Single-cell pan-omics, environmental neurology, and artificial intelligence: the time for holistic brain health research

The brain, with its trillions of neural connections, different cellular types, and molecular complexities, presents a formidable challenge for researchers aiming to comprehend the multifaceted nature of neural health. As traditional methods have provided valuable insights, emerging technologies offer unprecedented opportunities to delve deeper into the underpinnings of brain function. In the ever-evolving landscape of neuroscience, the quest to unravel the mysteries of the human brain is bound to take a leap forward thanks to new technological improvements and bold interpretative frameworks. Indeed, as our understanding of the intricacies of the human brain advances, so does the need for comprehensive and integrative approaches to studying brain health in a synergistic manner, bridging the gap between biology (e.g., molecular pathways), interactions (e.g., environmental influences), and advanced computational analyses (e.g., explainable artificial intelligence). Brain health, therefore, is not just about understanding brain function in and of itself; it is about comprehending its complex relational networks with the surrounding environment (represented by physiological, psychological, or external stimuli) and putting this information at the service of personalized well-being through precision medicine.

Advancements in whole genome sequencing and single-cell technologies have revolutionized our ability to scrutinize the molecular landscape of individual cells within the brain (Bagger et al., 2024). Techniques such as single-cell RNA sequencing, single-cell epigenomics, and single-cell proteomics enable researchers to capture the heterogeneity that exists among cells at drastically different levels of detail (Zhang et al., 2024). By examining gene expression, epigenetic modifications, and protein profiles at a single-cell resolution, scientists can unravel the intricacies of cellular diversity in the brain (Rizzuti et al., 2023). The application of single-cell omics in brain health research offers insights into cell types, states, and interactions, providing a nuanced understanding of neural development, neurodegenerative disorders, and the molecular dynamics underlying cognitive functions. Additionally, this approach facilitates the identification of rare cell populations and transitional states that may play crucial roles in brain health (Kwok et al., 2024). Indeed, the traditional lens through which the brain was studied could often detect just the general outlines of pathological mechanisms but lacked the resolution to discern finer features. With single-cell omics, we have become able to scrutinize individual cells with unprecedented detail. So, single-cell RNA sequencing, epigenomics, and proteomics enable scientists to decipher the unique molecular signatures of diverse cell types, shedding light on the cellular diversity within the brain but also unraveling the intricate relationship of genes, proteins, and epigenetic modifications that influence our neural landscape. In this scenario, pan-omic approaches applied at the single-cell level (i.e. the study of the totality of specific biological molecules in a collection of brain cells) seem to be the definitive tool to unravel the specificities of brain health and disease, by establishing the paramount contribution of each cell, and cell type alterations, to physiological and pathological states, even allowing the delineation of neuropsychological correlates (i.e. the physical brain structures which activity changes significantly in relation to a psychological change) at cellular detail (Abondio et al., 2024; Groza et al., 2024).

Indeed, the brain does not exist in isolation: it is constantly influenced by the surroundings in which it operates. Polymorphic environmental factors, ranging from pollutants and toxins to lifestyle and socioeconomic status, contribute to the complex interplay that shapes brain health. Environmental neurology recognizes that the well-being of our brains is intricately connected to what happens around us, both in the short and the long term, and seeks to elucidate how these external factors impact the brain at molecular, cellular, and systemic levels (Reis et al., 2023). Studying the environmental determinants of brain health requires interdisciplinary collaboration, incorporating expertise from environmental science, epidemiology, and neuroscience. By identifying and understanding environmental determinants of brain health, researchers can develop targeted interventions and preventive strategies. Integration with single-cell pan-omics provides a further dimension to this holistic perspective, connecting specific molecular alterations with environmental exposures, and paving the way for personalized approaches to brain health conservation. Given that we all live in a hyper-accelerated modernity defined by rapidly changing natural, socioeconomic, industrial, digital, technological, and interpersonal variables, unraveling the immediate impact of socio-environmental factors on brain health is paramount. From air pollution to lifestyle choices, everything leaves an imprint on the brain: delving into the complex interplay between environmental exposures and neurological well-being in extreme detail not only would frame an understanding of the impact of these effectors but would also inform the willful construction of stable environments and restorative interventions that nurture our brain health, rather than causing harm (Hachinski 2023).

The sheer volume and complexity of data generated by single-cell omics and the multidisciplinary approach of environmental neurology necessitate advanced analytical tools. Artificial intelligence (AI), with its capacity for pattern recognition, machine learning, and predictive modeling, emerges as a crucial component in the analysis of high-dimensional multifaceted datasets (Kim et al., 2023). Machine learning algorithms can discern subtle patterns within single-cell data, identifying molecular signatures associated with specific brain states or disease conditions. Integrating environmental data into AI models further enables the identification of complex relationships between environmental exposures and molecular alterations in the brain. Additionally, AI-driven predictive modeling holds the potential to anticipate individualized risk profiles for cognitive decline or neurodegenerative disorders based on a person’s unique molecular and environmental characteristics (Vodovotz 2023). Advanced AI architectures of particular interest include deep-learning models such as transformers, which have been developed primarily for natural language processing and computer vision data, and in this context, they can be used for integrated decision-making processes supported by neuroimaging information (Wang et al., 2024). A characteristic feature of these models implies an attention mechanism, which allows for the simultaneous analysis of all input data by contextualizing each analyzed object among the rest of the provided information and differentially weighting them, so that only the most relevant pieces of data will supply an amplified signal throughout the model. With an explosion of data from single-cell studies and the number of neurological and biopsychosocial factors that can be considered variables (and not merely corrective values in a simplified equation), deep-learning AI responds to the need for a digital structure that helps in making sense of such high-dimensional landscapes. Indeed, machine learning algorithms can spot patterns in the data that human eyes might miss; they can predict how certain environmental factors might influence specific cellular responses, offering a glimpse into the future of personalized brain health (Kalyakulina et al., 2024). This, of course, does not demote the experience, expertise, and decision-making of the clinician, but may be valuable to support an expert opinion in borderline cases.

So, the integration of single-cell pan-omics, environmental neurology, and artificial intelligence (Figure 1) holds the promise to create a powerful synergy that transcends the limitations of individual approaches. This integrated framework allows researchers to examine the molecular signatures of specific cell types in response to environmental stimuli, uncovering subtle but critical alterations that may underlie variations in brain health. For example, the impact of air pollution on neuronal subpopulations can be delineated at a single-cell level, elucidating the differential vulnerability of specific cell types to environmental stressors. A recently developed paradigm in this direction is the study of single-cell exposomics, which systematically investigates cell-environment interactions by modeling both transient and life-long environmental exposures as significant determinants of health at a cellular and molecular level (Tamiz et al., 2022). Environmental and behavioral monitoring may be enacted to assess critical exposures and collect both discrete and continuous data, while an associated -omic biomarker analysis can relay information on the internal exposome and the individual biological response, favoring a holistic interpretation of risk mitigation for public health. This approach may also have wider repercussions on environmental policy through a One Health approach, by elevating the standards for a healthy ecosystem which would in turn favor healthier living conditions in urban and rural areas and, by extension, healthier individuals (Wood et al., 2024). Integrating AI models can then predict the long-term consequences of such exposures, offering insights into potential interventions and personalized strategies to mitigate adverse effects. Ideally, the following paradigm could be delineated: single-cell studies reveal the molecular nuances within specific cell types, environmental neurology identifies the external players influencing the brain, and artificial intelligence determines the relationships that refer to the individual and the community. Indeed, personalized (precision) medicine implies understanding how environmental stressors affect the molecular signatures of different neurons, predicting individualized risks based on a combination of genetic predispositions and environmental exposures, and devising interventions that are unique to each individual or group of people with shared characteristics.

Figure 1.

Integrative framework for holistic brain research.

Schematic representation of the interplay among environmental neurology, artificial intelligence and single-cell omics to synergystically inform brain health. Created with Adobe Illustrator 2020.

While the proposed integrative approach holds great promise, several challenges must be addressed to fully realize its potential. Technical drawbacks in single-cell pan-omics, such as data noise and resolution limitations, require ongoing refinement, as well as standardization of experimental protocols and data analysis pipelines, which is crucial for ensuring comparability across studies, as well as repeatability. Moreover, the sheer size of the data that should be analyzed, integrated, stored, and managed requires physical infrastructures of great magnitude and computational power. While some material obstacles may be easier to bypass to achieve the goal of integrating single-cell omic data with environmental factors through AI, there are also limitations in algorithmic complexity and digital computation that must be considered. Environmental neurology, especially as a new, emerging interdisciplinary field of study at the crossroads of ecology, brain sciences, biology, and public health, faces challenges in accurately quantifying and characterizing diverse environmental exposures and other parameters that may be used as variables, rather than correctors, in AI predictive, generative and explainable models. Developing comprehensive databases that catalog environmental factors and their interactions with genetic and epigenetic determinants of health will be essential for advancing this field. In reference to artificial intelligence, ethical concerns, interpretability of models, and the need for robust validation present ongoing challenges; in particular, ensuring the responsible and transparent use of AI in brain health research is imperative to foster trust in the results and their potential applications.

In conclusion, the integration of single-cell pan-omics, environmental neurology, and artificial intelligence heralds a promising new era in brain health research. A synergistic approach could enable researchers to unravel the connections between molecular determinants, environmental influences, and individualized brain health outcomes. As technology continues to advance, the analyzed framework holds the potential to not only deepen our understanding of brain health but also pave the way for personalized interventions and preventive strategies. By embracing the complexity of the brain and its interactions with the environment, we can move closer to a holistic understanding of brain health, ultimately contributing to the development of targeted therapies for a diverse range of neurological conditions. It is essential to recognize the potentials (and the limitations) of this integrated approach not just in understanding the brain but in reshaping how we perceive and prioritize brain health as a holistic experience for the benefit of the individual, as well as the community.

Additional file: Open peer review report 1 ^{(76.3KB, pdf)} .