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

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 learningbased 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 costeffective,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.

Computational cytometer based on magnetically modulated coherent imaging and deep learning

Detecting rare cells within blood has numerous applications in disease diagnostics.Existing rare cell detection techniques are typically hindered by their high cost and low throughput.Here,we present a computational cytometer based on magnetically modulated lensless speckle imaging,which introduces oscillatory motion to the magneticbead-conjugated rare cells of interest through a periodic magnetic force and uses lensless time-resolved holographic speckle imaging to rapidly detect the target cells in three dimensions(3D).In addition to using cell-specific antibodies to magnetically label target cells,detection specificity is further enhanced through a deep-learning-based classifier that is based on a densely connected pseudo-3D convolutional neural network(P3D CNN),which automatically detects rare cells of interest based on their spatio-temporal features under a controlled magnetic force.To demonstrate the performance of this technique,we built a high-throughput,compact and cost-effective prototype for detecting MCF7 cancer cells spiked in whole blood samples.Through serial dilution experiments,we quantified the limit of detection(LoD)as 10 cells per millilitre of whole blood,which could be further improved through multiplexing parallel imaging channels within the same instrument.This compact,cost-effective and high-throughput computational cytometer can potentially be used for rare cell detection and quantification in bodily fluids for a variety of biomedical applications.