Diagnosis: Frontiers in blood testing

Sohn, Emily

doi:10.1038/549s16a

Cited by 22 publications

(19 citation statements)

References 7 publications

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“…The detection of enzymes overexpressed in tumor cells is one of the most widely applied strategies for the early diagnosis of cancers, providing more opportunities for early treatment of cancer. Methods for detecting tumor‐related enzymes have been widely developed, including endoscopy, [51] histopathology, [52] blood testing, [53] molecular imaging, [54] etc., but these methods lack intuitiveness. Thus, by virtue of its in‐situ as well as real‐time features, bioluminescence imaging of tumor‐related enzymes is of great research interest.…”

Section: D‐luciferin‐based Probes For Bioimagingmentioning

confidence: 99%

Activity‐Based Luciferase‐Luciferin Bioluminescence System for Bioimaging Applications

et al. 2021

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There is growing interest in applying bioluminescence for imaging varies biological processes in life sciences due to its high resolution, selectivity, and signal/noise ratio (no need of external light excitation). Among the diverse bioluminescence systems, firefly luciferin–luciferase system is considered to be the most popular one for bioimaging applications both in vitro and in vivo. The general design strategy of this system is to cage luciferin (i. e., free luciferin is protected with distinctive functional groups). When the protecting moieties are removed by their corresponding analytes, bioluminescence signal turns “on”, enabling people to understand their biological processes. Considering that d‐luciferin‐luciferase bioluminescence imaging system is now becoming a hot and cutting‐edge research topic, in this minireview, we briefly explain the bioluminescence mechanism and summarize the biological molecule‐responsive, d‐luciferin‐based bioluminescence imaging probes. Moreover, the attempts to optimize the photochemical properties of d ‐luciferin are also introduced. Current challenges and future perspectives for activity‐based luciferase‐luciferin bioluminescence system are also outlooked. With the understanding of the structure−property relationships and working mechanisms of these bioluminescence probes, we hope that this minireview might provide effective guidance for the development of bioluminescence imaging probes and even their clinical translations.

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Section: D‐luciferin‐based Probes For Bioimagingmentioning

confidence: 99%

Activity‐Based Luciferase‐Luciferin Bioluminescence System for Bioimaging Applications

et al. 2021

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“…FLIM has been used to study the difference between precancerous and cancerous cells [7] and to detect various malignancies in unstained human tissues [8][9][10][11][12]. As an important medium circulating in the whole body, blood carries a lot of information about infection, brain injuries, and cancers [13], including protein markers and circulating tumor DNA. The fluorescence spectroscopy of blood plasma has been reported as fingerprints of glucose metabolism in a rat model [14].…”

Section: Introductionmentioning

confidence: 99%

Metabolic Changes in Maternal and Cord Blood in One Case of Pregnancy-Associated Breast Cancer Seen by Fluorescence Lifetime Imaging Microscopy

et al. 2021

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Pregnancy-associated breast cancer (PABC) is a rare disease, which is frequently diagnosed at an advanced stage due to limitations in current diagnostic methods. In this study, fluorescence lifetime imaging microscopy (FLIM) was used to study the metabolic changes by measuring maternal blood and umbilical cord blood via the autofluorescence of coenzymes, reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H), and flavin adenine dinucleotide (FAD). The NAD(P)H data showed that a PABC case had significant differences compared with normal cases, which may indicate increased glycolysis. The FAD data showed that both maternal and cord blood of PABC had shorter mean lifetimes and higher bound-FAD ratios. The significant differences suggested that FLIM testing of blood samples may be a potential method to assist in PABC non-radiative screening.

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“…Blood is the pipeline of the human organism's physiology. The accessibility and minimal invasiveness during sampling has made it a feasible resource in scientific research and clinical diagnostics as they could replace more invasive and risky tests (Sohn, 2017). Because of utility and simplicity, blood transcriptome investigations on genome-wide scales have gained in popularity over the past few years.…”

Section: Introductionmentioning

confidence: 99%

The Human Blood Transcriptome in a Large Population Cohort and Its Relation to Aging and Health

et al. 2020

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Background: The blood transcriptome is expected to provide a detailed picture of an organism's physiological state with potential outcomes for applications in medical diagnostics and molecular and epidemiological research. We here present the analysis of blood specimens of 3,388 adult individuals, together with phenotype characteristics such as disease history, medication status, lifestyle factors, and body mass index (BMI). The size and heterogeneity of this data challenges analytics in terms of dimension reduction, knowledge mining, feature extraction, and data integration. Methods: Self-organizing maps (SOM)-machine learning was applied to study transcriptional states on a population-wide scale. This method permits a detailed description and visualization of the molecular heterogeneity of transcriptomes and of their association with different phenotypic features. Results: The diversity of transcriptomes is described by personalized SOM-portraits, which specify the samples in terms of modules of co-expressed genes of different functional context. We identified two major blood transcriptome types where type 1 was found more in men, the elderly, and overweight people and it upregulated genes associated with inflammation and increased heme metabolism, while type 2 was predominantly found in women, younger, and normal weight participants and it was associated with activated immune responses, transcriptional, ribosomal, mitochondrial, and telomere-maintenance cell-functions. We find a striking overlap of signatures shared by multiple diseases, aging, and obesity driven by an underlying common pattern, which was associated with the immune response and the increase of inflammatory processes. Conclusions: Machine learning applications for large and heterogeneous omics data provide a holistic view on the diversity of the human blood transcriptome. It provides a tool for comparative analyses of transcriptional signatures and of associated phenotypes in population studies and medical applications.

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Diagnosis: Frontiers in blood testing

Cited by 22 publications

References 7 publications

Activity‐Based Luciferase‐Luciferin Bioluminescence System for Bioimaging Applications

Activity‐Based Luciferase‐Luciferin Bioluminescence System for Bioimaging Applications

Metabolic Changes in Maternal and Cord Blood in One Case of Pregnancy-Associated Breast Cancer Seen by Fluorescence Lifetime Imaging Microscopy

The Human Blood Transcriptome in a Large Population Cohort and Its Relation to Aging and Health

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