This application is the national phase entry of International Application No. PCT/TR2017/050481, filed on Oct. 6, 2017, which is based upon and claims priority to Turkish Patent Application No. 2016/15290, filed on Oct. 31, 2016, the entire contents of which are incorporated herein by reference.
This invention is about a cellular diagnostic system which is composed of micro/nano patterned surfaces that can be integrated into a microfluidics device which reveals the nuclear shape deformability of the cells; which can be imaged with the help of an optical system and can be quantified with a software algorithm.
There are two device categories readily available in the market about cytological and histological diagnosis. FocalPoint (Becton Dickinson) and ThinPrep (Hologic) devices are in cytological specimens category and can analyze PAP cytology specimens for cervical cancer diagnosis. These devices prepare smears of the sample on a microscope slide, stain the samples and using a special algorithm analyze the difference in morphology and staining of the squamous and glandular epithelial cells using widefield light microscopy.
Other automated diagnostic devices in the market include image based detection software that work with paraffin/cryo embedded sectioned and stained samples imaged using widefield light microscopy.
Current technical problems arise from the fact that these methods are static, which means they can only give information about the condition of cells at the moment of fixation, embedding or staining. These sectioning and cytology techniques detect morphological changes resulting from the disease. The method of this invention allows detection of changes in nuclear elasticity of the cells caused by the conditions and makes these changes in the nuclear elasticity visible and detectable. Introduction of the nano/micropatterned interface, the invention allows cells to show properties that were otherwise hidden allowing the detection and diagnosis of disease (e.g. cancer).
The invention has advantages over the current techniques due to the properties mentioned below:
The invention described here, uses nano/micropatterned surfaces to force nuclei of the cell; which otherwise have similar morphology under the light microscope; to deform and accentuate minute differences in nuclear deformability and elasticity of the cell nucleus. The algorithm specifically designed to detect changes in the nuclear shape quantifies the extent of nuclear deformation. Using this quantification data, a diagnostic tool is devised. All the steps of the invention can work with live cells and does not require fixation which allows employing the same cells/tissue for further analysis (genetic analysis, etc.).
The invention includes nano/micro pillar surfaces prepared with defined height, dimension, and interpillar spacing, seeding these surfaces with cells and deformation of the cell nuclei. The extent of the nuclear deformation of the cell nucleus depends on disease state of the cell, hydrophobicity of the surface and distribution and size of the micropillars. The algorithm described in the invention quantifies the extent of nuclear deformation and generates a deformation score for diagnosis.
The cellular diagnostic system described in the invention has the following steps for diagnosis method:
a) Preparation of Nano/Micropatterned Surfaces:
Polymers that require chemical or photocrosslinking have an additional crosslinking step for nano/micropatterned surface preparation.
Nano/micropatterned array aims to deform cell nuclei. It includes nine surfaces. The surface is decorated with square prism micropillars. Micropillars dimensions are between 2-20 μm (Ex: A square prism micropillar with 4 μm edge and 4×4 μm cross sectional area). On each surface interpillar distance of the micropillars is different. Interpillar distances range between 2-20 μm (Ex: 4×4 micropillars separated from one another by an 8 μm spacing).
Nano/micropatterned silicon chips are prepared by photolithography, ion beam lithography or laser lithography methods. These silicon chips are designed to allow either duplicating the surface features directly or through the use of an intermediate mold (silicone).
There are two methods to produce nano/micropatterned test surfaces: (A) directly from silicon chips, (B) by preparing a secondary silicone mold. These molds (A, B) can be employed several ways: (i) using a polymer solution and solvent evaporation, (ii) hot embossing with a pre-made polymer film, (iii) UV crosslinking a polymer solution or (iv) chemical cross-linking.
b) Cell Seeding:
c) Imaging:
d) Analysis:
The nano/micropatterned surfaces are integrated into a microfluidics chip to obtain a disposable diagnostic system product with a shelf life. This microfluidics chips design includes channels for feeding cells and growth media into the nano/micropatterned reservoir and output channels for excess fluids. DNA dye reaches to the cells when the growth media dissolves the dye in the dye chamber. Cells are stained alive with the dye, and nuclear deformations become visible. Nano/micropatterned surface integrated microfluidics chip can be imaged with the desired microscopy tool (any microscope sensitive to fluorescence). The dye is excited with the excitation wavelength of the dye, and the emitted light goes through a dichroic mirror to meet the detector. After obtaining the digital image analysis is performed according to the
After the processing steps mentioned above, two parameters were calculated using the image of each cell nucleus:
The nano/micropatterned diagnostics chip that quantifies deformation of the cell nuclei has the following properties:
Calculation of Surface which Supplies the Most Nucleus Deformation on the Micropatterned Chip by Using Algorithm.
Using only the P4G4 surface, which induced the highest level of nuclear deformation, we evaluated the diagnostic performance of our software algorithm using six different cell types: L929, Saos-2, hOB, MCF-7, and SH-SY5Y. To accurately distinguish undeformed cell populations from deformed ones, we developed a finer scoring rubric than the one we used for surface selection. We defined a deformation score (DS), which combined how much the shape of a cell deviated from an ideal circle with whether it stayed more compact (e.g., like an ellipse) or obtained a bent shape (e.g., the shape of the letter “L”). To derive the parameters of this rubric, we created a database of synthesized cell nuclei. We designed 11 main cell templates (i.e., variants of deformed cells) we expected to see on cells on the P4G4 surface, with 50 examples of each (550 cells total) (
R1 (R1, CV1≤0.1, 0.1): no deformation,
R2 (0.1, 0.1<R2, CV2≤0.2, 0.2): low deformation-more compact,
R3 (0.2, 0<R3, CV3≤0.5, 0.3) low deformation-less compact,
R4 (0, 0.3<R4, CV4≤0.2, 0.5) high deformation-more compact and
R5 (0.2, 0.3<R5, CV5≤0.5, 0.5) high deformation-less compact (
Following this, we assigned weights to each cell based on the region it fell in wi=1:5, where i=R1 to R5. These weights indicate the deformation score at an individual cell level and are used as input to calculate population-level deformation score as follows:
DS=w*p′/100, (5)
where p′ is a vector representing the percentage of cells that fall in each of the regions R1-R5. An undeformed cell population would have minimally or none deformed cells (e.g., theoretically, 100% of its cells would fall gating area #1 and receive the minimum score of DS=1. Similarly, a deformed cell population would have cells that deform extensively (e.g., theoretically 100% of its cells should fall in the gating area #5) and receive the maximum score of DS=5. Based on this convention, the ‘undeformed’ and ‘deformed’ classes in the Surface Selection section correspond to DS≤3 and DS>3, respectively. We adopted this threshold as the cut-off between undeformed and deformed populations.
Number | Date | Country | Kind |
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2016/15290 | Oct 2016 | TR | national |
Filing Document | Filing Date | Country | Kind |
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PCT/TR2017/050481 | 10/6/2017 | WO |
Publishing Document | Publishing Date | Country | Kind |
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WO2018/182556 | 10/4/2018 | WO | A |
Number | Name | Date | Kind |
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20100303722 | Jin et al. | Dec 2010 | A1 |
Number | Date | Country |
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2013162482 | Oct 2013 | WO |
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Number | Date | Country | |
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20190291105 A1 | Sep 2019 | US |