Browsing by Author "Coole, Jackson B."

Now showing 1 - 5 of 5
  • Thumbnail Image
    Item
    Allele-Specific Recombinase Polymerase Amplification to Detect Sickle Cell Disease in Low-Resource Settings
    (American Chemical Society, 2021) Natoli, Mary E.; Chang, Megan M.; Kundrod, Kathryn A.; Coole, Jackson B.; Airewele, Gladstone E.; Tubman, Venée N.; Richards-Kortum, Rebecca R.; Bioengineering
    Sickle cell disease (SCD) is a group of common, life-threatening disorders caused by a point mutation in the β globin gene. Early diagnosis through newborn and early childhood screening, parental education, and preventive treatments are known to reduce mortality. However, the cost and complexity of conventional diagnostic methods limit the feasibility of early diagnosis for SCD in resource-limited areas worldwide. Although several point-of-care tests are commercially available, most are antibody-based tests, which cannot be used in patients who have recently received a blood transfusion. Here, we describe the development of a rapid, low-cost nucleic acid test that uses real-time fluorescence to detect the point mutation encoding hemoglobin S (HbS) in one round of isothermal recombinase polymerase amplification (RPA). When tested with a set of clinical samples from SCD patients and healthy volunteers, our assay demonstrated 100% sensitivity for both the βA globin and βS globin alleles and 94.7 and 97.1% specificities for the βA globin allele and βS globin allele, respectively (n = 91). Finally, we demonstrate proof-of-concept sample-to-answer genotyping of genomic DNA from capillary blood using an alkaline lysis procedure and direct input of diluted lysate into RPA. The workflow is performed in <30 min at a cost of <$5 USD on a commercially available benchtop fluorimeter and an open-source miniature fluorimeter. This study demonstrates the potential utility of a rapid, sample-to-answer nucleic acid test for SCD that may be implemented near the point of care and could be adapted to other disease-causing point mutations in genomic DNA.
  • Thumbnail Image
    Item
    Deep learning extended depth-of-field microscope for fast and slide-free histology
    (PNAS, 2020) Jin, Lingbo; Tang, Yubo; Wu, Yicheng; Coole, Jackson B.; Tan, Melody T.; Zhao, Xuan; Badaoui, Hawraa; Robinson, Jacob T.; Williams, Michelle D.; Gillenwater, Ann M.; Richards-Kortum, Rebecca R.; Veeraraghavan, Ashok; Bioengineering; Electrical and Computer Engineering
    Microscopic evaluation of resected tissue plays a central role in the surgical management of cancer. Because optical microscopes have a limited depth-of-field (DOF), resected tissue is either frozen or preserved with chemical fixatives, sliced into thin sections placed on microscope slides, stained, and imaged to determine whether surgical margins are free of tumor cells—a costly and time- and labor-intensive procedure. Here, we introduce a deep-learning extended DOF (DeepDOF) microscope to quickly image large areas of freshly resected tissue to provide histologic-quality images of surgical margins without physical sectioning. The DeepDOF microscope consists of a conventional fluorescence microscope with the simple addition of an inexpensive (less than $10) phase mask inserted in the pupil plane to encode the light field and enhance the depth-invariance of the point-spread function. When used with a jointly optimized image-reconstruction algorithm, diffraction-limited optical performance to resolve subcellular features can be maintained while significantly extending the DOF (200 µm). Data from resected oral surgical specimens show that the DeepDOF microscope can consistently visualize nuclear morphology and other important diagnostic features across highly irregular resected tissue surfaces without serial refocusing. With the capability to quickly scan intact samples with subcellular detail, the DeepDOF microscope can improve tissue sampling during intraoperative tumor-margin assessment, while offering an affordable tool to provide histological information from resected tissue specimens in resource-limited settings.
  • Thumbnail Image
    Item
    DeepDOF-SE: affordable deep-learning microscopy platform for slide-free histology
    (Springer Nature, 2024) Jin, Lingbo; Tang, Yubo; Coole, Jackson B.; Tan, Melody T.; Zhao, Xuan; Badaoui, Hawraa; Robinson, Jacob T.; Williams, Michelle D.; Vigneswaran, Nadarajah; Gillenwater, Ann M.; Richards-Kortum, Rebecca R.; Veeraraghavan, Ashok; Bioengineering; Electrical and Computer Engineering
    Histopathology plays a critical role in the diagnosis and surgical management of cancer. However, access to histopathology services, especially frozen section pathology during surgery, is limited in resource-constrained settings because preparing slides from resected tissue is time-consuming, labor-intensive, and requires expensive infrastructure. Here, we report a deep-learning-enabled microscope, named DeepDOF-SE, to rapidly scan intact tissue at cellular resolution without the need for physical sectioning. Three key features jointly make DeepDOF-SE practical. First, tissue specimens are stained directly with inexpensive vital fluorescent dyes and optically sectioned with ultra-violet excitation that localizes fluorescent emission to a thin surface layer. Second, a deep-learning algorithm extends the depth-of-field, allowing rapid acquisition of in-focus images from large areas of tissue even when the tissue surface is highly irregular. Finally, a semi-supervised generative adversarial network virtually stains DeepDOF-SE fluorescence images with hematoxylin-and-eosin appearance, facilitating image interpretation by pathologists without significant additional training. We developed the DeepDOF-SE platform using a data-driven approach and validated its performance by imaging surgical resections of suspected oral tumors. Our results show that DeepDOF-SE provides histological information of diagnostic importance, offering a rapid and affordable slide-free histology platform for intraoperative tumor margin assessment and in low-resource settings.
  • Thumbnail Image
    Item
    Development of a multimodal mobile colposcope for real-time cervical cancer detection
    (Optica Publishing Group, 2022) Coole, Jackson B.; Brenes, David; Possati-Resende, Júlio César; Antoniazzi, Márcio; Fonseca, Bruno de Oliveira; Maker, Yajur; Kortum, Alex; Vohra, Imran S.; Schwarz, Richard A.; Carns, Jennifer; Souza, Karen Cristina Borba; Santana, Iara Viana Vidigal; Kreitchmann, Regis; Salcedo, Mila P.; Salcedo, Mila P.; Ramanujam, Nirmala; Schmeler, Kathleen M.; Richards-Kortum, Rebecca; Bioengineering
    Cervical cancer remains a leading cause of cancer death among women in low-and middle-income countries. Globally, cervical cancer prevention programs are hampered by a lack of resources, infrastructure, and personnel. We describe a multimodal mobile colposcope (MMC) designed to diagnose precancerous cervical lesions at the point-of-care without the need for biopsy. The MMC integrates two complementary imaging systems: 1) a commercially available colposcope and 2) a high speed, high-resolution, fiber-optic microendoscope (HRME). Combining these two image modalities allows, for the first time, the ability to locate suspicious cervical lesions using widefield imaging and then to obtain co-registered high-resolution images across an entire lesion. The MMC overcomes limitations of high-resolution imaging alone; widefield imaging can be used to guide the placement of the high-resolution imaging probe at clinically suspicious regions and co-registered, mosaicked high-resolution images effectively increase the field of view of high-resolution imaging. Representative data collected from patients referred for colposcopy at Barretos Cancer Hospital in Brazil, including 22,800 high resolution images and 9,900 colposcope images, illustrate the ability of the MMC to identify abnormal cervical regions, image suspicious areas with subcellular resolution, and distinguish between high-grade and low-grade dysplasia.
  • Thumbnail Image
    Item
    Multimodal optical imaging with real-time projection of cancer risk and biopsy guidance maps for early oral cancer diagnosis and treatment
    (SPIE, 2023) Coole, Jackson B.; Brenes, David R.; Mitbander, Ruchika; Vohra, Imran S.; Hou, Huayu; Kortum, Alex; Tang, Yubo; Maker, Yajur; Schwarz, Richard A.; Carns, Jennifer L.; Badaoui, Hawraa; Williams, Michelle D.; Vigneswaran, Nadarajah; Gillenwater, Ann M.; Richards-Kortum, Rebecca; Bioengineering
    Significance: Despite recent advances in multimodal optical imaging, oral imaging systems often do not provide real-time actionable guidance to the clinician who is making biopsy and treatment decisions. Aim: We demonstrate a low-cost, portable active biopsy guidance system (ABGS) that uses multimodal optical imaging with deep learning to directly project cancer risk and biopsy guidance maps onto oral mucosa in real time. Approach: Cancer risk maps are generated based on widefield autofluorescence images and projected onto the at-risk tissue using a digital light projector. Microendoscopy images are obtained from at-risk areas, and multimodal image data are used to calculate a biopsy guidance map, which is projected onto tissue.ResultsRepresentative patient examples highlight clinically actionable visualizations provided in real time during an imaging procedure. Results show multimodal imaging with cancer risk and biopsy guidance map projection offers a versatile, quantitative, and precise tool to guide biopsy site selection and improve early detection of oral cancers. Conclusions: The ABGS provides direct visible guidance to identify early lesions and locate appropriate sites to biopsy within those lesions. This represents an opportunity to translate multimodal imaging into real-time clinically actionable visualizations to help improve patient outcomes.