PANAMA-enabled high-sensitivity dual nanoflow LC-MS metabolomics and proteomics analysis.

High-sensitivity nanoflow liquid chromatography (nLC) is seldom employed in untargeted metabolomics because current sample preparation techniques are inefficient at preventing nanocapillary column performance degradation. Here, we describe an nLC-based tandem mass spectrometry workflow that enables seamless joint analysis and integration of metabolomics (including lipidomics) and proteomics from the same samples without instrument duplication. This workflow is based on a robust solid-phase micro-extraction step for routine sample cleanup and bioactive molecule enrichment. Our method, termed proteomic and nanoflow metabolomic analysis (PANAMA), improves compound resolution and detection sensitivity without compromising the depth of coverage as compared with existing widely used analytical procedures. Notably, PANAMA can be applied to a broad array of specimens, including biofluids, cell lines, and tissue samples. It generates high-quality, information-rich metabolite-protein datasets while bypassing the need for specialized instrumentation.

PPARγ/NF-κB axis contributes to cold-induced resolution of experimental colitis and preservation of intestinal barrier.

BACKGROUND: Environmental stress is a significant contributor to the development of inflammatory bowel disease (IBD). The involvement of temperature stimulation in the development of IBD remains uncertain. Our preliminary statistical data suggest that the prevalence of IBD is slightly lower in colder regions compared to non-cold regions. The observation indicates that temperature changes may play a key role in the occurrence and progression of IBD. Here, we hypothesized that cold stress has a protective effect on IBD. METHODS: The cold exposure model for mice was placed in a constant temperature and humidity chamber, maintained at a temperature of 4 °C. Colitis models were induced in the mice using TNBS or DSS. To promote the detection methods more clinically, fluorescence confocal endoscopy was used to observe the mucosal microcirculation status of the colon in the live model. Changes in the colonic wall of the mice were detected using 9.4 T Magnetic Resonance Imaging (MRI) imaging and in vivo fluorescence imaging. Hematoxylin and eosin (H&E) and Immunofluorescence (IF) staining confirmed the pathological alterations in the colons of sacrificed mice. Molecular changes at the protein level were assessed through Western blotting and Enzyme-Linked Immunosorbent Assay (ELISA) assays. RNA sequencing (RNA-seq) and metabolomics (n = 18) were jointly analyzed to investigate the biological changes in the colon of mice treated by cold exposure. RESULTS: Cold exposure decreased the pathologic and disease activity index scores in a mouse model. Endomicroscopy revealed that cold exposure preserved colonic mucosal microcirculation, and 9.4 T MRI imaging revealed alleviation of intestinal wall thickness. In addition, the expression of the TLR4 and PP65 proteins was downregulated and epithelial cell junctions were strengthened after cold exposure. Intriguingly, we found that cold exposure reversed the decrease in ZO-1 and occludin protein levels in dextran sulfate sodium (DSS)- and trinitrobenzenesulfonic acid-induced colitis mouse models. Multi-omics analysis revealed the biological landscape of DSS-induced colitis under cold exposure and identified that the peroxisome proliferator-activated receptor (PPAR) signaling pathway mediates the effects of cold on colitis. Subsequent administration of rosiglitazone (PPAR agonist) enhanced the protective effect of cold exposure on colitis, whereas GW9662 (PPAR antagonist) administration mitigated these protective effects. Overall, cold exposure ameliorated the progression of mouse colitis through the PPARγ/NF-κB signaling axis and preserved the intestinal mucosal barrier. CONCLUSION: Our study provides a mechanistic link between intestinal inflammation and cold exposure, providing a theoretical framework for understanding the differences in the prevalence of IBD between the colder regions and non-cold regions, and offering new insights into IBD therapy.

Comprehensive transcriptomic and metabolomic insights into simultaneous CO2 sequestration and nitrate removal by the Chlorella vulgaris and Pseudomonas sp. consortium.

Simultaneous CO2 sequestration and nitrate removal can be achieved by co-cultivation of Chlorella vulgaris with Pseudomonas sp. However, a comprehensive understanding of the synergistic mechanism between C. vulgaris and Pseudomonas sp. remains unknown. In this study, transcriptomics and metabolomics analysis were employed to elucidate the synergistic mechanism of C. vulgaris and Pseudomonas sp. Transcriptomic and metabolomic analyses identified 3664 differentially expressed genes and 314 metabolites. Transcriptome analysis revealed that co-culture with Pseudomonas sp. promoted the photosynthesis of C. vulgaris by promoting the synthesis of photosynthetic pigments and photosynthesis-antenna proteins. Furthermore, it stimulated pathways associated with energy metabolism from carbon sources, such as the Calvin cycle, glycolytic pathway, and TCA cycle. Additionally, Pseudomonas sp. reduced nitrate levels in the co-culture system by denitrification, and microalgae regulated nitrate uptake by down-regulating the transcript levels of nitrate transporter genes. Metabolomic analysis indicated that nutrient exchange was conducted between algae and bacteria, and amino acids, phytohormones, and organic heterocyclic compounds secreted by the bacteria promoted the growth metabolism of microalgae. After supplementation with differential metabolites, the carbon fixation rate and nitrate removal rate of the co-culture system reached 0.549 g L-1 d-1 and 135.4 mg L-1 d-1, which were increased by 20% and 8%, respectively. This study provides a theoretical insight into microalgae-bacteria interaction and its practical application, as well as a novel perspective on flue gas treatment management.

A cross-omics data analysis strategy for metabolite-microbe pair identification.

Given the pivotal roles of metabolomics and microbiomics, numerous data mining approaches aim to uncover their intricate connections. However, the complex many-to-many associations between metabolome-microbiome profiles yield numerous statistically significant but biologically unvalidated candidates. To address these challenges, we introduce BiOFI, a strategic framework for identifying metabolome-microbiome correlation pairs (Bi-Omics). BiOFI employs a comprehensive scoring system, incorporating intergroup differences, effects on feature correlation networks, and organism abundance. Meanwhile, it establishes a built-in database of metabolite-microbe-KEGG functional pathway linking relationships. Furthermore, BiOFI can rank related feature pairs by combining importance scores and correlation strength. Validation on a dataset of cesarean-section infants confirms the strategy's validity and interpretability. The BiOFI R package is freely accessible at https://github.com/chentianlu/BiOFI.

Action of circulating and infiltrating B cells in the immune microenvironment of colorectal cancer by single-cell sequencing analysis.

BACKGROUND: The complexity of the immune microenvironment has an impact on the treatment of colorectal cancer (CRC), one of the most prevalent malignancies worldwide. In this study, multi-omics and single-cell sequencing techniques were used to investigate the mechanism of action of circulating and infiltrating B cells in CRC. By revealing the heterogeneity and functional differences of B cells in cancer immunity, we aim to deepen our understanding of immune regulation and provide a scientific basis for the development of more effective cancer treatment strategies. AIM: To explore the role of circulating and infiltrating B cell subsets in the immune microenvironment of CRC, explore the potential driving mechanism of B cell development, analyze the interaction between B cells and other immune cells in the immune microenvironment and the functions of communication molecules, and search for possible regulatory pathways to promote the anti-tumor effects of B cells. METHODS: A total of 69 paracancer (normal), tumor and peripheral blood samples were collected from 23 patients with CRC from The Cancer Genome Atlas database (https://portal.gdc.cancer.gov/). After the immune cells were sorted by multicolor flow cytometry, the single cell transcriptome and B cell receptor group library were sequenced using the 10X Genomics platform, and the data were analyzed using bioinformatics tools such as Seurat. The differences in the number and function of B cell infiltration between tumor and normal tissue, the interaction between B cell subsets and T cells and myeloid cell subsets, and the transcription factor regulatory network of B cell subsets were explored and analyzed. RESULTS: Compared with normal tissue, the infiltrating number of CD20+B cell subsets in tumor tissue increased significantly. Among them, germinal center B cells (GCB) played the most prominent role, with positive clone expansion and heavy chain mutation level increasing, and the trend of differentiation into memory B cells increased. However, the number of plasma cells in the tumor microenvironment decreased significantly, and the plasma cells secreting IgA antibodies decreased most obviously. In addition, compared with the immune microenvironment of normal tissues, GCB cells in tumor tissues became more closely connected with other immune cells such as T cells, and communication molecules that positively regulate immune function were significantly enriched. CONCLUSION: The role of GCB in CRC tumor microenvironment is greatly enhanced, and its affinity to tumor antigen is enhanced by its significantly increased heavy chain mutation level. Meanwhile, GCB has enhanced its association with immune cells in the microenvironment, which plays a positive anti-tumor effect.

Mitophagy and clear cell renal cell carcinoma: insights from single-cell and spatial transcriptomics analysis.

Background: Clear Cell Renal Cell Carcinoma (ccRCC) is the most common type of kidney cancer, characterized by high heterogeneity and complexity. Recent studies have identified mitochondrial defects and autophagy as key players in the development of ccRCC. This study aims to delve into the changes in mitophagic activity within ccRCC and its impact on the tumor microenvironment, revealing its role in tumor cell metabolism, development, and survival strategies. Methods: Comprehensive analysis of ccRCC tumor tissues using single cell sequencing and spatial transcriptomics to reveal the role of mitophagy in ccRCC. Mitophagy was determined to be altered among renal clear cells by gene set scoring. Key mitophagy cell populations and key prognostic genes were identified using NMF analysis and survival analysis approaches. The role of UBB in ccRCC was also demonstrated by in vitro experiments. Results: Compared to normal kidney tissue, various cell types within ccRCC tumor tissues exhibited significantly increased levels of mitophagy, especially renal clear cells. Key genes associated with increased mitophagy levels, such as UBC, UBA52, TOMM7, UBB, MAP1LC3B, and CSNK2B, were identified, with their high expression closely linked to poor patient prognosis. Particularly, the ubiquitination process involving the UBB gene was found to be crucial for mitophagy and its quality control. Conclusion: This study highlights the central role of mitophagy and its regulatory factors in the development of ccRCC, revealing the significance of the UBB gene and its associated ubiquitination process in disease progression.

Subclassification of lung adenocarcinoma through comprehensive multi-omics data to benefit survival outcomes.

OBJECTIVES: Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer. Understanding the molecular mechanisms underlying tumor progression is of great clinical significance. This study aims to identify novel molecular markers associated with LUAD subtypes, with the goal of improving the precision of LUAD subtype classification. Additionally, optimization efforts are directed towards enhancing insights from the perspective of patient survival analysis. MATERIALS AND METHODS: We propose an innovative feature-selection approach that focuses on LUAD classification, which is comprehensive and robust. The proposed method integrates multi-omics data from The Cancer Genome Atlas (TCGA) and leverages a synergistic combination of max-relevance and min-redundancy, least absolute shrinkage and selection operator, and Boruta algorithms. These selected features were deployed in six machine-learning classifiers: logistic regression, random forest, support vector machine, naive Bayes, k-Nearest Neighbor, and XGBoost. RESULTS: The proposed approach achieved an area under the receiver operating characteristic curve (AUC) of 0.9958 for LR. Notably, the accuracy and AUC of a composite model incorporating copy number, methylation, as well as RNA- sequencing data for expression of exons, genes, and miRNA mature strands surpassed the accuracy and AUC metrics of models with single-omics data or other multi-omics combinations. Survival analyses, revealed the SVM classifier to elicit optimal classification, outperforming that achieved by TCGA. To enhance model interpretability, SHapley Additive exPlanations (SHAP) values were utilized to elucidate the impact of each feature on the predictions. Gene Ontology (GO) enrichment analysis identified significant biological processes, molecular functions, and cellular components associated with LUAD subtypes. CONCLUSION: In summary, our feature selection process, based on TCGA multi-omics data and combined with multiple machine learning classifiers, proficiently identifies molecular subtypes of lung adenocarcinoma and their corresponding significant genes. Our method could enhance the early detection and diagnosis of LUAD, expedite the development of targeted therapies and, ultimately, lengthen patient survival.

Histone Lactylation Dynamics: Unlocking the Triad of Metabolism, Epigenetics, and Immune Regulation in Metastatic Cascade of Pancreatic Cancer.

Cancer cells rewire metabolism to sculpt the immune tumor microenvironment (TME) and propel tumor advancement, which intricately tied to post-translational modifications. Histone lactylation has emerged as a novel player in modulating protein functions, whereas little is known about its pathological role in pancreatic ductal adenocarcinoma (PDAC) progression. Employing a multi-omics approach encompassing bulk and single-cell RNA sequencing, metabolomics, ATAC-seq, and CUT&Tag methodologies, we unveiled the potential of histone lactylation in prognostic prediction, patient stratification and TME characterization. Notably, "LDHA-H4K12la-immuno-genes" axis has introduced a novel node into the regulatory framework of "metabolism-epigenetics-immunity," shedding new light on the landscape of PDAC progression. Furthermore, the heightened interplay between cancer cells and immune counterparts via Nectin-2 in liver metastasis with elevated HLS unraveled a positive feedback loop in driving immune evasion. Simultaneously, immune cells exhibited altered HLS and autonomous functionality across the metastatic cascade. Consequently, the exploration of innovative combination strategies targeting the metabolism-epigenetics-immunity axis holds promise in curbing distant metastasis and improving survival prospects for individuals grappling with challenges of PDAC.

Dysregulated proteasome activity and steroid hormone biosynthesis are associated with mortality among patients with acute COVID-19.

The persistence of coronavirus disease 2019 (COVID-19)-related hospitalization severely threatens medical systems worldwide and has increased the need for reliable detection of acute status and prediction of mortality. We applied a systems biology approach to discover acute-stage biomarkers that could predict mortality. A total 247 plasma samples were collected from 103 COVID-19 (52 surviving COVID-19 patients and 51 COVID-19 patients with mortality), 51 patients with other infectious diseases (IDCs) and 41 healthy controls (HCs). Paired plasma samples were obtained from survival COVID-19 patients within 1 day after hospital admission and 1-3 days before discharge. There were clear differences between COVID-19 patients and controls, as well as substantial differences between the acute and recovery phases of COVID-19. Samples from patients in the acute phase showed suppressed immunity and decreased steroid hormone biosynthesis, as well as elevated inflammation and proteasome activation. These findings were validated by enzyme-linked immunosorbent assays and metabolomic analyses in a larger cohort. Moreover, excessive proteasome activity was a prominent signature in the acute phase among patients with mortality, indicating that it may be a key cause of poor prognosis. Based on these features, we constructed a machine learning panel, including four proteins [C-reactive protein (CRP), proteasome subunit alpha type (PSMA)1, PSMA7, and proteasome subunit beta type (PSMB)1)] and one metabolite (urocortisone), to predict mortality among COVID-19 patients (area under the receiver operating characteristic curve: 0.976) on the first day of hospitalization. Our systematic analysis provides a novel method for the early prediction of mortality in hospitalized COVID-19 patients.

Progress based on a multi-omics research strategy in the biosynthesis and modernization of active ingredients of Herpetospermum pedunculosum seeds.

Herpetospermum pedunculosum seeds also known as Herpetospermum caudigerum Wall. is the mature seed of the Herpetospermum pedunculosum(Ser.) C. B. Clarke,Cucurbitaceae. Modern pharmacological studies have shown that H. pedunculosum has hepatoprotective, anti-inflammatory, anti-gout and antibacterial pharmacological activities. The biologically active chemical components include lignin compounds such as Herpetin, Herpetetrone, Herpetoriol and so on. The natural product displays considerable skeletal diversity and structural complexity, offering significant opportunities for novel drug discovery. Based on the multi-omics research strategy and the 'gene-protein-metabolite' research framework, the biosynthetic pathway of terpenoids and lignans in H. pedunculosum has has been elucidated at multiple levels. These approaches provide comprehensive genetic information for cloning and identification of pertinent enzyme genes. Furthermore, the application of multi-omics integrative approaches provides a scientific means to elucidate entire secondary metabolic pathways. We investigated the biosynthetic pathways of lignin and terpene components in H. pedunculosum and conducted bioinformatics analysis of the crucial enzyme genes involved in the biosynthetic process using genomic and transcriptomic data. We identified candidate genes for six key enzymes in the biosynthetic pathway. This review reports on the current literature on pharmacological investigations of H. pedunculosum, proposing its potential as an antidiabetic agent. Moreover, we conclude, for the first time, the identification of key enzyme genes potentially involved in the biosynthesis of active compounds in H. pedunculosum. This review provides a scientific foundation for the discovery of novel therapeutic agents from natural sources.

Advances in mass spectrometry imaging for plant metabolomics-Expanding the analytical toolbox.

Mass spectrometry imaging (MSI) has become increasingly popular in plant science due to its ability to characterize complex chemical, spatial, and temporal aspects of plant metabolism. Over the past decade, as the emerging and unique features of various MSI techniques have continued to support new discoveries in studies of plant metabolism closely associated with various aspects of plant function and physiology, spatial metabolomics based on MSI techniques has positioned it at the forefront of plant metabolic studies, providing the opportunity for far higher resolution than was previously available. Despite these efforts, profound challenges at the levels of spatial resolution, sensitivity, quantitative ability, chemical confidence, isomer discrimination, and spatial multi-omics integration, undoubtedly remain. In this Perspective, we provide a contemporary overview of the emergent MSI techniques widely used in the plant sciences, with particular emphasis on recent advances in methodological breakthroughs. Having established the detailed context of MSI, we outline both the golden opportunities and key challenges currently facing plant metabolomics, presenting our vision as to how the enormous potential of MSI technologies will contribute to progress in plant science in the coming years.

Quantification and Profiling of Early and Late Differentiation Stage T Cells in Mantle Cell Lymphoma Reveals Immunotherapeutic Targets in Subsets of Patients.

With the aim to advance the understanding of immune regulation in MCL and to identify targetable T-cell subsets, we set out to combine image analysis and spatial omic technology focused on both early and late differentiation stages of T cells. MCL patient tissue (n = 102) was explored using image analysis and GeoMx spatial omics profiling of 69 proteins and 1812 mRNAs. Tumor cells, T helper (TH) cells and cytotoxic (TC) cells of early (CD57-) and late (CD57+) differentiation stage were analyzed. An image analysis workflow was developed based on fine-tuned Cellpose models for cell segmentation and classification. TC and CD57+ subsets of T cells were enriched in tumor-rich compared to tumor-sparse regions. Tumor-sparse regions had a higher expression of several key immune suppressive proteins, tentatively controlling T-cell expansion in regions close to the tumor. We revealed that T cells in late differentiation stages (CD57+) are enriched among MCL infiltrating T cells and are predictive of an increased expression of immune suppressive markers. CD47, IDO1 and CTLA-4 were identified as potential targets for patients with T-cell-rich MCL TIME, while GITR might be a feasible target for MCL patients with sparse T-cell infiltration. In subgroups of patients with a high degree of CD57+ TC-cell infiltration, several immune checkpoint inhibitors, including TIGIT, PD-L1 and LAG3 were increased, emphasizing the immune-suppressive features of this highly differentiated T-cell subset not previously described in MCL.

Integrating Omics Data and AI for Cancer Diagnosis and Prognosis.

Cancer is one of the leading causes of death, making timely diagnosis and prognosis very important. Utilization of AI (artificial intelligence) enables providers to organize and process patient data in a way that can lead to better overall outcomes. This review paper aims to look at the varying uses of AI for diagnosis and prognosis and clinical utility. PubMed and EBSCO databases were utilized for finding publications from 1 January 2020 to 22 December 2023. Articles were collected using key search terms such as "artificial intelligence" and "machine learning." Included in the collection were studies of the application of AI in determining cancer diagnosis and prognosis using multi-omics data, radiomics, pathomics, and clinical and laboratory data. The resulting 89 studies were categorized into eight sections based on the type of data utilized and then further subdivided into two subsections focusing on cancer diagnosis and prognosis, respectively. Eight studies integrated more than one form of omics, namely genomics, transcriptomics, epigenomics, and proteomics. Incorporating AI into cancer diagnosis and prognosis alongside omics and clinical data represents a significant advancement. Given the considerable potential of AI in this domain, ongoing prospective studies are essential to enhance algorithm interpretability and to ensure safe clinical integration.

Corrigendum: Transcriptome-wide analyses of early immune responses in lumpfish leukocytes upon stimulation with poly(I:C).

[This corrects the article DOI: 10.3389/fimmu.2023.1198211.].

DeepIDA-GRU: a deep learning pipeline for integrative discriminant analysis of cross-sectional and longitudinal multiview data with applications to inflammatory bowel disease classification.

Biomedical research now commonly integrates diverse data types or views from the same individuals to better understand the pathobiology of complex diseases, but the challenge lies in meaningfully integrating these diverse views. Existing methods often require the same type of data from all views (cross-sectional data only or longitudinal data only) or do not consider any class outcome in the integration method, which presents limitations. To overcome these limitations, we have developed a pipeline that harnesses the power of statistical and deep learning methods to integrate cross-sectional and longitudinal data from multiple sources. In addition, it identifies key variables that contribute to the association between views and the separation between classes, providing deeper biological insights. This pipeline includes variable selection/ranking using linear and nonlinear methods, feature extraction using functional principal component analysis and Euler characteristics, and joint integration and classification using dense feed-forward networks for cross-sectional data and recurrent neural networks for longitudinal data. We applied this pipeline to cross-sectional and longitudinal multiomics data (metagenomics, transcriptomics and metabolomics) from an inflammatory bowel disease (IBD) study and identified microbial pathways, metabolites and genes that discriminate by IBD status, providing information on the etiology of IBD. We conducted simulations to compare the two feature extraction methods.

gNOMO2: a comprehensive and modular pipeline for integrated multi-omics analyses of microbiomes.

BACKGROUND: In recent years, omics technologies have offered an exceptional chance to gain a deeper insight into the structural and functional characteristics of microbial communities. As a result, there is a growing demand for user-friendly, reproducible, and versatile bioinformatic tools that can effectively harness multi-omics data to provide a holistic understanding of microbiomes. Previously, we introduced gNOMO, a bioinformatic pipeline tailored to analyze microbiome multi-omics data in an integrative manner. In response to the evolving demands within the microbiome field and the growing necessity for integrated multi-omics data analysis, we have implemented substantial enhancements to the gNOMO pipeline. RESULTS: Here, we present gNOMO2, a comprehensive and modular pipeline that can seamlessly manage various omics combinations, ranging from 2 to 4 distinct omics data types, including 16S ribosomal RNA (rRNA) gene amplicon sequencing, metagenomics, metatranscriptomics, and metaproteomics. Furthermore, gNOMO2 features a specialized module for processing 16S rRNA gene amplicon sequencing data to create a protein database suitable for metaproteomics investigations. Moreover, it incorporates new differential abundance, integration, and visualization approaches, enhancing the toolkit for a more insightful analysis of microbiomes. The functionality of these new features is showcased through the use of 4 microbiome multi-omics datasets encompassing various ecosystems and omics combinations. gNOMO2 not only replicated most of the primary findings from these studies but also offered further valuable perspectives. CONCLUSIONS: gNOMO2 enables the thorough integration of taxonomic and functional analyses in microbiome multi-omics data, offering novel insights in both host-associated and free-living microbiome research. gNOMO2 is available freely at https://github.com/muzafferarikan/gNOMO2.

Model-based multifacet clustering with high-dimensional omics applications.

High-dimensional omics data often contain intricate and multifaceted information, resulting in the coexistence of multiple plausible sample partitions based on different subsets of selected features. Conventional clustering methods typically yield only one clustering solution, limiting their capacity to fully capture all facets of cluster structures in high-dimensional data. To address this challenge, we propose a model-based multifacet clustering (MFClust) method based on a mixture of Gaussian mixture models, where the former mixture achieves facet assignment for gene features and the latter mixture determines cluster assignment of samples. We demonstrate superior facet and cluster assignment accuracy of MFClust through simulation studies. The proposed method is applied to three transcriptomic applications from postmortem brain and lung disease studies. The result captures multifacet clustering structures associated with critical clinical variables and provides intriguing biological insights for further hypothesis generation and discovery.

A multi-epigenomic map of endurance exercise training.

The Molecular Transducers of Physical Activity Consortium (MoTrPAC) aims to comprehensively map molecular alterations in response to acute exercise and chronic training. In one of a recent series of papers from MoTrPAC, Nair et al. provide the first multi-epigenomic and transcriptomic integration across eight tissues in both sexes following adaptation to endurance exercise training (EET).

TimiGP-Response: the pan-cancer immune landscape associated with response to immunotherapy.

Accumulating evidence suggests that the tumor immune microenvironment (TIME) significantly influences the response to immunotherapy, yet this complex relationship remains elusive. To address this issue, we developed TimiGP-Response (TIME Illustration based on Gene Pairing designed for immunotherapy Response), a computational framework leveraging single-cell and bulk transcriptomic data, along with response information, to construct cell-cell interaction networks associated with responders and estimate the role of immune cells in treatment response. This framework was showcased in triple-negative breast cancer treated with immune checkpoint inhibitors targeting the PD-1:PD-L1 interaction, and orthogonally validated with imaging mass cytometry. As a result, we identified CD8+ GZMB+ T cells associated with responders and its interaction with regulatory T cells emerged as a potential feature for selecting patients who may benefit from these therapies. Subsequently, we analyzed 3,410 patients with seven cancer types (melanoma, non-small cell lung cancer, renal cell carcinoma, metastatic urothelial carcinoma, hepatocellular carcinoma, breast cancer, and esophageal cancer) treated with various immunotherapies and combination therapies, as well as several chemo- and targeted therapies as controls. Using TimiGP-Response, we depicted the pan-cancer immune landscape associated with immunotherapy response at different resolutions. At the TIME level, CD8 T cells and CD4 memory T cells were associated with responders, while anti-inflammatory (M2) macrophages and mast cells were linked to non-responders across most cancer types and datasets. Given that T cells are the primary targets of these immunotherapies and our TIME analysis highlights their importance in response to treatment, we portrayed the pan-caner landscape on 40 T cell subtypes. Notably, CD8+ and CD4+ GZMK+ effector memory T cells emerged as crucial across all cancer types and treatments, while IL-17-producing CD8+ T cells were top candidates associated with immunotherapy non-responders. In summary, this study provides a computational method to study the association between TIME and response across the pan-cancer immune landscape, offering resources and insights into immune cell interactions and their impact on treatment efficacy.

Advancing personalized medicine: Integrating statistical algorithms with omics and nano-omics for enhanced diagnostic accuracy and treatment efficacy.

Medical laboratory services enable precise measurement of thousands of biomolecules and have become an inseparable part of high-quality healthcare services, exerting a profound influence on global health outcomes. The integration of omics technologies into laboratory medicine has transformed healthcare, enabling personalized treatments and interventions based on individuals' distinct genetic and metabolic profiles. Interpreting laboratory data relies on reliable reference values. Presently, population-derived references are used for individuals, risking misinterpretation due to population heterogeneity, and leading to medical errors. Thus, personalized references are crucial for precise interpretation of individual laboratory results, and the interpretation of omics data should be based on individualized reference values. We reviewed recent advancements in personalized laboratory medicine, focusing on personalized omics, and discussed strategies for implementing personalized statistical approaches in omics technologies to improve global health and concluded that personalized statistical algorithms for interpretation of omics data have great potential to enhance global health. Finally, we demonstrated that the convergence of nanotechnology and omics sciences is transforming personalized laboratory medicine by providing unparalleled diagnostic precision and innovative therapeutic strategies.