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PubMed2026

Unlocking the Full Potential of Spatial Omics in Plants: Practical Challenges, Solutions, and a Path Forward.

Spatial omics technologies are providing new opportunities for plant biology by enabling molecular profiling within structurally intact tissues, revealing spatially organised cell states, developmental gradients, and regulatory interactions. While spatial transcriptomics has driven early advances, the field is rapidly expanding toward integrated spatial multi-omics by combining single-cell and spatial transcriptomic, epigenomic, proteomic, and metabolomic data. These approaches offer new opportunities to study development, physiology, and plant biotic and abiotic interactions in spatially preserved cellular contexts. However, despite rapid adoption, the field remains constrained by plant-specific challenges when applying technologies largely developed for animal systems. Compared with animal systems, plant tissues pose additional challenges due to rigid cell walls, and diverse chemistries, complicating sample preparation, cell and subcellular segmentation, signal detection, and data integration. As a result, many studies rely on bespoke protocols and analysis pipelines that are often difficult to reproduce or generalise. Here, we provide a practical, solution-oriented synthesis of current bottlenecks across experimental and computational pipelines, highlight emerging strategies to overcome these limitations, and propose a roadmap for community-driven protocol sharing, benchmarking, and integration across spatial and multi-omics modalities. Addressing these challenges will be essential to establish spatial omics as a routine and scalable tool for plant biology.

Journal Article↗

PubMed2026

Pathogenesis of psoriasis and psoriatic arthritis: Insights from animal models and single-cell and spatial transcriptomic analyses of skin, synovium and entheses.

Psoriasis (PsO) and psoriatic arthritis (PsA) are immune-mediated diseases characterized by chronic systemic inflammation, including inflammation of the skin and joints. Recent advances in animal models, single-cell transcriptomics, spatial transcriptomics, and proteomics have greatly enhanced our understanding of disease pathogenesis. Mouse models exhibit key features of skin and joint inflammation, facilitating analysis of molecular pathways, and identification of therapeutic targets. Single-cell and spatial transcriptomic analyses have revealed cell-type-specific contributions to inflammation, highlighting interactions between keratinocytes, T cells, fibroblasts, and dendritic cells that drive psoriatic pathology. In psoriatic synovium, type 17 tissue-resident memory T cells, monocytes, and fibroblasts contribute to local inflammation and joint damage, whereas the roles of B cells and plasma cells are less clear. Proteomic and metabolomic profiling in patients with PsA has identified circulating protein signatures and metabolites associated with disease progression, sex-specific differences, and response to therapy. The integration of these multiomic approaches provides a detailed map of immune-stromal-epithelial crosstalk across skin, synovium, and entheses, uncovering mechanisms that were previously inaccessible. These insights have implications for predicting disease progression, identifying novel therapeutic targets, and optimizing treatment strategies. Collectively, advances in animal models and multiomic profiling are reshaping our understanding of PsO and PsA, providing a framework for future research, disease monitoring, and therapeutic development.

Animals↗

PubMed2024

Digital pathology and spatial omics in steatohepatitis: Clinical applications and discovery potentials.

Steatohepatitis with diverse etiologies is the most common histological manifestation in patients with liver disease. However, there are currently no specific histopathological features pathognomonic for metabolic dysfunction-associated steatotic liver disease, alcohol-associated liver disease, or metabolic dysfunction-associated steatotic liver disease with increased alcohol intake. Digitizing traditional pathology slides has created an emerging field of digital pathology, allowing for easier access, storage, sharing, and analysis of whole-slide images. Artificial intelligence (AI) algorithms have been developed for whole-slide images to enhance the accuracy and speed of the histological interpretation of steatohepatitis and are currently employed in biomarker development. Spatial biology is a novel field that enables investigators to map gene and protein expression within a specific region of interest on liver histological sections, examine disease heterogeneity within tissues, and understand the relationship between molecular changes and distinct tissue morphology. Here, we review the utility of digital pathology (using linear and nonlinear microscopy) augmented with AI analysis to improve the accuracy of histological interpretation. We will also discuss the spatial omics landscape with special emphasis on the strengths and limitations of established spatial transcriptomics and proteomics technologies and their application in steatohepatitis. We then highlight the power of multimodal integration of digital pathology augmented by machine learning (ML)algorithms with spatial biology. The review concludes with a discussion of the current gaps in knowledge, the limitations and premises of these tools and technologies, and the areas of future research.

Humans↗

PubMed2026

Follicular Lymphoma Transformation is Characterized by Cytokine-associated Remodeling of Stromal and Macrophage Compartments.

Across cancer, one of the most frequent examples of histologic transformation is the evolution of follicular lymphoma (FL) to an aggressive large cell lymphoma. Despite recent progress, understanding of the molecular and cellular underpinnings of transformation remains incomplete. Here, we dissect the interplay of tumor and microenvironment cell populations across transformation through a multimodal investigation of 95 FL and transformed FL (tFL) samples, including single-cell and bulk RNA-sequencing alongside spatial transcriptomics and proteomics, and validate findings across independent FL-tFL pairs. Upon transformation, fibroblasts and GPNMB+ macrophages increase while lymph-node organizing follicular dendritic and CCL21+ fibroblastic reticular cells were lost, resulting in an altered spatial distribution of cytokines that impacts T cell infiltration and macrophage differentiation and function. Secreted stromal and macrophage signals were further evident by non-invasive plasma proteomics. Taken together, our data reveal expansion of macrophages and fibroblasts as key features of transformation with potential diagnostic and therapeutic implications.

Journal Article↗

PubMed2006

Direct-tissue SELDI-TOF mass spectrometry analysis: a new application for clinical proteomics.

BACKGROUND: New molecular profiling technologies can aid in analysis of small pathologic samples obtained by minimally invasive biopsy and may enable the discovery of key biomarkers synergistic with anatomopathologic analysis related to prognosis, therapeutic response, and innovative target validation. Thus proteomic analysis at the histologic level in healthy and pathologic settings is a major issue in the field of clinical proteomics. METHODS: We used surface-enhanced laser desorption ionization-time-of-flight mass spectrometry (SELDI-TOF MS) technology with surface chromatographic subproteome enrichment and preservation of the spatial distribution of proteomic patterns to detect discrete modifications of protein expression. We performed in situ proteomic profiling of mouse tissue and samples of human cancer tissue, including brain and lung cancer. RESULTS: This approach permitted the discrimination of glioblastomas from oligodendrogliomas and led to the identification of 3 potential markers. CONCLUSION: Direct tissue proteomic analysis is an original application of SELDI-TOF MS technology that can expand the use of clinical proteomics as a complement to the anatomopathological diagnosis.

Animals↗

PubMed2026

AI proteomics: from protein identification to virtual cells.

Artificial intelligence (AI) is transforming scientific research, including proteomics. In this Perspective, we highlight key mass spectrometry (MS)-based proteomics areas where AI is driving innovation, ranging from protein identification to building AI virtual cells. These include improving peptide and protein identification and quantification; characterizing protein-protein interactions and protein complexes; advancing spatial and perturbation proteomics; integrating multi-omics data; and, ultimately, enabling AI virtual cells. Finally, we call for global collaboration among data producers, data consumers and other stakeholders to establish an AI-friendly ecosystem for MS-based proteomics, laying the foundation for transformative advancements in proteomics driven by AI.

Proteomics↗

PubMed2026

Unraveling lung cancer complexity: Spatial omics in tumor microenvironment characterization and precision medicine.

Heterogeneous tumor microenvironment (TME) in lung cancer plays a crucial role in disease progression and resistance to therapy. Despite advances in single-cell and bulk omics profiling, these methods often overlook spatial context, which is vital for understanding cell-cell interactions and regional heterogeneity. In recent years, spatial omics technologies-including spatial genomics, transcriptomics, proteomics, and metabolomics-have revolutionized the ability to map molecular landscapes while maintaining tissue architecture. These advancements have become essential components of next-generation lung cancer management. By providing unprecedented resolution in characterizing the lung cancer TME, spatial omics could reveal prognostic and predictive biomarkers and identify new therapeutic vulnerabilities. This review will provide the first critical evaluation of spatial multi-omics approaches for lung cancer prognosis. It will also assess various integration strategies for multi-omics data to explore the clinical translational potential of these tools for therapy selection and patient stratification. Therefore, a deeper understanding of spatial omics technologies and their application in lung cancer can significantly improve precision diagnostics and therapeutic decision-making.

Lung cancer↗

PubMed2006

Quantitative proteomics analysis of the secretory pathway.

We report more than 1400 proteins of the secretory-pathway proteome and provide spatial information on the relative presence of each protein in the rough and smooth ER Golgi cisternae and Golgi-derived COPI vesicles. The data support a role for COPI vesicles in recycling and cisternal maturation, showing that Golgi-resident proteins are present at a higher concentration than secretory cargo. Of the 1400 proteins, 345 were identified as previously uncharacterized. Of these, 230 had their subcellular location deduced by proteomics. This study provides a comprehensive catalog of the ER and Golgi proteomes with insight into their identity and function.

Animals↗

PubMed2026

Proteomic and Phosphoproteomic Signatures Link Molecular Remodeling to Behavioral Outcomes Following Elderberry and DHA Supplementation in Aging Mice.

Background: Aging is a risk factor for Alzheimer's disease and related dementias, which are associated with synaptic dysfunction and cognitive decline. Elderberry (Sambucus spp.) is rich in anthocyanins with antioxidant and anti-inflammatory properties. Docosahexaenoic acid (DHA), an essential fatty acid, plays a key role in neuronal membrane integrity during brain aging. However, it remains unclear whether elderberry and DHA exert overlapping or distinct effects on brain aging and how these relate to molecular signaling. This study aimed to characterize molecular signatures induced by dietary supplementation and to determine their relationships with behavioral outcomes. Methods: 44-week-old male C57BL/6J mice were randomly assigned to control, elderberry, DHA, or combined diets for 12 weeks. Behavioral testing assessed anxiety-like behavior, spatial learning and memory. Brain tissues underwent proteomic and phosphoproteomic profiling and fatty-acid analysis. Data were analyzed using Ingenuity Pathway Analysis to identify enriched pathways, upstream regulators, and functional associations. Results: Elderberry as well as DHA supplementation induced targeted remodeling of the proteome and phosphoproteome, with pathway enrichment involving synaptogenesis, glutamatergic signaling, and long-term potentiation. Upstream-regulator analysis predicted elderberry-associated CDK5 signaling, accompanied by reduced MAPT/Tau phosphorylation at selected sites, whereas DHA supplementation was associated with CAMK-related signaling. DHA supplementation altered fatty-acid composition, increasing the n-3/n-6 ratio. Elderberry reduced anxiety-like behavior and improved target-directed search during the Barnes maze probe test. Molecular signatures were examined in relation to the measured behavioral outcomes. Conclusions: Elderberry and DHA are associated with distinct molecular networks related to synaptic function and behavioral outcomes in the aging male mouse brain. These findings support further investigation of elderberry and DHA as dietary interventions targeting molecular and behavioral features of brain aging.

Animals↗

PubMed2026

RNF43 Mutations Are Associated With the Classical Molecular Subtype, Vigorous Antitumor Immune Responses, and Prolonged Survival in Pancreatic Adenocarcinoma.

RNF43 mutations were correlated with microsatellite status in colorectal cancer and with fewer and later recurrences in pancreatic ductal adenocarcinoma (PDAC). Here, we undertake a detailed assessment of RNF43 mutations in PDAC. A total of 313 PDACs (308 microsatellite stable [MSS] and 5 microsatellite-instable [MSI] cases) underwent next-generation sequencing (Oncomine Tumor Mutation Load assay; Thermo Fisher). Spatial analyses (NanoString) classified PDACs according to their transcriptomic and proteomic immune signaling. Fluorescent imaging was used to define spatial compartments (tumor: pancytokeratin+/CD45- and leukocytes: pancytokeratin-/CD45+). Each of 20 PDACs with RNF43 mutations (RNF43mut) and without RNF43 mutations (RNF43wt) underwent multiplex immunofluorescence analysis to determine immune status. A total of 153 PDACs (22 RNF43mut and 131 RNF43wt cases) underwent bulk RNA sequencing to assign into molecular subtypes. Overall, 24 RNF43 mutations were identified (22 MSS PDACs and 2 MSI PDACs). The incidence of RNF43 mutations in MSS PDACs (7.1%) was consistent with The Cancer Genome Atlas (6.7%). However, RNF43 mutations were more frequent among MSI PDACs (40%). Additionally, RNF43mut had differential frequencies of other mutations (including Wnt pathway genes), higher tumor mutational burden values (5.5 mut/mb vs 1.67 mut/mb; P < .01), and significantly longer overall survival (47 vs 18 months; P < .0001) than RNF43wt. Moreover, RNF43mut exhibited significantly higher densities of CD8+ T lymphocytes, dendritic cells, and B lymphocytes (P < .001) and an upregulation of ITGAX, CD11c, CD8, and HLA-DR compared with RNF43wt. Patients with RNF43mut PDACs were more often of the classical molecular subtype (20/22, 90.9%). RNF43mut PDACs showed high tumor mutational burden values, suggesting increased neoantigen load coupled with an abundance of antigen-presenting immune cells and an upregulation of immune determinants promoting antigen presentation. All this contributes to stronger antitumor immune responses and improved clinical outcomes.

Humans↗

PubMed2025

GZMK+CD8+ T cells target a specific acinar cell type in Sjögren's disease.

OBJECTIVES: Sjögren's disease (SjD) is a systemic autoimmune disorder characterized by dysfunction of exocrine glands, particularly the salivary and lacrimal glands, with no clear etiology or effective therapy. This study explores the complex interplay of varied cell types in the salivary glands and their role in the pathology of Sjögren's disease. METHODS: Utilizing single-cell and spatial transcriptomics alongside spatial immunophenotyping to analyze human minor salivary glands, we developed a comprehensive understanding of the cellular landscape of non-SjD salivary glands and how that landscape changes in SjD patients. In vitro cellular assays and novel patient-derived primary epithelial cells were co-cultured with autologous T cells to confirm effector states and the delivery and effect of disease-associated granzymes. RESULTS: We identified previously unrecognized heterogeneity among acinar cells, including a PRR4⁺CST3⁺WFDC2⁻ seromucous acinar population that is selectively lost in Sjögren's disease. Expression and organizational changes were linked to clinical features: (i) T cells in the glands of SSA⁺, high-focus score patients showed increased transcriptional signatures of activation, antigen presentation, and apoptosis resistance compared with patients with mild or moderate disease, and (ii) patients with low immune infiltration exhibited distinct epithelial organization. Notably, GZMK⁺CD8⁺ T cells, which accumulate with disease severity, displayed a cytotoxic transcriptional program, degranulated upon stimulation ex vivo, and localized spatially with immune-engaged epithelial cells. Functional assays demonstrated that GZMK activates interferon signaling in vitro, and autologous co-cultures of patient-derived T cells and epithelial cells validated these findings. CONCLUSIONS: Using single-cell and spatial transcriptomics and proteomics, this study identifies a selective loss of PRR4⁺CST3⁺WFDC2⁻ seromucous acinar cells and a rise in GZMK⁺CD8⁺ T cells in Sjögren's disease, revealing distinct immune-mediated epithelial remodeling and interferon-driven dysfunction across diverse clinical presentations. These findings uncover a novel sub-cytolytic effector mechanism by which GZMK⁺CD8⁺ T cells impair mitochondrial integrity and activate innate immune signaling, linking epithelial injury to type I interferon responses and offering new therapeutic targets.

Humans↗

PubMed2026

p21-senescent cells drive pancreatic islet dysfunction through targetable paracrine signaling in type 2 diabetes.

Cellular senescence is an irreversible stress response, which leads to loss of cellular function and remodeling of the cellular secretory profile. In humans, pancreatic β cells undergo cellular senescence during the progression to type 2 diabetes (T2D). However, the mechanism linking β cell senescence to islet dysfunction remains unknown, and thus the therapeutic potential of targeting senescent cells in T2D is not established. Herein, we identified a subpopulation of senescent β cells expressing p21, which emerged early in the progression of T2D in humans and mice. Spatial transcriptomics and proteomics analyses confirmed senescence and loss of cellular identity in this subpopulation in humans. Functional analysis revealed lack of glucose responsiveness, high basal insulin secretion, and transcription of senescence-associated secretory phenotype (SASP) factors. SASP factors from p21+ β cells induced secondary senescence in neighboring cells, characterized by dysfunction and loss of identity. JAK inhibitors counteracted the induction of secondary senescence and restored β cell function in islets from humans with T2D and in mice fed a high-fat diet. These findings reveal the critical role of p21+ β cells in T2D pathogenesis and the therapeutic potential of targeting this pathophysiological process.

Animals↗

PubMed2026

Dissecting spatial patterning and signaling with directional diffusion in spatial multi-omics.

Spatial multi-omics sequencing enables the simultaneous profiling of transcriptomics, proteomics, and epigenomics at a spatial resolution, offering insights into complex tissue organization and molecular regulation. However, the effective integration of multiple omics modalities in a spatial context remains a major challenge. Here, we present SpaDDM, a spatial multi-omics integration framework based on directional diffusion models (DDMs), which supports spatial pattern identification, cross-omics alignment, and inter-and intracellular signaling flow analysis. SpaDDM employs DDM-based graph networks to learn omics-specific representations by jointly incorporating spatial coordinates and molecular measurements within each modality, followed by an attention mechanism to align features across modalities. We benchmarked SpaDDM on diverse spatial multi-omics datasets, including transcriptomics-epigenomics and transcriptomics-proteomics combinations across multiple tissues and species. SpaDDM consistently outperformed existing methods by more accurately deciphering spatial tissue patterns and effectively reducing the boundary noise between spatial regions. Moreover, the learned low-dimensional coembedded representations of individual cells serve as integral mediators for inferring the signaling flows that underlie spatial patterning. Finally, we demonstrated that SpaDDM alignment of complementary information across multi-omics layers facilitates cross-omics translation and significantly improves the prediction of cell state alignments.

Multiomics↗

PubMed2007

Spatial learning induces predominant downregulation of cytosolic proteins in the rat hippocampus.

Spatial learning is known to depend on protein synthesis in the hippocampus. Whereas the role of the hippocampus in spatial memory is established, the biochemical and molecular mechanisms underlying this process are poorly understood. To comprehend the complex pattern of protein expression induced by spatial learning, we analyzed alterations in the rat hippocampus proteome after 7 days of spatial learning in the Morris water maze. Forty Wistar rats were randomized into two groups. Animals of group A learned to localize a hidden platform in the water maze. Animals of group B served as controls and spent exactly the same time in the water maze as animals of group A. However, no platform was used in this test and the rats could not learn to localize the target. After the last trial, hydrophilic proteins from the hippocampus were isolated. A proteome-wide study was performed, based on two-dimensional gel electrophoresis and mass spectrometry. Compared with non-learning animals, 53 (70%) proteins were downregulated and 23 (30%) proteins were upregulated after 7 days in rats with spatial learning. The overall changes in protein expression, as quantified by the induction factor, ranged from -1.62 (downregulation to 62%) to 2.10 (upregulation by 110%) compared with controls (100%). Most identified proteins exhibit known functions in vesicle transport, cytoskeletal architecture, and metabolism as well as neurogenesis. These findings indicate that learning in the Morris water maze has a morphological correlate on the proteome level in the hippocampus.

Animals↗

PubMed2006

Quantitative proteomics analysis of detergent-resistant membranes from chemical synapses: evidence for cholesterol as spatial organizer of synaptic vesicle cycling.

Synaptic vesicles (SVs) in the central nervous system upon stimulation undergo rapid calcium-triggered exoendocytic cycling within the nerve terminal that at least in part depends on components of the clathrin- and dynamin-dependent endocytosis machinery. How exocytic SV fusion and endocytic retrieval are temporally and spatially coordinated is still an open question. One possibility is that specialized membrane microdomains characterized by their high content in membrane cholesterol may assist in the spatial coordination of synaptic membrane protein recycling. Quantitative proteomics analysis of detergent-resistant membranes (DRMs) isolated from rat brain synapses or cholesterol-depleted control samples by liquid chromatography-tandem mass spectrometry identified a total of 159 proteins. Among these 122 proteins were classified as cholesterol-dependent DRM or DRM-associated proteins, many of which with proven or hypothesized functions in exoendocytic vesicle cycling including clathrin, the clathrin adaptor complex AP-2, and a variety of SV proteins. In agreement with this, SV membrane and endocytic proteins displayed a partial resistance to extraction with cold Triton X-100 in cultured rat hippocampal neurons where they co-localized with labeled cholera toxin B, a marker for cholesterol-enriched DRMs. Moreover SV proteins formed cholesterol-dependent complexes in CHAPS-extracted synaptic membrane lysates. Our combined data suggest that lipid microdomains may act as spatial coordinators for exoendocytic vesicle cycling at synapses.

Amino Acid Sequence↗

PubMed2025

Multi-omics analysis reveals distinct spatial compartmentalization of lung repair niches in pediatric ARDS.

BACKGROUND: Pediatric acute respiratory distress syndrome (PARDS), often triggered by viral infections, is a life-threatening condition. Despite its severity, children demonstrate significantly better survival rates and superior lung repair compared to adults. However, the mechanisms underlying this age-specific advantage remain incompletely understood. PATIENTS AND METHODS: We conducted a pilot multi-omics study of influenza-associated PARDS integrating single-cell RNA sequencing (scRNA-seq) of pediatric lung tissue and bronchoalveolar lavage fluid (BALF), spatial transcriptomics, and plasma proteomics. Analyses were harmonized with the Human Lung Cell Atlas (HLCA) reference, reanalysis of public pediatric PARDS airway scRNA-seq, and contextual comparisons to adult lethal COVID-19 lung. RESULTS: Tissue scRNA-seq and spatial data indicated outcome-linked divergence in PARDS. Survivor showed spatially restricted repair with preserved alveolar type II (AT2) cells, AT2-to-alveolar type I (AT1) differentiation signatures, and higher KRT17, whereas fatal case and adults exhibited diffuse immune activation with pro-fibrotic and pro-apoptotic signaling. In BALF, KRT17-positive airway stress–repair epithelial cells (hillock-like) increased from the acute to recovery phase, and plasma proteomics showed higher circulating KRT17 in survivors. HLCA-based label transfer strengthened cell-type definitions and enabled pediatric–adult comparisons suggesting biological and developmental differences; the adult lethal COVID-19 atlas provided a benchmark with attenuated epithelial repair and prominent collagen CTHRC1-pathologic fibroblasts. Fibroblast programs were regionally compartmentalized, with injury-enriched CTHRC1+ states versus alveolar fibroblasts in preserved areas, and showed stronger injury–homeostasis anti-correlation in fatalities. Myeloid remodeling included BALF transitions from FCN1-high inflammatory states toward FABP4-positive resident-like states, consistent with public pediatric datasets showing reduced inflammatory and interferon-stimulated gene (ISG) modules and severity-linked increases in aged neutrophils. CONCLUSIONS: This pilot multi-omics case series outlines putative pediatric lung repair niches in influenza-associated PARDS. KRT17-positive transitional epithelium, preserved AT2 differentiation, and restoration of resident-like macrophages may align with recovery, whereas diffuse immune activation and CTHRC1-enriched fibroblast programs may accompany worse outcomes. HLCA-guided annotations and adult benchmarks indicate possible age-related differences, warranting validation in larger multi-center cohorts.

Humans↗

PubMed2024

The burgeoning spatial multi-omics in human gastrointestinal cancers.

The development and progression of diseases in multicellular organisms unfold within the intricate three-dimensional body environment. Thus, to comprehensively understand the molecular mechanisms governing individual development and disease progression, precise acquisition of biological data, including genome, transcriptome, proteome, metabolome, and epigenome, with single-cell resolution and spatial information within the body's three-dimensional context, is essential. This foundational information serves as the basis for deciphering cellular and molecular mechanisms. Although single-cell multi-omics technology can provide biological information such as genome, transcriptome, proteome, metabolome, and epigenome with single-cell resolution, the sample preparation process leads to the loss of spatial information. Spatial multi-omics technology, however, facilitates the characterization of biological data, such as genome, transcriptome, proteome, metabolome, and epigenome in tissue samples, while retaining their spatial context. Consequently, these techniques significantly enhance our understanding of individual development and disease pathology. Currently, spatial multi-omics technology has played a vital role in elucidating various processes in tumor biology, including tumor occurrence, development, and metastasis, particularly in the realms of tumor immunity and the heterogeneity of the tumor microenvironment. Therefore, this article provides a comprehensive overview of spatial transcriptomics, spatial proteomics, and spatial metabolomics-related technologies and their application in research concerning esophageal cancer, gastric cancer, and colorectal cancer. The objective is to foster the research and implementation of spatial multi-omics technology in digestive tumor diseases. This review will provide new technical insights for molecular biology researchers.

Humans↗

PubMed2026

An Instrumental Optimization of a Label-Free Proteomic Method for Trace Protein Input.

Liquid chromatography-mass spectrometry (LC-MS)-based proteomics of trace-level samples, such as tens of cells or spatially resolved tissue regions, offers unique biological insights but is often constrained by the requirement for specialized, costly instrumentation. In this study, we developed a scalable workflow for the deep proteomic analysis of low- to ultralow-input samples by systematically optimizing a widely adopted Orbitrap and UHPLC platform to maximize sensitivity, precision, and throughput. This optimized workflow identified over 5600 proteins from 5 ng of peptides and 3400 proteins from 20 sorted cells, achieving a throughput of 30 analyses per day while maintaining deep proteome coverage and high quantitative reproducibility. Furthermore, by applying this method to spatially resolved proteomics, we identified over 6100 proteins from microscale regions of interest (ROIs) within a formalin-fixed, paraffin-embedded (FFPE) tissue. A data-driven normalization strategy was employed to correct for variable cellularity across tissue regions, effectively revealing intratumor heterogeneity and distinct molecular and functional signatures, including pathway activations not apparent in parallel spatial transcriptomic analysis. Ultimately, this accessible, high-performance method substantially lowers the instrumentation barrier for the deep proteomic profiling of trace-level biological samples.

Proteomics↗