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Deterministic scRNA-seq captures variation in intestinal crypt and organoid composition

Nature Methods volume 19, pages 323–330 (2022)Cite this article

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Abstract

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.

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Fig. 1: Overview and critical feature assessment of the DisCo system.
Fig. 2: Use of DisCo to map intestinal organoid cell identities.
Fig. 3: Use of DisCo to map intestinal organoid cell identity along development.
Fig. 4: Cell type distribution and marker gene expression across individual intestinal organoids.
Fig. 5: Cell type distribution across individual intestinal crypts.

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Data availability

The GEO (Gene Expression Omnibus) accession number for scRNA-seq data reported in this paper is GSE148093. The raw data and count matrices for Fig. 1h and Extended Data Fig. 2c are stored under the access code GSM4454017. The raw data and count matrices for Fig. 1i and Extended Data Fig. 2a are available under the access code GSM4454017. The raw data and count matrices for Fig. 1j are stored under the access codes GSM4454012–GSM4454016. The raw data and count matrices for Extended Data Fig. 2e,f are stored under the access codes GSM5567775–GSM5567779. The raw data and count matrices for Extended Data Fig. 2g are stored under the access codes GSM5567571–GSM5567730. The raw data and count matrices for Extended Data Fig. 4 are stored under the access codes GSM5567845–GSM5567854. The raw data for intestinal organoids embedded in Figs. 2 and 3, Extended Data Figs. 3–5, Figs. 4a,e and 5d,e and Extended Data Figs. 7, 9 and 10 are stored under access codes GSM4453981–GSM4454011. The raw data and count matrices for intestinal crypts embedded in Fig. 5 and Extended Data Figs. 8–10 are stored under the access codes GSM5567818–GSM5567844. Additionally, dataset GSM1544799 and data from ref. 23 (https://doi.org/10.1039/C9LC00014C, data available on request) were used for Fig. 1i and Extended Data Fig. 2a. In this study the following reference genomes were used: hg38 (GCF_000001405.26), mm10 (GCF_000001635.20) and mixed reference genome (GSE63269) of hg19 combined with mm10.

Code availability

This technology has been developed as an open source platform, therefore all required information for its implementation is publicly available. The source code for the machine-vision software is available on github (https://github.com/DeplanckeLab/DisCo_source) and the barcode merging script is supplied as Supplementary Software 1.

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Acknowledgements

The authors thank the former members of the Deplancke laboratory (EPFL) for their support: W. Chen and P.C. Schwalie for constructive discussions, and V. Braman for help in establishing the intestinal organoid culture. Furthermore, the authors thank G. Sorrentino from K. Schoonjans’ laboratory (EPFL) for valuable advice and support during organoid culture establishment; L. Aeberli and G. Muller from SEED Biosciences for cell sorting support; and the EPFL CMi, GECF, BIOP, FCCF, Histology Core Facility, SCITAS, and UNIL VITAL-IT for device fabrication, sequencing, imaging, sorting, histology, and computational support, respectively. The authors also thank J. Sordet-Dessimoz from the Histology Core Facility for her support with the RNAscope assay. This research was supported by the Swiss National Science Foundation Grant (IZLIZ3_156815) and a Precision Health and Related Technologies (PHRT-502) grant (to B.D.), the Swiss National Science Foundation SPARK initiative (CRSK-3_190627) and the EuroTech PostDoc Programme co-funded by the European Commission under its framework program Horizon 2020 (754462, to J.P.), as well as by the EPFL SV Interdisciplinary PhD Funding Program (to B.D. and E.A.). Y.S. is an ISAC Marylou Ingram scholar.

Author information

Author notes

  1. These authors contributed equally: J. Bues, M. Biočanin, J. Pezoldt.

Authors and Affiliations

  1. Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

    Johannes Bues, Marjan Biočanin, Joern Pezoldt, Riccardo Dainese, Wouter Saelens, Vincent Gardeux, Rita Sarkis, Julie Russeil & Bart Deplancke

  2. Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland

    Johannes Bues, Marjan Biočanin, Joern Pezoldt, Riccardo Dainese, Wouter Saelens, Vincent Gardeux, Julie Russeil & Bart Deplancke

  3. Laboratory for Stem Cell Bioengineering, Institute of Bioengineering, School of Life Sciences École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

    Antonius Chrisnandy, Saba Rezakhani & Matthias P. Lutolf

  4. Data Mining and Modelling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium

    Wouter Saelens & Yvan Saeys

  5. Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium

    Wouter Saelens & Yvan Saeys

  6. Internal Medicine I, University Hospital Tübingen, Faculty of Medicine, University of Tübingen, Tübingen, Germany

    Revant Gupta & Manfred Claassen

  7. Soft Materials Laboratory, Institute of Materials, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

    Esther Amstad

  8. Department of Computer Science, University of Tübingen, Tübingen, Germany

    Manfred Claassen

  9. Roche Institute for Translational Bioengineering (ITB), Roche Pharma Research and Early Development, Basel, Switzerland

    Matthias P. Lutolf

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  1. Johannes Bues
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Contributions

B.D., J.B. and M.B. designed the study. B.D., J.B., M.B. and J.P. wrote the manuscript. J.B. and R.D. designed and fabricated the microfluidic chips. J.B. developed the machine-vision integration for DisCo. J.B. and M.B. benchmarked the system and performed all single-cell RNA-seq experiments. J.P., J.B., M.B., W.S., V.G. and R.G. performed data analysis related to single organoid and crypt scRNA-seq experiments. J.B., S.R. and M.B. performed all organoid and cell culture assays. J.B., A.C., J.R. and R.S. performed all imaging assays. M.B. and J.R. isolated intestinal crypts, and M.B. picked and dissociated crypts. Y.S. supervised data analysis aspects and reviewed the manuscript. E.A. provided critical comments regarding microfluidic chip design and fabrication. M.C. provided critical comments on intestinal organoid scRNA-seq data analysis. M.P.L. provided critical comments regarding intestinal organoid scRNA-seq data and the design of critical confirmation experiments. All authors read, discussed and approved the final manuscript.

Corresponding author

Correspondence to Bart Deplancke.

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Competing interests

B.D., J.B., M.B. and R.D. have filed a patent application for the deterministic co-encapsulation system (patent no. US20190240664A1). All other authors have no competing interests.

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Nature Methods thanks Jarrett Camp and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Lei Tang was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 DisCo device features and their performance.

(a) Schematic of the DisCo device design (blue: flow layer, green: control layer). 1. oil valve, 2. oil inlet, 3. cell inlet, 4. bead inlet, 5. cell valve, 6. dropleting valve, 7. bead valve, 8. sample valve, 9. waste valve, 10. sample outlet, 11. waste outlet. (b) Visualization of real-time image processing for particle detection. (c) Particle positioning by valve oscillation. Approaching particles are detected in the detection zone. Once a particle is detected, the channel valve is oscillated to induce discrete movements of particles. Oscillation is terminated once correct placement of a particle is achieved. (d) Stopping accuracy in a defined window. Beads (n = 744) were positioned using valve oscillation, their position was manually determined within the stopping area. Scale was approximated from channel width. (e) Volume-defined droplet on-demand generation by valve pressurization. Droplets (n = 68, ~8 per condition, 1 experiment) were produced by pressurizing the dropleting valve at different pressures. Size was determined by imaging the dropleting process. Volumes were calculated from the imaging data based on droplet length and channel geometry. Thus, they should be considered an approximation. Points represent mean values, error bars +/-SD. The channel width of displayed images is 250 μm.

Extended Data Fig. 2 Quality assessment of DisCo scRNA-seq data.

(a) Cumulative reads per barcode (n = 500) for DisCo and two Drop-seq experiments2,23. (b) Hamming distances between all 12 nt barcodes of a Drop-seq experiment and generated 12 nt random barcode sequences representing the probability density for each set of barcodes. (c) Species purity (bars) and doublet ratio (dots) for unmerged (n = 949) and merged barcodes (n = 274). Data represent mean values, error bars standard deviation. (d-e) HEK 293T cells were processed with DisCo at 22 °C after 20, 40 or 60 min or stored on ice for 120 or 180 min and subsequently processed. (d) UMAP embedding of all profiled HEK 293T cells from the five sampling time points, color-coded by sampling time. (e) Violin plots showing the percentage of UMIs per cell of heat-shock-protein (HSP), mitochondrial protein-coding (MT), or ribosomal protein-coding (RPL) genes. (f) Correlation of the number of manually counted cells by fluorescence microscopy and the number of cells quantified by the DISPENCELL platform. (g) A quantified number of HEK 293T cells was processed with the Fluidigm C1 system. Processing efficiency was calculated as the percentage of cells retrieved from the sequencing data respective to the quantified number of input cells. The red line represents 100% efficiency, and samples were colored according to the recovery efficiency after sequencing.

Extended Data Fig. 3 DisCo performance on individual intestinal organoids and data analysis.

(a) Representative bright-field image of a differentiated organoid culture from single LGR5+ cells, as performed for experiments shown in Fig. 2. (b) Correlation of encapsulated cells on-chip with the number of cells detected after sequencing (cells passing QC, filtered above 800 genes/cell). (c) UMAP embedding colored by the number of detected genes (nFeature) per cell, the number of detected UMIs (nCount) per cell, the percentage of mitochondrial (mt) reads per cell, and the percentage of reads mapping to genes coding for respectively heat-shock proteins (Hsp), and ribosomal proteins (Rpl) per cell. (d) UMAP embedding colored by expression of selected marker genes (Clu, Anxa1, Spink4, ChgB, ChgA, Agr2, Clca1, and Fcgbp). (e) UMAP embedding for each of the three independent experimental batches colored by cluster annotation.

Extended Data Fig. 4 Batch effect assessment on organoids by split organoid experiment.

(a) UMAP embedding of cells collected from nine additional individual organoids (under maintenance conditions) for the purpose of evaluating batch effects. Left: All 748 processed cells clustered with k-means clustering, after which clusters were annotated according to marker gene expression. Right: Expression dot plot of selected marker genes. (b) Projection of cells (colored by cell type) derived from one organoid that was split into two independent samples (split organoid) on the reference UMAP shown in a). Organoid ‘S2_2’ was split into two batches, which were processed subsequently, with a one-hour delay, during which the second batch was stored at 4 °C.

Extended Data Fig. 5 Marker genes and YAP1 target gene activity of intestinal organoid cells.

(a) Heatmap of top DE genes per annotated cluster. (b) YAP1 target gene activity on a UMAP embedding. The expression of genes that are positively regulated by YAP127 was calculated as the cumulative Z-score and projected on the UMAP embedding of all sequenced cells.

Extended Data Fig. 6 Control images of DisCo- and RNAscope-processed organoids.

(a) Selected organoids from Fig. 4, imaged in microwell plates before dissociation to single cells. Scale bar: 50 μm. (b) RNAscope controls for organoids shown in Fig. 4b,f. Positive control (PpiB), and negative control (Duplex negative). Scale bar: 50 μm.

Extended Data Fig. 7 Marker gene expression per individual organoid.

(a) Violin plots showing marker gene expression (Fabp1, Muc2, Sox9, Olfm4, Reg3b, Ly6a) per organoid. (b) Violin plots showing the expression of selected genes (Defa24, Gip, Vnn1, Zg16) identified via psupertime analysis per individual organoid.

Extended Data Fig. 8 DisCo performance on individual intestinal crypts and data analysis.

(a) Processing efficiency of DisCo for individual and bulk intestinal crypts. All cells processed with DisCo were manually counted during the experiment, and compared to cell numbers after quality filtering (>500 UMIs). The red line represents 100% efficiency, and samples are colored according to sample type. (b) Expression dot plot of marker genes for clusters shown in Fig. 5a. (c) Gene activity represented as the cumulative Z-score and projected on the UMAP embedding of all sequenced cells using the expression of Top: Paneth cell-associated genes encompassing Lyz1, Defa17, Defa24 and Ang4 and Bottom: genes that are positively regulated by YAP127. (d) Projection of cell types onto the reference UMAP of cells derived from the 21 individual crypts. Cells per single crypt were colored according to their global clustering and highlighted on the UMAP embedding of all sequenced cells. Enterocytes (Entero), PIC (Potential intermediate cells), RegStem, (Regenerative Stem), TA (Transit amplifying cells; G1: G1/S and G2: G2/M cell cycle phase).

Extended Data Fig. 9 Analysis of combined intestinal organoid and crypt data.

(a) Combined UMAP embedding (as shown in Fig. 5d) stratified by the five individual batches of intestinal crypt samples and the three independent experimental batches of intestinal organoid differentiation samples, collectively embedded and colored by cluster annotation. (b) UMAP-based visualization of the expression of specific markers that were used for cluster annotation. (c) Bar graph depicting the cumulative Z-score of the expression of genes that are indicated within the respective bar graph. CanStem: canonical stem cell, RegStem: regenerative stem cell.

Extended Data Fig. 10 Individual organoids and crypts mapped onto the denominator UMAP.

Projection of cell types onto the reference UMAP of the ex vivo cell preparation for the 21 individual intestinal crypts and bulk samples embedded together with the 31 individual intestinal organoids. Cells per single crypt or organoid are colored according to their global clustering and highlighted on the UMAP embedding of all sequenced cells.

Supplementary information

Supplementary Information

Supplementary Fig. 1

Reporting Summary

Supplementary Video 1

Video showing on-demand droplet production with varying dropleting valve pressures.

Supplementary Video 2

Annotated video showing the operational DisCo system.

Supplementary Data 1

CAD file of the microfluidic chip control layer and CAD file of the microfluidic chip flow layer.

Supplementary Software 1

R-script for cell barcode merging and R-script used for scRNA-seq data analysis.

Supplementary Tables 1–3

Supplementary Table 1: Cellular yield per intestinal organoid and intestinal crypt. Supplementary Table 2: DE genes for cell clusters (as shown in Fig. 2B). Supplementary Table 3: Buffer-dependent-dissociation efficiencies for intestinal crypts.

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Bues, J., Biočanin, M., Pezoldt, J. et al. Deterministic scRNA-seq captures variation in intestinal crypt and organoid composition. Nat Methods 19, 323–330 (2022). https://doi.org/10.1038/s41592-021-01391-1

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