Hyperspectral enhanced dark field microscopy for imaging blood cells

Specific spectral sub-images for machine learning evaluation of optical differences between carbon ion and X ray radiation effects

Advances in radiotherapy, particularly the exploration of alternative radiation types such as carbon ions have updated our understanding of its effects and applicability on chondrosarcoma cells. Here we compare the optical effects produced by carbon ions (CI) and X-rays (XR) radiations on chondrosarcoma cells nuclei and set an automated method for evaluating the radiation-induced alterations without the need of chemical marking. Hyperspectral images (HSI) of SW1353 chondrosarcoma line carry detectable optical changes of the cells irradiated either with CI or XR compared to non-irradiated ones (REF). The differences between the spectral profiles of CI, XR and REF nuclei classes led to partitioning the HSIs into spectral sub-images. The changes are detected by support vector machine (SVM) classifiers whose performances are evaluated by the most used point metrics: sensitivity (SEN), accuracy (ACC), and precision (PREC), applied on spatial feature values. Specific interaction mechanisms by radiation type reveal distinct subintervals where HSIs changes are more prominent, and the classifiers perform at best. For CI the best classifiers are obtained for sub-images in the interval (424-436 nm), while for XR the best classifiers are obtained for sub-images in the interval (436-445 nm). The classifiers work better with texture features than roughness features in both cases. The classifier with the best SEN point metric in the testing phase is the most suitable to measure the irradiation efficiency irrespective of the radiation type. The altered nuclei are easier to discriminate when irradiated with CI than with XR. The study proves that SVM with optical data offers a rapid, automated, and label-free method for evaluating radiation-induced alterations in chondrosarcoma nuclei, thereby enabling effective analysis of extensive data.

Deep learning-assisted smartphone-based quantitative microscopy for label-free peripheral blood smear analysis

Hematologists evaluate alterations in blood cell enumeration and morphology to confirm peripheral blood smear findings through manual microscopic examination. However, routine peripheral blood smear analysis is both time-consuming and labor-intensive. Here, we propose using smartphone-based autofluorescence microscopy (Smart-AM) for imaging label-free blood smears at subcellular resolution with automatic hematological analysis. Smart-AM enables rapid and label-free visualization of morphological features of normal and abnormal blood cells (including leukocytes, erythrocytes, and thrombocytes). Moreover, assisted with deep-learning algorithms, this technique can automatically detect and classify different leukocytes with high accuracy, and transform the autofluorescence images into virtual Giemsa-stained images which show clear cellular features. The proposed technique is portable, cost-effective, and user-friendly, making it significant for broad point-of-care applications.

Visible and NIR microscopic hyperspectrum reconstruction from RGB images with deep convolutional neural networks

We investigate the microscopic hyperspectral reconstruction from RGB images with a deep convolutional neural network (DCNN) in this paper. Based on the microscopic hyperspectral imaging system, a homemade dataset consisted of microscopic hyperspectral and RGB image pairs is constructed. For considering the importance of spectral correlation between neighbor spectral bands in microscopic hyperspectrum reconstruction, the 2D convolution is replaced by 3D convolution in the DCNN framework, and a metric (weight factor) used to evaluate the performance reconstructed hyperspectrum is also introduced into the loss function used in training. The effects of the dimension of convolution kernel and the weight factor in the loss function on the performance of the reconstruction model are studied. The overall results indicate that our model can show better performance than the traditional models applied to reconstruct the hyperspectral images based on DCNN for the public and the homemade microscopic datasets. In addition, we furthermore explore the microscopic hyperspectrum reconstruction from RGB images in infrared region, and the results show that the model proposed in this paper has great potential to expand the reconstructed hyperspectrum wavelength range from the visible to near infrared bands.

Deep learning-based label-free hematology analysis framework using optical diffraction tomography

Hematology analysis, a common clinical test for screening various diseases, has conventionally required a chemical staining process that is time-consuming and labor-intensive. To reduce the costs of chemical staining, label-free imaging can be utilized in hematology analysis. In this work, we exploit optical diffraction tomography and the fully convolutional one-stage object detector or FCOS, a deep learning architecture for object detection, to develop a label-free hematology analysis framework. Detected cells are classified into four groups: red blood cell, abnormal red blood cell, platelet, and white blood cell. In the results, the trained object detection model showed superior detection performance for blood cells in refractive index tomograms (0.977 mAP) and also showed high accuracy in the four-class classification of blood cells (0.9708 weighted F1 score, 0.9712 total accuracy). For further verification, mean corpuscular volume (MCV) and mean corpuscular hemoglobin (MCH) were compared with values obtained from reference hematology equipment, with our results showing reasonable correlation in both MCV (0.905) and MCH (0.889). This study provides a successful demonstration of the proposed framework in detecting and classifying blood cells using optical diffraction tomography for label-free hematology analysis.

Optimization of Tyrosine Kinase Inhibitor-Loaded Gold Nanoparticles for Stimuli-Triggered Antileukemic Drug Release

Tyrosine kinase inhibitor (TKI) therapy is gaining attraction in advanced cancer therapeutics due to the ubiquity of kinases in cell survival and differentiation. Great progress was made in the past years in identifying tyrosine kinases that can function as valuable molecular targets and for the entrapment of their corresponding inhibitors in delivery compounds for triggered release. Herein we present a class of drug-delivery nanocompounds based on TKI Midostaurin-loaded gold nanoparticles that have the potential to be used as theranostic agents for the targeting of the FMS-like tyrosine kinase 3 (FLT3) in acute myeloid leukemia. We optimized the nanocompounds' formulation with loading efficiency in the 84-94% range and studied the drug release behavior in the presence of stimuli-responsive polymers. The therapeutic activity of MDS-loaded particles, superior to that of the free drug, was confirmed with toxicities depending on specific dosage ranges. No effect was observed on FLT3-negative cells or for the unloaded particles. Beyond druggability, we can track this type of nanocarrier inside biological structures as demonstrated via dark field microscopy. These properties might contribute to the facilitation of personalized drug dosage administration, critical for attaining a maximal therapeutic effect.

3D full-wave multi-scattering forward solver for coherent microscopes

A rigorous forward model solver for conventional coherent microscope is presented. The forward model is derived from Maxwell's equations and models the wave behaviour of light matter interaction. Vectorial waves and multiple-scattering effect are considered in this model. Scattered field can be calculated with given distribution of the refractive index of the biological sample. Bright field images can be obtained by combining the scattered field and reflected illumination, and experimental validation is included. Insights into the utility of the full-wave multi-scattering (FWMS) solver and comparison with the conventional Born approximation based solver are presented. The model is also generalizable to the other forms of label-free coherent microscopes, such as quantitative phase microscope and dark-field microscope.

Compact and low-cost deep-ultraviolet microscope system for label-free molecular imaging and point-of-care hematological analysis

Deep-ultraviolet (UV) microscopy enables label-free, high-resolution, quantitative molecular imaging and enables unique applications in biomedicine, including the potential for fast hematological analysis at the point-of-care. UV microscopy has been shown to quantify hemoglobin content and white blood cells (five-part differential), providing a simple alternative to the current gold standard, the hematological analyzer. Previously, however, the UV system comprised a bulky broadband laser-driven plasma light source along with a large and expensive camera and 3D translation stage. Here, we present a modified deep-UV microscope system with a compact footprint and low-cost components. We detail the novel design with simple, inexpensive optics and hardware to enable fast and accurate automated imaging. We characterize the system, including a modified low-cost web-camera and custom automated 3D translation stage, and demonstrate its ability to scan and capture large area images. We further demonstrate the capability of the system by imaging and analyzing blood smears, using previously trained networks for automatic segmentation, classification (including 5-part white blood cell differential), and colorization. The developed system is approximately 10 times less expensive than previous configurations and can serve as a point-of-care hematology analyzer, as well as be applied broadly in biomedicine as a simple compact, low-cost, quantitative molecular imaging system.

Label-free deep-UV microscopy detection and grading of neutropenia using a passive microfluidic device

Neutropenia is a condition comprising an abnormally low number of neutrophils, a type of white blood cell, which puts patients at an increased risk of severe infections. Neutropenia is especially common among cancer patients and can disrupt their treatment or even be life-threatening in severe cases. Therefore, routine monitoring of neutrophil counts is crucial. However, the current standard of care to assess neutropenia, the complete blood count (CBC), is resource-intensive, time-consuming, and expensive, thereby limiting easy or timely access to critical hematological information such as neutrophil counts. Here, we present a simple technique for fast, label-free neutropenia detection and grading via deep-ultraviolet (deep-UV) microscopy of blood cells in polydimethylsiloxane (PDMS)-based passive microfluidic devices. The devices can potentially be manufactured in large quantities at a low cost, requiring only 1 μL of whole blood for operation. We show that the absolute neutrophil counts (ANC) obtained from our proposed microfluidic device-enabled deep-UV microscopy system are highly correlated with those from CBCs using commercial hematology analyzers in patients with moderate and severe neutropenia, as well as healthy donors. This work lays the foundation for the development of a compact, easy-to-use UV microscope system to track neutrophil counts that is suitable for low-resource, at-home, or point-of-care settings.

Evaluation of intracellular distribution of folate functionalized silica nanoparticles using fluorescence and hyperspectral enhanced dark field microscopy

Using nanoparticles as carriers for drug delivery systems has become a widely applied strategy in therapeutics and diagnostics. However, the pattern of their intracellular distribution is yet to be clarified. Here we present an in vitro study on the incorporation of mesoporous silica nanoparticles conjugated with folate and loaded with a cytotoxic drug, Irinotecan. The nanoparticles count and distribution within the cell frame were evaluated by means of enhanced dark field microscopy combined with hyperspectral imagery and 3D reconstructions from double-labeled fluorescent samples. An original post-processing procedure was developed to emphasize the nanoparticles' localization in 3D reconstruction of cellular compartments. By these means, it has been shown that the conjugation of mesoporous silica nanoparticles with folate increases the efficiency of nanoparticles entering the cell and their preferential localization in the close vicinity of the nucleus. As revealed by metabolic viability assays, the nanoparticles functionalized with folate enhance the cytotoxic efficiency of Irinotecan.

Label-free hematology analysis method based on defocusing phase-contrast imaging under illumination of 415 nm light

Label-free imaging technology is a trending way to simplify and improve conventional hematology analysis by bypassing lengthy and laborious staining procedures. However, the existing methods do not well balance system complexity, data acquisition efficiency, and data analysis accuracy, which severely impedes their clinical translation. Here, we propose defocusing phase-contrast imaging under the illumination of 415 nm light to realize label-free hematology analysis. We have verified that the subcellular morphology of blood components can be visualized without complex staining due to the factor that defocusing can convert the second-order derivative distribution of samples' optical phase into intensity and the illumination of 415 nm light can significantly enhance the contrast. It is demonstrated that the defocusing phase-contrast images for the five leucocyte subtypes can be automatically discriminated by a trained deep-learning program with high accuracy (the mean F1 score: 0.986 and mean average precision: 0.980). Since this technique is based on a regular microscope, it simultaneously realizes low system complexity and high data acquisition efficiency with remarkable quantitative analysis ability. It supplies a label-free, reliable, easy-to-use, fast approach to simplifying and reforming the conventional way of hematology analysis.

Virtual Staining, Segmentation, and Classification of Blood Smears for Label-Free Hematology Analysis

Objective and Impact Statement. We present a fully automated hematological analysis framework based on single-channel (single-wavelength), label-free deep-ultraviolet (UV) microscopy that serves as a fast, cost-effective alternative to conventional hematology analyzers. Introduction. Hematological analysis is essential for the diagnosis and monitoring of several diseases but requires complex systems operated by trained personnel, costly chemical reagents, and lengthy protocols. Label-free techniques eliminate the need for staining or additional preprocessing and can lead to faster analysis and a simpler workflow. In this work, we leverage the unique capabilities of deep-UV microscopy as a label-free, molecular imaging technique to develop a deep learning-based pipeline that enables virtual staining, segmentation, classification, and counting of white blood cells (WBCs) in single-channel images of peripheral blood smears. Methods. We train independent deep networks to virtually stain and segment grayscale images of smears. The segmented images are then used to train a classifier to yield a quantitative five-part WBC differential. Results. Our virtual staining scheme accurately recapitulates the appearance of cells under conventional Giemsa staining, the gold standard in hematology. The trained cellular and nuclear segmentation networks achieve high accuracy, and the classifier can achieve a quantitative five-part differential on unseen test data. Conclusion. This proposed automated hematology analysis framework could greatly simplify and improve current complete blood count and blood smear analysis and lead to the development of a simple, fast, and low-cost, point-of-care hematology analyzer.

Continuous live cell imaging using dark field microscopy

The optical observation of live cell behavior is critical for biological and biomedical studies. The development of techniques for long-term cell and tissue imaging is, however, hindered by phototoxicity induced by excited fluorophores. We, herein, propose a methodology to capture live cell behavior using dark field microscopy (DM). Since the light intensity of DM is merely ∼0.1% of bright-field microscopy (BM) and ∼0.5% of fluorescence microscopy (FM), it allows super long and frequent live cell imaging. Our results demonstrate that continuous exposure to DM light for 48 h brings about no observable effect on the growth rate of 3T3 fibroblasts and HepG2 hepatoma cells, indicating minimum photo-toxicity. Moreover, DM images show contrast comparable to FM, which does not depend on the probes and staining efficiency. We, therefore, conclude that the proposed approach is suitable for long-term live cell imaging with super-high temporal resolution.

High-resolution dark-field confocal microscopy based on radially polarized illumination

Dark-field confocal microscopy (DFCM) facilitates the 3D detection and localization of surface and subsurface defects in high-precision optical components. The spatial resolution of conventional DFCM is commonly undermined owing to complementary aperture detection. We employed a radially polarized (RP) beam for illumination in DFCM. The RP beam creates a sub-diffraction-sized longitudinal optical component after being focused and effectively enhances the lateral resolution by 30.33% from 610 nm to 425 nm. The resolution improvement was verified by imaging a 2D sample containing sparsely distributed gold nanorods along with a 3D neodymium glass containing surface and subsurface defects.

Label-free automated neutropenia detection and grading using deep-ultraviolet microscopy

Neutropenia is a condition identified by an abnormally low number of neutrophils in the bloodstream and signifies an increased risk of severe infection. Cancer patients are particularly susceptible to this condition, which can be disruptive to their treatment and even life-threatening in severe cases. Thus, it is critical to routinely monitor neutrophil counts in cancer patients. However, the standard of care to assess neutropenia, the complete blood count (CBC), requires expensive and complex equipment, as well as cumbersome procedures, which precludes easy or timely access to critical hematological information, namely neutrophil counts. Here we present a simple, low-cost, fast, and robust technique to detect and grade neutropenia based on label-free multi-spectral deep-UV microscopy. Results show that the developed framework for automated segmentation and classification of live, unstained blood cells in a smear accurately differentiates patients with moderate and severe neutropenia from healthy samples in minutes. This work has significant implications towards the development of a low-cost and easy-to-use point-of-care device for tracking neutrophil counts, which can not only improve the quality of life and treatment-outcomes of many patients but can also be lifesaving.

Autofluorescence of blood and its application in biomedical and clinical research

Autofluorescence of blood has been explored as a label free approach for detection of cell types, as well as for diagnosis and detection of infection, cancer, and other diseases. Although blood autofluorescence is used to indicate the presence of several physiological abnormalities with high sensitivity, it often lacks disease specificity due to use of a limited number of fluorophores in the detection of several abnormal conditions. In addition, the measurement of autofluorescence is sensitive to the type of sample, sample preparation, and spectroscopy method used for the measurement. Therefore, while current blood autofluorescence detection approaches may not be suitable for primary clinical diagnosis, it certainly has tremendous potential in developing methods for large scale screening that can identify high risk groups for further diagnosis using highly specific diagnostic tests. This review discusses the source of blood autofluorescence, the role of spectroscopy methods, and various applications that have used autofluorescence of blood, to explore the potential of blood autofluorescence in biomedical research and clinical applications.

Label-Free White Blood Cell Classification Using Refractive Index Tomography and Deep Learning

Objective and Impact Statement. We propose a rapid and accurate blood cell identification method exploiting deep learning and label-free refractive index (RI) tomography. Our computational approach that fully utilizes tomographic information of bone marrow (BM) white blood cell (WBC) enables us to not only classify the blood cells with deep learning but also quantitatively study their morphological and biochemical properties for hematology research. Introduction. Conventional methods for examining blood cells, such as blood smear analysis by medical professionals and fluorescence-activated cell sorting, require significant time, costs, and domain knowledge that could affect test results. While label-free imaging techniques that use a specimen's intrinsic contrast (e.g., multiphoton and Raman microscopy) have been used to characterize blood cells, their imaging procedures and instrumentations are relatively time-consuming and complex. Methods. The RI tomograms of the BM WBCs are acquired via Mach-Zehnder interferometer-based tomographic microscope and classified by a 3D convolutional neural network. We test our deep learning classifier for the four types of bone marrow WBC collected from healthy donors (n = 10 ): monocyte, myelocyte, B lymphocyte, and T lymphocyte. The quantitative parameters of WBC are directly obtained from the tomograms. Results. Our results show >99% accuracy for the binary classification of myeloids and lymphoids and >96% accuracy for the four-type classification of B and T lymphocytes, monocyte, and myelocytes. The feature learning capability of our approach is visualized via an unsupervised dimension reduction technique. Conclusion. We envision that the proposed cell classification framework can be easily integrated into existing blood cell investigation workflows, providing cost-effective and rapid diagnosis for hematologic malignancy.

Lensless, reflection-based dark-field microscopy (RDFM) on a CMOS chip

We present for the first time a lens-free, oblique illumination imaging platform for on-sensor dark- field microscopy and shadow-based 3D object measurements. It consists of an LED point source that illuminates a 5-megapixel, 1.4 µm pixel size, back-illuminated CMOS sensor at angles between 0° and 90°. Analytes (polystyrene beads, microorganisms, and cells) were placed and imaged directly onto the sensor. The spatial resolution of this imaging system is limited by the pixel size (∼1.4 µm) over the whole area of the sensor (3.6×2.73 mm). We demonstrated two imaging modalities: (i) shadow imaging for estimation of 3D object dimensions (on polystyrene beads and microorganisms) when the illumination angle is between 0° and 85°, and (ii) dark-field imaging, at >85° illumination angles. In dark-field mode, a 3-4 times drop in background intensity and contrast reversal similar to traditional dark-field imaging was observed, due to larger reflection intensities at those angles. With this modality, we were able to detect and analyze morphological features of bacteria and single-celled algae clusters.

Dark-field hyperspectral imaging for label free detection of nano-bio-materials

Nanomaterials are playing an increasingly important role in cancer diagnosis and treatment. Nanoparticle (NP)-based technologies have been utilized for targeted drug delivery during chemotherapies, photodynamic therapy, and immunotherapy. Another active area of research is the toxicity studies of these nanomaterials to understand the cellular uptake and transport of these materials in cells, tissues, and environment. Traditional techniques such as transmission electron microscopy, and mass spectrometry to analyze NP-based cellular transport or toxicity effect are expensive, require extensive sample preparation, and are low-throughput. Dark-field hyperspectral imaging (DF-HSI), an integration of spectroscopy and microscopy/imaging, provides the ability to investigate cellular transport of these NPs and to quantify the distribution of them within bio-materials. DF-HSI also offers versatility in non-invasively monitoring microorganisms, single cell, and proteins. DF-HSI is a low-cost, label-free technique that is minimally invasive and is a viable choice for obtaining high-throughput quantitative molecular analyses. Multimodal imaging modalities such as Fourier transform infrared and Raman spectroscopy are also being integrated with HSI systems to enable chemical imaging of the samples. HSI technology is being applied in surgeries to obtain molecular information about the tissues in real-time. This article provides brief overview of fundamental principles of DF-HSI and its application for nanomaterials, protein-detection, single-cell analysis, microbiology, surgical procedures along with technical challenges and future integrative approach with other imaging and measurement modalities. This article is categorized under: Diagnostic Tools > in vitro Nanoparticle-Based Sensing Diagnostic Tools > in vivo Nanodiagnostics and Imaging Implantable Materials and Surgical Technologies > Nanoscale Tools and Techniques in Surgery.

Label-free hematology analysis using deep-ultraviolet microscopy

Hematological analysis, via a complete blood count (CBC) and microscopy, is critical for screening, diagnosing, and monitoring blood conditions and diseases but requires complex equipment, multiple chemical reagents, laborious system calibration and procedures, and highly trained personnel for operation. Here we introduce a hematological assay based on label-free molecular imaging with deep-ultraviolet microscopy that can provide fast quantitative information of key hematological parameters to facilitate and improve hematological analysis. We demonstrate that this label-free approach yields 1) a quantitative five-part white blood cell differential, 2) quantitative red blood cell and hemoglobin characterization, 3) clear identification of platelets, and 4) detailed subcellular morphology. Analysis of tens of thousands of live cells is achieved in minutes without any sample preparation. Finally, we introduce a pseudocolorization scheme that accurately recapitulates the appearance of cells under conventional staining protocols for microscopic analysis of blood smears and bone marrow aspirates. Diagnostic efficacy is evaluated by a panel of hematologists performing a blind analysis of blood smears from healthy donors and thrombocytopenic and sickle cell disease patients. This work has significant implications toward simplifying and improving CBC and blood smear analysis, which is currently performed manually via bright-field microscopy, and toward the development of a low-cost, easy-to-use, and fast hematological analyzer as a point-of-care device and for low-resource settings.

Hyperspectral and multispectral imaging in digital and computational pathology: a systematic review [Invited]

Hyperspectral imaging (HSI) and multispectral imaging (MSI) technologies have the potential to transform the fields of digital and computational pathology. Traditional digitized histopathological slides are imaged with RGB imaging. Utilizing HSI/MSI, spectral information across wavelengths within and beyond the visual range can complement spatial information for the creation of computer-aided diagnostic tools for both stained and unstained histological specimens. In this systematic review, we summarize the methods and uses of HSI/MSI for staining and color correction, immunohistochemistry, autofluorescence, and histopathological diagnostic research. Studies include hematology, breast cancer, head and neck cancer, skin cancer, and diseases of central nervous, gastrointestinal, and genitourinary systems. The use of HSI/MSI suggest an improvement in the detection of diseases and clinical practice compared with traditional RGB analysis, and brings new opportunities in histological analysis of samples, such as digital staining or alleviating the inter-laboratory variability of digitized samples. Nevertheless, the number of studies in this field is currently limited, and more research is needed to confirm the advantages of this technology compared to conventional imagery.

Programmable hyperspectral microscopy for high-contrast biomedical imaging in a snapshot

SIGNIFICANCE

Hyperspectral microscopy has been intensively explored in biomedical applications. However, due to its huge three-dimensional hyperspectral data cube, it typically suffers from slow data acquisition, mass data transmission and storage, and computationally expensive postprocessing.

AIM

To overcome the above limitations, a programmable hyperspectral microscopy technique was developed, which can perform hardware-based hyperspectral data postprocessing by the physical process of optical imaging in a snapshot.

APPROACH

A programmable hyperspectral microscopy system was developed to collect coded microscopic images from samples under multiplexed illumination. Principal component analysis followed by linear discriminant analysis scheme was coded into multiplexed illumination and realized by the physical process of optical imaging. The contrast enhancement was evaluated on two representative types of microscopic samples, i.e., tissue section and cell samples.

RESULTS

Compared to the microscopic images collected under white light illumination, the contrasts of coded microscopic images were significantly improved by 41% and 59% for tissue section and cell samples, respectively.

CONCLUSIONS

The proposed method can perform hyperspectral data acquisition and postprocessing simultaneously by its physical process, while preserving the most important spectral information to maximize the difference between the target and background, thus opening a new avenue for high-contrast microscopic imaging in a snapshot.

Automated image analysis and hyperspectral imagery with enhanced dark field microscopy applied to biochars produced at different temperatures

Biochar from agricultural biomasses and solid wastes represents a win-win solution for a rational waste management. Its sustainable usage requires the identification and standardization of biochar characteristics. The aim of this work was to identify the physical-chemical and spatial characteristics of biochars from pruning residues (PR), poultry litter (PL), and anaerobic cattle digestate (CD) at two pyrolysis temperatures (350 °C and 550 °C). The biochar characterization was carried out by applying emerging imaging techniques, 2D automated optical image analysis and hyperspectral enhanced dark-field microscopy (EDFM), and by SEM analysis. As predictable, the feedstock composition and the pyrolysis temperature strongly influence the physical structures of the biochar samples. Irrespective of charring temperature, PR biochar was mainly characterized by a broken and fragmented structure with an irregular and rough particle surface, completely different from the original PR wood cell. The EDFM imaging analysis evidenced the thermal degradation of PR vegetal products, composed primarily of hemicellulose, cellulose and lignin. On the contrary, small and regular particles with a smooth surface were produced by the PL pyrolysis, especially at 550 °C, due to the lower PL morphological homogeneity in comparison with the other biomasses. Finally, CD charring at both temperatures was characterized by changes in chemical composition, suggested by a lower pixel intensity. In conclusion, the emerging imaging techniques used in this study proved to be very effective in analyzing some properties of biochars, and can, therefore be considered as promising experimental strategies for detecting the feedstock and pyrolysis temperature of biochar.

Contamination detection by optical measurements in a real-life environment: A hospital case study

Organic dirt on touch surfaces can be biological contaminants (microbes) or nutrients for those but is often invisible by the human eye causing challenges for evaluating the need for cleaning. Using hyperspectral scanning algorithm, touch surface cleanliness monitoring by optical imaging was studied in a real-life hospital environment. As the highlight, a human eye invisible stain from a dirty chair armrest was revealed manually with algorithms including threshold levels for intensity and clustering analysis with two excitation lights (green and red) and one bandpass filter (wavelength λ = 500 nm). The same result was confirmed by automatic k-means clustering analysis from the entire dirty data of visible light (red, green and blue) and filters 420 to 720 nm with 20 nm increments. Overall, the collected touch surface samples (N = 156) indicated the need for cleaning in some locations by the high culturable bacteria and adenosine triphosphate counts despite the lack of visible dirt. Examples of such locations were toilet door lock knobs and busy registration desk armchairs. Thus, the studied optical imaging system utilizing the safe visible light area shows a promising method for touch surface cleanliness evaluation in real-life environments.

Label-free characterization of white blood cells using fluorescence lifetime imaging and flow-cytometry: molecular heterogeneity and erythrophagocytosis [Invited]

Blood cell analysis is one of the standard clinical tests. Despite the widespread use of exogenous markers for blood cell quantification, label-free optical methods are still of high demand due to their possibility for in vivo application and signal specific to the biochemical state of the cell provided by native fluorophores. Here we report the results of blood cell characterization using label-free fluorescence imaging techniques and flow-cytometry. Autofluorescence parameters of different cell types - white blood cells, red blood cells, erythrophagocytic cells - are assessed and analyzed in terms of molecular heterogeneity and possibilities of differentiation between different cell types in vitro and in vivo.

Gold nanoparticle uptake is enhanced by estradiol in MCF-7 breast cancer cells

Purpose: In the present study, we investigated the effects of 17β-estradiol (E₂) on membrane roughness and gold nanoparticle (AuNP) uptake in MCF-7 breast cancer cells. Methods: Estrogen receptor (ER)-positive breast cancer cells (MCF-7) were exposed to bare 20 nm AuNPs in the presence and absence of 1×10^-9 M E₂ for different time intervals for up to 24 hrs. The effects of AuNP incorporation and E₂ incubation on the MCF-7 cell surface roughness were measured using atomic force microscopy (AFM). Endocytic vesicle formation was studied using confocal laser scanning microscopy (CLSM). Finally, the results were confirmed by hyperspectral optical microscopy. Results: High-resolution AFM images of the surfaces of MCF-7 membranes (up to 250 nm²) were obtained. The incubation of cells for 12 hrs with AuNP and E₂ increased the cell membrane roughness by 95% and 30% compared with the groups treated with vehicle (ethanol) or AuNPs only, respectively. This effect was blocked by an ER antagonist (7α,17β-[9-[(4,4,5,5,5-Pentafluoropentyl)sulfinyl]nonyl]estra-1,3,5(10)-triene-3,17-diol [ICI] 182,780). Higher amounts of AuNPs were localized inside MCF-7 cells around the nucleus, even after 6 hrs of E₂ incubation, compared with vehicle-treated cells. Endolysosome formation was induced by E₂, which may be associated with an increase in AuNP-uptake. Conclusions: E₂ enhances AuNP incorporation in MCF-7 cells by modulating of plasma membrane roughness and inducing lysosomal endocytosis. These findings provide new insights into combined nanotherapies and hormone therapies for breast cancer.

Multidomain-Based Responsive Materials with Dual-Mode Optical Readouts

Responsive materials designed to generate signals for both surface-enhanced Raman spectroscopy (SERS) and phosphorescence lifetime-"dual-mode"-measurements are described. To demonstrate this concept, we incorporated pH-sensitive and oxygen-sensitive microdomains into a single hydrogel that could be interrogated via SERS and phosphorescence lifetime, respectively. Microdomains consisted two populations of discrete microcapsules containing either (1) gold nanoparticles capped with pH-sensitive Raman molecules or (2) oxygen-sensitive benzoporphyrin phosphors. While the microdomain-embedded hydrogels presented an expected background luminescence, the pH-sensitive SERS signal was distinguishable for all tested conditions. Response characteristics of the dual sensor showed no significant difference when compared to standalone single-mode pH and oxygen sensors. In addition, the feasibility of redundant multimode sensing was proven by observing the reaction produced by glucose oxidase chemically cross-linked within the corresponding alginate matrix. Each optical mode showed a signal change proportional to glucose concentration with an opposite signal directionality. These results support the promise of micro-/nanocomposite materials to improve measurement accuracy using intrinsic multimode responses and built-in redundancy, concepts that have broad appeal in the chemical sensing and biosensing fields.

Adverse Interactions of Luminescent Semiconductor Quantum Dots with Liposomes and Shewanella oneidensis

Cadmium-containing luminescent quantum dots (QD) are increasingly used in display, bioimaging, and energy technologies; however, significant concerns have been raised about their potentially adverse impact on human health and the environment. This study makes use of a broad toolkit of analytical methods to investigate and increase our understanding of the interactions of luminescent cadmium-containing (CdSe) and cadmium-free (ZnSe) QD, with and without a passivating higher bandgap energy ZnS shell, with phospholipid vesicles (liposomes), which model bacterial membranes, and with Shewanella oneidensis MR-1, an environmentally relevant bacteria. A unique feature of this study is that all QD types have the same surface chemistry, being capped with uncharged poly(ethylene glycol) ligands. This enables focusing the study on the impact of the QD core on liposomes and bacterial cells. The study reveals that QD association with liposome and bacterial cell membranes is imperative for their adverse impact on liposomes and bacterial cells. The QD' concentration-dependent association with liposomes and bacterial cells destabilizes the membranes mechanically, which leads to membrane disruption and lysis in liposomes and to bacterial cell death. The study also shows that cadmium-containing QD exhibit a higher level of membrane disruption in bacterial cells than cadmium-free QD. ZnSe QD have low membrane impact, and coating them with a ZnS shell decreases their membrane disruption activity. In contrast, CdSe QD exhibit a high level of membrane impact, and coating them with a ZnS shell does not decrease, but in fact further increases, their membrane disruption activity. This behavior might be attributed to higher affinity and association of CdSe/ZnS QD with liposomes and bacterial cells and to a contribution of dissolved zinc ions from the ZnS shell to increased membrane disruption activity.

Comparison of hyperspectral classification methods for the analysis of cerium oxide nanoparticles in histological and aqueous samples

Hyperspectral imaging (HSI) and classification are established methods that are being applied in new ways to the analysis of nanoscale materials in a variety of matrices. Typically, enhanced darkfield microscopy (EDFM)-based HSI data (also known as image datacubes) are collected in the wavelength range of 400-1000 nm for each pixel in a datacube. Utilising different spectral library (SL) creation methods, spectra from pixels in the datacube corresponding to known materials can be collected into reference spectral libraries (RSLs), which can be used to classify materials in datacubes of experimental samples using existing classification algorithms. In this study, EDFM-HSI was used to visualise and analyse industrial cerium oxide (CeO₂ ; ceria) nanoparticles (NPs) in rat lung tissues and in aqueous suspension. Rats were exposed to ceria NPs via inhalation, mimicking potential real-world occupational exposures. The lung tissues were histologically prepared: some tissues were stained with hematoxylin and eosin (H&E) and some were left unstained. The goal of this study was to determine how HSI and classification results for ceria NPs were influenced by (1) the use of different RSL creation and classification methods and (2) the application of those methods to samples in different matrices (stained tissue, unstained tissue, or aqueous solution). Three different RSL creation methods - particle filtering (PF), manual selection, and spectral hourglass wizard (SHW) - were utilised to create the RSLs of known materials in unstained and stained tissue, and aqueous suspensions, which were then used to classify the NPs in the different matrices. Two classification algorithms - spectral angle mapper (SAM) and spectral feature fitting (SFF) - were utilised to determine the presence or absence of ceria NPs in each sample. The results from the classification algorithms were compared to determine how each influenced the classification results for samples in different matrices. The results showed that sample matrix and sample preparation significantly influenced the NP classification thresholds in the complex matrices. Moreover, considerable differences were observed in the classification results when utilising each RSL creation and classification method for each type of sample. Results from this study illustrate the importance of appropriately selecting HSI algorithms based on specific material and matrix characteristics in order to obtain optimal classification results. As HSI is increasingly utilised for NP characterisation for clinical, environmental and health and safety applications, this investigation is important for further refining HSI protocols while ensuring appropriate data collection and analysis.

Hyperspectral-Enhanced Dark Field Microscopy for Single and Collective Nanoparticle Characterization in Biological Environments

We review how the hyperspectral dark field analysis gives us quantitative insights into the manner that different nanoscale materials interact with their environment and how this relationship is directly expressed in an optical readout. We engage classification tools to identify dominant spectral signatures within a scene or to qualitatively characterize nanoparticles individually or in populations based on their composition and morphology. Moreover, we follow up the morphological evolution of nanoparticles over time and in different biological environments to better understand and establish a link between the observed nanoparticles' changes and cellular behaviors.

Single-Cell Analysis Using Hyperspectral Imaging Modalities

Almost a decade ago, hyperspectral imaging (HSI) was employed by the NASA in satellite imaging applications such as remote sensing technology. This technology has since been extensively used in the exploration of minerals, agricultural purposes, water resources, and urban development needs. Due to recent advancements in optical re-construction and imaging, HSI can now be applied down to micro- and nanometer scales possibly allowing for exquisite control and analysis of single cell to complex biological systems. This short review provides a description of the working principle of the HSI technology and how HSI can be used to assist, substitute, and validate traditional imaging technologies. This is followed by a description of the use of HSI for biological analysis and medical diagnostics with emphasis on single-cell analysis using HSI.

The Fate of Inhaled Nanoparticles: Detection and Measurement by Enhanced Dark-field Microscopy

Assessing the potential health risks for newly developed nanoparticles poses a significant challenge. Nanometer-sized particles are not generally detectable with the light microscope. Electron microscopy typically requires high-level doses, above the physiologic range, for particle examination in tissues. Enhanced dark-field microscopy (EDM) is an adaption of the light microscope that images scattered light. Nanoparticles scatter light with high efficiency while normal tissues do not. EDM has the potential to identify the critical target sites for nanoparticle deposition and injury in the lungs and other organs. This study describes the methods for EDM imaging of nanoparticles and applications. Examples of EDM application include measurement of deposition and clearance patterns. Imaging of a wide variety of nanoparticles demonstrated frequent situations where nanoparticles detected by EDM were not visible by light microscopy. EDM examination of colloidal gold nanospheres (10-100 nm diameter) demonstrated a detection size limit of approximately 15 nm in tissue sections. EDM determined nanoparticle volume density was directly proportional to total lung burden of exposed animals. The results confirm that EDM can determine nanoparticle distribution, clearance, transport to lymph nodes, and accumulation in extrapulmonary organs. Thus, EDM substantially improves the qualitative and quantitative microscopic evaluation of inhaled nanoparticles.

High predictive values of RBC membrane-based diagnostics by biophotonics in an integrated approach for Autism Spectrum Disorders

Membranes attract attention in medicine, concerning lipidome composition and fatty acid correlation with neurological diseases. Hyperspectral dark field microscopy (HDFM), a biophotonic imaging using reflectance spectra, provides accurate characterization of healthy adult RBC identifying a library of 8 spectral end-members. Here we report hyperspectral RBC imaging in children affected by Autism Spectrum Disorder (ASD) (n = 21) compared to healthy age-matched subjects (n = 20), investigating if statistically significant differences in their HDFM spectra exist, that can comprehensively map a membrane impairment involved in disease. A significant difference concerning one end-member (spectrum 4) was found (P value = 0.0021). A thorough statistical treatment evidenced: i) diagnostic performance by the receiving operators curve (ROC) analysis, with cut-offs and very high predictive values (P value = 0.0008) of spectrum 4 for identifying disease; ii) significant correlations of spectrum 4 with clinical parameters and with the RBC membrane deficit of the omega-3 docosahexaenoic acid (DHA) in ASD patients; iii) by principal component analysis, very high affinity values of spectrum 4 to the factor that combines behavioural parameters and the variable “cc” discriminating cases and controls. These results foresee the use of biophotonic methodologies in ASD diagnostic panels combining with molecular elements for a correct neuronal growth.

Spectral-spatial feature-based neural network method for acute lymphoblastic leukemia cell identification via microscopic hyperspectral imaging technology

Microscopic examination is one of the most common methods for acute lymphoblastic leukemia (ALL) diagnosis. Most traditional methods of automized blood cell identification are based on RGB color or gray images captured by light microscopes. This paper presents an identification method combining both spectral and spatial features to identify lymphoblasts from lymphocytes in hyperspectral images. Normalization and encoding method is applied for spectral feature extraction and the support vector machine-recursive feature elimination (SVM-RFE) algorithm is presented for spatial feature determination. A marker-based learning vector quantization (MLVQ) neural network is proposed to perform identification with the integrated features. Experimental results show that this algorithm yields identification accuracy, sensitivity, and specificity of 92.9%, 93.3%, and 92.5%, respectively. Hyperspectral microscopic blood imaging combined with neural network identification technique has the potential to provide a feasible tool for ALL pre-diagnosis.

Optical hyperspectral imaging in microscopy and spectroscopy - a review of data acquisition

Rather than simply acting as a photographic camera capturing two-dimensional (x, y) intensity images or a spectrometer acquiring spectra (λ), a hyperspectral imager measures entire three-dimensional (x, y, λ) datacubes for multivariate analysis, providing structural, molecular, and functional information about biological cells or tissue with unprecedented detail. Such data also gives clinical insights for disease diagnosis and treatment. We summarize the principles underpinning this technology, highlight its practical implementation, and discuss its recent applications at microscopic to macroscopic scales. Datacube acquisition strategies in hyperspectral imaging x, y, spatial coordinates; λ, wavelength.

A Promising Raman Spectroscopy Technique for the Investigation of trans and cis Cholesteryl Ester Isomers in Biological Samples

Lipid geometry is an important issue in biology and medicine. The cis-trans geometry conversion of double bonds in lipids is an endogenous process that can be mediated by sulfur-centered free radicals. Trans isomers of polyunsaturated fatty acids can be used as biological markers of free radical stress, and their presence in biological samples can be determined by synthesis and characterization of appropriate reference compounds. Fractions of plasma lipids, such as cholesteryl linoleate and arachidonate esters, are interesting targets because of their connection with membrane phospholipid turnover and their roles in cardiovascular health. In this context, Raman spectroscopy can provide a useful contribution, since Raman analysis can be performed directly on the lipid extracts without any derivatization reaction, is nondestructive, and can rapidly supply biochemical information. This study focused on the build up of Raman spectral libraries of different cis and trans isomers of cholesteryl esters to be used as references for the examination of complex biological samples and to facilitate isomer recognition. Unsaturated cholesteryl esters obtained by chemical synthesis and with different alkyl chain lengths, double bond numbers, or both, were analyzed. The potential of Raman analysis for trans isomer detection in biological samples was successfully tested on some cholesteryl ester lipid fractions from human serum. The data suggest promising applications of Raman spectroscopy in metabolomics and lipidomics.