Immune checkpoint blockade (ICB) targeting PD-1/PD-L1 axis has transformed breast cancer treatment, yet how therapy reshapes the tumor microenvironment (TME) through cell-cell communication (CCC) remains unclear. Existing CCC inference methods relying on correlations have difficulty distinguishing genuine signaling from confounded associations. Here, we present a causal inference framework that uses single-cell data and leverages treatment as an instrumental variable to identify genuine CCC networks, referred to as scIVCCC, which infers causal signal transduction across cell types. Applying scIVCCC to single-cell RNA-seq data from 31 breast cancer patients before and after anti-PD-1 therapy, we constructed causal CCC networks linking exhausted T cells to tumor-associated macrophages (TAMs). Our analysis reveals a dual role of T cell-macrophage crosstalk: CD4+ and CD8+ exhausted T cells drive anti-tumor M1-like TAMs activation via TNF-TNFRSF1A, TNFSF14-LTBR, and ICAM1-ITGAL/ITGB2. Conversely, they also induce immunosuppressive M2-like polarization through pathways such as TNF-TNFRSF1B (TNFR2), TNFSF14-TNFRSF14 (HVEM), and RPS19-C5AR1, which likely contribute to therapeutic resistance. Our causal modeling suggests that receptors within these networks, such as C5AR1, TNFR2, and CSF1R, may serve as potential candidates for combination therapies to enhance anti-PD-1 efficacy. Collectively, these findings demonstrate that scIVCCC offers a robust framework for dissecting treatment-induced CCC dynamics and prioritizing actionable targets for clinical translation.

The majority of mortality during viral infections occurs in older males; however, underlying mechanisms by which age and sex shape antiviral immunity and pathological inflammatory responses remain incompletely understood. Here, we performed time-resolved single-cell RNA sequencing across 16 conditions spanning age, sex, and four stages of influenza infection in mice, generating a high-resolution atlas. Aged mice demonstrate delayed antiviral and inflammatory responses in multiple myeloid cells, impairing viral clearance, which delays recovery. Similarly, endothelial cells from aging mice show prolonged inflammatory and antiviral gene signatures. Altered gene signatures in immune and endothelial cells result in a shift in endothelial-immune interactions in the aged lung. Further, the infection status of the cell is a major driver of transcriptional state, with infected myeloid cells exhibiting broad upregulation of genes, including interferon-stimulated, inflammatory, complement, and oxidative stress-related genes. To assess whether these age-associated transcriptional patterns are conserved in humans, we examined BAL cells obtained from healthy individuals and COVID-19 patients, and found that immune cells from aged COVID-19 patients had elevated antiviral and pro-inflammatory gene expression compared to cells from young patients. Our analyses of sex differences identified that multiple myeloid cell types in aged male mice, but not in young male mice, show persistent inflammatory responses at later stages of infection, a likely mechanism contributing to elevated mortality in older males. These data reveal how infection status of the cell, age, and sex interact to drive persistent inflammation and impaired resolution, providing a foundational resource for designing age- and sex-specific therapeutic strategies.

MOTIVATION:Gene set analysis (GSA) is a foundational approach for interpreting genomic data of diseases by linking genes to biological processes. However, conventional GSA methods overlook clinical context of the analyses, often generating long lists of enriched pathways with redundant, nonspecific, or irrelevant results. Interpreting these requires extensive, ad-hoc manual effort, reducing both reliability and reproducibility. RESULTS:We introduce cGSA, a novel AI-driven framework that enhances GSA by incorporating context-aware pathway prioritization. cGSA integrates gene cluster detection, enrichment analysis, and large language models to identify pathways that are not only statistically significant but also biologically meaningful. Benchmarking on 102 curated gene sets across 19 diseases and ten disease-related biological mechanisms shows that cGSA outperforms baseline methods by over 30%, with expert validation confirming its increased precision and interpretability. Two independent case studies in melanoma and breast cancer further demonstrate its potential to uncover context-specific insights and support targeted hypothesis. AVAILABILITY AND IMPLEMENTATION:The demo website is publicly available at https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/cGSA/, while the data and code can be accessed at https://github.com/ncbi-nlp/cGSA.

Although postharvest starch degradation was extensively studied, its initiation site and distribution patterns remain unclear. This study investigated the spatiotemporal dynamics of starch metabolism in sweetpotato tubers along vertical (top, middle, bottom) and radial directions (phloem, outer xylem, inner xylem) under 4 kV·m-1 high voltage alternating electric field (HVAEF) treatment stored at 13 °C for 8 weeks. Results illustrated that starch degradation was first detected in the middle region during early storage, and extended to the bottom and top, whereas HVAEF's inhibitory effect concentrated in the middle and top zones. HVAEF exhibited tissue- and stage-dependent preservation effects, reducing starch content loss by up to 30% in outer xylem at week 4 and reducing soluble sugar accumulation by up to 40% in middle phloem at week 6-8 and middle outer xylem at week 4. Meanwhile, HVAEF was associated with a more uniform spatial distribution of soluble sugar, better preservation of starch granule integrity and cell wall structure, and lower amylase activities. Moreover, HVAEF correlated with downregulated INT1 expression, which showed relatively higher expression tendency in the middle xylem, and upregulated SWEET1 expression, which showed relatively higher expression tendency in the top phloem, suggesting a correlation between HVAEF treatment and tissue-specific carbohydrate redistribution during storage. This study revealed the spatiotemporal characteristics of HVAEF-regulated starch metabolism and highlights its potential as a green physical technology for postharvest quality preservation.

Cells within a tissue microenvironment communicate through intricate cell-cell communication (CCC) networks. In this meta-analysis of eight single-cell cohorts encompassing 153 patients and 279 samples, we advance the understanding of CCC networks in colorectal cancers through a novel analytical framework. Employing hierarchical topic modeling, we identify gene expression modules (GEMs) that mirror single-cell signaling states, crucial for deciphering the complexity of intercellular interactions. By applying causal discovery methods, we systematically uncover GEMs likely regulated by ligand-receptor signaling and cross-cell-type communication. This analysis reveals cross-cell-type CCC programs, marked by highly correlated GEMs across various cell types, shedding light on the intricate CCC networks within the tumor microenvironment. Spatial transcriptomics further validate these findings by demonstrating the co-localization of GEMs within CCC programs in distinct spatial domains, emphasizing the spatial dynamics of tumor intercellular communication. Our interactive website ( http://44.192.10.166:3838/ ) and analytical framework equip researchers with powerful tools to explore these complex mechanisms, potentially uncovering novel drug targets and refining strategies for precision immunotherapies. This comprehensive study not only presents a detailed catalog of CCC networks driven by ligand-receptor interactions in colorectal cancer but also highlights the significance of integrating multi-sample and patient data to unravel the molecular underpinnings of cancer communication pathways.

Cancer is mainly caused by a relatively small portion of somatic genome alterations (SGAs), called cancer drivers. Despite success in identifying a good number of cancer drivers, many more remain to be discovered to explain various cancers. Moreover, limited tools are available to identify potential interactions among cancer drivers for a better understanding of oncogenesis. To tackle these challenges, we have developed a novel approach called individualized Bayesian inference using a decision tree (IBI-DT). IBI-DT recognizes the genetic heterogeneity among cancer patients, where different individuals or patient subgroups of distinct genomic makeup may have different drivers. IBI-DT works by constructing smaller subgroups with similar genetic makeup (i.e. patient-like-me subgroups) using a decision tree structure and analyzing multiple trees to identify the SGAs that play a significant role in regulating downstream gene expression patterns at the subgroup and individual levels. This is distinct from population-based approaches, which tend to evaluate the influence of an SGA for the entire population, thereby likely missing low-frequency SGAs that may well explain a small subgroup of cancer patients. Also importantly, IBI-DT can efficiently identify cancer drivers that may have functional interactions. We applied IBI-DT to identify cancer drivers regulating the downstream differential gene expression in cancer patients and compared it to the standard, population-based method of expression quantitative trait loci analysis. Our results show that IBI-DT performs well in identifying both important cancer drivers, especially the low-frequency drivers, and their interactions, allowing for a better understanding of the cancer signaling pathways.

e14585 Background: Immune checkpoint blockade (ICB) targeting PD-1/PD-L1 is crucial in treating breast cancer. Despite clinical success, the mechanisms by which ICB therapies reshape the tumor microenvironment (TME) and enhance anti-tumor activity remain unclear. Recent studies reveal that anti-PD-1 therapies broadly alter TME cells beyond PD-1+ T cells, highlighting extensive cell-cell communication (CCC) as a key factor. Understanding CCC changes in response to anti-PD-1 therapy can illuminate its mechanisms of action (MOA) and TME dynamics. This study applies causal inference methodology to investigate CCC between T cells and non-T cells in the TME of breast cancer patients receiving anti-PD-1 therapy. Methods: We analyzed single-cell RNA-seq data from 31 breast cancer patients pre- and on-treatment with anti-PD-1 (pembrolizumab) reported by Bassez et al., 2021. We identified differentially expressed genes (DEGs) across cell types. We applied the instrumental variable method to identify causal relationships between signals in T-cell and non-T-cell DEGs to uncover treatment-induced CCC. We further searched for ligand-receptor pairs mediating CCC and identified gene expression modules (GEMs) regulated by these ligand-receptors. Finally, we constructed a CCC network from T to non-T cells. Results: Anti-PD1 treatment induced broad gene expression changes in diverse cell populations. Major T cell pathways influenced by anti-PD-1 therapy include NF-κB, interferon-γ and interleukins. CD4 + and CD8 + exhausted cells, expressing high levels of PDCD1 (encoding PD-1), exhibited distinct activated pathways. We identified the CCC network from T cells to non-T cells via ligand-receptor interactions. For example, anti-PD1 treatment activated cellular stress, apoptosis, and pro-inflammatory cytokine signaling pathways in T cells, altering the expression of a GEM that included TSC22D3 and TXNIP . This initiated signaling to myeloid cells via RPS19 - C5AR1 , leading to NF-κB activation. CD4+ exhausted T cells primarily signaled to monocytes, enhancing helper functions to recruit and activate monocytes, thereby promoting immune regulation and amplification. In contrast, CD8+ exhausted T cells primarily interacted with macrophages, intensifying cytotoxic responses that facilitated effective tumor-cell killing. Conclusions: Our analyses provide insights into the dynamic interplay of cells within the TMEs during anti-PD-1 therapy. These findings could facilitate the identification of new biomarkers for predicting heterogeneous treatment responses to anti-PD-1 regimens, potentially enhancing the design and customization of immunotherapeutic strategies for breast cancer patients. Finally, this study demonstrated the utility of causal inference methodologies for mechanistic studies of CCC.

Understanding tumor heterogeneity at the resolution of individualized cell-cell communication networks (CCCNs) remains a major computational challenge in precision oncology. Existing inference methods largely rely on population-level correlation and thus fail to capture patient-specific signaling patterns across diverse cell types. To address this limitation, we developed an integrative computational framework combining the nested hierarchical Dirichlet process (nHDP) model for identifying hierarchically structured gene expression modules, with instance-specific Greedy Fast Causal Inference (iGFCI) for inferring individualized CCCNs (iCCCNs) in colorectal cancer (CRC). Applied to large-scale single-cell RNA-seq data from over 625,000 cells, our model successfully decomposed complex gene expression modules GEMs, potentially representing the cell lineage and cellular signaling states, and uncovered iCCCNs across detailed cell subtypes. We further used TCGA bulk RNA-seq data with survival data to validate the clinical relevance of these individualized gene expression module causal interactions, demonstrating their potential as robust prognostic signatures in CRC. Finally, we used principled causal inference methods to search for ligand-receptor pairs that mediate cell-cell communication. This framework enables mechanistic insights into immune evasion. Our computational method represents a significant advance toward realizing personalized oncology, enabling precise patient stratification and identification of actionable biomarkers for improved therapeutic targeting in cancers.

High Voltage Alternating Electric Field (HVAEF), as an emerging postharvest preservation technology, is environmentally friendly and has garnered significant attention from scholars. This study investigated the effects of HVAEF on the postharvest quality and metabolite changes in 'Shine Muscat' grapes stored at near-freezing temperatures. The results demonstrated that HVAEF halved the weight loss and decay rate, inhibited browning by preserving polyphenol content, and reduced polyphenol oxidase and peroxidase activities, thereby extending the storage time by more than 20 days. Metabolomic analysis of the grape berries from the upper, middle, and bottom parts of the bunch revealed that the upper berries contained higher sugar levels and were more significantly influenced by HVAEF treatment. Additionally, HVAEF notably reduced the accumulation of amino acids, such as valine, leucine, and lysine, compared to the control. This study provides new insights into the enhancement of quality in fresh fruits and vegetables through HVAEF technology.

Pumpkin is rich in nutritional value, and it can be eaten as a vegetable or as a staple food, making it popular among modern consumers. However, after fresh cutting, pumpkins are susceptible to moisture loss, softening, microbial contamination, and browning, all of which significantly compromise their quality during storage. Therefore, it is essential to develop effective preservation techniques for maintaining the quality of fresh-cut pumpkins. Nisin, a safe natural preservative, has not yet been studied for use on fresh-cut pumpkins. This study examines the effects of nisin treatment on the quality of fresh-cut pumpkins and then explores preservation mechanisms based on physiological and metabolomic analysis. Results show that 0.4 g/L nisin treatment effectively delays surface browning without impacting odor and maintains microbial safety throughout storage. Additionally, nisin significantly enhances the activities of phenylalanine ammonia-lyase, cinnamate-4-hydroxylase, 4-coumarate-CoA ligase, and cinnamyl alcohol dehydrogenase, thereby promoting the accumulation of total phenols and carotenoids. The result of the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment of differential metabolites between control and nisin-treated groups reveals that the most significant pathways affected by nisin treatment are amino acid metabolism and phenylpropanoid metabolism, which suggests that nisin enhances preservation by modulating phenylpropanoid metabolism and alleviating amino acid metabolism. This study provides a theoretical basis and offers new insights into improving the storage quality of fresh-cut pumpkins.

The quality deterioration of sweetpotatoes easily during postharvest storage owing to their high respiration rate and accumulation of reactive oxygen species (ROS). In this study, an edible coating (MCW) assembled using montmorillonite, chitosan, and whey protein isolate was fabricated to improve the postharvest quality of sweetpotatoes, and the regulatory mechanism of the edible coating treatment on sweetpotato quality has investigated. The results confirmed that edible coating treatment had a positive effect on maintaining the appearance, firmness, colour, and sensory quality of sweetpotatoes, especially in maintaining free water, starch and soluble protein content, inhibiting respiration rate and delaying weight loss of sweetpotatoes stored at 25 degrees C for 30 d. Meanwhile, this study illuminated the MCW coating regulates ROS metabolism by upregulating superoxide dismutase, ascorbate peroxidase, and glutathione reductase gene expression, resulting in lower O-2(-center dot) content in postharvest sweetpotato. Furthermore, the relative expression of the genes encoding lipoxygenase, phospholipase C, and phospholipase D was downregulated by the edible coating treatment and contributed to lower enzyme activity, electrolyte leakage and malondialdehyde content, which further reduced the degree of membrane lipid peroxidation and effectively maintained the structure of plasma membrane and then protected the cell integrity of sweetpotato. Collectively, this study provides useful information at the transcriptional level that MCW coating can enhance the post-harvest quality of sweetpotatoes by regulating ROS and membrane lipid metabolism.

Sweet potatoes are extremely vulnerable to mechanical wounds during harvesting and postharvest handling. It is highly necessary to take measures to accelerate wound healing. The effect of 20 g L−1 of ascorbic acid (AA) treatment on the wound healing of sweet potatoes and its mechanisms were studied. The results validated that AA treatment significantly reduced the weight loss rate and disease index. AA treatment effectively enhanced the formation speed of lignin and SPP at the wound sites, decreased the MDA content, and maintained the cell membrane integrity. AA enhanced the activities of PAL, C4H, 4CL, CAD, and POD and increased the contents of chlorogenic acid, caffeic acid, sinapic acid, ferulic acid, cinnamic acid, p-coumaryl alcohol, sinapyl alcohol, coniferyl alcohol, and lignin. Based on a transcriptomic analysis, a total of 1200 genes were differentially expressed at the sweet potato wound sites by the AA treatment, among which 700 genes were upregulated and 500 genes were downregulated. The KEGG pathway analysis showed that the differentially expressed genes were mainly involved in phenylalanine, tyrosine, and tryptophan biosynthesis; phenylpropanoid biosynthesis; and other wound healing-related pathways. As verified by a qRT-PCR, the AA treatment significantly upregulated the gene expression levels of IbSKDH, IbADT/PDT, IbPAL, and Ib4CL at the wound sties.

Dietary fibers have attracted much attention due to their multiple benefits on gut health. In this work, the protective mechanism of dietary fiber from sweetpotato residues (SRDF) on the high-fat diet (HFD)-induced intestinal barrier injury was investigated using microbiome-metabolomics-based approach. The physicochemical property analysis demonstrated a thermal stability below 200 °C and porous pectin-polysaccharide structure of SRDF with high in vitro functional activities. The biochemical analysis indicated that SRDF significantly ameliorated intestinal barrier function by improving intestinal morphology and permeability and inhibiting inflammatory response. Microbiome analysis demonstrated that SRDF significantly reversed the HFD-induced dysbacteriosis, decreased the ratio of Firmicutes/Bacteroides and enhanced the relative abundance of probiotics, such as Muribaculaceae and Bifidobacteriaceae. Metabolomics analysis showed that SRDF also significantly altered the metabolic profile in the colon, wherein the differential metabolites were mainly involved in amino acid metabolism (especially tryptophan). Pearson correlation coefficient identified the beneficial relationship between intestinal microbiome and metabolome induced by SRDF. The limitation of this study was that the mouse model may not fully replicate the human intestinal responses due to the difference between the standard environmental conditions and natural world. Generally, our results implied the great potential of SRDF as a functional food ingredient.

Immune checkpoint blockade (ICB) has revolutionized the current immuno-oncology and significantly improved clinical outcome for cancer treatment. Despite the advancement in clinics, only a small subset of patients derives immune response to the ICB therapy. Therefore, a robust predictive biomarker that identifies potential candidate becomes increasingly crucial in delivering this technology to the public. In this review, we first discuss the biomarkers that focus on tumor genome, tumor microenvironment and tumor-host interaction. Then, we compare existing databases for biomarker discovery for ICB response. We also present IOhub - an interactive web portal that incorporates 36 bulk and 10 single-cell transcriptome datasets for benchmark analysis of the current biomarkers. Finally, we highlight the trending interest in antibody drug conjugate and combination treatment and their use in precision immuno-oncology.

59 Background: Colorectal cancer (CRC) is the 3rd most common cancer. The consensus molecular subtypes among CRC tumors reflects the heterogeneity of composition and functional states of cells in the tumor microenvironments (TME), which in turn may underlie diverse behaviors of disease progression and responses to anticancer treatments. Cells within a TME communicate through intricate cell-cell communication (CCC) networks. The altered state of one cell can influence the states of other cells through ligand-receptor-mediated signal transduction. Gaining insight into the cellular states of cells and understanding their complex communications in a TME is vital for uncovering the heterogeneous mechanisms governing tumor growth, immune evasion, and therapy resistance. Methods: In this meta-analysis of eight single-cell cohorts encompassing 153 patients and 279 samples with more than 600,000 cells, we advance the understanding of CCC networks in CRC through a novel analytical framework. Employing hierarchical language modeling, we identify gene expression modules (GEMs) that mirror single-cell signaling states, crucial for deciphering the complexity of intercellular interactions. By applying causal discovery methods, we systematically uncover GEMs likely regulated by ligand-receptor signaling and cross-cell-type communication. We further validate the discovered CCC using spatial transcriptomic data by testing the spatial co-localization. Results: This analysis reveals nine cross-cell-type CCC programs, marked by highly correlated GEMs across various cell types, shedding light on the intricate CCC networks within the TME. The discovered CCC programs include malignant program, proliferation program, stromal interactions, stromal-myeloid interactions, innate immunity interactions, regulatory interactions, epithelial-lymphocyte interactions and exhaustion program. Each program is composed of GEMs from various cell types. Spatial transcriptomics further validate these findings by demonstrating the co-localization of GEMs within CCC programs in distinct spatial domains, emphasizing the spatial dynamics of tumor intercellular communication. We successfully construct the CCC subnetworks connected by ligand-receptor signaling. Our interactive website and analytical framework equip researchers with powerful tools to explore complex mechanisms, potentially uncovering novel drug targets and refining strategies for precision immunotherapies. Conclusions: This study provides an in-depth analysis of colorectal cancer by: 1) Cataloging GEMs that precisely depict the transcriptomic processes unique to individual cell clusters or shared among multiple cell clusters. 2) Presenting the CCC networks driven by ligand-receptor interactions within the TME supported by both single-cell RNA-seq data and spatial transcriptomic data.

Interactions between tumor cells and immune cells in the tumor microenvironment (TME) play a vital role the mechanisms of immune evasion, by which cancer cells escape immune elimination. Thus, the characterization and quantification of different components in the TME is a hot topic in molecular biology and drug discovery. Since the development of transcriptome sequencing in bulk tissue, single cells and spatial dimensions, there are increasing methods emerging to deconvolute and subtype the TME. This review discusses and compares such computational strategies and downstream subtyping analyses. Integrative analyses of the transcriptome with other data, such as epigenetics and T-cell receptor sequencing, are needed to obtain comprehensive knowledge of the dynamic TME.

The inhibitory properties and underlying mechanism of chlorine dioxide (ClO2) fumigation on the pathogen Ceratocystis fimbriata (C. fimbriata) and resultant sweetpotato black rot were investigated in vitro and in vivo. Results revealed that the ClO2 fumigation effectively inhibited fungal growth and induced obvious morphological variation of C. fimbriata mycelia. Furthermore, the mycelial membrane suffered damage, as evidenced by a significant increase in malondialdehyde content and the leakage of protein and nucleic acid from mycelia cells, accompanied by a marked decrease in ergosterol content. Additionally, ClO2 fumigation caused spores cell membrane damage, a notable decrease in spore viability, and induced cell apoptosis as indicated by reductions in spore germination rate, two fluorescence staining observations, and flow cytometry analysis. Moreover, the decay diameter of sweetpotato black rot lesions decreased significantly after ClO2 fumigation, and the growth of C. fimbriata was also inhibited. These findings present a novel and effective technology for inhibiting the progression of sweetpotato black rot.

Bulk transcriptomics in tissue samples reflects the average expression levels across different cell types and is highly influenced by cellular fractions. As such, it is critical to estimate cellular fractions to both deconfound differential expression analyses and infer cell type-specific differential expression. Since experimentally counting cells is infeasible in most tissues and studies, in silico cellular deconvolution methods have been developed as an alternative. However, existing methods are designed for tissues consisting of clearly distinguishable cell types and have difficulties estimating highly correlated or rare cell types. To address this challenge, we propose hierarchical deconvolution (HiDecon) that uses single-cell RNA sequencing references and a hierarchical cell-type tree, which models the similarities among cell types and cell differentiation relationships, to estimate cellular fractions in bulk data. By coordinating cell fractions across layers of the hierarchical tree, cellular fraction information is passed up and down the tree, which helps correct estimation biases by pooling information across related cell types. The flexible hierarchical tree structure also enables estimating rare cell fractions by splitting the tree to higher resolutions. Through simulations and real data applications with the ground truth of measured cellular fractions, we demonstrate that HiDecon outperforms existing methods and accurately estimates cellular fractions. Finally, we show the utility of HiDecon estimates in identifying the associations between cellular fractions and Alzheimer's disease.

Cancers result from aberrations in cellular signaling systems, typically resulting from driver somatic genome alterations (SGAs) in individual tumors. Precision oncology requires understanding the cellular state and selecting medications that induce vulnerability in cancer cells under such conditions. To this end, we developed a computational framework consisting of two components: 1) A representation-learning component, which learns a representation of the cellular signaling systems when perturbed by SGAs, using a biologically-motivated and interpretable deep learning model. 2) A drug-response-prediction component, which predicts the response to drugs by leveraging the information of the cellular state of the cancer cells derived by the first component. Our cell-state-oriented framework significantly enhances the accuracy of genome-informed prediction of drug responses in comparison to models that directly use SGAs as inputs. Importantly, our framework enables the prediction of response to chemotherapy agents based on SGAs, thus expanding genome-informed precision oncology beyond molecularly targeted drugs.

Changes in the content of trans-caryophyllene could identify whether sweetpotatoes are infected with black spot disease. Therefore, in this study, a quartz crystal microbalance (QCM) sensor based on modified CAU-1@ZIF-8 was used for non-destructive detection of sweetpotato black spot. The experimental results showed that the constructed sensor performed best when both modified CAU-1 and ZIF-8 were present. The sensor showed a linear trend (R-2 = 0.996, limit of detection (LOD) = 0.55 ppm) in response to trans-caryophyllene in the concentration range of 20-340 ppm with a sensitivity of -0.489 Hz (ppm)(-1). The developed gas sensor was successfully used for the detection of trans-caryophyllene in real samples of sweetpotato. The experimental data showed a significant correlation with the results of gas chromatography-mass spectrometry (GC-MS) with a correlation coefficient of 0.970 (p < 0.01). Therefore, it could be used for early warning of black spot disease in sweetpotato.