A scalable, low-cost, sample hashing workflow for multiomic single-cell analysis using the Seq-Well S3 platform

A scalable, low-cost, sample hashing workflow for multiomic single-cell analysis using the Seq-Well S3 platform

  • Andreatta, M. et al. Interpretation of T cell states from single-cell transcriptomics data using reference atlases. Nat. Commun. 12, 2965 (2021.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Raghavan, S. et al. Microenvironment drives cell state, plasticity, and drug response in pancreatic cancer. Cell 184, 6119–6137.e26 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kotliar, D. et al. Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq. eLife 8, e43803 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhu, H. et al. Human PBMC scRNA-seq-based aging clocks reveal ribosome to inflammation balance as a single-cell aging hallmark and super longevity. Sci. Adv. 9, eabq7599 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Jeffries, A. M. et al. Single-cell transcriptomic and genomic changes in the ageing human brain. Nature 646, 657–666 (2025).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yang, X., Hou, X., Zhang, J., Liu, Z. & Wang, G. Research progress on the application of single-cell sequencing in autoimmune diseases. Genes Immun. 24, 220–235 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Scheinecker, C., Göschl, L. & Bonelli, M. Treg cells in health and autoimmune diseases: new insights from single cell analysis. J. Autoimmun. 110, 102376 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Zhao, M. et al. The application of single-cell RNA sequencing in studies of autoimmune diseases: a comprehensive review. Clin. Rev. Allergy Immunol. 60, 68–86 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Huang, W., Wang, D. & Yao, Y.-F. Understanding the pathogenesis of infectious diseases by single-cell RNA sequencing. Microb. Cell 8, 208–222 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wang, B. et al. Single-cell massively-parallel multiplexed microbial sequencing (M3-seq) identifies rare bacterial populations and profiles phage infection. Nat. Microbiol. 8, 1846–1862 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Swaminath, S. & Russell, A. B. The use of single-cell RNA-seq to study heterogeneity at varying levels of virus–host interactions. PLOS Pathog. 20, e1011898 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Nofech-Mozes, I., Soave, D., Awadalla, P. & Abelson, S. Pan-cancer classification of single cells in the tumour microenvironment. Nat. Commun. 14, 1615 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Liu, Z. L. et al. Single cell deciphering of progression trajectories of the tumor ecosystem in head and neck cancer. Nat. Commun. 15, 2595 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kang, J. et al. Systematic dissection of tumor-normal single-cell ecosystems across a thousand tumors of 30 cancer types. Nat. Commun. 15, 4067 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Heumos, L. et al. Best practices for single-cell analysis across modalities. Nat. Rev. Genet. 24, 550–572 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sidhaye, J. et al. Integrated transcriptome and proteome analysis reveals posttranscriptional regulation of ribosomal genes in human brain organoids. eLife 12, e85135 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bathke, J., Konzer, A., Remes, B., McIntosh, M. & Klug, G. Comparative analyses of the variation of the transcriptome and proteome of Rhodobacter sphaeroides throughout growth. BMC Genomics 20, 358 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Genshaft, A. S. et al. Single-cell RNA sequencing of liver fine-needle aspirates captures immune diversity in the blood and liver in chronic hepatitis B patients. Hepatology 78, 1525 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Liu, N. et al. Scalable, compressed phenotypic screening using pooled perturbations. Nat. Biotechnol 43, 1324–1336 (2025).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Morang’a, C. M. et al. scRNA-seq reveals elevated interferon responses and TNF-α signaling via NFkB in monocytes in children with uncomplicated malaria. Exp. Biol. Med. 249, 10233 (2025).

    Article 

    Google Scholar
     

  • Kotliar, D. et al. Single-Cell Profiling of ebola virus disease in vivo reveals viral and host dynamics. Cell 183, 1383–1401.e19 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Tarazona, S. et al. Harmonization of quality metrics and power calculation in multi-omic studies. Nat. Commun. 11, 3092 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hughes, T. K. et al. Second-strand synthesis-based massively parallel scRNA-seq reveals cellular states and molecular features of human inflammatory skin pathologies. Immunity 53, 878–894.e7 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Drake, R. S. et al. in Single Cell Transcriptomics: Methods and Protocols (eds. Calogero, R. A. & Benes, V.) 57–104 (Springer US, 2023); https://doi.org/10.1007/978-1-0716-2756-3_3

  • Mead, B. E. et al. Screening for modulators of the cellular composition of gut epithelia via organoid models of intestinal stem cell differentiation. Nat. Biomed. Eng. 6, 476–494 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ziegler, C. G. K. et al. SARS-CoV-2 receptor ACE2 is an interferon-stimulated gene in human airway epithelial cells and is detected in specific cell subsets across tissues. Cell 181, 1016–1035.e19 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Trombetta, J. J. et al. Preparation of single-cell RNA-seq libraries for next generation sequencing. Curr. Protoc. Mol. Biol. 107, 4.22.1–4.22.17 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cardiello, J. F. et al. Evaluation of genetic demultiplexing of single-cell sequencing data from model species. Life Sci. Alliance 6, e202301979 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kang, H. M. et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat. Biotechnol. 36, 89–94 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Brown, D. V. et al. A risk-reward examination of sample multiplexing reagents for single cell RNA-seq. Genomics 116, 110793 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Gideon, H. P. et al. Multimodal profiling of lung granulomas in macaques reveals cellular correlates of tuberculosis control. Immunity 55, 827–846.e10 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wilk, A. J. et al. Pro-inflammatory feedback loops define immune responses to pathogenic Lentivirus infection. Genome Med. 16, 24 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ordovas-Montanes, J. et al. Allergic inflammatory memory in human respiratory epithelial progenitor cells. Nature 560, 649–654 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Tzouanas, C. N. et al. Hepatic adaptation to chronic metabolic stress primes tumorigenesis. Cell 189, 35–460 (2026).

    Article 

    Google Scholar
     

  • Nyquist, S. K. et al. Cellular and transcriptional diversity over the course of human lactation. Proc. Natl Acad. Sci. USA 119, e2121720119 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cheng, J., Liao, J., Shao, X., Lu, X. & Fan, X. Multiplexing methods for simultaneous large-scale transcriptomic profiling of samples at single-cell resolution. Adv. Sci. 8, 2101229 (2021).

    Article 
    CAS 

    Google Scholar
     

  • Regev, A. et al. The Human Cell Atlas. eLife 6, e27041 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Pai, J. A. & Satpathy, A. T. High-throughput and single-cell T cell receptor sequencing technologies. Nat. Methods 18, 881–892 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mhanna, V. et al. Adaptive immune receptor repertoire analysis. Nat. Rev. Methods Primer 4, 1–25 (2024).


    Google Scholar
     

  • Irac, S. E., Soon, M. S. F., Borcherding, N. & Tuong, Z. K. Single-cell immune repertoire analysis. Nat. Methods 21, 777–792 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Ding, J. et al. Systematic comparison of single-cell and single-nucleus RNA-sequencing methods. Nat. Biotechnol. 38, 737–746 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Jovic, D. et al. Single-cell RNA sequencing technologies and applications: a brief overview. Clin. Transl. Med. 12, e694 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Rosenberg, A. B. et al. SPLiT-seq reveals cell types and lineages in the developing brain and spinal cord. Science 360, 176–182 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gierahn, T. M. et al. Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Nat. Methods 14, 395–398 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Fan, H. C., Fu, G. K. & Fodor, S. P. A. Combinatorial labeling of single cells for gene expression cytometry. Science 347, 1258367 (2015).

    Article 
    PubMed 

    Google Scholar
     

  • Chen, H. et al. High-throughput Microwell-seq 2.0 profiles massively multiplexed chemical perturbation. Cell Discov. 7, 1–4 (2021).

    Article 

    Google Scholar
     

  • Han, X. et al. Mapping the Mouse Cell Atlas by Microwell-Seq. Cell 172, 1091–1107.e17 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Ding, Y. et al. A deep learning model to predict a diagnosis of Alzheimer disease by using 18F-FDG PET of the brain. Radiology 290, 456–464 (2019).

    Article 
    PubMed 

    Google Scholar
     

  • Jaiswal, S. et al. Identification and characterization of the T cell receptor (TCR) repertoire of the cynomolgus macaque (Macaca Fascicularis). BMC Genomics 23, 647 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Prakadan, S. M. et al. Genomic and transcriptomic correlates of immunotherapy response within the tumor microenvironment of leptomeningeal metastases. Nat. Commun. 12, 5955 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mastering biology to advance human health. 10x Genomics https://www.10xgenomics.com/ (2025).

  • TotalSeqTM–a cell surface protein and hashing single-cell staining guide. BioLegend https://www.biolegend.com/nl-be/protocols/totalseq-a-cell-surface-protein-and-hashing-single-cell-staining-guide (2025).

  • Gonye, A. L. K. et al. Protocol for bulk RNA sequencing of enriched human neutrophils from whole blood and estimation of sample purity. STAR Protoc. 4, 102125 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hao, Y. et al. Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nat. Biotechnol. 42, 293–304 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Neavin, D. et al. Demuxafy: improvement in droplet assignment by integrating multiple single-cell demultiplexing and doublet detection methods. Genome Biol. 25, 94 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • AMPure XP Beads Protocol | PCR & NGS Library Cleanup. Beckman Coulter Life Sciences https://www.beckman.com/reagents/genomic/cleanup-and-size-selection/pcr/ampure-xp-protocol (2025).

  • Kaminow, B., Yunusov, D. & Dobin, A. STARsolo: accurate, fast and versatile mapping/quantification of single-cell and single-nucleus RNA-seq data. Preprint at bioRxiv https://doi.org/10.1101/2021.05.05.442755 (2021).

  • Roelli, P., bbimber, Flynn, B., santiagorevale & Gui, G. Hoohm/CITE-seq-Count: 1.4.2. Zenodo https://doi.org/10.5281/zenodo.2590196 (2019).

  • Picard Tools—by Broad Institute. Github https://broadinstitute.github.io/picard/ (2025).

  • Van der Auwera, G. & O’Connor, B. D. Genomics in the Cloud: Using Docker, GATK, and WDL in Terra (O’Reilly Media, Incorporated, 2020).

  • Van der Auwera, G. A. et al. From FastQ data to high-confidence variant calls: the genome analysis toolkit best practices pipeline. Curr. Protoc. Bioinforma. 43, 11.10.1–11.10.33 (2013).


    Google Scholar
     

  • Poplin, R. et al. Scaling accurate genetic variant discovery to tens of thousands of samples. Preprint at bioRxiv https://doi.org/10.1101/201178 (2018).

  • Vogel, C. & Marcotte, E. M. Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat. Rev. Genet. 13, 227–232 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Triana, S. et al. Single-cell proteo-genomic reference maps of the hematopoietic system enable the purification and massive profiling of precisely defined cell states. Nat. Immunol. 22, 1577–1589 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar