Single-cell Gene Expression Research Articles

Biclustering analysis on tree-shaped time-series single cell gene expression data of Caenorhabditis elegans

BackgroundIn recent years, gene clustering analysis has become a widely used tool for studying gene functions, efficiently categorizing genes with similar expression patterns to aid in identifying gene functions. Caenorhabditis elegans is commonly used in embryonic research due to its consistent cell lineage from fertilized egg to adulthood. Biologists use 4D confocal imaging to observe gene expression dynamics at the single-cell level. However, on one hand, the observed tree-shaped time-series datasets have characteristics such as non-pairwise data points between different individuals. On the other hand, the influence of cell type heterogeneity should also be considered during clustering, aiming to obtain more biologically significant clustering results.ResultsA biclustering model is proposed for tree-shaped single-cell gene expression data of Caenorhabditis elegans. Detailedly, a tree-shaped piecewise polynomial function is first employed to fit non-pairwise gene expression time series data. Then, four factors are considered in the objective function, including Pearson correlation coefficients capturing gene correlations, p-values from the Kolmogorov-Smirnov test measuring the similarity between cells, as well as gene expression size and bicluster overlapping size. After that, Genetic Algorithm is utilized to optimize the function.ConclusionThe results on the small-scale dataset analysis validate the feasibility and effectiveness of our model and are superior to existing classical biclustering models. Besides, gene enrichment analysis is employed to assess the results on the complete real dataset analysis, confirming that the discovered biclustering results hold significant biological relevance.

Cell coupling compensates for changes in single-cell Her6 dynamics and provides phenotypic robustness.

We investigate the effect of altering the protein expression dynamics of the bHLH transcription factor Her6 at the single-cell level in the embryonic zebrafish telencephalon. Using an homozygote endogenous Her6:Venus reporter and 4D single-cell tracking, we show that Her6 oscillates in neural telencephalic progenitors and that fusion of protein destabilisation domain (PEST) alters its expression dynamics, causing most cells to downregulate Her6 prematurely. However, counterintuitively, oscillatory cells increase with some expressing Her6 at high levels, resulting in increased heterogeneity of Her6 expression in the population. These tissue-levels changes appear to be an emergent property of coupling between single-cells as revealed by experimentally disrupting Notch signalling and by computationally modelling alterations in Her6 protein stability. Despite the profound differences in the single-cell Her6 dynamics, the size of the telencephalon is only transiently altered, and differentiation markers do not exhibit significant differences early on, while a small increase is observed at later developmental stages. Our study suggests that cell coupling provides a compensation strategy, whereby an almost normal phenotype is maintained even though single-cell gene expression dynamics are abnormal, granting phenotypic robustness.

Adult Human, but Not Rodent, Spermatogonial Stem Cells Retain States with a Foetal-like Signature.

Spermatogenesis involves a complex process of cellular differentiation maintained by spermatogonial stem cells (SSCs). Being critical to male reproduction, it is generally assumed that spermatogenesis starts and ends in equivalent transcriptional states in related species. Based on single-cell gene expression profiling, it has been proposed that undifferentiated human spermatogonia can be subclassified into four heterogenous subtypes, termed states 0, 0A, 0B, and 1. To increase the resolution of the undifferentiated compartment and trace the origin of the spermatogenic trajectory, we re-analysed the single-cell (sc) RNA-sequencing libraries of 34 post-pubescent human testes to generate an integrated atlas of germ cell differentiation. We then used this atlas to perform comparative analyses of the putative SSC transcriptome both across human development (using 28 foetal and pre-pubertal scRNA-seq libraries) and across species (including data from sheep, pig, buffalo, rhesus and cynomolgus macaque, rat, and mouse). Alongside its detailed characterisation, we show that the transcriptional heterogeneity of the undifferentiated spermatogonial cell compartment varies not only between species but across development. Our findings associate 'state 0B' with a suppressive transcriptomic programme that, in adult humans, acts to functionally oppose proliferation and maintain cells in a ready-to-react state. Consistent with this conclusion, we show that human foetal germ cells-which are mitotically arrested-can be characterised solely as state 0B. While germ cells with a state 0B signature are also present in foetal mice (and are likely conserved at this stage throughout mammals), they are not maintained into adulthood. We conjecture that in rodents, the foetal-like state 0B differentiates at birth into the renewing SSC population, whereas in humans it is maintained as a reserve population, supporting testicular homeostasis over a longer reproductive lifespan while reducing mutagenic load. Together, these results suggest that SSCs adopt differing evolutionary strategies across species to ensure fertility and genome integrity over vastly differing life histories and reproductive timeframes.

ScGAL: unmask tumor clonal substructure by jointly analyzing independent single-cell copy number and scRNA-seq data

BackgroundAccurately deciphering clonal copy number substructure can provide insights into the evolutionary mechanism of cancer, and clustering single-cell copy number profiles has become an effective means to unmask intra-tumor heterogeneity (ITH). However, copy numbers inferred from single-cell DNA sequencing (scDNA-seq) data are error-prone due to technically confounding factors such as amplification bias and allele-dropout, and this makes it difficult to precisely identify the ITH.ResultsWe introduce a hybrid model called scGAL to infer clonal copy number substructure. It combines an autoencoder with a generative adversarial network to jointly analyze independent single-cell copy number profiles and gene expression data from same cell line. Under an adversarial learning framework, scGAL exploits complementary information from gene expression data to relieve the effects of noise in copy number data, and learns latent representations of scDNA-seq cells for accurate inference of the ITH. Evaluation results on three real cancer datasets suggest scGAL is able to accurately infer clonal architecture and surpasses other similar methods. In addition, assessment of scGAL on various simulated datasets demonstrates its high robustness against the changes of data size and distribution. scGAL can be accessed at: https://github.com/zhyu-lab/scgal.ConclusionsJoint analysis of independent single-cell copy number and gene expression data from a same cell line can effectively exploit complementary information from individual omics, and thus gives more refined indication of clonal copy number substructure.

Single-Cell Gene-Regulatory Networks of Advanced Symptomatic Atherosclerosis.

While our understanding of the single-cell gene expression patterns underlying the transformation of vascular cell types during the progression of atherosclerosis is rapidly improving, the clinical and pathophysiological relevance of these changes remains poorly understood. Single-cell RNA sequencing data generated with SmartSeq2 (≈8000 genes/cell) in nearly 19 000 single cells isolated during atherosclerosis progression in Ldlr-/-Apob100/100 mice with human-like plasma lipoproteins and from humans with asymptomatic and symptomatic carotid plaques was clustered into multiple subtypes. For clinical and pathophysiological context, the advanced-stage and symptomatic subtype clusters were integrated with 135 tissue-specific (atherosclerotic aortic wall, mammary artery, liver, skeletal muscle, and visceral and subcutaneous, fat) gene-regulatory networks (GRNs) inferred from 600 coronary artery disease patients in the STARNET (Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task) study. Advanced stages of atherosclerosis progression and symptomatic carotid plaques were largely characterized by 3 smooth muscle cells (SMCs), and 3 macrophage subtype clusters with extracellular matrix organization/osteogenic (SMC), and M1-type proinflammatory/Trem2-high lipid-associated (macrophage) phenotypes. Integrative analysis of these 6 clusters with STARNET revealed significant enrichments of 3 arterial wall GRNs: GRN33 (macrophage), GRN39 (SMC), and GRN122 (macrophage) with major contributions to coronary artery disease heritability and strong associations with clinical scores of coronary atherosclerosis severity (SYNTAX/Duke scores). The presence and pathophysiological relevance of GRN39 were verified in 5 independent RNAseq data sets obtained from the human coronary and aortic artery, and primary SMCs and by targeting its top-key drivers, FRZB and ALCAM, in cultured human vascular SMCs. By identifying and integrating the most gene-rich single-cell subclusters of atherosclerosis to date with a coronary artery disease framework of GRNs, GRN39 was identified and independently validated as being critical for the transformation of contractile SMCs into an osteogenic phenotype promoting advanced-stage, symptomatic atherosclerosis.

An orchestra of machine learning methods reveals landmarks in single-cell data exemplified with aging fibroblasts.

In this work, a Python framework for characteristic feature extraction is developed and applied to gene expression data of human fibroblasts. Unlabeled feature selection objectively determines groups and minimal gene sets separating groups. ML explainability methods transform the features correlating with phenotypic differences into causal reasoning, supported by further pipeline and visualization tools, allowing user knowledge to boost causal reasoning. The purpose of the framework is to identify characteristic features that are causally related to phenotypic differences of single cells. The pipeline consists of several data science methods enriched with purposeful visualization of the intermediate results in order to check them systematically and infuse the domain knowledge about the investigated process. A specific focus is to extract a small but meaningful set of genes to facilitate causal reasoning for the phenotypic differences. One application could be drug target identification. For this purpose, the framework follows different steps: feature reduction (PFA), low dimensional embedding (UMAP), clustering ((H)DBSCAN), feature correlation (chi-square, mutual information), ML validation and explainability (SHAP, tree explainer). The pipeline is validated by identifying and correctly separating signature genes associated with aging in fibroblasts from single-cell gene expression measurements: PLK3, polo-like protein kinase 3; CCDC88A, Coiled-Coil Domain Containing 88A; STAT3, signal transducer and activator of transcription-3; ZNF7, Zinc Finger Protein 7; SLC24A2, solute carrier family 24 member 2 and lncRNA RP11-372K14.2. The code for the preprocessing step can be found in the GitHub repository https://github.com/AC-PHD/NoLabelPFA, along with the characteristic feature extraction https://github.com/LauritzR/characteristic-feature-extraction.

ScPRAM accurately predicts single-cell gene expression perturbation response based on attention mechanism.

With the rapid advancement of single-cell sequencing technology, it becomes gradually possible to delve into the cellular responses to various external perturbations at the gene expression level. However, obtaining perturbed samples in certain scenarios may be considerably challenging, and the substantial costs associated with sequencing also curtail the feasibility of large-scale experimentation. A repertoire of methodologies has been employed for forecasting perturbative responses in single-cell gene expression. However, existing methods primarily focus on the average response of a specific cell type to perturbation, overlooking the single-cell specificity of perturbation responses and a more comprehensive prediction of the entire perturbation response distribution. Here we present scPRAM, a method for predicting Perturbation Responses in single-cell gene expression based on Attention Mechanisms. Leveraging variational autoencoders and optimal transport, scPRAM aligns cell states before and after perturbation, followed by accurate prediction of gene expression responses to perturbations for unseen cell types through attention mechanisms. Experiments on multiple real perturbation datasets involving drug treatments and bacterial infections demonstrate that scPRAM attains heightened accuracy in perturbation prediction across cell types, species, and individuals, surpassing existing methodologies. Furthermore, scPRAM demonstrates outstanding capability in identifying differentially expressed genes under perturbation, capturing heterogeneity in perturbation responses across species, and maintaining stability in the presence of data noise and sample size variations. https://github.com/jiang-q19/scPRAM and https://doi.org/10.5281/zenodo.10935038. Supplementary data are available at Bioinformatics online.

Simultaneous single-cell three-dimensional genome and gene expression profiling uncovers dynamic enhancer connectivity underlying olfactory receptor choice.

The simultaneous measurement of three-dimensional (3D) genome structure and gene expression of individual cells is critical for understanding a genome's structure-function relationship, yet this is challenging for existing methods. Here we present 'Linking mRNA to Chromatin Architecture (LiMCA)', which jointly profiles the 3D genome and transcriptome with exceptional sensitivity and from low-input materials. Combining LiMCA and our high-resolution scATAC-seq assay, METATAC, we successfully characterized chromatin accessibility, as well as paired 3D genome structures and gene expression information, of individual developing olfactory sensory neurons. We expanded the repertoire of known olfactory receptor (OR) enhancers and discovered unexpected rules of their dynamics: OR genes and their enhancers are most accessible during early differentiation. Furthermore, we revealed the dynamic spatial relationship between ORs and enhancers behind stepwise OR expression. These findings offer valuable insights into how 3D connectivity of ORs and enhancers dynamically orchestrate the 'one neuron-one receptor' selection process.

Inferring gene regulatory networks from single-cell multiome data using atlas-scale external data.

Existing methods for gene regulatory network (GRN) inference rely on gene expression data alone or on lower resolution bulk data. Despite the recent integration of chromatin accessibility and RNA sequencing data, learning complex mechanisms from limited independent data points still presents a daunting challenge. Here we present LINGER (Lifelong neural network for gene regulation), a machine-learning method to infer GRNs from single-cell paired gene expression and chromatin accessibility data. LINGER incorporates atlas-scale external bulk data across diverse cellular contexts and prior knowledge of transcription factor motifs as a manifold regularization. LINGER achieves a fourfold to sevenfold relative increase in accuracy over existing methods and reveals a complex regulatory landscape of genome-wide association studies, enabling enhanced interpretation of disease-associated variants and genes. Following the GRN inference from reference single-cell multiome data, LINGER enables the estimation of transcription factor activity solely from bulk or single-cell gene expression data, leveraging the abundance of available gene expression data to identify driver regulators from case-control studies.

SingmiR: a single-cell miRNA alignment and analysis tool.

Single-cell RNA sequencing(RNA-seq) has revolutionized our understanding of cell biology, developmental and pathophysiological molecular processes, paving the way toward novel diagnostic and therapeutic approaches. However, most of the gene regulatory processes on the single-cell level are still unknown, including post-transcriptional control conferred by microRNAs (miRNAs). Like the established single-cell gene expression analysis, advanced computational expertise is required to comprehensively process newly emerging single-cell miRNA-seq datasets. A web server providing a workflow tailored for single-cell miRNA-seq data with a self-explanatory interface is currently not available. Here, we present SingmiR, enabling the rapid (pre-)processing and quantification of human miRNAs from noncoding single-cell samples. It performs read trimming for different library preparation protocols, generates automated quality control reports and provides feature-normalized count files. Numerous standard and advanced analyses such as dimension reduction, clustered feature heatmaps, sample correlation heatmaps and differential expression statistics are implemented. We aim to speed up the prototyping pipeline for biologists developing single-cell miRNA-seq protocols on small to medium-sized datasets. SingmiR is freely available to all users without the need for a login athttps://www.ccb.uni-saarland.de/singmir.

Modeling type 1 diabetes progression using machine learning and single-cell transcriptomic measurements in human islets.

Type 1 diabetes (T1D) is a chronic condition in which beta cells are destroyed by immune cells. Despite progress in immunotherapies that could delay T1D onset, early detection of autoimmunity remains challenging. Here, we evaluate the utility of machine learning for early prediction of T1D using single-cell analysis of islets. Using gradient-boosting algorithms, we model changes in gene expression of single cells from pancreatic tissues in T1D and non-diabetic organ donors. We assess if mathematical modeling could predict the likelihood of T1D development in non-diabetic autoantibody-positive donors. While most autoantibody-positive donors are predicted to be non-diabetic, select donors with unique gene signatures are classified as T1D. Our strategy also reveals a shared gene signature in distinct T1D-associated models across cell types, suggesting a common effect of the disease on transcriptional outputs of these cells. Our study establishes a precedent for using machine learning in early detection of T1D.

Secondary bone marrow graft loss after third-party virus-specific T cell infusion: Case report of a rare complication

Virus-specific T cells (VST) from partially-HLA matched donors have been effective for treatment of refractory viral infections in immunocompromised patients in prior studies with a good safety profile, but rare adverse events have been described. Here we describe a unique and severe adverse event of VST therapy in an infant with severe combined immunodeficiency, who receives, as part of a clinical trial (NCT03475212), third party VSTs for treating cytomegalovirus viremia following bone marrow transplantation. At one-month post-VST infusion, rejection of graft and reversal of chimerism is observed, as is an expansion of T cells exclusively from the VST donor. Single-cell gene expression and T cell receptor profiling demonstrate a narrow repertoire of predominantly activated CD4+ T cells in the recipient at the time of rejection, with the repertoire overlapping more with that of peripheral blood from VST donor than the infused VST product. This case thus demonstrates a rare but serious side effect of VST therapy.

Stochastic modeling of a gene regulatory network driving B cell development in germinal centers.

Germinal centers (GCs) are the key histological structures of the adaptive immune system, responsible for the development and selection of B cells producing high-affinity antibodies against antigens. Due to their level of complexity, unexpected malfunctioning may lead to a range of pathologies, including various malignant formations. One promising way to improve the understanding of malignant transformation is to study the underlying gene regulatory networks (GRNs) associated with cell development and differentiation. Evaluation and inference of the GRN structure from gene expression data is a challenging task in systems biology: recent achievements in single-cell (SC) transcriptomics allow the generation of SC gene expression data, which can be used to sharpen the knowledge on GRN structure. In order to understand whether a particular network of three key gene regulators (BCL6, IRF4, BLIMP1), influenced by two external stimuli signals (surface receptors BCR and CD40), is able to describe GC B cell differentiation, we used a stochastic model to fit SC transcriptomic data from a human lymphoid organ dataset. The model is defined mathematically as a piecewise-deterministic Markov process. We showed that after parameter tuning, the model qualitatively recapitulates mRNA distributions corresponding to GC and plasmablast stages of B cell differentiation. Thus, the model can assist in validating the GRN structure and, in the future, could lead to better understanding of the different types of dysfunction of the regulatory mechanisms.

ScINRB: single-cell gene expression imputation with network regularization and bulk RNA-seq data.

Single-cell RNA sequencing (scRNA-seq) facilitates the study of cell type heterogeneity and the construction of cell atlas. However, due to its limitations, many genes may be detected to have zero expressions, i.e. dropout events, leading to bias in downstream analyses and hindering the identification and characterization of cell types and cell functions. Although many imputation methods have been developed, their performances are generally lower than expected across different kinds and dimensions of data and application scenarios. Therefore, developing an accurate and robust single-cell gene expression data imputation method is still essential. Considering to maintain the original cell-cell and gene-gene correlations and leverage bulk RNA sequencing (bulk RNA-seq) data information, we propose scINRB, a single-cell gene expression imputation method with network regularization and bulk RNA-seq data. scINRB adopts network-regularized non-negative matrix factorization to ensure that the imputed data maintains the cell-cell and gene-gene similarities and also approaches the gene average expression calculated from bulk RNA-seq data. To evaluate the performance, we test scINRB on simulated and experimental datasets and compare it with other commonly used imputation methods. The results show that scINRB recovers gene expression accurately even in the case of high dropout rates and dimensions, preserves cell-cell and gene-gene similarities and improves various downstream analyses including visualization, clustering and trajectory inference.

Abstract 7041: Tumor-immune interactions and cisplatin resistance in localized muscle-invasive bladder cancer

Abstract Background: Cisplatin-based chemotherapy followed by radical cystectomy remains the standard-of-care treatment for patient with localized muscle-invasive bladder cancer (MIBC). Neoadjuvant clinical trials studying the combination of cisplatin-based chemotherapy and immune checkpoint blockade (ICB) in MIBC reported encouraging pathologic complete response rates. However, the molecular mechanisms and cellular programs involved in response and resistance to cisplatin and ICB in MIBC remain incompletely characterized. Our goal is to define cellular transcriptional programs and chromatin accessible regions in cancer cells and infiltrating immune cells associated with response and resistance to neoadjuvant systemic therapy. Methods: We performed single-cell Multiome ATAC and Gene expression sequencing (simultaneous identification of gene expression and open chromatin regions within the same cell) of tumors that had a complete response – “responders” – and tumors that had no response or progressed – “non-responders” – through neoadjuvant cisplatin-based chemotherapy (n=15). Single cell suspensions were prepared following the 10x Genomics protocol. Cell Ranger ARC was used for sequence alignment, peak calling, and generation of count matrices. Downstream single-cell gene expression analysis was performed with Scanpy and ATAC-seq with Seurat. Results: We identified two distinct urothelial cell populations in "responders" vs "non-responders" based on single cell gene expression and genome wide chromatin accessibility regions. Cancer cells in “non-responders” preferentially express basal cell gene expression signatures. “Non-responders” were significantly enriched in suppressive tumor-associated macrophages (TAMs). Interestingly, SPP1+ TAMs known to have a pro-tumoral role and promote resistance to ICB were exclusively identified in “non-responders”. Conclusion: Single cell and genome wide chromatin accessibility regions identify heterogenous urothelial cell populations expressing basal cell markers in tumors resistant to neoadjuvant cisplatin-based chemotherapy. “Non-responders” are significantly enriched with TAMs implicated in resistance to ICB. Future studies will better define the implications of these findings in cancer therapeutics for patients with MIBC. Citation Format: Filipe La Fuente De Carvalho, Jihyun Lee, Kevin Bi, Breanna Titchen, Konrad Stawiski, Jihye Park, Amanda Garza, Daniel Michaud, Jennifer Guerriero, Elizabeth Mittendorf, Kent Mouw, Eliezer Van Allen. Tumor-immune interactions and cisplatin resistance in localized muscle-invasive bladder cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7041.

Abstract 995: Preservation of patient whole blood preserves cancer immune cell biology at single cell resolution

Abstract Blood is an easily accessible clinical sample type that helps inform treatments, investigate disease mechanisms, and monitor outcomes. Logistical challenges in the transportation and processing of blood samples impact data output from single cell RNA sequencing (scRNA-seq). Recent scRNA-seq studies have shown that significant changes in gene expression and immune cell populations occur in as little as a few hours post collection. Thus, there is a need for rapid preservation of blood upon collection before biological changes can occur. Existing preservation approaches compromise the cell membrane, making samples incompatible with single cell transcriptomic analyses. Here, we describe a novel workflow for fixation and long-term storage of whole blood, and isolation of peripheral blood mononuclear cells (PBMCs) from the fixed blood using a readily available, low-cost, high-throughput procedure. We then profile the gene expression of these cells using the fixation-compatible Single Cell Gene Expression Flex workflow. Importantly, this novel blood collection and preparation workflow eases blood transportation logistical constraints, allowing distributed sample collection and batched shipping of blood samples for scRNA-seq and potentially enabling large-scale translational research studies. We compared whole blood fixed immediately after collection using our workflow with clinical blood samples stored at ambient temperature. This revealed that immune cell populations and biological pathways shift significantly as early as five hours following sample collection, if not immediately stabilized. Across a time course of ambient temperature storage of whole blood without immediate stabilization, we observe significant upregulation of stress-related genes (FOS, JUN, CIRBP) in samples using Single Cell Gene Expression Flex profiling. Additionally, expression of a proto-oncogene TNFAIP3 was upregulated in whole blood stored at 20°C compared to samples stored at 4°C for the same lengths of time, unless samples were stabilized using the novel workflow shown here. This novel whole blood fixation method, coupled with automatable cell isolation methods and Single Cell Gene Expression Flex-enabled multiplexed single cell sequencing, will usher in a new age of economical, large-scale translational research studies that result in valuable clinical insights. Citation Format: Kelly Martin, Connor Kunihiro, Jawad Abousoud, Tingsheng Drennon, Yina Li, Sean Marrache, Nandhini Raman, Veronica Rodriguez, Paul Lund, Jens Durruthy Durruthy, Sarah Taylor, Andrew Kohlway, Peter Smibert, Dagmar Walter, Jill Herschleb. Preservation of patient whole blood preserves cancer immune cell biology at single cell resolution [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 995.

Abstract 2278: Unraveling immune dynamics of CD4+ in non-small cell lung cancer through single-cell analysis and gene expression profiling

Abstract Non-small cell lung cancer (NSCLC) is a type of epithelial lung cancer other than small cell lung cancer and represents the majority of cases in lung cancer. The immune response against NSCLC relies significantly on the vital role by CD4+ T cells, contributing to the regulation of immune system and the suppression of tumor growth. Various CD4+ T cell subtypes, such as T conventional cells (Tconv cells) and T regulatory cells (Treg cells), perform distinct functions within the immune system. Tconv cells boost immune responses against specific pathogens, while Treg cells play a role in suppressing immune reactions. Investigating the correlation between Tconv cells and Treg cells in NSCLC could serve a valuable biomarker for evaluating the immune status of lung cancer patients. We examine the connection between particular CD4+ T cells in the lung and specific gene expressions associated with NSCLC. Through single-cell analysis, we classified the proportions of Treg cells and Tconv cells within CD4+ T cells in both the lungs and spleen of mice. Our discovery revealed that the proportion of Tconv cells in the lungs exceeded that in the spleen. From the differential gene expression (DEG) analysis using RNA-Seq data for Treg cells and Tconv cells in NSCLC, compared to the control group of Peripheral Blood Mononuclear Cells (PMBC), we identified 1823 genes uniquely expressed in NSCLC Treg cells and 711 genes in Tconv cells. Within these genes, we verified the expression of IL2RA and CTLA4, known to be expressed in NSCLC. Additionally, we identified high expression of genes TM4SF1 and KRT6A. By comparing the genes specific to Tconv and Treg cells in the lungs with those significantly expressed in NSCLC, we were able to identify 114 co-expressed genes in Treg cells and 71 in Tconv cells. We conducted GO and KEGG pathway analyses on these genes and examined the interaction networks among the associated genes. Furthermore, we found receptor interactions between Tconv cell subtypes (Th1, Th2, Th17, and Tfh) and Treg cells through cell-cell interaction analysis. We explored the mouse phenotypes associated with these genes. These discoveries unveiled connections within lung-specific gene networks of Treg and Tconv cells associated with NSCLC, highlighting candidate genes for targeted therapy in cancer. Citation Format: Hyunmin Woo, JiHye Yang, Siyoung Yang, Seong-il Eyun. Unraveling immune dynamics of CD4+ in non-small cell lung cancer through single-cell analysis and gene expression profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2278.

Abstract 3952: Multiomic single-cell tumor evolution models of minimal residual disease in pediatric B-cell acute lymphoblastic leukemia

Abstract Detection of minimal residual disease (MRD) after initial diagnosis (IDX) and therapy is a strong prognostic marker for relapse in pediatric B-cell acute lymphoblastic leukemia (B-ALL). Previous studies have explored clonal genetic and gene expression changes between initial diagnosis and relapse. However, studying tumor cells present at MRD is challenging due to their low abundance. Examining the genomic profiles of leukemia cells that resist induction chemotherapy and understanding their evolutionary relationship to cells present at diagnosis may lead to a better understanding of the cells that eventually seed relapse. We analyzed somatic point mutations, indels, and copy number variants from 41 pediatric B-ALL patients with high-risk B-ALL. We specifically focused on patients from the following four common high-risk subtypes: KMT2A-rearranged, Ph+, Ph-like, and iAMP21, who had detectable disease at the end of induction. Leukemic burden at end-induction (day 29) ranged from 0.03% to 66.00%. Data were generated in partnership with the National Cancer Institute’s Human Tumor Atlas Network and the Children’s Oncology Group. Our data set of 41 patients includes 39 with paired initial diagnostic and end-induction samples that we used to create tumor evolution models based on single cells across disease stages. We produced single-cell multiome gene expression (scRNA) and chromatin accessibility (scATAC) profiles (10x Genomics) with a median of 7428 leukemic blast cells at IDX (range: 2490-14219) and 1907 at MRD (range: 240-10167) after filtering. We called somatic mutations from scRNA and scATAC using SComatic and Monopogen, and we detected copy number changes using Numbat and EpiAneufinder. We also designed a targeted DNA sequencing panel including 335 variants based on B-ALL literature and database searches to produce scDNA (Mission Bio) for 24 patients (16 IDX-MRD pairs) with a median of 2346 cells profiled at IDX (range: 340-8849) and 1828 at MRD (range: 596-5133). We built tumor phylogenies based on somatic SNVs and CNVs with COMPASS. Using multiple tools and complementary single-cell omics data types to detect somatic changes improved our confidence in the events used for tumor evolution tree construction. Our approach allowed us to define recurrent genomic events and patterns across pediatric B-ALL subtypes and to describe the clonal evolution of cells measured at initial diagnostic and end-induction disease stages. We were also able to link cell-specific mutation events to their transcriptional and epigenomic signatures, including mapping mutation events along the B-cell development trajectory and within differentially expressed gene pathways. Some cases showed dramatic shifts in subclonal structure between IDX and MRD that may be related to therapy-induced clonal selection, such as a loss of NRAS hotspot mutation. Citation Format: Steven M. Foltz, Avi Loren, Changya Chen, Rushabh Mehta, Elizabeth Li, Jason Xu, Fatemeh Alikarami, Kathrin M. Bernt, Kai Tan. Multiomic single-cell tumor evolution models of minimal residual disease in pediatric B-cell acute lymphoblastic leukemia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3952.

Abstract 313: Benchmarking immune repertoire sequencing technologies

Abstract In the past decade, single cell gene expression technologies have transformed basic and translational research and are more frequently found in the toolbox of immunologists alongside the standard bulk B-cell receptor (BCR) sequencing and transcriptome profiling assays. While conventional bulk BCR assays capture the frequency of single chains, single-cell immune profiling reveals the frequency of paired heavy and light chains along with the cells transcriptional profile. Here we present the results of a single-blind benchmarking experiment conducted in collaboration with the U.S. Food and Drug Administration (FDA) that compares the performance of standard bulk DNA and RNA BCR repertoire sequencing assays, with the 10x Genomics Single Cell Immune Profiling solution. The FDA selected nine unambiguously monoclonal BCR-expressing cell lines to generate a reference mixture that was shared with each study participant, without revealing the input proportions. This reference cell line mixture was also diluted to 10% and 1% with B-cells sorted from peripheral blood mononuclear cells (PBMC) to further test the limits of clonotype detection. For each experiment, we targeted ~10,000 cells and in aggregate generated a paired gene expression and V(D)J dataset from ~100,000 cells. BCR clonotypes were detected in 78% of the captured cells across all samples profiled. Each of the 9 top unique clonotypes observed in our dataset corresponded to a cell line in the reference mixture, allowing us to decode the mixing percentages of each BCR-expression cell line. In addition, we were able to identify rare cell line clonotypes to as low as 0.01% in the dilution experiments. Furthermore, the multimodal nature of the single cell data also resolved the transcriptional profile of each cell line into its own cluster thus enabling independent verification of clonotype proportions. In summary, we have generated a resource for the immunology community to evaluate and compare data generated with different repertoire sequencing technologies. Citation Format: Shamoni Maheshwari, Carolyn Morrison, Sarah Taylor, BCR-SEQC consortium. Benchmarking immune repertoire sequencing technologies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 313.

Abstract 1273: Tumor progression and tumor microenvironment of pancreas cancer ascites revealed by scRNA-seq and spatial transcriptomics

Abstract Peritoneal metastasis of pancreas cancer is one of the ultimate modes of spread in the cancer. Little is known about how cancer cells spread into abdominal cavity and what is different from hematogenous metastasis. It is also not well characterized how tumor microenvironment prevent cancer cells from spreading in abdominal cavity. We performed single-cell RNA-seq analysis and special transcriptomic analysis to clarify the progression and environment of the cancer. We collected and processed ascites samples of 20 PDAC samples from 19 patients. Sixteen ascites samples were obtained from abdominal paracentesis and two cases in the postmortem period. For one patient ascites was collected both pre and postmortem. All but one patient received chemotherapy prior to ascites collection. Routine clinical cytology evaluation of ascites samples from 17 patients indicated it was malignant in 11 patients and nonmalignant in six patients. All ascites samples were purified by gradient centrifuge using Ficoll-Paque Plus to remove erythrocytes and necrotic debris. For the platform, we adopted the 10x Genomics Chromium Single Cell Gene Expression platform for single-cell RNA-seq library preparation. Spatial transcriptomics was performed for eight FFPE samples from the two autopsy patients with paired cancer ascites using 10x Visium. The CytAssist was used to transfer transcripts of pre-sectioned and pre-stained tissues with 11 × 11 mm Visium Capture Area slides. The sample locations were metastatic tumors to bowel wall, rib bone, mesentery, iliac nerve, diaphragm, peritoneum, pelvis, and primary pancreas tumor. A total of 248,263 cells were analyzed in single-cell RNA-seq. Cell population in pancreas cancer ascites consisted of macrophages, lymphocytes, dendritic cells, plasma cells, plasmacytoid dendritic cells, mesothelial cells, and cancer cells. Cancer cells were detected in twelve samples whereas no cancer cells were detected in eight samples. Gene ontology and gene set enrichment analyses showed macrophages in ascites with cancer cells involved neutrophil activation while those in ascites without cancer cells retained antigen presentation capacity and inducing interferon gamma pathway. Mesothelial cells in ascites with cancer cells showed more apoptosis and lymphocyte proliferation while those in ascites without cancers cells triggered fibrosis and mitosis. These data suggest that macrophage and mesothelial cells could be categorized into anti-tumor cells and tumor associated cells. Ongoing investigations are exploring ligand-receptor relationships. We are going to compare the expression profile of tumor cells in ascites fluid and in tissue from various organs by integrating single cell RNA-seq data and special transcriptomics data. Citation Format: Shigeaki Umeda, Elias-Ramzey Karnoub, Fiyinfolu Balogun, Alejandro Jiménez-Sánchez, Christine A. Iacobuzio-Donahue. Tumor progression and tumor microenvironment of pancreas cancer ascites revealed by scRNA-seq and spatial transcriptomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1273.