Xinmin Li

The cellular and molecular mechanisms of acupuncture have been investigated across various tissues in multiple animal models. However, the dynamic cellular and molecular changes at human acupuncture points remain unexplored. The primary challenge preventing such a study is the practical difficulty of obtaining sufficient cells from acupoints. To address this, we developed a new needle manipulation technique that enables the collection of sufficient cell number from the acupuncture needle during the treatment. Using this approach and single-cell technology, we identified eight cell types at the acupoint BL23: inflammatory fibroblast, myofibroblast, skeletal muscle cell, endothelial cell, smooth muscle cell, adipocyte, macrophage, and a novel cell type characterized by marker genes CNTNAP2 and CSMD1 . Remarkably, this novel cell population was significantly enriched during the pain relief phase compared to the pain state, while the other seven cell types were significantly reduced following acupuncture analgesia. Transcriptomic analysis suggested that these novel cells are involved in synapse assembly and synaptic plasticity. This study presents the first characterization of cellular and transcriptional dynamics at the acupoint BL23, offering new insights into the mechanism underlying acupuncture-induced pain relief.

Spiders are important predatory natural enemies in agricultural and forestry ecosystems, yet the role of vision in their predatory behavior remains unclear. In this study, we screened three opsin genes—corresponding to ultraviolet-sensitive and medium-to-long wavelength-sensitive opsins—from the transcriptome sequencing database of Pardosa astrigera. All three genes possess seven transmembrane topological structures and a lysine residue on the second transmembrane domain, which are typical characteristics of opsins. Using quantitative real-time PCR (RT-qPCR), we analyzed the expression patterns of these opsin genes in different tissues, developmental stages, and under the induction of light at three wavelengths. The results showed that all three opsin genes were significantly expressed in the cephalothorax and expressed across developmental stages with no significant differences. Under light induction, their relative expression first increased and then decreased in both male and female adult spiders. Subsequently, RNA interference (RNAi) was used to individually knock down each opsin gene, confirming their involvement in color vision. These results suggest that the three opsin genes are involved in spider vision, laying the foundation for further elucidating the role of vision in spider predation, and offering a new perspective for reducing the unintended killing of natural enemies by insect traps.

Spatial transcriptomics technology enables the mapping of gene expression within tissues, allowing researchers to visualize the spatial distribution of RNA molecules and gain insights into cellular organization, interactions, and functions in their native environments. A variety of spatial technologies are now commercially available, each offering distinct technical parameters such as cellular resolution, detection sensitivity, gene coverage, and throughput. This wide range of options can make it challenges or create confusion for researchers to select the most appropriate platform for their specific research objectives. In this paper, we will analyze and compare seven major commercially available spatial platforms to guide researchers in choosing the most suitable option for their needs.

Background: Overall survival of advanced colorectal cancer (CRC) patients remains poor, and gene expression analysis could potentially complement detection of clinically relevant mutations to personalize CRC treatments. Methods: We performed RNA sequencing of formalin-fixed, paraffin-embedded (FFPE) cancer tissue samples of 23 CRC patients and interpreted the data obtained using bioinformatic method Oncobox for expression-based rating of targeted therapeutics. Oncobox ranks cancer drugs according to the efficiency score calculated using target genes expression and molecular pathway activation data. The patients had primary and metastatic CRC with metastases in liver, peritoneum, brain, adrenal gland, lymph nodes and ovary. Two patients had mutations in NRAS, seven others had mutated KRAS gene. Patients were treated by aflibercept, bevacizumab, bortezomib, cabozantinib, cetuximab, crizotinib, denosumab, panitumumab and regorafenib as monotherapy or in combination with chemotherapy, and information on the success of totally 39 lines of therapy was collected. Results: Oncobox drug efficiency score was effective biomarker that could predict treatment outcomes in the experimental cohort (AUC 0.77 for all lines of therapy and 0.91 for the first line after tumor sampling). Separately for bevacizumab, it was effective in the experimental cohort (AUC 0.87) and in 3 independent literature CRC datasets, n = 107 (AUC 0.84–0.94). It also predicted progression-free survival in univariate (Hazard ratio 0.14) and multivariate (Hazard ratio 0.066) analyses. Difference in AUC scores evidences importance of using recent biosamples for the prediction quality. Conclusion: Our results suggest that RNA sequencing analysis of tumor FFPE materials may be helpful for personalizing prescriptions of targeted therapeutics in CRC.

The LIM protein Ajuba has been implicated in the development of human cancers. To date, its expression pattern and biological significance in breast cancers (BC) have not been fully investigated. In the current study, we examined Ajuba protein levels in 93 invasive ductal carcinoma specimens by immunohistochemistry. The Ajuba expression level was elevated in breast cancer tissue compared with normal tissue. Ajuba overexpression is correlated with advanced tumor‐node‐metastasis (TNM) stage, positive node status, and adverse patient outcomes. The Ajuba protein level was also higher in BC cell lines compared to normal breast epithelial cell line MCF‐10A. Ectopically expressed Ajuba in MCF‐7 cells stimulated in vitro and in vivo cell growth, invasion, cell cycle progression, and decreased paclitaxel‐induced apoptosis. RNA‐sequencing (RNA‐seq) followed by gene set enrichment analysis (GSEA) analysis showed that Ajuba overexpression regulated the Hippo signaling pathway. Ajuba overexpression also increased glucose uptake and increased expression of TAZ, GLUT3, and Survivin. TAZ knockdown abolished the role of Ajuba on GLUT3 and Survivin induction. The ChIP assay showed that TEAD4, a major TAZ binding transcription factor, could bind to the GLUT3 and Survivin promoter regions. In conclusion, our data demonstrated that elevated Ajuba expression is correlated with poor BC prognosis and regulated malignant behavior through TAZ‐GLUT3/Survivin signaling in BC cells.

RNA sequencing (RNAseq) can reveal gene fusions, splicing variants, mutations/indels in addition to differential gene expression, thus providing a more complete genetic picture than DNA sequencing. This most widely used technology in genomics tool box has evolved from classic bulk RNA sequencing (RNAseq), popular single cell RNA sequencing (scRNAseq) to newly emerged spatial RNA sequencing (spRNAseq). Bulk RNAseq studies average global gene expression, scRNAseq investigates single cell RNA biology up to 20,000 individual cells simultaneously, while spRNAseq has ability to dissect RNA activities spatially, representing next generation of RNA sequencing. This article highlights these technologies, characteristic features and suitable applications in precision oncology.

EGFR, BRAF, PIK3CA, and KRAS genes play major roles in EGFR pathway, and accommodate activating mutations that predict response to many targeted therapeutics. However, connections between these mutations and EGFR pathway expression patterns remain unexplored. Here, we investigated transcriptomic associations with these activating mutations in three ways. First, we compared expressions of these genes in the mutant and wild type tumors, respectively, using RNA sequencing profiles from The Cancer Genome Atlas project database (n = 3660). Second, mutations were associated with the activation level of EGFR pathway. Third, they were associated with the gene signatures of differentially expressed genes from these pathways between the mutant and wild type tumors. We found that the upregulated EGFR pathway was linked with mutations in the BRAF (thyroid cancer, melanoma) and PIK3CA (breast cancer) genes. Gene signatures were associated with BRAF (thyroid cancer, melanoma), EGFR (squamous cell lung cancer), KRAS (colorectal cancer), and PIK3CA (breast cancer) mutations. However, only for the BRAF gene signature in the thyroid cancer we observed strong biomarker diagnostic capacity with AUC > 0.7 (0.809). Next, we validated this signature on the independent literature-based dataset (n = 127, fresh-frozen tissue samples, AUC 0.912), and on the experimental dataset (n = 42, formalin fixed, paraffin embedded tissue samples, AUC 0.822). Our results suggest that the RNA sequencing profiles can be used for robust identification of the replacement of Valine at position 600 with Glutamic acid in the BRAF gene in the papillary subtype of thyroid cancer, and evidence that the specific gene expression levels could provide information about the driver carcinogenic mutations.

DNA methylation-based biomarkers of aging have been developed for many mammals but not yet for the vervet monkey (Chlorocebus sabaeus), which is a valuable non-human primate model for biomedical studies. We generated novel DNA methylation data from vervet cerebral cortex, blood, and liver using highly conserved mammalian CpGs represented on a custom array (HorvathMammalMethylChip40). We present six DNA methylation-based estimators of age: vervet multi-tissue epigenetic clock and tissue-specific clocks for brain cortex, blood, and liver. In addition, we developed two dual species clocks (human-vervet clocks) for measuring chronological age and relative age, respectively. Relative age was defined as ratio of chronological age to maximum lifespan to address the species differences in maximum lifespan. The high accuracy of the human-vervet clocks demonstrates that epigenetic aging processes are evolutionary conserved in primates. When applying these vervet clocks to tissue samples from another primate species, rhesus macaque, we observed high age correlations but strong offsets. We characterized CpGs that correlate significantly with age in the vervet. CpG probes that gain methylation with age across tissues were located near the targets of Polycomb proteins SUZ12 and EED and genes possessing the trimethylated H3K27 mark in their promoters. The epigenetic clocks are expected to be useful for anti-aging studies in vervets.

A patient’s response to immune checkpoint inhibitors (ICIs) is a complex quantitative trait, and determined by multiple intrinsic and extrinsic factors. Three currently FDA-approved predictive biomarkers (progra1mmed cell death ligand-1 (PD-L1); microsatellite instability (MSI); tumor mutational burden (TMB)) are routinely used for patient selection for ICI response in clinical practice. Although clinical utility of these biomarkers has been demonstrated in ample clinical trials, many variables involved in using these biomarkers have poised serious challenges in daily practice. Furthermore, the predicted responders by these three biomarkers only have a small percentage of overlap, suggesting that each biomarker captures different contributing factors to ICI response. Optimized use of currently FDA-approved biomarkers and development of a new generation of predictive biomarkers are urgently needed. In this review, we will first discuss three widely used FDA-approved predictive biomarkers and their optimal use. Secondly, we will review four novel gene signature biomarkers: T-cell inflamed gene expression profile (GEP), T-cell dysfunction and exclusion gene signature (TIDE), melanocytic plasticity signature (MPS) and B-cell focused gene signature. The GEP and TIDE have shown better predictive performance than PD-L1, and PD-L1 or TMB, respectively. The MPS is superior to PD-L1, TMB, and TIDE. The B-cell focused gene signature represents a previously unexplored predictive biomarker to ICI response. Thirdly, we will highlight two combined predictive biomarkers: TMB+GEP and MPS+TIDE. These integrated biomarkers showed improved predictive outcomes compared to a single predictor. Finally, we will present a potential nucleic acid biomarker signature, allowing DNA and RNA biomarkers to be analyzed in one assay. This comprehensive signature could represent a future direction of developing robust predictive biomarkers, particularly for the cold tumors, for ICI response.