Single-cell Proteomics Research Articles

Single-Cell Proteomics Uncovers Dual Traits of Dermal Sheath Cells in Wound Repair.

Wound healing is a dynamic process involving multiple cell types and signaling pathways. Dermal sheath cells (DSCs), residing surrounding hair follicles, play a critical role in tissue repair, yet their regulatory mechanisms remain unclear. This study used single-cell proteomics with the AcanCreER;R26LSL-tdTomato-DTR mouse model to explore DSC function across different healing stages. All animal procedures were conducted in accordance with the Animal Research: Reporting of In Vivo Experiments guidelines. Gene set enrichment analysis (GSEA) and temporal clustering (Mfuzz) were employed to reveal dynamic functional shifts. GSEA identified enriched gene sets related to interferon-gamma response, inflammatory response, ultraviolet response, myogenesis, and xenobiotic metabolism. Temporal clustering revealed eight distinct clusters: clusters associated with the early contracting and proliferative phases were linked to metabolic activation and oxidative stress, while clusters from the later remodeling phase emphasized extracellular matrix remodeling and structural reorganization. The dynamic expression of epithelial-mesenchymal transition-related genes and keratins supported DSCs' dual epithelial and mesenchymal traits. Additionally, keratins, collagens, integrins, and actin proteins emerged as promising markers or signature molecules for DSCs. This study reveals DSCs' dual traits during wound repair, providing a basis for therapies to enhance healing.

Cell Storage Conditions Impact Single-Cell Proteomic Landscapes.

Single cell transcriptomics (SCT) has revolutionized our understanding of cellular heterogeneity, yet the emergence of single cell proteomics (SCP) promises a more functional view of cellular dynamics. A challenge is that not all mass spectrometry facilities can perform SCP, and not all laboratories have access to cell sorting equipment required for SCP, which together motivate an interest in sending bulk cell samples through the mail for sorting and SCP analysis. Shipping requires cell storage, which has an unknown effect on SCP results. This study investigates the impact of cell storage conditions on the proteomic landscape at the single cell level, utilizing Data-Independent Acquisition (DIA) coupled with Parallel Accumulation Serial Fragmentation (diaPASEF). Three storage conditions were compared in 293T cells: (1) 37 °C (control), (2) 4 °C overnight, and (3) -196 °C storage followed by liquid nitrogen preservation. Both cold and frozen storage induced significant alterations in the cell diameter, elongation, and proteome composition. By elucidating how cell storage conditions alter cellular morphology and proteome profiles, this study contributes foundational technical information about SCP sample preparation and data quality.

Natural killer cells occupy unique spatial neighborhoods in HER2- and HER2+ human breast cancers

Tumor-infiltrating lymphocytes are considered clinically beneficial in breast cancer, but the significance of natural killer (NK) cells is less well characterized. As increasing evidence has demonstrated that the spatial organization of immune cells in tumor microenvironments is a significant parameter for impacting disease progression as well as therapeutic responses, an improved understanding of tumor-infiltrating NK cells and their location within tumor contextures is needed to improve the design of effective NK cell-based therapies. In this study, we developed a multiplex immunohistochemistry (mIHC) antibody panel designed to quantitatively interrogate leukocyte lineages, focusing on NK cells and their phenotypes, in two independent breast cancer patient cohorts (n = 26 and n = 30). Owing to the clinical evidence supporting a significant role for NK cells in HER2+ breast cancer in mediating responses to Trastuzumab, we further evaluated HER2- and HER2+ specimens separately. Consistent with literature, we found that CD3+ T cells were the dominant leukocyte subset across breast cancer specimens. In comparison, NK cells, identified by CD56 or NKp46 expression, were scarce in all specimens with low granzyme B expression indicating reduced cytotoxic functionality. Whereas NK cell density and phenotype did not appear to be influenced by HER2 status, spatial analysis revealed distinct NK cells phenotypes regarding their proximity to neoplastic tumor cells that associated with HER2 status. Spatial cellular neighborhood analysis revealed multiple unique neighborhood compositions surrounding NK cells, where NK cells from HER2- tumors were more frequently found proximal to neoplastic tumor cells, whereas NK cells from HER2+ tumors were instead more frequently found proximal to CD3+ T cells. This study establishes the utility of quantitative mIHC to evaluate NK cells at the single-cell spatial proteomics level and illustrates how spatial characteristics of NK cell neighborhoods vary within the context of HER2- and HER2+ breast cancers.

Multiplexed single-cell imaging reveals diverging subpopulations with distinct senescence phenotypes during long-term senescence induction.

Cellular senescence is a phenotypic state that contributes to the progression of age-related disease through secretion of pro-inflammatory factors known as the senescence-associated secretory phenotype (SASP). Understanding the process by which healthy cells become senescent and develop SASP factors is critical for improving the identification of senescent cells and, ultimately, understanding tissue dysfunction. Here, we reveal how the duration of cellular stress modulates the SASP in distinct subpopulations of senescent cells. We used multiplex, single-cell imaging to build a proteomic map of senescence induction in human epithelial cells induced to senescence over the course of 31 days. We map how the expression of SASP proteins increases alongside other known senescence markers such as p53, p21, and p16INK4a. The aggregated population of cells responded to etoposide with an accumulation of stress response factors over the first 11 days, followed by a plateau in most proteins. At the single-cell level, however, we identified two distinct senescence cell populations, one defined primarily by larger nuclear area and the second by higher protein concentrations. Trajectory inference suggested that cells took one of two discrete molecular paths from unperturbed healthy cells, through a common transitional subpopulation, and ending at the discrete terminal senescence phenotypes. Our results underscore the importance of using single-cell proteomics to identify the mechanistic pathways governing the transition from senescence induction to a mature state of senescence characterized by the SASP.

Single Cell Proteomics Reveals Specific Cellular Subtypes in Cardiomyocytes Derived from Human iPSCs and Adult Hearts.

Single cell proteomics was performed on human induced pluripotent stem cells (iPSCs), iPSC-derived cardiomyocytes, and adult cardiomyocytes. Over 700 proteins could be simultaneously measured in each cell revealing unique subpopulations. A sub-set of iPSCs expressed higher levels of Lin28a and Tra-1-60 towards the outer edge of cell colonies. In the cardiomyocytes, two distinct populations were found that exhibited complementary metabolic profiles. Cardiomyocytes from iPSCs showed a glycolysis profile while adult cardiomyocytes were enriched in proteins involved with fatty acid metabolism. Interestingly, rare single cells also co-expressed markers of both cardiac and neuronal lineages, suggesting there maybe a novel hybrid cell type in the human heart.

P0010 Characterization of proliferating memory CD4⁺ T cells reveals a distinct immune signature in anti-α4β7 integrin therapy non-responders Inflammatory Bowel Disease patients

Abstract Background Failure to respond to the anti-α4β7 integrin therapy, vedolizumab, is observed in a subset of inflammatory bowel disease (IBD) patients, potentially linked to distinct T cell activation profiles. This study aims to characterize the transcriptional and phenotypic features of circulating memory CD4⁺ T cells in vedolizumab non-responders and their association with treatment resistance. Methods We performed multidimensional flow cytometry and single-cell RNA sequencing to analyze IBD patients' circulating memory CD4⁺ T cell compartment, comparing pre- and post-treatment peripheral blood samples from vedolizumab responders and non-responders. Proteogenomic analysis and TCR repertoire profiling of CD4⁺ T cells were conducted using matched peripheral blood samples. Flow cytometry characterized T cell subsets based on surface markers, migration receptors, transcription factors, and cytokine expression, focusing on differences in activation and migration profiles. Results Ki67⁺ cells were enriched in the memory CD4⁺ T cell population of vedolizumab non-responders in both UC and CD, confirmed by two independent cohorts. TCR repertoire analysis showed increased diversity in memory CD4⁺ T cells post-treatment. Single-cell sequencing and proteomics revealed these Ki67⁺ cells had a high activation and proliferation profile (HLA-DR, CD40LG, MKI67) with reduced IL7R and SELL expression. They displayed elevated integrins (α4, β1, β7) and gut-homing receptors (CXCR3, CCR6). Upregulated T-bet, EOMES, and RORγt and increased IL-17A, indicated a pro-inflammatory Th1/Th17 phenotype in non-responders. Conclusion Ki67⁺ memory CD4⁺ T cells are significantly enriched in the peripheral blood of vedolizumab non-responders, displaying a distinct transcriptional and phenotypic profile characterized by Th1/Th17 polarization, high integrin α4β1 expression, and enhanced gut-homing capacity. These findings indicate that proliferating effector memory CD4⁺ T cells could contribute to therapy resistance in IBD, emphasizing the need for alternative therapeutic strategies targeting these cells in anti-α4β7 integrin therapy non-responders.

DOP030 Mucosal deficiency of mitochondria-driven humoral immunity is linked to Crohn’s Disease

Abstract Background Secretory IgA (sIgA) is critical for the maintenance of the mucosal barrier function, preventing bacterial translocation and reducing the risk of chronic inflammation. Several decades of research have revealed a dysregulation of the B-cell compartment in inflammatory bowel disease (IBD) of yet unknown origin1,2. While increased serum levels of (auto)antibodies against S. cerevisiae or the intestinal M-cell expressed FimH receptor glycoprotein 2 (GP2) are observed in patients with Crohn’s disease (CD)3, some studies indicated an impaired generation of intestinal immunoglobulin-secreting plasma cells (PCs)4. To unravel the role of mucosal humoral immunity in CD pathogenesis, we aimed to provide an in-depth characterisation of colonic PC differentiation in patients with CD. Methods In-depth phenotyping of patients with CD in remission and non-IBD controls (hospitalised normals) was performed using multi-omics analyses (spatial single-cell proteomics, proteomics, metabolomics, and transcriptomics) of plasma or colon biopsy samples. Colonic switched memory B cells (sMBCs) were sorted from lamina propria mononuclear cells (LPMNCs) by magnetic-activated cell sorting and ex vivo differentiated into PCs. The impact of mitochondrial function on colonic PC maturation was studied in mitochondrial respiratory chain complex V-deficient ATP8 mutant mice and mice that underwent nutritional intervention. Results Despite a hyperactive colonic B-cell compartment, patients with CD displayed significantly diminished mucosal dimeric IgA (dIgA) and fecal IgA compared to non-IBD controls. Polymeric immunoglobulin receptor (PIGR) expression did not differ, excluding a defective epithelial transcytosis of dIgA. Spatial single-cell proteomics uncovered that colonic plasmablasts and immature CD19+CD45+ PCs were increased at the expense of the fully mature CD19-CD45- phenotype. Consistently, PCs generated ex vivo from CD-derived sMBCs revealed impaired capacity to differentiate into IgA-secreting PCs. Metabolic phenotyping indicated a colonic mitochondrial dysfunction in patients with CD that was also reflected in colonic PCs by increased expression of the cell-surface NADase CD38 in concert with decreased expression of mitochondrially encoded transcripts. Of note, splenic PCs isolated from ATP8 mutant mice displayed a hyperproliferative and low-differentiated phenotype, while colonic IgA-secreting PCs were reduced. Conversely, feeding mice an isocaloric glucose-free, high-protein diet, which promotes mitochondrial activity, increased colonic IgA levels. Conclusion In summary, this study links mitochondrial dysfunction to impaired PC differentiation in patients with CD, resulting in reduced levels of sIgA and thereby compromised mucosal barrier integrity.

Single Cell-Pair Proteomics for Decoding Immune-Cancer Cell Interactions.

The efficacy of cancer immunotherapy is significantly influenced by the heterogeneity of individual tumors and immune responses. To investigate this phenomenon, a microfluidic platform is constructed for profiling immune-cancer cell interactions at the single-cell proteomics level for the first time. Based on the platform, a comprehensive workflow is proposed for achieving accurate single-cell pairing of an immune cell and a cancer cell with low cell damage and high success rate up to 95%, cell pair co-culture, and real-time microscopic monitoring of the cell-pair interactions, cell pair retrieval, mass spectrometry-based proteomic analysis of singe cell pairs, and decoupling of the proteomic information for each cell within the cell pair with the stable-isotope labeling method. With the workflow, the interactions of single natural killer (NK) cells and single K562 tumor cells are investigated based on real-time images and single cell-pair proteomics. Notably, an identification depth of over 1000 protein groups in a single cell-pair is achieved, leading to the discovery of sub-clusters of NK cells with different functions and the identification of important biomarkers for cancer treatments. This demonstrates the unique capability of the present platform in providing substantial and comprehensive datasets for profiling immune-cancer cell interactions, discovering heterogeneous immune responses, and predicting biomarkers in the study of cancer immunotherapy.

P0101 Phenotyping of human UC colonic tissue reveals inflammatory pathway gene expression in PD-1+ conventional and regulatory T cells which overlap with those regulated by rosnilimab in a mouse model of colitis

Abstract Background PD-1 co-inhibitory receptor agonists, such as rosnilimab, are a promising class of therapeutics that leverage immune homeostatic mechanisms to regulate T cell activity for treatment of inflammatory diseases, including ulcerative colitis (UC). In UC lamina propria, multiple T cell subtypes express PD-1, including peripheral helper T cells (Tph) and effector memory T cells, and the reduction of PD-1+ Tph cells is correlated with remission.1 Also, PD-1+ Tregs may comprise a subpopulation of pro-inflammatory Tregs in the UC colon. In a mouse model of colitis (induced by transfer of CD4+CD45Rb+ T cells from human PD-1 transgenic mice), rosnilimab treatment of established disease showed reduced body weight loss, reduced inflammation of the colon, and reduced infiltration of CD4 T cells.2 Methods A secondary analysis of active UC patient-derived colonic tissue characterized PD-1+ T cell subsets via a CD3-sorted single-cell proteomics and transcriptomics dataset.3 Pathway activity was estimated using Progeny.4 Bulk RNA-sequencing, gene expression analysis, and enrichment of human UC significant Reactome5 pathways was performed on colonic tissue from mice treated with rosnilimab in the colitis model. Results In human UC colonic tissue, PD-1+ CD4 conventional T (Tcon) cells and PD-1+ Tregs had increased JAK-STAT, TNF, and NF-κB estimated pathway activity compared to PD-1(-) cells. PD-1+ Tregs had increased expression of genes associated with a pro-inflammatory phenotype, including TNF, TBX21(TBET), and IL12RB2, and decreased expression of IL2RA (CD25). RNA-Seq of mouse colitis identified upregulated genes, compared to naive animals, in pathways that were also involved in human UC (“Signaling by interleukins” (e.g., Il6) and “Chemokine receptors bind chemokines” (e.g., Ccl20)). Expression levels of these genes in rosnilimab treated mice were similar to naive control animals. Additionally, the barrier gene Muc3 and fibrosis-associated Mmp9 were dysregulated in colitis but normal in rosnilimab-treated mice. Conclusion In UC colonic tissue, PD-1+ CD4 Tcon cells demonstrated increased inflammatory gene expression and PD-1+ Tregs exhibited a pro-inflammatory phenotype, suggesting that targeting both PD-1+ CD4 Tcon and PD-1+ Tregs may contribute to the therapeutic efficacy of rosnilimab in UC. These additional analyses support preclinical findings and suggest beneficial modulation of gene expression pathways of human UC, including reduction of pro-inflammatory cytokines and fibrosis, and upregulation of barrier function genes. These data demonstrate that PD-1 is upstream of multiple clinically validated drivers of UC and support the scientific rationale for studying rosnilimab in an ongoing Phase 2 study in UC (NCT06127043).

Enhanced sensitivity and scalability with a Chip-Tip workflow enables deep single-cell proteomics.

Single-cell proteomics (SCP) promises to revolutionize biomedicine by providing an unparalleled view of the proteome in individual cells. Here, we present a high-sensitivity SCP workflow named Chip-Tip, identifying >5,000 proteins in individual HeLa cells. It also facilitated direct detection of post-translational modifications in single cells, making the need for specific post-translational modification-enrichment unnecessary. Our study demonstrates the feasibility of processing up to 120 label-free SCP samples per day. An optimized tissue dissociation buffer enabled effective single-cell disaggregation of drug-treated cancer cell spheroids, refining overall SCP analysis. Analyzing nondirected human-induced pluripotent stem cell differentiation, we consistently quantified stem cell markers OCT4 and SOX2 in human-induced pluripotent stem cells and lineage markers such as GATA4 (endoderm), HAND1 (mesoderm) and MAP2 (ectoderm) in different embryoid body cells. Our workflow sets a benchmark in SCP for sensitivity and throughput, with broad applications in basic biology and biomedicine for identification of cell type-specific markers and therapeutic targets.

Challenging the Astral mass analyzer to quantify up to 5,300 proteins per single cell at unseen accuracy to uncover cellular heterogeneity.

Despite significant advancements in sample preparation, instrumentation and data analysis, single-cell proteomics is currently limited by proteomic depth and quantitative performance. Here we demonstrate highly improved depth of proteome coverage as well as accuracy and precision for quantification of ultra-low input amounts. Using a tailored library, we identify up to 7,400 protein groups from as little as 250 pg of HeLa cell peptides at a throughput of 50 samples per day. Using a two-proteome mix, we check for optimal parameters of quantification and show that fold change differences of 2 can still be successfully determined at single-cell-level inputs. Eventually, we apply our workflow to A549 cells, yielding a proteome coverage ranging from 1,801 to a maximum of >5,300 protein groups from a single cell depending on cell size and search strategy used, which allows for the study of dependencies between cell size and cell cycle phase. Additionally, our workflow enables us to distinguish between in vitro analogs of two human blastocyst lineages: naive human pluripotent stem cells (epiblast) and trophectoderm-like cells. Our data harmoniously align with transcriptomic data, indicating that single-cell proteomics possesses the capability to identify biologically relevant differences within the blastocyst.

Navigating the landscape of plant proteomics.

In plants, proteins are fundamental to virtually all biological processes, such as photosynthesis, signal transduction, metabolic regulation, and stress responses. Studying protein distribution, function, modifications, and interactions at the cellular and tissue levels is critical for unraveling the complexities of these biological pathways. Protein abundance and localization are highly dynamic and vary widely across the proteome, presenting a challenge for global protein quantification and analysis. Mass spectrometry-based proteomics approaches have proven to be powerful tools for addressing this complex issue. In this review, we summarize recent advancements in proteomics research and their applications in plant biology, with an emphasis on the current state and challenges of studying post-translational modifications, single-cell proteomics, and protein-protein interactions. Additionally, we discuss future prospects for plant proteomics, highlighting potential opportunities that proteomics technologies offer in advancing plant biology research.

Benchmark of Data Integration in Single-Cell Proteomics.

Single-cell proteomics (SCP) detected based on different technologies always involves batch-specific variations because of differences in sample processing and other potential biases. How to integrate SCP data effectively has become a great challenge. Integration of SCP data not only requires the conservation of true biological variances, but also realizes the removal of unwanted batch effects. In this study, benchmarking analysis of popular data integration methods was conducted to determine the most suitable method for SCP data. To comprehensively evaluate the performance of these integration methods, a novel evaluation system was proposed for integrating SCP data. This evaluation system consists of three objective measures from different perspectives: category (a), the efficacy of correcting batch effects; category (b), the power of conserving biological variances; and category (c), the ability to identify consistent markers. For this comprehensive evaluation, five benchmark data sets under different scenarios (containing substantial proteins, substantial cells, multiple batches, multiple cell types, and unbalanced data) were utilized for selecting the most suitable data integration method. As a result, three methods, ComBat, Scanorama, and Seurat version 3 CCA, were identified as the most recommended methods for integrating SCP data. Overall, this systematic evaluation might provide valuable guidance in choosing the appropriate method for data integration in the SCP.

Highly-Multiplexed Immunofluorescence PhenoCycler Panel for Murine FFPE Yields Insight into Tumor Microenvironment Immunoengineering

Spatial proteomics profiling is an emerging set of technologies that has the potential to elucidate the cell types, interactions, and molecular signatures that make up complex tissue microenvironments, with applications in the study of cancer, immunity, and much more. An emerging technique in the field is Co-Detection-by-indEXing (CODEX), recently renamed as the PhenoCycler system. This is a highly-multiplexed immunofluorescence imaging technology that relies on oligonucleotide-barcoded antibodies and cyclic immunofluorescence to visualize many antibody markers in a single specimen while preserving tissue architecture. Existing PhenoCycler panels are primarily designed for fresh-frozen tissues. Formalin-fixed paraffin-embedded (FFPE) blocks offer several advantages in preclinical research, but few antibody clones have been identified in this setting for PhenoCycler imaging. Here, we present a novel PhenoCycler panel of 28 validated antibodies for murine FFPE tissues. We describe our workflow for selecting and validating clones, barcoding antibodies, designing our panel, and performing multiplex imaging. We further detail our analysis pipeline for comparing marker expressions, clustering and phenotyping single-cell proteomics data, and quantifying spatial relationships. We then apply our panel and analysis protocol to profile the effects of three gene-delivery nanoparticle formulations, in combination with systemic anti-PD1, on the murine melanoma tumor immune microenvironment. Intralesional delivery of genes expressing the costimulatory molecule 4-1BBL and the cytokine IL-12 led to a shift towards intratumoral M1 macrophage polarization and promoted closer associations between intratumoral CD8 T cells and macrophages. Delivery of IFNγ, in addition to 4-1BBL and IL-12, further increased markers of antigen presentation on tumor cells and intratumoral antigen-presenting cells but also promoted greater expression of checkpoint marker PD-L1 and closer associations between intratumoral CD8 T cells and PD-L1-expressing tumor cells. These findings help to explain the benefits of 4-1BBL and IL-12 delivery while offering additional mechanistic insights into the limitations of IFNγ therapeutic efficacy.

Single-cell multi-omics analysis of in vitro post-ovulatory aged oocytes revealed aging-dependent protein degradation

Once ovulated, the oocyte has to be fertilized in a short time window, or it will undergo post-ovulation aging (POA), whose underlying mechanisms are still not elucidated. Here, we optimized single-cell proteomics methods and performed single-cell transcriptomic, proteomic and phosphoproteomic analysis of fresh, POA, and melatonin-treated POA oocytes. POA oocytes showed down-regulation of most differentially expressed proteins, with little correlation with mRNA expression, and the protein changes can be rescued by melatonin treatment. MG132 treatment rescued the decreased fertilization and polyspermy rates, and up-regulated fragmentation and parthenogenesis rates of POA oocytes. MG132-treated oocytes displayed health status at proteome, phosphoproteome and fertilization ability similar to fresh oocytes, suggesting that protein stabilization might be the underlying mechanism for melatonin to rescue POA. The important roles of proteasome-mediated protein degradation during oocyte POA revealed by single-cell multi-omics analyses offer new perspectives for increasing oocyte quality during POA, and improving assisted reproduction technologies.

Preclinical Models for Functional Precision Lung Cancer Research.

Patient-centered precision oncology strives to deliver individualized cancer care. In lung cancer, preclinical models and technological innovations have become critical in advancing this approach. Preclinical models enable deeper insights into tumor biology and enhance the selection of appropriate systemic therapies across chemotherapy, targeted therapies, immunotherapies, antibody-drug conjugates, and emerging investigational treatments. While traditional human lung cancer cell lines offer a basic framework for cancer research, they often lack the tumor heterogeneity and intricate tumor-stromal interactions necessary to accurately predict patient-specific clinical outcomes. Patient-derived xenografts (PDXs), however, retain the original tumor's histopathology and genetic features, providing a more reliable model for predicting responses to systemic therapeutics, especially molecularly targeted therapies. For studying immunotherapies and antibody-drug conjugates, humanized PDX mouse models, syngeneic mouse models, and genetically engineered mouse models (GEMMs) are increasingly utilized. Despite their value, these in vivo models are costly, labor-intensive, and time-consuming. Recently, patient-derived lung cancer organoids (LCOs) have emerged as a promising in vitro tool for functional precision oncology studies. These LCOs demonstrate high success rates in growth and maintenance, accurately represent the histology and genomics of the original tumors and exhibit strong correlations with clinical treatment responses. Further supported by advancements in imaging, spatial and single-cell transcriptomics, proteomics, and artificial intelligence, these preclinical models are reshaping the landscape of drug development and functional precision lung cancer research. This integrated approach holds the potential to deliver increasingly accurate, personalized treatment strategies, ultimately enhancing patient outcomes in lung cancer.

Same-Slide Spatial Multi-Omics Integration Reveals Tumor Virus-Linked Spatial Reorganization of the Tumor Microenvironment.

The advent of spatial transcriptomics and spatial proteomics have enabled profound insights into tissue organization to provide systems-level understanding of diseases. Both technologies currently remain largely independent, and emerging same slide spatial multi-omics approaches are generally limited in plex, spatial resolution, and analytical approaches. We introduce IN-situ DEtailed Phenotyping To High-resolution transcriptomics (IN-DEPTH), a streamlined and resource-effective approach compatible with various spatial platforms. This iterative approach first entails single-cell spatial proteomics and rapid analysis to guide subsequent spatial transcriptomics capture on the same slide without loss in RNA signal. To enable multi-modal insights not possible with current approaches, we introduce k-bandlimited Spectral Graph Cross-Correlation (SGCC) for integrative spatial multi-omics analysis. Application of IN-DEPTH and SGCC on lymphoid tissues demonstrated precise single-cell phenotyping and cell-type specific transcriptome capture, and accurately resolved the local and global transcriptome changes associated with the cellular organization of germinal centers. We then implemented IN-DEPTH and SGCC to dissect the tumor microenvironment (TME) of Epstein-Barr Virus (EBV)-positive and EBV-negative diffuse large B-cell lymphoma (DLBCL). Our results identified a key tumor-macrophage-CD4 T-cell immunomodulatory axis differently regulated between EBV-positive and EBV-negative DLBCL, and its central role in coordinating immune dysfunction and suppression. IN-DEPTH enables scalable, resource-efficient, and comprehensive spatial multi-omics dissection of tissues to advance clinically relevant discoveries.

High-Recovery Desalting Tip Columns for a Wide Variety of Peptides in Mass Spectrometry-Based Proteomics.

In mass spectrometry-based proteomics, loss-minimized peptide purification techniques play a key role in improving sensitivity and coverage. We have developed a desalting tip column packed with thermoplastic polymer-coated chromatographic particles, named ChocoTip, to achieve high recoveries in peptide purification by pipet-tip-based LC with centrifugation (tipLC). ChocoTip identified more than twice as many peptides from 20 ng of tryptic peptides from Hela cell lysate compared to a typical StageTip packed with chromatographic particles entangled in a Teflon mesh in tipLC. The high recovery of ChocoTip in tipLC was maintained for peptides with a wide variety of physical properties over the entire retention time range of the LC-MS/MS analysis, and was especially noteworthy for peptides with long retention times. These excellent properties are attributable to the unique morphology of ChocoTip, in which the thermoplastic polymer covers the pores, thereby inhibiting irreversible adsorption of peptides into mesopores of the chromatographic particles. ChocoTip is expected to find applications, especially in clinical proteomics and single-cell proteomics, where sample amounts are limited.

Electrophoresis-Correlative Ion Mobility Deepens Single-cell Proteomics in Capillary Electrophoresis Mass Spectrometry.

Detection of trace-sensitive signals is a current challenge in single-cell mass spectrometry (MS) proteomics. Separation prior to detection improves the fidelity and depth of proteome identification and quantification. We recently recognized capillary electrophoresis (CE) electrospray ionization (ESI) for ordering peptides into mass-to-charge (m/z)-dependent series, introducing electrophoresis-correlative (Eco) data-independent acquisition. Here, we demonstrate that these correlations based on electrophoretic mobility (μef) in the liquid phase are transferred into the gas phase, essentially temporally ordering the peptide ions into charge-dependent ion mobility (IM, 1/K0) trends (ρ > 0.97). Rather than sampling the entire IM region broadly, we pursued these predictable correlations to schedule narrower frames. Compared to classical ddaPASEF, Eco-framing significantly enhanced the resolution of IM MS (IMS) on a trapped ion mobility mass spectrometer (timsTOF PRO). This approach returned ∼50% more proteins from HeLa proteome digests approximating to one-to-two cells, identifying ∼962 proteins from ∼200 pg in <20 min of effective electrophoresis, without match-between-runs. As a proof of principle, we deployed Eco-IMS on 1,157 proteins by analyzing <4% of the total proteome in single, yolk-laden embryonic stem cells (∼80-μm) that were isolated from the animal cap of the South African clawed frog (Xenopus laevis). Quantitative profiling of 9 different blastomeres revealed detectable differences among these cells, which are normally fated to form the ectoderm but retain pluripotentiality. Eco-framing effectively deepens the proteome sensitivity in IMS using ddaPASEF, facilitating the proteome-driven classification of embryonic cell differentiation, as demonstrated in this report.

SPE-CZE-MS quantifies zeptomole concentrations of phosphorylated peptides.

Capillary zone electrophoresis (CZE) is gaining attention in the field of single-cell proteomics for its ultra-low-flow and high-resolution separation abilities. Even more sample-limited yet rich in biological information are phosphoproteomics experiments, as the phosphoproteome composes only a fraction of the whole cellular proteome. Rapid analysis, high sensitivity, and maximization of sample utilization are paramount for single-cell analysis. Some challenges of coupling CZE analysis with mass spectrometry analysis (MS) of complex mixtures include 1. sensitivity due to volume loading limitations of CZE and 2. incompatibility of MS duty cycles with electropherographic timescales. Here, we address these two challenges as applied to single-cell equivalent phosphoproteomics experiments by interfacing a microchip-based CZE device integrated with a solid-phase-extraction (SPE) bed with the Orbitrap Astral mass spectrometer. Using 225 phosphorylated peptide standards and phosphorylated peptide-enriched mouse brain tissue, we investigate microchip-based SPE-CZE functionality, quantitative performance, and complementarity to nano-LC-MS (nLC-MS) analysis. We highlight unique SPE-CZE separation mechanisms that can empower fit-for-purpose applications in single-cell-equivalent phosphoproteomics.