Identification of DAPI-stained normal, inflammatory, and carcinoma hepatic cells based on hyperspectral microscopy.

Gross chromatin imbalance and high DNA content are distinct features of various types of cancer cells. However, severe inflammation can also produce similar symptoms in cells. In this study, normal, inflammatory, and carcinoma hepatic cells were stained with 4',6-diamidino-2-phenylindole (DAPI) and investigated by hyperspectral microscopy. DAPI is a DNA-sensitive fluorochrome. Therefore, the differences in the cellular DNA of the samples can be revealed by the corresponding fluorescence. Our experimental results demonstrate that although chromosomal disorder and high DNA content both occur in severely inflammatory and carcinoma hepatic cells, there is still a slight difference in their DNA, making their fluorescent intensity and even their spectral shapes distinguishable. Based on these spectral features, we developed a method for the precise identification of normal, inflammatory, and carcinoma hepatic cells in the field of view. The identification accuracy for these three types of cells was 99.8%. We believe that examination that combines DAPI staining with hyperspectral microscopy is a potential method for the identification and investigation of various types of cancer tissues.

Hyperspectral microscopy combined with DAPI staining for the identification of hepatic carcinoma cells.

In this study, the DAPI staining is firstly reported for use in the identification of hepatic carcinoma cells based on hyperspectral microscopy. Nuclei in cancer cells usually contain more aneuploidies than that in normal cells, leading to the change of DNA content. Here, we stain hepatic carcinoma tissues and normal hepatic tissues with 4',6-diamidino-2-phenylindole (DAPI) which is sensitive to the DNA content as a fluorochrome binds to DNA. Consequently, the difference in DNA content between hepatic carcinoma cells and normal hepatic cells can be identified by the fluorescent spectral characteristics. Harnessing the hyperspectral microscopy, we find that the fluorescent properties of these two kinds of cells are different not only in the intensity but also in the spectral shape. These properties are exploited to train a support vector machine (SVM) model for classifying cells. The results show that the sensitivity and specificity for the identification of 1000 hepatic carcinoma samples are 99.3% and 99.1%, respectively.

Classification of two species of Gram-positive bacteria through hyperspectral microscopy coupled with machine learning.

Gram stain is one of the most common techniques used to visualize bacteria under microscopy and classify bacteria into two large groups (Gram-positive and Gram-negative). However, such an inaccurate classification is unfavorable for bacterial research. For instance, soil-rhizosphere bacteria, Bacillus megaterium (B. megaterium) and Bacillus cereus (B. cereus) have different effects on plants, nonetheless, they are both Gram-positive and difficult to be differentiated. Here, we present a method to precisely classify Gram-positive bacteria via hyperspectral microscopy. The pH-value differences in the intracellular environment of various types of bacteria can lead to different ionization of the auxochrome of crystal violet (CV) molecules during the Gram stain process. Consequently, there is a subtle difference in the absorption peak of Gram-stained bacteria. Harnessing hyperspectral microscopy can capture this subtle difference and enable precise classification. Besides the spectral features, the spatial features were also used to improve the quality of bacterial identification. The results show that the classification accuracy of two species of Gram-positive bacteria, B. megaterium and B. cereus, is up to 98.06%. We believe this method can be used for other Gram-positive bacteria and Gram-negative bacteria, realizing a more elaborate classification for Gram-stained bacteria.