Minimally invasive skin sampling and transcriptome analysis using microneedles for skin type biomarker research

Development of a biomarker-based platform for comprehensive skin characterization using minimally invasive skin sampling and quantitative real-time PCR

BACKGROUND

Classifying diverse skin types is crucial for promoting skin health. However, efficiently identifying and analyzing relevant biomarkers from a vast array of available genetic data is challenging. Therefore, this study aimed to develop a precise and efficient platform for analyzing specific skin biomarkers using quantitative real-time PCR (qRT-PCR) with the minimal invasive skin sampling method (MISSM).

MATERIALS AND METHODS

MISSM was used for RNA extraction from skin samples, followed by qRT-PCR analysis to quantify the expression of 20 biomarkers associated with skin characteristics (four biomarkers each for five skin characteristics). Noninvasive measurements from 299 Korean participants were utilized to correlate biomarker expression with skin parameters. Statistical analyses were conducted between biomarker expression levels and noninvasive skin measurements to select the relatively best-performing biomarker for each skin characteristic.

RESULTS

Collagen type 1 alpha 1 (COL1A1) and moesin (MSN) were identified as skin aging biomarkers. Krüppel-like factor 4 (KLF4) and serine peptidase inhibitor Kazal type 5 (SPINK5) were identified as skin dryness biomarkers, whereas melan-A (MLANA) was selected as a biomarker for understanding pigmentation dynamics. Myelin protein zero like 3 (MPZL3) and high mobility group box 2 (HMGB2) were identified as markers of oily skin and skin sensitivity, respectively. Statistically significant correlations were found between the biomarker expression levels and noninvasive skin characteristic measurements.

CONCLUSION

This study successfully developed a platform for the precise evaluation of individual skin characteristics using MISSM and qRT-PCR biomarker analysis. By selecting biomarkers that correlate with noninvasive measurements of skin characteristics, we demonstrated the platform's efficacy in assessing diverse skin conditions.

A variational autoencoder trained with priors from canonical pathways increases the interpretability of transcriptome data

Interpreting transcriptome data is an important yet challenging aspect of bioinformatic analysis. While gene set enrichment analysis is a standard tool for interpreting regulatory changes, we utilize deep learning techniques, specifically autoencoder architectures, to learn latent variables that drive transcriptome signals. We investigate whether simple, variational autoencoder (VAE), and beta-weighted VAE are capable of learning reduced representations of transcriptomes that retain critical biological information. We propose a novel VAE that utilizes priors from biological data to direct the network to learn a representation of the transcriptome that is based on understandable biological concepts. After benchmarking five different autoencoder architectures, we found that each succeeded in reducing the transcriptomes to 50 latent dimensions, which captured enough variation for accurate reconstruction. The simple, fully connected autoencoder, performs best across the benchmarks, but lacks the characteristic of having directly interpretable latent dimensions. The beta-weighted, prior-informed VAE implementation is able to solve the benchmarking tasks, and provide semantically accurate latent features equating to biological pathways. This study opens a new direction for differential pathway analysis in transcriptomics with increased transparency and interpretability.

Withania somnifera root extract inhibits MGO-induced skin fibroblast cells dysfunction via ECM-integrin interaction

ETHNOPHARMACOLOGICAL RELEVANCE

Withania somnifera (L.) Dunal, known as Ashwagandha, has long been used in traditional medicine in Ayurveda, India, a representative adaptogen. The main active constituents of W. somnifera are withanolides, and the root is often used as a medicine with a wide range of pharmacological activities, which can be used to treat insomnia, neurasthenia, diabetes mellitus and skin cancer.

AIM OF THE STUDY

Whole-component qualitative and quantitative analyses were performed on W. somnifera. We explored the ameliorative effect of the adaptogen representative plant W. somnifera on the senescence events of MGO-injured fibroblasts and its action mechanism and verified the hypotheses that WS can inhibit the accumulation of AGEs and regulate the dynamic balance among the components of the ECM by modulating the expression of integrin β1 receptor; as a result, WS maintains cellular behavioural and biological functions in a normal range and retards the aging of skin from the cellular level.

MATERIALS AND METHODS

In this study, the components of WS were first qualitatively and quantitatively analysed by HPLC fingerprinting and LC-MS detection. Second, a model of MGO-induced injury of CML-overexpressing fibroblasts was established. ELISA was used to detect CML expression and the synthesis of key extracellular matrix ECM protein components COL1, FN1, LM5 and TNC synthesis; CCK-8 was used to detect cell viability; EDU was used to detect cell proliferation capacity; fluorescence was used to detect cell adhesion capacity; and migration assay were used to detect cell migration capacity; qRT-PCR was used to detect the regulatory pathway TGF-β1 and MMP-2, MMP-9 in ECMs; immunofluorescence was used to detect the expression of ITGB1; and WB was used to detect the expression of COL1, FN1, LM5, Tnc, TGF-β1, MMP-2, MMP-9 and ITGB1.

RESULTS

In total, 27 active ingredients were analysed from WS, which mainly consisted of withanolide components, such as withaferin A and withanolide A. Based on the model of MGO-induced fibroblast senescence injury, WS significantly inhibited CML synthesis. By up-regulating the expression of integrin β1, it upregulated the expression of the TGF-β1 gene, which is closely related to the generation of ECMs, downregulated the expression of the MMP-2 and MMP-9 genes, which are closely related to the degradation of ECMs, maintained the dynamic balance of the four types of ECMs, and improved cell viability as well as proliferation, migration and adhesion abilities.

CONCLUSIONS

WS can prevent cellular behavioural dysfunction and delay skin ageing by reducing the accumulation of CML, upregulating the expression of the ITGB1 receptor, maintaining the normal function of ECM-integrin receptor interaction and preventing an imbalance between the production and degradation of protein components of ECMs. The findings reported in this study suggest that WS as a CML inhibitor can modulate ECM-integrin homeostasis and has great potential in the field of aging retardation.

Microneedles: A novel clinical technology for evaluating skin characteristics

BACKGROUND

Current methods for evaluating efficacy of cosmetics have limitations because they cannot accurately measure changes in the dermis. Skin sampling using microneedles allows identification of skin-type biomarkers, monitoring treatment for skin inflammatory diseases, and evaluating efficacy of anti-aging and anti-pigmentation products.

MATERIALS AND METHODS

Two studies were conducted: First, 20 participants received anti-aging treatment; second, 20 participants received anti-pigmentation treatment. Non-invasive devices measured skin aging (using high-resolution 3D-imaging in the anti-aging study) or pigmentation (using spectrophotometry in the anti-pigmentation study) at weeks 0 and 4, and adverse skin reactions were monitored. Skin samples were collected with biocompatible microneedle patches. Changes in expression of biomarkers for skin aging and pigmentation were analyzed using qRT-PCR.

RESULTS

No adverse events were reported. In the anti-aging study, after 4 weeks, skin roughness significantly improved in 17 out of 20 participants. qRT-PCR showed significantly increased expression of skin-aging related biomarkers: PINK1 in 16/20 participants, COL1A1 in 17/20 participants, and MSN in 16/20 participants. In the anti-pigmentation study, after 4 weeks, skin lightness significantly improved in 16/20 participants. qRT-PCR showed significantly increased expression of skin-pigmentation-related biomarkers: SOD1 in 15/20 participants and Vitamin D Receptor (VDR) in 15/20 participants. No significant change in TFAP2A was observed.

CONCLUSION

Skin sampling and mRNA analysis for biomarkers provides a novel, objective, quantitative method for measuring changes in the dermis and evaluating the efficacy of cosmetics. This approach complements existing evaluation methods and has potential application in assessing the effectiveness of medical devices, medications, cosmeceuticals, healthy foods, and beauty devices.

An ensemble-based deep learning model for detection of mutation causing cutaneous melanoma

When the mutation affects the melanocytes of the body, a condition called melanoma results which is one of the deadliest skin cancers. Early detection of cutaneous melanoma is vital for raising the chances of survival. Melanoma can be due to inherited defective genes or due to environmental factors such as excessive sun exposure. The accuracy of the state-of-the-art computer-aided diagnosis systems is unsatisfactory. Moreover, the major drawback of medical imaging is the shortage of labeled data. Generalized classifiers are required to diagnose melanoma to avoid overfitting the dataset. To address these issues, blending ensemble-based deep learning (BEDLM-CMS) model is proposed to detect mutation of cutaneous melanoma by integrating long short-term memory (LSTM), Bi-directional LSTM (BLSTM) and gated recurrent unit (GRU) architectures. The dataset used in the proposed study contains 2608 human samples and 6778 mutations in total along with 75 types of genes. The most prominent genes that function as biomarkers for early diagnosis and prognosis are utilized. Multiple extraction techniques are used in this study to extract the most-prominent features. Afterwards, we applied different DL models optimized through grid search technique to diagnose melanoma. The validity of the results is confirmed using several techniques, including tenfold cross validation (10-FCVT), independent set (IST), and self-consistency (SCT). For validation of the results multiple metrics are used which include accuracy, specificity, sensitivity, and Matthews's correlation coefficient. BEDLM gives the highest accuracy of 97% in the independent set test whereas in self-consistency test and tenfold cross validation test it gives 94% and 93% accuracy, respectively. Accuracy of in self-consistency test, independent set test, and tenfold cross validation test is LSTM (96%, 94%, 92%), GRU (93%, 94%, 91%), and BLSTM (99%, 98%, 93%), respectively. The findings demonstrate that the proposed BEDLM-CMS can be used effectively applied for early diagnosis and treatment efficacy evaluation of cutaneous melanoma.

Assessment of Treatment-Relevant Immune Biomarkers in Psoriasis and Atopic Dermatitis: Toward Personalized Medicine in Dermatology

Immunologically targeted therapies have revolutionized the treatment of inflammatory dermatoses, including atopic dermatitis and psoriasis. Although immunologic biomarkers hold great promise for personalized classification of skin disease and tailored therapy selection, there are no approved or widely used approaches for this in dermatology. This review summarizes the translational immunologic approaches to measuring treatment-relevant biomarkers in inflammatory skin conditions. Tape strip profiling, microneedle-based biomarker patches, molecular profiling from epidermal curettage, RNA in situ hybridization tissue staining, and single-cell RNA sequencing have been described. We discuss the advantages and limitations of each and open questions for the future of personalized medicine in inflammatory skin disease.

Where Microneedle Meets Biomarkers: Futuristic Application for Diagnosing and Monitoring Localized External Organ Diseases

Extracellular tissue fluids are interesting biomatrices that have recently attracted scientists' interest. Many significant biomarkers for localized external organ diseases have been isolated from this biofluid. In the diagnostic and disease monitoring context, measuring biochemical entities from the fluids surrounding the diseased tissues may give more important clinical value than measuring them at a systemic level. Despite all these facts, pushing tissue fluid-based diagnosis and monitoring forward to clinical settings faces one major problem: its accessibility. Most extracellular tissue fluid, such as interstitial fluid (ISF), is abundant but hard to collect, and the currently available technologies are invasive and expensive. This is where novel microneedle technology can help tackle this significant obstacle. The ability of microneedle technology to minimally invasively access tissue fluid-containing biomarkers will enable ISF and other tissue fluid utilization in the clinical diagnosis and monitoring of localized diseases. This review attempts to present the current pursuit of the application of microneedle systems as a diagnostic and monitoring platform, along with the recent progress of biomarker detection in diagnosing and monitoring localized external organ diseases. Then, the potential use of various microneedles in future clinical diagnostics and monitoring of localized diseases is discussed by presenting the currently studied cases.