High space-time bandwidth product imaging in low coherence quantitative phase microscopy.

Current low coherence quantitative phase microscopy (LC-QPM) systems suffer from either reduced field of view (FoV) or reduced temporal resolution due to the short temporal coherence (TC) length of the light source. Here, we propose a hybrid, experimental and numerical approach to address this core problem associated with LC-QPM. We demonstrate high spatial resolution and high phase sensitivity in LC-QPM at high temporal resolution. High space-time bandwidth product is achieved by employing incoherent light source for sample illumination in QPM to increase the spatial resolution and single-shot Hilbert spiral transform (HST) based phase recovery algorithm to enhance the temporal resolution without sacrificing spatial resolution during the reconstruction steps. The high spatial phase sensitivity comes by default due to the use of incoherent light source in QPM which has low temporal coherence length and does not generate speckle noise and coherent noise. The spatial resolution achieved by the HST is slightly inferior to the temporal phase-shifting (TPS) method when tested on a specimen but surpasses that of the single-shot Fourier transform (FT) based phase recovery method. Contrary to HST method, FT method requires high density fringes for lossless phase recovery, which is difficult to achieve in LC-QPM over entire FoV. Consequently, integration of HST algorithm with LC-QPM system makes an attractive route. Here, we demonstrate scalable FoV and resolution in single-shot LC-QPM and experimentally corroborate it on a test object and on both live and fixed biological specimen such as MEF, U2OS and human red blood cells (RBCs). LC-QPM system with HST reconstruction offer high-speed single-shot QPM imaging at high phase sensitivity and high spatial resolution enabling us to study sub-cellular dynamic inside U2OS for extended duration (3 h) and observe high-speed (50 fps) dynamics of human RBCs. The experimental results validate the effectiveness of the present approach and will open new avenues in the domain of biomedical imaging in the future.

SERS Nanowire Chip and Machine Learning-Enabled Classification of Wild-Type and Antibiotic-Resistant Bacteria at Species and Strain Levels.

Antimicrobial resistance (AMR) is a major health threat worldwide and the culture-based bacterial detection methods are slow. Surface-enhanced Raman spectroscopy (SERS) can be used to identify target analytes in real time with sensitivity down to the single-molecule level, providing a promising solution for the culture-free bacterial detection. We report the fabrication of SERS substrates having tightly packed silver (Ag) nanoparticles loaded onto long silicon nanowires (Si NWs) grown by the metal-assisted chemical etching (MACE) method for the detection of bacteria. The optimized SERS chips exhibited sensitivity down to 10-12 M concentration of R6G molecules and detected reproducible Raman spectra of bacteria down to a concentration of 100 colony forming units (CFU)/mL, which is a thousand times lower than the clinical threshold of bacterial infections like UTI (105 CFU/mL). A Siamese neural network model was used to classify SERS spectra from bacteria specimens. The trained model identified 12 different bacterial species, including those which are causative agents for tuberculosis and urinary tract infection (UTI). Next, the SERS chips and another Siamese neural network model were used to differentiate AMR strains from susceptible strains of Escherichia coli (E. coli). The enhancement offered by SERS chip-enabled acquisitions of Raman spectra of bacteria directly in the synthetic urine by spiking the sample with only 103 CFU/mL E. coli. Thus, the present study lays the ground for the identification and quantification of bacteria on SERS chips, thereby offering a potential future use for rapid, reproducible, label-free, and low limit detection of clinical pathogens.

Lulworthinone: In Vitro Mode of Action Investigation of an Antibacterial Dimeric Naphthopyrone Isolated from a Marine Fungus.

Treatment options for infections caused by antimicrobial-resistant bacteria are rendered ineffective, and drug alternatives are needed-either from new chemical classes or drugs with new modes of action. Historically, natural products have been important contributors to drug discovery. In a recent study, the dimeric naphthopyrone lulworthinone produced by an obligate marine fungus in the family Lulworthiaceae was discovered. The observed potent antibacterial activity against Gram-positive bacteria, including several clinical methicillin-resistant Staphylococcus aureus (MRSA) isolates, prompted this follow-up mode of action investigation. This paper aimed to characterize the antibacterial mode of action (MOA) of lulworthinone by combining in vitro assays, NMR experiments and microscopy. The results point to a MOA targeting the bacterial membrane, leading to improper cell division. Treatment with lulworthinone induced an upregulation of genes responding to cell envelope stress in Bacillus subtilis. Analysis of the membrane integrity and membrane potential indicated that lulworthinone targets the bacterial membrane without destroying it. This was supported by NMR experiments using artificial lipid bilayers. Fluorescence microscopy revealed that lulworthinone affects cell morphology and impedes the localization of the cell division protein FtsZ. Surface plasmon resonance and dynamic light scattering assays showed that this activity is linked with the compound's ability to form colloidal aggregates. Antibacterial agents acting at cell membranes are of special interest, as the development of bacterial resistance to such compounds is deemed more difficult to occur.

Chip-based multimodal super-resolution microscopy for histological investigations of cryopreserved tissue sections.

Histology involves the observation of structural features in tissues using a microscope. While diffraction-limited optical microscopes are commonly used in histological investigations, their resolving capabilities are insufficient to visualize details at subcellular level. Although a novel set of super-resolution optical microscopy techniques can fulfill the resolution demands in such cases, the system complexity, high operating cost, lack of multi-modality, and low-throughput imaging of these methods limit their wide adoption for histological analysis. In this study, we introduce the photonic chip as a feasible high-throughput microscopy platform for super-resolution imaging of histological samples. Using cryopreserved ultrathin tissue sections of human placenta, mouse kidney, pig heart, and zebrafish eye retina prepared by the Tokuyasu method, we demonstrate diverse imaging capabilities of the photonic chip including total internal reflection fluorescence microscopy, intensity fluctuation-based optical nanoscopy, single-molecule localization microscopy, and correlative light-electron microscopy. Our results validate the photonic chip as a feasible imaging platform for tissue sections and pave the way for the adoption of super-resolution high-throughput multimodal analysis of cryopreserved tissue samples both in research and clinical settings.

Multi-moded high-index contrast optical waveguide for super-contrast high-resolution label-free microscopy.

The article elucidates the physical mechanism behind the generation of superior-contrast and high-resolution label-free images using an optical waveguide. Imaging is realized by employing a high index contrast multi-moded waveguide as a partially coherent light source. The modes provide near-field illumination of unlabeled samples, thereby repositioning the higher spatial frequencies of the sample into the far-field. These modes coherently scatter off the sample with different phases and are engineered to have random spatial distributions within the integration time of the camera. This mitigates the coherent speckle noise and enhances the contrast (2-10) × as opposed to other imaging techniques. Besides, the coherent scattering of the different modes gives rise to fluctuations in intensity. The technique demonstrated here is named chip-based Evanescent Light Scattering (cELS). The concepts introduced through this work are described mathematically and the high-contrast image generation process using a multi-moded waveguide as the light source is explained. The article then explores the feasibility of utilizing fluctuations in the captured images along with fluorescence-based techniques, like intensity-fluctuation algorithms, to mitigate poor-contrast and diffraction-limited resolution in the coherent imaging regime. Furthermore, a straight waveguide is demonstrated to have limited angular diversity between its multiple modes and therefore, for isotropic sample illumination, a multiple-arms waveguide geometry is used. The concepts introduced are validated experimentally via high-contrast label-free imaging of weakly scattering nanosized specimens such as extra-cellular vesicles (EVs), liposomes, nanobeads and biological cells such as fixed and live HeLa cells.

Deriving high contrast fluorescence microscopy images through low contrast noisy image stacks.

Contrast in fluorescence microscopy images allows for the differentiation between different structures by their difference in intensities. However, factors such as point-spread function and noise may reduce it, affecting its interpretability. We identified that fluctuation of emitters in a stack of images can be exploited to achieve increased contrast when compared to the average and Richardson-Lucy deconvolution. We tested our methods on four increasingly challenging samples including tissue, in which case results were comparable to the ones obtained by structured illumination microscopy in terms of contrast.

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

High 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.

High space-bandwidth in quantitative phase imaging using partially spatially coherent digital holographic microscopy and a deep neural network.

Quantitative phase microscopy (QPM) is a label-free technique that enables monitoring of morphological changes at the subcellular level. The performance of the QPM system in terms of spatial sensitivity and resolution depends on the coherence properties of the light source and the numerical aperture (NA) of objective lenses. Here, we propose high space-bandwidth quantitative phase imaging using partially spatially coherent digital holographic microscopy (PSC-DHM) assisted with a deep neural network. The PSC source synthesized to improve the spatial sensitivity of the reconstructed phase map from the interferometric images. Further, compatible generative adversarial network (GAN) is used and trained with paired low-resolution (LR) and high-resolution (HR) datasets acquired from the PSC-DHM system. The training of the network is performed on two different types of samples, i.e. mostly homogenous human red blood cells (RBC), and on highly heterogeneous macrophages. The performance is evaluated by predicting the HR images from the datasets captured with a low NA lens and compared with the actual HR phase images. An improvement of 9× in the space-bandwidth product is demonstrated for both RBC and macrophages datasets. We believe that the PSC-DHM + GAN approach would be applicable in single-shot label free tissue imaging, disease classification and other high-resolution tomography applications by utilizing the longitudinal spatial coherence properties of the light source.

Highly temporal stable, wavelength-independent, and scalable field-of-view common-path quantitative phase microscope.

SIGNIFICANCE: High temporal stability, wavelength independency, and scalable field of view (FOV) are the primary requirements of a quantitative phase microscopy (QPM) system. The high temporal stability of the system provides accurate measurement of minute membrane fluctuations of the biological cells that can be an indicator of disease diagnosis. AIM: The main aim of this work is to develop a high temporal stable technique that can accurately quantify the cell's dynamics such as membrane fluctuations of human erythrocytes. Further, the technique should be capable of acquiring scalable FOV and resolution at multiple wavelengths to make it viable for various biological applications. APPROACH: We developed a single-element nearly common path, wavelength-independent, and scalable resolution/FOV QPM system to obtain temporally stable holograms/interferograms of the biological specimens. RESULTS: With the proposed system, the temporal stability is obtained ∼15 mrad without using any vibration isolation table. The capability of the proposed system is first demonstrated on USAF resolution chart and polystyrene spheres (4.5-µm diameter). Further, the system is implemented for single shot, wavelength-independent quantitative phase imaging of human red blood cells (RBCs) with scalable resolution using color CCD camera. The membrane fluctuation of healthy human RBCs is also measured and was found to be around 47 nm. CONCLUSIONS: Contrary to its optical counterparts, the present system offers an energy efficient, cost effective, and simple way of generating object and reference beam for the development of common-path QPM. The present system provides the flexibility to the user to acquire multi-wavelength quantitative phase images at scalable FOV and resolution.

Quantitative assessment of morphology and sub-cellular changes in macrophages and trophoblasts during inflammation.

In pregnancy during an inflammatory condition, macrophages present at the feto-maternal junction release an increased amount of nitric oxide (NO) and pro-inflammatory cytokines such as TNF-α and INF-γ, which can disturb the trophoblast functions and pregnancy outcome. Measurement of the cellular and sub-cellular morphological modifications associated with inflammatory responses are important in order to quantify the extent of trophoblast dysfunction for clinical implication. With this motivation, we investigated morphological, cellular and sub-cellular changes in externally inflamed RAW264.7 (macrophage) and HTR-8/SVneo (trophoblast) using structured illumination microscopy (SIM) and quantitative phase microscopy (QPM). We monitored the production of NO, changes in cell membrane and mitochondrial structure of macrophages and trophoblasts when exposed to different concentrations of pro-inflammatory agents (LPS and TNF-α). In vitro NO production by LPS-induced macrophages increased 22-fold as compared to controls, whereas no significant NO production was seen after the TNF-α challenge. Under similar conditions as with macrophages, trophoblasts did not produce NO following either LPS or the TNF-α challenge. Super-resolution SIM imaging showed changes in the morphology of mitochondria and the plasma membrane in macrophages following the LPS challenge and in trophoblasts following the TNF-α challenge. Label-free QPM showed a decrease in the optical thickness of the LPS-challenged macrophages while TNF-α having no effect. The vice-versa is observed for the trophoblasts. We further exploited machine learning approaches on a QPM dataset to detect and to classify the inflammation with an accuracy of 99.9% for LPS-challenged macrophages and 98.3% for TNF-α-challenged trophoblasts. We believe that the multi-modal advanced microscopy methodologies coupled with machine learning approach could be a potential way for early detection of inflammation.

Deep learning architecture "LightOCT" for diagnostic decision support using optical coherence tomography images of biological samples.

Optical coherence tomography (OCT) is being increasingly adopted as a label-free and non-invasive technique for biomedical applications such as cancer and ocular disease diagnosis. Diagnostic information for these tissues is manifest in textural and geometric features of the OCT images, which are used by human expertise to interpret and triage. However, it suffers delays due to the long process of the conventional diagnostic procedure and shortage of human expertise. Here, a custom deep learning architecture, LightOCT, is proposed for the classification of OCT images into diagnostically relevant classes. LightOCT is a convolutional neural network with only two convolutional layers and a fully connected layer, but it is shown to provide excellent training and test results for diverse OCT image datasets. We show that LightOCT provides 98.9% accuracy in classifying 44 normal and 44 malignant (invasive ductal carcinoma) breast tissue volumetric OCT images. Also, >96% accuracy in classifying public datasets of ocular OCT images as normal, age-related macular degeneration and diabetic macular edema. Additionally, we show ∼96% test accuracy for classifying retinal images as belonging to choroidal neovascularization, diabetic macular edema, drusen, and normal samples on a large public dataset of more than 100,000 images. The performance of the architecture is compared with transfer learning based deep neural networks. Through this, we show that LightOCT can provide significant diagnostic support for a variety of OCT images with sufficient training and minimal hyper-parameter tuning. The trained LightOCT networks for the three-classification problem will be released online to support transfer learning on other datasets.

Sub-nanometer height sensitivity by phase shifting interference microscopy under environmental fluctuations.

Phase shifting interferometric (PSI) techniques are among the most sensitive phase measurement methods. Owing to its high sensitivity, any minute phase change caused due to environmental instability results into, inaccurate phase measurement. Consequently, a well calibrated piezo electric transducer (PZT) and highly-stable environment is mandatory for measuring accurate phase map using PSI implementation. Here, we present an inverse approach, which can retrieve phase maps of the samples with negligible errors under environmental fluctuations. The method is implemented by recording a video of continuous temporally phase shifted interferograms and phase shifts were calculated between all the data frames using Fourier transform algorithm with a high accuracy ≤ 5.5 × 10-4 π rad. To demonstrate the robustness of the proposed method, a manual translation of the stage was employed to introduce continuous temporal phase shift between data frames. The developed algorithm is first verified by performing quantitative phase imaging of optical waveguide and red blood cells using uncalibrated PZT under the influence of vibrations/air turbulence and compared with the well calibrated PZT results. Furthermore, we demonstrated the potential of the proposed approach by acquiring the quantitative phase imaging of an optical waveguide with a rib height of only 2 nm and liver sinusoidal endothelial cells (LSECs). By using 12-bit CMOS camera the height of shallow rib waveguide is measured with a height sensitivity of 4 Å without using PZT and in presence of environmental fluctuations.vn.

Relationship between the source size at the diffuser plane and the longitudinal spatial coherence function of the optical coherence microscopy system.

Coherence properties of light sources are indispensable for optical coherence microscopy/tomography as they greatly influence the signal-to-noise ratio, axial resolution, and penetration depth of the system. In the present paper, we report the investigation of longitudinal spatial coherence properties of a pseudothermal light source (PTS) as a function of the laser spot size at the rotating diffuser plate. The laser spot size is varied by translating a microscope objective lens toward or away from the diffuser plate. The longitudinal spatial coherence length, which governs the axial resolution of the coherence microscope, is found to be minimum for the beam spot size of 3.5 mm at the diffuser plate. The axial resolution of the system is found to be equal to an $\sim{13}\,\,{\rm \unicode{x00B5}{\rm m}}$∼13µm at 3.5 mm beam spot size. The change in the axial resolution of the system is confirmed by performing the experiments on standard gauge blocks of a height difference of 15 µm by varying the spot size at the diffuser plate. Thus, by appropriately choosing the beam spot size at the diffuser plane, any monochromatic laser light source can be utilized to obtain high axial resolution irrespective of the source's temporal coherence length. It can provide speckle-free tomographic images of multilayered biological specimens with large penetration depth. In addition, a PTS avoids the use of any chromatic-aberration-corrected optics and dispersion-compensation mechanism unlike conventional setups.

Author Correction: Partially spatially coherent digital holographic microscopy and machine learning for quantitative analysis of human spermatozoa under oxidative stress condition.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.

Effect on the longitudinal coherence properties of a pseudothermal light source as a function of source size and temporal coherence.

In the present Letter, a synthesized pseudothermal light source having high temporal coherence (TC) and low spatial coherence (SC) properties is used. The longitudinal coherence (LC) properties of the spatially extended monochromatic light source are systematically studied. The pseudothermal light source is generated from two different monochromatic laser sources: He-Ne (at 632 nm) and DPSS (at 532 nm). It was found that the LC length of such a light source becomes independent of the parent laser's TC length for a large source size. For the chosen lasers, the LC length becomes constant to about 30 µm for a laser source size of ≥3.3 mm. Thus, by appropriately choosing the source size, any monochromatic laser light source depending on the biological window can be utilized to obtain high axial resolution in an optical coherence tomography (OCT) system irrespective of its TC length. The axial resolution of 650 nm was obtained using a 1.2 numerical aperture objective lens at a 632 nm wavelength. These findings pave the path for widespread penetration of pseudothermal light into existing OCT systems with enhanced performance. A pseudothermal light source with high TC and low SC properties could be an attractive alternative light source for achieving high axial resolution without needing dispersion compensation as compared to a broadband light source.

Low coherence quantitative phase microscopy with machine learning model and Raman spectroscopy for the study of breast cancer cells and their classification.

Early-stage detection of breast cancer is the primary requirement in modern healthcare as it is the most common cancer among women worldwide. Histopathology is the most widely preferred method for the diagnosis of breast cancer, but it requires long processing time and involves qualitative assessment of cancer by a trained person/doctor. Here, we present an alternate technique based on white light interference microscopy (WLIM) and Raman spectroscopy, which has the capability to differentiate between cancerous and normal breast tissue. WLIM provides quantitative phase information about the biological tissues/cells, whereas Raman spectroscopy can detect changes in their molecular structure and chemical composition during cancer growth. Further, both the techniques can be implemented very quickly without staining the sample. The present technique is employed to perform ex vivo study on a total of 80 normal and cancerous tissue samples collected from 16 different patients. A generalized machine learning model is developed for the classification of normal and cancerous tissues, which is based on texture features obtained from phase maps with an accuracy of 90.6%. The correlation of outcomes from these two techniques can open a new avenue for fast and accurate detection of cancer without any trained personnel.

Volumetric analysis of breast cancer tissues using machine learning and swept-source optical coherence tomography.

In breast cancer, 20%-30% of cases require a second surgery because of incomplete excision of malignant tissues. Therefore, to avoid the risk of recurrence, accurate detection of the cancer margin by the clinician or surgeons needs some assistance. In this paper, an automated volumetric analysis of normal and breast cancer tissue is done by a machine learning algorithm to separate them into two classes. The proposed method is based on a support-vector-machine-based classifier by dissociating 10 features extracted from the A-line, texture, and phase map by the swept-source optical coherence tomographic intensity and phase images. A set of 88 freshly excised breast tissue [44 normal and 44 cancers (invasive ductal carcinoma tissues)] samples from 22 patients was used in our study. The algorithm successfully classifies the cancerous tissue with sensitivity, specificity, and accuracy of 91.56%, 93.86%, and 92.71% respectively. The present computational technique is fast, simple, and sensitive, and extracts features from the whole volume of the tissue, which does not require any special tissue preparation nor an expert to analyze the breast cancer as required in histopathology. Diagnosis of breast cancer by extracting quantitative features from optical coherence tomographic images could be a potentially powerful method for cancer detection and would be a valuable tool for a fine-needle-guided biopsy.

Partially spatially coherent digital holographic microscopy and machine learning for quantitative analysis of human spermatozoa under oxidative stress condition.

Semen quality assessed by sperm count and sperm cell characteristics such as morphology and motility, is considered to be the main determinant of men's reproductive health. Therefore, sperm cell selection is vital in assisted reproductive technology (ART) used for the treatment of infertility. Conventional bright field optical microscopy is widely utilized for the imaging and selection of sperm cells based on the qualitative analysis by experienced clinicians. In this study, we report the development of a highly sensitive quantitative phase microscopy (QPM) using partially spatially coherent light source, which is a label-free, non-invasive and high-resolution technique to quantify various biophysical parameters. The partial spatial coherence nature of light source provides a significant improvement in spatial phase sensitivity and hence reconstruction of the phase of the entire sperm cell is demonstrated, which was otherwise not possible using highly spatially coherent light source. High sensitivity of the system enables quantitative phase imaging of the specimens having very low refractive index contrast with respect to the medium like tail of the sperm cells. Further, it also benefits with accurate quantification of 3D-morphological parameters of sperm cells which might be helpful in the infertility treatment. The quantitative analysis of more than 2500 sperm cells under hydrogen peroxide (H2O2) induced oxidative stress condition is demonstrated. It is further correlated with motility of sperm cell to study the effect of oxidative stress on healthy sperm cells. The results exhibit a decrease in the maximum phase values of the sperm head as well as decrease in the sperm cell's motility with increasing oxidative stress, i.e., H2O2 concentration. Various morphological and texture parameters were extracted from the phase maps and subsequently support vector machine (SVM) based machine learning algorithm is employed for the classification of the control and the stressed sperms cells. The algorithm achieves an area under the receiver operator characteristic (ROC) curve of 89.93% based on the all morphological and texture parameters with a sensitivity of 91.18%. The proposed approach can be implemented for live sperm cells selection in ART procedure for the treatment of infertility.

Characterization of color cross-talk of CCD detectors and its influence in multispectral quantitative phase imaging.

Multi-spectral quantitative phase imaging (QPI) is an emerging imaging modality for wavelength dependent studies of several biological and industrial specimens. Simultaneous multi-spectral QPI is generally performed with color CCD cameras. Here, we present a new approach for accurately measuring the color crosstalk of 2D area detectors, without needing prior information about camera specifications. Color crosstalk is systematically studied and compared using compact interference microscopy on two different cameras commonly used in QPI, single chip CCD (1-CCD) and three chip CCD (3-CCD). The influence of color crosstalk on the fringe width and the visibility of the monochromatic constituents corresponding to three color channels of white light interferogram are studied both through simulations and experiments. It is observed that presence of color crosstalk changes the fringe width and visibility over the imaging field of view. This leads to an unwanted non-uniform background error in the multi-spectral phase imaging of the specimens. The color crosstalk of the detector is observed to be the limiting factor for phase measurement accuracy of simultaneous multi-spectral QPI systems.

Quantitative phase microscopy of red blood cells during planar trapping and propulsion.

Red blood cells (RBCs) have the ability to undergo morphological deformations during microcirculation, such as changes in surface area, volume and sphericity. Optical waveguide trapping is suitable for trapping, propelling and deforming large cell populations along the length of the waveguide. Bright field microscopy employed with waveguide trapping does not provide quantitative information about structural changes. Here, we have combined quantitative phase microscopy and waveguide trapping techniques to study changes in RBC morphology during planar trapping and transportation. By using interference microscopy, time-lapsed interferometric images of trapped RBCs were recorded in real-time and subsequently utilized to reconstruct optical phase maps. Quantification of the phase differences before and after trapping enabled study of the mechanical effects during planar trapping. During planar trapping, a decrease in the maximum phase values, an increase in the surface area and a decrease in the volume and sphericity of RBCs were observed. QPM was used to analyze the phase values for two specific regions within RBCs: the annular rim and the central donut. The phase value of the annular rim decreases whereas it increases for the central donut during planar trapping. These changes correspond to a redistribution of cytosol inside the RBC during planar trapping and transportation.