Deterministic barcoding for spatial omics sequencing

Abstract

In some embodiments, provided herein are compositions and methods for generating molecular expression profiles of biological samples using deterministic barcoding within tissues (DBiT-seq) for spatial omics sequencing.

Description

[0001] Related applications

[0002] This application claims priority under 35 U.S.C. §119(e) to U.S. Provisional Application No. 62 / 908,270, filed on September 30, 2019, which is incorporated herein by reference in its entirety. Background Art

[0003] Spatial gene expression heterogeneity plays crucial roles in a range of biological, physiological, and pathological processes, but performing high spatial resolution, genome-wide, unbiased biomolecular profiling across large areas of tissue remains a scientific challenge. SUMMARY OF THE INVENTION

[0005] The present disclosure provides a platform technology, referred to herein as Deterministic Barcoding in Tissue for spatialomics sequencing (DBiT-seq). As described herein, this high-spatial resolution (HSR) technology can be used to generate multi-omics maps in intact tissue sections, with at least the following advantages over existing technologies: (1) high spatial resolution; (2) high-throughput cell analysis capabilities; and (3) true omics sensitivity. The present invention demonstrates how to design microfluidic-based detection systems that meet each of the above criteria by utilizing microfluidic chips (e.g., as polynucleotide reagent delivery systems). In this model, downstream spatial reconstruction is achieved by confining reagents labeled with different polynucleotide barcodes to specific spatial regions of the tissue to be mapped. The spatial resolution achieved using the devices and methods provided herein is sufficient to distinguish the contribution of single cells (e.g., mammalian cells between 5-20 μm in size) to the analyte map (target biomolecules in the region of interest). Furthermore, the high-throughput HSR technology provided herein matches the analytical capabilities of non-spatial techniques, which typically analyze tens of thousands of cells per run. This technology is applicable to sliced ​​tissues and can be used to map large areas per run, so that multiple cells can be mapped per run. Furthermore, the HSR technology disclosed herein can be used to target entire classes of coding RNA molecules, such as messenger RNA (mRNA), rather than just targeted groups of RNA molecules, which is particularly useful for generating transcriptome maps.

[0006] In some aspects, parallel microfluidic channels (10 μm, 25 μm, or 50 μm in width) are used to deliver molecular barcodes to the surface of fixed (e.g., formaldehyde- or formalin-fixed) tissue sections in a spatially confined manner. Two sets of barcodes, A1-A50 and B1-B50, cross-flow and then connect in situ to produce a two-dimensional mosaic of tissue pixels, each containing a unique combination of complete barcodes AiBj (i = 1-50, j = 1-50). This allows for simultaneous barcoding of mRNA, proteins, and even other omics on fixed tissue sections, enabling the construction of high-spatial-resolution multi-omics maps using next-generation sequencing (NGS). Application to mouse embryonic tissue revealed all major tissue types of early organogenesis, distinguished brain microvascular networks, discovered novel developmental patterns in the forebrain, and demonstrated the ability to detect a single layer of melanocytes arranged along the optic vesicle, where asymmetric expression of RORB and ALDH1A1 may be implicated in the pathogenesis of the retina and lens, respectively. Dozens more developmental features were further identified using automated feature recognition based on spatial differential expression. DBiT-seq is a highly versatile technology that has the potential to become a universal method for spatially barcoding and sequencing a range of molecular information at high resolution and genome-wide. It can be easily adopted by biologists without experience in microfluidics or advanced imaging and could spread rapidly, having a broader impact in fields as diverse as developmental biology, cancer biology, neuroscience, and clinical pathology.

[0007] Some aspects of the present disclosure provide a method comprising: (a) delivering a first set of barcoded polynucleotides bound to nucleic acids from a fixed tissue section to a region of interest in a fixed section of mammalian tissue mounted on a substrate, wherein the first set of barcoded polynucleotides is delivered by a first microfluidic device clamped at the region of interest, wherein the first microfluidic device comprises 5-50 variable-width microchannels, each microchannel having (i) an inlet end and an outlet end, (ii) a width of 50-150 μm at the inlet end and the outlet end, and (iii) a width of 10-50 μm at the region of interest; (b) delivering a reverse transcription reagent to the region of interest to generate cDNA linked to the first set of barcoded polynucleotides; and (c) delivering a second set of barcoded polynucleotides to the region of interest, wherein the second set of barcoded polynucleotides is clamped at the region of interest. The invention relates to a method for delivering a first set of barcode polynucleotides to a second microfluidic device, wherein the second microfluidic device comprises 5-50 variable width microchannels, each microchannel having (i) an inlet end and an outlet end, (ii) a width of 50-150 μm at the inlet end and the outlet end, and (iii) a width of 10-50 μm at the region of interest, wherein the second microfluidic device is located on the region of interest perpendicular to the direction of the microchannels of the first microfluidic device; (d) delivering a linking reagent to the region of interest to link the first set of barcode polynucleotides to the second set of barcode polynucleotides; (e) imaging the region of interest to generate a sample image; (f) delivering a lysis buffer or a denaturing reagent to the region of interest to generate a lysed or denatured tissue sample; and (g) extracting cDNA from the lysed or denatured tissue sample.

[0008] Other aspects of the present disclosure provide a method comprising: (a) delivering a binder DNA tag conjugate to a region of interest in a fixed section of mammalian tissue mounted on a substrate, wherein the binder DNA tag conjugate comprises (i) a binder molecule that specifically binds to a protein of interest, and (ii) a DNA tag, wherein the DNA tag comprises a binder barcode and a polyadenylation (polyA) sequence; (b) delivering a first set of barcode polynucleotides bound to nucleic acids in the fixed tissue section to the region of interest, wherein the first set of barcode polynucleotides is delivered via a first microfluidic device clamped at the region of interest, optionally wherein the first microfluidic device comprises 5-50 variable width microchannels, each microchannel having (i) an inlet end and an outlet end, (ii) a width of 50-150 μm at the inlet end and the outlet end, and (iii) a width of 10-50 μm at the region of interest; (c) delivering a reverse transcription reagent to the region of interest to produce (d) delivering a second set of barcode polynucleotides to the region of interest, wherein the second set of barcode polynucleotides is delivered by a second microfluidic device clamped at the region of interest, optionally wherein the second microfluidic device comprises 5-50 variable width microchannels, each microchannel having (i) an inlet end and an outlet end, (ii) a width of 50-150 μm at the inlet end and the outlet end, and (iii) a width of 10-50 μm at the region of interest, wherein the second microfluidic device is positioned over the region of interest perpendicular to the direction of the microchannels of the first microfluidic device; (e) delivering a ligation reagent to the region of interest to ligate the first set of barcode polynucleotides to the second set of barcode polynucleotides; (f) imaging the region of interest to generate a sample image; (g) delivering a lysis buffer or a denaturing reagent to the region of interest to generate a lysed or denatured tissue sample; and (h) extracting cDNA from the lysed or denatured tissue sample.

[0009] In some embodiments, the method further comprises sequencing the cDNA to generate cDNA reads.

[0010] In some embodiments, the sequencing comprises template switching the cDNA to add a second PCR handle end sequence at the opposite end from the first PCR handle end sequence, amplifying the cDNA (e.g., polymerase chain reaction (PCR)), generating a sequencing construct by tagging (an initial step in library preparation in which unfragmented DNA is cut and tagged for analysis), and sequencing the sequencing construct (e.g., by next-generation sequencing (NGS)) to generate cDNA reads.

[0011] In some embodiments, the method further comprises constructing a spatial molecular expression map of the tissue section by matching spatially addressable barcode conjugates to corresponding cDNA reads.

[0012] In some embodiments, the method further comprises identifying the anatomical location of the nucleic acid by correlating the spatial molecular expression map with the sample image.

[0013] In some embodiments, the fixed tissue sections mounted on slides are produced by: slicing formalin-fixed paraffin-embedded (FFPE) tissue, optionally dividing it into 5-10 μm slices, and mounting the tissue sections on a substrate, optionally a poly-L-lysine-coated slide; applying a washing solution (optionally a xylene solution) to the tissue sections to dewax the tissue sections; applying a rehydration solution to the tissue sections to rehydrate the tissue sections; applying an enzyme solution (optionally a proteinase K solution) to the tissue sections to permeabilize the tissue sections; and applying formalin to the tissue sections to post-fix the tissue sections.

[0014] In some embodiments, the first and / or second microfluidic devices are made of polydimethylsiloxane (PDMS).

[0015] In some embodiments, the first and / or second microfluidic devices comprise 40 to 60, optionally 50, microchannels.

[0016] In some embodiments, each microchannel of the first and second microfluidic devices has a width of 10 μm and a height of 12-15 μm, a width of 25 μm and a height of 17-22 μm, or a width of 50 μm and a height of 20-100 μm.

[0017] In some embodiments, the delivery of the first set of barcode polynucleotides is performed through the first microfluidic device using a negative pressure system, and / or the delivery of the second set of barcode polynucleotides is performed through the second microfluidic device using a negative pressure system.

[0018] In some embodiments, the lysis buffer or denaturing reagent is delivered directly to the tissue section, optionally through an aperture in a device clamped to the substrate, wherein the aperture is directly over the region of interest.

[0019] In some embodiments, the first set of barcode polynucleotides comprises a linker sequence, a spatial barcode sequence, and a polyT sequence (eg, ˜1-100, such as 25, 50, 75, 100 consecutive thymine (T) nucleotides).

[0020] In some embodiments, the second set of barcode polynucleotides comprises a linker sequence, a spatial barcode sequence, a unique molecular identifier (UMI) sequence, and a first PCR handle end sequence, optionally wherein the first PCR handle end sequence is end-functionalized with biotin.

[0021] In some embodiments, the first and / or second set of barcode polynucleotides comprises at least 50 barcode polynucleotides.

[0022] In some embodiments, the binding agent molecule is an antibody, optionally selected from a whole antibody, a Fab antibody fragment, a F(ab')2 antibody fragment, a monospecific Fab2 fragment, a bispecific Fab2 fragment, a trispecific Fab3 fragment, a single chain variable fragment (scFvs), a bispecific diabody, a trispecific diabody, a scFv-Fc molecule and a minibody.

[0023] In some embodiments, the nucleic acid of the biological sample is selected from: (i) ribonucleic acids (RNAs), optionally messenger RNAs (mRNAs), and (ii) deoxyribonucleic acids (DNAs), optionally genomic DNAs (gDNAs).

[0024] In some embodiments, (i) the second set of barcode polynucleotides are conjugated to a universal linker, or (ii) the method further comprises delivering a universal linker sequence to the biological sample, wherein the universal linker comprises a sequence complementary to a linker sequence of the first set of barcode polynucleotides and comprises a sequence complementary to a linker sequence of the second set of barcode polynucleotides.

[0025] In some embodiments, the imaging is performed using light or fluorescence microscopy.

[0026] In some embodiments, the substrate is a microscope slide, optionally a glass microscope slide, optionally polyamine coated, and optionally having dimensions of 25 mm x 75 mm.

[0027] Liu, Y., Yang, M., Deng, Y., Su, G., Guo, C.C., Zhang, D., Kim, D., Bai, Z., Xiao, Y. & Fan, R., “High-Spatial-Resolution Multi-Omics Atlas Sequencing of Mouse Embryos via Deterministic Barcoding in Tissue,” bioRxiv, 788992 (biorxiv.org / content / 10.1101 / 788992v2) (August 3, 2019), is hereby incorporated by reference in its entirety.

[0028] BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 .Spatial parameters of microfluidics-based spatial imaging detectors.

[0030] Figure 2 A graph depicting device performance versus channel width. This graph depicts the tradeoff between spatial resolution and mappable area in a microfluidic detector compared to a biological benchmark. It is assumed that the tissue has been mounted on a standard 25 mm x 75 mm microscope slide, as is standard practice in pathology, thus allowing space for approximately 50 inlets, outlets, and associated channel wiring areas.

[0031] Figure 3 Example schedule for dynamically varying microchannel width. Dynamically varying the microchannel width in a 10 μm device can reduce the incidence of clogging due to dust and lower the overall device flow resistance per unit length (estimated by the resistance proportional to 12 / (1-0.63hω)(1 / h^3ω)). Significantly increasing the channel cross section reduces flow resistance, enabling gentle vacuum pull, thereby reducing the chance of tissue damage or channel clogging. Other schedules are possible, following the general principle that the channel should remain as wide as possible for as long as possible.

[0032] Figure 4 Three design innovations significantly improve device performance and reduce failure rates.

[0033] Figures 5A-5C. Design of the DBiT-seq platform. (Figure 5A) Schematic diagram of the workflow. Formaldehyde-fixed tissue sections are used as starting material and incubated with a mixture of antibody-derived DNA tags (ADTs) that recognize a set 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 section to introduce the first set of barcodes A1 to A50. Each barcode is connected to a linker and an oligodeoxythymidylate (oligo-dT) sequence for binding to the polyadenine (poly-A) tail of mRNA or ADT. Reverse transcription (RT) is then performed in situ to generate cDNA covalently linked to barcodes A1-A50. Afterwards, the microfluidic chip is removed and another microfluidic chip with 50 parallel microchannels perpendicular to the microchannels in the first microfluidic chip is placed on the tissue section to introduce the second set of DNA barcodes B1-B50. These barcodes contain a linker, a unique molecular identifier (UMI), and a PCR handle. After the barcodes B1-B50 and a universal complementary linker are introduced via a second microfluidic chip, barcodes A and B are linked together via the linker. The intersection of the microfluidic channels in the first and second PDMS chips then defines distinct pixels with unique combinations of A and B, thereby generating a two-dimensional array of spatial barcodes AiBj (i = 1-50, j = 1-50). The second PDMS chip is then removed, leaving the tissue intact, while all mRNAs and proteins of interest are spatially barcoded. The barcoded tissue is imaged under an optical or fluorescence microscope to visualize individual pixels. Finally, cDNA is extracted from the tissue sections, the template is converted to incorporate another PCR handle, and it is amplified by PCR to prepare a sequencing library by tagging. Paired-end sequencing is performed to read the spatial barcodes (AiBj) and cDNA sequences from the mRNA and ADT. Computational reconstruction of spatial mRNA or protein expression maps is achieved by matching the spatial barcodes AiBj to the corresponding cDNA reads using the UMI. Spatial omics maps can be correlated with tissue images taken during or after microfluidic barcoding to identify the spatial location of individual pixels and the corresponding tissue morphology. (Figure 5B) Schematic diagram of the biochemical protocol for adding spatial barcodes to tissue sections. Proteins of interest are labeled with antibody DNA tags (ADTs), each consisting of a unique antibody barcode (15-mer, see Table 1) and a polyadenine (poly-A) tail. Barcodes A1-A50 contain a linker (15-mer), a unique spatial barcode Ai (i = 1-50, 8-mer, see Table 3), and a poly-T sequence (16-mer), which detects mRNA and protein by binding to the polyadenine (poly-A) tail.After the barcodes A1-A50 are introduced into the tissue sections, reverse transcription is performed in situ to generate cDNA from the mRNA and antibody barcodes. The barcodes B1-B50 consist of a linker (15-mer), a unique spatial barcode Bj (j = 1-50, 8-mer, see Table 3), a unique molecular identifier (UMI) (10-mer), and a PCR handle (22-mer) functionalized with a biotin end, which facilitates purification using streptavidin-coated magnetic beads in subsequent steps. When orthogonal microfluidics delivery is used to introduce the barcodes B1-B50 into the tissue samples that have been barcoded by A1-A50, a complementary linker is also introduced and the covalent connection of the barcodes A and B is initiated, thereby generating a two-dimensional array of spatially different barcodes AiBj (i = 1-50 and j = 1-50). (. Figure 5C ) Detailed microfluidic device design (left) and barcode chemistry scheme (right). Left: Fresh frozen tissue sections are first allowed to warm to room temperature for 10 minutes. Then, 4% formaldehyde is added and the tissue is fixed at room temperature for 20 minutes. After fixation, a mixture of 22 antibody-DNA tags (ADTs) is added and incubated at 4°C for 30 minutes. After washing three times with PBS, the first PDMS chip is attached to a glass slide. Barcode A (A1-A50) flows through each channel along with the reverse transcription mixture. After reverse transcription, the first PDMS chip is removed and the second PDMS is attached. The ligation solution flows into each channel along with barcode B (B1-B50). Once completed, the second PDMS chip is removed and a PDMS gasket is attached to the glass slide. Lysis solution is added to the gasket and the lysate is collected. cDNA and ADT-derived cDNA are extracted using streptavidin-coated magnetic beads. Template switching and PCR are then performed. Finally, a sequencing library is constructed using standard markers. Right: DNA barcode A consists of a poly T region, a barcode region, and a linker region. The poly T region recognizes the poly A tail of mRNA and ADT. DNA barcode B consists of a linker region, a barcode region, a UMI region, and a PCR primer handle region. During the ligation process, the linker region is ligated to the linker region of barcode A. The cDNA product then undergoes template switching. The final product is further amplified by PCR.

[0034] Figure 6 Microfluidic device design for HSR. Top – Various failure modes resulting from incorrect channel aspect ratio selection. Bottom – Successful flow resulting from appropriate channel aspect ratio selection.

[0035] Figures 7A-7GValidation of DBiT. (Figure 7A) Microfluidic device used in DBiT-seq. A series of microfluidic chips were fabricated with 50 parallel microfluidic channels in the center, each with a width of 50 μm, 25 μm, or 10 μm. The PDMS chip containing 50 parallel channels was placed directly on a tissue slice, and the central area was clamped using two acrylic plates and screws to apply pressure in a controlled manner. All 50 inlets were open holes (diameter ∼2 mm) capable of accommodating ∼13 μL of solution. Different barcode reagents were pipetted into these inlets and drawn into the microchannels by vacuum applied to the outlet cap located on the other side of the PDMS chip. (Figure 7B) Validation of spatial barcoding using fluorescent DNA probes. The figure shows parallel lines of Cy3-labeled barcode A (left panel) on a tissue slice defined by the first flow, square pixels of FITC-labeled barcode B (right panel) corresponding to the intersection of the first and second flows, and an overlay of the two fluorescent colors (center panel). Because barcode B is linked to fixed barcode A in an orthogonal direction, it can only be detected at the intersection of the first set (A1-A50) and the second set (B1-B50) of microchannels. Channel width = 50 μm. (Figure 7C) Validation of leak-free flow barcoding using monolayers of cells cultured on glass slides. Human umbilical vein endothelial cells (HUVECs) grown on glass slides were stained with 4',6-diamidino-2-phenylindole (DAPI) during the first flow and with anti-human VE-cadherin during the second flow. As shown in the magnified image, the fluorescent staining was confined to the channel. Scale bar = 20 μm. (Figure 7D) Confocal microscopy image of a tissue section stained with fluorescent DNA barcode A. The three-dimensional stacked image shows no leakage through the thickness of the tissue between adjacent channels. Scale bar = 20 μm. (Figure 7E) Spatial barcoding validation with 10 μm pixels. Tissue sections were spatially barcoded, and the resulting pixels were visualized using optical (top left) and fluorescence imaging (top right) of the same tissue sample using FITC-labeled barcode B. Pressing the microfluidic channel against the tissue section produces slight deformation of the tissue matrix, which allows direct visualization of the topography of individual tissue pixels. A magnified view (bottom) further shows discrete barcoded tissue pixels with a 10 μm pixel size. (Figure 7F) Determination of cross-channel diffusion distance, pixel measurement size, and number of cells per pixel. Quantitative analysis of line profiles revealed diffusion of DNA oligomers through the dense tissue matrix as small as 0.9 μm, achieved using a 10 μm-wide microchannel and the application of an acrylic clamp. The measured pixel size was consistent with the microchannel dimensions. Using DAPI, a fluorescent dye used to stain nuclear DNA, the number of cells in a pixel can be identified. The average number of cells for a 10 μm pixel was 1.7, and the average number of cells for a 50 μm pixel was 25.1. (Figure 7AG) Distribution of gene and UMI counts.DBiT-seq was compared with Slide-seq, ST, and a commercial ST (Visium) with varying dot / pixel sizes. Formaldehyde-fixed mouse embryonic tissue sections were used in DBiT-seq. Fresh-frozen mouse brain tissue was used in Slide-seq, ST, and Visium.

[0036] Figures 8A-8FSpatial multi-omics map of the whole mouse embryo. (Figure 8A) Spatial expression maps of pan-mRNA and pan-protein panels reconstructed from DBiT-seq (50 μm pixel size) and H&E images of adjacent tissue sections. The whole-transcriptome pan-mRNA map correlates with anatomical tissue morphology and density. (Figure 8B) Comparison with "pseudo-bulk" RNA-seq data. Four embryonic samples (E10) analyzed by DBiT-seq and embryonic samples analyzed by single-cell RNA-seq (Cao et al., 2019) are correctly localized in the UMAP according to developmental stage. (Figure 8C) Unsupervised clustering analysis and spatial patterns. Left: UMAP showing clustering of the transcriptome at each tissue pixel. Middle: Spatial distribution of clusters. Right: Overlay of the spatial cluster map and tissue image (H&E). Because H&E staining was performed on adjacent tissue sections, minor differences are expected. (Figure 8D) Gene Ontology (GO) analysis of all 11 clusters. Selected GO terms are highlighted. (Figure 8E) Anatomical annotation of major tissue regions based on H&E images. (Figure 8F) Correlation between mRNA and protein in each anatomically annotated tissue region. The average expression levels of individual mRNAs and homologous proteins were compared. (Figure 8G) Spatial expression of four individual proteins and homologous mRNA transcripts in whole mouse embryos. These are Notch1 (Notch1), CD63 (Cd63), Pan-Endothelial-Cell Antigen (Plvap), and EpCAM (Epcam). Multi-omics DBiT-seq allows head-to-head comparison of the expression of a panel of proteins and homologous genes. It shows the consistency and discordance between selected pairs of mRNA and protein, but the spatial resolution is sufficient to resolve the fine structure in specific organs. (Figure 8H) Correlation between mRNA and protein in anatomically annotated tissue regions. The average expression levels of individual mRNAs and cognate proteins within each of 13 anatomically annotated tissue regions were compared (Figure 8I) with comparison to immunofluorescence tissue staining. Pan-endothelial antigen (PECA), which marks embryonic vascularization, is ubiquitously expressed at this stage (E.10), consistent with protein and mRNA expression revealed by DBiT-seq. EpCAM, an epithelial marker, is present but in a few highly localized regions, which were also identified by DBiT-seq (mRNA and protein). P2RY12, a marker for microglia in the CNS, delineates the spatial distribution of the nervous system.

[0037] Figures 9A-9GSpatial multi-omics mapping of the embryonic mouse brain. (Figure 9A) Brightfield optical image of a mouse embryonic (E10) brain region. (Figure 9B) Hematoxylin and eosin (H&E) image of a mouse embryonic (E10) brain region. This image was obtained on adjacent tissue sections. (Figure 9C) Spatial expression maps of pan-mRNA and pan-protein panels of a mouse embryonic (E10) brain region, obtained at a 25 μm pixel size. The spatial pattern of the whole transcriptome (pan-mRNA) correlates with cell density and morphology in the tissue. (Figure 9D) Spatial expression of four individual proteins: CD63, pan-endothelial cell antigen (PECA), EpCAM (CD326), and MAdCAM-1. Spatial protein expression heatmaps demonstrate brain tissue region-specific expression and the brain microvascular network. (Figure 9E) Immunofluorescence staining validation. Spatial expression of EpCAM and PECA reconstructed by DBiT-seq and immunofluorescence images of the same proteins were overlaid on the H&E images for comparison. As shown by the line profiles, the highly localized expression pattern of EpCAM correlated well with immunostaining. In DBiT-seq, the microvascular network revealed by EpCAM correlated with the immunostaining image. (Figure 9F) Gene expression heatmap of 11 clusters obtained by unsupervised cluster analysis. The top differentially expressed genes are shown within each cluster. (Figure 9G) Spatial mapping of clusters 1, 2, 5, and 9. GO analysis identified the main biological processes within each cluster, consistent with anatomical annotations.

[0038] Figures 10A-10N. Mapping gene expression in early eye development at single-cell layer resolution. (Figure 10A) Brightfield image of a whole mouse embryonic tissue section (E10). Red indicates pan-mRNA signal in the region of interest (ROI) analyzed by DBiT-seq (10μm pixel size). Scale bar (left panel) 500μm. Scale bar (right panel) 200μm. (Figure 10B) H&E staining performed on adjacent tissue sections. Scale bar = 200μm. (Figure 10C) Overlay of spatial expression maps of selected genes. It reveals the spatial correlation of different genes with high precision. For example, Pax6 is expressed throughout the optic vesicle, including the melanocyte monolayer marked by Pme1 and the optic nerve fiber bundle on the left. Six6 is expressed within the optic vesicle but does not significantly overlap with the melanocyte layer, despite their close proximity. Scale bar = 100μm. (Figure 10D) Spatial expression of Pmel, Pax6, and Six6 superimposed on darkfield tissue images of mouse embryo samples E10 and E11 (pixel size 10 μm). These genes are involved in early embryonic eye development. Pmel was detected in a single layer of melanocytes arranged along the optic vesicle. Pax6 and Six6 were detected primarily within the optic vesicle but were also found in other areas mapped in this data. (Figure 10E) Spatial expression of aldehyde dehydrogenase 1a1 (Aldh1a1) and aldehyde dehydrogenase 1a3 (Aldh1a3). ALDH1a1 is expressed in the dorsal retina of the early embryo, while ALDH1a3 is primarily expressed in the retinal pigment epithelium and ventral retina. (Figure 10F) Spatial expression of Msx1. It is primarily enriched in the ciliary body of the eye, including the ciliary muscle and ciliary epithelium that produce aqueous humor. (Figure 10G) Spatial expression of Gata3. It is essential for lens development and is primarily expressed in posterior lens fiber cells during embryogenesis. (Figure 10H) Integration of single-cell RNA sequencing (scRNA-seq) (Cao et al., 2019) and DBiT-seq data (10 μm pixel size). The data were combined using unsupervised cluster analysis and visualized using different colors for different samples. It reveals that DBiT-seq pixels conform to the clustering of the single-cell RNA sequencing data. (Figure 10I) Cluster analysis of the combined dataset (scRNA-seq and DBiT-seq) revealed 25 major clusters. (Figure 10J) Spatial patterns of selected clusters (0, 2, 4, 6, 7, 8, 14, 19, 20, 22) identified in UMAP (Figure 10I). (Figure 10K) Cell types identified by scRNA-seq (different colors) and comparison with DBiT-seq pixels (black). ( FIG. 10L ), ( FIG. 10M ), and ( FIG. 10N ) Spatial expression patterns of DBiT-seq pixels from selected clusters ( FIG. 10I ) are associated with identified cell types ( FIG. 10K ).

[0039] Figures 11A-11DGlobal cluster analysis of 11 mouse embryos from E10, E11, and E12. (Figure 11A) tSNE plot showing cluster analysis of DBiT-seq data for all 11 mouse embryonic tissue samples. (Figure 11B) Color-coded tSNE plots of different mouse embryonic tissue samples. (Figure 11C) Heatmap and GO analysis of differentially expressed genes in 20 clusters. Selected GO terms and top-ranked genes are shown for clusters related to the musculature, pigment metabolism system, vascular development, neuronal development, and telencephalon development. (Figure 11D) UMAP plot showing the cluster analysis results, with color coding for different samples (left) or developmental stages (right).

[0040] Figures 12A-12G Mapping internal organs in E11 mouse embryos. (Figure 12A) Zoomed-in UMAP cluster analysis, specifically focusing on the lower body sample of an E11 embryo. (Figure 12B) Spatial expression of the four selected clusters shown in Figure 12A. (Figure 12C) UMAP cluster analysis showing only the lower body sample of an E11 embryo. Figure 6 Tissue pixels for the four major clusters shown are circled in this UMAP, and additional subclusters were identified. (Figure 12D) Spatial map of all clusters shown in Figure 12C. (Figure 12E) Cell type annotation (SingleR) using scRNA-seq reference data from E10.5 mouse embryos (Cao et al., 2019). (Figure 12F) Spatial expression maps of individual genes. (Figure 12G) Tissue types for clusters a, b, c, and d shown in (A) overlaid on the tissue image. Identification of major organs such as the heart (atria and ventricles), liver, and neural tube is consistent with tissue anatomy. For example, red blood cell coagulation was detected by DBiT-seq in the dorsal aorta and atrial cavity. Scale bar = 250 μm.

[0041] Figures 13A-13C Spatial DE was used for automatic feature recognition. (Figure 13A) The main features identified in E10 mouse embryo samples (see Figure 4 In addition to the eye, several other tissue types were identified. Pixel size = 10 μm. Scale bar = 200 μm. (Figure 13B) Major features identified in the lower body of an E11 mouse embryonic tissue sample (see Figure 6 ), showing various tissue types developing in E11. Pixel size = 25 μm. Scale bar = 500 μm. (Figure 13C) Key features identified in the lower body of an E12 mouse embryo sample (see Table S4), showing more tissue types and developing organs at this embryonic age (E12). Pixel size = 50 μm. Scale bar = 1 mm.

[0042] Figures 14A-14H. DBiT-seq on fluorescent IHC-stained tissue samples. (Figure 14A) Fluorescent image of a pre-stained mouse embryonic tissue section. Stained with DAPI, phalloidin, and P2RY12. Scale bar = 200 μm. (Figure 14B) UMI count heatmap generated by DBiT-seq of the same tissue section pre-stained with fluorescent IHC. (Figure 14C) Brightfield image of the tissue sample before DBiT-seq. (Figure 14D) Overlay of the brightfield image and the UMI heatmap. (Figure 14E) Cell segmentation using ImageJ based on the fluorescence image. (Figure 14F) Overlay of the DBiT-seq pixel grid and the fluorescence image. (Figure 14G) Fluorescent image of a representative pixel. Pixels containing single cell nuclei can be easily identified. (Figure 14H) Gene expression patterns of representative pixels from (G).

[0043] Figure 15 .Single-cell deterministic barcoding. Figure 15 We describe the experimental steps for deterministic barcoding in cells (DBiC) to detect and ultimately sequence the transcriptome of single cells in a massively parallel and deterministic manner, meaning that each cell analyzed by sequencing has a known combinatorial barcode AiBj (i=1-50, j=1-50) and a known position on the substrate. Thus, other cellular features such as cell size, morphology, protein signaling, and migration can be imaged and directly correlated with the omics data obtained for the same single cell by sequencing. (1) Hydrodynamic capture of ~3000 single cells in a microfluidic chip. The cells are then fixed and permeabilized with 1% formaldehyde. (2) A barcode Ai(i=1-50) solution is flowed through in a horizontal direction. To confirm that the flow is leak-free, the barcodes introduced into adjacent microchannels are pre-labeled with fluorescent groups of different colors. (3) Imaging of fluorescently labeled barcodes Ai (i = 1-50), which have bound to mRNA in the cell through hybridization between the oligodeoxythymidylate (oligo-dT) tag of the barcode A chain and the polyadenine (poly-A) tail of the mRNA. (4) The microfluidic chip is removed, but the cells remain on the surface of the polyamine-coated slide. Another microfluidic chip is placed on the slide with the microfluidic channels perpendicular to the direction of the first flow. Then, a barcode Bj (j = 1-50) solution is introduced in the perpendicular direction. Similarly, the barcode B solution introduced into the adjacent microchannel contains a different fluorescent group and can be visualized to confirm that no leakage has occurred. This figure shows the fluorescent signal from barcode Bj (j = 1-50), confirming that the barcoding of each single cell was successful. Combining barcodes Ai and Bj, each cell has a unique and known barcode AiBj (i = 1-50 and j = 1-50).

[0044] Figures 16A-16B Definitive barcoding within tissues for chromatin accessibility analysis. (Figure 16A) Schematic workflow for spatially resolved analysis of chromatin accessibility by orthogonally barcoding a single DNA sequence in the Tn5 enzyme. (Figure 16B) Tissue and fluorescence images show successful merging of barcode Ai (i = 1-50) and barcode Bj (j = 1-50), again orthogonally creating a spatial mosaic of barcoded tissue pixels.

[0045] Figures 17A-17D DBiT-seq workflow for FFPE samples. (Figure 17A) DBiT-seq protocol for FFPE samples. FFPE tissue blocks stored at room temperature were cut into sections approximately 5-7 μm thick and placed on poly-L-lysine-coated slides. Dewaxing, rehydration, permeabilization, and post-fixation were performed sequentially before attachment to the first PDMS chip. Barcodes A1-A50 were loaded, and reverse transcription was performed within each channel. After washing, the first PDMS chip was removed, and a second PDMS chip with perpendicular channels was attached to the tissue section. The ligation reaction mixture and DNA barcodes B1-B50 were vacuum-processed through each of the 50 channels and reacted for 30 minutes. The tissue section was then completely lysed with proteinase K and collected for downstream processes, including template switching, PCR, and library preparation. (Figure 17B) Dewaxing of an E10 mouse embryo. The tissue section maintained its morphology, and tissue features were clearly visible. (Figure 17C) Plastic deformation of the tissue section after two consecutive microfluidic flows for DBiT-seq. ( FIG17D ) Comparison of gene and UMI counts of DBiT-seq on FFPE samples with Slide-seq, Slide-seqV2, and DBiT-seq on formalin-fixed fresh-frozen samples.

[0046] Figures 18A-18ESpatial transcriptome analysis of FFPE tissue sections from E10.5 mouse embryos. (Figure 18A) DBiT-seq was used to investigate two tissue regions of FFPE mouse embryos. One experiment (FFPE-1) covered the head region of the mouse embryo; the other experiment (FFPE-2) covered the mid-body region, with minimal overlap with FFPE-1. Two different tissue sections were used in this study. (Figure 18B) UMAP visualization of combined pixels from FFPE-1 and FFPE-2 using the Seurat software package. Left: UMAP labeled with sample name; Right: UMAP labeled with cluster number. A total of 10 clusters were identified. (Figure 18C) Histomorphology, anatomical annotation, and spatial mapping of the 10 clusters in (Figure 18B). (Figure 18D) GO enrichment analysis of the above 10 clusters. (Figure 18E) Comparison with "pseudo-batch" reference data. The aggregated transcriptome profiles of the two FFPE samples were fully consistent with data generated from scRNA-seq reference data from E9.5-E13.5 mouse embryos (Cao et al., 2019).

[0047] Figures 19A-19E Integration of FFPE mouse embryo DBiT-seq data with scRNA-seq data. (Figure 19A) Integration analysis of FFPE-1 and FFPE-2 with scRNA-seq data from E9.5-E13.5 mouse embryos (Cao et al., 2019). These two samples are highly concordant in the scRNA-seq data. (Figure 19B) UMAP showing the integrated data for 26 different clusters. (Figure 19C) Cell type annotation for each cluster using cell type information from scRNA-seq data. (Figure 19D) Spatial mapping of some representative clusters. (Figure 19E) Cell types identified in Figure 8C.

[0048] Figures 20A-20E Spatial transcriptome analysis of adult mouse aorta FFPE tissue sections. (Figure 20A) Brightfield image of adult mouse aorta. Scale bar, 500 μm. (Figure 20B) Per-pixel UMI and gene count plots. The average UMI count per pixel was ~1828 and the gene count was ~664. (Figure 20C) Clustering with scRNA-seq data. Pixels of aortic samples were highly consistent with scRNA-seq reference values. (Figure 20D) Spatial mapping of cell types annotated by integrating scRNA-seq data. The cell types included endothelial cells (ECs), arterial fibroblasts (Fibro), macrophages (Macro), monocytes (Mono), neurons, and vascular smooth muscle cells (VSMCs). (Figure 20E) Spatial mapping of individual cell types from Figure 9D.

[0049] Figures 21A-21F Spatial transcriptome profiles of mouse heart (atria and ventricles) from FFPE tissue sections. (Figure 21A) Brightfield image and gene heatmap of dewaxed mouse atrial tissue section. (Figure 21B) Brightfield image and gene heatmap of dewaxed mouse ventricular tissue section. (Figure 21C) Clustering of atrial data with reference scRNA-seq data. (Figure 21D) Spatial distribution of representative annotated cells in the atria. (Figure 21E) Clustering of ventricle data with reference scRNA-seq data. (Figure 21F) Spatial distribution of representative annotated cells in the cerebral ventricles. Detailed Description of the Invention

[0051] In multicellular systems, cells do not function in isolation but are strongly influenced by their spatial location and environment (Knipple et al., 1985; Scadden, 2014; van Vliet et al., 2018). Spatial gene expression heterogeneity plays a crucial role in a range of biological, physiological, and pathological processes (de Bruin et al., 2014; Fuchs et al., 2004; Yudushkin et al., 2007). For example, how stem cells differentiate and give rise to different tissue types is a spatially regulated process that controls the development of different tissue types and organs (Ivanovs et al., 2017; Slack, 2008). Organogenesis in the mouse embryo begins at the end of the first week after gastrulation and continues until birth (Mitiku and Baker, 2007). Due to the dynamic heterogeneity of tissues and cells during rapid development, the exact timing and manner of the emergence of different organs in the early embryo remains unclear. Embryonic organs at this stage can be anatomically and molecularly distinct from their adult counterparts. To dissect the onset of early organogenesis in the context of the entire embryo, it is highly desirable not only to identify genome-wide molecular maps defining the emerging cell types but also to investigate their spatial organization within tissues at high resolution.

[0052] Despite the recent emergence of massively parallel single-cell RNA sequencing (scRNA-seq) (Klein et al., 2015; Macosko et al., 2015), which has revealed striking cellular heterogeneity across many tissue types, including the profiling of all major cell types in developing mouse embryos from E9 to E14 (Cao et al., 2019; Pijuan-Sala et al., 2019), spatial information within the tissue context is missing from scRNA-seq data. The field of spatial transcriptomics has emerged to address this challenge. Early attempts were based on the versatile single-molecule fluorescence in situ hybridization (smFISH) with spectral barcoding and serial imaging (Pichon et al., 2018; Trcek et al., 2017). Over the past few years, it has rapidly evolved from detecting a small number of genes to hundreds or thousands of genes (e.g., seqFISH, MERFISH) (Chen et al., 2015; Lubeck et al., 2014), and more recently, to the whole transcriptome level (e.g., SeqFISH+) (Eng et al., 2019). However, these methods are technically demanding, requiring highly sensitive optical imaging systems, complex image analysis processes, and lengthy, repetitive imaging workflows to achieve high multiplexing (Perkel, 2019). Furthermore, they are all based on limited probe sets that hybridize to known mRNA sequences, limiting their potential for discovering novel sequences and variants. Fluorescence in situ sequencing methods (e.g., FISSEQ, STARmap) (Lee et al., 2015; Wang et al., 2018) have also been reported, but the number of genes that can be detected is limited, and their workflows are similar to those of sequential FISH, requiring a lengthy, repetitive, and technically demanding imaging process.

[0053] There is a great need to develop new methods for high spatial resolution, unbiased, genome-scale molecular mapping in intact tissues that do not require complex imaging but can instead leverage the power of high-throughput next-generation sequencing (NGS). This year, a method called Slide-seq was reported that uses a self-assembled monolayer of DNA-barcoded magnetic beads on a glass slide to capture mRNA released from a tissue section placed on top. It demonstrated spatial transcriptome sequencing at a resolution of 10 μm (Rodriques et al., 2019). A similar method called HDST uses 2 μm magnetic beads in a microwell array chip to further improve the nominal resolution (Vickovic et al., 2019). However, these emerging NGS-based methods have the following limitations: (a) decoding DNA barcoded magnetic bead arrays is performed by manual sequential hybridization or SOLiD sequencing, similar to seqFISH, which also requires long and repeated imaging processes; (b) the number of genes detected from Slide-seq data at 10 μm resolution is very low (~150 genes / pixel), so even if the pooled gene set can localize major cell types, it is difficult to visualize the spatial expression of individual genes in a meaningful way; (c) these methods, including the previously reported low spatial resolution (~150 μm) method (Stahl et al., 2016), are all based on the same mechanism—“barcoded solid-phase RNA capture” (Salmen et al., 2018), which requires freshly sectioned tissue to be carefully transferred to the magnetic bead or spot array and lysed to release mRNA; although mRNA may be captured only by the magnetic beads directly below, lateral diffusion of free mRNA is inevitable; and (d) all of these genome-scale methods are technically demanding and difficult to use in most biological laboratories. Finally, it is unclear how these methods can be extended to other omics measurements, or how easily they can be adopted by researchers in other fields. Thus, high-spatial-resolution omics remains a scientific challenge, but also an opportunity that, if fully realized (with high spatial resolution) and democratized, could change the research paradigm in many areas of biology and medicine. Current methods are either technically impractical or fundamentally limited by their own methodologies, preventing widespread adoption.

[0054] Inspired by the molecular barcoding of single cells in isolated droplets or microwells as a universal sample preparation method (Dura et al., 2019; Klein et al., 2015; Macosko et al., 2015) for barcoding single cells for massively parallel sequencing of mRNA, DNA, or chromatin states, the present inventors sought to develop a universal method for spatially barcoding tissues, generating a large number of barcoded tissue pixels, each containing a different molecular barcode. Similarly, barcoded mRNAs or proteins in tissue pixels can be retrieved, pooled, and amplified for next-generation sequencing (NGS), but in this case for generating spatial omics maps. The present inventors have previously developed microfluidic channel-guided deposition and arraying of DNA or antibodies on substrates for complex protein analysis (Lu et al., 2013; Lu et al., 2015). Building on this technology, they designed a microfluidic channel-guided delivery technology for high-resolution spatial barcoding.

[0055] The present disclosure provides a new technology for spatial omics - Deterministic Barcoding in Tissue for spatial omics sequencing (DBiT-seq). A microfluidic chip with parallel channels (10 μm, 25 μm or 50 μm wide) is placed directly on a fixed tissue slice, and in some embodiments, a specific clamping force is used to clamp it only to the area of ​​interest to introduce oligodeoxythymidylate (oligo-dT)-labeled DNA barcodes A1-A50, which bind to mRNA and initiate in situ reverse transcription. This step produces barcode cDNA stripes in the tissue slice. Subsequently, the first chip is removed and another microfluidic chip is placed perpendicular to the first flow direction to introduce a second set of DNA barcodes B1-B50, which are connected at the intersection to form a two-dimensional mosaic of tissue pixels, each of which has a unique combination of barcodes Ai and Bj (i=1-50, j=1-50). The tissue is then lysed, and the spatially barcoded cDNA is retrieved, pooled, template-switched, PCR-amplified, and tagged to prepare libraries for NGS sequencing. Similar to Ab-seq or CITE-seq (Shahi et al., 2017; Stoeckius et al., 2017), proteins can be co-measured by applying a cocktail of antibody-derived DNA tags (ADTs) to fixed tissue sections prior to flow barcoding.

[0056] Using DBiT-seq, the data presented here demonstrate high spatial resolution co-mapping of the whole transcriptome and a panel of 22 proteins in the mouse embryo. It faithfully detects all major tissue types during early organogenesis. Spatial gene expression and protein maps further identify differential patterns of embryonic forebrain development and microvascular networks. The 10 μm pixel resolution allows detection of a single layer of melanocytes surrounding the optic vesicle and reveals asymmetric gene expression within it, which has never been observed before. DBiT-seq does not require any DNA spot microarrays or decoding DNA barcode magnetic bead arrays. It is applicable to existing fixed tissue sections and does not require freshly prepared tissue sections required by other methods (Rodriques et al., 2019; Stahl et al., 2016). It is highly versatile, allowing different reagents to be combined for multi-omics measurements to generate spatial multi-omics maps. The inventors envision that this may become a universal method for spatially encoding a range of molecular information (including DNA, epigenetic state, non-coding RNA, protein modifications, or combinations thereof). The microfluidic chip clamps directly to the region of interest on the tissue section, and the barcoding flow steps require no microfluidics experience. Reagent dispensing is similar to pipetting into a microliter plate. Therefore, DBiT-seq has the potential to become a platform technology that can be easily adopted by researchers across a wide range of biological and biomedical research fields.

[0057] HSR microfluidics-based system

[0058] To achieve high spatial resolution in biological environments, detectors (e.g., microfluidic devices) should characterize individual cells and resolve spatial features small enough to meaningfully image the spatial arrangement patterns of individual cells and cell populations.

[0059] Single-cell resolution. A detector can delineate individual cells if its pixel size is approximately equal to or smaller than the cell. Given that mammalian cells range in size from about 5-20 micrometers (μm) in length, this necessitates the use of detectors with pixels of approximately the same length. Although cells in a sample vary in size, and some cells may be larger and some smaller than a detector pixel of constant size, the inventors discovered that by combining optical imaging with digital spatial reconstruction, they can select those pixels that delineate individual cells to achieve true single-cell resolution, even for only a subset of the reconstructed image.

[0060] Imaging multicellular motifs. In addition to analyzing individual cells, it is also useful to consider the imaging detector's ability to resolve spatial features, as these are determined by the center-to-center distances between imaging pixels. This perspective becomes even more relevant when examining structures or motifs composed of populations of cells rather than individual cells, such as developing organoids in mouse embryos, as demonstrated in the examples provided herein.

[0061] The standard specification used in data processing in the time and space domains is the Nyquist Criterion, which states that given a certain center distance in micrometers, the detector can only faithfully reproduce the imaging spatial features of approximately twice the center distance. Given that mammalian cells are between about 5-20 μm in size and are typically adjacent to each other face to face, the features of the cell neighborhood should vary over a distance equivalent to one or more cell lengths. Therefore, in order to resolve these features, in some embodiments, the HSR detector provided herein includes pixels with a center distance between pixels not exceeding several cell lengths, for example 10-50 μm.

[0062] Imaging systems with pixel sizes and center distances much larger than these values ​​cannot depict single cells, nor can they resolve cellular or multicellular features, and therefore cannot display HSR. For example, a detector with 1 mm sized pixels will detect distance scales of 1-2 mm or larger and cannot resolve single or multicellular features. As described elsewhere in this disclosure, pixels much smaller than this range (e.g., less than 1 micron) make it difficult to fit a suitable detector because the mappable area becomes very small and the logistics tasks (including reagent loading and delivery) become impractical. The inventors have found that there is a critical range for high throughput HSR detection when the channel width and spacing (near the region of interest) are, for example, between about 2.5-50 μm.

[0063] Microfluidic devices

[0064] In some embodiments, a microfluidic device (e.g., a chip) can be used to deliver barcode polynucleotides to a biological sample in a spatially defined manner. As described herein, systems based on intersecting microfluidic channels have several key parameters that largely determine the spatial resolution and mappable area of ​​the device. These parameters include: (1) the number of microfluidic channels (η / eta); (2) microchannel width (ω / omega), measured in microns, i.e., the width of the open space in each microfluidic channel (the tissue under these open spaces is imaged); and (3) microchannel spacing (Δ / delta), measured in microns, i.e., the width of the closed space between the end of one channel and the beginning of another channel (the tissue under these closed spaces is not imaged). Further discussion of key challenges and solutions related to the device parameters is provided in the Examples.

[0065] Device parameters

[0066] The microfluidic devices provided herein include a plurality of microchannels having a certain width, depth, and spacing. Surprisingly, the present disclosure demonstrates the critical ranges of several microchannel parameters required to achieve high spatial resolution at the single-cell level.

[0067] Figure 1 An exemplary detection scheme comprising two microfluidic devices is depicted. The first device causes the reagent to flow from left to right and is plotted as rows. The second device causes the reagent to flow from top to bottom and is plotted as columns. The pixels of the detector consist of the overlapping area between the two sets of shapes, as shown in the figure, such that the square has a side length of ω microns. As an illustrative example, assume that the detection scheme uses a microfluidic device with η = 50, ω = 10 microns, and Δ = 10 microns. Reference Figure 1 , the detector will have square pixels with a side length of 10 microns, and the distance between the squares in the horizontal and vertical directions is equal to 20 microns. This means that it can analyze single cells of about 10 microns or larger and resolve spatial features (such as features of cell neighborhoods) of 40 microns or larger. As we have seen in this example, independent of some of the details of the embodiment, such a microfluidic-based detector will exhibit certain performance characteristics determined by the design and design parameters. These performance characteristics include: (1) the ability to analyze single cells; (2) the minimum length scale at which spatial features are reproduced; and (3) the size of the mappable area.

[0068] These performance characteristics are mutually exclusive and therefore cannot be selected independently. For example, as reported elsewhere, by reducing ω and Δ, it is possible to design devices with arbitrarily fine spatial resolution, even down to the nanometer scale. However, doing so would not result in a practical detector for examining tissue sections with single-cell resolution, as the mappable area of ​​the device would be correspondingly small (see, e.g., Figure 2 On the other hand, increasing ω and Δ to very large values ​​such as 1-2 mm (also reported) will result in extremely coarse spatial resolution, which is not suitable for high spatial resolution imaging. Therefore, a trade-off must be made between these design parameters to achieve a detector with high spatial resolution and a suitably large mappable area to meet the needs of the research community when studying tissue samples that have spatial features as small as cells, but where the cell neighborhood can vary in a biologically meaningful way over distances of hundreds of microns.

[0069] One factor contributing to this discrepancy is that the number of channels in a single-layer microfluidic device, n, cannot be increased indefinitely. This is because each channel must be fed by an inlet and lead to an outlet, and must approach and recede from the region of interest without intersecting other channels on the same device. The inventors have discovered that approximately 50 inlets and outlets can be installed while ensuring that the device can still be manufactured and manually filled with reagents. Figure 2The performance characteristics of a 50-channel device with various microchannel widths are shown. In this example, the channel width and spacing (parameters ω and Δ) are also assumed to be equal. Obviously, even if it is feasible to create nanochannels with a width of less than 100 nanometers, such a device can only analyze a small part of the tissue section, which is 600 micrometers (for some tumor cores) to centimeters (for human biopsies, such as whole tumor sections). On the other hand, there are reported devices using macrochannels with a width of up to 2 mm. Although these channels can map a large area (much larger than most tissue sections), they cannot map such a large area at high spatial resolution.

[0070] Number of microchannels. In some embodiments, the first set of barcode polynucleotides is delivered via a first microfluidic chip comprising parallel microchannels located on a surface of a biological sample. In some embodiments, the first microfluidic chip comprises at least 5, at least 10, at least 20, at least 30, at least 40, or at least 50 parallel microchannels. In some embodiments, the first microfluidic chip comprises 5, 10, 20, 30, 40, or 50 parallel microchannels. In some embodiments, the first microfluidic chip comprises 5 to 100 parallel microchannels (e.g., 5-10, 5-25, 5-50, 5-75, 10-25, 10-50, 10-75, 10-100, 25-0, 25-27, 25-100, 50-75, or 50-100 parallel microchannels). In certain embodiments, the second group of barcode polynucleotides is delivered by a second microfluidic chip, which includes parallel microchannels, which are located on the biological sample, perpendicular to the direction of the first microfluidic chip microchannel. In certain embodiments, the second microfluidic chip includes at least 5, at least 10, at least 20, at least 30, at least 40 or at least 50 parallel microchannels. In certain embodiments, the second microfluidic chip includes 5, 10, 20, 30, 40 or 50 parallel microchannels. In certain embodiments, the second microfluidic chip includes 5 to 100 parallel microchannels (e.g., 5-10, 5-25, 5-50, 5-75, 10-25, 10-50, 10-75, 10-100, 25-0, 25-27, 25-100, 50-75 or 50-100 parallel microchannels).

[0071] Microchannel Width. The data disclosed herein indicate that while microchannels with a width of 5 μm can be reproducibly fabricated by, for example, soft lithography, such small dimensions can easily cause clogging and / or collisions with tissue sections. The data indicate that the highest resolution is achieved using microchannels with a width of at least 10 μm. Thus, in some embodiments, the width of the microchannel is at least 10 μm (e.g., at least 15 μm, at least 20 μm, at least 25 μm, at least 30 μm, at least 35 μm, at least 40 μm, or at least 50 μm). In some embodiments, the width of the microchannel is 10 μm, 15 μm, 20 μm, 25 μm, 30 μm, 35 μm, 40 μm, or 50 μm. In some embodiments, the microchannel has a width of 10 μm to 150 μm (eg, 10-125 μm, 10-100 μm, 25-150 μm, 25-125 μm, 25-100 μm, 50-150 μm, 50-125 μm, or 50-100 μm).

[0072] Variable Width. Early data indicate that microchannel devices with microchannels of constant width (e.g., microchannels of varying lengths all have the same width) are often susceptible to clogging by particulate matter (e.g., dust), thereby affecting flow or application of negative pressure, with such errors occurring more frequently in devices with narrower microchannels (e.g., about 10 μm). To overcome this complexity, the present invention provides variable width microchannels having a width at the outlet and inlet that is greater (e.g., at least greater than 10%, such as greater than 10-50%, such as greater than 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, or 50%) than the width of the microchannel near / at the region of interest (e.g., the width near the inlet and outlet, with the width gradually decreasing as the channel approaches the region of interest— Figure 3 ).

[0073] Variable channel width can also facilitate fluid flow through microfluidic channels. In a microchannel with a rectangular cross section, the hydrodynamic resistance per unit length is proportional to the approximate value of the formula 12 / (1-0.63hω)(1 / h^3ω), where h represents the channel height (e.g., Figure 3 The formula used to generate Figure 3 Approximate relative flow resistance values ​​are shown. For example, a 50 μm device may have 100 μm channels that taper to 50 μm only near the region of interest. As another example, a 25 μm device may have channels that are 100 μm, 50 μm, and then taper to 25 μm near the region of interest. As yet another example, a 10 μm device may have channels that range from 100 μm, 50 μm, 25 μm, and then 10 μm near the region of interest.

[0074] In some embodiments, the width of the microchannel near the inlet and outlet is 50 μm to 150 μm, and the width near the region of interest is 10 μm to 50 μm. For example, the width of the microchannel near the inlet and outlet is 100 μm, and the width near the region of interest is 50 μm. As another example, the microchannel may have a width of 100 μm near the inlet and outlet and a width of 25 μm near the region of interest. As yet another example, the microchannel may have a width of 100 μm near the inlet and outlet and a width of 10 μm near the region of interest. In some embodiments, the width of the microchannel near the inlet and outlet is 50 μm, 60 μm, 70 μm, 80 μm, 90 μm, 100 μm, 110 μm, 120 μm, 130 μm, 130 μm, 140 μm, or 150 μm. In some embodiments, the microchannel has a width of 10 μm, 20 μm, 30 μm, 40 μm, or 50 μm near the region of interest.

[0075] Microchannel Height. The data disclosed herein also indicate that the most stable and least error-prone microfluidic devices, at least those fabricated from PDMS, have microchannel heights that are approximately equal to (e.g., within 10% of) the microchannel width. In some embodiments, the microchannel has a height of at least 10 μm (e.g., at least 15 μm, at least 20 μm, at least 25 μm, at least 30 μm, at least 35 μm, at least 40 μm, or at least 50 μm). In some embodiments, the microchannel has a height of 10 μm, 15 μm, 20 μm, 25 μm, 30 μm, 35 μm, 40 μm, or 50 μm. In some embodiments, the microchannel has a height of 10 μm to 150 μm (e.g., 10-125 μm, 10-100 μm, 25-150 μm, 25-125 μm, 25-100 μm, 50-150 μm, 50-125 μm, or 50-100 μm). These heights have been tested and proven to be sufficient to clear dust or, for example, tissue blockages, and low enough to provide the required stiffness and prevent deformation of the channel during clamping and flow.

[0076] In some embodiments, the microchannel has a width of 10 μm and a height of 12-15 μm. In other embodiments, the microchannel has a width of 25 μm and a height of 17-22 μm. In other embodiments, the microchannel has a width of 50 μm and a height of 20-100 μm.

[0077] Microchannel Spacing. Spacing is the distance between microchannels of a microfluidic device (e.g., a chip). In some embodiments, the spacing of the microfluidic device is at least 10 μm (e.g., at least 15 μm, at least 20 μm, at least 25 μm, at least 30 μm, at least 35 μm, at least 40 μm, or at least 50 μm). In some embodiments, the spacing of the microfluidic device is 10 μm, 15 μm, 20 μm, 25 μm, 30 μm, 35 μm, 40 μm, or 50 μm. In some embodiments, the spacing of the microfluidic device is between 10 μm and 150 μm (e.g., 10-125 μm, 10-100 μm, 25-150 μm, 25-125 μm, 25-100 μm, 50-150 μm, 50-125 μm, or 50-100 μm).

[0078] Negative pressure system

[0079] Many microfluidic platforms utilize positive pressure to pump fluid from a reservoir into the device via syringe pumps, peristaltic pumps, and other types of positive-pressure pumps. Typically, a connecting element is used to connect the reservoir / pump assembly to the microfluidic device; this typically takes the form of tubing that plugs into terminal pins at the device's inlet. However, this type of system requires time-consuming and labor-intensive fine-tuning of the assembly process and comes with several drawbacks. For example, if the pin is not inserted deep enough into the inlet hole, or the pin diameter is too small relative to the inlet, fluid pressure can eject the tubing from the inlet upon pump activation. Alternatively, if the pin is inserted too deeply into the inlet hole, fluid pressure can separate the microfluidic device from the glass substrate upon pump activation, leading to leakage. While plasma or thermal bonding of the pins to the inlet and / or the microfluidic device to the substrate may address these drawbacks, these strategies make non-destructive system disassembly difficult, leading to component loss, and are impractical when the substrate contains sensitive materials such as tissue sections and / or antibodies.

[0080] In contrast, the methods and devices provided herein overcome the shortcomings associated with existing microfluidic platforms by using, in some embodiments, a negative pressure system that uses vacuum to pull liquid through the device from the back, rather than positive pressure to push liquid through the device from the front. This has several advantages, including, for example: (i) reducing the risk of leaks by pulling the device and substrate together, and (ii) improving efficiency and ease of use—vacuum can be applied to all outlets, unlike pins, which must be inserted individually into each inlet. Using a negative pressure system can save hours of fine-tuning and pin assembly during each run.

[0081] Thus, in some embodiments provided herein, the barcode polynucleotides are delivered to the region of interest using negative pressure (vacuum) through a microfluidic device (e.g., a chip). In some embodiments, the first set of barcode polynucleotides is delivered using a negative pressure system through a first microfluidic device. In some embodiments, the second set of barcode polynucleotides is delivered using a negative pressure system through a second microfluidic device.

[0082] Inlet and outlet ports

[0083] Data of the present invention further show that the microfluidic device with common outlet is easy to reflux reagent to the region of interest by incorrect microchannel, especially during device disassembly.This reflux can cause the incorrect addressing of target molecule, causes in the subsequent steps of described method (for example after sequencing), the spatial mapping of target molecule is carried out incorrect reconstruction. In order to limit the possibility of reagent reflux, in certain embodiments, microfluidic device provided herein comprises each microchannel with its own inlet end and outlet end. For example, the microchannel device with 50 microchannels has 50 inlet ends and 50 outlet ends. This device design eliminates reflux. Therefore, this design has reduced reconstruction error rate (for example crosstalk event) by at least 90% (at least 95%, at least 98% or 100%).

[0084] Inlet holes. Initial microfluidic device designs employed small (1 mm) inlet holes without filters and long, small-cross-section channels. This presented several challenges. First, punching holes in, for example, PDMS generates small particle debris, sometimes similar in size to the microfluidic channel cross-section. This debris often causes blockages and flow restrictions when it reaches the region of interest. By introducing a filter assembly with an opening of ~10 μm before each inlet hole, these types of errors were significantly reduced.

[0085] Inlet filter. Second, the extremely small (1 mm diameter) inlet hole area creates significant challenges in accurately drilling holes to deliver reagents to the inlet. It is also difficult to pipette reagents into the inlet hole. By increasing the hole diameter from 1 mm to 1.85 mm, chip fabrication and reagent loading can be greatly facilitated.

[0086] Microchannel length. Third, in the original microfluidic design, the channel section with the smallest cross-section was too long, resulting in a sharp increase in flow resistance. By increasing the length of the channel section with a large cross-section (e.g., 50-100 microns) and reducing the length of the channel section with a small cross-section (e.g., 10-25 microns), we were able to more reliably flow reagents at lower vacuum pressures.

[0087] Figure 4 Three design innovations are described that significantly improved device performance and reduced failure rates.

[0088] fixture

[0089] During the initial experiments for testing the microfluidic devices and methods provided herein, reagent leakage frequently occurred between the channels on the region of interest, as evidenced by fluorescent dye analysis (see Example 4, Figure 7F). Conventional fixtures have been shown to be very bulky and have brought difficulties when processing inlets and outlets. In order to solve the problems found, a new fixture has been developed that combines specific clamping parameters, including local clamping force and specific clamping force. A range of clamping forces have been studied - in some cases, the clamping force is not enough to prevent leakage, while in other cases, the clamping force is too large to cause the flow in some or all of the microchannels to be significantly reduced or even completely stopped. Without wishing to be bound by theory, it is believed that this is due to the channel cross section being deformed under the action of the clamping force, thereby reducing the cross-sectional area, making the channel more susceptible to clogging due to, for example, dust or tissue occupying the entire microchannel.

[0090] Surprisingly, when the microfluidic device is clamped to the substrate in a localized manner over only the region of interest using a clamping force in the range of 5 to 50 Newtons, leakage of reagents is reduced. In some embodiments, the clamping force is 5 to 50 Newtons or 5 to 100 Newtons (e.g., 5-75 Newtons, 5-50 Newtons, 5-25 Newtons, 10-100 Newtons, 10-75 Newtons, 10-50 Newtons, 10-25 Newtons, 25-100 Newtons, 25-75 Newtons, 25-50 Newtons, 50-100 Newtons, 50-75 Newtons, Newton-force or 75-100 Newton-force, for example, 5 Newton-force, 10 Newton-force, 15 Newton-force, 20 Newton-force, 25 Newton-force, 30 Newton-force, 35 Newton-force, 40 Newton-force, 45 Newton-force, 50 Newton-force, 55 Newton-force, 60 Newton-force, 65 Newton-force, 70 Newton-force, 75 Newton-force, 80 Newton-force, 85 Newton-force, 90 Newton-force, 95 Newton-force or 100 Newton-force).

[0091] In some embodiments, the microfluidic chip is made of polydimethylsiloxane (PDMS). Other substrates can also be used.

[0092] sample

[0093] In some embodiments, the sample is a biological sample. Non-limiting examples of biological samples include tissues, cells, and body fluids (e.g., blood, urine, saliva, cerebrospinal fluid, and semen). For example, the biological sample can be adult tissue, embryonic tissue, or fetal tissue. In some embodiments, the biological sample is from humans or other animals. For example, the biological sample can be obtained from rodents (e.g., mice or rats), felines (e.g., cats), canines (e.g., dogs), equines (e.g., horses), bovines (e.g., cattle), rabbits (e.g., rabbits), porcines (e.g., pigs), caprae (e.g., goats), bears (e.g., bears), or fish (e.g., fish). Other animals are also contemplated herein.

[0094] In certain embodiments, biological sample is fixed, therefore is referred to as fixed biological sample.Fixing (such as tissue fixation) refers to, for example, using chemical method to preserve the process of biological sample natural state, for subsequent histological analysis.A variety of fixatives are all conventionally used, including, for example, formalin (such as formalin fixed paraffin embedded (FFPE) tissue), formaldehyde, paraformaldehyde and glutaraldehyde, any of which can be used for fixing biological sample.Other fixing agents (fixatives) are also contemplated herein.In certain embodiments, the fixed tissue is FFPE tissue.

[0095] In some embodiments, the biological sample is a tissue. In some embodiments, the biological sample is a cell. In some embodiments, the biological sample (e.g., tissue or cell) is sliced ​​and mounted on a surface, such as a slide (e.g., a glass microscope slide, such as a polylysine-coated glass microscope slide). In these embodiments, the sample can be fixed before or after it is sliced. In some embodiments, the fixation process involves perfusion of the animal from which the sample was collected. In some embodiments, the fixation process involves formalin fixation followed by paraffin embedding.

[0096] Molecules of interest

[0097] The molecule of interest in a biological sample can be any molecule present in the sample. Non-limiting examples include polynucleotides, polypeptides (e.g., proteins), peptides, lipids, and carbohydrates. Examples of polynucleotides include, but are not limited to, DNA and RNA, such as messenger RNA (mRNA). Examples of polypeptides include, but are not limited to, proteins. The molecule of interest can be, for example, a receptor, a ligand, a cytokine, a growth hormone, a growth factor, a transcription factor, and an enzyme. Other molecules of interest are also contemplated herein.

[0098] Binder DNA tag conjugate

[0099] In some embodiments, barcoding a molecule of interest present in a biological sample comprises using a DNA-binding agent tag conjugate comprising: (i) a binding agent molecule that specifically binds to a molecule of interest (e.g., an antibody) and (ii) a DNA tag (e.g., a continuous stretch of nucleotides), wherein the DNA tag comprises a binding agent barcode and a polyA sequence (e.g., at least 50, at least 100, ~1-100, e.g., 25-100, 50-100, or 75-100 consecutive adenine (A) nucleotides).

[0100] The binding agent molecule is any molecule that can bind to a molecule of interest (e.g., a polynucleotide, polypeptide, lipid, and / or carbohydrate) within a time period sufficient to withstand the barcode method described herein (e.g., generating cDNA for sequencing reading). In some embodiments, the binding agent molecule is an antibody. Non-limiting examples of antibodies include whole antibodies, Fab antibody fragments, F(ab')2 antibody fragments, monospecific Fab2 fragments, bispecific Fab2 fragments, trispecific Fab3 fragments, single-chain variable fragments (scFvs), bispecific diabodies, trispecific diabodies, scFv-Fc molecules, and miniantibodies. Other binding agent molecules include ligands (e.g., detecting receptor molecules of interest) and receptors (e.g., detecting ligand molecules of interest). Other molecules that bind to polynucleotides, polypeptides, peptides, lipids, and / or carbohydrates are also contemplated herein.

[0101] barcoded polynucleotides

[0102] FIG5B shows non-limiting examples of barcode polynucleotides (e.g., barcode DNA) of the present invention. In some embodiments, the barcode polynucleotides (e.g., the first set of barcode polynucleotides) include a linker sequence, a spatial barcode sequence, and a polyT sequence. In some embodiments, the barcode polynucleotides (e.g., the second set of barcode polynucleotides) include a linker sequence, a spatial barcode sequence, a unique molecular identifier (UMI) sequence, and a first PCR handle end sequence. In some embodiments, the PCR handle end sequence is end-functionalized with biotin.

[0103] As described herein, a linker sequence is any sequence complementary to a universal linker sequence. The length of the linker sequence may be different. For example, the length of the linker sequence may be 5 to 50 nucleotides (e.g., 5 to 40, 5 to 30, 5 to 20, 5 to 10, 10 to 50, 10 to 40, 10 to 30, or 10 to 20 nucleotides). In certain embodiments, the linker sequence may have a length of 5, 10, 15, 20, 25, 30, 35, 40, 45, or 50 nucleotides. Longer linker sequences are also contemplated herein. In certain embodiments, the linker sequence of one group (e.g., a first group) of barcode polynucleotides is different from the linker sequence of another group (e.g., a second group) of barcode polynucleotides (e.g., having different nucleotide compositions and / or different lengths).

[0104] A barcode sequence is a unique sequence that can be used to distinguish a barcode polynucleotide in a biological sample from other barcode polynucleotides in the same biological sample. A spatial barcode sequence is a barcode sequence associated with a specific position in a biological sample (e.g., a tissue section mounted on a slide). Those skilled in the art are aware of the concept of "barcode" and the ability to attach barcodes to nucleic acids and other proteins and non-protein materials (see Liszczak G et al., Angewandte Chem Int Ed Engl, March 22, 2019; 58(13): 4144-4162). Therefore, it should be understood that the term "unique" refers to a molecule of a single biological sample, and also refers to "unique" in a specific molecule or molecule subset of the sample. Thus, a "pixel" (also called a "patch") that contains a unique spatially addressable barcode conjugate (or a unique subset of spatially addressable barcode conjugates) is the only pixel in the sample that contains that particular unique barcode polynucleotide (or a unique subset of barcode polynucleotides), such that the pixel (and any molecule in the pixel) can be identified based on the unique barcode conjugate (or unique subset of barcode conjugates).

[0105] For example, as shown in Figure 5A, the polynucleotides in subset A1 (of barcode A) are encoded with a specific barcode sequence, while the polynucleotides in subsets A2, A3, A4, and so on are each encoded with a different barcode sequence, with each barcode being specific to that subset. Similarly, the polynucleotides in subset B1 (of barcode B) are encoded with a specific barcode sequence, while the polynucleotides in subsets B2, B3, B4, and so on are each encoded with a different barcode sequence, with each barcode being specific to that subset. Thus, each overlapping tile (comprising a unique combination of barcode subset A and barcode subset B) contains a unique composite barcode (barcode A + barcode B). For example, an overlapping pixel (tile) containing barcodes A1 + B1 is uniquely encoded relative to its adjacent overlapping tiles (comprising barcodes A2 + B1, A1 + B2, A2 + B2, and so on).

[0106] The length of the spatial barcode sequence may vary. For example, the length of the spatial barcode sequence can be 5 to 50 nucleotides (e.g., 5 to 40, 5 to 30, 5 to 20, 5 to 10, 10 to 50, 10 to 40, 10 to 30, or 10 to 20 nucleotides). In some embodiments, the length of the spatial barcode sequence can be 5, 10, 15, 20, 25, 30, 35, 40, 45, or 50 nucleotides. Longer spatial barcode sequences are also contemplated herein.

[0107] A polyT sequence is simply a continuous sequence of thymine (T) residues. Similarly, a polyA sequence is simply a continuous sequence of adenine (A) residues. The length of a polyT or polyA sequence may vary. For example, the length of a polyT or polyA sequence may be 5 to 50 nucleotides (e.g., 5 to 40, 5 to 30, 5 to 20, 5 to 10, 10 to 50, 10 to 40, 10 to 30, or 10 to 20 nucleotides). In certain embodiments, the length of a polyT or polyA sequence may be 5, 10, 15, 20, 25, 30, 35, 40, 45, or 50 nucleotides. Longer polyT or polyA sequences are also contemplated herein.

[0108] As known in the art, a unique molecular identifier (UMI) is a molecular (e.g., DNA or RNA) tag that is commonly used to detect and quantify unique mRNA transcripts (see, e.g., Islam S et al., Nat Methods 2014 Feb;11(2):163-6; Smith T et al., Genome Res 2017 Mar;27(3):491-499; Liu D, Peer J 2019 Dec 16;7:e8275). In some embodiments, the UMI is a barcode sequence. For example, the UMI can be a degenerate nucleotide sequence of 5 to 50 nucleotides (e.g., 5 to 40, 5 to 30, 5 to 20, 5 to 10, 10 to 50, 10 to 40, 10 to 30, or 10 to 20 nucleotides) in length that can be used to distinguish a barcode polynucleotide or spatially addressable barcode conjugate from other polynucleotides (e.g., other barcode polynucleotides and / or conjugates) in a biological sample. In some embodiments, the UMI can be 5, 10, 15, 20, 25, 30, 35, 40, 45, or 50 nucleotides in length.

[0109] Universal linker

[0110] Also provided herein is a universal linker, which can be a polynucleotide, for example, comprising (i) a first nucleotide sequence that is complementary to and / or binds to a linker sequence of a barcode polynucleotide of a first group of barcode polynucleotides, and (ii) a second nucleotide sequence that is complementary to and / or binds to a linker sequence of a barcode polynucleotide of a second group of barcode polynucleotides. The purpose of the universal linker is to act as a bridge connecting barcode polynucleotides from two different groups (e.g., a first group comprising a linker sequence, a spatial barcode sequence, and a polyT sequence, a second group comprising a linker sequence, a spatial barcode sequence, a unique molecular identifier (UMI) sequence, and a first PCR handle end sequence). The length of the universal linker may vary. For example, the universal linker can have a length of 10 to 100 nucleotides (e.g., 10 to 90, 10 to 80, 10 to 70, 10 to 60, 10 to 50, 10 to 40, 10 to 30, 10 to 20, 20 to 100, 20 to 90, 20 to 80, 20 to 70, 20 to 60, 20 to 50, 20 to 40, or 20 to 30 nucleotides. In some embodiments, the universal linker can have a length of 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 nucleotides. Longer universal linkers are also contemplated herein.

[0111] The universal linker is typically added to the biological sample after delivery of the second set of barcode polynucleotides, although in some embodiments, the universal linker is annealed to the second set of barcode polynucleotides prior to delivery of the second set of barcode polynucleotides.

[0112] method

[0113] In some embodiments, the method comprises delivering a first set of barcode polynucleotides to a biological tissue. The first set can include any number of barcode polynucleotides. In some embodiments, the first set includes 5 to 1000 barcode polynucleotides. For example, the first set can include 5 to 900, 5 to 800, 5 to 700, 5 to 600, 5 to 500, 5 to 400, 5 to 300, 5 to 200, 5 to 100, 10 to 1000, 10 to 900, 10 to 800, 10 to 700, 10 to 600, 10 to 500, 10 to 400, 10 to 300, 10 to 200, 20 to 1 In some embodiments, the present invention provides a first set of barcode polynucleotides having at least 100 barcode polynucleotides. The first set of barcode polynucleotides can include at least 100 barcode polynucleotides, at least 2 ...

[0114] The data show that permeabilization helps to obtain cytoplasmic analytes, such as mRNA. However, the introduction of a permeabilization step by the first microfluidic device, for example before delivering the first set of barcode polynucleotides, results in an increase in the rate at which the reagent diffuses through the tissue matrix (including through the tissue directly below the device wall). This causes the reagent to leak from the microchannel to other microchannels under the microchannel wall, resulting in reconstruction errors. By modifying the experimental protocol to introduce a permeabilization reagent after applying the first microfluidic device, thereby only increasing the diffusion rate of the reagent through the tissue directly below the microfluidic microchannel (rather than the microchannel wall), we found that the incidence of crosstalk failure events in each device tested (10 micron, 25 micron and 50 micron channel devices) was greatly reduced. Therefore, in some embodiments, the method includes delivering a permeabilization reagent (e.g., a detergent such as Triton-X 100 or Tween-20) to the biological tissue. In some embodiments, the method includes delivering the first set of barcode polynucleotides to the biological tissue and then delivering the permeabilization reagent to the biological tissue.

[0115] In some embodiments, the method comprises generating cDNA linked to the first set of barcode polynucleotides. In some embodiments, the method comprises exposing the biological sample to a reverse transcription reaction. Methods for producing cDNA are known, and an exemplary protocol is provided herein.

[0116] In some embodiments, the method comprises delivering a second set of barcode polynucleotides to the biological sample. The second set can include any number of barcode polynucleotides. In some embodiments, the second set includes 5 to 1000 barcode polynucleotides. For example, the first set can include 5 to 900, 5 to 800, 5 to 700, 5 to 600, 5 to 500, 5 to 400, 5 to 300, 5 to 200, 5 to 100, 10 to 1000, 10 to 900, 10 to 800, 10 to 700, 10 to 600, 10 to 500, 10 to 400, 10 to 300, 10 to 200, 20 to 1 700, 20-600, 20-500, 20-400, 20-300, 20-200, 50-1000, 50-900, 50-800, 50-700, 50-600, 50-500, 50-400, 50-300, or 50-200 barcode polynucleotides. A second set of barcode polynucleotides of greater than 1000 barcode polynucleotides is also contemplated herein.

[0117] In some embodiments, the method comprises ligating a first set of barcode polynucleotides to a second set of barcode polynucleotides. In some embodiments, the method comprises exposing the biological sample to a ligation reaction, thereby generating a two-dimensional array of spatially addressable barcode conjugates bound to a molecule of interest, wherein the spatially addressable barcode conjugates comprise unique combinations of barcode polynucleotides from the first and second sets. Methods for ligation are known, and an exemplary protocol is provided herein.

[0118] In some embodiments, the method includes imaging the biological sample to generate an image of the sample. For example, the sample can be imaged using an optical microscope or a fluorescence microscope.

[0119] cDNA extraction

[0120] In some embodiments, the method includes extracting cDNA from a biological sample. Methods for nucleic acid extraction are known, and an example protocol is provided herein. However, unexpectedly, in some embodiments, simply lysing the entire biological sample introduces complexity to downstream processes. For example, because the flow patterns of the first and second stages intersect in areas outside the region of interest and areas within the region of interest, in some cases, lysing the entire tissue section or an area larger than the region of interest can lead to incorrect spatial reconstruction after sequencing. The presence of intersections outside the region of interest can cause the target analyte to be labeled with a valid spatial address, but its position no longer matches the reconstructed address, resulting in spatial reconstruction errors. Another complicating factor is the high viscosity of the lysis buffer, which makes it difficult to confine the buffer to the region of interest.

[0121] To address these complex issues, the present invention provides a custom clamp with an opening directly above the region of interest, enabling targeted delivery of the lysis buffer (or other extraction reagent) to the region of interest. Furthermore, experimental data indicate that, in certain circumstances, the clamping pressure of the device (e.g., a force of 10-100 Newtons) at least partially determines the extent of lysis buffer leakage from a tissue sample.

[0122] Sequencing

[0123] In some embodiments, the method provided herein includes a sequencing step. For example, a second-generation sequencing (NGS) method (or other sequencing method) can be used to sequence the molecules identified in the region of interest. For example, see Goodwin S et al., Nature Reviews Genetics (Nature Reviews Genetics) 2016; 17: 333-351, incorporated herein by reference. In some embodiments, the method includes preparing an NGS library in vitro. Therefore, in some embodiments, the method includes sequencing the cDNA to generate cDNA readings. Other sequencing methods are known, and an example scheme is provided herein.

[0124] In some embodiments, the sequencing comprises template switching the cDNA to add a second PCR handle end sequence at the opposite end from the first PCR handle end sequence, amplifying the cDNA, generating a sequencing construct by tagging, and sequencing the sequencing construct to generate cDNA reads. Template switching (also known as template-switching polymerase chain reaction (TS-PCR)) is a reverse transcription and polymerase chain reaction (PCR) amplification method that relies on the natural PCR primer sequence of the polyadenylation site, also known as the poly (A) tail, and adds a second primer through the activity of murine leukemia virus reverse transcriptase (see, e.g., Petalidis L et al., Nucleic Acids Research 2003;31(22):e142). Tagging refers to a modified transposition reaction, commonly used for library preparation, involving transposon cleavage and tagging of double-stranded DNA with universal overhangs. Tagging methods are known.

[0125] In some embodiments, the method comprises constructing a spatial molecular expression map of a biological sample by matching spatially addressable barcode conjugates with corresponding cDNA reads. In some embodiments, the method comprises identifying the location of a molecule of interest by correlating the spatial molecular expression map with an image of the sample. Examples of these method steps are described above and in the Examples section.

[0126] Composition

[0127] Also provided herein are intermediate compositions produced during methods such as constructing a molecular expression profile of a biological sample. In some embodiments, the composition comprises a biological sample containing messenger ribonucleic acids (mRNAs), wherein the messenger ribonucleic acids comprise a polyA tail and / or a protein attached to a binder DNA tag conjugate. In some embodiments, the composition comprises a spatially addressable barcode conjugate comprising a PCR handle sequence, a universal molecular identifier (UMI) sequence, a first spatial barcode sequence, a linker sequence, a second spatial barcode sequence, and a polyT sequence, wherein the spatially addressable barcode conjugate is bound to the mRNA and / or protein by hybridization of polyA and polyT sequences. In some embodiments, the composition comprises a polynucleotide comprising a universal complementary linker sequence bound to the linker sequence of (b).

[0128] Reagent test kit

[0129] Also provided herein are kits for generating, for example, molecular expression profiles of biological samples. In some embodiments, the kit comprises a first set of barcode polynucleotides comprising a linker sequence, a spatial barcode sequence, and a polyT sequence. In some embodiments, the kit comprises a second set of barcode polynucleotides comprising a linker sequence, a spatial barcode sequence, a unique molecular identifier (UMI) sequence, and a first PCR handle end sequence, wherein the first PCR handle end sequence is optionally functionalized with a biotin end. In some embodiments, the kit comprises a polynucleotide comprising a universal complementary linker sequence capable of binding to a linker sequence of the first and second groups of barcode polynucleotides.

[0130] In some embodiments, the kit comprises a collection of binder DNA tag conjugates comprising (i) a binding agent molecule that specifically binds to a molecule of interest, and (ii) a DNA tag comprising a binder barcode and a polyA sequence.

[0131] In some embodiments, the kit comprises at least one reagent selected from the group consisting of a tissue fixation reagent, a reverse transcription reagent, a ligation reagent, a polymerase chain reaction reagent, a template switching reagent, and a sequencing reagent.

[0132] In some embodiments, the kit includes tissue sections (eg, glass slides).

[0133] In some embodiments, the kit comprises at least one microfluidic chip comprising parallel microchannels.

[0134] Other embodiments

[0135] The present invention provides the following additional embodiments:

[0136] 1. A method for generating a molecular expression profile of a biological sample, the method comprising: (a) barcoding molecules of interest in the biological sample by delivering a spatially addressable barcode conjugate to the biological sample; and (b) generating a molecular expression profile of the biological sample by imaging the sample, sequencing the spatially addressable barcode conjugate, and associating the sequence of the spatially addressable barcode conjugate with the sample image.

[0137] 2. The method according to paragraph 1, wherein the biological sample is a fixed biological sample.

[0138] 3. The method of paragraph 1 or 2, wherein the biological sample comprises cells (optionally a cell population) and / or tissue.

[0139] 4. The method of any of paragraphs 1 to 3, wherein the molecule of interest is selected from ribonucleic acids (RNAs) (optionally messenger RNAs (mRNAs)), deoxyribonucleic acids (DNAs) (optionally genomic DNAs (gDNAs)) and proteins.

[0140] 5. The method of any of paragraphs 1-4, comprising delivering a binder DNA tag conjugate to the biological sample, wherein the binder DNA tag conjugate comprises (i) a binder molecule that specifically binds to a molecule of interest, and (ii) a DNA tag, wherein the DNA tag comprises a binder barcode and a polyA sequence.

[0141] 6. The method of paragraph 5, wherein the binding agent molecule is an antibody.

[0142] 7. The method of paragraph 6, wherein the antibody is selected from the group consisting of: a whole antibody, a Fab antibody fragment, a F(ab')2 antibody fragment, a monospecific Fab2 fragment, a bispecific Fab2 fragment, a trispecific Fab3 fragment, a single-chain variable fragment (scFvs), a bispecific diabody, a trispecific diabody, a scFv-Fc molecule, and a minibody.

[0143] 8. The method of any of paragraphs 1-7, comprising delivering a first set of barcode polynucleotides to the biological tissue.

[0144] 9. The method of paragraph 8, wherein the first set of barcode polynucleotides comprises a linker sequence, a spatial barcode sequence, and a polyT sequence.

[0145] 10. The method of paragraph 8 or 9, wherein the first set of barcode polynucleotides is delivered via a first microfluidic chip comprising parallel microchannels located on a surface of the biological sample.

[0146] 11. The method of paragraph 10, wherein the first microfluidic chip comprises at least 10, at least 20, at least 30, at least 40, or at least 50 parallel microchannels.

[0147] 12. The method of any of paragraphs 8-11, further comprising generating cDNA linked to a first set of barcode polynucleotides by exposing the biological sample to a reverse transcription reaction.

[0148] 13. The method of paragraph 12, further comprising delivering a second set of barcode polynucleotides to the biological sample.

[0149] 14. The method of paragraph 13, wherein the second set of barcode polynucleotides comprises a linker sequence, a spatial barcode sequence, a unique molecular identifier (UMI) sequence, and a first PCR handle end sequence, wherein the first PCR handle end sequence is optionally end-functionalized with biotin.

[0150] 15. The method of paragraph 13 or 14, wherein (i) the second set of barcode polynucleotides are conjugated to a universal linker, or (ii) the method further comprises delivering a universal linker sequence to the biological sample, wherein the universal linker comprises a sequence complementary to a linker sequence of the first set of barcode polynucleotides and comprises a sequence complementary to a linker sequence of the second set of barcode polynucleotides.

[0151] 16. The method of any of paragraphs 13-15, wherein the second set of barcode polynucleotides is delivered via a second microfluidic chip comprising parallel microchannels, wherein the parallel microchannels are located on the biological sample perpendicular to the direction of the microchannels of the first microfluidic chip.

[0152] 17. The method of paragraph 16, wherein the second microfluidic chip comprises at least 10, at least 20, at least 30, at least 40, or at least 50 parallel microchannels.

[0153] 18. The method of any of paragraphs 13-17, further comprising ligating the first set of barcode polynucleotides to the second set of barcode polynucleotides by exposing the biological sample to a ligation reaction, thereby generating a two-dimensional array of spatially addressable barcode conjugates that bind to the molecule of interest, wherein the spatially addressable barcode conjugates comprise unique combinations of barcode polynucleotides from the first set and the second set.

[0154] 19. The method of paragraph 18, further comprising imaging the biological sample to produce a sample image.

[0155] 20. The method of paragraph 19, wherein the imaging is performed using an optical or fluorescence microscope.

[0156] 21. The method of any of paragraphs 18-20, further comprising extracting cDNA from the biological sample.

[0157] 22. The method of paragraph 21, further comprising sequencing the cDNA to generate cDNA reads.

[0158] 23. The method of paragraph 22, wherein the sequencing comprises template switching the cDNA to add a second PCR handle end sequence at an end opposite to the first PCR handle end sequence, amplifying the cDNA, generating a sequencing construct by tagging, and sequencing the sequencing construct to generate cDNA reads.

[0159] 24. The method of paragraph 22 or 23, further comprising constructing a spatial molecular expression profile of the biological sample by matching spatially addressable barcode conjugates to corresponding cDNA reads.

[0160] 25. The method of paragraph 24, further comprising identifying the location of the molecule of interest by correlating the spatial molecular expression map with the sample image.

[0161] 26. A composition comprising:

[0162] (a) a biological sample comprising messenger RNAs (mRNAs) comprising a polyA tail and / or a protein linked to a binder-DNA tag conjugate, wherein the conjugate comprises (i) a binder molecule that specifically binds to a molecule of interest, and (ii) a DNA tag comprising a binder barcode and a polyA sequence; and

[0163] (b) a spatially addressable barcode conjugate comprising a PCR handle sequence, a universal molecular identifier (UMI) sequence, a first spatial barcode sequence, a linker sequence, a second spatial barcode sequence, and a polyT sequence, wherein the spatially addressable barcode conjugate binds to the mRNA and / or protein through hybridization of the polyA and polyT sequences.

[0164] 27. The composition of paragraph 26, further comprising a polynucleotide comprising a universal complementary linker sequence that binds to the linker sequence of (b).

[0165] 28. A kit comprising:

[0166] (a) a first set of barcode polynucleotides comprising a linker sequence, a spatial barcode sequence, and a polyT sequence; and

[0167] (b) a second set of barcode polynucleotides comprising a linker sequence, a spatial barcode sequence, a unique molecular identifier (UMI) sequence, and a first PCR handle end sequence, optionally wherein the first PCR handle end sequence is end-functionalized with biotin; and

[0168] A polynucleotide comprising a universal complementary linker sequence capable of binding to the linker sequences of (a) and (b).

[0169] 29. The kit of paragraph 28, further comprising a set of binder DNA tag conjugates, wherein the binder DNA tag conjugates include (i) a binder molecule that specifically binds to a molecule of interest, and (ii) a DNA tag comprising a binder barcode and a polyA sequence.

[0170] 30. The kit of paragraph 28 or 29, further comprising at least one reagent selected from the group consisting of a tissue fixation reagent, a reverse transcription reagent, a ligation reagent, a polymerase chain reaction reagent, a template switching reagent, and a sequencing reagent.

[0171] 31. The kit of any of paragraphs 28-30, further comprising a tissue section.

[0172] 32. The kit of any of paragraphs 28-31, further comprising at least one microfluidic chip comprising parallel microchannels. Example

[0173] We have developed a novel high-resolution (~10 μm) spatial transcriptomics sequencing technology. All early attempts at spatial transcriptomics were based on multiplexed fluorescence in situ hybridization (Chen et al., 2015; Eng et al., 2019; Lubeck et al., 2014; Perkel, 2019). Recently, a major breakthrough in this field has been the reconstruction of spatial transcriptome maps using high-throughput next-generation sequencing (NGS) (Rodriques et al., 2019; Stahl et al., 2016), an unbiased, genome-wide approach that is more accessible to a wider range of biological and biomedical research teams. The core mechanism by which these NGS-based methods achieve spatial transcriptomics is through a method called "barcoded solid-phase RNA capture" (Trcek et al., 2017). This method uses DNA barcoded spot arrays, such as ST-seq (Stahl et al., 2016), or barcoded magnetic bead arrays, such as Slide-seq (Rodriques et al., 2019), to capture mRNA from freshly sliced ​​tissue placed on top and then lysed to release the mRNA. These methods remain technically demanding, requiring a lengthy and complex step to decode the barcoded magnetic beads. Furthermore, at the 10 μm size level, mRNA capture efficiency and the number of genes per pixel that can be measured are significantly suboptimal. Furthermore, it remains unclear how these methods can be extended to other omics measurements. Here, spatial DBiT-seq is a fundamentally different approach. Tissue does not require lysis to release mRNA and is compatible with existing formaldehyde-fixed tissue sections. It is also versatile and easy to use. In some embodiments, it utilizes only a simple microchannel device and a set of reagents. No complex sequence hybridization or SOLiD sequencing is required to decode the barcoded beads before the experiment. This stand-alone device is very intuitive to use and does not require any microfluidics handling system, making it easy for biologists without microfluidics training to adopt it.

[0174] Using this technology, we generated spatial multi-omics maps (protein and mRNA) of whole mouse embryos, generating numerous new insights. Major tissue types in the mouse embryo can be identified during early organogenesis. Spatial protein and gene expression maps revealed differential patterns in the embryonic forebrain, defined by MAdCAM1 expression. Reconstructed spatial protein expression maps readily resolved brain microvascular networks, which are nearly indistinguishable in histological images. We further demonstrated the ability to resolve the monolayer of melanocytes surrounding the optic vesicle and discovered asymmetric gene expression patterns between Rorb and Aldh1a1 within the optic vesicle, which may contribute to the subsequent development of the retina and lens, respectively. Compared to Slide-seq, DBiT-seq not only offers high spatial resolution but also high-quality sequencing data, with higher genomic coverage and more genes detected per 10 μm pixel. This improvement enabled us to visualize the spatial expression of individual genes, whereas Slide-seq data are too sparse to interrogate in a meaningful way.

[0175] Due to the versatility of our technology, we can easily combine multiple omics profiles at the same pixel. As demonstrated in this study, we simultaneously measured the global mRNA transcriptome and a panel of 22 protein markers, allowing us to compare the spatial expression patterns of individual proteins and mRNAs. We demonstrated the ability to use high-quality spatial protein expression data to guide tissue region-specific transcriptome analysis for differential gene expression and pathway analysis, enabling a new approach to mechanistic discovery that cannot be provided by a single type of omics data alone. Furthermore, DBiT has the potential to serve as a universal sample preparation step for high-spatial-resolution mapping of numerous other molecular information. For example, it can be applied to barcoded DNA sequences for high-spatial-resolution transposase-accessible chromatin analysis (ATAC) (Chen et al., 2016) and potentially for the detection of chromatin modifications using the tissue-specific cleavage under targets and release using nuclease (Cut-Run) technology (Skene and Henikoff, 2017), followed by DBiT.

[0176] This spatial barcoding approach is not limited to tissue specimens but is also applicable to single cells distributed on a substrate for deterministic barcoding of massively parallel transcriptome, proteome, or epigenome sequencing. In this way, various cellular analyses, such as cell migration, morphology, signal transduction, drug response, etc., can be completed in advance and linked to omics data, enabling single-cell omics to be directly related to the function of living cells in each single cell. This may further solve a long-standing problem in the field of single-cell RNA sequencing - the inevitable perturbation of cell state, including protein and mRNA expression, during trypsin digestion and preparation of single-cell suspensions.

[0177] Like any emerging technology, DBiT-seq has limitations. First, although it approaches single-cell mapping, it cannot resolve single cells. However, because DBiT-seq has the unique ability to obtain precisely matched tissue images from the same tissue section, we believe that it will be possible to delineate the boundaries of individual cells using molecular imaging techniques such as immunohistochemistry (IHC) or fluorescent in situ hybridization (FISH). This will help identify how many and which cells are in each pixel. Large IHC or FISH databases on the same tissue type are used to train machine learning (ML) neural networks to predict the spatial expression of individual cells based on histology. The trained neural network can then be applied to DBiT-seq and matched histological images to computationally reconstruct single-cell spatial gene or protein expression maps. Second, there is a theoretical resolution limit. Based on our validation data, this limit is ~2 μm, which is challenging to run using microfluidic DBiT. However, we are optimistic that this limit can be reduced to ~5 μm, where most pixels contain one or fewer cells. Third, the current DBiT-seq method relies on a 50×50 orthogonal barcode array that produces a 1 mm mappable area at a 10 μm pixel size. But this can be expanded by increasing the number of barcode reagents to 100×100 or even 200×200 to cover a larger mappable area. Fourth, for the current DBiT device, in some embodiments, the tissue slices are relatively placed in the center of the slide (in a 10 mm×10 mm area). However, many stored tissue slices contain tissue slices at different locations on the slide. To address this problem, a microfluidic device can be made that has a large-scale reagent delivery handle chip bonded to a small flow barcode chip so that the area required to connect the microfluidic flow barcode area to the slide is much smaller and can be aligned with the tissue slice at any position on the slide.

[0178] In summary, we report an enabling and versatile technology, here termed microfluidic deterministic barcoding in tissue (DBiT), for high-resolution spatial barcoding, used to simultaneously measure the mRNA transcriptome and proteome on fixed tissue sections, for example, at high spatial resolution (10 μm) and in an unbiased manner, genome-wide. DBiT-seq represents a fundamentally different approach to spatial omics, with the potential to become a universal method for mapping a range of molecular information (proteins, transcriptomes, and epigenomes). The potential impact of this approach could be broad and profound across many different areas of basic and translational research, including embryology, neuroscience, cancer, and clinical pathology.

[0179] Example 1. DBiT-seq workflow

[0180] The DBiT-seq workflow is shown in Figure 5A. It does not require a freshly sectioned tissue slice to start with, as fixed and stored standard tissue slices are compatible with our method. If frozen tissue slices are the starting material, they can be transferred to poly-L-lysine-coated slides, fixed with formaldehyde, and stored at -80°C until use. A polydimethylsiloxane (PDMS) microfluidic chip containing parallel microchannels (less than 10 μm in width) is placed on the tissue slice to introduce a set of DNA barcode solutions ( Figure 5C). Each barcode consists of an oligodeoxythymidylate (oligo-dT) sequence for binding to mRNA and a different barcode Ai (i = 1 to 50). Reverse transcription is performed during the first flow process for in situ cDNA synthesis, which instantly introduces barcodes A1-A50. The PDMS chip is then removed and another PDMS chip is placed on the same tissue, where the microchannels of the other PDMS chip are perpendicular to the microchannels in the first flow barcode encoding. Next, a second set of barcodes Bj (j = 1 to 50) flows in to initiate in situ connections that occur only at intersections, thereby producing a mosaic of tissue pixels, where each pixel has a different combination of barcodes Ai and Bj (i = 1 to 50, j = 1 to 50). During each flow and after two flows, the tissue section being processed is imaged so that the exact tissue area containing each pixel can be clearly identified. To perform multi-omics measurements of proteins and mRNA, tissue sections were first stained with a mixture of antibody-derived tags (ADTs) prior to microfluidic flow barcoding (Stoeckius et al., 2017). ADTs have a polyadenylated tail, allowing for the detection of proteins using a workflow similar to that used for detecting mRNA. After forming spatially barcoded tissue mosaics, cDNA was collected, templates were converted, and PCR amplified to make sequencing libraries. Using 100 × 100 paired-end NGS sequencing, we could detect the spatial barcodes (A i B j , i = 1-200, j = 1-200) and the corresponding transcripts and proteins to computationally reconstruct spatial expression maps. Notably, unlike other methods, DBiT allows the use of microfluidic channels to image the same tissue section to precisely locate pixels and correlate tissue morphology and omics with high resolution and accuracy.

[0181] Example 2. Barcode Design and Chemistry

[0182] Figure 5B depicts the key elements of DNA barcoding and the chemistry behind DBiT. To detect proteins of interest, tissue is first labeled with ADT, each consisting of a unique antibody barcode (15-mer, see Table 1) and a poly-A tail. Barcode A comprises a 15-mer linker, a unique spatial barcode Ai (i = 1-50, 8-mer, see Table 3), and a 16-mer poly-T sequence, where the poly-T sequence binds to both mRNA and ADT by binding to the poly-A tail. After permeabilization, DNA barcodes A1-A50 are flowed in along with a reverse transcriptase mixture, reverse transcribed in situ to generate cDNA, and barcode A is introduced into tissue stripes within each microchannel. Barcode B consists of a 15-mer linker, a unique spatial barcode Bj (j = 1-50, 8mer, see Table 3), a 10-mer unique molecular identifier (UMI), and a 22-mer PCR handle functionalized with a biotin end, which is subsequently used for cDNA purification using streptavidin-coated magnetic beads. In the second flow used to introduce barcodes B1-B50, a complementary linker and T4 ligase are also introduced to initiate in situ ligation of barcodes A and B only at the intersection of the two flows, thereby completing the deterministic barcoding of the tissue section and generating a tissue pixel mosaic with a different barcode within each of the 50×50=2,500 pixels. This chemical process is versatile and can be easily expanded to larger pixel arrays (e.g., 100×100=10,000) or to other omics measurements by changing the binding chemistry from poly-T to, for example, splice site-specific sequences.

[0183] Example 3. Enabling Microfluidic Devices with HSR

[0184] To explore enabling HSR using the microfluidic device described here, we experimented with values ​​of ω and Δ of 10 μm, 25 μm, and 50 μm. Here, we review the main challenges we faced in enabling a device with these parameters and the solutions we invented to overcome them.

[0185] Aspect Ratio. We experimented with various aspect ratios for 10 μm, 25 μm, and 50 μm devices. While those skilled in the art will recognize that microchannels can generally exhibit a wide range of widths and heights, it was demonstrated that only a specific range of aspect ratios performed well when clamped to tissue (this was necessary for various reasons; see below).

[0186] Because the microfluidic devices described herein consist of open spaces (channels) followed by a solid layer of PDMS (walls), the walls can be considered pillars or columns with a width equal to Δ, a channel spacing, and a height equal to the depth of the mold used to create the PDMS device. For the SU-8 molds we used to fabricate our devices, the height typically ranged from a few microns to a hundred microns. However, we found that for each choice of Δ, choosing a height that was too small resulted in the channels becoming very susceptible to clogging (see Figure 6 , top left and top center). This is due to the tissue itself being forced into the channel during clamping and stopping or selectively restricting the flow. This can be avoided by using a very large height. However, this leads to instability of the channel walls, which then bend during clamping (see Figure 6 , top right). We tested a range of channel height values ​​and achieved a solution by creating channels deep enough to avoid clogging, but with walls stable enough to avoid buckling. Figure 6 The results are shown in the bottom figure.

[0187] Channel and wall width (micrometers) Minimum functional height (micrometers) Maximum functional height (micrometers) 10 12 15 25 17 22 50 20 100

[0188] Example 4. Microfluidic device for DBiT process

[0189] The PDMS microfluidic chip design in this example includes 50 centrally located parallel microchannels connected to an equal number of inlets and outlets on either side of the PDMS slab. It is made of silicone rubber that adheres to the surface of a glass slide and can be placed on a tissue section to introduce solutions without significant leakage unless positive pressure is applied. To further aid assembly, a simple clamp secures the PDMS to the slide over the tissue sample area (Figure 7A). An insertion (inlet) hole with a diameter of ~2 mm and a depth of 4 mm allows for the direct pipetting of ~5 μL of barcoding reagent without requiring any microfluidic handling setup. A global cap connected to the house vacuum is placed on top of the outlet hole to pull the reagent from the insertion hole (inlet) into the tissue area. Pulling the solution from the inlet to the outlet takes seconds for a 50 μm microfluidic chip, while a 10 μm microfluidic chip takes up to 3 minutes. After flow barcoding, the microfluidic chip is sonicated and rinsed with 0.5 M NaOH solution and deionized water for reuse. Therefore, the device does not require complex microfluidic control systems and can be easily assembled by scientists without microfluidics experience, and the workflow is easy to adopt in traditional biological laboratories.

[0190] Example 5. Evaluation of DBiT using fluorescence in situ hybridization (FISH)

[0191] Although no significant leakage between microchannels was observed during vacuum-driven flow barcoding, it was unclear whether the DNA barcode solution diffused through the tissue matrix and led to cross-contamination. Diffusion distances in aqueous solutions decrease significantly with increasing molecular size, which has been exploited for diffusion-limited reagent exchange in microfluidics for a variety of chemical reactions. We hypothesized that diffusion through dense matrices would be more restricted. We designed a validation experiment to monitor our workflow stepwise using fluorescent probes and assess the effects of diffusion beneath the microchannel walls (Figures 7B and 7C, and data not shown). We conjugated barcode A (1-50) to the fluorophore Cy3 and barcode B (1-50) to the fluorophore FITC and then imaged tissue during and after DBiT at 50 μm pixel resolution. The first flow should have produced streaks of Cy3 signal corresponding to barcode A for tissue mRNA in situ hybridization. We observed a distinct streak pattern with no visually apparent diffusion between streaks. The second flow added barcode B only to the intersections, producing a single square of FITC signal, which was exactly what we observed (Figure 7B). Due to tissue autofluorescence excited by blue light (488 nm), a faint fluorescence appeared between the squares, but the average intensity was an order of magnitude lower. We also used a layer of human umbilical vein endothelial cells (HUVECs) grown on a glass slide and fixed with formaldehyde to simulate a thin "tissue" section with higher surface roughness and serve as a rigorous model for evaluating leakage through the microchannel. The first and second flows were stained with the small molecule dye DAPI (4',6-diamidino-2-phenylindole, a nuclear DNA stain) and a fluorophore-labeled anti-human VE-cadherin (endothelial cell-cell junction stain, red), respectively. When the microchannel wall passed through a cell or a cell nucleus, a fluorescent signal was observed only within the half of the microchannel (Figure 7C, data not shown). To assess the possibility of DNA diffusion through the tissue matrix beneath the microchannel wall, three-dimensional fluorescence confocal images were collected, which confirmed negligible leakage signal throughout the thickness of the tissue section (Figure 7D). These images were acquired using a 50 μm fixture without clamps. To evaluate the feasibility of flow barcoding down to 10 μm, we performed complete DBiT using fluorescent barcode B with FITC and observed a clear pattern of fluorescent pixels (Figure 7E and data not shown). Interestingly, the FISH signal of this pan-mRNA in each tissue pixel was not uniform but reflected the underlying cell morphology. As described above, our method allows imaging of the same tissue section during and after flow barcoding. We found that the clamping step compressed the tissue beneath the microchannel wall and caused local plastic deformation. Therefore, bright-field optical images of tissue areas processed by cross-flow barcoding showed an imprinted topological pattern with easily distinguishable tissue pixels, which can be used to assist in the correlation of histological and spatial omics sequencing data.Compressed tissue regions beneath the microchannel walls have higher matrix density and can further reduce diffusion distances. We used fluorescence intensity line profiles (Figure 7E) to calculate the increase in half-maximum intensity, which represents a quantitative measure of the "diffusion" distance between microchannels. The results showed that the diffusion distance for a 10-μm flow channel operated with a clamp was only 0.9 ± 0.2 μm, while the diffusion distance for a 50-μm flow channel without a clamp was 4.5 ± 1 μm (Figure 7F). Therefore, we speculate that the theoretical limit of DBiT spatial resolution can reach ∼2 μm.

[0192] Example 6. DBiT-seq data quality assessment

[0193] Analysis of the cDNA size distribution of PCR amplicons revealed a peak at 900–1100 bp for samples fixed immediately after preparation (data not shown). Leaving frozen tissue sections at room temperature for 24 hours or longer resulted in significant degradation and a shift of the primary peak to ∼350 bp. However, after immobilization and flow barcoding, it still generated sequencing data useful for quantifying gene expression. HiSeq paired-end (100 × 100) sequencing was performed to identify spatial barcodes and protein and mRNA expression at each pixel. Alignment was performed using the DropSeq tool (Macosko et al., 2015), and UMIs, barcode A, and barcode B were extracted from read 2. Processed reads were collated, mapped to the mouse genome (GRCh38), and demultiplexed and annotated (Gencode release M11) using a previously reported spatial transcriptomics pipeline (Navarro et al., 2017). Based on this, similar to scRNA-seq quality assessment, we calculated the total number of transcript reads (UMIs) per pixel and the total number of detected genes (Figure 7G and data not shown). Compared to literature data from Slide-seq (Rodriques et al., 2019) and low-resolution spatial transcriptomics (ST) sequencing data (Stahl et al., 2016), our data from a 10 μm DBiT-seq experiment were able to detect a total of 22,969 genes, 2,068 genes per pixel. In contrast, Slide-seq, with the same pixel size (10 μm), detected ~150 genes per pixel (spot). It is worth noting that this significant improvement in data quality enables DBiT-seq to directly visualize the expression patterns of individual genes, something that Slide-seq cannot do in a meaningful way due to data sparseness. The number of UMIs or genes per pixel detected by the low-resolution ST method is similar to that of our method, but the pixel size in ST is ∼100–150 μm, which is ∼100× larger in area. This significant improvement in data quality may be attributed to the uniqueness of the flow barcoding method, which does not require a tissue lysis step to release mRNA and avoids the loss of released mRNA due to their lateral diffusion into the solution phase. Although it has long been recognized that extracting mRNA from fixed tissue specimens for NGS sequencing will reduce yield due to degradation, recent studies have shown that the quality of mRNA in tissue remains largely unchanged, but it is the tissue lysis and RNA extraction steps that lead to degradation and corresponding poor recovery.

[0194] Example 7. Spatial multi-omics mapping of whole mouse embryos

[0195] The dynamics of embryonic development, particularly the early formation of distinct organs (organogenesis), is intricately controlled both spatially and temporally. Results from numerous laboratories worldwide, using a range of techniques including FISH, immunohistochemistry (IHC), and RNAseq, have been integrated to generate relatively comprehensive mouse embryonic gene expression databases, such as the eMouseAtlas (Armit et al., 2017). Therefore, the developing mouse embryo is ideally suited for validating new spatial omics technologies by providing a known reference for comparison. We applied DBiT-seq to E.10 whole mouse embryonic tissue sections with a pixel size of 50 μm to computationally construct a spatial multi-omics atlas. Histological images from adjacent sections were stained with hematoxylin and eosin (H&E) (Figure 8A, left panel). The number of mRNA transcripts per pixel, equivalent to pan-mRNA detection, was displayed as a spatial heat map (Figure 8A, center panel) and was found to correlate well with tissue density and H&E morphology. The total read count for a set of 22 protein markers (see Table 1) combined in each pixel appeared more uniform and less dependent on tissue density and morphology (Figure 8A, right). The quality of the sequencing data was very good, with an average of ∼4,500 genes detected per pixel, higher than that of 10 μm pixel DBiT-seq data (Figure 7G), in part due to the larger pixel size and subsequent increased cell type diversity per pixel. To benchmark the DBiT-seq data, we aggregated the mRNA expression profiles of all pixels in each E10 embryo sample to generate “pseudo-bulk” data, which we compared using unsupervised clustering with “pseudo-bulk” data generated from scRNA-seq of mouse embryos from E9.5 to E13.5 (Cao et al., 2019) (Figure 8B). We observed consistent temporal developmental classification visualized in UMAP, with all four E10 DBiT-seq samples falling between E9.5 and E10.5 from the reference data (Cao et al., 2019). Unsupervised clustering of all pixels in the mRNA transcriptome revealed 11 major clusters (Figure 8C), which, as shown in the tSNE plot, were associated with the major tissue types at this stage, including the telencephalon (forebrain), mesencephalon (midbrain), rhombencephalon (hindbrain), branchial arches, spinal neural tube, heart, limb buds, and the ventral and dorsal sides of the body for early visceral organ development (Figure 8D). We anticipate that more tissue subtypes will be identified using higher-resolution DBiT-seq.Based on literature databases and the classic Kaufman's Atlas of Mouse Development (Baldock and Armit, 2017), we annotated 13 major tissue types (Figure 8E), 9 of which were identified by unsupervised clustering. Interestingly, even at this resolution (pixel size = 50 μm), some fine features identified by cluster analysis—such as a small cluster in the middle of the brain and a distinct pixel stripe between the dorsal and ventral layers of the body—are not easily distinguished in H&E. The former indicates early development of the eyes and ears, while the latter is less clear but may be related to the dorsal aorta.

[0196] Example 8. Correlation between spatial expression patterns of proteins and mRNA

[0197] While single-cell RNA / protein co-sequencing methods such as CITE-seq can directly compare the expression levels of individual proteins with their cognate mRNAs in cells, correlation between their spatial expression patterns within a tissue context is lacking. Here, high-quality spatial multi-omics data allowed for a head-to-head comparison of individual protein and mRNA transcripts on a pixel-by-pixel basis within tissues. Therefore, all 22 proteins analyzed were compared with their corresponding mRNAs (data not shown). Selected mRNA / protein pairs are discussed below (Figure 8G). Notch signaling plays a crucial role in regulating numerous embryonic developmental processes. Notch1 protein was found to be highly expressed throughout the embryo, consistent with the observed ubiquitous expression of Notch1 mRNA, although this expression appears to reflect tissue density. CD63 is a key player in controlling cell development, growth, proliferation, and motility. Its mRNA transcript is indeed ubiquitously expressed throughout the embryo, with higher expression in the hindbrain and heart. Pan-endothelial cell antigen (PECA) or MECA-32, as pan-endothelial markers, is expressed in many tissue regions, but its spatial pattern is difficult to discern at this resolution. EpCAM is a pan-epithelial marker, with highly localized expression of both its mRNA and protein, and highly consistent expression patterns. Several other genes are discussed below. Integrin subunit alpha 4 (ITGA4), known to be crucial for epicardial development, is indeed highly expressed in the embryonic epicardium but is also observed in many other tissue regions. Its protein expression is observed throughout the embryo. Many genes, such as NPR1, exhibit strong discordance between mRNA and protein expression. The pan-leukocyte protein marker CD45 is ubiquitously observed but is significantly enriched in the dorsal aorta region and brain, despite lower expression of its cognate mRNA, Ptprc. We further generated a comprehensive chart of tissue region-specific mRNA and protein by calculating average expression for each of 13 anatomically annotated tissue regions (Figure 8H). Next, to validate the DBiT-seq data, immunofluorescence staining was performed using antibodies to detect P2RY12 (microglia in the central nervous system), PECA (endothelial cells), and EpCAM (epithelial cells). We observed highly consistent EpCAM patterns between immunostaining and DBiT-seq (Figure 8I). Spatial transcriptome sequencing was repeated using separate tissue sections from E.10 embryos (without ADT) and the results were consistent (data not shown). Finally, “bulk” transcriptional profiles could be obtained from spatial DBiT-seq data and compared with scRNA-seq from mouse embryos E9.5-E13.5, demonstrating that our data are correctly localized in the UMap compared to literature data (Cao et al., 2019).

[0198] Example 9. Spatial multi-omics mapping of the embryonic brain

[0199] We performed DBiT-seq at 25 μm pixel size to analyze brain regions of E10 mouse embryos ( Figures 9A-9G ). Compared with the 50μm experiment ( Figures 8A-8F ), the pan-mRNA and ubiquitin UMI count maps (Figure 9C) showed a finer structure associated with tissue morphology (Figure 9B). We surveyed all 22 individual proteins (Figure S4A) and observed different expression patterns for at least 12 proteins, four of which are shown in Figure 9D. CD63 is widely expressed in other regions except for a part of the forebrain. PECA is a pan-endothelial cell marker that is clearly detected in brain microvessels, which are not easily distinguished in histology. EpCAM is localized to highly defined areas that are as fine as a single line of pixels (~25μm) with a high signal-to-noise ratio. MAdCAM is differentially expressed in forebrain regions with different gene expression characteristics (data not shown). To verify these observations, we used adjacent tissue sections from the same embryo for immunofluorescence staining to detect EpCAM and PECA. The spatial expression maps obtained by DBiT-seq and immunofluorescence staining were superimposed on the H&E images, and their line profiles were drawn for quantitative comparison (Figure 9E). The main peaks were consistent with each other, but some inconsistencies in the precise peak positions were observed due to the use of different tissue sections for DBiT-seq and immunofluorescence. Finally, we performed unsupervised clustering of all pixels using their mRNA expression profiles and identified 10 different clusters characterized by specific marker genes (Figure 9F). We then mapped the spatial distribution of pixels in four representative clusters for H&E images (Figure 9G). Pathway analysis of marker genes showed that cluster 1 was mainly involved in telencephalon development, cluster 2 was associated with red blood cells in blood vessels, cluster 3 involved axon formation, and cluster 4 corresponded to myocardial development, which was very consistent with anatomical annotations. Cluster 2, enriched in hemoglobin genes in red blood cells, was consistent with the expression of PECA proteins that delineate endothelial microvasculature. We further demonstrated that high-quality spatial protein mapping data can be used to guide genome-wide spatial gene expression analysis.

[0200] Example 10. High spatial resolution mapping of early eye development

[0201] We further performed spatial transcriptome mapping of the developing eye region of E10 mouse embryos using 10 μm microfluidic channels and overlaid the resulting pan-mRNA UMI heatmap onto whole-mouse embryo tissue images (Figure 10A). A magnified view of the mapped region shows the morphology and individual pixels of the imprint. H&E staining was performed on adjacent tissue sections (Figure 10B). At this stage (E10), eye development likely reaches the late optic vesicle stage. Four genes were identified within the optic vesicle with distinct but spatially correlated expression patterns (Figure 10C and data not shown). Pax6 is expressed in both the optic vesicle and the optic stalk (Heavner and Pevny, 2012; Smith et al., 2009). Pmel, a pigment cell-specific gene (Kwon et al., 1991) involved in the formation of the fiber layer, was observed around the optic vesicle. Six6, a gene known for its role in retinal cell specification and proliferation in vertebrate embryos, is primarily localized within the optic vesicle, rather than the optic stalk (Heavner and Pevny, 2012). Trpm1 is located along the optic vesicle, with minimal overlap with Six6. The retinal pigment epithelium (RPE) is known to consist of a single layer of melanocytes arranged around the optic vesicle, and this single layer was successfully detected using DBiT-seq using markers such as Pmel and Trpm1 (Mort et al., 2015). We further performed GO analysis to identify major pathways and signature genes (data not shown). Eye development and melanin pathways emerged as two major categories. Furthermore, we performed 10 μm DBiT-seq on E11 mouse embryos and compared them side-by-side with E10 embryos in the eye visual field region (Figure 10D). The expression patterns of Pmel, Pax6, and Six6 around the eye were similar between E10 and E11 embryos but showed spatial variation as the optic cup begins to form at E11 (Yun et al., 2009). Furthermore, we analyzed other genes known to be involved in early eye formation (Figures 10E, 10F, and 10G). Aldh1a1, encoding the enzyme aldehyde dehydrogenase 1 family member A1, was observed in the dorsal retina, while Aldh1a3 was primarily localized ventrally and in the RPE. The spatial distribution of Aldh1a1 and Aldh1a3 within the eye field and their changes from E10 to E11 are consistent with literature, suggesting that Aldh1a family genes differentially control dorsoventral polarization during embryonic eye development (Matt et al., 2005). We noted that Msx1, a gene highly expressed in the ciliary muscle and ciliary epithelium, which support the eye (Zhao et al., 2002), was primarily localized around the eye field in E10 and E11 embryos. Gata3, a key gene for eye closure, is enriched at the anterior end of the eye field to control eye shape during development.Our data allow for high spatial resolution visualization of genome-wide gene expression during early eye field development.

[0202] Example 11. Direct integration with single-cell RNA sequencing data

[0203] We observed additional tissue signatures based on the spatial expression patterns of 19 top-ranked genes (data not shown), but cell types could not be easily identified. Because the pixel size in this experiment (10 μm) is close to the cellular level, we reasoned that data from scRNA-seq and DBiT-seq could be directly integrated to infer cell types and visualize spatial distribution. scRNA-seq data from E9.5 and E10.5 mouse embryos (Cao et al., 2019) were combined with DBiT-seq data (10 μm pixel size) from E10 mouse embryos for unsupervised clustering (Figure 10H). We found that spatial pixels corresponded well to the single-cell transcriptomes and collectively identified 24 clusters in the combined dataset (Figure 10I). Each cluster was mapped back to its spatial distribution in the tissue (8 clusters are shown in Figure 10J). We further used scRNA-seq data as a reference for cell type annotation (Figure 10K) and compared the 53 reported cell types directly with DBiT-seq data in UMAP (black), allowing us to detect the predominant cell type within each pixel (10 μm). We could then associate the scRNA-seq-annotated cell types with the corresponding spatial pixels and visualize the cell type distribution across the tissue. First, we examined the spatial pixels in clusters 2, 8, and 22 (see Figure 10H, a) and found that the predominant cell types were retinal tracks, retinal epithelium, and oligodendrocytes. Mapping the cell type-annotated pixels onto the tissue image revealed that retinal tracks and retinal epithelium were indeed located within the optic vesicle, while oligodendrocytes were located in three tissue regions, one of which corresponded to the optic stalk immediately adjacent to the optic vesicle, consistent with the observation that multiple oligodendrocyte pixel subclusters existed (Figure 10L). Second, the spatial pixels in region b of Figure 10H were detected only in clusters 14 and 16, and were found to be dominated by erythrocytes and endothelial cells. Mapping them back to the tissue image revealed microvessels (corresponding to endothelial cells) and blood clots (corresponding to red blood cells) appearing in the upper right corner (Figure 10M). Third, we also analyzed the spatial pixels in Figure 10H, cf, and the corresponding clusters 0, 4, 19, and 20, respectively. Associating spatial pixels with cell types revealed that (c) connective tissue serves as structural support for eye formation; (d) epithelial cells form the pituitary gland and muscle cells; (e) surround the trigeminal sensory nerve for facial touch sensing and ganglion neurons; and (f) within the trigeminal sensor itself (Figure 10N). Therefore, DBiT-seq with a 10 μm pixel size can be directly integrated with scRNA-seq to infer cell types and visualize their spatial distribution within the tissue environment.

[0204] Example 12. Cluster analysis of 11 embryo samples at different stages (E10-12)

[0205] To further understand the early development of mouse embryos over time, we integrated data from three stages (E10, E11, and E12) Figures 11A-11D ) and performed unsupervised clustering, showing 20 clusters visualized by t-distributed stochastic neighbor embedding (t-SNE) (Figures 11A and 11B) and the top differentially expressed genes (Figure 11C). Cluster 2 is associated with muscle system processes that preferentially express the Myl gene family, and the pixels in this cluster are mainly from the three E11 tail samples (see Figure 11A). Although pixels from the same sample are clustered together without batch normalization, some samples such as "E11 Tail (25μm) 1" show multiple distant clusters (Figure 11D left), indicating that there are significant differences in tissue types within this sample. Large pixels (50μm) tend to be farther away from the origin of UMAP, presumably because they cover more cells and have a higher degree of cell diversity within a pixel. In contrast, 10 μm pixels clustered around the UMAP center, indicating convergence to single-cell gene expression. Although these samples are quite different, E10, E11, and E12 pixels were spaced along the same trajectory (from left to right) consistent with developmental stage, so they were mapped to different tissue regions (head vs. tail) with different pixel sizes (10 μm, 25 μm vs. 50 μm) (Figure 11D right).

[0206] Example 13. Spatial Mapping of Visceral Organ Development

[0207] The sample “E11 Tail (25 μm) 1” displayed multiple distinct subclusters in the global UMAP (Figure 11D left panel), which led us to wonder which cell types comprised these clusters (see the zoomed-in view in Figure 12A). Four subclusters (a, b, c, and d) were mapped back to the tissue image, which revealed distinct spatial patterns across all subclusters (Figure 12B). Cluster analysis of all pixels in this sample identified 13 clusters visualized in the UMAP (Figure 12C) and spatial atlas (Figure 12D). To reveal the identity of these spatial patterns, we again used scRNA-seq as a reference (Cao et al., 2019) and performed automatic cell type annotation with SingleR (Aran et al., 2019) (Figure 12E). The main cell types in these spatial clusters (a, b, c, and d) are associated with different visceral organs, such as the liver (cluster a), neural tube (cluster b), heart (cluster c), and blood vessels containing coagulated red blood cells (cluster d) (Figure 12G). We further visualized the spatial expression of 8 representative marker genes (Figure 12F). Myh6 is a gene encoding myosin heavy chain α that is highly expressed in the atrium, while Myh7 (encoding myosin heavy chain β) is the main isoform expressed in ventricular muscle, which can not only detect cardiomyocytes but also distinguish between the atria and ventricles of the embryonic heart. Pax6 is expressed in region-specific neural progenitors in the neural tube. Car3 encodes carbonic anhydrase III and is expressed in slow-twitch skeletal muscle, particularly depicting the formation of the notochord. Apoa2, encoding apolipoprotein E, is liver-specific. The gene encoding hemoglobin alpha, Hba.a2, is normally present in red blood cells and has been shown to clot red blood cells in large vessels such as the dorsal aorta, as well as in microvessels of multiple organs. It has also been found in blood clots within the atria. Col4a1 encodes a specific collagen, type IV alpha 1, which is produced by endothelial cells to form the basement membrane, which precisely lines the inner surface of the dorsal aorta, presumably composed of a single layer of endothelial cells. It is also expressed in the heart, presumably in the endocardium and coronary arteries. Actb, encoding β-actin (a widely used reference or housekeeping gene), is ubiquitously expressed throughout the embryo but has low expression in, for example, neural tissue. We also compiled "pseudo-bulk" expression data by aggregating pixels from three major organs (heart, liver, and neural tube) and compared them side-by-side with ENCODE bulk RNA-seq data, demonstrating excellent concordance (Pearson correlation coefficient = ∼0.8) (data not shown).

[0208] Example 14. Automatic feature recognition using spatial DE

[0209] In our study, we evaluated the spatial differential expression (Spatial DE) pipeline (Svensson et al., 2018a), previously developed for ST data analysis, to automatically discover spatial organizational features without cell type annotation using scRNA-seq. In addition to the main pathways associated with eye development in Figures 10A-10E, spatial DE identified 20 features (Figure 13A), including eyes, ears, muscles, forebrain, and epithelium, which were consistent with scRNA-seq-based cell type identification. In contrast, some features were barely distinguishable in the corresponding tissue images, such as the ear (probably due to being at too early a stage in development) and the forebrain (which had little coverage in the mapped tissue regions). Spatial DE was applied to Figures 12A-12G In the data, not only the heart, liver, dorsal aorta, and neural tube as previously described were detected, but also a small portion of the lung bud was detected that covered the mapped tissue area. Many visceral organs begin to develop at the E10 stage, but are barely distinguishable. To further evaluate the potential of spatial DE to detect more different organs or tissues, E12 mouse embryos were analyzed using DBiT-seq. Interestingly, in only 1 / 3 of the whole embryonic tissue sections, spatial DE identified 40 different features, including the heart, lungs, urogenital system, digestive system, and male gonads (testis) (see Figure 12C). Many of these features were too early to be identified based on tissue morphology. We also revisited E10 whole mouse embryos ( Figures 8A-8F ) and E11 lower body DBiT-seq data ( Figures 12A-12G ), and identified ∼20 and ∼25 different features, respectively (data not shown), which were lower than those in the E12 sample, suggesting that the newly discovered features in E12 are related to the developmental process and the appearance of visceral organs at this stage.

[0210] Example 15. Combining immunofluorescence staining and DBiT-seq on the same tissue section

[0211] Finally, we demonstrated DBiT-seq using immunofluorescence-stained tissue sections. E11 mouse embryonic tissue sections were stained with DAPI, phalloidin, and a red fluorescent-labeled antibody to P2RY12 (a G protein-coupled receptor). Figures 14A-14H). We then performed DBiT-seq. While the microfluidic chip was still on the tissue section, we imaged the microfluidic channels and tissue immunofluorescence. By staining the cell nuclei with DAPI, we could perform cell segmentation using ImageJ (Figure 14E). Immunostaining also allowed us to select pixels of interest, such as pixels containing single cells or pixels showing expression of specific proteins, to study the association between morphological features, protein expression, and transcriptome (Figures 14G and 14H). Immunofluorescence staining is widely used in tissue pathology to measure spatial protein expression at the cellular or subcellular level. Combining immunofluorescence with DBiT-seq at the cellular level (10μm pixel size) on the same tissue section can improve the mapping of spatial omics data to specific cell types.

[0212] Example 16

[0213] In clinical practice, tissue samples are usually prepared as formalin-fixed paraffin-embedded (FFPE) tissue blocks rather than fresh-frozen forms for the convenience of tissue processing, storage, and transportation. At the same time, for diagnostic purposes, the tissue morphology of FFPE samples is well preserved, especially after long-term storage. Therefore, hospitals and research institutions have a large number of stored clinical FFPE tissue samples readily available, which can be used as a source of molecular research. 1 However, during sample preparation and storage, RNA from FFPE tissues often loses its integrity and becomes partially degraded and fragmented. 2 Transcriptome studies are most commonly performed through bulk extraction and sequencing, but this loses detailed and important cellular and spatial information about the tissue. 3,4 The formalin fixation step also hinders the application of traditional microfluidics-based scRNA-seq technology in this field.

[0214] Recently, spatial transcriptomic techniques have emerged to study gene expression in tissue sections without the need for general tissue digestion procedures. To date, dozens of simple spatial RNA-seq techniques have been reported, whether by hybridization with fluorescent probes or by 5-8 , or the second generation sequencing based on reverse transcription 9-12 However, the main focus to date has been on fresh-frozen (FF) samples, which contain high-quality and non-cross-linked RNA.

[0215] As described above, we demonstrated DBiT-seq as a high-spatial-resolution multi-omics tool for analyzing PFA-fixed frozen tissue sections. In this example, we demonstrate that DBiT-seq can also be applied to FFPE tissue sections with some modifications. We first demonstrated whole-transcriptome analysis of E10.5 mouse embryos. The results showed that the number of genes identified per pixel was sufficient for downstream analysis. The new experimental protocol faithfully detected the major tissue types in the early mouse brain and midbody. Integrated analysis with publicly available scRNA-seq datasets revealed the major cell types in each organ. We then applied the new protocol to tissue sections of the adult mouse heart and circulatory system (aorta, atria, and ventricles) and obtained cell distribution maps.

[0216] result

[0217] DBiT-seq workflow for FFPE samples

[0218] The main workflow for FFPE samples is shown in Figure 17A. First, the stored FFPE tissue blocks are sliced ​​into 5-7 μm thick sections and then placed on poly-L-lysine slides. To reduce further RNA oxidative degradation caused by air exposure, the FFPE sections are stored at -80°C before use. Dewax is performed using xylene washes. Afterwards, the tissue sections are rehydrated and permeabilized with proteinase K and then post-fixed again with formalin. The dewaxed tissue sections then appear dark (Figure 17B) and are ready for DBiT-seq. Briefly, a first PDMS chip with 50 parallel channels is attached to the sections, and a set of DNA barcode A oligonucleotides flow through the channels along with reverse transcription reagents. Reverse transcription within the tissue produces cDNA with barcode A at the 3' end. After removing the first chip, a second PDMS chip with another 50 channels perpendicular to the first PDMS chip is placed on top of the tissue. Then, 50 different barcode B oligonucleotides and a universal linker matching a linker sequence from barcode A are flowed and ligated in each lane. Ligation occurs only at the intersection of the two flows. The tissue is then imaged and fully digested. The digest is collected and subjected to downstream steps, including cDNA extraction, template switching, PCR, and labeling, before next-generation sequencing.

[0219] DBiT-seq data quality

[0220] The connection between the PDMS chip and the "soft" tissue slice is achieved using a clamp, which deforms the tissue slice beneath the channel walls. Consequently, after two consecutive PDMS chip connections and flows, we observed an orderly square array on the tissue slice (Figure 17C), allowing precise identification of the location and topography of tissue pixels. We first analyzed the cDNA size of FFPE mouse embryo samples and compared them with fresh-frozen (FF) samples (fresh-frozen data not shown). We noted that the peak size for FFPE samples was between 400 and 500 bps, much shorter than that for fresh-frozen samples, which peaked at over 1000 bps. The average size was also similar, ranging from ~600 bps for FFPE samples to over 1400 bps for fresh-frozen samples. Clearly, degradation over time has compromised RNA integrity. Next, we calculated the total number of genes and unique molecular identifiers (UMIs) per pixel (Figure 17D). For FFPE samples, we observed significant variability in results across sample types. For mouse embryos, an average of 520 UMIs and 355 genes were identified per pixel. For the mouse aorta, the average number per pixel increased to 1830 UMIs and 663 genes. The average UMIs and genes per pixel in the FFPE mouse atria and ventricles were even higher, showing 3014 UMIs and 1040 genes in the atria and 2140 UMIs and 832 genes in the ventricles. In comparison, we revisited the dataset of fresh-frozen mouse embryonic samples analyzed by DBiT-seq, which showed an average of 4688 UMIs and 2100 genes. Comparison between FFPE samples and fresh-frozen samples clearly showed that the FFPE samples showed approximately 1 / 9 of the UMIs or approximately 1 / 6 of the genes per pixel in the fresh-frozen samples. We calculated the Pearson correlation coefficient between the FFPE “pseudo-batch” dataset and the fresh-frozen samples and found an r value of ∼0.88 (data not shown), indicating a high correlation between the two samples despite the large differences in tissue origin or lineage. We also compared to fresh-frozen skull hippocampus samples analyzed by the recent 10 μm spot-scale technologies Slide-seq and Slide-seqV2, which both analyzed samples with fewer than or approximately 280 UMIs per spot and fewer than or approximately 200 genes per spot.

[0221] Spatial transcriptome mapping of E10.5 mouse embryos

[0222] Using an E10.5 mouse embryo as an example (Figure 18A), we performed DBiT-seq on two nearby sections from the same mouse, focusing on two distinct regions: the head (FFPE-1) and the midbody (FFPE-2). Integrative cluster analysis of the two datasets using Seurat yielded 10 distinct clusters (Figure 18B). Mapping the clusters back to their spatial locations, we identified highly robust spatially distinct patterns that matched tissue anatomical annotations (Figure 18C). Cluster 0 primarily represented muscle structures in the embryo. Cluster 3 encompassed the neural tube, forehead, and associated nervous system. Cluster 4 was ganglion-specific, encompassing ganglia in the brain (Figure 18C, left) and dorsal root ganglia (Figure 18C, right). The high resolution also allowed us to visualize individual bone fragments in the backbone (Cluster 6). The liver was primarily represented by Cluster 7, while the heart was shown as two layers, with Cluster 8 showing the myocardium and Cluster 10 showing the epicardium. Cluster 9 was also interesting, embedded within the neural tube and likely representing a specialized type of neuron. Spatial clustering demonstrates the high resolution of DBiT-seq, which can resolve very fine structures. We further performed GO analysis for each cluster (Figure 18D), and the results were very consistent with the anatomical annotations. The top 10 differentially expressed genes (DEGs) are also displayed as heat maps (data not shown). We also performed similar analyses for each tissue separately and found consistent patterns (data not shown). The DEGs of each cluster can be analyzed directly (data not shown). For example, Stmn2 and Mapt2, which encode microtubule-associated proteins and are very important for neuronal development, are mainly expressed in the forebrain and neural tube. Fabp7 is a gene encoding a brain fatty acid binding protein that is mainly expressed in the hindbrain. The myosin-related genes Myl2, Myh7, and Myl3 are only expressed in the heart. Slc4a1 is a gene related to coagulation that is highly expressed in the liver, which produces most coagulation factors. Copx is a gene encoding a heme biosynthesis enzyme that is also produced in the liver. Afp is a gene that is highly expressed in the liver during embryonic development and is also observed only in the liver.

[0223] We then apply spatial DE, an unsupervised spatial pattern recognition tool, to study the DBiT-seq data. 14 Using default settings, we identified 30 features for each of the two FFPE embryonic tissues (data not shown). GO analysis of each pattern gene set revealed highly significant results. For example, for FFPE-1, pattern 0 represented neural progenitor cell proliferation, while pattern 7 was associated with eye morphogenesis. For FFPE-2, cluster 20 was specific to heme metabolism, and cluster 26 was for cardiac muscle contraction.

[0224] Integration with scRNA-seq reference

[0225] To annotate the cell type at each pixel, we performed an integrated analysis of our DBiT-seq mouse E10.5 embryo data with published scRNA-seq references. 15 We first compared the aggregated “pseudo-batch” data to the reference using unsupervised clustering (Figure 18E). The DBiT-seq pixel data from FFPE-1 and FFPE-2 clustered very closely with the E10.5 scRNA-seq data, demonstrating that FFPE samples can display the correct embryonic age even with a reduced number of genes. We then used Seurat to integrate the FFPE spatial transcriptome data with the scRNA-seq reference, using sentence class transformation (SCTransform) to remove technical variations. 16 . The DBiT-seq pixels matched the scRNA-seq data very well (Figure 19A), enabling the transfer of cell type annotations from scRNA-seq data to our spatial pixels. The spatial mapping of the cell types is shown in Figure 19D. In FFPE-1, the cells in cluster 3 are mainly oligodendrocytes. Epithelial cells (cluster 4) and neuroepithelial cells (cluster 13) are widely distributed around the tissue. The distribution of excitatory and inhibitory neurons is very similar, which makes sense because they are all neurons that differ in function due to the neurotransmitters they use. In addition, cluster 14, a primitive erythroid cell that is critical for the transition from embryo to fetus in developing mammals, is mainly present in the liver region of FFPE-2. 17 Cardiomyocytes were also correctly identified in the cardiac region. Integration of publicly available scRNA-seq data can provide more detailed biological identification information than general GO analysis and will be preferred when quality references are available.

[0226] Spatial transcriptome analysis of the adult mouse aorta

[0227] We next examined FFPE aortic tissue sections from adult mice (Figure 20A). The aorta was cross-sectioned, revealing the thin wall of the artery as well as supporting tissue. A heatmap of gene and UMI counts is shown in Figure 20B. Due to the lack of distinct tissue features and the predominance of cell types such as smooth muscle cells, unsupervised clustering did not provide rich information (data not shown). However, when integrated with the aorta sc-RNAseq data from the reference 18, we can clearly identify six different cell types, including endothelial cells (ECs), arterial fibroblasts (Fibro), macrophages (Macro), monocytes (Mono), neurons and vascular smooth muscle cells (VSMCs). The majority of cells are endothelial cells, VSMCs and arterial fibroblasts. We also noticed that there is a layer of smooth muscle cells enriched in the arterial wall, which is reported to be the main cell type in vascular tissue. 19 We also briefly ran the automated cell annotation package SingleR on the aorta samples, which contains a built-in reference for mouse single-cell data (data not shown). It is worth noting that adipocytes, which are typically present in the supporting tissue surrounding the arteries, could be easily identified. The adipocyte-specific genes Adipoq and Aoc3 were also found to be expressed at high levels (data not shown).

[0228] Spatial mapping of the atria and ventricles using DBiT-seq

[0229] Finally, we used DBiT-seq to analyze cross-sections of FFPE blocks of adult mouse atria and ventricles (Figures 21A-21B). Although cardiomyocytes only account for 30-40% of the total number of cardiac cells, the volume fraction of cardiomyocytes can reach 70-80%. 20 Indeed, we observed a ubiquitous presence of the muscle-associated Myh6 gene (data not shown), which encodes a protein called cardiac alpha (α)-myosin heavy chain. This abundance of cardiomyocytes would pose a challenge to spatial transcriptome analysis by masking other cell types. In this case, unsupervised clustering of atrial and ventricular pixels using Seurat failed to resolve distinct clusters (data not shown). However, when integrated with a mouse heart scRNA-seq reference, 21 , the DBiT-seq pixels of the atria and ventricles matched the reference quite well, showing a total of 14 clusters (Figures 21C, 21E). The clusters were then annotated using scRNA-seq cell type information (data not shown). After annotation, we noticed that cardiomyocytes remained the predominant cell type found in multiple clusters (Figures 21D, 21F), such as clusters 1, 4, and 8 in the atria. There were also a large number of endothelial cells. Other cell types, such as stromal cells and macrophages, appeared much less frequently.

[0230] in conclusion

[0231] In summary, we demonstrated that DBiT-seq can serve as a high-resolution tool for spatial transcriptome profiling of FFPE tissue sections. It generates useful transcriptome data from highly degraded mRNA. Application to mouse embryonic tissue samples yielded clear spatial patterns that closely matched anatomical patterns. Integration with published scRNA-seq data significantly improved our understanding of tissues by providing cell type information. DBiT-seq was also successfully used to analyze aorta, atrial, and ventricular samples, providing detailed cell type information. Because FFPE samples are readily available and more commonly used in the clinic, we envision that DBiT-seq will be feasible for deeper understanding and analysis of clinically important samples.

[0232] Methods of Examples 1-15

[0233] Microfluidic device fabrication and assembly

[0234] Use soft lithography to make microfluidic devices with polydimethylsiloxane (PDMS). Chromium masks with 10 μm, 25 μm and 50 μm channel widths were ordered from Front Range Photomasks (Lake Havasu City, Arizona). SU-8 negative photoresist was used for mold manufacturing and was carried out according to the following microfabrication process. According to the manufacturer's instructions, a thin layer of SU-8 photoresist (SU-82010, SU-82025 and SU-82050, Microchem) was spin-coated on a clean silicon wafer. For the 50 μm wide microfluidic channel device, the thickness of the photoresist was 50 μm, for the 25 μm wide device, the thickness of the photoresist was 28 μm, and for the 10 μm wide device, the thickness of the photoresist was 20 μm. The SU-8 photolithography, development, and hard bake steps were followed according to the manufacturer's (Microchem) recommendations to create silicon molds for PDMS replication.

[0235] PDMS microfluidic chips were then fabricated via a replica molding process. A PDMS precursor was prepared by mixing Part A and Part B of GE RTV PDMS in a 10:1 ratio. After stirring and degassing, the mixture was poured into the aforementioned mold, degassed again for 30 minutes, and cured at 75°C for approximately 2 hours or overnight. The cured PDMS slab was cut, peeled off, and perforated with inlet and outlet holes to complete the fabrication. The inlet holes had a diameter of ~2 mm and could accommodate up to 13 μL of solution. A pair of microfluidic chips with identical inlet and outlet locations but orthogonal microfluidic channels at their centers were fabricated as a complete device for flow barcoding of tissue sections. To this end, the PDMS slab was attached to a tissue slide and securely fixed to the tissue specimen using a custom-designed acrylic clamp to prevent leakage through the microfluidic channels without requiring harsh bonding processes such as thermal or plasma bonding (Temiz et al., 2015).

[0236] DNA barcoding and other key reagents

[0237] The oligonucleotides used are listed in Table S1 Antibody-Oligonucleotide Sequences and Table S2 DNA Oligonucleotides and DNA Barcodes. All other key reagents used are listed in Table S3.

[0238] Tissue processing

[0239] Formaldehyde fixed tissue sections or frozen tissue sections were obtained from commercial source Zyagen (San Diego, California). The steps used by Zyagen to prepare embryonic tissue sections were as follows. Pregnant mice (C57BL / 6NCrl) were raised and maintained by Charles River Laboratories. More information can be found in the information sheet. Timed pregnant mice (day 10 or day 12) were transported to Zyagen (San Diego, California) on the same day. Mice were sacrificed on the day of arrival to collect embryos. Embryonic sagittal frozen sections were prepared by Zyagen (San Diego, California) as follows: freshly dissected embryos were immersed in OCT and quickly frozen with liquid nitrogen. Before slicing, the frozen tissue blocks were heated to the temperature of the cryostat (-20°C) of the freezing microtome. The tissue blocks were then cut into a thickness of ~7 μm and placed on a poly-L-lysine coated slide (catalog number 63478-AS, electron microscopy sciences). The frozen sections were then fixed with 4% formaldehyde and stored directly at -80°C if long-term storage was required.

[0240] Histology and H&E staining

[0241] The same commercial resource also requested adjacent tissue sections that could be used for histological examination using H&E staining. Basically, fixed tissue sections were first washed with deionized water (DIwater) and the nuclei were stained with alum hematoxylin (Sigma) for 2 minutes. Afterwards, the sections were washed again in deionized water and incubated at room temperature for 45 seconds in bluing reagent (0.3% acid ethanol, Sigma). Finally, the sections were stained with eosin for more than 2 minutes. The stained embryo sections were examined immediately or stored in a -80°C refrigerator for future analysis.

[0242] Immunofluorescence staining

[0243] Immunofluorescence staining was performed on the same tissue section or adjacent sections to generate validation data. The three fluorescently labeled antibodies listed below were used to visualize the expression of the three target proteins: Alexa Fluor 647 anti-mouse CD326 (Ep-CAM) antibody, Alexa Fluor 488 anti-mouse pan-endothelial cell antigen antibody, and PE anti-P2RY12 antibody. The steps for staining mouse embryonic tissue sections were as follows: (1) Fresh frozen tissue sections were fixed with 4% formaldehyde for 20 minutes and washed three times with PBS. (2) 1% bovine serum albumin (BSA) was added to PBS to block the tissue and incubated at room temperature for 30 minutes. (3) The tissue was washed three times with PBS. (4) A mixture of the three antibodies (final concentration of 25 μg / mL in 1% BSA, PBS) was added to the tissue, approximately 50 μL. Incubate in the dark at room temperature for 1 hour. (5) The tissue was washed three times with PBS for 5 minutes each. (6) The tissue was briefly immersed in water and then air-dried. (7) The tissue was imaged using EVOS (Thermo Fisher Scientific EVOS fl model) at 10× magnification. The filters used were Cy5, RFP, and GFP.

[0244] Application of DNA-antibody conjugates on tissue sections

[0245] To obtain spatial proteomic information, we incubated fixed tissue sections with a mixture of DNA-antibody conjugates before microfluidic spatial barcoding. The mixture was prepared by mixing 0.1 μg of each DNA-antibody conjugate (see Table S1). Tissue sections were first blocked with 1% BSA / PBS plus RNase inhibitor and then incubated with the mixture at 4°C for 30 minutes. Subsequently, before attachment to the first PDMS microfluidic chip, the tissue sections were washed three times with a wash buffer containing 1% BSA and 0.01% Tween 20 in 1× PBS and once with deionized water.

[0246] Adding the first set of barcodes and reverse transcription

[0247] To spatially barcode mRNA for transcriptome mapping, slides were blocked with 1% BSA plus RNase inhibitor (0.05 U / μL, Enzymatics) for 30 minutes at room temperature. After a quick rinse with 1× PBS and deionized water, the first PDMS microfluidic chip was roughly aligned and placed on the tissue slide so that the center of the flow barcode area covered the tissue of interest. The tissue section was then permeabilized by loading PBS containing 0.5% Triton X-100 into each of the 50 channels, followed by incubation for 20 minutes, and finally by flowing 20 μL of 1× PBS to thoroughly clean the tissue section. One vial of RT mix was prepared as follows: 50 μL of RT buffer (5×, Maxima H Minus kit), 32.8 μL of RNase-free water, 1.6 μL of RNase inhibitor (Enzymatics), 3.1 μL of Superase In RNase inhibitor (Ambion), 12.5 μL of dNTPs (10 mM, Thermo Fisher), 25 μL of reverse transcriptase (Thermo Fisher), 100 μL of 0.5× PBS and inhibitor (0.05 U / μL, Enzymatics). For the first microfluidic barcoding step, we added 5 μL of a solution containing 4.5 μL of the RT mix and 0.5 μL of one of 50 DNA barcodes (A1-A50) (25 μM) to each insert and then pulled the solution in using a house vacuum in <3 minutes, depending on the channel width. Binding of the DNA oligomers to the tissue-immobilized mRNA occurred at room temperature for 30 minutes, followed by in situ reverse transcription by incubation at 42°C for 1.5 hours. To prevent evaporation of the solution within the channel, the entire device was kept in a sealed humidified chamber (Gervais and Delamarche, 2009). Finally, the channel was flushed by continuously flowing NEB buffer 3.1 (1×, New England Biolabs) supplemented with 1% RNase inhibitor (Enzymatics) for 10 minutes. During the flow barcoding step, optical images can be taken to record the exact position of these microfluidic channels relative to the tissue section undergoing spatial barcoding. This is done using an EVOS microscope (Thermo Fisher Scientific EVOS fl model (Thermo Fisher EVOS fl)) in brightfield or darkfield mode.The clamp was then removed, and the PDMS chip was removed from the tissue section and immersed in a 50 mL centrifuge tube containing RNase-free water to rinse off the remaining salts.

[0248] Add a second set of barcodes and connect

[0249] After drying the tissue sections, a second PDMS chip with a microfluidic channel was carefully aligned and attached to the tissue sections, wherein the microfluidic channel was perpendicular to the direction of the first PDMS chip in the tissue barcode area so that the microfluidic channel covered the tissue area of ​​interest. The ligation mixture was prepared as follows: 69.5 μL of RNase-free water, 27 μL of T4 DNA ligase buffer (10×, New England Biolabs), 11 μL of T4 DNA ligase (400 U / μL, New England Biolabs), 2.2 μL of RNase inhibitor (40 U / μL, Enzymatics), 0.7 μL of SUPERaseIn RNase inhibitor (20 U / μL, Ambion), and 5.4 μL of Triton X-100 (5%). For the second flow barcoding, a total of 5 μL of solution was added to each channel, including 2 μL of the above-mentioned ligation mixture, 2 μL of NEB buffer 3.1 (1×, New England Biolabs), and 1 μL of DNA barcode B (25 μM). The reaction was allowed to proceed at 37°C for 30 minutes, and then the microfluidic channels were washed by flowing 1× PBS supplemented with 0.1% Triton X-100 and 0.25% SUPERase In RNAse inhibitor for 10 minutes. Similarly, before peeling off the second PDMS chip, images showing the location of the microfluidic channels on the tissue section were taken under bright field or dark field of an optical microscope (ThermoFisher EVOS fl model) during the flow step.

[0250] cDNA collection and purification

[0251] We designed a square-hole PDMS gasket that can be aligned and placed on the tissue section, creating an open reservoir to load lysis buffer specifically into the flow-barcoded tissue area to collect the cDNA of interest. Depending on the area of ​​the area, the typical amount of buffer used is 10-100 μL of proteinase K lysis solution, which contains 2 mg / mL proteinase K (Thermo Fisher Scientific), 10 mM Tris (pH = 8.0), 200 mM NaCl, 50 mM EDTA, and 2% SDS. Lysis is performed at 55°C for 2 hours. The lysate is then collected and stored at -80°C until use. The cDNA in the lysate is purified using streptavidin magnetic beads (Dynabeads MyOne Streptavidin C1 magnetic beads, Thermo Fisher Scientific). The magnetic beads (40 μL) were first washed three times with 1× B&W buffer (see manufacturer's manual) containing 0.05% Tween-20 and then stored in 100 μL of 2× B&W buffer (containing 2 μL of SUPERaseIn RNAse inhibitor). To purify the stored tissue lysate, it was thawed and the volume was increased to 100 μL with RNAse-free water. Then, 5 μL of PMSF (100 μM, Sigma) was added to the lysate and incubated at room temperature for 10 minutes to inhibit the activity of proteinase K. Next, 100 μL of the washed streptavidin magnetic bead suspension was added to the lysate and incubated with gentle rotation for 60 minutes. The magnetic beads containing cDNA were further washed twice with 1× B&W buffer and then washed once with 1× Tris buffer (containing 0.1% Tween-20).

[0252] Template switching and PCR amplification

[0253] The cDNA bound to the magnetic beads was washed and resuspended in template switching solution. The template switching reaction mixture contained: 44 μL of 5× Maxima RT buffer (Thermo Fisher), 44 μL of 20% Ficoll PM-400 solution (Sigma), 22 μL of 10 mM dNTPs each (Thermo Fisher), 5.5 μL of RNase inhibitor (Enzymatics), 11 μL of Maxima HMinus reverse transcriptase (Thermo Fisher), and 5.5 μL of template switching primer (100 μM). The reaction was carried out at room temperature for 30 minutes and then incubated at 42°C for another 90 minutes. The magnetic beads were washed once with a buffer containing 10 mM Tris and 0.1% Tween-20, and then rinsed again with RNase-free water using a magnetic separation process. PCR is performed after these two steps. In the first step, a mixture containing 110 μL of Kapa High-Fidelity Hot Start Enzyme Master Mix (Kapa Biosystems), 8.8 μL of 10 μM stock solutions of Primers 1 and 2, and 92.4 μL of water is added to the washed magnetic beads. If protein detection is performed in conjunction with a process similar to CITE-seq, Primer 3 solution (1.1 μL, 10 μM) is also added in this step. PCR is then performed using the following conditions: an initial incubation at 95°C for 3 minutes, followed by five cycles of 98°C for 20 seconds, 65°C for 45 seconds, and 72°C for 3 minutes. The magnetic beads are then removed from the solution using a magnet. Evagreen (20×, Biotium) was added to the supernatant at a ratio of 1:20, and a vial of the resulting solution was loaded into a qPCR instrument (Bio-Rad) for the second PCR step: an initial incubation at 95°C for 3 minutes, followed by a cycle of 98°C for 20 seconds, 65°C for 20 seconds, and finally a 3-minute hold at 72°C. The reaction was stopped when the fluorescent signal just reached a plateau.

[0254] Amplicon purification, sequencing library preparation, and quality assessment

[0255] The PCR products were then purified at a ratio of 0.6× using Ampure XP magnetic beads (Beckman Coulter). mRNA-derived cDNA (>300bp) was then collected from the magnetic beads. If the cDNA was less than 300bp, it remained in the supernatant fraction. This fraction was used if protein detection was performed like CITE-seq. To sequence the cDNA derived from the antibody-DNA conjugate, we further purified the supernatant using 2× Ampure XP magnetic beads. The purified cDNA was then amplified using a PCR reaction mixture containing 45 μL of the purified cDNA fraction, 50 μL of 2× KAPA Hifi PCR Master Mix (Kapa Biosystems), 2.5 μL of 10 μM P7 primer, and 2.5 μL of 10 μM P5 cite primer. PCR was performed under the following conditions: first incubation at 95°C for 3 minutes, followed by 10 cycles of 95°C for 20 seconds, 60°C for 30 seconds, and 72°C for 20 seconds, and finally at 72°C for 5 minutes. The PCR products were further purified by 1.6×Ampure XP magnetic beads. For mRNA-derived cDNA sequencing, the quality of the amplicons was first analyzed using Qubit (Life Technologies) and then using the Agilent Bioanalyzer High Sensitivity Chip. Sequencing libraries were then constructed using the Nextera XT kit (Illumina) and sequenced using a HiSeq 4000 sequencer using a paired-end 100×100 mode. For joint analysis of proteins and mRNA, the DNA-antibody conjugate-derived sequencing library was combined with the mRNA-derived cDNA library in a 1:9 ratio, which was sufficient to detect a limited proteome and had minimal impact on the sequencing depth required for mRNA.

[0256] Fluorescent staining of tissue before DBiT-seq

[0257] Tissue sections can be fluorescently stained using common nuclear stains or fluorescently labeled antibodies prior to DBiT-seq to facilitate identification of tissue regions of interest. Following the DBiT-seq fixation step with formaldehyde, the entire tissue is permeabilized with 0.5% Triton X-100 in PBS for 20 minutes and washed three times with 1× PBS. A working solution mixture of DAPI and phalloidin (FITC-labeled) is added to the top of the tissue and incubated at room temperature for 20 minutes. After washing three times with 1× PBS, the tissue sections are blocked with 1% BSA for 30 minutes. Finally, a fluorescently labeled antibody (in this case, P2RY12) is added and incubated at room temperature for 1 hour. Tissue images are captured using an EVOS microscope (Thermo Fisher Scientific EVOS fl model) with a 10× objective. Filters used are DAPI, GFP, and RFP. After staining, the DBiT-seq barcoding step can be continued.

[0258] smFISH and comparison with DBiT-seq

[0259] Single molecular fish (smFISH) was performed using the HCR v3.0 kit (Molecular Instruments, Inc) according to the product instructions. The probes used in the current study included Ttn, sfrp2, Trf, and Dlk1. smFISH z-stack images were captured using a ZEISS LSM 880 confocal microscope equipped with a 60× oil immersion objective. smFISH quantification was performed using FISH-quant (https: / / biii.eu / fish-quant). mRNA transcript counts were the average of three fields of view, each measuring 306 × 306 μm. The sum of DBiT-seq transcript counts at the same locations was also calculated and compared side by side with the smFISH counts.

[0260] Counting the number of cells in each pixel

[0261] The number of cells per pixel was manually counted using tissue images stained with DAPI and ethidium homodimer-1 (Figure S1B). The total cell count was obtained by summing the number of nuclei in each pixel. If a nucleus appeared at the edge of the pixel, we counted it as 1 if more than half of the nucleus was within the pixel, and 0 otherwise. A total of 50 pixels were counted, and the average number is reported.

[0262] Quantitative and statistical analysis

[0263] Sequence alignment and generation of gene expression matrices

[0264] To obtain transcriptomic data, read 2 was processed by extracting UMIs, barcode A, and barcode B. The processed read 1 was cleaned, mapped to the mouse genome (GRCh38), demultiplexed, and annotated using the ST pipeline v1.7.2 (Navarro et al., 2017) (Gencode release M11), generating a digital gene expression matrix for downstream analysis. The rows of the gene matrix correspond to pixels, defined by their positional information (barcode A × barcode B), and the columns correspond to genes.

[0265] For proteomics data, read 2 was processed by extracting antibody-derived barcodes, spatial barcode A, and barcode B. The processed reads were collated and demultiplexed using the ST pipeline v1.7.2 (Navarro et al., 2017) to generate a gene-protein matrix for downstream analysis. Similar to the gene expression matrix, rows correspond to pixels defined by (barcode A × barcode B), while columns correspond to proteins.

[0266] Pan-mRNA and pan-protein heatmaps were generated using unnormalized raw UMI counts.

[0267] Data standardization and integration

[0268] The transcriptomic data of each pixel with regularized negative binomial regression were normalized and variance stabilized using a module in Seurat V3.2 called “SCTransform”. This process is similar to the process widely used for normalization of scRNA-seq data, with each “pixel” being treated as a “single cell”. The expression matrix of all pixels was SCTransformed (“NormalizeData”, “ScaleData”, and “FindVariableFeatures”). The integration of scRNA-seq reference data and spatial transcriptomic data was performed using Seurat V3.2 and the “SCTransform” module. Normalization of gene data was completed using Scran (V3.11) following the standard procedures recommended in the Seurat package.

[0269] Cluster analysis

[0270] Spatially variable genes were identified by spatial DE (Svensson et al., 2018b). The resulting differentially expressed gene lists were submitted to ToppGene (Chen et al., 2009) for GO and pathway enrichment analysis. The spatially variable genes generated by spatial DE were used for cluster analysis. Non-negative matrix factorization (NMF) was performed using the NNLM package in R after logarithmic transformation of the raw expression values. We chose k out of 11 for the mouse embryo DBiT-seq transcriptome data obtained with a pixel size of 50 μm. For each pixel, the maximum factor loaded from NMF was used to assign cluster membership. The NMF clustering of pixels was plotted by tSNE using the “Rtsne” package in R.

[0271] Comparison with ENCODE bulk sequencing data

[0272] Download public batch RNA-Seq datasets from ENCODE (liver, heart, and neural tube of mouse embryos E11.5) and normalize raw expression counts using FPKM. For DBiT-seq data, a “pseudo-batch” gene expression profile was obtained by summing the counts of each gene in each tissue region and dividing by the sum of the total UMI counts in that specific region, and then multiplying by 1 million. Scatter plots were performed using log 10 (FPKM+1) values ​​were used for bulk data and log10(pseudogene expression+1) values ​​were used for DBiT-seq data. Pairwise Pearson correlation coefficients were calculated. Good correlation (r>0.784) was observed between the two different data sets.

[0273] Gene length deviation analysis

[0274] Gene length bias is well understood in bulk RNA-seq data. We further analyzed our DBiT-seq and ST data using the GeneLengthBias reference package for RNAseq data (Phipson et al., 2019) following standard procedures.

[0275] Data analysis using a single-cell RNA-seq analysis workflow

[0276] Data analysis of E10-E12 tissue sections was performed using Seurat v3.2 (Butler et al., 2018; Stuart et al., 2019) according to standard procedures. Briefly, data normalization, transformation, and selection of variable genes were performed using the SCTransform function with default settings. Principal component analysis (PCA) was performed on the top 3,000 variable genes using the RunPCA function, and the top 30 principal components were used for shared nearest neighbor (SNN) map construction using the FindNeighbors function. Clusters were then identified using the FindClusters function. We used the Uniform Manifold Approximation and Projection (UMAP) to visualize the DBiT-seq data in a reduced two-dimensional space (McInnes et al., 2018). To identify differentially expressed genes for each cluster, pairwise comparisons of cells in a single cluster with all remaining cells were performed using the FindAllMarkers function (settings: min.pct = 0.25, logfc.threshold = 0.25). Expression heatmaps were then generated using the top 10 differentially expressed genes in each cluster.

[0277] Integrating data analysis and cell type identification

[0278] Automated cell type identification of the E11 mouse tail region was performed using SingleR (version 1.2.3) (Aran et al., 2019) following standard procedures. Single-cell RNA-seq data from E10.5 (Cao et al., 2019) were used as a reference. The 12 most common cell types were displayed in the UMAP, with smaller cell types indicated as "other."

[0279] Cell type identification in the E10 eye region was performed by integration with scRNA-seq reference data. We combined the DBiT-seq data with scRNA-seq data from mouse embryos E9.5 and E10.5 (Cao et al., 2019) using Seurat v3.2 and performed clustering after a “sentence-to-class transformation” (“SCTransform”) step. The DBiT-seq data showed a similar distribution to the scRNA-seq reference data. We then used the cell type information from the reference data to assign a cell type to each cluster (if two cell types were present in a cluster, the dominant cell type was assigned). The cell type of each pixel was then assigned by its cluster number.

[0280] References for Examples 1-15

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[0324] References for Example 16

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[0363] Additional references

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[0366] Cao, JY, Spielmann, M., Qiu, XJ, Huang, XF, Ibrahim, DM, Hill, AJ, Zhang, F., Mundlos, S., Christiansen, L., Steemers, FJ, et al. (2019). The single-cell transcriptional landscape of mammalian organogenesis. Nature, 566, 496-+.

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[0368] Chen, KH, Boettiger, AN, Moffitt, JR, Wang, SY, and Zhuang, XW (2015). Spatially resolved, highly multiplexed RNA profiling in single cells. Science, 348. Chen, X., Shen, Y., Draper, W., Buenrostro, JD, Litzenburger, U., Cho, SW, Satpathy, AT, Carter, AC, Ghosh, RP, East-Seletsky, A., et al. (2016). ATAC-see reveals the accessible genome by transposase-mediated imaging and sequencing. Nat Methods, 13, 1013-1020.

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[0371] Fuchs, E., Tumbar, T., and Guasch, G. (2004). Socializing with the neighbors: stem cells and their niche. Cell, 116, 769-778.

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[0402] Table 1

[0403] barcode Specificity clone Barcode sequence SEQ ID NO: 0012 CD117 (c-kit) 2B8 TGCATGTCATCGGTG 1 0078 CD49d R1-2 CGCTTGGACGCTTAA 2 0096 CD45 30-F11 TGGCTATGGAGCAGA 3 0104 CD102 3C4(MIC2 / 4) GATATTCAGTGCGAC 4 0115 FcεRIα MAR-1 AGTCACCTCGAAGCT 5 0118 NK-1.1 PK136 GTAACATTACTCGTC 6 0119 Siglec H 551 CCGCACCTACATTAG 7 0122 TER-119 / erythroid cells TER-119 GCGCGTTTGTGCTAT 8 0130 Ly-6A / E(Sca-1) D7 TTCCTTTCCTACGCA 9 0232 MAdCAM-1 MECA-367 TTGGGCGATTAAGAA 10 0381 pan-endothelial cell antigen MECA-32 CGTCCTAGTCATTGG 11 0415 P2RY12 S16007D TTGCTTATTTCCGCA 12 0439 CD201(EPCR) RCR-16 TATGATCTGCCCTTG 13 0442 Notch 1 HMN1-12 TCCGGTCACTCAGTA 14 0443 CD41 MWReg30 ACTTGGATGGACACT 15 0449 CD326 (Ep-CAM) G8.8 ACCCGCGTTAGTATG 16 0552 CD304 (neuropiliin 1) 3E12 CCAGCTCATTCAACG 17 0553 CD309 (VEGFR2, Flk-1) Avas12 ATAAGAGCCCACCAT 18 0558 CD55(DAF) RIKO-3 ATTGTTGTCAGACCA 19 0559 CD63 NVG-2 ATCCGACACGTATTA 20 0564 Folate receptor beta (FR-β) 10 / FR2 CTCAGATGCCCTTTA 21 0596 ESAM 1G8 / ESAM TATAGTTTCCGCCGT 22

[0404] Table 2. Reagents and resources

[0405]

[0406]

[0407] Table 3. DNA oligonucleotides for PCR and sequencing library preparation SEQ ID NOS: 23-31 (from top to bottom)

[0408]

[0409] Table 4. DNA barcode sequences SEQ ID NOS: 32-131 (from top to bottom)

[0410]

[0411]

[0412]

[0413]

[0414]

[0415]

[0416] All references, patents, and patent applications disclosed herein are incorporated by reference into the entire document for each cited subject matter.

[0417] The indefinite articles "a" and "an" as used in the specification and claims are to be understood to mean "at least one" unless expressly stated otherwise.

[0418] It should also be understood that in any method claimed herein that includes multiple steps or actions, the order of the method steps or actions is not necessarily limited to the order in which the method steps or actions are listed unless explicitly stated to the contrary.

[0419] In the claims and the foregoing description, all transitional phrases such as “comprising,” “including,” “carrying,” “having,” “containing,” “involving,” “holding,” “composed of,” etc. shall be construed as open-ended, meaning including, but not limited to, including. As set forth in Section 2111.03 of the United States Patent Office Manual of Patent Examining Procedures, only the transitional phrases “consisting of” and “consisting essentially of” shall be closed or semi-closed transitional phrases, respectively.

[0420] The terms "about" and "substantially" preceding a numerical value mean ±10% of the recited numerical value.

[0421] Where a numerical range is provided, every value between the upper and lower limit of that range is specifically contemplated and described herein. Sequence Listing <110> Yale University <120> Deterministic barcoding for spatial omics sequencing <130> Y0087.70152WO00 <140> Not yet allocated <141> Accompany here <150> US 62 / 908,270 <151> 2019-09-30 <160> 131 <170> PatentIn version 3.5 <210> 1 <211> 15 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <400> 1 tgcatgtcat cggtg 15 <210> 2 <211> 15 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <400> 2 cgcttggacg cttaa 15 <210> 3 <211> 15 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <400> 3 tggctatgga gcaga 15 <210> 4 <211> 15 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <400> 4 gatattcagt gcgac 15 <210> 5 <211> 15 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <400> 5 agtcacctcg aagct 15 <210> 6 <211> 15 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <400> 6 gtaacattac tcgtc 15 <210> 7 <211> 15 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <400> 7 ccgcacctac attag 15 <210> 8 <211> 15 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <400> 8 gcgcgtttgt gctat 15 <210> 9 <211> 15 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <400> 9 ttcctttcct acgca 15 <210> 10 <211> 15 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <400> 10 ttgggcgatt aagaa 15 <210> 11 <211> 15 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <400> 11 cgtcctagtc attgg 15 <210> 12 <211> 15 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <400> 12 ttgcttattt ccgca 15 <210> 13 <211> 15 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <400> 13 tatgatctgc ccttg 15 <210> 14 <211> 15 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <400> 14 tccggtcact cagta 15 <210> 15 <211> 15 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <400> 15 acttggatgg acact 15 <210> 16 <211> 15 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <400> 16 acccgcgtta gtatg 15 <210> 17 <211> 15 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <400> 17 ccagctcatt caacg 15 <210> 18 <211> 15 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <400> 18 ataagagccc accat 15 <210> 19 <211> 15 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <400> 19 attgttgtca gacca 15 <210> 20 <211> 15 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <400> 20 atccgacacg tatta 15 <210> twenty one <211> 15 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <400> twenty one ctcagatgcc cttta 15 <210> twenty two <211> 15 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <400> twenty two tatagtttcc gccgt 15 <210> twenty three <211> twenty two <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <400> twenty three caagcgttgg cttctcgcat ct 22 <210> twenty four <211> twenty three <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <400> twenty four aagcagtggt atcaacgcag agt 23 <210> 25 <211> 30 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <400> 25 cgaatgctct ggcctctcaa gcacgtggat 30 <210> 26 <211> 32 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <400> 26 aagcagtggt atcaacgcag agtgaatrgr gg 32 <210> 27 <211> 70 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <400> 27 aatgatacgg cgaccaccga gatctacact agatcgctcg tcggcagcgt cagatgtgta 60 taagagacag 70 <210> 28 <211> 88 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <400> 28 caagcagaag acggcatacg agattcgcct tagtctcgtg ggctcggaga tgtgtataag 60 agacagcaag cgttggcttc tcgcatct 88 <210> 29 <211> 88 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <400> 29 caagcagaag acggcatacg agatctagta cggtctcgtg ggctcggaga tgtgtataag 60 agacagcaag cgttggcttc tcgcatct 88 <210> 30 <211> 88 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <400> 30 caagcagaag acggcatacg agatttctgc ctgtctcgtg ggctcggaga tgtgtataag 60 agacagcaag cgttggcttc tcgcatct 88 <210> 31 <211> 88 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <400> 31 caagcagaag acggcatacg agatgctcag gagtctcgtg ggctcggaga tgtgtataag 60 agacagcaag cgttggcttc tcgcatct 88 <210> 32 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 32 aggccagagc attcgaacgt gattttttttttttttttvn 40 <210> 33 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 33 aggccagagc attcgaaaca tcgtttttttttttttttvn 40 <210> 34 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 34 aggccagagc attcgatgcc taatttttttttttttttvn 40 <210> 35 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 35 aggccagagc attcgagtgg tcattttttt ttttttttvn 40 <210> 36 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 36 aggccagagc attcgaccac tgtttttttt ttttttttvn 40 <210> 37 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 37 aggccagagc attcgacatt ggcttttttttttttttttvn 40 <210> 38 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 38 aggccagagc attcgcagat ctgtttttttttttttttvn 40 <210> 39 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 39 aggccagagc attcgcatca agttttttttttttttttvn 40 <210> 40 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 40 aggccagagc attcgcgctg atctttttttttttttttvn 40 <210> 41 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 41 aggccagagc attcgacaag ctatttttttttttttttvn 40 <210> 42 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 42 aggccagagc attcgctgta gcctttttttttttttttvn 40 <210> 43 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 43 aggccagagc attcgagtac aagtttttttttttttttvn 40 <210> 44 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 44 aggccagagc attcgaacaa ccatttttttttttttttvn 40 <210> 45 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 45 aggccagagc attcgaaccg agatttttttttttttttvn 40 <210> 46 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 46 aggccagagc attcgaacgc ttatttttttttttttttvn 40 <210> 47 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 47 aggccagagc attcgaagac ggatttttttttttttttvn 40 <210> 48 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 48 aggccagagc attcgaaggt acattttttt ttttttttvn 40 <210> 49 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 49 aggccagagc attcgacaca gaatttttttttttttttvn 40 <210> 50 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 50 aggccagagc attcgacagc agatttttttttttttttvn 40 <210> 51 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 51 aggccagagc attcgacctc caatttttttttttttttvn 40 <210> 52 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 52 aggccagagc attcgacgct cgatttttttttttttttvn 40 <210> 53 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 53 aggccagagc attcgacgta tcatttttttttttttttvn 40 <210> 54 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 54 aggccagagc attcgactat gcatttttttttttttttvn 40 <210> 55 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 55 aggccagagc attcgagagt caatttttttttttttttvn 40 <210> 56 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 56 aggccagagc attcgagatc gcatttttttttttttttvn 40 <210> 57 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 57 aggccagagc attcgagcag gaatttttttttttttttvn 40 <210> 58 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 58 aggccagagc attcgagtca ctatttttttttttttttvn 40 <210> 59 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 59 aggccagagc attcgatcct gtatttttttttttttttvn 40 <210> 60 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 60 aggccagagc attcgattga ggatttttttttttttttvn 40 <210> 61 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 61 aggccagagc attcgcaacc acatttttttttttttttvn 40 <210> 62 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 62 aggccagagc attcggacta gtatttttttttttttttvn 40 <210> 63 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 63 aggccagagc attcgcaatg gaatttttttttttttttvn 40 <210> 64 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 64 aggccagagc attcgcactt cgatttttttttttttttvn 40 <210> 65 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 65 aggccagagc attcgcagcg ttattttttt ttttttttvn 40 <210> 66 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 66 aggccagagc attcgcatac caatttttttttttttttvn 40 <210> 67 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 67 aggccagagc attcgccagt tcattttttt ttttttttvn 40 <210> 68 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 68 aggccagagc attcgccgaa gtatttttttttttttttvn 40 <210> 69 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 69 aggccagagc attcgccgtg agatttttttttttttttvn 40 <210> 70 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 70 aggccagagc attcgcctcc tgattttttt ttttttttvn 40 <210> 71 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 71 aggccagagc attcgcgaac ttatttttttttttttttvn 40 <210> 72 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 72 aggccagagc attcgcgact ggatttttttttttttttvn 40 <210> 73 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 73 aggccagagc attcgcgcat acatttttttttttttttvn 40 <210> 74 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 74 aggccagagc attcgctcaa tgattttttt ttttttttvn 40 <210> 75 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 75 aggccagagc attcgctgag ccatttttttttttttttvn 40 <210> 76 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 76 aggccagagc attcgctggc atatttttttttttttttvn 40 <210> 77 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 77 aggccagagc attcggaatc tgattttttt ttttttttvn 40 <210> 78 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 78 aggccagagc attcgcaaga ctatttttttttttttttvn 40 <210> 79 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 79 aggccagagc attcggagct gaatttttttttttttttvn 40 <210> 80 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 80 aggccagagc attcggagatag acatttttttttttttttvn 40 <210> 81 <211> 40 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-phosphorylation <220> <221> misc_feature <222> (40)..(40) <223> n is a, c, g or t <400> 81 aggccagagc attcggccac atatttttttttttttttvn 40 <210> 82 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 82 caagcgttgg cttctcgcat ctnnnnnnnn nnaacgtgat atccacgtgc ttgag 55 <210> 83 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 83 caagcgttgg cttctcgcat ctnnnnnnnn nnaaacatcg atccacgtgc ttgag 55 <210> 84 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 84 caagcgttgg cttctcgcat ctnnnnnnnn nnatgcctaa atccacgtgc ttgag 55 <210> 85 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 85 caagcgttgg cttctcgcat ctnnnnnnnn nnagtggtca atccacgtgc ttgag 55 <210> 86 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 86 caagcgttgg cttctcgcat ctnnnnnnnn nnaccactgt atccacgtgc ttgag 55 <210> 87 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 87 caagcgttgg cttctcgcat ctnnnnnnnn nncattggc atccacgtgc ttgag 55 <210> 88 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 88 caagcgttgg cttctcgcat ctnnnnnnnn nncagatctg atccacgtgc ttgag 55 <210> 89 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 89 caagcgttgg cttctcgcat ctnnnnnnnn nncatcaagt atccacgtgc ttgag 55 <210> 90 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 90 caagcgttgg cttctcgcat ctnnnnnnnn nncgctgatc atccacgtgc ttgag 55 <210> 91 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 91 caagcgttgg cttctcgcat ctnnnnnnnn nnacaagcta atccacgtgc ttgag 55 <210> 92 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 92 caagcgttgg cttctcgcat ctnnnnnnnn nnctgtagcc atccacgtgc ttgag 55 <210> 93 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 93 caagcgttgg cttctcgcat ctnnnnnnnn nnagtacaag atccacgtgc ttgag 55 <210> 94 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 94 caagcgttgg cttctcgcat ctnnnnnnnn nnaacaacca atccacgtgc ttgag 55 <210> 95 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 95 caagcgttgg cttctcgcat ctnnnnnnnn nnaaccgaga atccacgtgc ttgag 55 <210> 96 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 96 caagcgttgg cttctcgcat ctnnnnnnnn nnaacgctta atccacgtgc ttgag 55 <210> 97 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 97 caagcgttgg cttctcgcat ctnnnnnnnn nnaagacgga atccacgtgc ttgag 55 <210> 98 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 98 caagcgttgg cttctcgcat ctnnnnnnnn nnaaggtaca atccacgtgc ttgag 55 <210> 99 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 99 caagcgttgg cttctcgcat ctnnnnnnnn nnacacagaa atccacgtgc ttgag 55 <210> 100 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 100 caagcgttgg cttctcgcat ctnnnnnnnn nnacagcaga atccacgtgc ttgag 55 <210> 101 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 101 caagcgttgg cttctcgcat ctnnnnnnnn nnacctccaa atccacgtgc ttgag 55 <210> 102 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 102 caagcgttgg cttctcgcat ctnnnnnnnn nnacgctcga atccacgtgc ttgag 55 <210> 103 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 103 caagcgttgg cttctcgcat ctnnnnnnnn nnacgtatca atccacgtgc ttgag 55 <210> 104 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 104 caagcgttgg cttctcgcat ctnnnnnnnn nnactatgca atccacgtgc ttgag 55 <210> 105 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 105 caagcgttgg cttctcgcat ctnnnnnnnn nnagagtcaa atccacgtgc ttgag 55 <210> 106 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 106 caagcgttgg cttctcgcat ctnnnnnnnn nnagatcgca atccacgtgc ttgag 55 <210> 107 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 107 caagcgttgg cttctcgcat ctnnnnnnnn nnagcaggaa atccacgtgc ttgag 55 <210> 108 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 108 caagcgttgg cttctcgcat ctnnnnnnnn nnagtcacta atccacgtgc ttgag 55 <210> 109 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 109 caagcgttgg cttctcgcat ctnnnnnnnn nnatcctgta atccacgtgc ttgag 55 <210> 110 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 110 caagcgttgg cttctcgcat ctnnnnnnnn nnattgagga atccacgtgc ttgag 55 <210> 111 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 111 caagcgttgg cttctcgcat ctnnnnnnnn nncaaccaca atccacgtgc ttgag 55 <210> 112 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 112 caagcgttgg cttctcgcat ctnnnnnnnn nngactagta atccacgtgc ttgag 55 <210> 113 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 113 caagcgttgg cttctcgcat ctnnnnnnnn nncaatggaa atccacgtgc ttgag 55 <210> 114 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 114 caagcgttgg cttctcgcat ctnnnnnnnn nncacttcga atccacgtgc ttgag 55 <210> 115 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 115 caagcgttgg cttctcgcat ctnnnnnnnn nncagcgtta atccacgtgc ttgag 55 <210> 116 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 116 caagcgttgg cttctcgcat ctnnnnnnnn nncataccaa atccacgtgc ttgag 55 <210> 117 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 117 caagcgttgg cttctcgcat ctnnnnnnnn nnccagttca atccacgtgc ttgag 55 <210> 118 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 118 caagcgttgg cttctcgcat ctnnnnnnnn nnccgaagta atccacgtgc ttgag 55 <210> 119 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 119 caagcgttgg cttctcgcat ctnnnnnnnn nnccgtgaga atccacgtgc ttgag 55 <210> 120 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 120 caagcgttgg cttctcgcat ctnnnnnnnn nncctcctga atccacgtgc ttgag 55 <210> 121 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 121 caagcgttgg cttctcgcat ctnnnnnnnn nncgaactta atccacgtgc ttgag 55 <210> 122 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 122 caagcgttgg cttctcgcat ctnnnnnnnn nncgactgga atccacgtgc ttgag 55 <210> 123 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 123 caagcgttgg cttctcgcat ctnnnnnnnn nncgcataca atccacgtgc ttgag 55 <210> 124 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 124 caagcgttgg cttctcgcat ctnnnnnnnn nnctcaatga atccacgtgc ttgag 55 <210> 125 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 125 caagcgttgg cttctcgcat ctnnnnnnnn nnctgagcca atccacgtgc ttgag 55 <210> 126 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 126 caagcgttgg cttctcgcat ctnnnnnnnn nnctggcata atccacgtgc ttgag 55 <210> 127 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 127 caagcgttgg cttctcgcat ctnnnnnnnn nngaatctga atccacgtgc ttgag 55 <210> 128 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 128 caagcgttgg cttctcgcat ctnnnnnnnn nncaagacta atccacgtgc ttgag 55 <210> 129 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 129 caagcgttgg cttctcgcat ctnnnnnnnn nngagctgaa atccacgtgc ttgag 55 <210> 130 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 130 caagcgttgg cttctcgcat ctnnnnnnnn nngatagaca atccacgtgc ttgag 55 <210> 131 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> artificial synthesis <220> <221> misc_feature <222> (1)..(1) <223> 5'-Biotin modification <220> <221> misc_feature <222> (23)..(32) <223> n is a, c, g or t <400> 131 caagcgttgg cttctcgcat ctnnnnnnnn nngccacata atccacgtgc ttgag 55

Claims

1. A method comprising: (a) delivering a binder-DNA tag conjugate to a region of interest in a fixed section of mammalian tissue mounted on a substrate, wherein the binder-DNA tag conjugate comprises (i) a binder molecule that specifically binds to a protein of interest, and (ii) a DNA tag, wherein the DNA tag comprises a binder barcode and a polyA sequence; (b) delivering a first set of barcode polynucleotides bound to nucleic acids from the fixed tissue section to the region of interest, wherein the first set of barcode polynucleotides is delivered by a first microfluidic device clamped at the region of interest, wherein the first microfluidic device comprises a plurality of microchannels, each microchannel having an inlet end, an outlet end, and a width of at least 10 μm, and the first set of barcode polynucleotides comprises a linker sequence, a spatial barcode sequence, and a polyT sequence; (c) delivering reverse transcription reagents to the region of interest to generate cDNA linked to the first set of barcode polynucleotides; (d) delivering a second set of barcode polynucleotides to the region of interest, wherein the second set of barcode polynucleotides is delivered by a second microfluidic device clamped at the region of interest, wherein the second microfluidic device comprises a plurality of microchannels, each microchannel having an inlet end, an outlet end, and a width of at least 10 μm, wherein the second microfluidic device is positioned over the region of interest perpendicular to the direction of the microchannels of the first microfluidic device, and the second set of barcode polynucleotides comprises a linker sequence, a spatial barcode sequence, a unique molecular identifier sequence, and a first PCR handle end sequence; (e) delivering a ligation reagent to the region of interest to ligate the first set of barcode polynucleotides to the second set of barcode polynucleotides; (f) imaging the region of interest to generate a sample image; (g) delivering a lysis buffer or a denaturing agent to the region of interest to produce a lysed or denatured tissue sample; and (h) extracting cDNA from the lysed or denatured tissue sample.

2. The method according to claim 1, wherein The microchannels of the first microfluidic device are variable width microchannels, each microchannel having (i) a width of 50-150 μm at the inlet and outlet ends, and (ii) a width of 10-50 μm at the region of interest; The microchannels of the second microfluidic device are variable width microchannels, each microchannel having (i) a width of 50-150 μm at the inlet and outlet ends, and (ii) a width of 10-50 μm at the region of interest.

3. The method of claim 1 or 2, further comprising sequencing the cDNA to generate cDNA reads.

4. The method according to claim 3, wherein The sequencing includes template switching the cDNA to add a second PCR handle end sequence at the opposite end to the first PCR handle end sequence, amplifying the cDNA, generating a sequencing construct by tagging, and sequencing the sequencing construct to generate cDNA reads.

5. The method of claim 3, further comprising constructing a spatial molecular expression map of the tissue section by matching spatially addressable barcode conjugates with corresponding cDNA reads.

6. The method of claim 5, further comprising identifying the anatomical location of the nucleic acid by correlating the spatial molecular expression map with the sample image.

7. The method according to claim 1 or 2, wherein: The fixed tissue sections mounted on slides were produced by: Slicing formalin-fixed paraffin-embedded tissue and mounting the tissue slices on a substrate; applying a washing solution to the tissue section to dewax the tissue section; applying a rehydration solution to the tissue section to rehydrate the tissue section; applying an enzyme solution to the tissue section to permeabilize the tissue section; and Formalin was applied to the tissue sections to perform post-fixation on the tissue sections.

8. The method according to claim 7, wherein The formalin-fixed paraffin-embedded tissue sections were divided into 5-10 μm sections.

9. The method according to claim 7, wherein The substrate is a glass slide coated with poly-L-lysine.

10. The method according to claim 7, wherein: The washing solution is a xylene solution.

11. The method according to claim 7, wherein The enzyme solution is a proteinase K solution.

12. The method according to claim 1 or 2, wherein: The first and / or second microfluidic device is made of polydimethylsiloxane.

13. The method according to claim 1 or 2, wherein: The first and / or second microfluidic device comprises 5-100 microchannels.

14. The method according to claim 13, wherein The first and / or second microfluidic device comprises 50 microchannels.

15. The method according to claim 1, wherein Each microchannel of the first and second microfluidic devices has a width of 10 μm and a height of 12-15 μm, a width of 25 μm and a height of 17-22 μm, or a width of 50 μm and a height of 20-100 μm.

16. The method according to claim 1 or 2, wherein: The delivery of the first set of barcode polynucleotides is performed through the first microfluidic device using a negative pressure system, and / or the delivery of the second set of barcode polynucleotides is performed through the second microfluidic device using a negative pressure system.

17. The method according to claim 1 or 2, wherein: The lysis buffer or denaturing reagent is delivered directly to the tissue section.

18. The method according to claim 17, wherein The lysis buffer or denaturing reagent is delivered through a well in the device held on the substrate, wherein the well is directly over the region of interest.

19. The method according to claim 1 or 2, wherein: The first PCR handle end sequence is end-functionalized with biotin.

20. The method according to claim 1 or 2, wherein The first and / or second set of barcode polynucleotides comprises at least 50 barcode polynucleotides.

21. The method according to claim 1 or 2, wherein: The binding agent molecule is an antibody.

22. The method according to claim 1 or 2, wherein: The binding agent molecule is selected from the group consisting of: whole antibodies, Fab antibody fragments, F(ab')2 antibody fragments, monospecific Fab2 fragments, bispecific Fab2 fragments, trispecific Fab3 fragments, single chain variable fragments, bispecific diabodies, trispecific diabodies, scFv-Fc molecules and minibodies.

23. The method according to claim 1 or 2, wherein The nucleic acid of the fixed section of mammalian tissue is selected from the group consisting of: (i) ribonucleic acid, and (ii) deoxyribonucleic acid.

24. The method according to claim 23, wherein The ribonucleic acid is messenger ribonucleic acid.

25. The method of claim 23, wherein: The deoxyribonucleic acid is genomic deoxyribonucleic acid.

26. The method according to claim 1 or 2, wherein (i) the second set of barcode polynucleotides are conjugated to a universal linker, or (ii) the method further comprises delivering a universal linker sequence to the fixed section of mammalian tissue, wherein the universal linker comprises a sequence complementary to a linker sequence of the first set of barcode polynucleotides and comprises a sequence complementary to a linker sequence of the second set of barcode polynucleotides.

27. The method according to claim 1 or 2, wherein The imaging is performed using light or fluorescence microscopy.

28. The method according to claim 1 or 2, wherein The substrate is a microscope slide.

29. The method of claim 28, wherein The microscope slides are glass microscope slides.

30. The method of claim 28, wherein The microscope slides were polyamine coated.

31. The method of claim 28, wherein The microscope slides had dimensions of 25 mm x 75 mm.

32. The method of claim 1, wherein Each microchannel includes an aspect ratio that minimizes clogging, uneven flow, and / or tortuosity.

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