A ubiquitous system for disrupting how Western blot imaging and data processing is carried for clinical analysis

Informations projet

ODR

N° de convention de subvention: 739777

DOI

10.3030/739777

Projet clôturé

Date de signature de la CE 20 Février 2017

Date de début 1 Septembre 2017

Date de fin 31 Août 2018

Financé au titre de

• INDUSTRIAL LEADERSHIP - Innovation In SMEs

Coût total

€ 86 625,00

Contribution de l’UE

€ 86 625,00

Coordonné par

MERCARISTA SL
Spain

Periodic Reporting for period 1 - ODR (A ubiquitous system for disrupting how Western blot imaging and data processing is carried for clinical analysis)

Période du rapport: 2017-09-01 au 2018-08-31

Résumé du contexte et des objectifs généraux du projet

Western blotting (WB) is an imperative laboratory technique widely used in clinical and biology research for the detection and analysis of proteins in biological samples. It provides with information on the identity, size, and quantity of a target protein, information crucial for research studies involved in disease diagnosis; drug and vaccine development; and basic research, e.g. cancer. Overall, up to 60% of laboratories at research centers and companies private companies worldwide use WB at a daily basis. Due to its widespread application, and the importance of the processes for its use, it is important to obtain reliable data from WB results from which build sound scientific conclusions.
Current methodologies for the analysis of WB results present two major shortcomings:
1) The pipeline is not integrated, i.e. the workflow requires the use of specialized hardware and multiple software to cover the whole process. This aspect increases the total processing time and, therefore, reduces efficiency.
2) The pipeline is not automated, i.e. some key steps in the pipeline demands the direct interaction by the user in the analysis. It may increase the risk of introducing error in the analysis and, therefore, the risk of obtaining biased results that may compromise the quality of the scientific study.
As a consequence, there is a strong need for an integrated, time-efficient, and unbiased pipeline for the data analysis of WB results. A faster approach would allow researchers to obtain a more efficient data analysis, and lead to a better management of the financial resources from research institutions and public budget. Unbiased results are key to develop accurate treatments.
- Project goals:
The major goal for this project is to provide with a new method in order to enhance the data analysis processing of WB results by making use mobile technologies. For the 12-month period we aim at developing a minimal viable product (MVP) ready to be tested by researchers in a real environment. The trial period is essential to obtain feedback useful for the implementation of new features, and accelerate the production of a final product.

Travail effectué depuis le début du projet jusqu’à la fin de la période considérée dans le rapport et principaux résultats atteints jusqu’à présent

The project started on September 1st until August 31st. During this period we aimed at developing a software – called ODR – in a mobile App format for the processing of WB results. ODR consists of two parts: a mobile App (analytical part) and a cloud-based digital platform (data management).
The whole project was divided into different working packages: i) product design and development; ii) testing and data analysis; iii) marketing and dissemination (Figure 1).
i) Product and design development: These activities covered almost the entirety of the 12-month period. It entailed the design of the product and the development (coding) of the core parts. The last part of these activities were allocated to the implementation of new features based on the feedback obtained from the trial period.
Results: We developed a MVP of the software, i.e. a core product with basic functionalities, was produced for its further testing in a real environment.
ii) Testing and data analysis: A trial period to test the MVP was divided into two parts, i.e. internal and external testing. The internal testing activities were performed in-house to develop the MVP for external testing by researchers.
For the external testing, we established collaborations with research institutions to test ODR in order to give feedback. These research institutions were: Centro de Biología Molecular (CBMSO), Centro Nacional de Biotecnología (CNB), Universidad Autónoma de Madrid (UAM).
iii) Marketing and dissemination: We made use of different channels for the marketing and dissemination of the ODR project such as oral presentations, demo videos, infographic, and building landing page. We participated in a business congress where we communicated the project ODR through an oral presentation. We met with private investors and representatives from startup incubators to search for potential funding opportunities. Unfortunately, none of these meetings led to new funding.
Alternatively, we established collaborations with technological companies from different EU countries for side projects using the ODR technology.
Overall, during the 12-month period we achieved:
- To develop a MVP of the software ODR for its subsequent testing in a real environment.
- To establish collaboration with research institutions to undertake testing activities of ODR.
- To communicate and disseminate the project ODR through different channels to a specialized and broader audience.
- To establish collaborations with international partners and, therefore, open new business opportunities for side projects related to the technology developed for ODR.

Progrès au-delà de l’état des connaissances et impact potentiel prévu (y compris l’impact socio-économique et les conséquences sociétales plus larges du projet jusqu’à présent)

- Improvement of state-of-the-art: Current methodologies for the data analysis of results from laboratory techniques such as WB the use of multiple software for image processing and data analysis, and the manual data storage. Overall, the processing for a single image is time consuming, and in some cases the workflow entails the direct involvement of the user, which may lead to the introduction of error and affect data accuracy (Figure 2).
ODR allows performing the analysis with the same device (e.g. smartphone). Therefore, an image of the result obtained with the laboratory technique is digitized directly with the mobile device and processed through a bespoke image processing algorithm. The obtained results are analyzed and graphically represented. The data is uploaded real-time to a cloud-based digital platform for their storage and sharing with members from the same project (Figure 3).
The main technological advance with ODR is the implementation of mobile devices in the analysis of results obtained with laboratory techniques through an image processing algorithm. The workflow is faster than current methods and gives unbiased data due to the automated aspect. This algorithm processes images by recognizing and detecting automatically the regions to be analyzed, and calculate the data for subsequent analysis. In addition, the use of cloud technologies in ODR would allow researchers to organize and share more efficiently their findings and so speed collaboration.
During the 12-month period we prepared a MVP for ODR, which is currently tested by researchers in a real environment. The trial period would give us information on the robustness of the algorithm. This aspect is essential to refine the algorithm for the final product.
- Potential impacts from the ODR project:
With ODR we managed to reduce the processing time to a few minutes, with the possibility of processing multiple images in parallel. It leads to a substantial reduction in processing time and, therefore, increases efficiency in the analysis. For research institutions making common use of WB, reducing processing time may lead to reduction in costs and increased productivity. Accordingly, ODR would help use the financial resources more efficiently.
ODR presents a more automated workflow compared to current methodologies, i.e. a limited involvement by the user in the analysis. Automatization implies in obtaining unbiased results and, therefore, increased data accuracy and reproducibility. More reliable data would help obtain a better understanding of the biological processes and the discovery of potential target molecules for drug and vaccine development.

Schematics of ODR project with work packages and milestones

Comparison of current methods vs ODR approach

Visual representation of the ODR workflow