identification.

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Rusk, Nicole. „Multi-omics single-cell analysis“. Nature Methods 16, Nr. 8 (30.07.2019): 679. http://dx.doi.org/10.1038/s41592-019-0519-3.

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Chappell, Lia, Andrew J. C. Russell und Thierry Voet. „Single-Cell (Multi)omics Technologies“. Annual Review of Genomics and Human Genetics 19, Nr. 1 (31.08.2018): 15–41. http://dx.doi.org/10.1146/annurev-genom-091416-035324.

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Single-cell multiomics technologies typically measure multiple types of molecule from the same individual cell, enabling more profound biological insight than can be inferred by analyzing each molecular layer from separate cells. These single-cell multiomics technologies can reveal cellular heterogeneity at multiple molecular layers within a population of cells and reveal how this variation is coupled or uncoupled between the captured omic layers. The data sets generated by these techniques have the potential to enable a deeper understanding of the key biological processes and mechanisms driving cellular heterogeneity and how they are linked with normal development and aging as well as disease etiology. This review details both established and novel single-cell mono- and multiomics technologies and considers their limitations, applications, and likely future developments.

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Xu, Xing, Junxia Wang, Lingling Wu, Jingjing Guo, Yanling Song, Tian Tian, Wei Wang, Zhi Zhu und Chaoyong Yang. „Microfluidic Single‐Cell Omics Analysis“. Small 16, Nr. 9 (23.09.2019): 1903905. http://dx.doi.org/10.1002/smll.201903905.

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Wang, Le, und Bo Jin. „Single-Cell RNA Sequencing and Combinatorial Approaches for Understanding Heart Biology and Disease“. Biology 13, Nr. 10 (30.09.2024): 783. http://dx.doi.org/10.3390/biology13100783.

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By directly measuring multiple molecular features in hundreds to millions of single cells, single-cell techniques allow for comprehensive characterization of the diversity of cells in the heart. These single-cell transcriptome and multi-omic studies are transforming our understanding of heart development and disease. Compared with single-dimensional inspections, the combination of transcriptomes with spatial dimensions and other omics can provide a comprehensive understanding of single-cell functions, microenvironment, dynamic processes, and their interrelationships. In this review, we will introduce the latest advances in cardiac health and disease at single-cell resolution; single-cell detection methods that can be used for transcriptome, genome, epigenome, and proteome analysis; single-cell multi-omics; as well as their future application prospects.

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Mincarelli, Laura, Ashleigh Lister, James Lipscombe und Iain C. Macaulay. „Defining Cell Identity with Single-Cell Omics“. PROTEOMICS 18, Nr. 18 (28.05.2018): 1700312. http://dx.doi.org/10.1002/pmic.201700312.

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Yang, Xiaoxi, Yuqi Wen, Xinyu Song, Song He und Xiaochen Bo. „Exploring the classification of cancer cell lines from multiple omic views“. PeerJ 8 (18.08.2020): e9440. http://dx.doi.org/10.7717/peerj.9440.

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Background Cancer classification is of great importance to understanding its pathogenesis, making diagnosis and developing treatment. The accumulation of extensive omics data of abundant cancer cell line provide basis for large scale classification of cancer with low cost. However, the reliability of cell lines as in vitro models of cancer has been controversial. Methods In this study, we explore the classification on pan-cancer cell line with single and integrated multiple omics data from the Cancer Cell Line Encyclopedia (CCLE) database. The representative omics data of cancer, mRNA data, miRNA data, copy number variation data, DNA methylation data and reverse-phase protein array data were taken into the analysis. TumorMap web tool was used to illustrate the landscape of molecular classification.The molecular classification of patient samples was compared with cancer cell lines. Results Eighteen molecular clusters were identified using integrated multiple omics clustering. Three pan-cancer clusters were found in integrated multiple omics clustering. By comparing with single omics clustering, we found that integrated clustering could capture both shared and complementary information from each omics data. Omics contribution analysis for clustering indicated that, although all the five omics data were of value, mRNA and proteomics data were particular important. While the classifications were generally consistent, samples from cancer patients were more diverse than cancer cell lines. Conclusions The clustering analysis based on integrated omics data provides a novel multi-dimensional map of cancer cell lines that can reflect the extent to pan-cancer cell lines represent primary tumors, and an approach to evaluate the importance of omic features in cancer classification.

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Deng, Yanxiang, Amanda Finck und Rong Fan. „Single-Cell Omics Analyses Enabled by Microchip Technologies“. Annual Review of Biomedical Engineering 21, Nr. 1 (04.06.2019): 365–93. http://dx.doi.org/10.1146/annurev-bioeng-060418-052538.

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Single-cell omics studies provide unique information regarding cellular heterogeneity at various levels of the molecular biology central dogma. This knowledge facilitates a deeper understanding of how underlying molecular and architectural changes alter cell behavior, development, and disease processes. The emerging microchip-based tools for single-cell omics analysis are enabling the evaluation of cellular omics with high throughput, improved sensitivity, and reduced cost. We review state-of-the-art microchip platforms for profiling genomics, epigenomics, transcriptomics, proteomics, metabolomics, and multi-omics at single-cell resolution. We also discuss the background of and challenges in the analysis of each molecular layer and integration of multiple levels of omics data, as well as how microchip-based methodologies benefit these fields. Additionally, we examine the advantages and limitations of these approaches. Looking forward, we describe additional challenges and future opportunities that will facilitate the improvement and broad adoption of single-cell omics in life science and medicine.

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Rai, Muhammad Farooq, Chia-Lung Wu, Terence D. Capellini, Farshid Guilak, Amanda R. Dicks, Pushpanathan Muthuirulan, Fiorella Grandi, Nidhi Bhutani und Jennifer J. Westendorf. „Single Cell Omics for Musculoskeletal Research“. Current Osteoporosis Reports 19, Nr. 2 (09.02.2021): 131–40. http://dx.doi.org/10.1007/s11914-021-00662-2.

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Lv, Dekang, Xuehong Zhang und Quentin Liu. „Single-cell omics decipher tumor evolution“. Medicine in Omics 2 (September 2021): 100006. http://dx.doi.org/10.1016/j.meomic.2021.100006.

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Colomé-Tatché, M., und F. J. Theis. „Statistical single cell multi-omics integration“. Current Opinion in Systems Biology 7 (Februar 2018): 54–59. http://dx.doi.org/10.1016/j.coisb.2018.01.003.

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Kodzius, Rimantas, und Takashi Gojobori. „Single-cell technologies in environmental omics“. Gene 576, Nr. 2 (Februar 2016): 701–7. http://dx.doi.org/10.1016/j.gene.2015.10.031.

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Chen, Jiani, Wanzi Xiao, Eric Zhang und Xiang Chen. „Abstract 4943: Benchmarking unpaired single-cell RNA and single-cell ATAC integration“. Cancer Research 84, Nr. 6_Supplement (22.03.2024): 4943. http://dx.doi.org/10.1158/1538-7445.am2024-4943.

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Abstract The integration of single-cell RNA-sequencing (scRNA-seq) and single-cell ATAC-sequencing (scATAC-seq) data offers a unique opportunity to gain a comprehensive view of cellular identity with defining features and to infer gene-regulatory relationships. Despite the emergence of technologies that simultaneously capture both the gene expression and chromatin accessibility of individual cells (paired data), the practical challenges of these approaches (e.g., the unavailability in previous samples and prohibitive cost) have led researchers to turn to the existing trove of single-modality data generated from independent biological samples (unpaired data). Various computational tools have been developed to integrate these unpaired single modality datasets. However, the comparative performance of these tools has not been comprehensively evaluated, and a standard benchmark pipeline is still lacking. To address these challenges, we used pseudo-unpaired scRNA-seq and scATAC-seq data derived from publicly available paired single-cell multi-omics datasets to benchmark 14 publicly available integration methods. The primary goal of unpaired single-cell multi-omics integration is to narrow the omics gap while preserving cell type diversity. We therefore focused on pair-wise cell distance and cluster performance in the joint latent space constructed by various integration tools in our benchmarking pipeline. To ensure the robustness of these computational approaches, we examined their stability across a variety of scenarios, including variations in cell number, cell types, and biological and technical batch effects. A number of the integration methods tested produced promising results. While the widely used Seurat package was recently reported to have the best performance (Lee et al., Genome Biology 2023), other computational tools such as scVI, Cobolt, scJoint, scglue and scBridge performed equally well or better in reducing omics differences and facilitating the identification of cell clusters. Notably, scglue and Cobolt demonstrated strong performance in aligning the same cell from different modalities, and discrete clusters emerged in the joint latent space using scJoint and scBridge. These findings suggest that it may not be strictly necessary to use paired multi-omics data to guide integration to achieve favorable results. Our freely available benchmarking pipeline will empower researchers to identify the optimal data integration methods for their specific data, facilitate the benchmarking of new methods, and contribute to future method development in the field. Citation Format: Jiani Chen, Wanzi Xiao, Eric Zhang, Xiang Chen. Benchmarking unpaired single-cell RNA and single-cell ATAC integration [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4943.

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Lan, Wei, Tongsheng Ling, Qingfeng Chen, Ruiqing Zheng, Min Li und Yi Pan. „scMoMtF: An interpretable multitask learning framework for single-cell multi-omics data analysis“. PLOS Computational Biology 20, Nr. 12 (18.12.2024): e1012679. https://doi.org/10.1371/journal.pcbi.1012679.

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With the rapidly development of biotechnology, it is now possible to obtain single-cell multi-omics data in the same cell. However, how to integrate and analyze these single-cell multi-omics data remains a great challenge. Herein, we introduce an interpretable multitask framework (scMoMtF) for comprehensively analyzing single-cell multi-omics data. The scMoMtF can simultaneously solve multiple key tasks of single-cell multi-omics data including dimension reduction, cell classification and data simulation. The experimental results shows that scMoMtF outperforms current state-of-the-art algorithms on these tasks. In addition, scMoMtF has interpretability which allowing researchers to gain a reliable understanding of potential biological features and mechanisms in single-cell multi-omics data.

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Moarefian, Maryam, Antonia McDonnell Capossela, Ryan Eom und Kiana Aran. „Single-Cell Technologies: Advances in Single-Cell Migration and Multi-Omics“. GEN Biotechnology 1, Nr. 3 (01.06.2022): 246–61. http://dx.doi.org/10.1089/genbio.2022.0014.

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Bai, Dongsheng, Jinying Peng und Chengqi Yi. „Advances in single-cell multi-omics profiling“. RSC Chemical Biology 2, Nr. 2 (2021): 441–49. http://dx.doi.org/10.1039/d0cb00163e.

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Ji, Yuge, Mohammad Lotfollahi, F. Alexander Wolf und Fabian J. Theis. „Machine learning for perturbational single-cell omics“. Cell Systems 12, Nr. 6 (Juni 2021): 522–37. http://dx.doi.org/10.1016/j.cels.2021.05.016.

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Marx, Vivien. „How single-cell multi-omics builds relationships“. Nature Methods 19, Nr. 2 (Februar 2022): 142–46. http://dx.doi.org/10.1038/s41592-022-01392-8.

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Stein, Richard A. „Single-Cell Sequencing Sifts through Multiple Omics“. Genetic Engineering & Biotechnology News 39, Nr. 7 (Juli 2019): 32–36. http://dx.doi.org/10.1089/gen.39.07.10.

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Song, Yanling, Xing Xu, Wei Wang, Tian Tian, Zhi Zhu und Chaoyong Yang. „Single cell transcriptomics: moving towards multi-omics“. Analyst 144, Nr. 10 (2019): 3172–89. http://dx.doi.org/10.1039/c8an01852a.

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Dong, Xianjun, Chunyu Liu und Mikhail Dozmorov. „Review of multi-omics data resources and integrative analysis for human brain disorders“. Briefings in Functional Genomics 20, Nr. 4 (08.05.2021): 223–34. http://dx.doi.org/10.1093/bfgp/elab024.

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Abstract In the last decade, massive omics datasets have been generated for human brain research. It is evolving so fast that a timely update is urgently needed. In this review, we summarize the main multi-omics data resources for the human brains of both healthy controls and neuropsychiatric disorders, including schizophrenia, autism, bipolar disorder, Alzheimer’s disease, Parkinson’s disease, progressive supranuclear palsy, etc. We also review the recent development of single-cell omics in brain research, such as single-nucleus RNA-seq, single-cell ATAC-seq and spatial transcriptomics. We further investigate the integrative multi-omics analysis methods for both tissue and single-cell data. Finally, we discuss the limitations and future directions of the multi-omics study of human brain disorders.

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Loscalzo, Joseph. „Multi-Omics and Single-Cell Omics: New Tools in Drug Target Discovery“. Arteriosclerosis, Thrombosis, and Vascular Biology 44, Nr. 4 (April 2024): 759–62. http://dx.doi.org/10.1161/atvbaha.124.320686.

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Cao, Kai, Xiangqi Bai, Yiguang Hong und Lin Wan. „Unsupervised topological alignment for single-cell multi-omics integration“. Bioinformatics 36, Supplement_1 (01.07.2020): i48—i56. http://dx.doi.org/10.1093/bioinformatics/btaa443.

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Abstract Motivation Single-cell multi-omics data provide a comprehensive molecular view of cells. However, single-cell multi-omics datasets consist of unpaired cells measured with distinct unmatched features across modalities, making data integration challenging. Results In this study, we present a novel algorithm, termed UnionCom, for the unsupervised topological alignment of single-cell multi-omics integration. UnionCom does not require any correspondence information, either among cells or among features. It first embeds the intrinsic low-dimensional structure of each single-cell dataset into a distance matrix of cells within the same dataset and then aligns the cells across single-cell multi-omics datasets by matching the distance matrices via a matrix optimization method. Finally, it projects the distinct unmatched features across single-cell datasets into a common embedding space for feature comparability of the aligned cells. To match the complex non-linear geometrical distorted low-dimensional structures across datasets, UnionCom proposes and adjusts a global scaling parameter on distance matrices for aligning similar topological structures. It does not require one-to-one correspondence among cells across datasets, and it can accommodate samples with dataset-specific cell types. UnionCom outperforms state-of-the-art methods on both simulated and real single-cell multi-omics datasets. UnionCom is robust to parameter choices, as well as subsampling of features. Availability and implementation UnionCom software is available at https://github.com/caokai1073/UnionCom. Supplementary information Supplementary data are available at Bioinformatics online.

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Leenders, Floris, Eelco J. P. de Koning und Françoise Carlotti. „Pancreatic β-Cell Identity Change through the Lens of Single-Cell Omics Research“. International Journal of Molecular Sciences 25, Nr. 9 (26.04.2024): 4720. http://dx.doi.org/10.3390/ijms25094720.

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The main hallmark in the development of both type 1 and type 2 diabetes is a decline in functional β-cell mass. This decline is predominantly attributed to β-cell death, although recent findings suggest that the loss of β-cell identity may also contribute to β-cell dysfunction. This phenomenon is characterized by a reduced expression of key markers associated with β-cell identity. This review delves into the insights gained from single-cell omics research specifically focused on β-cell identity. It highlights how single-cell omics based studies have uncovered an unexpected level of heterogeneity among β-cells and have facilitated the identification of distinct β-cell subpopulations through the discovery of cell surface markers, transcriptional regulators, the upregulation of stress-related genes, and alterations in chromatin activity. Furthermore, specific subsets of β-cells have been identified in diabetes, such as displaying an immature, dedifferentiated gene signature, expressing significantly lower insulin mRNA levels, and expressing increased β-cell precursor markers. Additionally, single-cell omics has increased insight into the detrimental effects of diabetes-associated conditions, including endoplasmic reticulum stress, oxidative stress, and inflammation, on β-cell identity. Lastly, this review outlines the factors that may influence the identification of β-cell subpopulations when designing and performing a single-cell omics experiment.

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Nassar, Sam F., Khadir Raddassi und Terence Wu. „Single-Cell Multiomics Analysis for Drug Discovery“. Metabolites 11, Nr. 11 (25.10.2021): 729. http://dx.doi.org/10.3390/metabo11110729.

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Given the heterogeneity seen in cell populations within biological systems, analysis of single cells is necessary for studying mechanisms that cannot be identified on a bulk population level. There are significant variations in the biological and physiological function of cell populations due to the functional differences within, as well as between, single species as a result of the specific proteome, transcriptome, and metabolome that are unique to each individual cell. Single-cell analysis proves crucial in providing a comprehensive understanding of the biological and physiological properties underlying human health and disease. Omics technologies can help to examine proteins (proteomics), RNA molecules (transcriptomics), and the chemical processes involving metabolites (metabolomics) in cells, in addition to genomes. In this review, we discuss the value of multiomics in drug discovery and the importance of single-cell multiomics measurements. We will provide examples of the benefits of applying single-cell omics technologies in drug discovery and development. Moreover, we intend to show how multiomics offers the opportunity to understand the detailed events which produce or prevent disease, and ways in which the separate omics disciplines complement each other to build a broader, deeper knowledge base.

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Weiskittel, Taylor M., Cristina Correia, Grace T. Yu, Choong Yong Ung, Scott H. Kaufmann, Daniel D. Billadeau und Hu Li. „The Trifecta of Single-Cell, Systems-Biology, and Machine-Learning Approaches“. Genes 12, Nr. 7 (20.07.2021): 1098. http://dx.doi.org/10.3390/genes12071098.

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Together, single-cell technologies and systems biology have been used to investigate previously unanswerable questions in biomedicine with unparalleled detail. Despite these advances, gaps in analytical capacity remain. Machine learning, which has revolutionized biomedical imaging analysis, drug discovery, and systems biology, is an ideal strategy to fill these gaps in single-cell studies. Machine learning additionally has proven to be remarkably synergistic with single-cell data because it remedies unique challenges while capitalizing on the positive aspects of single-cell data. In this review, we describe how systems-biology algorithms have layered machine learning with biological components to provide systems level analyses of single-cell omics data, thus elucidating complex biological mechanisms. Accordingly, we highlight the trifecta of single-cell, systems-biology, and machine-learning approaches and illustrate how this trifecta can significantly contribute to five key areas of scientific research: cell trajectory and identity, individualized medicine, pharmacology, spatial omics, and multi-omics. Given its success to date, the systems-biology, single-cell omics, and machine-learning trifecta has proven to be a potent combination that will further advance biomedical research.

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Hsieh, James J., Natalia Miheecheva, Akshaya Ramachandran, Yang Lyu, Ilia Galkin, Viktor Svekolkin, Ekaterina Postovalova et al. „Integrated single-cell spatial multi-omics of intratumor heterogeneity in renal cell carcinoma.“ Journal of Clinical Oncology 38, Nr. 15_suppl (20.05.2020): e17106-e17106. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e17106.

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e17106 Background: Clear cell renal cell carcinoma (ccRCC) exhibits conspicuous intratumor heterogeneity (ITH) - a driver of tumor evolution and metastasis. ITH in RCC has been studied extensively with bulk tumor DNA sequencing, which lacks the ability to integrate single cell resolution data, spatial architecture, and microenvironment composition. Therefore, we analyzed primary ccRCC tumors at multiple biopsy sites with CyTOF, multiplex immunofluorescence (MxIF), whole exome sequencing (WES), RNA sequencing (RNA-seq), single nuclei RNA-seq (snRNA-seq), and whole genome bisulfite sequencing (WGBS). Methods: Primary ccRCC tumors collected from 6 patients (pts) were biopsied at multiple locations and subjected to CyTOF (n = 21 sites, 6 pts), MxIF (20 markers, n = 8 sites, 3 pts), WES (n = 8 sites, 3 pts), RNA-seq (n = 8 sites, 3 pts), snRNA-seq (n = 8 sites, 3 pts), and WGBS (n = 8 sites, 3 pts), enabling integrated multi-omics analysis. MxIF, CyTOF, and genomic/transcriptomic analyses were performed by BostonGene. Results: Genomic intratumor (IT) evolution of ccRCC cells was tracked with WES, and subclonal distribution of SETD2, STAG2, TSC2 and PBRM1 mutations was observed in different IT regions. Different regions of the same tumor were similar, whereas individual patient tumors were distinct according to tumor microenvironment cellular composition measured by CyTOF or deconvoluted from RNA-seq. The cellular deconvolution of the ccRCC tumors reconstructed from RNA-seq correlated with CyTOF, snRNA-seq and WGBS, showing high concordance among the methods. The promoter CpG island methylation levels, averaged across all genes, positively correlated with ccRCC grade. MxIF revealed spatial IT heterogeneity in the distribution of immune infiltrate components. Macrophages and T cells dispersed among malignant cells; whereas, T cells formed clusters at unique tumor margins. Conclusions: The utilization of multi-omics methods produced a high-resolution portrait of the ccRCC tumor composition and identified differential ITH among regions within the primary tumors or among individual primary tumors. This study demonstrated strong concordance among the different technologies, suggesting that tumor deconvolution by bulk RNA-seq might be clinically applicable for ccRCC tumors. MxIF analysis enabled a fine elucidation of the spatial relationships among the tumor and the immune and stromal cells, missed by common omic platforms. Integrated single cell multi-omics could render specific pathobiological and therapeutic insights that impact treatment decisions.

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Basso, K. „SINGLE CELL OMICS IN THE STUDY OF B CELL LYMPHOMA“. Hematological Oncology 41, S2 (Juni 2023): 37. http://dx.doi.org/10.1002/hon.3163_7.

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Adossa, Nigatu, Sofia Khan, Kalle T. Rytkönen und Laura L. Elo. „Computational strategies for single-cell multi-omics integration“. Computational and Structural Biotechnology Journal 19 (2021): 2588–96. http://dx.doi.org/10.1016/j.csbj.2021.04.060.

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Czarnewski, Paulo, Ahmed Mahfouz, Raffaele A. Calogero, Patricia M. Palagi, Laura Portell-Silva, Asier Gonzalez-Uriarte, Charlotte Soneson et al. „Community-driven ELIXIR activities in single-cell omics“. F1000Research 11 (29.07.2022): 869. http://dx.doi.org/10.12688/f1000research.122312.1.

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Single-cell omics (SCO) has revolutionized the way and the level of resolution by which life science research is conducted, not only impacting our understanding of fundamental cell biology but also providing novel solutions in cutting-edge medical research. The rapid development of single-cell technologies has been accompanied by the active development of data analysis methods, resulting in a plethora of new analysis tools and strategies every year. Such a rapid development of SCO methods and tools poses several challenges in standardization, benchmarking, computational resources and training. These challenges are in line with the activities of ELIXIR, the European coordinated infrastructure for life science data. Here, we describe the current landscape of and the main challenges in SCO data, and propose the creation of the ELIXIR SCO Community, to coordinate the efforts in order to best serve SCO researchers in Europe and beyond. The Community will build on top of national experiences and pave the way towards integrated long-term solutions for SCO research.

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Tang, Lin. „Arsenal of single-cell multi-omics methods expanded“. Nature Methods 18, Nr. 8 (August 2021): 858. http://dx.doi.org/10.1038/s41592-021-01245-w.

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Mauger, S., C. Monard, C. Thion und P. Vandenkoornhuyse. „Contribution of single-cell omics to microbial ecology“. Trends in Ecology & Evolution 37, Nr. 1 (Januar 2022): 67–78. http://dx.doi.org/10.1016/j.tree.2021.09.002.

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Zhu, Chenxu, Sebastian Preissl und Bing Ren. „Single-cell multimodal omics: the power of many“. Nature Methods 17, Nr. 1 (Januar 2020): 11–14. http://dx.doi.org/10.1038/s41592-019-0691-5.

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Efremova, Mirjana, und Sarah A. Teichmann. „Computational methods for single-cell omics across modalities“. Nature Methods 17, Nr. 1 (Januar 2020): 14–17. http://dx.doi.org/10.1038/s41592-019-0692-4.

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Wang, Daojing, und Steven Bodovitz. „Single cell analysis: the new frontier in ‘omics’“. Trends in Biotechnology 28, Nr. 6 (Juni 2010): 281–90. http://dx.doi.org/10.1016/j.tibtech.2010.03.002.

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Ye, Junran, Cuiqiyun Yang, Luojia Xia, Yinjie Zhu, Li Liu, Huansheng Cao und Yi Tao. „Protoplast Preparation for Algal Single-Cell Omics Sequencing“. Microorganisms 11, Nr. 2 (20.02.2023): 538. http://dx.doi.org/10.3390/microorganisms11020538.

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Single-cell sequencing (SCS) is an evolutionary technique for conducting life science research, providing the highest genome-sale throughput and single-cell resolution and unprecedented capabilities in addressing mechanistic and operational questions. Unfortunately, the current SCS pipeline cannot be directly applied to algal research as algal cells have cell walls, which makes RNA extraction hard for the current SCS platforms. Fortunately, effective methods are available for producing algal protoplasts (cells without cell walls), which can be directly fed into current SCS pipelines. In this review, we first summarize the cell wall structure and chemical composition of algal cell walls, particularly in Chlorophyta, then summarize the advances made in preparing algal protoplasts using physical, chemical, and biological methods, followed by specific cases of algal protoplast production in some commonly used eukaryotic algae. This review provides a timely primer to those interested in applying SCS in eukaryotic algal research.

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Pan, Lu, Paolo Parini, Roman Tremmel, Joseph Loscalzo, Volker M. Lauschke, Bradley A. Maron, Paola Paci et al. „Single Cell Atlas: a single-cell multi-omics human cell encyclopedia“. Genome Biology 25, Nr. 1 (19.04.2024). http://dx.doi.org/10.1186/s13059-024-03246-2.

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AbstractSingle-cell sequencing datasets are key in biology and medicine for unraveling insights into heterogeneous cell populations with unprecedented resolution. Here, we construct a single-cell multi-omics map of human tissues through in-depth characterizations of datasets from five single-cell omics, spatial transcriptomics, and two bulk omics across 125 healthy adult and fetal tissues. We construct its complement web-based platform, the Single Cell Atlas (SCA, www.singlecellatlas.org), to enable vast interactive data exploration of deep multi-omics signatures across human fetal and adult tissues. The atlas resources and database queries aspire to serve as a one-stop, comprehensive, and time-effective resource for various omics studies.

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Xu, Jing, De‐Shuang Huang und Xiujun Zhang. „scmFormer Integrates Large‐Scale Single‐Cell Proteomics and Transcriptomics Data by Multi‐Task Transformer“. Advanced Science, 14.03.2024. http://dx.doi.org/10.1002/advs.202307835.

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AbstractTransformer‐based models have revolutionized single cell RNA‐seq (scRNA‐seq) data analysis. However, their applicability is challenged by the complexity and scale of single‐cell multi‐omics data. Here a novel single‐cell multi‐modal/multi‐task transformer (scmFormer) is proposed to fill up the existing blank of integrating single‐cell proteomics with other omics data. Through systematic benchmarking, it is demonstrated that scmFormer excels in integrating large‐scale single‐cell multimodal data and heterogeneous multi‐batch paired multi‐omics data, while preserving shared information across batchs and distinct biological information. scmFormer achieves 54.5% higher average F1 score compared to the second method in transferring cell‐type labels from single‐cell transcriptomics to proteomics data. Using COVID‐19 datasets, it is presented that scmFormer successfully integrates over 1.48 million cells on a personal computer. Moreover, it is also proved that scmFormer performs better than existing methods on generating the unmeasured modality and is well‐suited for spatial multi‐omic data. Thus, scmFormer is a powerful and comprehensive tool for analyzing single‐cell multi‐omics data.

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Chen, Fuqun, Guanhua Zou, Yongxian Wu und Le Ou-Yang. „Clustering single-cell multi-omics data via graph regularized multi-view ensemble learning“. Bioinformatics, 28.03.2024. http://dx.doi.org/10.1093/bioinformatics/btae169.

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Abstract Motivation Single-cell clustering plays a crucial role in distinguishing between cell types, facilitating the analysis of cell heterogeneity mechanisms. While many existing clustering methods rely solely on gene expression data obtained from single-cell RNA sequencing techniques to identify cell clusters, the information contained in mono-omic data is often limited, leading to suboptimal clustering performance. The emergence of single-cell multi-omics sequencing technologies enables the integration of multiple omics data for identifying cell clusters, but how to integrate different omics data effectively remains challenging. Additionally, designing a clustering method that performs well across various types of multi-omics data poses a persistent challenge due to the data’s inherent characteristics. Results In this paper, we propose a graph-regularized multi-view ensemble clustering (GRMEC-SC) model for single-cell clustering. Our proposed approach can adaptively integrate multiple omics data and leverage insights from multiple base clustering results. We extensively evaluate our method on five multi-omics datasets through a series of rigorous experiments. The results of these experiments demonstrate that our GRMEC-SC model achieves competitive performance across diverse multi-omics datasets with varying characteristics. Availability and implementation Implementation of GRMEC-SC, along with examples, can be found on the GitHub repository: https://github.com/polarisChen/GRMEC-SC. Supplementary information Supplementary data are available at Bioinformatics online.

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Yuan, Musu, Liang Chen und Minghua Deng. „Clustering single-cell multi-omics data with MoClust“. Bioinformatics, 16.11.2022. http://dx.doi.org/10.1093/bioinformatics/btac736.

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Abstract Motivation Single-cell multi-omics sequencing techniques have rapidly developed in the past few years. Clustering analysis with single-cell multi-omics data may give us novel perspectives to dissect cellular heterogeneity. However, multi-omics data have the properties of inherited large dimension, high sparsity and existence of doublets. Moreover, representations of different omics from even the same cell follow diverse distributions. Without proper distribution alignment techniques, clustering methods will encounter less separable clusters easily affected by less informative omics data. Results We developed MoClust, a novel joint clustering framework that can be applied to several types of single-cell multi-omics data. A selective automatic doublet detection module that can identify and filter out doublets is introduced in the pretraining stage to improve data quality. Omics-specific autoencoders are introduced to characterize the multi-omics data. A contrastive learning way of distribution alignment is adopted to adaptively fuse omics representations into an omics-invariant representation. This novel way of alignment boosts the compactness and separableness of clusters, while accurately weighting the contribution of each omics to the clustering object. Extensive experiments, over both simulated and real multi-omics datasets, demonstrated the powerful alignment, doublet detection and clustering ability features of MoClust. Availability An implementation of MoClust is available from https://doi.org/10.5281/zenodo.7306504 Supplementary information Supplementary data are available at Bioinformatics online.

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Liu, Yufang, Yongkai Chen, Haoran Lu, Wenxuan Zhong, Guo-Cheng Yuan und Ping Ma. „Orthogonal multimodality integration and clustering in single-cell data“. BMC Bioinformatics 25, Nr. 1 (25.04.2024). http://dx.doi.org/10.1186/s12859-024-05773-y.

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AbstractMultimodal integration combines information from different sources or modalities to gain a more comprehensive understanding of a phenomenon. The challenges in multi-omics data analysis lie in the complexity, high dimensionality, and heterogeneity of the data, which demands sophisticated computational tools and visualization methods for proper interpretation and visualization of multi-omics data. In this paper, we propose a novel method, termed Orthogonal Multimodality Integration and Clustering (OMIC), for analyzing CITE-seq. Our approach enables researchers to integrate multiple sources of information while accounting for the dependence among them. We demonstrate the effectiveness of our approach using CITE-seq data sets for cell clustering. Our results show that our approach outperforms existing methods in terms of accuracy, computational efficiency, and interpretability. We conclude that our proposed OMIC method provides a powerful tool for multimodal data analysis that greatly improves the feasibility and reliability of integrated data.

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Scala, Giovanni, Luigi Ferraro, Aurora Brandi, Yan Guo, Barbara Majello und Michele Ceccarelli. „MoNETA: MultiOmics Network Embedding for SubType Analysis“. NAR Genomics and Bioinformatics 6, Nr. 4 (02.07.2024). http://dx.doi.org/10.1093/nargab/lqae141.

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Abstract Cells are complex systems whose behavior emerges from a huge number of reactions taking place within and among different molecular districts. The availability of bulk and single-cell omics data fueled the creation of multi-omics systems biology models capturing the dynamics within and between omics layers. Powerful modeling strategies are needed to cope with the increased amount of data to be interrogated and the relative research questions. Here, we present MultiOmics Network Embedding for SubType Analysis (MoNETA) for fast and scalable identification of relevant multi-omics relationships between biological entities at the bulk and single-cells level. We apply MoNETA to show how glioma subtypes previously described naturally emerge with our approach. We also show how MoNETA can be used to identify cell types in five multi-omic single-cell datasets.

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Wagle, Manoj M., Siqu Long, Carissa Chen, Chunlei Liu und Pengyi Yang. „Interpretable deep learning in single-cell omics“. Bioinformatics, 18.06.2024. http://dx.doi.org/10.1093/bioinformatics/btae374.

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Abstract Motivation Single-cell omics technologies have enabled the quantification of molecular profiles in individual cells at an unparalleled resolution. Deep learning, a rapidly evolving sub-field of machine learning, has instilled a significant interest in single-cell omics research due to its remarkable success in analysing heterogeneous high-dimensional single-cell omics data. Nevertheless, the inherent multi-layer nonlinear architecture of deep learning models often makes them ‘black boxes’ as the reasoning behind predictions is often unknown and not transparent to the user. This has stimulated an increasing body of research for addressing the lack of interpretability in deep learning models, especially in single-cell omics data analyses, where the identification and understanding of molecular regulators are crucial for interpreting model predictions and directing downstream experimental validations. Results In this work, we introduce the basics of single-cell omics technologies and the concept of interpretable deep learning. This is followed by a review of the recent interpretable deep learning models applied to various single-cell omics research. Lastly, we highlight the current limitations and discuss potential future directions. Supplementary information Supplementary data are available at Bioinformatics online.

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Wen, Lu, und Fuchou Tang. „Recent advances in single-cell sequencing technologies“. Precision Clinical Medicine 5, Nr. 1 (31.01.2022). http://dx.doi.org/10.1093/pcmedi/pbac002.

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Abstract Single-cell omics sequencing was first achieved for the transcriptome in 2009, which was followed by fast development of technologies for profiling the genome, DNA methylome, 3D genome architecture, chromatin accessibility, histone modifications, etc., in an individual cell. In this review we mainly focus on the recent progress in four topics in the single-cell omics field: single-cell epigenome sequencing, single-cell genome sequencing for lineage tracing, spatially resolved single-cell transcriptomics and third-generation sequencing platform-based single-cell omics sequencing. We also discuss the potential applications and future directions of these single-cell omics sequencing technologies for different biomedical systems, especially for the human stem cell field.

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Li, Yunfan, Dan Zhang, Mouxing Yang, Dezhong Peng, Jun Yu, Yu Liu, Jiancheng Lv, Lu Chen und Xi Peng. „scBridge embraces cell heterogeneity in single-cell RNA-seq and ATAC-seq data integration“. Nature Communications 14, Nr. 1 (28.09.2023). http://dx.doi.org/10.1038/s41467-023-41795-5.

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AbstractSingle-cell multi-omics data integration aims to reduce the omics difference while keeping the cell type difference. However, it is daunting to model and distinguish the two differences due to cell heterogeneity. Namely, even cells of the same omics and type would have various features, making the two differences less significant. In this work, we reveal that instead of being an interference, cell heterogeneity could be exploited to improve data integration. Specifically, we observe that the omics difference varies in cells, and cells with smaller omics differences are easier to be integrated. Hence, unlike most existing works that homogeneously treat and integrate all cells, we propose a multi-omics data integration method (dubbed scBridge) that integrates cells in a heterogeneous manner. In brief, scBridge iterates between i) identifying reliable scATAC-seq cells that have smaller omics differences, and ii) integrating reliable scATAC-seq cells with scRNA-seq data to narrow the omics gap, thus benefiting the integration for the rest cells. Extensive experiments on seven multi-omics datasets demonstrate the superiority of scBridge compared with six representative baselines.

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Ellis, Dorothy, Arkaprava Roy und Susmita Datta. „Clustering single-cell multimodal omics data with jrSiCKLSNMF“. Frontiers in Genetics 14 (09.06.2023). http://dx.doi.org/10.3389/fgene.2023.1179439.

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Introduction: The development of multimodal single-cell omics methods has enabled the collection of data across different omics modalities from the same set of single cells. Each omics modality provides unique information about cell type and function, so the ability to integrate data from different modalities can provide deeper insights into cellular functions. Often, single-cell omics data can prove challenging to model because of high dimensionality, sparsity, and technical noise.Methods: We propose a novel multimodal data analysis method called joint graph-regularized Single-Cell Kullback-Leibler Sparse Non-negative Matrix Factorization (jrSiCKLSNMF, pronounced “junior sickles NMF”) that extracts latent factors shared across omics modalities within the same set of single cells.Results: We compare our clustering algorithm to several existing methods on four sets of data simulated from third party software. We also apply our algorithm to a real set of cell line data.Discussion: We show overwhelmingly better clustering performance than several existing methods on the simulated data. On a real multimodal omics dataset, we also find our method to produce scientifically accurate clustering results.

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Eltager, Mostafa, Tamim Abdelaal, Ahmed Mahfouz und Marcel J. T. Reinders. „scMoC: single-cell multi-omics clustering“. Bioinformatics Advances 2, Nr. 1 (01.01.2022). http://dx.doi.org/10.1093/bioadv/vbac011.

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Abstract Motivation Single-cell multi-omics assays simultaneously measure different molecular features from the same cell. A key question is how to benefit from the complementary data available and perform cross-modal clustering of cells. Results We propose Single-Cell Multi-omics Clustering (scMoC), an approach to identify cell clusters from data with comeasurements of scRNA-seq and scATAC-seq from the same cell. We overcome the high sparsity of the scATAC-seq data by using an imputation strategy that exploits the less-sparse scRNA-seq data available from the same cell. Subsequently, scMoC identifies clusters of cells by merging clusterings derived from both data domains individually. We tested scMoC on datasets generated using different protocols with variable data sparsity levels. We show that scMoC (i) is able to generate informative scATAC-seq data due to its RNA-guided imputation strategy and (ii) results in integrated clusters based on both RNA and ATAC information that are biologically meaningful either from the RNA or from the ATAC perspective. Availability and implementation The data used in this manuscript is publicly available, and we refer to the original manuscript for their description and availability. For convience sci-CAR data is available at NCBI GEO under the accession number of GSE117089. SNARE-seq data is available at NCBI GEO under the accession number of GSE126074. The 10X multiome data is available at the following link https://www.10xgenomics.com/resources/datasets/pbmc-from-a-healthy-donor-no-cell-sorting-3-k-1-standard-2-0-0. Supplementary information Supplementary data are available at Bioinformatics Advances online.

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Vento-Tormo, Roser. „Single-cell omics meets organoid cultures“. Nature Reviews Genetics, 12.06.2023. http://dx.doi.org/10.1038/s41576-023-00622-9.

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„A focus on single-cell omics“. Nature Reviews Genetics 24, Nr. 8 (18.07.2023): 485. http://dx.doi.org/10.1038/s41576-023-00628-3.

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Kong, Siyuan, Rongrong Li, Yunhan Tian, Yaqiu Zhang, Yuhui Lu, Qiaoer Ou, Peiwen Gao, Kui Li und Yubo Zhang. „Single-cell omics: A new direction for functional genetic research in human diseases and animal models“. Frontiers in Genetics 13 (04.01.2023). http://dx.doi.org/10.3389/fgene.2022.1100016.

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Over the past decade, with the development of high-throughput single-cell sequencing technology, single-cell omics has been emerged as a powerful tool to understand the molecular basis of cellular mechanisms and refine our knowledge of diverse cell states. They can reveal the heterogeneity at different genetic layers and elucidate their associations by multiple omics analysis, providing a more comprehensive genetic map of biological regulatory networks. In the post-GWAS era, the molecular biological mechanisms influencing human diseases will be further elucidated by single-cell omics. This review mainly summarizes the development and trend of single-cell omics. This involves single-cell omics technologies, single-cell multi-omics technologies, multiple omics data integration methods, applications in various human organs and diseases, classic laboratory cell lines, and animal disease models. The review will reveal some perspectives for elucidating human diseases and constructing animal models.

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