Dataset Information

Multibow: digital spectral barcodes for cell tracing.

ABSTRACT: We introduce a multicolor labeling strategy (Multibow) for cell tracing experiments in developmental and regenerative processes. Building on Brainbow-based approaches that produce colors by differential expression levels of different fluorescent proteins, Multibow adds a layer of label diversity by introducing a binary code in which reporters are initially OFF and then probabilistically ON or OFF following Cre recombination. We have developed a library of constructs that contains seven different colors and three different subcellular localizations. Combining constructs from this library in the presence of Cre generates cells labeled with multiple independently expressed colors based on if each construct is ON or OFF following recombination. These labels form a unique "barcode" that allows the tracking of the cell and its clonal progenies in addition to expression level differences of each color. We tested Multibow in zebrafish which validates its design concept and suggests its utility for cell tracing applications in development and regeneration.

SUBMITTER: Xiong F

PROVIDER: S-EPMC4444339 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Multibow: digital spectral barcodes for cell tracing.

Xiong Fengzhu F Obholzer Nikolaus D ND Noche Ramil R RR Megason Sean G SG

PloS one 20150526 5

We introduce a multicolor labeling strategy (Multibow) for cell tracing experiments in developmental and regenerative processes. Building on Brainbow-based approaches that produce colors by differential expression levels of different fluorescent proteins, Multibow adds a layer of label diversity by introducing a binary code in which reporters are initially OFF and then probabilistically ON or OFF following Cre recombination. We have developed a library of constructs that contains seven different ...[more]

PMID: 26010570

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Project description:Digital contact tracing presents numerous advantages compared to manual contact tracing methods, especially in terms of enhanced speed and automation. Nevertheless, a lack of comprehensive evaluation regarding functionality, efficiency, benefits, and acceptance within communities remains. Here we primarily focus on the functionality of THEA-GS, an open-source digital contact tracing tool developed through consultation with stakeholders. Additionally, we provide insights from its implementation on a limited sample of haulage drivers in Uganda, serving as a representative case for a low- and middle-income country. THEA-GS comprises two primary components: (a) a smartphone application, and (b) a suite of server-programs responsible for data processing and analysis, including databases and a web-based interface featuring dashboards. In essence, the mobile application records the timestamped location of haulage drivers within the road network and identifies possible transmission hotspots by analyzing factors such as the duration of stops and the communities associated with them. The tool can be integrated with national infrastructure to compare drivers' diagnostic results and contact structure, thereby generating individual and community risk assessments relative to the road network. During the Omicron-variant wave of the COVID-19 pandemic, a total of 3,270 haulage drivers were enrolled between October 2021 and October 2022. Around 75% of these drivers utilized THEA-GS for approximately two months. Based on an analysis of 3,800 test results, which included 48 positive cases, 125 contacts, and 40 million time-stamped GPS points, THEA-GS shows a significant speed improvement, being approximately 90 times faster than MCT. For instance, the average time from sample collection to notifying a case and their contacts was approximately 70 and 80 min, respectively. The adoption of this tool encountered challenges, mainly due to drivers' awareness of its purpose and benefits for public health. THEA-GS is a place-based digital contact tracing tool specifically designed to assist National Public Health Institutions in managing infectious disease outbreaks involving the haulage industry as a high-risk group. While its utility, acceptance, and accuracy have not been fully evaluated, our preliminary tests conducted in Uganda indicate the tool's functionality is robust, but social acceptance and adoption are heavily reliant on establishing trust among users.

Project description:BackgroundMitigation of the spread of infection relies on targeted approaches aimed at preventing nonhousehold interactions. Contact tracing in the form of digital proximity tracing apps has been widely adopted in multiple countries due to its perceived added benefits of tracing speed and breadth in comparison to traditional manual contact tracing. Assessments of user responses to exposure notifications through a guided approach can provide insights into the effect of digital proximity tracing app use on managing the spread of SARS-CoV-2.ObjectiveThe aim of this study was to demonstrate the use of Venn diagrams to investigate the contributions of digital proximity tracing app exposure notifications and subsequent mitigative actions in curbing the spread of SARS-CoV-2 in Switzerland.MethodsWe assessed data from 4 survey waves (December 2020 to March 2021) from a nationwide panel study (COVID-19 Social Monitor) of Swiss residents who were (1) nonusers of the SwissCovid app, (2) users of the SwissCovid app, or (3) users of the SwissCovid app who received exposure notifications. A Venn diagram approach was applied to describe the overlap or nonoverlap of these subpopulations and to assess digital proximity tracing app use and its associated key performance indicators, including actions taken to prevent SARS-CoV-2 transmission.ResultsWe included 12,525 assessments from 2403 participants, of whom 50.9% (1222/2403) reported not using the SwissCovid digital proximity tracing app, 49.1% (1181/2403) reported using the SwissCovid digital proximity tracing app and 2.5% (29/1181) of the digital proximity tracing app users reported having received an exposure notification. Most digital proximity tracing app users (75.9%, 22/29) revealed taking at least one recommended action after receiving an exposure notification, such as seeking SARS-CoV-2 testing (17/29, 58.6%) or calling a federal information hotline (7/29, 24.1%). An assessment of key indicators of mitigative actions through a Venn diagram approach reveals that 30% of digital proximity tracing app users (95% CI 11.9%-54.3%) also tested positive for SARS-CoV-2 after having received exposure notifications, which is more than 3 times that of digital proximity tracing app users who did not receive exposure notifications (8%, 95% CI 5%-11.9%).ConclusionsResponses in the form of mitigative actions taken by 3 out of 4 individuals who received exposure notifications reveal a possible contribution of digital proximity tracing apps in mitigating the spread of SARS-CoV-2. The application of a Venn diagram approach demonstrates its value as a foundation for researchers and health authorities to assess population-level digital proximity tracing app effectiveness by providing an intuitive approach for calculating key performance indicators.

Dataset Information

Multibow: digital spectral barcodes for cell tracing.

Publications

Multibow: digital spectral barcodes for cell tracing.

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