Transcriptome in vivo analysis (TIVA) of spatially defined single cells in live tissue
Open accessAbstract
Transcriptome profiling of single cells resident in their natural microenvironment depends upon RNA capture methods that are both noninvasive and spatially precise. We engineered a transcriptome in vivo analysis (TIVA) tag, which upon photoactivation enables mRNA capture from single cells in live tissue. Using the TIVA tag in combination with RNA sequencing (RNA-seq), we analyzed transcriptome variance among single neurons in culture and in mouse and human tissue in vivo. Our data showed that the tissue microenvironment shapes the transcriptomic landscape of individual cells. The TIVA methodology is, to our knowledge, the first noninvasive approach for capturing mRNA from live single cells in their natural microenvironment.
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When a detailed reference map is not available, some spatial information can be gleaned from gene markers of spatial axes.
“When a detailed reference map is not available, some spatial information can be gleaned from gene markers of spatial axes163.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [163]
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“TIVA-tag synthesis was done according to the structure outlined in Figure 1 on either 1 µmol or 10 µmol scales with the following reagents (Glen Research): 2′-deoxy-2′fluoro-uridine phosphoramidite (10-3430), 2′-O-methyladenosine phosphoramidite (10-3100), Cy3 phosphoramidite (10-5913), Cy5 phosphoramidite (10-5915),…”
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“TIVA-tag synthesis was done according to the structure outlined in Figure 1 on either 1 μmol or 10 μmol scales with the following reagents (Glen Research): 2′-deoxy-2′fluoro-uridine phosphoramidite (10-3430), 2′-O-methyladenosine phosphoramidite (10-3100), Cy3 phosphoramidite (10-5913), Cy5 phosphoramidite (10-5915),…”
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Single Cell Isolation and Analysis
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Unravelling biology and shifting paradigms in cancer with single-cell sequencing
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Disentangling neural cell diversity using single-cell transcriptomics
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Stochastic electrotransport selectively enhances the transport of highly electromobile molecules
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Clinical and translational values of spatial transcriptomics
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“TIVA was applied on fresh living tissue by capturing cell-specific gene expressions through laser photoactivation.(17) While cell numbers can be analyzed simultaneously the real-time applications for living tissues are relatively limited.”
BackgroundUnderstanding tumor ecosystems by single-cell sequencing: promises and limitations
Ren, X., Kang, B., & Zhang, Z. (2018) · Genome biology · doi:10.1186/s13059-018-1593-z · Cited by 214 · see citing works
Barcoded solid-phase RNA capture for Spatial Transcriptomics profiling in mammalian tissue sections
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Single-cell sequencing in stem cell biology
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Advances in spatial transcriptomics and related data analysis strategies
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Advances in spatial transcriptomics and its applications in cancer research
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“1B), which can be achieved by introducing biotin tags and photoacti-vating them [8].”
BackgroundMicrofluidic Sample Preparation for Single Cell Analysis
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Recent advances in single-cell sequencing technologies
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Phenotypic clustering: a novel method for microglial morphology analysis
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Understanding development and stem cells using single cell-based analyses of gene expression
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Single Cell RNA Sequencing of Rare Immune Cell Populations
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“Other technologies for linking single cell transcriptomes with spatial positioning include spatial transcriptomics, which is performed on tissue sections (11), Seurat which links the in situ hybridization patterns of a series of landmark genes to the single cell gene expression profiles to generate a probability map o…”
BackgroundA single-cell transcriptomic and anatomic atlas of mouse dorsal raphe Pet1 neurons
Okaty, B. W., Sturrock, N., Lozoya, Y. E., Chang, Y., Senft, R. A., Lyon, K. A., Alekseyenko, O. V., & Dymecki, S. M. (2020) · eLife · doi:10.7554/elife.55523 · Cited by 117 · see citing works
“Single-cell RNA-sequencing, in particular, has become an indispensable approach, with different methods achieving different resolution of underlying cellular diversity (Bakken et al., 2018; Campbell et al., 2017; Hodge et al., 2019; Huang et al., 2019; Lovatt et al., 2014; Macosko et al., 2015; Okaty et al., 2015; Pou…”
MethodSingle-Cell Sequencing for Precise Cancer Research: Progress and Prospects
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“(i) Full-length Smart-Seq (30, 31) PTA/STA (32) (ii) UMI-based Islam’s UMI-WTA (34) (iii) in situ Seq FISSEQ (35) Padlock-RCA (36) smFISH (37, 38) TIVA (39) (iv) Multiplexed STRT-seq (40, 41) CEL-seq (42) Quartz-seq (43) Fiuidigm C1 (12, 44) DropSeq (45, 46) (i) Physical separation-based Han’s method (64) G&T-seq (65)…”
Computational solutions for spatial transcriptomics
Kleino, I., Frolovaitė, P., Suomi, T., & Elo, L. L. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.08.043 · Cited by 118 · see citing works
“In different approaches, the transcripts or cells on the target regions are tagged with barcode-oligos (TIVA tag, Zip-Seq) [12,13], which requires sequential labeling of each targeted region and, therefore, strongly limits the feasible number of samples.”
BackgroundQuantitative Single-Cell Approaches to Stem Cell Research
Etzrodt, M., Endele, M., & Schroeder, T. (2014) · Cell stem cell · doi:10.1016/j.stem.2014.10.015 · Cited by 113 · see citing works
“A variation of single-cell RNA-seq that relies on mRNA capture by photoactivation was used to capture mRNAs from selected single cells in complex tissues such as the brain in situ (Lovatt et al., 2014).”
MethodPlant Systems Biology at the Single-Cell Level
Libault, M., Pingault, L., Zogli, P., & Schiefelbein, J. (2017) · Trends in Plant Science · doi:10.1016/j.tplants.2017.08.006 · Cited by 113 · see citing works
From trans to cis: transcriptional regulatory networks in neocortical development
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From tissues to cell types and back: single-cell gene expression analysis of tissue architecture
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Droplet-based single cell RNAseq tools: a practical guide
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Optochemische Steuerung biologischer Vorgänge in Zellen und Tieren
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Perspectives on single-nucleus RNA sequencing in different cell types and tissues
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Neurotransmitter Switching? No Surprise
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Advances in single-cell RNA sequencing and its applications in cancer research
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A secretagogin locus of the mammalian hypothalamus controls stress hormone release
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Spatial transcriptomics technology in cancer research
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“TIVA LCM Live cells Cellular WT, >16,000 genes per cell (+) Full-length mRNA capture from single live cells (-) Low throughput and limited analysis of clinical samples (35) tomo-seq LCM Fresh-frozen Anatomical features WT, ~23,000 gene in total (+) Robust and high sensitivity; construction of 3D profiles (-) Limited a…”
BackgroundMethodInfluentialPlacing RNA in context and space – methods for spatially resolved transcriptomics
Strell, C., Hilscher, M. M., Laxman, N., Svedlund, J., Wu, C., Yokota, C., & Nilsson, M. F. (2018) · FEBS Journal · doi:10.1111/febs.14435 · Cited by 81 · see citing works
Spatial Transcriptomics: A Powerful Tool in Disease Understanding and Drug Discovery
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Single-cell omics: experimental workflow, data analyses and applications
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Network modeling of single-cell omics data: challenges, opportunities, and progresses
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Systems vaccinology and big data in the vaccine development chain
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“11 Transcriptomics of the complete genome is unbiased, relatively straightforward and is offered as a service by many contract laboratories.”
BackgroundVariation is function: Are single cell differences functionally important?
Dueck, H., Eberwine, J. H., & Kim, J. (2015) · BioEssays · doi:10.1002/bies.201500124 · Cited by 76 · see citing works
Self-citationWhat to compare and how: Comparative transcriptomics for Evo‐Devo
Roux, J., Rosikiewicz, M., & Robinson‐Rechavi, M. (2015) · Journal of Experimental Zoology Part B Molecular and Developmental Evolution · doi:10.1002/jez.b.22618 · Cited by 76 · see citing works
“…towards retrieving full transcriptomes of individual cells in situ, which proves particularly useful to study rare cell populations without compromising the quality of their transcriptomes (Battich et al., 2013; Avital et al., 2014; Lee et al., 2014; Lovatt et al., 2014; Crosetto et al., 2015).”
BackgroundKishi, J. Y., Liu, N., West, E. R., Sheng, K., Jordanides, J. J., Serrata, M. P., Cepko, C. L., Saka, S. K., & Yin, P. (2022) · Nature Methods · doi:10.1038/s41592-022-01604-1 · Cited by 78 · see citing works
Advances and Challenges in Spatial Transcriptomics for Developmental Biology
Choe, K., Pak, U., Pang, Y., Hao, W., & Yang, X. (2023) · Biomolecules · doi:10.3390/biom13010156 · Cited by 78 · see citing works
“This category typically includes RNA sequencing of individual cryosections [36], transcriptome in vivo analysis (TIVA) [37], RNA tomography (tomo-seq) [38,39], LCM coupled with Smart-Seq2 RNA sequencing (LCM-seq) [40,41], Geo-seq [42], NICHE-seq [43], polony (or DNA cluster)-indexed library-sequencing (PIXEL-seq) [44]…”
MethodPodocyte Dedifferentiation: A Specialized Process for a Specialized Cell
May, C., Saleem, M. A., & Welsh, G. I. (2014) · Frontiers in Endocrinology · doi:10.3389/fendo.2014.00148 · Cited by 75 · see citing works
Emerging approaches to study cell-cell interactions in tumor microenvironment
Nishida‐Aoki, N., & Gujral, T. S. (2019) · Oncotarget · doi:10.18632/oncotarget.26585 · Cited by 75 · see citing works
SRTsim: spatial pattern preserving simulations for spatially resolved transcriptomics
Zhu, J., Shang, L., & Zhou, X. (2023) · Genome biology · doi:10.1186/s13059-023-02879-z · Cited by 71 · see citing works
“ISS [12], and STARmap [13], while some are based on microdissection with examples including LCM [14] and TIVA [15].”
MethodThe Human Cell AtlasPreprint
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C. O., Birney, E., Bodenmiller, B., Campbell, P. J., Carninci, P., Clatworthy, M. R., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/121202 · Cited by 76 · see citing works
Self-citationSingle-Cell Transcriptomics in Cancer Immunobiology: The Future of Precision Oncology
Valdés‐Mora, F., Handler, K., Law, A. M. K., Salomon, R., Oakes, S. R., Ormandy, C. J., & Gallego‐Ortega, D. (2018) · Frontiers in Immunology · doi:10.3389/fimmu.2018.02582 · Cited by 68 · see citing works
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Weisenhorn, D. M. V., Giesert, F., & Wurst, W. (2016) · Journal of Neurochemistry · doi:10.1111/jnc.13670 · Cited by 67 · see citing works
“It has been shown that transcriptomes of neurons in particular are highly dynamic and exhibit a high number of genes with bimodal on or off expression (Lovatt et al. 2014; Dueck et al. 2015).”
BackgroundSpatial transcriptomics in development and disease
Zhou, R., Yang, G., Zhang, Y., & Wang, Y. (2023) · Molecular Biomedicine · doi:10.1186/s43556-023-00144-0 · Cited by 70 · see citing works
“Instead of labeling cells directly, TIVA-tag [17] is an engineered method that allows for the annealing of mRNA from ROIs in live tissue through photoactivation.”
MethodAdvances and applications in single-cell and spatial genomics
Wang, J., Ye, F., Chai, H., Jiang, Y., Wang, T., Ran, X., Xia, Q., Xu, Z., Fu, Y., Zhang, G., et al. (2024) · Science China Life Sciences · doi:10.1007/s11427-024-2770-x · Cited by 69 · see citing works
Recent advances in high-throughput single-cell transcriptomics and spatial transcriptomics
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Aptamer photoregulation in vivo
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Quantitative single-cell transcriptomics
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High-depth spatial transcriptome analysis by photo-isolation chemistry
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Can We Understand the Pathobiology of Bronchopulmonary Dysplasia?
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The role of beta cell heterogeneity in islet function and insulin release
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Single-Cell RNA Sequencing to Understand Host–Pathogen Interactions
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Single-Cell Applications of Next-Generation SequencingPreprint
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“(1992), in which a combined chemical and fluorescent probe injected into a cell can be photoactivated to “tag”mRNA molecules with biotin for later purification using streptavidin beads and sequenced (Lovatt et al. 2014).”
BackgroundMethodInfluentialRecent progress in single-cell cancer genomics
Tsoucas, D. M., & Yuan, G. (2017) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2017.01.002 · Cited by 52 · see citing works
Caged oligonucleotides for studying biological systems
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Self-citationCell-Penetrating Peptides and Transportan
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Gene expression profiling in the human alcoholic brain
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Single-cell RNA-sequencing resolves self-antigen expression during mTEC development
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RNA-Seq Reveals Novel Transcriptional Reorganization in Human Alcoholic Brain
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Interrogating islets in health and disease with single-cell technologies
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“From this study, the authors were able to demonstrate that the tissue microenvironment shapes the transcriptomic landscape of individual cells [106].”
Background
- Authors
- Lovatt, D., Ruble, B. K., Lee, J., Dueck, H., Kim, T. K., Fisher, S., Francis, C., Spaethling, J. M., Wolf, J. A., Grady, M. S., Ulyanova, A. V., Yeldell, S. B., Griepenburg, J. C., Buckley, P. T., Kim, J., Sul, J.-Y., Dmochowski, I. J., & Eberwine, J.
- Affiliations
- University of Pennsylvania (US)
- Corresponding author
- Eberwine, James
- ORCID
- Venue
- Nature Methods
- Published
- 2014-01-12
- Volume
- 11
- Issue
- 2
- Pages
- 190-196
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC3964595
- OpenAlex
- W2139357174
- Semantic Scholar
- e7b6e3520cfaf8a84c3b31af1f11c25242ce1c28
- Cited by
- 287
- References
- 43
- Citations by year
- 2026: 14 · 2025: 19 · 2024: 16 · 2023: 17 · 2022: 16 · 2021: 23 · 2020: 23 · 2019: 20 · 2018: 25 · 2017: 32 · 2016: 35 · 2015: 24 · 2014: 22 · 2013: 1
- FWCI
- 15.45
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Methods (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Pharmaceutical Research and Manufacturers of America Foundation
- Pennsylvania Department of Health
- McKnight Foundation
- University of California, Santa Cruz
- University of Pennsylvania
- National Institutes of Health (DP1OD004117)
- National Institute of Mental Health (U01MH098953, DP1MH099902)
- National Institute of General Medical Sciences (R01GM083030)
- National Center for Research Resources (S10RR021113)
- National Center for Chronic Disease Prevention and Health Promotion (U58 DP004117, DP004117)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Molecular Biology Techniques and Applications
- Keywords
- single-cell transcriptomics, spatial transcriptomics, tissue microenvironment, single neurons, transcriptome heterogeneity
- MeSH terms
- Animals, Brain, Hippocampus, Humans, Mice, Inbred C57BL, Neurons, RNA, Messenger, Gene Library, Sequence Analysis, RNA, Computational Biology, Gene Expression Profiling, Mice, High-Throughput Nucleotide Sequencing
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.
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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