Systems and methods for spatial analysis of analytes using fiducial alignment

Patent metadata

Field	Value
Publication number	US-11501440-B2
Application number	US-202016951843-A
Country	US
Kind code	B2
Filing date	Nov 18, 2020
Priority date	Nov 22, 2019
Publication date	Nov 15, 2022
Grant date	Nov 15, 2022

Abstract

Systems and methods for spatial analysis of analytes are provided. A data structure is obtained comprising an image, as an array of pixel values, of a sample on a substrate having a identifier, fiducial markers and a set of capture spots. The pixel values are used to identify derived fiducial spots. The substrate identifier identifies a template having reference positions for reference fiducial spots and a corresponding coordinate system. The derived fiducial spots are aligned with the reference fiducial spots using an alignment algorithm to obtain a transformation between the derived and reference fiducial spots. The transformation and the template corresponding coordinate system are used to register the image to the set of capture spots. The registered image is then analyzed in conjunction with spatial analyte data associated with each capture spot, thereby performing spatial analysis of analytes.

First claim

What is claimed is:

1. A method of spatial analysis of analytes comprising:
A) obtaining a data structure in electronic form comprising (i) an image of a sample on a substrate and (ii) a substrate identifier unique to the substrate, wherein:
the substrate includes a plurality of fiducial markers,
the substrate includes a set of capture spots, wherein the set of capture spots comprises at least 1000 capture spots; and
the image comprises an array of pixel values, wherein the array of pixel values comprises at least 100,000 pixel values;

B) analyzing the array of pixel values to identify a plurality of derived fiducial spots of the image;
C) using the substrate identifier of the data structure to select a first template in a plurality of templates, wherein each template in the plurality of templates comprises reference positions for a corresponding plurality of reference fiducial spots and a corresponding coordinate system;
D) aligning the plurality of derived fiducial spots of the image with the corresponding plurality of reference fiducial spots of the first template using an alignment algorithm to obtain a transformation between the plurality of derived fiducial spots of the image and the corresponding plurality of reference fiducial spots of the first template;
E) using the transformation and the coordinate system of the first template to register the image to the set of capture spots; and
F) analyzing the image after the using E) in conjunction with spatial analyte data associated with each capture spot, thereby performing spatial analysis of analytes.

2. The method of claim 1 , wherein the B) analyzing comprises:
identifying a plurality of candidate derived fiducial spots by thresholding the array of pixel values into a plurality of threshold images and identifying, within the plurality of threshold images, groups of pixels having white values,
clustering the plurality of candidate derived fiducial spots based on spot size, thereby distributing the plurality of candidate derived fiducial spots into a plurality of subsets of candidate derived fiducial spots, wherein each respective subset of candidate derived fiducial spots in the plurality of subsets of candidate derived fiducial spots has a characteristic size, and
selecting the subset of candidate derived fiducial spots in the plurality of subsets of candidate derived fiducial spots that has the largest characteristic size as the plurality of derived fiducial spots of the image.

3. The method of claim 2 , wherein the identifying further comprises merging respective pairs of candidate derived fiducial spots that are within a threshold distance of each other.

4. The method of claim 2 , wherein the identifying further comprises filtering out respective candidate derived fiducial spots that fail to satisfy a maximum or minimum size criterion.

5. The method of claim 2 , wherein the identifying further comprises filtering out respective candidate derived fiducial spots that fail to satisfy a circularity criterion, a convexity criterion, or an inertia ratio criterion.

6. The method of claim 1 , wherein the transformation comprises:
a similarity transform that comprises rotation, translation, and isotropic scaling of the plurality of derived fiducial spots of the image to minimize a residual error between the plurality of derived fiducial spots and the corresponding plurality of reference fiducial spots, or
a non-rigid transform that comprises anisotropic scaling and skewing of the plurality of derived fiducial spots of the image to minimize a residual error between the plurality of derived fiducial spots and the corresponding plurality of reference fiducial spots.

7. The method of claim 1 , wherein the transformation is a non-rigid transform and wherein the non-rigid transform is an affline transformation.

8. The method of claim 1 , wherein the alignment algorithm is a coherent point drift algorithm, an Iterative Closest Point algorithm, a Robust Point Matching algorithm, or a Thin-Plate-Spline Robust Point Matching algorithm.

9. The method of claim 1 , wherein the corresponding plurality of reference fiducial spots of the first template consists of between 100 spots and 1000 spots.

10. The method of claim 1 , wherein:
the sample is a sectioned tissue sample,
each respective capture spot in the set of capture spots is (i) at a different position in a two-dimensional array and (ii) associates with one or more analytes from the sectioned tissue sample, and
each respective capture spot in the set of capture spots is characterized by at least one unique spatial barcode in a plurality of spatial barcodes.

11. The method of claim 1 , wherein a capture spot in the set of capture spots comprises a capture domain or a cleavage domain.

12. The method of claim 1 , wherein each capture spot in the set of capture spots is attached directly or attached indirectly to the substrate.

13. The method of claim 10 , wherein the one or more analytes comprise five or more analytes, ten or more analytes, fifty or more analytes, one hundred or more analytes, five hundred or more analytes, 1000 or more analytes, 2000 or more analytes, or between 2000 and 10,000 analytes.

14. The method of claim 10 , wherein the unique spatial barcode encodes a unique predetermined value selected from the set {1, . . . , 1024}, {1, . . . , 4096}, {1, . . . , 16384}, {1, . . . , 65536}, {1, . . . , 262144}, {1, . . . , 1048576}, {1, . . . , 4194304}, {1, . . . , 16777216}, {1, . . . , 67108864}, or {1, . . . , 1×10 12 }.

15. The method of claim 1 , wherein each respective capture spot in the set of capture spots includes 1000 or more capture probes, 2000 or more capture probes, 10,000 or more capture probes, 100,000 or capture more probes, 1×10 6 or more capture probes, 2×10 6 or more capture probes, or 5×10 6 or more capture probes.

16. The method of claim 15 , wherein each capture probe in the respective capture spot includes a poly-A sequence or a poly-T sequence and a unique spatial barcode that characterizes the respective capture spot.

17. The method of claim 15 , wherein each capture probe in the respective capture spot includes the same spatial barcode from the plurality of spatial barcodes.

18. The method of claim 15 , wherein each capture probe in the respective capture spot includes a different spatial barcode from the plurality of spatial barcodes.

19. The method of claim 1 , wherein the sample is a sectioned tissue sample and wherein the sectioned tissue sample has a depth of 100 microns or less.

20. The method of claim 10 , wherein
the one or more analytes is a plurality of analytes,
a respective capture spot in the set of capture spots includes a plurality of capture probes, each probe in the plurality of capture probes including a capture domain that is characterized by a capture domain type in a plurality of capture domain types, and
each respective capture domain type in the plurality of capture domain types is configured to bind to a different analyte in the plurality of analytes.

21. The method of claim 20 , wherein the plurality of capture domain types comprises between 5 and 15,000 capture domain types and the respective capture spot includes at least five, at least 10, at least 100, or at least 1000 capture probes for each capture domain type in the plurality of capture domain types.

22. The method of claim 10 , wherein:
the one or more analytes is a plurality of analytes, and
a respective capture spot in the set of capture

Assignees

• 10X Genomics Inc

Inventors

• Weisenfeld Neil Ira
• Dshkhunyan Narek
• Shah Preyas

Classifications

• G06T7/33 Primary

  using feature-based methods · CPC title

• G06V20/695

  Preprocessing, e.g. image segmentation · CPC title

• G06T7/0014 Primary

  using an image reference approach · CPC title

• G06F18/22

  Matching criteria, e.g. proximity measures · CPC title

• G06T2207/10056

  Microscopic image · CPC title