Spatially Encoded Biological Assays Super-Linear Scaling
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Solution Overview
Problem
Existing spatially encoded biological assays face challenges in increasing the area sampled while maintaining adequate spatial resolution, particularly in analyzing larger regions of tissue without sacrificing single-cell resolution.
Innovation Solution
The method involves providing multiple sets of probes with distinct spatial barcodes to intersecting regions of a sample, allowing for the identification of molecules at specific intersections based on the combination of spatial barcodes, thereby enabling larger regions of interest to be analyzed with maintained resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If traditional spatially encoded assays are used to increase the area sampled, then the area of tissue analyzed increases, but the spatial resolution deteriorates and single-cell resolution is lost
Solution Approach 1:
The assay area is divided into multiple discrete regions, each covered by a separate probe set with unique spatial barcodes. This segmentation allows parallel analysis of multiple regions while maintaining the resolution needed to distinguish individual cells within each region.
Solution Approach 2:
The patent introduces an additional dimension of spatial encoding through the use of multiple probe sets that intersect across regions. By combining spatial barcodes from different probe sets, the system achieves super-linear scaling of assayed area while maintaining resolution through multi-dimensional barcode combination.
2Area of stationary object
If more probe sets are added to increase the region of interest, then the area sampled increases, but the device complexity increases
Solution Approach 1:
Multiple probe sets share common structural elements and barcode formats, allowing them to be processed through the same experimental workflow. Each probe set is universal in its design principles while being specific to its region, enabling scalable expansion without proportionally increasing operational complexity.
Solution Approach 2:
The system varies specific parameters such as barcode sequences and probe regions across different probe sets, while maintaining constant experimental conditions and processing methods. This parameter variation allows region-specific analysis without changing the overall device complexity or workflow.
Data Source
AI summary
The present disclosure generally relates to methods for increasing the region of interest of spatial multi-omics techniques while retaining single-cell resolution. These methods can improve the scaling of region of interest dimension with input/output channels from linear to super-linear. In some embodiments, the method is performed by providing a plurality of probes of a first type to a first region of a sample, wherein at least a subset of the probes of the first type includes a first spatial barcode, providing a plurality of probes of a second type to a second region of the sample, wherein at least a subset of the probes of the second type includes a second spatial barcode, and providing a probe of a third type to a third region of the sample, wherein the probe of the third type comprises a third spatial barcode.