Characterization Of Single Cells Research Articles

Integrative Transcriptomic and Single-Cell Protein Characterization of Colorectal Carcinoma Delineates Distinct Tumor Immune Microenvironments Associated with Overall Survival.

Colorectal carcinoma (CRC) is a heterogeneous group of tumors with varying therapeutic response and prognosis, and evidence suggests the tumor immune microenvironment (TIME) plays a pivotal role. Using advanced molecular and spatial biology technologies, we aimed to evaluate the TIME in patients with CRC to determine whether specific alterations in the immune composition correlated with prognosis. We identified primary and metastatic tumor samples from 31 consented patients, which were profiled with whole-exome sequencing and bulk RNA-seq. Immune cell deconvolution followed by gene set enrichment analysis and unsupervised clustering was performed. A subset of tumors underwent in situ analysis of the TIME spatial composition at single-cell resolution through Imaging Mass Mass Cytometry. Gene set enrichment analysis revealed two distinct groups of advanced CRC, one with an immune activated phenotype and the other with a suppressed immune microenvironment. The activated TIME phenotype contained increased Th1 cells, activated dendritic cells, tertiary lymphoid structures, and higher counts of CD8+ T cells whereas the inactive or suppressed TIME contained increased macrophages and a higher M2/M1 ratio. Our findings were further supported by RNA-seq data analysis from the TCGA CRC database, in which unsupervised clustering also identified two separate groups. The immunosuppressed CRC TIME had a lower overall survival probability (HR 1.66, p=0.007). This study supports the pertinent role of the CRC immune microenvironment in tumor progression and patient prognosis. We characterized the immune cell composition to better understand the complexity and vital role that immune activity states of the TIME play in determining patient outcome.

Biophysical essentials – A full stack open-source software framework for conserved and advanced analysis of patch-clamp recordings

Background and ObjectivesPatch-Clamp recordings allow for in depth electrophysiological characterization of single cells, their general biophysical properties as well as characteristics of voltage- and ligand-gated ionic currents. Different acquisition modes, such as whole-cell patch-clamp recordings in the current or voltage clamp configuration, capacitance measurements or single channel recordings from cultured cells as well as acute brain slices are routinely performed for these purposes. Nevertheless, multipurpose transparent and adaptable software tools to perform reproducible state-of-the-art analysis of multiple experiment types and to manage larger sets of experimental data are currently unavailable. MethodsBiophysical Essentials (BPE) was developed as an open-source full stack python software for transparent and reproducible analysis of electrophysiological recordings. For validation, BPE results were compared with manually analyzed single-cell patch-clamp data acquired from a human in vitro nociceptor-model and mouse dorsal root ganglia neurons. ResultsWhile initially designed to improve time consuming and repetitive analysis steps, BPE was further optimized as a technical software solution for entire workflow processing including data acquisition, data preprocessing, normalization and visualization and of single recordings up to stacked calculations and statistics of multiple experiments. BPE can operate with different file formats from different amplifier systems and producers. An in-process database logs all analysis steps reproducible review and serves as a central storage point for recordings. Statistical testing as well as advanced analysis functions like Boltzmann-fitting and dimensional reduction methods further support the researchers' needs in projects involving electrophysiology techniques. ConclusionsBPE extends beyond available patch-clamp specific, open source – and commercial analysis tools in particular because of reproducible and sharable analysis workflows. BPE enables full analysis from raw data acquisition to publication ready result visualizations – all within one single program. Thereby, BPE significantly enhances transparency in the analytical process of patch-clamp data analysis. BPEs function scope is completely accessible through an easy-to-use graphical user interface eliminating the need for programing language proficiency as required by many community patch-clamp analysis frameworks and algorithms.

Single-cell resolution characterization of myeloid-derived cell states with implication in cancer outcome

Tumor-associated myeloid-derived cells (MDCs) significantly impact cancer prognosis and treatment responses due to their remarkable plasticity and tumorigenic behaviors. Here, we integrate single-cell RNA-sequencing data from different cancer types, identifying 29 MDC subpopulations within the tumor microenvironment. Our analysis reveals abnormally expanded MDC subpopulations across various tumors and distinguishes cell states that have often been grouped together, such as TREM2+ and FOLR2+ subpopulations. Using deconvolution approaches, we identify five subpopulations as independent prognostic markers, including states co-expressing TREM2 and PD-1, and FOLR2 and PDL-2. Additionally, TREM2 alone does not reliably predict cancer prognosis, as other TREM2+ macrophages show varied associations with prognosis depending on local cues. Validation in independent cohorts confirms that FOLR2-expressing macrophages correlate with poor clinical outcomes in ovarian and triple-negative breast cancers. This comprehensive MDC atlas offers valuable insights and a foundation for futher analyses, advancing strategies for treating solid cancers.

Multi-omics and single cell characterization of cancer immunosenescence landscape

Cellular senescence (CS) is closely related to tumor progression. However, the studies about CS genes across human cancers have not explored the relationship between cancer senescence signature and telomere length. Additionally, single-cell analyses have not revealed the evolutionary trends of malignant cells and immune cells at the CS level. We defined a CS-associated signature, called “senescence signature”, and found that patients with higher senescence signature had worse prognosis. Higher senescence signature was related to older age, higher genomic instability, longer telomeres, increased lymphocytic infiltration, higher pro-tumor immune infiltrates (Treg cells and MDSCs), and could predict responses to immune checkpoint inhibitor therapy. Single-cell analysis further reveals malignant cells and immune cells share a consistent evolutionary trend at the CS level. MAPK signaling pathway and apoptotic processes may play a key role in CS, and senescence signature may effectively predict sensitivity of MEK1/2 inhibitors, ERK1/2 inhibitors and BCL-2 family inhibitors. We also developed a new CS prediction model of cancer survival and established a portal website to apply this model (https://bio-pub.shinyapps.io/cs_nomo/).

Genomic and single-cell characterization of patient-derived tumor organoid models of head and neck squamous cell carcinoma.

This study establishes patient-derived tumor organoids (PDOs) from 31 Head and Neck Squamous Cell Carcinoma (HNSCC) patients, faithfully recapitulating characteristics of primary tumors and accurately predicting clinical responses to cisplatin treatment. We reveal intertumoral heterogeneity within PDOs and a hybrid epithelial-mesenchymal transition (hEMT) program conferring cisplatin resistance, highlighting amphiregulin (AREG) as a regulator of cellular plasticity and potential therapeutic target for HNSCC treatment.

Liam tackles complex multimodal single-cell data integration challenges.

Multi-omics characterization of single cells holds outstanding potential for profiling the dynamics and relations of gene regulatory states of thousands of cells. How to integrate multimodal data is an open problem, especially when aiming to combine data from multiple sources or conditions containing both biological and technical variation. We introduce liam, a flexible model for the simultaneous horizontal and vertical integration of paired single-cell multimodal data and mosaic integration of paired with unimodal data. Liam learns a joint low-dimensional representation of the measured modalities, which proves beneficial when the information content or quality of the modalities differ. Its integration accounts for complex batch effects using a tunable combination of conditional and adversarial training, which can be optimized using replicate information while retaining selected biological variation. We demonstrate liam's superior performance on multiple paired multimodal data types, including Multiome and CITE-seq data, and in mosaic integration scenarios. Our detailed benchmarking experiments illustrate the complexities and challenges remaining for integration and the meaningful assessment of its success.

Age-related changes to macrophage subpopulations and TREM2 dysregulation characterize attenuated fracture healing in old mice.

Fracture healing complications increase with age, with higher rates of delayed unions and nonunions and an associated increase in morbidity and mortality in older adults. Macrophages have a dynamic role in fracture healing, and we have previously demonstrated that age-related changes in macrophages are associated with attenuated fracture repair in old mice. Here, we provide a single cell characterization of the immune cells involved in the early phase of fracture healing. We show that there were multiple transcriptionally distinct macrophage subpopulations present simultaneously within the healing tissue. Fracture healing was attenuated in old mice compared to young, and macrophages from the fracture callus of old mice demonstrated a pro-inflammatory phenotype compared to young. Interestingly, Trem2 expression was decreased in old macrophages compared to young. Young mice lacking Trem2 demonstrated attenuated fracture healing and inflammatory dysregulation similar to old mice. Trem2 dysregulation has previously been implicated in other age-related diseases, but its role in fracture healing is unknown. This work provides a robust characterization of the macrophage subpopulations involved in fracture healing, and further reveals the important role of Trem2 in fracture healing and may be a potential driver of age-related inflammatory dysregulation. Future work may further examine macrophages and Trem2 as potential therapeutic targets for management of fracture repair in older adults.

Single cell characterization of persistent cells upon immunotherapy treatment in colorectal and endometrial tumors: The SERPENTINE trial.

3139 Background: Immune checkpoint inhibitor (ICI) therapy has proved efficacy in MSI colorectal (CRC) and endometrial cancer (EC), but its use in MSS tumors remains a challenge. As the mechanisms of action of CTLA-4 and PD-1 are non-redundant, targeting both pathways may have additive or synergistic activity in comparison with single PD-1/PD-L1 inhibition in MSS and MSI CRC and EC tumors, while single cell characterization of persistent cells upon treatment would enable to describe cell populations and build a detailed single-cell map of the immune, tumor and stromal cell composition. A single dose of tremelimumab (Trem) 300mg, while maintaining a similar overall exposure, has 3- to 4-fold higher Cmax compared to 4 doses of Trem 1 mg/kg, which may have the potential for better anti-tumor activity while potentially avoiding cumulative toxicity. Here we present the results of patients (pts) with refractory ICI-naïve MSS EC and CRC treated with Dur plus Trem and the characterization of immune and persistent cells with longitudinal single-cell analyses. Methods: Pts with advanced MSS/pMMR CRC and EC that have progressed during or after standard therapy with measurable disease per RECIST 1.1, ECOG ≤1, were treated with Dur 1500mg plus Trem 300mg at Week 0, followed by Dur 1500 mg Q4W. Response rate was correlated with transcriptome and T cell repertoire dynamics measured at single-cell level from freshly processed biopsies at three different timepoints: baseline, upon treatment and at progression. Results: 24 samples for single-cell analysis were obtained (4 MSS CRC and 4 MSS EC). The response rate (RR) was 0%, while disease control rate was 12.5%. Despite the lack of response, we found a massive shift in the immune tumor microenvironments of all patients. Following treatment, immune cell composition shifted to increased frequency of effector-memory, proliferating CD8+ cells and CD4+ cells, surprisingly, with enriched T helper phenotypes. Both lineages showed strong signals of exhaustion. The integration of matched T cell receptor (TCR) data pointed to an infiltration of new clonotypes, rather than the expansion of pre-existing tumor-resident T cells, with distinct degrees of dysfunctional phenotypes between the clones. Intriguingly, expanded clonotypes could be detected and monitored in peripheral blood cells using ultra-deep TCR sequencing. Conclusions: Dual treatment with Dur and Trem does not result in an improved RR in patients with refractory MSS/pMMR CRC and EC, despite profound dynamics in the tumor infiltrating immune cells landscape. The infiltration of CD4+ T helper cells provides mechanistic insights of immune evasion, a potential avenue to treat MSS/pMMR tumors. Clinical trial information: 2021-004061-13 .

Multiplexed single-cell characterization of alternative polyadenylation regulators

Most mammalian genes have multiple polyA sites, representing a substantial source of transcript diversity regulated by the cleavage and polyadenylation (CPA) machinery. To better understand how these proteins govern polyA site choice, we introduce CPA-Perturb-seq, a multiplexed perturbation screen dataset of 42CPA regulators with a 3' scRNA-seq readout that enables transcriptome-wide inference of polyA site usage. We develop a framework to detect perturbation-dependent changes in polyadenylation and characterize modules of co-regulated polyA sites. We find groups of intronic polyA sites regulated by distinct components of the nuclear RNA life cycle, including elongation, splicing, termination, and surveillance. We train and validate a deep neural network (APARENT-Perturb) for tandem polyA site usage, delineating a cis-regulatory code that predicts perturbation response and reveals interactions between regulatory complexes. Our work highlights the potential for multiplexed single-cell perturbation screens to further our understanding of post-transcriptional regulation.

Biophysical phenotyping of single-cell based on impedance and application for individualized precision medicine

Single-cell biophysical characterization based on impedance measurement is an advantageous approach due to its label-free, high-efficiency, cost-effective and real-time capability. Biophysical phenotyping can yield timely and rich information on physiological and pathological state of cells for disease diagnosis, drug screening, precision medicine, etc. However, precise measurement on single-cell impedance is challenging, particularly hard to figure out the detailed biophysical parameters of single cell due to coupling and complexity of impedance model. Here, we propose an analytic determination method to decode single-cell electrophysiological parameters (including cell-substrate interface capacitance, cell membrane capacitance, cell membrane conductivity, and cytoplasm conductivity) from the impedances measured at optimized frequencies by using analytic solution rather than spectrum fitting. With this simple and fast analytic solution method, the physiological parameters of single cell in natural adhesion state can be accurately determined in real time. We validate this cell parameter determination method in monitoring the change of cell adhesion under hydraulic effects and exploring electrophysiological differences among MCF-7, HeLa, Huh7, and MDA-MB-231 cell lines. Particularly, we apply the approach to optimize tumor treating fields (TTFields) therapy, realizing individualized precision medicine. Our work provides an accurate and efficient approach for characterizing single-cell biophysical properties with real-time, in-situ, label-free, and less invasive advantages.

Programming-free Image-Analysis Workflows for IHC, IF and Spatial Proteomics: More than just Cell Counting

Quantitative analysis of cells using a specific protein marker is one of the most frequently performed tasks in both preclinical and clinical histopathology. The primary alternatives include immunohistochemistry and immunofluorescence. This article will briefly shed some light on the pros and cons of both methods, the main ones being that immunohistochemistry is widely available, but in many laboratories, it is limited to only one marker per image. Utilizing more than one marker can be achieved through methods such as establishing duplex or triplex stains, serial sections, or re-staining. However, immunofluorescence excels where a deep multi-marker characterization of single cells is required. Spatial proteomics systems have recently increased the plexity to as many as 100 parallel markers, allowing advanced co-expression, rare cells, and spatial neighbourhood analyses. Bioinformatic analysis aspects for both modalities are discussed, outlining two generic workflows as they are realised in a professional image analysis software used by biomedical researchers. While cell segmentation and typing are at the core, a number of pre- and post-processing steps, such as tissue detection, comparison of ROIs, hotspot search, spatial clustering or neighbourhood analysis should be performed to provide more comprehensive read-outs.

Transcriptomic characterization of single cells derived from immortalized human thyroid follicular epithelial cell lines

Transcriptomic characterization of single cells derived from immortalized human thyroid follicular epithelial cell lines

Abstract PO2-13-04: Single cell characterization of senescent CD8+ T cell promotes immunotherapy resistance in early-stage triple-negative breast cancer

Abstract Background: Immune checkpoint blockade (ICB) has been explored in triple-negative breast cancer (TNBC) and has been proven to improve patient survival in advanced TNBCs (IMpassion130) or increase the pathological complete response rate in early TNBCs (KEYNOTE522). However, ICB could only benefit a subset of patients, highlighting the urgent need to optimize patient selection. Unlike in advanced TNBCs, a predictive biomarker for ICB in early TNBCs is still lacking. The main reason might be that previous studies neglected the heterogeneity of tumor ecosystems between early and advanced TNBCs. There is an urgent need to understand the unique ecosystem of early-stage TNBC and develop biomarkers for precision immunotherapy. Objective: This study aims to characterize the micro-loop associated with the immune efficacy of early TNBC, so as to explain the mechanisms behind the differences in early and late efficacy markers, identify efficacy markers in early TNBC immunotherapy, and search for clinical targeting strategies to sensitize TNBC for neoadjuvant therapy. Methods: We conducted an in-depth analysis of 32 single-cell transcriptomes, comprising 26 early-stage TNBC enrolled in prospective clinical trials (NCT04418154 and NCT04613674), receiving neoadjuvant chemotherapy alone (n=8) or combined with anti-PD1 (n=18), and 6 advanced TNBC. We analyzed the microenvironmental differences between early and advanced tumors, and between different treatment response groups in early stages, and identified a subpopulation of cells specific to early stages and associated with immunotherapeutic efficacy. We used mIHC, flow-through, and validated the presence of this cell in clinical samples. We also explored its predictive performance on four independent cohorts of TNBCs with ICB treatment (GSE194040, NCT04129996, GSE169246, GSE124821). Furthermore, to understand the impact of ISG+ T cells on immunotherapy efficacy and explore targeting strategies, we employed a multi-faceted approach to assess their tumor-killing ability, response to Anti-PD1 treatment, and sensitization effect of drugs targeting ISG+ T cells. ISG+ T cells were sorted using flow cytometry for further experiment. In vitro, we established a co-culture model of primary TNBC patients-derived organoid (PDO) and tumor infiltrated CD8+T cells and evaluated the tumor cell viability as well as T cell cytotoxicity. In vivo, we established TS/A tumor cell bearing BalB/c mouse and E0771 tumor cell-bearing C57BL mouse and measured tumor size and tumor microenvironment by flow cytometry. Results: We obtained 236,017 high-quality cells and identified cell subsets across 32 samples with different stages, treatments and responses. We then uncovered an enriched subset of interferon-induced (ISG+) CD8+ T cells specific to early TNBC, serving as a predictive factor for ICB resistance. Mechanistically, the upregulation of the IFN pathway in early-stage TNBC triggers a transition in ISG+ CD8+ T cells, resulting in cellular senescence, diminished cytotoxicity, and impaired response to ICB. Encouragingly, we made a noteworthy discovery that Nicotinamide mononucleotide, an anti-aging drug, effectively restores the functionality of ISG+ CD8+ T cells, amplifying immunotherapy sensitivity. Conclusions: This study provided a comprehensive single-cell transcriptome atlas of early-stage TNBC and depicted stage-related ISG+CD8+ T cell for predicting ICB response in early TNBC. We emphasize the critical role of T cell dysfunction prompted by cellular senescence in driving ICB resistance during the early stages of TNBC and propose an approach for sensitizing immunotherapy with aging drugs. Citation Format: Tong Fu, Yi-Yu Chen, Xi Jin, Yi-Zhou Jiang, Zhi-Ming Shao. Single cell characterization of senescent CD8+ T cell promotes immunotherapy resistance in early-stage triple-negative breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-13-04.

Apolipoprotein E controls Dectin-1-dependent development of monocyte-derived alveolar macrophages upon pulmonary β-glucan-induced inflammatory adaptation

The lung is constantly exposed to the outside world and optimal adaptation of immune responses is crucial for efficient pathogen clearance. However, mechanisms that lead to lung-associated macrophages’ functional and developmental adaptation remain elusive. To reveal such mechanisms, we developed a reductionist model of environmental intranasal β-glucan exposure, allowing for the detailed interrogation of molecular mechanisms of pulmonary macrophage adaptation. Employing single-cell transcriptomics, high-dimensional imaging and flow cytometric characterization paired with in vivo and ex vivo challenge models, we reveal that pulmonary low-grade inflammation results in the development of apolipoprotein E (ApoE)-dependent monocyte-derived alveolar macrophages (ApoE+CD11b+ AMs). ApoE+CD11b+ AMs expressed high levels of CD11b, ApoE, Gpnmb and Ccl6, were glycolytic, highly phagocytic and produced large amounts of interleukin-6 upon restimulation. Functional differences were cell intrinsic, and myeloid cell-specific ApoE ablation inhibited Ly6c+ monocyte to ApoE+CD11b+ AM differentiation dependent on macrophage colony-stimulating factor secretion, promoting ApoE+CD11b+ AM cell death and thus impeding ApoE+CD11b+ AM maintenance. In vivo, β-glucan-elicited ApoE+CD11b+ AMs limited the bacterial burden of Legionella pneumophilia after infection and improved the disease outcome in vivo and ex vivo in a murine lung fibrosis model. Collectively these data identify ApoE+CD11b+ AMs generated upon environmental cues, under the control of ApoE signaling, as an essential determinant for lung adaptation enhancing tissue resilience.

Electrical sensing of single oocytes in microfluidic systems: A mini review

AbstractThe noninvasive and label‐free electrical characterization of single cells is increasingly gaining interest over the last years. Besides cancer cells, oocytes are in the focus of those investigations. The study of oocytes is relevant for two areas: on the one hand, the quality of mammalian oocytes is important in artificial reproduction technology for a high fertilization yield. On the other hand, amphibian oocytes are well established to be used as host cells for the characterization of ion‐channel kinetics for drug screening. For both areas, easy‐to‐perform and reliable characterization techniques are needed. This mini‐review summarizes important developments of characterizing oocytes with microfluidic systems in combination with observer‐independent noninvasive electrical sensing. Such miniaturized microfluidic systems can be improved to facilitate multiple oocyte characterizations simultaneously for higher throughput screening.

Single cell characterization of blood and expanded regulatory T cells in autoimmune polyendocrine syndrome type 1

Immune tolerance fails in autoimmune polyendocrine syndrome type 1 (APS-1) because of AIRE mutations. We have used single cell transcriptomics to characterize regulatory Tcells (Tregs) sorted directly from blood and from invitro expanded Tregs in APS-1 patients compared to healthy controls. We revealed only CD52 and LTB (down) and TXNIP (up) as consistently differentially expressed genes in the datasets. There were furthermore no large differences of the TCR-repertoire of expanded Tregs between the cohorts, but unique patients showed a more restricted use of specific clonotypes. We also found that invitro expanded Tregs from APS-1 patients had similar suppressive capacity as controls in co-culture assays, despite expanding faster and having more exhausted cells. Our results suggest that APS-1 patients do not have intrinsic defects in their Treg functionality, and that their Tregs can be expanded exvivo for potential therapeutic applications.

Abstract 2305: Label-free high dimensional single cell morphological profiling of different hematological malignancies by ghost cytometry

Abstract Characterizing the heterogeneity in human leukemias is critical for understanding mechanisms of disease onset, uncovering biomarkers for disease diagnosis, and guiding research into novel treatment approaches. While extensive research focus has been placed on characterizing the molecular heterogeneity in leukemia by modern omics technologies, comparatively less emphasis has been placed on studying the diversity in holistic morphological changes that occur in single cells in the context of disease. Here, we report on a novel approach to understanding and characterizing different hematological malignancies using ghost cytometry, a recently developed high-content flow cytometric method that leverages high-speed, artificial intelligence (AI) driven morphological characterization and analysis of single cells. We performed morphometric profiling on human bone marrow samples from patients with T and B-cell acute lymphoblastic leukemia (ALL, n=5), acute myeloid leukemia (AML, n=5), multiple myeloma (MM, n=5) and healthy bone marrow mononuclear cell (BM-MNC) controls (n=6). Morphological profiles from individual cells were analyzed by unsupervised machine learning (universal manifold approximation and projection, UMAP) and compared within disease subsets and against controls. We identified disease-specific cell populations from a mixture of healthy control and disease samples without any labeling, based on completely label-free information. For validation of the specific cell subsets, we overlaid known cell surface markers (e.g. CD45 for blast cells, CD10/CD19 for B-ALL, CD33/CD34 for AML, CD38/CD138 for MM). The intensity of CD45 was dimmer and disease-specific markers were highly expressed in the disease-specific cell population. We also identified a unique cell subpopulation in the disease-specific cell population that could not be distinguished by existing markers. We also analyzed three human myeloma cell lines (AMO1, KMS, and L363) for comparison to the primary MM samples. In the UMAP analysis using LF-GC data, in all three types of cell lines, Bortezomib-resistant cell lines exhibited different distributions from the control parental cell lines. In addition, we observed distinct morphological profiles between primary myeloma samples and myeloma cell lines. Here we present a novel cell characterization approach for human hematological diseases that leverages AI-based, label-free, high-speed morphological characterization of single cells. We demonstrate that the approach can be used to identify subtle morphological differences in blast cells from a range of blood cancer subtypes. Application of morphological profiling and AI can be used to identify measurable, disease-related changes in these diseases that have diagnostic, drug screening, and therapeutic monitoring potential. Citation Format: Asako Tsubouchi, Juho J. Miettinen, Keisuke Wagatsuma, Satoru Akai, Yuri An, Mika Uematsu, Philipp Sergeev, Dimitrios Tsallos, Markus J. Vähä-Koskela, Sadao Ota, Caroline A. Heckman. Label-free high dimensional single cell morphological profiling of different hematological malignancies by ghost cytometry [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 2305.

Abstract 1152: Pan-cancer single-cell and subcellular characterization of lymphoid aggregates and tertiary lymphoid structures using high-plex spatial molecular imaging

Abstract Tertiary lymphoid structures (TLSs), clusters of immune cells that form in non-lymphoid tissues, are frequently identified in a wide range of solid tumors. They function as immunological “hubs”, driving anti-tumor responses. Previous studies have demonstrated that the presence of TLSs is often associated with improved responses to immunotherapy and superior clinical outcomes. Despite these insights, our understanding of their phenotypic diversity, spatial organization, intercellular communication, and interactions with cancer cells and the tumor microenvironment remains incomplete. Existing methods lack the resolution, depth, and scale required for a thorough understanding of these complex structures. We have obtained in-house and public spatial multi-omics data generated on various tissues across 9 different cancer types using the cutting-edge single-cell spatial transcriptomics and high-plex spatial molecular imaging platforms. We have developed innovative computational approaches for the detailed characterization of TLSs at single-cell and subcellular resolution and carried out an extensive analysis of lymphoid aggregates (LAs) and TLSs in tissue sections across these different solid cancer types. We defined the phenotypic state of each individual cell within TLSs including B/plasma cells, follicular helper/regulatory/stressed/exhausted T cells, follicular dendritic cells, fibroblasts and high endothelial venules. Then, we performed unbiased phenotyping and classification of TLSs based on their cellular states, compositions and organization patterns across various types of tissues. Additionally, we performed spatial neighborhood analysis, characterized the neighboring cells of each TLS and analyzed their cell-cell communication networks. Our approach allowed an unprecedented examination of the spatial phenotypes of TLSs in large, complex tissues at super resolution, in ways that were not previously described. We comprehensively characterized TLSs across different solid tumor types. Our analysis revealed a high degree of spatial and phenotypic heterogeneity in LAs and TLSs. We created a comprehensive spatial atlas of LAs and TLSs across solid tumor types, profiled TLSs at various maturation states with distinct cellular composition and organization patterns, and discovered new TLS states. Notably, we observed distinct communication networks associated with TLS phenotypic states. Our innovative methods and analytical approaches offer a more comprehensive way to study LAs and TLSs, enhancing detection capabilities and providing deeper insights into their spatial interaction and functional phenotypes. This research holds the potential to significantly advance our understanding of TLSs and their roles in immunobiology and immunotherapy responses across a broad range of cancers and diseases. Citation Format: Yunhe Liu, Yuanyuan Zhang, Guangchun Han, Guangsheng Pei, Kyung Serk Cho, Enyu Dai, Yanshuo Chu, Jiahui Jiang, Ansam Sinjab, Luisa M. Soto, Alejandra Serrano, Jianjun Gao, Humam Kadara, Catherine Fridman, Wolf Herve Fridman, Linghua Wang. Pan-cancer single-cell and subcellular characterization of lymphoid aggregates and tertiary lymphoid structures using high-plex spatial molecular imaging [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 1152.

Abstract 4986: Single-cell multimodal characterization of ovarian cancer microenvironment under neoadjuvant niraparib treatment informs an effective therapeutic strategy of suppressing HRD-dependent eTreg hyper-expansion

Abstract Homologous recombination deficiency (HRD) is a widespread hallmark in cancer, rendering tumors susceptible to PARP inhibition. Despite this well-established tumor-intrinsic vulnerability, the effects of HRD and related therapies on the tumor microenvironment (TME) remain poorly understood. In this study, utilizing single-cell RNA and T cell receptor profiling (scRNA/TCR-seq), we characterized 79 high-grade serous ovarian cancer (HGSOC) samples, both pre- and post-treatment, from 46 patients treated with neoadjuvant niraparib or chemotherapy in a phase II clinical trial (NCT04507841). This effort established the most comprehensive HGSOC TME atlas to date, encompassing over 700,000 cells. By integrating multiplex immunofluorescence imaging, flow cytometry, and bulk TCR-seq data from additional tumors and matched peripheral blood samples, we systematically assessed HRD-induced and treatment-perturbed dynamics of tumor-infiltrating and circulating immune cells. Surprisingly, among over 120 cell states across 10 cell types, effector regulatory CD4+ T cells (eTreg) exhibited the strongest enrichment in treatment-naïve HRD tumors and a marked treatment-induced reduction. Further analyses of transcriptional states and clonal kinetics revealed HRD-driven functional coordination between eTregs and exhausted CD8+ T cells (Tex), both displaying uniquely heightened tumor reactivity signatures and clonal co-expansions. The intra-population heterogeneity analysis of Tregs and Texs further demonstrated an effectiveness gradient, wherein the terminal subsets had a greater presence in HRD and were reversed by both niraparib and chemotherapy treatments. Delineating the clonal evolution of Tregs and Texs, we identified their notable crosstalk with other tumor-infiltrating T cell populations while remaining remarkably isolated from the peripheral blood repertoire, in line with their terminally differentiated status. Furthermore, we observed widespread, chronic activation of interferon signaling across TME components, downstream of HRD. This orchestrated CD4+ T cell anergy induction into Tregs, mainly through direct engagement by cancer cells that upregulated MHC-II molecules but lacked essential co-stimulatory ligands. Finally, we experimentally validated a therapeutic strategy to conjunctively and effectively break down these factors in HGSOC by combining PARP inhibition and eTreg deletion through a humanized therapeutic anti-CCR8 antibody in multiple syngeneic HGSOC mouse models. Our study unravels a key immunoregulatory role of eTregs in the HGSOC TME amplified by HRD. These findings underscore the underappreciated value of eTreg-focused therapeutics as novel immunotherapies for HGSOC and other genomically unstable cancers. Citation Format: Yikai Luo, Yu Xia, Dan Liu, Xiong Li, Huayi Li, Yi Zhao, Ding Ma, Yong Fang, Han Liang, Qinglei Gao. Single-cell multimodal characterization of ovarian cancer microenvironment under neoadjuvant niraparib treatment informs an effective therapeutic strategy of suppressing HRD-dependent eTreg hyper-expansion [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 4986.

Abstract 4215: Single-cell proteomic characterization of the tumor microenvironment in humanized NOG-EXL patient-derived xenograft (PDX) models

Abstract Introduction: CD34+ hematopoietic stem cell (HSC) derived humanized mice engineered to express human cytokine transgenes are promising models for the evaluation of novel immunotherapeutic treatments. While flow cytometry-based analyses have confirmed the presence of many human immune cell populations within humanized mice bearing patient-derived tumor xenografts (PDX), comprehensive high-dimensional immunophenotyping of the tumor microenvironment (TME) of these models remains limited. Beyond this, the relative influence of PDX vs. donor CD34+ HSC populations on TME composition is poorly understood. Here, we characterize the quantity and diversity of immune cell subpopulations in the TME, bone marrow, and spleen of humanized NOG-EXL (huNOG-EXL) mice using Cytometry by Time-Of-Flight (CyTOF) data with > 60 features. Methods: We performed CyTOF analysis on 250 dissociated samples from primary tumors (n = 15; n = 6 head and neck cancer, n = 6 ovarian cancer, n = 3 renal cell carcinoma), as well as xenograft (n = 59), spleen (n = 88), and bone marrow (n = 88) samples from huNOG-EXL mice (Taconic). Each sample was profiled with two partially overlapping antibody panels; an innate immunity-focused panel (40 features), and an adaptive immunity-focused panel (40 features). Results: CyTOF data were collected from over 10 million live immune cells for each of the adaptive and innate antibody panels. Unbiased clustering of single-cell marker expression revealed clusters corresponding to all major lymphoid and myeloid cell types. Therapeutically relevant cell states were identified including exhausted CD8+ T cells marked by PD-1 and Tim-3 expression, and PD-L1+ macrophages. Hierarchical clustering of bone marrow and spleen samples suggested that systemically, immune composition was not driven by tumor-intrinsic factors alone with many samples clustering on the basis of CD34+ stem cell donor ID. Conversely, clustering PDX samples revealed strong consistency in xenografts originating from the same primary tumor sample regardless of the chosen stem cell donor. The immune composition of primary tumor samples showed greater similarity with matched PDX samples versus unmatched PDX samples, further supporting that tumor-intrinsic factors drive immune composition in xenografts. Conclusions: We provide a high-resolution characterization of the immune phenotypes present in huNOG-EXL PDX models across three cancer types. These data captured substantial heterogeneity in the immune composition of humanized xenograft samples, and we demonstrate that immune composition in humanized xenografts is strongly driven by tumor-intrinsic features. Overall, this resource data supports the use of these models for immuno-oncology studies with future work focused on functionally validating specific immune cell populations of interest. Citation Format: Daniel Stueckmann, Jalna Meens, Sally Zhang, Shirley Hui, Sujana Sivapatham, Sephane Chevrier, Jennifer Pfeil, Lisa Martin, Maria Komisarenko, Philip Dube, Susan Prendeville, Hartland Jackson, Antonio Finelli, Gary Bader, Bernd Bodenmiller, Keith A. Lawson, Laurie Ailles. Single-cell proteomic characterization of the tumor microenvironment in humanized NOG-EXL patient-derived xenograft (PDX) 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 4215.