Comment on “Neuron-specific transcriptomic dysregulation in methotrexate-induced cognitive impairment revealed by snRNA-seq”

HIGHLIGHTS The high methotrexate dose used in the study (100 mg/kg) is much higher than typical human doses, which may exaggerate cognitive impairment. Future studies should use doses that better reflect clinical regimens. The use of marker genes (e.g., Rbfox3, Snap25, Gad2) for cell annotation may lead to misclassification due to overlapping expression in different cell types. Additional methods should be considered for more accurate identification. Future studies should investigate the impact of different chemotherapy dosages and regimens on cognitive impairment to improve clinical relevance. Dear Editor, We read with great interest the recent article titled “Neuron-specific transcriptomic dysregulation in methotrexate-induced cognitive impairment revealed by snRNA-seq[1],” which I found highly intriguing. The use of single-nucleus RNA sequencing (snRNA-seq) to investigate the impact of methotrexate (MTX) on cognitive function in mice offers valuable insights into the underlying molecular mechanisms. However, I believe that there are a few areas where the study design and data analysis could benefit from further refinement. Specifically, I would like to highlight two points related to the chemotherapy dosage used and the methodology for annotating cell populations, both of which could be more robust with appropriate references to relevant literature. This correspondence is developed following the TITAN Guidelines 2025[2] for the declaration and use of artificial intelligence in scholarly publishing. No AI tools were employed in the conception, design, analysis, interpretation, or writing of this work. First, in the study, the authors use a 100 mg/kg dose of MTX for the chemotherapy regimen in mice, which is considerably higher than the doses typically administered to humans in clinical settings. Clinically, MTX is administered at much lower doses (usually around 15–25 mg/m2 per week for cancer patients), and the dosing regimens in humans are generally lower, with longer cycles and combinations with other chemotherapeutic agents[3]. The high dosage used in the mouse model may exaggerate cognitive impairment, but it does not necessarily reflect the more complex and subtler cognitive deficits observed in humans under clinical treatment conditions. To enhance the clinical relevance of the study and improve translational potential, it would be beneficial for future studies to explore a range of doses that more closely mimic clinical chemotherapy protocols. This approach could yield results that better represent the clinical scenario of chemotherapy-induced cognitive impairment (CICI) in humans. Second, the study uses established marker genes (e.g., Rbfox3, Snap25, Gad2) to annotate cell populations, which is a common and reasonable approach. However, in the context of the complexity of the nervous system, there may be overlap in the expression of these marker genes across different cell types[4]. For instance, Rbfox3, typically used to identify excitatory neurons, may also be expressed in certain inhibitory neurons, leading to potential misclassification in more heterogeneous regions of the brain. Additionally, some cell populations may express multiple markers, complicating the annotation process. To address this, I recommend considering additional methods for cell type identification, such as transcription factor-based analyses or functional characteristics of the cells, to supplement the marker-based approach. This would improve the specificity of cell population identification. Moreover, immunohistochemical validation or other complementary techniques could help confirm the annotations and ensure the accuracy of cell type classification[5]. We suggest future research to explore the potential impact of chemotherapy dosage, treatment regimens, and the combination of chemotherapeutic agents on CICI. This will provide a more comprehensive understanding of the factors influencing cognitive deficits in cancer patients and improve the translational relevance of the findings for clinical practice.