Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution
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Abstract
Gene expression at fine scale: Mapping gene expression at the single-cell level within tissues remains a technical challenge. Rodriques et al. developed a method called Slide-seq, whereby RNA was spatially resolved from tissue sections by transfer onto a surface covered with DNA-barcoded beads. Applying Slide-seq to regions of a mouse brain revealed spatial gene expression patterns in the Purkinje layer of the cerebellum and axes of variation across Purkinje cell compartments. The authors used this method to dissect the temporal evolution of cell type–specific responses in a mouse model of traumatic brain injury. Science , this issue p. 1463
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Spatial profiling provides information on tissue organization and three-dimensional architecture, which is essential for model validation.
“Spatial profiling provides further information on tissue organization and three-dimensional architecture, thereby being essential for model validation186,187”
From Human disease models in drug development (2023) · cites this paper as [187]
The paper’s main text (page unavailable in our copy) shows that Slide-seq resolves tissue organization and that co-registered hippocampal sections allow three-dimensional visualization. It does not say that spatial profiling is essential for model validation. (LLM, conf=0.85)
Experimental — accuracy not yet measuredSlide-seq captures transcripts on a slide equipped with random 10-μm DNA-barcoded beads and gains the positions of barcodes via in situ indexing, enabling spatial transcriptomic analysis at a near-cellular resolution.
“Slide-seq captures transcripts on a slide equipped with random 10-µm DNA-barcoded beads and gains the positions of barcodes via in situ indexing, enabling spatial transcriptomic analysis at a near-cellular resolution [79].”
From Single-cell sequencing to multi-omics: technologies and applications (2024) · cites this paper as [79]
The lack of single-cell resolution with ST and the low sensitivity of ISS requires further technological improvements.
“While the technologies used here do not require harsh dissociation procedures like single-cell or single-nuclei approaches and maintain spatial information, the lack of single-cell resolution with ST and the low sensitivity of ISS requires further technological improvements (Eng et al. , 2019; Rodriques et al. , 2019; Vickovic et al. , 2019; Stickels et al. , 2020).”
From Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer’s Disease (2020) · cites this paper as Rodriques et al., 2019
PET imaging of collagen and molecular imaging of fibrosis allow fast, non-invasive, whole-organ quantitative and longitudinal readouts of drug efficacy in antifibrotic clinical trials.
“PET imaging of collagen and molecular imaging of fibrosis185,186, which allows fast, non-invasive, and whole-organ-quantitative and longitudinal readouts of drug efficacy in antifibrotic clinical trials”
From Fibrosis: from mechanisms to medicines (2020) · cites this paper as [185]
Combining molecular imaging of fibrosis with single-cell genomics could improve patient diagnostics, staging, prognostication, stratification, and cohort enrichment, optimizing clinical trial design.
“combining molecular imaging of fibrosis185 with cutting edge omic approaches such as single cell genomics174–176 could markedly improve patient diagnostics, staging, prognostication, stratification and cohort enrichment, which would in turn optimize clinical trial design”
From Fibrosis: from mechanisms to medicines (2020) · cites this paper as [185]
Spatially resolved sequencing uses slides pre-patterned with beads carrying unique DNA barcodes to tag mRNA molecules based on location in brain slice, enabling whole-transcriptome sequencing with ~10 μm spatial resolution.
“Spatially resolved sequencing uses slides pre-patterned with beads carrying unique DNA barcodes to tag mRNA molecules based on their location in a brain slice, enabling whole-transcriptome sequencing with ~10 μm spatial resolution (Rodriques et al., 2019; Ståhl et al., 2016).”
From Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes (2021) · cites this paper as Rodriques et al., 2019
Slide-seq's ability to assign transcripts to single cells is limited by its ~10 μm spatial resolution.
“For Slide-seq (Rodriques et al., 2019), this is limited by spatial resolution (~10 μm)”
From Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes (2021) · cites this paper as Rodriques et al., 2019
Slide-seq and Slide-seqV2 barcode mRNA using barcoded reagents placed directly on glass slide in solution, creating a monolayer-packed array.
“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).”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [118]
The previously reported aging-related decline of OPC, NRP and ImmN was observed.
“Nonetheless, we did observe the previously reported aging-related decline of OPC 49 , NRP 35,36 and ImmN 36,50 .”
From Single-cell transcriptomic profiling of the aging mouse brain (2019) · cites this paper as [50]
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Cites
Object recognition from local scale-invariant features
Lowe, D. G. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790410 · Cited by 16,472 · see citing works
Salmon provides fast and bias-aware quantification of transcript expression
Patro, R., Duggal, G., Love, M. I., Irizarry, R. A., & Kingsford, C. (2017) · Nature Methods · doi:10.1038/nmeth.4197 · Cited by 12,700 · see citing works
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110 · see citing works
Self-citationGenome-wide atlas of gene expression in the adult mouse brain
Lein, E. S., Hawrylycz, M. J., Ao, N., Ayres, M., Bensinger, A., Bernard, A., Boe, A. F., Boguski, M. S., Brockway, K. S., Byrnes, E. J., et al. (2007) · Nature · doi:10.1038/nature05453 · Cited by 5,572 · see citing works
Self-citationVisualization and analysis of gene expression in tissue sections by spatial transcriptomics
Ståhl, P. L., Salmén, F., Vickovic, S., Lundmark, A., Navarro, J. F., Magnusson, J., Giacomello, S., Asp, M., Westholm, J. O., Huss, M., et al. (2016) · Science · doi:10.1126/science.aaf2403 · Cited by 3,834 · see citing works
A pyramid approach to subpixel registration based on intensity
Thévenaz, P., Ruttimann, U. E., & Unser, M. A. (1998) · IEEE Transactions on Image Processing · doi:10.1109/83.650848 · Cited by 3,097 · see citing works
Spatially resolved, highly multiplexed RNA profiling in single cells
Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S., & Zhuang, X. (2015) · Science · doi:10.1126/science.aaa6090 · Cited by 2,969 · see citing works
Conditional gene targeting in macrophages and granulocytes using LysMcre mice
Clausen, B. E., Burkhardt, C., Reith, W., Renkawitz, R., & Förster, I. (1999) · Transgenic Research · doi:10.1023/a:1008942828960 · Cited by 2,143 · see citing works
Molecular diversity and specializations among the cells of the adult mouse brain
Saunders, A., Macosko, E. Z., Wysoker, A., Goldman, M., Krienen, F. M., de Rivera, H., Bien, E., Baum, M., Bortolin, L., Wang, S., et al. (2018) · Cell · doi:10.1016/j.cell.2018.07.028 · Cited by 1,847 · see citing works
Self-citationStoodley, C. J., & Schmahmann, J. D. (2010) · Cortex · doi:10.1016/j.cortex.2009.11.008 · Cited by 1,360 · see citing works
Cocker, L. J. L. D., van Veluw, S. J., Fowkes, M. E., Luijten, P. R., Mali, W. P. T. M., & Hendrikse, J. (2013) · Cerebrovascular Diseases · doi:10.1159/000353668 · Cited by 1,163 · see citing works
Single-cell spatial reconstruction reveals global division of labour in the mammalian liver
Halpern, K. B., Shenhav, R., Matcovitch-Natan, O., Tóth, B., Lemze, D., Golan, M., Massasa, E. E., Baydatch, S., Landen, S., Moor, A. E., et al. (2017) · Nature · doi:10.1038/nature21065 · Cited by 1,059 · see citing works
Highly Multiplexed Subcellular RNA Sequencing in Situ
Lee, J. H., Daugharthy, E. R., Scheiman, J., Kalhor, R., Yang, J. L., Ferrante, T. C., Terry, R., Jeanty, S. S. F., Li, C., Amamoto, R., et al. (2014) · Science · doi:10.1126/science.1250212 · Cited by 973 · see citing works
New stereological methods for counting neurons
West, M. J. (1993) · Neurobiology of Aging · doi:10.1016/0197-4580(93)90112-o · Cited by 940 · see citing works
Shah, S., Lubeck, E., Zhou, W., & Cai, L. (2016) · Neuron · doi:10.1016/j.neuron.2016.10.001 · Cited by 671 · see citing works
The Tumor Microenvironment in the Response to Immune Checkpoint Blockade Therapies
Petitprez, F., Meylan, M., de Reyniès, A., Sautès‐Fridman, C., & Fridman, W. H. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.00784 · Cited by 604 · see citing works
Brochu, G., Maler, L., & Hawkes, R. B. (1990) · The Journal of Comparative Neurology · doi:10.1002/cne.902910405 · Cited by 530 · see citing works
Lee, J. H., Daugharthy, E. R., Scheiman, J., Kalhor, R., Ferrante, T. C., Terry, R., Turczyk, B. M., Yang, J. L., Lee, H. S., Aach, J., et al. (2015) · Nature Protocols · doi:10.1038/nprot.2014.191 · Cited by 529 · see citing works
Climbing fiber responses evoked in vestibulocerebellum of rabbit from visual system.
Maekawa, K., & Simpson, J. L. (1973) · Journal of Neurophysiology · doi:10.1152/jn.1973.36.4.649 · Cited by 494 · see citing works
The diversity and disparity of the glial scar
Adams, K. L., & Gallo, V. (2017) · Nature Neuroscience · doi:10.1038/s41593-017-0033-9 · Cited by 423 · see citing works
Cerebellar modules operate at different frequencies
Zhou, H., Lin, Z., Voges, K., Ju, C., Gao, Z., Bosman, L. W. J., Ruigrok, T. J. H., Hoebeek, F. E., Zeeuw, C. I. D., & Schonewille, M. (2014) · eLife · doi:10.7554/elife.02536 · Cited by 335 · see citing works
Decoding Randomly Ordered DNA ArraysPreprint
Gunderson, K. L., Kruglyak, S., Graige, M. S., Garcia, F., Kermani, B. G., Zhao, C., Che, D., Dickinson, T. A., Wickham‐Garcia, E., Bierle, J., et al. (2004) · Genome Research · doi:10.1101/gr.2255804 · Cited by 326 · see citing works
Molecular atlas of the adult mouse brain
Ortiz, C., Navarro, J. F., Jurek, A., Märtin, A., Lundeberg, J., & Meletis, K. (2020) · Science Advances · doi:10.1126/sciadv.abb3446 · Cited by 317 · see citing works
Redefining the cerebellar cortex as an assembly of non-uniform Purkinje cell microcircuits
Cerminara, N. L., Lang, E. J., Sillitoe, R. V., & Apps, R. (2015) · Nature reviews. Neuroscience · doi:10.1038/nrn3886 · Cited by 315 · see citing works
Davis, M. Y., Trinh, K., Thomas, R. E., Yu, S., Germanos, A. A., Whitley, B. N., Sardi, S. P., Montine, T. J., & Pallanck, L. J. (2016) · PLoS Genetics · doi:10.1371/journal.pgen.1005944 · Cited by 294 · see citing works
Kenney, A. M., & Kocsis, J. D. (1998) · Journal of Neuroscience · doi:10.1523/jneurosci.18-04-01318.1998 · Cited by 234 · see citing works
Distribution of the substance P receptor (NK-1 receptor) in the central nervous system
Maeno, H., Kiyama, H., & Tohyama, M. (1993) · Molecular Brain Research · doi:10.1016/0169-328x(93)90172-l · Cited by 202 · see citing works
A Single-Cell Transcriptional Atlas of the Developing Murine Cerebellum
Carter, R. A., Bihannic, L., Rosencrance, C. D., Hadley, J. L., Tong, Y., Phoenix, T. N., Natarajan, S., Easton, J. B., Northcott, P. A., & Gawad, C. (2018) · Current Biology · doi:10.1016/j.cub.2018.07.062 · Cited by 190 · see citing works
Gravel, C., & Hawkes, R. (1990) · The Journal of Comparative Neurology · doi:10.1002/cne.902910107 · Cited by 171 · see citing works
Sarna, J. R., Marzban, H., Watanabe, M., & Hawkes, R. B. (2006) · The Journal of Comparative Neurology · doi:10.1002/cne.20912 · Cited by 167 · see citing works
Shah, S., Lubeck, E., Zhou, W., & Cai, L. (2017) · Neuron · doi:10.1016/j.neuron.2017.05.008 · Cited by 146 · see citing works
Dendritic Control of Spontaneous Bursting in Cerebellar Purkinje Cells
Womack, M. D., & Khodakhah, K. (2004) · Journal of Neuroscience · doi:10.1523/jneurosci.0290-04.2004 · Cited by 145 · see citing works
Systematic Regional Variations in Purkinje Cell Spiking Patterns
Xiao, J., Cerminara, N. L., Kotsurovskyy, Y., Aoki, H., Burroughs, A., Wise, A. K., Luo, Y., Marshall, S. P., Sugihara, I., Apps, R., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0105633 · Cited by 103 · see citing works
Bravo González‐Blas, C., Quan, X., Duran‐Romaña, R., Taskiran, I. I., Koldere, D., Davie, K., Christiaens, V., Makhzami, S., Hulselmans, G., de Waegeneer, M., et al. (2020) · Molecular Systems Biology · doi:10.15252/msb.20209438 · Cited by 88 · see citing works
Lobule‐specific membrane excitability of cerebellar Purkinje cells
Kim, C., Oh, S. H., Lee, J. H., Chang, S. O., Kim, J., & Kim, S. J. (2011) · The Journal of Physiology · doi:10.1113/jphysiol.2011.221846 · Cited by 81 · see citing works
Dlk1 Promotes a Fast Motor Neuron Biophysical Signature Required for Peak Force Execution
Müller, D., Cherukuri, P., Henningfeld, K. A., Poh, C. H., Wittler, L., Grote, P., Schlüter, O. M., Schmidt, J. V., Laborda, J., Bauer, S. R., et al. (2014) · Science · doi:10.1126/science.1246448 · Cited by 78 · see citing works
Robinson, G. A. (1994) · Molecular Brain Research · doi:10.1016/0169-328x(94)90116-3 · Cited by 77 · see citing works
Focal brain injury induces multiple immediate early genes encoding zinc finger transcription factors
Honkaniemi, J., Sagar, S. M., Pyykönen, I., Hicks, K. J., & Sharp, F. R. (1995) · Molecular Brain Research · doi:10.1016/0169-328x(94)00200-x · Cited by 58 · see citing works
Roberts, M. T., & Trussell, L. O. (2010) · Journal of Neurophysiology · doi:10.1152/jn.00312.2010 · Cited by 57 · see citing works
Gene Deregulation and Underlying Mechanisms in Spinocerebellar Ataxias With Polyglutamine Expansion
Niewiadomska-Cimicka, A., Hache, A., & Trottier, Y. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00571 · Cited by 41 · see citing works
Chung, C., Elrick, M. J., Dell’Orco, J., Qin, Z. S., Sundaram, S. K., Chinnaiyan, A. M., Shakkottai, V. G., & Lieberman, A. P. (2016) · PLoS Genetics · doi:10.1371/journal.pgen.1006042 · Cited by 31 · see citing works
WYLIE, D. R. W., Brown, M., Winship, I. R., et al. (2003) · The Journal of Comparative Neurology · doi:10.1002/cne.10857 · Cited by 30 · see citing works
Demilly, A., Reeber, S. L., Gebre, S. A., et al. (2010) · The Cerebellum · doi:10.1007/s12311-010-0156-y · Cited by 29 · see citing works
Microcircuits of the Dorsal Cochlear Nucleus
Trussell, L. O., & Oertel, D. (2018) · Springer handbook of auditory research · doi:10.1007/978-3-319-71798-2_4 · Cited by 26 · see citing works
González‐Blas, C. B., Quan, X., Duran‐Romaña, R., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2019.12.19.882381 · Cited by 17 · see citing works
Storer, P. D., & Jones, K. J. (2003) · The Journal of Comparative Neurology · doi:10.1002/cne.10623 · Cited by 13 · see citing works
SCHMOLESKY, M. T., Wang, Y., Creel, D. J., & Leventhal, A. G. (2000) · The Journal of Comparative Neurology · doi:10.1002/1096-9861(20001113)427:2 · Cited by 13 · see citing works
Decoding the body language of immunity: Tackling the immune system at the organism level
Chevrier, N. (2019) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2019.10.010 · Cited by 12 · see citing works
The presence of rRNA sequences in polyadenylated RNA and its potential functions
Kong, Q., Stockinger, M., Chang, Y., et al. (2008) · Biotechnology Journal · doi:10.1002/biot.200800122 · Cited by 12 · see citing works
broadchenf/Slideseq: Slide-seq 2019 Archive
samrodriques, & linlinbroad (2019) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.2571615 · Cited by 1 · see citing works
broadchenf/Slideseq: Slide-seq 2019 Archive
Samrodriques, & Linlinbroad (2019) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.2571614 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- [30]Lowe DG, in Proceedings of the Seventh IEEE International Conference on Computer Vision (IEEE, 1999; http://ieeexplore.ieee.org/document/790410/), pp. 1150– 1157 vol. 2.
- [35]Xiao J, Systematic regional variations in Purkinje cell spiking patterns. PLoS One. 9, e105633 (2014). PMC4140808 25144311
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Hao, Y., Hao, S., Andersen-Nissen, E., Mauck, W. M., Zheng, S., Butler, A., Lee, M. J., Wilk, A. J., Darby, C., Zagar, M., et al. (2020) · doi:10.1101/2020.10.12.335331 · Cited by 15,508 · see citing works
Efremova, M., Vento-Tormo, M., Teichmann, S. A., & Vento-Tormo, R. (2020) · Nature Protocols · doi:10.1038/s41596-020-0292-x · Cited by 3,449 · see citing works
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Browaeys, R., Saelens, W., & Saeys, Y. (2020) · Nature Methods · doi:10.1038/s41592-019-0667-5 · Cited by 2,192 · see citing works
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Maynard, K. R., Collado-Torres, L., Weber, L. M., Uytingco, C., Barry, B. K., Williams, S. R., Catallini, J. L., Tran, M. N., Besich, Z., Tippani, M., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-020-00787-0 · Cited by 1,222 · see citing works
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Stickels, R. R., Murray, E., Kumar, P., Li, J., Marshall, J. L., Di Bella, D. J., Arlotta, P., Macosko, E. Z., & Chen, F. (2021) · Nature Biotechnology · doi:10.1038/s41587-020-0739-1 · Cited by 1,295 · see citing works
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Asp, M., Bergenstråhle, J., & Lundeberg, J. (2020) · BioEssays · doi:10.1002/bies.201900221 · Cited by 666 · see citing works
Microglia and Central Nervous System–Associated Macrophages—From Origin to Disease Modulation
Prinz, M., Masuda, T., Wheeler, M. A., & Quintana, F. J. (2021) · Annual Review of Immunology · doi:10.1146/annurev-immunol-093019-110159 · Cited by 584 · see citing works
Multi-omics single-cell data integration and regulatory inference with graph-linked embedding
Cao, Z.-J., & Gao, G. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01284-4 · Cited by 616 · see citing works
The expanding vistas of spatial transcriptomics
Tian, L., Chen, F., & Macosko, E. Z. (2023) · Nature Biotechnology · doi:10.1038/s41587-022-01448-2 · Cited by 539 · see citing works
Self-citationMicroscopic examination of spatial transcriptome using Seq-Scope
Cho, C.-S., Xi, J., Si, Y., Park, S.-R., Hsu, J.-E., Kim, M., Jun, G., Kang, H. M., & Lee, J. H. (2021) · Cell · doi:10.1016/j.cell.2021.05.010 · Cited by 520 · see citing works
Li, B., Zhang, W., Guo, C., Xu, H., Li, L., Fang, M., Hu, Y., Zhang, X., Yao, X., Tang, M., et al. (2022) · Nature Methods · doi:10.1038/s41592-022-01480-9 · Cited by 459 · see citing works
Self-citationSingle-cell multimodal omics: the power of many
Zhu, C., Preissl, S., & Ren, B. (2020) · Nature Methods · doi:10.1038/s41592-019-0691-5 · Cited by 386 · see citing works
Spatial profiling of chromatin accessibility in mouse and human tissues
Deng, Y., Bartosovic, M., Ma, S., Zhang, D., Kukanja, P., Xiao, Y., Su, G., Liu, Y., Qin, X., Rosoklija, G. B., et al. (2022) · Nature · doi:10.1038/s41586-022-05094-1 · Cited by 417 · see citing works
Cell segmentation in imaging-based spatial transcriptomics
Petukhov, V., Xu, R. J., Soldatov, R. A., Cadinu, P., Khodosevich, K., Moffitt, J. R., & Kharchenko, P. V. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01044-w · Cited by 412 · see citing works
An ex vivo tumor fragment platform to dissect response to PD-1 blockade in cancer
Voabil, P., de Bruijn, M., Roelofsen, L. M., Hendriks, S. H., Brokamp, S., van den Braber, M., Broeks, A., Sanders, J., Herzig, P., Zippelius, A., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01398-3 · Cited by 380 · see citing works
Spatial-CUT&Tag: Spatially resolved chromatin modification profiling at the cellular level
Deng, Y., Bartosovic, M., Kukanja, P., Zhang, D., Liu, Y., Su, G., Enninful, A., Bai, Z., Castelo-Branco, G., & Fan, R. (2022) · Science · doi:10.1126/science.abg7216 · Cited by 393 · see citing works
The triumphs and limitations of computational methods for scRNA-seq
Kharchenko, P. V. (2021) · Nature Methods · doi:10.1038/s41592-021-01171-x · Cited by 321 · see citing works
In vivo Perturb-Seq reveals neuronal and glial abnormalities associated with autism risk genes
Jin, X., Simmons, S. K., Guo, A., Shetty, A. S., Ko, M., Nguyen, L., Jokhi, V., Robinson, E., Oyler, P., Curry, N., et al. (2020) · Science · doi:10.1126/science.aaz6063 · Cited by 317 · see citing works
Human disease models in drug development
Loewa, A., Feng, J. J., & Hedtrich, S. (2023) · Nature Reviews Bioengineering · doi:10.1038/s44222-023-00063-3 · Cited by 331 · see citing works
Single-cell and spatial transcriptomics: deciphering brain complexity in health and disease
Piwecka, M., Rajewsky, N., & Rybak-Wolf, A. (2023) · Nature Reviews Neurology · doi:10.1038/s41582-023-00809-y · Cited by 320 · see citing works
Predicting cellular responses to complex perturbations in high‐throughput screens
Lotfollahi, M., Klimovskaia Susmelj, A., De Donno, C., Hetzel, L., Ji, Y., Ibarra, I. L., Srivatsan, S. R., Naghipourfar, M., Daza, R. M., Martin, B., et al. (2023) · Molecular Systems Biology · doi:10.15252/msb.202211517 · Cited by 307 · see citing works
Applying high-dimensional single-cell technologies to the analysis of cancer immunotherapy
Gohil, S. H., Iorgulescu, J. B., Braun, D. A., Keskin, D. B., & Livak, K. J. (2021) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-020-00449-x · Cited by 282 · see citing works
Single-cell sequencing techniques from individual to multiomics analyses
Kashima, Y., Sakamoto, Y., Kaneko, K., Seki, M., Suzuki, Y., & Suzuki, A. (2020) · Experimental & Molecular Medicine · doi:10.1038/s12276-020-00499-2 · Cited by 270 · see citing works
BANKSY unifies cell typing and tissue domain segmentation for scalable spatial omics data analysis
Singhal, V., Chou, N., Lee, J., Yue, Y., Liu, J., Chock, W. K., Lin, L., Chang, Y.-C., Teo, E. M. L., Aow, J., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01664-3 · Cited by 282 · see citing works
DestVI identifies continuums of cell types in spatial transcriptomics data
Lopez, R., Li, B., Keren-Shaul, H., Boyeau, P., Kedmi, M., Pilzer, D., Jelinski, A., Yofe, I., David, E., Wagner, A., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01272-8 · Cited by 276 · see citing works
“The convention for analyzing such pairs of datasets (as implemented by all existing pipelines, including NMFReg [14], RCTD [16], SPOTLight [17], Stereoscope [18], DSTG [19], and cell2location [20]) is to apply a two-step process.”
BackgroundMethodThe Human Lung Cell Atlas: A High-Resolution Reference Map of the Human Lung in Health and Disease
Schiller, H. B., Montoro, D. T., Simon, L. M., Rawlins, E. L., Meyer, K. B., Strunz, M., Vieira Braga, F. A., Timens, W., Koppelman, G. H., Budinger, G. R. S., et al. (2019) · American Journal of Respiratory Cell and Molecular Biology · doi:10.1165/rcmb.2018-0416tr · Cited by 245 · see citing works
Single-cell technologies in hepatology: new insights into liver biology and disease pathogenesis
Ramachandran, P., Matchett, K. P., Dobie, R., Wilson-Kanamori, J. R., & Henderson, N. C. (2020) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/s41575-020-0304-x · Cited by 253 · see citing works
Hotspot identifies informative gene modules across modalities of single-cell genomics
DeTomaso, D., & Yosef, N. (2021) · Cell Systems · doi:10.1016/j.cels.2021.04.005 · Cited by 267 · see citing works
Spatial genomics enables multi-modal study of clonal heterogeneity in tissues
Zhao, T., Chiang, Z. D., Morriss, J. W., LaFave, L. M., Murray, E. M., Del Priore, I., Meli, K., Lareau, C. A., Nadaf, N. M., Li, J., et al. (2022) · Nature · doi:10.1038/s41586-021-04217-4 · Cited by 268 · see citing works
Self-citationSingle-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes
Armand, E. J., Li, J., Xie, F., Luo, C., & Mukamel, E. A. (2021) · Neuron · doi:10.1016/j.neuron.2020.12.010 · Cited by 241 · see citing works
Big data in basic and translational cancer research
Jiang, P., Sinha, S., Aldape, K., Hannenhalli, S., Sahinalp, C., & Ruppin, E. (2022) · Nature Reviews Cancer · doi:10.1038/s41568-022-00502-0 · Cited by 254 · see citing works
Aging and Rejuvenation of Neural Stem Cells and Their Niches
Navarro Negredo, P., Yeo, R. W., & Brunet, A. (2020) · Cell Stem Cell · doi:10.1016/j.stem.2020.07.002 · Cited by 246 · see citing works
The diversity of GABAergic neurons and neural communication elements
Huang, Z. J., & Paul, A. (2019) · Nature Reviews Neuroscience · doi:10.1038/s41583-019-0195-4 · Cited by 201 · see citing works
Single-cell spatial transcriptome reveals cell-type organization in the macaque cortex
Chen, A., Sun, Y., Lei, Y., Li, C., Liao, S., Meng, J., Bai, Y., Liu, Z., Liang, Z., Zhu, Z., et al. (2023) · Cell · doi:10.1016/j.cell.2023.06.009 · Cited by 232 · see citing works
Slide-tags enables single-nucleus barcoding for multimodal spatial genomics
Russell, A. J. C., Weir, J. A., Nadaf, N. M., Shabet, M., Kumar, V., Kambhampati, S., Raichur, R., Marrero, G. J., Liu, S., Balderrama, K. S., et al. (2024) · Nature · doi:10.1038/s41586-023-06837-4 · Cited by 225 · see citing works
Self-citationZeng, Y., Wei, Z., Yu, W., Yin, R., Yuan, Y., Li, B., Tang, Z., Lu, Y., & Yang, Y. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac297 · Cited by 211 · see citing works
The landscape of cell–cell communication through single-cell transcriptomics
Almet, A. A., Cang, Z., Jin, S., & Nie, Q. (2021) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2021.03.007 · Cited by 197 · see citing works
SpaGE: Spatial Gene Enhancement using scRNA-seq
Abdelaal, T., Mourragui, S., Mahfouz, A., & Reinders, M. J. T. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa740 · Cited by 207 · see citing works
Systematic comparison of sequencing-based spatial transcriptomic methods
You, Y., Fu, Y., Li, L., Zhang, Z., Jia, S., Lu, S., Ren, W., Liu, Y., Xu, Y., Liu, X., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02325-3 · Cited by 220 · see citing works
Single-cell sequencing to multi-omics: technologies and applications
Wu, X., Yang, X., Dai, Y., Zhao, Z., Zhu, J., Guo, H., & Yang, R. (2024) · Biomarker Research · doi:10.1186/s40364-024-00643-4 · Cited by 195 · see citing works
“Slide-seq captures transcripts on a slide equipped with random 10-µm DNA-barcoded beads and gains the positions of barcodes via in situ indexing, enabling spatial transcriptomic analysis at a near-cellular resolution [79].”
MethodTemporal modelling using single-cell transcriptomics
Ding, J., Sharon, N., & Bar-Joseph, Z. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-021-00444-7 · Cited by 195 · see citing works
Super-resolved spatial transcriptomics by deep data fusion
Bergenstråhle, L., He, B., Bergenstråhle, J., Abalo, X., Mirzazadeh, R., Thrane, K., Ji, A. L., Andersson, A., Larsson, L., Stakenborg, N., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01075-3 · Cited by 188 · see citing works
Statistical and machine learning methods for spatially resolved transcriptomics data analysis
Zeng, Z., Li, Y., Li, Y., & Luo, Y. (2022) · Genome Biology · doi:10.1186/s13059-022-02653-7 · Cited by 186 · see citing works
Xu, Y., Su, G.-H., Ma, D., Xiao, Y., Shao, Z.-M., & Jiang, Y.-Z. (2021) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-021-00729-7 · Cited by 163 · see citing works
Machado, L., Geara, P., Camps, J., Dos Santos, M., Teixeira-Clerc, F., Van Herck, J., Varet, H., Legendre, R., Pawlotsky, J.-M., Sampaolesi, M., et al. (2021) · Cell Stem Cell · doi:10.1016/j.stem.2021.01.017 · Cited by 170 · see citing works
Polony gels enable amplifiable DNA stamping and spatial transcriptomics of chronic pain
Fu, X., Sun, L., Dong, R., Chen, J. Y., Silakit, R., Condon, L. F., Lin, Y., Lin, S., Palmiter, R. D., & Gu, L. (2022) · Cell · doi:10.1016/j.cell.2022.10.021 · Cited by 161 · see citing works
Wu, Z., Trevino, A. E., Wu, E., Swanson, K., Kim, H. J., D’Angio, H. B., Preska, R., Charville, G. W., Dalerba, P. D., Egloff, A. M., et al. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00951-w · Cited by 154 · see citing works
The evolving concept of cell identity in the single cell era
Morris, S. A. (2019) · Development · doi:10.1242/dev.169748 · Cited by 154 · see citing works
Analyzing microglial phenotypes across neuropathologies: a practical guide
Schwabenland, M., Brück, W., Priller, J., Stadelmann, C., Lassmann, H., & Prinz, M. (2021) · Acta Neuropathologica · doi:10.1007/s00401-021-02370-8 · Cited by 149 · see citing works
Recent advances in single-cell sequencing technologies
Wen, L., & Tang, F. (2022) · Precision Clinical Medicine · doi:10.1093/pcmedi/pbac002 · Cited by 127 · see citing works
Davidsson, M., Wang, G., Aldrin-Kirk, P., Cardoso, T., Nolbrant, S., Hartnor, M., Mudannayake, J., Parmar, M., & Björklund, T. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1910061116 · Cited by 142 · see citing works
Spatial mapping of cellular senescence: emerging challenges and opportunities
Gurkar, A. U., Gerencser, A. A., Mora, A. L., Nelson, A. C., Zhang, A. R., Lagnado, A. B., Enninful, A., Benz, C., Furman, D., Beaulieu, D., et al. (2023) · Nature Aging · doi:10.1038/s43587-023-00446-6 · Cited by 139 · see citing works
Mallach, A., Zielonka, M., van Lieshout, V., An, Y., Khoo, J. H., Vanheusden, M., Chen, W.-T., Moechars, D., Arancibia-Carcamo, I. L., Fiers, M., et al. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114216 · Cited by 135 · see citing works
Pang, M., Su, K., & Li, M. (2021) · doi:10.1101/2021.11.28.470212 · Cited by 130 · see citing works
Spatial proteomics in three-dimensional intact specimens
Bhatia, H. S., Brunner, A.-D., Öztürk, F., Kapoor, S., Rong, Z., Mai, H., Thielert, M., Ali, M., Al-Maskari, R., Paetzold, J. C., et al. (2022) · Cell · doi:10.1016/j.cell.2022.11.021 · Cited by 129 · see citing works
Development, wiring and function of dopamine neuron subtypes
Garritsen, O., van Battum, E. Y., Grossouw, L. M., & Pasterkamp, R. J. (2023) · Nature reviews. Neuroscience · doi:10.1038/s41583-022-00669-3 · Cited by 128 · see citing works
Single-cell transcriptomics for the assessment of cardiac disease
Miranda, A. M. A., Janbandhu, V., Maatz, H., Kanemaru, K., Cranley, J., Teichmann, S. A., Hübner, N., Schneider, M. D., Harvey, R. P., & Noseda, M. (2023) · Nature Reviews Cardiology · doi:10.1038/s41569-022-00805-7 · Cited by 117 · see citing works
Droplet-based single cell RNAseq tools: a practical guide
Salomon, R., Kaczorowski, D., Valdes-Mora, F., Nordon, R. E., Neild, A., Farbehi, N., Bartonicek, N., & Gallego-Ortega, D. (2019) · Lab on a Chip · doi:10.1039/c8lc01239c · Cited by 109 · see citing works
Spatially resolved transcriptomics in neuroscience
Close, J. L., Long, B. R., & Zeng, H. (2021) · Nature Methods · doi:10.1038/s41592-020-01040-z · Cited by 94 · see citing works
Mapping epigenetic modifications by sequencing technologies
Chen, X., Xu, H., Shu, X., & Song, C.-X. (2025) · Cell Death & Differentiation · doi:10.1038/s41418-023-01213-1 · Cited by 105 · see citing works
Glia in neurodegeneration: Drivers of disease or along for the ride?
Gleichman, A. J., & Carmichael, S. T. (2020) · Neurobiology of Disease · doi:10.1016/j.nbd.2020.104957 · Cited by 90 · see citing works
Alignment of spatial genomics data using deep Gaussian processes
Jones, A., Townes, F. W., Li, D., & Engelhardt, B. E. (2023) · Nature Methods · doi:10.1038/s41592-023-01972-2 · Cited by 85 · see citing works
Into the multiverse: advances in single-cell multiomic profiling
Ogbeide, S., Giannese, F., Mincarelli, L., & Macaulay, I. C. (2022) · Trends in Genetics · doi:10.1016/j.tig.2022.03.015 · Cited by 82 · see citing works
Computational methods for the integrative analysis of single-cell data
Forcato, M., Romano, O., & Bicciato, S. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa042 · Cited by 71 · see citing works
Network modeling of single-cell omics data: challenges, opportunities, and progresses
Blencowe, M., Arneson, D., Ding, J., Chen, Y.-W., Saleem, Z., & Yang, X. (2019) · Emerging Topics in Life Sciences · doi:10.1042/etls20180176 · Cited by 73 · see citing works
Computational methods for single-cell multi-omics integration and alignment
Stanojevic, S., Li, Y., Ristivojevic, A., & Garmire, L. X. (2022) · Genomics, Proteomics & Bioinformatics · doi:10.1016/j.gpb.2022.11.013 · Cited by 74 · see citing works
Spatiotemporal transcriptomics reveals pathogenesis of viral myocarditis
Mantri, M., Hinchman, M. M., McKellar, D. W., Wang, M. F. Z., Cross, S. T., Parker, J. S. L., & De Vlaminck, I. (2022) · Nature Cardiovascular Research · doi:10.1038/s44161-022-00138-1 · Cited by 74 · see citing works
Decoder-seq enhances mRNA capture efficiency in spatial RNA sequencing
Cao, J., Zheng, Z., Sun, D., Chen, X., Cheng, R., Lv, T., An, Y., Zheng, J., Song, J., Wu, L., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-02086-y · Cited by 71 · see citing works
Exploring single cells in space and time during tissue development, homeostasis and regeneration
Mayr, U., Serra, D., & Liberali, P. (2019) · Development · doi:10.1242/dev.176727 · Cited by 63 · see citing works
Lopez, R., Nazaret, A., Langevin, M., Samaran, J., Regier, J., Jordan, M. I., & Yosef, N. (2019) · arXiv · doi:10.48550/arxiv.1905.02269 · Cited by 105 · see citing works
Zhang, M., Eichhorn, S. W., Zingg, B., Yao, Z., Zeng, H., Dong, H., & Zhuang, X. (2020) · doi:10.1101/2020.06.04.105700 · Cited by 44 · see citing works
Review of multi-omics data resources and integrative analysis for human brain disorders
Dong, X., Liu, C., & Dozmorov, M. (2021) · Briefings in Functional Genomics · doi:10.1093/bfgp/elab024 · Cited by 47 · see citing works
Deep learning in spatial transcriptomics: Learning from the next next-generation sequencing
Heydari, A. A., & Sindi, S. S. (2023) · Biophysics Reviews · doi:10.1063/5.0091135 · Cited by 44 · see citing works
Peng, G., Cui, G., Ke, J., & Jing, N. (2020) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-120219-083220 · Cited by 42 · see citing works
Sample-multiplexing approaches for single-cell sequencing
Zhang, Y., Xu, S., Wen, Z., Gao, J., Li, S., Weissman, S. M., & Pan, X. (2022) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-022-04482-0 · Cited by 40 · see citing works
Seq-Scope: Repurposing Illumina sequencing flow cells for high-resolution spatial transcriptomics
Kim, Y., Cheng, W., Cho, C.-S., Hwang, Y., Si, Y., Park, A., Schrank, M., Hsu, J.-E., Anacleto, A., Xi, J., et al. (2025) · Nature Protocols · doi:10.1038/s41596-024-01065-0 · Cited by 44 · see citing works
Encoding Method of Single-cell Spatial Transcriptomics Sequencing
Zhou, Y., Jia, E., Pan, M., Zhao, X., & Ge, Q. (2020) · International Journal of Biological Sciences · doi:10.7150/ijbs.43887 · Cited by 32 · see citing works
From tissue architecture to clinical insights: Spatial transcriptomics in solid tumor studies
Sharma, A., Raut, S. S., Shukla, A., Gupta, S., Mishra, A., & Singh, A. (2025) · Seminars in Oncology · doi:10.1016/j.seminoncol.2025.152389 · Cited by 29 · see citing works
Ma, C., Chitra, U., Zhang, S., & Raphael, B. J. (2022) · Cell Systems · doi:10.1016/j.cels.2022.09.002 · Cited by 27 · see citing works
- Authors
- Rodriques, S. G., Stickels, R. R., Goeva, A., Martin, C. A., Murray, E., Vanderburg, C. R., Welch, J., Chen, L. M., Chen, F., & Macosko, E. Z.
- Affiliations
- Broad Institute (US)
- Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.
- Corresponding author
- Chen, Fei
- Macosko, Evan Z.
- ORCID
- Venue
- Science
- Published
- 2019-03-29
- Volume
- 363
- Issue
- 6434
- Pages
- 1463-1467
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC6927209
- OpenAlex
- W2915848182
- Cited by
- 2,424
- References
- 51
- Citations by year
- 2026: 385 · 2025: 391 · 2024: 415 · 2023: 319 · 2022: 300 · 2021: 318 · 2020: 212 · 2019: 80 · 2018: 1 · 2017: 1
- FWCI
- 75.91
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Science (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- NIH Office of the Director (DP2 AG058488-01, 1DP5OD024583)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Molecular Biology Techniques and Applications
- Keywords
- Slide-seq, spatial transcriptomics, cerebellum, hippocampus, traumatic brain injury, single-cell RNA sequencing, mouse model
- MeSH terms
- Brain Injuries, Traumatic, Animals, Cerebellum, Disease Models, Animal, Frozen Sections, Gene Expression Regulation, Hippocampus, Purkinje Cells, RNA, Messenger, Transcription, Genetic, Sequence Analysis, RNA, Cell Size, Mice, Genome-Wide Association Study, Single-Cell Analysis, High-Throughput Nucleotide Sequencing
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