Periodic Reporting for period 1 - READ-seq (Revealing the gene regulatory networks that govern cell mechanical properties by single cell microfluidics)
Période du rapport: 2023-09-01 au 2025-08-31
Our bodies are formed of microscopic cells, which underpin all our bodily functions, from white blood cells which recognise and destroy pathogens, to muscle cells which enable movement and brain cells which store memories. One of the central aims of biology is therefore to understand how cells achieve their numerous functions. However, cells are incredibly complex, and even individual cells within populations exhibit significant heterogenity, rendering this endeavour extremely challenging. In order to address these challenges and disentangle cellular function, we need to study large numbers of individual cells in high detail.
Recent technology advances mean that we now have a plethora of methods for studying the molecular components of cells, at the single cell level. For example, for each cell, we can determine gene sequences and their corresponding expression levels. We are also able to rapidly profile single cell behaviour and physical properties, such as their size, shape, and mechanical stiffness. However, the link between the molecular composition and the physical, functional properties of cells remains heavily underexplored, due to a lack of suitable methods.
This project therefore aimed to establish new technology which simultaneously profiles both the physical properties and molecular components of individual cells in high-throughput. This will allow us to understand how molecular composition determines physical properties, such as identifying genes which regulate cell size and mechanical stiffness. This technology can then be used in a range of scientific areas, from understanding the fundamental processes behind embryo development to probing mechanisms of cancer pathology.
Recent technology advances mean that we now have a plethora of methods for studying the molecular components of cells, at the single cell level. For example, for each cell, we can determine gene sequences and their corresponding expression levels. We are also able to rapidly profile single cell behaviour and physical properties, such as their size, shape, and mechanical stiffness. However, the link between the molecular composition and the physical, functional properties of cells remains heavily underexplored, due to a lack of suitable methods.
This project therefore aimed to establish new technology which simultaneously profiles both the physical properties and molecular components of individual cells in high-throughput. This will allow us to understand how molecular composition determines physical properties, such as identifying genes which regulate cell size and mechanical stiffness. This technology can then be used in a range of scientific areas, from understanding the fundamental processes behind embryo development to probing mechanisms of cancer pathology.
During this project, I established a new, high-throughput technology platform which simultaneously measures both physical properties and gene expression levels of single cells. This technology is based on microfluidics, a technique which creates microscale channels to control fluid flows, akin to circuits on a computer chip. Using microfluidics, we can generate of microscale droplets: aqueous droplets of 1 nL volume (1 billionth of 1 L) are stably suspended in oil, so that each droplet acts as an individual microreactor. A key advance was the use of such droplets for single cell analysis. By encapsulating individual cells in such droplets, the contents of each cell can be measured. However, a crucial limitation of droplets is that every droplet looks identical, meaning that we cannot easily track individual droplets between measurements. Here, I developed a method to uniquely tag droplets, allowing us to link imaging and sequencing data for single cells.
My droplet tagging technology introduces physical barcodes into each droplet with the cells. Each barcode is identifiable by both imaging and DNA sequencing, so that we can link the two data types for the accompanying cells. I developed methods to synthesise these barcodes: to make them optically identifiable, I edited the parameters of size, shape, and colour. In order to make them also identifiable by sequencing, I incorporated DNA molecules with controllable release mechanisms.
Having established a way to tag large numbers of individual droplets, I then developed the systems to read barcodes in droplets. I optimised a microfluidic chip design which carries out the whole workflow, comprising 1) characterisation of single cells by imaging, 2) production of droplets containing both barcodes and characterised cells, and 3) identification of barcodes within droplets with their accompanying cell. The barcoded droplets can then be collected and sequenced, to determine gene expression levels for each cell. In addition to these experimental aspects, I wrote software to analyse the resulting datasets, extracting cell properties from images and pairing these physical parameters (e.g. size, mechanical stiffness) with their gene expression profiles.
I validated the whole platform by application to known samples, including a mixture of two cell types. These cell types have known differences in both their physical properties (one cell type is larger than the other), and their gene expression patterns. This cell mixture thus demonstrated that the images and gene expression profiles are correctly linked for each single cell. I also applied the platform to investigate several cancer cell lines, to identify genes associated with physical properties and their universality across cell types.
My droplet tagging technology introduces physical barcodes into each droplet with the cells. Each barcode is identifiable by both imaging and DNA sequencing, so that we can link the two data types for the accompanying cells. I developed methods to synthesise these barcodes: to make them optically identifiable, I edited the parameters of size, shape, and colour. In order to make them also identifiable by sequencing, I incorporated DNA molecules with controllable release mechanisms.
Having established a way to tag large numbers of individual droplets, I then developed the systems to read barcodes in droplets. I optimised a microfluidic chip design which carries out the whole workflow, comprising 1) characterisation of single cells by imaging, 2) production of droplets containing both barcodes and characterised cells, and 3) identification of barcodes within droplets with their accompanying cell. The barcoded droplets can then be collected and sequenced, to determine gene expression levels for each cell. In addition to these experimental aspects, I wrote software to analyse the resulting datasets, extracting cell properties from images and pairing these physical parameters (e.g. size, mechanical stiffness) with their gene expression profiles.
I validated the whole platform by application to known samples, including a mixture of two cell types. These cell types have known differences in both their physical properties (one cell type is larger than the other), and their gene expression patterns. This cell mixture thus demonstrated that the images and gene expression profiles are correctly linked for each single cell. I also applied the platform to investigate several cancer cell lines, to identify genes associated with physical properties and their universality across cell types.
A patent has been filed for the new technology established in this project has been filed, to enable its commercialisation and widespread adoption. In addition, the manuscript detailing the development of this new technology is now available on bioRxiv, prior to its publication in a peer-reviewed journal. During the project, I also presented the project at several international conferences.