Systems and Methods for Spatial Alignment of Cellular Specimens and Applications Thereof

A method for yielding a spatially resolved map of a specimen, using a computational processing system, comprising:

• obtaining spatial omics data from a plurality of regions that cover a specimen, wherein the specimen comprises a plurality of cell types;
• estimating a number of cells per region from the spatial omics data
• and a fraction of each cell type from the spatial omics data
• querying referential single cell omics data to match a number of cells for each cell type to yield single cell omics data for spatial assignment;
• and
• based on a globally optimal solution, assigning single cells from the single cell omics data to spatial coordinates to yield a spatially resolved map of the specimen.

A method for yielding a spatially resolved map of a specimen, the method comprising:

• obtaining, using a computational processing system, spatial omics data from a plurality of regions that cover a specimen, wherein the specimen is a collection of cells that comprises a plurality of cell types;
• estimating, using the computational processing system, a number of cells per region of the plurality of regions;
• and
• based on a globally optimal solution, concurrently:
• determining a fraction of each cell type of the plurality of cells;
• querying, using the computational processing system, referential single cell omics data to match the number of cells for each cell type of the plurality of cell types to yield a set of single cell omics data for spatial assignment;
• and
• assigning, using the computational processing system, single cells from the set of single cell omics data to spatial coordinates to yield a spatially resolved map of the specimen.

A method for yielding a spatially resolved map of a specimen for a set of one or more cell types, the method comprising:

• obtaining, using a computational processing system, spatial omics data from a plurality of regions that cover a specimen, wherein the specimen is a collection of cells that comprises a plurality of cell types;
• estimating, using the computational processing system, a number of cells per region of the plurality of regions from the spatial omics data;
• estimating, for each region of the plurality regions, using the computational processing system, a fraction of each cell type of the plurality of cell types from the spatial omics data;
• querying, for each region comprising a fraction of a cell type of the set of one or more cell types, using the computational processing system, referential single cell omics data to match the number of cells for each cell type of the plurality of cell types to yield a set of single cell omics data for spatial assignment;
• and
• based on a globally optimal solution, assigning, for each region comprising a fraction of a cell type of the set of one or more cell types, using the computational processing system, single cells from the set of single cell omics data to spatial coordinates to yield a spatially resolved map of the specimen consisting of the cell types of the set of one or more cell types.

A method for yielding a spatially resolved map of a specimen via spatial transcriptomics, the method comprising:

• obtaining, using a computational processing system, spatial transcriptomics data from a plurality of regions that cover a specimen, wherein the specimen is a collection of cells that comprises a plurality of cell types;
• estimating, using the computational processing system, a number of cells per region of the plurality of regions from the spatial transcriptomics data;
• estimating, using the computational processing system, a fraction of each cell type of the plurality of cell types from the spatial transcriptomics data;
• querying, using the computational processing system, referential single cell transcriptomics data to match the number of cells for each cell type of the plurality of cell types to yield a set of single cell transcriptomics data for spatial assignment;
• and
• based on a globally optimal solution, assigning, using the computational processing system, single cells from the set of single cell transcriptomics data to spatial coordinates to yield a spatially resolved map of the specimen.

A method to diagnose a medical disorder based on spatial signatures, comprising:

• rendering a spatially resolved map of a tissue specimen extracted from a patient, wherein the rendering a spatially resolved map comprises:
• generating spatial omics data from a plurality of regions that cover the tissue specimen;
• querying, using a computational processing system, referential single cell omics data to yield a set of single cell omics data for spatial assignment;
• and
• based on a globally optimal solution, assigning, using the computational processing system, single cells from the set of single cell omics data to spatial coordinates to yield the spatially resolved map of the tissue specimen;
• assessing the spatially resolved map to detect a presence of a spatial signature, wherein the spatial signature is associated with a characteristic of a medical disorder;
• and determining the patient has the characteristic of the medical disorder by the
• presence of the spatial signature within the spatially resolved map.

A method to diagnose a cancer based on spatial signatures, comprising:

• rendering a spatially resolved map of a tumor specimen from a patient, wherein the rendering a spatially resolved map comprises:
• generating spatial omics data from a plurality of regions that cover the tumor specimen;
• querying, using a computational processing system, referential single cell omics data to yield a set of single cell omics data for spatial assignment;
• and
• based on a globally optimal solution, assigning, using the computational processing system, single cells from the set of single cell omics data to spatial coordinates to yield the spatially resolved map of the tumor specimen;
• assessing the spatially resolved map to detect a presence of a spatial signature, wherein the spatial signature is associated with a cancer characteristic;
• and
• determining the patient has the cancer characteristic by the presence of the spatial signature within the spatially resolved map.

A method for training a machine learning model to predict spatial signatures from spatially resolved maps, comprising:

• rendering a spatially resolved map of a plurality of multicellular specimens, wherein each multicellular specimen is associated with a biological characteristic;
• rendering a spatially resolved map of a plurality of multicellular control specimens, wherein each multicellular control specimen is not associated with the biological characteristic,
• wherein rendering of each spatially resolved map comprises:
• generating spatial omics data from a plurality of regions that cover the specimen;
• querying, using a computational processing system, referential single cell omics data to yield a set of single cell omics data for spatial assignment;
• based on a globally optimal solution, assigning, using the computational processing system, single cells from the set of single cell omics data to spatial coordinates to yield the spatially resolved map of the specimen;
• and
• training a machine learning model with each spatially resolved map of the plurality of multicellular specimens and of the plurality of multicellular control specimens to predict the biological characteristic from a spatially resolved map.