Spatial-Resolution Cell Type Proteome Profiling of Cancer Tissue by Fully Integrated Proteomics Technology

Clinical functional proteomics of intercellular signalling in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) has an atypical, highly stromal tumour microenvironment (TME) that profoundly contributes to its poor prognosis1. Here, to better understand the intercellular signalling between cancer and stromal cells directly in PDAC tumours, we developed a multidimensional proteomic strategy called TMEPro. We applied TMEPro to profile the glycosylated secreted and plasma membrane proteome of 100 human pancreatic tissue samples to a great depth, define cell type origins and identify potential paracrine cross-talk, especially that mediated through tyrosine phosphorylation. Temporal dynamics during pancreatic tumour progression were investigated in a genetically engineered PDAC mouse model. Functionally, we revealed reciprocal signalling between stromal cells and cancer cells mediated by the stromal PDGFR-PTPN11-FOS signalling axis. Furthermore, we examined the generic shedding mechanism of plasma membrane proteins in PDAC tumours and revealed that matrix-metalloprotease-mediated shedding of the AXL receptor tyrosine kinase ectodomain provides an additional dimension of intercellular signalling regulation in the PDAC TME. Importantly, the level of shed AXL has a potential correlation with lymph node metastasis, and inhibition of AXL shedding and its kinase activity showed a substantial synergistic effect in inhibiting cancer cell growth. In summary, we provide TMEPro, a generically applicable clinical functional proteomic strategy, and a comprehensive resource for better understanding the PDAC TME and facilitating the discovery of new diagnostic and therapeutic targets.

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.

Spatial Proteomic Profiling of Colorectal Cancer Revealed Its Tumor Microenvironment Heterogeneity

Colorectal cancer is a predominant malignancy with a second mortality worldwide. Despite its prevalence, therapeutic options remain constrained and surgical operation is still the most useful therapy. In this regard, a comprehensive spatially resolved quantitative proteome atlas was constructed to explore the functional proteomic landscape of colorectal cancer. This strategy integrates histopathological analysis, laser capture microdissection, and proteomics. Spatial proteome profiling of 200 tissue section samples facilitated by the fully integrated sample preparation technology SISPROT enabled the identification of more than 4000 proteins on the Orbitrap Exploris 240 from 2 mm2 × 10 μm tissue sections. Compared with normal adjacent tissues, we identified a spectrum of cancer-associated proteins and dysregulated pathways across various regions of colorectal cancer including ascending colon, transverse colon, descending colon, sigmoid colon, and rectum. Additionally, we conducted proteomic analysis on tumoral epithelial cells and paracancerous epithelium from early to advanced stages in hallmark rectum cancer and sigmoid colon cancer. Bioinformatics analysis revealed functional proteins and cell-type signatures associated with different regions of colorectal tumors, suggesting potential clinical implications. Overall, this study provides a comprehensive spatially resolved functional proteome landscape of colorectal cancer, serving as a valuable resource for exploring potential biomarkers and therapeutic targets.

Hierarchical Dendritic Photonic Crystal Beads for Efficient Isolation and Proteomic Analysis of Multiple Cell Types

Cell types with different morphology, and function collaborate to maintain organ function. As such, analyzing proteomic differences and connections between different types of cells forms the foundation for establishing functional connectomes and developing in vitro organoid simulation experiments. However, the efficiency of cell type isolation from organs is limited by time, equipment, and cost. Here, hierarchical dendritic photonic crystal beads (HDPCBs) featuring high-density functional groups via the self-assembly of dendritic mesoporous structure SiO2 nanoparticles (DM-SiO2) and grafting dendrimers onto the surface of dendritic mesoporous photonic crystal beads (DMPCBs) is developed. This platform integrates multitype cell separation with in situ protein cleavage processes. Efficient simultaneous isolation of Kupffer cells and Liver Sinusoidal Endothelial cells (LSECs) from liver, with high specificity and convenient operation in a short separation time are demonstrated. The results reveal 2832 and 3442 unique proteins identified in Kupffer cells and LSECs using only 50 HDPCBs, respectively. 764 and 629 over-expressed proteins associated with the function of Kupffer cells and LSECs are found, respectively. The work offers a new method for efficiently isolating multiple cell types from tissues and downstream proteomic analysis, ultimately facilitating the identification of primary cell compositions and functions.

Deep spatial proteomics reveals region-specific features of severe COVID-19-related pulmonary injury

As a primary target of severe acute respiratory syndrome coronavirus 2, lung exhibits heterogeneous histopathological changes following infection. However, comprehensive insight into their protein basis with spatial resolution remains deficient, which hinders further understanding of coronavirus disease 2019 (COVID-19)-related pulmonary injury. Here, we generate a region-resolved proteomic atlas of hallmark pathological pulmonary structures by integrating histological examination, laser microdissection, and ultrasensitive proteomics. Over 10,000 proteins are quantified across 71 post-mortem specimens. We identify a spectrum of pathway dysregulations in alveolar epithelium, bronchial epithelium, and blood vessels compared with non-COVID-19 controls, providing evidence for transitional-state pneumocyte hyperplasia. Additionally, our data reveal the region-specific enrichment of functional markers in bronchiole mucus plugs, pulmonary fibrosis, airspace inflammation, and alveolar type 2 cells, uncovering their distinctive features. Furthermore, we detect increased protein expression associated with viral entry and inflammatory response across multiple regions, suggesting potential therapeutic targets. Collectively, this study provides a distinct perspective for deciphering COVID-19-caused pulmonary dysfunction by spatial proteomics.

Development of a sample preparation method for micro-proteomics analysis of the formaldehyde-fixed paraffin-embedded liver tissue samples

Liver micro-proteomics based on the routinely used formaldehyde-fixed paraffin-embedded (FFPE) samples is valuable for innovative research, but the technical approach for sample preparation is often challenging. In this study, we aimed to develop a method for sample preparation for micro-proteomics on using the FFPE liver samples. We collected 2000 individual cells per batch from FFPE liver slices with laser capture microdissection and used them as test samples. We used the microscale fresh-frozen liver samples or HepG2 cells as control samples. For the FFPE samples, we first established a procedure for protein extraction. 2 h incubation at 95 °C in alkaline amine buffer supplemented with 4% sodium dodecyl sulfate allows improved production, efficiency, and quality of protein extraction. Then, we developed a dedicated protocol HDMSP for the micro-concentrated (<0.05 μg/μL) protein preparation for mass spectrometry (MS) based analysis, in which 2 μg/μL carboxyl magnetic beads and 70% acetonitrile are used to induce protein precipitation. For the 0.01 μg/μL protein control samples, protein recovery rate (PRR) by HDMSP is 72.1%, while the PRR is 5.9% if using a standard method solid phase-enhanced sample preparation. For the FFPE samples, the HDMSP PRR is 88.8%, and the subsequent MS analysis demonstrates increased depth, robustness, and quantitation accuracy for HDMSP relative to the control of in-gel digestion. Moreover, the physicochemical properties and subcellular location of the FFPE liver micro-proteome are comparable to those of the fresh-frozen control samples processed with filter-aided sample preparation (FASP). HDMSP is also comparable to FASP in terms of reproducibility and physicochemical properties in liver subcellular proteomes, and meanwhile reduces the sample preparation time by 15.9% and the experimental cost by 30.8%. Overall, the new method is simple and highly effective for preparing the microscale FFPE liver protein samples for MS analysis. This study provides a useful solution for FFPE liver micro-proteomics.

Proteomic analysis of glomeruli, tubules and renal interstitium in idiopathic membranous nephropathy (IMN): A statistically observational study

Idiopathic membranous nephropathy (IMN) is a common type of primary glomerulonephritis, which pathogenesis are highly involved protein and immune regulation. Therefore, we investigated protein expression in different microregions of the IMN kidney tissue. We used laser capture microdissection and mass spectrometry to identify the proteins in the kidney tissue. Using MSstats software to identify the differently expressed protein (DEP). Gene ontology analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis were used to predict and enrich the potential functions of the DEPs, and DEPs were compared to the Public data in the gene expression omnibus (GEO) database for screening biomarkers of IMN. Immune infiltration analysis was used to analyze the immune proportion in IMN. Three significantly up-regulated proteins were identified in the glomeruli of patients with IMN; 9 significantly up-regulated and 6 significantly down-regulated proteins were identified in the interstitium of patients with IMN. Gene ontology analysis showed that the DEPs in the glomerulus and interstitium were mostly enriched in "biological regulation, the immune system, and metabolic processes." Kyoto Encyclopedia of Genes and Genomes analysis showed that the DEPs in the glomerulus and interstitium were mostly enriched in the "immune system" and the "complement and coagulation cascades. " According to the public information of the GEO database, DEPs in our study, Coatomer subunit delta Archain 1, Laminin subunit alpha-5, and Galectin-1 were highly expressed in the IMN samples from the GEO database; in the immune infiltration analysis, the proportion of resting memory CD4 T cells and activated NK cells in IMN were significantly higher than in the normal group. This study confirmed that there were significant differences in protein expression in different micro-regions of patients with IMN, The protein Coatomer subunit delta Archain 1, Laminin subunit alpha 5, Galectin-1 are potential biomarkers of IMN, the memory T cells CD4 and NK cells, maybe involved in the immunologic mechanism in the development of IMN.

Direct 3D Sampling of the Embryonic Mouse Head: Layer-wise Nanosecond Infrared Laser (NIRL) Ablation from Scalp to Cortex for Spatially Resolved Proteomics

Common workflows in bottom-up proteomics require homogenization of tissue samples to gain access to the biomolecules within the cells. The homogenized tissue samples often contain many different cell types, thereby representing an average of the natural proteome composition, and rare cell types are not sufficiently represented. To overcome this problem, small-volume sampling and spatial resolution are needed to maintain a better representation of the sample composition and their proteome signatures. Using nanosecond infrared laser ablation, the region of interest can be targeted in a three-dimensional (3D) fashion, whereby the spatial information is maintained during the simultaneous process of sampling and homogenization. In this study, we ablated 40 μm thick consecutive layers directly from the scalp through the cortex of embryonic mouse heads and analyzed them by subsequent bottom-up proteomics. Extra- and intracranial ablated layers showed distinct proteome profiles comprising expected cell-specific proteins. Additionally, known cortex markers like SOX2, KI67, NESTIN, and MAP2 showed a layer-specific spatial protein abundance distribution. We propose potential new marker proteins for cortex layers, such as MTA1 and NMRAL1. The obtained data confirm that the new 3D tissue sampling and homogenization method is well suited for investigating the spatial proteome signature of tissue samples in a layerwise manner. Characterization of the proteome composition of embryonic skin and bone structures, meninges, and cortex lamination in situ enables a better understanding of molecular mechanisms of development during embryogenesis and disease pathogenesis.

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 important tissue heterogeneity, which make it impossible for proteome mapping of tissue microenvironment. Here we report an integrated wet collection of single tissue voxel 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 tissue voxel dissected by LCM into the PCR tube cap and MS-compatible surfactant-assisted one-pot voxel processing in the collection cap. This convenient method allows reproducible label-free quantification of ∼900 and ∼4,600 proteins for single voxel from fresh frozen human spleen tissue at 20 μm × 20 μm × 10 μm (close to single cells) and 200 μm × 200 μm × 10 μm (∼100 cells), respectively. 100s-1000s of protein signatures with differential expression levels were identified to be spatially resolved between spleen red and white pulp regions depending on the voxel size. Region-specific signaling pathways were enriched from single voxel proteomics data. Antibody-based CODEX imaging was used to validate label-free MS quantitation for single voxel analysis. The wcSOP-MS method paves the way for routine robust single voxel proteomics and spatial proteomics.

Functional and Clinical Proteomic Exploration of Pancreatic Cancer

Pancreatic cancer, most cases being pancreatic ductal adenocarcinoma (PDAC), is one of the most lethal cancers with a median survival time of less than 6 months. Therapeutic options are very limited for PDAC patients, and surgery is still the most effective treatment, making improvements in early diagnosis critical. One typical characteristic of PDAC is the desmoplastic reaction of its stroma microenvironment, which actively interacts with cancer cells to orchestrate key components in tumorigenesis, metastasis, and chemoresistance. Global exploration of cancer-stroma crosstalk is essential to decipher PDAC biology and design intervention strategies. Over the past decade, the dramatic improvement of proteomics technologies has enabled profiling of proteins, post-translational modifications (PTMs), and their protein complexes at unprecedented sensitivity and dimensionality. Here, starting with our current understanding of PDAC characteristics, including precursor lesions, progression models, tumor microenvironment, and therapeutic advancements, we describe how proteomics contributes to the functional and clinical exploration of PDAC, providing insights into PDAC carcinogenesis, progression, and chemoresistance. We summarize recent achievements enabled by proteomics to systematically investigate PTMs-mediated intracellular signaling in PDAC, cancer-stroma interactions, and potential therapeutic targets revealed by these functional studies. We also highlight proteomic profiling of clinical tissue and plasma samples to discover and verify useful biomarkers that can aid early detection and molecular classification of patients. In addition, we introduce spatial proteomic technology and its applications in PDAC for deconvolving tumor heterogeneity. Finally, we discuss future prospects of applying new proteomic technologies in comprehensively understanding PDAC heterogeneity and intercellular signaling networks. Importantly, we expect advances in clinical functional proteomics for exploring mechanisms of cancer biology directly by high-sensitivity functional proteomic approaches starting from clinical samples.

Recent advances in microfluidics for single-cell functional proteomics

Single-cell proteomics (SCP) reveals phenotypic heterogeneity by profiling individual cells, their biological states and functional outcomes upon signaling activation that can hardly be probed via other omics characterizations. This has become appealing to researchers as it enables an overall more holistic view of biological details underlying cellular processes, disease onset and progression, as well as facilitates unique biomarker identification from individual cells. Microfluidic-based strategies have become methods of choice for single-cell analysis because they allow facile assay integrations, such as cell sorting, manipulation, and content analysis. Notably, they have been serving as an enabling technology to improve the sensitivity, robustness, and reproducibility of recently developed SCP methods. Critical roles of microfluidics technologies are expected to further expand rapidly in advancing the next phase of SCP analysis to reveal more biological and clinical insights. In this review, we will capture the excitement of the recent achievements of microfluidics methods for both targeted and global SCP, including efforts to enhance the proteomic coverage, minimize sample loss, and increase multiplexity and throughput. Furthermore, we will discuss the advantages, challenges, applications, and future prospects of SCP.

In-Depth Mass Spectrometry-Based Proteomics of Formalin-Fixed, Paraffin-Embedded Tissues with a Spatial Resolution of 50-200 μm

Formalin-fixed, paraffin-embedded (FFPE) tissues are banked in large repositories to cost-effectively preserve valuable specimens for later study. With the rapid growth of spatial proteomics, FFPE tissues can serve as a more accessible alternative to more commonly used frozen tissues. However, extracting proteins from FFPE tissues is challenging due to cross-links formed between proteins and formaldehyde. Here, we have adapted the nanoPOTS sample processing workflow, which was previously applied to single cells and fresh-frozen tissues, to profile protein expression from FFPE tissues. Following the optimization of extraction solvents, times, and temperatures, we identified an average of 1312 and 3184 high-confidence master proteins from 10 μm thick FFPE-preserved mouse liver tissue squares having lateral dimensions of 50 and 200 μm, respectively. The observed proteome coverage for FFPE tissues was on average 88% of that achieved for similar fresh-frozen tissues. We also characterized the performance of our fully automated sample preparation and analysis workflow, termed autoPOTS, for FFPE spatial proteomics. This modified nanodroplet processing in one pot for trace samples (nanoPOTS) and fully automated processing in one pot for trace sample (autoPOTS) workflows provides the greatest coverage reported to date for high-resolution spatial proteomics applied to FFPE tissues. Data are available via ProteomeXchange with identifier PXD029729.

Proteomics approaches to characterize the immune responses in cancer

Despite the dynamic development of cancer research, annually millions of people die of cancer. The human immune system is the major 'guard' against tumor development. Unfortunately, cancer cells have the ability to evade the immune system and continue to grow. The proper understanding of the intricate immune response in tumorigenesis remains the holy grail of cancer immunology and designing effective immunotherapy. To decode the immune responses in cancer, in recent years, proteomics studies have received considerable attention. Proteomics studies focus on the detection and quantification of proteins, which are the effectors of biological functions, and as such, are proven to reflect the cell state more accurately, in comparison to genomic or transcriptomic studies. In this review, we discuss the proteomics studies applied to characterize the immune responses in cancer and tumor immune microenvironment heterogeneity. Further, we describe emerging single-cell proteomics approaches that have the potential to be applied in cancer immunity studies.

Hanging drop sample preparation improves sensitivity of spatial proteomics

Spatial proteomics holds great promise for revealing tissue heterogeneity in both physiological and pathological conditions. However, one significant limitation of most spatial proteomics workflows is the requirement of large sample amounts that blurs cell-type-specific or microstructure-specific information. In this study, we developed an improved sample preparation approach for spatial proteomics and integrated it with our previously-established laser capture microdissection (LCM) and microfluidics sample processing platform. Specifically, we developed a hanging drop (HD) method to improve the sample recovery by positioning a nanowell chip upside-down during protein extraction and tryptic digestion steps. Compared with the commonly-used sitting-drop method, the HD method keeps the tissue pixel away from the container surface, and thus improves the accessibility of the extraction/digestion buffer to the tissue sample. The HD method can increase the MS signal by 7 fold, leading to a 66% increase in the number of identified proteins. An average of 721, 1489, and 2521 proteins can be quantitatively profiled from laser-dissected 10 μm-thick mouse liver tissue pixels with areas of 0.0025, 0.01, and 0.04 mm², respectively. The improved system was further validated in the study of cell-type-specific proteomes of mouse uterine tissues.

Unbiased spatial proteomics with single-cell resolution in tissues

Mass spectrometry (MS)-based proteomics has become a powerful technology to quantify the entire complement of proteins in cells or tissues. Here, we review challenges and recent advances in the LC-MS-based analysis of minute protein amounts, down to the level of single cells. Application of this technology revealed that single-cell transcriptomes are dominated by stochastic noise due to the very low number of transcripts per cell, whereas the single-cell proteome appears to be complete. The spatial organization of cells in tissues can be studied by emerging technologies, including multiplexed imaging and spatial transcriptomics, which can now be combined with ultra-sensitive proteomics. Combined with high-content imaging, artificial intelligence and single-cell laser microdissection, MS-based proteomics provides an unbiased molecular readout close to the functional level. Potential applications range from basic biological questions to precision medicine.

Zwitter-ionic monolith-based spintip column coupled with Evosep One liquid chromatography for high-throughput proteomic analysis

A high-throughput proteomic workflow with good sensitivity and reproducibility is highly demanding for proteomic studies of large clinical cohorts. We present a workflow that seamlessly integrates the zwitter-ionic monolith-based spintip (ZIM-Tip) with the Evosep One liquid chromatography system to address this challenge. Disposable ZIM-Tips were prepared with satisfying permeability based on photo-initiated free radical polymerization. Sample preparation steps, including ion-exchange-based protein concentration, reduction, alkylation, and enzymatic digestion, were processed on the ZIM-Tips in 2 h with about 10% sample loss. The peptides recovered from ZIM-Tips were directly loaded on Evotips for desalting and proteomic data acquisition. In one-hour high performance liquid chromatography-MS/MS run, more than 4000 proteins were consistently identified from 1 µg of cell lysate using timsTOF Pro-mass spectrometer in data-dependent acquisition mode (DDA). At least 20 samples with protein amount of 1 µg could be processed each day. Good intra- and inter-day precision in quantification were demonstrated with median coefficient of variation (CV) values of less than 20% and 30%, respectively. The average Pearson correlation coefficients of each two sets of samples are 0.934 and 0.901, respectively. Collectively, the ZIM-Tip technology offers an useful solution for clinical cohort studies with demand for large sample amounts and low sample input while maintaining in-depth proteome coverage.

Laser capture microdissection-capillary zone electrophoresis-tandem mass spectrometry (LCM-CZE-MS/MS) for spatially resolved top-down proteomics: a pilot study of zebrafish brain

Mass spectrometry (MS)-based spatially resolved top-down proteomics (TDP) of tissues is crucial for understanding the roles played by microenvironmental heterogeneity in the biological functions of organs and for discovering new proteoform biomarkers of diseases. There are few published spatially resolved TDP studies. One of the challenges relates to the limited performance of TDP for the analysis of spatially isolated samples using, for example, laser capture microdissection (LCM) because those samples are usually mass-limited. We present the first pilot study of LCM-capillary zone electrophoresis (CZE)-MS/MS for spatially resolved TDP and used zebrafish brain as the sample. The LCM-CZE-MS/MS platform employed a non-ionic detergent and a freeze-thaw method for efficient proteoform extraction from LCM isolated brain sections followed by CZE-MS/MS without any sample cleanup step, ensuring high sensitivity. Over 400 proteoforms were identified in a CZE-MS/MS analysis of one LCM brain section via consuming the protein content of roughly 250 cells. We observed drastic differences in proteoform profiles between two LCM brain sections isolated from the optic tectum (Teo) and telencephalon (Tel) regions. Proteoforms of three proteins (npy, penkb, and pyya) having neuropeptide hormone activity were exclusively identified in the isolated Tel section. Proteoforms of reticulon, myosin, and troponin were almost exclusively identified in the isolated Teo section, and those proteins play essential roles in visual and motor activities. The proteoform profiles accurately reflected the main biological functions of the Teo and Tel regions of the brain. Additionally, hundreds of post-translationally modified proteoforms were identified.

Three-dimensional spatial transcriptomics uncovers cell type localizations in the human rheumatoid arthritis synovium

The inflamed rheumatic joint is a highly heterogeneous and complex tissue with dynamic recruitment and expansion of multiple cell types that interact in multifaceted ways within a localized area. Rheumatoid arthritis synovium has primarily been studied either by immunostaining or by molecular profiling after tissue homogenization. Here, we use Spatial Transcriptomics, where tissue-resident RNA is spatially labeled in situ with barcodes in a transcriptome-wide fashion, to study local tissue interactions at the site of chronic synovial inflammation. We report comprehensive spatial RNA-Seq data coupled to cell type-specific localization patterns at and around organized structures of infiltrating leukocyte cells in the synovium. Combining morphological features and high-throughput spatially resolved transcriptomics may be able to provide higher statistical power and more insights into monitoring disease severity and treatment-specific responses in seropositive and seronegative rheumatoid arthritis.

Laser Capture Proteomics: spatial tissue molecular profiling from the bench to personalized medicine

INTRODUCTION

Laser Capture Microdissection (LCM) uses a laser to isolate, or capture, specific cells of interest in a complex heterogeneous tissue section, under direct microscopic visualization. Recently, there has been a surge of publications using LCM for tissue spatial molecular profiling relevant to a wide range of research topics.

AREAS COVERED

We summarize the many advances in tissue Laser Capture Proteomics (LCP) using mass spectrometry for discovery, and protein arrays for signal pathway network mapping. This review emphasizes: a) transition of LCM phosphoproteomics from the lab to the clinic for individualized cancer therapy, and b) the emerging frontier of LCM single cell molecular analysis combining proteomics with genomic, and transcriptomic analysis. The search strategy was based on the combination of MeSH terms with expert refinement.

EXPERT OPINION

LCM is complemented by a rich set of instruments, methodology protocols, and analytical A.I. (artificial intelligence) software for basic and translational research. Resolution is advancing to the tissue single cell level. A vision for the future evolution of LCM is presented. Emerging LCM technology is combining digital and AI guided remote imaging with automation, and telepathology, to a achieve multi-omic profiling that was not previously possible.

Spatial proteomics for understanding the tissue microenvironment

The human body comprises rich populations of cells, which are arranged into tissues and organs with diverse functionalities. These cells exhibit a broad spectrum of phenotypes and are often organized as a heterogeneous but sophisticatedly regulated ecosystem - tissue microenvironment, inside which every cell interacts with and is reciprocally influenced by its surroundings through its life span. Therefore, it is critical to comprehensively explore the cellular machinery and biological processes in the tissue microenvironment, which is best exemplified by the tumor microenvironment (TME). The past decade has seen increasing advances in the field of spatial proteomics, the main purpose of which is to characterize the abundance and spatial distribution of proteins and their post-translational modifications in the microenvironment of diseased tissues. Herein, we outline the achievements and remaining challenges of mass spectrometry-based tissue spatial proteomics. Exciting technology developments along with important biomedical applications of spatial proteomics are highlighted. In detail, we focus on high-quality resources built by scalpel macrodissection-based region-resolved proteomics, method development of sensitive sample preparation for laser microdissection-based spatial proteomics, and antibody recognition-based multiplexed tissue imaging. In the end, critical issues and potential future directions for spatial proteomics are also discussed.

Spatially resolved proteomics identify biomarkers from endometrial sentinel lymph nodes

In this issue, Aboulouard et al. (2021) use spatially resolved shot-gun proteomics to characterize the sentinel lymph nodes (SLNs) of endometrial cancer patients at different pathological grades. They identify biomarkers that predict prognosis as well as tumor grade.

Dissecting the multi-omics atlas of the exosomes released by human lung adenocarcinoma stem-like cells

Lung adenocarcinoma is heterogeneous and hierarchically organized, with a subpopulation of stem-like cells (CSCs) that reside at the apex of the hierarchy, in which exosomes act as important mediators by transporting specific molecules among different cell populations. Although there have been numerous studies on tumor exosomes, the constituents and functional properties of CSC-derived exosomes are still poorly characterized. Here we present a detail transcriptome and proteome atlas of the exosomes released by human lung adenocarcinoma stem-like cells (LSLCs). The transcriptome analysis indicates the specific patterns of exosomal constituents, including the fragmentation of transcripts and the low-level presence of circular RNAs, and identifies multiple exosomal-enriched mRNAs and lncRNAs. Integrative analysis of transcriptome and proteome data reveals the diverse functions of exosomal-enriched RNAs and proteins, many of which are associated with tumorigenesis. Importantly, several LSLC markers we identified are highly expressed in LSLC-derived exosomes and associate with poor survival, which may serve as promising liquid biopsy biomarkers for lung adenocarcinoma diagnosis. Our study provides a resource for the future elucidation of the functions of tumor-derived exosomes and their regulatory mechanisms in mediating lung cancer development.

Combinatory strategy using nanoscale proteomics and machine learning for T cell subtyping in peripheral blood of single multiple myeloma patients

T cells play crucial roles in our immunity against hematological tumors by inducing sustained immune responses. Flow cytometry-based detection of a limited number of specific protein markers has been routinely applied for basic research and clinical investigation in this area. In this study, we combined flow cytometry with the simple integrated spintip-based proteomics technology (SISPROT) to characterize the proteome of primary T cell subtypes in the peripheral blood (PB) from single multiple myeloma (MM) patients. Taking advantage of the integrated high pH reversed-phase fractionation in the SISPROT device, the global proteomes of CD3⁺, CD4⁺ and CD8⁺ T cells were firstly profiled with a depth of >7 000 protein groups for each cell type. The sensitivity of single-shot proteomic analysis was dramatically improved by optimizing the SISPROT and data-dependent acquisition parameters for nanogram-level samples. Eight subtypes of T cells were sorted from about 4 mL PB of single MM patients, and the individual subtype-specific proteomes with coverage among 1 702 and 3 699 protein groups were obtained from as low as 70 ng and up to 500 ng of cell lysates. In addition, we developed a two-step machine learning-based subtyping strategy for proof-of-concept classifying eight T cell subtypes, independent of their cell numbers and individual differences. Our strategy demonstrates an easy-to-use proteomic analysis on immune cells with the potential to discover novel subtype-specific protein biomarkers from limited clinical samples in future large scale clinical studies.

Proteomic Profiling and Artificial Intelligence for Hepatocellular Carcinoma Translational Medicine

Hepatocellular carcinoma (HCC) is the most common primary cancer of the liver with high morbidity and mortality rates worldwide. Since 1963, when alpha-fetoprotein (AFP) was discovered as a first HCC serum biomarker, several other protein biomarkers have been identified and introduced into clinical practice. However, insufficient specificity and sensitivity of these biomarkers dictate the necessity of novel biomarker discovery. Remarkable advancements in integrated multiomics technologies for the identification of gene expression and protein or metabolite distribution patterns can facilitate rising to this challenge. Current multiomics technologies lead to the accumulation of a huge amount of data, which requires clustering and finding correlations between various datasets and developing predictive models for data filtering, pre-processing, and reducing dimensionality. Artificial intelligence (AI) technologies have an enormous potential to overcome accelerated data growth, complexity, and heterogeneity within and across data sources. Our review focuses on the recent progress in integrative proteomic profiling strategies and their usage in combination with machine learning and deep learning technologies for the discovery of novel biomarker candidates for HCC early diagnosis and prognosis. We discuss conventional and promising proteomic biomarkers of HCC such as AFP, lens culinaris agglutinin (LCA)-reactive L3 glycoform of AFP (AFP-L3), des-gamma-carboxyprothrombin (DCP), osteopontin (OPN), glypican-3 (GPC3), dickkopf-1 (DKK1), midkine (MDK), and squamous cell carcinoma antigen (SCCA) and highlight their functional significance including the involvement in cell signaling such as Wnt/β-catenin, PI3K/Akt, integrin αvβ3/NF-κB/HIF-1α, JAK/STAT3 and MAPK/ERK-mediated pathways dysregulated in HCC. We show that currently available computational platforms for big data analysis and AI technologies can both enhance proteomic profiling and improve imaging techniques to enhance the translational application of proteomics data into precision medicine.

Fully integrated protein absolute quantification platform for analysis of multiple tumor markers in human plasma

In this study, we developed a fully integrated protein absolute quantification platform for simultaneous analysis of multiple tumor markers in human plasma, by which multiple target proteins (alpha-fetoprotein, prostate-specific antigen, carcino-embryonic antigen and mucin-1) were firstly enriched by aptamers immobilized capillary column using graphene oxide modified polymer microsphere as the separation matrix, and then the eluted target proteins were online denatured, reduced, desalted and digested by our developed fully automated sample treatment device (FAST), finally the resulting peptides were analyzed by parallel reaction monitoring (PRM) on LTQ-orbitrap velos mass spectrometry. Compared to traditional ELISA assay, the platform exhibited significant advantages such as short analysis time, low limit of detection, and ease of automation. Furthermore, our developed platform was also applied in the absolute quantification of tumor markers from clinical human plasma samples, and the results were comparable to those obtained by clinical immunoassay. All the results demonstrated that such a platform could provide a promising tool for achieving high sensitivity, high accuracy, and high throughput detection of disease related protein markers in the routine physical examination and clinical disease diagnosis.

Unraveling Heterogeneity in Transcriptome and Its Regulation Through Single-Cell Multi-Omics Technologies

Cellular heterogeneity plays a pivotal role in tissue homeostasis and the disease development of multicellular organisms. To deconstruct the heterogeneity, a multitude of single-cell toolkits measuring various cellular contents, including genome, transcriptome, epigenome, and proteome, have been developed. More recently, multi-omics single-cell techniques enable the capture of molecular footprints with a higher resolution by simultaneously profiling various cellular contents within an individual cell. Integrative analysis of multi-omics datasets unravels the relationships between cellular modalities, builds sophisticated regulatory networks, and provides a holistic view of the cell state. In this review, we summarize the major developments in the single-cell field and review the current state-of-the-art single-cell multi-omic techniques and the bioinformatic tools for integrative analysis.

Spatial proteome profiling by immunohistochemistry-based laser capture microdissection and data-independent acquisition proteomics

Understanding the tumor heterogeneity through spatially resolved proteome profiling is important for biomedical research and clinical application. Laser capture microdissection (LCM) is a powerful technology for exploring local cell populations without losing spatial information. Conventionally, tissue sections are stained with hematoxylin and eosin (H&E) for cell-type identification before LCM. However, it generally requires experienced pathologists to distinguish different cell types, which limits the application of LCM to broad cancer research field. Here, we designed an immunohistochemistry (IHC)-based workflow for cell type-resolved proteome analysis of tissue samples. Firstly, targeted cell type was marked by IHC using antibody targeting cell-type specific marker to improve accuracy and efficiency of LCM. Secondly, to increase protein recovery from chemically crosslinked IHC tissues, we optimized a decrosslinking procedure to seamlessly combine with the integrated spintip-based sample preparation technology SISPROT. This newly developed approach, termed IHC-SISPROT, has comparable performance as H&E staining-based proteomic analysis. High sensitivity and reproducibility of IHC-SISPROT were achieved by combining with data independent acquisition proteomics. More than 3500 proteins were identified from only 0.2 mm² and 12 μm thickness of hepatocellular carcinoma (HCC) tissue section. Furthermore, using 5 mm² and 12 μm thickness of HCC tissue section, 6660 and 6052 protein groups were quantified from cancer cells and cancer-associated fibroblasts (CAFs) by the IHC-SISPROT workflow. Bioinformatic analysis revealed the enrichment of cell type-specific ligands and receptors and potentially new communications between cancer cells and CAFs by these signaling proteins. Therefore, IHC-SISPROT is a sensitive and accurate proteomic approach for spatial profiling of cell type-specific proteome from tissues.

Mutual regulation of JAG2 and PRAF2 promotes migration and invasion of colorectal cancer cells uncoupled from epithelial-mesenchymal transition

Background

Our previous studies revealed that Jagged 2 (JAG2) is involved in the regulation of migration and invasion of colon cancer cells without affecting cell proliferation. This study further explored the specific mechanism by which JAG2 promotes migration and invasion of colorectal cancer cells.

Methods

JAG2 mRNA expression in different clinical stages of colorectal cancer and normal intestinal tissues was detected by quantitative PCR (QPCR). QPCR and Western Blot were used to analyze the differential expression of JAG2 mRNA and protein between normal human colon tissue cells and various colorectal cancer cells. Co-expression status of JAG2 and epithelial–mesenchymal transition (EMT) markers in colon cancer tissues and cells was analyzed. The difference between TGF-β-induced EMT model and the JAG2 overexpression model were compared in promoting migration and invasion of HT29 cells. HT29 cells were treated with EMT pathway inhibitors (LY2157299 and Slug siRNA) to identify a cross-talk between the JAG2 effect and the Notch pathway. Co-expressed genes of JAG2 in colorectal cancer cells were identified using siRNA and transcriptome microarray technology. The mutual regulation of JAG2 and the co-expressed gene PRAF2 and the regulation of the paracrine effect of exosomes were analyzed.

Results

JAG2 was abnormally expressed in colorectal cancer tissues and directly related to clinical stages. Similar to the findings in tissues, the expression of both JAG2 mRNA and protein was significantly increased in the colorectal cancer cell lines compared with that of normal colorectal cell line CCD18-Co. It was shown in our cell model that JAG2 was involved in the regulation of migration and invasion independent of the canonical Notch signaling pathway. More interestingly, JAG2 also promoted the migration and invasion of colon cancer cells in a non-EMT pathway. Further analysis revealed the co-expression of JAG2 with PRAF2 in colorectal cancer cells. JAG2-rich exosomes were released from colorectal cancer cells in a PRAF2-dependent way, while these exosomes regulated the metastasis of colorectal cancer cells in a paracrine manner.

Conclusions

This is the evidence supporting the biological function of JAG2 through non-canonical Notch and non-EMT-dependent pathways and also the first demonstration of the functions of PRAF2 in colorectal cancer cells. These findings also provide theoretical basis for the development of small molecules or biological agents for therapeutic intervention targeting JAG2/PRAF2.

Electronic supplementary material

The online version of this article (10.1186/s12935-019-0871-5) contains supplementary material, which is available to authorized users.

Characterization of Sample Loss Caused by Competitive Adsorption of Proteins in Vials Using Sodium Dodecyl Sulfate-Polyacrylamide Gel Electrophoresis

Sample loss caused by competitive protein adsorption on solid surfaces from complex samples remains to be a major hurdle in sensitive analyses of proteins. No label-free techniques can easily quantify individual proteins adsorbed on irregular surfaces of Eppendorf vials or Falcon tubes, which are commonly used to contain complex biological samples. Multiplexed characterization of such adsorption by different proteins is technically challenging. Herein, we developed a direct protein analysis based on sodium dodecyl sulfate-polyacrylamide gel electrophoresis for the characterization of sample loss occurred on the curved surface with limited area. Using this simple and easily accessible method, we discovered the effect of ethylenediaminetetraacetic acid on surface adsorption of different milk proteins, specifically an augmented loss of milk proteins in low-binding sample vials. In this study, we also identified severe biases of silver staining and established proteomics-based mapping of protein distribution in biological samples for absolute quantification of competitive protein adsorption on irregular surfaces.

Fast quantitative urinary proteomic profiling workflow for biomarker discovery in kidney cancer

Background

Urine has evolved as a promising body fluids in clinical proteomics because it can be easily and noninvasively obtained and can reflect physiological and pathological status of the human body. Many efforts have been made to characterize more urinary proteins in recent years, but few have focused on the analysis throughput and detection reproducibility. Increasing the urine proteomic profiling throughput and reproducibility is urgently needed for discovering potential biomarker in large cohorts.

Methods

In this study, we developed a fast and robust workflow for streamlined urinary proteome analysis. The workflow integrate highly efficient sample preparation technique and urinary specific data-independent acquisition (DIA) approach. The performance of the workflow was systematically evaluated and the workflow was subsequently applied in a proof-of-concept urine proteome study of 21 kidney cancer (KC) patients and 22 healthy controls.

Results

With this workflow, the entire sample preparation process takes less than 3 h and allows multiplexing on standard centrifuges. Without pre-fractionation, our newly developed DIA method allows quantitative analysis of ~ 1000 proteins within 80 min of MS time (~ 15 samples/day). The quantitation accuracy of the whole workflow was excellent with median CV of 9.1%. The preliminary study on KC identified 125 significantly changed proteins.

Conclusions

The result suggested the feasibility of applying the high throughput workflow in extensive urinary proteome profiling and clinical relevant biomarker discovery.

Electronic supplementary material

The online version of this article (10.1186/s12014-018-9220-2) contains supplementary material, which is available to authorized users.