Method for the detection of cellular abnormalities using fourier transform infrared spectroscopy

• US 6,620,621 B1
• Filed: 11/03/1999
• Issued: 09/16/2003
• Est. Priority Date: 11/13/1995
• Status: Expired due to Fees

• Current Assignee: Agilent Technologies Australia (M) Pty., Ltd. (Agilent Technologies, Inc.)
• Original Assignee: Digilab, Inc.
• Inventors: Rigas, Basil, Cohenford, Menashi A., Bhandare, Prashant S., Krishnan, Krishnaswamy, Cahn, Frederick R.
• Primary Examiner(s): Wallenhorst, Maureen M.
• Application Number:
  US09/433,655
• Time in Patent Office: 1,413 Days
• Field of Search: 436/63, 436/64, 436/171, 436/172, 600/473, 600/475, 600/476, 600/478, 250/338.1, 250/339.01, 250/339.09, 250/339.12, 250/341.1, 250/341.8, 250/461.2
• US Class Current: 436/63
• CPC Class Codes: G01J 2003/2866 Markers; Calibrating of scan
• CPC Class Codes: G01J 3/28 Investigating the spectrum using colour filters G01J3/51
• CPC Class Codes: G01J 3/453 by correlation of the amplitudes
• CPC Class Codes: G01N 2021/3595 using FTIR
• CPC Class Codes: G01N 21/35 using infrared light G01N21/39 takes precedence

Abstract

This invention teaches a method to identify cellular abnormalities which are associated with disease states. The method utilizes infrared (IR) spectra of cell samples which are dried on an infrared-transparent matrix and scanned at the frequency range from 3000-950 cm⁻¹. The identification of samples is based on establishing a reference using a representative set of spectra of normal and/or diseased specimens. During the reference assembly process, multivariate techniques such as Principal Component Analysis (PCA) and/or Partial Least Squares (PLS) are used. PCA and PLS reduce the data based on maximum variations between the spectra, and generate clusters in a multidimensional space representing the different populations. The utilization of Mahalinobis distances, or linear regression (e.g., Principle Component Regression on the reduced data from PCA) form the basis for the discrimination. In one embodiment, the invention is a method to distinguish premalignant and malignant stages of cervical cancer from normal cervical cells. This method is simple to use and achieves statistically reliable distinction between the following groups of cervical smears: normal (individuals with no prior history of dysplasia), dysplasia and malignant samples. Further, this invention discloses a method to obtain the IR spectrum of individual cervical cells fixed on an infrared-transparent matrix and to use the spectra o the individual cells in the method described above. In another aspect, the invention is a method for using vibrational spectroscopic imaging to distinguish between normal and diseased cells. In another aspect, the invention is a method to identify women at a high risk for developing cervical dysplasia.

Claims

1. A method for identifying a patient at high risk for dysplasia utilizing single cell infrared microspectroscopy, said method comprising;

(a) creating a reference set of absorption spectra from cervical cell samples taken from women having no history of dysplasia, each of said samples having a first cell exhibiting a first spectrum pattern and a second cell exhibiting a second spectrum pattern;

(b) producing absorption data for a cervical cell sample acquired from said patient by directing a beam of infrared light at single cells in said cervical cell sample to produce absorption data for each of said single cells;

(c) comparing said absorption data for said cervical cell sample acquired from said patient with said reference spectra, thereby generating comparison data;

(d) generating scores for said absorption data using said comparison data;

(e) creating frequency distribution profiles from said scores; and

(f) detecting said high risk for dysplasia using said frequency distribution profiles.