The present work shows design, development, and testing of the multimodal optical setup for simultaneous auto-fluorescence imaging, spectroscopy and quantitative phase microscopy (SAF-QPM) from same field of view (FOV). The SAF-QPM combines the capabilities of autofluorescence, offering molecular insights into metabolic activities, with quantitative phase imaging, providing nanometres-level sensitivity to cellular morphology and refractive index distribution. The autofluorescence serves as a biomarker for abnormal cellular changes associated with cancer development. Simultaneous incorporation of quantitative phase microscopy of the same tissue section probes the refractive index-dependent changes in the cellular morphology associated with cancer development. Non-interferometric QPI technique is used to retrieve the phase information of the sample, which overcomes the limitations of instability and coherent noise associated with interferometric QPI techniques. The incorporation of simultaneous AF and QPM from the same field of view enhances the accuracy and specificity of label-free cancer diagnosis.
Structured illumination microscopy (SIM) is a most popular super-resolution technique used in cell biology and bio-imaging. Here, we present a novel approach to realize multiscale super-resolution SIM by swapping the non-linearity between instrumentation and reconstruction algorithm to achieve super-resolution. Our goal is to overcome two conventional limitations of SIM i.e., fixed resolution and the need of precise knowledge of illumination pattern. The optical system encodes higher order frequencies of the sample by projecting PSF-modulated binary patterns for illuminating the sample plane, which do not have clean Fourier peaks conventionally used in SIM. These patterns fold high frequency content of sample into the measurements in an obfuscated manner, which are de-obfuscated using multiple signal classification algorithm. Our approach eliminates the need of clean peaks in the illumination pattern, which have multiple advantages i.e., simple instrumentation and the flexibility of using different collection lenses. The reconstruction algorithm used in the proposed work does not require known illumination. Finally, we reduce the sensitivity of reconstruction algorithm to the signal to background ratio. Here, we acquired patterned illumination images of the same sample using different collection objective lenses, and obtained diffraction limited as well as super-resolved images, supporting 4 different resolution in the same system through SIM. Our experimental results with multiple collection objective lens show wider applicability of the proposed system at signal to background ration as small as <3.
Motility of cells plays an important role to determine the cell growth, health and to monitor the gene transformation. The aim of the current study is to propose a combination of a high-contrast label-free imaging method and a computational approach (conventionally used for super-resolution) which can be used as a tool in tracing the motion of the cells and organelles. Here, we integrate quantitative phase microscopy (QPM) with waveguide-based trapping (WT) and applied multiple signal classification algorithm (MUSICAL) to analyze the motion of the trapped particle. We successfully trapped and displaced a 1 µm polystyrene bead particle on a strip waveguide using a 1064 nm laser beam. While propelling the polystyrene bead particle, we recorded time-lapsed interferometric images using a partially spatially coherent (PSC) light-based off-axis QPM system. The reconstruction of time-lapsed phase images of the trapped particle is accomplished using the Fourier transform and transport of intensity algorithm, which further used in MUSICAL for the motion trace analysis. Here, we traced the motion of a trapped bead particle with scale finer than the size of the object i.e., diffraction limit of the system. We show super-resolved motion trace even though the particle’s image is itself diffraction limited in each frame. The proposed study could be useful in different biological applications such as cell monitoring, cell tracking, manipulation, and classification between healthy and unhealthy cells.
In this work we have explored the live-cell friendly nanoscopy method Multiple Signal Classification Algorithm (MUSICAL) for multi-colour imaging of various organelles and sub-cellular structures in the cardiomyoblast cell line H2c9. We have tested MUSICAL for fast (up to 230Hz), multi-colour time-lapse sequences of various sub-cellular structures (mitochondria, endoplasmic reticulum, microtubules, endosomes and nuclei) in living cells using low excitation-light dose. Challenges and opportunities with applying MUSICAL for studying rapid sub-cellular dynamics are discussed.
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