Single-cell RNA-seq Technologies Research Articles

Abstract 7504: Longitudinal multi-omics characterization of circulating tumor cells from ALK positive NSCLC patients

Abstract Introduction: Anaplastic lymphoma kinase positive non-small cell lung cancer (ALK+NSCLC) represents a distinct subtype of NSCLC characterized by oncogenic ALK gene mutations leading to ALK overexpression and activation in tumor cells. ALK+ NSCLC will frequently develop mutations during treatment, leading to nonresponse and eventually cancer relapse. Therefore, there is a critical need for sensitive technologies to monitor ALK+ NSCLC disease status in patients over time. To facilitate longitudinal monitoring, there has been a rapid evolution in diagnostic technologies leveraging circulating tumor cells (CTCs) as a liquid biopsy tool. CTCs can be collected non-invasively at various intervals throughout patient treatment. Moreover, they shed from solid tumors and disseminate into the bloodstream, which makes isolating CTCs a valuable source of genomic, transcriptomic, and proteomic insights pertaining to the primary tumor. In this study, we present a comprehensive workflow for conducting multi-omic analysis on CTCs derived from ALK+ NSCLC patients. Methods: Blood samples were collected from a cohort of ALK+ NSCLC patients, and CTCs were isolated using a high throughput, label free microfluidic device, Labyrinth. Enriched CTCs were immunofluorescently stained for cytokeratin, CD45, DAPI, epithelial cell adhesion molecule (EpCAM), and vimentin markers. Heterogeneous CTC populations with hybrid epithelial/mesenchymal (EM) phenotypes are reported. Fluorescence in situ hybridization (FISH) was performed on enriched CTC samples. DNA was extracted and sequenced using a 523 gene targeted sequencing panel. Single cell RNA-seq technology was performed in parallel on CTC samples to investigate the gene expression throughout treatment. Results: The median concentration of CTCs in the ALK+ NSCLC cohort (n=33) is 137 CTCs per ml of blood, and 64% of CTCs are in an EM hybrid state. In ALK+ NSCLC patients (n=3) with an average of 4.6 visits per sample, we observed a trend of decreasing CTC concentration for patients with partial response to the treatment. The presence of ALK fusion in ALK+ NSCLC patient’s CTC is confirmed by FISH. Oncogenic mutations (JAK1, SPEN, ERBB4, NTRK3) are observed in bulk CTC samples from two NSCLC patients. Single cell RNA sequencing was performed on three ALK+ NSCLC patients; 58, 199, and 700 potential CTCs were detected with positive cytokeratin expression and no white blood cell marker expression. Conclusions: This study examined the feasibility of using CTCs for studying and monitoring the clinical status of the ALK+ NSCLC patients as well as the genomic and transcriptomic signatures within their tumors. More patients will need to be processed in order to validate our workflow and reinforce our conclusions. Citation Format: Yuru Chen, Shamileh Fouladdel, Harrison Ball, Zhaoping Qin, Albert Liu, Xu Cheng, Liwei Bao, Habib Serhan, Bryce Vandenburg, Varun Kathawate, Larua Goo, Peter Ulintz, Nathan Merrill, Aaron Udager, Angel Qin, Sofia D. Merajver, Sunitha Nagrath. Longitudinal multi-omics characterization of circulating tumor cells from ALK positive NSCLC 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 7504.

SAT268 Regulation Of Stem/Progenitor Cells Of The HPA Axis During Stress Adaptation

Abstract Disclosure: P. Siatra: None. D. Cozma: None. I. Berger: None. I. Oikonomakos: None. M. Theodoropoulou: None. S.R. Bornstein: None. C.L. Andoniadou: None. C. Steenblock: None. Chronic stress is a pervasive concern in the modern society. A long-term hyper activation of the hypothalamic-pituitary-adrenal (HPA) axis leads to elevated amounts of stress hormones [e.g. ACTH and glucocorticoids (GCs)]. This can incur maladaptation that eventually contributes to mental illness, cardiovascular dysfunctions, diabetes, cancer and autoimmune diseases. Emerging key players for stress adaptation of the HPA axis are stem/progenitor cell populations. In this study, we aim to unravel the mechanisms involved in the regulation of stem/progenitor cells of the HPA axis during stress adaptation. To that aim, we incorporated an in vitro system of non-adherent spheroids from primary adrenocortical mouse cells that were stimulated with ACTH. The treatment resulted in the increased growth of the spheroids compared to unstimulated controls indicating a potential role of the hormone in cell proliferation and stem cell behaviour. This result is in line with the enlarged adrenals observed in patients with Cushing’s syndrome. To assess the contribution of stem cells during stress adaptation in the adrenal cortex in vivo, we performed immobilization stress and lineage tracing studies using the tamoxifen inducible Gli1:CreERT2/R26R:eYFP mouse line. By combining these results along with single-cell RNA-seq technology, we aim to shed light into molecular factors involved in the adult adrenal cortex regeneration and remodelling. To follow the effect of GC excesses on the negative feedback loop observed during a normal stress response on the pituitary gland, we stimulated adherent primary anterior pituitary colonies with dexamethasone and corticosterone. GC treatment led to downregulation of Pomc expression, a precursor polypeptide for ACTH and other hormones, in the treated colonies compared to the control. This downregulation caused by GCs could be mediated through different gene targets of the glucocorticoid receptor as well as via paracrine signalling from pituitary stem cells. Altogether, our results will provide us with more profound knowledge of mechanisms underlying interactions between stressors and stem/progenitor cells of the HPA axis along with novel therapeutic targets for stress-associated disorders. Presentation: Saturday, June 17, 2023

Systematic investigation of mitochondrial transfer between cancer cells and Tcells at single-cell resolution.

Mitochondria (MT) participate in most metabolic activities of mammalian cells. A near-unidirectional mitochondrial transfer from Tcells to cancer cells was recently observed to "metabolically empower" cancer cells while "depleting immune cells," providing new insights into tumor-T cell interaction and immune evasion. Here, we leverage single-cell RNA-seq technology and introduce MERCI, a statistical deconvolution method for tracing and quantifying mitochondrial trafficking between cancer and Tcells. Through rigorous benchmarking and validation, MERCI accurately predicts the recipient cells and their relative mitochondrial compositions. Application of MERCI to human cancer samples identifies a reproducible MT transfer phenotype, with its signature genes involved in cytoskeleton remodeling, energy production, and TNF-α signaling pathways. Moreover, MT transfer is associated with increased cell cycle activity and poor clinical outcome across different cancer types. In summary, MERCI enables systematic investigation of an understudied aspect of tumor-T cell interactions that may lead to the development of therapeutic opportunities.

MuDCoD: multi-subject community detection in personalized dynamic gene networks from single-cell RNA sequencing.

With the wide availability of single-cell RNA-seq (scRNA-seq) technology, population-scale scRNA-seq datasets across multiple individuals and time points are emerging. While the initial investigations of these datasets tend to focus on standard analysis of clustering and differential expression, leveraging the power of scRNA-seq data at the personalized dynamic gene co-expression network level has the potential to unlock subject and/or time-specific network-level variation, which is critical for understanding phenotypic differences. Community detection from co-expression networks of multiple time points or conditions has been well-studied; however, none of the existing settings included networks from multiple subjects and multiple time points simultaneously. To address this, we develop Multi-subject Dynamic Community Detection (MuDCoD) for multi-subject community detection in personalized dynamic gene networks from scRNA-seq. MuDCoD builds on the spectral clustering framework and promotes information sharing among the networks of the subjects as well as networks at different time points. It clusters genes in the personalized dynamic gene networks and reveals gene communities that are variable or shared not only across time but also among subjects. Evaluation and benchmarking of MuDCoD against existing approaches reveal that MuDCoD effectively leverages apparent shared signals among networks of the subjects at individual time points, and performs robustly when there is no or little information sharing among the networks. Applications to population-scale scRNA-seq datasets of human-induced pluripotent stem cells during dopaminergic neuron differentiation and CD4+ T cell activation indicate that MuDCoD enables robust inference for identifying time-varying personalized gene modules. Our results illustrate how personalized dynamic community detection can aid in the exploration of subject-specific biological processes that vary across time. MuDCoD is publicly available at https://github.com/bo1929/MuDCoD as a Python package. Implementation includes simulation and real-data experiments together with extensive documentation.

Single-cell RNA-sequencing data clustering using variational graph attention auto-encoder with self-supervised leaning.

The emergence of single-cell RNA-seq (scRNA-seq) technology makes it possible to capture their differences at the cellular level, which contributes to studying cell heterogeneity. By extracting, amplifying and sequencing the genome at the individual cell level, scRNA-seq can be used to identify unknown or rare cell types as well as genes differentially expressed in specific cell types under different conditions using clustering for downstream analysis of scRNA-seq. Many clustering algorithms have been developed with much progress. However, scRNA-seq often appears with characteristics of high dimensions, sparsity and even the case of dropout events', which make the performance of scRNA-seq data clustering unsatisfactory. To circumvent the problem, a new deep learning framework, termed variational graph attention auto-encoder (VGAAE), is constructed for scRNA-seq data clustering. In the proposed VGAAE, a multi-head attention mechanism is introduced to learn more robust low-dimensional representations for the original scRNA-seq data and then self-supervised learning is also recommended to refine the clusters, whose number can be automatically determined using Jaccard index. Experiments have been conducted on different datasets and results show that VGAAE outperforms some other state-of-the-art clustering methods.

Abstract B015: Understanding cell heterogeneity in single-cell renal cell carcinoma datasets

Abstract Studying the heterogeneity in RCC may provide insights for improved diagnosis and treatment. Cell heterogeneity may be reflected at the mRNA level, and cell deconvolution tools like CIBERSORT have been used to decipher cell-specific signals in bulk RNA interrogation. Single-cell RNA seq technologies (scRNA-seq) enable dissecting cell heterogeneity in tumor samples further. An exploratory scRNA-seq study was conducted on three subtypes of RCC: 2 samples of clear cell RCC (ccRCC1 and ccRCC2), 1 sample of type-2 papillary RCC (pRCC), and 1 sample of chromophobe RCC (chRCC) (Su et al., 2021). This study provided valuable insights, such as delineating new tumor markers and predicting drug responses. As we advance our understanding of scRNA-seq, additional tools allow annotating of more cell types and exploring ploidy levels. Here we reanalyze this dataset further to illustrate the tumor microenvironment heterogeneity in RCC samples. We downloaded the datasets (GSE152938) from Gene Expression Omnibus and reprocessed the data using Seurat. The cellular composition and ploidy levels were explored using Azimuth and CopyKAT. A total of 30261 high-quality single-cell transcriptomic information of the RCC samples were obtained for the downstream analysis. The RCC subtypes showed both intra and inter-tumoral heterogeneity. Within the clear cell carcinomas, the ccRCC2 sample showed about 63.57% immune cell infiltration compared to 45.68% in ccRCC1. Rare subpopulations of 0.86% fibroblast and 0.81% mast cells were observed only in ccRCC1. In contrast, 1.56% of B cells were observed only in ccRCC2. Using an improved cell deconvolution algorithm, we identified natural killer T cells, neutrophils, and plasma cells in the ccRCC samples that were not reported in the original publication. We also identified five subtypes of endothelial cells (instead of the two reported in the original article) which may further our understanding of angiogenesis and immune regulation in these ccRCC samples. Our analysis showed that the macrophages in the ccRCC and pRCC samples are predominantly M2 macrophages. In the chRCC sample, we also identified 0.2% B cells and 2.19% M2 macrophage subpopulations, which may further illuminate the immune landscape of the chRCC tumor microenvironment. We found the ccRCC sample to be the most immune-rich with about 56.49% immune infiltration, followed by the pRCC (46.88% infiltration) and chRCC (8.86% infiltration) samples. In the combined ccRCC samples, three of the four ccRCC cell clusters were predominantly diploids (85.6%, 97.8%, and 60%, respectively), whereas only one showed a higher proportion of aneuploidy (95.8%). Further studies with a larger sample are required to validate these observations. This reanalysis of previously published data may enrich the results of the original manuscript and provide additional information about the tumor microenvironment of these RCC subtypes. We will integrate these findings with our more extensive collection of scRNA-seq at our institution, for further interrogating and validating these results. Citation Format: Sadia Islam Kana, Lucas A. Salas. Understanding cell heterogeneity in single-cell renal cell carcinoma datasets [abstract]. In: Proceedings of the AACR Special Conference: Advances in Kidney Cancer Research; 2023 Jun 24-27; Austin, Texas. Philadelphia (PA): AACR; Cancer Res 2023;83(16 Suppl):Abstract nr B015.

Single-Cell RNA-seq Analysis of a Human Embryonic Stem Cell to Endothelial Cell System Based on Transcription Factor Overexpression.

Human embryonic stem cell (hESC)-derived endothelial cells (ECs) possess therapeutic potential in many diseases. Cytokine supplementation induction and transcription factor overexpression have become two mainstream methods of hESC-EC induction. Single-cell RNA-seq technology has been widely used to analyse dynamic processes during hESC-EC induction and components of induced endothelial cells. However, studies that used single-cell RNA-seq are mainly based on cytokine supplementation methods. In this study, we used a high-efficiency human embryonic stem cell-endothelial cell line (hESC-EC) called the "FLI1-PKC system" as a research model and employed single-cell RNA sequencing (scRNA-seq) to investigate the transcriptional landscape and cellular dynamics. The high-efficiency hESC-EC induction (FLI1-PKC) system was established in our previous study. We applied single-cell RNA sequencing (scRNA-seq) of the differentiated cells at different time points and investigated the gene expression profiles. The FLI1-PKC induction system can directionally differentiate hESCs into mature endothelial cells with all the requisite functions. Unlike other hES-EC induction protocols, the FLI1-PKC method follows a different induction route; nonetheless, the transcriptome of induced endothelial cells (iECs) remains the same. The elevated number of activated transcription factors may explain why the FLI1-PKC system is more effective than other hES-EC protocols. Our study has presented a single-cell transcriptional overview of a high-efficiency hESC-EC induction system, which can be used as a model and reference for further improvement in other hESC induction systems.

#3764 SINGLE-CELL RNA-SEQ REVEALS IMMUNE AND KIDNEY CELL TYPE–SPECIFIC MOLECULAR ASSOCIATIONS TO IGA NEPHROPATHY

Abstract Background and Aims IgA nephropathy is the most common primary glomerulonephritis worldwide. There is growing evidence of host immunity is closely related to the pathogenesis of IgAN, but the detailed mechanism in the early stages of IgAN remain largely unknown. In this study, we applied single-cell RNA-seq technology to investigate the transcriptomic changes in PBMCs and kidney tissues of early stage IgAN patients at the single-cell resolution. This study aims to establish a immune and kidney cell landscape of IgAN, and offers new insights for the diagnosis and treatment of IgAN. Method In strict accordance to the admission criteria, collect peripheral venous blood and kidney samples of healthy donors and newly diagnosed IgAN patients, prepare single cell solution, verify cell viability by flow cytometry, and perform single-cell RNA-seq using a BD Rhapsody platform. Meanwhile, the clinical data of healthy donors and IgAN patients were collected and analysed. Finally, the correlation and association of differential expressed genes (DEGs) and cell clusters-identified by single-cell RNA-seq with the clinical data of IgAN were investigated. Results First, we established immune cells and kidney cells landscape of IgAN. The differentially expressed genes (DEGs) between the control group and IgAN were mainly concentrated in the NK cell-mediated cytotoxicity and cell killing pathways. Interestingly, we found significant decreases in NK cell numbers and cytotoxicity, and NK cell numbers and marker genes were negatively correlated with clinical parameters, including urinary protein creatinine ratio (UPCR) and serum galactose-deficient IgA1 and IgA. In contrast, B cell DEGs were enriched in different viral infection pathways, and one specific B cell subgroup showed inhibition of NFκB signaling, which was positively correlated with clinical parameters. In addition, a subpopulation of monocytes expressing interferon-inducing genes was positively associated with clinical severity of IgAN. In case of the kidney tissue, compared with the control group, mesangial cells of IgAN patients up-regulated extracellular matrix, transcription factors, kidney development and genes related to the regulation of Wnt signaling pathway. The expression of structural protein ITGA8 on mesangial cells was significantly decreased, indicating the mesangial cells were damaged in early IgAN. Pseudo-time analysis indicated that extracellular matrix-related genes were involved in mesangial cell lesion. We also found that the patie'ts' glomerular endothelial cells expressed high level of angiogenesis and VEGF- related genes. Moreover, podocytes of IgAN patients expressed genes of cytoskeletal recombination and FGFR activation. Conclusion We successfully established the single-cell landscapes of peripheral blood immune cells and kidney cells in early IgAN patients, which revealed changes in the number and status of immune cells and kidney cells closely related to clinical manifestations and phenotypic transformation. Our study offers new insights for the diagnosis and treatment of IgAN.

Expression quantitative trait locus studies in the era of single-cell omics.

Genome-wide association studies have revealed that the regulation of gene expression bridges genetic variants and complex phenotypes. Profiling of the bulk transcriptome coupled with linkage analysis (expression quantitative trait locus (eQTL) mapping) has advanced our understanding of the relationship between genetic variants and gene regulation in the context of complex phenotypes. However, bulk transcriptomics has inherited limitations as the regulation of gene expression tends to be cell-type-specific. The advent of single-cell RNA-seq technology now enables the identification of the cell-type-specific regulation of gene expression through a single-cell eQTL (sc-eQTL). In this review, we first provide an overview of sc-eQTL studies, including data processing and the mapping procedure of the sc-eQTL. We then discuss the benefits and limitations of sc-eQTL analyses. Finally, we present an overview of the current and future applications of sc-eQTL discoveries.

Reconstruction of the cell pseudo-space from single-cell RNA sequencing data with scSpace

Tissues are highly complicated with spatial heterogeneity in gene expression. However, the cutting-edge single-cell RNA-seq technology eliminates the spatial information of individual cells, which contributes to the characterization of cell identities. Herein, we propose single-cell spatial position associated co-embeddings (scSpace), an integrative method to identify spatially variable cell subpopulations by reconstructing cells onto a pseudo-space with spatial transcriptome references (Visium, STARmap, Slide-seq, etc.). We benchmark scSpace with both simulated and biological datasets, and demonstrate that scSpace can accurately and robustly identify spatially variated cell subpopulations. When employed to reconstruct the spatial architectures of complex tissue such as the brain cortex, the small intestinal villus, the liver lobule, the kidney, the embryonic heart, and others, scSpace shows promising performance on revealing the pairwise cellular spatial association within single-cell data. The application of scSpace in melanoma and COVID-19 exhibits a broad prospect in the discovery of spatial therapeutic markers.

Single-cell transcriptomic survey of cell diversity and functional changes in yak hearts at different altitude.

The yak (Bos grunniens) is a species adapted to the hypoxic environment in the plateau area. The heart is a hypoxia-sensitive organ involved in this adaptation. Herein, we used single-cell RNA-seq technology and clustering to determine the presence of 11 cell populations in the yak heart. We analyzed gene expression differences and expression patterns in each cell subpopulation at different altitudes. The cells related to altitude changes are mainly smooth muscle cells and vascular endothelial cells. Of the four transcription factors (TFs, MEF2B, FOXP4, ARID5A, and HES4) found in smooth muscle cells, only MEF2B was specifically expressed in vascular smooth muscle cells. Three key TFs (HNF1B, DMRTA1, and ARNTL2) were also found in the cardiomyocyte module. Compared with data extracted from low-altitude yak, we observed that the high altitude yak has enhanced contraction and relaxation of vascular smooth muscle cells and an increased metabolic level of cardiomyocytes. These may be strategies for the yak to adapt to high-altitude hypoxia environments.

Return of the Tbx5; lineage-tracing reveals ventricular cardiomyocyte-like precursors in the injured adult mammalian heart

The single curative measure for heart failure patients is a heart transplantation, which is limited due to a shortage of donors, the need for immunosuppression and economic costs. Therefore, there is an urgent unmet need for identifying cell populations capable of cardiac regeneration that we will be able to trace and monitor. Injury to the adult mammalian cardiac muscle, often leads to a heart attack through the irreversible loss of a large number of cardiomyocytes, due to an idle regenerative capability. Recent reports in zebrafish indicate that Tbx5a is a vital transcription factor for cardiomyocyte regeneration. Preclinical data underscore the cardioprotective role of Tbx5 upon heart failure. Data from our earlier murine developmental studies have identified a prominent unipotent Tbx5-expressing embryonic cardiac precursor cell population able to form cardiomyocytes, in vivo, in vitro and ex vivo. Using a developmental approach to an adult heart injury model and by employing a lineage-tracing mouse model as well as the use of single-cell RNA-seq technology, we identify a Tbx5-expressing ventricular cardiomyocyte-like precursor population, in the injured adult mammalian heart. The transcriptional profile of that precursor cell population is closer to that of neonatal than embryonic cardiomyocyte precursors. Tbx5, a cardinal cardiac development transcription factor, lies in the center of a ventricular adult precursor cell population, which seems to be affected by neurohormonal spatiotemporal cues. The identification of a Tbx5-specific cardiomyocyte precursor-like cell population, which is capable of dedifferentiating and potentially deploying a cardiomyocyte regenerative program, provides a clear target cell population for translationally-relevant heart interventional studies.

Neighbor-specific gene expression revealed from physically interacting cells during mouse embryonic development

Development of multicellular organisms is orchestrated by persistent cell-cell communication between neighboring partners. Direct interaction between different cell types can induce molecular signals that dictate lineage specification and cell fate decisions. Current single-cell RNA-seq technology cannot adequately analyze cell-cell contact-dependent gene expression, mainly due to the loss of spatial information. To overcome this obstacle and resolve cell-cell contact-specific gene expression during embryogenesis, we performed RNA sequencing of physically interacting cells (PIC-seq) and assessed them alongside similar single-cell transcriptomes derived from developing mouse embryos between embryonic day (E) 7.5 and E9.5. Analysis of the PIC-seq data identified gene expression signatures that were dependent on the presence of specific neighboring cell types. Our computational predictions, validated experimentally, demonstrated that neural progenitor (NP) cells upregulate Lhx5 and Nkx2-1 genes, when exclusively interacting with definitive endoderm (DE) cells. Moreover, there was a reciprocal impact on the transcriptome of DE cells, as they tend to upregulate Rax and Gsc when in contact with NP cells. Using individual cell transcriptome data, we formulated a means of computationally predicting the impact of one cell type on the transcriptome of its neighboring cell types. We have further developed a distinctive spatial-t-distributed stochastic neighboring embedding to display the pseudospatial distribution of cells in a 2-dimensional space. In summary, we describe an innovative approach to study contact-specific gene regulation during embryogenesis.

Profiling Transcriptional Heterogeneity with Seq-Well S3: A Low-Cost, Portable, High-Fidelity Platform for Massively Parallel Single-Cell RNA-Seq.

Seq-Well is a high-throughput, picowell-based single-cell RNA-seq technology that can be used to simultaneously profile the transcriptomes of thousands of cells (Gierahn et al. Nat Methods 14(4):395-398, 2017). Relative to its reverse-emulsion-droplet-based counterparts, Seq-Well addresses key cost, portability, and scalability limitations. Recently, we introduced an improved molecular biology for Seq-Well to enhance the information content that can be captured from individual cells using the platform. This update, which we call Seq-Well S3 (S3: Second-Strand Synthesis), incorporates a second-strand-synthesis step after reverse transcription to boost the detection of cellular transcripts normally missed when running the original Seq-Well protocol (Hughes et al. Immunity 53(4):878-894.e7, 2020). This chapter provides details and tips on how to perform Seq-Well S3, along with general pointers on how to subsequently analyze the resultant single-cell RNA-seq data.

HUSCH: an integrated single-cell transcriptome atlas for human tissue gene expression visualization and analyses.

Understanding gene expression patterns across different human cell types is crucial for investigating mechanisms of cell type differentiation, disease occurrenceand progression. The recent development of single-cell RNA-seq (scRNA-seq) technologies significantly boosted the characterization of cell type heterogeneities in different human tissues. However, the huge number of datasets in the public domain also posed challenges in data integration and reuse. We present Human Universal Single Cell Hub (HUSCH, http://husch.comp-genomics.org), an atlas-scale curated database that integrates single-cell transcriptomic profiles of nearly 3 million cells from 185 high-quality human scRNA-seq datasets from 45 different tissues. All the data in HUSCH were uniformly processed and annotated with a standard workflow. In the single dataset module, HUSCH provides interactive gene expression visualization, differentially expressed genes, functional analyses, transcription regulatorsand cell-cell interaction analyses for each cell type cluster. Besides, HUSCH integrated different datasets in the single tissue module and performs data integration, batch correction, and cell type harmonization. This allows a comprehensive visualization and analysis of gene expression within each tissue based on single-cell datasets from multiple sources and platforms. HUSCH is a flexible and comprehensive data portal that enables searching, visualizing, analyzing, and downloading single-cell gene expression for the human tissue atlas.

Psupertime: supervised pseudotime analysis for time-series single-cell RNA-seq data.

MotivationImprovements in single-cell RNA-seq technologies mean that studies measuring multiple experimental conditions, such as time series, have become more common. At present, few computational methods exist to infer time series-specific transcriptome changes, and such studies have therefore typically used unsupervised pseudotime methods. While these methods identify cell subpopulations and the transitions between them, they are not appropriate for identifying the genes that vary coherently along the time series. In addition, the orderings they estimate are based only on the major sources of variation in the data, which may not correspond to the processes related to the time labels.ResultsWe introduce psupertime, a supervised pseudotime approach based on a regression model, which explicitly uses time-series labels as input. It identifies genes that vary coherently along a time series, in addition to pseudotime values for individual cells, and a classifier that can be used to estimate labels for new data with unknown or differing labels. We show that psupertime outperforms benchmark classifiers in terms of identifying time-varying genes and provides better individual cell orderings than popular unsupervised pseudotime techniques. psupertime is applicable to any single-cell RNA-seq dataset with sequential labels (e.g. principally time series but also drug dosage and disease progression), derived from either experimental design and provides a fast, interpretable tool for targeted identification of genes varying along with specific biological processes.Availability and implementationR package available at github.com/wmacnair/psupertime and code for results reproduction at github.com/wmacnair/psupplementary.Supplementary information Supplementary data are available at Bioinformatics online.

Abstract 1205: Modeling spatially resolved cell-type-specific gene expression by weighted regression with SPACER

Abstract Transcriptomic analysis has substantially advanced our understanding of human diseases, but the complex nature of tissues is often ignored. Recent development of single-cell RNA-seq (scRNA-seq) technologies has made it possible to characterize cellular heterogeneity of solid tissues. Along with analyzing gene expression patterns on a single-cell level, there is a critical need to explore the spatial patterns exhibited by the genes across the tissue sample. Spatially resolved transcriptomics (SRT) is key to understanding cellular functions in their morphological state. However, current barcoding-based SRT technologies, such as 10x Genomics Visium, lack single-cell resolution, greatly hindering the investigation of the spatial differences in gene expression profiles as failure to account for cell-type variations can lead to an obscured understanding of the spatial patterns detected across the tissue. The overall goal of this research is to integrate spatial transcriptomics and scRNA-seq data in order to infer the gene expression profiles for each of the bulk-level spots in the spatially resolved data. This will allow us to study spatial patterns of genes with cell-type level resolution. SPACER expands the spatial transcriptomics data into the individual cell types by utilizing the spatial similarities and high-resolution histology information as a weight in a non-negative least squares regression. Having this integrated understanding allows us to gain an additional level of information about the gene activity for each cell type present in the tissue. We have performed benchmark evaluations for our method based on data generated from the 10x Visium platform and have seen promising results. The evaluations have shown high correlations for the gene expression patterns predicted by SPACER for varying cell types on the benchmark evaluations. We next analyzed spatial transcriptomics data for pancreatic cancer, breast cancer, and melanoma tissue samples to better study the complex nature of cancerous tissues. A tumor is consistently interacting with its microenvironment, which can impact tumor growth and cell proliferation. Thus, it is important to study the spatial gene expression patterns for different cell types in cancerous tissues. During our analysis, we identify the boundary of the tumor region in the bulk-spatial transcriptomics data and perform differential expression analysis on the SPACER results to compare the expression patterns of the core tumor region to the infiltrated tumor regions for each individual cell type. Identifying genes that are differentially expressed for specific cell types in varying regions of the cancerous tissue can link gene expressions to clinically important morphological features, provide critical information about how cells are interacting with neighboring cells in their macroenvironment, and be used to target different tumor cells therapeutically. Citation Format: Amelia R. Schroeder, Kyle Coleman, Jian Hu, Mingyao Li. Modeling spatially resolved cell-type-specific gene expression by weighted regression with SPACER [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1205.

Entropy-based inference of transition states and cellular trajectory for single-cell transcriptomics.

The development of single-cell RNA-seq (scRNA-seq) technology allows researchers to characterize the cell types, states and transitions during dynamic biological processes at single-cell resolution. One of the critical tasks is to infer pseudo-time trajectory. However, the existence of transition cells in the intermediate state of complex biological processes poses a challenge for the trajectory inference. Here, we propose a new single-cell trajectory inference method based on transition entropy, named scTite, to identify transitional states and reconstruct cell trajectory from scRNA-seq data. Taking into account the continuity of cellular processes, we introduce a new metric called transition entropy to measure the uncertainty of a cell belonging to different cell clusters, and then identify cell states and transition cells. Specifically, we adopt different strategies to infer the trajectory for the identified cell states and transition cells, and combine them to obtain a detailed cell trajectory. For the identified cell clusters, we utilize the Wasserstein distance based on the probability distribution to calculate distance between clusters, and construct the minimum spanning tree. Meanwhile, we adopt the signaling entropy and partial correlation coefficient to determine transition paths, which contain a group of transition cells with the largest similarity. Then the transitional paths and the MST are combined to infer a refined cell trajectory. We apply scTite to four real scRNA-seq datasets and an integrated dataset, and conduct extensive performance comparison with nine existing trajectory inference methods. The experimental results demonstrate that the proposed method can reconstruct the cell trajectory more accurately than the compared algorithms. The scTite software package is available at https://github.com/dblab2022/scTite.

Tissue-Specific Diversity of Group 2 Innate Lymphoid Cells in the Skin.

Since the discovery of group 2 innate lymphoid cells (ILC2s), their developmental pathways, mechanisms of activation and regulation, and immunological roles in the steady state and in disease have been reported in various organs. ILC2s, which produce large amounts of IL-5 and IL-13 in response to tissue-derived factors and are essential in inducing and promoting allergic inflammation, have also been found to play multifaceted roles in maintaining tissue homeostasis. While T cells respond to foreign antigens, the activation of ILC2s is regulated by various tissue-derived factors, including cytokines, lipids, hormones, and neurotransmitters, and ILC2s show different phenotypes depending on the tissue in which they are present. In this review, we discuss tissue-specific characteristics of ILC2s in the skin. ILC2s, as defined in the lungs, intestinal tract, and adipose tissue, cannot be directly applied to cutaneous ILC biology, because skin ILC2s exhibit different aspects in the expression patterns of cell surface markers, the response to tissue-derived cytokines and the functions in both steady-state and inflammation. The skin contains ILCs with features of both ILC2s and ILC3s, and the plasticity between ILCs complicates their characters. Furthermore, the epidermis, dermis, and subcutaneous tissues contain ILCs with different characteristics; their localization has expanded our understanding of ILC function. Single-cell RNA-seq technology has further elucidated the role of ILCs in human skin and disease pathogenesis. Overall, this review discusses the phenotypical and functional heterogeneity of skin ILCs reported in recent years and highlights future directions within the field of ILC biology.

Heterogeneity of ILC2s in the Lungs.

Group 2 innate lymphoid cells (ILC2s) are GATA3-expressing type 2 cytokine-producing innate lymphocytes that are present in various organs throughout the body. Basically, ILC2s are tissue-resident cells associated with a variety of pathological conditions in each tissue. Differences in the tissue-specific properties of ILC2s are formed by the post-natal tissue environment; however, diversity exists among ILC2s within each localized tissue due to developmental timing and activation. Diversity between steady-state and activated ILC2s in mice and humans has been gradually clarified with the advancement of single-cell RNA-seq technology. Another layer of complexity is that ILC2s can acquire other ILC-like functions, depending on their tissue environment. Further, ILC2s with immunological memory and exhausted ILC2s are both present in tissues, and the nature of ILC2s varies with senescence. To clarify how ILC2s affect human diseases, research should be conducted with a comprehensive understanding of ILC2s, taking into consideration the diversity of ILC2s rather than a snapshot of a single section. In this review, we summarize the current understanding of the heterogeneity of ILC2s in the lungs and highlight a novel field of immunology.