The aim of the proposed research is the development of new methodology to aid classical cytology and pathology in the detection and diagnosis of disease. In classical cytology, visual inspection is used to detect changes in the morphology of cells obtained from body fluids, or by exfoliation or thin needle biopsy, while in classical pathology stained tissue sections are examined visually. The novel methods being developed in the PI's laboratory, referred to as spectral cytology and spectral pathology, are based on optical measurements, which detect variations in biochemical composition of cells quantitatively, and which use objective algorithms to quantify the spectral results in terms of disease. These diagnostic algorithms are trained using the correlation between spectral data and classical cytology/pathology as the gold standard. The efforts carried out in the Pi's laboratory, and results from other groups worldwide, have demonstrated that spectral cytology is an extraordinarily sensitive method for detecting variations in cellular properties. For example, spectral cytology at the single cell level can be used to distinguish normal from cancerous epithelial cells, normal lymphocytes from lymphoma cells, and even closely related normal cells from each other (e.g. B- and T-lymphocytes, activated and non-activated lymphocytes). Relevance: The methods of optical diagnoses being developed in the PI's laboratory will augment presently available methods of cytology and pathology to screen exfoliated cells for disease, and diagnose tissue sections from biopsies for the same diseases. The optical methods are entirely machine-based and computer-interpreted and thus, reduce the workload in diagnostic laboratories, increase the overall accuracy and decrease the time required to render medical diagnoses.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA090346-07
Application #
7687493
Study Section
Enabling Bioanalytical and Biophysical Technologies Study Section (EBT)
Program Officer
Sorbara, Lynn R
Project Start
2000-06-01
Project End
2011-07-31
Budget Start
2009-08-01
Budget End
2010-07-31
Support Year
7
Fiscal Year
2009
Total Cost
$449,168
Indirect Cost
Name
Northeastern University
Department
Chemistry
Type
Schools of Arts and Sciences
DUNS #
001423631
City
Boston
State
MA
Country
United States
Zip Code
02115
Diem, Max; Miljkovi?, MiloŇ°; Bird, Benjamin et al. (2016) Cancer screening via infrared spectral cytopathology (SCP): results for the upper respiratory and digestive tracts. Analyst 141:416-28
Chernenko, Tatyana; Buyukozturk, Fulden; Miljkovic, Milos et al. (2013) Label-Free Raman Microspectral Analysis for Comparison of Cellular Uptake and Distribution between Non-Targeted and EGFR-Targeted Biodegradable Polymeric Nanoparticles. Drug Deliv Transl Res 3:
Marcsisin, Ellen J; Uttero, Christina M; Mazur, Antonella I et al. (2012) Noise Adjusted Principal Component reconstruction to optimize infrared microspectroscopy of individual live cells. Analyst 137:2958-64
Mazur, Antonella I; Marcsisin, Ellen J; Bird, Benjamin et al. (2012) Evaluating different fixation protocols for spectral cytopathology, part 1. Anal Chem 84:1259-66
Mazur, Antonella I; Marcsisin, Ellen J; Bird, Benjamin et al. (2012) Evaluating different fixation protocols for spectral cytopathology, part 2: cultured cells. Anal Chem 84:8265-71
Chernenko, T; Sawant, R R; Miljkovic, M et al. (2012) Raman microscopy for noninvasive imaging of pharmaceutical nanocarriers: intracellular distribution of cationic liposomes of different composition. Mol Pharm 9:930-6
Bird, B; Miljkovi?, M; Laver, N et al. (2011) Spectral detection of micro-metastases and individual metastatic cells in lymph node histology. Technol Cancer Res Treat 10:135-44
Marcsisin, Ellen J Swain; Uttero, Christina M; Miljkovic, Milos et al. (2010) Infrared microspectroscopy of live cells in aqueous media. Analyst 135:3227-32
Miljkovic, Milos; Chernenko, Tatyana; Romeo, Melissa J et al. (2010) Label-free imaging of human cells: algorithms for image reconstruction of Raman hyperspectral datasets. Analyst 135:2002-13
Papamarkakis, Kostas; Bird, Benjamin; Schubert, Jennifer M et al. (2010) Cytopathology by optical methods: spectral cytopathology of the oral mucosa. Lab Invest 90:589-98

Showing the most recent 10 out of 28 publications