Classifying breast cancer and fibroadenoma tissue biopsies from paraffined stain-free slides by fractal biomarkers in Fourier Ptychographic Microscopy

Breast cancer is one of the most spread and monitored pathologies in high-income countries. After breast biopsy, histological tissue is stored in paraffin, sectioned and mounted. Conventional inspection of tissue slides under benchtop light microscopes involves paraffin removal and staining, typically with H&E. Then, expert pathologists are called to judge the stained slides. However, paraffin removal and staining are operator-dependent, time and resources consuming processes that can generate ambiguities due to non-uniform staining. Here we propose a novel method that can work directly on paraffined stain-free slides. We use Fourier Ptychography as a quantitative phase-contrast microscopy method, which allows accessing a very wide field of view (i.e., mm2) in one single image while guaranteeing high lateral resolution (i.e., 0.5 µm). This imaging method is multi-scale, since it enables looking at the big picture, i.e. the complex tissue structure and connections, with the possibility to zoom-in up to the single-cell level. To handle this informative image content, we introduce elements of fractal geometry as multi-scale analysis method. We show the effectiveness of fractal features in describing and classifying fibroadenoma and breast cancer tissue slides from ten patients with very high accuracy. We reach 94.0 ± 4.2% test accuracy in classifying single images. Above all, we show that combining the decisions of the single images, each patient's slide can be classified with no error. Besides, fractal geometry returns a guide map to help pathologist to judge the different tissue portions based on the likelihood these can be associated to a breast cancer or fibroadenoma biomarker. The proposed automatic method could significantly simplify the steps of tissue analysis and make it independent from the sample preparation, the skills of the lab operator and the pathologist.

Drop-on-Demand Pyro-Electrohydrodynamic Printing of Nematic Liquid Crystal Microlenses

Inkjet printing of liquid crystal (LC) microlens arrays is particularly appealing for the development of switchable 2D/3D organic light-emitting diode (OLED) displays, as the printing process ensures that the lenses can be deposited directly and on-demand onto the pixelated OLED layer without the need for additional steps, thus simplifying fabrication complexity. Even if different fabrication technologies have been employed and good results in LC direct printing have already been achieved, all the systems used require costly equipment and heated nozzles to reduce the LC solution's viscosity. Here, we present the direct printing of a nematic LC (NLC) lens by a Drop-on-Demand (DoD) inkjet printing by a pyro-electrohydrodynamic effect for the first time. The method works at ambient temperature and avoids dispensing nozzles, thus offering a noncontact manipulation approach of liquid with high resolution and good repeatability on different kinds of substrates. NLC microlenses are printed on different substrates and fully characterized. Polarization properties are evaluated for various samples, i.e., NLC lenses on unaligned and indium-tin oxide (ITO) aligned. Moreover, an in-depth characterization of the NLC lenses is reported by polarized optical microscopy and by analyzing the birefringence in digital holographic microscopy.

Multi-scale fractal Fourier Ptychographic microscopy to assess the dose-dependent impact of copper pollution on living diatoms

Accumulation of bioavailable heavy metals in aquatic environment poses a serious threat to marine communities and human health due to possible trophic transfers through the food chain of toxic, non-degradable, exogenous pollutants. Copper (Cu) is one of the most spread heavy metals in water, and can severely affect primary producers at high doses. Here we show a novel imaging test to assay the dose-dependent effects of Cu on live microalgae identifying stress conditions when they are still capable of sustaining a positive growth. The method relies on Fourier Ptychographic Microscopy (FPM), capable to image large field of view in label-free phase-contrast mode attaining submicron lateral resolution. We uniquely combine FPM with a new multi-scale analysis method based on fractal geometry. The system is able to provide ensemble measurements of thousands of diatoms in the liquid sample simultaneously, while ensuring at same time single-cell imaging and analysis for each diatom. Through new image descriptors, we demonstrate that fractal analysis is suitable for handling the complexity and informative power of such multiscale FPM modality. We successfully tested this new approach by measuring how different concentrations of Cu impact on Skeletonema pseudocostatum diatom populations isolated from the Sarno River mouth.

All-optical dual module platform for motility-based functional scrutiny of microencapsulated probiotic bacteria

Probiotic bacteria are widely used in pharmaceutics to offer health benefits. Microencapsulation is used to deliver probiotics into the human body. Capsules in the stomach have to keep bacteria constrained until release occurs in the intestine. Once outside, bacteria must maintain enough motility to reach the intestine walls. Here, we develop a platform based on two label-free optical modules for rapidly screening and ranking probiotic candidates in the laboratory. Bio-speckle dynamics assay tests the microencapsulation effectiveness by simulating the gastrointestinal transit. Then, a digital holographic microscope 3D-tracks their motility profiles at a single element level to rank the strains.

Investigation on lysosomal accumulation by a quantitative analysis of 2D phase-maps in digital holography microscopy

Lysosomes are the terminal end of catabolic pathways in the cell, as well as signaling centers performing important functions such as the recycling of macromolecules, organelles, and nutrient adaptation. The importance of lysosomes in human health is supported by the fact that the deficiency of most lysosomal genes causes monogenic diseases called as a group Lysosomal Storage Diseases (LSDs). A common phenotypic hallmark of LSDs is the expansion of the lysosomal compartment that can be detected by using conventional imaging methods based on immunofluorescence protocols or overexpression of tagged lysosomal proteins. These methods require the alteration of the cellular architecture (i.e., due to fixation methods), can alter the behavior of cells (i.e., by the overexpression of proteins), and require sample preparation and the accurate selection of compatible fluorescent markers in relation to the type of analysis, therefore limiting the possibility of characterizing cellular status with simplicity. Therefore, a quantitative and label-free methodology, such as Quantitative Phase Imaging through Digital Holographic (QPI-DH), for the microscopic imaging of lysosomes in health and disease conditions may represent an important advance to study and effectively diagnose the presence of lysosomal storage in human disease. Here we proof the effectiveness of the QPI-DH method in accomplishing the detection of the lysosomal compartment using mouse embryonic fibroblasts (MEFs) derived from a Mucopolysaccharidosis type III-A (MSP-IIIA) mouse model, and comparing them with wild-type (WT) MEFs. We found that it is possible to identify label-free biomarkers able to supply a first pre-screening of the two populations, thus showing that QPI-DH can be a suitable candidate to surpass fluorescent drawbacks in the detection of lysosomes dysfunction. An appropriate numerical procedure was developed for detecting and evaluate such cellular substructures from in vitro cells cultures. Results reported in this study are encouraging about the further development of the proposed QPI-DH approach for such type of investigations about LSDs.

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.

Beyond fluorescence: advances in computational label-free full specificity in 3D quantitative phase microscopy

Despite remarkable progresses in quantitative phase imaging (QPI) microscopes, their wide acceptance is limited due to the lack of specificity compared with the well-established fluorescence microscopy. In fact, the absence of fluorescent tag prevents to identify subcellular structures in single cells, making challenging the interpretation of label-free 2D and 3D phase-contrast data. Great effort has been made by many groups worldwide to address and overcome such limitation. Different computational methods have been proposed and many more are currently under investigation to achieve label-free microscopic imaging at single-cell level to recognize and quantify different subcellular compartments. This route promises to bridge the gap between QPI and FM for real-world applications.

Label-Free Intracellular Multi-Specificity in Yeast Cells by Phase-Contrast Tomographic Flow Cytometry

In-flow phase-contrast tomography provides a 3D refractive index of label-free cells in cytometry systems. Its major limitation, as with any quantitative phase imaging approach, is the lack of specificity compared to fluorescence microscopy, thus restraining its huge potentialities in single-cell analysis and diagnostics. Remarkable results in introducing specificity are obtained through artificial intelligence (AI), but only for adherent cells. However, accessing the 3D fluorescence ground truth and obtaining accurate voxel-level co-registration of image pairs for AI training is not viable for high-throughput cytometry. The recent statistical inference approach is a significant step forward for label-free specificity but remains limited to cells' nuclei. Here, a generalized computational strategy based on a self-consistent statistical inference to achieve intracellular multi-specificity is shown. Various subcellular compartments (i.e., nuclei, cytoplasmic vacuoles, the peri-vacuolar membrane area, cytoplasm, vacuole-nucleus contact site) can be identified and characterized quantitatively at different phases of the cells life cycle by using yeast cells as a biological model. Moreover, for the first time, virtual reality is introduced for handling the information content of multi-specificity in single cells. Full fruition is proofed for exploring and interacting with 3D quantitative biophysical parameters of the identified compartments on demand, thus opening the route to a metaverse for 3D microscopy.

Phenotyping neuroblastoma cells through intelligent scrutiny of stain-free biomarkers in holographic flow cytometry

To efficiently tackle certain tumor types, finding new biomarkers for rapid and complete phenotyping of cancer cells is highly demanded. This is especially the case for the most common pediatric solid tumor of the sympathetic nervous system, namely, neuroblastoma (NB). Liquid biopsy is in principle a very promising tool for this purpose, but usually enrichment and isolation of circulating tumor cells in such patients remain difficult due to the unavailability of universal NB cell-specific surface markers. Here, we show that rapid screening and phenotyping of NB cells through stain-free biomarkers supported by artificial intelligence is a viable route for liquid biopsy. We demonstrate the concept through a flow cytometry based on label-free holographic quantitative phase-contrast microscopy empowered by machine learning. In detail, we exploit a hierarchical decision scheme where at first level NB cells are classified from monocytes with 97.9% accuracy. Then we demonstrate that different phenotypes are discriminated within NB class. Indeed, for each cell classified as NB its belonging to one of four NB sub-populations (i.e., CHP212, SKNBE2, SHSY5Y, and SKNSH) is evaluated thus achieving accuracy in the range 73.6%-89.1%. The achieved results solve the realistic problem related to the identification circulating tumor cell, i.e., the possibility to recognize and detect tumor cells morphologically similar to blood cells, which is the core issue in liquid biopsy based on stain-free microscopy. The presented approach operates at lab-on-chip scale and emulates real-world scenarios, thus representing a future route for liquid biopsy by exploiting intelligent biomedical imaging.

AI-aided holographic flow cytometry for label-free identification of ovarian cancer cells in the presence of unbalanced datasets

Liquid biopsy is a valuable emerging alternative to tissue biopsy with great potential in the noninvasive early diagnostics of cancer. Liquid biopsy based on single cell analysis can be a powerful approach to identify circulating tumor cells (CTCs) in the bloodstream and could provide new opportunities to be implemented in routine screening programs. Since CTCs are very rare, the accurate classification based on high-throughput and highly informative microscopy methods should minimize the false negative rates. Here, we show that holographic flow cytometry is a valuable instrument to obtain quantitative phase-contrast maps as input data for artificial intelligence (AI)-based classifiers. We tackle the problem of discriminating between A2780 ovarian cancer cells and THP1 monocyte cells based on the phase-contrast images obtained in flow cytometry mode. We compare conventional machine learning analysis and deep learning architectures in the non-ideal case of having a dataset with unbalanced populations for the AI training step. The results show the capacity of AI-aided holographic flow cytometry to discriminate between the two cell lines and highlight the important role played by the phase-contrast signature of the cells to guarantee accurate classification.

Label-free liquid biopsy through the identification of tumor cells by machine learning-powered tomographic phase imaging flow cytometry

Image-based identification of circulating tumor cells in microfluidic cytometry condition is one of the most challenging perspectives in the Liquid Biopsy scenario. Here we show a machine learning-powered tomographic phase imaging flow cytometry system capable to provide high-throughput 3D phase-contrast tomograms of each single cell. In fact, we show that discrimination of tumor cells against white blood cells is potentially achievable with the aid of artificial intelligence in a label-free flow-cyto-tomography method. We propose a hierarchical machine learning decision-maker, working on a set of features calculated from the 3D tomograms of the cells' refractive index. We prove that 3D morphological features are adequately distinctive to identify tumor cells versus the white blood cell background in the first stage and, moreover, in recognizing the tumor type at the second decision step. Proof-of-concept experiments are shown, in which two different tumor cell lines, namely neuroblastoma cancer cells and ovarian cancer cells, are used against monocytes. The reported results allow claiming the identification of tumor cells with a success rate higher than 97% and with an accuracy over 97% in discriminating between the two cancer cell types, thus opening in a near future the route to a new Liquid Biopsy tool for detecting and classifying circulating tumor cells in blood by stain-free method.

Digital holographic approaches to the detection and characterization of microplastics in water environments

Microplastic (MP) pollution is seriously threatening the environmental health of the world, which has accelerated the development of new identification and characterization methods. Digital holography (DH) is one of the emerging tools to detect MPs in a high-throughput flow. Here, we review advances in MP screening by DH. We examine the problem from both the hardware and software viewpoints. Automatic analysis based on smart DH processing is reported by highlighting the role played by artificial intelligence for classification and regression tasks. In this framework, the continuous development and availability in recent years of field-portable holographic flow cytometers for water monitoring also is discussed.

On monocytes and lymphocytes biolens clustering by in flow holographic microscopy

Live cells act as biological lenses and can be employed as real-world optical components in bio-hybrid systems. Imaging at nanoscale, optical tweezers, lithography and also photonic waveguiding are some of the already proven functionalities, boosted by the advantage that cells are fully biocompatible for intra-body applications. So far, various cell types have been studied for this purpose, such as red blood cells, bacterial cells, stem cells and yeast cells. White Blood Cells (WBCs) play a very important role in the regulation of the human body activities and are usually monitored for assessing its health. WBCs can be considered bio-lenses but, to the best of our knowledge, characterization of their optical properties have not been investigated yet. Here, we report for the first time an accurate study of two model classes of WBCs (i.e., monocytes and lymphocytes) by means of a digital holographic microscope coupled with a microfluidic system, assuming WBCs bio-lens characteristics. Thus, quantitative phase maps for many WBCs have been retrieved in flow-cytometry (FC) by achieving a significant statistical analysis to prove the enhancement in differentiation among sphere-like bio-lenses according to their sizes (i.e., diameter d) exploiting intensity parameters of the modulated light in proximity of the cell optical axis. We show that the measure of the low intensity area (S: I z < I th z ) in a fixed plane, is a feasible parameter for cell clustering, while achieving robustness against experimental misalignments and allowing to adjust the measurement sensitivity in post-processing. 2D scatterplots of the identified parameters (d-S) show better differentiation respect to the 1D case. The results show that the optical focusing properties of WBCs allow the clustering of the two populations by means of a mere morphological analysis, thus leading to the new concept of cell-optical-fingerprint avoiding fluorescent dyes. This perspective can open new routes in biomedical sciences, such as the chance to find optical-biomarkers at single cell level for label-free diagnosis.

Beyond conventional microscopy: Observing kidney tissues by means of fourier ptychography

Kidney microscopy is a mainstay in studying the morphological structure, physiology and pathology of kidney tissues, as histology provides important results for a reliable diagnosis. A microscopy modality providing at same time high-resolution images and a wide field of view could be very useful for analyzing the whole architecture and the functioning of the renal tissue. Recently, Fourier Ptychography (FP) has been proofed to yield images of biology samples such as tissues and in vitro cells while providing high resolution and large field of view, thus making it a unique and attractive opportunity for histopathology. Moreover, FP offers tissue imaging with high contrast assuring visualization of small desirable features, although with a stain-free mode that avoids any chemical process in histopathology. Here we report an experimental measuring campaign for creating the first comprehensive and extensive collection of images of kidney tissues captured by this FP microscope. We show that FP microscopy unlocks a new opportunity for the physicians to observe and judge renal tissue slides through the novel FP quantitative phase-contrast microscopy. Phase-contrast images of kidney tissue are analyzed by comparing them with the corresponding renal images taken under a conventional bright-field microscope both for stained and unstained tissue samples of different thicknesses. In depth discussion on the advantages and limitations of this new stain-free microscopy modality is reported, showing its usefulness over the classical light microscopy and opening a potential route for using FP in clinical practice for histopathology of kidney.

Bilateral advancement O-to-H flap or H-plasty

A 68 old man presented with an enlarging lesion of the forehead. Clinical diagnosis of basal cell carcinoma was made, and surgical removal was scheduled.

Use of a rhombic transposition flap in mandibular dermatosurgery

A 82 year old woman presented with a growing lesion of the right mandibular border. A clinical diagnosis of basal cell carcinoma was made and removal with a suitable border was planned

Stain-free identification of cell nuclei using tomographic phase microscopy in flow cytometry

Quantitative Phase Imaging (QPI) has gained popularity in bioimaging because it can avoid the need for cell staining, which in some cases is difficult or impossible. However, as a result, QPI does not provide labelling of various specific intracellular structures. Here we show a novel computational segmentation method based on statistical inference that makes it possible for QPI techniques to identify the cell nucleus. We demonstrate the approach with refractive index tomograms of stain-free cells reconstructed through the tomographic phase microscopy in flow cytometry mode. In particular, by means of numerical simulations and two cancer cell lines, we demonstrate that the nucleus can be accurately distinguished within the stain-free tomograms. We show that our experimental results are consistent with confocal fluorescence microscopy (FM) data and microfluidic cytofluorimeter outputs. This is a significant step towards extracting specific three-dimensional intracellular structures directly from the phase-contrast data in a typical flow cytometry configuration.

Finding intracellular lipid droplets from the single-cell biolens' signature in a holographic flow-cytometry assay

In recent years, intracellular LDs have been discovered to play an important role in several pathologies. Therefore, detection of LDs would provide an in-demand diagnostic tool if coupled with flow-cytometry to give significant statistical analysis and especially if the diagnosis is made in full non-invasive mode. Here we combine the experimental results of in-flow tomographic phase microscopy with a suited numerical simulation to demonstrate that intracellular LDs can be easily detected through a label-free approach based on the direct analysis of the 2D quantitative phase maps recorded by a holographic flow cytometer. In fact, we demonstrate that the presence of LDs affects the optical focusing lensing features of the embracing cell, which can be considered a biological lens. The research was conducted on white blood cells (i.e., lymphocytes and monocytes) and ovarian cancer cells. Results show that the biolens properties of cells can be a rapid biomarker that aids in boosting the diagnosis of LDs-related pathologies by means of the holographic flow-cytometry assay for fast, non-destructive, and high-throughput screening of statistically significant number of cells.

Biological Cells as Natural Biophotonic Devices: Fundamental and Applications-introduction to the feature issue

This feature issue of Biomedical Optics Express presents a cross-section of interesting and emerging work of relevance to the use of biological cells or microorganisms in optics and photonics. The technologies demonstrated here aim to address challenges to meeting the optical imaging, sensing, manipulating and therapy needs in a natural or even endogenous manner. This collection of 15 papers includes the novel results on designs of optical systems or photonic devices, image-assisted diagnosis and treatment, and manipulation or sensing methods, with applications for both ex vivo and in vivo use. These works portray the opportunities for exploring the field crossing biology and photonics in which a natural element can be functionalized for biomedical applications.

Developing a Reliable Holographic Flow Cyto-Tomography Apparatus by Optimizing the Experimental Layout and Computational Processing

Digital Holographic Tomography (DHT) has recently been established as a means of retrieving the 3D refractive index mapping of single cells. To make DHT a viable system, it is necessary to develop a reliable and robust holographic apparatus in order that such technology can be utilized outside of specialized optics laboratories and operated in the in-flow modality. In this paper, we propose a quasi-common-path lateral-shearing holographic optical set-up to be used, for the first time, for DHT in a flow-cytometer modality. The proposed solution is able to withstand environmental vibrations that can severely affect the interference process. Furthermore, we have scaled down the system while ensuring that a full 360° rotation of the cells occurs in the field-of-view, in order to retrieve 3D phase-contrast tomograms of single cells flowing along a microfluidic channel. This was achieved by setting the camera sensor at 45° with respect to the microfluidic direction. Additional optimizations were made to the computational elements to ensure the reliable retrieval of 3D refractive index distributions by demonstrating an effective method of tomographic reconstruction, based on high-order total variation. The results were first demonstrated using realistic 3D numerical phantom cells to assess the performance of the proposed high-order total variation method in comparison with the gold-standard algorithm for tomographic reconstructions: namely, filtered back projection. Then, the proposed DHT system and the processing pipeline were experimentally validated for monocytes and mouse embryonic fibroblast NIH-3T3 cells lines. Moreover, the repeatability of these tomographic measurements was also investigated by recording the same cell multiple times and quantifying the ability to provide reliable and comparable tomographic reconstructions, as confirmed by a correlation coefficient greater than 95%. The reported results represent various steps forward in several key aspects of in-flow DHT, thus paving the way for its use in real-world applications.

Intelligent polarization-sensitive holographic flow-cytometer: Towards specificity in classifying natural and microplastic fibers

Micron size fiber fragments (MFFs), both natural and synthetic, are ubiquitous in our life, especially in textile clothes, being necessary in modern society. In the Earth's aquatic ecosystem, microplastic fibers account for ~91% of microplastic pollution, thus deserving notable attention as one of the most alarming ecological problems. Accurate automatic identification of MFFs discharges in specific upstream locations is highly demanded. Computational microscopy based on Digital Holography (DH) and machine learning has been demonstrated to identify microplastics in respect to microalgae. However, DH is a non-specific optical tool, meaning it cannot distinguish different types of plastic materials. On the other hand, materials-specific assessments are pivotal to establish the environmental impact of different textile products and production processes. Spectroscopic assays can be employed to identify microplastics for their intrinsic specificity, although they are generally low-throughput and require large concentrations to enable effective measurements. Conversely, MFFs are usually finely dispersed within a water sample. Here we rely on a polarization-resolved holographic flow cytometer in a Lab-on-Chip (LoC) platform for analysing MFFs. We demonstrate that two important objectives can be achieved, i.e. adding material specificity through polarization analysis while operating in a microfluidic stream modality. Through a machine learning numerical pipeline, natural fibers (i.e. cotton and wool) can be clearly separated from synthetic microfilaments, namely PA6, PA6.6, PET, PP. Moreover, the proposed system can accurately distinguish between different polymers under investigation, thus fulfilling the specificity goal. We extract and select different features from amplitude, phase and birefringence maps retrieved from the digital holograms. These are shown to typify MFFs without the need for sample pre-treatment or large concentrations. The simplicity of the DH method for identifying MFFs in LoC-based flow cytometers could promote the use of polarization resolved field-portable analysis systems suitable for studying pollution caused by washing processes of synthetic textiles.

Speeding up reconstruction of 3D tomograms in holographic flow cytometry via deep learning

Tomographic flow cytometry by digital holography is an emerging imaging modality capable of collecting multiple views of moving and rotating cells with the aim of recovering their refractive index distribution in 3D. Although this modality allows us to access high-resolution imaging with high-throughput, the huge amount of time-lapse holographic images to be processed (hundreds of digital holograms per cell) constitutes the actual bottleneck. This prevents the system from being suitable for lab-on-a-chip platforms in real-world applications, where fast analysis of measured data is mandatory. Here we demonstrate a significant speeding-up reconstruction of phase-contrast tomograms by introducing in the processing pipeline a multi-scale fully-convolutional context aggregation network. Although it was originally developed in the context of semantic image analysis, we demonstrate for the first time that it can be successfully adapted to a holographic lab-on-chip platform for achieving 3D tomograms through a faster computational process. We trained the network with input-output image pairs to reproduce the end-to-end holographic reconstruction process, i.e. recovering quantitative phase maps (QPMs) of single cells from their digital holograms. Then, the sequence of QPMs of the same rotating cell is used to perform the tomographic reconstruction. The proposed approach significantly reduces the computational time for retrieving tomograms, thus making them available in a few seconds instead of tens of minutes, while essentially preserving the high-content information of tomographic data. Moreover, we have accomplished a compact deep convolutional neural network parameterization that can fit into on-chip SRAM and a small memory footprint, thus demonstrating its possible exploitation to provide onboard computations for lab-on-chip devices with low processing hardware resources.

Kinematic analysis and visualization of Tetraselmis microalgae 3D motility by digital holography

A study on locomotion in a 3D environment of Tetraselmis microalgae by digital holographic microscopy is reported. In particular, a fast and semiautomatic criterion is revealed for tracking and analyzing the swimming path of a microalga (i.e., Tetraselmis species) in a 3D volume. Digital holography (DH) in a microscope off-axis configuration is exploited as a useful method to enable fast autofocusing and recognition of objects in the field of view, thus coupling DH with appropriate numerical algorithms. Through the proposed method we measure, simultaneously, the tri-dimensional paths followed by the flagellate microorganism and the full set of the kinematic parameters that describe the swimming behavior of the analyzed microorganisms by means of a polynomial fitting and segmentation. Furthermore, the method is capable to furnish the accurate morphology of the microorganisms at any instant of time along its 3D trajectory. This work launches a promising trend having as the main objective the combined use of DH and motility microorganism analysis as a label-free and non-invasive environmental monitoring tool, employable also for in situ measurements. Finally, we show that the locomotion can be visualized intriguingly by different modalities to furnish marine biologists with a clear 3D representation of all the parameters of the kinematic set in order to better understand the behavior of the microorganism under investigation.

Differential diagnosis of hereditary anemias from a fraction of blood drop by digital holography and hierarchical machine learning

Anemia affects about the 25% of the global population and can provoke severe diseases, ranging from weakness and dizziness to pregnancy problems, arrhythmias and hearth failures. About 10% of the patients are affected by rare anemias of which 80% are hereditary. Early differential diagnosis of anemia enables prescribing patients a proper treatment and diet, which is effective to mitigate the associated symptoms. Nevertheless, the differential diagnosis of these conditions is often difficult due to shared and overlapping phenotypes. Indeed, the complete blood count and unaided peripheral blood smear observation cannot always provide a reliable differential diagnosis, so that biomedical assays and genetic tests are needed. These procedures are not error-free, require skilled personnel, and severely impact the financial resources of national health systems. Here we show a differential screening system for hereditary anemias that relies on holographic imaging and artificial intelligence. Label-free holographic imaging is aided by a hierarchical machine learning decider that works even in the presence of a very limited dataset but is enough accurate for discerning between different anemia classes with minimal morphological dissimilarities. It is worth to notice that only a few tens of cells from each patient are sufficient to obtain a correct diagnosis, with the advantage of significantly limiting the volume of blood drawn. This work paves the way to a wider use of home screening systems for point of care blood testing and telemedicine with lab-on-chip platforms.

Instant in situ formation of a polymer film at the water–oil interface

The water–oil interface is an environment that is often found in many contexts of the natural sciences and technological arenas. This interface has always been considered a special environment as it is rich in different phenomena, thus stimulating numerous studies aimed at understanding the abundance of physico-chemical problems that occur there. The intense research activity and the intriguing results that emerged from these investigations have inspired scientists to consider the water–oil interface even as a suitable setting for bottom-up nanofabrication processes, such as molecular self-assembly, or fabrication of nanofilms or nano-devices. On the other hand, biphasic liquid separation is a key enabling technology in many applications, including water treatment for environmental problems. Here we show for the first time an instant nanofabrication strategy of a thin film of biopolymer at the water–oil interface. The polymer film is fabricated in situ, simply by injecting a drop of polymer solution at the interface. Furthermore, we demonstrate that with an appropriate multiple drop delivery it is also possible to quickly produce a large area film (up to 150 cm²). The film inherently separates the two liquids, thus forming a separation layer between them and remains stable at the interface for a long time. Furthermore, we demonstrate the fabrication with different oils, thus suggesting potential exploitation in different fields (e.g. food, pollution, biotechnology). We believe that the new strategy fabrication could inspire different uses and promote applications among the many scenarios already explored or to be studied in the future at this special interface environment.

A completely new method for easy and quick formation of a thin polymer film at the special setting of a stratified oil/water interface. Morphological SEM and quantitative full-field characterization have been reported using digital holography.

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.

Three-Dimensional Quantitative Intracellular Visualization of Graphene Oxide Nanoparticles by Tomographic Flow Cytometry

Interaction of nanoparticles (NPs) with cells is of fundamental importance in biology and biomedical sciences. NPs can be taken up by cells, thus interacting with their intracellular elements, modifying the life cycle pathways, and possibly inducing death. Therefore, there is a great interest in understanding and visualizing the process of cellular uptake itself or even secondary effects, for example, toxicity. Nowadays, no method is reported yet in which 3D imaging of NPs distribution can be achieved for suspended cells in flow-cytometry. Here we show that, by means of label-free tomographic flow-cytometry, it is possible to obtain full 3D quantitative spatial distribution of nanographene oxide (nGO) inside each single flowing cell. This can allow the setting of a class of biomarkers that characterize the 3D spatial intracellular deployment of nGO or other NPs clusters, thus opening the route for quantitative descriptions to discover new insights in the realm of NP-cell interactions.

Rolling angle recovery of flowing cells in holographic tomography exploiting the phase similarity

Holographic tomography allows the 3D mapping of the refractive index of biological samples thanks to reconstruction methods based on the knowledge of illumination directions or rotation angles of the imaged sample. Recently, phase contrast tomographic flow cytometry by digital holography has been demonstrated to reconstruct the three-dimensional refractive index distribution of single cells while they are flowing along microfluidic channels. In this system, the illumination direction is fixed while the sample's rotation is not deterministically known a priori but induced by hydrodynamic forces. We propose here a technique to retrieve the rolling angles, based on a new phase images similarity metric that is capable of identifying a cell's orientations from its 3D positioning while it is flowing along the microfluidic channel. The method is experimentally tested and also validated through appropriate numerical simulations. We provide demonstration of concept by achieving reconstruction of breast cancer cells tomography.

Biological Lenses as a Photomask for Writing Laser Spots into Ferroelectric Crystals

Red blood cells on the surface of a lithium niobate crystal can be used as optical lenses for direct writing of laser-induced refractive index changes. The writing process by such a photomask made of biological lenses is due to the photorefractive effect. Wavefront analysis by a digital holographic microscope is performed for deep and accurate evaluation of local refractive index changes. Different focusing properties can be imprinted on the crystal depending on which type of RBC is employed, discocytes or spherical-like RBCs. The possibility to fix into a solid material the optical fingerprint of the RBCs will have an impact on both diagnostics and cell\material interfacing.

Microfluidic engineering for continuous in-flow cyto-tomography

The possibility to investigate cells in microfluidic flow by using a full 3D morphometry analysis is highly demanded to achieve information about their healthiness. Recently, the tomographic flow cytometry by digital holography has been demonstrated to monitor red blood cells in microfluidics environment by simply applying flux pressure to induce random self-rotation of flowing cells. Here, we provide a microfluidic solution to engineer the flow with the aim to ensure the full 360 degree of angle rotation of all cells in the field of view. We test the proposed methods for circulating tumour cells.

In vitro cytotoxicity evaluation of cadmium by label-free holographic microscopy

Among all environmental pollutants, the toxic heavy metal cadmium is considered as a human carcinogen. Cadmium may induce cell death by apoptosis in various cell types, although the underlying mechanisms are still unclear. In this paper we show how a label-free digital holography (DH)-based technique is able to quantify the evolution of key biophysical parameters of cells during the exposure to cadmium for the first time. Murine embryonic fibroblasts NIH 3T3 are chosen here as cellular model for studying the cadmium effects. The results demonstrate that DH is able to retrieve the temporal evolution of different key parameters such as cell volume, projected area, cell thickness and dry mass, thus providing a full quantitative characterization of the cell physical behaviour during cadmium exposure. Our results show that the label-free character of the technique would allow biologists to perform systematic and reliable studies on cell death process induced by cadmium and we believe that more in general this can be easily extended to others heavy metals, thus avoiding the time-consuming, expensive and invasive label-based procedures used nowadays in the field. In fact, pollution by heavy metals is severe issue that needs rapid and reliable methods to be settled.

Label-Free Optical Marker for Red-Blood-Cell Phenotyping of Inherited Anemias

The gold-standard methods for anemia diagnosis are complete blood counts and peripheral-smear observations. However, these do not allow for a complete differential diagnosis as that requires biochemical assays, which are label-dependent techniques. On the other hand, recent studies focus on label-free quantitative phase imaging (QPI) of blood samples to investigate blood diseases by using video-based morphological methods. However, when sick cells are very similar to healthy ones in terms of morphometric features, identification of a blood disease becomes challenging even with QPI. Here, we introduce a label-free optical marker (LOM) to detect red-blood-cell (RBC) phenotypes, demonstrating that a single set of all-optical parameters can clearly identify a signature directly related to an erythrocyte disease through modeling each RBC as a biological lens. We tested this novel biophotonic analysis by proving that several inherited anemias, such as iron-deficiency anemia, thalassemia, hereditary spherocytosis, and congenital dyserythropoietic anemia, can be identified and sorted, thus opening a novel route for blood diagnosis on a completely different concept based on LOMs.

Full-angle tomographic phase microscopy of flowing quasi-spherical cells

We report a reliable full-angle tomographic phase microscopy (FA-TPM) method for flowing quasi-spherical cells along microfluidic channels. This method lies in a completely passive optical system, i.e. mechanical scanning or multi-direction probing of the sample is avoided. It exploits the engineered rolling of cells while they are flowing along a microfluidic channel. Here we demonstrate significant progress with respect to the state of the art of in-flow TPM by showing a general extension to cells having almost spherical shapes while they are flowing in suspension. In fact, the adopted strategy allows the accurate retrieval of rotation angles through a theoretical model of the cells' rotation in a dynamic microfluidic flow by matching it with phase-contrast images resulting from holographic reconstructions. So far, the proposed method is the first and the only one that permits to get in-flow TPM by probing the cells with full-angle, achieving accurate 3D refractive index mapping and the simplest optical setup, simultaneously. Proof of concept experiments were performed successfully on human breast adenocarcinoma MCF-7 cells, opening the way for the full characterization of circulating tumor cells (CTCs) in the new paradigm of liquid biopsy.

Investigating fibroblast cells under "safe" and "injurious" blue-light exposure by holographic microscopy

The exposure to visible light has been shown to exert various biological effects, such as erythema and retinal degeneration. However, the phototoxicity mechanisms in living cells are still not well understood. Here we report a study on the temporal evolution of cell morphology and volume during blue light exposure. Blue laser irradiation is switched during the operation of a digital holography (DH) microscope between what we call here "safe" and "injurious" exposure (SE & IE). The results reveal a behaviour that is typical of necrotic cells, with early swelling and successive leakage of the intracellular liquids when the laser is set in the "injurious" operation. In the phototoxicity investigation reported here the light dose modulation is performed through the very same laser light source adopted for monitoring the cell's behaviour by digital holographic microscope. We believe the approach may open the route to a deep investigation of light-cell interactions, with information about death pathways and threshold conditions between healthy and damaged cells when subjected to light-exposure. 3D Morphology and quantitative phase information from late stage of necrosis cell death.

Tomographic flow cytometry by digital holography

High-throughput single-cell analysis is a challenging task. Label-free tomographic phase microscopy is an excellent candidate to perform this task. However, in-line tomography is very difficult to implement in practice because it requires a complex set-up for rotating the sample and examining the cell along several directions. We demonstrate that by exploiting the random rolling of cells while they are flowing along a microfluidic channel, it is possible to obtain in-line phase-contrast tomography, if smart strategies for wavefront analysis are adopted. In fact, surprisingly, a priori knowledge of the three-dimensional position and orientation of rotating cells is no longer needed because this information can be completely retrieved through digital holography wavefront numerical analysis. This approach makes continuous-flow cytotomography suitable for practical operation in real-world, single-cell analysis and with a substantial simplification of the optical system; that is, no mechanical scanning or multi-direction probing is required. A demonstration is given for two completely different classes of biosamples: red blood cells and diatom algae. An accurate characterization of both types of cells is reported, despite their very different nature and material content, thus showing that the proposed method can be extended by adopting two alternate strategies of wavefront analysis to many classes of cells.

Investigation on dynamics of red blood cells through their behavior as biophotonic lenses

The possibility to adopt biological matter as photonic optical elements can open scenarios in biophotonics research. Recently, it has been demonstrated that a red blood cell (RBC) can be seen as an optofluidic microlens by showing its imaging capability as well as its focal tunability. Moreover, correlation between an RBC’s morphology and its behavior as a refractive optical element has been established and its exploitation for biomedical diagnostic purposes has been foreseen. In fact, any deviation from the healthy RBC morphology can be seen as additional aberration in the optical wavefront passing through the cell. By this concept, accurate localization of focal spots of RBCs can become very useful in the blood disorders identification. We investigate the three-dimensional positioning of such focal spots over time for samples with two different osmolarity conditions, i.e., when they assume discocyte and spherical shapes, respectively. We also demonstrate that a temporal variation of an RBC’s focal points along the optical axis is correlated to the temporal fluctuations in the RBC’s thickness maps. Furthermore, we show a sort of synchronization of the whole erythrocytes ensemble.

Nanomechanics of a fibroblast suspended using point-like anchors reveal cytoskeleton formation

Cells are suspended and stretched using two microbeads. The formation of inner cytoskeleton structures is reported using displacement, QPM phase change and fluorescent micrographs.

Holographic imaging of unlabelled sperm cells for semen analysis: a review

Male reproductive health in both humans and animals is an important research field in biological study. In order to characterize the morphology, the motility and the concentration of the sperm cells, which are the most important parameters to feature them, digital holography demonstrated to be an attractive technique. Indeed, it is a label-free, non-invasive and high-resolution method that enables the characterization of live specimen. The review is intended both for summarizing the state-of-art on the semen analysis and recent achievement obtained by means of digital holography and for exploring new possible applications of digital holography in this field. Quantitative phase maps of living swimming spermatozoa.

Improving holographic reconstruction by automatic Butterworth filtering for microelectromechanical systems characterization

Digital holographic microscopy is an important interferometric tool in optical metrology allowing the investigation of engineered surfaces with microscale lateral resolution and nanoscale axial precision. In particular, microelectromechanical systems (MEMS) surface analysis, conducted by holographic characterization, requires high accuracy for functional testing. The main issues related to MEMS inspection are the superficial roughness and the complex geometry resulting from the several fabrication steps. Here, an automatic procedure, particularly suited in the case of high-roughness surfaces, is presented to selectively filter the spectrum, providing very low-noise reconstructed images. The numerical procedure is based on Butterworth filtering, and the obtained results demonstrate a significant increase in the images' quality and in the accuracy of the measurements, making our technique highly applicable for quantitative phase imaging in MEMS analysis. Furthermore, our method is fully tunable to the spectrum under investigation and automatic. This makes it highly suitable for real-time applications. Several experimental tests show the suitability of the proposed approach.

Active accumulation of very diluted biomolecules by nano-dispensing for easy detection below the femtomolar range

Highly sensitive detection of biomolecules is of paramount interest in many fields including biomedicine, safety and eco-pollution. Conventional analyses use well-established techniques with detection limits ~1 pM. Here we propose a pyro-concentrator able to accumulate biomolecules directly onto a conventional binding surface. The operation principle is relatively simple but very effective. Tiny droplets are drawn pyro-electro-dynamically and released onto a specific site, thus increasing the sensitivity. The reliability of the technique is demonstrated in case of labelled oligonucleotides diluted serially. The results show the possibility to detect very diluted oligonucleotides, down to a few hundreds of attomoles. Excellent results are shown also in case of a sample of clinical interest, the gliadin, where a 60-fold improved detection limit is reached, compared with standard ELISA. This method could open the way to a mass-based technology for sensing molecules at very low concentrations, in environmental as well as in diagnostics applications.

3D morphometry of red blood cells by digital holography

Three dimensional (3D) morphometric analysis of flowing and not-adherent cells is an important aspect for diagnostic purposes. However, diagnostics tools need to be quantitative, label-free and, as much as possible, accurate. Recently, a simple holographic approach, based on shape from silhouette algorithm, has been demonstrated for accurate calculation of cells biovolume and displaying their 3D shapes. Such approach has been adopted in combination with holographic optical tweezers and successfully applied to cells with convex shape. Nevertheless, unfortunately, the method fails in case of specimen with concave surfaces. Here, we propose an effective approach to achieve correct 3D shape measurement that can be extended in case of cells having concave surfaces, thus overcoming the limit of the previous technique. We prove the new procedure for healthy red blood cells (RBCs) (i.e., discocytes) having a concave surface in their central region. Comparative analysis of experimental results with a theoretical 3D geometrical model of RBC is discussed in order to evaluate accuracy of the proposed approach. Finally, we show that the method can be also useful to classify, in terms of morphology, different varieties of RBCs.

Imaging adherent cells in the microfluidic channel hidden by flowing RBCs as occluding objects by a holographic method

Imaging through turbid media is a challenging topic. A liquid is considered turbid when dispersed particles provoke strong light scattering, thus destroying the image formation by any standard optical system. Generally, colloidal solutions belong to the class of turbid media since dispersed particles have dimensions ranging between 0.2 μm and 2 μm. However, in microfluidics, another relevant issue has to be considered in the case of flowing liquid made of a multitude of occluding objects, e.g. red blood cells (RBCs) flowing in veins. In such a case instead of severe scattering processes unpredictable phase delays occur resulting in a wavefront distortion, thus disturbing or even hindering the image formation of objects behind such obstructing layer. In fact RBCs can be considered to be thin transparent phase objects. Here we show that sharp amplitude imaging and phase-contrast mapping of cells hidden behind biological occluding objects, namely RBCs, is possible in harsh noise conditions and with a large field-of view by Multi-Look Digital Holography microscopy (ML-DH). Noteworthy, we demonstrate that ML-DH benefits from the presence of the RBCs, providing enhancement in terms of numerical resolution and noise suppression thus obtaining images whose quality is higher than the quality achievable in the case of a liquid without occlusive objects.

Holographic tracking of living cells by three-dimensional reconstructed complex wavefronts alignment

We propose here a new three-dimensional (3D) holographic tracking method capable to track, simultaneously and in a single step, all the spatial coordinates of micro-objects. The approach is based on the enhanced correlation coefficient (ECC) maximization method but applied, for the first time to the best of our knowledge, directly on the holographic reconstructed complex wave fields. The key novelty of the proposed strategy is its ability to calculate simultaneously the 3D coordinates of cells, without decoupling the contribution of amplitude and phase. The proposed strategy is tested on living cells (i.e., NIH 3T3 mouse fibroblast) flowing into a microfluidic channel and compared with classical holographic tracking approach. Theoretical description and experimental validation of the proposed strategy are reported.

Particle tracking by full-field complex wavefront subtraction in digital holography microscopy

The 3D tracking of micro-objects, based on digital holography, is proposed through the analysis of the complex wavefront of the light scattered by the micro-samples. Exploiting the advantages of the off-axis full-field holographic interferometry, the tracking of multiple objects is achieved by a direct wavefront analysis at the focal plane overcoming the limitation of the conventional back focal plane interferometry in which only one object at a time can be tracked. Furthermore, the method proposed and demonstrated here is a step forward with respect to other holographic tracking tools. The approach is tested in two experiments, the first investigates the Brownian motion of particles trapped by holographic optical tweezers, while the second relates to the cell motility in a 3D collagen matrix, thus showing its usefulness for lab-on-chip systems in typical bioassay testing.