Single-cell Sequencing Platforms Research Articles

Linking transcriptome and morphology in bone cells at cellular resolution with generative AI

Abstract Recent advancements in deep learning (DL) have revolutionized the capability of artificial intelligence (AI) by enabling the analysis of large-scale, complex datasets that are difficult for humans to interpret. However, large amounts of high-quality data are required to train such generative AI models successfully. With the rapid commercialization of single-cell sequencing and spatial transcriptomics platforms, the field is increasingly producing large-scale datasets such as histological images, single-cell molecular data, and spatial transcriptomic data. These molecular and morphological datasets parallel the multimodal text and image data used to train highly successful generative AI models for natural language processing and computer vision. Thus, these emerging data types offer great potential to train generative AI models that uncover intricate biological processes of bone cells at a cellular level. In this Perspective, we summarize the progress and prospects of generative AI applied to these datasets and their potential applications to bone research. In particular, we highlight three AI applications: predicting cell differentiation dynamics, linking molecular and morphological features, and predicting cellular responses to perturbations. To make generative AI models beneficial for bone research, important issues, such as technical biases in bone single-cell datasets, lack of profiling of important bone cell types, and lack of spatial information, need to be addressed. Realizing the potential of generative AI for bone biology will also likely require generating large-scale, high-quality cellular-resolution spatial transcriptomics datasets, improving the sensitivity of current spatial transcriptomics datasets, and thorough experimental validation of model predictions.

Deciphering cellular heterogeneity in Spodoptera frugiperda midgut cell line through single cell RNA sequencing

Using the 10× Genomics Chromium single-cell RNA sequencing (scRNA-seq) platform, we discovered unexpected heterogeneity in an established cell line developed from the midgut of the Fall armyworm, Spodoptera frugiperda, a major global pest. We analyzed the sequences of 18,794 cells and identified ten unique cellular clusters, including stem cells, enteroblasts, goblet cells and enteroendocrine cells, characterized by the expression of specific marker genes. Additionally, these studies addressed an important knowledge gap by investigating the expression of genes coding for respiratory and midgut membrane insecticide targets classified by the Insecticide Resistance Action Committee. Dual-fluorescence tagging method, fluorescence microscopy and fluorescence-activated cell sorting confirmed the expression of midgut cell type-specific genes. Stem cells were isolated from the heterogeneous population of SfMG-0617 cells. Our results, validated by KEGG and Gene Ontology analyses and supported by Monocle 3.0, advance the fields of midgut cellular biology and establish standards for scRNA-seq studies in non-model organisms.

Single Nucleus Total RNA Sequencing of Formalin-Fixed Paraffin-Embedded Gliomas.

Gliomas, the predominant form of brain cancer, comprise diverse malignant subtypes with limited curative therapies available. The insufficient understanding of their molecular diversity and evolutionary processes hinders the advancement of new treatments. Technical complexities associated with formalin-fixed paraffin-embedded (FFPE) clinical samples hinder molecular-level analyses of gliomas. Current single-cell RNA sequencing (scRNA-seq) platforms are inadequate for large-scale clinical applications. In this study, automated snRandom-seq is developed, a high-throughput single-nucleus total RNA sequencing platform optimized for archival FFPE samples. This platform integrates automated single-nucleus isolation and droplet barcoding systems with the random primer-based scRNA-seq chemistry, accommodating a broad spectrum of sample types. The automated snRandom-seq is applied to analyze 116492 single nuclei from 17 FFPE samples of various glioma subtypes, including rare clinical samples and matched primary-recurrent glioblastomas (GBMs). The study provides comprehensive insights into the molecular characteristics of gliomas at the single-cell level. Abundant non-coding RNAs (ncRNAs) with distinct expression profiles across different glioma clusters and uncovered promising recurrence-related targets and pathways in primary-recurrent GBMs are identified. These findings establish automated snRandom-seq as a robust tool for scRNA-seq of FFPE samples, enabling exploration of molecular diversities and tumor evolution. This platform holds significant implications for large-scale integrative and retrospective clinical research.

Patient-Derived Tumor Organoids Combined with Function-Associated ScRNA-Seq for Dissecting the Local Immune Response of Lung Cancer.

In vitro models coupled with multimodal approaches are needed to dissect the dynamic response of local tumor immune microenvironment (TIME) to immunotherapy. Here the patient-derived primary lung cancer organoids (pLCOs) are generated by isolating tumor cell clusters, including the infiltrated immune cells. A function-associated single-cell RNA sequencing (FascRNA-seq) platform allowing both phenotypic evaluation and scRNA-seq at single-organoid level is developed to dissect the TIME of individual pLCOs. The analysis of 171 individual pLCOs derived from seven patients reveals that pLCOs retain the TIME heterogeneity in the parenchyma of parental tumor tissues, providing models with identical genetic background but various TIME. Linking the scRNA-seq data of individual pLCOs with their responses to anti-PD-1 (αPD-1) immune checkpoint blockade (ICB) allows to confirm the central role of CD8+ T cells in anti-tumor immunity, to identify potential tumor-reactive T cells with a set of 10 genes, and to unravel the factors regulating T cell activity, including CD99 gene. In summary, the study constructs a joint phenotypic and transcriptomic FascRNA-seq platform to dissect the dynamic response of local TIME under ICB treatment, providing a promising approach to evaluate novel immunotherapies and to understand the underlying molecular mechanisms.

New Insight into Intestinal Mast Cells Revealed by Single-Cell RNA Sequencing.

Mast cells (MCs) are tissue-resident immune cells distributed in all tissues and strategically located close to blood and lymphatic vessels and nerves. Thanks to the expression of a wide array of receptors, MCs act as tissue sentinels, able to detect the presence of bacteria and parasites and to respond to different environmental stimuli. MCs originate from bone marrow (BM) progenitors that enter the circulation and mature in peripheral organs under the influence of microenvironment factors, thus differentiating into heterogeneous tissue-specific subsets. Even though MC activation has been traditionally linked to IgE-mediated allergic reactions, a role for these cells in other pathological conditions including tumor progression has recently emerged. However, several aspects of MC biology remain to be clarified. The advent of single-cell RNA sequencing platforms has provided the opportunity to understand MCs' origin and differentiation as well as their phenotype and functions within different tissues, including the gut. This review recapitulates how single-cell transcriptomic studies provided insight into MC development as well as into the functional role of intestinal MC subsets in health and disease.

Single-cell transcriptomics: background, technologies, applications, and challenges.

Single-cell sequencing was developed as a high-throughput tool to elucidate unusual and transient cell states that are barely visible in the bulk. This technology reveals the evolutionary status of cells and differences between populations, helps to identify unique cell subtypes and states, reveals regulatory relationships between genes, targets and molecular mechanisms in disease processes, tumor heterogeneity, the state of the immune environment, etc. However, the high cost and technical limitations of single-cell sequencing initially prevented its widespread application, but with advances in research, numerous new single-cell sequencing techniques have been discovered, lowering the cost barrier. Many single-cell sequencing platforms and bioinformatics methods have recently become commercially available, allowing researchers to make fascinating observations. They are now increasingly being used in various industries. Several protocols have been discovered in this context and each technique has unique characteristics, capabilities and challenges. This review presents the latest advancements in single-cell transcriptomics technologies. This includes single-cell transcriptomics approaches, workflows and statistical approaches to data processing, as well as the potential advances, applications, opportunities and challenges of single-cell transcriptomics technology. You will also get an overview of the entry points for spatial transcriptomics and multi-omics.

Abstract LB289: Single-cell CRISPR screens at scale to understand T cell biology

Abstract CRISPR screens have become the primary discovery engine in modern biology and are widely used to uncover novel targets in immuno-oncology. However, these genetic screens are usually coupled to rather simplistic read-outs such as cell fitness. In contrast, CROP-Seq (or “CRISPR Droplet” Sequencing) screens combine CRISPR perturbation with single-cell transcriptomics, enabling high-content phenotyping at single-cell resolution. Briefly, cells are perturbed with a pooled sgRNA library and transcriptomic profiles of each cell are recorded using conventional single-cell RNA sequencing platforms. Currently, the scale of these screens is limited to the perturbation of a couple of hundred genes, possibly up to 1.000 genes, because of increased costs for single-cell library preparation and next generation sequencing (NGS). At Myllia, we have built a platform allowing high-content CROP-Seq screens in both CRISPR-ready cancer cell lines as well as primary immune cells, e.g., human T cells. Using CRISPR KO, CRISPR interference or CRISPR activation workflows, we can help accelerate the identification of target genes and regulatory networks involved in immuno-oncology. Primary T cells are of great interest in the immunotherapy community as they are key players in autoimmune and inflammatory disease. Engineered T-lymphocytes such as CAR-T cells are currently developed as novel cellular medicines, yet many T cell-intrinsic features involved in CAR-T potency remain elusive. To enable the discovery of novel targets, we have performed multiple customized CROP-Seq CRISPR KO screens in primary human T cells investigating T cell stemness and effector phenotypes as well as CD4+ T helper cell differentiation towards Th1, Th2, or Th17 subsets. Here, we present screens for regulators of functional T cell plasticity and Th2 commitment in which we aimed to identify genes determining critical T cell fate decisions, potentially linking gene (dys)function to either autoimmune/inflammatory disease or functional persistence of cytotoxic T cells for cancer immunotherapy. Overall, the advanced CROP-Seq platform combining large-scale perturbations and targeted sequencing (TA) read-outs will catalyse a paradigm shift for CROP-Seq enabling high-throughput functional genomic screens that support the validation of drug targets in autoimmunity, inflammation and immuno-oncology. Citation Format: Nicole Untermoser, Johanna Irnstorfer, Anatoly Vasilyev, Nikola Vinko, Sumit Pawar, Anke Loregger, Adam Krejci, Henrik Schmidt, Tilmann Bürckstümmer. Single-cell CRISPR screens at scale to understand T cell biology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB289.

Abstract 6405: Identification of molecular targets in bladder cancer variants using single-cell analysis

Abstract Introduction and objectives: Variant subtypes in bladder cancer, also known as histologic variants, are clinically aggressive tumors associated with poor prognosis. The rarity of these tumors has made molecular characterization elusive using bulk transcriptional profiling. The aim of this study was to use single cell RNA sequencing to identify molecular features associated with variant subtypes that could be potential targets for therapy. Methods: Fresh tissue from bladder cancer patients undergoing surgery at our institution was sequenced using a bead-based single-cell RNA sequencing platform (Seq-Well) to generate an atlas of 9 tumors containing variants (micropapillary, nested, squamous differentiation, pleomorphic giant-cell like, plasmacytoid, small cell) and 3 tumors with pure urothelial carcinoma (UC) histology. Pathologic diagnoses were independently confirmed. Analysis was performed using the Seurat package in R Studio. Immunostaining validation was performed on a separately banked cohort. Bulk RNA sequencing data and clinical data were extracted and analyzed from The Cancer Genome Atlas (TCGA) database. Chimeric Antigen Receptor (CAR) T cells targeting TM4SF1 were generated by lentiviral transduction of primary human T cells and tested against publicly available bladder cancer cell lines. Results: Variant tumors were enriched in TM4SF1, a surface molecule that we demonstrate to be susceptible to CAR T therapy in vitro. Variants also share a cell state characterized by highly specific expression of MUC16 (CA125), which is enriched in metastatic sites, harbors transcriptional hallmarks of epithelial-mesenchymal transition and luminal-basal plasticity, and is associated with poor survival and resistance to chemotherapy. Finally, we find evidence of transcriptional mimicry between variants and similar appearing non-urothelial cells (e.g. plasmacytoid:plasma cell), suggesting common pathways between distinct cell types that could be exploited. Conclusion: We have identified several targetable molecular features within variant bladder cancer subtypes. These findings set a critical starting point for future therapeutic investigation. Funding: California Urology Foundation (HY)Conflicts of interest: noneKeywords: Single cell analysis, bladder cancer, variants, genomics, biomarkers Citation Format: Heiko Yang, Hanbing Song, Paul Allegakoen, Kevin Lu, Elizabeth Yip, Keliana Hui, Kevin Chang, Corynn Kasap, Janae Gayle, Bradley Stohr, Arun Wiita, Chien-Kuang C. Ding, Maxwell V. Meng, Jonathan Chou, Sima P. Porten, Franklin W. Huang. Identification of molecular targets in bladder cancer variants using single-cell analysis [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 6405.

An ultra high-throughput, massively multiplexable, single-cell RNA-seq platform in yeasts.

Yeasts are naturally diverse, genetically tractable, and easy to grow such that researchers can investigate any number of genotypes, environments, or interactions thereof. However, studies of yeast transcriptomes have been limited by the processing capabilities of traditional RNA sequencing techniques. Here we optimize a powerful, high-throughput single-cell RNA sequencing (scRNAseq) platform, SPLiT-seq (Split Pool Ligation-based Transcriptome sequencing), for yeasts and apply it to 43,388 cells of multiple species and ploidies. This platform utilizes a combinatorial barcoding strategy to enable massively parallel RNA sequencing of hundreds of yeast genotypes or growth conditions at once. This method can be applied to most species or strains of yeast for a fraction of the cost of traditional scRNAseq approaches. Thus, our technology permits researchers to leverage "the awesome power of yeast" by allowing us to survey the transcriptome of hundreds of strains and environments in a short period of time and with no specialized equipment. The key to this method is that sequential barcodes are probabilistically appended to cDNA copies of RNA while the molecules remain trapped inside of each cell. Thus, the transcriptome of each cell is labeled with a unique combination of barcodes. Since SPLiT-seq uses the cell membrane as a container for this reaction, many cells can be processed together without the need to physically isolate them from one another in separate wells or droplets. Further, the first barcode in the sequence can be chosen intentionally to identify samples from different environments or genetic backgrounds, enabling multiplexing of hundreds of unique perturbations in a single experiment. In addition to greater multiplexing capabilities, our method also facilitates a deeper investigation of biological heterogeneity, given its single-cell nature. For example, in the data presented here, we detect transcriptionally distinct cell states related to cell cycle, ploidy, metabolic strategies, and so forth, all within clonal yeast populations grown in the same environment. Hence, our technology has two obvious and impactful applications for yeast research: the first is the general study of transcriptional phenotypes across many strains and environments, and the second is investigating cell-to-cell heterogeneity across the entire transcriptome.

Tick innate immune responses to hematophagy and Ehrlichia infection at single-cell resolution.

Ticks rely on robust cellular and humoral responses to control microbial infection. However, several aspects of the tick's innate immune system remain uncharacterized, most notably that of the immune cells (called hemocytes), which are known to play a significant role in cellular and humoral responses. Despite the importance of hemocytes in regulating microbial infection, our understanding of their basic biology and molecular mechanisms remains limited. Therefore, we believe that a more detailed understanding of the role of hemocytes in the interactions between ticks and tick-borne microbes is crucial to illuminating their function in vector competence and to help identify novel targets for developing new strategies to block tick-borne pathogen transmission. This study examined hemocytes from the lone star tick (Amblyomma americanum) at the transcriptomic level using the 10X genomics single-cell RNA sequencing platform to analyze hemocyte populations from unfed, partially blood-fed, and Ehrlichia chaffeensis-infected ticks. The functional role of differentially expressed hemocyte markers in hemocyte proliferation and Ehrlichia dissemination was determined using an RNA interference approach. Our data exhibit the identification of fourteen distinct hemocyte populations. Our results uncover seven distinct lineages present in uninfected and Ehrlichia-infected hemocyte clusters. The functional characterization of hemocytin, cystatin, fibronectin, and lipocalin demonstrate their role in hemocyte population changes, proliferation, and Ehrlichia dissemination. Our results uncover the tick immune responses to Ehrlichia infection and hematophagy at a single-cell resolution. This work opens a new field of tick innate immunobiology to understand the role of hemocytes, particularly in response to prolonged blood-feeding (hematophagy), and tick-microbial interactions.

Advanced sequencing-based high-throughput and long-read single-cell transcriptome analysis.

Cells are the fundamental building blocks of living systems, exhibiting significant heterogeneity. The transcriptome connects the cellular genotype and phenotype, and profiling single-cell transcriptomes is critical for uncovering distinct cell types, states, and the interplay between cells in development, health, and disease. Nevertheless, single-cell transcriptome analysis faces daunting challenges due to the low abundance and diverse nature of RNAs in individual cells, as well as their heterogeneous expression. The advent and continuous advancements of next-generation sequencing (NGS) and third-generation sequencing (TGS) technologies have solved these problems and facilitated the high-throughput, sensitive, full-length, and rapid profiling of single-cell RNAs. In this review, we provide a broad introduction to current methodologies for single-cell transcriptome sequencing. First, state-of-the-art advancements in high-throughput and full-length single-cell RNA sequencing (scRNA-seq) platforms using NGS are reviewed. Next, TGS-based long-read scRNA-seq methods are summarized. Finally, a brief conclusion and perspectives for comprehensive single-cell transcriptome analysis are discussed.

DVsc: An Automated Framework for Efficiently Detecting Viral Infection from Single-cell Transcriptomics Data.

Single-cell RNA sequencing (scRNA-seq) has emerged as a valuable tool for studying cellular heterogeneity in various fields, particularly in virological research. By studying the viral and cellular transcriptomes, the dynamics of viral infection can be investigated at a single-cell resolution. However, limited studies have been conducted to investigate whether RNA transcripts from clinical samples contain substantial amounts of viral RNAs, and a specific computational framework for efficiently detecting viral reads based on scRNA-seq data has not been developed. Hence, we introduce DVsc, an open-source framework for precise quantitative analysis of viral infection from single-cell transcriptomics data. When applied to approximately 200 diverse clinical samples that were infected by more than 10 different viruses, DVsc demonstrated high accuracy in systematically detecting viral infection across a wide array of cell types. This innovative bioinformatics pipeline could be crucial for addressing the potential effects of surreptitiously invading viruses on certain illnesses, as well as for designing novel medicines to target viruses in specific host cell subsets and evaluating the efficacy of treatment. DVsc supports the FASTQ format as an input and is compatible with multiple single-cell sequencing platforms. Moreover, it could also be applied to sequences from bulk RNA sequencing data. DVsc is available at http://62.234.32.33:5000/DVsc.

Secretory GFP reconstitution labeling of neighboring cells interrogates cell–cell interactions in metastatic niches

Cancer cells inevitably interact with neighboring host tissue-resident cells during the process of metastatic colonization, establishing a metastatic niche to fuel their survival, growth, and invasion. However, the underlying mechanisms in the metastatic niche are yet to be fully elucidated owing to the lack of methodologies for comprehensively studying the mechanisms of cell–cell interactions in the niche. Here, we improve a split green fluorescent protein (GFP)-based genetically encoded system to develop secretory glycosylphosphatidylinositol-anchored reconstitution-activated proteins to highlight intercellular connections (sGRAPHIC) for efficient fluorescent labeling of tissue-resident cells that neighbor on and putatively interact with cancer cells in deep tissues. The sGRAPHIC system enables the isolation of metastatic niche-associated tissue-resident cells for their characterization using a single-cell RNA sequencing platform. We use this sGRAPHIC-leveraged transcriptomic platform to uncover gene expression patterns in metastatic niche-associated hepatocytes in a murine model of liver metastasis. Among the marker genes of metastatic niche-associated hepatocytes, we identify Lgals3, encoding galectin-3, as a potential pro-metastatic factor that accelerates metastatic growth and invasion.

Abstract LB249: A genome-scale CROP-seq screen reveals mediators of T cell signaling

Abstract CROP-seq/Perturb-seq screens combine CRISPR perturbation with single-cell RNA sequencing. In brief, cells are perturbed with a pooled sgRNA library and transcriptomic profiles of each cell are recorded using conventional single-cell RNA sequencing platforms. Currently, the scale of these screens is limited to the perturbation of a couple of hundred genes, possibly up to 1.000 genes, because costs for single-cell library preparation and next generation sequencing are high. Here, we aimed to conduct a genome-scale CRISPR screen within a reasonable budget. We chose Jurkat T cells for this experiment and focused on T cell activation because signaling pathways in these cells are well understood and we could utilize the published knowledge to benchmark the performance of our platform. To accomplish this, we utilized a CRISPR interference setup in which multiple sgRNAs can be delivered to the same cell, thus “squeezing” more perturbations into one cell. We targeted 18.595 human genes with four sgRNAs per gene and delivered this genome-scale library to Jurkat cells harboring dCas9-KRAB. Cells were stimulated with anti-TCR and anti-CD28 antibodies for 24 hours to activate T cell signaling. Following that, we processed 1.000.000 cells in one go, using the Chromium X platform that has recently been launched by 10X Genomics. Following single-cell library preparation, we amplified a selected set of 374 transcripts and submitted the corresponding library for NGS on one NovaSeq S4 flowcell. First, we confirmed that the perturbation of 374 marker genes by CRISPR interference led to the downregulation of the cognate targets, suggesting that the CRISPR perturbation workflow is functional. Then, we assessed whether T cell activation could be recapitulated from the chosen markers and found that activated Jurkat T cells can be distinguished from their unactivated counterparts using the signature in question. Finally, we assessed the phenotypes of gene knockouts introduced at genome-scale. Of the 18.595 genes perturbed, a set of 70 genes affected T cell activation, partitioning to 55 activators (whose knockout led to diminished signaling) and 15 inhibitors (whose knockout led to enhanced signaling). Of note, our screen recovered key signaling nodes that are proximal to the TCR and have been well described in the literature, including LAT, LCK, ZAP70, CD3E, ITK, RASGRP1 and VAV1. Overall, the screen presented here will catalyse a paradigm shift for CROP-seq/Perturb-seq type CRISPR screens towards genome scale. Citation Format: Anke Loregger, Nicole Untermoser, Anatoly Vasilyev, Adam Krejci, Henrik Schmidt, Tilmann Bürckstümmer. A genome-scale CROP-seq screen reveals mediators of T cell signaling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB249.

Abstract 4273: Integration with benchmark data of paired bulk and single-cell RNA sequencing data substantially improves the accuracy of bulk tissue deconvolution

Abstract The accuracy of current deconvolution methods largely relies on the quality of cell-type expression references. However, single-cell (sc) and single-nuclei (sn) RNA-seq data used for building the reference are usually generated from independent studies that are distinct from the bulk RNA-seq data to be deconvolved. This study design inherently introduces technical confounding factors as unwanted variations, which is not fully addressed by current methods. To evaluate the impact of this variation on deconvolution accuracy, we generated a benchmark dataset where bulk and snRNA-seq profiling were performed from the same aliquot of single-nuclei that were extracted from 24 healthy retina samples. All donor eye samples were collected within six hours post-mortem and were absent of any disease. This study design guarantees the matched sequencing data to present the same cell-type compositions, so that cross-platform technical artifacts become the remaining confounding factor. We used the benchmark dataset to evaluate the performance of seven current deconvolution methods and found they performed much worse in matched real-bulk data than in matched pseudo-bulks that were summations of the single-cell data. This finding suggests that none of these methods have fully addressed the major technical artifacts between bulk and single-cell sequencing platforms. We therefore propose DeMix.SC, a new deconvolution framework that optimizes deconvolution parameters using a small set of matched bulk and sc/snRNA-seq data from the same tissue type. DeMix.SC includes two major steps. First, we measure the technical variations across genes and across platforms using the benchmark data. Second, we introduce a new weight function for each gene that produces a ranking order that accounts for both the platform-specific technical variations and cell-type specific expressions at gene level. Using the benchmark data for retina, we applied DeMix.SC to previously published human retinal RNA-seq data from 523 individuals with different stages of age-related macular degeneration (AMD). We observed that DeMix.SC can accurately capture the cell-type composition shifts in the AMD retina. DeMix.SC revealed a significant drop of rod cells as well as increased astrocytes, bipolar cells, and Müller cells in the AMD retina compared to the non-AMD group. The proportion changes of the later three minor cell types were not identified by other methods, while DeMix.SC could reveal such tendency. In summary, DeMix.SC integrates benchmark data to improve the deconvolution accuracy in retina samples. Our method is generic and can be applied to other disease conditions, such as deciphering the cell-type heterogeneity in cancer. We expect DeMix.SC will help revolutionize the downstream cell-type specific analysis of bulk RNA-seq data and identify cellular targets of human diseases. Citation Format: Shuai Guo, Xuesen Cheng, Andrew Koval, Shuangxi Ji, Qingnan Liang, Yumei Li, Leah A. Owen, Ivana K. Kim, John Weinstein, Scott Kopetz, John Paul Shen, Margaret M. DeAngelis, Rui Chen, Wenyi Wang. Integration with benchmark data of paired bulk and single-cell RNA sequencing data substantially improves the accuracy of bulk tissue deconvolution. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4273.

PD02-04 SINGLE CELL TRANSCRIPTIONAL PROFILES OF BENIGN PROSTATIC HYPERPLASIA

You have accessJournal of UrologyCME1 Apr 2023PD02-04 SINGLE CELL TRANSCRIPTIONAL PROFILES OF BENIGN PROSTATIC HYPERPLASIA Rei Unno, Jon Akutagawa, Hanging Song, Heiko Yang, Thomas Chi, and Franklin W. Huang Rei UnnoRei Unno More articles by this author , Jon AkutagawaJon Akutagawa More articles by this author , Hanging SongHanging Song More articles by this author , Heiko YangHeiko Yang More articles by this author , Thomas ChiThomas Chi More articles by this author , and Franklin W. HuangFranklin W. Huang More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003219.04AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Benign prostatic hyperplasia (BPH) affects the majority of aging men and leads to lower urinary tract symptoms. Based on histological characteristics and bulk sequencing data, the pathophysiology and underlying mechanisms of BPH are poorly understood. The aim of this study was to investigate the transcriptional profile of BPH at single cell resolution to improve our understanding of the molecular features of BPH. METHODS: Fifteen BPH specimens were collected from patients undergoing holmium laser enucleation of the prostate. Cells from each specimen were isolated, captured, and sequenced using a bead-based single-cell RNA sequencing platform (Seq-well). Transcriptomic analysis of the gene expression matrices was performed using software including Scanpy and cellxgene. The absence of malignant pathologic diagnoses was independently confirmed. RESULTS: 16,234 cells from 15 BPH specimens were analyzed. By canonical cell type markers, we annotated three major cell types (epithelial, stromal, and immune cells). Using significantly upregulated genes in BPH compared to normal prostates previously derived from a bulk RNA sequencing comparison, the signature score of the bulk sequencing data was high only in the fibroblast populations. After performing a sub-clustering analysis of the 6249 epithelial cells, we identified four luminal cell subclusters showing similar levels of luminal epithelial markers KLK3, KLK2, and NKX3-1, as well as one distinct basal and one club cell population. Analysis of the differentially expressed genes among the four luminal subclusters revealed that luminal subcluster 4 was enriched in a specific signature gene set, including ribosomal genes and KLK4, which has been shown to be abundant in BPH, compared to the other subclusters. Gene ontology analysis using this gene set demonstrated that luminal subcluster 4 was associated with pathways such as oxidative phosphorylation and adipogenesis. Analyses of the BPH immune microenvironment showed a larger proportion of pro-inflammatory cells, such as M1 macrophages, compared to anti-inflammatory cells within the myeloid cell population. CONCLUSIONS: Single cell profiling of BPH identified epithelial cells, stromal cells, and immune cells. We confirmed the upregulation of previously derived BPH-associated genes in the fibroblast population and the enrichment of pro-inflammatory myeloid cells in the immune microenvironment. Within the epithelial cell population, we identified a luminal epithelial subcluster that may represent a cell state associated with BPH. Source of Funding: none © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e70 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Rei Unno More articles by this author Jon Akutagawa More articles by this author Hanging Song More articles by this author Heiko Yang More articles by this author Thomas Chi More articles by this author Franklin W. Huang More articles by this author Expand All Advertisement PDF downloadLoading ...

Validation of the application of gel beads-based single-cell genome sequencing platform to soil and seawater

Single-cell genomics is applied to environmental samples as a method to solve the problems of current metagenomics. However, in the fluorescence-activated cell sorting-based cell isolation and subsequent whole genome amplification, the sorting efficiency and the sequence quality are greatly affected by the type of target environment, limiting its adaptability. Here, we developed an improved single-cell genomics platform, named SAG-gel, which utilizes gel beads for single-cell isolation, lysis, and whole genome amplification. To validate the versatility of SAG-gel, single-cell genome sequencing was performed with model bacteria and microbial samples collected from eight environmental sites, including soil and seawater. Gel beads enabled multiple lysis treatments. The genome coverage with model bacteria was improved by 9.1–25%. A total of 734 single amplified genomes were collected from the diverse environmental samples, and almost full-length 16S rRNA genes were recovered from 57.8% of them. We also revealed two marine Rhodobacter strains harboring nearly identical 16S rRNA genes but having different genome contents. In addition, searching for viral sequences elucidated the virus-host linkage over the sampling sites, revealing the geographic distribution and diverse host range of viruses.

Unravelling Tumour Microenvironment in Melanoma at Single-Cell Level and Challenges to Checkpoint Immunotherapy.

Melanoma is known as one of the most immunogenic tumours and is often characterised by high mutation burden, neoantigen load and immune infiltrate. The application of immunotherapies has led to impressive improvements in the clinical outcomes of advanced stage melanoma patients. The standard of care immunotherapies leverage the host immunological influence on tumour cells, which entail complex interactions among the tumour, stroma, and immune cells at the tumour microenvironmental level. However, not all cancer patients can achieve a long-term durable response to immunotherapy, and a significant proportion of patients develops resistance and still die from their disease. Owing to the multi-faceted problems of tumour and microenvironmental heterogeneity, identifying the key factors underlying tumour progression and immunotherapy resistance poses a great challenge. In this review, we outline the main challenges to current cancer immunotherapy research posed by tumour heterogeneity and microenvironment complexities including genomic and transcriptomic variability, selective outgrowth of tumour subpopulations, spatial and temporal tumour heterogeneity and the dynamic state of host immunity and microenvironment orchestration. We also highlight the opportunities to dissect tumour heterogeneity using single-cell sequencing and spatial platforms. Integrative analyses of large-scale datasets will enable in-depth exploration of biological questions, which facilitates the clinical application of translational research.

Vasculogenesis in kidney organoids upon transplantation

Human induced pluripotent stem cell-derived kidney organoids have potential for disease modeling and to be developed into clinically transplantable auxiliary tissue. However, they lack a functional vasculature, and the sparse endogenous endothelial cells (ECs) are lost upon prolonged culture in vitro, limiting maturation and applicability. Here, we use intracoelomic transplantation in chicken embryos followed by single-cell RNA sequencing and advanced imaging platforms to induce and study vasculogenesis in kidney organoids. We show expansion of human organoid-derived ECs that reorganize into perfused capillaries and form a chimeric vascular network with host-derived blood vessels. Ligand-receptor analysis infers extensive potential interactions of human ECs with perivascular cells upon transplantation, enabling vessel wall stabilization. Perfused glomeruli display maturation and morphogenesis to capillary loop stage. Our findings demonstrate the beneficial effect of vascularization on not only epithelial cell types, but also the mesenchymal compartment, inducing the expansion of ´on target´ perivascular stromal cells, which in turn are required for further maturation and stabilization of the neo-vasculature. The here described vasculogenic capacity of kidney organoids will have to be deployed to achieve meaningful glomerular maturation and kidney morphogenesis in vitro.

ZINB-Based Graph Embedding Autoencoder for Single-Cell RNA-Seq Interpretations

Single-cell RNA sequencing (scRNA-seq) provides high-throughput information about the genome-wide gene expression levels at the single-cell resolution, bringing a precise understanding on the transcriptome of individual cells. Unfortunately, the rapidly growing scRNA-seq data and the prevalence of dropout events pose substantial challenges for cell type annotation. Here, we propose a single-cell model-based deep graph embedding clustering (scTAG) method, which simultaneously learns cell–cell topology representations and identifies cell clusters based on deep graph convolutional network. scTAG integrates the zero-inflated negative binomial (ZINB) model into a topology adaptive graph convolutional autoencoder to learn the low-dimensional latent representation and adopts Kullback–Leibler (KL) divergence for the clustering tasks. By simultaneously optimizing the clustering loss, ZINB loss, and the cell graph reconstruction loss, scTAG jointly optimizes cluster label assignment and feature learning with the topological structures preserved in an end-to-end manner. Extensive experiments on 16 single-cell RNA-seq datasets from diverse yet representative single-cell sequencing platforms demonstrate the superiority of scTAG over various state-of-the-art clustering methods.