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Journal articles on the topic 'Single cell multiomics'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Chappell, Lia, Andrew J. C. Russell, and Thierry Voet. "Single-Cell (Multi)omics Technologies." Annual Review of Genomics and Human Genetics 19, no. 1 (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 drivi

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Ingelfinger, Florian, Eduardo Beltrán, Lisa A. Gerdes, and Burkhard Becher. "Single-cell multiomics in neuroinflammation." Current Opinion in Immunology 76 (June 2022): 102180. http://dx.doi.org/10.1016/j.coi.2022.102180.

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Lee, Jeongwoo, Do Young Hyeon, and Daehee Hwang. "Single-cell multiomics: technologies and data analysis methods." Experimental & Molecular Medicine 52, no. 9 (2020): 1428–42. http://dx.doi.org/10.1038/s12276-020-0420-2.

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Abstract Advances in single-cell isolation and barcoding technologies offer unprecedented opportunities to profile DNA, mRNA, and proteins at a single-cell resolution. Recently, bulk multiomics analyses, such as multidimensional genomic and proteogenomic analyses, have proven beneficial for obtaining a comprehensive understanding of cellular events. This benefit has facilitated the development of single-cell multiomics analysis, which enables cell type-specific gene regulation to be examined. The cardinal features of single-cell multiomics analysis include (1) technologies for single-cell isol

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Demetci, Pinar, Rebecca Santorella, Björn Sandstede, William Stafford Noble, and Ritambhara Singh. "Single-Cell Multiomics Integration by SCOT." Journal of Computational Biology 29, no. 1 (2022): 19–22. http://dx.doi.org/10.1089/cmb.2021.0477.

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Macaulay, Iain C., Chris P. Ponting, and Thierry Voet. "Single-Cell Multiomics: Multiple Measurements from Single Cells." Trends in Genetics 33, no. 2 (2017): 155–68. http://dx.doi.org/10.1016/j.tig.2016.12.003.

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6

R Aroor, A. "Multiomics: Concepts, Methods and Applications." AJ Journal of Medical Sciences 1, no. 1 (2024): 12–15. https://doi.org/10.71325/ajjms.v1i1.arora.

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Multiomics is a high-throughput technology with multilayered system biology approach incorporating bioinformatic analysis of the data. At present it includes genomics, epigenomics, transcriptomics, proteomics, metabolomics, and gut microbiomics with advancement to the level of single cell analysis. Multiomics is an advancing approach (1) to understand the pathobiology of disease, (2) to identify sensitive biomarkers for the diagnosis of diseases as well as in monitoring and (3) to identify disease specific targets to treat the patient in the context of precision medicine as targeted therapeuti

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Nassar, Sam F., Khadir Raddassi, and Terence Wu. "Single-Cell Multiomics Analysis for Drug Discovery." Metabolites 11, no. 11 (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 propertie

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Ak, Cigdem, Nicole Szczepanski, Aaron Doe, and Gurkan Yardimci. "Abstract 5001: Multimodal topic modeling for single-cell multiomics." Cancer Research 85, no. 8_Supplement_1 (2025): 5001. https://doi.org/10.1158/1538-7445.am2025-5001.

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Abstract Single-cell (sc) multiomics assays simultaneously measure transcriptomic, epigenomic, and proteomic modalities in single cells. These multimodal assays enable deeper characterization of cells compared to single-omic assays by offering a more comprehensive measurement of the cell state. Additionally, they can identify cross modality associations, such as promoter-enhancer linkages. Recently, numerous computational methods for sc-multiomics data have been released; however, there is a dearth of interpretable methods While matrix factorization and deep learning approaches, such as MOFA+

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Blutt, Sarah E., Cristian Coarfa, Josef Neu, and Mohan Pammi. "Multiomic Investigations into Lung Health and Disease." Microorganisms 11, no. 8 (2023): 2116. http://dx.doi.org/10.3390/microorganisms11082116.

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Diseases of the lung account for more than 5 million deaths worldwide and are a healthcare burden. Improving clinical outcomes, including mortality and quality of life, involves a holistic understanding of the disease, which can be provided by the integration of lung multi-omics data. An enhanced understanding of comprehensive multiomic datasets provides opportunities to leverage those datasets to inform the treatment and prevention of lung diseases by classifying severity, prognostication, and discovery of biomarkers. The main objective of this review is to summarize the use of multiomics inv

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10

Perkel, Jeffrey M. "Single-cell analysis enters the multiomics age." Nature 595, no. 7868 (2021): 614–16. http://dx.doi.org/10.1038/d41586-021-01994-w.

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Hatami, Elham, Hye-Won Song, Hongduan Huang, et al. "Integration of single-cell transcriptomic and chromatin accessibility on heterogenicity of human peripheral blood mononuclear cells utilizing microwell-based single-cell partitioning technology." Journal of Immunology 212, no. 1_Supplement (2024): 1508_5137. http://dx.doi.org/10.4049/jimmunol.212.supp.1508.5137.

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Abstract Single-cell RNA sequencing (scRNA-Seq) deepens our understanding of cellular development and heterogeneity. However, limitations exist in unraveling cell states and gene regulatory programs. Chromatin state profiles assess gene expression potential and offer insights into transcriptional regulation. Integrated with gene expression data, chromatin accessibility region (CAR) profiles establish fundamental gene regulatory logic for cell fate. ATAC-seq (Assay for Transposase-Accessible Chromatin using Sequencing) is a highly potent approach for profiling genome-wide CARs. To investigate t

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Belhocine, Kamila, Laura DeMare, and Olivia Habern. "Single-Cell Multiomics: Simultaneous Epigenetic and Transcriptional Profiling." Genetic Engineering & Biotechnology News 41, no. 1 (2021): 66–68. http://dx.doi.org/10.1089/gen.41.01.17.

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13

Ayer, Aruna, and Cynthia Sakofsky. "Higher Throughput, More Flexible Single-Cell Multiomics Analysis." Genetic Engineering & Biotechnology News 43, no. 7 (2023): 46–47. http://dx.doi.org/10.1089/gen.43.07.17.

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Kashima, Yukie, Yoshitaka Sakamoto, Keiya Kaneko, Masahide Seki, Yutaka Suzuki, and Ayako Suzuki. "Single-cell sequencing techniques from individual to multiomics analyses." Experimental & Molecular Medicine 52, no. 9 (2020): 1419–27. http://dx.doi.org/10.1038/s12276-020-00499-2.

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Abstract Here, we review single-cell sequencing techniques for individual and multiomics profiling in single cells. We mainly describe single-cell genomic, epigenomic, and transcriptomic methods, and examples of their applications. For the integration of multilayered data sets, such as the transcriptome data derived from single-cell RNA sequencing and chromatin accessibility data derived from single-cell ATAC-seq, there are several computational integration methods. We also describe single-cell experimental methods for the simultaneous measurement of two or more omics layers. We can achieve a

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Bian, Shuhui, Yu Hou, Xin Zhou, et al. "Single-cell multiomics sequencing and analyses of human colorectal cancer." Science 362, no. 6418 (2018): 1060–63. http://dx.doi.org/10.1126/science.aao3791.

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Although genomic instability, epigenetic abnormality, and gene expression dysregulation are hallmarks of colorectal cancer, these features have not been simultaneously analyzed at single-cell resolution. Using optimized single-cell multiomics sequencing together with multiregional sampling of the primary tumor and lymphatic and distant metastases, we developed insights beyond intratumoral heterogeneity. Genome-wide DNA methylation levels were relatively consistent within a single genetic sublineage. The genome-wide DNA demethylation patterns of cancer cells were consistent in all 10 patients w

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Williams, Mark Elliott, and Christopher D. Scharer. "MANGO: Inferring gene regulatory networks from single cell multiomics." Journal of Immunology 210, no. 1_Supplement (2023): 83.04. http://dx.doi.org/10.4049/jimmunol.210.supp.83.04.

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Abstract Transcription factors (TFs) and their target gene regulatory networks (GRNs) control immune cell fate decisions during differentiation and responses to environmental cues and signals. To better understand how GRN control immune outcomes and are dysregulated in disease settings, we developed MANGO (Multiomics Aided Neural Graph Ontology), a computational method that integrates single cell gene expression and chromatin accessibility data to infer genome-wide, cell-type specific GRN. MANGO harnesses graph neural networks to generate representations of gene and regulatory link behavior an

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17

Chen, Song, and Sarah A. Teichmann. "Completing the cancer jigsaw puzzle with single-cell multiomics." Nature Cancer 2, no. 12 (2021): 1260–62. http://dx.doi.org/10.1038/s43018-021-00306-5.

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18

Klotz, Remi, Alexis Zukowski, Mohamed Kamal, Frank Attenelo, Srinivas Ramachandran, and Min Yu. "Abstract 1242: Single cell multiomic map reveals regulatory landscape of human brain metastases." Cancer Research 83, no. 7_Supplement (2023): 1242. http://dx.doi.org/10.1158/1538-7445.am2023-1242.

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Abstract Capturing comprehensive brain metastasis landscape is critical to the establishment of effective anti-tumor strategies. This study took advantage of single-cell multiomics sequencing to profile the molecular and cellular dynamics in tumor cells and associated microenvironment during brain metastasis. Among different primary cancer types, our data suggest that there are conserved yet distinct tumor cell subpopulations, governed by specific changes in gene expression, chromatin accessibility and tumor-stroma interactions. We characterized the conserved tumor subpopulations of brain meta

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Taguchi, Y.-h., and Turki Turki. "Tensor-Decomposition-Based Unsupervised Feature Extraction in Single-Cell Multiomics Data Analysis." Genes 12, no. 9 (2021): 1442. http://dx.doi.org/10.3390/genes12091442.

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Analysis of single-cell multiomics datasets is a novel topic and is considerably challenging because such datasets contain a large number of features with numerous missing values. In this study, we implemented a recently proposed tensor-decomposition (TD)-based unsupervised feature extraction (FE) technique to address this difficult problem. The technique can successfully integrate single-cell multiomics data composed of gene expression, DNA methylation, and accessibility. Although the last two have large dimensions, as many as ten million, containing only a few percentage of nonzero values, T

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Poverennaya, E. V., O. I. Kiseleva, E. A. Ponomarenko, S. N. Naryzhny, V. G. Zgoda, and A. V. Lisitsa. "Multiomics study of HepG2 cell line proteome." Biomeditsinskaya Khimiya 63, no. 5 (2017): 373–78. http://dx.doi.org/10.18097/pbmc20176305373.

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Current proteomic studies are generally focused on the most abundant proteoforms encoded by canonical nucleic sequences. Transcriptomic and proteomic data, accumulated in a variety of postgenome sources and coupled with state-of-art analytical technologies, allow to start the identification of aberrant (non-canonical) proteoforms. The main sources of aberrant proteoforms are alternative splicing, single nucleotide polymorphism, and post-translational modifications. The aim of this work was to estimate the heterogeneity of HepG2 proteome. We suggested multiomics approach, which combines transcr

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21

Kara, Nihan, Nikolay Samusik, Xiaoshan Shi, Chip Lomas, Stephanie Widmann, and Aaron J. Tyznik. "Single-cell trajectory analysis reveals a melanoma-driven distinct hematopoietic response in murine spleen." Journal of Immunology 206, no. 1_Supplement (2021): 107.12. http://dx.doi.org/10.4049/jimmunol.206.supp.107.12.

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Abstract Hematopoietic stem and progenitor cells (HSPCs) are a rare population of precursor cells residing in bone marrow that replenish blood cells throughout adult life. Growing evidence suggests that tumor progression can interfere with normal hematopoiesis, skew the host system to undergo myeloid biased changes and cause extramedullary hematopoiesis (EMH) in organs such as the spleen. In this study, we investigated tumor-driven phenotypic and molecular alterations in HSPCs during EMH using single-cell multiomics. We used a B16-F10 melanoma mouse model which showed splenomegaly and alterati

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Montagne, Janelle M., Elizabeth M. Jaffee, and Elana J. Fertig. "Multiomics Empowers Predictive Pancreatic Cancer Immunotherapy." Journal of Immunology 210, no. 7 (2023): 859–68. http://dx.doi.org/10.4049/jimmunol.2200660.

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Abstract Advances in cancer immunotherapy, particularly immune checkpoint inhibitors, have dramatically improved the prognosis for patients with metastatic melanoma and other previously incurable cancers. However, patients with pancreatic ductal adenocarcinoma (PDAC) generally do not respond to these therapies. PDAC is exceptionally difficult to treat because of its often late stage at diagnosis, modest mutation burden, and notoriously complex and immunosuppressive tumor microenvironment. Simultaneously interrogating features of cancer, immune, and other cellular components of the PDAC tumor m

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Kumar, Rashmi, Kevin J. Zemaitis, James M. Fulcher, and Ljiljana Paša-Tolić. "Advances in mass spectrometry-enabled multiomics at single-cell resolution." Current Opinion in Biotechnology 87 (June 2024): 103096. http://dx.doi.org/10.1016/j.copbio.2024.103096.

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24

Bisht, Vartika, Katrina Nash, Yuanwei Xu, et al. "Integration of the Microbiome, Metabolome and Transcriptomics Data Identified Novel Metabolic Pathway Regulation in Colorectal Cancer." International Journal of Molecular Sciences 22, no. 11 (2021): 5763. http://dx.doi.org/10.3390/ijms22115763.

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Integrative multiomics data analysis provides a unique opportunity for the mechanistic understanding of colorectal cancer (CRC) in addition to the identification of potential novel therapeutic targets. In this study, we used public omics data sets to investigate potential associations between microbiome, metabolome, bulk transcriptomics and single cell RNA sequencing datasets. We identified multiple potential interactions, for example 5-aminovalerate interacting with Adlercreutzia; cholesteryl ester interacting with bacterial genera Staphylococcus, Blautia and Roseburia. Using public single ce

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Chen, Xi, Yuan Wang, Antonio Cappuccio, et al. "Mapping disease regulatory circuits at cell-type resolution from single-cell multiomics data." Nature Computational Science 3, no. 7 (2023): 644–57. http://dx.doi.org/10.1038/s43588-023-00476-5.

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AbstractResolving chromatin-remodeling-linked gene expression changes at cell-type resolution is important for understanding disease states. Here we describe MAGICAL (Multiome Accessibility Gene Integration Calling and Looping), a hierarchical Bayesian approach that leverages paired single-cell RNA sequencing and single-cell transposase-accessible chromatin sequencing from different conditions to map disease-associated transcription factors, chromatin sites, and genes as regulatory circuits. By simultaneously modeling signal variation across cells and conditions in both omics data types, MAGIC

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Adey, Andrew C. "Single-cell multiomics to probe relationships between histone modifications and transcription." Nature Methods 18, no. 6 (2021): 602–3. http://dx.doi.org/10.1038/s41592-021-01147-x.

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Thibivilliers, Sandra, and Marc Libault. "Plant Single-Cell Multiomics: Cracking the Molecular Profiles of Plant Cells." Trends in Plant Science 26, no. 6 (2021): 662–63. http://dx.doi.org/10.1016/j.tplants.2021.03.001.

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28

Thompson, Kathryn, Benjamin Geller, Lubna Nousheen, et al. "A Multiomic, Single-Cell Measurable Residual Disease (scMRD) Assay for Simultaneous Assessment of DNA Mutations and Surface Immunophenotypes in Acute Myeloid Leukemia." Blood 144, Supplement 1 (2024): 6168. https://doi.org/10.1182/blood-2024-204025.

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The small population of cancerous cells that remain following treatment, known as measurable residual disease (MRD), is the major cause of relapse in acute myeloid leukemia (AML). Usually, these refractory cells have gained additional resistance mutations or changed their surface immunophenotypes in ways that preclude detection and phasing by current gold standard flow cytometry or bulk next-generation sequencing assays. For this reason, a multiomic single-cell MRD (scMRD) assay could offer a more comprehensive indicator of relapse and the potential for faster response. Here, we present a new

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Ors, Aysegul, Hisham Mohammed, Aaron R. Doe, Syber Haverlack, Mithila Handu, and Ryan Mulqueen. "Abstract 5288: Single-cell multiomics reveal divergent transcriptional and epigenetic cell states in breast cancer." Cancer Research 83, no. 7_Supplement (2023): 5288. http://dx.doi.org/10.1158/1538-7445.am2023-5288.

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Abstract Over the recent years, single-cell sequencing studies have been used to better describe the highly heterogeneous nature of breast cancers on transcriptional and genetic levels. It is also known that breast cancers are highly driven by estrogen receptor alpha (ER), a transcription factor that’s important for mammary tissue homeostasis. However, little is known about how estrogen signaling heterogeneity affects cancer progression and response to anti-estrogen therapy at a single-cell level. Leveraging single-omic and multi-omic single-cell sequencing technologies, we tracked estrogen re

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Jhaveri, Niyati, HaYeun Ji, Anushka Dikshit, et al. "Multiomic Spatial Phenotyping of the Tumor Immune Microenvironment at Single Cell Resolution." Journal of Immunology 210, no. 1_Supplement (2023): 249.18. http://dx.doi.org/10.4049/jimmunol.210.supp.249.18.

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Abstract The Tumor microenvironment (TME) is critical in cancer development, progression, and control. Immunological components within tumors, known as the tumor immune microenvironment (TIME), have been implicated in cancer progression. Effective strategies for cancer immunotherapies will require a deep understanding of factors that shape the TME and TIME. Here, we describe a spatial multiomics assay utilizing RNAscope™ ISH technology paired with high-plex whole-slide spatial phenotyping with the PhenoCycler ®-Fusion platform. This two-step approach is compatible with human FFPE tissues and e

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Wang, Lin, Jangham Jung, Husam Babikir, et al. "A single-cell atlas of glioblastoma evolution under therapy reveals cell-intrinsic and cell-extrinsic therapeutic targets." Nature Cancer 3, no. 12 (2022): 1534–52. http://dx.doi.org/10.1038/s43018-022-00475-x.

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AbstractRecent longitudinal studies of glioblastoma (GBM) have demonstrated a lack of apparent selection pressure for specific DNA mutations in recurrent disease. Single-cell lineage tracing has shown that GBM cells possess a high degree of plasticity. Together this suggests that phenotype switching, as opposed to genetic evolution, may be the escape mechanism that explains the failure of precision therapies to date. We profiled 86 primary-recurrent patient-matched paired GBM specimens with single-nucleus RNA, single-cell open-chromatin, DNA and spatial transcriptomic/proteomic assays. We foun

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Flynn, Emily, Ana Almonte-Loya, and Gabriela K. Fragiadakis. "Single-Cell Multiomics." Annual Review of Biomedical Data Science 6, no. 1 (2023). http://dx.doi.org/10.1146/annurev-biodatasci-020422-050645.

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Single-cell RNA sequencing methods have led to improved understanding of the heterogeneity and transcriptomic states present in complex biological systems. Recently, the development of novel single-cell technologies for assaying additional modalities, specifically genomic, epigenomic, proteomic, and spatial data, allows for unprecedented insight into cellular biology. While certain technologies collect multiple measurements from the same cells simultaneously, even when modalities are separately assayed in different cells, we can apply novel computational methods to integrate these data. The ap

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Xu, Xing, Qiannan Zhang, Mingyin Li, et al. "Microfluidic single‐cell multiomics analysis." VIEW, November 30, 2022, 20220034. http://dx.doi.org/10.1002/viw.20220034.

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Goss, Kyndal, and Edwin M. Horwitz. "Single-cell multiomics to advance cell therapy." Cytotherapy, October 2024. http://dx.doi.org/10.1016/j.jcyt.2024.10.009.

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Boyeau, Pierre, Stephen Bates, Can Ergen, Michael I. Jordan, and Nir Yosef. "VI-VS: calibrated identification of feature dependencies in single-cell multiomics." Genome Biology 25, no. 1 (2024). http://dx.doi.org/10.1186/s13059-024-03419-z.

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AbstractUnveiling functional relationships between various molecular cell phenotypes from data using machine learning models is a key promise of multiomics. Existing methods either use flexible but hard-to-interpret models or simpler, misspecified models. (Variational Inference for Variable Selection) balances flexibility and interpretability to identify relevant feature relationships in multiomic data. It uses deep generative models to identify conditionally dependent features, with false discovery rate control. is available as an open-source Python package, providing a robust solution to ide

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Ma, Jiaxiu, Chao Dong, Aibin He, and Haiqing Xiong. "Single-cell multiomics: a new frontier in drug research and development." Frontiers in Drug Discovery 4 (October 22, 2024). http://dx.doi.org/10.3389/fddsv.2024.1474331.

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Single-cell multiomics (sc-multiomics) is a burgeoning field that simultaneously integrates multiple layers of molecular information, enabling the characterization of dynamic cell states and activities in development and disease as well as treatment response. Studying drug actions and responses using sc-multiomics technologies has revolutionized our understanding of how small molecules intervene for specific cell types in cancer treatment and how they are linked with disease etiology and progression. Here, we summarize recent advances in sc-multiomics technologies that have been adapted and im

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Ma, Yuanyuan, Zexuan Sun, Pengcheng Zeng, Wenyu Zhang, and Zhixiang Lin. "JSNMF enables effective and accurate integrative analysis of single-cell multiomics data." Briefings in Bioinformatics, April 4, 2022. http://dx.doi.org/10.1093/bib/bbac105.

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Abstract The single-cell multiomics technologies provide an unprecedented opportunity to study the cellular heterogeneity from different layers of transcriptional regulation. However, the datasets generated from these technologies tend to have high levels of noise, making data analysis challenging. Here, we propose jointly semi-orthogonal nonnegative matrix factorization (JSNMF), which is a versatile toolkit for the integrative analysis of transcriptomic and epigenomic data profiled from the same cell. JSNMF enables data visualization and clustering of the cells and also facilitates downstream

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Hu, Haoran, Xinjun Wang, Site Feng, et al. "A unified model-based framework for doublet or multiplet detection in single-cell multiomics data." Nature Communications 15, no. 1 (2024). http://dx.doi.org/10.1038/s41467-024-49448-x.

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AbstractDroplet-based single-cell sequencing techniques rely on the fundamental assumption that each droplet encapsulates a single cell, enabling individual cell omics profiling. However, the inevitable issue of multiplets, where two or more cells are encapsulated within a single droplet, can lead to spurious cell type annotations and obscure true biological findings. The issue of multiplets is exacerbated in single-cell multiomics settings, where integrating cross-modality information for clustering can inadvertently promote the aggregation of multiplet clusters and increase the risk of erron

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Jeong, Yunhee, Jonathan Ronen, Wolfgang Kopp, Pavlo Lutsik, and Altuna Akalin. "scMaui: a widely applicable deep learning framework for single-cell multiomics integration in the presence of batch effects and missing data." BMC Bioinformatics 25, no. 1 (2024). http://dx.doi.org/10.1186/s12859-024-05880-w.

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AbstractThe recent advances in high-throughput single-cell sequencing have created an urgent demand for computational models which can address the high complexity of single-cell multiomics data. Meticulous single-cell multiomics integration models are required to avoid biases towards a specific modality and overcome sparsity. Batch effects obfuscating biological signals must also be taken into account. Here, we introduce a new single-cell multiomics integration model, Single-cell Multiomics Autoencoder Integration (scMaui) based on variational product-of-experts autoencoders and adversarial le

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Liao, Xian, Emilia Scheidereit, and Christoph Kuppe. "New tools to study renal fibrogenesis." Current Opinion in Nephrology & Hypertension, April 8, 2024. http://dx.doi.org/10.1097/mnh.0000000000000988.

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Purpose of the review Kidney fibrosis is a key pathological aspect and outcome of chronic kidney disease (CKD). The advent of multiomic analyses using human kidney tissue, enabled by technological advances, marks a new chapter of discovery in fibrosis research of the kidney. This review highlights the rapid advancements of single-cell and spatial multiomic techniques that offer new avenues for exploring research questions related to human kidney fibrosis development. Recent findings We recently focused on understanding the origin and transition of myofibroblasts in kidney fibrosis using single

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Wang, William, Xuanqi Liu, and Diane Catherine Wang. "Single‐cell and spatial alterations of neural cells and circuits in clinical and translational medicine." Clinical and Translational Medicine 14, no. 6 (2024). http://dx.doi.org/10.1002/ctm2.1696.

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AbstractThe spatiotemporal heterogeneity of neurons, circuits and regulators is being uncovered at a single‐cell level, from single‐cell gene expression to functional regulations. The classifications, architectonics and functional communications amongst neural cells and circuits within the brain can be clearly delineated using single‐cell multiomics and transomics. This Editorial highlights the spatiotemporal heterogeneity of neurons and circuits as well as regulators, initiates the translation of neuronal diversity and spatial organisation at single‐cell levels into clinical considerations, a

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Wang, William, Xuanqi Liu, and Diane Catherine Wang. "Single‐cell and spatial alterations of neural cells and circuits in clinical and translational medicine." Clinical and Translational Discovery 4, no. 3 (2024). http://dx.doi.org/10.1002/ctd2.298.

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AbstractThe spatiotemporal heterogeneity of neurons, circuits and regulators is being uncovered at a single‐cell level, from the single‐cell gene expression to functional regulations. The classifications, architectonics and functional communications amongst neural cells and circuits within the brain can be clearly delineated using single‐cell multiomics and transomics. This Editorial highlights the spatiotemporal heterogeneity of neurons and circuits as well as regulators, initiates the translation of neuronal diversity and spatial organisation at single‐cell levels into clinical consideration

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Aihara, Seishi, and Yoshiharu Muto. "Single-cell epigenetics and multiomics analysis in kidney research." Clinical and Experimental Nephrology, April 25, 2025. https://doi.org/10.1007/s10157-025-02679-8.

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Abstract The rapid evolution of single-cell sequencing technologies has significantly advanced our knowledge of cellular heterogeneity and the underlying molecular basis in healthy and diseased kidneys. While single-cell transcriptomic analysis excels in characterizing cell states in the heterogeneous population, the complex regulatory mechanisms governing the gene expressions are difficult to decipher using transcriptomic data alone. Single-cell sequencing technology has recently extended to include epigenome and other modalities, allowing single-cell multiomics analysis. Especially, the inte

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YU, Lei. "scONE-seq: A single-cell multi-omics method enables simultaneous dissection of phenotype and genotype heterogeneity from frozen tumors." July 5, 2022. https://doi.org/10.5281/zenodo.6796059.

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Single-cell multi-omics can provide a unique perspective on tumor cellular heterogeneity. Most previous single-cell-whole-genome-RNA-sequencing (scWGS-RNA-seq) methods require physical separation of DNA and RNA, which makes these methods labor-intensive and technically demanding, time-consuming, or requiring special devices. And they are not applicable to frozen samples that cannot generate intact single-cell suspensions. We have developed scONE-seq, a versatile method for simultaneous transcriptome and genome profiling of single cells or single nuclei. Compared with existing methods, scONE-se

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Zhang, Wenyu, and Zhixiang Lin. "iPoLNG—An unsupervised model for the integrative analysis of single-cell multiomics data." Frontiers in Genetics 14 (February 7, 2023). http://dx.doi.org/10.3389/fgene.2023.998504.

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Abstract:

Single-cell multiomics technologies, where the transcriptomic and epigenomic profiles are simultaneously measured in the same set of single cells, pose significant challenges for effective integrative analysis. Here, we propose an unsupervised generative model, iPoLNG, for the effective and scalable integration of single-cell multiomics data. iPoLNG reconstructs low-dimensional representations of the cells and features using computationally efficient stochastic variational inference by modelling the discrete counts in single-cell multiomics data with latent factors. The low-dimensional represe

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Sussman, Jonathan H., Jason Xu, Nduka Amankulor, and Kai Tan. "Dissecting the tumor microenvironment of epigenetically-driven gliomas: Opportunities for single-cell and spatial multiomics." Neuro-Oncology Advances, August 21, 2023. http://dx.doi.org/10.1093/noajnl/vdad101.

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Abstract Malignant gliomas are incurable brain neoplasms with dismal prognoses and near-universal fatality, with minimal therapeutic progress despite billions of dollars invested in research and clinical trials over the last two decades. Many glioma studies have utilized disparate histologic and genomic platforms to characterize the stunning genomic, transcriptomic, and immunologic heterogeneity found in gliomas. Single-cell and spatial omics technologies enable unprecedented characterization of heterogeneity in solid malignancies and provide a granular annotation of transcriptional, epigeneti

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Dimitriu, Maria A., Irina Lazar-Contes, Martin Roszkowski, and Isabelle M. Mansuy. "Single-Cell Multiomics Techniques: From Conception to Applications." Frontiers in Cell and Developmental Biology 10 (March 21, 2022). http://dx.doi.org/10.3389/fcell.2022.854317.

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Recent advances in methods for single-cell analyses and barcoding strategies have led to considerable progress in research. The development of multiplexed assays offers the possibility to conduct parallel analyses of multiple factors and processes for comprehensive characterization of cellular and molecular states in health and disease. These technologies have expanded extremely rapidly in the past years and constantly evolve and provide better specificity, precision and resolution. This review summarizes recent progress in single-cell multiomics approaches, and focuses, in particular, on the

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Diamante, Graciel, Sung Min Ha, Darren Wijaya, and Xia Yang. "Single Cell Multiomics Systems Biology for Molecular Toxicity." Current Opinion in Toxicology, May 2024, 100477. http://dx.doi.org/10.1016/j.cotox.2024.100477.

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Troulé, Kevin, Robert Petryszak, Batuhan Cakir, et al. "CellPhoneDB v5: inferring cell–cell communication from single-cell multiomics data." Nature Protocols, March 25, 2025. https://doi.org/10.1038/s41596-024-01137-1.

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Fu, Yifan, Jinxin Tao, Tao Liu, et al. "Unbiasedly decoding the tumor microenvironment with single-cell multiomics analysis in pancreatic cancer." Molecular Cancer 23, no. 1 (2024). http://dx.doi.org/10.1186/s12943-024-02050-7.

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AbstractPancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy with a poor prognosis and limited therapeutic options. Research on the tumor microenvironment (TME) of PDAC has propelled the development of immunotherapeutic and targeted therapeutic strategies with a promising future. The emergence of single-cell sequencing and mass spectrometry technologies, coupled with spatial omics, has collectively revealed the heterogeneity of the TME from a multiomics perspective, outlined the development trajectories of cell lineages, and revealed important functions of previously under

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