Team:BM-AMU/Description

Team:BM-AMU- 2019.igem.org







Inspiration & Description


The background we are interested in

Epithelial and mesenchymal cells represent distinct lineages, each with a unique gene expression profile that imparts attributes specific to each cell type. The loss of epithelial features is often accompanied by increased cell motility and expression of mesenchymal genes. This process, referred as epithelial to mesenchymal transition (EMT), plays an important role in embryonic development, chronic inflammation, tissue remodeling, cancer metastasis, and various fibrotic diseases.

Yet, rather than a binary switch, EMT is more like a progressive process. Most cells undergo EMT incompletely and then stay in an intermediate state, which is defined as the presence of both epithelial and mesenchymal features in the cell. In addition, the intermediate states of EMT are more common and complex in physiological and pathological processes. If these intermediate states can be clarified, then we will gain a deeper insight through the EMT process, further explaining embryonic development, tumor metastasis, etc.

Description

The problem we want to solve

In fact, few studies have tried to identify these complex transitory states, mainly due to the tremendous difficulty in observing the complete landscape of EMT’s intermediate states. After communicating with researchers, clinical pathologists and developers from biotech companies, We have also found that: Most of the studies usually neglect the progressive transcriptional alterations occurring during intermediate EMT states, which imply a range of phenotypic cellular heterogeneity.

In conclusion, we discovered a key problem inside: more accurate data on dynamic transcriptional landscapes are necessary to characterize the unclear intermediate states during EMT, and we can map these temporal transcriptome changes in the dynamic EMT process.

The solution we proposed

First, in order to accurately capture in intermediate EMT states, our designs are as follows : EMT markers are labeled with fluorescence as precise monitors to measure the degree of transdifferentiation from E to M. Then cells begin to undergo EMT under the action of controllers. Thus, we can accurately derive cells in different intermediate EMT states based on distinguished fluorescence expression.

Next, dynamic transcriptional landscapes of EMT from the engineered cells was captured by FACS-seq. After elementary data analysis, we can obtain that map mentioned above, which means the initial problem has been solved. However, we have another surprising new idea during this process.

A wonderful idea not in the plan

During our experimental process, we noticed that transcriptional landscapes based on FACS-seq are often expensive and time-consuming to obtain. However, various phenotypes, such as fluorescence intensity, can be low-cost alternative modalities. They convolve dynamic transcriptome into a complex image lacking of the readily access. Non-trivial relationships between them, however, have not been mined yet.

After feasibility analysis through interviews and publications, we designed a software predicting transcriptional landscapes during EMT! When we input a fluorescent image with EMT markers, Support Vector Machine (SVM) can output the most probable sketch map of transcriptome.

References:

1. Li Chunhe, Hong Tian, Nie Qing. Quantifying the landscape and kinetic paths for epithelial–mesenchymal transition from a core circuit. Physical chemistry chemical physics : PCCP, 2016, Vol.18 (27), pp.17949-56.

2. Adam L. MacLean, Tian Hong, Qing Nie. Exploring intermediate cell states through the lens of single cells. Current Opinion in Systems Biology, 2018, Vol.9 , pp.32-41.

3. Ana Sofia Ribeiro, Joana eParedes. P-cadherin linking breast cancer stem cells and invasion: a promising marker to identify an intermediate/intermediate EMT state. Frontiers in Oncology, 2015, Vol.4.

4. Simeoni Chiara, Dinicola Simona, Cucina Alessandra, Mascia Corrado, Bizzarri Mariano. Systems Biology Approach and Mathematical Modeling for Analyzing Phase-Space Switch During Epithelial-Mesenchymal Transition. Methods in molecular biology (Clifton, N.J.), 2018, Vol.1702 , pp.95-123.

5. Dongre, A. and R.A. Weinberg. New insights into the mechanisms of epithelial-mesenchymal transition and implications for cancer. Nat Rev Mol Cell Biol, 2019. 20(2): p. 69-84.

6. Pastushenko, I., et al. Identification of the tumour transition states occurring during EMT. Nature, 2018. 556(7702): p. 463-468.

7. Huang R Y-J, Wong M K, Tan T Z, et al. An EMT spectrum defines an anoikis-resistant and spheroidogenic intermediate mesenchymal state that is sensitive to e-cadherin restoration by a src-kinase inhibitor, saracatinib (AZD0530). Cell death & disease, 2013, Vol.4 , pp.e915.

8. Ta Catherine Ha, Nie Qing, Hong Tian. Controlling Stochasticity in Epithelial-Mesenchymal Transition Through Multiple Intermediate Cellular States. Discrete and continuous dynamical systems. Series B, 2016, Vol.21 (7), pp.2275-2291.

9. Gloushankova Natalya A, Rubtsova Svetlana N, Zhitnyak Irina Y. An In Vitro System to Study the Epithelial-Mesenchymal Transition In Vitro. Methods in molecular biology (Clifton, N.J.), 2018, Vol.1749 , pp.29-42.

10. D. V. Maltseva, J. A. Makarova, A. Yu. Khrisstichenko, I. M. Tsypina, E. A. Tonevitsky, S. A. Rodin. Epithelial to Mesenchymal Transition Marker in 2D and 3D Colon Cancer Cell Cultures in the Presence of Laminin 332 and 411. Molecular Biology, 2019, Vol.53 (2), pp.291-298.

11. Myung-Giun Noh, Se-Jeong Oh, Eun-Jung Ahn, et al. Prognostic significance of E-cadherin and N-cadherin expression in Gliomas. BMC Cancer, 2017, Vol.17 (1).

12. Qing Ji, Xuan Liu, Zhifen Han, et al. Resveratrol suppresses epithelial-to-mesenchymal transition in colorectal cancer through TGF-β1/Smads signaling pathway mediated Snail/E-cadherin expression. BMC Cancer, 2015, Vol.15 (1).