Micro-Raman Imaging for Biology with Multivariate Spectral Analysis

  • Federica Malvaso

Student thesis: Master's Thesis

Abstract

Raman spectroscopy is a noninvasive technique that can provide complex information on the vibrational state of the molecules. It defines the unique fingerprint that allow the identification of the various chemical components within a given sample. The aim of the following thesis work is to analyze Raman maps related to three pairs of different cells, highlighting differences and similarities through multivariate algorithms. The first pair of analyzed cells are human embryonic stem cells (hESCs), while the other two pairs are induced pluripotent stem cells (iPSCs) derived from T lymphocytes and keratinocytes, respectively. Although two different multivariate techniques were employed, ie Principal Component Analysis and Cluster Analysis, the same results were achieved: the iPSCs derived from T-lymphocytes show a higher content of genetic material both compared with the iPSCs derived from keratinocytes and the hESCs . On the other side, equally evident, was that iPS cells derived from keratinocytes assume a molecular distribution very similar to hESCs.
Date of AwardMay 5 2015
Original languageEnglish (US)
Awarding Institution
  • Physical Science and Engineering
SupervisorEnzo Di Fabrizio (Supervisor)

Keywords

  • Raman Spectroscopy
  • Principal Component Analysis
  • Cluster Analysis
  • Stem Cells
  • iPSCs
  • K-Means Algorithms

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