High-plex protein and whole transcriptome co-mapping at cellular resolution with spatial CITE-seq

Exploring the untapped potential of single-cell and spatial omics in plant biology

Advances in single-cell and spatial omics technologies have revolutionised biology by revealing the diverse molecular states of individual cells and their spatial organization within tissues. The field of plant biology has widely adopted single-cell transcriptome and chromatin accessibility profiling and spatial transcriptomics, which extend traditional cell biology and genomics analyses and provide unique opportunities to reveal molecular and cellular dynamics of tissues. Using these technologies, comprehensive cell atlases have been generated in several model plant species, providing valuable platforms for discovery and tool development. Other emerging technologies related to single-cell and spatial omics, such as multiomics, lineage tracing, molecular recording, and high-content genetic and chemical perturbation phenotyping, offer immense potential for deepening our understanding of plant biology yet remain underutilised due to unique technical challenges and resource availability. Overcoming plant-specific barriers, such as cell wall complexity and limited antibody resources, alongside community-driven efforts in developing more complete reference atlases and computational tools, will accelerate progress. The synergy between technological innovation and targeted biological questions is poised to drive significant discoveries, advancing plant science. This review highlights the current applications of single-cell and spatial omics technologies in plant research and introduces emerging approaches with the potential to transform the field.

Single-cell transcriptomic analysis identifies a stress response Schwann cell subtype

Background

Peripheral nerve injury can lead to sensory, motor, and autonomic nerve dysfunction, significantly impacting patients' quality of life. Schwann cells (SCs), as key components of the peripheral nervous system, play a crucial role in nerve repair. However, many functionally specialized and flexible SC subtypes remain unidentified. Recent advancements in single-cell transcriptomics have enabled a deeper understanding of SC heterogeneity during peripheral nervous system development.

Methods

In this study, we utilized single-cell transcriptomics to investigate SC heterogeneity in the dorsal root ganglia of both normal and spinal nerve injury mouse models.

Results

We identified a novel SC subtype associated with pressure sensation, which we termed stress response related SCs (SRSCs). These cells are terminally differentiated and highly express the pressure-sensing gene Npy. Following peripheral nerve injury, SRSCs function as stimulus receptors, receiving external stimuli and transmitting signals to typical repair SCs via the SPP1 signaling network. This interaction promotes dedifferentiation and facilitates injury repair.

Conclusion

Our findings enhance the understanding of SC heterogeneity and reveal SRSCs as key players in nerve repair. These insights provide potential targets for developing novel therapeutic strategies for peripheral nerve diseases.

SMOPCA: spatially aware dimension reduction integrating multi-omics improves the efficiency of spatial domain detection

Technological advances have enabled us to profile multiple omics layers with spatial information, significantly enhancing spatial domain detection and advancing a variety of biomedical research fields. Despite these advancements, there is a notable lack of effective methods for modeling spatial multi-omics data. We introduce SMOPCA, a Spatial Multi-Omics Principal Component Analysis method designed to perform joint dimension reduction on multimodal data while preserving spatial dependencies. Extensive experiments reveal that SMOPCA outperforms existing single-modal and multimodal dimension reduction and clustering methods, across both single-cell and spatial multi-omics datasets derived from diverse technologies and tissue structures.

Development and validation of a nomogram to predict bacterial blood stream infection

OBJECTIVE

To identify the risk factors of bacterial blood stream infection (BSI) and construct a nomogram to predict the occurrence of bacterial BSI.

METHODS

Blood stream infection is characterized by a systemic infection patient with positive blood culture and has one or more clinical symptoms, such as fever (body temperature > 38 °C) or hypothermia (body temperature < 36 °C), chills, hypotension, oliguria, or high lactic acid levels. The study dataset was randomly divided into a 70% training set and a 30% validation set. Univariate logistic analysis, least absolute shrinkage and selection operator (LASSO) regression analysis, and random forest algorithms were utilized to identify the potential risk factors for BSI. Independent risk factors identified by multivariate logistic analysis were used to construct a nomogram. The discriminative ability, calibrating ability, and clinical practicality of the nomogram were evaluated using the receiver operating characteristic curve, calibration curve, and decision curve analysis.

RESULTS

A total of 195 bacterial BSI patients were enrolled. gender, Acute Physiology and Chronic Health Evaluation-II (APACHEII) score, nCD64 index, erythrocyte sedimentation rate (ESR), procalcitonin (PCT), C-reactive protein (CRP), Interleukin-6 (IL-6), lymphocyte count, T-cell count, B-cell count, NK-cell count, Interleukin-8 (IL-8), Interleukin-10 (IL-10) and Interleukin-17A(IL-17A) were independent risk factors for BSI. The nomogram model exhibited excellent discrimination with an area under the curve (AUC) of 0.836 (95% CI 0.653-0.874) in the training set and 0.871 (95% CI 0.793-0.861) in the validation set. The calibration curve indicated satisfactory calibration ability of the predictive model. Decision curve analysis revealed that the nomogram model had good clinical utility in predicting bacterial BSI.

CONCLUSION

Overall, this study successfully identified five risk factors for BSI patients and developed a nomogram, offering individualized diagnosis and risk assessment to predict bacterial BSI in infected patients.

Spatial Insights in Cardiovascular Aging

Spatial omics provides unprecedented insights into how the cardiovascular system is spatially organized and how cellular phenotypes are distributed. Researchers have been able to clarify the complex spatial architecture of the cardiovascular system and how cellular phenotypes are distributed during the aging process by integrating data from spatial omics. In addition, this new technology has revealed previously hidden patterns of gene expression and cellular communication that were not detected using traditional bulk omics approaches. In this review, we explore the contribution of spatial omics in clarifying the molecular mechanisms that influence cardiovascular aging, highlighting the importance and application of spatial omics in unraveling the spatial heterogeneity within the aging cardiovascular system. This will help us understand the molecular mechanisms implicated in age-related cardiovascular diseases.

Advances in spatial transcriptomics and its application in the musculoskeletal system

While bulk RNA sequencing and single-cell RNA sequencing have shed light on cellular heterogeneity and potential molecular mechanisms in the musculoskeletal system in both physiological and various pathological states, the spatial localization of cells and molecules and intercellular interactions within the tissue context require further elucidation. Spatial transcriptomics has revolutionized biological research by simultaneously capturing gene expression profiles and in situ spatial information of tissues, gradually finding applications in musculoskeletal research. This review provides a summary of recent advances in spatial transcriptomics and its application to the musculoskeletal system. The classification and characteristics of data acquisition techniques in spatial transcriptomics are briefly outlined, with an emphasis on widely-adopted representative technologies and the latest technological breakthroughs, accompanied by a concise workflow for incorporating spatial transcriptomics into musculoskeletal system research. The role of spatial transcriptomics in revealing physiological mechanisms of the musculoskeletal system, particularly during developmental processes, is thoroughly summarized. Furthermore, recent discoveries and achievements of this emerging omics tool in addressing inflammatory, traumatic, degenerative, and tumorous diseases of the musculoskeletal system are compiled. Finally, challenges and potential future directions for spatial transcriptomics, both as a field and in its applications in the musculoskeletal system, are discussed.

Cancer-on-a-chip for precision cancer medicine

Many cancer therapies fail in clinical trials despite showing potent efficacy in preclinical studies. One of the key reasons is the adopted preclinical models cannot recapitulate the complex tumor microenvironment (TME) and reflect the heterogeneity and patient specificity in human cancer. Cancer-on-a-chip (CoC) microphysiological systems can closely mimic the complex anatomical features and microenvironment interactions in an actual tumor, enabling more accurate disease modeling and therapy testing. This review article concisely summarizes and highlights the state-of-the-art progresses in CoC development for modeling critical TME compartments including the tumor vasculature, stromal and immune niche, as well as its applications in therapying screening. Current dilemma in cancer therapy development demonstrates that future preclinical models should reflect patient specific pathophysiology and heterogeneity with high accuracy and enable high-throughput screening for anticancer drug discovery and development. Therefore, CoC should be evolved as well. We explore future directions and discuss the pathway to develop the next generation of CoC models for precision cancer medicine, such as patient-derived chip, organoids-on-a-chip, and multi-organs-on-a-chip with high fidelity. We also discuss how the integration of sensors and microenvironmental control modules can provide a more comprehensive investigation of disease mechanisms and therapies. Next, we outline the roadmap of future standardization and translation of CoC technology toward real-world applications in pharmaceutical development and clinical settings for precision cancer medicine and the practical challenges and ethical concerns. Finally, we overview how applying advanced artificial intelligence tools and computational models could exploit CoC-derived data and augment the analytical ability of CoC.

Cell-type deconvolution methods for spatial transcriptomics

Spatial transcriptomics is a powerful method for studying the spatial organization of cells, which is a critical feature in the development, function and evolution of multicellular life. However, sequencing-based spatial transcriptomics has not yet achieved cellular-level resolution, so advanced deconvolution methods are needed to infer cell-type contributions at each location in the data. Recent progress has led to diverse tools for cell-type deconvolution that are helping to describe tissue architectures in health and disease. In this Review, we describe the varied types of cell-type deconvolution methods for spatial transcriptomics, contrast their capabilities and summarize them in a web-based, interactive table to enable more efficient method selection.

Metformin and chloroquine enhanced the efficacy of cytarabine in acute lymphoblastic leukemia cell lines: a drug repositioning approach

Acute lymphoblastic leukemia (ALL) is the most common childhood cancer. Despite advances in the treatment of ALL, high disease recurrence and the impact of chemical toxicity on patients' quality of life persist. Drug repositioning has been proven to have antitumor and anti-inflammatory properties in leukemia. This study investigated the effects of metformin and chloroquine on the efficacy of cytarabine in NALM-6 cells. The growth inhibitory effects of metformin (Met) and chloroquine (CQ) on the response of NALM-6 cells to cytarabine (AraC) were determined via the MTT assay. To test the regeneration potential, a colony formation assay was performed. Apoptosis and cell cycle analyses were executed via flow cytometry. Oxidative stress markers and antioxidant activity were measured. Gene expression analysis and protein measurement of apoptotic and signaling pathways were performed. The administration of metformin and chloroquine increased the efficacy of cytarabine in suppressing NALM-6 cells, leading to decreased colony formation, increased apoptosis, and G1 phase cell cycle arrest. These effects are mediated by the upregulation of TP53, CASP3 and CASP8 genes and the reduction in BCL-2, NRAS and KRAS genes. Our data suggest that the combination of AraC with Met and CQ may be an effective approach for the treatment of B-ALL.

The dual effects of propofol down-regulating PD-L1 expression and inhibiting autophagy to reduce cerebral ischemia reperfusion injury

Propofol, an anesthetic, has shown neuroprotective effects in animal and cellular models of cerebral ischemia-reperfusion (I/R) injury, likely by suppressing I/R-induced excessive autophagy. PD-L1 and immune escape are implicated in experimental stroke outcomes, but their mechanisms remain unclear. Here, we demonstrate that oxygen-glucose deprivation/reoxygenation (OGD/R) in astrocytes upregulates PD-L1 expression and autophagy, effects that are attenuated by propofol treatment via the NF-κB pathway. In vivo, propofol protects mice from I/R injury by downregulating PD-L1, inflammatory factors, and autophagy. Clinically, craniotomy patients receiving propofol exhibited higher CD3+ and CD4+ T lymphocyte percentages and lower PD-L1 and inflammatory factor levels. Our findings elucidate propofol's dual role in downregulating PD-L1 and inhibiting autophagy in cerebral I/R injury, suggesting its potential as a novel therapeutic strategy to mitigate inflammation and immune dysregulation.

snCED-seq: high-fidelity cryogenic enzymatic dissociation of nuclei for single-nucleus RNA-seq of FFPE tissues

Recent advances have shown that single-nucleus RNA sequencing (snRNA-seq) can be applied to formalin-fixed, paraffin-embedded (FFPE) tissues, opening avenues for transcriptomic analysis of archived specimens. Yet, isolating intact nuclei remains difficult due to RNA cross-linking. Here, we introduce a cryogenic enzymatic dissociation (CED) strategy for rapid, high-yield and fidelity nuclei extraction from FFPE samples and validate its utility with snRandom-seq (snCED-seq) using male C57/BL6 mice. Compared with conventional approaches, CED delivers a tenfold increase in nuclei yield with significantly reduced hands-on time, while minimizing secondary RNA degradation and preserving intranuclear transcripts. snCED-seq enhances gene detection sensitivity, lowers mitochondrial and ribosomal contamination, and increases overall gene expression quantification. In Alzheimer's disease studies, it distinguished two astrocyte subpopulations, microglia, and oligodendrocytes, revealing cellular heterogeneity. Additionally, snCED-seq identify major cell types in a single 50 μm FFPE human lung section. Our results demonstrate that snCED-seq is robust for FFPE specimens and poised to enable multi-omics analyses of clinical samples.

Emerging Techniques in Spatial Multiomics: Fundamental Principles and Applications to Dermatology

Molecular pathology, such as high-throughput genomic and proteomic profiling, identifies precise disease targets from biopsies but require tissue dissociation, losing valuable histologic and spatial context. Emerging spatial multi-omic technologies now enable multiplexed visualization of genomic, proteomic, and epigenomic targets within a single tissue slice, eliminating the need for labeling multiple adjacent slices. Although early work focused on RNA (spatial transcriptomics), spatial technologies can now concurrently capture DNA, genome accessibility, histone modifications, and proteins with spatially-resolved single-cell resolution. This review outlines the principles, advantages, limitations, and potential for spatial technologies to advance dermatologic research. By jointly profiling multiple molecular channels, spatial multiomics enables novel studies of copy number variations, clonal heterogeneity, and enhancer dysregulation, replete with spatial context, illuminating the skin's complex heterogeneity.

Unravelling tumour spatiotemporal heterogeneity using spatial multimodal data

Analysing the genome, epigenome, transcriptome, proteome, and metabolome within the spatial context of cells has transformed our understanding of tumour spatiotemporal heterogeneity. Advances in spatial multi-omics technologies now reveal complex molecular interactions shaping cellular behaviour and tissue dynamics. This review highlights key technologies and computational methods that have advanced spatial domain identification and their pseudo-relations, as well as inference of intra- and inter-cellular molecular networks that drive disease progression. We also discuss strategies to address major challenges, including data sparsity, high-dimensionality, scalability, and heterogeneity. Furthermore, we outline how spatial multi-omics enables novel insights into disease mechanisms, advancing precision medicine and informing targeted therapies. KEY POINTS: Advancements in spatial multi-omics facilitate our understanding of tumour spatiotemporal heterogeneity. AI-driven multimodal models uncover complex molecular interactions that underlie cellular behaviours and tissue dynamics. Combining multi-omics technologies and AI-enabled bioinformatics tools helps predict critical disease stages, such as pre-cancer, advancing precision medicine, and informing targeted therapeutic strategies.

Advances in Spatial Omics Technologies

Rapidly developing spatial omics technologies provide us with new approaches to deeply understanding the diversity and functions of cell types within organisms. Unlike traditional approaches, spatial omics technologies enable researchers to dissect the complex relationships between tissue structure and function at the cellular or even subcellular level. The application of spatial omics technologies provides new perspectives on key biological processes such as nervous system development, organ development, and tumor microenvironment. This review focuses on the advancements and strategies of spatial omics technologies, summarizes their applications in biomedical research, and highlights the power of spatial omics technologies in advancing the understanding of life sciences related to development and disease.

Neuritin-specific antibody impedes the Treg-mediated suppression of anti-tumor immunity and enhances response to anti-PD1

Regulatory T cells (Tregs) and effector T cells play critical roles in tumor immunity, with Tregs suppressing immune responses and contributing to an immunosuppressive tumor microenvironment (TME). Neuritin-1 (Nrn), a neuropeptide, has been identified to enhance Treg expansion. However, its role in T cell biology and tumor development remains unclear. We demonstrated that Nrn is highly expressed in the in-vitro-induced Tregs (iTregs). Functionally, Nrn promoted iTreg differentiation in a dose-dependent manner, while Nrn deletion or anti-Nrn antibody treatment significantly inhibited iTreg differentiation. Additionally, Nrn suppressed IL-2 transcription and secretion in T cells, impairing T cell activation and pro-inflammatory cytokine production. Treg-specific Nrn knockout mice exhibited reduced B16 melanoma tumor growth, decreased Treg infiltration, and increased effector T cell infiltration. Conversely, overexpression of Nrn accelerated B16 melanoma tumor progression by enhancing Treg-mediated suppression. Importantly, we developed the first anti-Nrn antibody, which effectively reduced tumour growth, decreased Treg infiltration, and enhanced effector T-cell activity. Importantly, anti-Nrn synergistically worked with anti-PD1 and improved the anti-PD1 response by reducing Tregs and increasing effector function in tumor-infiltrated T cells, resulting in enhanced tumor regression. Our findings identify Nrn as a critical regulator of Treg differentiation and effector T cell suppression, contributing to tumor progression. Targeting Nrn alone or combined with anti-PD1 therapy represents a promising strategy to enhance anti-tumor immunity.

Advances and applications in single-cell and spatial genomics

The applications of single-cell and spatial technologies in recent times have revolutionized the present understanding of cellular states and the cellular heterogeneity inherent in complex biological systems. These advancements offer unprecedented resolution in the examination of the functional genomics of individual cells and their spatial context within tissues. In this review, we have comprehensively discussed the historical development and recent progress in the field of single-cell and spatial genomics. We have reviewed the breakthroughs in single-cell multi-omics technologies, spatial genomics methods, and the computational strategies employed toward the analyses of single-cell atlas data. Furthermore, we have highlighted the advances made in constructing cellular atlases and their clinical applications, particularly in the context of disease. Finally, we have discussed the emerging trends, challenges, and opportunities in this rapidly evolving field.

Learning tissue representation by identification of persistent local patterns in spatial omics data

Spatial omics data provide rich molecular and structural information on tissues. Their analysis provides insights into local heterogeneity of tissues and holds promise to improve patient stratification by associating clinical observations with refined tissue representations. We introduce Kasumi, a method for identifying spatially localized neighborhood patterns of intra- and intercellular relationships that are persistent across samples and conditions. The tissue representation based on these patterns can facilitate translational tasks, as we show for stratification of cancer patients for disease progression and response to treatment using data from different experimental platforms. On these tasks, Kasumi outperforms related approaches and offers explanations of spatial coordination and relationships at the cell-type or marker level. We show that persistent patterns comprise regions of different sizes, and that non-abundant, localized relationships in the tissue are strongly associated with unfavorable outcomes.

Prognostic significance of circulating tumor DNA in urothelial carcinoma patients undergoing immune checkpoint inhibitor therapy: a systematic review and meta-analysis

Background

Circulating tumor DNA (ctDNA) has emerged as a novel biomarker with the advantages of being non-invasive and enabling dynamic monitoring, providing significant clinical insights into the prognosis and management of malignancies. However, its prognostic role in patients with urothelial carcinoma (UC) receiving immune checkpoint inhibitors (ICI) remains controversial. This study aims to systematically review and perform a meta-analysis to evaluate the prognostic significance of ctDNA levels in this specific patient population.

Methods

We conducted a comprehensive search of the PubMed, Cochrane Library, CNKI, and EMBASE databases to include studies published up to November 14, 2024, assessing the prognostic value of ctDNA in UC patients treated with ICI. Fixed-effects or random-effects models were used to evaluate the association between ctDNA levels and overall survival (OS), progression-free survival (PFS)/disease-free survival (DFS). Funnel plots, Begg's test, and Egger's test were employed to assess publication bias.

Results

Nine studies from eight articles, comprising a total of 862 urothelial carcinoma (UC) patients treated with immune checkpoint inhibitors (ICIs), were included in this meta-analysis. Seven studies investigated the association between baseline circulating tumor DNA (ctDNA) status and clinical outcomes. Compared to patients without detectable ctDNA, those with elevated baseline ctDNA levels exhibited significantly shorter progression-free survival/disease-free survival (PFS/DFS) (HR = 2.75, 95% CI = 1.36-5.58, P = 0.005), though no statistically significant difference was observed in overall survival (OS) (HR = 2.08, 95% CI = 0.83-5.24, P = 0.119). Additionally, we evaluated the prognostic value of ctDNA dynamics during ICI therapy. A decline or clearance of ctDNA levels was significantly associated with improved clinical outcomes (OS: HR = 0.10, 95% CI = 0.02-0.47, P = 0.004; PFS/DFS: HR = 0.27, 95% CI = 0.16-0.45, P < 0.001).

Conclusions

This meta-analysis demonstrates that detectable ctDNA is significantly associated with PFS or DFS in patients with UC undergoing ICI therapy. Moreover, dynamic changes in ctDNA are strongly correlated with OS and PFS/DFS. Therefore, ctDNA serves as a valuable tool for pre-treatment diagnostic assessment and patient stratification and plays a crucial role in monitoring treatment response and tracking disease progression throughout therapy.

Systematic review registration

www.inplasy.com, identifier INPLASY202520058.

Tumor associated chromosomal instability drives colorectal adenoma to adenocarcinoma progression based on 17 year follow up evidence

Colorectal cancer (CRC) is a leading cause of cancer-related deaths globally. Adenomas, precursors to CRC, can be diagnosed early, but the genetic events leading to adenoma-adenocarcinoma conversion remain unclear. This study explored the role of chromosomal instabilities (CINs) in this conversion. Over a 17-year follow-up period, 119 adenomas were analyzed using low-coverage whole-genome sequencing (LC-WGS) and Ultrasensitive Chromosomal Aneuploidy Detector. Risk factors for adenocarcinoma development were identified through logistic regression analysis, and survival was assessed using Kaplan-Meier curves. CIN was found in 32% of adenomas, with a higher incidence in high-grade adenomas (P = 0.0359). Common chromosomal changes included loss of 18q, 1p, and 17p and gain of 8q (MYC), 20q, and 7p (EGFR). During the 17-year follow-up, 88 patients experienced recurrence, including 40 cases of adenomas and 48 cases of progression to adenocarcinoma. CIN was identified in 40% of progression cases, 33.6% of adenoma recurrence cases, and 26% of nonrecurrent cases. A strong genetic linkage was observed before and after tumor transformation, with a high match between the tumors and matched prior adenomas. CIN was significantly associated with disease progression (HR: 2.5, 95% CI: 1.4-4.5, P = 0.00162) and was an independent risk factor. Additionally, MFN2 gene copy number deletion was linked to recurrence and/or progression after resection, with reduced expression in tumor tissues. In conclusion, CIN is a key risk factor for adenoma recurrence and progression, and MFN2 gene copy number deletion is associated with adverse outcomes, providing insights for more accurate clinical prognostication of adenoma-to-adenocarcinoma transformation.

Monitoring Immune Responses to Vaccination: A Focus on Single-Cell Analysis and Associated Challenges

Monitoring the immune response to vaccination encompasses both significant challenges and promising opportunities for scientific advancement. The primary challenge lies in the inherent complexity and interindividual variability of immune responses, influenced by factors including age, genetic background, and prior immunological history. This variability necessitates the development of sophisticated, highly sensitive assays capable of accurately quantifying immune parameters such as antibody titers, T-cell responses, and cytokine profiles. Furthermore, the temporal dynamics of the immune response require comprehensive longitudinal studies to elucidate the durability and quality of vaccine-induced immunity. Challenges of this magnitude pave the way for immunological research advancements and diagnostic methodologies. Cutting-edge monitoring techniques, such as high-throughput sequencing and advanced flow cytometry, enable deeper insights into the mechanistic underpinnings of vaccine efficacy and contribute to the iterative design of more effective vaccines. Additionally, the integration of analytical tools holds the potential to predict immune responses and tailor personalized vaccination strategies. This will be addressed in this review to provide insight for enhancing public health outcomes and fortifying preparedness against future infectious disease threats.

Advances in Microfluidic Single-Cell RNA Sequencing and Spatial Transcriptomics

The development of micro- and nano-fabrication technologies has greatly advanced single-cell and spatial omics technologies. With the advantages of integration and compartmentalization, microfluidic chips are capable of generating high-throughput parallel reaction systems for single-cell screening and analysis. As omics technologies improve, microfluidic chips can now integrate promising transcriptomics technologies, providing new insights from molecular characterization for tissue gene expression profiles and further revealing the static and even dynamic processes of tissues in homeostasis and disease. Here, we survey the current landscape of microfluidic methods in the field of single-cell and spatial multi-omics, as well as assessing their relative advantages and limitations. We highlight how microfluidics has been adapted and improved to provide new insights into multi-omics over the past decade. Last, we emphasize the contributions of microfluidic-based omics methods in development, neuroscience, and disease mechanisms, as well as further revealing some perspectives for technological advances in translational and clinical medicine.

A systematic review of the avian antibody (IgY) therapeutic effects on human bacterial infections over the decade

The overuse of antibiotics worldwide, especially during the Coronavirus pandemic, has raised concerns about the rise of antibiotic resistance and its side effects. Immunoglobulin Y, a natural protein that specifically targets foreign antigens, holds promise as a potential therapeutic option, particularly for individuals with sensitive immune systems. Despite numerous studies on IgY, the optimal administration method, effective dose, target antigen, and potential side effects of this antibody remain areas of active research and challenge. This review selected and evaluated articles published in the last ten years from databases such as PubMed and Science Direct with appropriate keywords discussing the therapeutic effects of immunoglobulin Y in human infections in vivo. Out of all the reviewed articles, 35 articles met the inclusion criteria. The results showed that the specific antibody against dental, respiratory, and skin infections has an acceptable effectiveness. In contrast, some infections, such as neurological infections, including tetanus and botulism, still need further investigation due to the short survival time of mice. On the other hand, reporting side effects such as antibody-dependent enhancement in some infections limits its use.

Deciphering the tumor-suppressive role of RBMS3 in lung adenocarcinoma through genomic insights into prognosis and mechanisms

Lung adenocarcinoma (LUAD) is a leading cause of cancer-related mortality, underscoring the urgent need for novel biomarkers and therapeutic targets. Through transcriptomic analysis of 4456 genes in TCGA-LUAD cohorts, we identified RBMS3 as a significantly downregulated tumor suppressor (log2 fold change = -1.82, adjusted P = 3.2 × 10⁻5). Clinically, elevated RBMS3 expression was independently associated with improved overall survival (HR = 0.766, 95% CI 0.602-0.973, P = 0.029), validated by Kaplan-Meier analysis (Log-rank P = 0.027). Functionally, RBMS3 overexpression in A549 and PC-9 LUAD cells suppressed invasion (66.53% and 52.46% reduction, respectively; P < 0.01) and induced apoptosis (total apoptosis increased by 6.53% and 8.57%; P < 0.05). Cell cycle analysis revealed accelerated G1-to-S phase transition, with G1-phase proportions decreasing from 44.6 to 36.87% in A549 (P < 0.01) and from 49.83 to 37.13% in PC-9 (P < 0.01). TIMER-based correlation analysis demonstrated a positive association between RBMS3 expression and immune cell infiltration, with the regression line indicating significant correlations for B cells (cor = 0.16, P = 4.25 × 10⁻4), CD8 + T cells (cor = 0.214, P = 1.86 × 10⁻⁶), CD4 + T cells (cor = 0.24, P = 8.99 × 10⁻⁸), macrophages (cor = 0.341, P = 1.07 × 10⁻14), neutrophils (cor = 0.277, P = 5.71 × 10⁻10), and dendritic cells (cor = 0.369, P = 3.70 × 10⁻17). These findings underscore RBMS3's dual role in LUAD as a tumor suppressor and immune microenvironment modulator, offering novel insights for prognosis and therapy.

Spatially resolved genome-wide joint profiling of epigenome and transcriptome with spatial-ATAC-RNA-seq and spatial-CUT&Tag-RNA-seq

The epigenome of a cell is tightly correlated with gene transcription, which controls cell identity and diverse biological activities. Recent advances in spatial technologies have improved our understanding of tissue heterogeneity by analyzing transcriptomics or epigenomics with spatial information preserved, but have been mainly restricted to one molecular layer at a time. Here we present procedures for two spatially resolved sequencing methods, spatial-ATAC-RNA-seq and spatial-CUT&Tag-RNA-seq, that co-profile transcriptome and epigenome genome wide. In both methods, transcriptomic readouts are generated through tissue fixation, permeabilization and in situ reverse transcription. In spatial-ATAC-RNA-seq, Tn5 transposase is used to probe accessible chromatin, and in spatial-CUT&Tag-RNA-seq, the tissue is incubated with primary antibodies that target histone modifications, followed by Protein A-fused Tn5-induced tagmentation. Both methods leverage a microfluidic device that delivers two sets of oligonucleotide barcodes to generate a two-dimensional mosaic of tissue pixels at near single-cell resolution. A spatial-ATAC-RNA-seq or spatial-CUT&Tag-RNA-seq library can be generated in 3-5 d, allowing researchers to simultaneously investigate the transcriptomic landscape and epigenomic landscape of an intact tissue section. This protocol is an extension of our previous spatially resolved epigenome sequencing protocol and provides opportunities in multimodal profiling.

Association between hematological inflammatory markers and latent TB infection: insights from NHANES 2011-2012 and transcriptomic data

Background

Latent tuberculosis infection affects about one-quarter of the global population and can progress to active tuberculosis. Hematological inflammatory markers, such as the systemic immune-inflammation index, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and monocyte-to-lymphocyte ratio, reflect systemic inflammation and immune status but are understudied in latent tuberculosis infection. This study investigates the association between these markers and latent tuberculosis infection in a nationally representative sample.

Methods

Data from 7,042 participants in the 2011-2012 National Health and Nutrition Examination Survey and transcriptomic data from the GSE19491 dataset were analyzed. Latent tuberculosis infection was identified using the QuantiFERON-TB Gold assay. Hematological parameters were measured via complete blood counts, and inflammatory markers were calculated through these parameters. Statistical analyses included linear regression adjusted for confounders and subgroup analyses. Transcriptomic analyses involved immune cell profiling, gene set enrichment, and immune checkpoint gene expression.

Results

Individuals with latent tuberculosis infection had significantly lower systemic immune-inflammation index, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and monocyte-to-lymphocyte ratio. These associations remained significant after adjusting for age, gender, body mass index, diabetes, and hypertension. Transcriptomic analyses revealed heightened activation of memory CD4 and CD8 T cells, increased cytolytic activity, and upregulated T-cell co-inhibition pathways, alongside differential expression of immune checkpoint genes in individuals with latent tuberculosis infection.

Conclusions

A lower systemic immune-inflammation index and other related hematological inflammatory markers independently correlate with latent tuberculosis infection. These findings underscore the potential significance of hematological inflammatory markers in identifying and understanding latent tuberculosis infection. Further exploration of these markers may enhance diagnostic and therapeutic strategies of tuberculosis.

Comprehensive systems biology analysis reveals splicing factor contributions to cutaneous melanoma progression

Cutaneous melanoma (CM) is an aggressive skin cancer with high metastatic potential and poor prognosis. Splicing factors, which regulate pre-mRNA alternative splicing (AS) events, have been suggested as potential therapeutic targets in CM. The objective of this study was to identify candidate splicing factors involved in CM through a systems biology approach and to elucidate their roles in CM progression. 390 AS events associated with patient survival were identified using bivariate Cox regression and receiver operating characteristic (ROC) analyses. 121 splicing factors significantly associated with patient prognosis were screened by univariate Cox regression analysis. A bipartite association network between AS events and splicing factors was constructed using Spearman correlation analysis. Based on the network topology, five candidate splice factors were identified. Among them, U2SURP, a poorly characterized serine/arginine-rich protein family member, was selected for further analysis in CM. Results indicated that U2SURP gene expression was significantly negatively correlated with the Immune Infiltration Score, the infiltration levels of dendritic cells, gamma-delta T cells, natural killer (NK) cells, and cytotoxic cells, as well as the expression of the immune checkpoint gene PD-1, suggesting that U2SURP may serve as a potential target for CM immunotherapy. Experimental validation showed that U2SURP mRNA and protein were overexpressed in CM cells, and silencing of U2SURP using siRNA significantly reduced CM cell survival, proliferation and migration. Furthermore, single-cell functional analysis showed that U2SURP gene expression was positively correlated with CM cell proliferation and differentiation. This study systematically identified candidate splicing factors involved in CM and provided new insights into the role of U2SURP in CM progression. These findings contribute to a deeper understanding of the pathogenesis of CM and establish new approaches for identifying splicing-related cancer therapeutic targets.

Metabolic expression profiling analysis reveals pyruvate-mediated EPHB2 upregulation promotes lymphatic metastasis in head and neck squamous cell carcinomas

Lymphatic metastasis is a well-known factor for initiating distant metastasis of head and neck squamous cell carcinoma (HNSCC), which caused major death in most patients with cancer. Meanwhile, metabolic reprogramming to support metastasis is regarded as a prominent hallmark of cancers. However, how metabolic disorders drive in HNSCC remains unclear. We firstly established a new classification of HNSCC patients based on metabolism gene expression profiles from the TCGA and GEO database, and identified an enriched carbohydrate metabolism subgroup which was significantly associated with lymphatic metastasis and worse clinical outcome. Moreover, we found that highly activated pyruvate metabolism endowed tumors with EPHB2 upregulation and promoted tumor lymphangiogenesis independently of VEGF-C/VEGFR3 signaling pathway. Mechanically, high nuclear acetyl-CoA production from pyruvate metabolism promoted histone acetylation, which in turn transcriptionally upregulated EPHB2 expression and secretion in tumor cells. EPHB2 bound with EFNB1 in lymphatic endothelial cells promoted YAP/TAZ cytoplasmic retention, which alleviated YAP/TAZ-mediated prospero homeobox protein 1 (PROX1) transcriptional repression, and then triggered tumor lymphangiogenesis. Importantly, combined treatment with EFNB1-Fc and VEGFR3 inhibitor synergistic abrogated lymphangiogenesis in vitro and in vivo, suggesting that targeting EPHB2 might be a potential strategy to patients with no or slight response to VEGFR3 inhibitor. These findings uncover the mechanism by which pyruvate metabolism is linked to lymphatic metastasis of tumor and provides a promising therapeutic strategy for the prevention of HNSCC metastasis.

Association between triglyceride-glucose index and its combination with obesity indicators and depression: findings from NHANES 2005-2020

Background

The relationship between the triglyceride-glucose (TyG) index, its combination with obesity indicators, and depression remains understudied in the American population.

Methods

This cross-sectional study analyzed data from 10,423 adults in the National Health and Nutrition Examination Survey (NHANES) conducted between 2005 and 2020. We employed multivariable logistic regression analysis, smoothing techniques, generalized additive models, stratified analyses, and sensitivity analyses to examine the relationship between TyG, its combination (TyG-WC, TyG-WHtR, TyG-BMI) with obesity indicators, and depression.

Results

The results indicate that the TyG index, TyG-WC, TyG-WHtR, TyG-BMI, and depression exhibited a significant statistical association with depressive symptoms (all P for trend < 0.001). Specifically, a one-unit increase in the TyG index correlated with a 37% increase in the risk of depressive symptoms (95% CI: 1.21-1.55), a one-unit increase in TyG-WC correlated with a 3.26 times increase in the risk of depressive symptoms (95% CI: 2.22-4.80), a one-unit increase in TyG-WHtR correlated with a 27% increase in the risk of depressive symptoms (95% CI: 1.18-1.36), and a one-unit increase in TyG-BMI correlated with a 2.30 times increase in the risk of depressive symptoms (95% CI: 1.72-3.08). There was a significant nonlinear correlation between TyG-WC, TyG-WHtR, and TyG-BMI with depressive symptoms (all P for nonlinearity < 0.001), except for a linear correlation between the TyG index and depressive symptoms (P for linearity < 0.001).

Conclusion

Monitoring the TyG index, TyG-WC, TyG-WHtR, TyG-BMI may facilitate depression risk assessment and prevention.

Single-Cell Multi-Omics: Insights into Therapeutic Innovations to Advance Treatment in Cancer

Advances in single-cell multi-omics technologies have deepened our understanding of cancer biology by integrating genomic, transcriptomic, epigenomic, and proteomic data at single-cell resolution. These single-cell multi-omics technologies provide unprecedented insights into tumour heterogeneity, tumour microenvironment, and mechanisms of therapeutic resistance, enabling the development of precision medicine strategies. The emerging field of single-cell multi-omics in genomic medicine has improved patient outcomes. However, most clinical applications still depend on bulk genomic approaches, which fail to directly capture the genomic variations driving cellular heterogeneity. In this review, we explore the common single-cell multi-omics platforms and discuss key analytical steps for data integration. Furthermore, we highlight emerging knowledge in therapeutic resistance and immune evasion, and the potential of new therapeutic innovations informed by single-cell multi-omics. Finally, we discuss the future directions of the application of single-cell multi-omics technologies. By bridging the gap between technological advancements and clinical implementation, this review provides a roadmap for leveraging single-cell multi-omics to improve cancer treatment and patient outcomes.

From morphology to single-cell molecules: high-resolution 3D histology in biomedicine

High-resolution three-dimensional (3D) tissue analysis has emerged as a transformative innovation in the life sciences, providing detailed insights into the spatial organization and molecular composition of biological tissues. This review begins by tracing the historical milestones that have shaped the development of high-resolution 3D histology, highlighting key breakthroughs that have facilitated the advancement of current technologies. We then systematically categorize the various families of high-resolution 3D histology techniques, discussing their core principles, capabilities, and inherent limitations. These 3D histology techniques include microscopy imaging, tomographic approaches, single-cell and spatial omics, computational methods and 3D tissue reconstruction (e.g. 3D cultures and spheroids). Additionally, we explore a wide range of applications for single-cell 3D histology, demonstrating how single-cell and spatial technologies are being utilized in the fields such as oncology, cardiology, neuroscience, immunology, developmental biology and regenerative medicine. Despite the remarkable progress made in recent years, the field still faces significant challenges, including high barriers to entry, issues with data robustness, ambiguous best practices for experimental design, and a lack of standardization across methodologies. This review offers a thorough analysis of these challenges and presents recommendations to surmount them, with the overarching goal of nurturing ongoing innovation and broader integration of cellular 3D tissue analysis in both biology research and clinical practice.

Exploring scanning electrochemical probe microscopy in single-entity analysis in biology: Past, present, and future

Scanning Electrochemical Probe Microscopy (SEPM) shows significant potential promise for analyzing localized electrochemical activity at biological interfaces of single entities. Utilizing various SEPM probe manipulations allows real-time monitoring of the morphology and physiological activities of single biological entities, offering vital electrochemical insights into biological processes. This review focuses on the application of five SEPM techniques in imaging single biological entities, highlighting their unique advantages in the observation and quantitative evaluation of biological morphology. Specifically, these techniques not only enable high-resolution imaging of single biological structures but also allow for quantitative analysis of their response behavior. Additionally, the integration of Artificial Intelligence (AI) is discussed to improve data processing and image analysis, potentially advancing SEPM technology towards automation. Although still in an early stage, AI integration opens new avenues for deeper single-entity analysis. This review aims to offer an interdisciplinary perspective and encourage advancements in SEPM-based imaging and analytical techniques, contributing to the bioanalytical field.

Decoding the blueprints of embryo development with single-cell and spatial omics

Embryonic development is a complex and intricately regulated process that encompasses precise control over cell differentiation, morphogenesis, and the underlying gene expression changes. Recent years have witnessed a remarkable acceleration in the development of single-cell and spatial omic technologies, enabling high-throughput profiling of transcriptomic and other multi-omic information at the individual cell level. These innovations offer fresh and multifaceted perspectives for investigating the intricate cellular and molecular mechanisms that govern embryonic development. In this review, we provide an in-depth exploration of the latest technical advancements in single-cell and spatial multi-omic methodologies and compile a systematic catalog of their applications in the field of embryonic development. We deconstruct the research strategies employed by recent studies that leverage single-cell sequencing techniques and underscore the unique advantages of spatial transcriptomics. Furthermore, we delve into both the current applications, data analysis algorithms and the untapped potential of these technologies in advancing our understanding of embryonic development. With the continuous evolution of multi-omic technologies, we anticipate their widespread adoption and profound contributions to unraveling the intricate molecular foundations underpinning embryo development in the foreseeable future.

Resolving tissue complexity by multimodal spatial omics modeling with MISO

Spatial molecular profiling has provided biomedical researchers valuable opportunities to better understand the relationship between cellular localization and tissue function. Effectively modeling multimodal spatial omics data is crucial for understanding tissue complexity and underlying biology. Furthermore, improvements in spatial resolution have led to the advent of technologies that can generate spatial molecular data with subcellular resolution, requiring the development of computationally efficient methods that can handle the resulting large-scale datasets. MISO (MultI-modal Spatial Omics) is a versatile algorithm for feature extraction and clustering, capable of integrating multiple modalities from diverse spatial omics experiments with high spatial resolution. Its effectiveness is demonstrated across various datasets, encompassing gene expression, protein expression, epigenetics, metabolomics and tissue histology modalities. MISO outperforms existing methods in identifying biologically relevant spatial domains, representing a substantial advancement in multimodal spatial omics analysis. Moreover, MISO's computational efficiency ensures its scalability to handle large-scale datasets generated by subcellular resolution spatial omics technologies.

Multiplexed spatial mapping of chromatin features, transcriptome and proteins in tissues

The phenotypic and functional states of cells are modulated by a complex interactive molecular hierarchy of multiple omics layers, involving the genome, epigenome, transcriptome, proteome and metabolome. Spatial omics approaches have enabled the study of these layers in tissue context but are often limited to one or two modalities, offering an incomplete view of cellular identity. Here we present spatial-Mux-seq, a multimodal spatial technology that allows simultaneous profiling of five different modalities: two histone modifications, chromatin accessibility, whole transcriptome and a panel of proteins at tissue scale and cellular level in a spatially resolved manner. We applied this technology to mouse embryos and mouse brains, generating detailed multimodal tissue maps that identified more cell types and states compared to unimodal data. This analysis uncovered spatiotemporal relationships among histone modifications, chromatin accessibility, gene expression and protein levels during neuron differentiation, and revealed a radial glia niche with spatially dynamic epigenetic signals. Collectively, the spatial multi-omics approach heralds a new era for characterizing tissue and cellular heterogeneity that single-modality studies alone could not reveal.

Microfluidics for morpholomics and spatial omics applications

Creative designs, precise fluidic manipulation, and automation have supported the development of microfluidics for single-cell applications. Together with the advancements in detection technologies and artificial intelligence (AI), microfluidic-assisted platforms have been increasingly used for new modalities of single-cell investigations and in spatial omics applications. This review explores the use of microfluidic technologies for morpholomics and spatial omics with a focus on single-cell and tissue characterization. We emphasize how various fluid dynamic principles and unique design integrations enable highly precise fluid manipulation, enhancing sample handling in morpholomics. Additionally, we examine the use of microfluidics-assisted spatial barcoding with micrometer resolutions for the spatial profiling of tissue specimens. Finally, we discuss how microfluidics can serve as a bridge for integrating multiple unique fields in omics research and outline key challenges that these technologies may face in practical translation.

HMGCL activates autophagy in osteosarcoma through β-HB mediated inhibition of the PI3K/AKT/mTOR signaling pathway

BACKGROUND

3-hydroxy-3-methylglutaryl-coenzymOHBe A(HMG-CoA) lyase (HMGCL) catalyzes the cleavage of HMG-CoA into acetyl-CoA and acetoacetic acid and serves as a rate-limiting enzyme in the metabolism of ketone bodies. While HMGCL is involved in various biological processes, its specific role in osteosarcoma remains unclear.

METHODS

Using data from a public database of osteosarcoma patients, we investigated the expression and prognostic value of HMGCL. The effects of HMGCL on the proliferation, migration, and invasion of osteosarcoma cells were assessed using CCK-8 assays, wound healing tests, and transwell invasion assays. We explored and validated the specific molecular mechanisms by which HMGCL influences osteosarcoma through transcriptome sequencing. Finally, we established a subcutaneous tumor formation model in nude mice to investigate the function of HMGCL in vivo.

RESULTS

The expression of HMGCL is downregulated in osteosarcoma and correlates with the prognosis of osteosarcoma patients. Overexpression of HMGCL can inhibit the proliferation, migration, and invasion of osteosarcoma cells, as well as tumor growth in vivo. Through our investigation of the underlying mechanism, we found that HMGCL may inhibit the activation of the PI3K/AKT/mTOR signaling pathway via its product, β-HB. This inhibition promotes the phosphorylation of ULK1, thereby facilitating autophagy in osteosarcoma cells and enhancing the malignancy of the disease.

CONCLUSION

HMGCL inhibits the activation of the PI3K/AKT/mTOR signaling pathway mediated by β-HB, thereby reducing the proliferation, migration, and invasion of osteosarcoma cells while promoting autophagy. HMGCL may represent a new target for the treatment of osteosarcoma, offering new hope for patients with this disease.

Randomized Spatial PCA (RASP): a computationally efficient method for dimensionality reduction of high-resolution spatial transcriptomics data

Spatial transcriptomics (ST) provides critical insights into the spatial organization of gene expression, enabling researchers to unravel the intricate relationship between cellular environments and biological function. Identifying spatial domains within tissues is key to understanding tissue architecture and mechanisms underlying development and disease progression. Here, we present Randomized Spatial PCA (RASP), a novel spatially-aware dimensionality reduction method for ST data. RASP is designed to be orders-of-magnitude faster than existing techniques, scale to datasets with 100, 000+ locations, support flexible integration of non-transcriptomic covariates, and reconstruct de-noised, spatially-smoothed gene expression values. It employs a randomized two-stage PCA framework with sparse matrix operations and configurable spatial smoothing. RASP was compared to BASS, GraphST, SEDR, SpatialPCA, and STAGATE using diverse ST datasets (10x Visium, Stereo-Seq, MERFISH, 10x Xenium) on human and mouse tissues. RASP demonstrates comparable or superior tissue domain detection with substantial improvements in computational speed, enhancing exploration of high-resolution subcellular datasets.

Single-cell genomics and spatial transcriptomics in islet transplantation for diabetes treatment: advancing towards personalized therapies

Diabetes mellitus (DM) is a global health crisis affecting millions, with islet transplantation emerging as a promising treatment strategy to restore insulin production. This review synthesizes the current research on single-cell and spatial transcriptomics in the context of islet transplantation, highlighting their potential to revolutionize DM management. Single-cell RNA sequencing, offers a detailed look into the diversity and functionality within islet grafts, identifying specific cell types and states that influence graft acceptance and function. Spatial transcriptomics complements this by mapping gene expression within the tissue's spatial context, crucial for understanding the microenvironment surrounding transplanted islets and their interactions with host tissues. The integration of these technologies offers a comprehensive view of cellular interactions and microenvironments, elucidating mechanisms underlying islet function, survival, and rejection. This understanding is instrumental in developing targeted therapies to enhance graft performance and patient outcomes. The review emphasizes the significance of these research avenues in informing clinical practices and improving outcomes for patients with DM through more effective islet transplantation strategies. Future research directions include the application of these technologies in personalized medicine, developmental biology, and regenerative medicine, with the potential to predict disease progression and treatment responses. Addressing ethical and technical challenges will be crucial for the successful implementation of these integrated approaches in research and clinical practice, ultimately enhancing our ability to manage DM and improve patient quality of life.

HSP90 co-regulates the formation and nuclear distribution of the glycolytic output complex to promote resistance and poor prognosis in gastric cancer patients

BACKGROUND

Resistance to treatment is a critical factor contributing to poor prognosis in gastric cancer patients. HSP90 has emerged as a promising therapeutic target; however, its role in regulating tumor metabolic pathways, particularly glycolysis, remains poorly understood, which limits its clinical application.

METHODS

We identified proteins that directly interact with HSP90 using immunoprecipitation (IP) followed by mass spectrometry. The relationship between HSP90 and glycolysis was further investigated through transcriptomic analyses and in vitro experiments. Mechanistic insights were obtained through mass spectrometry, co-immunoprecipitation (Co-IP) assays, drug sensitivity tests, and bioinformatics analyses. Additionally, we developed a scoring system based on transcriptomic data to evaluate its prognostic significance and association with treatment resistance in gastric cancer patients.

RESULTS

Our multi-omics and in vitro studies revealed that HSP90 regulates glycolysis and influences the stemness properties of gastric cancer cells. Mechanistically, HSP90 facilitates the assembly of a glycolytic multi-enzyme complex, termed the HGEO complex, which enhances glycolytic metabolism. Mechanistically, HSP90 facilitates the formation of a multienzyme complex comprising key enzymes including PGK1, PKM2, ENO1, and LDHA, thereby facilitating the production of the final glycolytic products. We refer to this as the "HSP90-Glycolytic Output Complex" (HGEO Complex). We quantified this phenomenon with a scoring system (HGScore), finding that patients with a high HGScore exhibited more malignant signatures, increased resistance to treatment, and poorer prognoses. Furthermore, we demonstrated that the HGEO complex is localized in the nucleus, regulated by the nuclear lamina protein LMNA, which further contributes to treatment resistance and adverse outcomes. In vitro experiments indicated that inhibiting the formation of this complex sensitizes gastric cancer cells to chemotherapy.

CONCLUSION

Our findings suggest that HSP90 and LMNA mediated the formation and nuclear localization of the HGEO complex, thereby enhancing the malignant traits and resistance mechanisms in gastric cancer. Targeting this pathway may offer a novel therapeutic strategy to improve treatment outcomes.

Powerful and accurate case-control analysis of spatial molecular data with deep learning-defined tissue microniches

As spatial molecular data grow in scope and resolution, there is a pressing need to identify key spatial structures associated with disease. Current approaches often rely on hand-crafted features such as local abundances of manually annotated, discrete cell types, which may overlook important signals. Here we introduce variational inference-based microniche analysis (VIMA), a method that combines deep learning with principled statistics to discover associated spatial features with greater flexibility and precision. VIMA uses a variational autoencoder to extract numerical "fingerprints" from small tissue patches that capture their biological content. It uses these fingerprints to define a large number of "microniches" - small, potentially overlapping groups of tissue patches with highly similar biology that span multiple samples. It then uses rigorous statistics to identify microniches whose abundance correlates with case-control status. We show in simulations that VIMA is well calibrated and more powerful and accurate than other approaches. We then apply VIMA to a 140-gene spatial transcriptomics dataset in Alzheimer's dementia, a 54-marker CO-Detection by indEXing (CODEX) dataset in ulcerative colitis (UC), and a 7-marker immunohistochemistry dataset in rheumatoid arthritis (RA), in each case recapitulating known biology and identifying novel spatial features of disease.

Seq-Scope-eXpanded: Spatial Omics Beyond Optical Resolution

Sequencing-based spatial transcriptomics (sST) enables transcriptome-wide gene expression mapping but falls short of reaching the optical resolution (200-300 nm) of imaging-based methods. Here, we present Seq-Scope-X (Seq-Scope-eXpanded), which empowers submicrometer-resolution Seq-Scope with tissue expansion to surpass this limitation. By physically enlarging tissues, Seq-Scope-X minimizes transcript diffusion effects and increases spatial feature density by an additional order of magnitude. In liver tissue, this approach resolves nuclear and cytoplasmic compartments in nearly every single cell, uncovering widespread differences between nuclear and cytoplasmic transcriptome patterns. Independently confirmed by imaging-based methods, these results suggest that individual hepatocytes can dynamically switch their metabolic roles. Seq-Scope-X is also applicable to non-hepatic tissues such as brain and colon, and can be modified to perform spatial proteomic analysis, simultaneously profiling hundreds of barcode-tagged antibody stains at microscopic resolutions in mouse spleens and human tonsils. These findings establish Seq-Scope-X as a transformative tool for ultra-high-resolution whole-transcriptome and proteome profiling, offering unparalleled spatial precision and advancing our understanding of cellular architecture, function, and disease mechanisms.

High-resolution spatially resolved proteomics of complex tissues based on microfluidics and transfer learning

Despite recent advances in imaging- and antibody-based methods, achieving in-depth, high-resolution protein mapping across entire tissues remains a significant challenge in spatial proteomics. Here, we present parallel-flow projection and transfer learning across omics data (PLATO), an integrated framework combining microfluidics with deep learning to enable high-resolution mapping of thousands of proteins in whole tissue sections. We validated the PLATO framework by profiling the spatial proteome of the mouse cerebellum, identifying 2,564 protein groups in a single run. We then applied PLATO to rat villus and human breast cancer samples, achieving a spatial resolution of 25 μm and uncovering proteomic dynamics associated with disease states. This approach revealed spatially distinct tumor subtypes, identified key dysregulated proteins, and provided novel insights into the complexity of the tumor microenvironment. We believe that PLATO represents a transformative platform for exploring spatial proteomic regulation and its interplay with genetic and environmental factors.

A Temporal and Spatial Atlas of Adaptive Immune Responses in the Lymph Node Following Viral Infection

The spatial organization of adaptive immune cells within lymph nodes is critical for understanding immune responses during infection and disease. Here, we introduce AIR-SPACE, an integrative approach that combines high-resolution spatial transcriptomics with paired, high-fidelity long-read sequencing of T and B cell receptors. This method enables the simultaneous analysis of cellular transcriptomes and adaptive immune receptor (AIR) repertoires within their native spatial context. We applied AIR-SPACE to mouse popliteal lymph nodes at five distinct time points after Vaccinia virus footpad infection and constructed a comprehensive map of the developing adaptive immune response. Our analysis revealed heterogeneous activation niches, characterized by Interferon-gamma (IFN-γ) production, during the early stages of infection. At later stages, we delineated sub-anatomical structures within the germinal center (GC) and observed evidence that antibody-producing plasma cells differentiate and exit the GC through the dark zone. Furthermore, by combining clonotype data with spatial lineage tracing, we demonstrate that B cell clones are shared among multiple GCs within the same lymph node, reinforcing the concept of a dynamic, interconnected network of GCs. Overall, our study demonstrates how AIR-SPACE can be used to gain insight into the spatial dynamics of infection responses within lymphoid organs.

Understanding developing kidneys and Wilms tumors one cell at a time

Single-cell sequencing-based techniques are revolutionizing all fields of biomedical sciences, including normal kidney development and how this is disturbed in the development of Wilms tumor. The many different techniques and the differences between them can obscure which technique is best used to answer which question. In this review we summarize the techniques currently available, discuss which have been used in kidney development or Wilms tumor context, and which techniques can or should be combined to maximize the increase in biological understanding we can get from them.

Spatial‒temporal heterogeneities of liver cancer and the discovery of the invasive zone

Solid tumours are intricate and highly heterogeneous ecosystems, which grow in and invade normal organs. Their progression is mediated by cancer cells' interaction with different cell types, such as immune cells, stromal cells and endothelial cells, and with the extracellular matrix. Owing to its high incidence, aggressive growth and resistance to local and systemic treatments, liver cancer has particularly high mortality rates worldwide. In recent decades, spatial heterogeneity has garnered significant attention as an unfavourable biological characteristic of the tumour microenvironment, prompting extensive research into its role in liver tumour development. Advances in spatial omics have facilitated the detailed spatial analysis of cell types, states and cell‒cell interactions, allowing a thorough understanding of the spatial and temporal heterogeneities of tumour microenvironment and informing the development of novel therapeutic approaches. This review illustrates the latest discovery of the invasive zone, and systematically introduced specific macroscopic spatial heterogeneities, pathological spatial heterogeneities and tumour microenvironment heterogeneities of liver cancer.

Single-cell sequencing reveals the mechanisms of multiple myeloma progression: clarity or confusion?

Multiple myeloma (MM), the second most common hematologic malignancy, is characterized by the clonal expansion of myeloma cells and accumulation of genetic lesions. MM progression is accompanied by increased aggressiveness and drug resistance. Even the goal of "cure" remains hard to reach for most patients, advances in diagnosis and treatment have allowed some to achieve durable remissions and transition to plateau phase. Single-cell sequencing, with its powerful ability to analyze cellular heterogeneity and molecular patterns at ground-breaking resolution, is informative for deciphering tumors and their microenvironment. In this review, we summarize the new insights of studies facilitated by emerging single-cell sequencing into clonal evolution, myeloma-supported microenvironment transformation, epigenetic changes, and novel prognostic and therapeutic strategies for MM, revealing the key mechanisms underlying MM progression and the direction of future efforts. With the continuous expansion of the research scope and optimization of related technologies, single-cell sequencing is expected to revolutionize our understanding of the biology and evolutionary dynamics of MM and contribute to the radical and precise improvement of treatment.

Effects of yeast culture on growth performance, antioxidant capacity, immune function, and intestinal microbiota structure in Simmental beef cattle

Introduction

As functional feed additives, yeast cultures have been applied in animal husbandry and shown a wide range of good efficacy. This paper aimed to evaluate the effects of yeast culture (YC) on the growth performance, antioxidant capacity, immune function, and intestinal microbiota structure in beef cattle.

Methods

Forty Simmental bulls were randomly divided into two groups, including the control group fed with Total mixed ration (TMR) and YC group fed with TMR supplemented with 2% YC, for 60 days. Serum samples were collected on the 1st, 30th, and 60th days, respectively, while feces 3 days before the end of the test.

Results

Results showed that YC addition significantly elevated average daily gain and reduced feed to weight ratio of beef cattle. The enzyme activities of total superoxide dismutase, total antioxidant capacity, and glutathione peroxidase in the serum in YC group obviously increased, while the malondialdehyde content distinctly decreased. Furthermore, YC feeding significantly enhanced the immunoglobulin G (IgG), IgA, IgM levels, lysozyme content, alkaline phosphatase activity, as well as the contents of interleukin-1β (IL-1β), IL-6, and interferon-γ in the cattle serum. The Shannon and Observed species indexes of fecal samples in YC group were remarkably higher than those in the control group, with the former group exhibiting a significant increase in the relative abundance of Paraprevotellace_CF231 and Peptostreptococcaceae_Clostridium at the genus level, while the relative abundance of Spirochaetaceae_Treponema decreased significantly. Moreover, the abundance of Clostridium and CF231 was positively associated with the levels of serum antioxidant capacity and immune function indicator contents.

Discussion

In conclusion, YC could obviously improve the growth performance, antioxidant capacity, immune function, and intestinal microbiota structure in Simmental beef cattle. These results provide a theoretical basis for the clinical application of such yeast fermented preparations in beef cattle husbandry.

Metabolic reprogramming and immunological changes in the microenvironment of esophageal cancer: future directions and prospects

Background

Esophageal cancer (EC) is the seventh-most prevalent cancer worldwide and is a significant contributor to cancer-related mortality. Metabolic reprogramming in tumors frequently coincides with aberrant immune function alterations, and extensive research has demonstrated that perturbations in energy metabolism within the tumor microenvironment influence the occurrence and progression of esophageal cancer. Current treatment modalities for esophageal cancer primarily include encompass chemotherapy and a limited array of targeted therapies, which are hampered by toxicity and drug resistance issues. Immunotherapy, particularly immune checkpoint inhibitors (ICIs) targeting the PD-1/PD-L1 pathway, has exhibited promising results; however, a substantial proportion of patients remain unresponsive. The optimization of these immunotherapies requires further investigation. Mounting evidence underscores the importance of modulating metabolic traits within the tumor microenvironment (TME) to augment anti-tumor immunotherapy.

Methods

We selected relevant studies on the metabolism of the esophageal cancer tumor microenvironment and immune cells based on our searches of MEDLINE and PubMed, focusing on screening experimental articles and reviews related to glucose metabolism, amino acid metabolism, and lipid metabolism, as well their interactions with tumor cells and immune cells, published within the last five years. We analyzed and discussed these studies, while also expressing our own insights and opinions.

Results

A total of 137 articles were included in the review: 21 articles focused on the tumor microenvironment of esophageal cancer, 33 delved into research related to glucose metabolism and tumor immunology, 30 introduced amino acid metabolism and immune responses, and 17 focused on the relationship between lipid metabolism in the tumor microenvironment and both tumor cells and immune cells.

Conclusion

This article delves into metabolic reprogramming and immune alterations within the TME of EC, systematically synthesizes the metabolic characteristics of the TME, dissects the interactions between tumor and immune cells, and consolidates and harnesses pertinent immunotherapy targets, with the goal of enhancing anti-tumor immunotherapy for esophageal cancer and thereby offering insights into the development of novel therapeutic strategies.

The prognostic value of circulating tumor DNA in malignant melanoma patients treated with immune checkpoint inhibitors: a systematic review and meta-analysis

Background

Circulating tumor DNA (ctDNA) is an emerging biomarker in malignant melanoma(MM), and high levels of ctDNA may reflect a higher tumor load. However, its prognostic value for MM receiving immune checkpoint inhibitors(ICI) remains controversial. This meta-analysis aimed to elucidate the prognostic significance of ctDNA in this patient population.

Methods

We conducted a comprehensive search of the PubMed, Cochrane Library, CNKI, and EMBASE databases, including studies published up to August 15, 2024, to investigate the prognostic impact of ctDNA in MM patients treated with ICI. Using a fixed-effects model, we systematically evaluated the association between ctDNA levels and key survival outcomes, including overall survival (OS) and progression-free survival (PFS). Additionally, funnel plots, Begg's test, and Egger's test were employed to assess potential publication bias.

Results

Twelve studies from eleven articles, involving a total of 1063 eligible MM patients receiving ICI therapy, were included. The results indicated that patients with detectable ctDNA before initiating ICI therapy had significantly poorer OS (HR = 3.19, 95% CI = 2.22-4.58, P < 0.001) and PFS (HR = 2.08, 95% CI = 1.61-2.69, P < 0.001). Furthermore, the detectability of ctDNA during treatment was also significantly associated with worse OS (HR = 4.57, 95% CI = 3.03-6.91, P < 0.001) and PFS (HR = 3.79, 95% CI = 2.13-6.75, P < 0.001).

Conclusions

This meta-analysis indicates that in MM patients receiving ICI therapy, detectable and high levels of ctDNA are significantly associated with poorer OS and PFS. Therefore, ctDNA can serve as a diagnostic and stratification tool prior to treatment, as well as an effective indicator for monitoring treatment response and disease progression.

Systematic Review Registration

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