Yibo Zhang

SPIE Digital Library Proceedings

Early identification of pathogenic bacteria in food, water, and bodily fluids is very important and yet challenging, owing to sample complexities and large sample volumes that need to be rapidly screened. Existing screening methods based on plate counting or molecular analysis present various tradeoffs with regard to the detection time, accuracy/sensitivity, cost, and sample preparation complexity. Here, we present a computational live bacteria detection system that periodically captures coherent microscopy images of bacterial growth inside a 60-mm-diameter agar plate and analyses these time-lapsed holograms using deep neural networks for the rapid detection of bacterial growth and the classification of the corresponding species. The performance of our system was demonstrated by the rapid detection of Escherichia coli and total coliform bacteria (i.e., Klebsiella aerogenes and Klebsiella pneumoniae subsp. pneumoniae) in water samples, shortening the detection time by >12 h compared to the Environmental Protection Agency (EPA)-approved methods. Using the preincubation of samples in growth media, our system achieved a limit of detection (LOD) of ~1 colony forming unit (CFU)/L in ≤9 h of total test time. This platform is highly cost-effective (~$0.6/test) and has high-throughput with a scanning speed of 24 cm²/min over the entire plate surface, making it highly suitable for integration with the existing methods currently used for bacteria detection on agar plates. Powered by deep learning, this automated and cost-effective live bacteria detection platform can be transformative for a wide range of applications in microbiology by significantly reducing the detection time and automating the identification of colonies without labelling or the need for an expert.

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 magnetic-bead-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.

We present a deep learning framework based on a generative adversarial network (GAN) to perform super-resolution in coherent imaging systems. We demonstrate that this framework can enhance the resolution of both pixel size-limited and diffraction-limited coherent imaging systems. The capabilities of this approach are experimentally validated by super-resolving complex-valued images acquired using a lensfree on-chip holographic microscope, the resolution of which was pixel size-limited. Using the same GAN-based approach, we also improved the resolution of a lens-based holographic imaging system that was limited in resolution by the numerical aperture of its objective lens. This deep learning-based super-resolution framework can be broadly applied to enhance the space-bandwidth product of coherent imaging systems using image data and convolutional neural networks, and provides a rapid, non-iterative method for solving inverse image reconstruction or enhancement problems in optics.

The histological analysis of tissue samples, widely used for disease diagnosis, involves lengthy and laborious tissue preparation. Here, we show that a convolutional neural network trained using a generative adversarial-network model can transform wide-field autofluorescence images of unlabelled tissue sections into images that are equivalent to the bright-field images of histologically stained versions of the same samples. A blind comparison, by board-certified pathologists, of this virtual staining method and standard histological staining using microscopic images of human tissue sections of the salivary gland, thyroid, kidney, liver and lung, and involving different types of stain, showed no major discordances. The virtual-staining method bypasses the typically labour-intensive and costly histological staining procedures, and could be used as a blueprint for the virtual staining of tissue images acquired with other label-free imaging modalities.

Using a deep neural network, we demonstrate a digital staining technique, which we term PhaseStain, to transform the quantitative phase images (QPI) of label-free tissue sections into images that are equivalent to the brightfield microscopy images of the same samples that are histologically stained. Through pairs of image data (QPI and the corresponding brightfield images, acquired after staining), we train a generative adversarial network and demonstrate the effectiveness of this virtual-staining approach using sections of human skin, kidney, and liver tissue, matching the brightfield microscopy images of the same samples stained with Hematoxylin and Eosin, Jones’ stain, and Masson’s trichrome stain, respectively. This digital-staining framework may further strengthen various uses of label-free QPI techniques in pathology applications and biomedical research in general, by eliminating the need for histological staining, reducing sample preparation related costs and saving time. Our results provide a powerful example of some of the unique opportunities created by data-driven image transformations enabled by deep learning.

Porphyromonas gingivalis (P. gingivalis) is a periodontal pathogen that may accumulate with other organisms in subgingival plaque biofilms and is associated with periodontal disease. P. gingivalis fimbriae (FimA) is a filamentous structure on the surface of bacteria that is closely associated with bacterial adhesion to and colonization of host tissues, and serves an essential role in biofilm formation. The present study aimed to construct P. gingivalis FimA prokaryotic expression plasmids, purify a FimA fusion protein and explore the effect of a recombinant FimA protein on the inflammatory response in human peripheral blood mononuclear cells (PBMCs). P. gingivalis FimA prokaryotic expression plasmids were constructed by gene cloning and recombination technology. SDS‑PAGE was used to evaluate the purified recombinant FimA protein. The cell proliferation rate and inflammatory cytokine expression of PBMCs treated with the FimA fusion protein with or without transfection with toll‑like receptor 4 (TLR4) small interfering (si)RNA were detected by CCK‑8 assays and ELISAs, respectively. The expression levels of TLR4, nuclear factor kappa‑light‑chain‑enhancer of activated B cells (NF‑κB) and myeloid differentiation primary response 88 (MyD88) in PBMCs were detected by western blot analysis and reverse transcription quantitative polymerase chain reaction. A FimA fusion protein with high purity was obtained. FimA fusion protein treatment significantly increased PBMC proliferation and promoted the release of tumor necrosis factor‑α (TNF‑α), interleukin (IL)‑6, matrix metalloproteinase (MMP)‑8 and MMP‑9 in PBMCs. TLR4 interference reversed the effects of the FimA fusion protein on PBMC proliferation and inflammatory cytokine release. The expression levels of TLR4, NF‑κB and MyD88 in PBMCs were significantly increased following treatment with the FimA fusion protein, while the expression levels of these genes at the mRNA and protein levels decreased significantly in PBMCs following FimA fusion protein treatment and TLR4 interference. The FimA fusion protein increased PBMC proliferation and promoted the release of the inflammatory cytokines TNF‑α, IL‑6, MMP‑8 and MMP‑9 via the TLR4/NF‑κB signaling pathway. FimA may serve as a promising therapeutic strategy for periodontal disease.

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.

Holographic microscopy presents challenges for color reproduction due to the usage of narrow‐band illumination sources, which especially impacts the imaging of stained pathology slides for clinical diagnoses. Here, an accurate color holographic microscopy framework using absorbance spectrum estimation is presented. This method uses multispectral holographic images acquired and reconstructed at a small number (e.g., three to six) of wavelengths, estimates the absorbance spectrum of the sample, and projects it onto a color tristimulus. Using this method, the wavelength selection is optimized to holographically image 25 pathology slide samples with different tissue and stain combinations to significantly reduce color errors in the final reconstructed images. The results can be used as a practical guide for various imaging applications and, in particular, to correct color distortions in holographic imaging of pathology samples spanning different dyes and tissue types.

Conventional optical refractometry methods are often limited by a narrow measurement range, complex hardware, or relatively high cost. Here, we present a novel refractometry method to measure the bulk refractive index (RI) of materials (including solids and liquids) using lensless holographic on-chip imaging and autofocusing, which is simple, cost-effective, and has a large RI measurement range. As a proof of concept, two compact prototypes were built to measure the RIs of solid materials and liquids, respectively, and they were tested by measuring the RIs of a ZnSe plate and a microscopy immersion oil. Experimental results show that our devices have an average accuracy of ~3 × 10⁻⁴ RI unit (RIU) with an estimated precision of ~3 × 10⁻³ RIU for solids; and an average accuracy of ~1 × 10⁻⁴ RIU with an estimated precision of ~3 × 10⁻⁴ RIU for liquids. We believe that this cost-effective and portable RI measurement platform holds promise to be used in laboratory and industrial settings.