A Novel Dry-Stabilized Whole Blood Microsampling and Protein Extraction Method for Testing of SARS-CoV-2 Antibody Titers

McCarthy, Patrick; Pathakamuri, Joseph; Kuebler, Daniel; Neves, Jocelyn; Krohn, Madison; Rohall, Michael; Archibeque, Isaac; Giese, Heidi; Werner, Martina; Daviso, Eugenio; Thomann, Ulrich

doi:10.3390/vaccines10101760

Cited by 3 publications

(7 citation statements)

References 15 publications

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“…The second is the antibody test (AT) (Jonczyk et al 2022 ), which gauges the earlier presence of SARS-CoV-2, i.e., the previous infection. AT does not identify present infections (McCarthy et al 1760 ). AT is utilized for public health inspection and epidemiologic goals.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: ELUCNN for explainable COVID-19 diagnosis

et al. 2023

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COVID-19 is a positive-sense single-stranded RNA virus caused by a strain of coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Several noteworthy variants of SARS-CoV-2 were declared by WHO as Alpha, Beta, Gamma, Delta, and Omicron. Till 13/Dec/2022, it has caused 6.65 million death tolls, and over 649 million confirmed positive cases. Based on the convolutional neural network (CNN), this study first proposes a ten-layer CNN as the backbone model. Then, the exponential linear unit (ELU) is introduced to replace ReLU, and the traditional convolutional block is now transformed into conv-ELU. Finally, an ELU-based CNN (ELUCNN) model is proposed for COVID-19 diagnosis. Besides, the MDA strategy is used to enhance the size of the training set. We develop a mobile app integrating ELUCNN, and this web app is run on a client–server modeled structure. Ten runs of the tenfold cross-validation experiment show our model yields a sensitivity of $$94.41\pm 0.98$$ 94.41 ± 0.98 , a specificity of $$94.84\pm 1.21$$ 94.84 ± 1.21 , an accuracy of $$94.62\pm 0.96$$ 94.62 ± 0.96 , and an F1 score of $$94.61\pm 0.95$$ 94.61 ± 0.95 . The ELUCNN model and mobile app are effective in COVID-19 diagnosis and give better results than 14 state-of-the-art COVID-19 diagnosis models concerning accuracy.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: ELUCNN for explainable COVID-19 diagnosis

et al. 2023

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“…As compared to the standard method where proteins were extracted using heat-induced reversal of cross-linking and ultrasonication followed by overnight trypsin digestion (Standard), we decided to incorporate AFA technology in this workflow for extraction of proteins from LCM tissues (AFA overnight). AFA technology uses focused high-frequency sound waves to lyse cells and extract biomolecules . Following extraction, digestion of proteins with trypsin is ∼12–16 hour long process.…”

Section: Resultsmentioning

confidence: 99%

“…AFA technology uses focused high-frequency sound waves to lyse cells and extract biomolecules. 15 Following extraction, digestion of proteins with trypsin is ∼12−16 hour long process. Though methods to reduce the digestion time have been previously published (e.g., S-trap), they are not ideally suited for low abundance samples such as those obtained by LCM.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

A High-Throughput Workflow for FFPE Tissue Proteomics

Pujari

Mangalaparthi

Madden

et al. 2023

J. Am. Soc. Mass Spectrom.

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Laser capture microdissection (LCM) has become an indispensable tool for mass spectrometry-based proteomic analysis of specific regions obtained from formalin-fixed paraffinembedded (FFPE) tissue samples in both clinical and research settings. Low protein yields from LCM samples along with laborious sample processing steps present challenges for proteomic analysis without sacrificing protein and peptide recovery. Automation of sample preparation workflows is still under development, especially for samples such as laser-capture microdissected tissues. Here, we present a simplified and rapid workflow using adaptive focused acoustics (AFA) technology for sample processing for high-throughput FFPE-based proteomics. We evaluated three different workflows: standard extraction method followed by overnight trypsin digestion, AFA-assisted extraction and overnight trypsin digestion, and AFA-assisted extraction simultaneously performed with trypsin digestion. The use of AFA-based ultrasonication enables automated sample processing for high-throughput proteomic analysis of LCM-FFPE tissues in 96-well and 384-well formats. Further, accelerated trypsin digestion combined with AFA dramatically reduced the overall processing times. LC− MS/MS analysis revealed a slightly higher number of protein and peptide identifications in AFA accelerated workflows compared to standard and AFA overnight workflows. Further, we did not observe any difference in the proportion of peptides identified with missed cleavages or deamidated peptides across the three different workflows. Overall, our results demonstrate that the workflow described in this study enables rapid and high-throughput sample processing with greatly reduced sample handling, which is amenable to automation.

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“…Microsampling technologies have been evaluated in biomarker research studies for the diagnosis and prognosis of various conditions and the prediction and monitoring of response to an intervention 24,41,42,132–144 . Omics‐based approaches, such as metabolomics and proteomics, are increasingly employed for the generation of large amounts of data to facilitate the rapid discovery of biomarkers.…”

Section: Applicationsmentioning

confidence: 99%

“…However, a bias between TASSO‐SST samples and venous samples was found to differ significantly in D‐dimer, IL‐1B and IL‐1Ra, which could be attributed to differences in user proficiency, temperature control and the time lag between sample collection and processing 42 . Microsampling has also been used for the serological analysis of SARS‐CoV‐2 infection in three studies 142–144 …”

Section: Applicationsmentioning

confidence: 99%

Blood microsampling technologies: Innovations and applications in 2022

Thangavelu

Wouters

Kindt

et al. 2023

Analytical Science Advances

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With the development of highly sensitive bioanalytical techniques, the volume of samples necessary for accurate analysis has reduced. Microsampling, the process of obtaining small amounts of blood, has thus gained popularity as it offers minimal‐invasiveness, reduced logistical costs and biohazard risks while simultaneously showing increased sample stability and a potential for the decentralization of the approach and at‐home self‐sampling. Although the benefits of microsampling have been recognised, its adoption in clinical practice has been slow. Several microsampling technologies and devices are currently available and employed in research studies for various biomedical applications. This review provides an overview of the state‐of‐the‐art in microsampling technology with a focus on the latest developments and advancements in the field of microsampling. Research published in the year 2022, including studies (i) developing strategies for the quantitation of analytes in microsamples and (ii) bridging and comparing the interchangeability between matrices and choice of technology for a given application, is reviewed to assess the advantages, challenges and limitations of the current state of microsampling. Successful implementation of microsampling in routine clinical care requires continued efforts for standardization and harmonization. Microsampling has been shown to facilitate data‐rich studies and a patient‐centric approach to healthcare and is foreseen to play a central role in the future digital revolution of healthcare through continuous monitoring to improve the quality of life.

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A Novel Dry-Stabilized Whole Blood Microsampling and Protein Extraction Method for Testing of SARS-CoV-2 Antibody Titers

Cited by 3 publications

References 15 publications

RETRACTED ARTICLE: ELUCNN for explainable COVID-19 diagnosis

RETRACTED ARTICLE: ELUCNN for explainable COVID-19 diagnosis

A High-Throughput Workflow for FFPE Tissue Proteomics

Blood microsampling technologies: Innovations and applications in 2022

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