# US 2026/0088134 A1 · Systems and Methods for Spatial Alignment of Cellular Specimens and Applications Thereof

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- Number: US 2026/0088134 A1
- Document ID: US20260088134A1
- Status: Published application
- Kind: A1
- Application number: 18/866,981
- Filed: 2023-05-18
- Published: 2026-03-26
- Earliest claimed: 2022-05-18
- Assignees: The Board of Trustees of the Leland Stanford Junior University
- Applicants: The Board of Trustees of the Leland Stanford Junior University
- Inventors: Aaron M. Newman; Milad R. Vahid; Erin Brown; Chloe Steen
- Web page: https://patentgrep.com/patent/US20260088134A1

## CPC

- G16B 45/00: ICT specially adapted for bioinformatics-related data visualisation, e.g. displaying of maps or networks
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G16B 20/00: ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
- G16B 25/10: Gene or protein expression profiling; Expression-ratio estimation or normalisation
- G16H 10/40: for data related to laboratory analysis, e.g. patient specimen analysis
- G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

Processes to spatially align single cells to yield a specimen map with single cell resolution are provided. Methods can perform spatial omics on a specimen to yield spatial omics data. The spatial omics data can be used in combination with single cell omics data to assign single cells to spatial coordinates to yield a resolved specimen map.

## Claims

### Claim at position 1

1-13. (canceled)

### Claim 14

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.

### Claim 15 (depends on 14)

The method of claim 14, wherein the spatial omics is one of: spatial transcriptomics, spatial genomics, spatial epigenomics, spatial methylomics, spatial proteomics, or spatial metabolomics.

### Claim 16 (depends on 14)

The method of claim 14 further comprising: extracting source material to perform the spatial omics from each region of the plurality of regions, wherein the source material is extracted via laser capture microdissection, iterative microdigestion, or in situ capture.

### Claim 17 (depends on 14)

The method of claim 14, wherein the spatial omics is spatial transcriptomics, the method further comprising:
determining expression of a plurality of transcripts via in situ hybridization.

### Claim 18 (depends on 14)

The method of claim 14, wherein the step of estimating the number of cells per region comprises:
estimating, using the computational processing system, the number of cells per region of the plurality of regions based on an amount of source material derived from each region, as determined by the spatial omics data.

### Claim 19 (depends on 14)

The method of claim 14, wherein the step of estimating the number of cells per region comprises:
estimating, using the computational processing system, the number of cells per region via cell segmentation.

### Claim 20 (depends on 19)

The method of claim 19, wherein each region of the plurality of regions is examined for segmented nuclei or staining of cell membranes and the estimation of the number of cells is based on the nuclei count or cell membrane count.

### Claim 21 (depends on 14)

The method of claim 14, wherein estimating a fraction of each cell type of the plurality of cell types is estimated by a deconvolution method.

### Claim 22 (depends on 21)

The method of claim 21, wherein the deconvolution method is determined from the spatial omics data and an a priori defined reference.

### Claim 23 (depends on 21)

The method of claim 21, wherein the deconvolution method is determined by: Spatial Seurat, RCTD, SPOTlight, cell2location, or CIBERSORTx.

### Claim 24 (depends on 14)

The method of claim 14, wherein querying the referential single cell omics data to match the number of cells for each cell type of the plurality of cell types further comprises:
removing, using the computational processing system, single cell omics data of one or more single cells within the referential single cell omics data when the number of single cells within the referential single cell omics data is greater than the number of cells estimated within the specimen.

### Claim 25 (depends on 14)

The method of claim 14, wherein querying the referential single cell omics data to match the number of cells for each cell type of the plurality of cell types further comprises:
adding, using the computational processing system, single cell omics data of one or more single cells within the referential single cell omics data when the number of single cells within the referential single cell omics data is less than the number of cells estimated within the specimen.

### Claim 26 (depends on 25)

The method of claim 25, wherein adding single cell omics data of one or more single cells comprises duplicating single cell omics data of the single cell omics data.

### Claim 27 (depends on 25)

The method of claim 25, wherein adding single cell omics data of one or more single cells comprises generating single cell omics data representative of the single cell omics data.

### Claim 28 (depends on 14)

The method of claim 14, wherein each region comprises a number of subregions equal to with the number of cells estimated for each region.

### Claim 29 (depends on 28)

The method of claim 28, wherein assigning single cells from the set of single cell omics data to spatial coordinates comprises:
generating, using the computational processing system, matrix of single cell omics profiles with single cells and a matrix of specimen omics profiles with subregions; and
utilizing, using the computational processing system, the matrices to determine a globally optimal solution.

### Claim 30 (depends on 29)

The method of claim 29 further comprises:
determining, using the computational processing system, the globally optimal solution by summation of assignments of singles cells to subregions that minimizes a linear cost function.

### Claim 31 (depends on 30)

The method of claim 30, wherein the determining the globally optimal solution further comprises:
solving, using the computational processing system, the globally optimal solution via a shortest augmenting paths-based Jonker-Volgenant algorithm.

### Claim 32 (depends on 30)

The method of claim 30, wherein the determining the globally optimal solution comprises:
solving, using the computational processing system, the globally optimal solution via a cost scaling push-relabel method.

### Claim 33 (depends on 14)

The method of claim 14, wherein the spatially resolved map of the specimen has a single-cell resolution.

### Claim 34

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.

### Claim 35 (depends on 34)

The method of claim 34, wherein the spatial omics is one of: spatial transcriptomics, spatial genomics, spatial epigenomics, spatial methylomics, spatial proteomics, or spatial metabolomics.

### Claim 36 (depends on 34)

The method of claim 34 further comprising:
extracting source material to perform the spatial omics from each region of the plurality of regions, wherein the source material is extracted via laser capture microdissection, iterative microdigestion, or in situ capture.

### Claim 37 (depends on 34)

The method of claim 34, wherein the spatial omics is spatial transcriptomics, the method further comprising:
determining expression of a plurality of transcripts via in situ hybridization.

### Claim 38 (depends on 34)

The method of claim 34, wherein the step of estimating the number of cells per region comprises:
estimating, using the computational processing system, the number of cells per region of the plurality of regions based on an amount of source material derived from each region, as determined by the spatial omics data.

### Claim 39 (depends on 34)

The method of claim 34, wherein the step of estimating the number of cells per region comprises:
estimating, using the computational processing system, the number of cells per region via cell segmentation.

### Claim 40 (depends on 39)

The method of claim 39, wherein each region of the plurality of regions is examined for segmented nuclei or staining of cell membranes and the estimation of the number of cells is based on the nuclei count or cell membrane count.

### Claim 41 (depends on 34)

The method of claim 34, wherein querying the referential single cell omics data to match the number of cells for each cell type of the plurality of cell types further comprises:
removing, using the computational processing system, single cell omics data of one or more single cells within the referential single cell omics data when the number of single cells within the referential single cell omics data is greater than the number of cells estimated within the specimen.

### Claim 42 (depends on 34)

The method of claim 34, wherein querying the referential single cell omics data to match the number of cells for each cell type of the plurality of cell types further comprises: adding, using the computational processing system, single cell omics data of one or more single cells within the referential single cell omics data when the number of single cells within the referential single cell omics data is less than the number of cells estimated within the specimen.

### Claim 43 (depends on 42)

The method of claim 42, wherein adding single cell omics data of one or more single cells comprises duplicating single cell omics data of the single cell omics data.

### Claim 44 (depends on 42)

The method of claim 42, wherein adding single cell omics data of one or more single cells comprises generating single cell omics data representative of the single cell omics data.

### Claim 45 (depends on 34)

The method of claim 34, wherein each region comprises a number of subregions equal to with the number of cells estimated for each region.

### Claim 46 (depends on 45)

The method of claim 45, wherein assigning single cells from the set of single cell omics data to spatial coordinates comprises:
generating, using the computational processing system, matrix of single cell omics profiles with single cells and a matrix of specimen omics profiles with subregions; and
utilizing, using the computational processing system, the matrices to determine a globally optimal solution.

### Claim 47 (depends on 46)

The method of claim 46 further comprises:
determining, using the computational processing system, the globally optimal solution by summation of assignments of singles cells to subregions that minimizes a linear cost function.

### Claim 48 (depends on 47)

The method of claim 47, wherein the determining the globally optimal solution further comprises:
solving, using the computational processing system, the globally optimal solution via a shortest augmenting paths-based Jonker-Volgenant algorithm.

### Claim 49 (depends on 47)

The method of claim 47, wherein the determining the globally optimal solution further comprises:
solving, using the computational processing system, the globally optimal solution via a cost scaling push-relabel method.

### Claim 50 (depends on 34)

The method of claim 34, wherein the spatially resolved map of the specimen has a single-cell resolution.

### Claim 51

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.

### Claim 52 (depends on 51)

The method of claim 51, wherein the spatial omics is one of: spatial transcriptomics, spatial genomics, spatial epigenomics, spatial methylomics, spatial proteomics, or spatial metabolomics.

### Claim 53 (depends on 51)

The method of claim 51 further comprising:
extracting source material to perform the spatial omics from each region of the plurality of regions, wherein the source material is extracted via laser capture microdissection, iterative microdigestion, or in situ capture.

### Claim 54 (depends on 51)

The method of claim 51, wherein the spatial omics is spatial transcriptomics, the method further comprising:
determining expression of a plurality of transcripts via in situ hybridization.

### Claim 55 (depends on 51)

The method of claim 51, wherein the step of estimating the number of cells per region comprises:
estimating, using the computational processing system, the number of cells per region of the plurality of regions based on an amount of source material derived from each region, as determined by the spatial omics data.

### Claim 56 (depends on 51)

The method of claim 51, wherein the step of estimating the number of cells per region comprises:
estimating, using the computational processing system, the number of cells per region via cell segmentation.

### Claim 57 (depends on 56)

The method of claim 56, wherein each region of the plurality of regions is examined for segmented nuclei or staining of cell membranes and the estimation of the number of cells is based on the nuclei count or cell membrane count.

### Claim 58 (depends on 51)

The method of claim 51, wherein estimating a fraction of each cell type of the plurality of cell types is estimated by a deconvolution method.

### Claim 59 (depends on 58)

The method of claim 58, wherein the deconvolution method is determined from the spatial omics data and an a priori defined reference.

### Claim 60 (depends on 58)

The method of claim 58, wherein the deconvolution method is determined by: Spatial Seurat, RCTD, SPOTlight, cell2location, or CIBERSORTx.

### Claim 61 (depends on 51)

The method of claim 51, wherein querying the referential single cell omics data to match the number of cells for each cell type of the plurality of cell types further comprises:
removing, 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 cell omics data of one or more single cells within the referential single cell omics data when the number of single cells within the referential single cell omics data is greater than the number of cells estimated within the specimen.

### Claim 62 (depends on 51)

The method of claim 51, wherein querying the referential single cell omics data to match the number of cells for each cell type of the plurality of cell types further comprises:
adding, 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 cell omics data of one or more single cells within the referential single cell omics data when the number of single cells within the referential single cell omics data is less than the number of cells estimated within the specimen.

### Claim 63 (depends on 62)

The method of claim 62, wherein adding single cell omics data of one or more single cells comprises duplicating single cell omics data of the single cell omics data.

### Claim 64 (depends on 62)

The method of claim 62, wherein adding single cell omics data of one or more single cells comprises generating single cell omics data representative of the single cell omics data.

### Claim 65 (depends on 51)

The method of claim 51, wherein each region comprises a number of subregions equal to with the number of cells estimated for each region.

### Claim 66 (depends on 65)

The method of claim 65, wherein assigning single cells from the set of single cell omics data to spatial coordinates comprises:
generating, for each region comprising a fraction of a cell type of the set of one or more cell types, using the computational processing system, a matrix of single cell omics profiles with single cells and a matrix of specimen omics profiles with subregions; and
utilizing, using the computational processing system, the matrices to determine a globally optimal solution.

### Claim 67 (depends on 66)

The method of claim 66 further comprises:
determining, using the computational processing system, the globally optimal solution by summation of assignments of singles cells to subregions that minimizes a linear cost function.

### Claim 68 (depends on 67)

The method of claim 67, wherein the determining the globally optimal solution further comprises:
solving, using the computational processing system, the globally optimal solution via a shortest augmenting paths-based Jonker-Volgenant algorithm.

### Claim 69 (depends on 67)

The method of claim 67, wherein the determining the globally optimal solution further comprises:
solving, using the computational processing system, the globally optimal solution via a cost scaling push-relabel method.

### Claim 70 (depends on 51)

The method of claim 51, wherein the spatially resolved map of the specimen has a single-cell resolution.

### Claim 71

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.

### Claim 72 (depends on 71)

The method of claim 71 further comprising: extracting RNA from each region of the plurality of regions, wherein the RNA is extracted via in situ capture; and
sequencing the extracted RNA from each region of the plurality of regions to yield the spatial transcriptomics data from the plurality of regions.

### Claim 73 (depends on 72)

The method of claim 72, wherein the RNA is extracted from each region of the plurality of regions via 10xGenomics Visium or NanoString GeoMX.

### Claim 74 (depends on 72)

The method of claim 72, wherein the sequencing is performed by one of the following techniques: whole exome sequencing, capture targeted sequencing, amplification-based targeted sequencing, sequencing based on random priming, or end-biased sequencing.

### Claim 75 (depends on 71)

The method of claim 71 further comprising:
determining expression of a plurality of transcripts via in situ hybridization to yield the spatial transcriptomics data from the plurality of regions.

### Claim 76 (depends on 75)

The method of claim 75, wherein the expression of the plurality of transcripts is determined via Vizgen MERSCOPE, NanoString CosMX, 10×Genomics Xenium, or hybridization-based in situ sequencing.

### Claim 77 (depends on 71)

The method of claim 71, wherein the step of estimating the number of cells per region comprises:
estimating, using the computational processing system, the number of cells per region of the plurality of regions based on a number of detectably expressed genes.

### Claim 78 (depends on 77)

The method of claim 77, wherein the number of detectably expressed genes is determined by a number of unique molecular identifiers.

### Claim 79 (depends on 71)

The method of claim 71, wherein the step of estimating the number of cells per region comprises:
estimating, using the computational processing system, the number of cells per region via cell segmentation.

### Claim 80 (depends on 79)

The method of claim 79, wherein each region of the plurality of regions is examined for segmented nuclei or staining of cell membranes and the estimation of the number of cells is based on the nuclei count or cell membrane count.

### Claim 81 (depends on 71)

The method of claim 71, wherein estimating a fraction of each cell type of the plurality of cell types is estimated by a deconvolution method.

### Claim 82 (depends on 81)

The method of claim 81, wherein the deconvolution method is determined from the spatial transcriptomics data and an a priori defined reference.

### Claim 83 (depends on 81)

The method of claim 81, wherein the deconvolution method is determined by: Spatial Seurat, RCTD, SPOTlight, cell2location, or CIBERSORTx.

### Claim 84 (depends on 71)

The method of claim 71, wherein querying the referential single cell transcriptomics data to match the number of cells for each cell type of the plurality of cell types further comprises:
removing, using the computational processing system, single cell transcriptomics data of one or more single cells within the referential single cell transcriptomics data when the number of single cells within the referential single cell transcriptomics data is greater than the number of cells estimated within the specimen.

### Claim 85 (depends on 71)

The method of claim 71, wherein querying the referential single cell transcriptomics data to match the number of cells for each cell type of the plurality of cell types further comprises:
adding, using the computational processing system, single cell transcriptomics data of one or more single cells within the referential single cell transcriptomics data when the number of single cells within the referential single cell transcriptomics data is less than the number of cells estimated within the specimen.

### Claim 86 (depends on 85)

The method of claim 85, wherein adding single cell transcriptomics data of one or more single cells comprises duplicating single cell transcriptomics data of the single cell transcriptomics data.

### Claim 87 (depends on 85)

The method of claim 85, wherein adding single cell transcriptomics data of one or more single cells comprises generating single cell transcriptomics data representative of the single cell transcriptomics data.

### Claim 88 (depends on 71)

The method of claim 71, wherein each region comprises a number of subregions equal to with the number of cells estimated for each region.

### Claim 89 (depends on 88)

The method of claim 88, wherein assigning single cells from the set of single cell transcriptomics data to spatial coordinates comprises:
generating, using the computational processing system, matrix of single cell transcriptomics profiles with single cells and a matrix of specimen transcriptomics profiles with subregions; and
utilizing, using the computational processing system, the matrices to determine a globally optimal solution.

### Claim 90 (depends on 89)

The method of claim 89 further comprises:
determining, using the computational processing system, the globally optimal solution by summation of assignments of singles cells to subregions that minimizes a linear cost function.

### Claim 91 (depends on 90)

The method of claim 90, wherein the determining the globally optimal solution further comprises:
solving, using the computational processing system, the globally optimal solution via a shortest augmenting paths-based Jonker-Volgenant algorithm.

### Claim 92 (depends on 90)

The method of claim 90, wherein the determining the globally optimal solution further comprises:
solving, using the computational processing system, the globally optimal solution via a cost scaling push-relabel method.

### Claim 93 (depends on 71)

The method of claim 71, wherein the spatially resolved map of the specimen has a single cell resolution.

### Claim 94

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.

### Claim 95 (depends on 94)

The method of claim 94, wherein assessing the spatially resolved map to detect the presence of the spatial signature further comprises:
utilizing the rendered spatially resolved map of the tissue specimen as input in a trained machine learning model to yield a likelihood of the characteristic of medical disorder, wherein determining the patient has the characteristic of medical disorder is determined by the likelihood of the characteristic of medical disorder.

### Claim 96 (depends on 94)

The method of claim 94, wherein the characteristic of medical disorder is a response to therapy.

### Claim 97 (depends on 96)

The method of claim 96 further comprising:
administering the therapy based on a presence of the spatial signature.

### Claim 98 (depends on 94)

The method of claim 94, wherein the characteristic of medical disorder is a need for a further diagnostic technique to be performed.

### Claim 99 (depends on 98)

The method of claim 98 further comprising:
performing the further diagnostic technique based on a presence of the spatial signature.

### Claim 100 (depends on 94)

The method of claim 94 further comprising:
performing a spatial omics protocol using the tissue specimen extracted from the patient, wherein the spatial omics protocol is utilized to render the spatially resolved map.

### Claim 101 (depends on 100)

The method of claim 100 further comprising:
extracting the tissue specimen from the patient to perform the spatial omics protocol.

### Claim 102 (depends on 94)

The method of claim 94, wherein the tissue specimen comprises tissue of a tumor, of a multicellular organ, infiltrated by immune cells, infected with pathogens, interacting with microbiomes.

### Claim 103 (depends on 94)

The method of claim 94, wherein the medical disorder is cancer, a pathogenic infection, an organ dysfunction, an inflammatory disorder, an autoimmune disorder, diabetes, liver dysfunction, heart disease, or a neurodegenerative disorder.

### Claim 104 (depends on 94)

The method of claim 94, wherein the characteristic of the medical disorder is a particular pathology, a likelihood of success or failure of a therapy, a severity of the medical disorder, a need for a particular medical intervention, or a likelihood of a future medical complication.

### Claim 105

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.

### Claim 106 (depends on 105)

The method of claim 105, wherein assessing the spatially resolved map to detect the presence of the spatial signature further comprises:
utilizing the rendered spatially resolved map of the tumor specimen as input in a trained machine learning model to yield a likelihood of the cancer characteristic; wherein determining the patient has the cancer characteristic of medical disorder is determined by the likelihood of the cancer characteristic.

### Claim 107 (depends on 105)

The method of claim 105, wherein the cancer characteristic is a response to a therapy, a toxicity of a therapy, or a resistance to a therapy.

### Claim 108 (depends on 107)

The method of claim 107, wherein the cancer characteristic is the response to the therapy, the method further comprising:
administering the therapy based on a presence of the spatial signature, wherein the presence of the spatial signature indicates the patient will respond to the therapy.

### Claim 109 (depends on 107)

The method of claim 107, wherein the cancer characteristic is the response to the therapy, the method further comprising:
administering the therapy based on a lack of a presence of the spatial signature, wherein the presence of the spatial signature indicates the patient will not respond to the therapy.

### Claim 110 (depends on 107)

The method of claim 107, wherein the cancer characteristic is the toxicity of the therapy, the method further comprising:
administering the therapy based on a presence of the spatial signature, wherein the presence of the spatial signature indicates the therapy is not toxic to the patient.

### Claim 111 (depends on 107)

The method of claim 107, wherein the cancer characteristic is the toxicity of the therapy, the method further comprising:
administering the therapy based on a lack of a presence of the spatial signature, wherein the presence of the spatial signature indicates the therapy is toxic to the patient.

### Claim 112 (depends on 107)

The method of claim 107, wherein the cancer characteristic is the resistance to the therapy, the method further comprising:
administering the therapy based on a presence of the spatial signature, wherein the presence of the spatial signature indicates the patient will not be resistant to the therapy.

### Claim 113 (depends on 107)

The method of claim 107, wherein the cancer characteristic is the resistance to the therapy, the method further comprising:
administering the therapy based on a lack of a presence of the spatial signature, wherein the presence of the spatial signature indicates the patient will be resistant to the therapy.

### Claim 114 (depends on 107)

The method of claim 107, wherein the therapy comprises one of: immunotherapy, chemotherapy, radiotherapy, a targeted therapy, hormone therapy, or surgical resection.

### Claim 115 (depends on 105)

The method of claim 105 further comprising:
performing a spatial omics protocol using the tumor specimen extracted from the patient, wherein the spatial omics protocol is utilized to render the spatially resolved map.

### Claim 116 (depends on 115)

The method of claim 115 further comprising:
extracting the tumor specimen from the patient to perform the spatial omics protocol.

### Claim 117 (depends on 105)

The method of claim 105, wherein the cancer characteristic is cancer progression, a likelihood of metastasis, a transition from pre-invasive to invasive cancer, or a likelihood of recurrence.

### Claim 118

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.

### Claim 119 (depends on 118)

The method of claim 118, wherein the biological characteristic comprises a pathology, a medical disorder, a health status, a metabolic status, an organ status, an activation of multicellular communication, a multicellular transition, or a multicellular response to a stimulus.

### Claim 120 (depends on 118)

The method of claim 118, wherein each multicellular specimen is a tumor specimen and the biological characteristic is a cancer characteristic selected from: a response to a therapy, a toxicity of a therapy, or a resistance to a therapy.

### Claim 121 (depends on 120)

The method of claim 120, wherein the therapy comprises one of: immunotherapy, chemotherapy, radiotherapy, a targeted therapy, hormone therapy, or surgical resection.

### Claim 122 (depends on 118)

The method of claim 118, wherein each multicellular specimen is a tumor specimen and the biological characteristic is a cancer characteristic selected from: cancer progression, a likelihood of metastasis, a transition from pre-invasive to invasive cancer, or a likelihood of recurrence.

### Claim 123 (depends on 118)

The method of claim 118, wherein the machine learning model is a classifier.

### Claim 124 (depends on 118)

The method of claim 118, wherein the machine learning model is a regressor.

### Claim 125 (depends on 118)

The method of claim 118, wherein the machine learning model incorporates a deep neural network (DNN), a convolutional neural network (CNN), a graph neural network (GNN), a recurrent neural network, a long short-term memory (LSTM) network, a kernel ridge regression (KRR), or gradient-boosted random forest decision trees.

### Claim 126 (depends on 118)

The method of claim 118, wherein the machine learning model incorporates a spatial encoder.