Single-cell Expression Research Articles

Single-cell gene set scoring with nearest neighbor graph smoothed data (gssnng).

Gene set scoring (or enrichment) is a common dimension reduction task in bioinformatics that can be focused on the differences between groups or at the single sample level. Gene sets can represent biological functions, molecular pathways, cell identities, and more. Gene set scores are context dependent values that are useful for interpreting biological changes following experiments or perturbations. Single sample scoring produces a set of scores, one for each member of a group, which can be analyzed with statistical models that can include additional clinically important factors such as gender or age. However, the sparsity and technical noise of single-cell expression measures create difficulties for these methods, which were originally designed for bulk expression profiling (microarrays, RNAseq). This can be greatly remedied by first applying a smoothing transformation that shares gene measure information within transcriptomic neighborhoods. In this work, we use the nearest neighbor graph of cells for matrix smoothing to produce high quality gene set scores on a per-cell, per-group, level which is useful for visualization and statistical analysis. The gssnng software is available using the python package index (PyPI) and works with Scanpy AnnData objects. It can be installed using "pip install gssnng." More information and demo notebooks: see https://github.com/IlyaLab/gssnng.

Provably accurate and scalable linear classifiers in hyperbolic spaces

Many high-dimensional practical data sets have hierarchical structures induced by graphs or time series. Such data sets are hard to process in Euclidean spaces, and one often seeks low-dimensional embeddings in other space forms to perform the required learning tasks. For hierarchical data, the space of choice is a hyperbolic space because it guarantees low-distortion embeddings for tree-like structures. The geometry of hyperbolic spaces has properties not encountered in Euclidean spaces that pose challenges when trying to rigorously analyze algorithmic solutions. We propose a unified framework for learning scalable and simple hyperbolic linear classifiers with provable performance guarantees. The gist of our approach is to focus on Poincaré ball models and formulate the classification problems using tangent space formalisms. Our results include a new hyperbolic perceptron algorithm as well as an efficient and highly accurate convex optimization setup for hyperbolic support vector machine classifiers. Furthermore, we adapt our approach to accommodate second-order perceptrons, where data are preprocessed based on second-order information (correlation) to accelerate convergence, and strategic perceptrons, where potentially manipulated data arrive in an online manner and decisions are made sequentially. The excellent performance of the Poincaré second-order and strategic perceptrons shows that the proposed framework can be extended to general machine learning problems in hyperbolic spaces. Our experimental results, pertaining to synthetic, single-cell RNA-seq expression measurements, CIFAR10, Fashion-MNIST and mini-ImageNet, establish that all algorithms provably converge and have complexity comparable to those of their Euclidean counterparts.

A SINC-Seq Protocol for the Analysis of Subcellular Gene Expression in Single Cells.

Microfluidic devices offer precise control of single cells and molecules by liquid flows, downsizing tools to allow us to perform single-cell assays at unprecedented resolutions and minimizing contamination. In this chapter, we introduce an approach, called single-cell integrated nuclear and cytoplasmic RNA-sequencing (SINC-seq), which enables precise fractionation of cytoplasmic and nuclear RNA of single cells. This approach uses electric field control in microfluidics to manipulate single cells and RNA sequencing to dissect gene expression and RNA localization in subcellular compartments. The microfluidic system for SINC-seq exploits a hydrodynamic trap (a constriction in a microchannel) to isolate a single cell, selectively lyses its plasma membrane via a focused electric field, and retains the nucleus at the hydrodynamic trap during the electrophoretic extraction of cytoplasmic RNA. Here, we provide a step-by-step protocol from microfluidic RNA fractionation to off-chip preparation of RNA-sequencing libraries for full-length cDNA sequencing using both a short-read sequencer (Illumina) and a long-read sequencer (Oxford Nanopore Technologies).

Simultaneous Profiling of Chromosome Conformation and Gene Expression in Single Cells.

Rapid development in single-cell chromosome conformation capture technologies has provided valuable insights into the importance of spatial genome architecture for gene regulation. However, a long-standing technical gap remains in the simultaneous characterization of three-dimensional genomes and transcriptomes in the same cell. We have described an assay named Hi-C and RNA-seq employed simultaneously (HiRES), which integrates in situ reverse transcription and chromosome conformation capture (3C) for the parallel analysis of chromatin organization and gene expression. Here, we provide a detailed implementation of the assay, using mouse embryos and cerebral cortices as examples. The versatility of this method extends beyond these two samples, with the potential to be used in various other cell types. Key features • A multi-omics sequencing approach to profile 3D genome structure and gene expression simultaneously in single cells. • Compatible with animal tissues. • One-tube amplification of both DNA and RNA components. • Requires three days to complete.

Simultaneous profiling of chromosome conformation and gene expression in single cells

Simultaneous profiling of chromosome conformation and gene expression in single cells

Altered gene expression in excitatory neurons is associated with Alzheimer's disease and its higher incidence in women.

Alzheimer's disease (AD) is a neurodegenerative disorder involving interactions between different cell types in the brain. Previous single-cell and bulk expression Alzheimer's studies have reported conflicting findings about the key cell types and cellular pathways whose expression is primarily altered in this disease. We re-analyzed these data in a uniform, coherent manner aiming to resolve and extend past findings. Our analysis sheds light on the observation that females have higher AD incidence than males. We re-analyzed three single-cell transcriptomics datasets. We used the software Model-based Analysis of Single-cell Transcriptomics (MAST) to seek differentially expressed genes comparing AD cases to matched controls for both sexes together and each sex separately. We used the GOrilla software to search for enriched pathways among the differentially expressed genes. Motivated by the male/female difference in incidence, we studied genes on the X-chromosome, focusing on genes in the pseudoautosomal region (PAR) and on genes that are heterogeneous across individuals or tissues for X-inactivation. We validated findings by analyzing bulk AD datasets from the cortex in the Gene Expression Omnibus. Our results resolve a contradiction in the literature, showing that by comparing AD patients to unaffected controls, excitatory neurons have more differentially expressed genes than do other cell types. Synaptic transmission and related pathways are altered in a sex-specific analysis of excitatory neurons. PAR genes and X-chromosome heterogeneous genes, including, for example, BEX1 and ELK1, may contribute to the difference in sex incidence of Alzheimer's disease. GRIN1, stood out as an overexpressed autosomal gene in cases versus controls in all three single-cell datasets and as a functional candidate gene contributing to pathways upregulated in cases. Taken together, these results point to a potential linkage between two longstanding questions concerning AD pathogenesis, involving which cell type is the most important and why females have a higher incidence than males. By reanalyzing three, published, single-cell RNAseq datasets, we resolved a contradiction in the literature and showed that when comparing AD patients to unaffected controls, excitatory neurons have more differentially expressed genes than do other cell types.Further analysis of the published single-cell datasets showed that synaptic transmission and related pathways are altered in a sex-specific analysis of excitatory neurons.Combining analysis of single-cell datasets and publicly available bulk transcriptomics datasets revealed that X-chromosome genes, such as BEX1, ELK1, and USP11, whose X-inactivation status is heterogeneous may contribute to the higher incidence in females of Alzheimer's disease.

Innovations in hematopoietic stem-cell mobilization: a review of the novel CXCR4 inhibitor motixafortide.

Hematopoietic stem-cell transplantation (HCT) and stem-cell-based gene therapies rely on the ability to collect sufficient CD34+ hematopoietic stem and progenitor cells (HSPCs), typically via peripheral blood mobilization. Commonly used HSPC mobilization regimens include single-agent granulocyte colony-stimulating factor (G-CSF), plerixafor, chemotherapy, or a combination of these agents. These regimens, however, frequently require multiple days of injections and leukapheresis procedures to collect adequate HSPCs for HCT (minimum = >2 × 106 CD34+ cells/kg; optimal = 5-6 × 106 CD34+ cells/kg). In addition, these regimens frequently yield suboptimal CD34+ HSPC numbers for HSPC-based gene-edited therapies, given the significantly higher HSPC number needed for successful gene-editing and manufacturing. Meanwhile, G-CSF is associated with common adverse events such as bone pain as well as an increased risk of rare but potentially life-threatening splenic rupture. Moreover, G-CSF is unsafe in patients with sickle-cell disease, a key patient population that may benefit from autologous HSPC-based gene-edited therapies, where it has been associated with unacceptable rates of serious vaso-occlusive and thrombotic events. Motixafortide is a novel CXCR4 inhibitor with extended in vivo activity (>48 h) that has been shown in preclinical and clinical trials to rapidly mobilize robust numbers of HSPCs in preparation for HCT, while preferentially mobilizing increased numbers of more primitive HSPCs by immunophenotyping and single-cell RNA expression profiling. In this review, we present a history of stem-cell mobilization and update of recent innovations in novel mobilization strategies with a specific focus on the development of motixafortide, a long-acting CXCR4 inhibitor, as a novel HSPC mobilizing agent.

EVALUATING THE COMPLEXITY OF GENE COEXPRESSION ESTIMATION FOR SINGLE-CELL DATA

With the rapid advance of single-cell RNA sequencing (scRNA-seq) technology, understanding biological processes at a more refined single-cell level is becoming possible. Gene coexpression estimation is an essential step in this direction. It can annotate functionalities of unknown genes or construct the basis of gene regulatory network inference. This study thoroughly tests the existing gene coexpression estimation methods on simulation datasets with known ground truth coexpression networks. We generate these novel datasets using two simulation processes, NORmal-To-Anything (NORTA) and Single-cell ExpRession of Genes In silicO (SERGIO), that use the parameters learned from the experimental data. We demonstrate that these simulations better capture the underlying properties of the real-world single-cell datasets than previously tested simulations for the task. Our performance results on tens of simulated and eight experimental datasets show that all methods produce estimations with a high false discovery rate, potentially caused by high sparsity levels in the data. Finally, we find that commonly used preprocessing approaches, such as normalization and imputation, do not improve the coexpression estimation. Overall, our benchmark setup contributes to the coexpression estimator development, and our study provides valuable insights for the community for single-cell data analyses.

Nonparametric Bayesian Two-Level Clustering for Subject-Level Single-Cell Expression Data

The advent of single-cell sequencing opens new avenues for personalized treatment. In this paper, we address a two-level clustering problem of simultaneous subject subgroup discovery (subject level) and cell type detection (cell level) for single-cell expression data from multiple subjects. However, current statistical approaches either cluster cells without considering the subject heterogeneity or group subjects without using the single-cell information. To bridge the gap between cell clustering and subject grouping, we develop a nonparametric Bayesian model, Subject and Cell clustering for Single-Cell expression data (SCSC) model, to achieve subject and cell grouping simultaneously. SCSC does not need to prespecify the subject subgroup number or the cell type number. It automatically induces subject subgroup structures and matches cell types across subjects. Moreover, it directly models the single-cell raw count data by deliberately considering the data's dropouts, library sizes, and over-dispersion. A blocked Gibbs sampler is proposed for the posterior inference. Simulation studies and the application to a multi-subject iPSC scRNA-seq dataset validate the ability of SCSC to simultaneously cluster subjects and cells.

The eighth International Forum on Stem Cells: virtual meeting, October 20-21, 2022.

The eighth International Forum on Stem Cells: virtual meeting, October 20-21, 2022.

The gold-ringed octopus (Amphioctopus fangsiao) genome and cerebral single-nucleus transcriptomes provide insights into the evolution of karyotype and neural novelties

BackgroundColeoid cephalopods have distinctive neural and morphological characteristics compared to other invertebrates. Early studies reported massive genomic rearrangements occurred before the split of octopus and squid lineages (Proc Natl Acad Sci U S A 116:3030-5, 2019), which might be related to the neural innovations of their brain, yet the details remain elusive. Here we combine genomic and single-nucleus transcriptome analyses to investigate the octopod chromosome evolution and cerebral characteristics.ResultsWe present a chromosome-level genome assembly of a gold-ringed octopus, Amphioctopus fangsiao, and a single-nucleus transcriptome of its supra-esophageal brain. Chromosome-level synteny analyses estimate that the chromosomes of the ancestral octopods experienced multiple chromosome fission/fusion and loss/gain events by comparing with the nautilus genome as outgroup, and that a conserved genome organization was detected during the evolutionary process from the last common octopod ancestor to their descendants. Besides, protocadherin, GPCR, and C2H2 ZNF genes are thought to be highly related to the neural innovations in cephalopods (Nature 524:220–4, 2015), and the chromosome analyses pinpointed several collinear modes of these genes on the octopod chromosomes, such as the collinearity between PCDH and C2H2 ZNF, as well as between GPCR and C2H2 ZNF. Phylogenetic analyses show that the expansion of the octopod protocadherin genes is driven by a tandem-duplication mechanism on one single chromosome, including two separate expansions at 65 million years ago (Ma) and 8–14 Ma, respectively. Furthermore, we identify eight cell types (i.e., cholinergic and glutamatergic neurons) in the supra-esophageal brain of A. fangsiao, and the single-cell expression analyses reveal the co-expression of protocadherin and GPCR in specific neural cells, which may contribute to the neural development and signal transductions in the octopod brain.ConclusionsThe octopod genome analyses reveal the dynamic evolutionary history of octopod chromosomes and neural-related gene families. The single-nucleus transcriptomes of the supra-esophageal brain indicate their cellular heterogeneities and functional interactions with other tissues (i.e., gill), which provides a foundation for further octopod cerebral studies.

Multi-omics pan-cancer study of cuproptosis core gene FDX1 and its role in kidney renal clear cell carcinoma.

The mechanism of copper-induced cellular death was newly discovered and termed cuproptosis. Inducing cuproptosis in cancer cells is well anticipated for its curative potential in treating tumor diseases. However, ferredoxin 1 (FDX1), the core regulatory gene in cuproptosis, is rarely studied, and the regulation of FDX1 in tumor biology remains obscure. A comprehensive pan-cancer analysis of FDX1 is needed. Thirty-three types of tumors were included with paired normal tissues in The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) datasets. The interaction between transcription, protein, phosphorylation, and promoter methylation levels was analyzed. Survival, immune infiltration, single-cell FDX1 expression, FDX1-related tumor mutational burden (TMB), microsatellite instability (MSI), stemness, tumor immune dysfunction and exclusion (TIDE), and immunotherapy-related analyses were performed. FDX1 protein expression was assessed by kidney renal clear cell carcinoma (KIRC) tissue microarray immunohistochemistry. The function of FDX1 in KIRC was further explored by experiments in 786-O cell lines in vitro. FDX1 is highly expressed in 15 tumor types and lowly expressed in 11 tumor types. The corresponding changes in protein expression, phosphorylation, and promoter methylation level of FDX1 have been described in several tumors. Survival analysis showed that FDX1 was related to favorable or poor overall survival in eight tumors and progression-free survival in nine tumors. Immune infiltration and single-cell analysis indicated the indispensable role of FDX1 expression in macrophages and monocytes. Multiple established immunotherapy cohorts suggested that FDX1 may be a potential predictor of treatment effects for tumor patients. Tissue microarray analysis showed decreased FDX1 expression in KIRC patients' tumor tissues. Knockdown of FDX1 resulted in the downregulation of cuproptosis in kidney renal clear tumor cells. Mechanistically, the FDX1-associated gene expression signature in KIRC is related to the enrichment of genes involved in the tricarboxylic acid (TCA) cycle, NOTCH pathway, etc. Several NOTCH pathway genes were differentially expressed in the high- and low-FDX1 groups in KIRC. Our analysis showed that the central regulatory gene of cuproptosis, FDX1, has differential expression and modification levels in various tumors, which is associated with cellular function, immune modulation, and disease prognosis. Thus, FDX1-dependent cuproptosis may serve as a brand-new target in future therapeutic approaches against tumors.

Highly Multiplexed Immunofluorescence Imaging for Quantitative Spatial Analysis in Tissue Samples with ChipCytometry™.

Traditional immunofluorescence (IF) imaging assays are limited to the detection of just a few markers due to spectral overlap of fluorescent emission bands. Furthermore, standard fluorescent imaging instruments have a dynamic range that is too narrow to capture the full range of expression values seen in biology, precluding the accurate quantification of single-cell target expression. Here we describe a protocol for detection and quantification of dozens of protein targets with single-cell quantitative precision using an iterative staining approach called ChipCytometry™.

Gold nanoparticle delivery to solid tumors: a multiparametric study on particle size and the tumor microenvironment.

Nanoparticle (NP) delivery to solid tumors remains an actively studied field, where several recent studies have shed new insights into the underlying mechanisms and the still overall poor efficacy. In the present study, Au NPs of different sizes were used as model systems to address this topic, where delivery of the systemically administered NPs to the tumor as a whole or to tumor cells specifically was examined in view of a broad range of tumor-associated parameters. Using non-invasive imaging combined with histology, immunohistochemistry, single-cell spatial RNA expression and image-based single cell cytometry revealed a size-dependent complex interaction of multiple parameters that promoted tumor and tumor-cell specific NP delivery. Interestingly, the data show that most NPs are sequestered by tumor-associated macrophages and cancer-associated fibroblasts, while only few NPs reach the actual tumor cells. While perfusion is important, leaky blood vessels were found not to promote NP delivery, but rather that delivery efficacy correlated with the maturity level of tumor-associated blood vessels. In line with recent studies, we found that the presence of specialized endothelial cells, expressing high levels of CD276 and Plvap promoted both tumor delivery and tumor cell-specific delivery of NPs. This study identifies several parameters that can be used to determine the suitability of NP delivery to the tumor region or to tumor cells specifically, and enables personalized approaches for maximal delivery of nanoformulations to the targeted tumor.

ScFASTCORMICS: A Contextualization Algorithm to Reconstruct Metabolic Multi-Cell Population Models from Single-Cell RNAseq Data.

Tumours are composed of various cancer cell populations with different mutation profiles, phenotypes and metabolism that cause them to react to drugs in diverse manners. Increasing the resolution of metabolic models based on single-cell expression data will provide deeper insight into such metabolic differences and improve the predictive power of the models. scFASTCORMICS is a network contextualization algorithm that builds multi-cell population genome-scale models from single-cell RNAseq data. The models contain a subnetwork for each cell population in a tumour, allowing to capture metabolic variations between these clusters. The subnetworks are connected by a union compartment that permits to simulate metabolite exchanges between cell populations in the microenvironment. scFASTCORMICS uses Pareto optimization to simultaneously maximise the compactness, completeness and specificity of the reconstructed metabolic models. scFASTCORMICS is implemented in MATLAB and requires the installation of the COBRA toolbox, rFASTCORMICS and the IBM CPLEX solver.

Single cell biology to analyse cellular heterogeneity in the response to amyloid plaques in Alzheimer’s Disease

AbstractMicroglial activation and neuroinflammation are crucial in the pathogenesis of Alzheimer’s disease. However, studies in mouse models and human postmortem samples have yielded divergent results regarding transcriptional programs in microglia elicited by exposure to amyloid‐b pathology. Here, we investigate 127,000 single cell expression profiles of human microglia isolated freshly from a xenotransplantation model for AD. Human microglia adopt a different, more complex spectrum of cell states than mouse microglia. Besides a disease associated (DAM) profile, human microglia show a pronounced HLA‐response related to antigen presentation in response to amyloid‐b plaques, which is absent in mouse. Furthermore, oligomeric amyloid‐β present in this model induces a distinctive pro‐inflammatory CRM response characterized by the expression of cytokines and chemokines, also not seen in mice. TREM2 and APOE polymorphisms modulate the response of microglia to amyloid‐b plaques, much less to oligomeric Aβ. In conclusion, human microglia mount a multipronged ARM response to amyloid pathology in Alzheimer’s disease. This implies that therapeutic strategies targeting microglia need to analyse carefully the effect on these different cell states some of which seem protective.

Estimating heterogeneous gene regulatory networks from zero-inflated single-cell expression data

Inferring gene regulatory networks can elucidate how genes work cooperatively. The gene-gene collaboration information is often learned by Gaussian graphical models (GGM) that aim to identify whether the expression levels of any pair of genes are dependent, given other genes’ expression values. One basic assumption that guarantees the validity of GGM is data normality, and this often holds for bulk-level expression data which aggregate biological signals from a collection of cells. However, fine-grained cell-level expression profiles collected in single-cell RNA-sequencing (scRNA-seq) reveal nonnormality features—cellular heterogeneity and zero inflation. We propose a Bayesian latent mixture GGM to jointly estimate multiple gene regulatory networks accounting for the zero inflation and unknown heterogeneity of single-cell expression data. The proposed approach outperforms competing methods on synthetic data in terms of network structure and precision matrix estimation accuracy and provides biological insights when applied to two real-world scRNA-seq datasets. An R package implementing the proposed model is available on GitHub https://github.com/WgitU/BLGGM.

SRGS: sparse partial least squares-based recursive gene selection for gene regulatory network inference

BackgroundThe identification of gene regulatory networks (GRNs) facilitates the understanding of the underlying molecular mechanism of various biological processes and complex diseases. With the availability of single-cell RNA sequencing data, it is essential to infer GRNs from single-cell expression. Although some GRN methods originally developed for bulk expression data can be applicable to single-cell data and several single-cell specific GRN algorithms were developed, recent benchmarking studies have emphasized the need of developing more accurate and robust GRN modeling methods that are compatible for single-cell expression data.ResultsWe present SRGS, SPLS (sparse partial least squares)-based recursive gene selection, to infer GRNs from bulk or single-cell expression data. SRGS recursively selects and scores the genes which may have regulations on the considered target gene based on SPLS. When dealing with gene expression data with dropouts, we randomly scramble samples, set some values in the expression matrix to zeroes, and generate multiple copies of data through multiple iterations to make SRGS more robust. We test SRGS on different kinds of expression data, including simulated bulk data, simulated single-cell data without and with dropouts, and experimental single-cell data, and also compared with the existing GRN methods, including the ones originally developed for bulk data, the ones developed specifically for single-cell data, and even the ones recommended by recent benchmarking studies.ConclusionsIt has been shown that SRGS is competitive with the existing GRN methods and effective in the gene regulatory network inference from bulk or single-cell gene expression data. SRGS is available at: https://github.com/JGuan-lab/SRGS.

Single-cell and microarray chip analysis revealed the underlying pathogenesis of ulcerative colitis and validated model genes in diagnosis and drug response

The morbidity rate of ulcerative colitis (UC) in the world is increasing year by year, recurrent episodes of diarrhea, mucopurulent and bloody stools, and abdominal pain are the main symptoms, reducing the quality of life of the patient and affecting the productivity of the society. In this study, we sought to develop robust diagnostic biomarkers for UC, to uncover potential targets for anti-TNF-ɑ drugs, and to investigate their associated pathway mechanisms. We collected single-cell expression profile data from 9 UC or healthy samples and performed cell annotation and cell communication analysis. Revealing the possible pathogenesis of ulcerative colitis by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) analysis. Based on the disease-related modules obtained from weighted correlation network analysis (WGCNA) analysis, we used Lasso regression analysis and random forest algorithm to identify the genes with the greatest impact on disease (EPB41L3, HSD17B3, NDRG1, PDIA5, TRPV3) and further validated the diagnostic value of the model genes by various means. To further explore the relationship and mechanism between model genes and drug sensitivity, we collected gene expression profiles of 185 UC patients before receiving anti-tumor necrosis factor drugs, and we performed functional analysis based on the results of differential analysis between NR tissues and R tissues, and used single-sample GSEA (ssGSEA) and CIBERSORT algorithms to explore the important role of immune microenvironment on drug sensitivity. The results suggest that our model is not only helpful in aiding diagnosis, but also has implications for predicting drug efficacy; in addition, model genes may influence drug sensitivity by affecting immune cells. We suggest that this study has developed a diagnostic model with higher specificity and sensitivity, and also provides suggestions for clinical administration and drug efficacy prediction.

Abstract A058: Meta-analysis of single-cell RNA expression in genetically engineered mouse models of pancreatic ductal adenocarcinoma reveals inter-model heterogeneity

Abstract Background: Genetically engineered mouse models (GEMMs) are widely used in the study of pancreatic ductal adenocarcinoma (PDAC) because of their immune-competent tumor microenvironment (TME); however, the extent to which particular GEMMs recapitulate the tumor and TME observed in the patient population has not been systematically evaluated. In this study, we integrate single-cell RNA sequencing (sc-RNA-seq) data from multiple studies and multiple GEMM backgrounds to identify differences in the cellular compositions of popular PDAC GEMMs. Methods: A total of 49,191 cells were used from three studies, including normal mouse pancreas (N=2) and five different GEMM backgrounds (N=16). Data curation, integration, and analysis were based on the Seurat pipeline in R. SingelCellNet was used to train a random forest model on manually labeled human sc-RNA-seq data from 20 patients. To enable cross-species use, the classifier was trained using only genes with both human and mouse homologues. Cells classified as neoplastic were further clustered to quantify the number of classical and basal-like cells based on signature gene expression levels. The ratio of these subtypes in each GEMM and the relationship between the modified genes in each model were examined. Results: Ad-hoc clustering and a human-cell-trained single-cell classifier showed 79% agreement in an integrated data set of PDAC GEMMs. Cells identified by both methods as tumor (8,303 cells, 17% of total) were assessed for PDAC tumor subtype via subsequent clustering analysis (basal-like or classical). When comparing the ratio of differently subtyped tumor cells, we identified stark differences between GEMM genetic backgrounds. Among five different models, KIC (KrasLSL−G12D/+Ink4a/Arffl/flPtf1aCre/+), KPPCN (KrasLSL−G12D/+Trp53fl/flPdx1Cre/+Nsdhlfl/fl), and pdx1-KPC (KrasLSL−G12D/+Trp53LSL-R172H/+Pdx1Cre/+) exhibited a higher proportion of basal-like PDAC cells compared to KPfC/KPPC (KrasLSL−G12D/+Trp53fl/flPdx1Cre/+) and ptf1a-KPC (KrasLSL−G12D/+Trp53LSL-R172H/+Ptf1aCre/+). Interestingly, in the KIC model, which was harvested at early and late time points (40 or 60 days), classical PDAC was overrepresented in early models, and basal-like PDAC was more prevalent in the older tumors. While sample sizes are limited in this study, in Pdx1 driven models, we observed a bias towards basal-like phenotype in GEMMs using the Trp53LSL-R172H/+ method compared to those with Trp53fl/fl. Conclusions: In a comparison of publicly available sc-RNA-seq, we highlight potential biases in the molecular subtypes that arise from specific PDAC GEMMs. Because of the known link between tumor subtype and therapeutic response, these results suggest translational work may benefit from GEMM selection that considers transcriptomic diversity. Citation Format: Yun Jae Yoo, Ki H Oh, Luke A. Torre-Healy, Richard A. Moffitt. Meta-analysis of single-cell RNA expression in genetically engineered mouse models of pancreatic ductal adenocarcinoma reveals inter-model heterogeneity [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A058.