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Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2
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Measurement of the location of molecules in tissues is essential for understanding tissue formation and function. Previously, we developed Slide-seq, a technology that enables transcriptome-wide detection of RNAs with a spatial resolution of 10 μm. Here we report Slide-seqV2, which combines improvements in library generation, bead synthesis and array indexing to reach an RNA capture efficiency ~50% that of single-cell RNA-seq data (~10-fold greater than Slide-seq), approaching the detection efficiency of droplet-based single-cell RNA-seq techniques. First, we leverage the detection efficiency of Slide-seqV2 to identify dendritically localized mRNAs in neurons of the mouse hippocampus. Second, we integrate the spatial information of Slide-seqV2 data with single-cell trajectory analysis tools to characterize the spatiotemporal development of the mouse neocortex, identifying underlying genetic programs that were poorly sampled with Slide-seq. The combination of near-cellular resolution and high transcript detection efficiency makes Slide-seqV2 useful across many experimental contexts.
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“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 [119]
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Cites
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters
Yu, G., Wang, L.-G., Han, Y., & He, Q.-Y. (2012) · OMICS: A Journal of Integrative Biology · doi:10.1089/omi.2011.0118 · Cited by 36,259
Comprehensive Integration of Single-Cell Data
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453
Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L. (2014) · Nature Biotechnology · doi:10.1038/nbt.2859 · Cited by 7,314
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
Self-citationA Direct Approach to False Discovery Rates
Storey, J. D. (2002) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00346 · Cited by 5,603
The single-cell transcriptional landscape of mammalian organogenesis
Cao, J., Spielmann, M., Qiu, X., Huang, X., Ibrahim, D. M., Hill, A. J., Zhang, F., Mundlos, S., Christiansen, L., Steemers, F. J., et al. (2019) · Nature · doi:10.1038/s41586-019-0969-x · Cited by 4,490
La Manno, G., Soldatov, R., Zeisel, A., Braun, E., Hochgerner, H., Petukhov, V., Lidschreiber, K., Kastriti, M. E., Lönnerberg, P., Furlan, A., et al. (2018) · Nature · doi:10.1038/s41586-018-0414-6 · Cited by 4,118
Visualization 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
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
Cited by
Sorted by citations
Items 901–1,000 of 1,001
Qin, X., Liu, C., Gu, F., & Lan, X. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf674 · Cited by 1
Park, S. Y., & Kim, Y. H. (2025) · Journal of Digestive Cancer Research · doi:10.52927/jdcr.2025.13.3.269 · Cited by 1
Chen, X., Li, C., Zhou, Q., Cui, N., Huang, Y., Chen, C. J., Lan, J., Gu, S., Liu, H., Yang, C., et al. (2026) · Small Methods · doi:10.1002/smtd.202501812 · Cited by 1
Xu, R., Cheng, X., Ching, W., Wu, S., Zhang, Y., & Zhang, Y. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013893 · Cited by 1
ACID: Improving Ambiguous Cell Type Annotation in Spatial Transcriptomics
Zhang, Y., Zhou, Z., & Dai, Z. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11357194 · Cited by 1
Hafiz, F., Azim, R., & Shatabda, S. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag027 · Cited by 1
Lytan, I., Singha, B., Boruah, N., Gogoi, P. P., Longkumer, P., Pucho, M., Dkhar, B., & Sinha, U. B. (2026) · Mini-Reviews in Medicinal Chemistry · doi:10.2174/0113895575418750251204104229 · Cited by 1
Wu, J., Oshinjo, A. M., & Izzi, V. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag145 · Cited by 1
Spatial omics: current advances, biomedical applications, and future perspectives
Kim, M.-H., & Cha, H. (2026) · Kosin Medical Journal · doi:10.7180/kmj.26.165 · Cited by 1
Zhang, Z., Duan, H., & Gao, X. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag469 · Cited by 2
Kim, D., Lee, Y., Kim, M.-J., Lee, A. C., Kwon, S., & Kang, K. (2026) · Science Advances · doi:10.1126/sciadv.aea3814 · Cited by 1
Emerging Regenerative Medicine for Spinal Cord Injury: Spinal Cord Organoids-on-a-Chip
Khan, M., Choi, H., Ahmad, S., Lee, S., Park, J., & Han, I. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27157060 · Cited by 1
Li, Y., Zhou, X., & Cao, H. (2022) · Genetics · doi:10.1093/genetics/iyac095 · Cited by 1
Götz, T., Cong, X., Rauber, S., Angeli, M., Lang, E. W., Ramming, A. M., & Schmidkonz, C. (2024) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2024.100384 · Cited by 1
An overview of single-cell high-throughput technology in plants
Auroux, L., Liew, L. C., & Lewsey, M. G. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-23736-2.00001-0 · Cited by 1
Andrei, P., Grieco, M., Acha‐Sagredo, A., Becchi, T., Dhami, P., Fung, K., Rodriguez‐Justo, M., Cereda, M., & Ciccarelli, F. D. (2026) · doi:10.70401/exo.2025.0017 · Cited by 1
Martin, P. C. N., Wang, W., Kim, H., Holze, H., Fisher, P. B., Saavedra, A. P., Winn, R. A., Madan, E., Gogna, R., & Won, K. (2025) · Nature Communications · doi:10.1038/s41467-025-62782-y · Cited by 1
Integrating Graph Convolutional Networks for Missing Gene Expression Imputation
Zhang, Y., Yu, H., Yan, Z., Pan, T., Zhang, Y., Liu, Y., Li, S., Guo, Y., Song, J., & Yu, D. (2025) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbbio.2025.3605719 · Cited by 1
STADiffuser: high-fidelity simulation and full-view 3D modeling of spatial transcriptomics
Zhang, C., & Zhang, S. (2026) · Nature Communications · doi:10.1038/s41467-026-76829-1 · Cited by 1
Xiong, C., Huang, S., Zhou, M., Zhang, Y., Wu, W., Li, X., Yao, H., Chen, J., & Li, Y. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf471 · Cited by 1
CellSP enables module discovery and visualization for subcellular spatial transcriptomics data
Aggarwal, B., & Sinha, S. (2025) · Communications Biology · doi:10.1038/s42003-025-08891-2 · Cited by 1
You, X., & Liu, Y. (2025) · GenoMed Connect · doi:10.69709/genomc.2025.131100 · Cited by 1
S3RL: Enhancing Spatial Single‐Cell Transcriptomics With Separable Representation Learning
Fu, L., Wang, P., Xu, G., Lu, J., Peng, Q., Wu, D., & Sun, H. (2026) · Advanced Science · doi:10.1002/advs.202516178 · Cited by 1
From genomes to precision medicine: 25 years of breakthroughs in genetics and genomics
Hailu, S. G. (2026) · The Nucleus · doi:10.1007/s13237-025-00632-4 · Cited by 1
Advances in spatial omics for the analysis of prostate cancer
Xu, J., Wang, D., Di, X., & Liu, Y. (2026) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-026-06396-3 · Cited by 1
HRCHY-CytoCommunity identifies hierarchical tissue organization in cell-type spatial maps
Xie, R., Wang, Z., Liu, J., Xu, H., Xu, Y., Lin, J., Hu, Y., & Gao, L. (2026) · Nature Communications · doi:10.1038/s41467-026-70069-z · Cited by 1
Qian, J., Bao, H., Yang, H., Zhang, J., Shao, X., & Fan, X. (2026) · Targetome · doi:10.48130/targetome-0026-0009 · Cited by 1
Integrating multiomic layers to decode psychiatric disease mechanisms
Sheu, L., Girdhar, K., & Roussos, P. (2026) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2026.102451 · Cited by 1
2P-NucTag: On-demand phototagging for molecular analysis of functionally identified cortical neurons
Shi, J., Nutkovich, B., Kushinsky, D., Rao, B. Y., Herrlinger, S. A., Tsivourakis, E., Mihaila, T. S., Paredes, M. E. C., Malina, K. C.-K., O’Toole, C. K., et al. (2026) · Neuron · doi:10.1016/j.neuron.2026.03.016 · Cited by 1
INST-Align: Implicit Neural Alignment for Spatial Transcriptomics via Canonical Expression Fields
Han, B., Qi, C., Musialski, P., & Wei, Z. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38186-6_12 · Cited by 1
Battistoni, G., Torres-Garcia, S., Sia, C. Y., Corriero, S., Boquetale, C., Williams, E., Cregeen, A., Wasilewska, K., Alini, M., Hemmer, N., et al. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2527896123 · Cited by 1
Wang, C., Zhang, T., Sun, H., Wu, Z. W., Liang, S., Wang, X., Du, M., Liang, Y., Gao, X., Tang, Q., et al. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag333 · Cited by 1
Varela-Martínez, I., Pipicelli, F., & Hippenmeyer, S. (2026) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2026.102487 · Cited by 1
Gao, Q., Shang, J., Yuan, S., Li, F., & Wang, J. (2026) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-026-00832-9 · Cited by 1
Wang, R., Liu, X., Zhuo, L., Zhou, Z., Xu, J., Zou, Q., & Fu, X. (2026) · Genome biology · doi:10.1186/s13059-026-04166-z · Cited by 1
From candidate genes to omics: Unbiased approaches reshaping arthropod Evo-Devo
Vieira, J., & da Fonseca, R. N. (2026) · Genetics and Molecular Biology · doi:10.1590/1678-4685-gmb-2025-0217 · Cited by 1
Spatial Transcriptomics in Breast Cancer: Advances and Applications
Cao, Y., Xu, K., Li, X. Y., Zhang, J., Jin, W., & Liu, Y.-X. (2026) · Biology · doi:10.3390/biology15131061 · Cited by 1
Dissecting human organ development using spatial technologies
Zhang, X., Vandana, J. J., Greenspun, B. C., & Chen, S. (2026) · Developmental Cell · doi:10.1016/j.devcel.2026.05.004 · Cited by 1
차세은, Kim, J., Kim, J., Kim, D., Song, H., Lim, K. S., & Chae, S. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27146149 · Cited by 1
Li, X., Jia, X., Zhao, D., Xu, J., Du, G., Qi, Y., Wu, Y., Chen, Y., Zhu, J., Gu, J., et al. (2026) · Communications Biology · doi:10.1038/s42003-026-10667-1 · Cited by 1
NicheDeSig: niche-aware deconvolution and adaptive signature analysis for spatial transcriptomics
Xue, W., Zhang, J., Chen, T., Shen, W., Ma, J., Xu, Y., Wong, H., & Wu, S. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag578 · Cited by 1
Advances in single-cell and spatial multi-omics for plant development and stress responses
Xu, P., Wang, B., Hu, Y., Qiong, L., & Zhang, T. (2026) · Acta Botanica Yunnanica · doi:10.1016/j.pld.2026.07.015 · Cited by 1
Meng, Y., Sun, J., Kong, C., Ma, Y., & Yu, T. (2026) · Frontiers in Genetics · doi:10.3389/fgene.2026.1865685 · Cited by 1
Zahedi, R., Argha, A., Farbehi, N., Bakhshayeshi, I., Porntaveetus, T., Ye, Y., Lovell, N. H., & Alinejad‐Rokny, H. (2026) · Advanced Science · doi:10.1002/advs.77003 · Cited by 1
Shan, Y., Piao, Y., & Ge, Q. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag465 · Cited by 1
Decoding spatial domains and heterogeneities of complex tissues by multimodal deep learning
Meng, Y., Chen, Z., Zhang, G., Jin, S., 郎继东, Zhang, Z., Cui, F., Lv, K., Zeng, P., Hu, X., et al. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.115004 · Cited by 1
Mori, T., Takase, T., Lan, K.-C., Yamane, J., Alev, C., Kimura, A., Osafune, K., Yamashita, J. K., Akutsu, T., Kitano, H., et al. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05355-4 · Cited by 1
Identification and Localization of Cell Types in the Mouse Olfactory Bulb Using Slide-SeqV2
Fang, A., Petentler, K., Price, A. J., Malloy, S., Peterson, M. A., Maddera, L. E., Russell, J., Treese, M., Li, H. L., Wang, Y., et al. (2023) · Methods in molecular biology · doi:10.1007/978-1-0716-3425-7_13 · Cited by 1
Understanding Spondyloarthritis Pathogenesis: The Promise of Single-Cell Profiling
Ermann, J., Lefton, M., Wei, K. S., & Gutiérrez‐Arcelus, M. (2024) · Current Rheumatology Reports · doi:10.1007/s11926-023-01132-7 · Cited by 1
STGAT: Graph attention networks for deconvolving spatial transcriptomics data
Li, W., Zhang, H., Wang, L., Wang, P., & Yu, K. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108431 · Cited by 1
Decoding the causal drivers of spatial cellular topology
Shankar, P., Liang, H., Chitra, U., & Singh, R. (2026) · iScience · doi:10.1016/j.isci.2026.115794 · Cited by 1
Tu, W., & Zhang, L. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012948 · Cited by 1
Okoh, E. A. (2025) · International Journal of Research Publication and Reviews · doi:10.55248/gengpi.6.0625.2267 · Cited by 1
Spatial pattern enhanced cellular and tissue recognition for spatial transcriptomics
Wang, Y., Zhang, Z., & Li, G. (2025) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaf106 · Cited by 1
A Comprehensive Review of Cell-Type Deconvolution in Spatial Transcriptomic Data
Wang, L., Hu, Y., & Gao, L. (2025) · Big Data Mining and Analytics · doi:10.26599/bdma.2025.9020056 · Cited by 1
Verasztó, C., Roussel, Y., Kanari, L., Piluso, S., Markram, H., & Keller, D. X. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1014106 · Cited by 1
Single-cell multi-omics sequencing and its applications in studying the nervous system
Chaoyang, W., & Xiaoying, F. (2021) · Biophysics Reports · doi:10.52601/bpr.2021.210031 · Cited by 2
Keller, D. X., Verasztó, C., & Markram, H. (2023) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2023.1118170 · Cited by 1
Tiwari, V. K., & Lavin, D. P. (2021) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.739974427.793585290 · Cited by 1
Hirz, T., Mei, S., Sarkar, H., Kfoury, Y. S., Wu, S., Verhoeven, B. M., Subtelny, A. O., Zlatev, D. V., Wszolek, M. W., Salari, K., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.18.484781 · Cited by 1
Self-citationLi, Z., Chen, X., Zhang, X., Chen, S., & Jiang, R. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.09.515447 · Cited by 1
Xu, T., Zhu, E., Zhang, C., Calandrelli, R., Lin, P., & Zhong, S. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.06.12.544625 · Cited by 1
Liu, L., Bi, S., Wang, N., & Wang, M. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4791548 · Cited by 1
Li, L., Li, Z., Yin, X., & Xu, X. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.15.589470 · Cited by 1
ctQC improves biological inferences from single cell and spatial transcriptomics dataPreprint
Lakshmanan, V., Kahraman, M., Macalinao, D. C., Gunn, N. A. L., Venkatesh, P. N., Chang, M., Fu, C. W. P., Qiang, L. W., Tan, I. B., & Prabhakar, S. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.23.594978 · Cited by 1
Yu, J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.03.597195 · Cited by 1
CoDi: Contrastive distance cell type annotation for spatially resolved transcriptomicsPreprint
Kovačević, V., Bezulj, M., Milicevic, N., Josic, B., Fang, S., Zhang, Y., & Li, J. (2024) · Research Square · doi:10.21203/rs.3.rs-4495419/v1 · Cited by 1
Self-citationRapid and memory-efficient analysis and quality control of large spatial transcriptomics datasetsPreprint
Kövér, B., & Vigilante, A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.07.23.604776 · Cited by 1
Spatiotemporally restricted Hippo signalings instruct the fate and maturation of hepatobiliary cellsPreprint
Wang, Y., Zhong, Z., Wang, Y., Zhu, Y., Guan, K., & Yu, F. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.02.621695 · Cited by 1
STEP: Deciphering Spatial Atlas at Single-Cell Level with Whole-Transcriptome CoveragePreprint
Hu, Z., Zhu, Z., Cai, L., Zhan, Y., Yan, X., Chen, J., Sun, B., Du, S., Jiang, S., Wang, H., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.22.624797 · Cited by 1
Kim, J., Ravichandran, H., Yoffe, L., Bhinder, B., Finos, K., Singh, A., Pua, B. B., Bates, S., Huang, B. E., Rendeiro, A. F., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.19.637138 · Cited by 1
Nearest-neighbor nonnegative spatial factorization to study spatial and temporal transcriptomicsPreprint
Shrestha, P., Diaz, L. C., & Engelhardt, B. E. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.19.639134 · Cited by 1
A network regularized linear model to infer spatial expression pattern for single cellPreprint
Gu, C., Chen, H., & Liu, Z. (2025) · eLife · doi:10.7554/elife.105589 · Cited by 1
Kaur, E., Lopos, L. C., & Bilichak, A. (2025) · Wiley eBooks · doi:10.1002/9781394280049.ch1 · Cited by 1
Highly sensitive and scalable time-resolved RNA sequencing in single cells with scNT-seq2Preprint
Qiu, Q., Zhang, H., Gao, W. N. D., Li, F., Liang, D., & Wu, H. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.03.657745 · Cited by 1
Neuronal activity triggers widespread changes in RNA stabilityPreprint
Duffy, E. E., Patop, I. L., Kalaora, S., Assad, E. G., Koren, S. A., Traunmüller, L., Krüttner, S., Pajarillo, N. S., Finander, B., Barsdale, Z., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.05.674581 · Cited by 1
Avraham‐Davidi, I., Mages, S., Klughammer, J., Moriel, N., Imada, S., Hofree, M., Murray, E. M., Chen, J. H., Pelka, K., Mehta, A., et al. (2025) · eLife · doi:10.7554/elife.104815.2 · Cited by 1
Self-citationA network regularized linear model to infer spatial expression pattern for single cellPreprint
Gu, C., Chen, H., & Liu, Z. (2025) · eLife · doi:10.7554/elife.105589.2 · Cited by 1
Multi-tissue spatial transcriptomics reveals biological age hotspots in mouse and human agingPreprint
Vicente-Alvarez, V., de Magalhaes, C. G., Glubokov, D., Molière, A., Tyshkovskiy, A., & Gladyshev, V. N. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.23.689860 · Cited by 1
Panwar, P., Guo, B., Zhou, H., Hicks, S. C., & Ghazanfar, S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.11.30.691081 · Cited by 1
Kim, H.-J., Pendyala, S., Lin, J., Cooke, T., Li, G., Noble, W. S., Deng, X., Distèche, C. M., Trapnell, C., & Fowler, D. M. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.25.727721 · Cited by 1
Handa, D., Martin-Linares, C. P., Stein-O’Brien, G., Ling, J., & Chitra, U. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.01.742217 · Cited by 1
Single-cell analysis of mRNA splicing variants
Potts, J. L. (2020) · Digital Repository Service (Northeastern University) · doi:10.17760/d20382869
Spatial mapping of the tumor immune microenvironment
Wu, Y.-C., Pagacz, J., Emery, S. C., Kron, S. J., & Lee, S. S.-Y. (2021) · Elsevier eBooks · doi:10.1016/b978-0-323-90949-5.00009-7
Bouwers, E. G. (2011) · PUB – Publications at Bielefeld University (Bielefeld University)
New molecular techniques for exploring neuronal appetite pathways
Tadross, J. A., Lam, B., & Yeo, G. S. H. (2021) · Current Opinion in Endocrine and Metabolic Research · doi:10.1016/j.coemr.2021.100309
Wu, H., Li, J., Li, X., Gao, M., Tian, W., Chen, D., Zhou, Z., & Liu, S. (2022) · Research Square · doi:10.21203/rs.3.rs-1837739/v1
Self-citationJoshi, D. C., Patel, S. S., & Pillai, B. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-91810-7.00019-4
Spatial Transcriptomics in Inflammation: Dissecting the Immune Response in 3D in Complex Tissues
Hu, K. H. (2023) · Progress in inflammation research · doi:10.1007/978-3-031-23661-7_8
Tang, J., Ding, J., Venegas, J., Lu, Q., Wang, Y., Wu, L., Jin, W., Wen, H., Liu, R., Tang, W., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-2826640/v1
Ritter, U. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1155057
[Advances of spatial omics in the individualized diagnosis and treatment of head and neck cancer].
Xu, C., Wang, Y., Wei, D., Li, W., Qian, Y., Pan, X., & Lei, D. (2023) · PubMed Central · doi:10.13201/j.issn.2096-7993.2023.09.008
Guan, R., Sun, H., Zhang, T., Wu, Z., Du, M., Liang, Y., Wang, J., Tang, Q., Zeng, A., Feng, X., et al. (2024) · Research Square · doi:10.21203/rs.3.rs-3583635/v1
Analytical Tools to Quantitate Immune Mediated Effects
Pavelko, K. D., & Villasboas, J. C. (2024) · Wiley eBooks · doi:10.1002/9781119824572.ch26
Mori, T., Takase, T., Lan, K.-C., Yamane, J., Alev, C., Kimura, A., Osafune, K., Yamashita, J. K., Akutsu, T., Kitano, H., et al. (2023) · Figshare · doi:10.6084/m9.figshare.23529050
Xu, Y., & McCord, R. P. (2021) · Figshare · doi:10.6084/m9.figshare.15139724
Additional file 4 of Modeling zero inflation is not necessary for spatial transcriptomics
Zhao, P., Zhu, J., Ma, Y., & Zhou, X. (2022) · Figshare · doi:10.6084/m9.figshare.19793447
Mori, T., Takase, T., Lan, K.-C., Yamane, J., Alev, C., Kimura, A., Osafune, K., Yamashita, J. K., Akutsu, T., Kitano, H., et al. (2023) · Figshare · doi:10.6084/m9.figshare.23529053
Additional file 6 of Modeling zero inflation is not necessary for spatial transcriptomics
Zhao, P., Zhu, J., Ma, Y., & Zhou, X. Q. (2022) · Figshare · doi:10.6084/m9.figshare.19793453
Mori, T., Takase, T., Lan, K.-C., Yamane, J., Alev, C., Kimura, A., Osafune, K., Yamashita, J. K., Akutsu, T., Kitano, H., et al. (2023) · Figshare · doi:10.6084/m9.figshare.23529047
Metadata
- Authors
- Stickels, R. R., Murray, E., Kumar, P., Li, J., Marshall, J. L., Di Bella, D. J., Arlotta, P., Macosko, E. Z., & Chen, F.
- Affiliations
- Broad Institute (US)
- Massachusetts General Hospital (US)
- Corresponding author
- Macosko, Evan Z.
- Chen, Fei
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2020-12-07
- Volume
- 39
- Issue
- 3
- Pages
- 313-319
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC8606189
- OpenAlex
- W3112563877
- Cited by
- 1,295
- References
- 47
- Citations by year
- 2026: 247 · 2025: 277 · 2024: 304 · 2023: 192 · 2022: 186 · 2021: 88
- FWCI
- 38.93
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion)
- Funders
- U.S. Department of Health & Human Services | National Institutes of Health (DP2 AG058488-01, 1DP5OD024583)
- U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (R01HG010647)
- Burroughs Wellcome Fund
- 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
- spatial transcriptomics, dendritic mRNA localization, hippocampal neurons, neocortex development, spatiotemporal gene expression, single-cell trajectory analysis, array indexing, library construction
- MeSH terms
- Animals, Humans, RNA, Messenger, Sequence Analysis, RNA, Mice, Single-Cell Analysis, Transcriptome
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