Lipid-Modified Oligos for Live Cell Spatial Transcriptomics
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Solution Overview
Problem
Existing spatial transcriptomics technologies are not compatible with live cell cultures and lack spatial indexes to map single-cell RNAseq data to cell morphology, limiting the integration of single-cell morphology and gene expression.
Innovation Solution
A method involving the preparation of a cell culture substrate with single cells or micro-colonies, labeling with spatial barcodes using oligonucleotides, and subsequent sequencing to determine nucleic acid sequences while correlating with phenotypic or morphologic measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing spatial transcriptomics technologies are used, then spatial information can be obtained, but they are not compatible with live cell cultures and require specialized expensive substrates
Solution Approach 1:
The patent applies universality by designing a labeling substrate that can be used with any standard cell culture substrate, eliminating the need for specialized expensive substrates. The labeling substrate comprises oligonucleotides that can be incorporated into cells through standard culture conditions, making the system compatible with live cell cultures while working with conventional substrates.
Solution Approach 2:
The patent uses an intermediary approach by introducing a labeling substrate as a mediator between the cell culture substrate and the spatial barcode detection system. This labeling substrate comprises oligonucleotides that can be incorporated into cells during standard culture, serving as an intermediary carrier for spatial barcodes without requiring specialized substrates.
2Measurement precision
If manual cell picking methods are used to integrate morphology and gene expression, then spatial information can be obtained, but throughput is greatly limited
Solution Approach 1:
The patent applies segmentation by dividing the cell population into individually labeled cells, each receiving a unique spatial barcode. This allows parallel processing of multiple cells simultaneously through high-throughput sequencing, while maintaining the ability to link morphology and gene expression data for each individual cell through the barcode identifier.
Solution Approach 2:
The patent uses copying by creating a digital copy of spatial position information through barcodes that are sequenced alongside gene expression data. This digital copy allows computational linking of morphology and transcriptome information without requiring manual intervention, enabling high-throughput automated integration.
3Measurement precision
If in situ sequencing methods are used, then spatial information can be obtained, but detection efficiency and/or number of genes being probed is sacrificed
Solution Approach 1:
The patent extracts the spatial barcode information from the cell and incorporates it into a separate labeling substrate that can be processed alongside the cells through standard sequencing workflows. This extraction allows spatial information to be obtained without sacrificing detection efficiency, as the barcodes are incorporated during cell culture rather than requiring complex in situ sequencing.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables high-throughput integration of single-cell long-term culture, imaging, and RNAseq without specialized substrates, allowing for unambiguous linking of imaging-based phenotypic observations with transcriptome information.
Implementation Method 1
The labeling substrate comprises a lipid-modified oligonucleotide (LMO)
Data Source
AI summary
Existing spatial transcriptomics technologies require dead tissues and are not compatible with live cell cultures. The present disclosure provides materials and methods for sequencing a single cell from a cell culture sample and obtaining morphologic or phenotypic measurements and information by combining sequencing approaches and spatial hashing (e.g., barcoding) at a single cell level.