Feature Of The Week 10/21/12: Cell Death Detection by Quantitative Three-Dimensional Single-Cell Tomography
The aim of cancer therapies is mainly to stop cell proliferation or induce cell death. Cell death regulation is important for normal development and homeostasis. Cancer cells escape from death signals and continue their abnormal proliferation. Therefore, the ability to induce death in cancer cells has been a crucial biomarker for the efficacy of chemotherapeutic agents. However, individual cancer cells, even from the same population, vary greatly in their response to cell death stimuli. Measuring the response at the single-cell levels provide further pharmacokinetic and pharmacodynamics information, which aids drug development and regimen design. Developing a microscopic technology with non-invasive, in situ, label-free, single-cell spatial resolution may serve this long-term need. We report the detection of cell death at the single-cell level using ultrahigh-resolution optical coherence tomography (UR-OCT). An improvement in OCT technology currently provides axial resolution to approximately 1 μm and lateral resolution to 2 μm. We demonstrated that UR-OCT not only provides three-dimensional in situ single-cell imaging but is also able to delineate subcellular structure (i.e., the nucleus). Dead cells cannot be differentiated from live cells based merely on size. Many parametric analytic methods have been used to address this issue, including speckle fluctuation in time-lapse images. It was confirmed that back-scattering signals are lower in apoptotic cells, which is most likely due to the perturbation of mitochondria morphology during apoptosis. Nuclear disintegration after chromatin condensation provides high-signal-intensity peaks that facilitate the identification of apoptotic cells.
With the collaboration between Professor Sheng-Lung Huang’s team at National Taiwan University (NTU) and Professor Jeng-Wei Tjiu’s team at the NTU Hospital, a homemade UR-OCT system was developed to image single-cell basal cell carcinoma (BCC) in three dimensions and differentiate between live and dead BCC cells by not only morphological recognition but also parametric analysis. The BCC cell line was used because BCC is the most common skin cancer, and we are familiar with it. An image analysis approach was also developed to automatically extract deterministic information of a single cell.
For more information see recent Article. Courtesy Sheng-Lung Huang and Jeng-Wei Tjiu from National Taiwan University.