Effects of NaCl and iso-osmotic PEG stress on growth,osmolytes accumulation and antioxidant defense in cultured sugarcane cells

In order to discriminate between the ionic and osmotic components of salt stress, sugarcane (Saccharum officinarum L. cv. Co 86032) calli were cultured on media containing NaCl or polyethylene glycol (PEG) 8000 that exerted the same osmotic pressure (−0.7 MPa). PEG stress exposure for 15 days led to significant growth reduction and loss in water content than salt stressed and control tissues. Osmotic adjustment (OA) was observed in callus tissues grown on salt, but was not evident in callus grown on PEG. Oxidative damage to membranes, estimated in terms of accumulation of thiobarbituric acid reactive substances-TBARS and electrolytic leakage was significantly higher in both the stressed calli than the control however, the extent of damage was more in the PEG stressed calli. The stressed callus tissues showed inhibition of ascorbate peroxidase activity, while catalase activity was increased. These results indicate sensitivity of cells to PEG-mediated stress than salt stress and differences in their OA to these two stress conditions. The sensitivity to the osmotic stress indicate that expression of the stress tolerance response requires the coordinated action of different tissues in a plant and hence was not expressed at the cellular level.     

Focus: A Multifaceted Battle Against Cancer: Extracting Knowledge from Chemical Imaging Data Using Computational Algorithms for Digital Cancer Diagnosis

Fourier transform infrared (FTIR) spectroscopic imaging is an emerging microscopy modality for clinical histopathologic diagnoses as well as for biomedical research. Spectral data recorded in this modality are indicative of the underlying, spatially resolved biochemical composition but need computerized algorithms to digitally recognize and transform this information to a diagnostic tool to identify cancer or other physiologic conditions. Statistical pattern recognition forms the backbone of these recognition protocols and can be used for highly accurate results. Aided by biochemical correlations with normal and diseased states and the power of modern computer-aided pattern recognition, this approach is capable of combating many standing questions of traditional histology-based diagnosis models. For example, a simple diagnostic test can be developed to determine cell types in tissue. As a more advanced application, IR spectral data can be integrated with patient information to predict risk of cancer, providing a potential road to precision medicine and personalized care in cancer treatment. The IR imaging approach can be implemented to complement conventional diagnoses, as the samples remain unperturbed and are not destroyed. Despite high potential and utility of this approach, clinical implementation has not yet been achieved due to practical hurdles like speed of data acquisition and lack of optimized computational procedures for extracting clinically actionable information rapidly. The latter problem has been addressed by developing highly efficient ways to process IR imaging data but remains one that has considerable scope for progress. Here, we summarize the major issues and provide practical considerations in implementing a modified Bayesian classification protocol for digital molecular pathology. We hope to familiarize readers with analysis methods in IR imaging data and enable researchers to develop methods that can lead to the use of this promising technique for digital diagnosis of cancer.