Rapid advancements in high-throughput sequencing technologies allow for detailed and accurate measurement of omics features within their biological context. The integration of different omics types creates heterogeneous datasets, presenting challenges in analysis due to variations in measurement units, sample numbers, and features. Currently, there is a lack of generalized guidelines for making decisions in multi-omics study design (MOSD), such as selecting an appropriate number of samples and features, type of preprocessing and integration for robust analysis results. We propose a suggestive guideline for MOSD, involving nine important factors: sample size, feature selection, preprocessing strategy, noise characterization, class balance, number of classes, cancer subtype combination, omics combination, and clinical features. To assess the effectiveness of our proposed MOSD guidelines, we designed and conducted seven benchmark tests using 10 clustering methods on various TCGA cancer datasets with an objective of clustering cancer subtypes. The results indicated robust performance in terms of cancer subtype discrimination when adhering to the following criteria: 26 or more samples per class, selecting less than 10% of omics features, maintaining a sample balance under a 3:1 ratio, and keeping the noise level below 30%. Feature selection was particularly important, improving clustering performance by 34%. These findings provide evidence-based recommendations for MOSD, enabling researchers to optimize analytical approaches and enhance the reliability of results across cancer datasets. The proposed MOSD framework offers a suggestive guideline addressing both computational and biological factors for multi-omics data integration.

The pathogenesis of atopic dermatitis (AD) is multifactorial, including immune dysregulation and epidermal barrier defects, and a novel therapeutic modality that can simultaneously target multiple pathways is needed. We investigated the therapeutic effects of exosomes (IFN-γ-iExo) secreted from IFN-γ-primed induced pluripotent stem cell-derived mesenchymal stem cells (iMSC) in mice with Aspergillus fumigatus-induced AD. IFN-γ-iExo was epicutaneously administered to mice with AD-like skin lesions. The effects of IFN-γ-iExo treatment were investigated through clinical scores, transepidermal water loss (TEWL) measurements, and histopathology. To elucidate the therapeutic mechanism, we used an in vitro model of human keratinocyte HaCaT cells stimulated with IL-4 and IL-13 and performed extensive bioinformatics analysis of skin mRNA from mice. The expression of indoleamine 2,3-dioxygenase was higher in IFN-γ primed iMSCs than in iMSCs. In human keratinocyte HaCaT cells, treatment with IFN-γ-iExo led to decreases in the mRNA expression of thymic stromal lymphopoietin, IL-25, and IL-33 and increases in keratin 1, keratin 10, desmoglein 1, and ceramide synthase 3. IFN-γ-iExo treatment significantly improved clinical and histological outcomes in AD mice, including clinical scores, TEWL, inflammatory cell infiltration, and epidermal thickness. Bioinformatics analysis of skin mRNA from AD mice showed that IFN-γ-iExo treatment is predominantly involved in skin barrier function and T cell immune response. Treatment with IFN-γ-iExo improved the clinical and histological outcomes of AD mice, which were likely mediated by restoring proper skin barrier function and suppressing T cell-mediated immune response.

Multi-omics data is frequently measured to enrich the comprehension of biological mechanisms underlying certain phenotypes. However, due to the complex relations and high dimension of multi-omics data, it is difficult to associate omics features to certain biological traits of interest. For example, the clinically valuable breast cancer subtypes are well-defined at the molecular level, but are poorly classified using gene expression data. Here, we propose a multi-omics analysis method called MONTI (Multi-Omics Non-negative Tensor decomposition for Integrative analysis), which goal is to select multi-omics features that are able to represent trait specific characteristics. Here, we demonstrate the strength of multi-omics integrated analysis in terms of cancer subtyping. The multi-omics data are first integrated in a biologically meaningful manner to form a three dimensional tensor, which is then decomposed using a non-negative tensor decomposition method. From the result, MONTI selects highly informative subtype specific multi-omics features. MONTI was applied to three case studies of 597 breast cancer, 314 colon cancer, and 305 stomach cancer cohorts. For all the case studies, we found that the subtype classification accuracy significantly improved when utilizing all available multi-omics data. MONTI was able to detect subtype specific gene sets that showed to be strongly regulated by certain omics, from which correlation between omics types could be inferred. Furthermore, various clinical attributes of nine cancer types were analyzed using MONTI, which showed that some clinical attributes could be well explained using multi-omics data. We demonstrated that integrating multi-omics data in a gene centric manner improves detecting cancer subtype specific features and other clinical features, which may be used to further understand the molecular characteristics of interest. The software and data used in this study are available at: https://github.com/inukj/MONTI.

Drought tolerance is an important trait related to growth and yield in crop. Until now, drought related research has focused on coding genes. However, non-coding RNAs also respond significantly to environmental stimuli such as drought stress. Unfortunately, characterizing the role of siRNAs under drought stress is difficult since a large number of heterogenous siRNA species are expressed under drought stress and non-coding RNAs have very weak evolutionary conservation. Thus, to characterize the role of siRNAs, we need a well designed biological and bioinformatics strategy. In this paper, to characterize the function of siRNAs we developed and used a bioinformatics pipeline that includes a genomic-location based clustering technique and an evolutionary conservation tool. By comparing the wild type Nipponbare and two drought resistant rice varities, we found that 21 nt and 24 nt siRNAs are significantly expressed in the three rice plants but at different time points under a short-term (0, 1, and 6 hrs) drought treatment. siRNAs were up-regulated in the wild type at an early stage while the up-regulation was delayed in the two drought tolerant plants. Genes targeted by up-regulated siRNAs were related to oxidation reduction and proteolysis, which are well known to be associated with water deficit phenotypes. More interestingly, we found that siRNAs were located in intronic regions as clusters and were of high evolutionary conservation among monocot grass plants. In summary, we show that siRNAs are important respondents to drought stress and regulate genes related to the drought tolerance in water deficit conditions.

Understanding the immune response to COVID-19 is challenging due to its high variability among individuals. To identify differentially expressed cytokines between the deteriorating and recovering phases, we analyzed the Electronic Health Records (EHR) and cytokine profile data in a COVID-19 cohort of 444 infected patients and 145 non-infected healthy individuals. We categorized each patient’s progression into Deterioration Phase (DP) and Recovery Phase (RP) using longitudinal neutrophil, lymphocyte and lactate dehydrogenase levels. A random forest model was built using healthy and severe patients to compute the contribution of each cytokine toward disease progression using Shapley Additive Explanations (SHAP). SHAP values were used for supervised clustering to identify DP and RP-related samples and their associated cytokines. The identified clusters effectively discriminated DP and RP samples, suggesting that the cytokine profiles differed between deteriorating and recovering health conditions. Especially, CXCL10, GDF15, PTX3, and TNFSF10 were differentially expressed between the DP and RP samples, which are involved in the JAK-STAT, NF-\(\kappa\)B, and MAPK signaling pathways contributing to the inflammatory response. Collectively, we characterized the immune response in terms of disease progression of COVID-19 with deteriorating and recovering health conditions.