Computational identification of surface markers for isolating distinct subpopulations from heterogeneous cancer cell populations.

Intratumor heterogeneity reduces treatment efficacy and complicates our understanding of tumor progression. There is a pressing need to understand the functions of heterogeneous tumor cell subpopulations within a tumor, yet biological systems to study these processes in vitro are limited. With the advent of single-cell RNA sequencing (scRNA-seq), it has become clear that some cancer cell line models include distinct subpopulations. Heterogeneous cell lines offer a unique opportunity to study the dynamics and evolution of genetically similar cancer cell subpopulations in controlled experimental settings. Here, we present clusterCleaver, a computational package that uses metrics of statistical distance to identify candidate surface markers maximally unique to transcriptomic subpopulations in scRNA-seq which may be used for FACS isolation. clusterCleaver was experimentally validated using the MDA-MB-231 and MDA-MB-436 breast cancer cell lines. ESAM and BST2/tetherin were experimentally confirmed as surface markers which identify and separate major transcriptomic subpopulations within MDA-MB-231 and MDA-MB-436 cells, respectively. clusterCleaver is a computationally efficient and experimentally validated workflow for identification and enrichment of distinct subpopulations within cell lines which paves the way for studies on the coexistence of cancer cell subpopulations in well-defined in vitro systems.

Mathematical modelling of the dynamics of image-informed tumor habitats in a murine model of glioma.

Tumors exhibit high molecular, phenotypic, and physiological heterogeneity. In this effort, we employ quantitative magnetic resonance imaging (MRI) data to capture this heterogeneity through imaging-based subregions or "habitats" in a murine model of glioma. We then demonstrate the ability to model and predict the growth of the habitats using coupled ordinary differential equations (ODEs) in the presence and absence of radiotherapy. Female Wistar rats (N = 21) were inoculated intracranially with 106 C6 glioma cells, a subset of which received 20 Gy (N = 5) or 40 Gy (N = 8) of radiation. All rats underwent diffusion-weighted and dynamic contrast-enhanced MRI at up to seven time points. All MRI data at each visit were subsequently clustered using k-means to identify physiological tumor habitats. A family of four models consisting of three coupled ODEs were developed and calibrated to the habitat time series of control and treated rats and evaluated for predictive capability. The Akaike Information Criterion was used for model selection, and the normalized sum-of-square-error (SSE) was used to evaluate goodness-of-fit in model calibration and prediction. Three tumor habitats with significantly different imaging data characteristics (p < 0.05) were identified: high-vascularity high-cellularity, low-vascularity high-cellularity, and low-vascularity low-cellularity. Model selection resulted in a five-parameter model whose predictions of habitat dynamics yielded SSEs that were similar to the SSEs from the calibrated model. It is thus feasible to mathematically describe habitat dynamics in a preclinical model of glioma using biology-based ODEs, showing promise for forecasting heterogeneous tumor behavior.

LAMP-enabled diagnosis of Kaposi's sarcoma for sub-Saharan Africa.

Kaposi's sarcoma (KS) is an endothelial cancer caused by the Kaposi's sarcoma-associated herpesvirus (KSHV) and is one of the most common cancers in sub-Saharan Africa. In limited-resource settings, traditional pathology infrastructure is often insufficient for timely diagnosis, leading to frequent diagnoses at advanced-stage disease where survival is poor. In this study, we investigate molecular diagnosis of KS performed in a point-of-care device to circumvent the limited infrastructure for traditional diagnosis. Using 506 mucocutaneous biopsies collected from patients at three HIV clinics in Uganda, we achieved 97% sensitivity, 92% specificity, and 96% accuracy compared to gold standard U.S.-based pathology. The results presented in this manuscript show that LAMP-based quantification of KSHV DNA extracted from KS-suspected biopsies has the potential to serve as a successful diagnostic for the disease and that diagnosis may be accurately achieved using a point-of-care device, reducing the barriers to obtaining KS diagnosis while increasing diagnostic accuracy.

Integration of quantitative methods and mathematical approaches for the modeling of cancer cell proliferation dynamics.

Physiological processes rely on the control of cell proliferation, and the dysregulation of these processes underlies various pathological conditions, including cancer. Mathematical modeling can provide new insights into the complex regulation of cell proliferation dynamics. In this review, we first examine quantitative experimental approaches for measuring cell proliferation dynamics in vitro and compare the various types of data that can be obtained in these settings. We then explore the toolbox of common mathematical modeling frameworks that can describe cell behavior, dynamics, and interactions of proliferation. We discuss how these wet-laboratory studies may be integrated with different mathematical modeling approaches to aid the interpretation of the results and to enable the prediction of cell behaviors, specifically in the context of cancer.

Functionalized Lineage Tracing for the Study and Manipulation of Heterogeneous Cell Populations.

The ability to track and isolate unique cell lineages from large heterogeneous populations increases the resolution at which cellular processes can be understood under normal and pathogenic states beyond snapshots obtained from single-cell RNA sequencing (scRNA-seq). Here, we describe the Control of Lineages by Barcode Enabled Recombinant Transcription (COLBERT) method in which unique single guide RNA (sgRNA) barcodes are used as functional tags to identify and recall specific lineages of interest. An sgRNA barcode is stably integrated and actively transcribed, such that all cellular progeny will contain the parental barcode and produce a functional sgRNA. The sgRNA barcode has all the benefits of a DNA barcode and added functionalities. Once a barcode pertaining to a lineage of interest is identified, the lineage of interest can be isolated using an activator variant of Cas9 (such as dCas9-VPR) and a barcode-matched sequence upstream of a fluorescent reporter gene. CRISPR activation of the fluorescent reporter will only occur in cells producing the matched sgRNA barcode, allowing precise identification and isolation of lineages of interest from heterogeneous populations.

Integrating Quantitative Assays with Biologically Based Mathematical Modeling for Predictive Oncology.

We provide an overview on the use of biological assays to calibrate and initialize mechanism-based models of cancer phenomena. Although artificial intelligence methods currently dominate the landscape in computational oncology, mathematical models that seek to explicitly incorporate biological mechanisms into their formalism are of increasing interest. These models can guide experimental design and provide insights into the underlying mechanisms of cancer progression. Historically, these models have included a myriad of parameters that have been difficult to quantify in biologically relevant systems, limiting their practical insights. Recently, however, there has been much interest calibrating biologically based models with the quantitative measurements available from (for example) RNA sequencing, time-resolved microscopy, and in vivo imaging. In this contribution, we summarize how a variety of experimental methods quantify tumor characteristics from the molecular to tissue scales and describe how such data can be directly integrated with mechanism-based models to improve predictions of tumor growth and treatment response.

Integrating transcriptomics and bulk time course data into a mathematical framework to describe and predict therapeutic resistance in cancer.

A significant challenge in the field of biomedicine is the development of methods to integrate the multitude of dispersed data sets into comprehensive frameworks to be used to generate optimal clinical decisions. Recent technological advances in single cell analysis allow for high-dimensional molecular characterization of cells and populations, but to date, few mathematical models have attempted to integrate measurements from the single cell scale with other types of longitudinal data. Here, we present a framework that actionizes static outputs from a machine learning model and leverages these as measurements of state variables in a dynamic model of treatment response. We apply this framework to breast cancer cells to integrate single cell transcriptomic data with longitudinal bulk cell population (bulk time course) data. We demonstrate that the explicit inclusion of the phenotypic composition estimate, derived from single cell RNA-sequencing data (scRNA-seq), improves accuracy in the prediction of new treatments with a concordance correlation coefficient (CCC) of 0.92 compared to a prediction accuracy of CCC = 0.64 when fitting on longitudinal bulk cell population data alone. To our knowledge, this is the first work that explicitly integrates single cell clonally-resolved transcriptome datasets with bulk time-course data to jointly calibrate a mathematical model of drug resistance dynamics. We anticipate this approach to be a first step that demonstrates the feasibility of incorporating multiple data types into mathematical models to develop optimized treatment regimens from data.

Barriers to HIV Testing: Patient and Provider Perspectives in the Deep South.

Although CDC guidelines call for universal, "opt-out" HIV testing, barriers to testing continue to exist throughout the United States, with the rural South particularly vulnerable to both HIV infection and decreased awareness of status. Therefore, the objectives of this study were to evaluate uptake of "opt-out" HIV testing and barriers to testing within the primary care setting in the South. A concurrent triangulation design guided the collection of quantitative data from patients (N = 250) and qualitative data from providers (N = 10) across three primary health clinics in Alabama. We found that 30% of patients had never been tested for HIV, with the highest ranked barrier among patients being perceived costs, access to specialty care, and not feeling at risk. Significant differences existed in perceived barriers between patients and providers. Increased provider-patient engagement and the routine implementation of "opt-out" HIV testing would effectively reveal and mitigate barriers to testing, thus, increasing awareness of status.

A portable device for nucleic acid quantification powered by sunlight, a flame or electricity.

A decentralized approach to diagnostics can decrease the time to treatment of infectious diseases in resource-limited settings. Yet most modern diagnostic tools require stable electricity and are not portable. Here, we describe a portable device for isothermal nucleic-acid quantification that can operate with power from electricity, sunlight or a flame, and that can store heat from intermittent energy sources, for operation when electrical power is not available or reliable. We deployed the device in two Ugandan health clinics, where it successfully operated through multiple power outages, with equivalent performance when powered via sunlight or electricity. A direct comparison between the portable device and commercial qPCR (quantitative polymerase chain reaction) machines for samples from 71 Ugandan patients (29 of which were tested in Uganda) for the presence of Kaposi's sarcoma-associated herpesvirus DNA showed 94% agreement, with the four discordant samples having the lowest concentration of the herpesvirus DNA. The device's flexibility in power supply provides a needed solution for on-field diagnostics.

Solar-thermal complex sample processing for nucleic acid based diagnostics in limited resource settings.

The use of point-of-care (POC) devices in limited resource settings where access to commonly used infrastructure, such as water and electricity, can be restricted represents simultaneously one of the best application fits for POC systems as well as one of the most challenging places to deploy them. Of the many challenges involved in these systems, the preparation and processing of complex samples like stool, vomit, and biopsies are particularly difficult due to the high number and varied nature of mechanical and chemical interferents present in the sample. Previously we have demonstrated the ability to use solar-thermal energy to perform PCR based nucleic acid amplifications. In this work demonstrate how the technique, using similar infrastructure, can also be used to perform solar-thermal based sample processing system for extracting and isolating Vibrio Cholerae nucleic acids from fecal samples. The use of opto-thermal energy enables the use of sunlight to drive thermal lysing reactions in large volumes without the need for external electrical power. Using the system demonstrate the ability to reach a 95°C threshold in less than 5 minutes and maintain a stable sample temperature of +/- 2°C following the ramp up. The system is demonstrated to provide linear results between 10(4) and 10(8) CFU/mL when the released nucleic acids were quantified via traditional means. Additionally, we couple the sample processing unit with our previously demonstrated solar-thermal PCR and tablet based detection system to demonstrate very low power sample-in-answer-out detection.

KS-Detect - Validation of Solar Thermal PCR for the Diagnosis of Kaposi's Sarcoma Using Pseudo-Biopsy Samples.

Resource-limited settings present unique engineering challenges for medical diagnostics. Diagnosis is often needed for those unable to reach central healthcare systems, making portability and independence from traditional energy infrastructure essential device parameters. In 2014, our group presented a microfluidic device that performed a solar-powered variant of the polymerase chain reaction, which we called solar thermal PCR. In this work, we expand on our previous effort by presenting an integrated, portable, solar thermal PCR system targeted towards the diagnosis of Kaposi's sarcoma. We call this system KS-Detect, and we now report the system's performance as a diagnostic tool using pseudo-biopsy samples made from varying concentrations of human lymphoma cell lines positive for the KS herpesvirus (KSHV). KS-Detect achieved 83% sensitivity and 70% specificity at high (≥ 10%) KSHV+ cell concentrations when diagnosing pseudo-biopsy samples by smartphone image. Using histology, we confirm that our prepared pseudo-biopsies contain similar KSHV+ cell concentrations as human biopsies positive for KS. Through our testing of samples derived from human cell lines, we validate KS-Detect as a viable, portable KS diagnostic tool, and we identify critical engineering considerations for future solar-thermal PCR devices.

Expert holistic nurses' advice to nursing students.

PURPOSE: The purposes of this study were to describe the advice that expert holistic nurses gave to nursing students regarding the theory and practice of holistic nursing and to describe nursing students' experience and perceptions of their interaction with the experts. DESIGN: This was a qualitative descriptive study. METHODS: Nursing students who attended the 2008 and 2009 conferences of the American Holistic Nurses Association interviewed expert holistic nurses, asking them for advice for beginners. Students recorded the interviews on paper and wrote their perceptions of interacting with experts. The data were examined for recurring themes. FINDINGS: The most common advice from the experts was regarding the importance of self-care, person-centered care, touch, and lifelong learning. Students' perceptions of the interviews included feeling empowered to accomplish goals, gaining a greater understanding of holistic nursing, and feeling admiration for the holistic nurse experts. CONCLUSION: Giving students the opportunity to interact with nurse experts in an individual, informal setting is a useful educational strategy that increases knowledge, promotes socialization to the nursing profession, and offers students opportunities for professional networking.

Self-Directed Treatment of Intermittent Heartburn: A Randomized, Multicenter, Double-Blind, Placebo-Controlled Evaluation of Antacid and Low Doses of an H(2)-Receptor Antagonist (Famotidine).

BACKGROUND: Heartburn, a common symptom, is self-treated with oral antacids. Efficacy of antacids has not been demonstrated for individual, spontaneous heartburn episodes. METHODS: We conducted a double-blind, randomized, placebo-controlled, parallel-group study of self-directed treatment for episodic heartburn comparing famotidine (FAM) 5, 10, or 20 mg and antacid (11 mEq ANC) to placebo (PBO) during a 4-week period. Twenty-nine US investigators enrolled a total of 565 outpatients, ages 18--81 years (mean 44.1 years) with heartburn but not seeking care for heartburn. Treatment of spontaneous heartburn episodes was permitted as needed, up to twice daily, with self-administered test drug. An open-label, backup antacid was provided to use if test drug did not provide adequate relief. Patients assessed heartburn relief hourly and recorded use of backup antacid. Relief was defined as complete relief of symptoms without the use of backup antacid. RESULTS: The media proportion of episodes relieved was: PBO, 41%; FAM 5 mg, 59%, 0.05 less-than-or-equal p < 0.10; FAM 10 mg, 70%, p < 0.001; FAM 20 mg, 69%, p < 0.001; antacid, 62%, p < 0.05 (p-values versus PBO). Supplemental analyses incorporating time to relief confirmed that famotidine and antacid provided more rapid and more frequent relief than placebo (odds ratio for relief relative to PBO: FAM 5 mg, 1.55, p = 0.003; FAM 10 mg, 1.94, p < 0.001; FAM 20 mg, 2.13, p < 0.001; antacid 1.57, p = 0.003). The tolerability profile was similar with famotidine, antacid, and placebo. CONCLUSIONS: The positive results with antacid demonstrated for the first time the efficacy of antacid in self-treatment of individual heartburn episodes and provided internal validation of this study paradigm. Patients in this study self-medicated effectively using low doses of famotidine on an as needed basis for spontaneous episodes of heartburn.