Label-free, high-throughput detection of P. falciparum infection in sphered erythrocytes with digital holographic microscopy

Grading of glioma tumors using digital holographic microscopy

Gliomas are the most common type of cerebral tumors; they occur with increasing incidence in the last decade and have a high rate of mortality. For efficient treatment, fast accurate diagnostic and grading of tumors are imperative. Presently, the grading of tumors is established by histopathological evaluation, which is a time-consuming procedure and relies on the pathologists' experience. Here we propose a supervised machine learning procedure for tumor grading which uses quantitative phase images of unstained tissue samples acquired by digital holographic microscopy. The algorithm is using an extensive set of statistical and texture parameters computed from these images. The procedure has been able to classify six classes of images (normal tissue and five glioma subtypes) and to distinguish between gliomas types from grades II to IV (with the highest sensitivity and specificity for grade II astrocytoma and grade III oligodendroglioma and very good scores in recognizing grade III anaplastic astrocytoma and grade IV glioblastoma). The procedure bolsters clinical diagnostic accuracy, offering a swift and reliable means of tumor characterization and grading, ultimately the enhancing treatment decision-making process.

Screening for urothelial carcinoma cells in urine based on digital holographic flow cytometry through machine learning and deep learning methods

The incidence of urothelial carcinoma continues to rise annually, particularly among the elderly. Prompt diagnosis and treatment can significantly enhance patient survival and quality of life. Urine cytology remains a widely-used early screening method for urothelial carcinoma, but it still has limitations including sensitivity, labor-intensive procedures, and elevated cost. In recent developments, microfluidic chip technology offers an effective and efficient approach for clinical urine specimen analysis. Digital holographic microscopy, a form of quantitative phase imaging technology, captures extensive data on the refractive index and thickness of cells. The combination of microfluidic chips and digital holographic microscopy facilitates high-throughput imaging of live cells without staining. In this study, digital holographic flow cytometry was employed to rapidly capture images of diverse cell types present in urine and to reconstruct high-precision quantitative phase images for each cell type. Then, various machine learning algorithms and deep learning models were applied to categorize these cell images, and remarkable accuracy in cancer cell identification was achieved. This research suggests that the integration of digital holographic flow cytometry with artificial intelligence algorithms offers a promising, precise, and convenient approach for early screening of urothelial carcinoma.

Real-time 3D tracking of swimming microbes using digital holographic microscopy and deep learning

The three-dimensional swimming tracks of motile microorganisms can be used to identify their species, which holds promise for the rapid identification of bacterial pathogens. The tracks also provide detailed information on the cells' responses to external stimuli such as chemical gradients and physical objects. Digital holographic microscopy (DHM) is a well-established, but computationally intensive method for obtaining three-dimensional cell tracks from video microscopy data. We demonstrate that a common neural network (NN) accelerates the analysis of holographic data by an order of magnitude, enabling its use on single-board computers and in real time. We establish a heuristic relationship between the distance of a cell from the focal plane and the size of the bounding box assigned to it by the NN, allowing us to rapidly localise cells in three dimensions as they swim. This technique opens the possibility of providing real-time feedback in experiments, for example by monitoring and adapting the supply of nutrients to a microbial bioreactor in response to changes in the swimming phenotype of microbes, or for rapid identification of bacterial pathogens in drinking water or clinical samples.

Label-free cell classification in holographic flow cytometry through an unbiased learning strategy

Nowadays, label-free imaging flow cytometry at the single-cell level is considered the stepforward lab-on-a-chip technology to address challenges in clinical diagnostics, biology, life sciences and healthcare. In this framework, digital holography in microscopy promises to be a powerful imaging modality thanks to its multi-refocusing and label-free quantitative phase imaging capabilities, along with the encoding of the highest information content within the imaged samples. Moreover, the recent achievements of new data analysis tools for cell classification based on deep/machine learning, combined with holographic imaging, are urging these systems toward the effective implementation of point of care devices. However, the generalization capabilities of learning-based models may be limited from biases caused by data obtained from other holographic imaging settings and/or different processing approaches. In this paper, we propose a combination of a Mask R-CNN to detect the cells, a convolutional auto-encoder, used to the image feature extraction and operating on unlabelled data, thus overcoming the bias due to data coming from different experimental settings, and a feedforward neural network for single cell classification, that operates on the above extracted features. We demonstrate the proposed approach in the challenging classification task related to the identification of drug-resistant endometrial cancer cells.

Application of Digital Holographic Microscopy to Analyze Changes in T-Cell Morphology in Response to Bacterial Challenge

Quantitative phase imaging (QPI) is a non-invasive, label-free technique used to detect aberrant cell morphologies caused by disease, thus providing a useful diagnostic approach. Here, we evaluated the potential of QPI to differentiate specific morphological changes in human primary T-cells exposed to various bacterial species and strains. Cells were challenged with sterile bacterial determinants, i.e., membrane vesicles or culture supernatants, derived from different Gram-positive and Gram-negative bacteria. Timelapse QPI by digital holographic microscopy (DHM) was applied to capture changes in T-cell morphology over time. After numerical reconstruction and image segmentation, we calculated single cell area, circularity and mean phase contrast. Upon bacterial challenge, T-cells underwent rapid morphological changes such as cell shrinkage, alterations of mean phase contrast and loss of cell integrity. Time course and intensity of this response varied between both different species and strains. The strongest effect was observed for treatment with S. aureus-derived culture supernatants that led to complete lysis of the cells. Furthermore, cell shrinkage and loss of circular shape was stronger in Gram-negative than in Gram-positive bacteria. Additionally, T-cell response to bacterial virulence factors was concentration-dependent, as decreases in cellular area and circularity were enhanced with increasing concentrations of bacterial determinants. Our findings clearly indicate that T-cell response to bacterial stress depends on the causative pathogen, and specific morphological alterations can be detected using DHM.

Label-free viability assay using in-line holographic video microscopy

Total holographic characterization (THC) is presented here as an efficient, automated, label-free method of accurately identifying cell viability. THC is a single-particle characterization technology that determines the size and index of refraction of individual particles using the Lorenz-Mie theory of light scattering. Although assessment of cell viability is a challenge in many applications, including biologics manufacturing, traditional approaches often include unreliable labeling with dyes and/or time consuming methods of manually counting cells. In this work we measured the viability of Saccharomyces cerevisiae yeast in the presence of various concentrations of isopropanol as a function of time. All THC measurements were performed in the native environment of the sample with no dilution or addition of labels. Holographic measurements were made with an in-line holographic microscope using a 40[Formula: see text] objective lens with plane wave illumination. We compared our results with THC to manual counting of living and dead cells as distinguished with trypan blue dye. Our findings demonstrate that THC can effectively distinguish living and dead yeast cells by the index of refraction of individual cells.

Advances in Digital Holographic Interferometry

Holographic interferometry is a well-established field of science and optical engineering. It has a half-century history of successful implementation as the solution to numerous technical tasks and problems. However, fast progress in digital and computer holography has promoted it to a new level of possibilities and has opened brand new fields of its application. In this review paper, we consider some such new techniques and applications.

Automated wide-field malaria parasite infection detection using Fourier ptychography on stain-free thin-smears

Diagnosis of malaria in endemic areas is hampered by the lack of a rapid, stain-free and sensitive method to directly identify parasites in peripheral blood. Herein, we report the use of Fourier ptychography to generate wide-field high-resolution quantitative phase images of erythrocytes infected with malaria parasites, from a whole blood sample. We are able to image thousands of erythrocytes (red blood cells) in a single field of view and make a determination of infection status of the quantitative phase image of each segmented cell based on machine learning (random forest) and deep learning (VGG16) models. Our random forest model makes use of morphology and texture based features of the quantitative phase images. In order to label the quantitative images of the cells as either infected or uninfected before training the models, we make use of a Plasmodium berghei strain expressing GFP (green fluorescent protein) in all life cycle stages. By overlaying the fluorescence image with the quantitative phase image we could identify the infected subpopulation of erythrocytes for labelling purposes. Our machine learning model (random forest) achieved 91% specificity and 72% sensitivity while our deep learning model (VGG16) achieved 98% specificity and 57% sensitivity. These results highlight the potential for quantitative phase imaging coupled with artificial intelligence to develop an easy to use platform for the rapid and sensitive diagnosis of malaria.

Label-Free Assessment of the Drug Resistance of Epithelial Ovarian Cancer Cells in a Microfluidic Holographic Flow Cytometer Boosted through Machine Learning

About 75% of epithelial ovarian cancer (EOC) patients suffer from relapsing and develop drug resistance after primary chemotherapy. The commonly used clinical examinations and biological tumor tissue models for chemotherapeutic sensitivity are time-consuming and expensive. Research studies showed that the cell morphology-based method is promising to be a new route for chemotherapeutic sensitivity evaluation. Here, we offer how the drug resistance of EOC cells can be assessed through a label-free and high-throughput microfluidic flow cytometer equipped with a digital holographic microscope reinforced by machine learning. It is the first time that such type of assessment is performed to the best of our knowledge. Several morphologic and texture features at a single-cell level have been extracted from the quantitative phase images. In addition, we compared four common machine learning algorithms, including naive Bayes, decision tree, K-nearest neighbors, support vector machine (SVM), and fully connected network. The result shows that the SVM classifier achieves the optimal performance with an accuracy of 92.2% and an area under the curve of 0.96. This study demonstrates that the proposed method achieves high-accuracy, high-throughput, and label-free assessment of the drug resistance of EOC cells. Furthermore, it reflects strong potentialities to develop data-driven individualized chemotherapy treatments in the future.

Non-invasive and label-free identification of human natural killer cell subclasses by biophysical single-cell features in microfluidic flow

Natural killer (NK) cells are indicated as favorite candidates for innovative therapeutic treatment and are divided into two subclasses: immature regulatory NK CD56^bright and mature cytotoxic NK CD56^dim. Therefore, the ability to discriminate CD56^dim from CD56^bright could be very useful because of their higher cytotoxicity. Nowadays, NK cell classification is routinely performed by cytometric analysis based on surface receptor expression. Here, we present an in-flow, label-free and non-invasive biophysical analysis of NK cells through a combination of light scattering and machine learning (ML) for NK cell subclass classification. In this respect, to identify relevant biophysical cell features, we stimulated NK cells with interleukine-15 inducing a subclass transition from CD56^bright to CD56^dim. We trained our ML algorithm with sorted NK cell subclasses (≥86% accuracy). Next, we applied our NK cell classification algorithm to cells stimulated over time, to investigate the transition of CD56^bright to CD56^dim and their biophysical feature changes. Finally, we tested our approach on several proband samples, highlighting the potential of our measurement approach. We show a label-free way for the robust identification of NK cell subclasses based on biophysical features, which can be applied in both cell biology and cell therapy.

Label-free imaging and classification of live P. falciparum enables high performance parasitemia quantification without fixation or staining

Manual microscopic inspection of fixed and stained blood smears has remained the gold standard for Plasmodium parasitemia analysis for over a century. Unfortunately, smear preparation consumes time and reagents, while manual microscopy is skill-dependent and labor-intensive. Here, we demonstrate that deep learning enables both life stage classification and accurate parasitemia quantification of ordinary brightfield microscopy images of live, unstained red blood cells. We tested our method using both a standard light microscope equipped with visible and near-ultraviolet (UV) illumination, and a custom-built microscope employing deep-UV illumination. While using deep-UV light achieved an overall four-category classification of Plasmodium falciparum blood stages of greater than 99% and a recall of 89.8% for ring-stage parasites, imaging with near-UV light on a standard microscope resulted in 96.8% overall accuracy and over 90% recall for ring-stage parasites. Both imaging systems were tested extrinsically by parasitemia titration, revealing superior performance over manually-scored Giemsa-stained smears, and a limit of detection below 0.1%. Our results establish that label-free parasitemia analysis of live cells is possible in a biomedical laboratory setting without the need for complex optical instrumentation. We anticipate future extensions of this work could enable label-free clinical diagnostic measurements, one day eliminating the need for conventional blood smear analysis.

Multiparametric biophysical profiling of red blood cells in malaria infection

Biophysical separation promises label-free, less-invasive methods to manipulate the diverse properties of live cells, such as density, magnetic susceptibility, and morphological characteristics. However, some cellular changes are so minute that they are undetectable by current methods. We developed a multiparametric cell-separation approach to profile cells with simultaneously changing density and magnetic susceptibility. We demonstrated this approach with the natural biophysical phenomenon of Plasmodium falciparum infection, which modifies its host erythrocyte by simultaneously decreasing density and increasing magnetic susceptibility. Current approaches have used these properties separately to isolate later-stage infected cells, but not in combination. We present biophysical separation of infected erythrocytes by balancing gravitational and magnetic forces to differentiate infected cell stages, including early stages for the first time, using magnetic levitation. We quantified height distributions of erythrocyte populations-27 ring-stage synchronized samples and 35 uninfected controls-and quantified their unique biophysical signatures. This platform can thus enable multidimensional biophysical measurements on unique cell types.

Sensing morphogenesis of bone cells under microfluidic shear stress by holographic microscopy and automatic aberration compensation with deep learning

We present sensing time-lapse morphogenesis of living bone cells under micro-fluidic shear stress (FSS) by digital holographic (DH) microscopy. To remove the effect of aberrations on quantitative measurements, we propose a numerical and automatic method to compensate for aberrations based on a convolutional neural network (CNN). For the first time, the aberration compensation issue is considered as a regression task where optimal coefficients for constructing the phase aberration map act as responses corresponding to the input aberrated phase image. We adopted tens of thousands of living cells' phase images reconstructed from digital holograms for training the CNN. The experiments demonstrate that, based on the trained network, phase aberrations can be totally removed in real-time without any hypothesis of object and aberration phase, knowledge of the setup's physical parameters, and the operation of selecting background regions; hence, the morphogenesis of the bone cells under FSS is accurately detected and quantitatively analyzed. The results show that the proposed method could provide a highly efficient and versatile way to investigate the effects of micro-FSS on living biological cells in microfluidic lab-on-chip platforms thanks to the combination of phase-contrast label-free microcopy with artificial intelligence.

High-throughput label-free cell detection and counting from diffraction patterns with deep fully convolutional neural networks

SIGNIFICANCE

Digital holographic microscopy (DHM) is a promising technique for the study of semitransparent biological specimen such as red blood cells (RBCs). It is important and meaningful to detect and count biological cells at the single cell level in biomedical images for biomarker discovery and disease diagnostics. However, the biological cell analysis based on phase information of images is inefficient due to the complexity of numerical phase reconstruction algorithm applied to raw hologram images. New cell study methods based on diffraction pattern directly are desirable.

AIM

Deep fully convolutional networks (FCNs) were developed on raw hologram images directly for high-throughput label-free cell detection and counting to assist the biological cell analysis in the future.

APPROACH

The raw diffraction patterns of RBCs were recorded by use of DHM. Ground-truth mask images were labeled based on phase images reconstructed from RBC holograms using numerical reconstruction algorithm. A deep FCN, which is UNet, was trained on the diffraction pattern images to achieve the label-free cell detection and counting.

RESULTS

The implemented deep FCNs provide a promising way to high-throughput and label-free counting of RBCs with a counting accuracy of 99% at a throughput rate of greater than 288 cells per second and 200  μm  ×  200  μm field of view at the single cell level. Compared to convolutional neural networks, the FCNs can get much better results in terms of accuracy and throughput rate.

CONCLUSIONS

High-throughput label-free cell detection and counting were successfully achieved from diffraction patterns with deep FCNs. It is a promising approach for biological specimen analysis based on raw hologram directly.

Three-dimensional visualization and analysis of flowing droplets in microchannels using real-time quantitative phase microscopy

Recent years have witnessed the development of droplet-based microfluidics as a useful and effective tool for high-throughput analysis in biological, chemical and environmental sciences. Despite the flourishing development of droplet manipulation techniques, only a few methods allow for label-free and quantitative inspection of flowing droplets in microchannels in real-time and in three dimensions (3-D). In this work, we propose and demonstrate the application of a real-time quantitative phase microscopy (RT-QPM) technique for 3-D visualization of droplets, and also for full-field and label-free measurement of analyte concentration distribution in the droplets. The phase imaging system consists of a linear-CCD-based holographic microscopy configuration and an optofluidic phase-shifting element, which can be used for retrieving quantitative phase maps of flowing objects in the microchannels with a temporal resolution only limited to the frame rate of the CCD camera. To demonstrate the capabilities of the proposed imaging technique, we have experimentally validated the 3-D image reconstruction of the droplets generated in squeezing and dripping regimes and quantitatively investigated the volumetric and morphological variation of droplets as well as droplet parameters related to the depth direction under different flow conditions. We also demonstrated the feasibility of using this technique, as a refractive index sensor, for in-line quantitative measurement of carbamide analyte concentration within the flowing droplets.

Automatic detection and characterization of quantitative phase images of thalassemic red blood cells using a mask region-based convolutional neural network

SIGNIFICANCE

Label-free quantitative phase imaging is a promising technique for the automatic detection of abnormal red blood cells (RBCs) in real time. Although deep-learning techniques can accurately detect abnormal RBCs from quantitative phase images efficiently, their applications in diagnostic testing are limited by the lack of transparency. More interpretable results such as morphological and biochemical characteristics of individual RBCs are highly desirable.

AIM

An end-to-end deep-learning model was developed to efficiently discriminate thalassemic RBCs (tRBCs) from healthy RBCs (hRBCs) in quantitative phase images and segment RBCs for single-cell characterization.

APPROACH

Two-dimensional quantitative phase images of hRBCs and tRBCs were acquired using digital holographic microscopy. A mask region-based convolutional neural network (Mask R-CNN) model was trained to discriminate tRBCs and segment individual RBCs. Characterization of tRBCs was achieved utilizing SHapley Additive exPlanation analysis and canonical correlation analysis on automatically segmented RBC phase images.

RESULTS

The implemented model achieved 97.8% accuracy in detecting tRBCs. Phase-shift statistics showed the highest influence on the correct classification of tRBCs. Associations between the phase-shift features and three-dimensional morphological features were revealed.

CONCLUSIONS

The implemented Mask R-CNN model accurately identified tRBCs and segmented RBCs to provide single-RBC characterization, which has the potential to aid clinical decision-making.

Image-Based Single Cell Sorting Automation in Droplet Microfluidics

The recent boom in single-cell omics has brought researchers one step closer to understanding the biological mechanisms associated with cell heterogeneity. Rare cells that have historically been obscured by bulk measurement techniques are being studied by single cell analysis and providing valuable insight into cell function. To support this progress, novel upstream capabilities are required for single cell preparation for analysis. Presented here is a droplet microfluidic, image-based single-cell sorting technique that is flexible and programmable. The automated system performs real-time dual-camera imaging (brightfield & fluorescent), processing, decision making and sorting verification. To demonstrate capabilities, the system was used to overcome the Poisson loading problem by sorting for droplets containing a single red blood cell with 85% purity. Furthermore, fluorescent imaging and machine learning was used to load single K562 cells amongst clusters based on their instantaneous size and circularity. The presented system aspires to replace manual cell handling techniques by translating expert knowledge into cell sorting automation via machine learning algorithms. This powerful technique finds application in the enrichment of single cells based on their micrographs for further downstream processing and analysis.

Holographic Imaging of Insect Cell Cultures: Online Non-Invasive Monitoring of Adeno-Associated Virus Production and Cell Concentration

The insect cell-baculovirus vector system has become one of the favorite platforms for the expression of viral vectors for vaccination and gene therapy purposes. As it is a lytic system, it is essential to balance maximum recombinant product expression with harvest time, minimizing product exposure to detrimental proteases. With this purpose, new bioprocess monitoring solutions are needed to accurately estimate culture progression. Herein, we used online digital holographic microscopy (DHM) to monitor bioreactor cultures of Sf9 insect cells. Batches of baculovirus-infected Sf9 cells producing recombinant adeno-associated virus (AAV) and non-infected cells were used to evaluate DHM prediction capabilities for viable cell concentration, culture viability and AAV titer. Over 30 cell-related optical attributes were quantified using DHM, followed by a forward stepwise regression to select the most significant (p < 0.05) parameters for each variable. We then applied multiple linear regression to obtain models which were able to predict culture variables with root mean squared errors (RMSE) of 7 × 105 cells/mL, 3% for cell viability and 2 × 103 AAV/cell for 3-fold cross-validation. Overall, this work shows that DHM can be implemented for online monitoring of Sf9 concentration and viability, also permitting to monitor product titer, namely AAV, or culture progression in lytic systems, making it a valuable tool to support the time of harvest decision and for the establishment of controlled feeding strategies.

Real-time cholesterol sorting in Plasmodium falciparum-erythrocytes as revealed by 3D label-free imaging

Cholesterol, a necessary component of animal cell membranes, is also needed by the lethal human malaria parasite Plasmodium falciparum. Because P. falciparum lacks a cholesterol synthesis pathway and malaria patients have low blood cholesterol, we speculated that it scavenges cholesterol from them in some way. We used time-lapse holotomographic microscopy to observe cholesterol transport in live P. falciparum parasites and structurally investigate erythrocyte membranes, both during and after P. falciparum invasion of human erythrocytes. After P. falciparum initially acquired free cholesterol or inner erythrocytic membrane-derived cholesterol, we observed budding lipid membranes elongating into the cytosol and/or membrane segments migrating there and eventually fusing with the parasite membranes, presumably at the parasitophorous vacuole membrane (PVM). Finally, the cholesterol-containing segments were seen to surround the parasite nucleus. Our imaging data suggest that a novel membrane transport system operates in the cytosol of P. falciparum-infected erythrocytes as a cholesterol import system, likely between the PVM and the erythrocyte membrane, and that this transportation process occurs during the live erythrocyte stages of P. falciparum.

Machine Learning with Optical Phase Signatures for Phenotypic Profiling of Cell Lines

Robust and reproducible profiling of cell lines is essential for phenotypic screening assays. The goals of this study were to determine robust and reproducible optical phase signatures of cell lines for classification with machine learning and to correlate optical phase parameters to motile behavior. Digital holographic microscopy (DHM) reconstructed phase maps of cells from two pairs of cancer and non-cancer cell lines. Seventeen image parameters were extracted from each cell's phase map, used for linear support vector machine learning, and correlated to scratch wound closure and Boyden chamber chemotaxis. The classification accuracy was between 90% and 100% for the six pairwise cell line comparisons. Several phase parameters correlated with wound closure rate and chemotaxis across the four cell lines. The level of cell confluence in culture affected phase parameters in all cell lines tested. Results indicate that optical phase features of cell lines are a robust set of quantitative data of potential utility for phenotypic screening and prediction of motile behavior. © 2019 International Society for Advancement of Cytometry.