Grading schemes for breast cancer diagnosis are predominantly based on pathologists' qualitative assessment of altered nuclear structure from 2D brightfield microscopy images. However, cells are three-dimensional (3D) objects with features that are inherently 3D and thus poorly characterized in 2D. Our goal is to quantitatively characterize nuclear structure in 3D, assess its variation with malignancy, and investigate whether such variation correlates with standard nuclear grading criteria.
Methodology
We applied micro-optical computed tomographic imaging and automated 3D nuclear morphometry to quantify and compare morphological variations between human cell lines derived from normal, benign fibrocystic or malignant breast epithelium. To reproduce the appearance and contrast in clinical cytopathology images, we stained cells with hematoxylin and eosin and obtained 3D images of 150 individual stained cells of each cell type at sub-micron, isotropic resolution. Applying volumetric image analyses, we computed 42 3D morphological and textural descriptors of cellular and nuclear structure.
Principal Findings
We observed four distinct nuclear shape categories, the predominant being a mushroom cap shape. Cell and nuclear volumes increased from normal to fibrocystic to metastatic type, but there was little difference in the volume ratio of nucleus to cytoplasm (N/C ratio) between the lines. Abnormal cell nuclei had more nucleoli, markedly higher density and clumpier chromatin organization compared to normal. Nuclei of non-tumorigenic, fibrocystic cells exhibited larger textural variations than metastatic cell nuclei. At p<0.0025 by ANOVA and Kruskal-Wallis tests, 90% of our computed descriptors statistically differentiated control from abnormal cell populations, but only 69% of these features statistically differentiated the fibrocystic from the metastatic cell populations.
Conclusions
Our results provide a new perspective on nuclear structure variations associated with malignancy and point to the value of automated quantitative 3D nuclear morphometry as an objective tool to enable development of sensitive and specific nuclear grade classification in breast cancer diagnosis.
This dissertation presents the development of a laser lysis chip combined with a two-photon laser system to perform single-cell lysis of single cells in situ from three-dimensional (3D) cell spheroids followed by analysis of the cell lysate with two-step reverse transcription-quantitative polymerase chain reaction (RT-qPCR). The 3D spheroids were trapped in a well in the custom-designed laser lysis chip. Next, each single cell of interest in the 3D spheroid was identified and lysed one at a time utilizing a two-photon excited laser. After each cell lysis, the contents inside the target cell were released to the surrounding media and carried out to the lysate collector. Finally, the gene expression of each individual cell was measured by two-step RT-qPCR then spatially mapped back to its original location in the spheroids to construct a 3D gene expression map.
This novel technology and approach enables multiple gene expression measurements in single cells of multicellular organisms as well as cell-to-cell heterogeneous responses to the environment with spatial recognition. Furthermore, this method can be applied to study precancerous tissues for a better understanding of cancer progression and for identifying early tumor development.
In this dissertation project, an inexpensive, portable, low-energy consuming, and highly quantitative microbiological genomic sensor has been developed for in situ ocean-observing systems. A novel real-time colorimetric loop-mediated isothermal amplification (LAMP) technology has been developed for quantitative detection of microbial nucleic acids. This technology was implemented on a chip-level device with an embedded inexpensive imaging device and temperature controller to achieve quantitative detection within one hour. A bubble-free liquid handling approach was introduced to avoid bubble trapping during liquid loading, a common problem in microfluidic devices. An algorithm was developed to reject the effect of bubbles generated during the reaction process, to enable more accurate nucleic acid analysis. This genomic sensor has been validated at gene and gene expression levels using Synechocystis sp. PCC 6803 genomic DNA and total RNA. Results suggest that the detection limits reached 10 copies/μL and 100 fg/μL, respectively. This approach was highly quantitative, with linear standard curves down to 103 copies/μL and 1 pg/μL, respectively. In addition to environmental microbe characterization, this genomic sensor has been employed for viral RNA quantification during an infectious disease outbreak. As the Zika fever was spreading in America, a quantitative detection of Zika virus has been performed. The results show that the genomic sensor is highly quantitative from 10 copies/μL to 105 copies/μL. This suggests that the novel nucleic acid quantification technology is sensitive, quantitative, and robust. It is a promising candidate for rapid microbe detection and quantification in routine laboratories.
In the future, this genomic sensor will be implemented in in situ platforms as a core analytical module with minor modifications, and could be easily accessible by oceanographers. Deployment of this microbial genomic sensor in the field will enable new scientific advances in oceanography and provide a possible solution for infectious disease detection.
Although emerging evidence indicates that deep-sea water contains an untapped reservoir of high metabolic and genetic diversity, this realm has not been studied well compared with surface sea water. The study provided the first integrated meta-genomic and -transcriptomic analysis of the microbial communities in deep-sea water of North Pacific Ocean. DNA/RNA amplifications and simultaneous metagenomic and metatranscriptomic analyses were employed to discover information concerning deep-sea microbial communities from four different deep-sea sites ranging from the mesopelagic to pelagic ocean. Within the prokaryotic community, bacteria is absolutely dominant (~90%) over archaea in both metagenomic and metatranscriptomic data pools. The emergence of archaeal phyla Crenarchaeota, Euryarchaeota, Thaumarchaeota, bacterial phyla Actinobacteria, Firmicutes, sub-phyla Betaproteobacteria, Deltaproteobacteria, and Gammaproteobacteria, and the decrease of bacterial phyla Bacteroidetes and Alphaproteobacteria are the main composition changes of prokaryotic communities in the deep-sea water, when compared with the reference Global Ocean Sampling Expedition (GOS) surface water. Photosynthetic Cyanobacteria exist in all four metagenomic libraries and two metatranscriptomic libraries. In Eukaryota community, decreased abundance of fungi and algae in deep sea was observed. RNA/DNA ratio was employed as an index to show metabolic activity strength of microbes in deep sea. Functional analysis indicated that deep-sea microbes are leading a defensive lifestyle.