Smartphone-Based Cell Detection

  • Maria Maddalena Calabretta
  • Laura Montali
  • Antonia Lopreside
  • Aldo Roda
  • Elisa MicheliniEmail author
Living reference work entry


The smartphone integrated high-resolution complementary metal-oxide semiconductor (CMOS) sensors have been widely exploited to detect optical signals, relying on colorimetric, fluorescent (FL), and bio-chemiluminescent detections. In the last 5 years, there has been an exponential increase in the publications on the use of smartphones as stand-alone bioanalytical devices. Conversely, the use of smartphones to detect cells and cell biosensors has been seldomly explored. In this chapter, we will review the smartphone potential as portable detector, and we will provide an overview on the state-of-the-art of using smartphones to detect cells and cell biosensors. Major research trends, open issues, and limitations are also addressed to provide the reader a glance on this challenging research trend.


Smartphone Cell biosensor Bioluminescence Colorimetric detection Fluorescence Analytical device 


A recent estimate of the International Telecommunication Union 2018 reports a continuous upward trend in the access of information and communication technologies with a number of mobile-cellular telephone subscriptions that has now surpassed the total population (

It goes without saying that the availability of such disrupting technology provides an incredible potential especially for healthcare self-management and other markets. Smartphones can be considered a sort of evolution of the point-of-care (POC) concept. Several features of the smartphone can be exploited including processing power, camera, connectivity, enabling to perform tests outside laboratories. The smartphone integrated high-resolution complementary metal-oxide semiconductor (CMOS) sensors have been widely exploited to detect optical signals, relying on label-free, colorimetric, fluorescent (FL), and bio-chemiluminescent detections.

In the last 5 years, there has been an exponential increase in the publications on the use of smartphones as “all-in-one” bioanalytical devices. The continuous improvement of the smartphone electronics together with the availability of new apps turned smartphones into biosensors.

In this chapter, we will review the smartphone capabilities and compare them with portable detectors and an overview on the state-of-the-art of using smartphones to detect cell biosensors and cells will be provided. Major research trends, open issues, and limitations are also addressed to provide the reader a glance on this challenging approach.

Smartphone Camera as Light Detector

Several smartphone-based analytical devices have been reported in the past; however, few of them have been proposed to detect living cells. Since several approaches for detecting cells rely on the measurements of light, the main issue is related to the sensitivity of smartphone-embedded cameras. A side-by-side comparison was reported to evaluate the sensitivity of smartphone with that of low-light luminograph equipped with a thermoelectrically cooled CCD camera using a model LED light source. As expected, a good correlation between light intensity and measured signal was reported for both systems; however, the signal-to-noise ratios (S/N) of the luminograph images were approximately three orders of magnitude higher than those obtained with the smartphone camera (Roda et al. 2014b). Several factors explain this lower detectability of the smartphone-integrated camera, for instance, the smaller pixel size and the absence of a sensor cooling system, which causes higher thermal noise. For every 6 °C reduction in chip temperature, the dark-current shot noise, which is the major source of noise, is cut approximately in half (Christensen and Herron 2009). The variance due to dark-current shot noise is described by the following equation
$$ {\sigma}_d^2=2{qI}_d\Delta f $$
where σd2 is the variance of shot noise, q is the charge of an electron, Id is the amount of dark current, and Δf is the electronic bandwidth of the sensing system. The implementation of cooled systems into portable devices is not straightforward, since additional components are required to avoid water vapor condensation on the camera window and also a larger power supply.

The feasibility of using different portable CCD cameras as low-light detectors has been reported by cooling the sensor with a Peltier chamber down to −10 °C. This approach enabled the detection of different types of cells including magnetotactic bacteria and yeast; however, these approaches still require an external computer to handle the images and elaborate the signals (Roda et al. 2011, 2013).

A very recent comparison of different smartphone cameras was reported by Kim et al. together with an imaging-processing algorithm providing improved sensitivity of the smartphone cameras for low-light detection (Kim et al. 2017b). They compared five types of Android and iOS smartphones to measure the bioluminescent reporter Pseudomonas fluorescens M3A, and OnePlusOne provided the best performance with a limit of detection of about 106 colony forming units (CFU)/mL.

Recently, the implementation of smartphone-based platforms is appearing as a valuable tool to detect cells for diagnostics, food analysis, and forensic applications. However, there are very few reports of smartphone-based platforms that use cells as a sensing element. Living cells are a versatile platform for biosensing. They can be engineered to express biorecognition elements for several classes of analytes.

Smartphone-Based Fluorescence Platforms

Smartphone-based fluorescent (FL) devices have been developed both for qualitative and quantitative analysis of biological molecules. Thanks to the high potential sensitivity of photoluminescence and the availability of many FL dyes with different spectral properties, fluorescent biosensors provided valuable tools for multiplexed analysis. Conversely, these biosensors require an external excitation light source and the measurement cell is strongly influenced by specific parameters in terms of geometry and transparency.

An interesting application of smartphone-based imaging platforms is related to bacterial detection for various infectious diseases. Bacteria detection is generally time-consuming and requires skilled personnel and centralized lab facilities, so the availability of a mobile platform for rapid diagnostics would be highly valuable.

However, there are very few reports of fluorescent smartphone-based platforms to detect living cells. The first smartphone-based FL biosensor was developed in the 2012 by Ozcan’s group (Zhu et al. 2012) to detect Escherichia coli cells in liquid samples. A capture anti-E. coli 0157:H7 antibody was immobilized into a glass capillary support and a detection antibody labelled with a specific quantum dot was used for detection via LED light excitation (Fig. 1). An additional optical lens was inserted between the capillary and the smartphone camera for the acquisition of the FL signals. This cell phone-based fluorescent platform provided a detection limit of ∼5–10 CFU/mL in buffer solution and good performance also in complex food matrix such as fat-free milk samples. The same research group (Zhu et al. 2013) reported a multipurpose blood analysis smartphone microscope to measure the density of red and white blood cells and hemoglobin concentration in human blood samples. Images were processed and analyzed using a custom-developed app. Cell density or hemoglobin results were obtained within 10 s and could be stored on the smartphone or sent to a remote server for further analysis. These results were comparable with a standard commercial Sysmex KN21 hematology analyzer showing a 7% leukocyte counting error, a 5% erythrocyte counting error, and a 5% hemoglobin concentration measurement error.
Fig. 1

Cell phone-based fluorescent platform for E. coli detection (a and b): Scheme of the assay for E. coli detection and picture of the optical attachment on a cell phone exploiting the quantum dot-based sandwich assay in glass capillary tubes. This compact and light-weight unit can monitor 10 capillary tubes all in parallel. (Reprinted with permission from Zhu et al. 2012)

Fronczek et al. reported a smartphone-based protocol exploiting a paper microfluidic chip to detect Salmonella typhimurium in real poultry samples (Fronczek et al. 2014). In particular, the lysed sample was loaded on paper microfluidic chip able to extract FL genomic nucleic acids (DNA). A miniature FL microscope comprising two bandpass filters, two 10× objective lenses, a dichroic mirror, and a blue LED was assembled to an Iphone 4 camera for FL reflectance measurements. Cellulose paper was used for analyzing low and high concentrations of pathogen DNA while nitrocellulose paper for the mid-range concentrations obtaining a LOD of 103 CFU/mL and 104 CFU/mL, respectively. The use of two different papers in different channels for quantifying different concentration range makes the procedure relatively complex.

Another approach to detect food-borne bacterial pathogens Salmonella and E. coli 0157 was developed by Rajendran et al. (2014). Exploiting an alternative immunoassay format, the authors reported the use of silica nanoparticles doped with fluorescein isothiocyanate and Ru(bpy), conjugated to the respective antibodies. A smartphone-based fluorimeter, consisting of a simple optical module attached to the smartphone camera, was used for the acquisition of FL signals. Without any pretreatment of the sample, the device was able to detect pathogens at levels of 105 CFU/mL.

A microfluidic device coupled with a smartphone microscope was developed by Hutchison et al. (2015) to detect Bacillus anthracis spores, monitoring the entire growth process. Another example was reported by Shrivastava et al. for detecting S. aureus (Shrivastava et al. 2018). The smartphone microscope included a white LED, a 10× objective, and a dichroic mirror to excite the fluorescently labelled bacteria. The procedure consists to label the target bacteria by Sap-FMNPs, a S. aureus-specific aptamer (Sap) covalently attached to fluorescent magnetic nanoparticles (FMNPs), and successively load the cells into the bacterial detection cassette. Fluorescently labelled bacterial colonies were imaged with the smartphone microscope and analyzed with an external computer. The analytical performance of this system was compared to that obtained with conventional plate counting methods and showed promising results with the possibility to quantify a concentration range of 50–2000 CFU/mL of S. aureus cells.

Apart from these examples in which the smartphone is used to detect pathogens and cells as targets, another important area of research is related to the smartphone detection of cell biosensors; however, very few examples have been reported in the literature. In 2015, Mora et al. reported a smartphone-based fluorescent cell biosensor to quantitatively detect lactose and galactose in food products (Mora et al. 2015). In particular, living material-based analytical sensor (LiMBAS) composed by E. coli genetically engineered with a fluorescent reporter gene under the control of the T7/lac promoter and immobilized in polymeric matrix membrane was used to detect diffusion behavior of chemical stimuli under a blue-light source and a specific blue-light optical filter. Images were taken with a smartphone camera and analyzed with a suitable smartphone application able to calculate the analyte concentration. The proposed biosensor showed a good stability, at least 7 days at 4 °C, and a working range of 1–1000 mM for both lactose and galactose using a sample volume of 1–10 μL.

Smartphone-Based Bioluminescence Platforms

The implementation of bioluminescence (BL) in smartphone-based analytical platforms has been less explored in comparison to fluorescence detection. The main advantages of bioluminescence such as no need for external light source, zero background and high signal-to-noise ratio are counterbalanced by very weak signals. Therefore, BL detection generally requires highly sensitive instrumentation such as PMTs and cooled CCDs. Nowadays, thanks to the availability of several luciferase variants with improved emission properties, such as increased intensity, glow-type kinetics, and emission at different wavelengths (from 460 to 615 nm), it is possible to implement assays into portable formats using less sensitive detectors. More recently, BL has been used in smartphone-based assays for several applications. Thanks to the increasing accessibility of smartphones with high-performance sensors and the availability of several bioluminescent proteins, the landscape of point-of-need analysis has been completely reshaped.

The first application of a smartphone to detect analytes in biological samples using BL coupled with biospecific enzymatic reactions was reported in 2014 by Roda et al. (2014). As proof of concept, bile acids were detected using three enzymes (3α-steroid dehydrogenase, NADH/FMN oxidoreductase, and bacterial luciferase) co-immobilized on a cellulose disk. This demonstrated the versatility of this approach for analytes that are present in sufficiently high concentrations. The implementation of enzyme reactions into smartphone platforms is relatively easier than integrating living cells into these devices. In fact the first report on implementation of BL cell was published in 2016, relying on the integration of human cell lines used as “sentinel cells” into a smartphone-based platform with 3D-printed cartridges (Cevenini et al. 2016b). This toxicity cell biosensor was based on genetically engineered Hek293 cells, constitutively expressing a green-emitting luciferase and integrated in a 3D-printed device. This portable device includes all chemical reagents and droppers for the addition of sample and BL substrate and, thanks to the custom designed Tox-App, provides a stand-alone platform for user-friendly quantitative toxicity testing. The 3D-printed smartphone adaptors result in a very versatile approach thanks to the possibility to be easily produced at low-cost and designed for any kind of mobile device.

To improve cell viability during cell storage, future works will be focused on the immobilization of mammalian cells into suitable biocompatible matrices or on the use of alternative eukaryotic cell-lines which are less demanding in terms of culturing condition (e.g., trout cell lines). In addition, as direct 3D printing of living cells is an emerging approach for regenerative medicine and in vitro drug-screening and toxicology applications, the use of “bio-inks” (Ferris et al. 2013) could be also applied to enable a direct and robust deposition of “sentinel cells” into 3D-printed devices for the development of integrated biosensors.

The authors believe that this kind of cell phone biosensor could find many applications as rapid alerting tool for detecting the presence of harmful pollutants in civil and military water supplies, for terrorism surveillance, and for detection of health threats in drinking water in developing countries.

Currently, there is nothing like this validated biosensor available in the market and it could represent a turnkey solution for rapid, sensitive, portable toxicity sensor taking into consideration that huge efforts will be required to extend the lifespan of the integrated cells and to improve their responsiveness to reduce the time-to-response signal (Cevenini et al. 2016).

In the same year, a more efficient BL system by combining a novel imidazopyrazinone substrate (furimazine) with a new luciferase (NanoLuc) was reported by England et al. (2016). The authors used a blue-emitting luciferase from the deep-sea shrimp Oplophorus gracilirostris, which produces a reaction that is 100-fold brighter than firefly luciferase.

Exploiting the highly sensitive NanoLuc luciferase as reporter Cevenini et al. developed a 3D-printed bioluminescence smartphone-based cell biosensor for quantitative assessment of (anti)-inflammatory activity and toxicity of white grape pomace extracts. This cell phone biosensor was based on genetically engineered Hek293T cells. One of the most peculiar features offered by cell biosensors is that they provide quantitative information about the actual biological activity of analytes present in a sample. Indeed, living cells are able to respond either to the fraction of analyte that is able to enter into the cell and interact with intracellular molecular targets as well as to analytes that bind to specific membrane receptors activating signaling cascades. The use of mammalian cell lines is particularly attractive since they represent a better model of human physiology, providing more predictive biological information (Michelini et al. 2014; Class et al. 2015).

To obtain bioluminescent cell biosensors, a synthetic DNA construct in which the expression of a luciferase is under the control of a promoter is introduced into the cell. This promoter can be constitutive or regulated by response elements activated by specific receptors upon binding with target analytes (Raut et al. 2012; Michelini et al. 2008).

Thanks to NanoLuc’s small size (19 kDa) and the absence of posttranslational modifications, the synthesis and folding of the reporter enzyme was rapid, reducing the assay time.

Quantitative assessment of (anti)-inflammatory activity and toxicity of liquid samples was performed with a simple and rapid add-and-measure procedure. White grape pomace extracts, known to contain several bioactive compounds, were analyzed, confirming the suitability of the smartphone biosensing platform for analysis of untreated complex biological matrices.

The innovation of this cell-based and smartphone-based biosensing platform could be deployed as rapid screening tool for R&D activities of SME looking for new bioactive products and could help the small and medium enterprises not fully equipped with analytical laboratories for the first-level safety and efficacy tests which require expensive equipment. A valuable prescreening tool to select the best promising samples, e.g., pure molecules, active ingredients, food by-products, and vegetal extracts, is provided by the proposed approach, which prevents more specific and accurate analysis by external authorized laboratories. This first prescreening could reduce the number of samples to be sent to outsource analysis, and by providing real-time results, it could enable a continuous in-house management and tuning of procedures and protocols to obtain the most active products (Cevenini et al. 2016).

The use of smartphones in this type of application is not only as a detector but also as a wireless control system for analysis with remote control of on-site detector. In the following work, Cevenini et al. proposed a cell-based mobile platform that exploits a newly developed bioluminescent yeast-estrogen screen (nanoYES) and a low-cost compact camera as light detector. Saccharomyces cerevisiae cells were genetically engineered with a yeast codon-optimized variant of NanoLuc luciferase (yNLucP) under the regulation of human estrogen receptor α activation (Fig. 2). Optimizing a new procedure that enables to produce alginate slices with good reproducibility, Cevenini et al. were able to prepare ready-to-use 3D-printed cartridges with immobilized cells. Moreover, they obtain a portable device exploiting a compact camera and wireless connectivity enabling a rapid and quantitative evaluation (1-h incubation at room temperature) of total estrogenic activity in small sample volumes (50 μL) with a LOD of 0.08 nM for 17β-estradiol. Since presence of chemicals with estrogenic activity in surface, groundwater, and drinking water poses serious concerns for potential threats to human health and aquatic life, the developed portable analytical platform has found application for the evaluation of water samples spiked with different chemicals known to have estrogen-like activity. Thanks to the high sensitivity of the newly developed yeast biosensor and the possibility to wireless connect the camera with any smartphone model, the developed configuration is more versatile than previously reported smartphone- based devices and could find application for on-site analysis of endocrine disruptors (Cevenini et al. 2018).
Fig. 2

Ready-to-use cartridge with immobilized yeast biosensor and 3D-printed GoProHero5-based platform for bioluminescence signal acquisition (a). BL image obtained by incubating the yeast bioreporters with 17β-estradiol (concentration range from 0.05 to 10 nM) (b). Dose-response curve for 17β-estradiol (c). (Reprinted with permission from Cevenini et al. 2018)

To further improve predictivity and robustness of ready-to-use cartridge, 3D-cell culture models were also explored. In recent years, 3D-cell models have garnered great attention due to their capability to better mimic in vivo cellular responses to external stimuli. In order to obtain a predictive, sensitive, and robust yet low-cost 3D cell biosensor, a smartphone-based bioluminescent 3D cell biosensor platform for effect-based analysis was developed (Michelini et al. 2019). This biosensor exploited the nuclear factor-kappa B (NF-kB) signal transduction pathway which is induced by several types of stressors and is involved in the regulation of cell-cycle/growth, inflammation, apoptosis, and immunity (Fig. 3). Immobilized HEK293 spheroids genetically engineered with powerful red- and green-emitting luciferases were exploited as inflammation and viability reporters. The smartphone-based biosensor provides a limit of detection for tumor necrosis factor (TNFα) of 0.15 ± 0.05 ng/mL and could be a useful tool to initially screen environmental samples or other compounds on-site, especially for additional more accurate chemical analyses.
Fig. 3

Schematic illustration of the 3D-printed cartridge and inflammation smartphone-based biosensor integrating dual-color bioluminescent 3D spheroids. (Reprinted with permission from Michelini et al. 2019)

Smartphone-Based Colorimetric Platforms

In the last years, smartphone-based colorimetric biosensors have become quite popular thanks to their simplicity of use and low cost (Shen et al. 2012). Most of the reported applications use reflectance for measuring the generation of the color on a solid support while few applications use real colorimetric light absorption by a solution. One of the major disadvantages of this detection is related to quantitative information associated with a color change especially when there are slight changes in color, but the recent advanced computer techniques based on machine learning allow more accurate image processing (Kim et al. 2017).

A system for label-free, noninvasive, and long-term monitoring of cell viability was developed by Su et al. The system is composed by cell viability biosensor (CVBS), an illumination provider, and a smartphone and was able to assess the effect of okadaic acid toxins on HepG2 cell line’s viability (Su et al. 2017). Cells, cultured previously in a 96-well plate, were treated with cell counting kit-8 (CCK-8). This kit contains a water-soluble tetrazolium salt WST-8 (i.e., 2-(2-methoxy-4-nitrophenyl)-3-(4-nitrophenyl)-5-(2,4-disulfonic acid) benzene-2 h-tetrazole monosodium salt) which is easily reduced by the dehydrogenase in the cell to a highly water-soluble yellow formazan. This product is produced proportionally to the number of living cells, and images can be simply acquired with the smartphone camera.

More recently, Zhang et al. reported a simple, rapid, and sensitive colorimetric biosensors for the detection of Salmonella typhimurium pathogens by exploiting the multicolumn capillary for easy operation, the Fe-nanoclusters (FNCs) for signal amplification, and the smartphone app for image processing (Zhang et al. 2019). In particular, the proposed biosensor, showed in Fig. 4, consists of a multicolumn capillary in which the target bacteria were captured by magnetic nanoparticle column to form the MNP-bacteria complexes (magnetic bacteria) and then transferred into the FNCs column and conjugated with the FNCs to form the nanocluster bacteria. In the last column (HCl column), the nanocluster bacteria release the iron ions that react with potassium hexacyanoferrate to form the Prussian blue. The color changes were measured and analyzed using the hue-saturation-lightness color space-based smartphone app for quantitative determination of the target bacteria. Despite the proposed biosensor presents some limitations related to the volume of the samples not suitable for detecting pathogens in large volume and the fragility of the glass capillary, under optimal conditions shows a LOD of 14 CFU/mL.
Fig. 4

Portable smartphone-based system for okadaic acid detection using cell viability. (a) Action principle of CCK-8 kit. This kit contains WST-8 and 1-methoxy PMS. The conversion from NAD+ to NADH in cells is catalyzed by dehydrogenases, while the inverse conversion from NADH to NAD+ is accompanied by the conversion from 1-methoxy PMS to 1-methoxy PMS reduced form. In the extracellular microenvironment, 1-methoxy PMS reduced form reacts with WST-8 to generate the WST-8 formazan (orange water-soluble product) and 1-methoxy PMS. To reflect the living cell count or cell status, the color depth can be detected. (b) Construction of CVBS. (c) The portable smartphone-based system and (d) the main interface of the homemade software iPlate Monitor. (Reprinted with permission from Su et al. 2017)

Smartphone-Based Electrochemical Platforms

Electrochemical biosensors are widely used in several fields thanks to their potential reliability, portability, simplicity, and low cost, representing a promising tool for real-time and point-of-care (POCs) applications.

Many smartphone-based electrochemical platforms exploit the smartphone for controlling, recording, and displaying electrochemical signals. Generally, smartphone is mainly used for receiving and analyzing the incoming data even if an external electrochemical sensor is always required (Eltzov et al. 2015).

In fact, in contrast to optical techniques that are strongly influenced by the camera resolution, the focus and ambient light conditions, in electrochemistry, the measurements are independent of the smartphone’s capabilities.

Amperometric systems comprise different detecting techniques (chronoamperometry, cyclic voltammetry, differential pulse voltammetry, and square wave voltammetry) basically based on changes in the current resulting from the reduction or oxidation of an electroactive species involved in a biochemical reaction between the working electrode and counter electrode, in which the current is proportional to the concentration of the target compounds (Grieshaber et al. 2008; Su et al. 2011). A smartphone-based electrochemical cell biosensor was reported by Aronoff-Spencer et al. in which yeast cells were genetically modified to express specific antigens to detect target antibodies (Aronoff-Spencer et al. 2016). These “cell factories” display hepatitis C virus core antigen linked to gold-binding peptide repeats to create dual-affinity biobricks capable of concomitant immobilization on gold-surface electrodes and antibody capture (Fig. 5). To perform cyclic voltammetry measurements, a smartphone-based potentiostat interfaced through the audio jack was developed by the same group. Despite the procedure results laborious, the device provided comparable performances to laboratory grade instruments showing a high LOD for actual physiological testing, but the system has not yet been validated with real samples.
Fig. 5

Figure of yeast biobrick chimera enabled detection of anti-HCV core antibody with POC smartphone potentiostat platform. (a) Anti-HCV core antibody detected by fluorescence and electrochemical methods. (b) Illustration of smartphone-based potentiostat connected to a host device through the audio jack. (Reprinted with permission from Aronoff-Spencer et al. 2016)

As proof of concept, Olivo et al. developed an amperometric sensor for real-time monitoring of glucose and lactate in cell cultures media (Olivo et al. 2014). Thanks to an Android application “BlueCells,” the sensor is connected via Bluetooth to the smartphone allowing to the user to define the measurement setup and select the species to be monitored directly on the device screen.

Exploiting impedimetric measurement, a smartphone-based sensor was designed by Jiang et al. The authors reported a bacterial sensor based on electrical impedance spectroscopy wireless connected to a smartphone for real-time data transmission. The microfluidic system is designed to preconcentrate the bacteria, lowering the LOD to 10 bacterial cells per mL (Jiang et al. 2014).

Turning Smartphones into Microscopes

Thanks to the significant advancement of smartphone technologies, in the last years, there was an exponential growth of smartphone-based imaging applications related to cell-based disease diagnostics and cell identification (Liu et al. 2014; Skandarajah et al. 2014; Zhu et al. 2011). To this end, several mini-microscopes integrated into smartphones (Meng et al. 2016; Navruz et al. 2013), lens-free microscope (Tseng et al. 2010) and contact imaging systems (Lee and Yang 2014; Navruz et al. 2013) were developed for many mobile sensing applications.

A rapid imaging method based on smartphone detection was developed by Cui et al. to quantify viable bacteria by single-cell microdroplet turbidity (Cui et al. 2018). Cells, previously encapsulated in nanoliter-scale droplets and cultivated on-chip for several hours, were acquired via smartphone-based optical detector. A customized app was used to process images and to determine the number of live bacteria, based on the increase in microdroplet turbidity as a consequence of bacterial proliferation. E. coli (G−) and B. subtilis (G+) samples were quantified via smartphone camera showing a LOD of 100 CFU/mL.

Another interesting application of these smartphone-based imaging systems is related to detect cancer cells. In 2015, a smartphone-based imaging system was developed by Im et al. (2015) able to count cancer cells labelled with specific microbeads. Cells, placed on a glass slide for imaging, were imaged by the smartphone camera and the diffraction images analyzed by a cloud server that applied the Rayleigh–Sommerfeld diffraction principle. Exploiting this principle, microbead-labelled cells were identified and counted thanks to their transmittance and phase). The proposed platform was used for screening precancerous or cancerous cells in cervical specimens. An approach that involves the use of a smartphone for real-time quantification of cancer cells was developed by Amin et al. (2017). The authors suggested a 3D-printed smartphone-attachable continuous-flow magnetic focusing device able to separate and quantify in real-time different cell types based on their volumetric mass density, including cancer cells. The smartphone microscope provided a square capillary aligned to the smartphone camera and placed between two magnets with the same polar faces were placed oppositely. Cells images demonstrated the correct separation of different cancer cells from blood cells, providing a good separation distance about 100 μm.


Nowadays, smartphone-based biosensors can be considered a valid alternative tool suitable in several fields especially rapid diagnostics, forensic, environmental, and food monitoring. Recent scientific advancements in chemistry, biotechnology, synthetic biology, and technological improvements enabled the development of several prototypes for rapid detection of living cells or for detecting signals emitted by cell biosensors. Despite very promising results, several issues remain to be solved before these devices may enter the market. Indeed, cells are not as robust as enzymes or antibodies, and the detection of weak signals challenges the sensitivity of smartphone-integrated CMOSs.

Several research efforts are focused in this direction with the goal of providing new robust tools for point-of-care and point-of-need analysis. It is expected that 3D-cell models and robust microbial biosensors could be exploited for rapid on-site screening of environmental samples or toxic substances, thus providing a first-level screening for critical samples that require a more accurate analysis with conventional analytical techniques.


  1. Amin R, Knowlton S, Dupont J, Bergholz JS, Joshi A, Hart A, Yenilmez B, Yu CH, Wentworth A, Zhao JJ (2017) 3D-printed smartphone-based device for labelfree cell separation. J 3D Print Med 1(3):155–164CrossRefGoogle Scholar
  2. Aronoff-Spencer E, Venkatesh AG, Sun A, Brickner H, Looney D, Hall DA (2016) Detection of Hepatitis C core antibody by dual-affinity yeast chimera and smartphone-based electrochemical sensing. Biosens Bioelectron 86:690–696CrossRefGoogle Scholar
  3. Cevenini L, Calabretta MM, Lopreside A, Tarantino G, Tassoni A, Ferri M, Roda A, Michelini E (2016a) Exploiting NanoLuc luciferase for smartphone-based bioluminescence cell biosensor for (anti)-inflammatory activity and toxicity. Anal Bioanal Chem 408:8859–8868CrossRefGoogle Scholar
  4. Cevenini L, Calabretta MM, Tarantino G, Michelini E, Roda A (2016b) Smartphone-interfaced 3D printed toxicity biosensor integrating bioluminescent “sentinel cells”. Sensors Actuators B Chem 225:249–257CrossRefGoogle Scholar
  5. Cevenini L, Lopreside A. Calabretta MM D’Elia M, Simoni P, Michelini E, Roda A (2018) A novel bioluminescent NanoLuc yeast-estrogen screen biosensor (nanoYES) with a compact wireless camera for effect-based detection of endocrine-disrupting chemicals; Anal Bioanal Chem 410:1237–1246CrossRefGoogle Scholar
  6. Christensen DA, Herron JN (2009) Optical system design for biosensors based on CCD detection. In: Rasooly A, Herold KE (eds) Biosensors and biodetection. Methods in molecular biology, vol 503. Humana Press, TotowaGoogle Scholar
  7. Class B, Thorne N, Aguisanda F, Southall N, McKew JC, Zheng W (2015) High-throughput viability assay using an autonomously bioluminescent cell line with a bacterial Lux reporter. J Lab Autom 20:164–174CrossRefGoogle Scholar
  8. Cui X, Ren L, Shan Y, Wang X, Yang Z, Li C, Xua J, Bo M (2018) Smartphone-based rapid quantification of viable bacteria by single-cell microdroplet turbidity imaging. Analyst 143:330Google Scholar
  9. Eltzov E, Guttel S, Yuen Kei Adarina L, Dewi Sinawang P, Ionescu RE, Marks RS (2015) Lateral flow immunoassaysefrom paper strip to smartphone technology. Electroanalysis 27:2116–2130CrossRefGoogle Scholar
  10. England CG, Ehlerding EB, Cai W (2016) NanoLuc: a small luciferase is brightening up the field of bioluminescence. Bioconjug Chem 27(5):1175–1187CrossRefGoogle Scholar
  11. Ferris CJ, Gilmore KG, Wallace GG, Panhuis M (2013) Biofabrication: an overview of the approaches used for printing of living cells. Appl Microbiol Biotechnol 97:4243–4258CrossRefGoogle Scholar
  12. Fronczek CF, Park TS, Harshman DK, Nicolini AM, Yoon JY (2014) Paper microfluidic extraction and direct smartphone-based identification of pathogenic nucleic acids from field and clinical samples. RSC Adv 4:11103–11110CrossRefGoogle Scholar
  13. Grieshaber D, MacKenzie R, Voros J, Reimhult E (2008) Electrochemical biosensors sensor principles and architectures. Sensors 8:1400–1458CrossRefGoogle Scholar
  14. Hutchison JR, Erikson RL, Sheen AM, Ozanich RM, Kelly RT (2015) Reagent-free and portable detection of Bacillus anthracis spores using a microfluidic incubator and smartphone microscope. Analyst 140(18):6269–6276CrossRefGoogle Scholar
  15. Im H, Castro CM, Shao H, Liong M, Song J, Pathania D, Fexon L, Min C, AvilaWallace M, Zurkiya O (2015) Digital diffraction analysis enables low-cost molecular diagnostics on a smartphone. Proc Natl Acad Sci U S A 112(18):5613–5618CrossRefGoogle Scholar
  16. Jiang J, Wang X, Chao R, Ren Y, Hu C, Xu Z, Liu GL (2014) Smartphone based portable bacteria pre-concentrating microfluidic sensor and impedance sensing system. Sensors Actuators B 193:653–659CrossRefGoogle Scholar
  17. Kim H, Awofeso O, Choi S, Jung Y, Bae E (2017a) Colorimetric analysis of salivaealcohol test strips by smartphone-based instruments using machine-learning algorithms. Appl Opt 56:8492CrossRefGoogle Scholar
  18. Kim H, Jung Y, Doh IJ, Lozano-Mahecha RA, Applegate B, Bae E (2017b) Smartphone-based low light detection for bioluminescence application. Sci Rep 9(7):40203CrossRefGoogle Scholar
  19. Lee SA, Yang C (2014) A smartphone-based chip-scale microscope using ambient illumination. Lab Chip 14(16):3056–3063CrossRefGoogle Scholar
  20. Liu X, Lin TY, Lillehoj PB (2014) Smartphones for cell and biomolecular detection. Ann Biomed Eng 42(11):2205–2217CrossRefGoogle Scholar
  21. Meng X, Huang H, Yan K, Tian X, Yu W, Cui H, Kong Y, Xue L, Liu C, Wang S (2016) Smartphone based hand-held quantitative phase microscope using the transport of intensity equation method. Lab Chip 17(1):104–109CrossRefGoogle Scholar
  22. Michelini E, Cevenini L, Mezzanotte L, Ablamsky D, Southworth T, Branchini BR, Roda A (2008) Combining intracellular and secreted bioluminescent reporter proteins for multicolor cell-based assays. Photochem Photobiol Sci 7(2):212CrossRefGoogle Scholar
  23. Michelini E, Cevenini L, Calabretta MM, Calabria D, Roda A (2014) Exploiting in vitro and in vivo bioluminescence for the implementation of the three Rs principle (replacement, reduction, and refinement in drug discovery). Anal Bioanal Chem 406:5531–5539CrossRefGoogle Scholar
  24. Michelini E, Calabretta MM, Cevenini L, Lopreside A, Southworth T, Fontaine DM, Simoni P, Branchini BR, Roda A (2019) Smartphone-based multicolor bioluminescent 3D spheroid biosensors for T monitoring inflammatory activity. Biosens Bioelectron 123:269–277CrossRefGoogle Scholar
  25. Mora CA, Herzog AF, Raso RA, Stark WJ (2015) Programmable living material containing reporter micro-organisms permits quantitative detection of oligosaccharides. Biomaterials 61:1–9CrossRefGoogle Scholar
  26. Navruz I, Coskun AF, Wong J, Mohammad S, Tseng D, Nagi R, Phillips S, Ozcan A (2013) Smart-phone based computational microscopy using multi-frame contact imaging on a fiber-optic array. Lab Chip 13(20):4015–4023CrossRefGoogle Scholar
  27. Olivo J, Foglia L, Casulli MA, Boero C, Carrara S, De Micheli G (2014) Glucose and lactate monitoring in cell cultures with a wireless android interface. In: Biomedical circuits and systems conference (BioCAS). IEEE, Piscataway, pp 400–403Google Scholar
  28. Rajendran VK, Bakthavathsalam P, Ali PMJ (2014) Smartphone based bacterial detection using biofunctionalized fluorescent nanoparticles. Microchim Acta 181:1815–1821CrossRefGoogle Scholar
  29. Raut N, O’Connor G, Pasini P, Daunert S (2012) Engineered cells as biosensing systems in biomedical analysis. Anal Bioanal Chem 402:3147–3159CrossRefGoogle Scholar
  30. Roda A, Cevenini L, Michelini E, Branchini BR (2011) A portable bioluminescence engineered cell-based biosensor for on-site applications. Biosens Bioelectron 26(8):3647–3653CrossRefGoogle Scholar
  31. Roda A, Cevenini L, Borg S, Michelini E, Calabretta MM, Schüler D (2013) Bioengineered bioluminescent magnetotactic bacteria as a powerful tool for chip-based whole-cell biosensors. Lab Chip 13(24):4881–4889CrossRefGoogle Scholar
  32. Roda A, Guardigli M, Calabria D, Calabretta MM, Cevenini L, Michelini E (2014a) A 3D-printed device for a smartphone-based chemiluminescence biosensor for lactate in oral fluid and sweat. Analyst 139(24):6494–6501CrossRefGoogle Scholar
  33. Roda A, Michelini E, Cevenini L, Calabria D, Calabretta MM, Simoni P (2014b) Integrating biochemiluminescence detection on smartphones: mobile chemistry platform for point-of-need analysis. Anal Chem 86:7299–7304CrossRefGoogle Scholar
  34. Shen L, Hagen JA, Papautsky I (2012) Point-of-care colorimetric detection with a smartphone. Lab Chip 12:4240–4243CrossRefGoogle Scholar
  35. Shrivastava S, Lee WI, Lee NE (2018) Culture-free, highly sensitive, quantitative detection of bacteria from minimally processed samples using fluorescence imaging by smartphone. Biosens Bioelectron 109:90–97CrossRefGoogle Scholar
  36. Skandarajah A, Reber CD, Switz NA, Fletcher DA (2014) Quantitative imaging with a mobile phone microscope. PLoS One 9(5):96906CrossRefGoogle Scholar
  37. Su L, Jia W, Houb C, Lei YU (2011) Microbial biosensors: a review. Biosens Bioelectron 26:1788–1799CrossRefGoogle Scholar
  38. Su K, Pan Y, Wan Z, Zhong L, Fang J, Zou Q, Li H, Wang P (2017) Smartphone-based portable biosensing system using cell viability biosensor for okadaic acid detection. Sensors Actuators B Chem 251:134–143CrossRefGoogle Scholar
  39. Tseng D, Mudanyali O, Oztoprak C, Isikman SO, Sencan I, Yaglidere O, Ozcan A (2010) Lensfree microscopy on a cellphone. Lab Chip 10(14):1787–1792CrossRefGoogle Scholar
  40. Zhang H, Li X, Fengchun H, Siyuan W, Lei W, Ning L, Jianhan LB (2019) A capillary biosensor for rapid detection of Salmonella using Fe-nanocluster amplification and smart phone imaging. Biosens Bioelectron 127:142–149CrossRefGoogle Scholar
  41. Zhu HY, Mavandadi S, Coskun AF, Yaglidere O, Ozcan A (2011) Optofluidic fluorescent imaging cytometry on a cell phone. Anal Chem 83(17):6641–6647CrossRefGoogle Scholar
  42. Zhu HY, Sikora U, Ozcan A (2012) Quantum dot enabled detection of Escherichia coli using a cell-phone. Analyst 137:2541–2544CrossRefGoogle Scholar
  43. Zhu H, Sencan I, Wong J, Dimitrov S, Tseng D, Nagashima K, Ozcan A (2013) Cost-effective and rapid blood analysis on a cell-phone. Lab Chip 13(7):1282–1288CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Maria Maddalena Calabretta
    • 1
  • Laura Montali
    • 1
  • Antonia Lopreside
    • 1
  • Aldo Roda
    • 1
    • 2
  • Elisa Michelini
    • 1
    • 2
    • 3
    Email author
  1. 1.Department of Chemistry “G. Ciamician”University of BolognaBolognaItaly
  2. 2.INBB, Istituto Nazionale di Biostrutture e BiosistemiRomeItaly
  3. 3.Health Sciences and Technologies-Interdepartmental Center for Industrial Research (HST-ICIR)University of BolognaBolognaItaly

Section editors and affiliations

  • Isao Karube
    • 1
  • Sylvia Daunert
  • Gérald Thouand
    • 2
  1. 1.School of Bioscience and BiotechnologyTokyo University of TechnologyTokyoJapan
  2. 2.Technological InstituteUniversity of Nantes, CNRS GEPEALa Roche sur YonFrance

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