Speckle-free quantitative phase microscopy using pseudo-thermal light source for label-free imaging of biological cells and tissues with high temporal phase stability and spatial phase sensitivity

2020 ◽  
Author(s):  
Dalip S. Mehta ◽  
Veena Singh ◽  
Shilpa Tayal ◽  
Sunil Bhatt ◽  
Vishesh Kumar Dubey
Keyword(s):  
Light Source ◽  
Phase Stability ◽  
Phase Sensitivity ◽  
Biological Cells ◽  
Label Free ◽  
Quantitative Phase ◽  
Phase Microscopy ◽  
Spatial Phase ◽  
Temporal Phase ◽  
Thermal Light

scholarly journals High-throughput spatial sensitive quantitative phase microscopy using low spatial and high temporal coherent illumination

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Azeem Ahmad ◽  
Vishesh Dubey ◽  
Nikhil Jayakumar ◽  
Anowarul Habib ◽  
Ankit Butola ◽  
...  
Keyword(s):  
Light Source ◽  
High Throughput ◽  
High Speed ◽  
Light Sources ◽  
Single Shot ◽  
Phase Sensitivity ◽  
Quantitative Phase ◽  
Phase Microscopy ◽  
Spatial Phase ◽  
Quantitative Phase Microscopy

AbstractHigh space-bandwidth product with high spatial phase sensitivity is indispensable for a single-shot quantitative phase microscopy (QPM) system. It opens avenue for widespread applications of QPM in the field of biomedical imaging. Temporally low coherence light sources are implemented to achieve high spatial phase sensitivity in QPM at the cost of either reduced temporal resolution or smaller field of view (FOV). In addition, such light sources have low photon degeneracy. On the contrary, high temporal coherence light sources like lasers are capable of exploiting the full FOV of the QPM systems at the expense of less spatial phase sensitivity. In the present work, we demonstrated that use of narrowband partially spatially coherent light source also called pseudo-thermal light source (PTLS) in QPM overcomes the limitations of conventional light sources. The performance of PTLS is compared with conventional light sources in terms of space bandwidth product, phase sensitivity and optical imaging quality. The capabilities of PTLS are demonstrated on both amplitude (USAF resolution chart) and phase (thin optical waveguide, height ~ 8 nm) objects. The spatial phase sensitivity of QPM using PTLS is measured to be equivalent to that for white light source and supports the FOV (18 times more) equivalent to that of laser light source. The high-speed capabilities of PTLS based QPM is demonstrated by imaging live sperm cells that is limited by the camera speed and large FOV is demonstrated by imaging histopathology human placenta tissue samples. Minimal invasive, high-throughput, spatially sensitive and single-shot QPM based on PTLS will enable wider penetration of QPM in life sciences and clinical applications.

scholarly journals Characterization of Liposomes Using Quantitative Phase Microscopy (QPM)

Pharmaceutics ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 590
Author(s):  
Jennifer Cauzzo ◽  
Nikhil Jayakumar ◽  
Balpreet Singh Ahluwalia ◽  
Azeem Ahmad ◽  
Nataša Škalko-Basnet
Keyword(s):  
Rapid Development ◽  
Fluorescent Labeling ◽  
Label Free ◽  
Batch Mode ◽  
Full Field ◽  
Quantitative Phase ◽  
Phase Microscopy ◽  
Quantitative Phase Microscopy ◽  
Tracking Ability

The rapid development of nanomedicine and drug delivery systems calls for new and effective characterization techniques that can accurately characterize both the properties and the behavior of nanosystems. Standard methods such as dynamic light scattering (DLS) and fluorescent-based assays present challenges in terms of system’s instability, machine sensitivity, and loss of tracking ability, among others. In this study, we explore some of the downsides of batch-mode analyses and fluorescent labeling, while introducing quantitative phase microscopy (QPM) as a label-free complimentary characterization technique. Liposomes were used as a model nanocarrier for their therapeutic relevance and structural versatility. A successful immobilization of liposomes in a non-dried setup allowed for static imaging conditions in an off-axis phase microscope. Image reconstruction was then performed with a phase-shifting algorithm providing high spatial resolution. Our results show the potential of QPM to localize subdiffraction-limited liposomes, estimate their size, and track their integrity over time. Moreover, QPM full-field-of-view images enable the estimation of a single-particle-based size distribution, providing an alternative to the batch mode approach. QPM thus overcomes some of the drawbacks of the conventional methods, serving as a relevant complimentary technique in the characterization of nanosystems.

scholarly journals Rapid, label-free classification of tumor-reactive T cell killing with quantitative phase microscopy and machine learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Diane N. H. Kim ◽  
Alexander A. Lim ◽  
Michael A. Teitell
Keyword(s):  
Machine Learning ◽  
T Cell ◽  
Time Lapse ◽  
Cell Receptor ◽  
Model System ◽  
Cell Killing ◽  
Label Free ◽  
Receptor Model ◽  
Quantitative Phase ◽  
Phase Microscopy

AbstractQuantitative phase microscopy (QPM) enables studies of living biological systems without exogenous labels. To increase the utility of QPM, machine-learning methods have been adapted to extract additional information from the quantitative phase data. Previous QPM approaches focused on fluid flow systems or time-lapse images that provide high throughput data for cells at single time points, or of time-lapse images that require delayed post-experiment analyses, respectively. To date, QPM studies have not imaged specific cells over time with rapid, concurrent analyses during image acquisition. In order to study biological phenomena or cellular interactions over time, efficient time-dependent methods that automatically and rapidly identify events of interest are desirable. Here, we present an approach that combines QPM and machine learning to identify tumor-reactive T cell killing of adherent cancer cells rapidly, which could be used for identifying and isolating novel T cells and/or their T cell receptors for studies in cancer immunotherapy. We demonstrate the utility of this method by machine learning model training and validation studies using one melanoma-cognate T cell receptor model system, followed by high classification accuracy in identifying T cell killing in an additional, independent melanoma-cognate T cell receptor model system. This general approach could be useful for studying additional biological systems under label-free conditions over extended periods of examination.

Sign in / Sign up

Export Citation Format

Share Document