Cell Identity Factor Selection for Defined Cell Culture
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Solution Overview
Problem
Current methods lack a systematic approach to identify cell culture conditions and differentiation stimuli for maintaining and converting various human cell types, particularly in serum-free chemically-defined media, and there is a need for computational methods to predict signaling molecules for in vitro cell maintenance and conversion.
Innovation Solution
A method involving determining differential breadth and network scores of H3K4me3 modified regions and interactions between protein-coding genes to identify cell identity genes, prioritize genes for cell maintenance, and determine factors required for cell conversion by analyzing ChIP-seq data from databases like ENCODE and using protein-protein interaction networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional cell culture methods are used, then cells can be maintained in culture, but the culture conditions lack specificity to cell type and may cause cell transformation or death
Solution Approach 1:
The method performs preliminary computational analysis of gene expression data and epigenetic markers to predict cell-specific factors before establishing culture conditions. This preliminary identification of transcription factors and signalling molecules enables the design of tailored culture media that are specific to each cell type, preventing cell transformation or death while maintaining reliability.
Solution Approach 2:
The invention uses computational algorithms and bioinformatics tools as intermediaries to bridge the gap between raw genomic data and practical cell culture applications. These computational intermediaries analyze gene expression patterns and epigenetic modifications to identify cell-specific factors, which then serve as mediators to optimize culture conditions for specific cell types.
2Productivity
If serum-containing media are used for cell culture, then cell growth is supported, but chemically undefined conditions prevent systematic identification of cell-specific factors
Solution Approach 1:
The method extracts and identifies specific signalling molecules and transcription factors from complex serum-containing media through computational analysis of gene expression data. By taking out and individually characterizing these specific factors, the invention enables the development of chemically defined media that maintain cell growth support while achieving precise chemical definition for systematic identification of cell-specific requirements.
Solution Approach 2:
The invention changes the parameters of cell culture media from undefined serum-based formulations to chemically defined compositions by systematically varying and identifying the concentrations and types of signalling molecules. This parameter change enables both maintained productivity (cell growth) and improved measurement precision (chemical definition) through data-driven optimization.
3Adaptability or versatility
If computational approaches are developed for predicting transcription factors, then cell conversion can be guided, but no systematic method exists for identifying signalling molecules across multiple cell types
Solution Approach 1:
The invention creates a universal computational framework that can identify both transcription factors and signalling molecules across multiple cell types using the same methodological approach. This multi-functional system analyzes gene expression data and epigenetic markers to predict cell-specific factors regardless of cell type, enabling systematic identification for both cell maintenance and conversion applications across diverse cell types.
Data Source
AI summary
A method for maintaining a cell in vitro that comprises the steps of: providing a cell of interest in cell culture, determining differential breadth of histone 3 trimethylated at lysine 4 (H3K4me3) modified regions and a differential broadness score for each protein-coding gene in said cell, determining a network score for each protein-coding gene in said cell based on the differential broadness score and interactions between the protein products of each protein-coding gene over at least one network, and determining a cell identity score for each protein-coding gene in said cell based on a combination of the differential broadness score and network scores. The cell identity scores are used to prioritise protein-coding genes thereby identifying cell identity genes for said cell, which encode factors associated with cell identity. The cell of interest is cultured in the presence of said factors, thereby maintaining the cell identity of the cell of interest.