Micro-Anatomical Quantitative Imaging Towards Enabling Automated Diagnosis of Thick Tissues at the Point of Care

Histopathology is the clinical standard for tissue diagnosis. However, histopathology has several limitations including that it requires tissue processing, which can take 30 minutes or more, and requires a highly trained pathologist to diagnose the tissue. Additionally, the diagnosis is qualitative, and the lack of quantitation leads to possible observer-specific diagnosis. Taken together, it is difficult to diagnose tissue at the point of care using histopathology.

Several clinical situations could benefit from more rapid and automated histological processing, which could reduce the time and the number of steps required between obtaining a fresh tissue specimen and rendering a diagnosis. For example, there is need for rapid detection of residual cancer on the surface of tumor resection specimens during excisional surgeries, which is known as intraoperative tumor margin assessment. Additionally, rapid assessment of biopsy specimens at the point-of-care could enable clinicians to confirm that a suspicious lesion is successfully sampled, thus preventing an unnecessary repeat biopsy procedure. Rapid and low cost histological processing could also be potentially useful in settings lacking the human resources and equipment necessary to perform standard histologic assessment. Lastly, automated interpretation of tissue samples could potentially reduce inter-observer error, particularly in the diagnosis of borderline lesions.

To address these needs, high quality microscopic images of the tissue must be obtained in rapid timeframes, in order for a pathologic assessment to be useful for guiding the intervention. Optical microscopy is a powerful technique to obtain high-resolution images of tissue morphology in real-time at the point of care, without the need for tissue processing. In particular, a number of groups have combined fluorescence microscopy with vital fluorescent stains to visualize micro-anatomical features of thick (i.e. unsectioned or unprocessed) tissue. However, robust methods for segmentation and quantitative analysis of heterogeneous images are essential to enable automated diagnosis. Thus, the goal of this work was to obtain high resolution imaging of tissue morphology through employing fluorescence microscopy and vital fluorescent stains and to develop a quantitative strategy to segment and quantify tissue features in heterogeneous images, such as nuclei and the surrounding stroma, which will enable automated diagnosis of thick tissues.

To achieve these goals, three specific aims were proposed. The first aim was to develop an image processing method that can differentiate nuclei from background tissue heterogeneity and enable automated diagnosis of thick tissue at the point of care. A computational technique called sparse component analysis (SCA) was adapted to isolate features of interest, such as nuclei, from the background. SCA has been used previously in the image processing community for image compression, enhancement, and restoration, but has never been applied to separate distinct tissue types in a heterogeneous image. In combination with a high resolution fluorescence microendoscope (HRME) and a contrast agent acriflavine, the utility of this technique was demonstrated through imaging preclinical sarcoma tumor margins. Acriflavine localizes to the nuclei of cells where it reversibly associates with RNA and DNA. Additionally, acriflavine shows some affinity for collagen and muscle. SCA was adapted to isolate acriflavine positive features or APFs (which correspond to RNA and DNA) from background tissue heterogeneity. The circle transform (CT) was applied to the SCA output to quantify the size and density of overlapping APFs. The sensitivity of the SCA+CT approach to variations in APF size, density and background heterogeneity was demonstrated through simulations. Specifically, SCA+CT achieved the lowest errors for higher contrast ratios and larger APF sizes. When applied to tissue images of excised sarcoma margins, SCA+CT correctly isolated APFs and showed consistently increased density in tumor and tumor + muscle images compared to images containing muscle. Next, variables were quantified from images of resected primary sarcomas and used to optimize a multivariate model. The sensitivity and specificity for differentiating positive from negative ex vivo resected tumor margins was 82% and 75%. The utility of this approach was further tested by imaging the in vivo tumor cavities from 34 mice after resection of a sarcoma with local recurrence as a bench mark. When applied prospectively to images from the tumor cavity, the sensitivity and specificity for differentiating local recurrence was 78% and 82%. The results indicate that SCA+CT can accurately delineate APFs in heterogeneous tissue, which is essential to enable automated and rapid surveillance of tissue pathology.

Two primary challenges were identified in the work in aim 1. First, while SCA can be used to isolate features, such as APFs, from heterogeneous images, its performance is limited by the contrast between APFs and the background. Second, while it is feasible to create mosaics by scanning a sarcoma tumor bed in a mouse, which is on the order of 3-7 mm in any one dimension, it is not feasible to evaluate an entire human surgical margin. Thus, improvements to the microscopic imaging system were made to (1) improve image contrast through rejecting out-of-focus background fluorescence and to (2) increase the field of view (FOV) while maintaining the sub-cellular resolution needed for delineation of nuclei. To address these challenges, a technique called structured illumination microscopy (SIM) was employed in which the entire FOV is illuminated with a defined spatial pattern rather than scanning a focal spot, such as in confocal microscopy.

Thus, the second aim was to improve image contrast and increase the FOV through employing wide-field, non-contact structured illumination microscopy and optimize the segmentation algorithm for new imaging modality. Both image contrast and FOV were increased through the development of a wide-field fluorescence SIM system. Clear improvement in image contrast was seen in structured illumination images compared to uniform illumination images. Additionally, the FOV is over 13X larger than the fluorescence microendoscope used in aim 1. Initial segmentation results of SIM images revealed that SCA is unable to segment large numbers of APFs in the tumor images. Because the FOV of the SIM system is over 13X larger than the FOV of the fluorescence microendoscope, dense collections of APFs commonly seen in tumor images could no longer be sparsely represented, and the fundamental sparsity assumption associated with SCA was no longer met. Thus, an algorithm called maximally stable extremal regions (MSER) was investigated as an alternative approach for APF segmentation in SIM images. MSER was able to accurately segment large numbers of APFs in SIM images of tumor tissue. In addition to optimizing MSER for SIM image segmentation, an optimal frequency of the illumination pattern used in SIM was carefully selected because the image signal to noise ratio (SNR) is dependent on the grid frequency. A grid frequency of 31.7 mm-1 led to the highest SNR and lowest percent error associated with MSER segmentation.

Once MSER was optimized for SIM image segmentation and the optimal grid frequency was selected, a quantitative model was developed to diagnose mouse sarcoma tumor margins that were imaged ex vivo with SIM. Tumor margins were stained with acridine orange (AO) in aim 2 because AO was found to stain the sarcoma tissue more brightly than acriflavine. Both acriflavine and AO are intravital dyes, which have been shown to stain nuclei, skeletal muscle, and collagenous stroma. A tissue-type classification model was developed to differentiate localized regions (75x75 µm) of tumor from skeletal muscle and adipose tissue based on the MSER segmentation output. Specifically, a logistic regression model was used to classify each localized region. The logistic regression model yielded an output in terms of probability (0-100%) that tumor was located within each 75x75 µm region. The model performance was tested using a receiver operator characteristic (ROC) curve analysis that revealed 77% sensitivity and 81% specificity. For margin classification, the whole margin image was divided into localized regions and this tissue-type classification model was applied. In a subset of 6 margins (3 negative, 3 positive), it was shown that with a tumor probability threshold of 50%, 8% of all regions from negative margins exceeded this threshold, while over 17% of all regions exceeded the threshold in the positive margins. Thus, 8% of regions in negative margins were considered false positives. These false positive regions are likely due to the high density of APFs present in normal tissues, which clearly demonstrates a challenge in implementing this automatic algorithm based on AO staining alone.

Thus, the third aim was to improve the specificity of the diagnostic model through leveraging other sources of contrast. Modifications were made to the SIM system to enable fluorescence imaging at a variety of wavelengths. Specifically, the SIM system was modified to enabling imaging of red fluorescent protein (RFP) expressing sarcomas, which were used to delineate the location of tumor cells within each image. Initial analysis of AO stained panels confirmed that there was room for improvement in tumor detection, particularly in regards to false positive regions that were negative for RFP. One approach for improving the specificity of the diagnostic model was to investigate using a fluorophore that was more specific to staining tumor. Specifically, tetracycline was selected because it appeared to specifically stain freshly excised tumor tissue in a matter of minutes, and was non-toxic and stable in solution. Results indicated that tetracycline staining has promise for increasing the specificity of tumor detection in SIM images of a preclinical sarcoma model and further investigation is warranted.

In conclusion, this work presents the development of a combination of tools that is capable of automated segmentation and quantification of micro-anatomical images of thick tissue. When compared to the fluorescence microendoscope, wide-field multispectral fluorescence SIM imaging provided improved image contrast, a larger FOV with comparable resolution, and the ability to image a variety of fluorophores. MSER was an appropriate and rapid approach to segment dense collections of APFs from wide-field SIM images. Variables that reflect the morphology of the tissue, such as the density, size, and shape of nuclei and nucleoli, can be used to automatically diagnose SIM images. The clinical utility of SIM imaging and MSER segmentation to detect microscopic residual disease has been demonstrated by imaging excised preclinical sarcoma margins. Ultimately, this work demonstrates that fluorescence imaging of tissue micro-anatomy combined with a specialized algorithm for delineation and quantification of features is a means for rapid, non-destructive and automated detection of microscopic disease, which could improve cancer management in a variety of clinical scenarios.

Features of programmed cell death in intact Xenopus oocytes and early embryos revealed by near-infrared fluorescence and real-time monitoring.

Factors influencing apoptosis of vertebrate eggs and early embryos have been studied in cell-free systems and in intact embryos by analyzing individual apoptotic regulators or caspase activation in static samples. A novel method for monitoring caspase activity in living Xenopus oocytes and early embryos is described here. The approach, using microinjection of a near-infrared caspase substrate that emits fluorescence only after its proteolytic cleavage by active effector caspases, has enabled the elucidation of otherwise cryptic aspects of apoptotic regulation. In particular, we show that brief caspase activity (10 min) is sufficient to cause apoptotic death in this system. We illustrate a cytochrome c dose threshold in the oocyte, which is lowered by Smac, a protein that binds thereby neutralizing the inhibitor of apoptosis proteins. We show that meiotic oocytes develop resistance to cytochrome c, and that the eventual death of oocytes arrested in meiosis is caspase-independent. Finally, data acquired through imaging caspase activity in the Xenopus embryo suggest that apoptosis in very early development is not cell-autonomous. These studies both validate this assay as a useful tool for apoptosis research and reveal subtleties in the cell death program during early development. Moreover, this method offers a potentially valuable screening modality for identifying novel apoptotic regulators.

Time-Resolved Synchronous Fluorescence for Biomedical Diagnosis.

This article presents our most recent advances in synchronous fluorescence (SF) methodology for biomedical diagnostics. The SF method is characterized by simultaneously scanning both the excitation and emission wavelengths while keeping a constant wavelength interval between them. Compared to conventional fluorescence spectroscopy, the SF method simplifies the emission spectrum while enabling greater selectivity, and has been successfully used to detect subtle differences in the fluorescence emission signatures of biochemical species in cells and tissues. The SF method can be used in imaging to analyze dysplastic cells in vitro and tissue in vivo. Based on the SF method, here we demonstrate the feasibility of a time-resolved synchronous fluorescence (TRSF) method, which incorporates the intrinsic fluorescent decay characteristics of the fluorophores. Our prototype TRSF system has clearly shown its advantage in spectro-temporal separation of the fluorophores that were otherwise difficult to spectrally separate in SF spectroscopy. We envision that our previously-tested SF imaging and the newly-developed TRSF methods will combine their proven diagnostic potentials in cancer diagnosis to further improve the efficacy of SF-based biomedical diagnostics.

CLARITY and PACT-based imaging of adult zebrafish and mouse for whole-animal analysis of infections.

Visualization of infection and the associated host response has been challenging in adult vertebrates. Owing to their transparency, zebrafish larvae have been used to directly observe infection in vivo; however, such larvae have not yet developed a functional adaptive immune system. Cells involved in adaptive immunity mature later and have therefore been difficult to access optically in intact animals. Thus, the study of many aspects of vertebrate infection requires dissection of adult organs or ex vivo isolation of immune cells. Recently, CLARITY and PACT (passive clarity technique) methodologies have enabled clearing and direct visualization of dissected organs. Here, we show that these techniques can be applied to image host-pathogen interactions directly in whole animals. CLARITY and PACT-based clearing of whole adult zebrafish and Mycobacterium tuberculosis-infected mouse lungs enables imaging of mycobacterial granulomas deep within tissue to a depth of more than 1 mm. Using established transgenic lines, we were able to image normal and pathogenic structures and their surrounding host context at high resolution. We identified the three-dimensional organization of granuloma-associated angiogenesis, an important feature of mycobacterial infection, and characterized the induction of the cytokine tumor necrosis factor (TNF) within the granuloma using an established fluorescent reporter line. We observed heterogeneity in TNF induction within granuloma macrophages, consistent with an evolving view of the tuberculous granuloma as a non-uniform, heterogeneous structure. Broad application of this technique will enable new understanding of host-pathogen interactions in situ.

Direct In Vivo Manipulation and Imaging of Calcium Transients in Neutrophils Identify a Critical Role for Leading-Edge Calcium Flux.

Calcium signaling has long been associated with key events of immunity, including chemotaxis, phagocytosis, and activation. However, imaging and manipulation of calcium flux in motile immune cells in live animals remain challenging. Using light-sheet microscopy for in vivo calcium imaging in zebrafish, we observe characteristic patterns of calcium flux triggered by distinct events, including phagocytosis of pathogenic bacteria and migration of neutrophils toward inflammatory stimuli. In contrast to findings from ex vivo studies, we observe enriched calcium influx at the leading edge of migrating neutrophils. To directly manipulate calcium dynamics in vivo, we have developed transgenic lines with cell-specific expression of the mammalian TRPV1 channel, enabling ligand-gated, reversible, and spatiotemporal control of calcium influx. We find that controlled calcium influx can function to help define the neutrophil's leading edge. Cell-specific TRPV1 expression may have broad utility for precise control of calcium dynamics in other immune cell types and organisms.