BackgroundTriple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer (BC) characterized by a high risk of metastasis and poor prognosis. Current chemotherapy-based treatments are often limited by systemic toxicity and drug resistance. Brusatol (BRU), a natural compound with reported anti-tumor activity and low toxicity, has not been explored in the context of cancer metastasis or metabolic reprogramming. This study aimed to uncover the anti-metastatic mechanism of BRU by targeting the metabolic adaptation of detached TNBC cells.MethodsThe suppressive effect of BRU was assessed via LDH release assays, trypan blue staining, tumor spheroid culture and spontaneous metastasis assays. To elucidate the underlying mechanisms, global metabolomics analysis, NADPH/NADP+ measurements, intracellular ROS detection by flow cytometry, and quantitative PCR for metabolic gene expression were applied to evaluate metabolic alterations and redox imbalance.ResultsBRU promoted membrane damage and cell death in extracellular matrix (ECM)-detached TNBC cells and suppressed metastasis in vivo. Metabolomics analysis showed that BRU inhibited metabolic pathways, including the pentose phosphate pathway (PPP), glycolysis, and the tricarboxylic acid (TCA) cycle, while significantly reducing NADPH levels and exacerbating redox stress.ConclusionsThese findings suggest that BRU targets metabolic plasticity in TNBC cells, highlighting its potential as an adjuvant therapy to enhance anti-tumor efficacy while reducing chemotherapy-associated toxicity.

Abstract Recent advances in agentic systems have enabled the autonomous execution of research tasks across scientific domains. However, the rapid emergence of specialized scientific agents for areas such as computational pathology, microbiome research, gene editing, materials science, organic chemistry, and drug discovery has created a fragmented ecosystem of scientific capabilities. While these agents often demonstrate strong performance within their respective domains, limited interoperability makes it difficult to combine expertise across platforms and coordinate complex interdisciplinary workflows. Here we introduce GUIA (Guided-research Utilizing Intelligent Agents), an interoperable research-agent network built upon a flexible Agent-to-Agent (A2A) communication architecture. GUIA enables both in-house and third-party agents to collaborate within shared workflows, allowing scientific capabilities to accumulate through the integration of complementary expertise. We evaluated GUIA through four assessments spanning baseline benchmarking, third-party single-agent integration, third-party multi-agent integration, and cross-server agent collaboration. Furthermore, we demonstrate its practical utility through real-world applications involving therapeutic target discovery, drug discovery, and spatial proteomics analysis. Together, our results show that interoperable research-agent networks can coordinate specialized expertise across independently developed systems, providing a scalable framework for expanding scientific capabilities through collaboration.

Androgen receptor (AR) signaling is a primary oncogenic driver of castration-resistant prostate cancer (CRPC), yet the mechanism remains incompletely understood. Through proteomic profiling of CRPC and primary PCa cells, we identify G Protein Nucleolar 3 (GNL3) as a novel AR coregulator. GNL3 physically interacts with AR, enhances its chromatin occupancy, and directly coactivates transcriptional programs that promote cell proliferation, including NEK2 and CDC20. Concurrently, GNL3 functions as a corepressor of immune-responsive genes such as CXCL10 and TAP1 via class I histone deacetylases (HDACs), thereby facilitating CD8+ T cell elimination and establishing an immunosuppressive tumor microenvironment. GNL3 expression and AR-GNL3 complex formation progressively increase from normal prostate to CRPC and correlate with poor clinical outcomes. Functionally, GNL3 knockdown sensitizes CRPC cells to AR antagonists and impairs tumor growth and metastasis. Furthermore, we demonstrate that combinatorial inhibition of NEK2, class I HDACs, and AR signaling can be a potential therapeutic strategy for CRPC. Overall, these findings establish GNL3 as a dual-function AR coregulator and therapeutic target, providing mechanistic insights into transcriptional regulation and immune evasion in advanced PCa.

Obesity exacerbates rheumatoid arthritis (RA). However, the underlying mechanisms remain incompletely defined. Elucidating these mechanisms can help the identification of novel therapeutic targets. Herein, we used high-fat diet (HFD)-induced obese collagen-induced arthritis (CIA) mice to investigate these mechanisms. Immunohistochemistry revealed that obesity exacerbated joint inflammation and cartilage degradation. Next, integrated label-free quantitative proteomics and cytometry by time-of-flight (CyTOF) were used to characterize lymphocyte subsets. Proteomic profiling identified 26 differentially expressed proteins in obese versus lean CIA mice, including the transcription factors EOMES and KLF2, the TGFβ receptor (TGFβR) signaling component TGFBR2, and the tissue-resident memory (TRM) T cell marker CD103. CyTOF analysis revealed a robust 3.0-fold increase (P = 0.0043) in the proportion of CD103⁺ TRM cells among CD3⁺ T cells in obese CIA mice, characterized by a large effect size. Immunofluorescence results confirmed this increase in synovial tissues. Treatment with asiaticoside (a TGF-β/Smad-suppressing triterpenoid) significantly reduced TRM cell proportions (P < 0.05) and ameliorated symptoms in obese CIA mice. Collectively, these findings establish a novel mechanistic axis in which obesity-induced TGFβR-hyperactivation promotes TRM cell accumulation, which exacerbates arthritis severity in this RA model. Our findings provide a preclinical rationale for targeting TGFβR/TRM in human RA with obesity as a comorbidity.

Radiotherapy (RT) is a cornerstone of cancer treatment; however, its efficacy is frequently hampered by its adverse effects on normal tissues. By studying the effects of high-dose radiotherapy (HDRT) and low-dose radiotherapy (LDRT), we found that cancer cells adapt distinct responses to these doses to reduce cytotoxicity. Upon HDRT, cancThese authors contributed equally to this worker cells initiate a strong DNA damage response (DDR) to gain resistance through rapid production and/or activation of proteins for cell cycle arrest and DNA damage repair. In contrast, LDRT has a milder effect on the DDR and promotes resistance by triggering the synthesis of new proteins, including those essential for DNA repair and protein damage clearance. We showed that the inhibition of proteasome activity using a proteasome inhibitor (PI) result in the accumulation of damage to both proteins and DNA, leading to the profound death of cancer cells. Mechanistically, LDRT enhances protein synthesis through both increased mTOR signaling and 80S ribosome assembly. On the basis of these findings, we designed a chemoradiotherapy strategy that combines LDTR with PI to treat cancer while minimizing non-targeted toxicity.

Poly(ADP-ribose) polymerase 1 (PARP1) inhibition represents a promising targeted therapy for BRCA-deficient cancer patients based on the synthetic lethality theory. Recent evidence shows that the efficacy of DNA damage drugs depends on two aspects: DNA repair signaling and immune response. Applying a functional proteomics approach, we find that the function of the spliceosome is perturbed by PARP inhibitors via enhancing interaction between PARP1 and SF3B1, a key factor of the spliceosome. We demonstrate that differential alternative spliced mRNA and accumulation of double-stranded RNA (dsRNA) are induced by perturbation of the spliceosome upon PARP inhibitor treatment, resulting in triggering dsRNA antiviral mimicry innate immune response. Moreover, we identify a novel function of BRCA1, through which BRCA1 regulates innate immune response, leading to compromising of the innate immune signaling by downregulation of IRF3 in BRCA1-deficient breast cancer cells, which reduces the sensitivity to PARP inhibitors and causes intrinsic resistance. Polyinosinic-polycytidylic acid (poly(I:C)) is a dsRNA synthetic analog sensitizing PARP inhibitors through further triggering dsRNA signaling. Finally, we show that the combination of PARP inhibitors and poly(I:C) enhances anti-tumor efficiency in vivo. Overall, our study reveals that BRCA1 deficiency impedes tumor cell intrinsic innate immune response, inducing intrinsic resistance to PARP inhibitors that can be overcome when poly(I:C) is combined.

Biological data visualization is challenged by the growing complexity of datasets. Traditional single-data plots or simple juxtapositions often fail to fully capture dataset intricacies and interrelations. To address this, we introduce “cross-layout,” a novel visualization paradigm that integrates multiple plot types in a cross-like structure, with a central main plot surrounded by secondary plots for enhanced contextualization and interrelation insights. We also introduce “Marsilea,” a Python-based implementation of cross-layout visualizations, available in both programmatic and web-based interfaces to support users of all experience levels. This paradigm and its implementation offer a customizable, intuitive approach to advance biological data visualization.

Lead is a widespread environmental hazard that can adversely affect multiple biological functions. Blood cells are the initial targets that face lead exposure. However, a systematic assessment of lead dynamics in blood cells at single-cell resolution is still absent. Herein, C57BL/6 mice were fed with lead-contaminated food. Peripheral blood was harvested at different days. Extracted red blood cells and leukocytes were stained with 19 metal-conjugated antibodies and analyzed by mass cytometry. We quantified the time-lapse lead levels in 12 major blood cell subpopulations and established the distribution of lead heterogeneity. Our results show that the lead levels in all major blood cell subtypes follow lognormal distributions but with distinctively individual skewness. The lognormal distribution suggests a multiplicative accumulation of lead with stochastic turnover of cells, which allows us to estimate the lead lifespan of different blood cell populations by calculating the distribution skewness. These findings suggest that lead accumulation by single blood cells follows a stochastic multiplicative process.

Expansion microscopy (ExM) is an innovative super-resolution imaging technique that utilizes physical expansion to magnify biological samples, facilitating the visualization of cellular structures that are challenging to observe using traditional optical microscopes. The fundamental principle of ExM revolves around employing a specialized hydrogel to uniformly expand biological samples, thereby achieving super-resolution imaging under conventional optical imaging conditions. This technology finds application not only in various biological samples such as cells and tissue sections, but also enables super-resolution imaging of large biological molecules including proteins, nucleic acids, and metabolite molecules. In recent years, numerous researchers have delved into ExM, resulting in the continuous development of a range of derivative technologies that optimize experimental protocols and broaden practical application fields. This article presents a comprehensive review of these derivative technologies, highlighting the utilization of ExM for anchoring nucleic acids, proteins, and other biological molecules, as well as its applications in biomedicine. Furthermore, this review offers insights into the future development prospects of ExM technology.

Abstract Disclosure: E. Cheung: None. C. Zhang: None. Androgen receptor (AR) is a master transcriptional regulator in prostate cancer, interacting with and recruiting distinct sets of coregulator proteins to mediate the expression of direct target genes. What are the coregulators of AR and how they function in AR transcriptional activity and prostate cancer biology are still unclear. Using chromatin-immunoprecipitation coupled with mass spectrometry, we identified a set of AR interacting proteins that are present in multiple prostate cancer cell lines. We provide biochemical, cell-based, and genomic evidence to show that Nucleostemin, also known as Guanine nucleotide-binding protein-like 3, is a novel AR coregulator. Specifically, we demonstrate Nucleostemin has dual coregulator activities, functioning as both a coactivator and a corepressor of AR-dependent transcription to regulate the expression of genes that are involved for prostate cancer progression and immune response. From cellular and animal studies, we found that inhibiting Nucleostemin sensitizes prostate cancer cells to Enzalutamide. Clinical analyses of Nucleostemin from prostate cancer patient cohorts revealed that it is expressed significantly higher in cancer than normal in tissues. Finally, we found Nucleostemin expression is an excellent predictor of disease-free survival. In conclusion, our work suggests that Nucleostemin is a novel AR coregulator and is a promising diagnostic and therapeutic target for prostate cancer treatment. Presentation: 6/2/2024

ABSTRACT Integrin genes widely involve in tumorigenesis. Yet, a comprehensive characterization of integrin family and their interactome on the pan-cancer level is lacking. Here, we systematically dissect integrin family in nearly 10000 tumors across 32 cancer types. Globally, integrins represent a frequently altered and misexpressed pathway, with alteration and dysregulation overall being protumorigenic. Expression dysregulation, better than mutational landscape, of integrin family successfully identifies a subgroup of aggressive tumors demonstrating a high level of proliferation and stemness. We identify that several molecular mechanisms jointly regulate integrin expression in a context-dependent manner. For potential clinical usage, we construct a weighted score, integrinScore, to measure integrin signaling patterns in individual tumors. Remarkably, integrinScore consistently correlates with predefined molecular subtypes in multiple cancers, with integrinScore high tumors being more aggressive. Importantly, integrinScore is cancer-dependently and closely associated with proliferation, stemness, tumor microenvironment, metastasis, and immune signatures. IntegrinScore also predicts patient’s response to immunotherapy. By mining drug databases, we unravel an array of compounds that may serve as integrin signaling modulators. Finally, we build a user-friendly database to facilitate researchers to explore integrin-related knowledge. Collectively, we provide a comprehensive characterization of integrins across cancers, which offers gene- and cancer-specific rationales for developing integrin-targeted therapy.

Contemporary data visualization is challenged by the growing complexity and size of datasets, often comprising numerous interrelated features. Traditional visualization methods struggle to capture these complex relationships fully or are specialized to a domain requiring familiarity with multiple visualization tools. We introduce a novel and intuitive general visualization paradigm, termed “cross-layout visualization”, which integrates multiple plot types in a cross-like structure. This paradigm allows for a central main plot surrounded by secondary plots, each capable of layering additional features for enhanced context and understanding. To operationalize this paradigm, we present “Marsilea”, a Python library designed for creating complex visualizations with ease. Marsilea is notable for its modularity, diverse plot types, compatibility with various data formats, and is available in a coding-free web-based interface for users of all experience levels. We showcase its versatility and broad applicability by re-creating existing visualizations and creating novel visualizations that include elements such as heatmaps, sequence motifs, and set intersections that are typically beyond the scope of existing general visualization tools. The cross-layout paradigm, exemplified by Marsilea, offers a flexible, customizable, and intuitive approach to complex data visualization, promising to enhance data analysis across scientific domains.### Competing Interest StatementThe authors have declared no competing interest.

Although neoadjuvant chemoradiotherapy treatment followed by surgical resection is the recommended treatment for locally advanced rectal cancer (LARC), response rates remain poor. In proficient mismatch repair (pMMR) rectal cancer, combination (vs. monotherapy) immunotherapy has begun to show promise. This study involved 87 LARC patients undergoing short-course radiotherapy (SCRT), followed by CAPOX (capecitabine and oxaliplatin), in combination with the immune checkpoint inhibitor tislelizumab. Following neoadjuvant therapy, 81 patients underwent surgery, achieving an R0 resection rate of 98.7%. Pathological complete response (pCR) was observed in 41 patients (50.6%), with responders (patients with tumor regression grade TRG 0/TRG 1 or complete clinic response) constituting 69% (60/87). Grade 3 adverse events occurred in 11.5% of participants, and there was one case of grade 4 myasthenia gravis. Imaging Mass Cytometry (IMC) analysis demonstrated higher infiltration of M1 macrophages were in responders. Spatial analysis further identified significant aggregation of PD-L1+ myofibroblastic cancer-associated fibroblasts (MyoCAFs), a unique cell population, within a 10 µm radius to tumor cells, in non-responders; and dynamic analysis showed that post-treatment PD-L1+ MyoCAFs continued to increase in the non-responder group, who also had more exhausted CD8+T cells, possibly explaining their worse response. Our study affirms the efficacy and safety of neoadjuvant SCRT combined with immunochemotherapy in LARC, highlighting the importance of assessing the spatial distribution of immune cells in the tumor microenvironment (TME) for predicting treatment responses. ClinicalTrials.gov registration: NCT05515796.

Imaging mass cytometry (IMC) permits high-dimensional single-cell spatial proteomics by harnessing mass tags to replace conventional fluorescence tags. However, the current IMC technique commonly adopts metal-chelated polymer (MCP) tags, which are limited in sensitivity, multiplicity and data acquisition speed. Here, we demonstrate nanometal-organic framework (NMOF) tags, which could concurrently augment IMC's sensitivity, multiplicity, and acquisition speed. We designed and synthesized uniform-sized Zr-NMOFs (similar to 31 nm, PDI < 0.1) and then functionalized them with heterobifunctionalized aptamers containing phosphate groups and fluorescent moieties to generate Zr-NMOF_Aptamer probes. Such functionalization enabled direct ligand exchange with zirconium ions on Zr-NMOFs, thus allowing for concurrent fluorescence and mass signal acquisitions. The fluorescence signal enabled large-scale rapid imaging to quickly locate the region-of-interest, therefore significantly reducing IMC's blind scanning time and compensating for IMC's lower resolution. Meanwhile, the Zr-NMOF_Aptamer probe exhibited specific molecular recognition and a fourfold enhancement in signal amplification over the commercial MCP probe. Additionally, we showed that Zr-NMOF_Aptamer probes were compatible with commercial MCP probes for high-multiplex co-staining in IMC analysis. The Zr-NMOF_Aptamer probe represents a promising development of next-generation molecular probes for spatial proteomics with IMC.

To explore the feature of cancer cells and tumor subclones, we analyzed 101,065 single-cell transcriptomes from 12 colorectal cancer (CRC) patients and 92 single cell genomes from one of these patients. We found cancer cells, endothelial cells and stromal cells in tumor tissue expressed much more genes and had stronger cell-cell interactions than their counterparts in normal tissue. We identified copy number variations (CNVs) in each cancer cell and found correlation between gene copy number and expression level in cancer cells at single cell resolution. Analysis of tumor subclones inferred by CNVs showed accumulation of mutations in each tumor subclone along lineage trajectories. We found differentially expressed genes (DEGs) between tumor subclones had two populations: DEG CNV and DEG reg . DEG CNV , showing high CNV-expression correlation and whose expression differences depend on the differences of CNV level, enriched in housekeeping genes and cell adhesion associated genes. DEG reg , showing low CNV-expression correlation and mainly in low CNV variation regions and regions without CNVs, enriched in cytokine signaling genes. Furthermore, cell-cell communication analyses showed that DEG CNV tends to involve in cell-cell contact while DEG reg tends to involve in secreted signaling, which further support that DEG CNV and DEG reg are two regulatorily and functionally distinct categories.

Results of VCX3A immunohistochemical staining of tissue microarray samples of pediatric high-grade gliomas.

Spatial omics is a rapidly evolving approach for exploring tissue microenvironment and cellular networks by integrating spatial knowledge with transcript or protein expression information. However, there is a lack of databases for users to access and analyze spatial omics data. To address this limitation, we developed Aquila, a comprehensive platform for managing and analyzing spatial omics data. Aquila contains 107 datasets from 30 diseases, including 6500+ regions of interest, and 15.7 million cells. The database covers studies from spatial transcriptome and proteome analyses, 2D and 3D experiments, and different technologies. Aquila provides visualization of spatial omics data in multiple formats such as spatial cell distribution, spatial expression and colocalization of markers. Aquila also lets users perform many basic and advanced spatial analyses on any dataset. In addition, users can submit their own spatial omics data for visualization and analysis in a safe and secure environment. Finally, Aquila can be installed as an individual app on a desktop and offers the RESTful API service for power users to access the database. Overall, Aquila provides a detailed insight into transcript and protein expression in tissues from a spatial perspective. Aquila is available at https://aquila.cheunglab.org.

Supplementary Figure S1. H3.3K27M activates cancer/testis (CT) antigens in SF-188 cell line; Supplementary Figure S2. The putative CpGislands in the human VCX3A locus; Supplementary Figure S3. The genomic sequence of human glyceraldehyde 3-phosphate dehydrogenase (GAPDH), and an intragenic CpG rich region of GAPDH (S3A). No change in intragenic CpG methylation of GAPDH in Res259 cells carrying H3.3K27M (S3B); Supplementary Figure S4. Subcellular localization of VCX3A/B in Res259 cells; Supplementary Figure S5. The different isoforms of VCX3A/B cause similar gene expression changes; Supplementary Figure S6. The mRNA expression of HLA-A, -B, E, F and G in Res259 cells stably expressing H3.3K27M or empty vector.

The sequence analysis of different variants of VCX3A and VCX3B.