Interpreting Raman spectra using machine learning: towards a non-invasive method of characterizing single cells

Raman microscopy has the potential to non-destructively measure the biomolecular changes in single cells in a label-free manner. However, the extent to which Raman spectra can effectively infer biologically-relevant information is not well understood. In this thesis, we use machine learning methods to explore the ability of Raman microscopy data to infer cell states in microbes and the gene expression values of ten genes in mouse embryonic fibroblasts (MEFs) undergoing a dynamic cellular reprogramming process. Using a multi-modal, supervised learning approach, we provide evidence that Raman spectra can accurately resolve microbial cell types. This thesis also presents a robust computational pipeline to preprocess Raman spectra, calibrate multi-modal data, and segment nuclei; an analysis of methods to increase the signal-to-noise ratio of Raman spectra; and an analysis of Raman spectral features important for predicting microbial cell-type. Together, the results suggest Raman microscopy be considered as a useful modality for distinguishing cell-types and potentially tracking cellular dynamics, a common goal of many consortia including the Human Cell Atlas.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2021

Author dc:creator

• Sorenson, Taylor(Taylor M.)

Advisor dc:contributor.advisor

• Aviv Regev and Tommaso Biancalani.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/130714

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/130714