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.

Early detection and classification of live bacteria using time-lapse coherent imaging and deep learning

“…With further optimization of the hardware and control algorithms, an imaging throughput of >50 cm 2 /min can be reached. Alternatively, several image sensors can be installed and connected to a single computer for high-throughput parallel imaging 38 . In our proof-of-concept implementation, our image processing for each time interval takes~20 min and fits well into our 30 min measurement period between each scan.…”

The Use of Motion Analysis as Particle Biomarkers in Lensless Optofluidic Projection Imaging for Point of Care Urine Analysis

“…www.nature.com/scientificreports www.nature.com/scientificreports/ parasites in cerebrospinal fluid 21 . Their device is adaptable to Trichomonas vaginalis detection, though further testing must be done in spiked urine samples.…”

Perspectives on liquid biopsy for label‐free detection of “circulating tumor cells” through intelligent lab‐on‐chips

“…Although the human capability of delivering large volumes of clinically relevant data exponentially increased over the last decade, the capacity of effectively analyzing such data did not, being naturally limited by the skills of the pathologists called to judge based on their own experience. Thus, biology research, diagnostics, and medicine naturally started relying on AI‐based cellular image analysis 147‐186 . AI largely extends the variety of tasks that image analysis can accomplish.…”

“…Moreover, image‐activated sorting of SKNO‐1 acute myeloid leukemia cells from normal WBCs, as well as screening of HEK‐293, HeLa, and MCF‐7 cancer cells in heterogeneous mixtures with cells/s throughput, has been demonstrated 181 . Besides, a field portable computational cytometer has been recently presented that exploits magnetically modulated lensless speckle imaging to provide a contrast agent, based on motility, 182 to MCF‐7 cells spiked in whole blood 20 . Magnetic beads are attached to the tumor cells and move them under the action of an alternate magnetic field (magnetic enrichment).…”

“…Cross‐modality image transformation based on Generative Adversarial Networks allowed overcoming the diffraction limit and converting low‐resolution wide field images into the corresponding high‐resolution counterparts 183 . Super‐resolving wide‐field fluorescence images, recovering phase‐contrast information from the sole intensity information, transforming diffraction limited confocal microscopy images into super‐resolved stimulated emission depletion (STED) images, diffraction‐limited Total Internal Reflection Fluorescence (TIRF) images into TIRF‐SIM reconstructions, 182 and holographic images into Red Green Blue (RGB) bright‐field images 186 are few remarkable examples of this exciting pathway that promises to unlock new possibilities in the automated analysis of cells and tissues. Virtual staining of cells and histopathology slides is a recent breakthrough that allows converting images of unstained specimens into the corresponding images that would have been captured after applying a staining protocol to the samples 186 .…”