The utilization of biomarkers is becoming a viable method for noninvasive diagnosis of liver disease, monitoring of treatment response, and enabling personalized medicine. Identifying novel biomarkers for MASLD is crucial for advancing early disease diagnosis and improving clinical outcomes. Recent research has identified candidate serum biomarkers corresponding to each liver disease stage, including inflammatory cytokines, fatty acid transporter proteins, lipid droplet-associated proteins, and extracellular matrix proteins at various stages (30). Identifying these biomarkers can enhance the accuracy and speed of MASLD diagnosis, allowing physicians to develop patient-specific lifestyle and treatment plans (31).

Blood-based panels, such as NIS4^®, have become important for identifying patients at risk of severe MASLD or MASH without the need for invasive procedures. Recent improvements, including the NIS2 + ™ panel, have shown a potential to further reduce unnecessary biopsies and screening costs in clinical practice and trials (32, 33). Neither test has yet received FDA approval.

Biomarkers also play a vital role in treating fibrosis. Fibroblast Growth Factor 21 (FGF21), a liver-secreted hormone regulating energy and lipid metabolism, shows promise as a therapeutic target. FGF21 analogs, such as efruxifermin and pegozafermin, are currently in clinical trials for treating fibrosis in MASLD (12). These biomarkers identify fibrosis, guide FGF21-based therapies, and monitor treatment responses, which can lead to more personalized and effective liver disease management.

Biomarkers provide insight into the molecular mechanisms of liver disease, helping clinicians assess key processes such as fat deposition, oxidative stress, inflammation, and fibrosis. Application in noninvasive diagnostics, genetic profiling, and blood-based panels enhance early detection and risk stratification. By predicting disease progression, particularly fibrosis, biomarkers allow for optimized treatment strategies and real-time monitoring of therapeutic responses to metabolic and antifibrotic agents. Biomarkers are also important for developing targeted therapies and serve as surrogate endpoints, reducing the need for liver biopsies and allowing for faster evaluation of treatment efficacy.

Current imaging technologies offer advantages and disadvantages (cost, accessibility, resolution of imaging, sensitivity, and specificity) to diagnose and manage MASLD. To address these gaps, we focus on methods to detect early-stage cellular events and changes in protein expression and identify key pathways involved in MASLD progression. Mass spectrometry-based proteomics offers promising opportunities for novel biomarker discovery and elucidating the molecular mechanisms underlying MASLD development (34, 35). Proteomic studies identify circulating protein signatures associated with MASLD progression. A comprehensive proteo-transcriptomic analysis of 4,730 circulating proteins in patients with MASLD identified distinct signatures for active steatohepatitis and advanced fibrosis (36). Biomarkers are the future of MASLD diagnosis, treatment, and research.

Mass spectrometry is the gold standard for identifying and measuring proteins in proteomics, separating ions according to their mass-to-charge ratio (m/z), and enabling an evaluation of peptide masses. Tandem mass spectrometry (MS/MS) involves two stages of mass analyses with an intermediate fragmentation step in which nitrogen or helium gas fragments the ions into smaller masses for sequencing. Mass spectrometers are often coupled with High-Pressure Liquid Chromatography (HPLC), which separates peptides based on their interaction with a liquid mobile phase and a stationary phase. LC–MS/MS analysis offers an extremely selective and sensitive method for measuring peptides, as HPLC fractionation increases the number of peptide identifications (46). Spectra are collected in a 60-min run and stored as RAW files (uncompressed and unprocessed data). A single run can have over 12,000 spectra, requiring computational methods for database searching. These methods allow for identifying proteins by comparing the mass spectra to species-specific protein references (47, 48). One of the most widely used databases for this purpose is the UniProt Knowledgebase (UniProtKB). Pathway analysis can be applied to these large sets of identified proteins (Figure 1). This approach organizes large lists of proteins into smaller sets of proteins that function in the same pathways and biological processes (49). In the context of MASLD, annotated databases and pathway analyses can be utilized to identify specific proteins and pathways underlying this disease.

Traditional diagnostic methods often target a limited number of predefined validated markers and can underrepresent individuals from specific populations, comorbidities, ages, and health statuses. Proteomics offers several advantages for biomarker discovery, including improved accuracy, greater detection limits, depth of coverage for surrogate peptides, and broader applicability. One of the major advantages is global protein profiling, which makes it possible to analyze thousands of proteins in a biological sample at once (50). Another significant advantage includes elucidating post-translational modifications such as phosphorylation, glycosylation, acetylation, and ubiquitination. These modifications play crucial roles in disease processes and are not typically captured by traditional genomic or transcriptomic methods (51). Modern proteomic approaches facilitate quantifying protein expression across the proteome of both normal and diseased samples, providing valuable insights into protein composition, localization structure, function, interactions, and expression profiling (52).

Biomarker validation involves testing in larger and more diverse cohorts to ensure they are statistically significant and clinically relevant. This phase involves clinical sample testing, in which biomarkers are validated using clinical samples from well-characterized patient cohorts, often including various stages of the disease, to assess their diagnostic and prognostic utility (54). Validation across multiple laboratories ensures that biomarkers are reproducible in various settings and populations, confirming that candidate biomarkers are vetted prior to clinical trials and eventual regulatory approval.

The primary methods for diagnosing MASLD are histology-based. However, new diagnostic approaches are emerging and present a unique opportunity for monitoring disease progression in MASH stages, allowing for easily accessible noninvasive methods of identifying biomarkers (79–81). One such advancement involves using omics technologies to use extracellular vesicles (EVs) and exosomes as a noninvasive diagnostic approach to biomarker discovery.

EVs constitute a variety of membrane vesicles that are released from cells. They can be classified into apoptotic bodies, microvesicles (MVs), and exosomes (82). Apoptotic bodies are the largest EVs, 1,000–5,000 nm in size. They are released from apoptotic blebbing cells (external forms of the cells) that are undergoing cell death. MV’s range in size from 100 to 1,000 nm and are generated by external budding from the plasma membrane. Exosomes, the smallest of the EVs, have the smallest size of 30–100 nm and originate from the endosomal system (83). EVs are released into the extracellular space and deliver information to other cells. They also carry out essential cargoes used in cell communication (84). The cargo content comprises biologically important proteins, lipids, metabolites, and nucleic acids, making them critical targets in biological research (83, 85).

EVs can be explored in the pathological identification of rare diseases through cell communication’s cellular and molecular pathways, including homeostasis and other diseases such as cancer and neurodegenerative disorders (84, 86–88). EVs offer tremendous therapeutic opportunities in cancer, infectious diseases, and neurodegenerative disorders, including the potential to serve as important biologicals in drug delivery to targeted sites (89, 90). EVs also play direct roles as pathogenic elements, particularly in neurodegenerative disorders, cancer, and even microbial infections (91–94). The roles of EVs in homeostasis must be balanced since EVs secreted from cells can travel through the circulatory system to deliver information to neighboring cells or cells in different locations (95).

EVs can also carry metabolites, RNAs, DNA, and miRNA cargo that may serve as MASLD biomarkers. Studies have shown that circulating miR-135a-3p in exosomes may be potential noninvasive biomarkers for diagnosing MASLD (96). This miRNA is a more sensitive and specific biological marker for MASLD than ALT (96). EV-omics—a subset of proteomics—may provide insight into cell–cell communication in hepatocytes during MASLD progression (Figure 2), presenting an excellent opportunity for noninvasive biomarkers that can be isolated from urine (97), saliva (98), CSF (99), and plasma (100).

Industrial and manufacturing sectors (gas, chemical plastics, energy, emissions, heavy machinery) have contributed to large-scale pollution, impacting water, air, and soil. While many initiatives and mitigation strategies have helped reduce pollution, more research is needed to identify the various environmental and chemical toxicants that impact human health. An environmental toxicant can be viewed as any toxic agent or substance produced by humans or introduced into the environment by human activities. Toxicants come in diverse shapes and sizes and may emanate from natural and anthropogenic sources. Impact on the physical environment includes thermal/climatic stress, altitude (hypoxia), natural disasters, radiation, pollution, and metal exposure. Particulate matter, polyaromatic hydrocarbons, chemical solvents, mycotoxins, phthalates, lead, organophosphates mercury, perfluorocarbons, polychlorinated biphenyls, cadmium, and arsenic are the most studied toxicants due to health concerns and disease associations.

Cells are subject to chemical, molecular, and physical stresses that can become toxic to metabolic processes, alter macromolecular interactions and signaling, affect pH, and more. These external stresses can become internal molecular stresses, producing reactive metabolic byproducts such as reactive oxidative species (ROS) and uninitiated cell death pathways. Cells and tissues’ intrinsic and adaptative ability to modulate these molecular processes in response to stresses is essential to maintaining homeostasis (101). Cell hemostasis regulation relies on the heat shock response, unfolded protein response, oxidative stress response, and DNA damage response. While these response pathways have been widely studied, emerging evidence on extracellular vesicles as a mode of disease transmission has garnered attention.

Human environmental exposures, often called the “exposome,” represent a broad spectrum of external and internal factors, including chemical, physical, biological, and social elements that can influence human health outcomes. These environmental factors can enter the body through various routes, such as inhalation, ingestion, and dermal absorption, exerting detrimental effects on liver health. The development of fatty liver disease has been linked to exposure to environmental contaminants, including air pollutants, heavy metals, pesticides, endocrine-disrupting agents, and environmental toxins (102, 103). These environmental exposures can cause inflammation, oxidative stress, and abnormal lipid metabolism in the liver, leading to hepatocyte fat buildup (104). The risk of fatty liver disease is further increased by lifestyle factors influenced by environmental factors, such as diet, physical activity, and socioeconomic status.

The exposome emphasizes the importance of considering external and internal environmental exposures and their interactions with genetic and epigenetic factors in shaping individual susceptibility to fatty liver disease. Understanding the complex interactions between environmental exposures and liver function is essential to prevent and treat MASLD effectively (104, 105). Particulate matter air pollution (PM2.5), derived mainly from fossil fuel combustion, is associated with MASLD. A recent cross-sectional study conducted on hospitalized patients in the United States Nationwide Inpatient Sample (NIS) database found a link between ambient PM2.5 exposure and an increased risk of MASLD (106), emphasizing the need for further investigation to examine the impact of eleven past long-term PM2.5 exposure events on incident instances of MASLD.

Exposure to heavy metals such as mercury (107), lead (108), arsenic (109), and iron (110) is associated with a higher incidence of fatty liver disease and liver dysfunction, which has been implicated in the onset and progression of MASLD in various communities. Endocrine disruptors (such as organochlorine pesticides that alter lipid metabolism and cause oxidative stress in the liver) are linked to a higher risk of fatty liver disease and liver dysfunction (111). Genetic variants involved in detoxification pathways and lipid metabolism may influence a person’s vulnerability to environmental toxins and ability to metabolize and eliminate environmental exposures (112). In environmental exposures, lifestyle factors, including diet, exercise, and alcohol intake, can alter the risk of fatty liver disease. Socioeconomic differences worsen the impacts of environmental pollutants since those from underprivileged backgrounds are more likely to be exposed to pollution at higher levels and to be more vulnerable to its harmful effects on liver health. To reduce the incidence of fatty liver disease and improve liver health across various demographics, targeted therapies and public health initiatives must consider the intricate interactions between host variables and environmental exposures

In sophisticated biological systems such as the liver, characterized by its complex lobular architecture and numerous cell types, each type is assumed to have a unique function, lineage, and molecular profile, and assays of average cell populations adequately represent the fundamental biological processes within individual cells. Genetically identical cells, however, can differ significantly in function and composition, offering novel insights into hepatocellular dynamics. These discrepancies can profoundly affect the cell population’s functionality, contributing to MASLD progression. Mass spectrometry (MS)-based single-cell proteomics (SCP) provides in-depth revelations into cellular heterogeneity and has made remarkable advancements in recent decades. Initially focused on larger cells, recent improvements in MS experimental workflow, proof-of-concept studies, and sample preparation techniques enable the analysis of various cell types (113, 114). Modern SCP techniques can quantify approximately 1,000–1,500 proteins per single cell and up to 2,500 proteins across multiple cells (115). SCP could be highly beneficial in studying MASLD by enabling detailed mapping of protein expression in individual liver cells, thus allowing scientists to gain a deeper understanding of subpopulations of liver cells and identify dysregulated proteins and pathways that contribute to MASLD progression. SCP provides insights into lipid metabolism at the single-cell level. Abnormal lipid biosynthesis and metabolism are central to MASLD progression, and SCP can offer precise characterization of lipid isomers, including those differentiated by C=C bond and sn-position isomerism (115–117). By profiling lipid heterogeneity in liver cells, SCP could identify metabolic dysfunctions linked to MASLD, potentially revealing new biomarkers or therapeutic targets.

Single-cell proteomics presents considerable challenges due to the limited protein quantities and wide dynamic range of protein abundances within individual cells. Unlike nucleic acids, proteins cannot be amplified, necessitating highly sensitive analytical methods. Moreover, the technical demands of single-cell sampling and manipulation are exacerbated by small sample volumes and complex chemical environments, requiring precise extraction of analytes to prevent loss, dilution, or alterations to the cell’s native chemical profile (115). Currently, two key approaches to sample preparation—label-free and multiplexed methods—are undergoing active refinement (118). While challenges persist, such as the limited proteome depth achievable in each cell and the capacity to analyze only a few hundred cells per day, the potential of SCP remains immense (119). As these techniques evolve, SCP is poised to play a transformative role in shaping personalized medicine, advancing diagnostics, and driving therapeutic innovations (114, 118, 119). Label-free proteomics eliminates the need for chemical derivatization, a process that modifies proteins for detection, which can be complex and costly. Instead, it avoids using isotope-labeled reagents, making it more resource-efficient. In contrast, multiplexed single-cell proteomics employs tandem mass tags (TMTs) to label and analyze multiple samples in parallel, increasing throughput.

Another challenge to SCP, as in other omics areas, is standardizing the workflow and data processing to enable reproducible data (120, 121). Grégoire et al. (122) published a chapter detailing the R/Bioconductor package, scp, which provides a consistent framework for SCP data analysis using QFeatures and single-cell experiment structures. The approach includes a detailed protocol covering quality control, data aggregation, normalization, and batch correction, validated with controlled data sets, and fully outlines how to use the SCP package effectively. In a diet-induced MASH mouse model—known for mimicking the key features of human MASH, including steatosis, inflammation, and fibrosis—Ægidius et al. (123) integrated bulk RNA-seq, quantitative proteomics, and single-cell RNA-seq (scRNA-seq). The researchers developed a cell-type-specific map of liver pathology. They highlighted a disconnect between mRNA and protein levels in many cases, underscoring the importance of a multi-omics approach to fully capture the complexity of MASH. This liver single-cell atlas data is the closest clinical manifestation to human MASH. Due to liver inaccessibility, liver SCP studies are minimal. Weinberg et al. (124) stated, “Actual single-cell proteomics of human livers have not been done yet,” but stem cell and organoid technologies coupled with single-cell proteomics may help change that.

Proteogenomics is a multi-omics process that employs next-generation sequencing and mass spectrometry-based proteomics to integrate genomic, transcriptomic, and proteomic data to uncover novel proteins, improve disease processes, and identify potential biomarkers or therapeutic targets (125–127). This methodology bridges the gap between genotypic information and phenotypic protein expression, which enhances our ability to interpret variations in the genome by identifying how these changes manifest at the protein level, thus providing a more comprehensive view of cellular function and disease mechanisms.

Depending on the focus of the study and the available data, workflows are modifiable when integrating proteomics with transcriptomics. Generally, all workflows will normalize proteomic data and then combine the quantitative proteomics data with the quantitative transcriptomic data. Then, various outputs, like differential expression comparisons, network analysis, and functional annotations, allow researchers to gain a clearer picture of how proteins are expressed and how genetic variations influence disease (127, 128), detail the use of genomic data in proteogenomic biomarker discovery via several steps: Initially, the genomic sequencing data is aligned with a reference transcriptome to produce BAM files. Variants identified from these aligned reads are recorded in Variant Call Format (VCF) files and then translated into protein sequences FASTA files. Then, mass spectrometry techniques sequence proteins, employing data-dependent or independent acquisition methods. The resulting mass spectrometry data is matched against a custom-generated protein library based on genomic data. Potential biomarkers identified through this procedure are validated with targeted proteomics or antibody-based assays in extensive cohort studies. Proteogenomics requires sophisticated software tools for data integration, validation, and analysis because of the considerably sizeable datasets generated from DNA, RNA, and protein sequencing. This complexity presents a substantial challenge to the methodology. Conversely, this approach is precious in complex conditions such as MASLD, where genetic and molecular alterations play a critical role in disease progression and may lead to hepatocellular carcinoma (HCC) (129). The ability to identify specific protein variants in patients with MASLD offers essential insights into how these variants contribute to the disease’s advancement.

Research by Ægidius et al. (123) allows us to see how impactful proteogenomics is in their discovery of discrepancies between gene expression and protein levels for key molecules like Rbp4 and Erlin1, which are involved in lipid metabolism and inflammation. This underscores the significance of proteomic data in capturing post-transcriptional regulation, protein turnover, and other factors that influence protein abundance, which are critical for understanding the disease. Furthermore, this study by Ægidius et al. (123) is an excellent example of how the combination of proteogenomics technology enables researchers to capture the multifactorial complexity of the stages of MASLD and identify specific pathways involved in MASH pathogenesis, including those related to lipid metabolism, inflammation, mitochondrial dysfunction, and extracellular matrix production. In addition, Peiseler et al. (130) also validates how recent advances in technologies like proteogenomics have enhanced the understanding of the role immune cells, such as macrophages, T cells, and dendritic cells, play in MASLD. Their review identifies key immune cells contributing to different stages of MASLD progression by interacting with damaged hepatocytes, promoting fibrosis, and even influencing cancer development in MASH-associated HCC (130).