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

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.

Impact of rising seawater temperature on a phagocytic cell population during V. parahaemolyticus infection in the sea anemone E. pallida

Climate change is increasing ocean temperatures and consequently impacts marine life (e.g., bacterial communities). In this context, studying host-pathogen interactions in marine organisms is becoming increasingly important, not only for ecological conservation, but also to reduce economic loss due to mass mortalities in cultured species. In this study, we used Exaiptasia pallida (E. pallida), an anemone, as an emerging marine model to better understand the effect of rising temperatures on the infection induced by the pathogenic marine bacterium Vibrio parahaemolyticus. The effect of temperature on E. pallida was examined at 6, 24, or 30 h after bath inoculation with 108 CFU of V. parahaemolyticus expressing GFP (Vp-GFP) at 27°C (husbandry temperature) or 31°C (heat stress). Morphological observations of E. pallida and their Hsps expression demonstrated heat stress induced increasing damage to anemones. The kinetics of the infections revealed that Vp-GFP were localized on the surface of the ectoderm and in the mucus during the first hours of infection and in the mesenterial filaments thereafter. To better identify the E. pallida cells targeted by Vp-GFP infection, we used spectral flow cytometry. E. pallida cell types were identified based on their autofluorescent properties. corresponding to different cell types (algae and cnidocytes). We identified an AF10 population whose autofluorescent spectrum was identical to that of human monocytes/macrophage, suggesting that this spectral print could be the hallmark of phagocytic cells called "amebocytes''. AF10 autofluorescent cells had a high capacity to phagocytize Vp-GFP, suggesting their possible role in fighting infection. This was confirmed by microscopy using sorted AF10 and GFP-positive cells (AF10+/GFP+). The number of AF10+/GFP+ cells were reduced at 31°C, demonstrating that increased temperature not only damages tissue but also affects the immune response of E. pallida. In conclusion, our study provides a springboard for more comprehensive studies of immune defense in marine organisms and paves the way for future studies of the dynamics, activation patterns, and functional responses of immune cells when encountering pathogens.

Probing organoid metabolism using fluorescence lifetime imaging microscopy (FLIM): The next frontier of drug discovery and disease understanding

Organoid models have been used to address important questions in developmental and cancer biology, tissue repair, advanced modelling of disease and therapies, among other bioengineering applications. Such 3D microenvironmental models can investigate the regulation of cell metabolism, and provide key insights into the mechanisms at the basis of cell growth, differentiation, communication, interactions with the environment and cell death. Their accessibility and complexity, based on 3D spatial and temporal heterogeneity, make organoids suitable for the application of novel, dynamic imaging microscopy methods, such as fluorescence lifetime imaging microscopy (FLIM) and related decay time-assessing readouts. Several biomarkers and assays have been proposed to study cell metabolism by FLIM in various organoid models. Herein, we present an expert-opinion discussion on the principles of FLIM and PLIM, instrumentation and data collection and analysis protocols, and general and emerging biosensor-based approaches, to highlight the pioneering work being performed in this field.

A Quencher-Based Blood-Autofluorescence-Suppression Strategy Enables the Quantification of Trace Analytes in Whole Blood

Precise quantification of trace components in whole blood via fluorescence is of great significance. However, the applicability of current fluorescent probes in whole blood is largely hindered by the strong blood autofluorescence. Here, we proposed a blood autofluorescence-suppressed sensing strategy to develop an activable fluorescent probe for quantification of trace analyte in whole blood. Based on inner filter effect, by screening fluorophores whose absorption overlapped with the emission of blood, a redshift BODIPY quencher with an absorption wavelength ranging from 600-700 nm was selected for its superior quenching efficiency and high brightness. Two 7-nitrobenzo[c] [1,2,5] oxadiazole ether groups were introduced onto the BODIPY skeleton for quenching its fluorescence and the response of H2 S, a gas signal molecule that can hardly be quantified because of its low concentration in whole blood. Such detection system shows a pretty low background signal and high signal-to-back ratio, the probe thus achieved the accurate quantification of endogenous H2 S in 20-fold dilution of whole blood samples, which is the first attempt of quantifying endogenous H2 S in whole blood. Moreover, this autofluorescence-suppressed sensing strategy could be expanded to other trace analytes detection in whole blood, which may accelerate the application of fluorescent probes in clinical blood test.

Noninvasive, continuous fluorescence monitoring of bilirubin photodegradation

Nowadays phototherapy is widely used for treatment of various diseases. However, efficient application of phototherapy requires an understanding of light interactions with main endogenous chromophores (e.g., hemoglobin, bilirubin, and water) in tissue. In particular, bilirubin is the target chromophore in the treatment of neonatal jaundice, which is the most common disease affecting up to 80% of preterm infants. The most frequently recommended treatment technique for this disease is phototherapy with blue light in combination with conventional drug therapy. To follow threshold total serum bilirubin (TSB) concentration guidelines, it is essential to estimate TSB concentration accurately. The gold standard biochemical analysis is invasive and bulky. Moreover, noninvasive methods do not provide sufficient reproducibility and accuracy. In this research, the fluorescence sensing of bilirubin with human serum albumin complexes was studied. The fluorescence time course during light irradiation (central wavelength: 467 nm and power density: 12.13 mW cm^-2) was demonstrated to depend on the initial concentration. Specifically, for the bilirubin concentration C = 18.65 μM, an insignificant fluorescence signal increase was observed during the first 30 minutes of light irradiation, while for bilirubin concentration C = 373 μM, the fluorescence signal did not reach maximum during 2.5 hours of light irradiation. Thus, fluorescence sensing might show increased accuracy when used with other noninvasive bilirubin sensing methods.

Autofluorescence lifetime imaging classifies human lymphocyte activation and subtype

New non-destructive tools are needed to reliably assess lymphocyte function for immune profiling and adoptive cell therapy. Optical metabolic imaging (OMI) is a label-free method that measures the autofluorescence intensity and lifetime of metabolic cofactors NAD(P)H and FAD to quantify metabolism at a single-cell level. Here, we investigate whether OMI can resolve metabolic changes between human quiescent versus IL4/CD40 activated B cells and IL12/IL15/IL18 activated memory-like NK cells. We found that quiescent B and NK cells were more oxidized compared to activated cells. Additionally, the NAD(P)H mean fluorescence lifetime decreased and the fraction of unbound NAD(P)H increased in the activated B and NK cells compared to quiescent cells. Machine learning classified B cells and NK cells according to activation state (CD69+) based on OMI parameters with up to 93.4% and 92.6% accuracy, respectively. Leveraging our previously published OMI data from activated and quiescent T cells, we found that the NAD(P)H mean fluorescence lifetime increased in NK cells compared to T cells, and further increased in B cells compared to NK cells. Random forest models based on OMI classified lymphocytes according to subtype (B, NK, T cell) with 97.8% accuracy, and according to activation state (quiescent or activated) and subtype (B, NK, T cell) with 90.0% accuracy. Our results show that autofluorescence lifetime imaging can accurately assess lymphocyte activation and subtype in a label-free, non-destructive manner.

Unlocking autofluorescence in the era of full spectrum analysis: Implications for immunophenotype discovery projects

Understanding the complex elements affecting signal resolution in cytometry is key for quality experimental design and data. In this study, we incorporate autofluorescence as a contributing factor to our understanding of resolution in cytometry and corroborate its impact in fluorescence signal detection through mathematical predictions supported by empirical evidence. Our findings illustrate the critical importance of autofluorescence extraction via full spectrum unmixing in unmasking dim signals and delineating the expression and subset distribution of low abundance markers in discovery projects. We apply our findings to the precise definition of the tissue and cellular distribution of a weakly expressed fluorescent protein that reports on a low-abundance immunological gene. Exploiting the full spectrum coverage enabled by Aurora 5L, we describe a novel approach to the isolation of pure cell subset-specific autofluorescence profiles based on high dimensionality reduction algorithms. This method can also be used to unveil differences in the autofluorescent fingerprints of tissues in homeostasis and after immunological challenges.

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.

Detection of HOCl-driven degradation of the pericardium scaffolds by label-free multiphoton fluorescence lifetime imaging

Artificial biomaterials can significantly increase the rate of tissue regeneration. However, implantation of scaffolds leads not only to accelerated tissue healing but also to an immune response of the organism, which results in the degradation of the biomaterial. The synergy of the immune response and scaffold degradation processes largely determines the efficiency of tissue regeneration. Still, methods suitable for fast, accurate and non-invasive characterization of the degradation degree of biomaterial are highly demandable. Here we show the possibility of monitoring the degradation of decellularized bovine pericardium scaffolds under conditions mimicking the immune response and oxidation processes using multiphoton tomography combined with fluorescence lifetime imaging (MPT-FLIM). We found that the fluorescence lifetimes of genipin-induced cross-links in collagen and oxidation products of collagen are prominent markers of oxidative degradation of scaffolds. This was verified in model experiments, where the oxidation was induced with hypochlorous acid or by exposure to activated neutrophils. The fluorescence decay parameters also correlated with the changes of micromechanical properties of the scaffolds as assessed using atomic force microscopy (AFM). Our results suggest that FLIM can be used for quantitative assessments of the properties and degradation of the scaffolds essential for the wound healing processes in vivo.

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

The ability to visualize complex and dynamic physiological interactions between numerous cell types and the extracellular matrix (ECM) within a live tumor microenvironment is an important step toward understanding mechanisms that regulate tumor progression. While this can be accomplished through current intravital imaging techniques, it remains challenging due to the heterogeneous nature of tissues and the need for spatial context within the experimental observation. To this end, we have developed an intravital imaging workflow that pairs collagen second harmonic generation imaging, endogenous fluorescence from the metabolic co-factor NAD(P)H, and fluorescence lifetime imaging microscopy (FLIM) as a means to non-invasively compartmentalize the tumor microenvironment into basic domains of the tumor nest, the surrounding stroma or ECM, and the vasculature. This non-invasive protocol details the step-by-step process ranging from the acquisition of time-lapse images of mammary tumor models to post-processing analysis and image segmentation. The primary advantage of this workflow is that it exploits metabolic signatures to contextualize the dynamically changing live tumor microenvironment without the use of exogenous fluorescent labels, making it advantageous for human patient-derived xenograft (PDX) models and future clinical use where extrinsic fluorophores are not readily applicable.

Fluorescence lifetime activated droplet sorting (FLADS) for label-free sorting of Synechocystis sp. PCC6803

This study presents the label-free sorting of cyanobacterial cells in droplets with single-cell sensitivity based on their fluorescence lifetime. We separated living and dead cyanobacteria (Synechocystis sp. PCC6803) using fluorescence lifetime signals of the photopigment autofluorescence to indicate their photosynthetic activity. We developed a setup and a chip design to achieve live/dead sorting accuracies of more than 97% at a droplet frequency of 100 Hz with a PDMS-based chip system and standard optics using fluorescence lifetime as the sorting criterion. The obtained sorting accuracies could be experimentally confirmed by cell plating and observing the droplet sorting process via a high-speed camera. The herein presented results demonstrate the capabilities of the developed system for studying the effects of stressors on cyanobacterial physiology and the subsequent deterministic sorting of different stress-response phenotypes. This technology eliminates the need for tedious staining of cyanobacterial cells, which makes it particularly attractive for its application in the field of phototrophic microbial bio(techno)logic and in the context of cell secretion studies.

Electrodeformation of White Blood Cells Enriched with Gold Nanoparticles

The elasticity of white blood cells (WBCs) provides valuable insight into the condition of the cells themselves, the presence of some diseases, as well as immune system activity. In this work, we describe a novel process of refined control of WBCs’ elasticity through a combined use of gold nanoparticles (AuNPs) and the microelectrode array device. The capture and controlled deformation of gold nanoparticles enriched white blood cells in vitro are demonstrated and quantified. Gold nanoparticles enhance the effect of electrically induced deformation and make the DEP-related processes more controllable.

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.

Image3C, a multimodal image-based and label-independent integrative method for single-cell analysis

Image-based cell classification has become a common tool to identify phenotypic changes in cell populations. However, this methodology is limited to organisms possessing well-characterized species-specific reagents (e.g., antibodies) that allow cell identification, clustering, and convolutional neural network (CNN) training. In the absence of such reagents, the power of image-based classification has remained mostly off-limits to many research organisms. We have developed an image-based classification methodology we named Image3C (Image-Cytometry Cell Classification) that does not require species-specific reagents nor pre-existing knowledge about the sample. Image3C combines image-based flow cytometry with an unbiased, high-throughput cell clustering pipeline and CNN integration. Image3C exploits intrinsic cellular features and non-species-specific dyes to perform de novo cell composition analysis and detect changes between different conditions. Therefore, Image3C expands the use of image-based analyses of cell population composition to research organisms in which detailed cellular phenotypes are unknown or for which species-specific reagents are not available.

A Review of New High-Throughput Methods Designed for Fluorescence Lifetime Sensing From Cells and Tissues

Though much of the interest in fluorescence in the past has been on measuring spectral qualities such as wavelength and intensity, there are two other highly useful intrinsic properties of fluorescence: lifetime (or decay) and anisotropy (or polarization). Each has its own set of unique advantages, limitations, and challenges in detection when it comes to use in biological studies. This review will focus on the property of fluorescence lifetime, providing a brief background on instrumentation and theory, and examine the recent advancements and applications of measuring lifetime in the fields of high-throughput fluorescence lifetime imaging microscopy (HT-FLIM) and time-resolved flow cytometry (TRFC). In addition, the crossover of these two methods and their outlooks will be discussed.

Covalently Labeled Fluorescent Exosomes for In Vitro and In Vivo Applications

The vertiginous increase in the use of extracellular vesicles and especially exosomes for therapeutic applications highlights the necessity of advanced techniques for gaining a deeper knowledge of their pharmacological properties. Herein, we report a novel chemical approach for the robust attachment of commercial fluorescent dyes to the exosome surface with covalent binding. The applicability of the methodology was tested on milk and cancer cell-derived exosomes (from U87 and B16F10 cancer cells). We demonstrated that fluorescent labeling did not modify the original physicochemical properties of exosomes. We tested this nanoprobe in cell cultures and healthy mice to validate its use for in vitro and in vivo applications. We confirmed that these fluorescently labeled exosomes could be successfully visualized with optical imaging.

Label-free metabolic clustering through unsupervised pixel classification of multiparametric fluorescent images

Autofluorescence microscopy is a promising label-free approach to characterize NADH and FAD metabolites in live cells, with potential applications in clinical practice. Although spectrally resolved lifetime imaging techniques can acquire multiparametric information about the biophysical and biochemical state of the metabolites, these data are evaluated at the whole-cell level, thus providing only limited insights in the activation of metabolic networks at the microscale. To overcome this issue, here we introduce an artificial intelligence-based analysis that, leveraging the multiparametric content of spectrally resolved lifetime images, allows to detect and classify, through an unsupervised learning approach, metabolic clusters, which are regions having almost uniform metabolic properties. This method contextually detects the cellular mitochondrial turnover and the metabolic activation state of intracellular compartments at the pixel level, described by two functions: the cytosolic activation state (CAF) and the mitochondrial activation state (MAF). This method was applied to investigate metabolic changes elicited in the breast cancer cell line MCF-7 by specific inhibitors of glycolysis and electron transport chain, and by the deregulation of a specific mitochondrial enzyme (ACO2) leading to defective aerobic metabolism associated with tumor growth. In this model, mitochondrial fraction undergoes to a 13% increase upon ACO2 overexpression and the MAF function changes abruptly by altering the metabolic state of about the 25% of the mitochondrial pixels.

Assessment of Fibrinogen Macromolecules Interaction with Red Blood Cells Membrane by Means of Laser Aggregometry, Flow Cytometry, and Optical Tweezers Combined with Microfluidics

An elevated concentration of fibrinogen in blood is a significant risk factor during many pathological diseases, as it leads to an increase in red blood cells (RBC) aggregation, resulting in hemorheological disorders. Despite the biomedical importance, the mechanisms of fibrinogen-induced RBC aggregation are still debatable. One of the discussed models is the non-specific adsorption of fibrinogen macromolecules onto the RBC membrane, leading to the cells bridging in aggregates. However, recent works point to the specific character of the interaction between fibrinogen and the RBC membrane. Fibrinogen is the major physiological ligand of glycoproteins receptors IIbIIIa (GPIIbIIIa or αIIββ3 or CD41/CD61). Inhibitors of GPIIbIIIa are widely used in clinics for the treatment of various cardiovascular diseases as antiplatelets agents preventing the platelets’ aggregation. However, the effects of GPIIbIIIa inhibition on RBC aggregation are not sufficiently well studied. The objective of the present work was the complex multimodal in vitro study of the interaction between fibrinogen and the RBC membrane, revealing the role of GPIIbIIIa in the specificity of binding of fibrinogen by the RBC membrane and its involvement in the cells’ aggregation process. We demonstrate that GPIIbIIIa inhibition leads to a significant decrease in the adsorption of fibrinogen macromolecules onto the membrane, resulting in the reduction of RBC aggregation. We show that the mechanisms underlying these effects are governed by a decrease in the bridging components of RBC aggregation forces.

Unsupervised clustering of multiparametric fluorescent images extends the spectrum of detectable cell membrane phases with sub-micrometric resolution

Solvatochromic probes undergo an emission shift when the hydration level of the membrane environment increases and are commonly used to distinguish between solid-ordered and liquid-disordered phases in artificial membrane bilayers. This emission shift is currently limited in unraveling the broad spectrum of membrane phases of natural cell membranes and their spatial organization. Spectrally resolved fluorescence lifetime imaging can provide pixel-resolved multiparametric information about the biophysical state of the membranes, like membrane hydration, microviscosity and the partition coefficient of the probe. Here, we introduce a clustering based analysis that, leveraging the multiparametric content of spectrally resolved lifetime images, allows us to classify through an unsupervised learning approach multiple membrane phases with sub-micrometric resolution. This method extends the spectrum of detectable membrane phases allowing to dissect and characterize up to six different phases, and to study real-time phase transitions in cultured cells and tissues undergoing different treatments. We applied this method to investigate membrane remodeling induced by high glucose on PC-12 neuronal cells, associated with the development of diabetic neuropathy. Due to its wide applicability, this method provides a new paradigm in the analysis of environmentally sensitive fluorescent probes.

Spectroscopic Evaluation of Red Blood Cells of Thalassemia Patients with Confocal Microscopy: A Pilot Study

Hemoglobinopathies represent the most common single-gene defects in the world and pose a major public health problem, particularly in tropical countries, where they occur with high frequency. Diagnosing hemoglobinopathies can sometimes be difficult due to the coexistence of different causes of anemia, such as thalassemia and iron deficiency, and blood transfusions, among other factors, and requires expensive and complex molecular tests. This work explores the possibility of using spectral confocal microscopy as a diagnostic tool for thalassemia in pediatric patients, a disease caused by mutations in the globin genes that result in changes of the globin chains that form hemoglobin-in pediatric patients. Red blood cells (RBCs) from patients with different syndromes of alpha-thalassemia and iron deficiency (including anemia) as well as healthy (control) subjects were analyzed under a Leica TCS SP8 confocal microscope following different image acquisition protocols. We found that diseased RBCs exhibited autofluorescence when excited at 405 nm and their emission was collected in the spectral range from 425 nm to 790 nm. Three experimental descriptors calculated from the mean emission intensities at 502 nm, 579 nm, 628 nm, and 649 nm allowed us to discriminate between diseased and healthy cells. According to the results obtained, spectral confocal microscopy could serve as a tool in the diagnosis of thalassemia.

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.

The Oxidation-Induced Autofluorescence Hypothesis: Red Edge Excitation and Implications for Metabolic Imaging

Endogenous autofluorescence of biological tissues is an important source of information for biomedical diagnostics. Despite the molecular complexity of biological tissues, the list of commonly known fluorophores is strictly limited. Still, the question of molecular sources of the red and near-infrared excited autofluorescence remains open. In this work we demonstrated that the oxidation products of organic components (lipids, proteins, amino acids, etc.) can serve as the molecular source of such red and near-infrared excited autofluorescence. Using model solutions and cell systems (human keratinocytes) under oxidative stress induced by UV irradiation we demonstrated that oxidation products can contribute significantly to the autofluorescence signal of biological systems in the entire visible range of the spectrum, even at the emission and excitation wavelengths higher than 650 nm. The obtained results suggest the principal possibility to explain the red fluorescence excitation in a large class of biosystems-aggregates of proteins and peptides, cells and tissues-by the impact of oxidation products, since oxidation products are inevitably presented in the tissue. The observed fluorescence signal with broad excitation originated from oxidation products may also lead to the alteration of metabolic imaging results and has to be taken into account.

Classifying T cell activity in autofluorescence intensity images with convolutional neural networks

The importance of T cells in immunotherapy has motivated developing technologies to improve therapeutic efficacy. One objective is assessing antigen-induced T cell activation because only functionally active T cells are capable of killing the desired targets. Autofluorescence imaging can distinguish T cell activity states in a non-destructive manner by detecting endogenous changes in metabolic co-enzymes such as NAD(P)H. However, recognizing robust activity patterns is computationally challenging in the absence of exogenous labels. We demonstrate machine learning methods that can accurately classify T cell activity across human donors from NAD(P)H intensity images. Using 8260 cropped single-cell images from six donors, we evaluate classifiers ranging from traditional models that use previously-extracted image features to convolutional neural networks (CNNs) pre-trained on general non-biological images. Adapting pre-trained CNNs for the T cell activity classification task provides substantially better performance than traditional models or a simple CNN trained with the autofluorescence images alone. Visualizing the images with dimension reduction provides intuition into why the CNNs achieve higher accuracy than other approaches. Our image processing and classifier training software is available at https://github.com/gitter-lab/t-cell-classification.

Imaging Platelet Processes and Function-Current and Emerging Approaches for Imaging in vitro and in vivo

Platelets are small anucleate cells that are essential for many biological processes including hemostasis, thrombosis, inflammation, innate immunity, tumor metastasis, and wound healing. Platelets circulate in the blood and in order to perform all of their biological roles, platelets must be able to arrest their movement at an appropriate site and time. Our knowledge of how platelets achieve this has expanded as our ability to visualize and quantify discreet platelet events has improved. Platelets are exquisitely sensitive to changes in blood flow parameters and so the visualization of rapid intricate platelet processes under conditions found in flowing blood provides a substantial challenge to the platelet imaging field. The platelet's size (~2 μm), rapid activation (milliseconds), and unsuitability for genetic manipulation, means that appropriate imaging tools are limited. However, with the application of modern imaging systems to study platelet function, our understanding of molecular events mediating platelet adhesion from a single-cell perspective, to platelet recruitment and activation, leading to thrombus (clot) formation has expanded dramatically. This review will discuss current platelet imaging techniques in vitro and in vivo, describing how the advancements in imaging have helped answer/expand on platelet biology with a particular focus on hemostasis. We will focus on platelet aggregation and thrombus formation, and how platelet imaging has enhanced our understanding of key events, highlighting the knowledge gained through the application of imaging modalities to experimental models in vitro and in vivo. Furthermore, we will review the limitations of current imaging techniques, and questions in thrombosis research that remain to be addressed. Finally, we will speculate how the same imaging advancements might be applied to the imaging of other vascular cell biological functions and visualization of dynamic cell-cell interactions.