Todd H. Stokes

Semiconductor quantum dots for bioimaging and biodiagnostic applications

Semiconductor quantum dots (QDs) are light-emitting particles on the nanometer scale that have emerged as a new class of fluorescent labels for chemical analysis, molecular imaging, and biomedical diagnostics. Compared with traditional fluorescent probes, QDs have unique optical and electronic properties such as size-tunable light emission, narrow and symmetric emission spectra, and broad absorption spectra that enable the simultaneous excitation of multiple fluorescence colors. QDs are also considerably brighter and more resistant to photobleaching than are organic dyes and fluorescent proteins. These properties are well suited for dynamic imaging at the single-molecule level and for multiplexed biomedical diagnostics at ultrahigh sensitivity. Here, we discuss the fundamental properties of QDs; the development of next-generation QDs; and their applications in bioanalytical chemistry, dynamic cellular imaging, and medical diagnostics. For in vivo and clinical imaging, the potential toxicity of QDs remains a major concern. However, the toxic nature of cadmium-containing QDs is no longer a factor for in vitro diagnostics, so the use of multicolor QDs for molecular diagnostics and pathology is probably the most important and clinically relevant application for semiconductor QDs in the immediate future.

Pathology imaging informatics for quantitative analysis of whole-slide images

Objectives With the objective of bringing clinical decision support systems to reality, this article reviews histopathological whole-slide imaging informatics methods, associated challenges, and future research opportunities. Target audience This review targets pathologists and informaticians who have a limited understanding of the key aspects of whole-slide image (WSI) analysis and/or a limited knowledge of state-of-the-art technologies and analysis methods. Scope First, we discuss the importance of imaging informatics in pathology and highlight the challenges posed by histopathological WSI. Next, we provide a thorough review of current methods for: quality control of histopathological images; feature extraction that captures image properties at the pixel, object, and semantic levels; predictive modeling that utilizes image features for diagnostic or prognostic applications; and data and information visualization that explores WSI for de novo discovery. In addition, we highlight future research directions and discuss the impact of large public repositories of histopathological data, such as the Cancer Genome Atlas, on the field of pathology informatics. Following the review, we present a case study to illustrate a clinical decision support system that begins with quality control and ends with predictive modeling for several cancer endpoints. Currently, state-of-the-art software tools only provide limited image processing capabilities instead of complete data analysis for clinical decision-making. We aim to inspire researchers to conduct more research in pathology imaging informatics so that clinical decision support can become a reality.

k-Nearest neighbor models for microarray gene expression analysis and clinical outcome prediction.

In the clinical application of genomic data analysis and modeling, a number of factors contribute to the performance of disease classification and clinical outcome prediction. This study focuses on the k-nearest neighbor (KNN) modeling strategy and its clinical use. Although KNN is simple and clinically appealing, large performance variations were found among experienced data analysis teams in the MicroArray Quality Control Phase II (MAQC-II) project. For clinical end points and controls from breast cancer, neuroblastoma and multiple myeloma, we systematically generated 463 320 KNN models by varying feature ranking method, number of features, distance metric, number of neighbors, vote weighting and decision threshold. We identified factors that contribute to the MAQC-II project performance variation, and validated a KNN data analysis protocol using a newly generated clinical data set with 478 neuroblastoma patients. We interpreted the biological and practical significance of the derived KNN models, and compared their performance with existing clinical factors.

Convergence of biomarkers, bioinformatics and nanotechnology for individualized cancer treatment

Recent advances in biomarker discovery, biocomputing and nanotechnology have raised new opportunities in the emerging fields of personalized medicine (in which disease detection, diagnosis and therapy are tailored to each individuals molecular profile) and predictive medicine (in which genetic and molecular information is used to predict disease development, progression and clinical outcome). Here, we discuss advanced biocomputing tools for cancer biomarker discovery and multiplexed nanoparticle probes for cancer biomarker profiling, in addition to the prospects for and challenges involved in correlating biomolecular signatures with clinical outcome. This bio-nano-info convergence holds great promise for molecular diagnosis and individualized therapy of cancer and other human diseases.

Automatic batch-invariant color segmentation of histological cancer images

We propose an automatic color segmentation system that (1) incorporates domain knowledge to guide histological image segmentation and (2) normalizes images to reduce sensitivity to batch effects. Color segmentation is an important, yet difficult, component of image-based diagnostic systems. User-interactive guidance by domain experts—i.e., pathologistsߞoften leads to the best color segmentation or “ground truth” regardless of stain color variations in different batches. However, such guidance limits the objectivity, reproducibility and speed of diagnostic systems. Our system uses knowledge from pre-segmented reference images to normalize and classify pixels in patient images. The system then refines the segmentation by re-classifying pixels in the original color space. We test our system on four batches of H&E stained images and, in comparison to a system with no normalization (39% average accuracy), we obtain an average segmentation accuracy of 85%.

Informatics and Standards for Nanomedicine Technology

There are several issues to be addressed concerning the management and effective use of information (or data), generated from nanotechnology studies in biomedical research and medicine. These data are large in volume, diverse in content, and are beset with gaps and ambiguities in the description and characterization of nanomaterials. In this work, we have reviewed three areas of nanomedicine informatics: information resources; taxonomies, controlled vocabularies, and ontologies; and information standards. Informatics methods and standards in each of these areas are critical for enabling collaboration; data sharing; unambiguous representation and interpretation of data; semantic (meaningful) search and integration of data; and for ensuring data quality, reliability, and reproducibility. In particular, we have considered four types of information standards in this article, which are standard characterization protocols, common terminology standards, minimum information standards, and standard data communication (exchange) formats. Currently, because of gaps and ambiguities in the data, it is also difficult to apply computational methods and machine learning techniques to analyze, interpret, and recognize patterns in data that are high dimensional in nature, and also to relate variations in nanomaterial properties to variations in their chemical composition, synthesis, characterization protocols, and so on. Progress toward resolving the issues of information management in nanomedicine using informatics methods and standards discussed in this article will be essential to the rapidly growing field of nanomedicine informatics.

chip artifact CORRECTion (caCORRECT): A Bioinformatics System for Quality Assurance of Genomics and Proteomics Array Data

Quality assurance of high throughput “-omics” data is a major concern for biomedical discovery and translational medicine, and is considered a top priority in bioinformatics and systems biology. Here, we report a web-based bioinformatics tool called caCORRECT for chip artifact detection, analysis, and CORRECTion, which removes systematic artifactual noises that are commonly observed in microarray gene expression data. Despite the development of major databases such as GEO arrayExpress, caArray, and the SMD to manage and distribute microarray data to the public, reproducibility has been questioned in many cases, including high-profile papers and datasets. Based on both archived and synthetic data, we have designed the caCORRECT to have several advanced features: (1) to uncover significant, correctable artifacts that affect reproducibility of experiments; (2) to improve the integrity and quality of public archives by removing artifacts; (3) to provide a universal quality score to aid users in their selection of suitable microarray data; and (4) to improve the true-positive rate of biomarker selection verified by test data. These features are expected to improve the reproducibility of Microarray study. caCORRECT is freely available at: http://caCORRECT.bme.gatech.edu.

Diagnostic biomarkers for renal cell carcinoma: selection using novel bioinformatics systems for microarray data analysis

The differential diagnosis of clear cell, papillary, and chromophobe renal cell carcinoma is clinically important, because these tumor subtypes are associated with different pathobiology and clinical behavior. For cases in which histopathology is equivocal, immunohistochemistry and quantitative reverse transcriptase-polymerase chain reaction can assist in the differential diagnosis by measuring expression of subtype-specific biomarkers. Several renal tumor biomarkers have been discovered in expression microarray studies. However, due to heterogeneity of gene and protein expression, additional biomarkers are needed for reliable diagnostic classification. We developed novel bioinformatics systems to identify candidate renal tumor biomarkers from the microarray profiles of 45 clear cell, 16 papillary, and 10 chromophobe renal cell carcinomas; the microarray data was derived from 2 independent published studies. The ArrayWiki biocomputing system merged the microarray data sets into a single file, so gene expression could be analyzed from a larger number of tumors. The caCORRECT system removed non-random sources of error from the microarray data, and the omniBioMarker system analyzed data with several gene-ranking algorithms to identify algorithms effective at recognizing previously described renal tumor biomarkers. We predicted these algorithms would also be effective at identifying unknown biomarkers that could be verified by independent methods. We selected 6 novel candidate biomarkers from the omniBioMarker analysis and verified their differential expression in formalin-fixed paraffin-embedded tissues by quantitative reverse transcriptase-polymerase chain reaction and immunohistochemistry. The candidate biomarkers were carbonic anhydrase IX, ceruloplasmin, schwannomin-interacting protein 1, E74-like factor 3, cytochrome c oxidase subunit 5a, and acetyl-CoA acetyltransferase 1. Quantitative reverse transcriptase-polymerase chain reaction was performed on 17 clear cell, 13 papillary and 7 chromophobe renal cell carcinoma. Carbonic anhydrase IX and ceruloplasmin were overexpressed in clear cell renal cell carcinoma; schwannomin-interacting protein 1 and E74-like factor 3 were overexpressed in papillary renal cell carcinoma; and cytochrome c oxidase subunit 5a and acetyl-CoA acetyltransferase 1 were overexpressed in chromophobe renal cell carcinoma. Immunohistochemistry was performed on tissue microarrays containing 66 clear cell, 16 papillary, and 12 chromophobe renal cell carcinomas. Cytoplasmic carbonic anhydrase IX staining was significantly associated with clear cell renal cell carcinoma. Strong cytoplasmic schwannomin-interacting protein 1 and cytochrome c oxidase subunit 5a staining were significantly more frequent in papillary and chromophobe renal cell carcinoma, respectively. In summary, we developed a novel process for identifying candidate renal tumor biomarkers from microarray data, and verifying differential expression in independent assays. The tumor biomarkers have potential utility as a multiplex expression panel for classifying renal cell carcinoma with equivocal histology. Biomarker expression assays are increasingly important for renal cell carcinoma diagnosis, as needle core biopsies become more common and different therapies for tumor subtypes continue to be developed.

SickleREMOTE: A two-way text messaging system for pediatric sickle cell disease patients

Sickle cell disease, the most common hemo-globinopathy in the world, affects patient lives from early childhood. Effective care of sickle cell disease requires frequent medical monitoring, such as tracking the frequency, severity, and duration of painful events. Conventional monitoring includes paper- or web-based reporting diaries. These systems require that patients carry forms, which are easily lost, or laptop computers, which are impractical to scale to large populations. Both are prone to sporadic use by older adolescents due to lack of reminders. In this paper, we design and prototype a Sickle cell disease REporting and MOnitoring TElemedicine system (SickleREMOTE), aiming to resolve limitations of conventional monitoring diaries. This monitoring system is configured as automated short message service text (SMS-text) messages that arrive at a mobile phone anywhere on a cellular network. The messages may be reminders to encourage treatment adherence or questionnaires to collect self-assessed clinical data relating to treatment adjustments. Patients respond to the messages using pre-determined templates and a cloud database parses and stores messages automatically. Providers use a web-based interface to view, analyze, and download collected data. SickleREMOTE is developed by Georgia Institute of Technology in conjunction with Childrens Healthcare of Atlanta (CHOA). System effectiveness will be evaluated using a trial of 30 adolescents with sickle cell disease and measured by response rate, time to response, error rate, and correspondence with data collected by telephone calls.

iACT - An interactive mHealth monitoring system to enhance psychotherapy for adolescents with sickle cell disease

Sickle cell disease (SCD) is the most common inherited disease, and SCD symptoms impact functioning and well-being. For example, adolescents with SCD have a higher tendency of psychological problems than the general population. Acceptance and Commitment Therapy (ACT), a cognitive-behavioral therapy, is an effective intervention to promote quality of life and functioning in adolescents with chronic illness. However, traditional visit-based therapy sessions are restrained by challenges, such as limited follow-up, insufficient data collection, low treatment adherence, and delayed intervention. In this paper, we present Instant Acceptance and Commitment Therapy (iACT), a system designed to enhance the quality of pediatric ACT. iACT utilizes text messaging technology, which is the most popular cell phone activity among adolescents, to conduct real-time psychotherapy interventions. The system is built on cloud computing technologies, which provides a convenient and cost-effective monitoring environment. To evaluate iACT, a trial with 60 adolescents with SCD is being conducted in conjunction with the Georgia Institute of Technology, Childrens Healthcare of Atlanta, and Georgia State University.