Systems and methods for deconvolving cell types in histology slide images, using super-resolution spatial transcriptomics data

A computer-implemented method for classifying cells in an histology image, the method comprising:

• receiving, via one or more processors, the histology image and a cell segmentation map;
• processing the histology image and the cell segmentation map using a trained machine learning model to generate a cell expression map including a plurality of cell contours, each including a respective predicted aggregated gene expression,
• wherein the machine learning model is trained using training data;
• and
• for each of the cell contours, processing the cell contour to generate a respective predicted cell type using a model trained to predict cell types based on aggregated gene expression information.

A computing system for training a machine learning model to classify cells in a histology image, comprising:

• one or more processors;
• and
• one or more memories having stored thereon computer-executable instructions that, when executed by the one or more processors, cause the computing system to:
• receive, via one or more processors, training data;
• and
• train a machine learning model, using the training data, to generate a cell expression map corresponding to a cropped portion of a super-resolved gene expression heat map,
• wherein the cell expression map includes a plurality of cell contours, each including respective aggregated gene expression values.

A computing system for training a machine learning model to classify cells in a histology image, comprising:

• one or more processors;
• and
• one or more memories having stored thereon computer-executable instructions that, when executed by the one or more processors, cause the computing system to:
• receive, via one or more processors, the histology image and a cell segmentation map;
• process the histology image and the cell segmentation map using a trained machine learning model to generate a cell expression map including a plurality of cell contours, each including a respective predicted aggregated gene expression,
• wherein the machine learning model is trained using training data;
• and
• for each of the cell contours, process the cell contour to generate a respective predicted cell type using a model trained to predict cell types based on aggregated gene expression information.

A non-transitory computer-readable medium having stored thereon computer-executable instructions that, when executed by one or more processors, cause a computer to:

• receive, via one or more processors, training data;
• and
• train a machine learning model, using the training data, to generate a cell expression map corresponding to a cropped portion of a super-resolved gene expression heat map,
• wherein the cell expression map includes a plurality of cell contours, each including respective aggregated gene expression values.

A non-transitory computer-readable medium having stored thereon computer-executable instructions that, when executed by one or more processors, cause a computer to:

• receive, via one or more processors, a histology image and a cell segmentation map;
• process the histology image and the cell segmentation map using a trained machine learning model to generate a cell expression map including a plurality of cell contours, each including a respective predicted aggregated gene expression,
• wherein the machine learning model is trained using training data;
• and
• for each of the cell contours, process the cell contour to generate a respective predicted cell type using a model trained to predict cell types based on aggregated gene expression information.