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Deciphering Organoids: High-Dimensional Analysis of Biomimetic Cultures

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The Comprehensive “Omics” Approach from Metabolomics to Advanced Omics for Development of Immune Checkpoint Inhibitors: Potential Strategies for Next Generation of Cancer Immunotherapy

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Engineering spinal cord repair

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MOJITOO: a fast and universal method for integration of multimodal single-cell data

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Dissecting the Heterogeneity of Human Thoracic Aortic Aneurysms Using Single-Cell Transcriptomics

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Comprehensive benchmarking of CITE-seq versus DOGMA-seq single cell multimodal omics

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Microfluidics-based single cell analysis: from transcriptomics to spatiotemporal multi-omics

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Development and application of transcriptomics technologies in plant science

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Single-cell multimodal transcriptomics to study neuronal diversity in human stem cell-derived brain tissue and organoid models

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Immune Regulation in Time and Space: The Role of Local- and Long-Range Genomic Interactions in Regulating Immune Responses

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Machine Learning Approaches on High Throughput NGS Data to Unveil Mechanisms of Function in Biology and Disease

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A combinatorial indexing strategy for low-cost epigenomic profiling of plant single cells

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Exploring long non-coding RNA networks from single cell omics data

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Single-cell omics: A new direction for functional genetic research in human diseases and animal models

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Applications of single-cell genomics and computational strategies to study common disease and population-level variationPreprint

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GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership

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Single-cell multiomics decodes regulatory programs for mouse secondary palate development

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The Power of Single‐Cell Analysis for the Study of Liver Pathobiology

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Current progress and potential opportunities to infer single-cell developmental trajectory and cell fate

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scEnhancer: a single-cell enhancer resource with annotation across hundreds of tissue/cell types in three species

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Transcriptional Regulation of the Hippo Pathway: Current Understanding and Insights from Single-Cell Technologies

Paul, S., Xie, S., Yao, X., & Dey, A. (2022) · Cells · doi:10.3390/cells11142225 · Cited by 25

Epigenetic Regulations in Mammalian Cells: Roles and Profiling Techniques

Kim, U., & Lee, D.-S. (2023) · Molecules and Cells · doi:10.14348/molcells.2023.0013 · Cited by 27

Comprehensive characterization of tissue-specific chromatin accessibility in L2 Caenorhabditis elegans nematodesPreprint

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txci-ATAC-seq: a massive-scale single-cell technique to profile chromatin accessibility

Zhang, H., Mulqueen, R. M., Iannuzo, N., Farrera, D. O., Polverino, F., Galligan, J. J., Ledford, J. G., Adey, A., & Cusanovich, D. A. (2024) · Genome biology · doi:10.1186/s13059-023-03150-1 · Cited by 25

Current Epigenetic Insights in Kidney Development

Chan, K., & Li, X. (2021) · Genes · doi:10.3390/genes12081281 · Cited by 26

Revealing the heterogeneity of CD4+ T cells through single-cell transcriptomics

Morgan, D. M., Shreffler, W. G., & Love, J. C. (2022) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2022.08.010 · Cited by 26

Unbiased integration of single cell multi-omics dataPreprint

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CMOT: cross-modality optimal transport for multimodal inference

Alatkar, S. A., & Wang, D. (2023) · Genome biology · doi:10.1186/s13059-023-02989-8 · Cited by 24

Systems Biology and Experimental Model Systems of Cancer

Yalçın, G. D., Danisik, N., Baygin, R. C., & Acar, A. (2020) · Journal of Personalized Medicine · doi:10.3390/jpm10040180 · Cited by 24

Network-based integrative analysis of single-cell transcriptomic and epigenomic data for cell types

Wu, W., Zhang, W., & Ma, X. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab546 · Cited by 24

Single-cell multi-omics topic embedding reveals cell-type-specific and COVID-19 severity-related immune signatures

Zhou, M., Zhang, H., Bai, Z., Mann‐Krzisnik, D., Wang, F., & Li, Y. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100563 · Cited by 24

Decoding the complexities of human malaria through systems immunology

Tran, T. M., & Crompton, P. D. (2019) · Immunological Reviews · doi:10.1111/imr.12817 · Cited by 25

Improving plant gene regulatory network inference by integrative analysis of multi-omics and high resolution data sets

Qian, Y., & Huang, S. C. (2020) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2020.07.010 · Cited by 26

Neuronal differentiation strategies: insights from single-cell sequencing and machine learning

Konstantinides, N., & Desplan, C. (2020) · Development · doi:10.1242/dev.193631 · Cited by 23

DRAGON: Determining Regulatory Associations using Graphical models on multi-Omic Networks

Shutta, K. H., Weighill, D. A., Burkholz, R., Guebila, M. B., DeMeo, D. L., Zacharias, H. U., Quackenbush, J. F., & Altenbuchinger, M. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac1157 · Cited by 23

Evaluating microglial phenotypes using single-cell technologies

Sankowski, R., Monaco, G., & Prinz, M. (2021) · Trends in Neurosciences · doi:10.1016/j.tins.2021.11.001 · Cited by 25

Single cell‐ and spatial ‘Omics revolutionize physiology

Conrad, T., & Altmüller, J. (2022) · Acta Physiologica · doi:10.1111/apha.13848 · Cited by 22

Frontiers in single cell analysis: multimodal technologies and their clinical perspectives

Källberg, J., Xiao, W., Assche, D. V., Baret, J., & Taly, V. (2022) · Lab on a Chip · doi:10.1039/d2lc00220e · Cited by 24

An update on renal fibrosis: from mechanisms to therapeutic strategies with a focus on extracellular vesicles

Wang, C., Li, S., Zhong, X., Liu, B., & Lv, L. (2023) · Kidney Research and Clinical Practice · doi:10.23876/j.krcp.22.159 · Cited by 25

Epstein-Barr virus evades restrictive host chromatin closure by subverting B cell activation and germinal center regulatory loci

SoRelle, E. D., Reinoso-Vizcaíno, N. M., Dai, J., Barry, A. P., Chan, C. C. W., & Luftig, M. A. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112958 · Cited by 22

Glutaraldehyde: A review of its fixative effects on nucleic acids, proteins, lipids, and carbohydratesPreprint

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SHARE-Topic: Bayesian interpretable modeling of single-cell multi-omic data

Kazwini, N. E., & Sanguinetti, G. (2024) · Genome biology · doi:10.1186/s13059-024-03180-3 · Cited by 22

scConfluence: single-cell diagonal integration with regularized Inverse Optimal Transport on weakly connected features

Samaran, J., Gabriel Peyré, A., & Cantini, L. (2024) · Nature Communications · doi:10.1038/s41467-024-51382-x · Cited by 22

Interpretable deep learning of single-cell and epigenetic data reveals novel molecular insights in aging

Li, Z., Du, Z., Huang, D.-S., & Teschendorff, A. E. (2025) · Scientific Reports · doi:10.1038/s41598-025-89646-1 · Cited by 24

Dissecting endothelial cell heterogeneity with new tools

Zhong, J., Gao, R., Zhang, X., Yang, J., Liu, Y., Ma, J., & Chen, Q. (2025) · Cell Regeneration · doi:10.1186/s13619-025-00223-3 · Cited by 24

Single-cell transcriptomics: a novel precision medicine technique in nephrology

Kim, J., & Park, J. (2020) · The Korean Journal of Internal Medicine · doi:10.3904/kjim.2020.415 · Cited by 22

CHIPIN: ChIP-seq inter-sample normalization based on signal invariance across transcriptionally constant genes

Polit, L., Kerdivel, G., Gregoricchio, S., Esposito, M., Guillouf, C., & Boeva, V. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04320-3 · Cited by 23

Semi-supervised Single-Cell Cross-modality Translation Using Polarbear

Zhang, R., Meng-Papaxanthos, L., Vert, J., & Noble, W. S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-04749-7_2 · Cited by 24

Construction of the axolotl cell landscape using combinatorial hybridization sequencing at single-cell resolution

Ye, F., Zhang, G., Weigao, E., Chen, H., Yu, C., Yang, L. X., Fu, Y., Li, J., Fu, S., Sun, Z., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-31879-z · Cited by 21

A multi-view latent variable model reveals cellular heterogeneity in complex tissues for paired multimodal single-cell data

Wang, Y., Lian, B., Zhang, H., Zhong, Y., He, J. Y., Wu, F., Reinert, K., Shang, X., Yang, H., & Hu, J. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad005 · Cited by 24

Inhibitor of PD-1/PD-L1: a new approach may be beneficial for the treatment of idiopathic pulmonary fibrosis

Tan, J., Xue, Q., Hu, X., & Yang, J. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-04884-7 · Cited by 23

CASB: a concanavalin A‐based sample barcoding strategy for single‐cell sequencing

Fang, L., Li, G., Sun, Z., Zhu, Q., Cui, H., Li, Y., Zhang, J., Liang, W., Wei, W., Hu, Y., et al. (2021) · Molecular Systems Biology · doi:10.15252/msb.202010060 · Cited by 20

GLUER: integrative analysis of single-cell omics and imaging data by deep neural networkPreprint

Peng, T., Chen, G. M., & Tan, K. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.25.427845 · Cited by 21

scMultiSim: simulation of multi-modality single cell data guided by cell-cell interactions and gene regulatory networksPreprint

Li, H., Zhang, Z., Squires, M., Chen, X. X., & Zhang, X. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.15.512320 · Cited by 22

Practical bioinformatics pipelines for single-cell RNA-seq data analysis

He, J., Lihui, L., & Jiekai, C. (2022) · Biophysics Reports · doi:10.52601/bpr.2022.210041 · Cited by 23

Current and future perspectives of single-cell multi-omics technologies in cardiovascular research

Tan, W. L. W., Seow, W. Q., Zhang, A., Rhee, S., Wong, W. H., Greenleaf, W. J., & Wu, J. C. (2023) · Nature Cardiovascular Research · doi:10.1038/s44161-022-00205-7 · Cited by 22

Cancer systems immunology

Reticker-Flynn, N. E., & Engleman, E. G. (2020) · eLife · doi:10.7554/elife.53839 · Cited by 23

New discoveries in the field of metabolism by applying single-cell and spatial omics

Xie, B., Gao, D., Zhou, B., Chen, S., & Wang, L. (2023) · Journal of Pharmaceutical Analysis · doi:10.1016/j.jpha.2023.06.002 · Cited by 21

Combinatorial single-cell profiling of major chromatin types with MAbID

Lochs, S. J. A., van der Weide, R. H., de Luca, K. L., Korthout, T., van Beek, R. E., Kimurâ, H., & Kind, J. (2023) · Nature Methods · doi:10.1038/s41592-023-02090-9 · Cited by 20

New frameworks for hematopoiesis derived from single-cell genomics

Safina, K. R., & van Galen, P. (2024) · Blood · doi:10.1182/blood.2024024006 · Cited by 23

Recent advances in single-cell epigenomics

Harada, A., Kimurâ, H., & Ohkawa, Y. (2021) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2021.06.010 · Cited by 21

Multi-omic approaches to acute kidney injury and repair

Gerhardt, L. M. S., & McMahon, A. P. (2021) · Current Opinion in Biomedical Engineering · doi:10.1016/j.cobme.2021.100344 · Cited by 20

Encoding and decoding NF-κB nuclear dynamics

Wibisana, J. N., & Okada, M. (2022) · Current Opinion in Cell Biology · doi:10.1016/j.ceb.2022.102103 · Cited by 22

Integrating Micro and Nano Technologies for Cell Engineering and Analysis: Toward the Next Generation of Cell Therapy Workflows

Mukherjee, P., Park, S. H., Pathak, N., Patino, C. A., Bao, G., & Espinosa, H. D. (2022) · ACS Nano · doi:10.1021/acsnano.2c05494 · Cited by 20

Integrative insights and clinical applications of single-cell sequencing in cancer immunotherapy

Liu, Z., Li, H., Dang, Q., Weng, S., Duo, M., Lv, J., & Han, X. (2022) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-022-04608-4 · Cited by 19

Single-Cell Multimodal Prediction via Transformers

Tang, W., Wen, H., Liu, R., Ding, J., Jin, W., Xie, Y., Liu, H., & Tang, J. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3615061 · Cited by 23

The Use of Single-Cell RNA-Sequencing and Spatial Transcriptomics in Understanding the Pathogenesis and Treatment of Skin Diseases

Houser, A. E., Kazmi, A., Nair, A., & Ji, A. L. (2023) · JID Innovations · doi:10.1016/j.xjidi.2023.100198 · Cited by 23

Revealing gene function with statistical inference at single-cell resolution

Trapnell, C. (2024) · Nature Reviews Genetics · doi:10.1038/s41576-024-00750-w · Cited by 19

High-throughput single-cell omics using semipermeable capsules

Baronas, D., Žvirblytė, J., Norvaisis, S., Leonavičienė, G., Goda, K., Mikulenaite, V., Kašėta, V., Šablauskas, K., Griškevičius, L., Juzėnas, S., et al. (2025) · Science · doi:10.1126/science.ady7227 · Cited by 22

Comparative Transcriptomic Analysis of the Hematopoietic System between Human and Mouse by Single Cell RNA Sequencing

Gao, S., Wu, Z., Kannan, J., Mathews, L., Feng, X., Kajigaya, S., & Young, N. S. (2021) · Cells · doi:10.3390/cells10050973 · Cited by 19

Mediators of Capillary-to-Venule Conversion in the Chronic Inflammatory Skin Disease Psoriasis

He, Y., Kim, J., Tacconi, C., Moody, J., Dieterich, L. C., Anzengruber, F., Maul, J., Gousopoulos, E., Restivo, G., Levesque, M. P., et al. (2022) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2022.05.1089 · Cited by 20

The Analysis of the Human Megakaryocyte and Platelet Coding Transcriptome in Healthy and Diseased Subjects

Wispelaere, K. D., & Freson, K. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23147647 · Cited by 21

Single-cell transcriptomics is revolutionizing the improvement of plant biotechnology research: recent advances and future opportunities

Kaur, H., Jha, P., Ochatt, S., & Kumar, V. (2023) · Critical Reviews in Biotechnology · doi:10.1080/07388551.2023.2165900 · Cited by 22

Con-AAE: contrastive cycle adversarial autoencoders for single-cell multi-omics alignment and integration

Wang, X., Hu, Z., Yu, T., Wang, Y., Wang, R., Wei, Y., Shu, J., Ma, J., & Li, Y. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad162 · Cited by 23

GTAC enables parallel genotyping of multiple genomic loci with chromatin accessibility profiling in single cells

Turkalj, S., Jakobsen, N. A., Groom, A., Metzner, M., Riva, S. G., Gur, E., Usukhbayar, B., Salazar, M. A., Hentges, L. D., Mickute, G., et al. (2023) · Cell stem cell · doi:10.1016/j.stem.2023.04.012 · Cited by 19

scMHNN: a novel hypergraph neural network for integrative analysis of single-cell epigenomic, transcriptomic and proteomic data

Li, W., Xiang, B., Yang, F., Rong, Y., Yin, Y., Yao, J., & Zhang, H. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad391 · Cited by 22

Overloading And unpacKing (OAK) - droplet-based combinatorial indexing for ultra-high throughput single-cell multiomic profiling

Wu, B. C., Bennett, H. M., Ye, X., Sridhar, A., Eidenschenk, C., Everett, C., Nazarova, E. V., Chen, H.-H., Kim, I. K., DeAngelis, M. M., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-53227-z · Cited by 18

scPair: Boosting single cell multimodal analysis by leveraging implicit feature selection and single cell atlases

Hu, H., & Quon, G. (2024) · Nature Communications · doi:10.1038/s41467-024-53971-2 · Cited by 19

Applying single‐cell technologies to clinical pathology: progress in nephropathology

Stewart, B. J., & Clatworthy, M. R. (2020) · The Journal of Pathology · doi:10.1002/path.5417 · Cited by 18

Single-Cell Virtual Cytometer allows user-friendly and versatile analysis and visualization of multimodal single cell RNAseq datasets

Pont, F., Tosolini, M., Gao, Q., Perrier, M., Madrid-Mencía, M., Huang, T. S., Neuvial, P., Ayyoub, M., Nazor, K. L., & Fournié, J. (2020) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaa025 · Cited by 19

Ultra-high throughput single-cell analysis of proteins and RNAs by split-pool synthesis

O’Huallachain, M., Bava, F.-A., Shen, M., Dallett, C., Paladugu, S. R., Samusik, N., Yu, S., Hussein, R., Hillman, G. R., Higgins, S., et al. (2020) · Communications Biology · doi:10.1038/s42003-020-0896-2 · Cited by 19

Single-cell ‘omic profiles of human aortic endothelial cells in vitro and human atherosclerotic lesions ex vivo reveal heterogeneity of endothelial subtype and response to activating perturbations

Adelus, M. L., Ding, J., Tran, B. T., Conklin, A. C., Golebiewski, A. K., Stolze, L. K., Whalen, M. B., Cusanovich, D. A., & Romanoski, C. E. (2023) · eLife · doi:10.7554/elife.91729 · Cited by 21

Single-Cell Sequencing in Cancer: Recent Applications to Immunogenomics and Multi-omics Tools

Sierant, M. C., & Choi, J. (2018) · Genomics & Informatics · doi:10.5808/gi.2018.16.4.e17 · Cited by 15

Genomic and epigenomic active vitamin D responses in human colonic organoids

Li, J., Witonsky, D., Sprague, E., Alleyne, D., Bielski, M. C., Lawrence, K. M., & Kupfer, S. S. (2021) · Physiological Genomics · doi:10.1152/physiolgenomics.00150.2020 · Cited by 18

Staggered starts in the race to T cell activation

Richard, A. C., Frazer, G. L., Ma, C. Y., & Griffiths, G. M. (2021) · Trends in Immunology · doi:10.1016/j.it.2021.09.004 · Cited by 15

Haematopoiesis in Zebrafish (Danio Rerio)

Stosik, M. P., Tokarz–Deptuła, B., & Deptuła, W. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.902941 · Cited by 18

The role of single-cell genomics in human genetics

Sreenivasan, V. K. A., Balachandran, S., & Spielmann, M. (2022) · Journal of Medical Genetics · doi:10.1136/jmedgenet-2022-108588 · Cited by 19

Single-cell technologies in multiple myeloma: new insights into disease pathogenesis and translational implications

Chen, M., Jiang, J., & Hou, J. (2023) · Biomarker Research · doi:10.1186/s40364-023-00502-8 · Cited by 19

Pooled CRISPR screens with joint single-nucleus chromatin accessibility and transcriptome profiling

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