Autonomous Cell Imaging With Label-Free Computational Models

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Solution Overview

Problem

Current technologies for studying cell processes such as cell differentiation, disease modeling, and genetic and chemical screening are laborious, resource-intensive, and time-consuming due to the reliance on toxic fluorescence biomarkers, destructive methods like scRNA-seq, and heterogeneous imaging modalities that generate biased datasets.

Innovation Solution

An autonomous cell imaging and modeling platform using label-free, high-content computational imaging techniques and self-supervised learning models to continuously study live biological cells, generating high-content images and embeddings that capture positional and morphological characteristics without destruction, enabling efficient analysis and optimization of cellular processes.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If fluorescence biomarkers are used to study cell differentiation and screen drug candidates, then imaging capability is improved, but cell toxicity increases

Engineering Contradiction:

Improveimaging capabilityVSAvoidcell toxicity

Core Design Contradiction:

Measurement precisionVSObject-affected harmful factors

Solution Approach 1:

The patent extracts and removes the harmful fluorescence biomarkers from the imaging system, replacing them with label-free computational imaging techniques that analyze intrinsic optical properties of cells, thereby eliminating cell toxicity while preserving imaging capability

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent substitutes the chemical/biological fluorescence labeling mechanism with a physical/optical computational imaging mechanism that uses light interference and machine learning algorithms to extract cellular information without chemical labels

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Loss of information

If single-cell RNA sequencing is used to study cellular processes, then molecular-level insight is improved, but cell destruction is required

Engineering Contradiction:

Improvemolecular-level insightVSAvoidcell destruction

Core Design Contradiction:

Loss of informationVSReliability

Solution Approach 1:

The patent creates optical copies and computational representations of cellular molecular states through label-free imaging, generating virtual molecular-level information that mirrors what would be obtained from destructive sequencing without requiring physical cell destruction

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The patent changes the measurement parameters from direct molecular extraction (RNA sequencing) to optical property measurement (computational imaging), allowing molecular-level insights to be obtained through parameter transformation rather than physical destruction

Inventive Principle:

Principle #35Parameter changes

3Measurement precision

If heterogeneous imaging modalities are used to collect datasets, then measurement coverage is improved, but data bias increases

Engineering Contradiction:

Improvemeasurement coverageVSAvoiddata bias

Core Design Contradiction:

Measurement precisionVSStability of the object's composition

Solution Approach 1:

The patent develops a universal label-free computational imaging platform that performs multiple measurement functions (morphological analysis, metabolic state assessment, differentiation tracking) through a single consistent imaging modality, eliminating the need for heterogeneous techniques and their associated biases

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent applies homogeneous imaging conditions and processing pipelines across all samples and time points, ensuring consistent data collection that eliminates batch effects and measurement biases introduced by heterogeneous modalities

Inventive Principle:

Principle #33Homogeneity

4Measurement precision

If destructive imaging methods are used to obtain phenotypic insights, then phenotypic resolution is improved, but continuous monitoring capability is lost

Engineering Contradiction:

Improvephenotypic resolutionVSAvoidcontinuous monitoring capability

Core Design Contradiction:

Measurement precisionVSDuration of action of stationary object

Solution Approach 1:

The patent implements continuous, non-destructive label-free computational imaging that maintains cell viability throughout the experiment, enabling uninterrupted longitudinal monitoring of cellular processes from early differentiation stages through terminal phenotypes without sampling interruptions

Inventive Principle:

Principle #20Continuity of useful action

Data Source

PatentUS12462386B2Autonomous cell imaging and modeling system

Publication Date: 2025.11.04 INSITRO INC

AI summary

The present disclosure relates generally to an autonomous cell imaging and modeling platform, and more specifically to machine-learning techniques for using microscopy imaging data to continuously study live biological cells. The autonomous cell imaging and modeling platform can be applied to evaluate various cellular processes, such as cellular differentiation, optimization of cell culture (e.g., in-plate cytometry), disease modeling, histopathology imaging, and genetic and chemical screening, using a dynamic universal imaging system. In some embodiments, the platform comprises a set of label-free computational imaging techniques, self-supervised learning models, and robotic devices configured in an autonomous imaging system to study positional and morphological characteristics in particular cellular substructures of a cell culture in an efficient and non-destructive manner over time.