1
|
Tripathy SP, Ponnapati M, Bhat S, Jacobson J, Chatterjee P. Femtomolar detection of SARS-CoV-2 via peptide beacons integrated on a miniaturized TIRF microscope. SCIENCE ADVANCES 2022; 8:eabn2378. [PMID: 36001655 PMCID: PMC9401610 DOI: 10.1126/sciadv.abn2378] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 07/13/2022] [Indexed: 05/29/2023]
Abstract
The novel coronavirus SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) continues to pose a substantial global health threat. Along with vaccines and targeted therapeutics, there is a critical need for rapid diagnostic solutions. In this work, we use computational protein modeling tools to suggest molecular beacon architectures that function as conformational switches for high-sensitivity detection of the SARS-CoV-2 spike protein receptor binding domain (S-RBD). Integrating these beacons on a miniaturized total internal reflection fluorescence (mini-TIRF) microscope, we detect the S-RBD and pseudotyped SARS-CoV-2 with limits of detection in the femtomolar range. We envision that our designed mini-TIRF platform will serve as a robust platform for point-of-care diagnostics for SARS-CoV-2 and future emergent viral threats.
Collapse
Affiliation(s)
- Soumya P. Tripathy
- Center for Bits and Atoms, Massachusetts Institute of Technology, Cambridge, MA, USA
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Manvitha Ponnapati
- Center for Bits and Atoms, Massachusetts Institute of Technology, Cambridge, MA, USA
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Suhaas Bhat
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Joseph Jacobson
- Center for Bits and Atoms, Massachusetts Institute of Technology, Cambridge, MA, USA
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Pranam Chatterjee
- Center for Bits and Atoms, Massachusetts Institute of Technology, Cambridge, MA, USA
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| |
Collapse
|
2
|
George BJ, Gains-Germain L, Broms K, Black K, Furman M, Hays MD, Thomas KW, Simmons JE. Censoring Trace-Level Environmental Data: Statistical Analysis Considerations to Limit Bias. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:3786-3795. [PMID: 33625843 PMCID: PMC8224532 DOI: 10.1021/acs.est.0c02256] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
Trace-level environmental data typically include values near or below detection and quantitation thresholds where health effects may result from low-concentration exposures to one chemical over time or to multiple chemicals. In a cook stove case study, bias in dibenzo[a,h]anthracene concentration means and standard deviations (SDs) was assessed following censoring at thresholds for selected analysis approaches: substituting threshold/2, maximum likelihood estimation, robust regression on order statistics, Kaplan-Meier, and omitting censored observations. Means and SDs for gas chromatography-mass spectrometry-determined concentrations were calculated after censoring at detection and calibration thresholds, 17% and 55% of the data, respectively. Threshold/2 substitution was the least biased. Measurement values were subsequently simulated from two log-normal distributions at two sample sizes. Means and SDs were calculated for 30%, 50%, and 80% censoring levels and compared to known distribution counterparts. Simulation results illustrated (1) threshold/2 substitution to be inferior to modern after-censoring statistical approaches and (2) all after-censoring approaches to be inferior to including all measurement data in analysis. Additionally, differences in stove-specific group means were tested for uncensored samples and after censoring. Group differences of means tests varied depending on censoring and distributional decisions. Investigators should guard against censoring-related bias from (explicit or implicit) distributional and analysis approach decisions.
Collapse
Affiliation(s)
- Barbara Jane George
- Center for Public Health and Environmental Assessment,
Office of Research and Development, U.S. EPA, Research Triangle Park, North Carolina
27711, United States
| | | | - Kristin Broms
- Neptune and Company, Inc., Lakewood, Colorado 80215, United
States
| | - Kelly Black
- Neptune and Company, Inc., Lakewood, Colorado 80215, United
States
| | - Marschall Furman
- Oak Ridge Institute for Science and Education (ORISE)
Research Participant at U.S. EPA, Office of Research and Development, Center for
Public Health and Environmental Assessment, Research Triangle Park, North Carolina
27711, United States
| | - Michael D. Hays
- Center for Environmental Measurement and Modeling, Office
of Research and Development, U.S. EPA, Research Triangle Park, North Carolina 27711,
United States
| | - Kent W. Thomas
- Center for Public Health and Environmental Assessment,
Office of Research and Development, U.S. EPA, Research Triangle Park, North Carolina
27711, United States
| | - Jane Ellen Simmons
- Center for Public Health and Environmental Assessment,
Office of Research and Development, U.S. EPA, Research Triangle Park, North Carolina
27711, United States
| |
Collapse
|
3
|
Togun T, Hoggart CJ, Agbla SC, Gomez MP, Egere U, Sillah AK, Saidy B, Mendy F, Pai M, Kampmann B. A three-marker protein biosignature distinguishes tuberculosis from other respiratory diseases in Gambian children. EBioMedicine 2020; 58:102909. [PMID: 32711253 PMCID: PMC7381493 DOI: 10.1016/j.ebiom.2020.102909] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Revised: 07/02/2020] [Accepted: 07/08/2020] [Indexed: 01/30/2023] Open
Abstract
Background Our study aimed to identify a host cytokine biosignature that could distinguish childhood tuberculosis (TB) from other respiratory diseases (OD). Methods Cytokine responses in prospectively recruited children with symptoms suggestive of TB were measured in whole blood assay supernatants, harvested after overnight incubation, using a Luminex platform. We used logistic regression models with Least Absolute Shrinkage and Selection Operator (LASSO) penalty to identify the optimal biosignature associated with confirmed TB disease in the training set. We subsequently assessed its performance in the test set. Findings Of the 431 children included in the study, 44 had bacteriologically confirmed TB, 60 had clinically diagnosed TB while 327 had OD. All children were HIV-negative. Application of LASSO regression models to the training set (n = 260) resulted in the combination of IL-1ra, IL-7 and IP-10 from unstimulated samples as the optimally discriminant cytokine biosignature associated with bacteriologically confirmed TB. In the test set (n = 171), this biosignature distinguished children diagnosed with TB disease, irrespective of microbiological confirmation, from OD with area under the receiver operator characteristic curve (AUC) of 0•74 (95% CI: 0•67, 0•81), and demonstrated sensitivity and specificity of 72•2% (95% CI: 60•4, 82•1%) and 75•0% (95% CI: 64•9, 83•4%) respectively, with its performance independent of their age group and their age- and sex-adjusted nutritional status. Interpretation This novel biosignature of childhood TB derived from unstimulated supernatants is promising. Independent validation with further optimisation will improve its performance and translational potential. Funding Steinberg Fellowship (McGill University); Grand Challenges Canada; MRC Program Grant.
Collapse
Affiliation(s)
- Toyin Togun
- Vaccines and Immunity Theme, Medical Research Council Unit The Gambia at the London School of Hygiene & Tropical Medicine, Atlantic Boulevard, Fajara, The Gambia; The Vaccine Centre, and Clinical Research Department, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London, United Kingdom.
| | - Clive J Hoggart
- Section of Paediatrics, Imperial College London, St Mary's Campus, London, United Kingdom; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Schadrac C Agbla
- Department of Health Data Sciences, University of Liverpool, Liverpool, United Kingdom; Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, Keppel Street, London, United Kingdom
| | - Marie P Gomez
- Vaccines and Immunity Theme, Medical Research Council Unit The Gambia at the London School of Hygiene & Tropical Medicine, Atlantic Boulevard, Fajara, The Gambia
| | - Uzochukwu Egere
- Community Health Systems Group, Department of International Public Health, Liverpool School of Tropical Medicine, Liverpool, United Kingdom
| | - Abdou K Sillah
- Vaccines and Immunity Theme, Medical Research Council Unit The Gambia at the London School of Hygiene & Tropical Medicine, Atlantic Boulevard, Fajara, The Gambia
| | - Binta Saidy
- Vaccines and Immunity Theme, Medical Research Council Unit The Gambia at the London School of Hygiene & Tropical Medicine, Atlantic Boulevard, Fajara, The Gambia
| | - Francis Mendy
- Vaccines and Immunity Theme, Medical Research Council Unit The Gambia at the London School of Hygiene & Tropical Medicine, Atlantic Boulevard, Fajara, The Gambia
| | - Madhukar Pai
- McGill International TB Centre, and Department of Epidemiology, Biostatistics and Occupational Health, Faculty of Medicine, McGill University, Montreal, Quebec, Canada; Manipal-McGill Centre for Infectious Diseases, Manipal University, Manipal, India
| | - Beate Kampmann
- Vaccines and Immunity Theme, Medical Research Council Unit The Gambia at the London School of Hygiene & Tropical Medicine, Atlantic Boulevard, Fajara, The Gambia; The Vaccine Centre, and Clinical Research Department, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London, United Kingdom.
| |
Collapse
|
4
|
The Statistical Value of Raw Fluorescence Signal in Luminex xMAP Based Multiplex Immunoassays. Sci Rep 2016; 6:26996. [PMID: 27243383 PMCID: PMC4886638 DOI: 10.1038/srep26996] [Citation(s) in RCA: 75] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2015] [Accepted: 05/04/2016] [Indexed: 01/08/2023] Open
Abstract
Tissue samples (plasma, saliva, serum or urine) from 169 patients classified as either normal or having one of seven possible diseases are analysed across three 96-well plates for the presences of 37 analytes using cytokine inflammation multiplexed immunoassay panels. Censoring for concentration data caused problems for analysis of the low abundant analytes. Using fluorescence analysis over concentration based analysis allowed analysis of these low abundant analytes. Mixed-effects analysis on the resulting fluorescence and concentration responses reveals a combination of censoring and mapping the fluorescence responses to concentration values, through a 5PL curve, changed observed analyte concentrations. Simulation verifies this, by showing a dependence on the mean florescence response and its distribution on the observed analyte concentration levels. Differences from normality, in the fluorescence responses, can lead to differences in concentration estimates and unreliable probabilities for treatment effects. It is seen that when fluorescence responses are normally distributed, probabilities of treatment effects for fluorescence based t-tests has greater statistical power than the same probabilities from concentration based t-tests. We add evidence that the fluorescence response, unlike concentration values, doesn’t require censoring and we show with respect to differential analysis on the fluorescence responses that background correction is not required.
Collapse
|