Single-Cell RNA Sequencing of Brain Tissue Samples from Severe Traumatic Brain Injury Patients: A Protocol for Cellular Heterogeneity and Transcriptional Alteration Study

Abstract

Background

Severe traumatic brain injury (sTBI) leads to significant morbidity and mortality, often complicated by cerebral edema and raised intracranial pressure (ICP). Understanding the molecular impact of these pathophysiological changes on the injured and adjacent noninjured brain is crucial for improving patient outcomes. Single-cell ribonucleic acid sequencing (scRNA-seq) offers a high-resolution approach to studying cellular heterogeneity and transcriptional alterations in TBI.

Objective

This study aims to utilize scRNA-seq to analyze injured and noninjured brain tissue in patients with sTBI, identify differentially expressed genes, and characterize the cellular response to injury and its relation to ICP.

Materials and Methods

A cross-sectional study will be conducted, and brain tissue samples will be collected from sTBI patients. Tissue samples will be obtained during decompressive craniectomy, processed, and analyzed using the 10× Chromium system. Libraries will be sequenced on Illumina NovaSeq 6000, and transcriptomic data will be analyzed using Seurat and other bioinformatics tools. Bulk RNA sequencing will be performed for validation.

Results

This study is expected to identify unique gene expression patterns associated with cerebral edema, characterize cellular responses in injured versus noninjured brain regions, and provide insights into molecular pathways contributing to secondary brain injury.

Conclusion

By leveraging scRNA-seq, this study will provide a deeper understanding of the transcriptomic changes in sTBI. The findings may aid in discovering novel biomarkers and therapeutic targets, ultimately improving clinical management strategies for TBI patients.

Keywords

severe traumatic brain injury - single-cell RNA sequencing - cerebral edema - intracranial pressure - transcriptomics - biomarker discovery

Introduction

Traumatic brain injury (TBI) is a major global health problem and a leading cause of morbidity and mortality worldwide.[1] In India, TBI mortality remains significantly high compared with global averages.[2] TBI can result in significant neurological deficits, posing social and economic burdens on patients and their caregivers.[3] Cerebral edema (CE) and the subsequent elevation of intracranial pressure (ICP) are major clinical concerns in the management of TBI, frequently leading to secondary brain injury.[4]

CE and the subsequent elevation of ICP are major clinical concerns in the management of TBI, frequently leading to secondary brain injury. CE following TBI is a complex, multifactorial, and heterogeneous process. Its development involves several key factors, including disruption of the blood–brain barrier (BBB), ionic imbalances, and inflammatory responses, all of which contribute to increased ICP and a reduction in cerebral perfusion pressure, potentially resulting in secondary brain damage. While the molecular mechanisms underlying CE in TBI are progressively being elucidated, a comprehensive understanding remains elusive. The clinical significance of the relationship between CE, intracranial hypertension, and functional outcomes in TBI has long been recognized. However, current therapeutic strategies are largely nonspecific and reactive, and a deeper understanding of the precise molecular mechanisms driving CE and its effects on the surrounding noninjured brain tissue is needed.[4] [5] [6] Although advances in multimodal monitoring and imaging techniques have provided valuable insights into TBI, these methods often fall short in capturing the intricate cellular heterogeneity and transcriptomic alterations that characterize CE. This limitation underscores the importance of developing targeted therapies that can effectively address the molecular contributors and pathways involved in CE.

Single-cell ribonucleic acid sequencing (scRNA-seq) has emerged as a powerful tool to dissect the complex cellular and molecular changes following TBI at an unprecedented resolution. By enabling the examination of individual cells, scRNA-seq overcomes the limitations of bulk RNA sequencing, revealing cellular heterogeneity, identifying rare cell types, and uncovering dynamic shifts in cellular phenotypes.[7] Recent studies employing scRNA-seq in TBI research have provided novel insights into the condition's neuropathology.[8] [9] [10] [11] Experimental studies have identified key cell types and transcriptional changes associated with TBI,[10] uncovered enhanced noncanonical neurotrophic factor signaling in the subacute phase of TBI,[8] and mapped blast-induced TBI in the mouse hippocampus.[9] These studies highlight the capacity of scRNA-seq to refine our understanding of TBI's complex mechanisms and identify potential therapeutic targets.

This protocol outlines the methodology adopted for scRNA-seq to analyze human brain tissue from severe TBI (sTBI) patients. Our approach focuses on characterizing the differences in cellular heterogeneity between injured and adjacent noninjured tissue. This study has the potential to identify therapeutic targets for improving outcomes following TBI.

Objectives

1. ScRNA-seq-based cellular and transcriptional characterization of injured and adjacent noninjured brain tissue following sTBI.

2. To delineate differential gene expression patterns and identify distinct cellular subpopulations and molecular signatures associated with sTBI pathophysiology.

3. To explore the potential relationship between these cellular responses and gene expression patterns and ICP following sTBI.

Materials and Methods

Study Design

Cross-sectional study.

Study Site

In this prospective study, all patients with sTBI admitted to the Department of Neurosurgery of Jai Prakash Narayan Apex Trauma Center (JPNATC), All India Institute of Medical Sciences (AIIMS), New Delhi, India, will be screened for recruitment. The scRNA-seq/single-nucleus RNA (snRNA) samples will be analyzed by the CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), New Delhi, India.

Sample Collection and Categorization

Brain tissue samples will be collected from patients undergoing craniectomy following sTBI. Samples will be categorized into three groups to investigate the cellular response to injury:

1. Injured tissue: Brain tissue obtained from the primary injury site during craniectomy.

2. Surrounding noninjured tissue: Samples of brain tissue adjacent to the primary injury site, with no obvious traumatic injury.

3. Edematous tissue: Brain tissue samples will be obtained from noninjured areas during secondary decompressive craniectomy for ICP.

Sample Size

Determining an appropriate sample size is crucial in scRNA-seq studies to ensure sufficient statistical power for detecting meaningful biological differences. The calculation of sample size in scRNA-seq studies is complex, as it depends on several factors, including the anticipated effect size, variability within the data, desired statistical power, and the inherent cellular heterogeneity of the tissue under investigation.[11] To achieve adequate statistical power, we will sample 15,000 cells and sequence approximately 20,000 reads per cell for each brain tissue sample. For this study, we anticipate that four human subjects from each of the three categories (injured tissue, surrounding noninjured tissue, and edematous tissue) will provide greater than 80% power at a 0.1 level of significance, assuming a Cohen's effect size of 0.65 or greater. This effect size is based on previously published scRNA-seq studies in neurological injury. These previous studies have demonstrated the ability to identify significant cellular and transcriptomic changes with sample sizes ranging from 3 to 5 biological replicates per group.[8] [9] In addition to statistical considerations, we have also taken into account the practical limitations associated with obtaining human brain tissue, which can be challenging due to availability and ethical considerations.

In light of these factors, we have chosen a sample size of 50 brain tissue samples for scRNA-seq in sTBI patients. This sample size reflects a balance between statistical rigor, precedent in the field, and the feasibility of obtaining the necessary tissue samples.

Inclusion Criteria

1. Patients with sTBI (Glasgow Coma Scale ≤ 8) exhibiting raised ICP (≥ 20 mm Hg) and CE (based on radiology [plain computed tomography head]) requiring craniectomy within ≤ 24 hours of injury.

2. Age group of 12 to 70 years.

Exclusion Criteria

1. Patients referred from other hospitals where prior surgical intervention or prolonged medical management may have altered the natural progression of brain injury.

2. Patients with multiple systemic injuries (e.g., chest, abdominal, or extremity trauma).

3. Patients with preexisting neurological conditions (e.g., stroke, neurodegenerative disorders, epilepsy), systemic infections, metabolic disorders (e.g., uncontrolled diabetes, renal failure), or coagulopathies or preexisting systemic conditions (e.g., uncontrolled diabetes, metastatic cancer, active infections) that could interfere with gene expression analysis and bias study results.

4. Penetrating TBI.

5. Time since injury outside the study's specified window.

6. History of active substance abuse.

7. Age outside the defined range.

8. History of prior brain surgery (excluding the craniectomy performed as part of TBI treatment).

Tissue Collection and Processing

Immediately following surgical removal, tissue samples will be placed in a preservative (obtained from 10× Chromium, California, United States) snap-frozen in liquid nitrogen, and stored at –80°C to preserve RNA integrity until further processing. This rapid freezing method is crucial for minimizing RNA degradation and maintaining the in vivo transcriptional state of the cells.[11]

Single-Cell RNA Sequencing

For scRNA-seq, the frozen tissue samples will be thawed and minced into small pieces. Tissue dissociation will be performed using enzymatic digestion with 0.2% collagenase and 0.2% collagenase V in plain medium for 1 hour at 37°C and strained through a 70-μM mesh to obtain a single-cell suspension.[12] If the yield of neuronal cells is low, snRNA sequencing (snRNA-seq) will be performed as an alternative. Single-cell or single-nucleus libraries will be prepared using the 10× Chromium system. The generated libraries will be sequenced on the Illumina NovaSeq 6000 platform (Illumina, Inc. San Diego, California, United States), utilizing paired-end sequencing to generate (using 150 cycles in 28 × 10 × 10 × 90 base pair read configuration). Following sequencing, the raw reads will be aligned to the human genome (hg38) using the Cell Ranger pipeline. This pipeline will perform demultiplexing, alignment, and quality filtering of the reads to generate digital expression matrices, representing the gene expression profiles of individual cells or nuclei[4] [7] [12] [13] ([Fig. 1]).

Fig. 1 Single-cell ribonucleic acid sequencing (scRNA-seq) analysis workflow.

Data Analysis

The generated digital expression matrices will undergo a series of quality control, normalization, and downstream analyses. Individual samples will be merged to generate the digital expression matrix using Cell Ranger. Initial quality control will involve filtering out cells or nuclei with low gene counts (< 100 genes) or a high percentage of mitochondrial gene expression (> 25%), as these are indicative of poor quality or dying cells. Cells with < 100 genes and > 25% mitochondrial expression will be removed from further analysis. Normalization of the raw counts will be performed using Seurat's NormalizeData function. Variable genes will be identified using the FindVariableGenes function. The Seurat ScaleData function will be used to scale and center expression values in the data set for dimensional reduction.

Following normalization, the data will be subjected to dimensionality reduction techniques, including principal component analysis, t-distributed Stochastic Neighbor Embedding, and uniform manifold approximation and projection. These methods reduce the complexity of the data and allow for the visualization of cellular relationships in a lower-dimensional space. The first two dimensions will be used in plots.

Clustering analysis will be performed using Seurat's FindClusters function to identify distinct cell populations based on their gene expression profiles. Marker genes for each cluster, which define the cell types, will be identified using Seurat FindAllMarkers function.

To validate the scRNA-seq findings, bulk RNA sequencing will be performed on these samples to run a comparative analysis for correlation purposes.[12] [14] [15]

Statistical Analysis

Differential expression analysis will be conducted to identify genes that are significantly differentially expressed between experimental groups. The FindMarkers function will be used to determine the differentially expressed genes between two groups of cells in the three different sets of brain tissue as described earlier. Statistical significance will be assessed using adjusted p-values, with a threshold of < 0.05 considered statistically significant. Genes with adjusted p-value < 0.05 will be considered significantly differentially expressed.[15]

Study Outcome

We hypothesize that the effect of primary and secondary insults on the brain will have different inflammatory transcriptomic profiles and these findings will lead us to postulate specific pathways involved in TBI where areas of injury drive the inflammatory response in the uninjured brain.

Preliminary Findings

As of the current reporting period, we have successfully recruited four patients with sTBI and collected tissue samples. These initial efforts represent a critical step in establishing the feasibility of our study protocol. However, we have encountered challenges in the sample collection and transportation protocol. Specifically, initial attempts to preserve tissue integrity using snap freezing and liquid nitrogen methods have yielded inconsistent results, necessitating further optimization. Current efforts are focused on standardizing the tissue processing workflow to ensure the acquisition of high-quality samples suitable for scRNA-seq analysis.

Discussion

CE is a hallmark of sTBI and a major contributor to secondary brain injury. While its impact on the injured brain is well-documented, the downstream effects on the noninjured, adjacent brain regions remain poorly understood. Current research has established that the disruption of the BBB, excitotoxicity, oxidative stress, and neuroinflammation play pivotal roles in the pathogenesis of TBI. However, the extent to which these mechanisms influence cellular function and homeostasis in noninjured brain tissue remains an unmet scientific need. Understanding the transcriptional and cellular changes in both injured and noninjured regions is crucial to deciphering the broader impact of CE and its contribution to secondary injury progression. This study leverages scRNA-seq to bridge this knowledge gap, offering an unprecedented, high-resolution perspective on the molecular and cellular responses to TBI.

Recent advances in scRNA-seq and snRNA-seq have revealed distinct cellular responses to TBI, particularly in glial populations such as microglia, astrocytes, and oligodendrocytes. Qiu et al[8] demonstrated that in the subacute phase of TBI, microglia and astrocytes exhibit upregulation of neurotrophic factors such as midkine (MDK), pleiotrophin (PTN), and prosaposin (PSAP), which are known to facilitate neural repair and neuroprotection. Similarly, Arneson et al[16] identified mt-Rnr2 (humanin) as a neuroprotective factor in astrocyte-mediated interactions after mild TBI ([Table 1]). These studies suggest that glial-driven metabolic and inflammatory responses are key determinants of recovery and secondary injury outcomes. However, existing research has largely focused on the injured brain, leaving a critical gap in understanding how these responses extend to noninjured regions in the presence of CE.

Table 1 Summary of single-cell RNA sequencing studies in traumatic brain injury: key findings, methodologies, and implications

Study	Objective	Study population	Sample size	Technique used for scRNA-seq	Methodology	Key findings	Implications
Qiu et al, 2023[8]	Investigated noncanonical neurotrophic factor signaling in the subacute phase of TBI	Mouse model of TBI	17,278 nuclei	10× Genomics Chromium, snRNA-seq	scRNA-seq analysis of microglia and astrocytes in mouse models; validation with in vitro models	Upregulation of MDK, PTN, and PSAP in microglia/astrocytes; MDK and PTN promoted neural progenitor proliferation, while PSAP enhanced neurite growth	Suggests that neurotrophic factors play a key role in neuroregeneration post-TBI
Garza et al, 2023[13]	Examined the transcriptional response of oligodendroglia in human TBI	Human postmortem TBI brain tissue	15 patients	10× Genomics Chromium, snRNA-seq	Single-nucleus RNA sequencing (snRNA-seq) of human TBI brain tissue	Identified activation of an interferon response in oligodendroglia; correlated with transcriptional activation of endogenous retroviruses (ERVs)	Suggests ERVs contribute to neuroinflammation in TBI and could be therapeutic targets
Arneson et al, 2022[16]	Studied the spatiotemporal transcriptomic response of mild TBI	Mouse model of mild TBI	24 mice	10× Genomics Chromium, scRNA-seq	scRNA-seq of hippocampus, frontal cortex, and blood leukocytes at 24 h and 7 days postinjury	Astrocytes regulate post-TBI cell–cell interactions; mt-Rnr2 (humanin) identified as a key target for cognitive recovery	Identifies metabolic pathways in astrocytes as potential intervention points for mTBI treatment
Addison and Obafemi-Ajayi, 2022[17]	Evaluated different clustering methods for identifying TBI marker genes	Publicly available TBI data sets	N/A (Computational)	Various clustering algorithms applied to scRNA-seq data	Comparison of clustering algorithms on scRNA-seq data from TBI models	Different clustering methods yield varied results; optimal clustering improves marker gene identification	Highlights the importance of computational methods in analyzing TBI transcriptomics
Shi et al, 2024[12]	Reviewed the application of scRNA-seq in stroke and TBI	Literature review	N/A	Multiple platforms including 10× Genomics, Smart-seq	Literature review of scRNA-seq and spatial transcriptomics applications	scRNA-seq reveals cellular heterogeneity in TBI; spatial transcriptomics improves cell–cell interaction mapping	Supports integration of multiomics approaches for TBI research

Abbreviations: MDK, midkine; mTBI, mild TBI; N/A, not available; PSAP, prosaposin; PTN, pleiotrophin; snRNA-seq, single-cell ribonucleic acid sequencing; TBI, traumatic brain injury.

Another emerging theme from recent scRNA-seq studies is the role of neuroinflammation and endogenous retrovirus (ERV) activation in driving secondary brain injury. As summarized in the [Table 1], Garza et al[13] employed snRNA-seq on human postmortem TBI tissue and uncovered an interferon-driven inflammatory response in oligodendrocytes, potentially triggered by ERV activation ([Table 1]). This highlights an underexplored therapeutic target, where modulation of ERV-mediated immune activation could mitigate neuroinflammation-induced white matter damage. Such findings further underscore the potential for scRNA-seq in identifying novel targets for intervention.

While scRNA-seq has revolutionized the study of TBI, its power is further amplified when combined with spatial transcriptomics. As shown in the ROL table, Shi et al[12] emphasized that spatial transcriptomics allows researchers to map cell–cell interactions and molecular gradients within brain tissue, offering an added layer of spatial resolution to scRNA-seq-derived cellular profiles ([Table 1]). This highlights an essential next step in TBI research, where integrating multiomics approaches—including proteomics, metabolomics, and epigenomics—can provide a holistic view of injury pathophysiology and facilitate the discovery of targetable pathways for therapeutic intervention.

Novelty and Significance of the Proposed Study

Despite the wealth of knowledge gained from scRNA-seq studies in TBI, significant gaps remain. Current studies have largely focused on the injured brain and its direct cellular pathology, neglecting the noninjured adjacent regions, which remain susceptible to secondary damage due to CE, ischemic stress, and BBB breakdown. The novelty of this study lies in its ability to compare the transcriptomic landscape of injured and noninjured brain regions from the same patient, providing a systematic evaluation of how CE alters cellular function across heterogeneous brain regions. By utilizing single-cell transcriptomics, this study will capture rare and transitional cell populations, allowing for a granular understanding of cellular fate decisions in response to injury.

Additionally, by incorporating bulk RNA sequencing for comparative validation, this study ensures that single-cell findings are robust and reproducible across different levels of biological resolution. This dual approach will not only uncover cell-type-specific injury responses but also identify molecular signatures that may serve as early biomarkers of secondary brain injury. These insights have direct translational implications as they could guide the development of precision medicine approaches for TBI management, targeting specific cellular pathways that drive secondary injury.

The SWOT analysis provides a strategic overview of the strengths, weaknesses, opportunities, and threats associated with applying scRNA-seq in TBI research ([Fig. 2]). One of the key strengths of this approach is its ability to resolve cellular heterogeneity and characterize transcriptional changes at an unprecedented resolution. However, technical challenges, including tissue dissociation artifacts and computational demands, remain major weaknesses. The SWOT analysis also highlights significant opportunities, particularly in integrating spatial transcriptomics and multiomics to refine our understanding of TBI pathophysiology. Nevertheless, challenges such as high costs, data interpretation complexity, and limited sample availability pose threats to large-scale implementation.

Fig. 2 SWOT (strengths, weaknesses, opportunities, and threats) analysis of single-cell ribonucleic acid sequencing (scRNA-seq) research in severe traumatic brain injury (TBI).

Limitations and Future Directions

While scRNA-seq provides unparalleled resolution in characterizing TBI pathophysiology, several challenges must be addressed. Technical hurdles, including tissue dissociation biases and RNA degradation in postmortem samples, can impact data quality. Additionally, high costs and computational complexity remain barriers to the widespread adoption of single-cell and spatial transcriptomic approaches. Future studies should focus on optimizing tissue preservation protocols to enhance the recovery of viable cells, integrating deep-learning-based bioinformatics tools to refine cell-type classification, and expanding patient cohort sizes to improve the generalizability of findings.

Furthermore, incorporating longitudinal scRNA-seq analyses will be crucial in tracking the evolution of cellular responses over time, allowing researchers to identify dynamic gene expression changes that drive recovery or exacerbate injury. This will enable more precise targeting of therapeutic interventions aimed at modulating neuroinflammation, synaptic plasticity, and metabolic dysfunction in TBI patients.

Conclusion

This study represents a significant advancement in TBI research, leveraging scRNA-seq to uncover novel cellular and molecular mechanisms underlying CE and secondary brain injury. By systematically comparing injured and noninjured brain tissue, it addresses a critical gap in the current literature, providing high-resolution insights into how CE propagates secondary injury at the cellular level. The findings of this study have the potential to reshape therapeutic strategies for TBI by identifying precise molecular targets for early intervention and neuroprotection. Ultimately, this work paves the way for integrating multiomics and spatial transcriptomics into TBI research, laying the foundation for precision medicine approaches that could improve long-term neurological outcomes in TBI patients.

Conflict of Interest

None declared.

Ethical Approval

This study will be conducted under ethical guidelines and regulations. Ethical approval was obtained from the All India Institute of Medical Sciences (AIIMS) Institutional Ethics Committee before the commencement of the study [AIIMS AI1320 dates 04–05–2024]. Informed consent will be obtained from all patients or their legal representatives before any study-related procedures are performed. The rights and privacy of the patients will be protected throughout the study.

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Publication History

Article published online:
10 May 2025