MaltSci 麦伴科研

Appearance

Popular Reports / proteomics

This report is written by MaltSci based on the latest literature and research findings

What are the applications of single-cell proteomics? ​

Abstract ​

Single-cell proteomics (SCP) is a transformative technology that enables the analysis of protein expression at the individual cell level, providing unprecedented insights into cellular heterogeneity and function. This advancement represents a significant shift from traditional bulk proteomics, allowing researchers to explore the complexities of biological systems and disease mechanisms with greater resolution. SCP has shown remarkable applications across multiple domains, including cancer research, where it aids in understanding tumor heterogeneity, identifying unique biomarkers for diagnosis and prognosis, and elucidating treatment resistance mechanisms. In precision medicine, SCP facilitates the development of targeted therapies by revealing specific proteomic signatures associated with different cell types and disease states. Furthermore, SCP plays a critical role in immunology by profiling immune cell populations and their responses to pathogens and therapies, enhancing our understanding of immune dynamics. In neuroscience, SCP contributes to the study of neuronal heterogeneity, neurodegenerative diseases, and brain development, providing insights into the molecular underpinnings of these conditions. The integration of SCP with spatial proteomics and other omics technologies further enriches the understanding of cellular interactions and signaling pathways. Despite its potential, challenges such as data analysis, technology standardization, and sample throughput need to be addressed to fully realize the benefits of SCP. The future of single-cell proteomics is poised for significant advancements, with the promise of transforming our approach to biomedical research and clinical applications.

Outline ​

This report will discuss the following questions.

  • 1 Introduction
  • 2 Overview of Single-Cell Proteomics
    • 2.1 Definition and Importance
    • 2.2 Technologies and Methodologies
  • 3 Applications in Cancer Research
    • 3.1 Tumor Heterogeneity
    • 3.2 Biomarkers for Diagnosis and Prognosis
    • 3.3 Treatment Resistance Mechanisms
  • 4 Applications in Immunology
    • 4.1 Immune Cell Profiling
    • 4.2 Understanding Immune Responses
    • 4.3 Autoimmunity and Inflammation
  • 5 Applications in Neuroscience
    • 5.1 Neuronal Heterogeneity
    • 5.2 Neurodegenerative Diseases
    • 5.3 Brain Development
  • 6 Future Directions and Challenges
    • 6.1 Technological Advancements
    • 6.2 Data Analysis and Interpretation
    • 6.3 Integration with Other Omics
  • 7 Summary

1 Introduction ​

Single-cell proteomics is an innovative and rapidly evolving field that allows for the detailed analysis of protein expression at the individual cell level. This advancement marks a significant departure from traditional proteomics techniques, which typically analyze bulk samples and may obscure the inherent heterogeneity present within cellular populations. The ability to examine the proteomic profiles of single cells offers unprecedented insights into the complexities of biological systems, revealing variations in cellular function, disease mechanisms, and therapeutic responses. As the understanding of cellular heterogeneity deepens, the implications for biomedical research and clinical applications become increasingly profound.

The significance of single-cell proteomics is underscored by its potential to transform our approach to precision medicine. By identifying unique proteomic signatures associated with specific cell types or disease states, researchers can develop more accurate biomarkers for diagnosis and prognosis, tailor therapeutic strategies, and monitor treatment responses more effectively. Recent studies have highlighted the role of single-cell proteomics in various domains, including cancer research, immunology, and neuroscience, demonstrating its capacity to elucidate tumor heterogeneity, immune cell dynamics, and neuronal function [1][2][3].

Current advancements in single-cell proteomics are largely driven by technological innovations in mass spectrometry, microfluidics, and data analysis. These developments have enhanced the sensitivity, throughput, and reproducibility of proteomic analyses, allowing for the quantification of thousands of proteins within single cells [4][5]. Furthermore, the integration of spatial proteomics techniques enables researchers to explore the spatial organization of proteins within tissues, providing a more comprehensive understanding of cellular interactions and functions [1][5].

The organization of this review is structured to provide a comprehensive overview of single-cell proteomics and its applications across various fields. The first section will define single-cell proteomics and discuss its importance, followed by an exploration of the technologies and methodologies that underpin this field. Subsequent sections will delve into specific applications in cancer research, focusing on tumor heterogeneity, biomarkers for diagnosis and prognosis, and mechanisms of treatment resistance. The review will also examine applications in immunology, including immune cell profiling and the understanding of immune responses, as well as the implications for neuroscience, particularly in the study of neuronal heterogeneity and neurodegenerative diseases.

Finally, we will address future directions and challenges facing single-cell proteomics, including technological advancements, data analysis and interpretation, and the integration of multi-omics approaches. By synthesizing current methodologies, key findings, and future perspectives, this review aims to underscore the transformative potential of single-cell proteomics in advancing our understanding of complex biological systems and improving patient outcomes in clinical practice. Through a detailed examination of this field, we hope to highlight the critical role that single-cell proteomics will play in the future of biomedical research and healthcare.

2 Overview of Single-Cell Proteomics ​

2.1 Definition and Importance ​

Single-cell proteomics (SCP) represents a significant advancement in biological research, allowing for the detailed analysis of protein expression and modifications at the individual cell level. This methodology is particularly crucial in understanding the heterogeneity of cell populations, which is often masked in bulk analyses. The applications of single-cell proteomics span various fields, contributing to insights in normal physiology, disease mechanisms, and therapeutic interventions.

One of the primary applications of single-cell proteomics is in cancer research. SCP enables the identification of distinct cellular subpopulations within tumors, which can exhibit varied responses to therapies. For instance, single-cell chemical proteomics (SCCP) has been utilized to investigate the heterogeneous response of cancer cells to anticancer drugs, revealing time-resolved responses that indicate early emergence of subpopulations committed to cell death and those that are not [6]. This capability is essential for developing targeted therapies that consider the unique proteomic profiles of individual cancer cells, thereby enhancing treatment efficacy and minimizing resistance.

In addition to cancer research, single-cell proteomics has applications in precision medicine. By providing comprehensive protein profiling in heterogeneous tissues, SCP can inform the diagnosis and treatment selection for various diseases. The ability to analyze inter- and intracellular signaling pathways at a single-cell resolution aids in elucidating the complex biological processes that underlie disease pathogenesis [7]. For example, advancements in highly multiplexed single-cell protein analysis have allowed for accurate disease diagnosis and the identification of biomarkers that guide therapeutic choices [7].

SCP also plays a vital role in developmental biology, where it aids in understanding cellular differentiation and function during various stages of development. By examining the proteomic landscape of single cells, researchers can gain insights into the regulatory mechanisms that govern cell fate decisions and tissue formation [1]. This is particularly important in the study of stem cells and their differentiation pathways, where understanding the proteomic changes can lead to breakthroughs in regenerative medicine.

Moreover, single-cell proteomics has significant implications in immunology. The technology allows for the detailed characterization of immune cell populations, revealing how different cells respond to pathogens or therapies. This knowledge is critical for the development of vaccines and immunotherapies [5]. SCP can uncover variations in immune cell activation states and functions, contributing to the understanding of autoimmune diseases and chronic inflammatory conditions.

The integration of spatial proteomics with single-cell analysis further enhances the understanding of cellular interactions within their microenvironment. This combination allows researchers to visualize and quantify protein expression in specific tissue locations, providing context to cellular functions and their roles in health and disease [5].

In summary, single-cell proteomics is a transformative tool that provides unparalleled insights into cellular biology. Its applications extend across cancer research, precision medicine, developmental biology, and immunology, making it an essential component of modern biomedical research. The ongoing advancements in SCP methodologies promise to further enhance its utility, enabling researchers to tackle complex biological questions and develop innovative therapeutic strategies.

2.2 Technologies and Methodologies ​

Single-cell proteomics (SCP) has emerged as a pivotal technology in biological research, offering profound insights into cellular processes at an unprecedented resolution. The applications of SCP span various fields, including cancer research, precision medicine, and developmental biology, among others. This technology enables researchers to analyze thousands of proteins within individual cells, thereby facilitating a deeper understanding of cellular heterogeneity, signaling pathways, and disease mechanisms.

One of the primary applications of single-cell proteomics is in cancer research. SCP allows for the identification of rare cell types and the characterization of tumor microenvironments. For instance, highly multiplexed molecular imaging of different protein biomarkers in patient biopsies aids in accurate disease diagnosis and guides treatment selection (Mondal et al. 2018). Moreover, SCP has proven instrumental in studying the heterogeneous responses of cancer cells to therapies, providing insights into cellular subpopulations that are committed or uncommitted to cell death when exposed to anticancer drugs (Végvári et al. 2022).

In the realm of precision medicine, single-cell proteomics enhances our understanding of disease pathogenesis by enabling comprehensive protein profiling in individual cells. This capability allows for the examination of inter- and intracellular signaling pathways and the cellular compositions of normal versus diseased tissues (Ahmad & Budnik 2023). Such insights are critical for developing targeted therapies and personalized treatment strategies.

SCP also plays a significant role in developmental biology, where it helps elucidate the differentiation processes of stem cells and the functional status of various cell types during development (Truong & Kelly 2024). The ability to profile proteins at the single-cell level allows researchers to uncover hidden cell lineages and functions that are often obscured in bulk analyses, thus revealing important biological insights regarding cellular behavior and interactions.

Moreover, the technological advancements in single-cell proteomics have led to improved methodologies, such as label-free and multiplexed approaches, which enhance sensitivity and throughput (Petrosius & Schoof 2023). These innovations enable robust statistical methods for data interpretation and facilitate the integration of single-cell proteomics with other omics technologies, thereby advancing our understanding of complex biological systems (Chen et al. 2020).

In conclusion, the applications of single-cell proteomics are vast and transformative, providing critical insights into cellular heterogeneity, disease mechanisms, and therapeutic responses. As the field continues to evolve, SCP is expected to become an integral component of multi-omics platforms, significantly contributing to our understanding of health and disease.

3 Applications in Cancer Research ​

3.1 Tumor Heterogeneity ​

Single-cell proteomics has emerged as a transformative approach in cancer research, particularly in understanding tumor heterogeneity. This technique enables the detailed analysis of protein expression at the individual cell level, providing insights that are often obscured in bulk analyses. The applications of single-cell proteomics in the context of tumor heterogeneity are multifaceted and significant.

Firstly, single-cell proteomics allows for the identification and characterization of diverse cell populations within tumors. Cancer is known for its extensive cellular heterogeneity, where different subpopulations of cells may exhibit varying behaviors, including differences in proliferation rates, metastatic potential, and responses to therapies. By analyzing protein expression at the single-cell level, researchers can uncover these variations, leading to a more nuanced understanding of tumor biology and progression. For instance, the ability to monitor the heterogeneous expression of surface proteins in circulating tumor cells (CTCs) can reveal insights into the mechanisms of drug resistance and treatment failure [8].

Secondly, single-cell proteomics can enhance the detection of tumor microenvironment interactions. The tumor microenvironment plays a critical role in influencing tumor behavior and therapeutic responses. By employing single-cell proteomics, researchers can assess how different tumor cells interact with their microenvironment and how these interactions contribute to tumor heterogeneity. For example, the analysis of secretory and intracellular proteins in response to various treatments can help identify which cancer cells survive and adapt during therapy, thereby highlighting potential targets for intervention [9].

Additionally, this technology facilitates the development of personalized treatment strategies. As tumor heterogeneity often results in differential responses to therapies, single-cell proteomics can help in the identification of specific biomarkers associated with treatment resistance. By profiling the proteomes of individual cells, researchers can develop prognostic biomarkers that inform treatment decisions and potentially lead to more effective personalized therapies [10].

Moreover, single-cell proteomics is instrumental in studying the dynamics of cancer cell plasticity. Tumor cells can undergo phenotypic changes in response to therapeutic pressures, leading to increased heterogeneity and adaptability. By dissecting the proteomic profiles of individual cells, researchers can gain insights into the molecular mechanisms driving these changes, ultimately aiding in the design of therapies that can preemptively counteract such plasticity [11].

In conclusion, single-cell proteomics represents a powerful tool in cancer research, particularly in elucidating tumor heterogeneity. Its applications extend from identifying diverse cellular populations and understanding microenvironment interactions to developing personalized treatment strategies and studying cancer cell plasticity. As the technology continues to evolve, it holds the potential to significantly enhance our understanding of cancer biology and improve therapeutic outcomes for patients.

3.2 Biomarkers for Diagnosis and Prognosis ​

Single-cell proteomics (SCP) has emerged as a pivotal technology in cancer research, particularly in the discovery and validation of biomarkers for diagnosis and prognosis. This approach allows for the detailed analysis of protein expression at the single-cell level, which is crucial given the heterogeneity observed in cancer cell populations. Traditional bulk proteomics often masks the complexities of individual cell behavior, leading to an incomplete understanding of tumor biology.

One of the primary applications of SCP in cancer research is the identification of biomarkers that can predict patient outcomes. The dysregulated proteome is a significant contributor to carcinogenesis, affecting critical processes such as uncontrolled proliferation, metastasis, and resistance to therapies [10]. By analyzing the proteomic profiles of individual cancer cells, researchers can uncover unique molecular signatures that are indicative of specific cancer types or stages, thereby enhancing diagnostic accuracy.

For instance, SCP has the potential to reveal previously undocumented cell types within the tumor microenvironment, which may serve as novel biomarkers for early detection and risk assessment [12]. The identification of these biomarkers is particularly important for stratifying patients based on their risk of disease progression and tailoring treatment strategies accordingly.

Moreover, SCP facilitates the exploration of cellular states that are influenced by perturbed intracellular signaling pathways, allowing for the identification of functional mutations recurrent in various cancer subsets [13]. This capability is vital for understanding the molecular underpinnings of cancer and for developing targeted therapies that are more likely to succeed based on the specific proteomic landscape of an individual's tumor.

Recent advancements in single-cell mass spectrometry have significantly improved the sensitivity and throughput of proteomic analyses, enabling the detection of thousands of proteins within single cells [2]. This technological progress is crucial for enhancing the quality of data obtained, which in turn supports the development of robust biomarkers for clinical use.

Furthermore, SCP is increasingly being integrated with spatial proteomics, allowing researchers to map the distribution of proteins within the tumor microenvironment. This spatial information can provide insights into how cellular interactions and local environments influence cancer progression and therapeutic responses [5]. Such integrative approaches can lead to the identification of spatially distinct biomarkers that correlate with disease states, thus improving diagnostic and prognostic capabilities.

In summary, the applications of single-cell proteomics in cancer research are vast and transformative. By enabling the identification of precise biomarkers for diagnosis and prognosis, SCP not only enhances our understanding of tumor biology but also paves the way for personalized medicine approaches that can significantly improve patient outcomes in oncology. The ongoing developments in this field promise to further refine the tools available for biomarker discovery and therapeutic target identification, ultimately contributing to more effective cancer management strategies [14].

3.3 Treatment Resistance Mechanisms ​

Single-cell proteomics has emerged as a pivotal technology in cancer research, particularly in elucidating treatment resistance mechanisms. This approach allows for the detailed analysis of protein expression at the individual cell level, providing insights into the heterogeneous nature of cancer cells and their responses to therapies.

One significant application of single-cell proteomics is in the characterization of drug-resistant cancer cells. For instance, a study by Woo et al. (2025) utilized single-cell proteomics to investigate drug-resistant prostate cancer cells, revealing distinct proteomic signatures associated with cellular morphology and therapeutic resistance. The researchers employed a high-throughput data-independent acquisition (DIA) method, allowing them to quantify over 1300 proteins per cell on average, thus identifying unique molecular signatures that could inform therapeutic strategies [15].

Moreover, single-cell proteomics has been instrumental in understanding the mechanisms of radiotherapy resistance. Perico and Mauri (2025) highlighted that various factors such as DNA repair mechanisms, hypoxia, and metabolic reprogramming contribute to radioresistance. By employing proteomic technologies, researchers can identify biomarkers predictive of radiosensitivity and radioresistance, which is crucial for optimizing radiotherapy regimens [16].

Additionally, single-cell proteomics facilitates the exploration of the tumor microenvironment's role in treatment resistance. Cheng et al. (2025) emphasized the importance of tumor heterogeneity and the interactions between cancer cells and their microenvironment in influencing drug efficacy. Single-cell and spatial omics technologies provide high-resolution insights into gene expression and protein interactions, revealing the underlying pathologies and mechanisms that contribute to therapeutic resistance [17].

The integration of single-cell proteomics with other analytical techniques, such as single-cell RNA sequencing, further enhances the understanding of treatment resistance. For example, the application of multi-omic analyses in pediatric high-grade glioma has the potential to uncover novel resistance mechanisms, which could lead to improved therapeutic strategies for this challenging cancer type [18].

In summary, single-cell proteomics is a transformative tool in cancer research, particularly for dissecting the complex mechanisms underlying treatment resistance. By providing detailed insights into the proteomic landscape of individual cancer cells, this technology holds promise for advancing precision medicine and improving treatment outcomes for cancer patients.

4 Applications in Immunology ​

4.1 Immune Cell Profiling ​

Single-cell proteomics is a transformative technology that significantly enhances the understanding of immune cell profiling and the complexities of the immune system. The application of this technology in immunology has several critical dimensions, particularly in the analysis of immune cell diversity, functionality, and interactions within various biological contexts.

One of the primary applications of single-cell proteomics in immunology is the detailed characterization of immune cell populations. Immune cells are inherently diverse, and traditional bulk analysis methods often mask this heterogeneity. Single-cell proteomics enables researchers to profile proteins at the individual cell level, allowing for the assessment of cellular signaling pathways and the identification of specific immune cell subtypes based on their proteomic signatures. This is particularly crucial for understanding the roles of different immune cells in health and disease, including autoimmune disorders and cancer (He et al. 2022; Pham et al. 2021).

Moreover, single-cell proteomics facilitates the exploration of immune responses to various stimuli, such as infections or therapeutic interventions. By quantifying protein expression changes in response to specific challenges, researchers can uncover the mechanisms underlying immune activation, tolerance, and dysfunction. For instance, single-cell analysis can reveal how different immune cells respond to vaccines or immunotherapies, providing insights into the efficacy and mechanisms of action of these treatments (Xie & Ding 2022; Kim et al. 2023).

In the context of cancer immunology, single-cell proteomics allows for the examination of the tumor microenvironment, revealing interactions between tumor cells and immune cells. This application is vital for identifying potential biomarkers for disease progression and therapeutic response, thus aiding in the development of personalized medicine approaches. The ability to analyze immune cell subsets within tumors can inform strategies for enhancing anti-tumor immunity and overcoming resistance to therapies (Jia et al. 2022; Lohani et al. 2023).

Additionally, the integration of single-cell proteomics with other omics technologies, such as single-cell RNA sequencing, enhances the understanding of immune cell dynamics. This multi-omics approach enables a more comprehensive view of cellular functions, revealing how gene expression correlates with protein profiles and how these relationships impact immune cell behavior (Brunner et al. 2022; Zheng 2023).

Furthermore, single-cell proteomics can also be applied to study immune cell development and differentiation. By analyzing protein expression at various stages of immune cell maturation, researchers can gain insights into the regulatory networks that govern immune cell lineage decisions and functional specialization (Efremova et al. 2020; Pham et al. 2021).

In summary, single-cell proteomics is a powerful tool in immunology that facilitates the in-depth profiling of immune cells, enhances the understanding of immune responses, and contributes to the development of targeted therapies. Its applications span from characterizing immune cell diversity to evaluating therapeutic interventions, making it an essential component of modern immunological research.

4.2 Understanding Immune Responses ​

Single-cell proteomics has emerged as a transformative approach in immunology, enabling researchers to gain unprecedented insights into immune responses at the individual cell level. This technique allows for the detailed analysis of protein expression, post-translational modifications, and cellular interactions, which are crucial for understanding the complexity of immune cell behavior in various contexts.

One significant application of single-cell proteomics is in tracking the immune response to pathogens and vaccines. The immune system comprises a diverse array of cell types, each playing specialized roles in orchestrating responses to infections or malignancies. By employing single-cell proteomics, researchers can delineate the activation states and differentiation pathways of individual immune cells in response to stimuli, thereby elucidating how different immune cell populations contribute to the overall immune response. For instance, single-cell proteomics has facilitated the profiling of T cells, allowing scientists to track clonal T cell responses over time and space, which is essential for understanding vaccine-induced immunity and responses to infections [19].

Moreover, single-cell proteomics has proven invaluable in the study of immune cell heterogeneity and plasticity. The immune response is not uniform; rather, it consists of a dynamic spectrum of activation states that reflect the physiological environment and stimuli encountered by the cells. By analyzing proteins at the single-cell level, researchers can uncover the variability in protein expression and signaling pathways among cells of the same type, providing insights into how individual cells may respond differently to the same immunological challenge [20]. This capability is particularly important in the context of immunotherapy, where understanding the heterogeneity of immune cell responses can inform the development of more effective treatment strategies [21].

Additionally, the integration of single-cell proteomics with other omics technologies, such as genomics and transcriptomics, enhances the understanding of immune responses. This multi-omic approach allows for a comprehensive view of how genetic and transcriptomic profiles correlate with protein expression and functional outcomes, thereby revealing intricate networks that govern immune cell behavior [22]. Such insights can lead to the identification of novel biomarkers for disease progression and therapeutic response, ultimately aiding in the personalization of immunotherapies [23].

In summary, single-cell proteomics serves as a powerful tool in immunology, enabling the detailed exploration of immune responses through the characterization of individual cell proteomes. Its applications span from tracking immune responses to understanding cellular heterogeneity and integrating multi-omic data, all of which contribute to advancing the field of immunology and improving clinical outcomes in immunotherapy.

4.3 Autoimmunity and Inflammation ​

Single-cell proteomics (SCP) has emerged as a pivotal tool in immunology, particularly in the study of autoimmunity and inflammation. Its applications are extensive, as it allows researchers to dissect the heterogeneous nature of immune responses at the single-cell level, thereby providing insights into the mechanisms underlying various autoimmune diseases.

One significant application of SCP is in the characterization of cellular heterogeneity within immune cell populations. This is crucial for understanding how genetically and phenotypically identical immune cells can exhibit diverse functions and protein secretion profiles. For instance, microfluidic chip-based SCP enables the detailed characterization of immune responses at different cellular and molecular layers. This technology has demonstrated that polyfunctional T cells, which can produce multiple proteins simultaneously, play a key role in developing potent and durable cellular immunity against pathogens and cancers [24].

Moreover, SCP has been instrumental in elucidating the mechanisms of autoimmune diseases by identifying disease-associated cell states and communication networks among cells. For example, the application of single-cell multiomics techniques has provided insights into the molecular events triggering autoimmune responses, thereby revealing the effector cells involved in these processes [25]. By mapping autoreactive lymphocytes and understanding their interactions, researchers can better comprehend the pathogenesis of autoimmune conditions [25].

In the context of inflammatory diseases, SCP allows for an unbiased analysis of immune cell activation and differentiation. This is particularly relevant in chronic inflammatory conditions where the immune microenvironment is complex. Single-cell analysis can capture the simultaneous immune features of different cell types within inflamed tissues, thereby providing a comprehensive understanding of the immune response in diseases such as inflammatory bowel disease (IBD) [23]. Recent studies utilizing SCP have identified key immune players and explored functional heterogeneity within cellular subsets, enhancing our understanding of the mucosal immune system in health and disease [23].

Additionally, single-cell proteomics facilitates the development of predictive biomarkers for therapeutic responses. By assessing the functional proteomic profiles of immune cells, researchers can identify correlates that are crucial for optimizing immunotherapeutic products and tailoring treatments to individual patients [24]. This personalized approach is vital for improving clinical outcomes in autoimmune and inflammatory diseases.

In summary, the applications of single-cell proteomics in immunology, particularly in the realms of autoimmunity and inflammation, are profound. SCP not only enhances our understanding of cellular heterogeneity and immune mechanisms but also aids in the identification of biomarkers and the development of targeted therapies, ultimately contributing to more effective management of autoimmune conditions.

5 Applications in Neuroscience ​

5.1 Neuronal Heterogeneity ​

Single-cell proteomics (SCP) has emerged as a powerful tool for investigating neuronal heterogeneity, providing insights into the diverse functional states of individual neurons and their roles in health and disease. The ability to analyze proteins at the single-cell level allows researchers to uncover the complexities of neuronal populations that are often masked in bulk analyses.

One significant application of SCP in neuroscience is the study of spatiotemporal heterogeneity among neurons and neural circuits. Recent advancements in single-cell multiomics enable detailed profiling of gene expression, protein interactions, and functional regulations within individual neurons. This capability allows for the classification of neuronal types based on their unique proteomic signatures, which can reveal differences in cellular behavior and responses to stimuli. Such insights are crucial for understanding the underlying mechanisms of neurological disorders and developing targeted therapies (Wang et al. 2024) [26].

Furthermore, SCP facilitates the exploration of cellular responses in the context of neurodegenerative diseases. For instance, it can identify specific proteomic alterations in neuronal populations affected by conditions such as Alzheimer's disease. By comparing the proteomes of neurons derived from patients with genetic mutations associated with neurodegeneration to those from healthy controls, researchers can pinpoint novel therapeutic targets and gain a deeper understanding of disease pathology (Ghatak et al. 2024) [27].

Additionally, SCP is instrumental in dissecting the interactions among various cell types within the brain. By analyzing individual neurons in their native environments, SCP can elucidate how different neuronal subtypes communicate and interact with one another, shedding light on the dynamic processes that govern brain function (Lohani et al. 2023) [5]. This is particularly relevant in the context of developing new therapeutic strategies for neurological disorders, as understanding these interactions may lead to more effective interventions.

Moreover, the application of SCP in studying neuronal responses to external perturbations, such as pharmacological treatments or environmental changes, provides insights into the functional dynamics of neurons. For example, ultra-high sensitivity mass spectrometry techniques enable the quantification of proteomic changes in single neurons following treatment, revealing how specific proteins contribute to neuronal excitability and signaling pathways (Brunner et al. 2022) [28].

In summary, single-cell proteomics offers a comprehensive approach to studying neuronal heterogeneity by enabling the detailed analysis of individual neurons. This technology not only enhances our understanding of neuronal diversity and function but also holds promise for advancing the diagnosis and treatment of neurological diseases through the identification of novel biomarkers and therapeutic targets.

5.2 Neurodegenerative Diseases ​

Single-cell proteomics (SCP) has emerged as a transformative tool in the field of neuroscience, particularly in the study of neurodegenerative diseases. This technology allows for the detailed analysis of protein expression at the single-cell level, providing insights into the cellular heterogeneity and molecular mechanisms underlying various neurodegenerative conditions.

One significant application of SCP in neurodegenerative diseases is the identification of specific proteomic signatures associated with diseased cells. This is crucial for understanding the progression of conditions such as Alzheimer's disease (AD), Parkinson's disease (PD), and Huntington's disease (HD). Traditional proteomic methods often provide an averaged view of protein expression across a population of cells, which can obscure critical differences in cellular responses and disease mechanisms. In contrast, SCP enables researchers to analyze individual cells, thus revealing unique protein expression profiles that are indicative of disease states [5].

Moreover, SCP is instrumental in elucidating the underlying biological pathways involved in neurodegeneration. By combining single-cell proteomics with advanced techniques such as mass spectrometry, researchers can identify differentially expressed proteins and their functional roles in neurodegenerative processes. For instance, a study highlighted the use of SCP to analyze excitatory neurons derived from induced pluripotent stem cells (iPSCs) with AD mutations. This approach allowed for the correlation of proteomic data with electrophysiological changes, leading to the identification of novel therapeutic targets [27].

SCP also plays a vital role in understanding the interactions between neurodegenerative diseases and the immune system. Recent advances in single-cell multiomics approaches have facilitated the exploration of neuro-immune interactions, paving the way for new therapeutic strategies. By applying SCP to patient samples, researchers can investigate the cellular origins of protein changes and their implications for neuroinflammatory responses, thereby enhancing our understanding of the complex interplay between neurodegeneration and immune activation [29].

In addition to elucidating disease mechanisms, SCP holds promise for improving diagnostics and therapeutic interventions in neurodegenerative diseases. The identification of specific biomarkers through single-cell analysis can aid in early diagnosis and monitoring of disease progression. Furthermore, SCP can inform the development of targeted therapies by identifying proteins that are altered in disease states, thereby guiding the design of interventions aimed at restoring normal cellular function [30].

Overall, the applications of single-cell proteomics in the context of neurodegenerative diseases are vast and multifaceted. By providing a granular view of cellular proteomes, SCP is enhancing our understanding of the molecular underpinnings of neurodegeneration, facilitating the discovery of biomarkers, and informing the development of novel therapeutic strategies. As this field continues to evolve, it holds significant potential for transforming the diagnosis and treatment of neurodegenerative disorders.

5.3 Brain Development ​

Single-cell proteomics (SCP) has emerged as a transformative tool in neuroscience, particularly in the study of brain development. This approach allows researchers to analyze the proteomic signatures of individual cells, thereby uncovering insights that are often masked in bulk analyses. SCP facilitates the identification of specific protein expressions linked to various cell types and developmental stages, which is crucial for understanding the complex processes involved in brain development and associated neurological disorders.

One of the primary applications of SCP in neuroscience is the detailed analysis of cellular heterogeneity within the brain. Traditional methods may overlook the diversity of cell populations, but SCP enables the resolution of distinct protein profiles that correspond to specific cell types, including neurons, glial cells, and other supporting cells. This specificity is essential for deciphering the roles of different cell types in brain function and development, as highlighted by Wilson and Nairn (2018), who emphasized the importance of cell-type-specific proteomics in understanding developmental and behavioral disorders [31].

Moreover, SCP has significant implications for elucidating the temporal maturation trajectories of cells during brain development. By employing single-cell analyses, researchers can track how protein expression changes over time, providing insights into the developmental pathways that lead to the formation of mature neural circuits. This aspect is particularly relevant for understanding neurodevelopmental disorders, where deviations in typical protein expression patterns may contribute to pathologies [32].

The integration of SCP with spatial omics technologies further enhances its applicability. Spatial proteomics allows for the examination of protein distributions within the context of the brain's architecture, thereby revealing how spatial organization influences cellular interactions and functions. This integration is pivotal for understanding the complex interplay between different cell types in developing brain tissue and how these interactions contribute to overall brain health and disease [2].

In the context of specific neurological conditions, SCP can be utilized to identify proteomic changes associated with diseases such as Alzheimer's. For instance, novel methodologies combining single-cell patch-clamp techniques with proteomics have been developed to study excitatory neurons derived from induced pluripotent stem cells (iPSCs) carrying Alzheimer's-related mutations. This innovative approach not only provides insights into the electrophysiological characteristics of these neurons but also correlates these findings with proteomic data, thus identifying potential therapeutic targets [27].

Furthermore, SCP enables researchers to explore gene-environment interactions at a single-cell level, offering a deeper understanding of how external factors influence brain development and contribute to neurodiversity. The concept of "in vitro epidemiology," which leverages brain organoids and multiplexing approaches, exemplifies how SCP can model population-scale cohorts and dissect the molecular underpinnings of neurodevelopment [33].

In summary, single-cell proteomics is revolutionizing our understanding of brain development by providing a high-resolution view of cellular heterogeneity, maturation trajectories, and the spatial organization of proteins within the brain. These advancements are paving the way for new therapeutic strategies and a better understanding of the molecular basis of neurological disorders. The ongoing development of SCP methodologies promises to further enhance our ability to address complex biological questions in neuroscience.

6 Future Directions and Challenges ​

6.1 Technological Advancements ​

Single-cell proteomics (SCP) has emerged as a transformative approach in biological research, enabling the analysis of protein expression at the individual cell level. This capability is critical for understanding cellular heterogeneity, disease mechanisms, and the functional status of cells in various biological contexts. The applications of SCP are vast and continue to expand as technological advancements progress.

One significant application of single-cell proteomics is in the characterization of cellular heterogeneity within tissues. Traditional bulk measurements often obscure the variations present among individual cells, but SCP allows for a comprehensive understanding of how different cell types contribute to overall tissue function and disease pathology. For instance, SCP has been employed to identify rare cell types and characterize the distinct proteomic profiles associated with various states of cellular differentiation and response to stimuli [1].

Furthermore, SCP is increasingly being utilized in precision medicine, where it plays a crucial role in diagnosing diseases and guiding treatment decisions. By providing detailed protein profiles from individual cells within patient biopsies, SCP can reveal specific biomarkers that inform the selection of targeted therapies [7]. The high multiplexing capabilities of recent SCP technologies enable the simultaneous analysis of multiple protein biomarkers, enhancing diagnostic accuracy [5].

In addition to clinical applications, SCP is pivotal in developmental biology, where it helps elucidate the dynamic processes governing cell differentiation and organism development. This approach allows researchers to track changes in protein expression during critical developmental stages, thereby providing insights into normal physiological processes and potential developmental disorders [5].

As for future directions, the field of single-cell proteomics is poised for significant advancements driven by ongoing technological innovations. Recent developments in mass spectrometry, including improvements in sensitivity, throughput, and multiplexing capabilities, are expected to enhance the scope and resolution of proteomic analyses [2]. The integration of SCP with other omics technologies, such as single-cell transcriptomics and spatial proteomics, will likely lead to more comprehensive multi-omics platforms that can provide deeper biological insights [34].

However, several challenges remain that the scientific community must address to fully realize the potential of single-cell proteomics. Key among these is the need for standardized protocols and robust statistical methods for data interpretation, which are essential for ensuring the reproducibility and verifiability of findings [1]. Additionally, enhancing sample throughput while maintaining high sensitivity and resolution is crucial for broadening the applicability of SCP in both basic research and clinical settings [3].

In conclusion, single-cell proteomics holds great promise for advancing our understanding of complex biological systems and disease mechanisms. As technological advancements continue to emerge, the applications of SCP are expected to expand, paving the way for novel discoveries in biology and medicine. Addressing the existing challenges will be critical for the successful integration of SCP into routine research and clinical practice.

6.2 Data Analysis and Interpretation ​

Single-cell proteomics (SCP) has emerged as a powerful tool in biomedical research, enabling the analysis of protein expression at the individual cell level. This advancement allows for a more nuanced understanding of cellular heterogeneity and the underlying biological processes, with numerous applications across various fields.

Applications of single-cell proteomics are extensive. SCP has shown significant promise in cancer research, where it can help identify unique proteomic signatures associated with different tumor types and stages, providing insights into disease mechanisms and potential therapeutic targets. Additionally, SCP facilitates biomarker discovery, allowing for the identification of proteins that can serve as indicators of disease progression or response to treatment. In developmental biology, SCP aids in understanding cellular differentiation processes by profiling proteins in specific cell types at various developmental stages, thus elucidating the pathways involved in cell lineage decisions[2].

Furthermore, the integration of spatial proteomics with single-cell resolution enhances our understanding of cellular interactions within tissues, enabling researchers to study how microenvironments influence cellular behavior and phenotypic diversity[1]. The combination of SCP with multi-omics approaches allows for a comprehensive exploration of cellular signaling processes, incorporating data from transcriptomics and metabolomics to provide a holistic view of cellular function[34].

Despite the promising applications, there are significant challenges and future directions that need to be addressed in the field of single-cell proteomics. One of the primary challenges is the need for robust statistical methods for data interpretation, as working with small sample volumes can lead to data sparsity and variability[1]. Additionally, current SCP technologies often struggle with issues such as batch effects, high noise levels, and limited throughput, which can hinder the analysis and reproducibility of results[35].

To overcome these challenges, there is a pressing need for the development of standardized protocols and comprehensive databases that facilitate data sharing and accessibility[36]. Initiatives like the Single-cell Proteomic DataBase (SPDB) aim to provide a centralized resource for single-cell proteomic data, enhancing the ability to conduct comparative studies and validate findings across different research groups[36].

Moreover, advancements in computational tools, such as SCPline, which offers a framework for data preprocessing and analysis, are essential for improving the accessibility of SCP technologies to researchers with varying levels of computational expertise[37]. Future research should focus on enhancing sample throughput and sensitivity, allowing for the analysis of larger populations of single cells to enable biological applications beyond proof-of-concept[3].

In summary, single-cell proteomics holds great potential for advancing our understanding of cellular biology and disease mechanisms. However, to fully realize its capabilities, ongoing efforts must be directed towards addressing current limitations in data analysis, interpretation, and technology standardization.

6.3 Integration with Other Omics ​

Single-cell proteomics (SCP) has emerged as a powerful tool in the study of biological systems, particularly due to its ability to reveal phenotypic heterogeneity and provide insights into individual cell states and functional outcomes upon signaling activation. This level of analysis is critical for understanding complex biological processes, disease mechanisms, and cellular responses that cannot be effectively studied using bulk analyses. The applications of single-cell proteomics span various domains, including disease modeling, biomarker identification, and therapeutic development.

One of the primary applications of SCP is in the realm of disease research, where it enables the identification of unique protein expression profiles associated with specific cellular states in diseases such as cancer, neurodegenerative disorders, and cardiovascular diseases. For instance, SCP has facilitated the characterization of different cell states in tumor microenvironments, allowing for the identification of novel therapeutic targets and biomarkers that can guide precision medicine approaches [38].

In addition to disease research, SCP is also pivotal in developmental biology and stem cell research, where it helps elucidate the dynamics of cell differentiation and lineage tracing. By profiling individual cells at the proteomic level, researchers can gain insights into the regulatory networks governing cell fate decisions and the interactions between different cell types within a developing tissue [39].

Furthermore, SCP is increasingly integrated with other omics technologies, such as genomics, transcriptomics, and metabolomics, to provide a comprehensive understanding of cellular function and regulation. This integration allows for the simultaneous analysis of multiple molecular modalities from the same single cell, revealing complex interactions between proteins, RNAs, and metabolites that govern cellular behavior [40]. The combination of single-cell proteomics with spatial transcriptomics, for example, enables researchers to explore the spatial organization of proteins within tissues while correlating these findings with gene expression patterns [41].

Despite its promise, several challenges remain in the field of single-cell proteomics. The sensitivity and resolution of current SCP methods need to be improved to detect low-abundance proteins and to minimize sample loss during analysis. Additionally, the integration of multi-omics data poses computational challenges, as researchers must develop robust algorithms to handle the complexity and volume of data generated from single-cell analyses [42].

Future directions in single-cell proteomics are likely to focus on enhancing methodological advancements, such as automated sample preparation and the development of high-throughput platforms. These improvements aim to increase the sensitivity, robustness, and reproducibility of SCP techniques, thereby expanding their applications in clinical settings [38]. Moreover, ongoing efforts to integrate SCP with other omics approaches will continue to enhance our understanding of cellular systems, leading to breakthroughs in biomarker discovery and targeted therapies [43].

In conclusion, single-cell proteomics stands at the forefront of modern biological research, with applications that span disease characterization, developmental biology, and precision medicine. Its integration with other omics technologies holds the potential to unlock new insights into cellular dynamics and regulatory mechanisms, although addressing existing challenges will be crucial for its continued advancement.

7 Conclusion ​

Single-cell proteomics (SCP) has emerged as a groundbreaking technology that revolutionizes our understanding of biological systems by allowing for the analysis of protein expression at the individual cell level. Its applications span across diverse fields, including cancer research, precision medicine, immunology, and neuroscience, each benefiting from the detailed insights into cellular heterogeneity and function that SCP provides. Key findings highlight the ability of SCP to uncover tumor heterogeneity, identify novel biomarkers for diagnosis and prognosis, elucidate mechanisms of treatment resistance, and enhance our understanding of immune responses and neuronal diversity. The current landscape of single-cell proteomics is characterized by rapid technological advancements, particularly in mass spectrometry and data analysis, which are enhancing the sensitivity and throughput of proteomic analyses. However, challenges such as data interpretation, integration with other omics, and the need for standardized protocols remain. Future research directions should focus on addressing these challenges, promoting the integration of multi-omics approaches, and expanding the clinical applications of SCP. The ongoing evolution of this field holds significant promise for advancing precision medicine and improving patient outcomes across various diseases.

References ​

  • [1] Rushdy Ahmad;Bogdan Budnik. A review of the current state of single-cell proteomics and future perspective.. Analytical and bioanalytical chemistry(IF=3.8). 2023. PMID:37285026. DOI: 10.1007/s00216-023-04759-8.
  • [2] Thy Truong;Ryan T Kelly. What's new in single-cell proteomics.. Current opinion in biotechnology(IF=7.0). 2024. PMID:38359605. DOI: 10.1016/j.copbio.2024.103077.
  • [3] Alyssa A Nitz;Jose Humberto Giraldez Chavez;Zachary G Eliason;Samuel H Payne. Are We There Yet? Assessing the Readiness of Single-Cell Proteomics to Answer Biological Hypotheses.. Journal of proteome research(IF=3.6). 2025. PMID:38981598. DOI: 10.1021/acs.jproteome.4c00091.
  • [4] Benjamin C Orsburn;Yuting Yuan;Namandjé N Bumpus. Insights into protein post-translational modification landscapes of individual human cells by trapped ion mobility time-of-flight mass spectrometry.. Nature communications(IF=15.7). 2022. PMID:36433961. DOI: 10.1038/s41467-022-34919-w.
  • [5] Vartika Lohani;Akhiya A R;Soumen Kundu;Md Quasid Akhter;Swarnendu Bag. Single-Cell Proteomics with Spatial Attributes: Tools and Techniques.. ACS omega(IF=4.3). 2023. PMID:37251119. DOI: 10.1021/acsomega.3c00795.
  • [6] Ákos Végvári;Jimmy E Rodriguez;Roman A Zubarev. Single-Cell Chemical Proteomics (SCCP) Interrogates the Timing and Heterogeneity of Cancer Cell Commitment to Death.. Analytical chemistry(IF=6.7). 2022. PMID:35731985. DOI: 10.1021/acs.analchem.2c00413.
  • [7] Manas Mondal;Renjie Liao;Jia Guo. Highly Multiplexed Single-Cell Protein Analysis.. Chemistry (Weinheim an der Bergstrasse, Germany)(IF=3.7). 2018. PMID:29194810. DOI: 10.1002/chem.201705014.
  • [8] K Kamil Reza;Shuvashis Dey;Alain Wuethrich; Jing Wang;Andreas Behren;Fiach Antaw;Yuling Wang;Abu Ali Ibn Sina;Matt Trau. In Situ Single Cell Proteomics Reveals Circulating Tumor Cell Heterogeneity during Treatment.. ACS nano(IF=16.0). 2021. PMID:34225455. DOI: 10.1021/acsnano.0c10008.
  • [9] Yugyung Jung;Minkook Son;Yu Ri Nam;Jongchan Choi;James R Heath;Sung Yang. Microfluidic Single-Cell Proteomics Assay Chip: Lung Cancer Cell Line Case Study.. Micromachines(IF=3.0). 2021. PMID:34683198. DOI: 10.3390/mi12101147.
  • [10] Maomao Li;Jing Zuo;Kailin Yang;Ping Wang;Shengtao Zhou. Proteomics mining of cancer hallmarks on a single-cell resolution.. Mass spectrometry reviews(IF=6.6). 2024. PMID:37051664. DOI: 10.1002/mas.21842.
  • [11] Deshen Pan;Deshui Jia. Application of Single-Cell Multi-Omics in Dissecting Cancer Cell Plasticity and Tumor Heterogeneity.. Frontiers in molecular biosciences(IF=4.0). 2021. PMID:34722635. DOI: 10.3389/fmolb.2021.757024.
  • [12] Jacob Kagan;Robert L Moritz;Richard Mazurchuk;Je Hyuk Lee;Peter Vasili Kharchenko;Orit Rozenblatt-Rosen;Eytan Ruppin;Fredrik Edfors;Fiona Ginty;Yury Goltsev;James A Wells;John LaCava;Jessica L Riesterer;Ronald N Germain;Tujin Shi;Mark S Chee;Bogdan A Budnik;John R Yates;Brian T Chait;Jeffery R Moffitt;Richard D Smith;Sudhir Srivastava. National Cancer Institute Think-Tank Meeting Report on Proteomic Cartography and Biomarkers at the Single-Cell Level: Interrogation of Premalignant Lesions.. Journal of proteome research(IF=3.6). 2020. PMID:32163288. DOI: 10.1021/acs.jproteome.0c00021.
  • [13] Sonia Gavasso;Stein-Erik Gullaksen;Jørn Skavland;Bjørn T Gjertsen. Single-cell proteomics: potential implications for cancer diagnostics.. Expert review of molecular diagnostics(IF=3.6). 2016. PMID:26895397. DOI: 10.1586/14737159.2016.1156531.
  • [14] Yong Chiang Tan;Teck Yew Low;Pey Yee Lee;Lay Cheng Lim. Single-cell proteomics by mass spectrometry: Advances and implications in cancer research.. Proteomics(IF=3.9). 2024. PMID:38727198. DOI: 10.1002/pmic.202300210.
  • [15] Jongmin Woo;Michael Loycano;Md Amanullah;Jiang Qian;Sarah R Amend;Kenneth J Pienta;Hui Zhang. Single-Cell Proteomic Characterization of Drug-Resistant Prostate Cancer Cells Reveals Molecular Signatures Associated with Morphological Changes.. Molecular & cellular proteomics : MCP(IF=5.5). 2025. PMID:40090465. DOI: 10.1016/j.mcpro.2025.100949.
  • [16] Davide Perico;Pierluigi Mauri. Deciphering Radiotherapy Resistance: A Proteomic Perspective.. Proteomes(IF=3.6). 2025. PMID:40559998. DOI: 10.3390/proteomes13020025.
  • [17] Xiaoxia Cheng;Ting Peng;Tian Chu;Yiqun Yang;Jia Liu;Qinglei Gao;Canhui Cao;Juncheng Wei. Application of single-cell and spatial omics in deciphering cellular hallmarks of cancer drug response and resistance.. Journal of hematology & oncology(IF=40.4). 2025. PMID:40605007. DOI: 10.1186/s13045-025-01722-1.
  • [18] Rebecca L Murdaugh;Jamie N Anastas. Applying single cell multi-omic analyses to understand treatment resistance in pediatric high grade glioma.. Frontiers in pharmacology(IF=4.8). 2023. PMID:37205910. DOI: 10.3389/fphar.2023.1002296.
  • [19] Kathryn E Yost;Howard Y Chang;Ansuman T Satpathy. Tracking the immune response with single-cell genomics.. Vaccine(IF=3.5). 2020. PMID:31859202. DOI: 10.1016/j.vaccine.2019.11.035.
  • [20] Peer W F Karmaus. Application of Single Cell Methods in Immunometabolism and Immunotoxicology.. Current opinion in toxicology(IF=3.6). 2024. PMID:39091379. DOI: 10.1016/j.cotox.2024.100488.
  • [21] Qingzhu Jia;Han Chu;Zheng Jin;Haixia Long;Bo Zhu. High-throughput single-сell sequencing in cancer research.. Signal transduction and targeted therapy(IF=52.7). 2022. PMID:35504878. DOI: 10.1038/s41392-022-00990-4.
  • [22] Martijn Cordes;Karin Pike-Overzet;Erik B Van Den Akker;Frank J T Staal;Kirsten Canté-Barrett. Multi-omic analyses in immune cell development with lessons learned from T cell development.. Frontiers in cell and developmental biology(IF=4.3). 2023. PMID:37091971. DOI: 10.3389/fcell.2023.1163529.
  • [23] Hengqi Betty Zheng. Application of single-cell omics in inflammatory bowel disease.. World journal of gastroenterology(IF=5.4). 2023. PMID:37576705. DOI: 10.3748/wjg.v29.i28.4397.
  • [24] Weiming Ni;Edward X Han;Matthew Cyr;Sean Mackay;Jing Zhou. Applying single-cell highly multiplexed secretome proteomics to characterize immunotherapeutic products and predict clinical responses.. Proteomics(IF=3.9). 2023. PMID:36786585. DOI: 10.1002/pmic.202200242.
  • [25] Altea Gjurgjaj;Cecilia Domínguez Conde. Narrowing Down Key Players in Autoimmunity via Single-Cell Multiomics.. European journal of immunology(IF=3.7). 2025. PMID:40534591. DOI: 10.1002/eji.202451233.
  • [26] William Wang;Xuanqi Liu;Diane Catherine Wang. Single-cell and spatial alterations of neural cells and circuits in clinical and translational medicine.. Clinical and translational medicine(IF=6.8). 2024. PMID:38812092. DOI: 10.1002/ctm2.1696.
  • [27] Swagata Ghatak;Jolene K Diedrich;Maria Talantova;Nivedita Bhadra;Henry Scott;Meetal Sharma;Matthew Albertolle;Nicholas J Schork;John R Yates;Stuart A Lipton. Single-Cell Patch-Clamp/Proteomics of Human Alzheimer's Disease iPSC-Derived Excitatory Neurons Versus Isogenic Wild-Type Controls Suggests Novel Causation and Therapeutic Targets.. Advanced science (Weinheim, Baden-Wurttemberg, Germany)(IF=14.1). 2024. PMID:38773714. DOI: 10.1002/advs.202400545.
  • [28] Andreas-David Brunner;Marvin Thielert;Catherine Vasilopoulou;Constantin Ammar;Fabian Coscia;Andreas Mund;Ole B Hoerning;Nicolai Bache;Amalia Apalategui;Markus Lubeck;Sabrina Richter;David S Fischer;Oliver Raether;Melvin A Park;Florian Meier;Fabian J Theis;Matthias Mann. Ultra-high sensitivity mass spectrometry quantifies single-cell proteome changes upon perturbation.. Molecular systems biology(IF=7.7). 2022. PMID:35226415. DOI: 10.15252/msb.202110798.
  • [29] Rotem Shalita;Ido Amit. The industrial genomic revolution: A new era in neuroimmunology.. Neuron(IF=15.0). 2022. PMID:36257321. DOI: 10.1016/j.neuron.2022.09.030.
  • [30] Ramavati Pal;Guido Alves;Jan Petter Larsen;Simon Geir Møller. New insight into neurodegeneration: the role of proteomics.. Molecular neurobiology(IF=4.3). 2014. PMID:24323427. DOI: 10.1007/s12035-013-8590-8.
  • [31] Rashaun S Wilson;Angus C Nairn. Cell-Type-Specific Proteomics: A Neuroscience Perspective.. Proteomes(IF=3.6). 2018. PMID:30544872. DOI: 10.3390/proteomes6040051.
  • [32] Niklas Binder;Ashkan Khavaran;Roman Sankowski. Primer on machine learning applications in brain immunology.. Frontiers in bioinformatics(IF=3.9). 2025. PMID:40313869. DOI: 10.3389/fbinf.2025.1554010.
  • [33] Manuel Lessi;Nicolò Caporale;Giuseppe Testa. From Cells to Selves and Back: Human Neurodiversity at Single-Cell Resolution.. Physiology (Bethesda, Md.)(IF=10.3). 2026. PMID:40531508. DOI: 10.1152/physiol.00010.2025.
  • [34] Haiyang Xie;Xianting Ding. The Intriguing Landscape of Single-Cell Protein Analysis.. Advanced science (Weinheim, Baden-Wurttemberg, Germany)(IF=14.1). 2022. PMID:35199955. DOI: 10.1002/advs.202105932.
  • [35] Wei Li;Fan Yang;Fang Wang;Yu Rong;Linjing Liu;Bingzhe Wu;Han Zhang;Jianhua Yao. scPROTEIN: a versatile deep graph contrastive learning framework for single-cell proteomics embedding.. Nature methods(IF=32.1). 2024. PMID:38504113. DOI: 10.1038/s41592-024-02214-9.
  • [36] Fang Wang;Chunpu Liu;Jiawei Li;Fan Yang;Jiangning Song;Tianyi Zang;Jianhua Yao;Guohua Wang. SPDB: a comprehensive resource and knowledgebase for proteomic data at the single-cell resolution.. Nucleic acids research(IF=13.1). 2024. PMID:37953313. DOI: 10.1093/nar/gkad1018.
  • [37] Shangwei Guo;Shengming Zhou;Guohua Wang;Fang Wang. SCPline: An interactive framework for the single-cell proteomics data preprocessing.. Briefings in bioinformatics(IF=7.7). 2025. PMID:40462514. DOI: 10.1093/bib/bbaf256.
  • [38] Sofani Tafesse Gebreyesus;Gul Muneer;Chih-Cheng Huang;Asad Ali Siyal;Mihir Anand;Yu-Ju Chen;Hsiung-Lin Tu. Recent advances in microfluidics for single-cell functional proteomics.. Lab on a chip(IF=5.4). 2023. PMID:36811978. DOI: 10.1039/d2lc01096h.
  • [39] Valdemaras Petrosius;Erwin M Schoof. Recent advances in the field of single-cell proteomics.. Translational oncology(IF=4.1). 2023. PMID:36270102. DOI: 10.1016/j.tranon.2022.101556.
  • [40] Xuefei Wang;Xinchao Wu;Ni Hong;Wenfei Jin. Progress in single-cell multimodal sequencing and multi-omics data integration.. Biophysical reviews(IF=3.7). 2024. PMID:38495443. DOI: 10.1007/s12551-023-01092-3.
  • [41] Sabine Seeler;Kristjan Arnarsson;Martina Dreßen;Markus Krane;Stefanie A Doppler. Beyond the Heartbeat: Single-Cell Omics Redefining Cardiovascular Research.. Current cardiology reports(IF=3.3). 2024. PMID:39158785. DOI: 10.1007/s11886-024-02117-3.
  • [42] Sooyeon Kim;Latiefa Kamarulzaman;Yuichi Taniguchi. Recent methodological advances towards single-cell proteomics.. Proceedings of the Japan Academy. Series B, Physical and biological sciences(IF=4.6). 2023. PMID:37673661. DOI: 10.2183/pjab.99.021.
  • [43] Tatsuya Nobori. Exploring the untapped potential of single-cell and spatial omics in plant biology.. The New phytologist(IF=8.1). 2025. PMID:40398874. DOI: 10.1111/nph.70220.

MaltSci Intelligent Research Services ​

Search for more papers on MaltSci.com ​

Single-cell proteomics · Tumor heterogeneity · Biomarkers · Immune cell profiling · Neuronal heterogeneity

© 2025 MaltSci