Non-invasive diagnostic tool for Parkinson’s disease by sebum RNA profile with machine learning

Uehara, Yuya; Ueno, Shinichi; Amano-Takeshige, Haruka; Suzuki, Shuji; Imamichi, Yoko; Fujimaki, Mitsuhisa; Ota, Noriyasu; Murase, Takatoshi; Inoue, Tsukasa; Saiki, Sachio

doi:10.1038/s41598-021-98423-9

Cited by 18 publications

(23 citation statements)

References 45 publications

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“…This comprehensive analysis method using SSL-RNAs is expected to contribute to solving these clinical and research issues owing to advantageous features such as non-invasiveness and ease of sampling. Further, since the skin is called the disease-sensor organ and is believed to reflect the conditions inside the body 60 , 61 , the SSL-RNA analysis may have wide applicability to understand various pathologies of human body.…”

Section: Discussionmentioning

confidence: 99%

Non-invasive human skin transcriptome analysis using mRNA in skin surface lipids

et al. 2022

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Non-invasive acquisition of mRNA data from the skin can be extremely useful for understanding skin physiology and diseases. Inspired by the holocrine process, in which the sebaceous glands secrete cell contents into the sebum, we focused on the possible presence of mRNAs in skin surface lipids (SSLs). We found that measurable levels of human mRNAs exist in SSLs, where the sebum protects them from degradation by RNases. The AmpliSeq transcriptome analysis was modified to measure SSL-RNA levels, and our results revealed that the SSL-RNAs predominantly comprised mRNAs derived from sebaceous glands, the epidermis, and hair follicles. Analysis of SSL-RNAs non-invasively collected from patients with atopic dermatitis revealed increased expression of inflammation-related genes and decreased expression of terminal differentiation-related genes, consistent with the results of previous reports. Further, we found that lipid synthesis-related genes were downregulated in the sebaceous glands of patients with atopic dermatitis. These results indicate that the analysis of SSL-RNAs is a promising strategy to understand the pathophysiology of skin diseases.

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Section: Discussionmentioning

confidence: 99%

Non-invasive human skin transcriptome analysis using mRNA in skin surface lipids

et al. 2022

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“…Another research group 22 who used sensory array to detect the volatile organic compounds to identify patients with PD found sensitivity, specificity, and accuracy values of 79%, 84%, and 81%, respectively, in a small sample (29 PD, 19 control subjects). Uehara and colleagues 23 observed that by comparing sebum RNA profiles between patients with PD (23 drug naïve and 42 medicated) and controls (N = 65) they can efficiently discriminate these two groups of subjects, with similar sensitivity and specificity values, both being 69%. Our study, with double-blinding, well-matched controls, and a larger sample size, seems more robust.…”

Section: Discussionmentioning

confidence: 99%

Sensitivity of Sniffer Dogs for a Diagnosis of Parkinson's Disease: A Diagnostic Accuracy Study

Gao

Wang

et al. 2022

Movement Disorders

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BackgroundThe diagnostic criteria for Parkinson's disease (PD) remain complex, which is especially problematic for nonmovement disorder experts. A test is required to establish a diagnosis of PD with improved accuracy and reproducibility.ObjectiveThe study aimed to investigate the sensitivity and specificity of tests using sniffer dogs to diagnose PD.MethodsA prospective, diagnostic case‐control study was conducted in four tertiary medical centers in China to evaluate the accuracy of sniffer dogs to distinguish between 109 clinically established medicated patients with PD, 654 subjects without PD, 37 drug‐naïve patients with PD, and 185 non‐PD controls. The primary outcomes were sensitivity and specificity of sniffer dog's identification.ResultsIn the study with patients who were medicated, when two or all three sniffer dogs yielded positive detection results in a sample tested, the index test sensitivity, specificity, and positive and negative likelihood ratios were 91% (95% CI: 84%–96%), 95% (95% CI: 93%–97%), and 19.16 (95% CI: 13.52–27.16) and 0.10 (95% CI: 0.05–0.17), respectively. The corresponding sensitivity, specificity, and positive and negative likelihood ratios in patients who were drug‐naïve were 89% (95% CI: 75%–96%), 86% (95% CI: 81%–91%), and 6.6 (95% CI: 4.51–9.66) and 0.13 (95% CI: 0.05–0.32), respectively.ConclusionsTests using sniffer dogs may be a useful, noninvasive, fast, and cost‐effective method to identify patients with PD in community screening and health prevention checkups as well as in neurological practice. © 2022 International Parkinson and Movement Disorder Society.

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“…The collection of SSLs and purification of SSL‐RNAs were performed according to previous reports with slight modifications 19,24 . In brief, sebum specimens were collected from whole faces of the study participants by wiping the skin surface for <1 min using a single sheet of oil‐blotting film (5.0 × 8.0 cm; 3 M Japan, Tokyo, Japan) per participant.…”

Section: Methodsmentioning

confidence: 99%

“…Data analyses were performed according to previous reports 19,24 . The normalization and statistical analysis of the AmpliSeq transcriptomic data were performed using R language.…”

Section: Methodsmentioning

confidence: 99%

“…The collection of SSLs and purification of SSL-RNAs were performed according to previous reports with slight modifications. 19,24 In brief, sebum specimens were collected from whole faces of the study participants by wiping the skin surface for <1 min using a single sheet of oil-blotting film (5.0 9 8.0 cm; 3 M Japan, Tokyo, Japan) per participant. The film was then cut into small pieces and RNA was extracted using QIAzol reagent (QIAGEN, Hilden, Germany), followed by purification using the RNeasy Mini kit (QIAGEN).…”

Section: Collection Of Ssls Rna Preparation and Ampliseq Transcriptom...mentioning

confidence: 99%

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Non‐invasive transcriptomic analysis using mRNAs in skin surface lipids obtained from children with mild‐to‐moderate atopic dermatitis

Shima

Inoue

Uehara

et al. 2022

Acad Dermatol Venereol

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Background Specimens for analysing the molecular pathology of skin disease are generally obtained through invasive methods, such as biopsy. However, less burdensome methods are desirable for paediatric patients. We recently established a method that comprehensively analyses RNA present in sebum (skin surface lipid–RNAs: SSL‐RNAs) using a next‐generation sequencer. Using this method, biological information can be obtained from the skin in a completely non‐invasive manner. Objectives To verify the applicability of the SSL‐RNA method for analysis of paediatric skin and analyse the molecular pathology of mild‐to‐moderate atopic dermatitis (AD) in children. Methods We collected sebum specimens from the whole faces of 23 healthy children and 16 children with mild‐to‐moderate AD (eczema area and severity index (EASI) score: 5.9 ± 2.6) ranging in age from 6 months to 5 years, using an oil‐blotting film. We then extracted SSL‐RNAs from the samples and performed an AmpliSeq transcriptomic analysis. Results The expressions of genes related to keratinization (LCE, PSORS1C2, IVL and KRT17), triglyceride synthesis and storage (PLIN2, DGAT2 and CIDEA), wax synthesis (FAR2), ceramide synthesis (GBA2, SMPD3 and SPTLC3), antimicrobial peptides (DEFB1) and intercellular adhesion (CDSN), all of which are related to the skin barrier, are lower in children with AD than in healthy children. The children with AD also have higher expression of CCL17, a Th2‐cytokine and an increased Th2‐immune response as demonstrated by a gene set variation analysis. Moreover, KRT17 and CCL17 expression levels are significantly correlated with the EASI score. Conclusions Molecular changes associated with abnormal immune responses and the epidermal barrier in children with mild‐to‐moderate AD can be determined using the SSL‐RNA method. This non‐invasive method could therefore be a useful means for understanding the molecular pathology of paediatric AD.

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Non-invasive diagnostic tool for Parkinson’s disease by sebum RNA profile with machine learning

Cited by 18 publications

References 45 publications

Non-invasive human skin transcriptome analysis using mRNA in skin surface lipids

Non-invasive human skin transcriptome analysis using mRNA in skin surface lipids

Sensitivity of Sniffer Dogs for a Diagnosis of Parkinson's Disease: A Diagnostic Accuracy Study

Non‐invasive transcriptomic analysis using mRNAs in skin surface lipids obtained from children with mild‐to‐moderate atopic dermatitis

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