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Cited by 1,037Single-cell and spatial transcriptomicsBioinformatics and Genomic NetworksGenomics and Chromatin Dynamics

The technological landscape and applications of single-cell multi-omics

Open access
Baysoy, Alev, Bai, Zhiliang, Satija, Rahul, Fan, Rong·2023·Nature Reviews Molecular Cell Biology
DOI 10.1038/s41580-023-00615-wPMC PMC10242609
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Abstract

Single-cell multi-omics technologies and methods characterize cell states and activities by simultaneously integrating various single-modality omics methods that profile the transcriptome, genome, epigenome, epitranscriptome, proteome, metabolome and other (emerging) omics. Collectively, these methods are revolutionizing molecular cell biology research. In this comprehensive Review, we discuss established multi-omics technologies as well as cutting-edge and state-of-the-art methods in the field. We discuss how multi-omics technologies have been adapted and improved over the past decade using a framework characterized by optimization of throughput and resolution, modality integration, uniqueness and accuracy, and we also discuss multi-omics limitations. We highlight the impact that single-cell multi-omics technologies have had in cell lineage tracing, tissue-specific and cell-specific atlas production, tumour immunology and cancer genetics, and in mapping of cellular spatial information in fundamental and translational research. Finally, we discuss bioinformatics tools that have been developed to link different omics modalities and elucidate functionality through the use of better mathematical modelling and computational methods. Single-cell multi-omics methods are essential for characterizing cell states and types. The past decade has ushered in improvements in spatial resolution and computational data integration and in new omics modalities. Consequently, single-cell multi-omics have advanced fundamental and translational research, including, for example, in production of cell atlases and in tumour immunology therapeutics.

AI snapshot

Written from the paper’s full text.

TL;DR

This comprehensive Review surveys single-cell multi-omics technologies, their decade of development, spatial extensions, applications, and computational integration tools. It argues these methods are transforming cell lineage tracing, atlas production, tumour immunology/cancer genetics, and spatial mapping, while highlighting benchmarking, cost, coverage, and throughput limitations.

Key findings

  • The review organizes multi-omics around transcriptome-anchored pairings with genome, epigenome, proteome, and emerging modalities; Table 1 lists methods from 2014 to 2023, including spatial resolutions from 100 μm (spatial transcriptomics) to 2 μm (HDST) and DBiT-seq at 10–50 μm.
  • Throughput has increased markedly: SPLiT-seq uniquely labelled over 100,000 nuclei in one experiment; Paired-seq processed 1 million nuclei; Perturb-seq analysed 200,000 cells; Sci-CAR-seq profiled over 11,200 nuclei; SNARE-seq co-profiled over 10,000 mRNA transcripts with chromatin accessibility; TEA-seq measured thousands of single cells.
  • Applications include lineage tracing, cell atlases, tumour immunology and cancer genetics: TCGA characterizes over 20,000 primary cancer and matched normal samples from 33 cancer types; HCA launched in 2017; CXCL13+CD8+ T cells correlated with favourable ICB response; RUNX1 was identified as a potential oncogene in mixed-phenotype acute leukaemia; CAR T ALL analysis implicated TH2 pathways and related genes in 5-year remission.
  • Computational integration is classified as matched versus unmatched; examples include Seurat v3 and LIGER, GLUE for triple-omics integration, Seurat v4 weighted nearest-neighbour analysis, DeepMAPS, SCENIC+, FigR, MIRA, CellOracle, and MultiVelo.

Methods

  • Design: comprehensive Review, not primary experimental research; synthesizes established and cutting-edge single-cell multi-omics technologies and bioinformatics tools.
  • Data or sample: no primary experimental sample; based on published literature and three summary tables — Table 1 (single-cell and spatial omics methods by date), Table 2 (multi-omics contributions to cell atlases), and Table 3 (computational methods).
  • Main analysis: narrative synthesis organized by modality integration (genome–transcriptome, transcriptome–epigenome, transcriptome–proteome, other modalities), spatial multi-omics, research and clinical applications, and computational integration.

Limitations

  • Stated in the paper: benchmarking emerging computational tools is crucial; matched or same-experiment datasets and gold-standard datasets are difficult to obtain; high sequencing costs and limited coverage of single cells; long-read technologies face low sequencing accuracy and difficulty obtaining intact DNA and full-length RNA from clinical samples; most spatial technologies are low throughput and have relatively small capture areas, limiting 3D tissue profiling; pseudotime methods are not always accurate in reconstructing complex developmental trajectories.
  • Evident from the text: as a review, it presents no new experimental data or meta-analysis, and its coverage is selective rather than systematic — the authors state they “only highlight atlases encompassing multi-omics datasets” and that described technologies are “just some of the many examples.”

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1038/s41580-023-00615-w

Cites

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Statistics

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Claims the paper makes

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  • Premisecitation

    Many scRNA-seq approaches introduced over the last ten years are now key for measuring cell-to-cell variation and its effects on function across populations.

    “Several scRNA-seq methods developed in the recent decade have become essential for capturing intercellular variation and its impact on cell functions at the population scale15–19.”
    Quote in the paperNot a substantial claim0 of 5 sources checked
    • cites [15] Hashimshony, T., Wagner, F., Sher, N. & Yanai, I. CEL-seq: single-cell RNA-seq by multiplexed linear amplification. Cell Rep. 2, 666–673 (2012). source

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    • cites [16] Islam, S. et al. Highly multiplexed and strand-specific single-cell RNA 5′ end sequencing. Nat. Protoc. 7, 813–828 (2012). source

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    • cites [19] Sasagawa, Y. et al. Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity. Genome Biol. 14, R31 (2013). source

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  • Premisecitation

    Current methods permit joint measurement of the genome, epigenome, transcriptome, proteome and additional omics layers within individual cells.

    “Methodological and technological advances now allow the simultaneous profiling of genome, epigenome, transcriptome, proteome and other (emerging) omics modalities in an effort to better understand biological mechanisms and genotype-to-phenotype relationships.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [1] Lee, J., Hyeon, D. Y. & Hwang, D. Single-cell multiomics: technologies and data analysis methods. Exp. Mol. Med. 52, 1428–1442 (2020). source

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  • Premisenone given

    During the last ten years, methods for single-cell multi-omics have been created that directly measure phenotypes previously only inferred from single-omics approaches.

    “Over the past decade, single-cell multi-omics technologies have been developed to directly characterize the phenotypes that are usually inferred only from single-omics methods.”
    Quote in the paperSubstantial claim
  • Interpretationreasoning

    Measuring genome and transcriptome together in one cell can reveal how the genome's transcriptional state is expressed.

    “Parallel profiling of the genome and transcriptome of the same single cell can reflect the transcriptional state of the genome.”
    Quote in the paperSubstantial claim
  • Interpretationreasoning

    Combining single-cell epigenome assays with transcriptomics can directly reveal DNA methylation, accessibility and histone marks alongside the RNA they accompany.

    “Single-cell epigenome analysis in tandem with transcriptomics allows the direct elucidation of DNA epigenetic features such as DNA methylation, DNA accessibility and histone modifications in relation to the transcriptome they produce.”
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    Premisenone given

    Among single-cell omics approaches, transcriptomics is the most established and is frequently combined with other omics to examine links between expression and phenotypic diversity without bias.

    “The most mature of the single-cell omics methods, single-cell transcriptomics, is often paired with other omics to study the connection between gene expression and phenotypic heterogeneity in an unbiased manner (Fig. 2).”
    Quote in the paperNot a substantial claim

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    Interpretationnone given

    Progress in multiplexing, throughput, resolution and accuracy for single-cell multi-omics has helped map a cell's genetic landscape more fully and improved identification of cellular differences and relationships.

    “Continuous advancements in multiplexing, throughput, resolution and accuracy in single-cell multi-omics technologies have aided the comprehensive delineation of the genetic landscape of a cell and revolutionized our capacity to identify cellular heterogeneities and relationships (Table 1).”
    Quote in the paperSubstantial claim

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    Premisecitation

    Single cells can be isolated into separate compartments using LCM, robotic micromanipulation, FACS or microfluidic platforms, where multiple molecule types are barcoded, enabling genome-scale characterization of cell-type-specific genomic information at high resolution.

    “By using laser capture microdissection (LCM), robotic micromanipulation, fluorescence-activated cell sorting or microfluidic platforms1, single cells can be isolated into individual compartments where they undergo barcoding of multiple types of molecules, thereby enabling genome-scale characterization of cell-type-specific genomic information at an unprecedented resolution.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [1] Lee, J., Hyeon, D. Y. & Hwang, D. Single-cell multiomics: technologies and data analysis methods. Exp. Mol. Med. 52, 1428–1442 (2020). source

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    Premisecitation

    The earliest method for genome-wide mRNA sequencing of individual cells appeared in 2009.

    “In 2009, the first single-cell genome-wide mRNA sequencing (scRNA-seq) method was reported6, thanks in part to the emergence of next-generation sequencing (NGS).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [6] Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009). source

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    Premisecitation

    In 2013, single-cell sequencing received the title Method of the Year because it was accessible, versatile and robust.

    “Single-cell sequencing was designated ‘Method of the Year’ in 2013 owing to its availability, versatility and robustness14.”
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    • cites [14] No Authors Listed. Method of the year 2013. Nat. Methods 11, 1 (2014). source

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    Premisecitation

    The single-cell transcriptomics field is divided into two major categories: microfluidics droplet-based and microwell plate-based.

    “The field has rapidly developed ever since and is divided into two major categories: microfluidics droplet-based transcriptomics7–10 and microwell plate-based11–13 transcriptomics (Box 1).”
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    • cites [7] Gierahn, T. M. et al. Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Nat. Methods 14, 395–398 (2017). source

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    • cites [8] Klein, A. M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015). source

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    • cites [9] Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015). source

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    • cites [10] Zheng, G. X. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017). source

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    • cites [11] Hagemann-Jensen, M. et al. Single-cell RNA counting at allele and isoform resolution using Smart-seq3. Nat. Biotechnol. 38, 708–714 (2020). source

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    • cites [12] Natarajan, K. N. Single-cell tagged reverse transcription (STRT-seq). Methods Mol. Biol. 1979, 133–153 (2019). source

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    • cites [13] Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014). source

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    Premisecitation

    Broad implementation of single-cell multi-omics has been transformative in enabling cell lineage tracing, producing cell-type atlases of various organs, and dissecting mechanisms in tumour immunology and cancer genetics.

    “The broad implementation of these technologies has been transformative in enabling applications such as tracing cell lineages3, producing cell-type atlases of various organs4 and dissecting mechanisms in disease settings such as tumour immunology and cancer genetics5.”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [3] Wagner, D. E. & Klein, A. M. Lineage tracing meets single-cell omics: opportunities and challenges. Nat. Rev. Genet. 21, 410–427 (2020). source

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    • cites [4] Elmentaite, R., Domínguez Conde, C., Yang, L. & Teichmann, S. A. Single-cell atlases: shared and tissue-specific cell types across human organs. Nat. Rev. Genet. 23, 395–410 (2022). source

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    • cites [5] Yofe, I., Dahan, R. & Amit, I. Single-cell genomic approaches for developing the next generation of immunotherapies. Nat. Med. 26, 171–177 (2020). source

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    Premisereasoning

    New techniques that map gene expression in tissue space led to the idea of spatial multi-omics.

    “As single-cell technologies have continually advanced, emerging methods capable of spatially mapping gene expression within the tissue drove the conception of ‘spatial multi-omics’.”
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    Interpretationreasoning

    These approaches yield extensive datasets that support uncovering regulatory mechanisms, protein dynamics, epigenetic differences and perturbation models, changing views of cellular heterogeneity in health and disease.

    “These technologies have generated a huge amount of data that enable the discovery of mechanisms of gene regulation, protein expression dynamics, epigenetic variation and cell perturbation models, which have revolutionized how we understand cell heterogeneity in health and disease (Fig. 1).”
    Quote in the paperSubstantial claim

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    Interpretationnone given

    scRNA-seq by itself misses important mutations and cannot link genome to transcriptome in cells, a link needed to find new disease-related expression programmes.

    “On its own, scRNA-seq is unable to provide coverage of key mutations and does not provide genome–transcriptome correlations in cells that ultimately lead to the discovery of novel gene-expression programmes in disease.”
    Quote in the paperSubstantial claim

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    Premisenone given

    Methods for single-cell DNA methylation at base resolution fall into two main types, RRBS and WGBS, both relying on bisulfite conversion and next-generation sequencing.

    “Single-cell DNA methylation profiling methods with single-base resolution can be categorized according to two core technologies: reduced-range bisulfite sequencing (RRBS) and whole-genome bisulfite sequencing (WGBS), both using bisulfite conversion followed by NGS.”
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    Premisecitation

    Methylation of CpG islands commonly controls gene expression.

    “DNA methylation at CpG islands generally regulates gene expression25.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [25] Moore, D. DNA methylation and its basic function. Neuropsychopharmacology 38, 23–38 (2013). source

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    Premisenone given

    WGBS covers more CpG islands, while RRBS costs less.

    “The primary differences between the two categories are coverage and cost: WGBS has higher coverage of CpG islands in the genome whereas RRBS is less expensive.”
    Quote in the paperNot a substantial claim

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    Premisereasoning

    Microfluidic droplet methods process thousands of cells at once, but they read only transcript ends, so allele-specific expression and other isoforms are missed.

    “Droplet-based methods can analyse single cells at a much higher throughput (thousands of cells profiled at once) by utilizing microfluidic devices, but only from the 5′ or 3′ end of the transcript, thereby omitting the detection of allele-specific expression and other isoforms.”
    Quote in the paperNot a substantial claim

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    Premisereasoning

    Plate-based transcriptomics can produce full-length transcripts, which helps detect genes with low expression and rare transcripts.

    “Plate-based methods can generate full-length transcripts that carry ample information to allow detection of genes that are not highly expressed and other rare transcripts.”
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    Interpretationnone given

    Accessible chromatin is central to reading genomic activity, since it exposes enhancers, transcription factor binding sites and other regulatory DNA.

    “Chromatin accessibility is integral to detecting genomic activity as it reveals enhancer activity, transcription factor binding sites and other DNA regulatory elements.”
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    Premisecitation

    scNMT-seq and scNOMeRe-seq show single-cell nucleosome occupancy mapping, measuring methylation, accessibility and RNA from one DNA molecule.

    “Adding to the comprehensive tri-omics methods and WGBS technologies is single-cell nucleosome occupancy mapping, which is demonstrated in scNMT-seq29 and scNOMeRe-seq36 and by which DNA methylation, chromatin accessibility and transcripts from the same DNA molecule can be profiled.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [29] Clark, S. J. et al. scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells. Nat. Commun. 9, 781 (2018). source

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    • cites [36] Wang, Y. et al. Single-cell multiomics sequencing reveals the functional regulatory landscape of early embryos. Nat. Commun. 12, 1247 (2021). source

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    Premisenone given

    Droplet-based approaches need many cells and high cell quality.

    “These methods require both high quantity and high quality of cells.”
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    Premisecitation

    scNOMe-seq measures nucleosome position, chromatin accessibility and DNA methylation by recovering reads even with signal or allele dropout, separating them from inaccessible-chromatin reads.

    “scNOMe-seq characterizes nucleosome positioning, chromatin accessibility and DNA methylation by uniquely recovering reads regardless of signal and allelic drop-outs, thereby distinguishing them from reads of inaccessible chromatin35.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [35] Pott, S. Simultaneous measurement of chromatin accessibility, DNA methylation, and nucleosome phasing in single cells. eLife https://doi.org/10.7554/eLife.23203 (2017). source

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    Interpretationreasoning

    In both G&T-seq and DR-seq, DNA and RNA are separated physically; this can lose material or sensitivity and introduce amplification bias, making small pathogenic changes harder to detect confidently.

    “Both methods are similar in that DNA and RNA must be physically separated, which may result in loss of genetic material and sensitivity or in amplification bias, which limits the ability to confidently detect small pathogenic alterations.”
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    Premisecitation

    Two methods, G&T-seq and DR-seq, appeared the following year.

    “The next year, genome and transcriptome sequencing (G&T-seq)21 and gDNA–mRNA sequencing (DR-seq)22 were reported.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [21] Macaulay, I. C. et al. G&T-seq: parallel sequencing of single-cell genomes and transcriptomes. Nat. Methods 12, 519–522 (2015). source

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    • cites [22] Dey, S. S., Kester, L., Spanjaard, B., Bienko, M. & van Oudenaarden, A. Integrated genome and transcriptome sequencing of the same cell. Nat. Biotechnol. 33, 285–289 (2015). source

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    Premisecitation

    TARGET-seq, a plate-based approach, raised throughput to about 5,000 cells per run by barcoding and pooling in smaller reactions, and protein digestion improved mutation coverage.

    “In 2019, TARGET-seq, a plate-based method, demonstrated increased throughput of ~5,000 single cells profiled per run through the use of barcoding and pooling of libraries in reduced reaction volumes and was set apart from previously mentioned methods by improving mutation coverage using protein digestion, which improves the release of DNA and RNA during cell lysis23.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [23] Rodriguez-Meira, A. et al. Unravelling intratumoral heterogeneity through high-sensitivity single-cell mutational analysis and parallel RNA sequencing. Mol. Cell 73, 1292 (2019). source

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  • May combine two claims.

    Premisenone given

    Multi-omics methods that extend scRNA-seq retain transcriptome profiling while also measuring other layers and raising throughput via multiplexing, tagging, hashing and related strategies.

    “Building on scRNA-seq, multi-omics technologies preserve the progress made in characterizing transcriptomic information while simultaneously capturing other modalities and increasing throughput through cell multiplexing, cell tagging, cell hashing and other methods (Fig. 3).”
    Quote in the paperNot a substantial claim

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    Interpretationreasoning

    Plate-based approaches face constraints from the number of wells and the number of cells that can be analysed.

    “However, these methods are limited by plate size and the quantity of cells available for analysis.”
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    Premisecitation

    scM&T-seq was the first WGBS-based multi-omics method; it used G&T-seq and scBS-seq to assess methylation heterogeneity and Smart-seq2 to obtain RNA data together with genome-wide methylation.

    “The first method of WGBS multi-omics, single-cell genome-wide methylome and transcriptome sequencing (scM&T-seq)28, borrowed already-established single-omics techniques from G&T-seq21 and scBS-seq26 to measure DNA methylation heterogeneity in single cells, combined with Smart-seq2 to generate transcriptional data concurrently with genome-wide methylation data.”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [28] Angermueller, C. et al. Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity. Nat. Methods 13, 229–232 (2016). source

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    • cites [21] Macaulay, I. C. et al. G&T-seq: parallel sequencing of single-cell genomes and transcriptomes. Nat. Methods 12, 519–522 (2015). source

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    • cites [26] Smallwood, S. A. et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat. Methods 11, 817–820 (2014). source

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    Interpretationnone given

    Single-cell histone modification data help reveal epigenetic programmes and differentiation paths, supporting prediction of cell states.

    “Histone modifications at single-cell resolution are informative in understanding epigenetic programmes and differentiation trajectories of cells, thereby aiding in cell-state prediction.”
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    Premisecitation

    CUT&Tag, introduced in 2019, uses Tn5 transposase to tagment directly at antibody-bound sites.

    “In addition to the well-established chromatin immunoprecipitation followed by sequencing (ChIP–seq)45 and CUT&RUN46 methods, cleavage under targets and tagmentation (CUT&Tag), which was developed in 2019, uses Tn5 transposase to directly tagment the antibody binding site47.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [47] Kaya-Okur, H. S. et al. CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nat. Commun. 10, 1930 (2019). source

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    Premisecitation

    ATAC-seq can find open chromatin regions linked to transcription even with few cells, and it is simple and fast because it skips extraction and enzymatic digestion.

    “ATAC-seq allows the identification of regions within the genome that have open chromatin states associated with transcription in low numbers of cells and is an easy and time-friendly method as it does not include extraction or enzymatic-digestion steps.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [37] Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013). source

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    Premisecitation

    Methods of medium throughput, including scCAT-seq and ASTAR-seq, work with whole cells instead of nuclei and measure epigenome and transcriptome together.

    “Medium-throughput methods involving manipulation of intact cells rather than nuclei and profiling both epigenome and transcriptome such as scCAT-seq53 and assay for single-cell transcriptome and accessibility regions (ASTAR-seq)54 have been developed.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [53] Liu, L. et al. Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity. Nat. Commun. 10, 470 (2019). source

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    • cites [54] Xing, Q. R. et al. Parallel bimodal single-cell sequencing of transcriptome and chromatin accessibility. Genome Res. 30, 1027–1039 (2020). source

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    Premisecitation

    scCUT&Tag and scCUT&Tag2for1 use antibody-guided tagmentation to map active and silenced regulatory elements across the genome by targeting RNA polymerase II and Polycomb complexes, respectively.

    “This method has been modified further by other groups to profile histone modifications at the single-cell level: methods such as scCUT&Tag48 and scCUT&Tag2for149 use tagmentation directed by antibodies to profile active and silenced regulatory elements genome-wide by targeting domains bound by, respectively, RNA polymerase II and Polycomb repressive complexes.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [48] Bartosovic, M., Kabbe, M. & Castelo-Branco, G. Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues. Nat. Biotechnol. 39, 825–835 (2021). source

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    • cites [49] Janssens, D. H. et al. CUT&Tag2for1: a modified method for simultaneous profiling of the accessible and silenced regulome in single cells. Genome Biol. 23, 81 (2022). source

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    Premisecitation

    SHARE-seq uses several rounds of hybridization blocking to label mRNA and chromatin fragments in single cells.

    “Increasing throughput, SHARE-seq was built based on SPLiT-seq and Paired-seq and uses several rounds of hybridization blocking to label mRNA and chromatin fragments in single cells60.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [60] Ma, S. et al. Chromatin potential identified by shared single-cell profiling of RNA and chromatin. Cell 183, 1103 (2020). source

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    Premisecitation

    Paired-seq used three rounds of ligation-based combinatorial indexing to process 1 million nuclei in a single experiment for parallel transcriptome and chromatin accessibility analyses.

    “This strategy was implemented shortly after by Paired-seq, which used three rounds of ligation-based combinatorial indexing to process 1 million nuclei in a single experiment for parallel transcriptome and chromatin accessibility analyses59.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [59] Zhu, C. et al. An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome. Nat. Struct. Mol. Biol. 26, 1063–1070 (2019). source

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    Premisecitation

    ISSAAC-seq uses a two-tagmentation strategy for chromatin and cDNA.

    “Another recent high-throughput method, ISSAAC-seq (in situ sequencing hetero RNA–DNA-hybrid after assay for transposase-accessible chromatin sequencing), used a two-tagmentation strategy: an initial tagmentation reaction on accessible chromatin followed by reverse transcription and another tagmentation reaction on cDNA–RNA hybrids62.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [62] Xu, W. et al. ISSAAC-seq enables sensitive and flexible multimodal profiling of chromatin accessibility and gene expression in single cells. Nat. Methods 19, 1243–1249 (2022). source

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    Premisecitation

    Mosaic-seq systematically perturbs enhancers and measures their endogenous activities while jointly measuring transcriptome at single-cell resolution.

    “Another method, Mosaic-seq, was developed to systematically perturb enhancers and measure their endogenous activities while jointly measuring the transcriptome at single-cell resolution86.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [86] Xie, S., Duan, J., Li, B., Zhou, P. & Hon, G. C. Multiplexed engineering and analysis of combinatorial enhancer activity in single cells. Mol. Cell 66, 285–299.e5 (2017). source

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    Premisecitation

    CRISP-seq captures mRNA and genomic perturbations from CRISPR-pooled screens and elucidated function of multiple regulatory factors in a single experiment.

    “In 2016, CRISP-seq, a method to capture mRNA and genomic perturbations from CRISPR-pooled screens emerged, elucidating the function of multiple regulatory factors in a single experiment85.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [85] Jaitin, D. A. et al. Dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-seq. Cell 167, 1883–1896.e15 (2016). source

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    Premisecitation

    Spear-ATAC-seq characterized over 100,000 single-cell epigenetic states and over 400 CRISPR perturbations.

    “CRISPR screens have been coupled with other methods such as scATAC-seq — for example, in developing Spear-ATAC-seq, which characterized over 100,000 single-cell epigenetic states and over 400 CRISPR perturbations87.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [87] Pierce, S. E., Granja, J. M. & Greenleaf, W. J. High-throughput single-cell chromatin accessibility CRISPR screens enable unbiased identification of regulatory networks in cancer. Nat. Commun. 12, 2969 (2021). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    TCR/BCR repertoire data can provide understanding of immunity spectra and suggest how receptor configurations contribute to adaptive immunity, autoimmunity and tumour growth.

    “These data can provide an in-depth understanding of immunity spectra in individuals and suggest how various receptor configurations contribute to adaptive immunity responses, autoimmunity and tumour growth88.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [88] Pai, J. A. & Satpathy, A. T. High-throughput and single-cell T cell receptor sequencing technologies. Nat. Methods 18, 881–892 (2021). source

      Not checked in the free run.

    • Rests on an assumption: TCRs and BCRs are crucial components of adaptive immunity.

  • May combine two claims.

    Premisecitation

    ECCITE-seq incorporates CRISPR-compatible transcriptome, immunity repertoire and proteome indexing, including clonotype profiling and CRISPR-directed perturbations at high sensitivity and single-cell resolution.

    “Finally, expanded CRISPR-compatible CITE-seq (ECCITE-seq) expanded the protein multi-omics field to incorporate CRISPR-compatible transcriptome, immunity repertoire and proteome indexing, including both clonotype profiling and CRISPR-directed perturbations at high sensitivity and single-cell resolution81.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [81] Mimitou, E. P. et al. Multiplexed detection of proteins, transcriptomes, clonotypes and CRISPR perturbations in single cells. Nat. Methods 16, 409–412 (2019). source

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    Antibody-free single-cell proteomics methods produce more comprehensive characterization of the cellular proteome and can profile thousands of proteins in single cells.

    “Other single-cell proteomics methods that do not rely on antibodies produce a more comprehensive characterization of the cellular proteome, as they are not limited by antigen-specific reagents and can profile thousands of proteins in single cells.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    SCoPE-MS and SCoPE2 use mass spectrometry and have no available integration method with other omics.

    “SCoPE-MS and SCoPE2 are such methods using mass spectrometry, but they have no available integration method with other omics, leaving room for future development72,73.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [72] Budnik, B., Levy, E., Harmange, G. & Slavov, N. SCoPE-MS: mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation. Genome Biol. 19, 161 (2018). source

      Not checked in the free run.

    • cites [73] Specht, H. et al. Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity using SCoPE2. Genome Biol. 22, 50 (2021). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    RNAscope hybridizes DNA with metal-tagged probes and uses metal-conjugated antibodies and mass cytometry to assess RNA and proteins.

    “To assess both RNA and proteins, RNAscope, commercialized in 2011 by Advanced Cell Diagnostics103, hybridizes DNA with probes tagged with metal instead of fluorophores, followed by the addition to the tissue section of metal-conjugated antibodies and mass-cytometry measurements of metal abundance104,105.”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [103] Bio-Techne. Bio-Techne announces commercial release of RNAscope® HiPlex Assay: a multiplex in situ hybridization assay for tissues, https://investors.bio-techne.com/press-releases/detail/148/bio-techne-announces-commercial-release-of-rnascope (2016).

      Not checked in the free run.

    • cites [104] Schulz, D. et al. Simultaneous multiplexed imaging of mRNA and proteins with subcellular resolution in breast cancer tissue samples by mass cytometry. Cell Syst. 6, 531 (2018). source

      Not checked in the free run.

    • cites [105] Wang, F. et al. RNAscope: a novel in situ RNA analysis platform for formalin-fixed, paraffin-embedded tissues. J. Mol. Diagn. 14, 22–29 (2012). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Single-cell multi-omics methods require dissociation and lose information about physical interactions, despite computational efforts.

    “The aforementioned single-cell multi-omics methods require dissociation of cells from their tissue and thus lose information about physical interactions within and between cells, despite several computational efforts made to infer cell–cell interactions96,97.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [96] Efremova, M., Vento-Tormo, M., Teichmann, S. A. & Vento-Tormo, R. CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes. Nat. Protoc. 15, 1484–1506 (2020). source

      Not checked in the free run.

    • cites [97] Browaeys, R., Saelens, W. & Saeys, Y. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat. Methods 17, 159–162 (2020). source

      Not checked in the free run.

    • Rests on an assumption: Organization of cellular compartments and intercellular interactions is fundamental to the function of multicellular organisms.

  • May combine two claims.

    Premisecitation

    CODEX uses multiplexed cytometric imaging to quantitatively characterize protein expression and define tissue architecture.

    “The spatial proteomics field has also expanded with the development of technologies such as co-detection by indexing (CODEX), which uses a multiplexed cytometric imaging approach to quantitatively characterize protein expression and define tissue architecture112.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [112] Goltsev, Y. et al. Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell 174, 968–981.e15 (2018). source

      Not checked in the free run.

  • May combine two claims.

    Premisereasoning

    LCM-based methods allow full gene/protein profiling at single-cell level with positional information, but assay only a small number of cells.

    “LCM-based methods allow full gene or protein profiling at a single-cell level including positional information, but they can assay only a small number of cells.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Slide-TCR-seq sequences whole transcriptomes and TCRs by integrating Slide-seqV2 with rhTCR-seq, revealing interaction between clonality, neighbouring cell types and gene expression at single-cell resolution.

    “Expanding to spatial mapping of T-cell clonotype repertoires, Slide-TCR-seq was recently reported to sequence whole transcriptomes and TCRs by integrating Slide-seqV2 with rhTCR-seq131 (a method used to amplify TCR transcripts), revealing the interaction between clonality, neighbouring cell types and gene expression in tissues at single-cell resolution132.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [131] Li, S. et al. RNase H-dependent PCR-enabled T-cell receptor sequencing for highly specific and efficient targeted sequencing of T-cell receptor mRNA for single-cell and repertoire analysis. Nat. Protoc. 14, 2571–2594 (2019). source

      Not checked in the free run.

    • cites [132] Liu, S. et al. Spatial maps of T cell receptors and transcriptomes reveal distinct immune niches and interactions in the adaptive immune response. Immunity 55, 1940–1952.e5 (2022). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    SM-Omics and SPOTS achieved co-analysis of transcriptome and proteins in tissue sections, not at single-cell resolution.

    “Combined spatial transcriptomics or the Visium platform with CITE-seq chemistry, SM-Omics129 and SPOTS130 also achieved co-analysis of transcriptome and a panel of proteins in tissue sections, although not at single-cell resolution.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [129] Vickovic, S. et al. SM-Omics is an automated platform for high-throughput spatial multi-omics. Nat. Commun. 13, 795 (2022). source

      Not checked in the free run.

    • cites [130] Ben-Chetrit, N. et al. Integration of whole transcriptome spatial profiling with protein markers. Nat. Biotechnol. https://doi.org/10.1038/s41587-022-01536-3 (2023). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    GeoMx uses targeted FISH detection and can detect up to 10,000 genes in a select microscopic region.

    “GeoMx (from Nanostring) uses a targeted FISH detection method rather than direct sequencing of transcripts, allowing the detection of up to 10,000 genes in a select microscopic region of tissue through photocleaving and sequencing the DNA probes, which are hybridized to target genes, and the probe panel is now approaching a whole-transcriptome level of coverage121.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [121] Merritt, C. R. et al. Multiplex digital spatial profiling of proteins and RNA in fixed tissue. Nat. Biotechnol. 38, 586–599 (2020). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Spatial transcriptomics was introduced in 2016 with 100-μm spot size on a microarray slide.

    “In 2016, spatial transcriptomics was introduced116, demonstrating a 100-μm spot size on a microarray slide with arrayed oligonucleotides to capture spatial gene expression information.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [116] Ståhl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    DBiT-seq has been extended to spatial co-profiling of mRNA and hundreds of proteins, and transcriptome and epigenome.

    “Recently, DBiT-seq has further demonstrated its versatility by spatial co-profiling of mRNA transcriptome and hundreds of cellular proteins as part of spatial CITE-seq126 and of transcriptome and epigenome as part of spatial ATAC–RNA-seq127, spatial CUT&Tag–RNA-seq127 and (reported in a preprint) SPARC-seq128.”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [126] Liu, Y. et al. High-plex protein and whole transcriptome co-mapping at cellular resolution with spatial CITE-seq. Nat. Biotechnol. https://doi.org/10.1038/s41587-023-01676-0 (2023). source

      Not checked in the free run.

    • cites [127] Zhang, D. et al. Spatial epigenome–transcriptome co-profiling of mammalian tissues. Nature 616, 113–122 (2023). source

      Not checked in the free run.

    • cites [128] Jiang, F. et al. Simultaneously spatiotemporal gene expression and chromatin accessibility for mouse brain development. Preprint at bioRxiv, https://doi.org/10.1101/2022.03.22.485333 (2022). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    LCM can simultaneously characterize multiple modalities, as demonstrated by co-profiling mRNAs, microRNAs, DNA methylation and protein expression.

    “Multiple modalities can be characterized simultaneously based on LCM, as demonstrated in a work that constructed dynamic regulatory networks by co-profiling of mRNAs, microRNAs, DNA methylation and protein expression133.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [133] Ding, J. et al. Integrating multiomics longitudinal data to reconstruct networks underlying lung development. Am. J. Physiol. Lung Cell Mol. Physiol. 317, L556–L568 (2019). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    ENCODE defines and annotates functional elements in human and mouse genomes using multi-omics sequencing.

    “The Encyclopedia of DNA Elements (ENCODE) project consortium was piloted in 2003 and has since scaled up to define and annotate functional elements in both human and mouse genomes using multi-omics sequencing148.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [148] Dunham, I. et al. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    iTracer combines reporter barcodes with inducible CRISPR-Cas9 in iPSC-derived organoids and is compatible with single-cell and spatial transcriptomics.

    “The recently developed iTracer combines reporter barcodes with inducible CRISPR–Cas9 in induced pluripotent stem cell-derived organoids, and is compatible with single-cell and spatial transcriptomics140.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [140] He, Z. Lineage recording in human cerebral organoids. Nat. Methods 19, 90–99 (2022). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Combining genome, transcriptome and lineage reporter methods enables lineage tracing in diseases such as cancer, as shown by study on glioblastoma drug resistance.

    “Finally, combining genome, transcriptome and lineage reporter methods enables lineage tracing in diseases such as cancer, in which elucidating the persistence of epigenetic states together with genetic mutations can lay a foundation for rational design of therapeutics, as shown by a study elucidating the relevance of single-cell lineages in glioblastoma drug resistance142.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [142] Eyler, C. E. et al. Single-cell lineage analysis reveals genetic and epigenetic interplay in glioblastoma drug resistance. Genome Biol. 21, 174 (2020). source

      Not checked in the free run.

    • Rests on an assumption: Clonal information can be encoded by DNA oligonucleotide barcodes to record all cell division events within an organism.

  • May combine two claims.

    Premisecitation

    TCGA is a cancer-specific multi-omics resource containing over 20,000 primary cancer and matched normal samples from 33 cancer types, with genomic, epigenomic, transcriptomic and proteomic data.

    “The Cancer Genome Atlas (TCGA) is a cancer-specific multi-omics data resource characterizing over 20,000 primary cancer and corresponding normal samples from 33 cancer types, representing data from genomic, epigenomic, transcriptomic and proteomic modalities.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [147] Cancer Genome Atlas Research Network et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 45, 1113–1120 (2013). source

      Not checked in the free run.

  • May combine two claims.

    Interpretationnone given

    Single-cell multi-omics has had a large impact on cell lineage classification, tumour classification, and understanding evolution of cell states and types.

    “Altogether, single-cell multi-omics has had a large impact on cell lineage classification for disease, tumour classification and our understanding of evolution of cell states and types.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Integrative analysis of metabolomics, transcriptomics and genetic data confirmed metabolic pathways in non-small-cell lung cancer and elucidated metabolites and genes contributing to platelet activation, haemostasis, angiogenesis and cell proliferation.

    “Another group performed integrative analysis of metabolomics, transcriptomics and genetic data to confirm metabolic pathways in non-small-cell lung cancer and elucidate metabolites and genes contributing to platelet activation, haemostasis, angiogenesis and cell proliferation159.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [159] Hoang, L. T. et al. Metabolomic, transcriptomic and genetic integrative analysis reveals important roles of adenosine diphosphate in haemostasis and platelet activation in non-small-cell lung cancer. Mol. Oncol. 13, 2406–2421 (2019). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Integrated transcriptomics and metabolomics revealed that CPT1A overexpression is detrimental to individuals with prostate cancer and may support disease progression.

    “Integrated analysis of transcriptomics and metabolomics revealed that CPT1A overexpression is detrimental to individuals with prostate cancer and may support disease progression.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [158] Joshi, M. CPT1A over-expression increases reactive oxygen species in the mitochondria and promotes antioxidant defenses in prostate cancer. Cancers 12, 3431 (2020). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    slide-DNA-seq applied to a mouse metastasis model and primary human cancer revealed spatially distinct clonal populations and genes associated with clonal variation and the local tumour microenvironment.

    “The method was applied to a mouse metastasis model and to primary human cancer, revealing spatially distinct clonal populations and uncovering genes associated with clonal variation and the local tumour microenvironment.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [157] Zhao, T. et al. Spatial genomics enables multi-modal study of clonal heterogeneity in tissues. Nature 601, 85–91 (2022). source

      Not checked in the free run.

  • May combine two claims.

    Interpretationnone given

    More comprehensive and transformative insights were generated in immuno-oncology research, including defining tumour and immune cell states, revealing their interplay, inferring antigen-immune receptor dynamics, identifying predictive biomarkers of treatment response, and providing directions for therapeutics for multiple cancer types.

    “However, more comprehensive and transformative insights were generated in immuno-oncology research, including but not limited to defining tumour and immune cell states and revealing the interplay between them in specific disease contexts, inferring the complex nature of antigen–immune receptor dynamics, identifying predictive biomarkers of treatment response and providing directions for the”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Interpretationnone given

    Different omics layers contain distinct feature spaces that can reveal mechanisms of regulation and functionality of diverse cell types, providing a comprehensive understanding of cellular processes.

    “Different omics layers contain distinct feature spaces that can reveal the underlying mechanisms of the regulation and functionality of diverse cell types, thereby providing a comprehensive understanding of cellular processes (Fig. 4c).”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Premisereasoning

    A general solution to this obstacle is to project cells into a co-embedded space or nonlinear manifold to find commonality between all cells.

    “A general solution to this obstacle is to project cells into a co-embedded space or nonlinear manifold to find a commonality between all cells.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationcitation

    Paired experimental methods are easier computationally because modalities can be tethered to a single cell or spot, but sequencing capacity of each modality decreases overall sample size.

    “By contrast, paired experimental methods (10x Multiome, SHARE-seq60, DBiT-seq123, SNARE-seq57, CITE-seq68 and others) are easier to work with computationally as different modalities can be tethered to a single cell or a single spot; however, the sequencing capacity of each modality causes the overall sample size to decrease.”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [60] Ma, S. et al. Chromatin potential identified by shared single-cell profiling of RNA and chromatin. Cell 183, 1103 (2020). source

      Not checked in the free run.

    • cites [123] Liu, Y. et al. High-spatial-resolution multi-omics sequencing via deterministic barcoding in tissue. Cell 183, 1665–1681.e18 (2020). source

      Not checked in the free run.

    • cites [57] Chen, S., Lake, B. B. & Zhang, K. High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell. Nat. Biotechnol. 37, 1452–1457 (2019). source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    By using bona fide multi-omics profiles, these methods can outperform unmatched integration strategies and facilitate cross-modality experimental design where multi-omics datasets are collected only in a subset of experiments.

    “By making use of bona fide multi-omics profiles, these methods can outperform unmatched integration strategies and facilitate an experimental design for cross-modality in which multi-omics datasets are only collected in a subset of experiments.”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [170] Ghazanfar, S. StabMap: Mosaic single cell data integration using non-overlapping features. Preprint at bioRxiv https://doi.org/10.1101/2022.02.24.481823 (2022). source

      Not checked in the free run.

    • cites [171] Gong, B. Cobolt: integrative analysis of multimodal single-cell sequencing data. Genome Biol. 22, 351 (2021). source

      Not checked in the free run.

    • cites [172] Ashuach, T. MultiVI: deep generative model for the integration of multi-modal data. Preprint at bioRxiv https://doi.org/10.1101/2021.08.20.457057 (2021). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    GLUE achieves triple-omics integration while simultaneously inferring regulatory interactions.

    “A recent unmatched method, graph-linked unified embedding (GLUE), achieves triple-omics integration while simultaneously inferring regulatory interactions.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [169] Cao, Z. J. & Gao, G. Multi-omics single-cell data integration and regulatory inference with graph-linked embedding. Nat. Biotechnol. 40, 1458–1466 (2022). source

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    High sequencing costs and limited coverage of single cells mean each omics layer can only be partially profiled, resulting in loss of important data.

    “Other challenges include high sequencing costs and limited coverage of single cells, for which each omics layer can only be partially profiled, thus resulting in loss of important data.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    CellOracle leverages multi-omics data, collected in parallel or integrated computationally, to learn gene regulatory networks and infer results of in silico perturbations.

    “Similarly, CellOracle179 leverages multi-omics data, either collected in parallel or integrated computationally, to learn gene regulatory networks and infer the results of in silico perturbations, whereas in silico ChIP–seq predicts transcription factor binding at individual cis-regulatory elements179.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [179] Kamimoto, K. Dissecting cell identity via network inference and in silico gene perturbation. Nature 614, 742–751 (2023). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    DeepMAPS uses a five-step framework to preprocess data, generate an integrated cell-gene matrix, use a heterogeneous graph transformer to score gene-to-cell significance, generate clusters, and elucidate biological networks for each cell type.

    “Another method, DeepMAPS (deep learning-based multi-omics analysis platform for single-cell data), uses a five-step framework to preprocess data, generate an integrated cell–gene matrix specific to multi-omics data types, use a heterogeneous graph transformer model to generate a score for each cell indicating its gene-to-cell significance, generate clusters and ultimately elucidate diverse”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [175] Ma, A. et al. Single-cell biological network inference using a heterogeneous graph transformer. Nat. Commun. 14, 964 (2023). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    Single-cell multi-omics have become integral methods for elucidating the complexity of biological processes, especially for rare diseases and cell types.

    “Single-cell multi-omics have already become integral methods for elucidating the complexity of biological processes, especially when facing rare diseases and cell types.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationnone given

    Despite advancements in computational integration tools, analysis of biological functionality of cell types and gene regulatory networks remains a computational challenge.

    “Despite advancements in the many computational integration tools for multi-omics and multi-layered data, analysis of biological functionality of cell types and gene regulatory networks remains a computational challenge.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Interpretationnone given

    Most current spatial technologies are low throughput in the number of tissues they can process simultaneously and therefore cannot fully capture the 3D architecture of a tissue.

    “Most of the current spatial technologies are low throughput in the number of tissues they can process simultaneously and therefore cannot fully capture the 3D architecture of a tissue.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    In silico ChIP-seq predicts transcription factor binding at individual cis-regulatory elements.

    “whereas in silico ChIP–seq predicts transcription factor binding at individual cis-regulatory elements179.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [179] Kamimoto, K. Dissecting cell identity via network inference and in silico gene perturbation. Nature 614, 742–751 (2023). source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    Long-read technologies can advance detection of genetic variation in cells but face limitations of low sequencing accuracy and difficulty obtaining intact DNA and full-length RNA from clinical samples.

    “In the effort to capture more informative and complete genomic data, long-read technologies can advance the detection of genetic variation in cells, but still face limitations of low sequencing accuracy and difficulty in obtaining intact DNA and full-length RNA from clinical samples181.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [181] Sakamoto, Y., Sereewattanawoot, S. & Suzuki, A. A new era of long-read sequencing for cancer genomics. J. Hum. Genet. 65, 3–10 (2020). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    It is difficult to obtain matched or same-experiment datasets characterizing different omics layers along with gold-standard datasets.

    “Furthermore, it is difficult to obtain matched or same-experiment datasets characterizing different omics layers along with gold-standard datasets, posing an urgent need for benchmarking multi-omics studies and generating guidelines for data analysis.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    Many computational methods for pseudotime analysis of scRNA-seq data are not always accurate in reconstructing cell trajectories through developmental processes, which are complex and dynamic.

    “Finally, although many computational methods are offered for pseudotime analysis of scRNA-seq data, these methods are not always accurate in reconstructing the trajectory of cells through their developmental processes, which are complex and dynamic in nature.”
    Quote in the paperNot a substantial claim
74 of 74

  • The paper relies on this without support.

    Nuclear proteins are actively involved in gene regulation.

    “Nuclear proteins are actively involved in gene regulation”

  • The paper relies on this without support.

    TCRs and BCRs are crucial components of adaptive immunity.

    “T cell receptors (TCRs) and B cell receptors (BCRs), which are crucial components of adaptive immunity”

  • The paper relies on this without support.

    Organization of cellular compartments and intercellular interactions is fundamental to the function of multicellular organisms.

    “The organization of cellular compartments and macro-structures and of intercellular interactions is fundamental to the function of multicellular organisms.”

  • The paper relies on this without support.

    Clonal information can be encoded by DNA oligonucleotide barcodes to record all cell division events within an organism.

    “Clonal information can be encoded by DNA oligonucleotide barcodes to record all cell division events within an organism”
4 of 4

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • scTrio-seq is assigned two different reference numbers

    Minor

    The lineage-tracing section cites scTrio-seq as reference 28, but the earlier description identifies it as reference 33. Reference 28 is identified earlier as scM&T-seq.

  • scNMT-seq is assigned two different reference numbers

    Minor

    The lineage-tracing section cites scNMT-seq as reference 26, whereas the earlier description cites reference 29. Reference 26 is identified earlier as a single-cell bisulfite-sequencing method.

2 of 2

Cites

Flagged first, then by citations

Research works printed but not linked: 2

  1. Oxford Nanopore Technologies. Oxford Nanopore Technologies announces new collaboration with 10x Genomics to make single-cell and spatial full-length isoform transcript sequencing accessible to any laboratory, https://nanoporetech.com/about-us/news/oxford-nanopore-technologies-announces-new-collaboration-10x-genomics-make-single (27 October 2022).
  2. Bio-Techne. Bio-Techne announces commercial release of RNAscope® HiPlex Assay: a multiplex in situ hybridization assay for tissues, https://investors.bio-techne.com/press-releases/detail/148/bio-techne-announces-commercial-release-of-rnascope (2016).

Cited by

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Related

Authors
Baysoy, A., Bai, Z., Satija, R., & Fan, R.
Affiliations
  • Yale University (US)
  • New York Genome Center (US)
  • Yale Cancer Center (US)
Corresponding author
Fan, Rong
ORCID
Venue
Nature Reviews Molecular Cell Biology
Publisher
Springer Science and Business Media LLC
Published
2023-06-06
Volume
24
Issue
10
Pages
695-713
ISSN
  • 1471-0072 (ISSN-L)
  • 1471-0080
DOI
10.1038/s41580-023-00615-w
PMC
PMC10242609
OpenAlex
W4379521442
Semantic Scholar
da7c7d871d905c60ababe567260d597cc07919f0
Cited by
1,037
References
211
Citations by year
2026: 290 · 2025: 415 · 2024: 301 · 2023: 29 · 2022: 2
FWCI
27.36
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Reviews Molecular Cell Biology (publishedVersion, other-oa)
  • PubMed Central (publishedVersion, other-oa)
  • PubMed (publishedVersion)
Funders
  • Yale University
  • National Institutes of Health (1rf1mh128876-01)
  • National Institute on Aging (U54AG076043, U54AG079759)
  • National Institute of Mental Health (RF1 MH128876)
  • National Cancer Institute (UH3CA257393, UG3CA257393, R01CA245313, U54CA274509)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
Keywords
single-cell multi-omics, single-cell transcriptomics, single-cell proteomics, single-cell metabolomics, single-cell epigenomics, lineage tracing, cell atlases, tumor immunology, cancer genetics, spatial omics, multi-omics integration, bioinformatics software
MeSH terms
Epigenome, Multiomics, Cell Lineage, Computational Biology, Metabolome

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-07.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-07.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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