High definition infrared spectroscopic imaging for lymph node histopathology

• Published in: PloS one 2015-06, Vol.10 (6), p.e0127238-e0127238
• Main Authors: Leslie, L Suzanne, Wrobel, Tomasz P, Mayerich, David, Bindra, Snehal, Emmadi, Rajyasree, Bhargava, Rohit
• Format: Article
• Language: English
• Subjects: Adult; Aged; Algorithms; B-Lymphocytes - pathology; Bioengineering; Biopsy; Breast cancer; Care and treatment; Chemistry; Computer engineering; Connective tissues; Diagnosis; Erythrocytes; Erythrocytes - pathology; Female; Flow cytometry; Fourier transforms; High definition; Histopathology; Historical account; Humans; Immunological memory; Infrared imaging; Infrared spectroscopy; Lymph nodes; Lymph Nodes - pathology; Lymphatic diseases; Lymphatic system; Lymphocytes; Lymphocytes B; Lymphocytes T; Lymphoma; Memory cells; Microscopy; Middle Aged; Molecular absorption; Molecular chains; Molecular Imaging - instrumentation; Molecular Imaging - methods; Morphology; Muscles; Neoplasms - pathology; Noise; Object recognition; Optical microscopy; Sentinel Lymph Node Biopsy; Smooth muscle; Spectroscopy, Fourier Transform Infrared; Stains; T-Lymphocytes - pathology; Wavelengths
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0127238

Chemical imaging is a rapidly emerging field in which molecular information within samples can be used to predict biological function and recognize disease without the use of stains or manual identification. In Fourier transform infrared (FT-IR) spectroscopic imaging, molecular absorption contrast provides a large signal relative to noise. Due to the long mid-IR wavelengths and sub-optimal instrument design, however, pixel sizes have historically been much larger than cells. This limits both the accuracy of the technique in identifying small regions, as well as the ability to visualize single cells. Here we obtain data with micron-sized sampling using a tabletop FT-IR instrument, and demonstrate that the high-definition (HD) data lead to accurate identification of multiple cells in lymph nodes that was not previously possible. Highly accurate recognition of eight distinct classes - naïve and memory B cells, T cells, erythrocytes, connective tissue, fibrovascular network, smooth muscle, and light and dark zone activated B cells was achieved in healthy, reactive, and malignant lymph node biopsies using a random forest classifier. The results demonstrate that cells currently identifiable only through immunohistochemical stains and cumbersome manual recognition of optical microscopy images can now be distinguished to a similar level through a single IR spectroscopic image from a lymph node biopsy.