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. 2020 Dec 10;183(6):1665-1681.e18.
doi: 10.1016/j.cell.2020.10.026. Epub 2020 Nov 13.

High-Spatial-Resolution Multi-Omics Sequencing via Deterministic Barcoding in Tissue

Yang Liu  1 , Mingyu Yang  1 , Yanxiang Deng  1 , Graham Su  1 , Archibald Enninful  2 , Cindy C Guo  2 , Toma Tebaldi  3 , Di Zhang  2 , Dongjoo Kim  2 , Zhiliang Bai  2 , Eileen Norris  2 , Alisia Pan  2 , Jiatong Li  2 , Yang Xiao  2 , Stephanie Halene  3 , Rong Fan  4
Affiliations

High-Spatial-Resolution Multi-Omics Sequencing via Deterministic Barcoding in Tissue

Yang Liu et al. Cell. .

Abstract

We present deterministic barcoding in tissue for spatial omics sequencing (DBiT-seq) for co-mapping of mRNAs and proteins in a formaldehyde-fixed tissue slide via next-generation sequencing (NGS). Parallel microfluidic channels were used to deliver DNA barcodes to the surface of a tissue slide, and crossflow of two sets of barcodes, A1-50 and B1-50, followed by ligation in situ, yielded a 2D mosaic of tissue pixels, each containing a unique full barcode AB. Application to mouse embryos revealed major tissue types in early organogenesis as well as fine features like microvasculature in a brain and pigmented epithelium in an eye field. Gene expression profiles in 10-μm pixels conformed into the clusters of single-cell transcriptomes, allowing for rapid identification of cell types and spatial distributions. DBiT-seq can be adopted by researchers with no experience in microfluidics and may find applications in a range of fields including developmental biology, cancer biology, neuroscience, and clinical pathology.

Keywords: high spatial resolution; in situ barcoding; mouse embryo; next-generation sequencing; spatial multi-omics.

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Conflict of interest statement

Declaration of Interests R.F., Y.L., and Y.D. are inventors of a patent application related to this work. R.F. is a co-founder of IsoPlexis, Singleron Biotechnologies, and AtlasXomics and a member of their scientific advisory boards with financial interests, which could affect or have the perception of affecting the author’s objectivity. The interests of R.F. were reviewed and managed by Yale University Provost’s Office in accordance with the University’s conflict of interest policies.

Figures

Figure 1.
Figure 1.. Design and validation of DBiT-seq
(A) Schematic workflow. A formaldehyde-fixed tissue slide is used as the starting material, which is incubated with a cocktail of antibody-derived DNA tags (ADTs) that recognize a panel of proteins of interest. A custom-designed PDMS microfluidic device with 50 parallel microchannels in the center of the chip is aligned and placed on the tissue slide to introduce the 1st set of barcodes A1 to A50. Each barcode is tethered with a ligation linker and an oligo-dT sequence for binding the poly-A tail of mRNAs or ADTs. Then, reverse transcription (RT) is conducted in situ to yield cDNAs which are covalently linked to barcodes A1–A50. Afterwards, this microfluidic chip is removed and another microfluidic chip with 50 parallel microchannels perpendicular to those in the first microfluidic chip is placed on the tissue slide to introduce the 2nd set of DNA barcodes B1–B50. These barcodes contain a ligation linker, a unique molecular identifier (UMI) and a PCR handle. After introducing barcodes B1–B50 and a universal complementary ligation linker through the second microfluidic chip, the barcodes A and B are joined through ligation and then the intersection region of microfluidic channels in the first and second PDMS chips defines a distinct pixel with a unique combination of A and B, giving rise to a 2D array of spatial barcodes AiBj (i=1–50, j=1–50). Afterwards, the second PDMS chip is removed and the tissue remains intact while spatially barcoded for all mRNAs and the proteins of interest. The barcoded tissue is imaged under an optical or fluorescence microscope to visualize individual pixels. Finally, cDNAs are extracted from the tissue slide, template switched to incorporate another PCR handle, and amplified by PCR for preparation of sequencing library via tagmentation. A paired-end sequencing is performed to read the spatial barcodes (AiBj) and cDNA sequences from mRNAs and ADTs. Computational reconstruction of a spatial mRNA or protein expression map is realized by matching the spatial barcodes AiBj to the corresponding cDNA reads using UMIs. The spatial omics map can be correlated to the tissue image taken during or after microfluidic barcoding to identify the spatial location of individual pixels and the corresponding tissue morphology. (B) Microfluidic device used in DBiT-seq. A series of microfluidic chips were fabricated with 50 parallel microfluidic channels in the center that are 50μm, 25μm, or 10μm in width, respectively. The PDMS chip containing 50 parallel channels is placed directly on a tissue slide and the center region is clamped using two acrylic plates and screws to apply the pressing force in a controlled manner. All 50 inlets are open holes (~2mm in diameter) capable of holding ~13μL of solution. Different barcode reagents are pipetted to these inlets and drawn into the microchannels by vacuum applied to the roof cap of the outlets situated on the other side of the PDMS chip. (C) Validation of spatial barcoding using fluorescent DNA probes. The images show parallel lines of Cy3-labelled barcode A (red, left panel) on the tissue slide defined by the first flow, the square pixels of FITC-labeled barcode B (green, right panel) corresponding to the intersection of the first and the second flows, and the overlay of both fluorescence colors (middle). Because barcode B is ligated to the immobilized barcode A in an orthogonal direction, it is detectable only at the intersection of the first set (A1–A50) and the second set (B1–B50) of microchannels. Channel width = 50μm. (D) Validation of leak-free flow barcoding using a layer of cells cultured on a glass slide. HUVECs grown on a glass slide were stained by DAPI (blue) during the 1st flow and anti-human VE-cadherin (red) during the 2nd flow. As shown in the enlarged figures, fluorescence staining was confined within the channels. Scale bar = 20 μm. (E) Confocal microscopy image of a tissue slide stained with fluorescent DNA barcode A. The 3D stacked image shows no leakage between adjacent channels throughput the tissue thickness. Scale bar =20 μm. (F) Validation of spatial barcoding for 10μm pixels. A tissue slide was subjected to spatial barcoding and the resultant pixels were visualized by optical (upper left) and fluorescent imaging (upper right) of the same tissue sample using FITC-labeled barcode B. Pressing microfluidic channels against the tissue section resulted in a slight deformation of the tissue matrix, which allowed for directly visualizing the topography of individual tissue pixels. Enlarged views (low panels) further show discrete barcoded tissue pixels with 10μm pixel size. (G) Qualification of the cross-channel diffusion distance, the measured size of pixels, and the number of cells per pixel. Quantitative analysis of the line profile revealed the diffusion of DNA oligomers through the dense tissue matrix is as small as 0.9μm, which was obtained with the 10μm-wide microchannels with the application of an acrylic clamp. The measured pixel size agreed with the microchannel size. Using DAPI, a fluorescent dye for nuclear DNA staining, the number of cells in a pixel can be identified. The average cell number is 1.7 in a 10μm pixel and 25.1 in a 50μm pixel. (H) Gene and UMI count distribution. DBiT-seq is compared to Slide-seq, ST, and the commercialized ST (Visium) with different spot/pixel sizes. Formaldehyde-fixed mouse embryo tissue slides were used in DBiT-seq. Fresh frozen mouse brain tissues were used in Slide-seq, ST, and Visium. See also Figure S1 and S2.
Figure 2.
Figure 2.. Spatial multi-omics mapping of whole mouse embryos
(A) Pan-mRNA and pan-protein-panel spatial expression maps (pixel size 50μm) reconstructed from DBiT-seq, alongside the H&E image from an adjacent tissue section. Whole transcriptome pan-mRNA map correlated with anatomic tissue morphology and density. (B) Comparison to “pseudo bulk” RNA-seq data. Four embryo samples (E10) analyzed by DBiT-seq correctly situated in the UMAP in relation to those analyzed by single-cell RNA-seq (Cao et al., 2019) in terms of the developmental stage. (C) Unsupervised clustering analysis and spatial pattern. Left: UMAP showing the clusters of tissue pixel transcriptomes. Middle: spatial distribution of the clusters. Right: overlay of spatial cluster map and tissue image(H&E). Because the H&E staining was conducted on an adjacent tissue section, minor differences were anticipated. (D) Gene Ontology (GO) analysis of all 11 clusters. Selected GO terms are highlighted. (E) Anatomic annotation of major tissue regions based on the H&E image. (F) Correlation between mRNAs and proteins in each of the anatomically annotated tissue regions. The average expression levels of individual mRNAs and cognate proteins are compared. See also Figure S3.
Figure 3.
Figure 3.. Spatial multi-omics mapping of an embryonic mouse brain.
(A) Bright field optical image of the brain region of a mouse embryo (E10). (B) H&E image of the mouse embryo brain region (E10). It was obtained on an adjacent tissue section. (C) Pan-mRNA and pan-protein-panel spatial expression maps of the brain region of a mouse embryo (E10) obtained with 25μm pixel size. The spatial pattern of whole transcriptome (pan-mRNA) correlated with cell density and morphology in the tissue. (D) Spatial expression of four individual proteins: CD63, Pan-endothelial cell antigen (PECA), EpCAM (CD326) and MAdCAM-1. Spatial protein expression heatmaps revealed brain tissue region-specific expression and the brain microvascular network. (E) Validation by immunofluorescence staining. Spatial expression of EpCAM and PECA reconstructed from DBiT-seq and the immunofluorescence image of the same proteins were superimposed onto the H&E image for comparison. A highly localized expression pattern of EpCAM is in strong correlation with immunostaining as seen by the line profile. The network of microvasculature revealed by PECA in DBiT-seq is correlated with the immunostaining image. (F) Gene expression heatmap of 11 clusters obtained by unsupervised clustering analysis. Top ranked differentially expressed genes are shown in each cluster. (G) Spatial map of clusters 1, 2, 5 and 9. GO analysis identified the major biological processes within each cluster, in agreement with anatomical annotation. See also Figure S4.
Figure 4.
Figure 4.. Spatial gene expression mapping of early eye development.
(A) Bright field image of a whole mouse embryo tissue section (E10). Red indicates pan-mRNA signal in a region of interest (ROI) analyzed by DBiT-seq (10 μm pixel size). Scale bar (left panel) 500 μm. Scale bar (right panel) 200 μm. (B) H&E staining performed on an adjacent tissue section. Scale bar = 200 μm. (C) Overlay of spatial expression maps for selected genes. It revealed spatial correlation of different genes with high accuracy. For example, Pax6 is expressed in whole optic vesicle including a single-cell-layer of melanocytes marked by Pmel and the optical nerve fiber bundle on the left. Six6 is expressed within the optic vesicle but does not overlap significantly with the melanocyte layer although they are in proximity. Scale bar = 100 μm. (D) Pmel, Pax6 and Six6 spatial expression superimposed onto the darkfield tissue images of the mouse embryo samples E10 and E11 (pixel size 10μm). These genes are implicated in early stage embryonic eye development. Pmel was detected in a layer of melanocytes lining the optical vesicle. Pax6 and Six6 were mainly detected inside the optical vesicle but also seen in other regions mapped in this data. (E) Spatial expression of Aldh1a1 and Aldh1a3. Aldh1a1 is expressed in dorsal retina of early embryo, and meanwhile, Adlh1a3 is mainly expressed in retinal pigmented epithelium and in ventral retina. (F) Spatial expression of Msx1. It is mainly enriched in the ciliary body of an eye, including the ciliary muscle and the ciliary epithelium, which produces the aqueous humor. (G) Spatial expression of Gata3. It is essential for lens development and mainly expressed in posterior lens fiber cells during embryogenesis. (H) Integration of scRNA-seq (Cao et al., 2019) and DBiT-seq data (10 μm pixel size). The combined data were analyzed with unsupervised clustering and visualized with different colors for different samples. It revealed that DBiT-seq pixels conformed into the clusters of scRNA-seq data. (I) Clustering analysis of the combined dataset (scRNA-seq and DBiT-seq) revealed 25 major clusters. (J) Spatial pattern of select clusters (0, 2, 4, 6, 7, 8, 14, 19, 20, 22) identified in UMAP (I). (K) Cell types (different colors) identified by scRNA-seq and comparison with DBiT-seq pixels (black). (L), (M) & (N) Spatial expression pattern of DBiT-seq pixels from select clusters (I) in relation to cell types identified(K). See also Figure S5.
Figure 5.
Figure 5.. Global clustering analysis of 11 mouse embryos from E10, E11 to E12.
(A) tSNE plot showing the clustering analysis of DBiT-seq data from all 11 mouse embryo tissue samples. (B) tSNE plot color-coded for different mouse embryo tissue samples. (C) Heatmap of differentially expressed genes in 20 clusters and GO analysis. Select GO terms and top ranked genes are shown for the clusters implicated in muscle system, pigment metabolic system, blood vessel development, neuron development and telencephalon development. (D) UMAP plot showing the cluster analysis result, color-coded for different samples (left) or the developmental stages (right). See Table S4.
Figure 6.
Figure 6.. Mapping internal organs in a E11 mouse embryo.
(A) Enlarged view of UMAP clustering of Figure 5D with a specific focus on the E11 embryo lower body sample. (B) Spatial expression of four select clusters indicated in Figure 6A. (C) UMAP showing the clustering analysis of the E11 embryo lower body sample only. The tissue pixels from four major clusters shown in Figure 6A&B are circled in this UMAP with more sub-clusters identified. (D) Spatial map of all the clusters shown in (C). (E) Cell type annotation (SingleR) using scRNA-seq reference data from E10.5 mouse embryo (Cao et al., 2019). (F) Spatial expression maps of individual genes. (G) Tissue types identified for clusters a, b, c, and d indicated in (A) overlaid onto the tissue image. Major organs such as heart (atrium and ventricle), liver and neutral tube were identified, in agreement with the tissue anatomy. Erythrocyte coagulation was detected by DBiT-seq, for example, within the dorsal aorta and the atrial chamber. Scale bar = 250 μm. See also Figure S6.
Figure 7.
Figure 7.. SpatialDE for automated feature identification
(A) Major features identified in a E10 mouse embryo sample (see Figure 4). It revealed several additional tissue types in addition to eye. Pixel size = 10μm. Scale bar = 200 μm. (B) Major features identified in the lower body of a E11 mouse embryo tissue sample (see Figure 6), which showed a variety of tissue types developed in E11. Pixel size = 25μm. Scale bar = 500 μm. (C) Major features identified in the lower body of a E12 mouse embryo sample (see Table S4), which showed more tissue types and developing organs at this embryonic age (E12). Pixel size = 50μm. Scale bar = 1 mm. See also Figure S7.
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