Non-Invasive Label-free Analysis Pipeline for In Situ Characterization of Differentiation in 3D Brain Organoid Models.

Brain organoids provide a unique opportunity to model organ development in a system similar to human organogenesis in vivo. Brain organoids thus hold great promise for drug screening and disease modeling. Conventional approaches to organoid characterization predominantly rely on molecular analysis methods, which are expensive, time-consuming, labor-intensive, and involve the destruction of the valuable 3D architecture of the organoids. This reliance on end-point assays makes it challenging to assess cellular and subcellular events occurring during organoid development in their 3D context. As a result, the long developmental processes are not monitored nor assessed. The ability to perform non-invasive assays is critical for longitudinally assessing features of organoid development during culture. In this paper, we demonstrate a label-free high-content imaging approach for observing changes in organoid morphology and structural changes occurring at the cellular and subcellular level. Enabled by microfluidic-based culture of 3D cell systems and a novel 3D quantitative phase imaging method, we demonstrate the ability to perform non-destructive high-resolution imaging of the organoid. The highlighted results demonstrated in this paper provide a new approach to performing live, non-destructive monitoring of organoid systems during culture.

AI algorithm combined with RNA editing-based blood biomarkers to discriminate bipolar from major depressive disorders in an external validation multicentric cohort.

Bipolar disorder (BD) is a leading cause of disability worldwide, as it can lead to cognitive and functional impairment and premature mortality. The first episode of BD is usually a depressive episode and is often misdiagnosed as major depressive disorder (MDD). Growing evidence indicates that peripheral immune activation and inflammation are involved in the pathophysiology of BD and MDD. Recently, by developing a panel of RNA editing-based blood biomarkers able to discriminate MDD from depressive BD, we have provided clinicians a new tool to reduce the misdiagnosis delay observed in patients suffering from BD. The present study aimed at validating the diagnostic value of this panel in an external independent multicentric Switzerland-based cohort of 143 patients suffering from moderate to major depression. The RNA-editing based blood biomarker (BMK) algorithm developped allowed to accurately discriminate MDD from depressive BD in an external cohort, with high accuracy, sensitivity and specificity values (82.5 %, 86.4 % and 80.8 %, respectively). These findings further confirm the important role of RNA editing in the physiopathology of mental disorders and emphasize the possible clinical usefulness of the biomarker panel for optimization treatment delay in patients suffering from BD.

Label-free functional analysis of root-associated microbes with dynamic quantitative oblique back-illumination microscopy.

The increasing global demand for food, coupled with concerns about the environmental impact of synthetic fertilizers, underscores the urgency of developing sustainable agricultural practices. Nitrogen-fixing bacteria, known as diazotrophs, offer a potential solution by converting atmospheric nitrogen into bioavailable forms, reducing the reliance on synthetic fertilizers. However, a deeper understanding of their interactions with plants and other microbes is needed. In this study, we introduce a recently developed label-free 3D quantitative phase imaging technology called dynamic quantitative oblique back-illumination microscopy (DqOBM) to assess the functional dynamic activity of diazotrophs in vitro and in situ. Our experiments involved three different diazotrophs (Sinorhizobium meliloti, Azotobacter vinelandii, and Rahnella aquatilis) cultured on media with amendments of carbon and nitrogen sources. Over 5 days, we observed increased dynamics in nutrient-amended media. These results suggest that the observed bacterial dynamics correlate with their metabolic activity. Furthermore, we applied qOBM to visualize microbial dynamics within the root cap and elongation zone of Arabidopsis thaliana primary roots. This allowed us to identify distinct areas of microbial infiltration in plant roots without the need for fluorescent markers. Our findings demonstrate that DqOBM can effectively characterize microbial dynamics and provide insights into plant-microbe interactions in situ, offering a valuable tool for advancing our understanding of sustainable agriculture.

Label-Free Functional Analysis of Root-Associated Microbes with Dynamic Quantitative Oblique Back-illumination Microscopy.

The increasing global demand for food, coupled with concerns about the environmental impact of synthetic fertilizers, underscores the urgency of developing sustainable agricultural practices. Nitrogen-fixing bacteria, known as diazotrophs, offer a potential solution by converting atmospheric nitrogen into bioavailable forms, reducing the reliance on synthetic fertilizers. However, a deeper understanding of their interactions with plants and other microbes is needed. In this study, we introduce a recently developed label-free 3D quantitative phase imaging technology called dynamic quantitative oblique back-illumination microscopy (DqOBM) to assess the dynamic activity of diazotrophs in vitro and in situ. Our experiments involved three different diazotrophs (Sinorhizobium meliloti, Azotobacter vinelandii, and Rahnella aquatilis) cultured on media with amendments of carbon and nitrogen sources. Over five days, we observed increased dynamic activity in nutrient-amended media. These results suggest that the observed bacterial dynamics correlate with their metabolic activity. Furthermore, we applied qOBM to visualize bacterial activity within the root cap and elongation zone of Arabidopsis thaliana primary roots. This allowed us to identify distinct areas of microbial infiltration in plant roots without the need for fluorescent markers. Our findings demonstrate that DqOBM can effectively characterize microbial activity and provide insights into plant-microbe interactions in situ, offering a valuable tool for advancing our understanding of sustainable agriculture.

Real-time semantic segmentation and anomaly detection of functional images for cell therapy manufacturing.

BACKGROUND AIMS: Cell therapy is a promising treatment method that uses living cells to address a variety of diseases and conditions, including cardiovascular diseases, neurologic disorders and certain cancers. As interest in cell therapy grows, there is a need to shift to a more efficient, scalable and automated manufacturing process that can produce high-quality products at a lower cost. METHODS: One way to achieve this is using non-invasive imaging and real-time image analysis techniques to monitor and control the manufacturing process. This work presents a machine learning-based image analysis pipeline that includes semantic segmentation and anomaly detection capabilities. RESULTS/CONCLUSIONS: This method can be easily implemented even when given a limited dataset of annotated images, is able to segment cells and debris and can identify anomalies such as contamination or hardware failure.

Analysis of structural effects of sickle cell disease on brain vasculature of mice using three-dimensional quantitative phase imaging.

Significance: Although the molecular origins of sickle cell disease (SCD) have been extensively studied, the effects of SCD on the vasculature-which can influence blood clotting mechanisms, pain crises, and strokes-are not well understood. Improving this understanding can yield insight into the mechanisms and wide-ranging effects of this devastating disease. Aim: We aim to demonstrate the ability of a label-free 3D quantitative phase imaging technology, called quantitative oblique back-illumination microscopy (qOBM), to provide insight into the effects of SCD on brain vasculature. Approach: Using qOBM, we quantitatively analyze the vasculature of freshly excised, but otherwise unaltered, whole mouse brains. We use Townes sickle transgenic mice, which closely recapitulate the pathophysiology of human SCD, and sickle cell trait mice as controls. Two developmental time points are studied: 6-week-old mice and 20-week-old mice. Quantitative structural and biophysical parameters of the vessels (including the refractive index (RI), which is linearly proportional to dry mass) are extracted from the high-resolution images and analyzed. Results: qOBM reveals structural differences in the brain blood vessel thickness (thinner for SCD in particular brain regions) and the RI of the vessel wall (higher and containing a larger variation throughout the brain for SCD). These changes were only significant in 20-week-old mice. Further, vessel breakages are observed in SCD mice at both time points. The vessel wall RI distribution near these breaks, up to 350 µm away from the breaking point, shows an erratic behavior characterized by wide RI variations. Vessel diameter, tortuosity, texture within the vessel, and structural fractal patterns are found to not be statistically different. As with vessel breaks, we also observe blood vessel blockages only in mice brains with SCD. Conclusions: qOBM provides insight into the biophysical and structural composition of brain blood vessels in mice with SCD. Data suggest that the RI may be an indirect indicator of vessel rigidity, vessel strength, and/or tensions, which change with SCD. Future ex vivo and in vivo studies with qOBM could improve our understanding of SCD.

Euthymic and depressed bipolar patients are characterized by different RNA editing patterns in blood.

Bipolar disorder (BD) is a worldwide leading cause of disability. Inflammation roles in this disease is well established. ADAR1-mediated RNA editing is one of the key mechanisms regulating the inflammatory response. We have identified a panel of RNA editing-based blood biomarkers which allowed to discriminate unipolar from BD depression with high accuracy. We confirmed here the diagnostic value of this panel in a new cohort of BD patients recruited in Brazil. We also identified new combinations which allow a clear discrimination of BD from healthy controls and among BD subgroups, confirming that RNA editing is a key mechanism in BD.

Label- and slide-free tissue histology using 3D epi-mode quantitative phase imaging and virtual H&E staining.

Histological staining of tissue biopsies, especially hematoxylin and eosin (H&E) staining, serves as the benchmark for disease diagnosis and comprehensive clinical assessment of tissue. However, the process is laborious and time-consuming, often limiting its usage in crucial applications such as surgical margin assessment. To address these challenges, we combine an emerging 3D quantitative phase imaging technology, termed quantitative oblique back illumination microscopy (qOBM), with an unsupervised generative adversarial network pipeline to map qOBM phase images of unaltered thick tissues (i.e., label- and slide-free) to virtually stained H&E-like (vH&E) images. We demonstrate that the approach achieves high-fidelity conversions to H&E with subcellular detail using fresh tissue specimens from mouse liver, rat gliosarcoma, and human gliomas. We also show that the framework directly enables additional capabilities such as H&E-like contrast for volumetric imaging. The quality and fidelity of the vH&E images are validated using both a neural network classifier trained on real H&E images and tested on virtual H&E images, and a user study with neuropathologists. Given its simple and low-cost embodiment and ability to provide real-time feedback in vivo, this deep learning-enabled qOBM approach could enable new workflows for histopathology with the potential to significantly save time, labor, and costs in cancer screening, detection, treatment guidance, and more.

Analysis of the Asymmetry between Both Eyes in Early Diagnosis of Glaucoma Combining Features Extracted from Retinal Images and OCTs into Classification Models.

This study aims to analyze the asymmetry between both eyes of the same patient for the early diagnosis of glaucoma. Two imaging modalities, retinal fundus images and optical coherence tomographies (OCTs), have been considered in order to compare their different capabilities for glaucoma detection. From retinal fundus images, the difference between cup/disc ratio and the width of the optic rim has been extracted. Analogously, the thickness of the retinal nerve fiber layer has been measured in spectral-domain optical coherence tomographies. These measurements have been considered as asymmetry characteristics between eyes in the modeling of decision trees and support vector machines for the classification of healthy and glaucoma patients. The main contribution of this work is indeed the use of different classification models with both imaging modalities to jointly exploit the strengths of each of these modalities for the same diagnostic purpose based on the asymmetry characteristics between the eyes of the patient. The results show that the optimized classification models provide better performance with OCT asymmetry features between both eyes (sensitivity 80.9%, specificity 88.2%, precision 66.7%, accuracy 86.5%) than with those extracted from retinographies, although a linear relationship has been found between certain asymmetry features extracted from both imaging modalities. Therefore, the resulting performance of the models based on asymmetry features proves their ability to differentiate healthy from glaucoma patients using those metrics. Models trained from fundus characteristics are a useful option as a glaucoma screening method in the healthy population, although with lower performance than those trained from the thickness of the peripapillary retinal nerve fiber layer. In both imaging modalities, the asymmetry of morphological characteristics can be used as a glaucoma indicator, as detailed in this work.

Partially coherent broadband 3D optical transfer functions with arbitrary temporal and angular power spectra.

Optical diffraction tomography is a powerful technique to produce 3D volumetric images of biological samples using contrast produced by variations in the index of refraction in an unlabeled specimen. While this is typically performed with coherent illumination from a variety of angles, interest has grown in partially coherent methods due to the simplicity of the illumination and the computation-free axial sectioning provided by the coherence window of the source. However, such methods rely on the symmetry or discretization of a source to facilitate quantitative analysis and are unable to efficiently handle arbitrary illumination that may vary asymmetrically in angle and continuously in the spectrum, such as diffusely scattered or thermal sources. A general broadband theory may expand the scope of illumination methods available for quantitative analysis, as partially coherent sources are commonly available and may benefit from the effects of spatial and temporal incoherence. In this work, we investigate partially coherent tomographic phase microscopy from arbitrary sources regardless of angular distribution and spectrum by unifying the effects of spatial and temporal coherence into a single formulation. This approach further yields a method for efficient computation of the overall systems' optical transfer function, which scales with O(n 3), down from O(mn 4) for existing convolutional methods, where n 3 is the number of spatial voxels in 3D space and m is the number of discrete wavelengths in the illumination spectrum. This work has important implications for enabling partially coherent 3D quantitative phase microscopy and refractive index tomography in virtually any transmission or epi-illumination 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.

Optimization of a flexible fiber-optic probe for epi-mode quantitative phase imaging.

Quantitative oblique back-illumination microscopy (qOBM) is an emerging label-free optical imaging technology that enables 3D, tomographic quantitative phase imaging (QPI) with epi-illumination in thick scattering samples. In this work, we present a robust optimization of a flexible, fiber-optic-based qOBM system. Our approach enables in silico optimization of the phase signal-to-noise-ratio over a wide parameter space and obviates the need for tedious experimental optimization which could easily miss optimal conditions. Experimental validations of the simulations are also presented and sensitivity limits for the probe are assessed. The optimized probe is light-weight (∼40g) and compact (8mm in diameter) and achieves a 2µm lateral resolution, 6µm axial resolution, and a 300µm field of view, with near video-rate operation (10Hz, limited by the camera). The phase sensitivity is <20nm for a single qOBM acquisition (at 10Hz) and a lower limit of ∼3 nm via multi-frame averaging. Finally, to demonstrate the utility of the optimized probe, we image (1) thick, fixed rat brain samples from a 9L gliosarcoma tumor model and (2) freshly excised human brain tissues from neurosurgery. Acquired qOBM images using the flexible fiber-optic probe are in excellent agreement with those from a free-space qOBM system (both in-situ), as well as with gold-standard histopathology slices (after tissue processing).

Functional imaging with dynamic quantitative oblique back-illumination microscopy.

SIGNIFICANCE: Quantitative oblique back-illumination microscopy (qOBM) is a recently developed label-free imaging technique that enables 3D quantitative phase imaging of thick scattering samples with epi-illumination. Here, we propose dynamic qOBM to achieve functional imaging based on subcellular dynamics, potentially indicative of metabolic activity. We show the potential utility of this novel technique by imaging adherent mesenchymal stromal cells (MSCs) grown in bioreactors, which can help address important unmet needs in cell manufacturing for therapeutics. AIM: We aim to develop dynamic qOBM and demonstrate its potential for functional imaging based on cellular and subcellular dynamics. APPROACH: To obtain functional images with dynamic qOBM, a sample is imaged over a period of time and its temporal signals are analyzed. The dynamic signals display an exponential frequency response that can be analyzed with phasor analysis. Functional images of the dynamic signatures are obtained by mapping the frequency dynamic response to phasor space and color-coding clustered signals. RESULTS: Functional imaging with dynamic qOBM provides unique information related to subcellular activity. The functional qOBM images of MSCs not only improve conspicuity of cells in complex environments (e.g., porous micro-carriers) but also reveal two distinct cell populations with different dynamic behavior. CONCLUSIONS: In this work we present a label-free, fast, and scalable functional imaging approach to study and intuitively display cellular and subcellular dynamics. We further show the potential utility of this novel technique to help monitor adherent MSCs grown in bioreactors, which can help achieve quality-by-design of cell products, a significant unmet need in the field of cell therapeutics. This approach also has great potential for dynamic studies of other thick samples, such as organoids.

Prostate cancer histopathology using label-free multispectral deep-UV microscopy quantifies phenotypes of tumor aggressiveness and enables multiple diagnostic virtual stains.

Identifying prostate cancer patients that are harboring aggressive forms of prostate cancer remains a significant clinical challenge. Here we develop an approach based on multispectral deep-ultraviolet (UV) microscopy that provides novel quantitative insight into the aggressiveness and grade of this disease, thus providing a new tool to help address this important challenge. We find that UV spectral signatures from endogenous molecules give rise to a phenotypical continuum that provides unique structural insight (i.e., molecular maps or "optical stains") of thin tissue sections with subcellular (nanoscale) resolution. We show that this phenotypical continuum can also be applied as a surrogate biomarker of prostate cancer malignancy, where patients with the most aggressive tumors show a ubiquitous glandular phenotypical shift. In addition to providing several novel "optical stains" with contrast for disease, we also adapt a two-part Cycle-consistent Generative Adversarial Network to translate the label-free deep-UV images into virtual hematoxylin and eosin (H&E) stained images, thus providing multiple stains (including the gold-standard H&E) from the same unlabeled specimen. Agreement between the virtual H&E images and the H&E-stained tissue sections is evaluated by a panel of pathologists who find that the two modalities are in excellent agreement. This work has significant implications towards improving our ability to objectively quantify prostate cancer grade and aggressiveness, thus improving the management and clinical outcomes of prostate cancer patients. This same approach can also be applied broadly in other tumor types to achieve low-cost, stain-free, quantitative histopathological analysis.

A game changer for bipolar disorder diagnosis using RNA editing-based biomarkers.

In clinical practice, differentiating Bipolar Disorder (BD) from unipolar depression is a challenge due to the depressive symptoms, which are the core presentations of both disorders. This misdiagnosis during depressive episodes results in a delay in proper treatment and a poor management of their condition. In a first step, using A-to-I RNA editome analysis, we discovered 646 variants (366 genes) differentially edited between depressed patients and healthy volunteers in a discovery cohort of 57 participants. After using stringent criteria and biological pathway analysis, candidate biomarkers from 8 genes were singled out and tested in a validation cohort of 410 participants. Combining the selected biomarkers with a machine learning approach achieved to discriminate depressed patients (n = 267) versus controls (n = 143) with an AUC of 0.930 (CI 95% [0.879-0.982]), a sensitivity of 84.0% and a specificity of 87.1%. In a second step by selecting among the depressed patients those with unipolar depression (n = 160) or BD (n = 95), we identified a combination of 6 biomarkers which allowed a differential diagnosis of bipolar disorder with an AUC of 0.935 and high specificity (Sp = 84.6%) and sensitivity (Se = 90.9%). The association of RNA editing variants modifications with depression subtypes and the use of artificial intelligence allowed developing a new tool to identify, among depressed patients, those suffering from BD. This test will help to reduce the misdiagnosis delay of bipolar patients, leading to an earlier implementation of a proper treatment.

Performances of rapid and connected salivary RT-LAMP diagnostic test for SARS-CoV-2 infection in ambulatory screening.

In the context of social events reopening and economic relaunch, sanitary surveillance of SARS-CoV-2 infection is still required. Here, we evaluated the diagnostic performances of a rapid, extraction-free and connected reverse-transcription loop-mediated isothermal amplification (RT-LAMP) assay on saliva. Nasopharyngeal (NP) swabs and saliva from 443 outpatients were collected simultaneously and tested by reverse-transcription quantitative PCR (RT-qPCR) as reference standard test. Seventy-one individuals (16.0%) were positive by NP and/or salivary RT-qPCR. Sensitivity and specificity of salivary RT-LAMP were 85.9% (95%CI 77.8-94.0%) and 99.5% (98.7-100%), respectively. Performances were similar for symptomatic and asymptomatic participants. Moreover, SARS-CoV-2 genetic variants were analyzed and no dominant mutation in RT-LAMP primer region was observed during the period of the study. We demonstrated that this RT-LAMP test on self-collected saliva is reliable for SARS-CoV-2 detection. This simple connected test with optional automatic results transfer to health authorities is unique and opens the way to secure professional and social events in actual context of economics restart.

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.

Deep UV Microscopy Identifies Prostatic Basal Cells: An Important Biomarker for Prostate Cancer Diagnostics.

Objective and Impact Statement. Identifying benign mimics of prostatic adenocarcinoma remains a significant diagnostic challenge. In this work, we developed an approach based on label-free, high-resolution molecular imaging with multispectral deep ultraviolet (UV) microscopy which identifies important prostate tissue components, including basal cells. This work has significant implications towards improving the pathologic assessment and diagnosis of prostate cancer. Introduction. One of the most important indicators of prostate cancer is the absence of basal cells in glands and ducts. However, identifying basal cells using hematoxylin and eosin (H&E) stains, which is the standard of care, can be difficult in a subset of cases. In such situations, pathologists often resort to immunohistochemical (IHC) stains for a definitive diagnosis. However, IHC is expensive and time-consuming and requires more tissue sections which may not be available. In addition, IHC is subject to false-negative or false-positive stains which can potentially lead to an incorrect diagnosis. Methods. We leverage the rich molecular information of label-free multispectral deep UV microscopy to uniquely identify basal cells, luminal cells, and inflammatory cells. The method applies an unsupervised geometrical representation of principal component analysis to separate the various components of prostate tissue leading to multiple image representations of the molecular information. Results. Our results show that this method accurately and efficiently identifies benign and malignant glands with high fidelity, free of any staining procedures, based on the presence or absence of basal cells. We further use the molecular information to directly generate a high-resolution virtual IHC stain that clearly identifies basal cells, even in cases where IHC stains fail. Conclusion. Our simple, low-cost, and label-free deep UV method has the potential to improve and facilitate prostate cancer diagnosis by enabling robust identification of basal cells and other important prostate tissue components.

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.