Methods and systems for predicting neurodegenerative disease state
The present disclosure provides automated methods and systems for implementing a pipeline involving the training and deployment of a predictive model for predicting cellular diseased state (e.g., neurodegenerative disease state such as presence or absence of Parkinson's Disease). Such a predictive model distinguishes between morphological cellular phenotypes e.g., morphological cellular phenotypes elucidated using Cell Paint, exhibited by cells of different diseased states.
1 . A method comprising:
obtaining or having obtained a cell;
capturing one or more images of the cell; and
analyzing the one or more images using a predictive model to predict a neurodegenerative disease state of the cell, the predictive model trained to distinguish between morphological profiles of cells of different neurodegenerative disease states, and the morphological profile being extracted from a layer of a deep learning neural network or being an embedding representing a dimensionally reduced representation of values of the layer of the deep learning neural network.
2 . The method of claim 1 , further comprising:
prior to capturing the one or more images of the cell, providing a perturbation to the cell;
subsequent to analyzing the one or more images, comparing the predicted neurodegenerative disease state of the cell to a neurodegenerative disease state of the cell known before providing the perturbation; and
based on the comparison, identifying the perturbation as having one of a therapeutic effect, a detrimental effect, or no effect.
3 . The method of claim 1 , wherein the predictive model is one of a neural network, random forest, or regression model.
4 . The method of claim 1 , wherein each of the morphological profiles of cells of different neurodegenerative disease states comprises values of imaging features or a transformed representation of images that define a neurodegenerative disease state of a cell.
5 . The method of claim 1 , wherein:
the imaging features comprise one or more of cell features or non-cell features,
the cell features comprise one or more of cellular shape, cellular size, cellular organelles, object-neighbors features, mass features, intensity features, quality features, texture features, and global features, and
the non-cell features comprise well density features, background versus signal features, and percent of touching cells in a well.
6 . The method of claim 1 , wherein the predicted neurodegenerative disease state of the cell predicted by the predictive model is a classification of at least two categories.
7 . The method of claim 1 , wherein the neurodegenerative disease is any one of Parkinson's Disease (PD), Alzheimer's Disease, Amyotrophic Lateral Sclerosis (ALS), Infantile Neuroaxonal Dystrophy (INAD), Multiple Sclerosis (MS), Amyotrophic Lateral Sclerosis (ALS), Batten Disease, Charcot-Marie-Tooth Disease (CMT), Autism, post-traumatic stress disorder (PTSD), schizophrenia, frontotemporal dementia (FTD), multiple system atrophy (MSA), and a synucleinopathy.
8 . The method of claim 1 , wherein the cell is one of a stem cell, partially differentiated cell, or terminally differentiated cell.
9 . The method of claim 1 , wherein the cell is a somatic cell selected from a fibroblast or a peripheral blood mononuclear cell (PBMC).
10 . The method of claim 1 , wherein the predictive model is trained by:
obtaining or having obtained a cell of a known neurodegenerative disease state;
capturing one or more images of the cell of the known neurodegenerative disease state; and
using the one or more images of the cell of the known neurodegenerative disease state, to train the predictive model to distinguish between morphological profiles of cells of different diseased states.
11 . The method of claim 10 , wherein the known neurodegenerative disease state of the cell serves as a reference ground truth for training the predictive model.
12 . The method of claim 1 , further comprising:
prior to capturing the one or more images of the cell, staining or having stained the cell using one or more fluorescent dyes,
wherein the one or more fluorescent dyes are Cell Paint dyes for staining one or more of a cell nucleus, cell nucleoli, plasma membrane, cytoplasmic RNA, endoplasmic reticulum, actin, Golgi apparatus, and mitochondria.
13 . The method of claim 1 , wherein:
each of the one or more images correspond to a fluorescent channel, and wherein
obtaining the cell and capturing the one or more images of the cell are performed in a high-throughput format using an automated array.
14 . The method of claim 1 , wherein analyzing the one or more images using the predictive model comprises:
dividing the one or more images into a plurality of tiles; and
analyzing the plurality of tiles using the predictive model on a per-tile basis.
15 . A non-transitory computer readable medium comprising instructions that, when executed by a processor, cause the processor to:
capture one or more images of a cell; and
analyze the one or more images using a predictive model to predict a neurodegenerative disease state of the cell, the predictive model trained to distinguish between morphological profiles of cells of different neurodegenerative disease states, and the morphological profile being extracted from a layer of a deep learning neural network or being an embedding representing a dimensionally reduced representation of values of the layer of the deep learning neural network.
16 . The non-transitory computer readable medium of claim 15 , further comprising instructions that, when executed by the processor, cause the processor to:
subsequent to analyze the one or more images, compare the predicted neurodegenerative disease state of the cell to a neurodegenerative disease state of the cell known before a perturbation was provided to the cell; and
based on the comparison, identify the perturbation as having one of a therapeutic effect, a detrimental effect, or no effect.
17 . The non-transitory computer readable medium of claim 15 , wherein the predictive model is trained by:
capture one or more images of a cell of the known neurodegenerative disease state; and
using the one or more images of the cell of the known neurodegenerative disease state to train the predictive model to distinguish between morphological profiles of cells of different diseased states.
18 . The non-transitory computer readable medium of claim 15 ,
wherein the cell was previously stained using one or more fluorescent dyes, and
wherein the one or more fluorescent dyes are Cell Paint dyes for staining one or more of a cell nucleus, cell nucleoli, plasma membrane, cytoplasmic RNA, endoplasmic reticulum, actin, Golgi apparatus, and mitochondria.
19 . The non-transitory computer readable medium of claim 15 , wherein each of the one or more images correspond to a fluorescent channel.
20 . The non-transitory computer readable medium of claim 15 , wherein the instructions that cause the processor to analyze the one or more images further comprises instructions that, when executed by the processor, cause the processor to:
divide the one or more images into a plurality of tiles; and
analyze the plurality of tiles using the predictive model on a per-tile basis.