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

In the paper

“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).”

What the sources say

• Not checked yetcites [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
• Supported

  The citation is attached to the droplet-based transcriptomics category, not the microwell plate-based category. The Introduction and Results (page numbers not provided) identify this paper's inDrop method as droplet-microfluidic RNA sequencing of single cells. (LLM, conf=0.93)

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

  The citation is attached to the droplet-based transcriptomics category, not specifically to the claim that the field has exactly two categories. The Introduction and Results identify Drop-Seq as a single-cell transcriptomics method that uses a microfluidic device to encapsulate cells in droplets. (LLM, conf=0.93)

  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
• Supported

  This source is cited in the group attached to “microfluidics droplet-based transcriptomics,” not the separate group attached to plate-based methods. The Results section (“Droplet-based platform enables barcoding of cells”) describes its scRNA-seq platform as using an 8-channel microfluidic chip to generate droplets, so it is an example of the cited category. (LLM, conf=0.93)

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

  The citation is attached to the microwell plate-based category, and this paper describes Smart-seq3 as a plate-based single-cell transcriptomics method. The Discussion says its implementation uses 384-well plates, and the Supplementary Methods describe distributing individual cells into 96- or 384-well plates. This supports the citation as an example of that category, rather than as evidence for the field-wide two-category division. (LLM, conf=0.90)

  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
• Not checked yetcites [12] Natarajan, K. N. Single-cell tagged reverse transcription (STRT-seq). Methods Mol. Biol. 1979, 133–153 (2019). source
• Not checked yetcites [13] Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014). source

In the paper

“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.”

What the sources say

• Partly supported

  The Results (‘CUT&Tag profiling of single cells’; page unavailable) confirm that scCUT&Tag uses antibody-directed tagmentation, and the paper profiles histone marks associated with active and repressive genomic regions. But the Online Methods (‘Antibodies’; page unavailable) list histone marks, Rad21 and Olig2—not RNA polymerase II. This source also does not describe scCUT&Tag2for1, which has a separate citation in the citing sentence. (LLM, conf=0.88)

  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). sourceExperimental — accuracy not yet measured
• Not checked yetcites [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

In the paper

“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).”

In the paper

“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).”

In the paper

“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.”

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

“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).”

What the sources say

• Not checked yetcites [6] Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009). source

In the paper

“Single-cell sequencing was designated ‘Method of the Year’ in 2013 owing to its availability, versatility and robustness14.”

What the sources say

• Not checked yetcites [14] No Authors Listed. Method of the year 2013. Nat. Methods 11, 1 (2014). source

In the paper

“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.”

What the sources say

• Not checked yetcites [3] Wagner, D. E. & Klein, A. M. Lineage tracing meets single-cell omics: opportunities and challenges. Nat. Rev. Genet. 21, 410–427 (2020). source
• Not checked yetcites [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
• Not checked yetcites [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

In the paper

“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’.”

In the paper

“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).”

In the paper

“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.”

In the paper

“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.”

In the paper

“DNA methylation at CpG islands generally regulates gene expression25.”

What the sources say

• Not checked yetcites [25] Moore, D. DNA methylation and its basic function. Neuropsychopharmacology 38, 23–38 (2013). source

In the paper

“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.”

In the paper

“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.”

In the paper

“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.”

In the paper

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

In the paper

“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.”

What the sources say

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

In the paper

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

In the paper

“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.”

What the sources say

• Not checked yetcites [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

In the paper

“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.”

In the paper

“The next year, genome and transcriptome sequencing (G&T-seq)21 and gDNA–mRNA sequencing (DR-seq)22 were reported.”

What the sources say

• Not checked yetcites [21] Macaulay, I. C. et al. G&T-seq: parallel sequencing of single-cell genomes and transcriptomes. Nat. Methods 12, 519–522 (2015). source
• Not checked yetcites [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

In the paper

“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.”

What the sources say

• Not checked yetcites [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

In the paper

“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).”

In the paper

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

In the paper

“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.”

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

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

In the paper

“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.”

What the sources say

• Not checked yetcites [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

In the paper

“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.”

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

“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.”

What the sources say

• Not checked yetcites [53] Liu, L. et al. Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity. Nat. Commun. 10, 470 (2019). source
• Not checked yetcites [54] Xing, Q. R. et al. Parallel bimodal single-cell sequencing of transcriptome and chromatin accessibility. Genome Res. 30, 1027–1039 (2020). source

In the paper

“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.”

What the sources say

• Not checked yetcites [60] Ma, S. et al. Chromatin potential identified by shared single-cell profiling of RNA and chromatin. Cell 183, 1103 (2020). source

In the paper

“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.”

What the sources say

• Not checked yetcites [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

In the paper

“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.”

What the sources say

• Not checked yetcites [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

In the paper

“Another method, Mosaic-seq, was developed to systematically perturb enhancers and measure their endogenous activities while jointly measuring the transcriptome at single-cell resolution86.”

What the sources say

• Not checked yetcites [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

In the paper

“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.”

What the sources say

• Not checked yetcites [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

In the paper

“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.”

What the sources say

• Not checked yetcites [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

In the paper

“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.”

What the sources say

• Not checked yetcites [88] Pai, J. A. & Satpathy, A. T. High-throughput and single-cell T cell receptor sequencing technologies. Nat. Methods 18, 881–892 (2021). source

In the paper

“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.”

What the sources say

• Not checked yetcites [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

In the paper

“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.”

In the paper

“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.”

What the sources say

• Not checked yetcites [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 yetcites [73] Specht, H. et al. Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity using SCoPE2. Genome Biol. 22, 50 (2021). source

In the paper

“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.”

What the sources say

• Not checked yetcites [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 yetcites [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 yetcites [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

In the paper

“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.”

What the sources say

• Not checked yetcites [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 yetcites [97] Browaeys, R., Saelens, W. & Saeys, Y. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat. Methods 17, 159–162 (2020). source

In the paper

“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.”

What the sources say

• Not checked yetcites [112] Goltsev, Y. et al. Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell 174, 968–981.e15 (2018). source

In the paper

“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.”

In the paper

“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.”

What the sources say

• Not checked yetcites [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 yetcites [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

In the paper

“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.”

What the sources say

• Not checked yetcites [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 yetcites [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

In the paper

“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.”

What the sources say

• Not checked yetcites [121] Merritt, C. R. et al. Multiplex digital spatial profiling of proteins and RNA in fixed tissue. Nat. Biotechnol. 38, 586–599 (2020). source

In the paper

“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.”

What the sources say

• Not checked yetcites [116] Ståhl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016). source

In the paper

“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.”

What the sources say

• Not checked yetcites [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 yetcites [127] Zhang, D. et al. Spatial epigenome–transcriptome co-profiling of mammalian tissues. Nature 616, 113–122 (2023). source
• Not checked yetcites [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

In the paper

“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.”

What the sources say

• Not checked yetcites [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

In the paper

“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.”

What the sources say

• Not checked yetcites [148] Dunham, I. et al. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012). source

In the paper

“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.”

What the sources say

• Not checked yetcites [140] He, Z. Lineage recording in human cerebral organoids. Nat. Methods 19, 90–99 (2022). source

In the paper

“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.”

What the sources say

• Not checked yetcites [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

In the paper

“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.”

What the sources say

• 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

“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.”

In the paper

“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.”

What the sources say

• Not checked yetcites [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

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

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

“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.”

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

“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”

In the paper

“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).”

In the paper

“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.”

In the paper

“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.”

What the sources say

• Not checked yetcites [60] Ma, S. et al. Chromatin potential identified by shared single-cell profiling of RNA and chromatin. Cell 183, 1103 (2020). source
• 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 [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

“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.”

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

“A recent unmatched method, graph-linked unified embedding (GLUE), achieves triple-omics integration while simultaneously inferring regulatory interactions.”

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

“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.”

In the paper

“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.”

What the sources say

• Not checked yetcites [179] Kamimoto, K. Dissecting cell identity via network inference and in silico gene perturbation. Nature 614, 742–751 (2023). source

In the paper

“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”

What the sources say

• Not checked yetcites [175] Ma, A. et al. Single-cell biological network inference using a heterogeneous graph transformer. Nat. Commun. 14, 964 (2023). source

In the paper

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

In the paper

“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.”

In the paper

“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.”

In the paper

“whereas in silico ChIP–seq predicts transcription factor binding at individual cis-regulatory elements179.”

What the sources say

• Not checked yetcites [179] Kamimoto, K. Dissecting cell identity via network inference and in silico gene perturbation. Nature 614, 742–751 (2023). source

In the paper

“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.”

What the sources say

• Not checked yetcites [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

In the paper

“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.”

In the paper

“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.”