A framework for ultra-low-input spatial tissue proteomics

Simple In-Cell Processing Enables Deep Proteome Analysis of Low-Input Caenorhabditis elegans

Caenorhabditis elegans is a widely used genetic model organism; however, the worm cuticle complicates extraction of intracellular proteins, a prerequisite for typical bottom-up proteomics. Conventional physical disruption procedures are not only time-consuming but can also cause significant sample loss, making it difficult to perform proteomics with low-input samples. Here, for the first time, we present an on-filter in-cell (OFIC) processing approach that can digest C. elegans proteins directly in the cells of the organism after methanol fixation. With OFIC processing and single-shot LC-MS analysis, we identified over 9400 proteins from a sample of only 200 worms, the largest C. elegans proteome reported to date that did not require fractionation or enrichment. We systematically evaluated the performance of the OFIC approach by comparing it to conventional lysis-based methods. Our data suggest superior performance of OFIC processing for C. elegans proteome identification and quantitation. We further evaluated the OFIC approach with even lower-input samples, including single worms. Then, we used this method to determine how the proteome is impacted by loss of superoxide dismutase sod-1, the ortholog of human SOD1, a gene associated with amyotrophic lateral sclerosis. Analysis of 8800 proteins from only 50 worms as the initial input showed that loss of sod-1 affects the abundance of proteins required for stress response, ribosome biogenesis, and metabolism. In conclusion, our streamlined OFIC approach, which can be broadly applied to other systems, minimizes sample loss while offering the simplest workflow reported to date for C. elegans proteomics.

Mass Spectrometry-Based Proteomics for Next-Generation Precision Oncology

Cancer is the leading cause of death worldwide characterized by patient heterogeneity and complex tumor microenvironment. While the genomics-based testing has transformed modern medicine, the challenge of diverse clinical outcomes highlights unmet needs for precision oncology. As functional molecules regulating cellular processes, proteins hold great promise as biomarkers and drug targets. Mass spectrometry (MS)-based clinical proteomics has illuminated the molecular features of cancers and facilitated discovery of biomarkers or therapeutic targets, paving the way for innovative strategies that enhance the precision of personalized treatment. In this article, we introduced the tools and current achievements of MS-based proteomics, choice of discovery and targeted MS from discovery to validation phases, profiling sensitivity from bulk samples to single-cell level and tissue to liquid biopsy specimens, current regulatory landscape of MS-based protein laboratory-developed tests (LDTs). The challenges, success and future perspectives in translating research MS assay into clinical applications are also discussed. With well-designed validation studies to demonstrate clinical benefits and meet the regulatory requirements for both analytical and clinical performance, the future of MS-based assays is promising with numerous opportunities to improve cancer diagnosis, treatment, and monitoring.

Molecularly-guided spatial proteomics captures single-cell identity and heterogeneity of the nervous system

Single-cell proteomics is an emerging field with significant potential to characterize heterogeneity within biological tissues. It offers complementary insights to single-cell transcriptomics by revealing unbiased proteomic changes downstream of the transcriptome. Recent advancements have focused on enhancing proteome coverage and depth, mostly in cultured cell lines, and a few recent studies have explored the potential of analyzing tissue micro-samples but were limited to homogenous peripheral tissues. In this current work, we utilize the power of spatial single cell-proteomics through immunostaining-guided laser capture microdissection (LCM) coupled with LC-MS to investigate the heterogenous central nervous system. We used this method to compare neuronal populations from cortex and substantia nigra, two brain regions associated with motor and cognitive function and various neurological disorders. Moreover, we used the technique to understand the neuroimmune changes associated with stab wound injury. Finally, we focus our application on the peripheral nervous system, where we compare the proteome of the myenteric plexus cell ganglion to the nerve bundle. This study demonstrates the utility of spatial single-cell proteomics in neuroscience research toward understanding fundamental biology and the molecular drivers of neurological conditions.

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

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

Mass Spectrometry-Based Spatial Multiomics Revealed Bioaccumulation Preference and Region-Specific Responses of PFOS in Mice Cardiac Tissue

The distribution and bioaccumulation of environmental pollutants are essential to understanding their toxicological mechanism. However, achieving spatial resolution at the subtissue level is still challenging. Perfluorooctanesulfonate (PFOS) is a persistent environmental pollutant with widespread occurrence. The bioaccumulation behavior of PFOS is complicated by its dual affinity for phospholipids and protein albumin. It is intriguing to visualize the distribution preference of PFOS and investigate the differential microenvironment responses at a subtissue level. Herein, we developed a mass-spectrometry (MS)-based spatial multiomics workflow, integrating matrix-assisted laser desorption/ionization MS imaging, laser microdissection, and liquid chromatography MS analysis. This integrated workflow elucidates the spatial distribution of PFOS in mouse cardiac tissue, highlighting its preferential accumulation in the pericardium over the myocardium. This distribution pattern results in greater toxicity to the pericardium, significantly altering cardiolipin levels and disrupting energy metabolism and lipid transport pathways. Our integrated approach provides novel insights into the bioaccumulation behavior of PFOS and demonstrates significant potential for revealing complex molecular mechanisms underlying the health impacts of environmental pollutants.

Integral-Omics: Serial Extraction and Profiling of Metabolome, Lipidome, Genome, Transcriptome, Whole Proteome and Phosphoproteome Using Biopsy Tissue

The integrative multiomics characterization of minute amounts of clinical tissue specimens has become increasingly important. Here, we present an approach called Integral-Omics, which enables sequential extraction of metabolites, lipids, genomic DNA, total RNA, proteins, and phosphopeptides from a single biopsy-level tissue specimen. We benchmarked this method with various samples, applied the workflow to perform multiomics profiling of tissues from six patients with colorectal cancer, and found that tumor tissues exhibited suppressed ferroptosis pathways at multiomics levels. Together, this study presents a methodology that enables sequential extraction and profiling of metabolomics, lipidomics, genomics, transcriptomics, proteomics, and phosphoproteomics using biopsy tissue specimens.

An accessible workflow for high-sensitivity proteomics using parallel accumulation-serial fragmentation (PASEF)

Deep and accurate proteome analysis is crucial for understanding cellular processes and disease mechanisms; however, it is challenging to implement in routine settings. In this protocol, we combine a robust chromatographic platform with a high-performance mass spectrometric setup to enable routine yet in-depth proteome coverage for a broad community. This entails tip-based sample preparation and pre-formed gradients (Evosep One) combined with a trapped ion mobility time-of-flight mass spectrometer (timsTOF, Bruker). The timsTOF enables parallel accumulation-serial fragmentation (PASEF), in which ions are accumulated and separated by their ion mobility, maximizing ion usage and simplifying spectra. Combined with data-independent acquisition (DIA), it offers high peak sampling rates and near-complete ion coverage. Here, we explain how to balance quantitative accuracy, specificity, proteome coverage and sensitivity by choosing the best PASEF and DIA method parameters. The protocol describes how to set up the liquid chromatography-mass spectrometry system and enables PASEF method generation and evaluation for varied samples by using the py_diAID tool to optimally position isolation windows in the mass-to-charge and ion mobility space. Biological projects (e.g., triplicate proteome analysis in two conditions) can be performed in 3 d with ~3 h of hands-on time and minimal marginal cost. This results in reproducible quantification of 7,000 proteins in a human cancer cell line in quadruplicate 21-min injections and 29,000 phosphosites for phospho-enriched quadruplicates. Synchro-PASEF, a highly efficient, specific and novel scan mode, can be analyzed by Spectronaut or AlphaDIA, resulting in superior quantitative reproducibility because of its high sampling efficiency.

Robust collection and processing for label-free single voxel proteomics

With advanced mass spectrometry (MS)-based proteomics, genome-scale proteome coverage can be achieved from bulk tissues. However, such bulk measurement lacks spatial resolution and obscures tissue heterogeneity, precluding proteome mapping of tissue microenvironment. Here we report an integrated wet collection of single microscale tissue voxels and Surfactant-assisted One-Pot voxel processing method termed wcSOP for robust label-free single voxel proteomics. wcSOP capitalizes on buffer droplet-assisted wet collection of single voxels dissected by LCM to the tube cap and SOP voxel processing in the same collection cap. This method enables reproducible, label-free quantification of approximately 900 and 4600 proteins for single voxels at 20 µm × 20 µm × 10 µm (~1 cell region) and 200 µm × 200 µm × 10 µm (~100 cell region) from fresh frozen human spleen tissue, respectively. It can reveal spatially resolved protein signatures and region-specific signaling pathways. Furthermore, wcSOP-MS is demonstrated to be broadly applicable for OCT-embedded and FFPE human archived tissues as well as for small-scale 2D proteome mapping of tissues at high spatial resolutions. wcSOP-MS may pave the way for routine robust single voxel proteomics and spatial proteomics.

diaTracer enables spectrum-centric analysis of diaPASEF proteomics data

Data-independent acquisition has become a widely used strategy for peptide and protein quantification in liquid chromatography-tandem mass spectrometry-based proteomics studies. The integration of ion mobility separation into data-independent acquisition analysis, such as the diaPASEF technology available on Bruker's timsTOF platform, further improves the quantification accuracy and protein depth achievable using data-independent acquisition. We introduce diaTracer, a spectrum-centric computational tool optimized for diaPASEF data. diaTracer performs three-dimensional (mass to charge ratio, retention time, ion mobility) peak tracing and feature detection to generate precursor-resolved "pseudo-tandem mass spectra", facilitating direct ("spectral-library free") peptide identification and quantification from diaPASEF data. diaTracer is available as a stand-alone tool and is fully integrated into the widely used FragPipe computational platform. We demonstrate the performance of diaTracer and FragPipe using diaPASEF data from triple-negative breast cancer, cerebrospinal fluid, and plasma samples, data from phosphoproteomics and human leukocyte antigens immunopeptidomics experiments, and low-input data from a spatial proteomics study. We also show that diaTracer enables unrestricted identification of post-translational modifications from diaPASEF data using open/mass-offset searches.

Towards routine proteome profiling of FFPE tissue: insights from a 1,220-case pan-cancer study

Proteome profiling of formalin-fixed paraffin-embedded (FFPE) specimens has gained traction for the analysis of cancer tissue for the discovery of molecular biomarkers. However, reports so far focused on single cancer entities, comprised relatively few cases and did not assess the long-term performance of experimental workflows. In this study, we analyze 1220 tumors from six cancer entities processed over the course of three years. Key findings include the need for a new normalization method ensuring equal and reproducible sample loading for LC-MS/MS analysis across cohorts, showing that tumors can, on average, be profiled to a depth of >4000 proteins and discovering that current software fails to process such large ion mobility-based online fractionated datasets. We report the first comprehensive pan-cancer proteome expression resource for FFPE material comprising 11,000 proteins which is of immediate utility to the scientific community, and can be explored via a web resource. It enables a range of analyses including quantitative comparisons of proteins between patients and cohorts, the discovery of protein fingerprints representing the tissue of origin or proteins enriched in certain cancer entities.

Digital Microfluidics for Sample Preparation in Low-Input Proteomics

Low-input proteomics, also referred to as micro- or nanoproteomics, has become increasingly popular as it allows one to elucidate molecular processes in rare biological materials. A major prerequisite for the analytics of minute protein amounts, e.g., derived from low cell numbers, down to single cells, is the availability of efficient sample preparation methods. Digital microfluidics (DMF), a technology allowing the handling and manipulation of low liquid volumes, has recently been shown to be a powerful and versatile tool to address the challenges in low-input proteomics. Here, an overview is provided on recent advances in proteomics sample preparation using DMF. In particular, the capability of DMF to isolate proteomes from cells and small model organisms, and to perform all necessary chemical sample preparation steps, such as protein denaturation and proteolytic digestion on-chip, are highlighted. Additionally, major prerequisites to making these steps compatible with follow-up analytical methods such as liquid chromatography-mass spectrometry will be discussed.

High-throughput proteomic and phosphoproteomic analysis of formalin-fixed paraffin-embedded tissue

Formalin-fixed, paraffin-embedded (FFPE) patient tissues are a valuable resource for proteomic studies with the potential to associate the derived molecular insights with clinical annotations and outcomes. Here we present an optimized, partially automated workflow for FFPE proteomics combining pathology-guided macro-dissection, Adaptive Focused Acoustics (AFA) sonication for lysis and decrosslinking, S-Trap digestion to peptides, and liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis using Orbitrap, Astral or timsTOF HT instrumentation. The workflow enables analysis of up to 96 dissected FFPE tissue samples or 10 μm scrolls, identifying 8,000-10,000 unique proteins per sample with median CVs <20%. Key optimizations include improved tissue lysis strategies, protein quantification for normalization, and peptide cleanup prior to LC-MS/MS analysis. Application to lung adenocarcinoma (LUAD) FFPE blocks confirms the platform's effectiveness in processing complex, clinically relevant samples, achieving deep proteome coverage and quantitative robustness comparable to TMT-based methods. Using the newly released Orbitrap Astral with short, 24-minute gradients, the workflow identifies up to ~10,000 unique proteins and ~11,000 localized phosphosites in LUAD FFPE tissue. This high-throughput, scalable workflow advances biomarker discovery and proteomic research in archival tissue samples.

Spatial proteomics of single cells and organelles on tissue slides using filter-aided expansion proteomics

Hydrogel-based tissue expansion combined with mass spectrometry (MS) offers an emerging spatial proteomics approach. Here, we present a filter-aided expansion proteomics (FAXP) strategy for spatial proteomics analysis of archived formalin-fixed paraffin-embedded (FFPE) specimens. Compared to our previous ProteomEx method, FAXP employed a customized tip device to enhance both the stability and throughput of sample preparation, thus guaranteeing the reproducibility and robustness of the workflow. FAXP achieved a 14.5-fold increase in volumetric resolution. It generated over 8 times higher peptide yield and a 255% rise in protein identifications while reducing sample preparation time by 50%. We also demonstrated the applicability of FAXP using human colorectal FFPE tissue samples. Furthermore, for the first time, we achieved bona fide single-subcellular proteomics under image guidance by integrating FAXP with laser capture microdissection.

diaTracer enables spectrum-centric analysis of diaPASEF proteomics data

Data-independent acquisition (DIA) has become a widely used strategy for peptide and protein quantification in mass spectrometry-based proteomics studies. The integration of ion mobility separation into DIA analysis, such as the diaPASEF technology available on Bruker's timsTOF platform, further improves the quantification accuracy and protein depth achievable using DIA. We introduce diaTracer, a new spectrum-centric computational tool optimized for diaPASEF data. diaTracer performs three-dimensional (m/z, retention time, ion mobility) peak tracing and feature detection to generate precursor-resolved "pseudo-MS/MS" spectra, facilitating direct ("spectral-library free") peptide identification and quantification from diaPASEF data. diaTracer is available as a stand-alone tool and is fully integrated into the widely used FragPipe computational platform. We demonstrate the performance of diaTracer and FragPipe using diaPASEF data from triple-negative breast cancer (TNBC), cerebrospinal fluid (CSF), and plasma samples, data from phosphoproteomics and HLA immunopeptidomics experiments, and low-input data from a spatial proteomics study. We also show that diaTracer enables unrestricted identification of post-translational modifications from diaPASEF data using open/mass-offset searches.

Spatial lung imaging in clinical and translational settings

For many severe lung diseases, non-invasive biomarkers from imaging could improve early detection of lung injury or disease onset, establish a diagnosis, or help follow-up disease progression and treatment strategies. Imaging of the thorax and lung is challenging due to its size, respiration movement, transferred cardiac pulsation, vast density range and gravitation sensitivity. However, there is extensive ongoing research in this fast-evolving field. Recent improvements in spatial imaging have allowed us to study the three-dimensional structure of the lung, providing both spatial architecture and transcriptomic information at single-cell resolution. This fast progression, however, comes with several challenges, including significant image file storage and network capacity issues, increased costs, data processing and analysis, the role of artificial intelligence and machine learning, and mechanisms to combine several modalities. In this review, we provide an overview of advances and current issues in the field of spatial lung imaging.

An Automated and Fast Sample Preparation Workflow for Laser Microdissection Guided Ultrasensitive Proteomics

Spatial tissue proteomics integrating whole-slide imaging, laser microdissection, and ultrasensitive mass spectrometry is a powerful approach to link cellular phenotypes to functional proteome states in (patho)physiology. To be applicable to large patient cohorts and low sample input amounts, including single-cell applications, loss-minimized and streamlined end-to-end workflows are key. We here introduce an automated sample preparation protocol for laser microdissected samples utilizing the cellenONE robotic system, which has the capacity to process 192 samples in 3 h. Following laser microdissection collection directly into the proteoCHIP LF 48 or EVO 96 chip, our optimized protocol facilitates lysis, formalin de-crosslinking, and tryptic digest of low-input archival tissue samples. The seamless integration with the Evosep ONE LC system by centrifugation allows 'on-the-fly' sample clean-up, particularly pertinent for laser microdissection workflows. We validate our method in human tonsil archival tissue, where we profile proteomes of spatially-defined B-cell, T-cell, and epithelial microregions of 4000 μm2 to a depth of ∼2000 proteins and with high cell type specificity. We finally provide detailed equipment templates and experimental guidelines for broad accessibility.

Integrating single-cell multi-omics and prior biological knowledge for a functional characterization of the immune system

The immune system comprises diverse specialized cell types that cooperate to defend the host against a wide range of pathogenic threats. Recent advancements in single-cell and spatial multi-omics technologies provide rich information about the molecular state of immune cells. Here, we review how the integration of single-cell and spatial multi-omics data with prior knowledge-gathered from decades of detailed biochemical studies-allows us to obtain functional insights, focusing on gene regulatory processes and cell-cell interactions. We present diverse applications in immunology and critically assess underlying assumptions and limitations. Finally, we offer a perspective on the ongoing technological and algorithmic developments that promise to get us closer to a systemic mechanistic understanding of the immune system.