JP2020134179A - Non-destructive cell analysis method - Google Patents

Abstract

To provide a non-destructive cell analysis method for analyzing cell type, etc. of pluripotent cells such as differentiated cells, mesenchymal stem cells, or the like. as well as to provide a non-destructive cell analysis method for analyzing the cell status, such as aging, activity, pluripotency, differentiation orientation, efficacy, etc.SOLUTION: The cell type or cell state can be analyzed without destroying the cells by obtaining and analyzing a sugar chain profile of functional factors secreted from cells in a medium using a microarray with immobilized sugar chain-binding proteins.SELECTED DRAWING: None

Description

The present invention relates to an inspection device and an analysis method for non-destructively inspecting the state of cells. The examination and analysis of the present invention relate to the classification and analysis of cell types such as pluripotent cells such as differentiated cells and stem cells. In addition, the state of cells to be examined and analyzed by the present invention includes aging and activity, pluripotency, differentiation orientation, and effectiveness.

Various inspection devices and analysis methods have been conventionally known for non-destructive inspection devices and analysis methods for the state of cells.

In Patent Document 1, the gene expression mode of cells and biological tissues, the sample chip analyzer for analyzing the antigen-antibody reaction, and the analysis method are bound to the sample without being affected by the excitation light or the disturbance light of the fluorescent substance. A sample chip analyzer and an analysis method capable of reliably and highly accurately detecting light emission from a fluorescent substance labeled on a test sample to streamline the analysis work of the test sample are described.

Patent Document 2 proposes a method for analyzing the interaction between a protein and a sugar chain, which opens the way to the practical use of a lectin array. A test sugar fluorescently labeled on a substrate on which a protein that interacts with a sugar chain, such as a lectin, an enzyme protein having a sugar-binding domain, a cytokine having an affinity for a sugar chain, or an antibody that interacts with a sugar chain, is immobilized. The interaction between the protein and the sugar chain is analyzed by contacting the chain or the test complex sugar, not washing the substrate, allowing excitation light (evanescent wave) to act, and measuring the intensity of the excited fluorescence. How to do it is described.

In Patent Document 3, light is introduced into a substrate made of a light-guiding material in which a plurality of sugar chain-binding proteins such as a sugar chain recognition antibody and a lectin belonging to the IgM class are arranged and fixed, and a side end surface of the substrate. A sugar chain having a means for generating an evanescent wave on the surface of the substrate to excite a fluorescent label and a means for measuring the fluorescence intensity for measuring the intensity of fluorescence generated by the means at each position of the sugar chain-binding protein. Alternatively, a complex sugar analyzer is described.

On the other hand, the following three items are defined as the minimum necessary evaluation conditions for mesenchymal stem cells as quality control items (Non-Patent Document 1).
1. 1. Must be able to adhere to plastic and be cultured. Regarding the expression of cell surface markers, CD73, CD90, CD105 are positive, and CD45, CD34, CD14 or CD11b, CD79a or CD19, HLA-DR are negative. Has the ability to differentiate into osteoblasts, adipocytes, and chondrocytes

However, the potency test takes a very long time to carry out. In addition, although the test of cell surface markers can be performed with a flow cytometer, it is relatively easy, but the specificity of these markers is not high, and it has been pointed out that there is a limit to the cells that can be identified. .. Specifically, it is extremely difficult to distinguish between mesenchymal stem cells and fibroblasts (Non-Patent Document 2).

In addition, the evaluation method for mesenchymal stem cell preparations recommended by the International Cell Therapy Association includes evaluation of immunomodulatory function, and cytokine stimulation of IFNγ, TNFα, etc. and gene expression analysis are recommended (Non-Patent Document 3). ..

It is said that the anti-inflammatory effect of mesenchymal stem cells can be predicted to some extent by what factors are induced by cytokine stimulation and how much, and in products such as regenerative medicine using mesenchymal stem cells. This is because it is considered to be linked to effectiveness (Non-Patent Document 4).

It has been pointed out that the evaluation of immunomodulatory function also requires a long test time because it requires cytokine stimulation and further culture of cells, which increases the cost of the test. Since this evaluation requires nucleic acid extraction, cells will be destroyed. A method of recovering exosomes from a culture supernatant and performing sugar chain profiling is known (Non-Patent Document 5), but recovery of exosomes requires a concentration operation using an ultracentrifugation, which is a highly convenient method. I can't say.

Therefore, in the development of products such as regenerative medicine using mesenchymal stem cells, there is room for improvement in terms of time, cost, etc. in the "quality control" method.

JP-A-2003-172701 International Publication No. 2005/064333 JP-A-2007-3357

M. Dominici, K. Le Blanc, I. Mueller, I. Slaper-Cortenbach, F. Marini, D. Krause, R. Deans, A. Keating, Dj. Prockop, E. Horwitz, Minimal criteria for defining multipotent mesenchymal stromal cells. The International Society for Cellular Therapy position statement, Cytotherapy, 2006; 8 (4), pp315-317 E. Alt, Y. Yan, S. Gehmert, YH Song, A. Altman, S Gehmert, D. Vykoukai, X. Bai, Fibroblasts share mesenchymal phenotypes with stem cells, but lack their differentiation and colony-forming potential, Biol. Cell., 2011 Apr; 103 (4) pp.198-208 Cytotherapy, 2016 February; 18 (2): 151-159. doi: 10.1016 / j / jcyt. 2015.11.008 Galipeau et al., 2016 Cytotherapy; & Chinnadurai et al., 2018 Cell Reports A. Shimoda et al., BBRC 491 (2017), pp.701-707

An object of the present invention is to provide a method for nondestructively inspecting and analyzing cell types such as pluripotent cells such as differentiated cells and mesenchymal stem cells. Another object of the present invention is to provide a method for nondestructively examining and analyzing a cell state, for example, aging, activity, pluripotency, differentiation orientation, efficacy and the like.

As a result of diligent studies to solve the above problems, the present inventors obtained and analyzed the sugar chain profile from functional factors secreted from cells using a microarray on which a sugar chain-binding protein was immobilized. , Found that it is possible to analyze cell type or cell state without destroying cells. The present invention has been completed based on these findings. That is, the inventions made to solve the above problems are as follows.

[1]
A method of obtaining and analyzing a sugar chain profile from a functional factor secreted from cells in a medium using a microarray on which a sugar chain-binding protein is immobilized.

[2]
The method for analyzing [1], wherein the cell is a human cell.

[3]
The analysis method of [1] or [2], wherein the step of acquiring the sugar chain profile is performed using an evanescent wave fluorescence excitation device.

[4]
The analysis method according to any one of [1] to [3], wherein the medium is a serum-free medium.

[5]
The analysis method according to any one of [1] to [4], wherein the functional factor is a glycoprotein.

According to the present invention, it is possible to provide a method for non-destructively examining and analyzing cell types such as pluripotent cells such as differentiated cells and mesenchymal stem cells. The present invention can also provide a non-destructive method for examining and analyzing cell states such as aging, activity, pluripotency, differentiation orientation, efficacy and the like. Since the inspection and analysis method of the present invention can be inspected and analyzed without performing any pretreatment, the time required for quality control and the cost can be reduced in the development of products such as regenerative medicine using cells. be able to.

Comparison diagram of sugar chain profiling The figure explaining the flow from the input layer to the output layer of the deep learning used

The analysis method of the present invention will be described in detail below.

The test and analysis method of the present invention is to non-destructively analyze the characteristics of cells by using sugar chain profiling using a lectin microarray of glycoproteins secreted into a cell culture medium.

"Non-destructive analysis of cell characteristics" is to analyze cell type or cell state without destroying cells.

According to the inspection and analysis method of the present invention, the characteristics of cells can be analyzed at low cost by using a medium that is originally discarded by medium replacement or the like without using the cells to be inspected.

In the test and analysis method of the present invention, the state of cells is analyzed non-destructively by obtaining the sugar chain profile from the functional factors secreted in the medium using a microarray on which a sugar chain-binding protein is immobilized. To do.

In the analysis method of the present invention, the step of acquiring the sugar chain profile can be performed using an evanescent wave fluorescence excitation device.

The evanescent wave fluorescence exciter is a functional factor that generates an evanescent wave (proximity field) on the surface of a microarray on which a sugar chain-binding protein is immobilized and is secreted into the medium from the cell to be analyzed. It is a device that can detect the interaction with the sugar chain-binding protein in a non-destructive manner without washing or drying the surface of the microarray.

Examples of the evanescent wave fluorescence excitation device include Glyco Station Reader and Glyco Lite of Glyco Technica Co., Ltd.

In the present invention, the cells to be examined and analyzed are not particularly limited as long as the effects of the present invention can be obtained, and examples thereof include human cells. In the present invention, the cell type to be examined and analyzed is not particularly limited as long as the effects of the present invention can be obtained, and examples thereof include pluripotent cells such as differentiated cells and mesenchymal stem cells.

In the present invention, the state of the cell to be examined and analyzed is not particularly limited as long as the effect of the present invention can be obtained, and examples thereof include aging and activity, pluripotency, differentiation orientation, and effectiveness. Be done.

The medium used in the present invention is not particularly limited as long as it is a medium capable of culturing cells, and examples thereof include a serum-free medium.

The functional factors used in the present invention are not particularly limited as long as the effects of the present invention can be obtained, and examples thereof include glycoproteins.

The serum-free medium is a medium for culturing animal cells that does not contain serum, and is about 5 to 20% in the medium in order to supplement components such as growth factors necessary for artificially culturing animal cells. Compared with the medium to which serum was added, the same effect was realized without adding serum.

In the present invention, supervised learning can be used for analysis, among other machine learning. "Supervised learning" is a learning method that iteratively learns from data and finds a unique procedure from the patterns hidden in it, without hard-coding the procedure to derive the desired result in software.

Deep learning can be used as machine learning for the analysis method of the present invention. "Deep learning" is a supervised learning method that analyzes data through the learning of data by stacking multiple layers of neural networks that refer to information transmission between nerve cells.

The inspection and analysis method of the present invention may use an evanescent wave fluorescence excitation scanner for detecting weak bonds with sugar chains without omission, a sample chip analyzer, and an evanescent wave fluorescence excitation device for acquiring a sugar chain profile. it can.

As the inspection and analysis method of the present invention, the method for analyzing the interaction between a protein and a sugar chain described in Patent Documents 1, 2 and 3 can be used. As the inspection and analysis method of the present invention, the sugar chain or complex sugar analyzer described in Patent Documents 1, 2 and 3 can be used. Other prior art techniques can be used for the inspection and analysis methods of the present invention.

Hereinafter, examples of the present invention will be described, but the present invention is not limited to the above-described embodiments and the following examples, and can be variously changed within the technical scope grasped from the description of the claims. Is.

Human adipose tissue-derived mesenchymal stem cells (hereinafter referred to as "AD"), human umbilical band-derived mesenchymal stem cells (hereinafter referred to as "UC"), human lung fibroblasts (hereinafter referred to as "Lung"), human liver-derived Fibroblasts (hereinafter referred to as "Liver") and human artery-derived cells (hereinafter referred to as "Aorta") were cultured in a serum-free medium for mesenchymal stem cells (manufactured by Rohto Pharmaceutical Co., Ltd.).

20 μL of the culture supernatant was collected 2 and 4 days after the culture, and the sample was pretreated according to the following protocol.

1. 1. 20 μL of each sample and 100 μg labeling of Cy3 Mono-Reactive dye (GE Healthcare, PA23001 divided into 100 μg labeling) were mixed and reacted at room temperature in the dark for 1 hour.

2. 2. Desalted columns (Zeba ™ Spin Desalting Columns, 7K MWCO (Thermo SCIENTIFIC, 89882)) were centrifuged at 1,500 xg for 1 minute at 4 ° C.

3. 3. Apply TBS = 300 μL to the desalting column and centrifuge at 1,500 × g for 1 minute at 4 ° C (column wash). This process was repeated twice.

4. The whole amount of each sample and T = 25 μL were applied to a desalting column and centrifuged at 1,500 × g for 2 minutes at 4 ° C. to remove unreacted Cy3.

5. Probing Solution 450 μL was applied to each sample, and the sample was increased to 500 μL / tube.

6. After washing LecChip (registered trademark) (GlycoTechnica lectin microarray) with Probing Solution (100 μL / well) (GlycoTechnica probing solution for LecChip) three times, apply each sample (100 μL / well) and apply LecChip (registered trademark). The reaction was carried out at 20 ° C. for 17 hours or more.

7. LecChip (registered trademark) in the liquid phase state in which the sample was reacted was measured with GlycoStation (registered trademark) Reader 1200 (Evanescent wave fluorescence excitation scanner manufactured by GlycoTechnica) (measurement conditions, number of integrations: 4, exposure time: 133 msec) , Camera gain: 75, 85, 95, 105, 115, 125).

8. Quantification and data integration (gain integration) were performed using GlycoStaion (registered trademark) Tools Pro Suite 1.5 (GlycoTechnica sugar chain analysis software).

9. For the numerical data that was data-integrated (gain-integrated), all the digitized data was exported to the Microsoft (registered trademark) Excel sheet. It may be exported to a spreadsheet of spreadsheet software.

It is obtained by scanning the pretreated sample as described above with a sugar chain profiler (GlycoStation (registered trademark) Reader 1200) using a lectin microarray (LecChip (registered trademark)) equipped with 45 types of lectins. Sugar chain profiling based on the fluorescence intensity (sugar chain profile) was performed. A comparative diagram of the results is shown in FIG. In FIG. 1, Before is a medium in which cells are not cultured, and After is sugar chain profiling in the medium 2 days after cell culture.

No signals other than STL and UDA were detected in the supernatant of the medium in which the cells were not cultured, and many lectin signals were detected in the supernatant of the medium 2 days after the cell culture. In addition, although not shown in the figure, the signal of lectin detected from the supernatant of the medium 4 days after the cell culture is increased as compared with the supernatant of the medium 2 days after the cell culture, and the cell culture It was confirmed that the signals from many lectins seen later were factors secreted from cells in the medium.

Fluorescence intensity (sugar chain) obtained by scanning with a sugar chain profiler (GlycoStation (registered trademark) Reader 1200) using a lectin microarray (LecChip (registered trademark)) equipped with 45 types of lectins as shown in FIG. From the profile), a feature vector consisting of 45 elements was created for common use in both unsupervised learning and supervised learning.

By adding 1.0 to the fluorescence intensity and taking the common logarithm, the difference in small fluorescence intensity is emphasized, the difference in large fluorescence intensity is reduced, and the minimum value is set to 0 and the maximum value is set to 1. Normalization was done as follows.

Normalization method (x: measured fluorescence intensity, x _min : measured minimum fluorescence intensity, x _max : measured maximum fluorescence intensity, y: after normalization)
y = (log (x + 1.0) --log (x _min + 1.0)) / (log (x _max + 1.0) --log (x _min + 1.0))

Supervised learning is a framework that supports linear classification and deep learning using the multi-value classification function Classifier included in the distributed processing framework Jubatus (http://jubat.us/) for online machine learning. TensorFlow (http) Deep learning was performed using //tensorflow.org/) and its interface Keras (http://keras.io/).

Since the number of samples is not large, the optimization of hyperparameters in these was performed by cross-validation without one. The recognition accuracy of the classifier was output as many as the number of samples, and the settings were changed repeatedly to improve the final recognition accuracy.

Although linear classification cannot be expected to have the same recognition accuracy as deep learning, it was used for this purpose because it is possible to know how much the fluorescence intensity of each lectin contributed to the classification. In order to maximize the recognition accuracy, we selected algorithms such as AROW to be used in the Classifier, and further optimized the regularization weight value that can be set by those algorithms. From the completed linear classifier, a ranking of lectins that contributed to each classification was created and used to complement the results obtained in deep learning described later.

Deep learning consisted of stacking hidden layers by affine layers (fully connected layers). The ReLU function was used as the activation function, and the initial value of He was used as the initial value of the weight. In the case of two classifications, the output of the output layer can be classified into two categories according to whether it is close to 0 or 1 by using the Sigmoid function of a single node. In the case of 3 or more classifications, the number of nodes in the output layer can be classified according to the number of classifications. The number of hidden layers is about 2 to 7, the number of nodes in each layer is about 4 to 60, and hyperparameter optimization such as batch size, use of batch normalization, dropout value, learning rate, and examination of parameter update methods other than SGD. went.

Specifically, deep learning was performed with a configuration from the input layer to the output layer as shown in FIG.

The accuracy of cell type recognition by deep learning is as shown in Table 1. The number of samples used is AD = 105, UC = 30, Lung = 6, Liver = 6, Aorta = 6 samples.

From Table 1, it was found that mesenchymal stem cells (AD, UD) were recognized with an accuracy of more than 90%, and significant performance was obtained in determining the cell type that is the basis of quality confirmation. This result emphasizes that the cells of interest were non-destructive and were obtained from a previously discarded medium without any special pretreatment.

INDUSTRIAL APPLICABILITY The present invention provides a method for nondestructively inspecting and analyzing cell types such as pluripotent cells such as differentiated cells and mesenchymal stem cells. The present invention also provides a method for non-destructively examining and analyzing cell states such as aging, activity, pluripotency, differentiation orientation, efficacy and the like.

Claims (5)

A method of obtaining and analyzing a sugar chain profile from a functional factor secreted from cells in a medium using a microarray on which a sugar chain-binding protein is immobilized. The analysis method according to claim 1, wherein the cell is a human cell. The analysis method according to claim 1 or 2, wherein the step of acquiring the sugar chain profile is performed using an evanescent wave fluorescence excitation device. The analysis method according to any one of claims 1 to 3, wherein the medium is a serum-free medium. The analysis method according to any one of claims 1 to 4, wherein the functional factor is glycoprotein.

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