Decoding complexity: the need to enhance precision and streamline spatial understanding in neuroscience

Neuroscience is the ultimate frontier in our quest for a comprehensive understanding of human behavior. Since its launch in 2009, the Human Connectome Project has emerged as a pioneering force, making heroic strides in elucidating the intricate correlation between structural information and the functioning of the human brain. However, this ambitious endeavor is just one facet of a broader and ongoing active exploration that seeks to provide insight into the underlying cellular mechanisms that govern the functional dynamics of neural circuits where cellular morphology, function, and gene expression profiles reflect the heterogeneity and diversity of cell types in the central nervous system.

Various nervous system components arise from convergent functional specialization, wherein identical neuronal types are situated across diverse brain regions, underscoring the critical role of spatial information. Innovative methods such as cultured brain organoids have emerged as powerful tools that model critical features of the fetal human brain, including cytoarchitecture and cell diversity (Chiaradia and Lancaster, 2020). In vitro approaches such as these allow researchers to delve into neurons’ developmental and maturation processes, enabling them to decipher the dynamic interplay between specific neuronal types and their neighboring cells through which researchers can gain valuable insights into newly differentiated neurons’ complex organization and orientation.

Conventional analytical techniques, such as bulk sequencing, provide data that reflects the average functional output at a broad scale, which may render observation of intrinsic differences among individual cells elusive. Consequently, gaining insight into the inherent distinctions among subpopulations at a single cellular level becomes imperative for a comprehensive understanding of the dynamics and intricacies of a complex neuronal network. There is a pressing need to transition from bulk-level gene expression profiling to unveiling the subtleties in single-cell transcriptomic profiles in different conditions (disease or control) and ultimately to advanced gene editing applications, leading to the democratization of technologies within the single-cell biology realm. This democratization has significantly expanded our capabilities, empowering researchers to scrutinize and understand the responses of individual cells, including, but not limited to, those beyond the commonly studied two-dimensional model systems. The added spatial perspective enhances our ability to discern the finer details of cellular interactions and enriches the broader view of the cellular landscape within a tissue.

Based on the research question, it is essential to study single cells before exploring the complexity embedded in the network. At the same time, it is imperative to elucidate the function, transcriptomic expression patterns, and responses of cells based on their position to other cell types in different brain regions. This perspective article highlights a few technical challenges and potential solutions that may guide us to determine the extent and pace at which single-cell technology can be incorporated into neuroscience research.

The current reliance on two-dimensional model systems: In neuroscience research, the need for efficient sample preparation techniques has become increasingly evident in recent years. A critical limitation of most recent studies has been the inefficient and challenging process of single-cell isolation from human post-mortem or rodent brain tissues (Piwecka et al., 2023). The current protocols to isolate single cells from brain tissues are often labor-intensive, result in significant structural damage to the cell, and require complex, expensive instrumentation. Most neural cells’ delicate and intricate nature makes it challenging to isolate them without causing damage. In addition, potential data distortion due to cellular stress responses is often triggered while isolating live single cells from tissues, which encourages some researchers to perform single nuclei isolation and sequencing to avoid introducing data bias from broken or ruptured cells.

Considering these limitations, other researchers have adapted to using two-dimensional in-vitro model systems like human-induced pluripotent stem cell (iPSCs) derived cells. iPSCs can be reprogrammed from either skin or blood samples and offer a renewable, personalized source for studying neuronal development, and be subsequently patterned to differentiate into various neuronal cell types by induction with small molecules or upregulation of specific transcription factors to model neurological diseases and their corresponding therapeutic explorations (Mahajani et al., 2019). A two-dimensional model also allows genetic modifications to be performed in a simplified yet controlled and replicable system, thereby facilitating the development of novel treatments for complex neurological disorders.

Based on the research question, human iPSCs-derived cells could offer distinct advantages over cells isolated from post-mortem tissue by enabling researchers to study dynamic processes by observing neuronal development and disease progression over time instead of providing a snapshot of a cell in a fixed, after-the-fact time point.

Importance of selecting stress-free stem cells: Due to the above reasons, researchers often turn to patient-derived iPSCs. These iPSCs serve as a valuable model for studying many neurological disorders, such as patterning them to differentiate into dopaminergic neurons to study the effect of overexpression of an aggregated protein, alpha-synuclein (Mahajani et al., 2019). This enables researchers to elucidate complex relationships between genetic and environmental factors. However, due to the complex makeup of the sample origin, it is imperative to begin such studies with a single clone of iPSCs to maintain genetic homogeneity and avoid mosaicism to ensure the fidelity and relevance of the research outcomes (Chehelgerdi et al., 2023). This crucial step must be revised to ensure the experiment observation and attempt to isolate the effects of specific genetic variations associated with the target neurological condition.

Researchers have recently employed CRISPR/Cas9 genomic editing to create genetic models for diseases. For instance, reports have implicated genetic mutations in the LRRK2 gene to cause both sporadic and familial Parkinson’s disease. The differentiated dopaminergic neurons from CRISPR/Cas9-edited iPSCs demonstrated reduced neurite length and arborization, highlighting the post-translational modification of mutated alpha-synuclein in these neurites (Wakhloo et al., 2022). Moreover, researchers can also correct genetic anomalies in iPSCs to make personalized therapies more accessible. However, due to the variability of editing efficiency and the frequency of off-target effects, isolation and analysis of single clonal populations of the correct genotype(s) are imperative to avoid working with cells with less editing efficiency or containing genetic mutations caused by off-target editing.

Traditional flow cytometry methods for isolating single clones, such as BD Bioscience’s Aria cell sorters, have been extensively utilized. Flow cytometry facilitates the isolation of individual cells based on specific markers, ensuring a relatively homogenous population (Robinson et al., 2023). However, high shear forces correlated to the sorting pressures used with conventional flow cytometry methods can be highly detrimental to iPSCs. The stress induced during sorting, known as sorter induced cell stress, can lead to gene expression changes and cell behavior alterations and compromise the patterning potential, ultimately impacting cell viability and pluripotency (Andra et al., 2020). Furthermore, sorting-induced stress can affect the genetic stability of iPSCs by disrupting the balance of pluripotency, resulting in genetic mutations or the introduction of undesired epigenetic modifications. These alterations compromise the reliability and consistency of iPSCs, posing challenges and risk of misinterpretation of data from downstream applications. Sorter induced cell stress affects a wide range of cell types, including immune cells. For instance, researchers demonstrated the sorting-induced activation of signaling cascades like MAPK p38 in human primary T-cells (Andra et al., 2020). MAPK p38 signaling cascade has been reported to be induced due to environmental stress, which could be misinterpreted as a hallmark of a disease.

After recognizing the limitations of traditional sorting techniques, the field has looked towards innovative methods to address these concerns. Microfluidic sorting platforms like the NX One from Nodexus have emerged as promising sorting and dispensing alternatives. The new wave of cutting-edge technology offers a much gentler approach to sorting by utilizing microfluidic chips that allow for lower sorting pressure, which helps preserve pluripotency, genetic stability, and overall health of iPSCs. The reduced cellular stress associated with microfluidic sorting translates to more reliable and consistent results in downstream applications.

Selecting a single clone of iPSCs while minimizing environmental insults such as sorter induced cell stress is critical in neuroscience research, particularly when investigating patient-derived cells. The variability introduced by mosaicism and genetic heterogeneity within iPSC populations can confound results and hinder the identification of actual disease-associated factors. Adopting advanced methodologies, like the gentle sorting offered by the Nodexus NX One, ensures the better overall health of iPSCs, which should be a prerequisite for all experiments trying to understand the complexities of neurological disorders.

Need to streamline spatial information: Neurons and other cells present in the brain operate within a complex network where their functions and responses are closely tied to their spatial arrangements. As informative as the experiments involving iPSCs-derived neuronal cells can be, it is also essential to establish their relative spatial orientation to each other, the environment, and the localization of abnormally aggregated proteins before one can decipher these cells’ responses to different therapeutic approaches. Techniques like single-cell RNA or single nuclei RNA sequencing are being utilized extensively to identify molecular identities of different cell types in the brain and offer a way for grouping and “mapping” cells according to the phenotype or readout under investigation. This method has also been able to help identify novel neuronal types in distinct brain regions based on their expression profiles (Lake et al., 2016).

The three-dimensional organization of the brain plays a crucial role in shaping its function, as neurons form connections with specific spatial precision, creating complex neuronal circuits (Chiaradia and Lancaster, 2020). The importance of spatial context in neuroscience research would extend across various aspects, including neuronal development, circuitry, connectivity, plasticity, and the disruptions observed in neurological disorders.

The importance of spatial information is much more pronounced when studying neurological disorders like Alzheimer’s disease and Parkinson’s disease. For instance, in the context of Alzheimer’s disease, it has been widely reported that extracellular amyloid plaques and intracellular neurofibrillary tangles (i.e., tau protein aggregates) are observed in various brain regions at different Braak stages of disease progression (Otero-Garcia et al., 2022). Moreover, researchers have also demonstrated the distinct transcriptomic patterns of certain neuronal and glial cell types based on their proximity to an amyloid plaque. It is similar to differential neuronal transcriptomic profiles in the presence or absence of intracellular neurofibrillary tangles (Otero-Garcia et al., 2022).

Spatial context via spatial transcriptomic analysis could be studied using human post-mortem brain tissues in late-stage disorders. However, this strategy cannot be employed for studying brain development and maturation. To achieve this, three-dimensional brain organoids are being used. These simplified versions of the brain have enabled researchers to replicate key features of brain development, maturation, and neuronal circuit formation in a controlled and reproducible environment (Eichmüller and Knoblich, 2022). Nonetheless, there are still certain limitations to this culture system, such as the higher cost of generating organoids, the longer duration required for maturation, and the need for better imaging capabilities.

Considering the spatial heterogeneity evident in disease progression in different brain regions of multiple donors, there exists a need to streamline the spatial information. A recently explored idea of spatial sorting, where researchers can sort cells of interest in a spatial format to study cell-to-cell interaction while maintaining a two-dimensional culture system, has also emerged. With this method, researchers can reproducibly generate a map of where each cell type is sorted (or placed) with higher accuracy rather than depending on the chance of finding these cells close to each other. Current conventional sorting methods are considered non-spatial as there is a need to eliminate the structure of the tissue during processing. Contrary to the existing method, this new approach allows researchers to spatially sort cells of interest, study their interactions in live two-dimensional culture systems, and subject these to transcriptomic analysis if required. This would enable researchers to focus on particular scientific questions rather than perform exploratory research, thereby reducing downstream sequencing costs.

Conclusion: The landscape of neuroscience research is accelerating tremendously. Interpreting data gathered based on conventional two-dimensional model systems with an added spatial perspective is crucial as the field progresses. The importance of incorporating three-dimensional spatial data into neuroscience research cannot be overstated, as it provides a critical piece of information that complements insights obtained from two-dimensional cell culture studies and facilitates our better understanding across various aspects of the field, from studying the complexities of neuronal development, pathfinding, and forming connections and circuits to abnormal disruptions in disease states (Figure 1).

Figure 1.

Iterative neuroscience research cycle, where Nodexus’s NX One enables gentle and efficient single-cell sorting for 2D culture systems and single-cell transcriptomics studies.

Created with BioRender.com.

The shift towards single-cell technologies provides a promising alternative as we navigate the hindrances posed by conventional analytical techniques and the limitations of two-dimensional model systems. Although conventional two-dimensional cultures demonstrate homogeneity, the lack of network complexity must be addressed. Three-dimensional brain organoids have been reported to be a suitable next-generation model system, and the emergence of spatial transcriptomics analysis is the missing link in our quest for a complete understanding of the complex cellular architecture of the human brain.

Researchers constantly choose between traditional methods and innovative solutions in this dynamic landscape. The imperative lies in overcoming technical challenges and embracing and integrating advanced methodologies in their current research framework to answer new questions. As we continue to push the boundaries of neuroscience research, the collaborative efforts of researchers, coupled with cutting-edge technologies, hold the promise of unlocking new dimensions in our understanding of the complexities of the human brain.

We thank Karthik Balakrishnan and Anand Kesavaraju from Nodexus Inc., USA for their support during the revision of this manuscript.