Smartphone-based sickle cell disease detection and monitoring for point-of-care settings

https://doi.org/10.1016/j.bios.2020.112417Get rights and content

Highlights

  • Simple, reliable, and user-friendly smartphone-based diagnostic platform.

  • Potential utility for sickle cell disease (SCD) detection at POC settings.

  • Enables SCD detection directly from a drop of whole blood.

  • Useful in assessing the SCD severity, and can guide adjusting the dose of medication.

Abstract

Sickle cell disease (SCD) is a worldwide hematological disorder causing painful episodes, anemia, organ damage, stroke, and even deaths. It is more common in sub-Saharan Africa and other resource-limited countries. Conventional laboratory-based diagnostic methods for SCD are time-consuming, complex, and cannot be performed at point-of-care (POC) and home settings. Optical microscope-based classification and counting demands a significant amount of time, extensive setup, and cost along with the skilled human labor to distinguish the normal red blood cells (RBCs) from sickled cells. There is an unmet need to develop a POC and home-based test to diagnose and monitor SCD and reduce mortality in resource-limited settings. An early-stage and timely diagnosis of SCD can help in the effective management of the disease. In this article, we utilized a smartphone-based image acquisition method for capturing RBC images from the SCD patients in normoxia and hypoxia conditions. A computer algorithm is developed to differentiate RBCs from the patient's blood before and after cell sickling. Using the developed smartphone-based technique, we obtained similar percentage of sickle cells in blood samples as analyzed by conventional method (standard microscope). The developed method of testing demonstrates the potential utility of the smartphone-based test for reducing the overall cost of screening and management for SCD, thus increasing the practicality of smartphone-based screening technique for SCD in low-resource settings. Our setup does not require any special storage requirements. This is the characteristic advantage of our technique as compared to other hemoglobin-based POC diagnostic techniques.

Introduction

Sickle cell disease (SCD) is a common worldwide genetic disorder caused by the single point mutation in the beta-globin gene (Alapan et al., 2016; Modell and Darlison, 2008; Knowlton et al., 2015). The β-6 glutamic acid is substituted by Valine, leading to the transformation of normal hemoglobin into HbS (Oyenike et al., 2019). At low levels of oxygen, the HbS polymerizes, and results in sickled shape RBCs (Liu et al., 2018; Darrow et al., 2016). This sickling of cells makes them hard and sticky and as a result, severely affects their oxygen transport efficiency and blood circulation. Patient experiences acute vaso-occlusive pain in children as well as in adults (Liu et al., 2019; Rees et al., 2010). The children born in resource-limited settings are at a greater risk of SCD (Kumar et al., 2014). Centers for Disease Control (CDC) has reported about 100,000 cases of individuals with homozygous genotype from a 2008 census in the USA population mainly in African Americans (Adenmosun et al., 2017). SCD is the most prevalent disease in sub-Saharan Africa with the highest incidence of deaths in children under 5 years of age (Makani et al., 2011). Approximately 700 children in Africa are born with SCD every day (Modell and Darlison, 2008; Makani et al., 2011). Over half of them die due to lack of diagnosis and treatment of SCD. This disease can damage any part of the body, especially spleen (Al-Salem, 2010). Children having SCD are liable to the development of systemic infections due to loss of splenic functions. Another major organ affected in SCD is the lung (Gladwin and Vichinsky, 2008). Patients with SCD are at high risk of pulmonary hypertension at a very young age, which crucially increases mortality rates in children. Cerebrovascular disorders are also responsible for much morbidity and mortality with SCD (De Montalembert and Wang, 2013; Lynch, 2004). The most common risk factors associated with SCD are stroke and silent infarction. The likelihood of a child with SCD having a risk of stroke is 200 times greater than a healthy one with a top incidence of ischemic stroke (caused by a blood clot that blocks a blood vessel in the brain) between 2 and 5 years of age.

An early-stage detection of SCD can be quite effective in managing the disease, especially in home-based, point-of-care (POC), and other resource-limited settings (Vichinsky et al., 1988). Hemoglobin electrophoresis and high-performance liquid chromatography (HPLC) are gold standard methods for the detection of SCD (Kanter et al., 2015). They require heavy laboratory equipment, a continuous supply of electricity, approximately 1 mL patient blood sample, and trained staff to operate and interpret the test. Other existing techniques rely heavily on the use of optical microscopes. The morphological changes in the sickle cells are observed during their oxygenated (normoxia) and deoxygenated (hypoxia) states. This use of bulky and expensive microscopes is a time-consuming process that can only be performed at a diagnostic lab. These standards are often impossible to meet in many parts of sub-Saharan Africa and other low-resource countries.

Microfluidic devices have been widely utilized in the detection of several microorganisms such as bacterial cells (Escherichia coli, Salmonella spp, Vibrio cholerae, and Mycobacterium tuberculosis), viruses (Human Immunodeficiency Virus type 1, Hepatitis B virus, and Zika virus) and other tumor markers (Neuron-specific enolase in small cell lung cancer) (Nasseri et al., 2018; Fan et al., 2017; Herrada et al., 2018; Kabir et al., 2020a). Several researchers have developed electrical impedance spectroscopy (EIS) based methods for the detection of SCD using microfluidic chips (Dona et al., 2018). This combination of EIS and microfluidic has resulted in a reliable, accurate, and efficient method that can easily distinguish normal and sickled RBC and offers several advantages like label-free and non-invasiveness. The variations in the measured electrical impedance differential of sickle RBCs can work as a new biomarker of SCD. Another electrical impedance-based microflow cytometry technique with oxygen control seems potentially useful for SCD diagnosis (Liu et al., 2018).

Optical methods for sickle cell detection are based on the measurement of number of cells and different form factors. Electrical methods rely on the cellular dielectric properties, cell size, and difference in cell interior, Hb types and concentrations. Advanced image processing is not required for these methods, however, they do not provide any information on the disease severity. The equipment used for EIS (impedance analyzer) and oxygen control is relatively expensive and large in size, and not suitable for home-based and POC settings. It is vital to make sure that SCD diagnostic device must fulfill the World Health Organization's criteria of being affordable, sensitive, specific, user-friendly, rapid and robust, equipment free, and delivered to those who need it, leading to the acronym “ASSURED” (Sher et al., 2017). The further addition of features like real-time testing, communication of results and ease of sample collection leading to the acronym “REASSURED” can make such SCD devices even more suitable (Land et al., 2019). A POC SCD device developed on REASSURED criteria can diagnose SCD in newborn babies and adults and can significantly reduce the associated pain episodes and mortality (Steele et al., 2019). A variety of techniques such as HemeChip and μPADs have been reported in this effort. See reference (Ilyas et al., 2020) for a comprehensive review.

The widespread use of smartphones worldwide has opened new avenues for home and POC-based biomedical diagnostics (Knowlton et al., 2015). A myriad of attachments has been developed to integrate with smartphones to enhance their imaging capabilities and observe medical conditions. Smartphone-based techniques have been used for rapid imaging, detection, quantification, and monitoring of infections and diseases (Natesan et al., 2018; Neill et al., 2019; Calabretta et al., 2020; Coleman et al., 2019a, 2019b; Kabir et al., 2020b). Various researchers have developed mobile phone-based fluorescence microscopy devices for POC diagnosis (Snow et al., 2019; Shrivastava et al., 2018). These devices have demonstrated comparable performance to standard laboratory methods. A magnetic levitation-based platform was developed by researchers to detect sickled cells (Knowlton et al., 2015). The developed setup eliminates the need for expensive centrifuge machines and microscopes for SCD detection. It rather utilizes magnetic levitation and a smartphone to capture the images of RBC levitating in the magnetic field using a smartphone camera. The sample is illuminated by an external LED and a lens is utilized for image enhancement purposes. Sickle cell levitation patterns are inherently different than those of normal RBC and this criterion may be used to distinguish the disease. This technique is limited to SCD detection and may be further developed to detect disease severity or sickle cell trait (SCT).

In this article, we have developed a POC and home-based portable and standalone setup for the diagnosis and treatment monitoring of SCD based on shape change in RBCs under hypoxia. It consists of a custom-designed 3D structure that can be easily attached to the smartphone camera. This setup supports an external lens to enhance the image quality along with the microchip to contain the blood sample. The sample is illuminated with an external LED and images of cells are captured using the smartphone camera. The captured images are further analyzed using a computer algorithm written in MATLAB. The normal RBCs can be automatically distinguished from the sickled cells based on their morphology. The developed setup is cost-effective that significantly reduces the per-test cost and can easily be utilized in any home-based settings. It can diagnose the SCD and can also be utilized for monitoring the treatment. Using our technique, it is possible to determine the percentage of sickled blood cells and may potentially adjust the dose of medication. The whole diagnostic process can be completed within 16 min time with minimal user input.

Section snippets

Microfluidic device design and fabrication

The microfluidic device was developed using previously reported method utilizing 1.5 mm thick Poly-(methylmethacrylate) (PMMA) sheets (McMaster-Carr, Atlanta, GA), the double-sided adhesive tape (DSA) (3M, St. Paul, MN, 20 μm thick) and microscope slides (Fisherbrand plain, pre-cleaned glass slides) (Asghar et al., 2019; Rappa et al., 2018; Sher and Asghar, 2019; Coarsey et al., 2019). The design for the device was developed using AutoCAD software. PMMA sheets were machined with VLS 2.30 laser

Results

The smartphone setup used for screening sickled cells is illustrated in Fig. 2. The whole platform comprised of an aspheric lens, printed stage, and a light source to capture the images of RBCs inside microfluidic channel. The stage was designed to stabilize the smartphone upright and the sample was input into microfluidic chamber through an inlet. The smartphone camera captured images of the microfluidic chip through the aspheric lens that is pasted on the smartphone camera lens to focus the

Discussion

Here, we present the development and evaluation of a smartphone-based optical setup for the detection and quantification of sickled cells using disposable microfluidic chip containing a small volume of blood ~1 μL. The main advantages of using microchip are the ease of utilization, the requirement of only finger prick blood volume and high uniformity of blood cells in microfluidic device. Blood smears are frequently utilized in hematological analysis where the uniformity of the cells may be

Conclusion

We have demonstrated a simple, rapid, and cost-effective smartphone-based SCD detection method. This developed platform utilizes an external lens that can be easily attached to the smartphone camera to record images of various blood samples inside a microchip. The captured images are rapidly processed using a MATLAB program and the total number of sickled cells is automatically counted. To evaluate the performance of our setup, we used normal blood sample as well as the SCD patients' samples.

CRediT authorship contribution statement

Shazia Ilyas: Formal analysis, Writing - original draft. Mazhar Sher: Formal analysis, Writing - original draft. E. Du: Formal analysis. Waseem Asghar: Formal analysis, Writing - original draft, Supervision.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

We acknowledge research support from NIH R15AI127214, R56AI138659, Institute for Sensing and Embedded Networking Systems Engineering (I-SENSE) Research Initiative Award, FAU Faculty Mentoring Award, Humanity in Science Award, and a start-up research support from College of Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL. Author E.D. acknowledges support from NIH grant OT2HL152638 and NSF grant 1635312.

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