Motility-based label-free detection of parasites in bodily fluids using holographic speckle analysis and deep learning

Abstract

Parasitic infections constitute a major global public health issue. Existing screening methods that are based on manual microscopic examination often struggle to provide sufficient volumetric throughput and sensitivity to facilitate early diagnosis. Here, we demonstrate a motility-based label-free computational imaging platform to rapidly detect motile parasites in optically dense bodily fluids by utilizing the locomotion of the parasites as a specific biomarker and endogenous contrast mechanism. Based on this principle, a cost-effective and mobile instrument, which rapidly screens ~3.2 mL of fluid sample in three dimensions, was built to automatically detect and count motile microorganisms using their holographic time-lapse speckle patterns. We demonstrate the capabilities of our platform by detecting trypanosomes, which are motile protozoan parasites, with various species that cause deadly diseases affecting millions of people worldwide. Using a holographic speckle analysis algorithm combined with deep learning-based classification, we demonstrate sensitive and label-free detection of trypanosomes within spiked whole blood and artificial cerebrospinal fluid (CSF) samples, achieving a limit of detection of ten trypanosomes per mL of whole blood (~five-fold better than the current state-of-the-art parasitological method) and three trypanosomes per mL of CSF. We further demonstrate that this platform can be applied to detect other motile parasites by imaging Trichomonas vaginalis, the causative agent of trichomoniasis, which affects 275 million people worldwide. With its cost-effective, portable design and rapid screening time, this unique platform has the potential to be applied for sensitive and timely diagnosis of neglected tropical diseases caused by motile parasites and other parasitic infections in resource-limited regions.

Fig. 1. High-throughput bodily fluid screening device, which screens and analyzes ~3.2 mL of fluid sample within ~20 min.

a Schematic of the device based on lensless holographic time-resolved speckle imaging. b Photograph of the device, controlled by a laptop, which is also used for processing the acquired data

Fig. 2

Sample preparation and imaging process

Fig. 3. Image processing steps.

Raw holographic speckle patterns are processed by the CMA algorithm with OFN, followed by deep learning-based identification, for sensitive and label-free detection of trypanosomes in lysed blood. The steps are performed in the same order as listed here

Fig. 4. Experimental demonstration of applying the CMA algorithm and OFN to a lysed blood sample spiked with motile trypanosome parasites, over an FOV of ~14.7 mm².

a A time-sequence of the diffraction patterns of the sample is captured. b, c One of the frames in the raw image sequence is shown. The diffraction pattern is severely speckled due to the light scattering by the cell debris, which renders the parasites invisible (yellow arrows in (c)). d, e After being processed by the CMA algorithm, motile parasites can be detected. The amplitude and phase movies of the three trypanosomes in e (indicated by white arrows) are also shown in Supplementary Movie 2

Fig. 5. Quantification of the LoD of our platform for detecting trypanosomes in lysed whole blood and artificial CSF samples.

a Calibration curve for trypanosome detection in lysed whole blood (logarithmic scale). Dashed line indicates expected values (y = x). Measured values are indicated by blue data points. Error bars show the standard deviation of 3 independent measurements. b Zoom-in of (a) shows the low concentration measurements (linear scale), including the negative control (no trypanosomes). No false positives were found in the three negative control samples. c, d Calibration curves for trypanosome detection in artificial CSF, similar to (a, b). Orange dashed line in d corresponds to the mean +3 × standard deviation of the negative control result