Single-cell Gene Expression Data Research Articles

Highlights from Recent Cancer Literature

Highlights from Recent Cancer Literature

Single Cell Gene Expression Predicts Plasma Cell Leukemia Patient Response to Daratumumab

The promise of precision medicine is to identify the ideal treatment for each patient, eliminating failed treatment cycles and reducing treatment burdens on patients and payors. While gene sequencing can predict effective treatment options in some cases, studies have estimated that only 10-15% of cancer patients are treated with genotype matched drugs. Gene expression is theoretically an attractive alternative to predicting therapeutic response because, it provides a direct readout of dysregulated cellular activities including druggable pathways. However, the use of bulk gene expression to predict therapeutic response is hampered by tumor heterogeneity; that is, a biopsied tumor contains multiple cells types and different tumor clones and a bulk analysis only generates an average gene expression signal across all of the cells, leading to a decreased sensitivity for identifying biomarkers found only in specific cell subgroups. More recently, single-cell gene expression analysis has shown promise in improving precision medicine through a focused look into the biology of the tumor cells themselves. For example, we previously showed (Scolnick, et al ASCO 2022) that a model trained on single-cell gene expression data collected from 17 Multiple Myeloma (MM) patient's plasma cells (PCs) could be used to predict the therapeutic response of seven independent MM patient samples at 100% accuracy. Here we combine single-cell gene expression analysis with Singleron Biotechnology's MapResponse™ machine learning algorithms to extend our predictions to data from peripheral blood mononuclear cells (PBMCs) of plasma cell leukemia patients who were treated with daratumumab. Our model accurately predicted the response to daratumumab treatment for both patients based on the PCs collected from blood samples. These early findings point to the potential value of combining single-cell RNA sequencing with machine learning methods such as MapResponse™ to bring precision medicine to more patients based on minimal invasive assessment of PBMCs.

EPCO-14. ISOSCELES: AN INTEGRATIVE FRAMEWORK FOR THE PREDICTION OF TREATMENT RESISTANT GLIOBLASTOMA CELLS

Abstract Glioblastoma (GBM) remains the most common and lethal adult primary brain cancer. Two of the most significant issues preventing the development of effective GBM treatments are inter- and intra-tumor heterogeneity. To address these issues, we developed a novel platform termed ISOSCELES (Inferred cell Sensitivity Operating on the integration of Single-Cell Expression and L1000 Expression Signatures). ISOSCELES integrates single-cell gene expression data in individual GBM tumors with perturbation-response data derived from the NIH Library of Integrated Network-Based Cellular Signatures (LINCS) L1000 dataset to predict sensitive and resistant tumor cell populations. Importantly, we analyzed the predictive power of ISOSCELES in an in vivo xenograft model and demonstrated that ISOSCELES reveals the GBM cell identities primed for lineage expansion during treatment with the aurora kinase inhibitor alisertib. These studies suggest that ISOSCELES can be used to identify sensitive and resistant cell populations to targeted therapies in GBM, which can inform treatment decisions in ongoing and future clinical trials.

Pathway trajectory analysis with tensor imputation reveals drug-induced single-cell transcriptomic landscape

Genome-wide identification of single-cell transcriptomic responses of drugs in various human cells is a challenging issue in medical and pharmaceutical research. Here we present a computational method, tensor-based imputation of gene-expression data at the single-cell level (TIGERS), which reveals the drug-induced single-cell transcriptomic landscape. With this algorithm, we predict missing drug-induced single-cell gene-expression data with tensor imputation, and identify trajectories of regulated pathways considering intercellular heterogeneity. Tensor imputation outperformed existing imputation methods for data completion, and provided cell-type-specific transcriptomic responses for unobserved drugs. For example, TIGERS correctly predicted the cell-type-specific expression of maker genes for pancreatic islets. Pathway trajectory analysis of the imputed gene-expression profiles of all combinations of drugs and human cells identified single-cell-specific drug activities and pathway trajectories that reflect drug-induced changes in pathway regulation. The proposed method is expected to expand our understanding of the single-cell mechanisms of drugs at the pathway level.

Time-Varying Gene Expression Network Analysis Reveals Conserved Transition States in Hematopoietic Differentiation between Human and Mouse.

(1) Background: analyses of gene networks can elucidate hematopoietic differentiation from single-cell gene expression data, but most algorithms generate only a single, static network. Because gene interactions change over time, it is biologically meaningful to examine time-varying structures and to capture dynamic, even transient states, and cell-cell relationships. (2) Methods: a transcriptomic atlas of hematopoietic stem and progenitor cells was used for network analysis. After pseudo-time ordering with Monocle 2, LOGGLE was used to infer time-varying networks and to explore changes of differentiation gene networks over time. A range of network analysis tools were used to examine properties and genes in the inferred networks. (3) Results: shared characteristics of attributes during the evolution of differentiation gene networks showed a “U” shape of network density over time for all three branches for human and mouse. Differentiation appeared as a continuous process, originating from stem cells, through a brief transition state marked by fewer gene interactions, before stabilizing in a progenitor state. Human and mouse shared hub genes in evolutionary networks. (4) Conclusions: the conservation of network dynamics in the hematopoietic systems of mouse and human was reflected by shared hub genes and network topological changes during differentiation.

Single-nuclei transcriptome analysis of the shoot apex vascular system differentiation in Populus.

Differentiation of stem cells in the plant apex gives rise to aerial tissues and organs. Presently, we lack a lineage map of the shoot apex cells in woody perennials - a crucial gap considering their role in determining primary and secondary growth. Here, we used single-nuclei RNA-sequencing to determine cell type-specific transcriptomes of the Populus vegetative shoot apex. We identified highly heterogeneous cell populations clustered into seven broad groups represented by 18 transcriptionally distinct cell clusters. Next, we established the developmental trajectories of the epidermis, leaf mesophyll and vascular tissue. Motivated by the high similarities between Populus and Arabidopsis cell population in the vegetative apex, we applied a pipeline for interspecific single-cell gene expression data integration. We contrasted the developmental trajectories of primary phloem and xylem formation in both species, establishing the first comparison of vascular development between a model annual herbaceous and a woody perennial plant species. Our results offer a valuable resource for investigating the principles underlying cell division and differentiation conserved between herbaceous and perennial species while also allowing us to examine species-specific differences at single-cell resolution.

DynDeepDRIM: a dynamic deep learning model to infer direct regulatory interactions using time-course single-cell gene expression data.

Time-course single-cell RNA sequencing (scRNA-seq) data have been widely used to explore dynamic changes in gene expression of transcription factors (TFs) and their target genes. This information is useful to reconstruct cell-type-specific gene regulatory networks (GRNs). However, the existing tools are commonly designed to analyze either time-course bulk gene expression data or static scRNA-seq data via pseudo-time cell ordering. A few methods successfully utilize the information from multiple time points while also considering the characteristics of scRNA-seq data. We proposed dynDeepDRIM, a novel deep learning model to reconstruct GRNs using time-course scRNA-seq data. It represents the joint expression of a gene pair as an image and utilizes the image of the target TF-gene pair and the ones of the potential neighbors to reconstruct GRNs from time-course scRNA-seq data. dynDeepDRIM can effectively remove the transitive TF-gene interactions by considering neighborhood context and model the gene expression dynamics using high-dimensional tensors. We compared dynDeepDRIM with six GRN reconstruction methods on both simulation and four real time-course scRNA-seq data. dynDeepDRIM achieved substantially better performance than the other methods in inferring TF-gene interactions and eliminated the false positives effectively. We also applied dynDeepDRIM to annotate gene functions and found it achieved evidently better performance than the other tools due to considering the neighbor genes.

Identification of spatially variable genes with graph cuts

Single-cell gene expression data with positional information is critical to dissect mechanisms and architectures of multicellular organisms, but the potential is limited by the scalability of current data analysis strategies. Here, we present scGCO, a method based on fast optimization of hidden Markov Random Fields with graph cuts to identify spatially variable genes. Comparing to existing methods, scGCO delivers a superior performance with lower false positive rate and improved specificity, while demonstrates a more robust performance in the presence of noises. Critically, scGCO scales near linearly with inputs and demonstrates orders of magnitude better running time and memory requirement than existing methods, and could represent a valuable solution when spatial transcriptomics data grows into millions of data points and beyond.

SciCAN: single-cell chromatin accessibility and gene expression data integration via cycle-consistent adversarial network

The boom in single-cell technologies has brought a surge of high dimensional data that come from different sources and represent cellular systems from different views. With advances in these single-cell technologies, integrating single-cell data across modalities arises as a new computational challenge. Here, we present an adversarial approach, sciCAN, to integrate single-cell chromatin accessibility and gene expression data in an unsupervised manner. We benchmarked sciCAN with 5 existing methods in 5 scATAC-seq/scRNA-seq datasets, and we demonstrated that our method dealt with data integration with consistent performance across datasets and better balance of mutual transferring between modalities than the other 5 existing methods. We further applied sciCAN to 10X Multiome data and confirmed that the integrated representation preserves biological relationships within the hematopoietic hierarchy. Finally, we investigated CRISPR-perturbed single-cell K562 ATAC-seq and RNA-seq data to identify cells with related responses to different perturbations in these different modalities.

Research progress on application of single-cell TCR/BCR sequencing technology to the tumor immune microenvironment, autoimmune diseases, and infectious diseases.

Single-cell omics is the profiling of individual cells through sequencing and other technologies including high-throughput analysis for single-cell resolution, cell classification, and identification as well as time series analyses. Unlike multicellular studies, single-cell omics overcomes the problem of cellular heterogeneity. It provides new methods and perspectives for in-depth analyses of the behavior and mechanism of individual cells in the cell population and their relationship with the body, and plays an important role in basic research and precision medicine.Single-cell sequencing technologies mainly include single-cell transcriptome sequencing, single-cell assay for transposase accessible chromatin with high-throughput sequencing, single-cell immune profiling (single-cell T-cell receptor [TCR]/B-cell receptor [BCR] sequencing), and single-cell transcriptomics. Single-cell TCR/BCR sequencing can be used to obtain a large amount of single-cell gene expression and immunomics data at one time, and combined with transcriptome sequencing and TCR/BCR diversity data, can resolve immune cell heterogeneity. This paper summarizes the progress in applying single-cell TCR/BCR sequencing technology to the tumor immune microenvironment, autoimmune diseases, infectious diseases, immunotherapy, and chronic inflammatory diseases, and discusses its shortcomings and prospects for future application.

Engineering naturally occurring CD7− T cells for the immunotherapy of hematological malignancies

AbstractChimeric antigen receptor (CAR) T-cell therapy targeting T-cell acute lymphoblastic leukemia (T-ALL) faces limitations such as antigen selection and limited T-cell persistence. CD7 is an attractive antigen for targeting T-ALL, but overlapping expression on healthy T cells leads to fratricide of CD7-CAR T cells, requiring additional genetic modification. We took advantage of naturally occurring CD7− T cells to generate CD7-CAR (CD7-CARCD7−) T cells. CD7-CARCD7− T cells exhibited a predominantly CD4+ memory phenotype and had significant antitumor activity upon chronic antigen exposure in vitro and in xenograft mouse models. Based on these encouraging results, we next explored the utility of CD7− T cells for the immunotherapy of CD19+ hematological malignancies. Direct comparison of nonselected (bulk) CD19-CAR and CD19-CARCD7− T cells revealed that CD19-CARCD7− T cells had enhanced antitumor activity compared with their bulk counterparts in vitro and in vivo. Lastly, to gain insight into the behavior of CD19-CAR T cells with low levels of CD7 gene expression (CD7lo) in humans, we mined single-cell gene and T-cell receptor (TCR) expression data sets from our institutional CD19-CAR T-cell clinical study. CD19-CARCD7lo T cells were present in the initial CD19-CAR T-cell product and could be detected postinfusion. Intriguingly, the only functional CD4+ CD19-CAR T-cell cluster observed postinfusion exhibited CD7lo expression. Additionally, samples from patients responsive to therapy had a higher proportion of CD7lo T cells than nonresponders (NCT03573700). Thus, CARCD7− T cells have favorable biological characteristics and may present a promising T-cell subset for adoptive cell therapy of T-ALL and other hematological malignancies.

The scINSIGHT Package for Integrating Single-Cell RNA-Seq Data from Different Biological Conditions.

Data integration is a critical step in the analysis of multiple single-cell RNA sequencing samples to account for heterogeneity due to both biological and technical variability. scINSIGHT is a new integration method for single-cell gene expression data, and can effectively use the information of biological condition to improve the integration of multiple single-cell samples. scINSIGHT is based on a novel non-negative matrix factorization model that learns common and condition-specific gene modules in samples from different biological or experimental conditions. Using these gene modules, scINSIGHT can further identify cellular identities and active biological processes in different cell types or conditions. Here we introduce the installation and main functionality of the scINSIGHT R package, including how to preprocess the data, apply the scINSIGHT algorithm, and analyze the output.

Comprehensive Analysis of CRIP1 Expression in Acute Myeloid Leukemia.

Acute myeloid leukemia (AML) is a highly heterogeneous hematological malignancy that imposes great challenges in terms of drug resistance and relapse. Previous studies revealed heterogeneous leukemia cells and their relevant gene markers, such as CRIP1 as clinically prognostic in t (8;21) AML patients. However, the expression and role of CRIP1 in AML are poorly understood. We used the single-cell RNA sequencing and gene expression data from t (8;21) AML patients to analyze the immune and regulation networks of CRIP1. Two independent cohorts from GSE37642 and The Cancer Genome Atlas (TCGA) datasets were employed as validation cohorts. In addition, the methylation data from TCGA were used to analyze the methylation effect of the CRIP1 expression. Gene expression profile from t (8;21) AML patients showed that the CRIP1-high group exhibited an enrichment of immune-related pathways, including tumor necrosis factor (TNF)α signaling via nuclear factor kappa B (NFκB) pathways. Further studies using CIBERSORT showed that the CRIP1-high group had a significantly higher infiltration of exhausted CD8 T cells and activated mast cells. The CRIP1 expression was validated in the GSE37642-GPL96, GSE37642-GPL570, and TCGA datasets. In addition, with the methylation data, four CpG probes of CRIP1 (cg07065217, cg04411625, cg25682097, and 11763800) were identified as negatively associated with the CRIP1 gene expression in AML patients. Our data provide a comprehensive overview of the regulation of CRIP1 expression in AML patients. The evaluation of the TNFα-NFκB signaling pathway as well as the immune heterogeneity might provide new insights for exploring improvements in AML treatment.

Integration of single-cell multi-omics data by regression analysis on unpaired observations

Despite recent developments, it is hard to profile all multi-omics single-cell data modalities on the same cell. Thus, huge amounts of single-cell genomics data of unpaired observations on different cells are generated. We propose a method named UnpairReg for the regression analysis on unpaired observations to integrate single-cell multi-omics data. On real and simulated data, UnpairReg provides an accurate estimation of cell gene expression where only chromatin accessibility data is available. The cis-regulatory network inferred from UnpairReg is highly consistent with eQTL mapping. UnpairReg improves cell type identification accuracy by joint analysis of single-cell gene expression and chromatin accessibility data.

Common Trajectories of Highly Effective CD19-Specific CAR T Cells Identified by Endogenous T-cell Receptor Lineages.

Utilizing clonal trajectories to define transcriptional potential, we find a unique signature of CAR T-cell effector precursors present in preinfusion cell products. Functional assessment of cells with this signature indicated early effector potential and resistance to exhaustion, consistent with postinfusion cellular patterns observed in patients. This article is highlighted in the In This Issue feature, p. 2007.

The SRPK inhibitor N-(2-(piperidin-1-yl)-5-(trifluoromethyl)phenyl) isonicotinamide (SRPIN340) increases the immune response against metastatic melanoma in mice

Cancers have a strong relationship with immune cells in their microenvironment, which significantly influences tumor proliferation and progression. Thus, pharmacological strategies that stimulate the immune system to combat tumor cells are promising for better therapeutic efficacy. Deregulated expression of the splicing regulatory serine arginine protein kinases (mostly SRPK1 and SRPK2) has been found in different cancer types, leading to the expression of isoforms related to tumor growth and metastasis. The microenvironment of melanoma exhibits a strong presence of immune cells, which significantly influences tumor progression, and around 50% of cutaneous melanoma patients benefit from targeted immunotherapy. Here, we analyzed human malignant melanoma single-cell gene expression data and observed that SRPK1/2 overexpression correlates with immune system pathway alterations. In further analysis, we observed an increased presence of immune cells in biopsies from mice bearing metastatic melanoma treated with SRPIN340, a well-known SRPK1/2 pharmacological inhibitor. Local treatments increased the expression of proinflammatory cytokines at the tumor lesions and the activity of the spleen, accompanied by reduced pulmonary metastasis foci, edema formation, and alveolar congestion. In in vitro assays, SRPIN340 also potentiated immunological susceptibility, by increasing the expression of the antigen presenting MHCI and MHCII molecules and by increasing the ability of B16F10 cells to attract splenic cells in transwell assays. Taken together, these results reveal that the antimetastatic effect of SRPIN340 can also involve an increased immune response, which suggests additional functional clues for SRPKs in tumor biology.

Discovering a Four-Gene Prognostic Model Based on Single-Cell Data and Gene Expression Data of Pancreatic Adenocarcinoma.

BackgroundPancreatic cancer has a 5-year overall survival lower than 8%. Pancreatic adenocarcinoma (PAAD) is the most common type. This study attempted to explore novel molecular subtypes and a prognostic model through analyzing tumor microenvironment (TME).Materials and MethodsSingle-cell RNA sequencing (scRNA-seq) data and expression profiles from public databases were downloaded. Three PAAD samples with single-cell data and 566 samples with gene expression data were included. Seurat was used to identify cell subsets. SVA merged and removed batch effects from multichip datasets. CIBERSORT was used to evaluate the components of different cells in transcriptome, ConsensusClusterPlus was used to identify molecular subtypes, and gene set enrichment analysis was used for functional enrichment analysis. LASSO Cox was performed to construct dimensionality reduction and prognosis model.ResultsMemory B cells (MBCs) were identified to be significantly with PAAD prognosis. Two immune subtypes (IS1 and IS2) with distinct overall survival were constructed. Forty-one DEGs were identified between IS1 and IS2. Four prognostic genes (ANLN, ARNTL2, SERPINB5, and DKK1) were screened to develop a prognostic model. The model was effective in classifying samples into high-risk and low-risk groups with distinct prognosis. Three subgroups of MBCs were identified, where MBC_0 and MBC_1 were differentially distributed between IS1 and IS2, high-risk and low-risk groups.ConclusionsMBCs were closely involved in PAAD progression, especially MBC_0 and MBC_1 subgroups. The four-gene prognostic model was predictive of overall survival and could guide immunotherapy for patients with PAAD.

Abstract 3392: Rapid, scalable isolation of human tumor nuclei for single cell genomics

Abstract Sample preparation remains a significant roadblock to the implementation and scaling of single cell technologies in clinical and translational research. Time consuming, custom dissociation protocols performed on fresh tissue by experts are often utilized to obtain high quality single cell data. However, these complex protocols result in low sample throughput and high variability driven by logistical challenges and user variability. Additionally, many samples that are of interest for clinical and translational studies arrive in research labs snap frozen, leaving nuclei extraction as the only viable path to generate single cell/nuclei data. To overcome these challenges, we present a scalable, easy to use, spin column-based nuclei isolation platform that generates single nuclei that are ready for input into downstream 10x Genomics single cell assays in under an hour. We processed eight unique human tumors in parallel (skin melanoma, breast, ovary, prostate, lung, kidney, colorectal, and pancreas). Starting from snap frozen tissue, we were able to generate single nuclei suspensions for all samples, in parallel, within 90 minutes. The resulting single nuclei were run through the 10x Genomics Single Cell Gene Expression, Single Cell Immune Profiling, and Single Cell ATAC workflows. Single cell gene expression data retained high data complexity across all tumor types tested. Expected cell type clusters, including tissue-specific tumor cells, surrounding/infiltrating immune cells and stromal cells were easily identified and profiled via gene expression analysis. Even fragile cell types often missing in single-cell data (e.g. mature adipocytes) were preserved and profiled in this experiment. Additionally, epigenetic changes as measured by Single Cell ATAC profiling were consistent with both general and tissue-specific epigenetics of each cancer type analyzed. In summary, this platform streamlines sample preparation for single cell studies from snap frozen tissue, enabling these samples to be used much more routinely and unlock biobanked sample cohorts. The findings presented here demonstrate that this fast and easy to use nuclei isolation system provides a consistent platform for generating high quality single nuclei data at scale for clinical, translational and basic research studies alike. Citation Format: Michael Gibbons, Alaina Puleo, Jill Herschleb, Sarah Taylor. Rapid, scalable isolation of human tumor nuclei for single cell genomics [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 3392.

DIRECT-NET: An efficient method to discover cis-regulatory elements and construct regulatory networks from single-cell multiomics data.

The emergence of single-cell multiomics data provides unprecedented opportunities to scrutinize the transcriptional regulatory mechanisms controlling cell identity. However, how to use those datasets to dissect the cis-regulatory element (CRE)–to–gene relationships at a single-cell level remains a major challenge. Here, we present DIRECT-NET, a machine-learning method based on gradient boosting, to identify genome-wide CREs and their relationship to target genes, either from parallel single-cell gene expression and chromatin accessibility data or from single-cell chromatin accessibility data alone. By extensively evaluating and characterizing DIRECT-NET’s predicted CREs using independent functional genomics data, we find that DIRECT-NET substantially improves the accuracy of inferring CRE-to-gene relationships in comparison to existing methods. DIRECT-NET is also capable of revealing cell subpopulation–specific and dynamic regulatory linkages. Overall, DIRECT-NET provides an efficient tool for predicting transcriptional regulation codes from single-cell multiomics data.

Ultra-rare and common genetic variant analysis converge to implicate negative selection and neuronal processes in the aetiology of schizophrenia

Both common and rare genetic variants (minor allele frequency >1% and <0.1% respectively) have been implicated in the aetiology of schizophrenia. In this study, we integrate single-cell gene expression data with publicly available Genome-Wide Association Study (GWAS) and exome sequenced data in order to investigate in parallel, the enrichment of common and (ultra-)rare variants related to schizophrenia in several functionally relevant gene-sets. Four types of gene-sets were constructed 1) protein-truncating variant (PTV)-intolerant (PI) genes 2) genes expressed in brain cell types and neurons ascertained from mouse and human brain tissue 3) genes defined by synaptic function and location and 4) intersection genes, i.e., PI genes that are expressed in the human and mouse brain cell gene-sets. We show that common as well as ultra-rare schizophrenia-associated variants are overrepresented in PI genes, in excitatory neurons from the prefrontal cortex and hippocampus, medium spiny neurons, and genes enriched for synaptic processes. We also observed stronger enrichment in the intersection genes. Our findings suggest that across the allele frequency spectrum, genes and genetic variants likely to be under stringent selection, and those expressed in particular brain cell types, are involved in the same biological pathways influencing the risk for schizophrenia.