Single-cell Gene Expression Data Research Articles

TDC-2: Multimodal Foundation for Therapeutic Science.

Therapeutics Data Commons (tdcommons.ai) is an open science initiative with unified datasets, AI models, and benchmarks to support research across therapeutic modalities and drug discovery and development stages. The Commons 2.0 (TDC-2) is a comprehensive overhaul of Therapeutic Data Commons to catalyze research in multimodal models for drug discovery by unifying single-cell biology of diseases, biochemistry of molecules, and effects of drugs through multimodal datasets, AI-powered API endpoints, new multimodal tasks and model frameworks, and comprehensive benchmarks. TDC-2 introduces over 1,000 multimodal datasets spanning approximately 85 million cells, pre-calculated embeddings from 5 state-of-the-art single-cell models, and a biomedical knowledge graph. TDC-2 drastically expands the coverage of ML tasks across therapeutic pipelines and 10+ new modalities, spanning but not limited to single-cell gene expression data, clinical trial data, peptide sequence data, peptidomimetics protein-peptide interaction data regarding newly discovered ligands derived from AS-MS spectroscopy, novel 3D structural data for proteins, and cell-type-specific protein-protein interaction networks at single-cell resolution. TDC-2 introduces multimodal data access under an API-first design using the model-view-controller paradigm. TDC-2 introduces 7 novel ML tasks with fine-grained biological contexts: contextualized drug-target identification, single-cell chemical/genetic perturbation response prediction, protein-peptide binding affinity prediction task, and clinical trial outcome prediction task, which introduce antigen-processing-pathway-specific, cell-type-specific, peptide-specific, and patient-specific biological contexts. TDC-2 also releases benchmarks evaluating 15+ state-of-the-art models across 5+ new learning tasks evaluating models on diverse biological contexts and sampling approaches. Among these, TDC-2 provides the first benchmark for context-specific learning. TDC-2, to our knowledge, is also the first to introduce a protein-peptide binding interaction benchmark.

ProBac-seq, a bacterial single-cell RNA sequencing methodology using droplet microfluidics and large oligonucleotide probe sets.

Methods that measure the transcriptomic state of thousands of individual cells have transformed our understanding of cellular heterogeneity in eukaryotic cells since their introduction in the past decade. While simple and accessible protocols and commercial products are now available for the processing of mammalian cells, these existing technologies are incompatible with use in bacterial samples for several fundamental reasons including the absence of polyadenylation on bacterial messenger RNA, the instability of bacterial transcripts and the incompatibility of bacterial cell morphology with existing methodologies. Recently, we developed ProBac sequencing (ProBac-seq), a method that overcomes these technical difficulties and provides high-quality single-cell gene expression data from thousands of bacterial cells by using messenger RNA-specific probes. Here we provide details for designing large oligonucleotide probe sets for an organism of choice, amplifying probe sets to produce sufficient quantities for repeated experiments, adding unique molecular indexes and poly-A tails to produce finalized probes, in situ probe hybridization and single-cell encapsulation and library preparation. This protocol, from the probe amplification to the library preparation, requires ~7 d to complete. ProBac-seq offers several advantages over other methods by capturing only the desired target sequences and avoiding nondesired transcripts, such as highly abundant ribosomal RNA, thus enriching for signal that better informs on cellular state. The use of multiple probes per gene can detect meaningful single-cell signals from cells expressing transcripts to a lesser degree or those grown in minimal media and other environmentally relevant conditions in which cells are less active. ProBac-seq is also compatible with other organisms that can be profiled by in situ hybridization techniques.

Biclustering analysis on tree-shaped time-series single cell gene expression data of Caenorhabditis elegans

BackgroundIn recent years, gene clustering analysis has become a widely used tool for studying gene functions, efficiently categorizing genes with similar expression patterns to aid in identifying gene functions. Caenorhabditis elegans is commonly used in embryonic research due to its consistent cell lineage from fertilized egg to adulthood. Biologists use 4D confocal imaging to observe gene expression dynamics at the single-cell level. However, on one hand, the observed tree-shaped time-series datasets have characteristics such as non-pairwise data points between different individuals. On the other hand, the influence of cell type heterogeneity should also be considered during clustering, aiming to obtain more biologically significant clustering results.ResultsA biclustering model is proposed for tree-shaped single-cell gene expression data of Caenorhabditis elegans. Detailedly, a tree-shaped piecewise polynomial function is first employed to fit non-pairwise gene expression time series data. Then, four factors are considered in the objective function, including Pearson correlation coefficients capturing gene correlations, p-values from the Kolmogorov-Smirnov test measuring the similarity between cells, as well as gene expression size and bicluster overlapping size. After that, Genetic Algorithm is utilized to optimize the function.ConclusionThe results on the small-scale dataset analysis validate the feasibility and effectiveness of our model and are superior to existing classical biclustering models. Besides, gene enrichment analysis is employed to assess the results on the complete real dataset analysis, confirming that the discovered biclustering results hold significant biological relevance.

ScGAL: unmask tumor clonal substructure by jointly analyzing independent single-cell copy number and scRNA-seq data

BackgroundAccurately deciphering clonal copy number substructure can provide insights into the evolutionary mechanism of cancer, and clustering single-cell copy number profiles has become an effective means to unmask intra-tumor heterogeneity (ITH). However, copy numbers inferred from single-cell DNA sequencing (scDNA-seq) data are error-prone due to technically confounding factors such as amplification bias and allele-dropout, and this makes it difficult to precisely identify the ITH.ResultsWe introduce a hybrid model called scGAL to infer clonal copy number substructure. It combines an autoencoder with a generative adversarial network to jointly analyze independent single-cell copy number profiles and gene expression data from same cell line. Under an adversarial learning framework, scGAL exploits complementary information from gene expression data to relieve the effects of noise in copy number data, and learns latent representations of scDNA-seq cells for accurate inference of the ITH. Evaluation results on three real cancer datasets suggest scGAL is able to accurately infer clonal architecture and surpasses other similar methods. In addition, assessment of scGAL on various simulated datasets demonstrates its high robustness against the changes of data size and distribution. scGAL can be accessed at: https://github.com/zhyu-lab/scgal.ConclusionsJoint analysis of independent single-cell copy number and gene expression data from a same cell line can effectively exploit complementary information from individual omics, and thus gives more refined indication of clonal copy number substructure.

Inferring gene regulatory networks from single-cell multiome data using atlas-scale external data.

Existing methods for gene regulatory network (GRN) inference rely on gene expression data alone or on lower resolution bulk data. Despite the recent integration of chromatin accessibility and RNA sequencing data, learning complex mechanisms from limited independent data points still presents a daunting challenge. Here we present LINGER (Lifelong neural network for gene regulation), a machine-learning method to infer GRNs from single-cell paired gene expression and chromatin accessibility data. LINGER incorporates atlas-scale external bulk data across diverse cellular contexts and prior knowledge of transcription factor motifs as a manifold regularization. LINGER achieves a fourfold to sevenfold relative increase in accuracy over existing methods and reveals a complex regulatory landscape of genome-wide association studies, enabling enhanced interpretation of disease-associated variants and genes. Following the GRN inference from reference single-cell multiome data, LINGER enables the estimation of transcription factor activity solely from bulk or single-cell gene expression data, leveraging the abundance of available gene expression data to identify driver regulators from case-control studies.

ScINRB: single-cell gene expression imputation with network regularization and bulk RNA-seq data.

Single-cell RNA sequencing (scRNA-seq) facilitates the study of cell type heterogeneity and the construction of cell atlas. However, due to its limitations, many genes may be detected to have zero expressions, i.e. dropout events, leading to bias in downstream analyses and hindering the identification and characterization of cell types and cell functions. Although many imputation methods have been developed, their performances are generally lower than expected across different kinds and dimensions of data and application scenarios. Therefore, developing an accurate and robust single-cell gene expression data imputation method is still essential. Considering to maintain the original cell-cell and gene-gene correlations and leverage bulk RNA sequencing (bulk RNA-seq) data information, we propose scINRB, a single-cell gene expression imputation method with network regularization and bulk RNA-seq data. scINRB adopts network-regularized non-negative matrix factorization to ensure that the imputed data maintains the cell-cell and gene-gene similarities and also approaches the gene average expression calculated from bulk RNA-seq data. To evaluate the performance, we test scINRB on simulated and experimental datasets and compare it with other commonly used imputation methods. The results show that scINRB recovers gene expression accurately even in the case of high dropout rates and dimensions, preserves cell-cell and gene-gene similarities and improves various downstream analyses including visualization, clustering and trajectory inference.

Abstract 6348: Unveiling diverse trimeric forms of STEAP1 in human prostate cancer cells: Exploring STEAP1-STEAP2 interdependence for expression and plasma membrane localization

Abstract Six-Transmembrane Epithelial Antigen of the Prostate 1 (STEAP1) is an emerging therapeutic target that is a member of the STEAP1-4 family of metalloreductases. We recently reported the preclinical characterization of xaluritamig (AMG 509), a STEAP1 XmAb® 2+1 T-cell engager molecule that is being developed for the treatment of prostate cancer (Nolan-Stevaux et al, 2023). Xaluritamig is designed to preferentially target tumor cells that express high levels of STEAP1. However, prostate tumor cells also express high levels of STEAP2. Here, we characterize the interactions between STEAP1 and STEAP2 in prostate cancer cell lines and the impact to xaluritamig activity. Bulk mRNA as well as single-cell gene expression data (scRNAseq) reveal a strong correlation between STEAP1 and STEAP2 expression in human prostate cancer, suggesting that the STEAP proteins may form both homotrimers and heterotrimers in prostate tumors. To test this idea, we investigated the structural behavior of STEAP1/2 trimers in human prostate cancer cell lines that endogenously express STEAP1 and STEAP2. Using xaluritamig and the parental anti-STEAP1 antibody, we show that STEAP1 primarily forms heterotrimers with STEAP2 in prostate cancer cells but can also assemble as homotrimers in the absence of STEAP2. Both STEAP1/2 heterotrimers and STEAP1 homotrimers can be targeted by xaluritamig, leading to T cell-dependent cellular cytotoxicity. Similarly, we found that anti-STEAP2 antibodies detected STEAP2 homotrimers when STEAP1 expression was ablated in cells, and that both STEAP1/2 heterotrimers and STEAP2 homotrimers were targeted by a STEAP2-specific T-cell engager molecule. We also demonstrate that neither STEAP1 nor STEAP2 significantly influences the mRNA or protein expression of the other. Our data elucidates the versatility of STEAP family members in forming both homotrimers and heterotrimers on the plasma membrane of human prostate cancer cells and the potential for therapeutic targeting with STEAP1/2-directed therapies. Characterizing the capability of existing STEAP1 or STEAP2 targeting therapeutics to bind their respective targets, whether within a homotrimer or heterotrimer, is crucial for a comprehensive understanding of the therapeutic potential of these drugs. Further research will help delineate the mechanism underlying the formation of STEAP1/2 trimers in cancer cells and continue advancing our understanding of this intriguing biological relationship. Citation Format: Cong Li, Lingming Liang, Joonhee Park, Olga Pryshchep, Binnaz Staley, Peng Li, Ben Alba, Xuan Liu, Fei Li, Gregory L. Moore, Matthew J. Bernett, Umesh Muchhal, John Desjarlais, Angela Coxon, Julie M. Bailis. Unveiling diverse trimeric forms of STEAP1 in human prostate cancer cells: Exploring STEAP1-STEAP2 interdependence for expression and plasma membrane localization [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 6348.

Label-aware distance mitigates temporal and spatial variability for clustering and visualization of single-cell gene expression data

Clustering and visualization are essential parts of single-cell gene expression data analysis. The Euclidean distance used in most distance-based methods is not optimal. The batch effect, i.e., the variability among samples gathered from different times, tissues, and patients, introduces large between-group distance and obscures the true identities of cells. To solve this problem, we introduce Label-Aware Distance (Lad), a metric using temporal/spatial locality of the batch effect to control for such factors. We validate Lad on simulated data as well as apply it to a mouse retina development dataset and a lung dataset. We also found the utility of our approach in understanding the progression of the Coronavirus Disease 2019 (COVID-19). Lad provides better cell embedding than state-of-the-art batch correction methods on longitudinal datasets. It can be used in distance-based clustering and visualization methods to combine the power of multiple samples to help make biological findings.

Interrogations of single-cell RNA splicing landscapes with SCASL define new cell identities with physiological relevance

RNA splicing shapes the gene regulatory programs that underlie various physiological and disease processes. Here, we present the SCASL (single-cell clustering based on alternative splicing landscapes) method for interrogating the heterogeneity of RNA splicing with single-cell RNA-seq data. SCASL resolves the issue of biased and sparse data coverage on single-cell RNA splicing and provides a new scheme for classifications of cell identities. With previously published datasets as examples, SCASL identifies new cell clusters indicating potentially precancerous and early-tumor stages in triple-negative breast cancer, illustrates cell lineages of embryonic liver development, and provides fine clusters of highly heterogeneous tumor-associated CD4 and CD8 T cells with functional and physiological relevance. Most of these findings are not readily available via conventional cell clustering based on single-cell gene expression data. Our study shows the potential of SCASL in revealing the intrinsic RNA splicing heterogeneity and generating biological insights into the dynamic and functional cell landscapes in complex tissues.

MarkerMap: nonlinear marker selection for single-cell studies

Single-cell RNA-seq data allow the quantification of cell type differences across a growing set of biological contexts. However, pinpointing a small subset of genomic features explaining this variability can be ill-defined and computationally intractable. Here we introduce MarkerMap, a generative model for selecting minimal gene sets which are maximally informative of cell type origin and enable whole transcriptome reconstruction. MarkerMap provides a scalable framework for both supervised marker selection, aimed at identifying specific cell type populations, and unsupervised marker selection, aimed at gene expression imputation and reconstruction. We benchmark MarkerMap’s competitive performance against previously published approaches on real single cell gene expression data sets. MarkerMap is available as a pip installable package, as a community resource aimed at developing explainable machine learning techniques for enhancing interpretability in single-cell studies.

Integrating leiomyoma genetics, epigenomics, and single-cell transcriptomics reveals causal genetic variants, genes, and cell types

Uterine fibroids (UF), that can disrupt normal uterine function and cause significant physical and psychological health problems, are observed in nearly 70% of women of reproductive age. Although heritable genetics is a significant risk factor, specific genetic variations and gene targets causally associated with UF are poorly understood. Here, we performed a meta-analysis on existing fibroid genome-wide association studies (GWAS) and integrated the identified risk loci and potentially causal single nucleotide polymorphisms (SNPs) with epigenomics, transcriptomics, 3D chromatin organization from diverse cell types as well as primary UF patient’s samples. This integrative analysis identifies 24 UF-associated risk loci that potentially target 394 genes, of which 168 are differentially expressed in UF tumors. Critically, integrating this data with single-cell gene expression data from UF patients reveales the causal cell types with aberrant expression of these target genes. Lastly, CRISPR-based epigenetic repression (dCas9-KRAB) or activation (dCas9-p300) in a UF disease-relevant cell type further refines and narrows down the potential gene targets. Our findings and the methodological approach indicate the effectiveness of integrating multi-omics data with locus-specific epigenetic editing approaches for identifying gene- and celt type-targets of disease-relevant risk loci.

Abstract B063: Investigation of tumor heterogeneity using integrated single-cell RNA sequence data based on HER2 status in patients with breast cancer

Abstract [Introduction] Human epidermal growth factor receptor 2 (HER2), which is characterized by ERBB2 amplification, is one of the important markers for treatment decision related to breast cancer. Many targeted therapies for HER2-positive (immunohistochemistry [IHC] scores of 3+ or 2+ with an in situ hybridization [ISH] gene amplification) or HER2-low (IHC score 1+ or 2+ with no ISH gene amplification) breast cancers have been extensively developed thus far. Meanwhile, tumoral heterogeneity is considered one of the mechanisms for drug resistance; however, it has not been fully elucidated on each HER2 status at single-cell level. Therefore, an integrated analysis of the breast cancer single-cell gene expression data of both public datasets and our cohort was used to investigate tumor heterogeneity based on HER2 status. [Methods] We collected 21 and 6 scRNA-seq samples of primary breast cancer from the public Gene Expression Omnibus (GEO) and our institution, respectively. A total of 27 samples included HER2-positive cases (pure HER2 cases: estrogen receptor [ER]-negative/HER2-positive and Luminal-HER2 cases: ER-positive/HER2-positive) and Luminal cases (ER-positive/HER2-negative). These datasets were imported into R software version 4.2.0. and transformed into Seurat objects with the package Seurat version 4.3.0. UMAP plots, which is a non-linear dimension reduction method, were used for clustering analyses. Seurat in R was used to generate UMAP, feature, and violin plots. Additionally, we performed pathway enrichment analysis with the significant gene list from each cluster between the ERBB2-high and ERBB2-low groups. [Results] Clustering analysis revealed heterogeneous distribution in each gene related to breast cancer (ESR1, PGR, ERBB2, and MKI67). One of the clusters revealed high MKI67 expression. ERBB2 expressions were diffusely distributed on each cluster of pure HER2 cases; however, the expressions were considerably higher in one of the clusters in cases of Luminal-HER2. The ERBB2 expression in Luminal cases, which included HER2-low status, was lower than that in HER2-positive cases, albeit slight expression was observed. Cell proliferation factors, including IGF, IGF1R, and EGF, were included in the ERBB2-high expression group compared with the ERBB2-low expression group for pathway analysis. Further, we examined typical gene expressions which are associated with markers related to breast cancer, cancer stem cell, and epithelial-to-mesenchymal transition in each case and revealed heterogeneous patterns across patients. [Conclusion] Heterogeneous ERBB2 expression distribution was observed in HER2-positive cases, and slight ERBB2 expression was identified in the Luminal cases. Moreover, Luminal-HER2 cases could be considered a more heterogeneous subtype compared with pure HER2 cases. Additionally, gene expressions of typical gene markers varied across patients. These results indicated that breast cancer displays heterogeneous patterns on each HER2 status not only intra-tumoral heterogeneity but also inter-patient heterogeneity. Citation Format: Sho Shiino, Momoko Tokura, Jun Nakayama, Masayuki Yoshida, Akihiko Suto, Yusuke Yamamoto. Investigation of tumor heterogeneity using integrated single-cell RNA sequence data based on HER2 status in patients with breast cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr B063.

MSIsensor-RNA: Microsatellite Instability Detection for Bulk and Single-cell Gene Expression Data

Abstract Microsatellite instability (MSI) is an indispensable biomarker in cancer immunotherapy. Currently, MSI scoring methods by high-throughput omics methods have gained popularity and demonstrated better performance than the gold standard method for MSI detection. However, the MSI detection method on expression data, especially single-cell expression data, is still lacking, limiting the scope of clinical application and prohibiting the investigation of MSI at a single-cell level. Herein, we developed MSIsensor-RNA, an accurate, robust, adaptable, and standalone software to detect MSI status based on expression values of MSI-associated genes. We demonstrated the favorable performance and promise of MSIsensor-RNA in both bulk and single-cell gene expression data in multiplatform technologies including RNA sequencing (RNA-seq), microarray, and single-cell RNA-seq. MSIsensor-RNA is a versatile, efficient, and robust method for MSI status detection from both bulk and single-cell gene expression data in clinical studies and applications. MSIsensor-RNA is available at https://github.com/xjtu-omics/msisensor-rna.

ScBiG for representation learning of single-cell gene expression data based on bipartite graph embedding.

Analyzing single-cell RNA sequencing (scRNA-seq) data remains a challenge due to its high dimensionality, sparsity and technical noise. Recognizing the benefits of dimensionality reduction in simplifying complexity and enhancing the signal-to-noise ratio, we introduce scBiG, a novel graph node embedding method designed for representation learning in scRNA-seq data. scBiG establishes a bipartite graph connecting cells and expressed genes, and then constructs a multilayer graph convolutional network to learn cell and gene embeddings. Through a series of extensive experiments, we demonstrate that scBiG surpasses commonly used dimensionality reduction techniques in various analytical tasks. Downstream tasks encompass unsupervised cell clustering, cell trajectory inference, gene expression reconstruction and gene co-expression analysis. Additionally, scBiG exhibits notable computational efficiency and scalability. In summary, scBiG offers a useful graph neural network framework for representation learning in scRNA-seq data, empowering a diverse array of downstream analyses.

Chromosome-specific maturation of the epigenome in the Drosophila male germline.

Spermatogenesis in the Drosophila male germline proceeds through a unique transcriptional program controlled both by germline-specific transcription factors and by testis-specific versions of core transcriptional machinery. This program includes the activation of genes on the heterochromatic Y chromosome, and reduced transcription from the X chromosome, but how expression from these sex chromosomes is regulated has not been defined. To resolve this, we profiled active chromatin features in the testes from wildtype and meiotic arrest mutants and integrate this with single-cell gene expression data from the Fly Cell Atlas. These data assign the timing of promoter activation for genes with germline-enriched expression throughout spermatogenesis, and general alterations of promoter regulation in germline cells. By profiling both active RNA polymerase II and histone modifications in isolated spermatocytes, we detail widespread patterns associated with regulation of the sex chromosomes. Our results demonstrate that the X chromosome is not enriched for silencing histone modifications, implying that sex chromosome inactivation does not occur in the Drosophila male germline. Instead, a lack of dosage compensation in spermatocytes accounts for the reduced expression from this chromosome. Finally, profiling uncovers dramatic ubiquitinylation of histone H2A and lysine-16 acetylation of histone H4 across the Y chromosome in spermatocytes that may contribute to the activation of this heterochromatic chromosome.

Chromosome-specific maturation of the epigenome in the Drosophila male germline

Spermatogenesis in the Drosophila male germline proceeds through a unique transcriptional program controlled both by germline-specific transcription factors and by testis-specific versions of core transcriptional machinery. This program includes the activation of genes on the heterochromatic Y chromosome, and reduced transcription from the X chromosome, but how expression from these sex chromosomes is regulated has not been defined. To resolve this, we profiled active chromatin features in the testes from wildtype and meiotic arrest mutants and integrate this with single-cell gene expression data from the Fly Cell Atlas. These data assign the timing of promoter activation for genes with germline-enriched expression throughout spermatogenesis, and general alterations of promoter regulation in germline cells. By profiling both active RNA polymerase II and histone modifications in isolated spermatocytes, we detail widespread patterns associated with regulation of the sex chromosomes. Our results demonstrate that the X chromosome is not enriched for silencing histone modifications, implying that sex chromosome inactivation does not occur in the Drosophila male germline. Instead, a lack of dosage compensation in spermatocytes accounts for the reduced expression from this chromosome. Finally, profiling uncovers dramatic ubiquitinylation of histone H2A and lysine-16 acetylation of histone H4 across the Y chromosome in spermatocytes that may contribute to the activation of this heterochromatic chromosome.

Tumor-Infiltrating Normal B Lymphocytes Have Remarkable Prognostic Effects and Are Crucial for Antitumor Immune Responses in Diffuse Large B-Cell Lymphoma

Introduction: In recent years, increasing evidence suggests an important role of tumor-infiltrating B lymphocytes (TIL-Bs) in cancer immunity. In various types of solid tumors, high density of TIL-Bs and presence of mature tertiary lymphoid structures consistently correlate with better prognosis and response to immune checkpoint blockade therapies. Diffuse large B-cell lymphoma (DLBCL) is a type of aggressive lymphoma arising from mature B cells. TIL-Bs have been identified in DLBCL based on single-cell gene-expression data in several previous studies, but their prognostic significance in DLBCL is unknown. In this study, we determined the clinical significance of TIL-B abundance and investigated the potential underlying mechanisms. Patients and Methods: This abstract includes a discovery study in which we distinguished TIL-Bs from malignant B-cells by immunoglobulin gene (IG) clonotype analysis and a validation study using flow cytometry. In the discovery study, a molecularly well-characterized DLBCL cohort assembled by the International DLBCL R-CHOP Consortium Program was analyzed. DNA samples from DLBCL patients were sequenced by high-throughput ultra-deep sequencing assays using multiplexed forward and reverse primers. Based on the IG heavy chain (IGH) sequencing results, malignant B-cell and TIL-B VDJ clonotypes were identified for 138 patients, and the frequencies and numbers of TIL-B clonotypes in individual sequence repertoires were calculated. In 79 DLBCL cases, a DJ-only sequence was predominant and no productive VDJ sequences met the criteria for diagnostic sequences. Total frequencies of productive VDJ sequences in the sequence repertoires were then used to access high and low TIL-B frequencies. TIL-B frequencies were also evaluated by IG light chain clonotypes for these 217 patients and additional 52 patients. The TIL-B frequencies were correlated with patient survival, gene-expression profiling (GEP) data GSE31312, and frequencies of DLBCL-infiltrating immune cells quantified by fluorescent multiplex immunohistochemistry (mIHC). To validate the prognostic significance of TIL-B frequencies, we assembled a validation cohort of 94 DLBCL patients treated at Duke University Medical Center. TIL-Bs in tissues were quantified by flow cytometry immunophenotyping. Ratios of TIL-Bs to abnormal B cells were assessed for prognostic effects. Results: First, we found thathigh frequencies of TIL-Bs and clonotypic diversity were associated with significantly favorable prognostic effects in 138 DLBCL patients with their malignant B-cell IGH-VDJ clonotypes identified (Figure A). The prognostic effects of TIL-Bs outweighed those of the germinal center B-cell-like/activated B-cell-like classification (Figure B). Second, patients with high frequencies of TIL-Bs compared with those without had significant upregulation of immune response genes by GEP (Figure C), higher CD4 T cell infiltration quantified by fluorescent mIHC (Figure D), and enrichment of EcoTyper-defined specific cell states, including significantly increased frequencies of B cell state S2 (pre-memory), CD4 T cell state S3 (naïve), follicular helper T cell state S3, and endothelial cell state S3, whereas lower frequencies of B cell state S5 (plasmablast) and follicular helper T cell state S1. Third, high TIL-B abundance in 79 ‘DJ-only’ clonotype cases and additional 52 cases assessed by light chain clonotype analysis showed similar favorable prognostic effects and associations with upregulated immune gene expression and CD4 T cell infiltration. Fourth, the identified GEP signatures, prominent with genes involved in T-dependent B cell activation and T cell activation and differentiation, showed significant prognostic effects in two GEO datasets. Fifth, TIL-B abundance showed significant prognostic effects in DLBCL cases infiltrated with T-cells in both the discovery study (Figure E) and validation study (Figure F, flow cytometry cohort). Conclusions: TIL-B frequencies and clonotypic diversity have remarkable prognostic and biological effects in DLBCL, which has significant implications for developing novel therapeutic strategies. Our study identified a new prognostic biomarker in DLBCL and an important player in anti-lymphoma immune responses, providing a new point of view to analyze the tumor biology in aggressive B-cell lymphoma and precisely identify prognostic determinants.

Gene-based association study of rare variants in children of diverse ancestries implicates TNFRSF21 in the development of allergic asthma

BackgroundMost genetic studies of asthma and allergy have focused on common variation in individuals primarily of European ancestry. Studying the role of rare variation in quantitative phenotypes and in asthma phenotypes in populations of diverse ancestries can provide additional, important insights into the development of these traits. ObjectiveThe goal of this study was to examine the contribution of rare variants to different asthma- or allergy-associated quantitative traits in children with diverse ancestries and explore their role in asthma phenotypes. MethodsWe examined whole-genome sequencing (WGS) data from children participants in longitudinal studies of asthma (n=1,035; parent-identified as 67% Black and 25% Hispanic) to identify rare variants (minor allele frequency < 0.01). We assigned variants to genes and tested for associations using an omnibus variant-set test between each of 24,902 genes and eight asthma-associated quantitative traits. Combining our results with external data on predicted gene expression in humans and mouse knockout studies, three candidate genes were identified. A burden of rare variants in each gene and in a combined 3-gene score was tested for its associations with clinical phenotypes of asthma. Finally, published single cell gene expression data in lower airway mucosal cells after allergen challenge was used to assess transcriptional responses to allergen. ResultsRare variants in USF1 were significantly associated with blood neutrophil count (p=2.18x10-7); rare variants in TNFRSF21 with total IgE (p=6.47x10-6) and PIK3R6 with eosinophil count (p=4.10x10-5) reached suggestive significance. These three findings were supported by independent data from human and mouse studies. A burden of rare variants in TNFRSF21 and in a 3-gene score were associated with allergy-related phenotypes in cohorts of children with mild and severe asthma. Furthermore, TNFRSF21 was significantly upregulated in bronchial basal epithelial cells from adults with allergic asthma but not in adults with allergies (but not asthma) after allergen challenge. ConclusionWe report novel associations between rare variants in genes and allergic and inflammatory phenotypes in children with diverse ancestries, highlighting TNFRSF21 as contributing to the development of allergic asthma.

Non-Negative Low-Rank Representation With Similarity Correction for Cell Type Identification in scRNA-Seq Data.

Single-cell RNA sequencing (scRNA-Seq) technology has emerged as a powerful tool to investigate cellular heterogeneity within tissues, organs, and organisms. One fundamental question pertaining to single-cell gene expression data analysis revolves around the identification of cell types, which constitutes a critical step within the data processing workflow. However, existing methods for cell type identification through learning low-dimensional latent embeddings often overlook the intercellular structural relationships. In this paper, we present a novel non-negative low-rank similarity correction model (NLRSIM) that leverages subspace clustering to preserve the global structure among cells. This model introduces a novel manifold learning process to address the issue of imbalanced neighbourhood spatial density in cells, thereby effectively preserving local geometric structures. This procedure utilizes a position-sensitive hashing algorithm to construct the graph structure of the data. The experimental results demonstrate that the NLRSIM surpasses other advanced models in terms of clustering effects and visualization experiments. The validated effectiveness of gene expression information after calibration by the NLRSIM model has been duly ascertained in the realm of relevant biological studies. The NLRSIM model offers unprecedented insights into gene expression, states, and structures at the individual cellular level, thereby contributing novel perspectives to the field.

PLAE Web App Enables Powerful Searching and Multiple Visualizations Across One Million Unified Single-Cell Ocular Transcriptomes.

To create a high-performance reactive web application to query single-cell gene expression data across cell type, species, study, and other factors. We updated the content and structure of the underlying data (single cell Eye in a Disk [scEiaD]) and wrote the web application PLAE (https://plae.nei.nih.gov) to visualize and explore the data. The new portal provides quick visualization of over a million individual cells from vertebrate eye and body transcriptomes encompassing four species, 60 cell types, six ocular tissues, and 23 body tissues across 35 publications. To demonstrate the value of this unified pan-eye dataset, we replicated known neurogenic and cone macula markers in addition to proposing six new cone human region markers. The PLAE web application offers the eye community a powerful and quick means to test hypotheses related to gene expression across a highly diverse, community-derived database. The PLAE resource enables any researcher or clinician to study and research gene expression patterning across a wide variety of curated ocular cell types with a responsive web app.