Neuroblastoma is a highly heterogeneous disease not only in the clinical presentation of individual patients, but also in the cellular composition of any given tumor. Insights into this diversity have only recently been enabled due to advancements in single cell technologies, which have facilitated investigation of this disease at unprecedented resolution and detail. Coinciding with the growing number of scRNA-seq technologies, so too are the number of single cell datasets encompassing neuroblastoma patients across several institutions. However, due to the rarity of the affliction and sample access, the cohort pool in each aforementioned scRNA-seq study is limited to a reduced representation of the spectrum of disease classifications, which limits the ability of any single study to draw conclusions about neuroblastoma as a whole. Moreover, inconsistencies in data acquisition and analytical approaches across these studies have led to diverging interpretations. As such, we decided to amass the entirety of publicly available neuroblastoma scRNA-seq studies, representing a more comprehensive cross-section of patient presentations, towards the goal of conducting an exhaustive meta-analysis of the underlying data. To this end, we have implemented a generalizable non-negative matrix factorization (NMF)-based framework targeted at discovering conserved gene expression programs in malignant neuroblastoma as well as the supporting microenvironment. Using graph-based network analyses for classification of gene expression programs, we have identified conserved signatures of malignant and non-tumor cell types in neuroblastoma. In addition to defining the landscape of expression programs in human neuroblastoma patients, we have also utilized the NMF analysis to assess the alignment of several preclinical models to human signatures. We have identified gene expression programs that align to malignant human expression programs as well as signatures more closely related to non-tumor cell types. These include previously characterized divergent mesenchymal and adrenergic programs, as well as undescribed liver/metabolic, neuronal, and glial signatures. When considering the affinity of neuroblastoma models to malignant human profiles we observed specific agreement between certain preclinical signatures and subtype classifications found in patient samples. Careful consideration of these results will allow researchers to guide preclinical studies by cross-referencing neuroblastoma models of interest with patient profiles. Overall, we characterize the most updated view of the landscape of neuroblastoma by documenting the full repertoire of gene expression programs across patient and preclinical models. Citation Format: Richard Chapple, Charlie Wright, Min Pan, Paul Geeleher. Meta-analysis of neuroblastoma single cell RNA-seq datasets identifies conserved and divergent gene expression programs across human and preclinical models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4075.