<p>Hemozoin, the heme detoxification end product in malaria parasites during their growth in the red blood cells (RBCs), serves as an important marker for diagnosis and treatment target of malaria disease. However, the current method for hemozoin-targeted drug screening mainly relies on <italic>in-vitro</italic> β-hematin inhibition assays, which may lead to false-positive events due to under-representation of the real hemozoin crystal. Quantitative <italic>in-situ</italic> imaging of hemozoin is highly desired for high-throughput screening of antimalarial drugs and for elucidating the mechanisms of antimalarial drugs. We present transient absorption (TA) imaging as a high-speed single-cell analysis platform with chemical selectivity to hemozoin. We first demonstrated that TA microscopy is able to identify β-hematin, the artificial form of hemozoin, from the RBCs. We further utilized time-resolved TA imaging to <italic>in situ</italic> discern hemozoin from malaria-infected RBCs with optimized imaging conditions. Finally, we quantitatively analyzed the hemozoin amount in RBCs at different infection stages by single-shot TA imaging. These results highlight the potential of TA imaging for efficient antimalarial drug screening and drug mechanism investigation.</p>
Hyperspectral stimulated Raman scattering (SRS) microscopy allows imaging of complex chemical mixtures and analysis cellular metabolites with high specificity. However, current SRS imaging is not implemented to address the cell heterogeneity issue, which can only be resolved by statistical analysis of a large amount of cells through cytometry. We developed a high-speed hyperspectral SRS image cytometry platform based on multiplex excitation, acquiring a Raman spectrum of 200 wavenumbers in 5 microseconds. This platform enables measurement of <100 cells per second. Multiple chemical signatures, featuring different cellular organelles such as lipids, endoplasmic reticulum, nucleus, and cytoplasm can be segmented. Statistical analysis over a large amount of cells reveals unprecedented details about cell metabolic changes after drug treatment.
Voltage imaging has become an emerging technique to record membrane potential change in living cells. Yet, compared to the conventional electrophysiology, imaging approaches are still limited to relative membrane potential changes, losing important information conveyed by absolute value of membrane voltage. This challenge comes from several factors affecting the signal intensity, such as concentration, illumination intensity, and photobleaching. Spectroscopy is a quantitative method that shows potential to report the state of molecules in situ. Here, we apply electronic pre-resonance stimulated Raman scattering (SRS) imaging to detect near-infrared absorbing microbial rhodopsin voltage sensors in E. coli. The use of newly developed near-infrared microbial rhodopsins (Ganapathy et. al. 2017. JACS, 2017, 139(6):2338- 44) enables electronic pre-resonance SRS imaging with single cell sensitivity. By spectral profile analysis, we identified voltage-sensitive SRS peaks. The spectral signature can be used as part of a quantitative approach to measure membrane potential and enable mapping of absolute voltage in a neural network.
Stimulated Raman scattering (SRS) microscopy is a promising technique for label-free imaging of living systems. We demonstrate microsecond-scale SRS spectral imaging by tuning two spectrally focused pulses temporally through a resonant delay-line. Our platform acquired an SRS spectrum within 42 microseconds and formed a spectral image composed of 40,000 pixels in real-time.
A hyperspectral image corresponds to a data cube with two spatial dimensions and one spectral dimension. Through linear un-mixing, hyperspectral images can be decomposed into spectral signatures of pure components as well as their concentration maps. Due to this distinct advantage on component identification, hyperspectral imaging becomes a rapidly emerging platform for engineering better medicine and expediting scientific discovery. Among various hyperspectral imaging techniques, hyperspectral stimulated Raman scattering (HSRS) microscopy acquires data in a pixel-by-pixel scanning manner. Nevertheless, current image acquisition speed for HSRS is insufficient to capture the dynamics of freely moving subjects. Instead of reducing the pixel dwell time to achieve speed-up, which would inevitably decrease signal-to-noise ratio (SNR), we propose to reduce the total number of sampled pixels. Location of sampled pixels are carefully engineered with triangular wave Lissajous trajectory. Followed by a model-based image in-painting algorithm, the complete data is recovered for linear unmixing. Simulation results show that by careful selection of trajectory, a fill rate as low as 10% is sufficient to generate accurate linear unmixing results. The proposed framework applies to any hyperspectral beam-scanning imaging platform which demands high acquisition speed.