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Machine learning phenomics (MLP) combining deep learning with time-lapse-microscopy for monitoring colorectal adenocarcinoma cells gene expression and drug-response.
D'Orazio, M; Murdocca, M; Mencattini, A; Casti, P; Filippi, J; Antonelli, G; Di Giuseppe, D; Comes, M C; Di Natale, C; Sangiuolo, F; Martinelli, E.
Affiliation
  • D'Orazio M; Department of Electronic Engineering, University of Rome Tor Vergata, via del Politecnico 1, 00133, Rome, Italy.
  • Murdocca M; Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (ICLOC), University of Rome Tor Vergata, 00133, Rome, Italy.
  • Mencattini A; Department of Biomedicine and Prevention, Tor Vergata University, Rome, Italy.
  • Casti P; Department of Electronic Engineering, University of Rome Tor Vergata, via del Politecnico 1, 00133, Rome, Italy. mencattini@ing.uniroma2.it.
  • Filippi J; Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (ICLOC), University of Rome Tor Vergata, 00133, Rome, Italy. mencattini@ing.uniroma2.it.
  • Antonelli G; Department of Electronic Engineering, University of Rome Tor Vergata, via del Politecnico 1, 00133, Rome, Italy.
  • Di Giuseppe D; Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (ICLOC), University of Rome Tor Vergata, 00133, Rome, Italy.
  • Comes MC; Department of Electronic Engineering, University of Rome Tor Vergata, via del Politecnico 1, 00133, Rome, Italy.
  • Di Natale C; Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (ICLOC), University of Rome Tor Vergata, 00133, Rome, Italy.
  • Sangiuolo F; Department of Electronic Engineering, University of Rome Tor Vergata, via del Politecnico 1, 00133, Rome, Italy.
  • Martinelli E; Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (ICLOC), University of Rome Tor Vergata, 00133, Rome, Italy.
Sci Rep ; 12(1): 8545, 2022 05 20.
Article in En | MEDLINE | ID: mdl-35595808
ABSTRACT
High-throughput phenotyping is becoming increasingly available thanks to analytical and bioinformatics approaches that enable the use of very high-dimensional data and to the availability of dynamic models that link phenomena across levels from genes to cells, from cells to organs, and through the whole organism. The combination of phenomics, deep learning, and machine learning represents a strong potential for the phenotypical investigation, leading the way to a more embracing approach, called machine learning phenomics (MLP). In particular, in this work we present a novel MLP platform for phenomics investigation of cancer-cells response to therapy, exploiting and combining the potential of time-lapse microscopy for cell behavior data acquisition and robust deep learning software architectures for the latent phenotypes extraction. A two-step proof of concepts is designed. First, we demonstrate a strict correlation among gene expression and cell phenotype with the aim to identify new biomarkers and targets for tailored therapy in human colorectal cancer onset and progression. Experiments were conducted on human colorectal adenocarcinoma cells (DLD-1) and their profile was compared with an isogenic line in which the expression of LOX-1 transcript was knocked down. In addition, we also evaluate the phenotypic impact of the administration of different doses of an antineoplastic drug over DLD-1 cells. Under the omics paradigm, proteomics results are used to confirm the findings of the experiments.
Subject(s)

Full text: 1 Collection: 01-internacional Health context: 1_ASSA2030 Database: MEDLINE Main subject: Colorectal Neoplasms / Adenocarcinoma / Deep Learning Type of study: Prognostic_studies Limits: Humans Language: En Journal: Sci Rep Year: 2022 Document type: Article

Full text: 1 Collection: 01-internacional Health context: 1_ASSA2030 Database: MEDLINE Main subject: Colorectal Neoplasms / Adenocarcinoma / Deep Learning Type of study: Prognostic_studies Limits: Humans Language: En Journal: Sci Rep Year: 2022 Document type: Article