Cell Type Composition Research Articles

A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex.

The molecular organization of the human neocortex historically has been studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally defined spatial domains that move beyond classic cytoarchitecture. We used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex. Integration with paired single-nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we mapped the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains.

Spotless, a reproducible pipeline for benchmarking cell type deconvolution in spatial transcriptomics.

Spatial transcriptomics (ST) technologies allow the profiling of the transcriptome of cells while keeping their spatial context. Since most commercial untargeted ST technologies do not yet operate at single-cell resolution, computational methods such as deconvolution are often used to infer the cell type composition of each sequenced spot. We benchmarked 11 deconvolution methods using 63 silver standards, 3 gold standards, and 2 case studies on liver and melanoma tissues. We developed a simulation engine called synthspot to generate silver standards from single-cell RNA-sequencing data, while gold standards are generated by pooling single cells from targeted ST data. We evaluated methods based on their performance, stability across different reference datasets, and scalability. We found that cell2location and RCTD are the top-performing methods, but surprisingly, a simple regression model outperforms almost half of the dedicated spatial deconvolution methods. Furthermore, we observe that the performance of all methods significantly decreased in datasets with highly abundant or rare cell types. Our results are reproducible in a Nextflow pipeline, which also allows users to generate synthetic data, run deconvolution methods and optionally benchmark them on their dataset (https://github.com/saeyslab/spotless-benchmark).

Spotless, a reproducible pipeline for benchmarking cell type deconvolution in spatial transcriptomics

Spatial transcriptomics (ST) technologies allow the profiling of the transcriptome of cells while keeping their spatial context. Since most commercial untargeted ST technologies do not yet operate at single-cell resolution, computational methods such as deconvolution are often used to infer the cell type composition of each sequenced spot. We benchmarked 11 deconvolution methods using 63 silver standards, 3 gold standards, and 2 case studies on liver and melanoma tissues. We developed a simulation engine called synthspot to generate silver standards from single-cell RNA-sequencing data, while gold standards are generated by pooling single cells from targeted ST data. We evaluated methods based on their performance, stability across different reference datasets, and scalability. We found that cell2location and RCTD are the top-performing methods, but surprisingly, a simple regression model outperforms almost half of the dedicated spatial deconvolution methods. Furthermore, we observe that the performance of all methods significantly decreased in datasets with highly abundant or rare cell types. Our results are reproducible in a Nextflow pipeline, which also allows users to generate synthetic data, run deconvolution methods and optionally benchmark them on their dataset (https://github.com/saeyslab/spotless-benchmark).

Single-cell and spatial transcriptomics analysis of non-small cell lung cancer

Lung cancer is the second most frequently diagnosed cancer and the leading cause of cancer-related mortality worldwide. Tumour ecosystems feature diverse immune cell types. Myeloid cells, in particular, are prevalent and have a well-established role in promoting the disease. In our study, we profile approximately 900,000 cells from 25 treatment-naive patients with adenocarcinoma and squamous-cell carcinoma by single-cell and spatial transcriptomics. We note an inverse relationship between anti-inflammatory macrophages and NK cells/T cells, and with reduced NK cell cytotoxicity within the tumour. While we observe a similar cell type composition in both adenocarcinoma and squamous-cell carcinoma, we detect significant differences in the co-expression of various immune checkpoint inhibitors. Moreover, we reveal evidence of a transcriptional “reprogramming” of macrophages in tumours, shifting them towards cholesterol export and adopting a foetal-like transcriptional signature which promotes iron efflux. Our multi-omic resource offers a high-resolution molecular map of tumour-associated macrophages, enhancing our understanding of their role within the tumour microenvironment.

Validating reference-based algorithms to determine cell-type heterogeneity in ovarian cancer DNA methylation studies

Information about cell composition in tissue samples is crucial for biomarker discovery and prognosis. Specifically, cancer tissue samples present challenges in deconvolution studies due to mutations and genetic rearrangements. Here, we optimized a robust, DNA methylation-based protocol, to be used for deconvolution of ovarian cancer samples. We compared several state-of-the-art methods (HEpiDISH, MethylCIBERSORT and ARIC) and validated the proposed protocol in an in-silico mixture and in an external dataset containing samples from ovarian cancer patients and controls. The deconvolution protocol we eventually implemented is based on MethylCIBERSORT. Comparing deconvolution methods, we paid close attention to the role of a reference panel. We postulate that a possibly high number of samples (in our case: 247) should be used when building a reference panel to ensure robustness and to compensate for biological and technical variation between samples. Subsequently, we tested the performance of the validated protocol in our own study cohort, consisting of 72 patients with malignant and benign ovarian disease as well as in five external cohorts. In conclusion, we refined and validated a reference-based algorithm to determine cell type composition of ovarian cancer tissue samples to be used in cancer biology studies in larger cohorts.

Reliable detection of stochastic epigenetic mutations and associations with cardiovascular aging.

Stochastic epigenetic mutations (SEMs) have been proposed as novel aging biomarkers to capture heterogeneity in age-related DNA methylation changes. SEMs are defined as outlier methylation patterns at cytosine-guanine dinucleotide sites, categorized as hypermethylated (hyperSEM) or hypomethylated (hypoSEM) relative to a reference. Because SEMs are defined by their outlier status, it is critical to differentiate extreme values due to technical noise or data artifacts from those due to real biology. Using technical replicate data, we found SEM detection is not reliable: across 3 datasets, 24 to 39% of hypoSEM and 46 to 67% of hyperSEM are not shared between replicates. We identified factors influencing SEM reliability-including blood cell type composition, probe beta-value statistics, genomic location, and presence of SNPs. We used these factors in a training dataset to build a machine learning-based filter that removes unreliable SEMs, and found this filter enhances reliability in two independent validation datasets. We assessed associations between SEM loads and aging phenotypes in the Framingham Heart Study and discovered that associations with aging outcomes were in large part driven by hypoSEMs at baseline methylated probes and hyperSEMs at baseline unmethylated probes, which are the same subsets that demonstrate highest technical reliability. These aging associations were preserved after filtering out unreliable SEMs and were enhanced after adjusting for blood cell composition. Finally, we utilized these insights to formulate best practices for SEM detection and introduce a novel R package, SEMdetectR, which uses parallel programming for efficient SEM detection with comprehensive options for detection, filtering, and analysis.

Rethinking eco‐evo studies of gene expression for non‐model organisms in the genomic era

AbstractRecent advances in genomic technology, including the rapid development of long‐read sequencing technology and single‐cell RNA‐sequencing methods, are poised to significantly expand the kinds of studies that are feasible in ecological genomics. In this perspective, we review these new technologies and discuss their potential impact on gene expression studies in non‐model organisms. Although traditional RNA‐sequencing methods have been an extraordinarily powerful tool to apply functional genomics in an ecological context, bulk RNA‐seq approaches often rely on de novo transcriptome assembly, and cannot capture expression changes in rare cell populations or distinguish shifts in cell type abundance. Advancements in genome assembly technology, particularly long‐read sequencing, and improvements in the scalability of single‐cell RNA‐sequencing (scRNA‐seq) offer unprecedented resolution in understanding cellular heterogeneity and gene regulation. We discuss the potential of these technologies to enable disentangling differential gene regulation from cell type composition differences and uncovering subtle expression patterns masked by bulk RNA‐seq. The integration of these approaches provides a more nuanced understanding of the ecological and evolutionary dynamics of gene expression, paving the way for refined models and deeper insights into the generation of biodiversity.

Insights into Disease Progression with spatial transcriptomics

In this work we present a pipeline (DeepTME) that integrates single cells transcription, spatial location of cells, cell type composition, and tissue morphology, using deep learning. DeepTME classifies the tissue microenvironments (TME) at single cell resolution by learning low-dimensional features from all transcription profiles, images similarity, neighboring information, and cell type composition, The proposed approach is able to capture important TME features than can used to quantify disease progression and severity and ultimately identify the molecular components of disease progression and obtain insight into their mechanisms. National Institutes of Health. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Associations in cell type-specific hydroxymethylation and transcriptional alterations of pediatric central nervous system tumors

Although intratumoral heterogeneity has been established in pediatric central nervous system tumors, epigenomic alterations at the cell type level have largely remained unresolved. To identify cell type-specific alterations to cytosine modifications in pediatric central nervous system tumors, we utilize a multi-omic approach that integrated bulk DNA cytosine modification data (methylation and hydroxymethylation) with both bulk and single-cell RNA-sequencing data. We demonstrate a large reduction in the scope of significantly differentially modified cytosines in tumors when accounting for tumor cell type composition. In the progenitor-like cell types of tumors, we identify a preponderance differential Cytosine-phosphate-Guanine site hydroxymethylation rather than methylation. Genes with differential hydroxymethylation, like histone deacetylase 4 and insulin-like growth factor 1 receptor, are associated with cell type-specific changes in gene expression in tumors. Our results highlight the importance of epigenomic alterations in the progenitor-like cell types and its role in cell type-specific transcriptional regulation in pediatric central nervous system tumors.

Ultrasensitive and Multiplexed Protein Imaging with Clickable and Cleavable Fluorophores.

Single-cell spatial proteomic analysis holds great promise to advance our understanding of the composition, organization, interaction, and function of the various cell types in complex biological systems. However, the current multiplexed protein imaging technologies suffer from low detection sensitivity, limited multiplexing capacity, or are technically demanding. To tackle these issues, here, we report the development of a highly sensitive and multiplexed in situ protein profiling method using off-the-shelf antibodies. In this approach, the protein targets are stained with horseradish peroxidase (HRP) conjugated antibodies and cleavable fluorophores via click chemistry. Through repeated cycles of target staining, fluorescence imaging, and fluorophore cleavage, many proteins can be profiled in single cells in situ. Applying this approach, we successfully quantified 28 different proteins in human formalin-fixed paraffin-embedded (FFPE) tonsil tissue, which represents the highest multiplexing capacity among the tyramide signal amplification (TSA) methods. Based on their unique protein expression patterns and their microenvironment, ∼820,000 cells in the tissue are classified into distinct cell clusters. We also explored the cell-cell interactions between these varied cell clusters and observed that different subregions of the tissue are composed of cells from specific clusters.

Epigenetic age oscillates during the day.

Since their introduction, epigenetic clocks have been extensively used in aging, human disease, and rejuvenation studies. In this article, we report an intriguing pattern: epigenetic age predictions display a 24-h periodicity. We tested a circadian blood sample collection using 17 epigenetic clocks addressing different aspects of aging. Thirteen clocks exhibited significant oscillations with the youngest and oldest age estimates around midnight and noon, respectively. In addition, daily oscillations were consistent with the changes of epigenetic age across different times of day observed in an independant populational dataset. While these oscillations can in part be attributed to variations in white blood cell type composition, cell count correction methods might not fully resolve the issue. Furthermore, some epigenetic clocks exhibited 24-h periodicity even in the purified fraction of neutrophils pointing at plausible contributions of intracellular epigenomic oscillations. Evidence for circadian variation in epigenetic clocks emphasizes the importance of the time-of-day for obtaining accurate estimates of epigenetic age.

SPADE: spatial deconvolution for domain specific cell-type estimation

Understanding gene expression in different cell types within their spatial context is a key goal in genomics research. SPADE (SPAtial DEconvolution), our proposed method, addresses this by integrating spatial patterns into the analysis of cell type composition. This approach uses a combination of single-cell RNA sequencing, spatial transcriptomics, and histological data to accurately estimate the proportions of cell types in various locations. Our analyses of synthetic data have demonstrated SPADE’s capability to discern cell type-specific spatial patterns effectively. When applied to real-life datasets, SPADE provides insights into cellular dynamics and the composition of tumor tissues. This enhances our comprehension of complex biological systems and aids in exploring cellular diversity. SPADE represents a significant advancement in deciphering spatial gene expression patterns, offering a powerful tool for the detailed investigation of cell types in spatial transcriptomics.

Abstract P64: Adipose-enriched Peri-tumoral Stroma Prognosticates Poorer Survival in Breast Cancers

Abstract Background: Breast cancers display a large degree of diversity between and within tumors. Intra-tumoral heterogeneity is contributed by variations in tumor cells and non-malignant stromal and immune cells of the tumor microenvironment (TME). So far, studies on the TME have focused on intra-tumoral stroma, whereas little attention has been given to the peri-tumoral microenvironment, extending beyond the tumor margins into the surrounding histologically normal-appearing tissues. Here, we hypothesized that intrinsic intra-tumoral heterogeneity impacts the more distal peri-tumoral stroma, which may in turn affect tumor growth and development. Methods: A systematic, multi-regional transcriptomic profiling analysis of the tumor and peri-tumoral regions from 8 ER+/PR+/HER2− invasive breast carcinomas was performed using the human Clariom™ S Assay. For each patient, 5 spatially distinct tumor samples were obtained, while peri-tumoral samples were collected from 4 different sides and at varying distances up to 7 cm from tumor margins. Pathway enrichment analyses and cellular deconvolution were performed to determine their molecular characteristics and cell type compositions. Gene expression signatures identified from peri-tumoral samples were applied to tumor-adjacent normal RNA-seq samples from 50 TCGA ER+/PR+/HER2− breast cancer patients to determine their prognostic value. Results: The 8 tumors displayed varying levels of spatial diversity in their biological properties, including 3 tumors with basal-classified regions. Four peri-tumoral clusters with distinct pathway activities and cell compositions were identified, but were independent of distance and location to the tumor. Two clusters reflect the major breast peri-tumoral states – a pro-inflammatory, adipose-enriched phenotype which was significantly associated with poorer overall survival (HR=4.28, p-value=0.02), and an adaptive immune cell- and myofibroblast-enriched phenotype. Conclusion: This suggests that the peri-tumoral stroma may be an important determinant in cancer progression. Moreover, independent of spatially-defined patterns, the peri-tumoral phenotype appears to be driven by inter-individual variations in the proportion of fibroglandular and fat tissue of the breast. Citation Format: Hannah Si Hui Lau, Veronique Kiak Mien Tan, Benita Kiat Tee Tan, Yirong Sim, Jelmar Quist, Aye Aye Thike, Puay Hoon Tan, Shazib Pervaiz, Anita Grigoriadis, Kanaga Sabapathy. Adipose-enriched Peri-tumoral Stroma Prognosticates Poorer Survival in Breast Cancers [abstract]. In: Proceedings of Frontiers in Cancer Science; 2023 Nov 6-8; Singapore. Philadelphia (PA): AACR; Cancer Res 2024;84(8_Suppl):Abstract nr P64.

GTADC: A Graph-Based Method for Inferring Cell Spatial Distribution in Cancer Tissues.

The heterogeneity of tumors poses a challenge for understanding cell interactions and constructing complex ecosystems within cancer tissues. Current research strategies integrate spatial transcriptomics (ST) and single-cell sequencing (scRNA-seq) data to thoroughly analyze this intricate system. However, traditional deep learning methods using scRNA-seq data tend to filter differentially expressed genes through statistical methods. In the context of cancer tissues, where cancer cells exhibit significant differences in gene expression compared to normal cells, this heterogeneity renders traditional analysis methods incapable of accurately capturing differences between cell types. Therefore, we propose a graph-based deep learning method, GTADC, which utilizes Silhouette scores to precisely capture genes with significant expression differences within each cell type, enhancing the accuracy of gene selection. Compared to traditional methods, GTADC not only considers the expression similarity of genes within their respective clusters but also comprehensively leverages information from the overall clustering structure. The introduction of graph structure effectively captures spatial relationships and topological structures between the two types of data, enabling GTADC to more accurately and comprehensively resolve the spatial composition of different cell types within tissues. This refinement allows GTADC to intricately reconstruct the cellular spatial composition, offering a precise solution for inferring cell spatial composition. This method allows for early detection of potential cancer cell regions within tissues, assessing their quantity and spatial information in cell populations. We aim to achieve a preliminary estimation of cancer occurrence and development, contributing to a deeper understanding of early-stage cancer and providing potential support for early cancer diagnosis.

Phosphorylation of the compartmentalized PKA substrate TAF15 regulates RNA-protein interactions.

Spatiotemporal-controlled second messengers alter molecular interactions of central signaling nodes for ensuring physiological signal transmission. One prototypical second messenger molecule which modulates kinase signal transmission is the cyclic-adenosine monophosphate (cAMP). The main proteinogenic cellular effectors of cAMP are compartmentalized protein kinase A (PKA) complexes. Their cell-type specific compositions precisely coordinate substrate phosphorylation and proper signal propagation which is indispensable for numerous cell-type specific functions. Here we present evidence that TAF15, which is implicated in the etiology of amyotrophic lateral sclerosis, represents a novel nuclear PKA substrate. In cross-linking and immunoprecipitation experiments (iCLIP) we showed that TAF15 phosphorylation alters the binding to target transcripts related to mRNA maturation, splicing and protein-binding related functions. TAF15 appears to be one of multiple PKA substrates that undergo RNA-binding dynamics upon phosphorylation. We observed that the activation of the cAMP-PKA signalingaxis caused a change in the composition of a collection of RNA species that interact with TAF15. This observation appears to be a broader principle in the regulation of molecular interactions, as we identified a significant enrichment of RNA-binding proteins within endogenous PKA complexes. We assume that phosphorylation of RNA-binding domains adds another layer of regulation to binary protein-RNAs interactions with consequences to RNA features including binding specificities, localization, abundance and composition.

DNA methylation signatures support the role of neutrophils and monocytes in depression

IntroductionResearch repeatedly linked inflammation with major depressive disorder (MDD). The presence of an inflammatory subtype of depression is supported by molecular findings as well as imaging reports. We investigated the cell type composition estimated by using epigenome-wide DNA methylation markers in a sample of depressed individuals showing high or low inflammation levels measured by hsCRP. We aimed to understand the connection between depression and inflammation, specifically differences in cell type compositions between high and low inflammation groups at baseline.Objectives119 individuals with MDD were included for this analysis. Following quality control procedures, 113 participants were included in the analysis (Mage= 47 years, 57.98% women). The sample consisted of 37 individuals with high hsCRP (hsCRP > 1.5, Mage=45, MhsCRP=8.2, MMADRS=28, 70% women) and 76 individuals with low hsCRP (hsCRP < 1.5, Mage= 44, MhsCRP=0.99, MMADRS=28, 49% women).MethodsThe Illumina Infinium MethylationEPIC 850k BeadChip was used for analyzing whole blood derived DNA. Data processing and cell type estimation was conducted using the RnBeads package. We applied the Houseman method to estimate cell type composition through epigenome-wide DNA methylation signatures, resulting in six cell types: neutrophils, natural killer cells, B cells, CD4+ T cells, CD8+ T cells and monocytes. Comparisons between both groups were tested using ANOVA.ResultsHigh and low hsCRP groups were compared for each of the six cell types estimated. A statistically significant difference was seen for monocytes (p=0.0316) and a trend for neutrophils (p=0.0742). The mean values for neutrophils in patients without inflammation were found to be 60%, while in patients with inflammation, it was 63%. For monocytes, the mean values for patients without inflammation and those with inflammation were 10% and 9.4%, respectively, with a smaller range (4.5%-14.3%) for individuals with inflammation as compared to patients without inflammation (5.3%-20.7%). None of the other four cell types showed a statistically significant difference.Conclusions We identified differences in the cell type composition between groups of depressed patients with high versus low inflammation. These results align with the existing body of knowledge reported in established academic literature. Our study emphasizes the role of specific cells like neutrophils and monocytes in inflammation and depression. These findings offer valuable insights for improving depression treatment strategies as inflammation state may be relevant for treatment response. We also show the merit of DNA methylation signatures for the profiling of patients’ inflammation status, i.e., immunomethylomics.Disclosure of InterestNone Declared

Abstract 6249: Single-cell RNA sequencing investigation of multi-stage NASH-related HCC mouse model

Abstract Hepatocellular carcinoma (HCC) is one of the most frequent malignancies and a leading cause of cancer-related death worldwide. Its incidence is also one of the most rapidly increasing in the West. Most (>80%) of HCCs are diagnosed at advanced stage and therefore not operable. Even after surgical resection, the long-term prognosis of HCC remains unsatisfactory due to high recurrence rates. It is also an extremely difficult-to-treat cancer. Most HCC patients have a background of chronic liver disease, including chronic viral infection, excessive alcohol consumption or non-alcoholic fatty liver disease NAFLD)/steatohepatitis (NASH). In June 2023, new nomenclature of NAFLD and NASH have been internationally announced as metabolic dysfunction-associated steatotic liver disease (MASLD) and metabolic dysfunction-associated steatohepatitis, respectively. Due to the enforcement of neonatal HBV vaccination program and the availability of effective antiviral treatments on HBV and HCV, the global incidence of viral hepatitis-related HCC has been declining, whereas NAFLD/NASH-related incidence of HCC has been increasing. We established NASH-related HCC mouse model using Western diet (WD) plus carbon tetrachloride (CCl4) method. This model demonstrated steatosis, fibrosis, obesity and HCC with high incidence. It has also been reported to accurately portrait the key pathological features of the patients. Next, we collected liver tissue samples at different timepoints (normal; Week 12: NAFLD/NASH; Week 18: fibrosis/cirrhosis; Week 32: HCC) and analyzed them using single-cell RNA sequencing (scRNA-seq). Single-cell sequencing offers an unprecedented platform to perform systematic and in-depth investigation of the admixed tumor microenvironment (TME) in HCC tumor. We have taken advantage of the unique multi-dimensional capacity of scRNA-seq to delineate multifaceted landscapes and cell-cell communication in multi-stage NASH-related HCC mouse model. After removing the low-quality cells, our multi-stage dataset consisted of >130,000 cells. Using typical cell type markers, we identified major cell types, namely T cells, B cells, myeloid cells, liver sinusoidal endothelial cells, hepatic stellate cells, hepatocytes and HCC tumor cells. We observed a change in the cell type composition during disease progression. Throughout our study, we closely examined the communication level between different cell types to identify any irregularities or disturbances that could lead to NASH-related HCC. Our findings concluded that hepatic stellate cells, despite existing in low proportion at all stages, exhibited the strongest level of interaction with other cell types. Moreover, our data also established the comprehensive multi-stage cellular landscape of NASH-relative HCC. Citation Format: Tina Suoangbaji, Vanilla Xin Zhang, Daniel Wai-Hung Ho, Irene Oi-Lin Ng. Single-cell RNA sequencing investigation of multi-stage NASH-related HCC mouse model [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 6249.

Monkey multi-organ cell atlas exposed to estrogen

Abstract Awareness of estrogen’s effects on health is broadening rapidly. The effects of long-term high levels of estrogen on the body involve multiple organs. Here, we used both single-cell chromatin accessibility and RNA sequencing data to analyze the potential effect of estrogen on major organs. The integrated cell map enabled in-depth dissection and comparison of molecular dynamics, cell-type compositions, and cellular heterogeneity across multiple tissues and organs under estrogen stimulation. We also inferred pseudotime cell trajectories and cell–cell communications to uncover key molecular signatures underlying their cellular processes in major organs in response to estrogen. For example, estrogen could induce the differentiation of IFIT3+ neutrophils into S100A9+ neutrophils involved in the function of endosome-to-lysosome transport and the multivesicular body sorting pathway in liver tissues. Furthermore, through integration with human genome-wide association study data, we further identified a subset of risk genes during disease development that were induced by estrogen, such as AKT1 (related to endometrial cancer), CCND1 (related to breast cancer), HSPH1 (related to colorectal cancer), and COVID-19 and asthma-related risk genes. Our work uncovers the impact of estrogen on the major organs, constitutes a useful resource, and reveals the contribution and mechanism of estrogen to related diseases.

Abstract 7044: Transcriptome and TCR repertoire in human telomerase reverse transcriptase peptide vaccine treated metastatic prostate cancer patients

Abstract Human telomerase reverse transcriptase (hTERT) vaccine has found to be effective for most of the participated de novo metastatic prostate cancer patients in clinical studies. Nevertheless, the molecular mechanisms behind the immune response induced by the hTERT vaccine are not fully elucidated. Among the cohort of 21 patients treated with androgen deprivation therapy, radiotherapy and hTERT vaccine, we used RNA-sequencing and TCR-sequencing (20 and 14 pre-treatment and 20 and 21 post-treatment samples, respectively) to inspect the changes in transcriptome and TCR-repertoire after the hTERT vaccine treatment and differences between patients that had early response and patients that had late or no response. We found multiple hallmarks being shifted after the treatment and being different between early and late responders. Also, a change in activity of multiple gene set signatures was detected after the treatment and activity of part of the gene set signatures predicted survival. Deconvolution of cell type composition from RNA-sequencing data revealed changes in immune cell abundances after the treatment. TCR-sequencing revealed changes in composition and diversity of TCR repertoires after the treatment and these differed between early and late responders. The results of this study will help to predict patients’ responses to the cancer vaccine and to further understand the mechanisms behind successful cancer vaccines. Citation Format: Reetta Nätkin, Julia Elisabeth Simensen, Nikolai Engedal, Espen Basmo Ellingsen, Marita Westhrin, Ingunn Westgaard, Eivind Hovig, Wolfgang Lilleby, Matti Nykter, Alfonso Urbanucci. Transcriptome and TCR repertoire in human telomerase reverse transcriptase peptide vaccine treated metastatic prostate cancer patients [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 7044.

Graded FGF activity patterns distinct cell types within the apical sensory organ of the sea anemone Nematostella vectensis

Bilaterian animals have evolved complex sensory organs comprised of distinct cell types that function coordinately to sense the environment. Each sensory unit has a defined architecture built from component cell types, including sensory cells, non-sensory support cells, and dedicated sensory neurons. Whether this characteristic cellular composition is present in the sensory organs of non-bilaterian animals is unknown. Here, we interrogate the cell type composition and gene regulatory networks controlling development of the larval apical sensory organ in the sea anemone Nematostella vectensis. Using single cell RNA sequencing and imaging approaches, we reveal two unique cell types in the Nematostella apical sensory organ, GABAergic sensory cells and a putative non-sensory support cell population. Further, we identify the paired-like (PRD) homeodomain gene prd146 as a specific sensory cell marker and show that Prd146+ sensory cells become post-mitotic after gastrulation. Genetic loss of function approaches show that Prd146 is essential for apical sensory organ development. Using a candidate gene knockdown approach, we place prd146 downstream of FGF signaling in the apical sensory organ gene regulatory network. Further, we demonstrate that an aboral FGF activity gradient coordinately regulates the specification of both sensory and support cells. Collectively, these experiments define the genetic basis for apical sensory organ development in a non-bilaterian animal and reveal an unanticipated degree of complexity in a prototypic sensory structure.