In-Depth Proteomic Characterization of Classical and Non-Classical Monocyte Subsets

Comprehensive proteomics of monocytes indicates oxidative imbalance functionally related to inflammatory response in chronic kidney disease-related atherosclerosis

Atherosclerosis-induced cardiovascular events are the leading cause of mortality in chronic kidney disease (CKD) patients. Monocytes are involved in the formation of atherosclerotic plaques and mediate in the overproduction of ROS, promoting inflammation and oxidative stress. However, the relationship between monocytes, inflammation, and oxidative status in CKD-associated atherosclerosis has not been thoroughly investigated. Monocytes and plasma derived from two groups of CKD patients with varying degrees of atherosclerosis and two groups of patients with cardiovascular disease (CVD) and non-CKD atherosclerosis were analyzed. This study was designed to perform a comprehensive proteomic analysis of monocytes in combination with functional bioinformatics. In addition, a targeted investigation of oxidative stress- and inflammatory-related factors to explore CKD-associated atherosclerosis was applied. Dysregulation of proteins involved in lipid oxidation, cell survival, ROS synthesis and metabolism, and inflammatory responses has been revealed. The characteristic disturbances in the monocyte proteome changed with the progression of CKD. A closer examination of oxidative stress's triggers, mediators, and effects on protein and lipid levels showed alterations in the oxidative imbalance between CKD and CVD. CKD monocytes demonstrated a significant increase of oxidized glutathione without changing the level of its reduced form. Evaluation of enzymatic antioxidants, sources of ROS, and modifications caused by ROS also revealed significant alterations between the study groups. In CKD, inflammation and oxidative imbalance correlated and drove each other. However, in CVD, oxidative stress-related factors were associated with each other but not to inflammatory proteins. Moreover, lipid abnormalities were more specific to classical CVD and unrelated to CKD. Such a comprehensive characterization of monocytes and oxidative stress in CKD and CVD patients has never been presented so far. Obtained results support the involvement of distinct mechanisms underlying the acceleration of atherosclerotic and non-atherosclerotic CKD.

Mass Spectrometry-Based Proteogenomics: New Therapeutic Opportunities for Precision Medicine

Proteogenomics refers to the integration of comprehensive genomic, transcriptomic, and proteomic measurements from the same samples with the goal of fully understanding the regulatory processes converting genotypes to phenotypes, often with an emphasis on gaining a deeper understanding of disease processes. Although specific genetic mutations have long been known to drive the development of multiple cancers, gene mutations alone do not always predict prognosis or response to targeted therapy. The benefit of proteogenomics research is that information obtained from proteins and their corresponding pathways provides insight into therapeutic targets that can complement genomic information by providing an additional dimension regarding the underlying mechanisms and pathophysiology of tumors. This review describes the novel insights into tumor biology and drug resistance derived from proteogenomic analysis while highlighting the clinical potential of proteogenomic observations and advances in technique and analysis tools.

The slan antigen identifies the prototypical non-classical CD16+-monocytes in human blood

Introduction

Peripheral monocytes in humans are conventionally divided into classical (CL, CD14++CD16-), intermediate (INT, CD14++CD16+) and non-classical (NC, CD14dim/-CD16++) cells, based on their expression levels of CD14 and CD16. A major fraction of the NC-monocytes has been shown to express the 6-sulfo LacNAc (slan) antigen, but whether these slan+/NC-monocytes represent the prototypical non-classical monocytes or whether they are simply a sub-fraction with identical features as the remainder of NC monocytes is still unclear.

Methods

We analyzed transcriptome (by bulk and single cell RNA-seq), proteome, cell surface markers and production of discrete cytokines by peripheral slan+/NC- and slan-/NC-monocytes, in comparison to total NC-, CL- and INT- monocytes.

Results

By bulk RNA-seq and proteomic analysis, we found that slan+/NC-monocytes express higher levels of genes and proteins specific of NC-monocytes than slan-/NC-monocytes do. Unsupervised clustering of scRNA-seq data generated one cluster of NC- and one of INT-monocytes, where all slan+/NC-monocytes were allocated to the NC-monocyte cluster, while slan-/NC-monocytes were found, in part (13.4%), within the INT-monocyte cluster. In addition, total NC- and slan-/NC-monocytes, but not slan+/NC-monocytes, were found by both bulk RNA-seq and scRNA-seq to contain a small percentage of natural killer cells.

Conclusion

In addition to comparatively characterize total NC-, slan-/NC- and slan+/NC-monocyte transcriptomes and proteomes, our data prove that slan+/NC-, but not slan-/NC-, monocytes are more representative of prototypical NC-monocytes.

Quantitative proteomics of small numbers of closely-related cells: Selection of the optimal method for a clinical setting

Mass spectrometry (MS)-based proteomics profiling has undoubtedly increased the knowledge about cellular processes and functions. However, its applicability for paucicellular sample analyses is currently limited. Although new approaches have been developed for single-cell studies, most of them have not (yet) been standardized and/or require highly specific (often home-built) devices, thereby limiting their broad implementation, particularly in non-specialized settings. To select an optimal MS-oriented proteomics approach applicable in translational research and clinical settings, we assessed 10 different sample preparation procedures in paucicellular samples of closely-related cell types. Particularly, five cell lysis protocols using different chemistries and mechanical forces were combined with two sample clean-up techniques (C18 filter- and SP3-based), followed by tandem mass tag (TMT)-based protein quantification. The evaluation was structured in three phases: first, cell lines from hematopoietic (THP-1) and non-hematopoietic (HT-29) origins were used to test the approaches showing the combination of a urea-based lysis buffer with the SP3 bead-based clean-up system as the best performer. Parameters such as reproducibility, accessibility, spatial distribution, ease of use, processing time and cost were considered. In the second phase, the performance of the method was tested on maturation-related cell populations: three different monocyte subsets from peripheral blood and, for the first time, macrophages/microglia (MAC) from glioblastoma samples, together with T cells from both tissues. The analysis of 50,000 cells down to only 2,500 cells revealed different protein expression profiles associated with the distinct cell populations. Accordingly, a closer relationship was observed between non-classical monocytes and MAC, with the latter showing the co-expression of M1 and M2 macrophage markers, although pro-tumoral and anti-inflammatory proteins were more represented. In the third phase, the results were validated by high-end spectral flow cytometry on paired monocyte/MAC samples to further determine the sensitivity of the MS approach selected. Finally, the feasibility of the method was proven in 194 additional samples corresponding to 38 different cell types, including cells from different tissue origins, cellular lineages, maturation stages and stimuli. In summary, we selected a reproducible, easy-to-implement sample preparation method for MS-based proteomic characterization of paucicellular samples, also applicable in the setting of functionally closely-related cell populations.

Single-Cell and Bulk RNA-Sequencing Reveal Differences in Monocyte Susceptibility to Influenza A Virus Infection Between Africans and Europeans

There is considerable inter-individual and inter-population variability in response to viruses. The potential of monocytes to elicit type-I interferon responses has attracted attention to their role in viral infections. Here, we use single-cell RNA-sequencing to characterize the role of cellular heterogeneity in human variation of monocyte responses to influenza A virus (IAV) exposure. We show widespread inter-individual variability in the percentage of IAV-infected monocytes. Notably, individuals with high cellular susceptibility to IAV are characterized by a lower activation at basal state of an IRF/STAT-induced transcriptional network, which includes antiviral genes such as IFITM3, MX1 and OAS3. Upon IAV challenge, we find that cells escaping viral infection display increased mRNA expression of type-I interferon stimulated genes and decreased expression of ribosomal genes, relative to both infected cells and those never exposed to IAV. We also uncover a stronger resistance of CD16⁺ monocytes to IAV infection, together with CD16⁺ -specific mRNA expression of IL6 and TNF in response to IAV. Finally, using flow cytometry and bulk RNA-sequencing across 200 individuals of African and European ancestry, we observe a higher number of CD16 ⁺ monocytes and lower susceptibility to IAV infection among monocytes from individuals of African-descent. Based on these data, we hypothesize that higher basal monocyte activation, driven by environmental factors and/or weak-effect genetic variants, underlies the lower cellular susceptibility to IAV infection of individuals of African ancestry relative to those of European ancestry. Further studies are now required to investigate how such cellular differences in IAV susceptibility translate into population differences in clinical outcomes and susceptibility to severe influenza.

Biomaterials as Tools to Decode Immunity

The immune system has remarkable capabilities to combat disease with exquisite selectivity. This feature has enabled vaccines that provide protection for decades and, more recently, advances in immunotherapies that can cure some cancers. Greater control over how immune signals are presented, delivered, and processed will help drive even more powerful options that are also safe. Such advances will be underpinned by new tools that probe how immune signals are integrated by immune cells and tissues. Biomaterials are valuable resources to support this goal, offering robust, tunable properties. The growing role of biomaterials as tools to dissect immune function in fundamental and translational contexts is highlighted. These technologies can serve as tools to understand the immune system across molecular, cellular, and tissue length scales. A common theme is exploiting biomaterial features to rationally direct how specific immune cells or organs encounter a signal. This precision strategy, enabled by distinct material properties, allows isolation of immunological parameters or processes in a way that is challenging with conventional approaches. The utility of these capabilities is demonstrated through examples in vaccines for infectious disease and cancer immunotherapy, as well as settings of immune regulation that include autoimmunity and transplantation.

Comprehensive cell surface proteomics defines markers of classical, intermediate and non-classical monocytes

Monocytes are a critical component of the cellular innate immune system, and can be subdivided into classical, intermediate and non-classical subsets on the basis of surface CD14 and CD16 expression. Classical monocytes play the canonical role of phagocytosis, and account for the majority of circulating cells. Intermediate and non-classical cells are known to exhibit varying levels of phagocytosis and cytokine secretion, and are differentially expanded in certain pathological states. Characterisation of cell surface proteins expressed by each subset is informative not only to improve understanding of phenotype, but may also provide biological insights into function. Here we use highly multiplexed Tandem-Mass-Tag (TMT)-based mass spectrometry with selective cell surface biotinylation to characterise the classical monocyte surface proteome, then interrogate the phenotypic differences between each monocyte subset to identify novel protein markers.

Identification of Novel Human Monocyte Subsets and Evidence for Phenotypic Groups Defined by Interindividual Variations of Expression of Adhesion Molecules

Monocytes contribute to immune responses as a source for subsets of dendritic cells and macrophages. Human blood monocytes are classified as classical, non-classical and intermediate cells. However, the particular functions of these subsets have been hard to define, with conflicting results and significant overlaps. One likely reason for these ambiguities is in the heterogeneity of these monocyte subsets regrouping cells with divergent functions. To better define monocyte populations, we have analysed expression of 17 markers by multicolour flow cytometry in samples obtained from 28 control donors. Data acquisition was tailored to detect populations present at low frequencies. Our results reveal the existence of novel monocyte subsets detected as larger CD14⁺ cells that were CD16⁺ or CD16^neg. These large monocytes differed from regular, smaller monocytes with respect to expression of various cell surface molecules, such as FcR, chemokine receptors, and adhesion molecules. Unsupervised multidimensional analysis confirmed the existence of large monocytes and revealed interindividual variations that were grouped according to unique patterns of expression of adhesion molecules CD62L, CD49d, and CD43. Distinct inflammatory responses to TLR agonists were found in small and large monocytes. Overall, refining the definition of monocyte subsets should lead to the identification of populations with specific functions.

Proteogenomics of Colorectal Cancer Liver Metastases: Complementing Precision Oncology with Phenotypic Data

Hotspot testing for activating KRAS mutations is used in precision oncology to select colorectal cancer (CRC) patients who are eligible for anti-EGFR treatment. However, even for KRAS^wildtype tumors anti-EGFR response rates are <30%, while mutated-KRAS does not entirely rule out response, indicating the need for improved patient stratification. We performed proteogenomic phenotyping of KRAS^wildtype and KRAS^G12V CRC liver metastases (mCRC). Among >9000 proteins we detected considerable expression changes including numerous proteins involved in progression and resistance in CRC. We identified peptides representing a number of predicted somatic mutations, including KRAS^G12V. For eight of these, we developed a multiplexed parallel reaction monitoring (PRM) mass spectrometry assay to precisely quantify the mutated and canonical protein variants. This allowed phenotyping of eight mCRC tumors and six paired healthy tissues, by determining mutation rates on the protein level. Total KRAS expression varied between tumors (0.47–1.01 fmol/µg total protein) and healthy tissues (0.13–0.64 fmol/µg). In KRAS^G12V-mCRC, G12V-mutation levels were 42–100%, while one patient had only 10% KRAS^G12V but 90% KRAS^wildtype. This might represent a missed therapeutic opportunity: based on hotspot sequencing, the patient was excluded from anti-EGFR treatment and instead received chemotherapy, while PRM-based tumor-phenotyping indicates the patient might have benefitted from anti-EGFR therapy.

Monocyte Subsets: Phenotypes and Function in Tuberculosis Infection

Monocytes are critical defense components that play an important role in the primary innate immune response. The heterogeneous nature of monocytes and their ability to differentiate into either monocyte-derived macrophages or monocyte-derived dendritic cells allows them to serve as a bridge between the innate and adaptive immune responses. Current studies of monocytes based on immunofluorescence, single-cell RNA sequencing and whole mass spectrometry finger printing reveals different classification systems for monocyte subsets. In humans, three circulating monocyte subsets are classified based on relative expression levels of CD14 and CD16 surface proteins, namely classical, intermediate and non-classical subsets. Transcriptomic analyses of these subsets help to define their distinct functional properties. Tuberculosis (TB) is a disease instigated by the deadly pathogen Mycobacterium tuberculosis. Current research on monocytes in TB has indicated that there are alterations in the frequency of intermediate and non-classical subsets suggesting their impact in bacterial persistence. In this review, we will focus on these monocyte subsets, including their classification, frequency distribution, cytokine profiles, role as a biomarker and will comment on future directions for understanding the salient phenotypic and functional properties relevant to TB pathogenesis.

Monocyte Subsets Have Distinct Patterns of Tetraspanin Expression and Different Capacities to Form Multinucleate Giant Cells

Monocytes are able to undergo homotypic fusion to produce different types of multinucleated giant cells, such as Langhans giant cells in response to M. tuberculosis infection or foreign body giant cells in response to implanted biomaterials. Monocyte fusion is highly coordinated and complex, with various soluble, intracellular, and cell-surface components mediating different stages of the process. Tetraspanins, such as CD9, CD63, and CD81, are known to be involved in cell:cell fusion and have been suggested to play a role in regulating homotypic monocyte fusion. However, peripheral human monocytes are not homogenous: they exist as a heterogeneous population consisting of three subsets, classical (CD14⁺⁺CD16^-), intermediate (CD14⁺⁺CD16⁺), and non-classical (CD14⁺CD16⁺), at steady state. During infection with mycobacteria, the circulating populations of intermediate and non-classical monocytes increase, suggesting they may play a role in the disease outcome. Human monocytes were separated into subsets and then induced to fuse using concanavalin A. The intermediate monocytes were able to fuse faster and form significantly larger giant cells than the other subsets. When antibodies targeting tetraspanins were added, the intermediate monocytes responded to anti-CD63 by forming smaller giant cells, suggesting an involvement of tetraspanins in fusion for at least this subset. However, the expression of fusion-associated tetraspanins on monocyte subsets did not correlate with the extent of fusion or with the inhibition by tetraspanin antibody. We also identified a CD9^High and a CD9^Low monocyte population within the classical subset. The CD9^High classical monocytes expressed higher levels of tetraspanin CD151 compared to CD9^Low classical monocytes but the CD9^High classical subset did not exhibit greater potential to fuse and the role of these cells in immunity remains unknown. With the exception of dendrocyte-expressed seven transmembrane protein, which was expressed at higher levels on the intermediate monocyte subset, the expression of fusion-related proteins between the subsets did not clearly correlate with their ability to fuse. We also did not observe any clear correlation between giant cell formation and the expression of pro-inflammatory or fusogenic cytokines. Although tetraspanin expression appears to be important for the fusion of intermediate monocytes, the control of multinucleate giant cell formation remains obscure.