Deterministic scRNA-seq captures variation in intestinal crypt and organoid composition
Open accessAbstract
Single-cell RNA sequencing (scRNA-seq) approaches have transformed our ability to resolve cellular properties across systems, but are currently tailored toward large cell inputs (>1,000 cells). This renders them inefficient and costly when processing small, individual tissue samples, a problem that tends to be resolved by loading bulk samples, yielding confounded mosaic cell population read-outs. Here, we developed a deterministic, mRNA-capture bead and cell co-encapsulation dropleting system, DisCo, aimed at processing low-input samples (<500 cells). We demonstrate that DisCo enables precise particle and cell positioning and droplet sorting control through combined machine-vision and multilayer microfluidics, enabling continuous processing of low-input single-cell suspensions at high capture efficiency (>70%) and at speeds up to 350 cells per hour. To underscore DisCo's unique capabilities, we analyzed 31 individual intestinal organoids at varying developmental stages. This revealed extensive organoid heterogeneity, identifying distinct subtypes including a regenerative fetal-like Ly6a+ stem cell population that persists as symmetrical cysts, or spheroids, even under differentiation conditions, and an uncharacterized 'gobloid' subtype consisting predominantly of precursor and mature (Muc2+) goblet cells. To complement this dataset and to demonstrate DisCo's capacity to process low-input, in vivo-derived tissues, we also analyzed individual mouse intestinal crypts. This revealed the existence of crypts with a compositional similarity to spheroids, which consisted predominantly of regenerative stem cells, suggesting the existence of regenerating crypts in the homeostatic intestine. These findings demonstrate the unique power of DisCo in providing high-resolution snapshots of cellular heterogeneity in small, individual tissues.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Droplet microfluidics can print one organoid per well and enable rapid generation of intra-organoid heterogeneity.
“Droplet microfluidics can print one organoid per well and enable the rapid generation of intra-organoid heterogeneity75.”
From Organoids (2022) · cites this paper as [75]
The Results describe a droplet-microfluidic system that encapsulates cells and beads for single-cell RNA sequencing, not one that prints organoids into wells. The Introduction and Results describe measuring heterogeneity in existing intestinal organoids; they do not address rapid generation of intra-organoid heterogeneity. Searches for one organoid per well and rapid generation found no supporting passage (page numbers unavailable). (LLM, conf=0.76)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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 · see citing works
HTSeq—a Python framework to work with high-throughput sequencing data
Anders, S., Pyl, P. T., & Huber, W. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btu638 · Cited by 21,464 · see citing works
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 · see citing works
Massively parallel digital transcriptional profiling of single cells
Zheng, G. X. Y., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., et al. (2017) · Nature Communications · doi:10.1038/ncomms14049 · Cited by 7,353 · see citing works
Single Lgr5 stem cells build crypt-villus structures in vitro without a mesenchymal niche
Sato, T., Vries, R. G., Snippert, H. J., van de Wetering, M., Barker, N., Stange, D. E., van Es, J. H., Abo, A., Kujala, P., Peters, P. J., et al. (2009) · Nature · doi:10.1038/nature07935 · Cited by 7,204 · 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
Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y., & Greenleaf, W. J. (2013) · Nature Methods · doi:10.1038/nmeth.2688 · Cited by 6,422 · see citing works
Full-length RNA-seq from single cells using Smart-seq2
Picelli, S., Faridani, O. R., Björklund, Å. K., Winberg, G., Sagasser, S., & Sandberg, R. (2014) · Nature Protocols · doi:10.1038/nprot.2014.006 · Cited by 4,137 · see citing works
Monolithic Microfabricated Valves and Pumps by Multilayer Soft Lithography
Unger, M., Chou, H.-P., Thorsen, T., Scherer, A., & Quake, S. R. (2000) · Science · doi:10.1126/science.288.5463.113 · Cited by 4,016 · see citing works
mRNA-Seq whole-transcriptome analysis of a single cell
Tang, F., Barbacioru, C., Wang, Y., Nordman, E., Lee, C., Xu, N., Wang, X., Bodeau, J., Tuch, B. B., Siddiqui, A., et al. (2009) · Nature Methods · doi:10.1038/nmeth.1315 · Cited by 3,969 · see citing works
Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells
Klein, A. M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, D. A., & Kirschner, M. W. (2015) · Cell · doi:10.1016/j.cell.2015.04.044 · Cited by 3,356 · see citing works
Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris
The Tabula Muris Consortium, coordination, O., coordination, L., processing, O. C. A., sequencing, L. P. A., analysis, C. D., annotation, C. T., Writing group, Supplemental text writing group, & Principal investigators (2018) · Nature · doi:10.1038/s41586-018-0590-4 · Cited by 3,326 · see citing works
Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics
Street, K., Risso, D., Fletcher, R. B., Das, D., Ngai, J., Yosef, N., Purdom, E., & Dudoit, S. (2018) · BMC Genomics · doi:10.1186/s12864-018-4772-0 · Cited by 3,159 · see citing works
Single-cell RNA sequencing technologies and bioinformatics pipelines
Hwang, B., Lee, J. H., & Bang, D. (2018) · Experimental & Molecular Medicine · doi:10.1038/s12276-018-0071-8 · Cited by 1,793 · see citing works
A single-cell survey of the small intestinal epithelium
Haber, A. L., Biton, M., Rogel, N., Herbst, R. H., Shekhar, K., Smillie, C., Burgin, G., Delorey, T. M., Howitt, M. R., Katz, Y., et al. (2017) · Nature · doi:10.1038/nature24489 · Cited by 1,750 · see citing works
A comparison of single-cell trajectory inference methods
Saelens, W., Cannoodt, R., Todorov, H., & Saeys, Y. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0071-9 · Cited by 1,670 · see citing works
Self-citationMapping the Mouse Cell Atlas by Microwell-Seq
Han, X., Wang, R., Zhou, Y., Fei, L., Sun, H., Lai, S., Saadatpour, A., Zhou, Z., Chen, H., Ye, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.001 · Cited by 1,597 · see citing works
Designer matrices for intestinal stem cell and organoid culture
Gjorevski, N., Sachs, N., Manfrin, A., Giger, S., Bragina, M. E., Ordóñez-Morán, P., Clevers, H., & Lutolf, M. P. (2016) · Nature · doi:10.1038/nature20168 · Cited by 1,426 · see citing works
Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding
Rosenberg, A. B., Roco, C. M., Muscat, R. A., Kuchina, A., Sample, P., Yao, Z., Graybuck, L. T., Peeler, D. J., Mukherjee, S., Chen, W., et al. (2018) · Science · doi:10.1126/science.aam8999 · Cited by 1,422 · see citing works
Single-cell messenger RNA sequencing reveals rare intestinal cell types
Grün, D., Lyubimova, A., Kester, L., Wiebrands, K., Basak, O., Sasaki, N., Clevers, H., & van Oudenaarden, A. (2015) · Nature · doi:10.1038/nature14966 · Cited by 1,234 · see citing works
CEL-seq2: sensitive highly-multiplexed single-cell RNA-Seq
Hashimshony, T., Senderovich, N., Avital, G., Klochendler, A., de Leeuw, Y., Anavy, L., Gennert, D., Li, S., Livak, K. J., Rozenblatt-Rosen, O., et al. (2016) · Genome Biology · doi:10.1186/s13059-016-0938-8 · Cited by 1,125 · see citing works
Construction of a human cell landscape at single-cell level
Han, X., Zhou, Z., Fei, L., Sun, H., Wang, R., Chen, Y., Chen, H., Wang, J., Tang, H., Ge, W., et al. (2020) · Nature · doi:10.1038/s41586-020-2157-4 · Cited by 1,087 · see citing works
A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications
Haque, A., Engel, J., Teichmann, S. A., & Lönnberg, T. (2017) · Genome Medicine · doi:10.1186/s13073-017-0467-4 · Cited by 1,071 · see citing works
Stoeckius, M., Zheng, S., Houck-Loomis, B., Hao, S., Yeung, B. Z., Mauck, W. M., Smibert, P., & Satija, R. (2018) · Genome Biology · doi:10.1186/s13059-018-1603-1 · Cited by 1,046 · see citing works
Lun, A. T. L., Riesenfeld, S. J., Andrews, T. S., Dao, T. P., Gomes, T., Jamboree, P. I. T. 1. H. C. A., & Marioni, J. C. (2019) · Genome biology · doi:10.1186/s13059-019-1662-y · Cited by 1,044 · see citing works
Pollen, A. A., Nowakowski, T. J., Shuga, J., Wang, X., Leyrat, A. A., Lui, J. H., Li, N., Szpankowski, L., Fowler, B., Chen, P., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.2967 · Cited by 920 · see citing works
Massively parallel digital transcriptional profiling of single cellsPreprint
Zheng, G. X. Y., Terry, J. M., Belgrader, P., et al. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/065912 · Cited by 897 · see citing works
Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
Ding, J., Adiconis, X., Simmons, S. K., Kowalczyk, M. S., Hession, C. C., Marjanovic, N. D., Hughes, T. K., Wadsworth, M. H., Burks, T., Nguyen, L. T., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0465-8 · Cited by 871 · see citing works
Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput
Gierahn, T. M., Wadsworth, M. H., Hughes, T. K., Bryson, B. D., Butler, A., Satija, R., Fortune, S. M., Love, J. C., & Shalek, A. K. (2017) · Nature Methods · doi:10.1038/nmeth.4179 · Cited by 855 · see citing works
Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo
Wagner, D. E., Weinreb, C., Collins, Z. M., Briggs, J. A., Megason, S. G., & Klein, A. M. (2018) · Science · doi:10.1126/science.aar4362 · Cited by 849 · see citing works
Fly Cell Atlas: A single-nucleus transcriptomic atlas of the adult fruit fly
Li, H., Janssens, J., Waegeneer, M. D., Kolluru, S. S., Davie, K., Gardeux, V., Saelens, W., David, F. P. A., Brbić, M., Spanier, K. I., et al. (2022) · Science · doi:10.1126/science.abk2432 · Cited by 847 · see citing works
Self-citationNew developments in goblet cell mucus secretion and function
Birchenough, G. M. H., Johansson, M., Gustafsson, J. K., Bergström, J. H., & Hansson, G. C. (2015) · Mucosal Immunology · doi:10.1038/mi.2015.32 · Cited by 820 · see citing works
Whole-organism lineage tracing by combinatorial and cumulative genome editing
McKenna, A., Findlay, G. M., Gagnon, J. A., Horwitz, M. S., Schier, A. F., & Shendure, J. (2016) · Science · doi:10.1126/science.aaf7907 · Cited by 766 · see citing works
Denisenko, E., Guo, B. B., Jones, M., Hou, R., de Kock, L., Lassmann, T., Poppe, D., Clément, O., Simmons, R. K., Lister, R., et al. (2020) · Genome Biology · doi:10.1186/s13059-020-02048-6 · Cited by 686 · see citing works
YAP/TAZ-Dependent Reprogramming of Colonic Epithelium Links ECM Remodeling to Tissue Regeneration
Yui, S., Azzolin, L., Maimets, M., Pedersen, M. T., Fordham, R. P., Hansen, S. L., Larsen, H. L., Guiu, J., Alves, M. R. P., Rundsten, C. F., et al. (2017) · Cell stem cell · doi:10.1016/j.stem.2017.11.001 · Cited by 678 · see citing works
Yap-dependent reprogramming of Lgr5+ stem cells drives intestinal regeneration and cancer
Gregorieff, A., Liu, Y., Inanlou, M. R., Khomchuk, Y., & Wrana, J. L. (2015) · Nature · doi:10.1038/nature15382 · Cited by 647 · see citing works
MULTI-seq: sample multiplexing for single-cell RNA sequencing using lipid-tagged indices
McGinnis, C. S., Patterson, D. M., Winkler, J., Conrad, D. N., Hein, M. Y., Srivastava, V., Hu, J. L., Murrow, L. M., Weissman, J. S., Werb, Z., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0433-8 · Cited by 643 · see citing works
Niche-independent high-purity cultures of Lgr5+ intestinal stem cells and their progeny
Yin, X., Farin, H. F., van Es, J. H., Clevers, H. C., Langer, R., & Karp, J. M. (2013) · Nature Methods · doi:10.1038/nmeth.2737 · Cited by 571 · see citing works
Single-cell transcriptomes of the regenerating intestine reveal a revival stem cell
Ayyaz, A., Kumar, S., Sangiorgi, B., Ghoshal, B., Gosio, J., Ouladan, S., Fink, M., Barutcu, S., Trcka, D., Shen, J., et al. (2019) · Nature · doi:10.1038/s41586-019-1154-y · Cited by 569 · see citing works
Self-organization and symmetry breaking in intestinal organoid development
Serra, D., Mayr, U., Boni, A., Lukonin, I., Rempfler, M., Challet Meylan, L., Stadler, M. B., Strnad, P., Papasaikas, P., Vischi, D., et al. (2019) · Nature · doi:10.1038/s41586-019-1146-y · Cited by 561 · see citing works
A lineage-resolved molecular atlas of C. elegans embryogenesis at single-cell resolution
Packer, J. S., Zhu, Q., Huynh, C., Sivaramakrishnan, P., Preston, E. A., Dueck, H. R., Stefanik, D. J., Tan, K., Trapnell, C., Kim, J., et al. (2019) · Science · doi:10.1126/science.aax1971 · Cited by 539 · see citing works
Comparative Analysis of Droplet-Based Ultra-High-Throughput Single-Cell RNA-Seq Systems
Zhang, X., Li, T., Liu, F., Chen, Y., Yao, J., Li, Z., Huang, Y., & Wang, J. (2018) · Molecular Cell · doi:10.1016/j.molcel.2018.10.020 · Cited by 380 · see citing works
Paracrine orchestration of intestinal tumorigenesis by a mesenchymal niche
Roulis, M., Kaklamanos, A., Schernthanner, M., Bielecki, P., Zhao, J., Kaffe, E., Frommelt, L.-S., Qu, R., Knapp, M. S., Henriques, A. R., et al. (2020) · Nature · doi:10.1038/s41586-020-2166-3 · Cited by 298 · see citing works
Mustata, R. C., Vasile, G., Vallone, V. B. F., Strollo, S., Lefort, A., Libert, F., Monteyne, D., Pérez-Morga, D. L., Vassart, G., & Garcia, M. (2013) · Cell Reports · doi:10.1016/j.celrep.2013.09.005 · Cited by 291 · see citing works
Slingshot: Cell lineage and pseudotime inference for single-cell transcriptomicsPreprint
Street, K., Risso, D., Fletcher, R. B., Das, D., Ngai, J., Yosef, N., Purdom, E. A., & Dudoit, S. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/128843 · Cited by 290 · see citing works
Phenotypic landscape of intestinal organoid regeneration
Lukonin, I., Serra, D., Challet Meylan, L., Volkmann, K., Baaten, J., Zhao, R., Meeusen, S., Colman, K., Maurer, F., Stadler, M. B., et al. (2020) · Nature · doi:10.1038/s41586-020-2776-9 · Cited by 283 · see citing works
Gérard, A., Woolfe, A., Mottet, G., Reichen, M., Castrillon, C. E., Menrath, V., Ellouze, S., Poitou, A., Doineau, R., Briseño-Roa, L., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0466-7 · Cited by 257 · see citing works
Cheng, Y.-H., Chen, Y., Lin, E., Brien, R., Jung, S., Chen, Y.-T., Lee, W., Hao, Z., Sahoo, S., Kang, H. M., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-10122-2 · Cited by 229 · see citing works
Profiling lymphocyte interactions at the single-cell level by microfluidic cell pairing
Dura, B., Dougan, S. K., Barisa, M., Hoehl, M. M., Lo, C. T., Ploegh, H. L., & Joel Voldman, B. (2015) · Nature Communications · doi:10.1038/ncomms6940 · Cited by 185 · see citing works
Sequencing metabolically labeled transcripts in single cells reveals mRNA turnover strategies
Battich, N., Beumer, J., de Barbanson, B., Krenning, L., Baron, C. S., Tanenbaum, M. E., Clevers, H. C., & van Oudenaarden, A. (2020) · Science · doi:10.1126/science.aax3072 · Cited by 166 · see citing works
Massively parallel nanowell-based single-cell gene expression profiling
Goldstein, L. D., Chen, Y.-J. J., Dunne, J., Mir, A. A., Hubschle, H., Guillory, J., Yuan, W., Zhang, J., Stinson, J., Jaiswal, B. S., et al. (2017) · BMC Genomics · doi:10.1186/s12864-017-3893-1 · Cited by 126 · see citing works
Highly multiplexed single-cell RNA-seq by DNA oligonucleotide tagging of cellular proteins
Gehring, J., Hwee Park, J., Chen, S., Thomson, M., & Pachter, L. (2020) · Nature Biotechnology · doi:10.1038/s41587-019-0372-z · Cited by 122 · see citing works
Highly parallel and efficient single cell mRNA sequencing with paired picoliter chambers
Zhang, M., Zou, Y. W., Xu, X. J., Zhang, X., Gao, M., Song, J. T., Huang, P., Chen, Q., Zhu, Z., Lin, W., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15765-0 · Cited by 80 · see citing works
Chung, M. T., Núñez, D., Cai, D., & Kurabayashi, K. (2017) · Lab on a Chip · doi:10.1039/c7lc00745k · Cited by 78 · see citing works
Choi, J. H., Yarishkin, O. V., Kim, E., Bae, Y., Kim, A., Kim, S. C., Ryoo, K., Cho, C., Hwang, E. M., & Park, J. (2018) · Experimental & Molecular Medicine · doi:10.1038/s12276-018-0172-4 · Cited by 75 · see citing works
Fly Cell Atlas: a single-cell transcriptomic atlas of the adult fruit flyPreprint
Li, H., Janssens, J., Waegeneer, M. D., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.07.04.451050 · Cited by 72 · see citing works
Inference and effects of barcode multiplets in droplet-based single-cell assays
Lareau, C. A., Ma, S., Duarte, F. M., & Buenrostro, J. D. (2020) · Nature Communications · doi:10.1038/s41467-020-14667-5 · Cited by 61 · see citing works
The Use of the Fluidigm C1 for RNA Expression Analyses of Single Cells
DeLaughter, D. M. (2018) · Current Protocols in Molecular Biology · doi:10.1002/cpmb.55 · Cited by 46 · see citing works
Wang, Y. J., Schug, J., Lin, J., Wang, Z. P., Kossenkov, A. V., & Kaestner, K. H. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/541433 · Cited by 33 · see citing works
Comparative analysis of droplet-based ultra-high-throughput single-cell RNA-seq systemsPreprint
Zhang, X., Li, T., Liu, F., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/313130 · Cited by 30 · see citing works
Biočanin, M., Bues, J., Dainese, R., Amstad, E., & Deplancke, B. (2019) · Lab on a Chip · doi:10.1039/c9lc00014c · Cited by 24 · see citing works
Self-citationPheno-seq – linking visual features and gene expression in 3D cell culture systems
Tirier, S. M., Park, J., Preußer, F., Amrhein, L., Gu, Z., Steiger, S., Mallm, J., Krieger, T. G., Waschow, M., Eismann, B., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-48771-4 · Cited by 24 · see citing works
Real Time Analysis of Metabolic Profile in Ex Vivo Mouse Intestinal Crypt Organoid Cultures
Bas, T., & Augenlicht, L. H. (2014) · Journal of Visualized Experiments · doi:10.3791/52026 · Cited by 17 · see citing works
psupertime: supervised pseudotime inference for single cell RNA-seq data with sequential labelsPreprint
Macnair, W., & Claassen, M. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/622001 · Cited by 17 · see citing works
Self-citationMultimodal and multisensory coding in the Drosophila larval peripheral gustatory centerPreprint
Maier, G. L., Biočanin, M., Bues, J., Meyenhofer, F., Avalos, C. B., Kwon, J. Y., Deplancke, B., & Sprecher, S. G. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.21.109959 · Cited by 2 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- Macnair, W. & Claassen, M. psupertime: supervised pseudotime inference for single cell RNA-seq data with sequential labels. bioRxiv 622001 (2019). doi:10.1101/622001 · DOI
Cited by
Sorted by citations
Zhao, Z., Chen, X., Dowbaj, A. M., Sljukic, A., Bratlie, K., Lin, L., Fong, E. L. S., Balachander, G. M., Chen, Z., Soragni, A., et al. (2022) · Nature Reviews Methods Primers · doi:10.1038/s43586-022-00174-y · Cited by 1,001 · see citing works
An oncogenic phenoscape of colonic stem cell polarization
Qin, X., Rodriguez, F. C., Sufi, J., Vlckova, P., Claus, J., & Tape, C. J. (2023) · Cell · doi:10.1016/j.cell.2023.11.004 · Cited by 120 · see citing works
Middle-out methods for spatiotemporal tissue engineering of organoids
Blatchley, M. R., & Anseth, K. S. (2023) · Nature Reviews Bioengineering · doi:10.1038/s44222-023-00039-3 · Cited by 85 · see citing works
Applications of human organoids in the personalized treatment for digestive diseases
Wang, Q., Guo, F., Jin, Y., & Ma, Y. (2022) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-022-01194-6 · Cited by 82 · see citing works
Microfluidic Biochips for Single‐Cell Isolation and Single‐Cell Analysis of Multiomics and Exosomes
Wang, C., Qiu, J., Liu, M., Wang, Y., Yu, Y., Liu, H., Zhang, Y., & Han, L. G. (2024) · Advanced Science · doi:10.1002/advs.202401263 · Cited by 81 · see citing works
Advances in Microfluidics: Technical Innovations and Applications in Diagnostics and Therapeutics
Aubry, G., Lee, H. J., & Lu, H. (2023) · Analytical Chemistry · doi:10.1021/acs.analchem.2c04562 · Cited by 75 · see citing works
Recent advances in high-throughput single-cell transcriptomics and spatial transcriptomics
Shen, X., Zhao, Y., Wang, Z., & Shi, Q. (2022) · Lab on a Chip · doi:10.1039/d2lc00633b · Cited by 65 · see citing works
spinDrop: a droplet microfluidic platform to maximise single-cell sequencing information content
Jonghe, J. D., Kamiński, T. S., Morse, D. B., Tabaka, M., Ellermann, A. L., Kohler, T. N., Amadei, G., Handford, C. E., Findlay, G. M., Zernicka‐Goetz, M., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-40322-w · Cited by 61 · see citing works
Tang, T., Zhao, H., Shen, S., Yang, L., & Lim, C. T. (2024) · Microsystems & Nanoengineering · doi:10.1038/s41378-023-00631-y · Cited by 58 · see citing works
Plastic persisters: revival stem cells in colorectal cancer
Tape, C. J. (2023) · Trends in cancer · doi:10.1016/j.trecan.2023.11.003 · Cited by 58 · see citing works
Everything You Always Wanted to Know About Organoid-Based Models (and Never Dared to Ask)
Hautefort, I., Poletti, M., Papp, D., & Korcsmaros, T. D. (2022) · Cellular and Molecular Gastroenterology and Hepatology · doi:10.1016/j.jcmgh.2022.04.012 · Cited by 46 · see citing works
High-throughput microfluidic droplets in biomolecular analytical system: A review
Zhang, L., Parvin, R., Chen, M., Hu, D., Fan, Q., & Ye, F. (2023) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2023.115213 · Cited by 39 · see citing works
Tresenrider, A. M., Sridhar, A., Eldred, K. C., Cuschieri, S. A., Hoffer, D., Trapnell, C., & Reh, T. A. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100548 · Cited by 37 · see citing works
Organoid‐Guided Precision Medicine: From Bench to Bedside
Tao, B., Li, X., Hao, M., Tian, T., Li, Y., Li, X., Yang, C., Li, Q., Feng, Q., Zhou, H., et al. (2025) · MedComm · doi:10.1002/mco2.70195 · Cited by 38 · see citing works
Approaches for single-cell RNA sequencing across tissues and cell types
Sant, P., Rippe, K., & Mallm, J. (2023) · Transcription · doi:10.1080/21541264.2023.2200721 · Cited by 34 · see citing works
Liu, C., Li, K., Sui, X., Zhao, T., Zhang, T., Chen, Z., Wu, H., Li, C. R., Li, H., Yang, F., et al. (2024) · Advanced Science · doi:10.1002/advs.202400185 · Cited by 32 · see citing works
Intestinal cellular heterogeneity and disease development revealed by single-cell technology
Wang, Y., Song, W., Yu, S., Liu, Y., & Chen, Y. (2022) · Cell Regeneration · doi:10.1186/s13619-022-00127-6 · Cited by 29 · see citing works
Kobayashi, S., Ogasawara, N., Watanabe, S., Yoneyama, Y., Kirino, S., Hiraguri, Y., Inoue, M., Nagata, S., Okamoto‐Uchida, Y., Kofuji, S., et al. (2022) · Inflammation and Regeneration · doi:10.1186/s41232-022-00237-3 · Cited by 30 · see citing works
Microfluidics-based single cell analysis: from transcriptomics to spatiotemporal multi-omics
Cao, J., Chen, X., Huang, S., Shi, W., Fan, Q., Gong, Y., Peng, Y., Wu, L., & Yang, C. J. (2022) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2022.116868 · Cited by 29 · see citing works
Huang, K., Li, M., Li, Q., Chen, Z., Zhang, Y., & Gu, Z. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108322 · Cited by 27 · see citing works
An Automated Single‐Cell Droplet‐Digital Microfluidic Platform for Monoclonal Antibody Discovery
Ahmadi, F., Tran, H., Letourneau, N., Little, S. R., Fortin, A., Moraitis, A. N., & Shih, S. C. C. (2024) · Small · doi:10.1002/smll.202308950 · Cited by 24 · see citing works
Microfluidic single‐cell multiomics analysis
Xu, X., Zhang, Q., Li, M., Lin, S., Liang, S., Cai, L., Zhu, H., Su, R. J., & Yang, C. J. (2022) · Open Research (University of Surrey) · doi:10.1002/viw.20220034 · Cited by 23 · see citing works
Gene-expression memory-based prediction of cell lineages from scRNA-seq datasets
Eisele, A. S., Tarbier, M., Dormann, A. A., Pelechano, V., & Suter, D. M. (2024) · Nature Communications · doi:10.1038/s41467-024-47158-y · Cited by 26 · see citing works
Stem cell heterogeneity, plasticity, and regulation
Cui, Z., Wei, H., Goding, C. R., & Cui, R. (2023) · Life Sciences · doi:10.1016/j.lfs.2023.122240 · Cited by 21 · see citing works
Mukherjee, P., Park, S. H., Pathak, N., Patino, C. A., Bao, G., & Espinosa, H. D. (2022) · ACS Nano · doi:10.1021/acsnano.2c05494 · Cited by 20 · see citing works
Stress-relaxing granular bioprinting materials enable complex and uniform organoid self-organization
Graham, A. J., Khoo, M. W. L., Srivastava, V., Viragova, S., Kim, H., Parekh, K., Hennick, K. M., Bird, M., Goldhammer, N., Zeng, J., et al. (2026) · Nature Materials · doi:10.1038/s41563-026-02519-4 · Cited by 24 · see citing works
High precision, high throughput generation of droplets containing single cells
Zhou, J., Wei, A., Bertsch, A., & Renaud, P. (2022) · Lab on a Chip · doi:10.1039/d2lc00841f · Cited by 17 · see citing works
Engineering next-generation microfluidic technologies for single-cell phenomics
Lambert, C. L. G., van Mierlo, G., Bues, J., Guillaume-Gentil, O. J., & Deplancke, B. (2025) · Nature Genetics · doi:10.1038/s41588-025-02198-y · Cited by 17 · see citing works
Self-citationBacterial ADP-heptose triggers stem cell regeneration in the intestinal epithelium following injury
Goyal, S., Guo, C. X., Singh, O., Ranger, A., Harrigan, C. F., Meade, J. J., Luchak, A., Tsang, D. K., Gaisano, H. Y., Gao, N., et al. (2025) · Cell stem cell · doi:10.1016/j.stem.2025.06.009 · Cited by 18 · see citing works
Next-Generation Sequencing-Based Spatial Transcriptomics: A Perspective from Barcoding Chemistry
Shi, W., Zhang, J., Huang, S., Fan, Q., Cao, J., Zeng, J., Wu, L., & Yang, C. J. (2024) · JACS Au · doi:10.1021/jacsau.4c00118 · Cited by 15 · see citing works
NOTCH-driven oscillations control cell fate decisions during intestinal homeostasisPreprint
Weterings, S. D. C., Eto, H., de Leede-Scheven, J.-D., Giladi, A., Hoekstra, M. E., Beijk, W. F., Liefting, E. J. M., van den Anker, K. B., van Rheenen, J., van Oudenaarden, A., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.26.609553 · Cited by 16 · see citing works
Advanced sequencing-based high-throughput and long-read single-cell transcriptome analysis
Huang, S., Shi, W., Li, S., Fan, Q., Yang, C. J., Cao, J., & Wu, L. (2024) · Lab on a Chip · doi:10.1039/d4lc00105b · Cited by 14 · see citing works
Post, F., Hausmann, A., Kabatnik, S., Steigerwald, S., Brand, A., Clement, D. L., Skov, J., Lõhmussaar, K., Larsen, H. L., Maimets, M., et al. (2025) · Cell Systems · doi:10.1016/j.cels.2025.101396 · Cited by 13 · see citing works
An extracellular matrix niche secreted by epithelial cells drives intestinal organoid formation
Chrisnandy, A., & Lütolf, M. P. (2025) · Developmental Cell · doi:10.1016/j.devcel.2025.06.026 · Cited by 11 · see citing works
Self-citationApplication and challenges of tumor organoid technology in precision immunotherapy
Zhao, X., Gao, J., Liu, Y., Shi, Y., Zhang, M., Zhang, C., & Chen, J. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1723995 · Cited by 12 · see citing works
Komarov, N., Fritsch, C., Maier, G. L., Bues, J., Biočanin, M., Avalos, C. B., Dodero, A., Kwon, J. Y., Deplancke, B., & Sprecher, S. G. (2025) · PLoS Biology · doi:10.1371/journal.pbio.3002730 · Cited by 9 · see citing works
Self-citationTime-resolved single-cell transcriptomic sequencing
Xu, X., Wen, Q. J., Lan, T., Zeng, L., Zeng, Y., Lin, S., Qiu, M., Na, X., & Yang, C. J. (2024) · Chemical Science · doi:10.1039/d4sc05700g · Cited by 10 · see citing works
Biomimetic Gland Models with Engineered Stratagems
Lin, X., Sun, L., Lu, M., & Zhao, Y. (2023) · Research · doi:10.34133/research.0232 · Cited by 8 · see citing works
Organoid Cultures In Silico: Tools or Toys?
Thalheim, T., Aust, G., & Galle, J. (2022) · Bioengineering · doi:10.3390/bioengineering10010050 · Cited by 7 · see citing works
Liu, Z., Zhang, Y., Li, J., Chen, S., Zhao, H. Q., Zhao, X. J., & Sun, D. B. (2025) · Small · doi:10.1002/smll.202500520 · Cited by 6 · see citing works
Barcode-free prediction of cell lineages from scRNA-seq datasetsPreprint
Eisele, A. S., Tarbier, M., Dormann, A. A., Pelechano, V., & Suter, D. M. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.20.508646 · Cited by 6 · see citing works
A Single-cell Perturbation Landscape of Colonic Stem Cell PolarisationPreprint
Qin, X., Rodriguez, F. C., Sufi, J., Vlckova, P., Claus, J., & Tape, C. J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.02.15.528008 · Cited by 6 · see citing works
Li, W., Bao, Z., Huang, H., Ma, Y., Sun, Y., Lin, X., Sun, W., Wang, S., Cui, Z., Chen, Y., et al. (2025) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2025.118385 · Cited by 5 · see citing works
Jin, Y., Park, S., & Cho, S. (2026) · Nature Reviews Bioengineering · doi:10.1038/s44222-025-00384-5 · Cited by 6 · see citing works
Pan, J., Chen, S., Jin, L., Chen, K., Ye, Z., & Li, B. (2026) · Biochimica et Biophysica Acta (BBA) - Reviews on Cancer · doi:10.1016/j.bbcan.2026.189544 · Cited by 5 · see citing works
Nutrient sensing in intestinal stem cell: Linking dietary nutrients to cellular metabolic regulation
Li, W., Yuan, X.-Y., Wang, Z., & Lin, R. (2025) · World Journal of Stem Cells · doi:10.4252/wjsc.v17.i7.107770 · Cited by 4 · see citing works
A deep learning-based computational pipeline predicts developmental outcome in retinal organoids
Afting, C., Bhatti, N., Schlagheck, C., Salvador, E. S., Herrera, M., Agarwal, R., Suzuki, R., Hackert, N. S., Lorenz, H., Žílová, L., et al. (2026) · PLoS Biology · doi:10.1371/journal.pbio.3003597 · Cited by 3 · see citing works
Dang, K., Zhao, Y., Ye, K., Guo, Y., Wang, W., Ge, Q., & Zhao, X. (2023) · Biotechnology Journal · doi:10.1002/biot.202300294 · Cited by 3 · see citing works
Deep learning predicts tissue outcomes in retinal organoidsPreprint
Afting, C., Bhatti, N., Schlagheck, C., Salvador, E. S., Herrera, M., Agarwal, R., Suzuki, R., Hackert, N. S., Lorenz, H., Žílová, L., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.19.639061 · Cited by 3 · see citing works
Single-cell phenomics through integrated imaging and molecular profilingPreprint
Bues, J., Pezoldt, J., Lambert, C. L. G., Hale, B. D., Bugani, E., Viñas, R., Ferrari, T., Grenningloh, N., Gardeux, V., Wen, S., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.28.690954 · Cited by 3 · see citing works
Self-citationHIV-1 transmission: modelling and direct visualization in the third dimension
Coomer, C. A., & Padilla‐Parra, S. (2023) · Microscopy · doi:10.1093/jmicro/dfad014 · Cited by 2 · see citing works
Nguyen, L., Vallejos, C. A., Mill, P., & Megaw, R. D. (2025) · Genes · doi:10.3390/genes16091088 · Cited by 2 · see citing works
Assessing differential cell composition in single-cell studies using voomCLR
Assefa, A. T., Verbist, B. M. P., & den Berge, K. V. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf637 · Cited by 2 · see citing works
Recent progress in co-detection of single-cell transcripts and proteins
Huang, S., Fan, Q., Wang, Y., Huang, Z., Shi, W., Gong, Y., Yang, T., Wang, J., Wu, L., & Yang, C. J. (2023) · Nano Research · doi:10.1007/s12274-023-5924-3 · Cited by 2 · see citing works
spinDrop: a droplet microfluidic platform to maximise single-cell sequencing information contentPreprint
Jonghe, J. D., Kamiński, T. S., Morse, D. B., Tabaka, M., Ellermann, A. L., Kohler, T. N., Amadei, G., Handford, C. E., Findlay, G. M., Zernicka‐Goetz, M., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.12.523500 · Cited by 2 · see citing works
Okotel, M., & Huang, C. (2025) · Critical Reviews™ in Oncogenesis · doi:10.1615/critrevoncog.2025060830 · Cited by 1 · see citing works
Precancerous microenvironment: A signalling perspective
Qin, X. (2026) · Current Opinion in Cell Biology · doi:10.1016/j.ceb.2025.102611 · Cited by 1 · see citing works
Cai, Y., Su, F., Han, C., Yu, D., Luo, Y., & Xing, X. (2026) · Sensors and Actuators A Physical · doi:10.1016/j.sna.2026.117624 · Cited by 1 · see citing works
Liu, C., Li, K., Sui, X., Zhao, T., Zhang, T., Chen, Z., Wu, H., Li, C., Li, H., Yang, F., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.11.571047 · Cited by 1 · see citing works
Single-cell, single-organoid phenotypic landscapes
Camp, J. G., & Lukonin, I. (2022) · Nature Methods · doi:10.1038/s41592-022-01416-3
High precision, high throughput generation of droplets containing single cellsPreprint
Zhou, J., Wei, A., Bertsch, A., & Renaud, P. (2022) · Research Square · doi:10.21203/rs.3.rs-1963392/v1
High precision, high throughput generation of droplets containing single cellsPreprint
Zhou, J., Wei, A., Bertsch, A., & Renaud, P. (2022) · Research Square · doi:10.21203/rs.3.rs-1963392/v2
Bacterial ADP-heptose initiates a revival stem cell program in the intestinal epitheliumPreprint
Goyal, S., Guo, C. X., Ranger, A., Tsang, D. K. L., Singh, O., Harrigan, C. F., Zaslaver, O., Röst, H. L., Gaisano, H. Y., Yuzwa, S. A., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.15.575749
Komarov, N., Fritsch, C., Maier, G. L., Bues, J., Biočanin, M., Avalos, C. B., Dodero, A., Kwon, J. Y., Deplancke, B., & Sprecher, S. G. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.07.04.602043
Self-citationDroplet Microfluidic Systems for Multistep Single-Cell Sequencing Assays
Olszewska, Z., Pyzik, A. A., Malkowski, M., & Kamiński, T. S. (2024) · Bioanalysis · doi:10.1007/978-981-97-6540-9_14
GEMLI: Gene Expression Memory-Based Lineage Inference from Single-Cell RNA-Sequencing Datasets
Eisele, A. S., & Suter, D. M. (2025) · Methods in molecular biology · doi:10.1007/978-1-0716-4310-5_19
High-Quality Cell Capture Reveals Transcriptomic Changes After Single-Axon Injury of Mauthner CellsPreprint
Song, Z., Zhu, L., Shen, Y., Yao, H., Cai, Y., Shi, L., Chen, X., Han, A., Zhao, Z.-A., Qu, K., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.17.643681
Shirai, Y., Iguchi, T., Fujimoto, K., Imaoka, M., Hasegawa, Y., & Chiba, K. (2025) · Toxicology Letters · doi:10.1016/j.toxlet.2025.111805
Quantitative imaging of dynamic processes in intestinal organoids
Spoelstra, W. K., Zheng, X., Kok, R. N. U., Huelsz‐Prince, G., Betjes, M. A., Ender, P., Goos, Y. J., Kuentzelmann, K. A., Klumpe, H. E., Krueger, D., et al. (2026) · Nature Protocols · doi:10.1038/s41596-026-01432-z
Frequency encoding regulates cell type composition in the small intestine
Weterings, S. D. C., Eto, H., de Leede-Scheven, J.-D., Giladi, A., Monster, J. L., Hoekstra, M. E., Beijk, W. F., Liefting, E. J. M., van den Anker, K. B., van Rheenen, J., et al. (2026) · Science · doi:10.1126/science.adt1928
- Authors
- Bues, J., Biočanin, M., Pezoldt, J., Dainese, R., Chrisnandy, A., Rezakhani, S., Saelens, W., Gardeux, V., Gupta, R., Sarkis, R., Russeil, J., Saeys, Y., Amstad, E., Claassen, M., Lutolf, M. P., & Deplancke, B.
- Affiliations
- SIB Swiss Institute of Bioinformatics (CH)
- École Polytechnique Fédérale de Lausanne (CH)
- Universitätsklinikum Tübingen (DE)
- Ghent University (BE)
- Corresponding author
- Deplancke, Bart
- ORCID
- Venue
- Nature Methods
- Published
- 2022-02-14
- Volume
- 19
- Issue
- 3
- Pages
- 323-330
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W4212799764
- Cited by
- 71
- References
- 63
- Citations by year
- 2026: 8 · 2025: 18 · 2024: 13 · 2023: 16 · 2022: 16
- FWCI
- 5.15
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- Nature Methods (publishedVersion)
- PubMed (publishedVersion)
- Ghent University Academic Bibliography (Ghent University) (acceptedVersion, other-oa)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Funders
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (IZLIZ3_156815, CRSK-3_190627)
- Precision Health & related Technologies (PHRT-502) EPFL SV Interdisciplinary PhD Funding Program
- EC | Horizon 2020 Framework Programme (754462)
- ISAC Marylou Ingram scholar
- EPFL SV Interdisciplinary PhD Funding Program
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Physical Sciences › Engineering › Biomedical Engineering › Innovative Microfluidic and Catalytic Techniques Innovation
- Physical Sciences › Engineering › Biomedical Engineering › Microfluidic and Bio-sensing Technologies
- Keywords
- single-cell RNA sequencing, intestinal organoids, intestinal crypts, organoid heterogeneity, goblet cells, intestinal stem cells, cysts
- MeSH terms
- Animals, Cell Differentiation, Intestinal Mucosa, Organoids, Stem Cells, Mice, Single-Cell Analysis
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 ›
Are you an author, or do you see a problem with this page? Request a review