Single-cell Transcriptomics Research Articles

Leveraging cell type-specificity for gene set analysis of single cell transcriptomics.

Although single cell RNA-sequencing (scRNA-seq) provides unprecedented insights into the biology of complex tissues, analyzing such data on a gene-by-gene basis is challenging due to the large number of tested hypotheses and consequent low statistical power and difficult interpretation. These issues are magnified by the increased noise, significant sparsity and multi-modal distributions characteristic of single cell data. One promising approach for addressing these challenges is gene set testing, or pathway analysis. Unfortunately, statistical and biological differences between single cell and bulk transcriptomic data make it challenging to use existing gene set collections, which were developed for bulk tissue analysis, on scRNA-seq data. In this paper, we describe a procedure for customizing gene set collections originally created for bulk tissue analysis to reflect the structure of gene activity within specific cell types. Our approach leverages information about mean gene expression in the 81 human cell types profiled via scRNA-seq by the Human Protein Atlas (HPA) Single Cell Type Atlas. This HPA information is used to compute cell type-specific gene and gene set weights that can be used to filter or weight gene set collections. As demonstrated through the analysis of immune cell scRNA-seq data using gene sets from the Molecular Signatures Database (MSigDB), accounting for cell type-specificity can significantly improve gene set testing power and interpretability. An example vignette along with gene and gene set weights for the 81 HPA SCTA cell types and the MSigDB collections are available at https://hrfrost.host.dartmouth.edu/SCGeneSetOpt/ .

Dynamic transcriptomic and regulatory networks underpinning the transition from fetal primordial germ cells to spermatogonia in mice.

The transition from fetal primordial germ cells (PGCs) to spermatogonia (SPG) is critical for male germ cell development; however, the detailed transcriptomic dynamics and regulation underlying this transition remain poorly understood. Here by interrogating the comprehensive transcriptome atlas dataset of mouse male germ cells and gonadal cells development, we elucidated the regulatory networks underlying this transition. Our single-cell transcriptome analysis revealed that the transition from PGCs to SPG was characterized by global hypertranscription. A total of 315 highly active regulators were identified to be potentially involved in this transition, among which a non-transcription factor (TF) regulator TAGLN2 was validated to be essential for spermatogonial stem cells (SSCs) maintenance and differentiation. Metabolism profiling analysis also revealed dynamic changes in metabolism-related gene expression during PGC to SPG transition. Furthermore, we uncovered that intricate cell-cell communication exerted potential functions in the regulation of hypertranscription in germ cells by collaborating with stage-specific active regulators. Collectively, our work extends the understanding of molecular mechanisms underlying male germ cell development, offering insights into the recapitulation of germ cell generation in vitro.

Introducing synthetic thermostable RNase inhibitors to single-cell RNA-seq

Single-cell RNA-sequencing (scRNAseq) is revolutionizing biomedicine, propelled by advances in methodology, ease of use, and cost reduction of library preparation. Over the past decade, there have been remarkable technical improvements in most aspects of single-cell transcriptomics. Yet, little to no progress has been made in advancing RNase inhibition despite maintained RNA integrity being critical during cell collection, storage, and cDNA library generation. Here, we demonstrate that a synthetic thermostable RNase inhibitor (SEQURNA) yields single-cell libraries of equal or superior quality compared to ubiquitously used protein-based recombinant RNase inhibitors (RRIs). Importantly, the synthetic RNase inhibitor provides additional unique improvements in reproducibility and throughput, enables new experimental workflows including retained RNase inhibition throughout heat cycles, and can reduce the need for dry-ice transports. In summary, replacing RRIs represents a substantial advancement in the field of single-cell transcriptomics.

Mouse Wound Models and Preparation of Single-Cell Suspensions.

The management of acute full-thickness skin injuries presents a considerable challenge in clinical practice. The complexity of wound healing, involving diverse cell populations and the intricate wound microenvironment, complicates the assessment of treatment effects and their interactions using traditional experimental approaches. Single-cell transcriptome sequencing technology has revolutionized the understanding of cellular functions and mechanisms during wound healing. However, the variability in methodologies for creating mouse wound models introduces confounding factors that can impact experimental results. Furthermore, the process of dissociating mouse skin tissues presents significant technical hurdles, highlighting the critical need for high-quality single-cell suspensions for accurate transcriptome sequencing. Therefore, this study aims to optimize the construction of mouse wound models to accurately assess changes in wound closure while eliminating variables that could influence the subsequent sequencing result. Additionally, the preparation of single-cell suspensions was performed using both enzyme preparation protocols and test kit protocols to optimize cost and effectiveness.

SpaTopic: A statistical learning framework for exploring tumor spatial architecture from spatially resolved transcriptomic data.

Tumor tissues exhibit a complex spatial architecture within the tumor microenvironment (TME). Spatially resolved transcriptomics (SRT) is promising for unveiling the spatial structures of the TME at both cellular and molecular levels, but identifying pathology-relevant spatial domains remains challenging. Here, we introduce SpaTopic, a statistical learning framework that harmonizes spot clustering and cell-type deconvolution by integrating single-cell transcriptomics and SRT data. Through topic modeling, SpaTopic stratifies the TME into spatial domains with coherent cellular organization, facilitating refined annotation of the spatial architecture with improved performance. We assess SpaTopic across various tumor types and show accurate prediction of tertiary lymphoid structures and tumor boundaries. Moreover, marker genes derived from SpaTopic are transferrable and can be applied to mark spatial domains in other datasets. In addition, SpaTopic enables quantitative comparison and functional characterization of spatial domains across SRT datasets. Overall, SpaTopic presents an innovative analytical framework for exploring, comparing, and interpreting tumor SRT data.

ScEMB: Learning context representation of genes based on large-scale single-cell transcriptomics.

The rapid advancement of single-cell transcriptomic technologies has led to the curation of millions of cellular profiles, providing unprecedented insights into cellular heterogeneity across various tissues and developmental stages. This growing wealth of data presents an opportunity to uncover complex gene-gene relationships, yet also poses significant computational challenges. We present scEMB, a transformer-based deep learning model developed to capture context-aware gene embeddings from large-scale single-cell transcriptomics data. Trained on over 30 million single-cell transcriptomes, scEMB utilizes an innovative binning strategy that integrates data across multiple platforms, effectively preserving both gene expression hierarchies and cell-type specificity. In downstream tasks such as batch integration, clustering, and cell type annotation, scEMB demonstrates superior performance compared to existing models like scGPT and Geneformer. Notably, scEMB excels in silico correlation analysis, accurately predicting gene perturbation effects in CRISPR-edited datasets and microglia state transition, identifying a few known Alzheimer's disease (AD) risks genes in top gene list. Additionally, scEMB offers robust fine-tuning capabilities for domain-specific applications, making it a versatile tool for tackling diverse biological problems such as therapeutic target discovery and disease modeling. scEMB represents a powerful tool for extracting biologically meaningful insights from complex gene expression data. Its ability to model in silico perturbation effects and conduct correlation analyses in the embedding space highlights its potential to accelerate discoveries in precision medicine and therapeutic development.

Muscle-resident mesenchymal progenitors sense and repair peripheral nerve injury via the GDNF-BDNF axis.

Fibro-adipogenic progenitors (FAPs) are muscle-resident mesenchymal progenitors that can contribute to muscle tissue homeostasis and regeneration, as well as postnatal maturation and lifelong maintenance of the neuromuscular system. Recently, traumatic injury to the peripheral nerve was shown to activate FAPs, suggesting that FAPs can respond to nerve injury. However, questions of how FAPs can sense the anatomically distant peripheral nerve injury and whether FAPs can directly contribute to nerve regeneration remained unanswered. Here, utilizing single-cell transcriptomics and mouse models, we discovered that a subset of FAPs expressing GDNF receptors Ret and Gfra1 can respond to peripheral nerve injury by sensing GDNF secreted by Schwann cells. Upon GDNF sensing, this subset becomes activated and expresses Bdnf. FAP-specific inactivation of Bdnf (Prrx1Cre; Bdnffl/fl) resulted in delayed nerve regeneration owing to defective remyelination, indicating that GDNF-sensing FAPs play an important role in the remyelination process during peripheral nerve regeneration. In aged mice, significantly reduced Bdnf expression in FAPs was observed upon nerve injury, suggesting the clinical relevance of FAP-derived BDNF in the age-related delays in nerve regeneration. Collectively, our study revealed the previously unidentified role of FAPs in peripheral nerve regeneration, and the molecular mechanism behind FAPs' response to peripheral nerve injury.

Heterogeneity in Dental Tissue-Derived MSCs Revealed by Single-Cell RNA-seq.

Mesenchymal stromal cells (MSCs) are multipotent, progenitor cells that reside in tissues across the human body, including the periodontal ligament (PDL) and gingiva. They are a promising therapeutic tool for various degenerative and inflammatory diseases. However, different heterogeneity levels caused by tissue-to-tissue and donor-to-donor variability, and even intercellular differences within a given MSCs population, restrict their therapeutic potential. There are considerable efforts to decipher these heterogeneity levels using different "omics" approaches, including single-cell transcriptomics. Previous studies applied this approach to compare MSCs isolated from various tissues of different individuals, but distinguishing between donor-to-donor and tissue-to-tissue variability is still challenging. In this study, MSCs were isolated from the PDL and gingiva of 5 periodontally healthy individuals and cultured in vitro. A total of 3,844 transcriptomes were generated using single-cell mRNA sequencing. Clustering across the 2 different tissues per donor identified PDL- and gingiva-specific and tissue-spanning MSCs subpopulations with unique upregulated gene sets. Gene/pathway enrichment and protein-protein interaction (PPI) network analysis revealed differences restricted to several cellular processes between tissue-specific subpopulations, indicating a limited tissue-of-origin variability in MSCs. Gene expression, pathway enrichment, and PPI network analysis across all donors' PDL- or gingiva-specific subpopulations showed significant but limited donor-to-donor differences. In conclusion, this study demonstrates tissue- and donor-specific variabilities in the transcriptome level of PDL- and gingiva-derived MSCs, which seem restricted to specific cellular processes. Identifying tissue-specific and tissue-spanning subpopulations highlights the intercellular differences in dental tissue-derived MSCs. It could be reasonable to control MSCs at a single-cell level to ensure their properties before transplantation.

Network Medicine Approach Unravels Endophenotype Signature in Alzheimer's Disease through Large-Scale Comparative Proteomics Analysis: Vascular Dysfunction as a Prime Example.

Alzheimer's disease (AD) is the most common neurodegenerative disease burdening public health. We proposed a network-based infrastructure to identify protein signatures for five AD pathological endophenotypes: amyloidosis, tauopathy, vascular dysfunction, lysosomal dysfunction, and neuroinflammation. We analyzed 23 proteomic data sets from AD patients and transgenic mouse models, using network proximity to measure associations between endophenotype modules and differentially expressed proteins (DEPs) in the integrated AD proteome. We focused on the vascular dysfunction signature with 21 DEPs by integrating RNA-seq, single-cell transcriptomics, GWAS, and literature. Experiments on APP/PS1 and MCAO models highlighted three proteins (SEPT5, SNAP25, STXBP1) as novel AD biomarker candidates. This study demonstrates a network medicine framework for deciphering endophenotype signatures in AD.

Single-cell multi-omics map of human fetal blood in Down syndrome

Down syndrome predisposes individuals to haematological abnormalities, such as increased number of erythrocytes and leukaemia in a process that is initiated before birth and is not entirely understood1–3. Here, to understand dysregulated haematopoiesis in Down syndrome, we integrated single-cell transcriptomics of over 1.1 million cells with chromatin accessibility and spatial transcriptomics datasets using human fetal liver and bone marrow samples from 3 fetuses with disomy and 15 fetuses with trisomy. We found that differences in gene expression in Down syndrome were dependent on both cell type and environment. Furthermore, we found multiple lines of evidence that haematopoietic stem cells (HSCs) in Down syndrome are ‘primed’ to differentiate. We subsequently established a Down syndrome-specific map linking non-coding elements to genes in disomic and trisomic HSCs using 10X multiome data. By integrating this map with genetic variants associated with blood cell counts, we discovered that trisomy restructured regulatory interactions to dysregulate enhancer activity and gene expression critical to erythroid lineage differentiation. Furthermore, as mutations in Down syndrome display a signature of oxidative stress4,5, we validated both increased mitochondrial mass and oxidative stress in Down syndrome, and observed that these mutations preferentially fell into regulatory regions of expressed genes in HSCs. Together, our single-cell, multi-omic resource provides a high-resolution molecular map of fetal haematopoiesis in Down syndrome and indicates significant regulatory restructuring giving rise to co-occurring haematological conditions.

Neural network-assisted humanisation of COVID-19 hamster transcriptomic data reveals matching severity states in human disease

Translating findings from animal models to human disease is essential for dissecting disease mechanisms, developing and testing precise therapeutic strategies. The coronavirus disease 2019 (COVID-19) pandemic has highlighted this need, particularly for models showing disease severity-dependent immune responses. Single-cell transcriptomics (scRNAseq) is well poised to reveal similarities and differences between species at the molecular and cellular level with unprecedented resolution. However, computational methods enabling detailed matching are still scarce. Here, we provide a structured scRNAseq-based approach that we applied to scRNAseq from blood leukocytes originating from humans and hamsters affected with moderate or severe COVID-19. Integration of data from patients with COVID-19 with two hamster models that develop moderate (Syrian hamster, Mesocricetus auratus) or severe (Roborovski hamster, Phodopus roborovskii) disease revealed that most cellular states are shared across species. A neural network-based analysis using variational autoencoders quantified the overall transcriptomic similarity across species and severity levels, showing highest similarity between neutrophils of Roborovski hamsters and patients with severe COVID-19, while Syrian hamsters better matched patients with moderate disease, particularly in classical monocytes. We further used transcriptome-wide differential expression analysis to identify which disease stages and cell types display strongest transcriptional changes. Consistently, hamsters' response to COVID-19 was most similar to humans in monocytes and neutrophils. Disease-linked pathways found in all species specifically related to interferon response or inhibition of viral replication. Analysis of candidate genes and signatures supported the results. Our structured neural network-supported workflow could be applied to other diseases, allowing better identification of suitable animal models with similar pathomechanisms across species. This work was supported by German Federal Ministry of Education and Research, (BMBF) grant IDs: 01ZX1304B, 01ZX1604B, 01ZX1906A, 01ZX1906B, 01KI2124, 01IS18026B and German Research Foundation (DFG) grant IDs: 14933180,431232613.

A novel method for characterizing cell-cell interactions at single-cell resolution reveals unique signatures in blood T cell-monocyte complexes during infection.

Communication between immune cells through direct contact is a critical feature of immune responses. Here, we developed a novel high-throughput method to study the transcriptome and adaptive immune receptor repertoire of single cells forming complexes without needing bioinformatic deconvolution. We found that T cells and monocytes forming complexes in blood during active tuberculosis (TB) and dengue hold unique transcriptomic signatures indicative of TCR/MCH-II immune synapses. Additionally, T cells in complexes showed enrichment for effector phenotypes, imaging and transcriptomic features of active TCR signaling, and increased immune activity at diagnosis compared to after anti-TB therapy. We also found evidence for bidirectional RNA exchange between T cells and monocytes, since complexes were markedly enriched for "dual-expressing" cells (i.e., co-expressing T cell and monocyte genes). Thus, studying immune cell complexes at a single-cell resolution offers novel perspectives on immune synaptic interactions occurring in blood during infection.

Targeting SHCBP1 Inhibits Tumor Progression by Restoring Ciliogenesis in Ductal Carcinoma

Abstract Primary cilia detect and transmit environmental signals into cells. Primary cilia are absent in a subset of ductal carcinomas characterized by distinctive biological activities, and recovery of cilia with normal functionality has been shown to have therapeutic potential in some cancer types. Therefore, elucidation of the underlying mechanism and clinical significance of ciliary loss in ductal carcinomas could help develop effective treatment strategies. Here, we identified a link between SHCBP1 and cilia in ductal carcinomas. Shcbp1 knockout in transgenic mice profoundly impeded tumor progression and metastasis, prolonging survival. Single-cell transcriptome analysis revealed a functional connection between SHCBP1 deficiency and increased tumor ciliogenesis. SHCBP1 ablation restored ciliogenesis in unciliated ductal carcinoma by promoting the proximity between the midbody remnant (MBR) and centrosome through enhanced Rab8 GTPase activity and Rab8GTP positioning within the MBR. Inhibition of tumor progression by SHCBP1 loss relied on the recovery of ciliogenesis. Analysis of a large cohort of patients with ductal carcinoma revealed a negative correlation between SHCBP1-induced ciliary loss and patient prognosis. Restoring ciliogenesis via SHCBP1 ablation elicited therapeutic effects in patient-derived xenograft models. Together, this study delineates that induction of MBR-centrosome proximity through SHCBP1-deficiency reactivates ciliogenesis, offering unique opportunities for the treatment of unciliated ductal carcinomas.

A Strategy Involving Microporous Microneedles Integrated with CAR-TREM2-Macrophages for Scar Management by Regulating Fibrotic Microenvironment.

Dipeptidyl peptidase 4 (DPP4) positive fibroblasts play a pivotal role in scar development following skin injury. Heterogeneous vascular endothelial cells (ECs) within scarred areas retain the capacity to drive tissue regeneration and repair. Simultaneously, TREM2 macrophages play a crucial role in the progression and resolution of fibrosis by engaging in mutual regulation with ECs. However, effective strategies to inhibit scar formation through multi-factor regulation of the scar microenvironment remain a challenge. Here, CAR-TREM2-macrophages (CAR-TREM2-Ms) capable of targeting DPP4+ fibroblasts and modulating ECs subtype within the scar microenvironment are engineered to effectively prevent scarring. Hydrogel microporous microneedles (mMNs) are employed to deliver CAR-TREM2-Ms, which can effectively alleviate scar. Single-cell transcriptome sequencing (scRNA-seq) analysis reveals that CAR-TREM2-Ms can modify ECs fibrotic phenotype and regulate fibrosis by suppressing the profibrotic gene leucine-rich-alpha-2-glycoprotein 1 (Lrg1). In vitro experiments further demonstrate that CAR-TREM2-Ms improve the scar microenvironment by phagocytosing DPP4+ fibroblasts and suppressing TGFβ secretion. This, in turn, inhibits the phenotypic conversion of LRG1 ECs and provides multifactorial way of alleviating scars. This study uncovers the evidence that mMNs attached to CAR-TREM2-Ms may exert vital influences on skin scarring through the regulation of the skin scar microenvironment, providing a promising approach for treating posttraumatic scarring.

ScDFN: enhancing single-cell RNA-seq clustering with deep fusion networks.

Single-cell ribonucleic acid sequencing (scRNA-seq) technology can be used to perform high-resolution analysis of the transcriptomes of individual cells. Therefore, its application has gained popularity for accurately analyzing the ever-increasing content of heterogeneous single-cell datasets. Central to interpreting scRNA-seq data is the clustering of cells to decipher transcriptomic diversity and infer cell behavior patterns. However, its complexity necessitates the application of advanced methodologies capable of resolving the inherent heterogeneity and limited gene expression characteristics of single-cell data. Herein, we introduce a novel deep learning-based algorithm for single-cell clustering, designated scDFN, which can significantly enhance the clustering of scRNA-seq data through a fusion network strategy. The scDFN algorithm applies a dual mechanism involving an autoencoder to extract attribute information and an improved graph autoencoder to capture topological nuances, integrated via a cross-network information fusion mechanism complemented by a triple self-supervision strategy. This fusion is optimized through a holistic consideration of four distinct loss functions. A comparative analysis with five leading scRNA-seq clustering methodologies across multiple datasets revealed the superiority of scDFN, as determined by better the Normalized Mutual Information (NMI) and the Adjusted Rand Index (ARI) metrics. Additionally, scDFN demonstrated robust multi-cluster dataset performance and exceptional resilience to batch effects. Ablation studies highlighted the key roles of the autoencoder and the improved graph autoencoder components, along with the critical contribution of the four joint loss functions to the overall efficacy of the algorithm. Through these advancements, scDFN set a new benchmark in single-cell clustering and can be used as an effective tool for the nuanced analysis of single-cell transcriptomics.

Single-cell transcriptomics reveal distinctive patterns of fibroblast activation in heart failure with preserved ejection fraction

AbstractInflammation, fibrosis and metabolic stress critically promote heart failure with preserved ejection fraction (HFpEF). Exposure to high-fat diet and nitric oxide synthase inhibitor N[w]-nitro-l-arginine methyl ester (L-NAME) recapitulate features of HFpEF in mice. To identify disease-specific traits during adverse remodeling, we profiled interstitial cells in early murine HFpEF using single-cell RNAseq (scRNAseq). Diastolic dysfunction and perivascular fibrosis were accompanied by an activation of cardiac fibroblast and macrophage subsets. Integration of fibroblasts from HFpEF with two murine models for heart failure with reduced ejection fraction (HFrEF) identified a catalog of conserved fibroblast phenotypes across mouse models. Moreover, HFpEF-specific characteristics included induced metabolic, hypoxic and inflammatory transcription factors and pathways, including enhanced expression of Angiopoietin-like 4 (Angptl4) next to basement membrane compounds, such as collagen IV (Col4a1). Fibroblast activation was further dissected into transcriptional and compositional shifts and thereby highly responsive cell states for each HF model were identified. In contrast to HFrEF, where myofibroblast and matrifibrocyte activation were crucial features, we found that these cell states played a subsidiary role in early HFpEF. These disease-specific fibroblast signatures were corroborated in human myocardial bulk transcriptomes. Furthermore, we identified a potential cross-talk between macrophages and fibroblasts via SPP1 and TNFɑ with estimated fibroblast target genes including Col4a1 and Angptl4. Treatment with recombinant ANGPTL4 ameliorated the murine HFpEF phenotype and diastolic dysfunction by reducing collagen IV deposition from fibroblasts in vivo and in vitro. In line, ANGPTL4, was elevated in plasma samples of HFpEF patients and particularly high levels associated with a preserved global-longitudinal strain. Taken together, our study provides a comprehensive characterization of molecular fibroblast activation patterns in murine HFpEF, as well as the identification of Angiopoietin-like 4 as central mechanistic regulator with protective effects.

Classifying cell cycle states and a quiescent-like G0 state using single-cell transcriptomics.

Single-cell transcriptomics has unveiled a vast landscape of cellular heterogeneity in which the cell cycle is a significant component. We trained a high-resolution cell cycle classifier (ccAFv2) using single cell RNA-seq (scRNA-seq) characterized human neural stem cells. The ccAFv2 classifies six cell cycle states (G1, Late G1, S, S/G2, G2/M, and M/Early G1) and a quiescent-like G0 state (qG0), and it incorporates a tunable parameter to filter out less certain classifications. The ccAFv2 classifier performed better than or equivalent to other state-of-the-art methods even while classifying more cell cycle states, including G0. We demonstrate that the ccAFv2 classifier is generalizable across cell types and all three germ layers by applying it to developing fetal cells. We showcased the versatility of ccAFv2 by successfully applying it to classify cells, nuclei, and spatial transcriptomics data in humans and mice, using various normalization methods and gene identifiers. We provide methods to regress the cell cycle expression patterns out of single cell or nuclei data to uncover underlying biological signals. The classifier can be used either as an R package integrated with Seurat or a PyPI package integrated with scanpy. We proved that ccAFv2 has enhanced accuracy, flexibility, and adaptability across various experimental conditions, establishing ccAFv2 as a powerful tool for dissecting complex biological systems, unraveling cellular heterogeneity, and deciphering the molecular mechanisms by which proliferation and quiescence affect cellular processes.

Placozoan secretory cell types implicated in feeding, innate immunity and regulation of behavior.

Placozoa are millimeter-sized, flat, irregularly shaped ciliated animals that crawl on surfaces in warm oceans feeding on biofilms, which they digest externally. They stand out from other animals due to their simple body plans. They lack organs, body cavities, muscles and a nervous system and have only seven broadly defined morphological cell types, each with a unique distribution. Analyses of single cell transcriptomes of four species of placozoans revealed greater diversity of secretory cell types than evident from morphological studies, but the locations of many of these new cell types were unknown and it was unclear which morphological cell types they represent. Furthermore, there were contradictions between the conclusions of previous studies and the single cell RNAseq studies. To address these issues, we used mRNA probes for genes encoding secretory products expressed in different metacells in Trichoplax adhaerens to localize cells in whole mounts and in dissociated cell cultures, where their morphological features could be visualized and identified. The nature and functions of their secretory granules were further investigated with electron microscopic techniques and by imaging secretion in live animals during feeding episodes. We found that two cell types participate in disintegrating prey, one resembling a lytic cell type in mammals and another combining features of zymogen gland cells and enterocytes. We identified secretory epithelial cells expressing glycoproteins or short peptides implicated in defense. We located seven peptidergic cell types and two types of mucocytes. Our findings reveal mechanisms that placozoans use to feed and protect themselves from pathogens and clues about neuropeptidergic signaling. We compare placozoan secretory cell types with cell types in other animal phyla to gain insight about general evolutionary trends in cell type diversification, as well as pathways leading to the emergence of synapomorphies.

Abstract C121: Racial differences in expression of the oxygen-sensor BACH1 in breast cancer

Abstract Hypoxia is an important hallmark of aggressive solid tumors and a key driver of metastasis and resistance to therapy. Hypoxic stress induces the activation of various factors, such as hypoxia- inducible factors (HIFs), which facilitate cellular adaptation and promote tumorigenesis. Given the complexity of this mechanism, identifying novel targets that sensitize hypoxic tumors to therapy could have significant clinical impact. We demonstrate HIF-independent induction of BACH1 within multiple TNBC (Triple-Negative Breast Cancer) cell lines and patient-derived organoids under hypoxic conditions. Utilizing ChIP-seq, bulk, single-cell and spatial transcriptomics across diverse BACH1 knockout tumor models, our study revealed that BACH1 selectively and directly modulates the transcriptional response associated with hypoxia and stress in TNBC. TGCA analysis reveals racial disparities in BACH1 mRNA expression in TNBC BACH1 mRNA and activity across all breast cancer subtypes (Basal, Her, LumA, LumB). Our findings suggest that BACH1 is an oxygen-sensitive mediator of hypoxia and a promising therapeutic target for mitigating hypoxia-induced pro-metastatic signaling and treatment resistance. These results also highlight the necessity of accounting for racial differences in BACH1 expression and function as a means of developing targeted therapy that takes into account racial disparities in TNBC. Citation Format: Long C. Nguyen, Christopher Dann, Dongbo Yang, Madeline Henn, Emily Shi, Thomas Li, Kent Schechter, Letícia Stock, Wenchao Liu, Margarite Matossian, Andrea Valdespino, Yoo Jane Han, Jing Zhang, Geetha Priyanka Yerradoddi, Yan Li, Mitsuyo Matsumoto, Olufunmilayo I Olopade, Kazuhiko Igarashi, Marsha R Rosner. Racial differences in expression of the oxygen-sensor BACH1 in breast cancer [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C121.

Prognostic modeling and Emerging therapeutic targets Unveiled through single-cell sequencing in esophageal squamous Cell carcinoma

ESCC presents a significant global health challenge due to its high mortality rates and varying responses to treatment. This underscores the critical need for novel diagnostic and predictive biomarkers to improve treatment outcomes.Initially, we conducted single-cell transcriptome sequencing on a total of 128,688 cells obtained from 10 patients as part of our research. Utilizing machine learning and cross-validation techniques, we developed a model incorporating 12 genes that distinguish malignant cells from non-malignant ones. In vitro, we explored the effects of IGFBP2 knockdown on the proliferation, invasion, and migration of ESCC cells. The clinical relevance of IGFBP2 was confirmed through IHC and Kaplan-Meier survival analyses. Furthermore, using bioinformatics tools such as GSVA and xCell on public databases, we discovered that high expression of IGFBP2 is associated with an immunosuppressive tumor microenvironment in ESCC, characterized by reduced CD8+ T cell infiltration. This was validated then through IHC.In summary, our study integrates single-cell sequencing and sophisticated computational techniques to highlight IGFBP2 as a promising biomarker and therapeutic target in ESCC.