DNA-Programmed Four-Bit Quaternary Fluorescence Encoding (FLUCO) Enables 51-Colored Bioimaging Analysis.

Color encoding plays a crucial role in painting, digital photography, and spectral analysis. Achieving accurate, target-responsive color encoding at the molecular level has the potential to revolutionize scientific research and technological innovation, but significant challenges persist. Here, we propose a multibit DNA self-assembly system based on computer-aided design (CAD) technology, enabling accurate, target-responsive, amplified color encoding at the molecular level, termed fluorescence encoding (FLUCO). As a model, we establish a quaternary FLUCO system using four-bit DNA self-assembly, which can accurately encode 51 colors, presenting immense potential in applications such as spatial proteomic imaging and multitarget analysis. Notably, FLUCO enables the simultaneous imaging of multiple targets exceeding the limitations of channels using conventional imaging equipment, and marks the integration of computer science for molecular encoding and decoding. Overall, our work paves the way for target-responsive, controllable molecular encoding, facilitating spatial omics analysis, exfoliated cell analysis, and high-throughput liquid biopsy.

Tumor-associated antigen-specific cell imaging based on upconversion luminescence and nucleic acid rolling circle amplification.

Tumor-associated antigen (TAA)-based diagnosis has gained prominence for early tumor screening, treatment monitoring, prognostic assessment, and minimal residual disease detection. However, limitations such as low sensitivity and difficulty in extracting non-specific binding membrane proteins still exist in traditional detection methods. Upconversion luminescence (UCL) exhibits unique physical and chemical properties under wavelength near-infrared light excitation. Rolling circle amplification (RCA) is an efficient DNA amplification technique with amplification factors as high as 105. Therefore, the above two excellent techniques can be employed for highly accurate imaging analysis of tumor cells. Herein, we developed a novel nanoplatform for TAA-specific cell imaging based on UCL and RCA technology. An aptamer-primer complex selectively binds to Mucin 1 (MUC1), one of TAA on cell surface, to trigger RCA reaction, generating a large number of repetitive sequences. These sequences provide lots of binding sites for complementary signal probes, producing UCL from lanthanide-doped upconversion nanoparticles (UCNPs) after releasing quencher group. The experimental results demonstrate the specific attachment of upconversion nanomaterials to cancer cells which express a high level of MUC1, indicating the potential of UCNPs and RCA in tumor imaging.

Dynamic Addressing Molecular Robot (DAMR): An Effective and Efficient Trial-and-Error Approach for the Analysis of Single Nucleotide Polymorphisms.

Accurate and efficient molecular recognition plays a crucial role in the fields of molecular detection and diagnostics. Conventional trial-and-error-based molecular recognition approaches have always been challenged in distinguishing minimal differences between targets and non-targets, such as single nucleotide polymorphisms (SNPs) of oligonucleotides. To address these challenges, here, a novel concept of dynamic addressing analysis is proposed. In this concept, by dissecting the regions of the target and creating a corresponding recognizer, it is possible to eliminate the inaccuracy and inefficiency of recognition. To achieve this concept, a Dynamic Addressing Molecular Robot (DAMR), a DNA-based dynamic addressing device is developed which is capable of dynamically locating targets. DAMR is designed to first bind to the conserved region of the target while addressing the specific region dynamically until accurate recognition is achieved. DAMR has provided an approach for analyzing low-resolution targets and has been used for analyzing SNP of miR-196a2 in both cell and serum samples, which has opened new avenues for effective and efficient molecular recognition.

Orthogonal DNA Self-Assembly-Based Expansion Microscopy Platform for Amplified, Multiplexed Biomarker Imaging.

Expansion microscopy (ExM) facilitates nanoscale imaging under conventional microscopes, but it frequently encounters challenges such as fluorescence losses, low signal-to-noise ratio (SNR), and limited detection throughput. To address these issues, a method of orthogonal DNA self-assembly-based ExM (o-DAExM) platform is developed, which employs hybridization chain reaction instead of conventional fluorescence labeling units, showcasing signal amplification efficacy, enhancement of SNR, and expandable multiplexing capability at any stage of the ExM process. In this work, o-DAExM has been applied to compare with immunofluorescence-based ExM for cellular cytoskeleton imaging, and the resolved nanoscale spatial distributions of cytoskeleton show outstanding performance and reliability of o-DAExM. Furthermore, the study demonstrates the utility of o-DAExM in accurately revealing exosome heterogeneous information and multiplexed analysis of protein targets in single cells, which provides infinite possibilities in super-resolution imaging of cells and other samples. Therefore, o-DAExM offers a straightforward expansion and signal labeling method, highlighting future prospects to study nanoscale structures and functional networks in biological systems.

Metal-organic framework integrated hydrogel bioreactor for smart detection of metal ions.

Bioreactors with environment responsiveness for smart detection has attracted widespread interest. Bioreactors that operate in liquid have excellent reaction speed and sensitivity, and those that operate at a solid interface have unique portability and stability. However, bioreactors that can simultaneously take advantage of both properties are still limited. Here, we developed a metal-organic framework (MOF) integrated hydrogel bioreactor that can accommodate both solid and liquid properties by using a hydrogel as a quasi-liquid medium. To enhance the stability and intelligence of the hydrogel bioreactor, we have opted for the utilization of europium metal-organic framework (Eu-MOF) as the optical output to withstand long-term storage challenges, and DNA as the highly programmable substance for intelligent target response. On this basis, smart detection of metal ions and biological micro-molecules have been achieved. Notably, this quasi-liquid hydrogel bioreactor has effectively tackled the intrinsic issues of inadequate dispersion stability of Eu-MOF in liquid systems and poor stability of DNA against environmental interference. Moreover, this MOF integrated hydrogel bioreactor has been applied to the construction of a portable hydrogel bioreactor, which enables platform-free and arrayed target detection via a smartphone, providing a new perspective for further promoting the application of quasi-liquid hydrogel bioreactors and intelligent nanobiological sensors.

Combined Consideration of Tumor-Associated Immune Cell Density and Immune Checkpoint Expression in the Peritumoral Microenvironment for Prognostic Stratification of Non-Small-Cell Lung Cancer Patients.

Given the complexity and highly heterogeneous nature of the microenvironment and its effects on antitumor immunity and cancer immune evasion, the prognostic value of a single immune marker is limited. Here, we show how the integration of immune checkpoint molecule expression and tumor-associated immune cell distribution patterns can influence prognosis prediction in non-small-cell lung cancer (NSCLC) patients. We analyzed tissue microarray (TMA) data derived from multiplex immunohistochemistry results and measured the densities of tumor-infiltrating CD8+ and FOXP3+ immune cells and tumor cells (PanCK+), as well as the densities of programmed cell death 1 (PD-1)+ and programmed cell death ligand 1 (PD-L1)+ cells in the peritumor and intratumor subregions. We found a higher density of infiltrating CD8+ and FOXP3+ immune cells in the peritumoral compartment than in the intratumoral compartment. In addition, unsupervised hierarchical clustering analysis of these markers revealed that the combination of high CD8/FOXP3 expression, low PD-1 and PD-L1 immune checkpoint expression, and lack of epidermal growth factor receptor (EGFR) mutation could be a favorable predictive marker. On the other hand, based on the clustering analysis, low CD8/FOXP3 and immune checkpoint (PD-1 and PD-L1) expression might be a marker for patients who are likely to respond to strategies targeting regulatory T (Treg) cells. Furthermore, an immune risk score model was established based on multivariate Cox regression, and the risk score was determined to be an independent prognostic factor for NSCLC patients. These results indicate that the immune context is heterogeneous because of the complex interactions of different components and that using multiple factors in combination might be promising for predicting the prognosis of and stratifying NSCLC patients.

Cell graph neural networks enable the precise prediction of patient survival in gastric cancer.

Gastric cancer is one of the deadliest cancers worldwide. An accurate prognosis is essential for effective clinical assessment and treatment. Spatial patterns in the tumor microenvironment (TME) are conceptually indicative of the staging and progression of gastric cancer patients. Using spatial patterns of the TME by integrating and transforming the multiplexed immunohistochemistry (mIHC) images as Cell-Graphs, we propose a graph neural network-based approach, termed Cell-Graph Signature or CGSignature, powered by artificial intelligence, for the digital staging of TME and precise prediction of patient survival in gastric cancer. In this study, patient survival prediction is formulated as either a binary (short-term and long-term) or ternary (short-term, medium-term, and long-term) classification task. Extensive benchmarking experiments demonstrate that the CGSignature achieves outstanding model performance, with Area Under the Receiver Operating Characteristic curve of 0.960 ± 0.01, and 0.771 ± 0.024 to 0.904 ± 0.012 for the binary- and ternary-classification, respectively. Moreover, Kaplan-Meier survival analysis indicates that the "digital grade" cancer staging produced by CGSignature provides a remarkable capability in discriminating both binary and ternary classes with statistical significance (P value < 0.0001), significantly outperforming the AJCC 8th edition Tumor Node Metastasis staging system. Using Cell-Graphs extracted from mIHC images, CGSignature improves the assessment of the link between the TME spatial patterns and patient prognosis. Our study suggests the feasibility and benefits of such an artificial intelligence-powered digital staging system in diagnostic pathology and precision oncology.

Overexpression of PLXDC2 in Stromal Cell-Associated M2 Macrophages Is Related to EMT and the Progression of Gastric Cancer.

The tumor microenvironment (TME) comprises distinct cell types, including stromal types such as fibroblast cells and macrophage cells, which have recently become a critical factor in tumor development and progression. Here, we identified the TME-related gene, plexin domain containing 2 (PLXDC2), in a high-stromal-score population. And we revealed that this gene was related to poor survival and advanced (tumor-node-metastasis) stage in gastric cancer (GC) patients from The Cancer Genome Atlas database. An integrated gene profile and functional analysis of the proportions of tumor-infiltrating immune cells revealed that the expression of the M2 macrophages cell marker CD163 was positively correlated with PLXDC2 expression. In addition, the M2 macrophages gene signature and high PLXDC2 expression were associated with the inflammatory signaling pathway and the epithelial-to-mesenchymal transition (EMT)-related gene signature. Single-cell study of GC identified PLXDC2 was enriched specifically in fibroblasts and monocytes/macrophages populations, which supported its important role in the stroma. Furthermore, according to a tissue microarray immunohistochemistry analysis, the expression of PLXDC2 elevated in human GC stromal specimens compared to tumor tissue specimens. Moreover, PLXDC2 overexpression in the stromal compartment was associated with CD163-positive regulatory M2 macrophages, and its functions were related to the pathogenesis of GC. Multiplexed immunohistochemistry verified PLXDC2's correlation with EMT markers. Our data suggested that PLXDC2 was expressed in stromal cells and that its crosstalk with tumor-associated macrophages could contribute to cancer biology by inducing the EMT process.