Assessing characteristics of RNA amplification methods for single cell RNA sequencing.

BACKGROUND: Recently, measurement of RNA at single cell resolution has yielded surprising insights. Methods for single-cell RNA sequencing (scRNA-seq) have received considerable attention, but the broad reliability of single cell methods and the factors governing their performance are still poorly known. RESULTS: Here, we conducted a large-scale control experiment to assess the transfer function of three scRNA-seq methods and factors modulating the function. All three methods detected greater than 70% of the expected number of genes and had a 50% probability of detecting genes with abundance greater than 2 to 4 molecules. Despite the small number of molecules, sequencing depth significantly affected gene detection. While biases in detection and quantification were qualitatively similar across methods, the degree of bias differed, consistent with differences in molecular protocol. Measurement reliability increased with expression level for all methods and we conservatively estimate measurements to be quantitative at an expression level greater than ~5-10 molecules. CONCLUSIONS: Based on these extensive control studies, we propose that RNA-seq of single cells has come of age, yielding quantitative biological information.

Selection of DNA aptamers for ovarian cancer biomarker HE4 using CE-SELEX and high-throughput sequencing.

The development of novel affinity probes for cancer biomarkers may enable powerful improvements in analytical methods for detecting and treating cancer. In this report, we describe our use of capillary electrophoresis (CE) as the separation mechanism in the process of selecting DNA aptamers with affinity for the ovarian cancer biomarker HE4. Rather than the conventional use of cloning and sequencing as the last step in the aptamer selection process, we used high-throughput sequencing on an Illumina platform. This data-rich approach, combined with a bioinformatics pipeline based on freely available computational tools, enabled the entirety of the selection process-and not only its endpoint-to be characterized. Affinity probe CE and fluorescence anisotropy assays demonstrate the binding affinity of a set of aptamer candidates identified through this bioinformatics approach. Graphical Abstract A population of candidate aptamers is sequenced on an Illumina platform, enabling the process by which aptamers are selected over multiple SELEX rounds to be characterized. Bioinformatics tools are used to identify enrichment of selected aptamers and groupings into clusters based on sequence and structural similarity. A subset of sequenced aptamers may be intelligently chosen for in vitro testing.

Design and Integration of a Texting Tool to Keep Patients' Family Members Updated During Hospitalization: Family Members' Perspectives.

While there is an evolving literature on the benefits of texting and patient-centered technologies, texting initiatives have not focused on family members. We sought to identify patients' family members' perspectives on facilitators and barriers to using 1 digital texting innovation to promote family-centered care during patients' hospitalizations. This qualitative study was conducted at a tertiary care center in Houston, consisting of 7 hospitals (1 academic hospital and 6 community hospitals), involving analyzation of 3137 comments from family members who used the digital texting technology. Thematic analysis methods were used. The data analysis for loved ones' feedback resulted in 4 themes as facilitators: (1) inpatient text messaging keeps loved ones updated and connected (n = 611); (2) inpatient text messaging allows for stronger continuity of communication (n = 69); (3) messaging promotes a sense of staff compassion and service (n = 245); and (4) messaging reduces phone calls (n = 65). The data analysis resulted in 4 themes as barriers to text messaging helpfulness: (1) messages could feel generic (n = 31); (2) inpatient texting was not needed if all loved ones were regularly at bedside (n = 6); (3) messages could have a perceived delay (n = 37); and (4) security features could impact convenience (n = 29). Our findings indicate that family members and loved ones value inpatient text messages, not only for the information the messages provide, but also because the act of writing text messages and preparing loved ones shows inclusiveness, compassion, and family-centered care.

Design and Integration of a Texting Tool to Keep Patients' Family Members Updated During Hospitalization: Clinicians' Perspectives.

An important gap in the literature is how clinicians feel about patient-centered technologies and how clinicians experience patient-centered technologies in their workflows. Our goal was to identify clinician users' perspectives on facilitators (pros) and barriers (cons) to using 1 digital texting innovation to promote family centered care during patients' hospitalizations. This qualitative study was conducted at a tertiary care center in Houston, consisting of 7 hospitals (1 academic hospital and 6 community hospitals), involving analyzation of 3 focus groups of 18 physicians, 5 advanced practice providers, and 10 nurse directors and managers, as well as a content analysis of 156 real-time alerts signaling family dissatisfaction on the nursing unit/floor. Thematic analysis methods were used. We selected these participants by attending their regularly scheduled service-line meetings. Clinician feedback from focus groups resulted in 3 themes as facilitators: (a) texting platforms must be integrated within the electronic medical record; (b) texting reduces outgoing phone calls; (c) texting reduces incoming family phone calls. Clinician feedback resulted in 3 themes as barriers: (a) best practice alerts can be disruptive; (b) real-time alerts can create hopelessness; and (c) scale-up is challenging. The analyzation of facilitators (pros) and barriers (cons) pertains only to the clinician's feedback. We also analyzed real-time alerts signaling family dissatisfaction (defined as "service recovery escalation" throughout this manuscript). The most common selection for the source of family dissatisfaction, as reflected through the real-time alerts was, "I haven't heard from physicians enough," appearing in 52 out of 156 alerts (33%). The second most common selection for the source of dissatisfaction was "perceived inconsistent or incomplete information provided by team members," which was selected in 48 cases (31%). Our findings indicate that clinicians value inpatient texting, not only for its ability to quickly relay updates to multiple family members with 1 click, but also because, when used intentionally and meaningfully, texting decreases family phone calls.

Multi-Dimensional Mapping of Brain-Derived Extracellular Vesicle MicroRNA Biomarker for Traumatic Brain Injury Diagnostics.

The diagnosis and prognosis of traumatic brain injury (TBI) is complicated by variability in the type and severity of injuries and the multiple endophenotypes that describe each patient's response and recovery to the injury. It has been challenging to capture the multiple dimensions that describe an injury and its recovery to provide clinically useful information. To address this challenge, we have performed an open-ended search for panels of microRNA (miRNA) biomarkers, packaged inside of brain-derived extracellular vesicles (EVs), that can be combined algorithmically to accurately classify various states of injury. We mapped GluR2+ EV miRNA across a variety of injury types, injury intensities, history of injuries, and time elapsed after injury, and sham controls in a pre-clinical murine model (n = 116), as well as in clinical samples (n = 36). We combined next-generation sequencing with a technology recently developed by our lab, Track Etched Magnetic Nanopore (TENPO) sorting, to enrich for GluR2+ EVs and profile their miRNA. By mapping and comparing brain-derived EV miRNA between various injuries, we have identified signaling pathways in the packaged miRNA that connect these biomarkers to underlying mechanisms of TBI. Many of these pathways are shared between the pre-clinical model and the clinical samples, and present distinct signatures across different injury models and times elapsed after injury. Using this map of EV miRNA, we applied machine learning to define a panel of biomarkers to successfully classify specific states of injury, paving the way for a prognostic blood test for TBI. We generated a panel of eight miRNAs (miR-150-5p, miR-669c-5p, miR-488-3p, miR-22-5p, miR-9-5p, miR-6236, miR-219a.2-3p, miR-351-3p) for injured mice versus sham mice and four miRNAs (miR-203b-5p, miR-203a-3p, miR-206, miR-185-5p) for TBI patients versus healthy controls.

Assessing the Impact of Patient-Facing Mobile Health Technology on Patient Outcomes: Retrospective Observational Cohort Study.

BACKGROUND: Despite the growth of and media hype about mobile health (mHealth), there is a paucity of literature supporting the effectiveness of widespread implementation of mHealth technologies. OBJECTIVE: This study aimed to assess whether an innovative mHealth technology system with several overlapping purposes can impact (1) clinical outcomes (ie, readmission rates, revisit rates, and length of stay) and (2) patient-centered care outcomes (ie, patient engagement, patient experience, and patient satisfaction). METHODS: We compared all patients (2059 patients) of participating orthopedic surgeons using mHealth technology with all patients of nonparticipating orthopedic surgeons (2554 patients). The analyses included Wilcoxon rank-sum tests, Kruskal-Wallis tests for continuous variables, and chi-square tests for categorical variables. Logistic regression models were performed on categorical outcomes and a gamma-distributed model for continuous variables. All models were adjusted for patient demographics and comorbidities. RESULTS: The inpatient readmission rates for the nonparticipating group when compared with the participating group were higher and demonstrated higher odds ratios (ORs) for 30-day inpatient readmissions (nonparticipating group 106/2636, 4.02% and participating group 54/2048, 2.64%; OR 1.48, 95% CI 1.03 to 2.13; P=.04), 60-day inpatient readmissions (nonparticipating group 194/2636, 7.36% and participating group 85/2048, 4.15%; OR 1.79, 95% CI 1.32 to 2.39; P<.001), and 90-day inpatient readmissions (nonparticipating group 261/2636, 9.90% and participating group 115/2048, 5.62%; OR 1.81, 95% CI 1.40 to 2.34; P<.001). The length of stay for the nonparticipating cohort was longer at 1.90 days, whereas the length of stay for the participating cohort was 1.50 days (mean 1.87, SD 2 vs mean 1.50, SD 1.37; P<.001). Patients treated by participating surgeons received and read text messages using mHealth 83% of the time and read emails 84% of the time. Patients responded to 60% of the text messages and 53% of the email surveys. Patients were least responsive to digital monitoring questions when the hospital asked them to do something, and they were most engaged with emails that did not require action, including informational content. A total of 96% (558/580) of patients indicated high satisfaction with using mHealth technology to support their care. Only 0.40% (75/2059) patients opted-out of the mHealth technology program after enrollment. CONCLUSIONS: A novel, multicomponent, pathway-driven, patient-facing mHealth technology can positively impact patient outcomes and patient-reported experiences. These technologies can empower patients to play a more active and meaningful role in improving their outcomes. There is a deep need, however, for a better understanding of the interactions between patients, technology, and health care providers. Future research is needed to (1) help identify, address, and improve technology usability and effectiveness; (2) understand patient and provider attributes that support adoption, uptake, and sustainability; and (3) understand the factors that contribute to barriers of technology adoption and how best to overcome them.

Selection of DNA Aptamers for Ovarian Cancer Biomarker CA125 Using One-Pot SELEX and High-Throughput Sequencing.

CA125 is a mucin glycoprotein whose concentration in serum correlates with a woman's risk of developing ovarian cancer and also indicates response to therapy in diagnosed patients. Accurate detection of this large, complex protein in patient samples is of great clinical relevance. We suggest that powerful new diagnostic tools may be enabled by the development of nucleic acid aptamers with affinity for CA125. Here, we report on our use of One-Pot SELEX to isolate single-stranded DNA aptamers with affinity for CA125, followed by high-throughput sequencing of the selected oligonucleotides. This data-rich approach, combined with bioinformatics tools, enabled the entire selection process to be characterized. Using fluorescence anisotropy and affinity probe capillary electrophoresis, the binding affinities of four aptamer candidates were evaluated. Two aptamers, CA125_1 and CA125_12, both without primers, were found to bind to clinically relevant concentrations of the protein target. Binding was differently influenced by the presence of Mg2+ ions, being required for binding of CA125_1 and abrogating binding of CA125_12. In conclusion, One-Pot SELEX was found to be a promising selection method that yielded DNA aptamers to a clinically important protein target.