Single-cell Technologies Research Articles

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.

Modeling single cell trajectory using forward-backward stochastic differential equations.

Recent advances in single-cell sequencing technology have provided opportunities for mathematical modeling of dynamic developmental processes at the single-cell level, such as inferring developmental trajectories. Optimal transport has emerged as a promising theoretical framework for this task by computing pairings between cells from different time points. However, optimal transport methods have limitations in capturing nonlinear trajectories, as they are static and can only infer linear paths between endpoints. In contrast, stochastic differential equations (SDEs) offer a dynamic and flexible approach that can model non-linear trajectories, including the shape of the path. Nevertheless, existing SDE methods often rely on numerical approximations that can lead to inaccurate inferences, deviating from true trajectories. To address this challenge, we propose a novel approach combining forward-backward stochastic differential equations (FBSDE) with a refined approximation procedure. Our FBSDE model integrates the forward and backward movements of two SDEs in time, aiming to capture the underlying dynamics of single-cell developmental trajectories. Through comprehensive benchmarking on multiple scRNA-seq datasets, we demonstrate the superior performance of FBSDE compared to other methods, highlighting its efficacy in accurately inferring developmental trajectories.

Precision isolation and cultivation of single cells by vortex and flat-top laser ejection

Single-cell isolation stands as a critical step in single-cell studies, and single-cell ejection technology based on laser induced forward transfer technology (LIFT) is considered one of the most promising methods in this regard for its ability of visible isolating single cell from complex samples. In this study, we improve the LIFT technology and introduce optical vortex laser-induced forward transfer (OV-LIFT) and flat-top laser-induced forward transfer (FT-LIFT) by utilizing spatial light modulator (SLM), aiming to enhance the precision of single-cell sorting and the cell’s viability after ejection. Experimental results demonstrate that applying vortex and flat-top beams during the sorting and collection process enables precise retrieval of single cells within diameter ranges of 50 μm and 100 μm, respectively. The recovery rates of Saccharomyces cerevisiae and Escherichia coli DH5α single cell ejected by vortex beam are 89 and 78%, by flat-top beam are 85 and 57%. When employing Gaussian beam sorting, the receiving range extends to 400 μm, with cultivation success rates of S. cerevisiae and E. coli DH5α single cell are 48 and 19%, respectively. This marks the first application of different mode beams in the ejection and cultivation of single cells, providing a novel and effective approach for the precise isolation and improving the viability of single cells.

Mapping Human Immunity and the Education of Waldeyer's Ring.

The development and deployment of single-cell genomic technologies have driven a resolution revolution in our understanding of the immune system, providing unprecedented insight into the diversity of immune cells present throughout the body and their function in health and disease. Waldeyer's ring is the collective name for the lymphoid tissue aggregations of the upper aerodigestive tract, comprising the palatine, pharyngeal (adenoids), lingual, and tubal tonsils. These tonsils are the first immune sentinels encountered by ingested and inhaled antigens and are responsible for mounting the first wave of adaptive immune response. An effective mucosal immune response is critical to neutralizing infection in the upper airway and preventing systemic spread, and dysfunctional immune responses can result in ear, nose, and throat pathologies. This review uses Waldeyer's ring to demonstrate how single-cell technologies are being applied to advance our understanding of the immune system and highlight directions for future research.

Droplet-based microfluidic platform for detecting agonistic peptides that are self-secreted by yeast expressing a G-protein-coupled receptor

BackgroundSingle-cell droplet microfluidics is an important platform for high-throughput analyses and screening because it provides an independent and compartmentalized microenvironment for reaction or cultivation by coencapsulating individual cells with various molecules in monodisperse microdroplets. In combination with microbial biosensors, this technology becomes a potent tool for the screening of mutant strains. In this study, we demonstrated that a genetically engineered yeast strain that can fluorescently sense agonist ligands via the heterologous expression of a human G-protein-coupled receptor (GPCR) and concurrently secrete candidate peptides is highly compatible with single-cell droplet microfluidic technology for the high-throughput screening of new agonistically active peptides.ResultsThe water-in-oil microdroplets were generated using a flow-focusing microfluidic chip to encapsulate engineered yeast cells coexpressing a human GPCR [i.e., angiotensin II receptor type 1 (AGTR1)] and a secretory agonistic peptide [i.e., angiotensin II (Ang II)]. The single yeast cells cultured in the droplets were then observed under a microscope and analyzed using image processing incorporating machine learning techniques. The AGTR1-mediated signal transduction elicited by the self-secreted Ang II peptide was successfully detected via the expression of a fluorescent reporter in single-cell yeast droplet cultures. The system could also distinguish Ang II analog peptides with different agonistic activities. Notably, we further demonstrated that the microenvironment of the single-cell droplet culture enabled the detection of rarely existing positive (Ang II-secreting) yeast cells in the model mixed cell library, whereas the conventional batch-culture environment using a shake flask failed to do so. Thus, our approach provided compartmentalized microculture environments, which can prevent the diffusion, dilution, and cross-contamination of peptides secreted from individual single yeast cells for the easy identification of GPCR agonists.ConclusionsWe established a droplet-based microfluidic platform that integrated an engineered yeast biosensor strain that concurrently expressed GPCR and self-secreted the agonistic peptides. This offers individually isolated microenvironments that allow the culture of single yeast cells secreting these peptides and gaging their signaling activities, for the high-throughput screening of agonistic peptides. Our platform base on yeast GPCR biosensors and droplet microfluidics will be widely applicable to metabolic engineering, environmental engineering, and drug discovery.

Abstract LB314: Organ-specific roles of CD83+ macrophages in breast cancer

Abstract The purpose of this study is to understand how heterogeneity in organ-specific immune infiltrates can lead to Triple Negative Breast Cancer (TNBC) progression and immunotherapies resistance in metastasis. Both innate and adaptive immune responses are essential for breast cancer progression. Using single-cell multiplexing technology on human and murine models of primary and metastatic breast cancers, we investigated the pattern of immune cell infiltration in bone and lung metastases in a temporal manner. The parallel profiling of metastasis sites revealed a strong involvement of the innate immunity in restraining tumor progression with an acute increase in Mrc1+ Cd83+ tumor-associated macrophages at the early stage of lung colonization. Interestingly, the increase was modest in bone metastasis and remained consistent overtime despite bone metastasis progression. In contrast, we saw an increase in the number of Ly6G+ Lrg1+ granulocytes. Profiling of paired TNBC patient samples from the primary and metastatic sites have shown an immunologically quiescent tumor microenvironment in metastasis as compared to the primary tumors. Hence, to further explore the role of Mrc1+ Cd83+ macrophages on therapeutic resistance, we profiled the orthotopic claudin-low TNBC models, T11 and T12, following their exposure to chemotherapy. While responsive tumors displayed a decreased infiltration of Mrc1+ Cd83+ macrophages, the opposite phenotype was observed in non-responders, suggesting their involvement in tumor progression. Overall, our results suggest differential responses in untreated lung/bone metastases and primary tumors indicating divergent roles for Cd83+ TAMs in restraining or promoting tumor progression, while also indicating some crosstalk between neutrophils and stromal cells that aid in bone metastatic progression. These site-specific differences in immune cell phenotype indicate that there is a great need to thoroughly characterize the tumor immune compartment in both the primary and the metastatic setting to improve treatment responses. Considering the inhibitory effect of macrophages on T cells in these models, this study highlights a tissue-specific reprogramming of immune cells in breast cancer metastasis. Citation Format: Swarnima Singh, Igor Bado. Organ-specific roles of CD83+ macrophages in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB314.

The Role of Immune Cells in DKD: Mechanisms and Targeted Therapies.

Diabetic kidney disease (DKD), is a common microvascular complication and a major cause of death in patients with diabetes. Disorders of immune cells and immune cytokines can accelerate DKD development of in a number of ways. As the kidney is composed of complex and highly differentiated cells, the interactions among different cell types and immune cells play important regulatory roles in disease development. Here, we summarize the latest research into the molecular mechanisms underlying the interactions among various immune and renal cells in DKD. In addition, we discuss the most recent studies related to single cell technology and bioinformatics analysis in the field of DKD. The aims of our review were to explore immune cells as potential therapeutic targets in DKD and provide some guidance for future clinical treatments.

Deep Learning in Single-cell Analysis

Single-cell technologies are revolutionizing the entire field of biology. The large volumes of data generated by single-cell technologies are high dimensional, sparse, and heterogeneous and have complicated dependency structures, making analyses using conventional machine learning approaches challenging and impractical. In tackling these challenges, deep learning often demonstrates superior performance compared to traditional machine learning methods. In this work, we give a comprehensive survey on deep learning in single-cell analysis. We first introduce background on single-cell technologies and their development, as well as fundamental concepts of deep learning including the most popular deep architectures. We present an overview of the single-cell analytic pipeline pursued in research applications while noting divergences due to data sources or specific applications. We then review seven popular tasks spanning different stages of the single-cell analysis pipeline, including multimodal integration, imputation, clustering, spatial domain identification, cell-type deconvolution, cell segmentation, and cell-type annotation. Under each task, we describe the most recent developments in classical and deep learning methods and discuss their advantages and disadvantages. Deep learning tools and benchmark datasets are also summarized for each task. Finally, we discuss the future directions and the most recent challenges. This survey will serve as a reference for biologists and computer scientists, encouraging collaborations.

DELVE: feature selection for preserving biological trajectories in single-cell data

Single-cell technologies can measure the expression of thousands of molecular features in individual cells undergoing dynamic biological processes. While examining cells along a computationally-ordered pseudotime trajectory can reveal how changes in gene or protein expression impact cell fate, identifying such dynamic features is challenging due to the inherent noise in single-cell data. Here, we present DELVE, an unsupervised feature selection method for identifying a representative subset of molecular features which robustly recapitulate cellular trajectories. In contrast to previous work, DELVE uses a bottom-up approach to mitigate the effects of confounding sources of variation, and instead models cell states from dynamic gene or protein modules based on core regulatory complexes. Using simulations, single-cell RNA sequencing, and iterative immunofluorescence imaging data in the context of cell cycle and cellular differentiation, we demonstrate how DELVE selects features that better define cell-types and cell-type transitions. DELVE is available as an open-source python package: https://github.com/jranek/delve.

FixNCut: single-cell genomics through reversible tissue fixation and dissociation

The use of single-cell technologies for clinical applications requires disconnecting sampling from downstream processing steps. Early sample preservation can further increase robustness and reproducibility by avoiding artifacts introduced during specimen handling. We present FixNCut, a methodology for the reversible fixation of tissue followed by dissociation that overcomes current limitations. We applied FixNCut to human and mouse tissues to demonstrate the preservation of RNA integrity, sequencing library complexity, and cellular composition, while diminishing stress-related artifacts. Besides single-cell RNA sequencing, FixNCut is compatible with multiple single-cell and spatial technologies, making it a versatile tool for robust and flexible study designs.

Clustering single-cell multi-omics data via graph regularized multi-view ensemble learning.

Single-cell clustering plays a crucial role in distinguishing between cell types, facilitating the analysis of cell heterogeneity mechanisms. While many existing clustering methods rely solely on gene expression data obtained from single-cell RNA sequencing techniques to identify cell clusters, the information contained in mono-omic data is often limited, leading to suboptimal clustering performance. The emergence of single-cell multi-omics sequencing technologies enables the integration of multiple omics data for identifying cell clusters, but how to integrate different omics data effectively remains challenging. Additionally, designing a clustering method that performs well across various types of multi-omics data poses a persistent challenge due to the data's inherent characteristics. In this paper, we propose a graph-regularized multi-view ensemble clustering (GRMEC-SC) model for single-cell clustering. Our proposed approach can adaptively integrate multiple omics data and leverage insights from multiple base clustering results. We extensively evaluate our method on five multi-omics datasets through a series of rigorous experiments. The results of these experiments demonstrate that our GRMEC-SC model achieves competitive performance across diverse multi-omics datasets with varying characteristics. Implementation of GRMEC-SC, along with examples, can be found on the GitHub repository: https://github.com/polarisChen/GRMEC-SC. Supplementary data are available at Bioinformatics online.

SEGCECO: Subgraph Embedding of Gene expression matrix for prediction of CEll-cell COmmunication.

Recent advances in single-cell RNA sequencing technology have eased analyses of signaling networks of cells. Recently, cell-cell interaction has been studied based on various link prediction approaches on graph-structured data. These approaches have assumptions about the likelihood of node interaction, thus showing high performance for only some specific networks. Subgraph-based methods have solved this problem and outperformed other approaches by extracting local subgraphs from a given network. In this work, we present a novel method, called Subgraph Embedding of Gene expression matrix for prediction of CEll-cell COmmunication (SEGCECO), which uses an attributed graph convolutional neural network to predict cell-cell communication from single-cell RNA-seq data. SEGCECO captures the latent and explicit attributes of undirected, attributed graphs constructed from the gene expression profile of individual cells. High-dimensional and sparse single-cell RNA-seq data make converting the data into a graphical format a daunting task. We successfully overcome this limitation by applying SoptSC, a similarity-based optimization method in which the cell-cell communication network is built using a cell-cell similarity matrix which is learned from gene expression data. We performed experiments on six datasets extracted from the human and mouse pancreas tissue. Our comparative analysis shows that SEGCECO outperforms latent feature-based approaches, and the state-of-the-art method for link prediction, WLNM, with 0.99 ROC and 99% prediction accuracy. The datasets can be found at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE84133 and the code is publicly available at Github https://github.com/sheenahora/SEGCECO and Code Ocean https://codeocean.com/capsule/8244724/tree.

Analysis of the effect of CCR7 on the microenvironment of mouse oral squamous cell carcinoma by single-cell RNA sequencing technology

BackgroundStudies have shown that CCR7, an important inflammatory factor, can promote the proliferation and metastasis of oral squamous cell carcinoma (OSCC), but its role in the tumor microenvironment (TME) remains unclear. This paper explores the role of CCR7 in the TME of OSCC.MethodsIn this work, we constructed CCR7 gene knockout mice and OSCC mouse models. Single-cell RNA sequencing (scRNA-seq) and bioinformatics were used to analyze the differences in the OSCC microenvironment between three CCR7 gene knockout mice (KO) and three wild-type mice (WT). Immunohistochemistry, immunofluorescence staining, and flow cytometry were used to analyze the expression of key genes in significantly different cell types between the KO and WT groups. An in vitro experiment was used to verify the effect of CCR7 on M2 macrophage polarization.ResultsIn the mouse OSCC models, the tumor growth rate in the KO group was significantly lower than that in the WT group. Eight main cell types (including tumor cells, fibroblasts, macrophages, granulocytes, T cells, endothelial cells, monocytes, and B cells) were identified by Seurat analysis. The scRNA-seq results showed that the proportion of tumor cells was lower, but the proportion of inflammatory cells was significantly higher in the KO group than in the WT group. CellPhoneDB analysis results indicated a strong interaction relationship between tumor cells and macrophages, T cells, fibroblasts, and endothelial cells. Functional enrichment results indicated that the expression level of the Dusp1 gene in the KO group was generally higher than that in the WT group in various cell types. Macrophage subclustering results indicated that the proportion of M2 macrophages in the KO group was lower than that in the WT group. In vitro experimental results showed that CCR7 can promote M2 macrophage polarization, thus promoting the proliferation, invasion and migration of OSCC cells.ConclusionsCCR7 gene knockout can significantly inhibit the growth of mouse oral squamous cell carcinoma by promoting the polarization of M2 macrophages.

Optimization of mouse kidney digestion protocols for single-cell applications.

Single-cell technologies such as flow cytometry and single-cell RNA sequencing (scRNAseq) have allowed for comprehensive characterisation of the kidney cellulome. However, there is disparity in the various protocols for preparing kidney single-cell suspensions. We aimed to address this limitation by characterising kidney cellular heterogeneity using three previously published single-cell preparation protocols. Single-cell suspensions were prepared from male and female C57BL/6 kidneys using the following kidney tissue dissociation protocols: (P1) a scRNAseq protocol; (P2) a multi-tissue digestion kit from Miltenyi Biotec; and (P3) a protocol established in our laboratory. Following dissociation, flow cytometry was used to identify known major cell types including leukocytes (myeloid and lymphoid), vascular cells (smooth-muscle and endothelial), nephron epithelial cells (intercalating, principal, proximal and distal tubule cells), podocytes, and fibroblasts. Of the protocols tested, P2 yielded significantly less leukocytes and type B-intercalating cells compared to the other techniques. P1 and P3 produced similar yields for most cell types, however, endothelial and myeloid-derived cells were significantly enriched using P1. Significant sex differences were detected in only two cell types: granulocytes (increased in males) and smooth muscle cells (increased in females). Future single-cell studies that aim to enrich for specific kidney cell types, may benefit from this comparative analysis.

Cryptosporidium Genomics — Current Understanding, Advances, and Applications

Abstract Purpose of Review Here we highlight the significant contribution that genomics-based approaches have had on the field of Cryptosporidium research and the insights these approaches have generated into Cryptosporidium biology and transmission. Recent Findings There are advances in genomics, genetic manipulation, gene expression, and single-cell technologies. New and better genome sequences have revealed variable sub-telomeric gene families and genes under selection. RNA expression data now include single-cell and post-infection time points. These data have provided insights into the Cryptosporidium life cycle and host–pathogen interactions. Antisense and ncRNA transcripts are abundant. The critical role of the dsRNA virus is becoming apparent. Summary The community’s ability to identify genomic targets in the abundant, yet still lacking, collection of genomic data, combined with their increased ability to assess function via gene knock-out, is revolutionizing the field. Advances in the detection of virulence genes, surveillance, population genomics, recombination studies, and epigenetics are upon us.

Abstract 1151: Reconstruction of the spatial ecosystem of glioblastoma reveals relationships between tumor cell states and microenvironment

Abstract Glioblastoma multiforme (GBM) is the most aggressive form of primary brain tumor, with no curative treatment options. Multiple studies have characterized at single cell resolution the GBM as being composed of transcriptional cell states interconnected with components in the tumor immune microenvironment (TME). Our group proposed and validated the first single cell guided functional classification of GBM in four tumor-intrinsic cell states which informed clinical outcome and delivered therapeutic options. However, single cell technologies are unable to unravel the spatial relationships among the cell states of GBM and between GBM cell states and TME. Spatially resolved transcriptomic technologies are emerging as powerful tools to reconstruct the spatial architecture of a tissue. We performed spatial transcriptomics of multicellular regions of interest (ROI) in 6 primary IDH wild-type GBM and 2 recurrent GBM with both CosMx Spatial Molecular Imager, which analyzes 1,000 RNA probes and 64 proteins at single cell resolution, and GeoMx Digital Spatial Profiler which profiles the whole transcriptome (~18,000 genes) at ROI resolution. The development of computational tools aimed to integrate spatial proximity and CosMx derived single-cell transcriptomics revealed spatial segregation of the tumor cell clones and cellular states and highlighted recurrent patterns of cell states, distinct TME cell types associated with coherent histopathological features across multiple samples. The development of a spatial informed intercellular communication algorithm and the reconstruction of ligand-receptor-target networks will allow the discovery of tumor cell states-TME cross-talks and the biological signaling regulated by these interactions that are driving the heterogeneity of GBM and therefore potentially therapeutically targetable. Analysis of matched regions of interest profiled by GeoMx and spatial proteomics with CosMx further cross-validated the spatial ecosystem of glioblastoma as reconstructed at single-cell resolution. Our studies established a scalable approach to resolve the transcriptional heterogeneity of GBM and reconstruct the architecture of GBM cell states and tumor microenvironment. Citation Format: Bruno Adabbo, Simona Migliozzi, Luciano Garofano, Fulvio D'Angelo, Pedro Davila, Sakir H. Gultekin, Daniel Bilbao Cortes, Benjamin Currall, Sion L. Williams, Marc Sanson, Franck Bielle, Anna Luisa Di Stefano, Michele Ceccarelli, Anna Lasorella, Antonio Iavarone. Reconstruction of the spatial ecosystem of glioblastoma reveals relationships between tumor cell states and microenvironment [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 1151.

Abstract 6934: Integrating deep learning morphology profiling, cell sorting, and scRNASeq reveals sample diversity and enriched sub-populations

Abstract Advances in single cell technologies have ushered in a new multi-omics era that has provided more granular information on cell types and functions at unprecedented resolution. Morpholomics (the information encoded by cell morphology) has traditionally been used as a readout of cell identity, state, and function; but scalability, interpretation, and sorting based on morphology has remained challenging. The Deepcell® platform, REM-I, performs high-dimensional morphology analysis of unlabeled, single cells using deep-learning on high-resolution bright-field images captured in microfluidic flow to profile and sort target cells in real-time and at scale. Morphology analysis of single cells is achieved by a self-supervised deep learning foundation model, ‘Human Foundation Model’ (HFM), which combines deep learning features with morphometrics. HFM determines cell identities using morphological features associated with distinct processes characterized by granules, vesicles, cell size, pigmentation. and others. To demonstrate the ability of HFM modeling to distinguish and characterize a diversity of cell types representative of tumor microenvironments, we processed various sample types on the REM-I platform. Human melanoma, immune cells, stromal cells, and dissociated biopsies were combined to recapitulate heterogeneous tumor samples. HFM analysis enabled morphology-based exploration of the resulting bright-field images, revealing the presence of various morphologically distinct populations within each sample. Tumor cells were morphologically distinct and clustered separately from non-tumor cells. Immune cells exhibited subtle but separable morphologies, including T cells of distinct activation states. To demonstrate the morphology-based sorting capabilities of REM-I, we applied it to a ‘reference sample’ consisting of several fixed cell lines, at proportions ranging from 1% to >90%. Using only brightfield images of single cells analyzed through HFM, we used the platform to enrich each cell line from the ‘reference sample’ with high yield and purity, and identified cell types constituting a small percentage (~1%) of the total population. We next showed that unlabeled cells enriched on REM-I that are characterized as unperturbed and viable are amenable to downstream molecular analyses. These cell populations were sorted and the retrieved live cells were analyzed by scRNA-Seq (10x Chromium), demonstrating enrichment of various cell types of the tumor microenvironment in each cell population relative to the pre-sorted sample. These results illustrate: 1) the ability to perform downstream molecular analyses on sorted cells from REM-I, 2) the platform can be used to parse the tumor microenvironment, and 3) the link between morphology, cell identity, and molecular signatures. Citation Format: Andreja Jovic, Christian Corona, Kiran Saini, Tiffine Pham, Ryan Carelli, Vivian Lu, Senzeyu Zhang, Stephane C. Boutet, Maddison Masaeli. Integrating deep learning morphology profiling, cell sorting, and scRNASeq reveals sample diversity and enriched sub-populations [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 6934.

Abstract 865: Single-cell sequencing identification of malignant cell subpopulations linked to recurrence in hepatocellular carcinoma

Abstract Introduction: Hepatocellular Carcinoma (HCC) is a highly malignant and challenging-to-treat tumor, with early recurrence affecting up to 35-50% of patients within two years after surgery, significantly impacting prognosis. Identifying high-risk patients for recurrence is critical for guiding postoperative adjuvant therapy. The rapidly advancing field of single-cell RNA sequencing technology offers an unprecedented opportunity to discover clinically relevant cell subpopulations. Here, we harnessed single-cell techniques to unveil a cell subpopulation closely linked with HCC recurrence, highlighting its clinical significance. Methods and Results: We conducted single-cell RNA sequencing on four HCC samples and harmonized data from three public datasets (GSE112271, GSE125449, CNP0000650) to elucidate the heterogeneity of malignant cells in HCC. Our investigation revealed a distinctive HCC subpopulation enriched in patients with early recurrence (11.4% vs. 3.8%, P=0.02). This subpopulation exhibited up-regulated STMN1 and EZH2, signifying high proliferative potential and a propensity for epithelial-mesenchymal transition (EMT). Applying the CIBERSORTx algorithm to the TCGA-LIHC dataset (N=323 patients), we inferred the proportion of this recurrence subpopulation among all malignant cells. Notably, patients with a higher proportion of this subpopulation (N=160) demonstrated significantly shorter recurrence-free survival (31.2 vs. 42.9 months, P=0.002), reduced median survival (45.9 vs 55.7 months, P=0.03) and a higher rate of microvascular invasion (35% vs. 23%, P=0.01). To validate this recurrence-related subpopulation in clinical samples, we conducted multiplex immunohistochemistry staining on paraffin-embedded (FFPE) HCC samples (including CD45, CD31, ARG1, STMN1, EZH2) and used machine learning for its identification. Our findings revealed that patients with a higher proportion of this recurrence subpopulation (N=40) experienced significantly shorter median survival (38.5 vs. 51.0 months, P<0.005) and progression-free survival (25.1 vs. 33.4 months, P=0.01) compared to a lower proportion (N=40). Multivariate Cox regression analysis confirmed that the proportion of this recurrence subpopulation is an independent predictor of worse prognosis (P=0.003). Conclusion: Through the power of single-cell RNA sequencing technology, we have unearthed and characterized a malignant cell subpopulation intricately connected with HCC recurrence. Importantly, this subpopulation can be identified in FFPE tissue samples and correlates robustly with patient prognosis. Our study underscores the transformative possibility of single-cell techniques in developing cell subpopulation-based biomarkers, emphasizing their clinical relevance and potential utility in guiding postoperative adjuvant therapy for HCC patients. Citation Format: Yucheng Dong, Xin Guo, Xuezhu Wang, Fan Yang, Yongchang Zheng, Yang Chen. Single-cell sequencing identification of malignant cell subpopulations linked to recurrence in hepatocellular carcinoma [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 865.

Abstract 3387: Spatial analysis revealed distinct molecular and immune landscape in genetic versus carcinogen-induced lung adenocarcinoma precancer mouse models

Abstract Introduction: Our understanding of early carcinogenesis of lung adenocarcinoma (LUAD) is still rudimentary. Single-cell technologies have unveiled the intricate interaction between tumor cells and tumor microenvironment (TME) with unprecedented precision. However, the spatial context of single-cell phenotypes within TME during LUAD early carcinogenesis remain poorly understood. Here, we sought to characterize human-relevant murine LUAD precancer models to elucidate the molecular and immune changes in the spatial context during early LUAD carcinogenesis to inform lung cancer interception strategies. Methods: We have developed genetically engineered mouse models (GEMMs) (129S4 KrasG12D) and carcinogen-induced LUAD precancer model (CITM) (129S4 Urethane). Lung tissues were collected at various time points after induction and analyzed using whole exome sequencing (WES), imaging mass cytometry (IMC), and spatial transcriptomics (10x Visium). Results: Pathological image analysis demonstrated a significantly higher tumor burden (p<0.01) in the 129S4 KrasG12D model compared to the 129S4 Urethane model across different time points. WES revealed a progressive increase in tumor mutation burden (TMB) from precancers to invasive LUADs in both models, with the CITM model having significantly higher TMB (p<0.05) at matched timepoints than GEMM model. IMC assessed 39 biomarkers in 284 regions of interest (ROIs) and identified 1.4 million individual cells with 17 major cell types. Macrophages were the most prevalent immune cell type in TME, representing 9.00% of total cells in GEMM and 8.29% in CITM. The percent immune cells of the total cells progressively decreased from early-stage hyperplasia to late-stage adenocarcinoma in both models. The CITM exhibited a significantly (p<0.05) higher immune proportion than the GEMM model at hyperplasia and adenoma stages, with no significant difference at the invasive adenocarcinoma tumor cores between these 2 models. On the other hand, CITM had a significantly higher overall immune proportion (p<0.05) and the CD8 T cell and B cell proportions than GEMM tumor adjacent lung tissues at the adenocarcinoma stage leading to a significantly higher immune cell gradient between tumor adjacent lung tissues and tumor cores in CITM models. Spatial transcriptomics (ST) data demonstrated an increased presence of immune response signaling at the tumor adjacent lung tissues compared to the tumor core in the latest stage. Conclusion: Spatial analysis revealed different molecular and immune landscapes in the genetic and carcinogen-induced LUAD precancer models, leading to distinct cellular spatial arrangements within the tumor microenvironment in each model. Overall, innate immunity, particularly macrophage, probably plays critical roles during early carcinogenesis of LUAD regardless of carcinogen exposure. Citation Format: Bo Zhu, Pingjun Chen, Jian-Rong Li, Muhammad Aminu, Chenyang Li, Hong Chen, Yanhua Tian, Frank R. Rojas, Parra Cuentas Edwin Roger, Ou Shi, Monique B. Nilsson, Alissa Poteete, Shawna M. Hubert, Khaja B. Khan, Wei Lu, Xingzhi Song, Jianhua Zhang, Don L. Gibbons, Luisa M. Solis Soto, Ignacio I. Wistuba, John V. Heymach, Chao Cheng, Jia Wu, Jianjun Zhang. Spatial analysis revealed distinct molecular and immune landscape in genetic versus carcinogen-induced lung adenocarcinoma precancer mouse models [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 3387.

Abstract 3644: Uncover spatial signatures of tumor microenvironment and oncogenic pathways using 6,000-plex single-cell spatial molecular imaging on FFPE skin squamous cell carcinoma

Abstract Background: Tumor progression and therapeutic response are regulated by the tumor microenvironment. Understanding the spatial association and architecture of molecular characteristics and composition of tumor immune microenvironment at single-cell and subcellular resolution encourages improvements in clinical prognosis and immunotherapy benefits. Single-cell Spatial profiling technologies permit the study of transcriptional activity at the spatial, single-cell level and provide abundant, high-resolution information required for the identification of clinical-related features in immuno-oncology. Methods: We performed an ultra-high-plex RNA assay to detect 6,000 targets simultaneously in situ on FFPE human skin squamous cell carcinoma using the CosMx™ Spatial Molecular Imager (SMI). For the selection of regions of interest, four protein markers and DAPI were co-detected on the same tissue slide. Tertiary analysis algorithms were developed for cell typing, co-localization of genes and ligands, cell-cell interaction, and pathway analysis. Results: Thousands of transcripts were simultaneously detected with high sensitivity and specificity on the FFPE skin squamous cell carcinoma tissue section at single-cell subcellular resolution. We investigated the tumor microenvironment including cell types, their spatial distribution and proportion of diversified immune cells in the tumor compartment. The cell typing results revealed many cancerous subpopulations, which resolved spatially from cells that lacked any defining marker genes. We also assessed distances between immune cells and their nearest functional-related neighbors. Moreover, we revealed the ligands co-localization, as well as spatial patterns of direct cell-cell interactions and signaling pathways. We found that some genes have elevated expression in macrophages which are near cancer compared to those are far from cancer cells, empowering the molecular investigation of novel signaling between immune and cancer cells in tumor immune microenvironment. Conclusions: Single-cell spatial measurements of 6,000 RNA and four proteins on the same tissue section, along with a large viewing area on archival FFPE tissue, provide a novel tool to reveal the spatial signature of tumor microenvironment and oncogenic pathways, facilitating the next level of cancer and therapeutic research. FOR RESEARCH USE ONLY. Not for use in diagnostic procedures. FOR RESEARCH USE ONLY. Not for use in diagnostic procedures. Citation Format: Michael Patrick, Shanshan He, Saskia Ilcisin, David Kroeppler, Patrick Danaher, Jason Reeves, Mark Gregory, Haiyan Zhai, Michael Rhodes, Joseph Beechem. Uncover spatial signatures of tumor microenvironment and oncogenic pathways using 6,000-plex single-cell spatial molecular imaging on FFPE skin squamous cell carcinoma [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 3644.