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

In the paper

“Overcoming these shortcomings, we reported the first amplification and sequencing of a whole genome and whole transcriptome from a single cell using a microfluidics-facilitated technology in 2014, which captured single cells using microvalve-based control channels, selectively lysed and separated cytoplasmic and nuclear contents, and then performed on-chip amplification, thereby enabling multiple”

What the sources say

• Partly supported

  The Abstract describes a microfluidic platform that separates and amplifies genomic DNA and mRNA from the same single cell, and says it could enable multiple genomic measurements. It also says sequencing was limited to a panel of genes and transcripts using off-chip PCR and Sanger sequencing; it does not report sequencing the whole genome and whole transcriptome. (LLM, conf=0.90)

  cites [20] Han, L. et al. Co-detection and sequencing of genes and transcripts from the same single cells facilitated by a microfluidics platform. Sci. Rep. 4, 6485 (2014). sourceExperimental — accuracy not yet measured

In the paper

“Subsequently, DNA methylation co-profiling technologies were developed for tri-omics analysis: single-cell triple omics sequencing (scTrio-seq) combined profiling of genome, methylome and transcriptome in 25 single cancer cells by applying scRRBS and whole-genome sequencing33;”

What the sources say

• Partly supported

  The Introduction and Results (page numbers not provided) say scTrio-seq analyzed 25 HCC cancer cells and obtained genomic CNV, methylome, and transcriptome information using scRRBS data. The CNV deduction Results section says whole-genome DNA amplification could not be performed alongside bisulfite conversion in a single cell; CNVs were instead inferred from RRBS data. Thus, the claim's whole-genome sequencing component is not supported. (LLM, conf=0.90)

  cites [33] Hou, Y. et al. Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas. Cell Res. 26, 304–319 (2016). sourceExperimental — accuracy not yet measured

In the paper

“For example, in 2016, the proximity ligation assay for RNA (PLAYR) was developed to simultaneously quantify more than 40 targeted mRNAs and proteins from 10,000 single cells in several cell types67.”

What the sources say

• Partly supported

  The Introduction (page unavailable) says PLAYR enables simultaneous quantitative measurement of more than 40 proteins and RNAs, and the Results describe its use with multiple cell types. The Discussion reports hundreds to thousands of intact cells measured per second, but our copy does not substantiate the specific figure of 10,000 single cells. (LLM, conf=0.85)

  cites [67] Frei, A. P. et al. Highly multiplexed simultaneous detection of RNAs and proteins in single cells. Nat. Methods 13, 269–275 (2016). sourceExperimental — accuracy not yet measured

In the paper

“Furthermore, this study led to the discovery of key factors in the gene–metabolite interaction network, metabolites, cell cycle genes and genes of the p53 signalling pathway, which may have important roles in disease mechanisms.”

What the sources say

• Supported

  The Discussion identifies key metabolites in the gene–metabolite interaction network as potentially important in disease mechanisms. It also identifies cell-cycle checkpoint and p53-signalling genes, particularly in LUSC tumours. (LLM, conf=0.94)

  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

In the paper

“These include SCENIC+176, functional inference of gene regulation (FigR)177, and probabilistic multimodal models for integrated regulatory analysis (MIRA)178, which model joint variation across paired transcriptomic and scATAC-seq measurements to link enhancers to genes and to identify transcription factors that drive cellular decisions.”

What the sources say

• Supported

  The SCENIC+ workflow (p. 16) combines chromatin accessibility and gene expression to predict enhancer-driven regulatory networks. The methods (p. 19) say it accepts single-cell multiome data or paired scRNA-seq and scATAC-seq from matching populations; the opening passage (p. 1) says it links enhancers to target genes, predicts cell-state-specific transcription factors, and studies regulation along differentiation trajectories. This verdict concerns SCENIC+ only, not the separately cited FigR and MIRA methods. (LLM, conf=0.91)

  cites [176] Gonzalez-Blas, C. SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks. Preprint at bioRxiv https://doi.org/10.1101/2022.08.19.504505 (2022). source
• Supported

  The Abstract identifies FigR as a framework that pairs scATAC-seq and scRNA-seq cells, connects distal regulatory elements to genes, and identifies candidate transcription-factor regulators. The Discussion says FigR uses variation in chromatin accessibility and RNA expression across single cells to infer regulatory relationships spanning cell states. Citation 177 identifies FigR specifically; this verdict does not assess the other two methods in the citing sentence. (LLM, conf=0.91)

  cites [177] Kartha, V. Functional inference of gene regulation using single-cell multi-omics. Cell Genom. 2, 100166 (2022). source
• Supported

  The Results section describes MIRA’s joint modeling of single-cell expression and chromatin accessibility and its gene-level analysis of how surrounding regulatory elements influence expression. It also says MIRA predicts key regulators, while the Discussion says it identifies regulators driving fate decisions at trajectory branch points. The citation to this paper is attached to MIRA, not to SCENIC+ or FigR. (LLM, conf=0.92)

  cites [178] Lynch, A. MIRA: joint regulatory modeling of multimodal expression and chromatin accessibility in single cells. Nat. Methods 19, 1097–1108 (2021). source

In the paper

“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.”

What the sources say

• Not checked yetcites [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
• Not checked yetcites [16] Islam, S. et al. Highly multiplexed and strand-specific single-cell RNA 5′ end sequencing. Nat. Protoc. 7, 813–828 (2012). source
• Not checked yetcites [17] Jaitin, D. A. et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343, 776–779 (2014). source
• Not checked yetcites [18] Ramskold, D. et al. Full-length mRNA-seq from single-cell levels of RNA and individual circulating tumor cells. Nat. Biotechnol. 30, 777–782 (2012). source
• Not checked yetcites [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

In the paper

“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.”

What the sources say

• Not checked yetcites [1] Lee, J., Hyeon, D. Y. & Hwang, D. Single-cell multiomics: technologies and data analysis methods. Exp. Mol. Med. 52, 1428–1442 (2020). source

In the paper

“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.”

In the paper

“Parallel profiling of the genome and transcriptome of the same single cell can reflect the transcriptional state of the genome.”

In the paper

“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.”

In the paper

“Many groups have built from this foundation to develop technologies that co-profile the DNA methylome and transcriptome28–31.”

What the sources say

• Not checked yetcites [28] Angermueller, C. et al. Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity. Nat. Methods 13, 229–232 (2016). source
• Not checked yetcites [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
• Not checked yetcites [30] Guo, F. et al. Single-cell multi-omics sequencing of mouse early embryos and embryonic stem cells. Cell Res. 27, 967–988 (2017). source
• Not checked yetcites [31] Hu, Y. et al. Simultaneous profiling of mRNA transcriptome and DNA methylome from a single cell. Methods Mol. Biol. 1979, 363–377 (2019). source

In the paper

“Most recent single-cell multi-omics technologies that profile chromatin accessibility are primarily adapted from assay for transposase-accessible chromatin sequencing (ATAC-seq37), which uses the transposase Tn5 to fragment open chromatin and tag adaptors to the DNA38–42.”

What the sources say

• Not checked yetcites [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

In the paper

“Chromatin accessibility paired with transcriptional analysis can reveal regulatory elements responsible for driving gene expression.”

In the paper

“Many multi-omics studies, in which protein and mRNA are co-profiled, rely on the use of predetermined, curated antibody panels to confer single-cell phenotypes, thereby losing novel and unexpected proteomic information63–66.”

What the sources say

• Not checked yetcites [63] Albayrak, C. et al. Digital quantification of proteins and mRNA in single mammalian cells. Mol. Cell 61, 914–924 (2016). source
• Not checked yetcites [64] Darmanis, S. et al. Simultaneous multiplexed measurement of RNA and proteins in single cells. Cell Rep. 14, 380–389 (2016). source
• Not checked yetcites [65] Genshaft, A. S. et al. Multiplexed, targeted profiling of single-cell proteomes and transcriptomes in a single reaction. Genome Biol. 17, 188 (2016). source
• Not checked yetcites [66] Stahlberg, A., Thomsen, C., Ruff, D. & Aman, P. Quantitative PCR analysis of DNA, RNAs, and proteins in the same single cell. Clin. Chem. 58, 1682–1691 (2012). source

In the paper

“These methods have been extended to characterize RNA and histone modifications using multi-omics techniques such as Paired-Tag51 and CoTECH52, which use combinatorial barcoding to allow high-throughput detection of both transcriptome and chromatin occupancy.”

What the sources say

• Not checked yetcites [51] Zhu, C. et al. Joint profiling of histone modifications and transcriptome in single cells from mouse brain. Nat. Methods 18, 283–292 (2021). source
• Not checked yetcites [52] Xiong, H., Luo, Y., Wang, Q., Yu, X. & He, A. Single-cell joint detection of chromatin occupancy and transcriptome enables higher-dimensional epigenomic reconstructions. Nat. Methods 18, 652–660 (2021). source

In the paper

“Split-pool ligation-based transcriptome sequencing (SPLiT-seq), a plate-based transcriptomics method, uniquely labelled over 100,000 nuclei in one experiment using four rounds of combinatorial barcoding58.”

What the sources say

• Not checked yetcites [58] Rosenberg, A. B. et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360, 176–182 (2018). source

In the paper

“method, uniquely labelled over 100,000 nuclei in one experiment using four rounds of combinatorial barcoding58”

What the sources say

• Not checked yetcites [58] Rosenberg, A. B. et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360, 176–182 (2018). source

In the paper

“Recently, TEA-seq, a method for trimodal single-cell measurement of transcripts, epitopes and chromatin accessibility, used the scATAC-seq workflow and the 10x Genomics Multiome ATAC Plus Gene expression kit to elucidate the modulators of gene regulation in thousands of single cells61.”

What the sources say

• Not checked yetcites [61] Swanson, E. et al. Simultaneous trimodal single-cell measurement of transcripts, epitopes, and chromatin accessibility using TEA-seq. eLife https://doi.org/10.7554/eLife.63632 (2021). source

In the paper

“As an alternative, single-nucleus chromatin accessibility and mRNA expression sequencing (SNARE-seq) used droplet-based microfluidics to co-profile over 10,000 mRNA transcripts with chromatin accessibility from the same single cell57.”

What the sources say

• Not checked yetcites [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

In the paper

“This methodology was further developed into scCUT&TAG-pro, which is a multimodal assay for profiling protein–DNA interactions and the abundance of surface proteins in single cells50.”

What the sources say

• Not checked yetcites [50] Zhang, B. et al. Characterizing cellular heterogeneity in chromatin state with scCUT&Tag-pro. Nat. Biotechnol. 40, 1220–1230 (2022). source

In the paper

“To achieve higher throughput in single-cell analysis, Sci-CAR-seq combined single-cell combinatorial indexing55 with ATAC-seq to provide deep insights into chromatin states jointly with gene expression in over 11,200 nuclei56.”

What the sources say

• Not checked yetcites [55] Vitak, S. A. et al. Sequencing thousands of single-cell genomes with combinatorial indexing. Nat. Methods 14, 302–308 (2017). source
• Not checked yetcites [56] Cao, J. et al. Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science 361, 1380–1385 (2018). source

In the paper

“An integrated protein and RNA detection method combining PEA and specific target amplification was also developed in 2016 to detect RNAs and proteins from the same single cell in a single reaction chamber of the Fluidigm C1 platform65.”

What the sources say

• Not checked yetcites [65] Genshaft, A. S. et al. Multiplexed, targeted profiling of single-cell proteomes and transcriptomes in a single reaction. Genome Biol. 17, 188 (2016). source

In the paper

“Technologies that can profile both intracellular and cell-surface proteins can better elucidate the signalling pathways that required for the function of a single cell75,76.”

What the sources say

• Not checked yetcites [75] Chung, H. et al. Joint single-cell measurements of nuclear proteins and RNA in vivo. Nat. Methods 18, 1204–1212 (2021). source
• Not checked yetcites [76] Katzenelenbogen, Y. et al. Coupled scRNA-seq and intracellular protein activity reveal an immunosuppressive role of TREM2 in cancer. Cell 182, 872–885.e19 (2020). source

In the paper

“The combination of sample multiplexing and droplet-based platforms such as 10x Genomics or microwell-based platforms such as BD Rhapsody71 have increased the throughput of mRNA and protein co-profiling.”

What the sources say

• Not checked yetcites [71] Shum, E. Y., Walczak, E. M., Chang, C. & Christina Fan, H. Quantitation of mRNA transcripts and proteins using the BD RhapsodyTM single-cell analysis system. Adv. Exp. Med. Biol. 1129, 63–79 (2019). source

In the paper

“RNA and immunodetection (RAID), a method developed for immunodetection of intracellular proteins or phosphorylated proteins together with mRNA profiling, enables correlation analysis of environmental stimuli with heterogeneous cellular responses77.”

What the sources say

• Not checked yetcites [77] Gerlach, J. P. et al. Combined quantification of intracellular (phospho-)proteins and transcriptomics from fixed single cells. Sci. Rep. 9, 1469 (2019). source

In the paper

“Similarly, RNA expression and protein sequencing assay (REAP-seq) demonstrated quantification of surface proteins with 82 antibodies and genome-scale mRNAs in a single workflow69.”

What the sources say

• Not checked yetcites [69] Peterson, V. M. et al. Multiplexed quantification of proteins and transcripts in single cells. Nat. Biotechnol. 35, 936–939 (2017). source

In the paper

“Perturb-seq is one of these methods, in which thousands of CRISPR-mediated perturbations and mRNA profiles are analysed in a single experiment of 200,000 cells82.”

What the sources say

• Not checked yetcites [82] Dixit, A. et al. Perturb-Seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167, 1853–1866.e17 (2016). source

In the paper

“making this method adaptable for integration with most scRNA-seq methods.”

In the paper

“Thus, multi-omics technologies have the capacity to measure the effects of cell perturbations and signalling pathways on the state of single cells.”

In the paper

“This multi-omics method allows profiling of thousands of single cells while reliably detecting cell-surface proteins, mitochondrial DNA genotypes and epigenomic modifications.”

What the sources say

• Not checked yetcites [74] Fiskin, E. et al. Single-cell profiling of proteins and chromatin accessibility using PHAGE-ATAC. Nat. Biotechnol. 40, 374–381 (2022). source

In the paper

“CROP-seq, another CRISPR-based genetic screening method combined with single-cell transcriptomics, directly links expression of a single guide RNA (sgRNA) to transcriptome responses in thousands of individual cells83.”

What the sources say

• Not checked yetcites [83] Datlinger, P. et al. Pooled CRISPR screening with single-cell transcriptome readout. Nat. Methods 14, 297–301 (2017). source

In the paper

“Recently, ATAC with select antigen profiling by sequencing (ASAP-seq) and DOGMA-seq78 demonstrated simultaneous profiling of chromatin accessibility, gene expression and protein expression by using reagents and protocols similar to those used in mtscATAC-seq79, CITE-seq68 and scATAC-seq43.”

What the sources say

• Not checked yetcites [78] Mimitou, E. P. et al. Scalable, multimodal profiling of chromatin accessibility, gene expression and protein levels in single cells. Nat. Biotechnol. 39, 1246–1258 (2021). source

In the paper

“This method combines highly multiplexed protein-marker detection with unbiased transcriptome profiling for thousands of single cells68.”

What the sources say

• Not checked yetcites [68] Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017). source

In the paper

“Incorporating even more modalities is NEAT-seq80, which co-profiles the abundance of nuclear protein epitopes, chromatin accessibility and the transcriptome in single cells.”

What the sources say

• Not checked yetcites [80] Chen, A. F. et al. NEAT-seq: simultaneous profiling of intra-nuclear proteins, chromatin accessibility and gene expression in single cells. Nat. Methods 19, 547–553 (2022). source

In the paper

“Another method used a homogeneous affinity-based proximity extension assay (PEA) to interrogate ~100 protein targets in single-cell lysates by linking matched pairs of oligonucleotide–antibody conjugates to target antigens and simultaneously co-detecting RNA by real-time PCR64.”

What the sources say

• Not checked yetcites [64] Darmanis, S. et al. Simultaneous multiplexed measurement of RNA and proteins in single cells. Cell Rep. 14, 380–389 (2016). source

In the paper

“Immunity repertoire sequencing, together with technological advances and integration with other omics modalities, has the capacity to provide deeper understanding of how T-cell and B cell clonality and specificity influence immune responses.”

In the paper

“Very recently, an automated and integrated platform, spatial molecular imager, was developed to resolve multi-omics spatial distribution of 980 RNAs and 108 proteins in formalin-fixed, paraffin-embedded tissues at single-cell and subcellular resolution, using a strategy similar to that of CODEX by performing multiple cycles of nucleic acid hybridization of fluorescent molecular barcodes113.”

What the sources say

• Not checked yetcites [113] He, S. et al. High-plex imaging of RNA and proteins at subcellular resolution in fixed tissue by spatial molecular imaging. Nat. Biotechnol. https://doi.org/10.1038/s41587-022-01483-z (2022). source

In the paper

“Sequential FISH (seqFISH+) demonstrated the ability to perform discovery-driven studies of the spatial organization of tissues with its capability of imaging mRNAs for 10,000 genes in single cells102.”

What the sources say

• Not checked yetcites [102] Eng, C. L. et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH. Nature 568, 235–239 (2019). source

In the paper

“A year later, MERFISH substantially increased the number of RNA molecules that could be simultaneously imaged in single cells by utilizing combinatorial labelling and sequential imaging of multiple readout hybridizations101.”

What the sources say

• Not checked yetcites [101] Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015). source

In the paper

“Several methods have been published pairing gene expression with TCR sequences in single cells89–92.”

What the sources say

• Not checked yetcites [89] Neal, J. T. et al. Organoid modeling of the tumor immune microenvironment. Cell 175, 1972–1988.e16 (2018). source
• Not checked yetcites [90] Singh, M. et al. High-throughput targeted long-read single cell sequencing reveals the clonal and transcriptional landscape of lymphocytes. Nat. Commun. 10, 3120 (2019). source
• Not checked yetcites [91] Tu, A. A. et al. TCR sequencing paired with massively parallel 3′ RNA-seq reveals clonotypic T cell signatures. Nat. Immunol. 20, 1692–1699 (2019). source
• Not checked yetcites [92] Zemmour, D. et al. Publisher correction: single-cell gene expression reveals a landscape of regulatory T cell phenotypes shaped by the TCR. Nat. Immunol. 19, 645 (2018). source

In the paper

“short-read scRNA-seq is not always sufficient to resolve the genetic complexity of these isoforms while preserving accurate gene expression quantification at the 3′ end of the transcripts90.”

What the sources say

• Not checked yetcites [90] Singh, M. et al. High-throughput targeted long-read single cell sequencing reveals the clonal and transcriptional landscape of lymphocytes. Nat. Commun. 10, 3120 (2019). source

In the paper

“Integration of specific TCR and BCR (DNA) clonotypes out of a cell population repertoire with scRNA-seq paves the way for a better understanding of the relationships between transcriptome, clone composition and unique sequence features of antigen receptors such as their CDR3 length and presence of specific sequence motifs93.”

What the sources say

• Not checked yetcites [93] Minervina, A., Pogorelyy, M. & Mamedov, I. T-cell receptor and B-cell receptor repertoire profiling in adaptive immunity. Transpl. Int. 32, 1111–1123 (2019). source

In the paper

“The V(D)J regions of rearranged TCR and BCR mRNAs are located in the first ~500 nucleotides of the 5′ end of the transcripts”

What the sources say

• Not checked yetcites [90] Singh, M. et al. High-throughput targeted long-read single cell sequencing reveals the clonal and transcriptional landscape of lymphocytes. Nat. Commun. 10, 3120 (2019). source

In the paper

“DBiT-seq was the first reported spatial multi-omics technology that demonstrated co-mapping of tens of proteins and with a whole transcriptome, and this method has further expanded to spatial epigenomics profiling of chromatin accessibility124 and histone modification125.”

What the sources say

• Not checked yetcites [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 yetcites [124] Deng, Y. et al. Spatial profiling of chromatin accessibility in mouse and human tissues. Nature 609, 375–383 (2022). source
• Not checked yetcites [125] Deng, Y. et al. Spatial-CUT&Tag: Spatially resolved chromatin modification profiling at the cellular level. Science 375, 681–686 (2022). source

In the paper

“This technology was later adapted by 10x Genomics as ‘10x Visium’, with increased resolution of 55 μm117.”

What the sources say

• Not checked yetcites [117] 10x Genomics. 10x Genomics acquires Spatial Transcriptomics, https://www.genomeweb.com/sequencing/10x-genomics-acquires-spatial-transcriptomics (2018).

In the paper

“Recently, LCM coupled with fluorescence imaging enabled deep proteomic characterization of formalin-fixed, paraffin-embedded tissues using mass spectrometry135.”

What the sources say

• Not checked yetcites [135] Mund, A. Deep visual proteomics defines single-cell identity and heterogeneity. Nat. Biotechnol. 40, 1231–1240 (2022). source

In the paper

“Single-cell proteomics methods contribute to our understanding of how protein levels change as cells differentiate by analysing lineage-specific transcription factors and their abundance over time, ultimately leading to the maintenance or emergence of a lineage trajectory139.”

What the sources say

• Not checked yetcites [139] Palii, C. G. et al. Single-cell proteomics reveal that quantitative changes in co-expressed lineage-specific transcription factors determine cell fate. Cell Stem Cell 24, 812–820.e5 (2019). source

In the paper

“Slide-seq, and later Slide-seqV2, barcode mRNA in tissues using a method conceptually similar to spatial transcriptomics, but a different strategy: instead of using arrayed barcoded reagents printed on slides, the barcoded reagents are placed directly onto the glass slide in solution, thereby creating a monolayer-packed array on a glass coverslip118,119 (Fig. 4a).”

What the sources say

• Not checked yetcites [118] Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019). source
• Not checked yetcites [119] Stickels, R. R. et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat. Biotechnol. 39, 313–319 (2021). source

In the paper

“Technologies for profiling the transcriptome in combination with chromatin accessibility, DNA methylation, histone modifications and nucleosome organization provide new insights into cell-type identity and elucidate epigenetic processes involved in lineage priming138.”

What the sources say

• Not checked yetcites [138] Woodworth, M. B., Girskis, K. M. & Walsh, C. A. Building a lineage from single cells: genetic techniques for cell lineage tracking. Nat. Rev. Genet. 18, 230–244 (2017). source

In the paper

“Methods discussed above such as scTRIO-seq28 and scNMT-seq26 can be used to simultaneously sample genomic copy number variations, the DNA methylome, nucleosome occupancy and the transcriptome of single cells to reveal new cell types and their roles in a studied lineage.”

What the sources say

• Not checked yetcites [28] Angermueller, C. et al. Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity. Nat. Methods 13, 229–232 (2016). source
• Not checked yetcites [26] Smallwood, S. A. et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat. Methods 11, 817–820 (2014). source

In the paper

“One group combined prostate cancer RNA-seq and SNP data to identify risk-modulating non-coding RNA, demonstrating the application of multi-omics TCGA data integration146,147.”

What the sources say

• Not checked yetcites [146] Guo, H. Modulation of long noncoding RNAs by risk SNPs underlying genetic predispositions to prostate cancer. Nat. Genet. 48, 1142–1150 (2016). source
• Not checked yetcites [147] Cancer Genome Atlas Research Network et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 45, 1113–1120 (2013). source

In the paper

“Recently, part of its bulk epigenomics data was deconvolved into specific cell-type assignments using scRNA-seq149, which further refines the data annotation.”

What the sources say

• Not checked yetcites [149] Abascal, F. et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature 583, 699–710 (2020). source

In the paper

“Recently, spatial multi-omics data have been used to add tissue context to cell genomic annotations through integration of multiple complementary technologies.”

What the sources say

• Not checked yetcites [153] Madissoon, E. et al. A spatially resolved atlas of the human lung characterizes a gland-associated immune niche. Nat. Genet. 55, 66–77 (2023). source

In the paper

“This combination adds a valuable parameter to atlas production as spatial distribution of cells can tie multi-omics signatures of specific cell types with their localization within the tissue153.”

What the sources say

• Not checked yetcites [153] Madissoon, E. et al. A spatially resolved atlas of the human lung characterizes a gland-associated immune niche. Nat. Genet. 55, 66–77 (2023). source

In the paper

“Tracing cell lineage can also provide valuable insights into how immune cells react to infection, how they differentiate and the mechanisms that underlie their fate, as shown in a recent study combining scRNA-seq and scTCR-seq for tracing clonal expansion and differentiation of CD8+ T cells, ultimately delineating pathways leading to cell exhaustion144,145.”

What the sources say

• Not checked yetcites [144] Kasmani, M. Y. et al. Clonal lineage tracing reveals mechanisms skewing CD8+ T cell fate decisions in chronic infection. J. Exp. Med. https://doi.org/10.1084/jem.20220679 (2023). source
• Not checked yetcites [145] Peng, G., Cui, G., Ke, J. & Jing, N. Using single-cell and spatial transcriptomes to understand stem cell lineage specification during early embryo development. Annu. Rev. Genomics Hum. Genet. 21, 163–181 (2020). source

In the paper

“Other consortia aim to provide comprehensive multi-omics atlases of the heart, lung, blood, sleep disorders and ageing, all incorporating thousands of datasets to identify cell types associated with disease onset and progression150.”

What the sources say

• Not checked yetcites [150] Taliun, D. et al. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature 590, 290–299 (2021). source

In the paper

“Another study combined single-cell transcriptome and DNA methylome data to construct a chronic lymphocytic leukaemia (CLL) lineage tree based on random DNA methylation changes, finding that certain CLL lineages were preferentially affected by drug treatment143.”

What the sources say

• Not checked yetcites [143] Gaiti, F. Epigenetic evolution and lineage histories of chronic lymphocytic leukaemia. Nature 569, 576–580 (2019). source

In the paper

“Another study applied single-cell multi-omics integration of DNA accessibility, gene expression and protein abundance to mixed-phenotype acute leukaemia models, determining that the gene RUNX1, encoding a transcription factor, is a potential oncogene in mixed-phenotype acute leukaemia associated with poor survival163.”

What the sources say

• Not checked yetcites [163] Granja, J. M. et al. Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia. Nat. Biotechnol. 37, 1458–1465 (2019). source

In the paper

“Study of the effects of inactivation of the signal transduction factor SMAD4 on the transcriptome, proteome and secretome led to the identification of three SMAD4-mediated processes that may promote metastasis in individuals with advanced colorectal cancer161.”

What the sources say

• Not checked yetcites [161] Dijkstra, J. J. Multiomics of colorectal cancer organoids reveals putative mediators of cancer progression resulting from SMAD4 inactivation. J. Proteome Res. 22, 138–151 (2022). source

In the paper

“Single-cell multi-omics technologies have added unprecedented breadth and depth into research of a variety of diseases including viral infections such as SARS-CoV-2 (ref. 154), cardiovascular diseases155, neurological disorders156 and others.”

What the sources say

• Not checked yetcites [154] Stephenson, E. et al. Single-cell multi-omics analysis of the immune response in COVID-19. Nat. Med. 27, 904–916 (2021). source
• Not checked yetcites [155] Miranda, A. M. A. et al. Single-cell transcriptomics for the assessment of cardiac disease. Nat. Rev. Cardiol. https://doi.org/10.1038/s41569-022-00805-7 (2022). source
• Not checked yetcites [156] Dong, X., Liu, C. & Dozmorov, M. Review of multi-omics data resources and integrative analysis for human brain disorders. Brief. Funct. Genomics 20, 223–234 (2021). source

In the paper

“Another study integrated the spatial phenotypes and immunity effectors panel of Akoya Biosciences with multiplexed immunofluorescent imaging, scRNA-seq and TCR repertoire analysis to elucidate T cell evasion pathways in response to ICB in triple-negative breast cancer165.”

What the sources say

• Not checked yetcites [165] Hammerl, D. et al. Spatial immunophenotypes predict response to anti-PD1 treatment and capture distinct paths of T cell evasion in triple negative breast cancer. Nat. Commun. 12, 5668 (2021). source

In the paper

“Additionally, peak-to-gene link analysis was performed by linking differentially accessible genomic regions, depicted as peaks, to known leukaemia genes, showing that specific peak-to-gene links were enriched in immunity regulation.”

What the sources say

• Not checked yetcites [163] Granja, J. M. et al. Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia. Nat. Biotechnol. 37, 1458–1465 (2019). source

In the paper

“Very recently, a newly developed spatial technology, slide-DNA-seq, was used to preserve local tumour architecture, enabling the discovery of distinct tumour clones and their copy number variations157.”

What the sources say

• Not checked yetcites [157] Zhao, T. et al. Spatial genomics enables multi-modal study of clonal heterogeneity in tissues. Nature 601, 85–91 (2022). source

In the paper

“Proteomics, genomics and phosphoproteomics methods were combined to classify hallmarks of lung cancer tumour progression in a large study of non-smoking individuals with early-stage tumours162.”

What the sources say

• Not checked yetcites [162] Chen, Y. J. et al. Proteogenomics of non-smoking lung cancer in East Asia delineates molecular signatures of pathogenesis and progression. Cell 182, 226–244.e17 (2020). source

In the paper

“The spatial immunophenotypes were assigned in triple-negative breast cancer through a spatial phenotype classifier, which predicted responses to treatment.”

What the sources say

• Not checked yetcites [165] Hammerl, D. et al. Spatial immunophenotypes predict response to anti-PD1 treatment and capture distinct paths of T cell evasion in triple negative breast cancer. Nat. Commun. 12, 5668 (2021). source

In the paper

“Specifically, proteomics clustering distinguished clinical features, biomarkers and druggable targets in early stages of lung adenocarcinoma.”

What the sources say

• Not checked yetcites [162] Chen, Y. J. et al. Proteogenomics of non-smoking lung cancer in East Asia delineates molecular signatures of pathogenesis and progression. Cell 182, 226–244.e17 (2020). source

In the paper

“One study resolved how tumour-reactive T cells respond to ICB in different tumour types by analysing previously published single-cell transcriptomics, epigenomics and TCR data, finding that the presence CXCL13+CD8+ T cells correlated with favourable response to ICB164.”

What the sources say

• Not checked yetcites [164] Liu, B., Zhang, Y., Wang, D., Hu, X. & Zhang, Z. Single-cell meta-analyses reveal responses of tumor-reactive CXCL13+ T cells to immune-checkpoint blockade. Nat. Cancer 3, 1123–1136 (2022). source

In the paper

“Another study applied gain and loss of function of CPT1A, which encodes a fatty acids metabolism enzyme, to elucidate genetic and metabolic vulnerabilities in prostate cancer158.”

What the sources say

• Not checked yetcites [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

In the paper

“In GLUE, a graph variational autoencoder learns feature embeddings from previous biological knowledge, which is then used to reconstruct and link omics data through an inner product with cell embeddings that are aligned further in subsequent steps169.”

What the sources say

• Not checked yetcites [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

In the paper

“Single-cell multi-omics has also been applied to assess chimeric antigen receptor (CAR) T cell therapy in acute lymphoblastic leukaemia, combining transcriptomics and proteomics data to determine the molecular basis of phenotypic differences between complete remission, non-responsive and relapsed individuals166.”

What the sources say

• Not checked yetcites [166] Bai, Z. et al. Single-cell antigen-specific landscape of CAR T infusion product identifies determinants of CD19-positive relapse in patients with ALL. Sci. Adv. 8, eabj2820 (2022). source

In the paper

“This analysis led to discovery of TH2 cell pathways and related genes as potential targets for maintaining 5-year remission following CAR T cell immunotherapy.”

What the sources say

• Not checked yetcites [166] Bai, Z. et al. Single-cell antigen-specific landscape of CAR T infusion product identifies determinants of CD19-positive relapse in patients with ALL. Sci. Adv. 8, eabj2820 (2022). source

In the paper

“Recently, Seurat v4 used a weighted nearest-neighbour analysis to integrate multiple modalities measured from a single cell and collectively define a cellular state174.”

What the sources say

• Not checked yetcites [174] Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021). source

In the paper

“Integration of unpaired multi-omics data is limited by the inherent information loss that comes from tying together distinct feature spaces of varying multi-omics modalities.”

In the paper

“Current tools used for the joint analysis of multiple single-cell omics modalities can be broadly classified into matched or unmatched pipelines (Table 3).”

What the sources say

• Not checked yetcites Table 3

In the paper

“These methods include StabMap170, Cobolt171, MultiVI172 and ‘Bridge Integration’ in Seurat v5 (ref. 173).”

What the sources say

• Not checked yetcites [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 yetcites [171] Gong, B. Cobolt: integrative analysis of multimodal single-cell sequencing data. Genome Biol. 22, 351 (2021). source
• Not checked yetcites [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

In the paper

“Despite the proliferation of and advancements in experimental methods that simultaneously profile more than one omics modality, a major hurdle in the field is the inherent complexity of multi-omics data integration.”

In the paper

“These methods can also be extended to model post-transcriptional regulation and predict future cellular states, such as MultiVelo, which integrates chromatin accessibility and gene expression to estimate chromatin accessibility switches and gene splicing states180.”

What the sources say

• Not checked yetcites [180] Li, C. Multi-omic single-cell velocity models epigenome–transcriptome interactions and improves cell fate prediction. Nat. Biotechnol. 41, 387–398 (2023). source

In the paper

“The emergence of spatial omics is transforming our understanding of the molecular mechanisms behind disease by providing spatial coordinates of cells, thus tracing back the location of cells (and their associated multi-omics readouts) within tissues.”

In the paper

“This unsupervised strategy learns cell-specific modality ‘weights’ that contain the information from each modality to determine its significance in subsequent analysis steps.”

What the sources say

• Not checked yetcites [174] Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021). source

In the paper

“Furthermore, the relatively small capture area limits their application in the complete profiling of most human tissue sections.”