The Functional Immunogenomics and Transcriptomics Database (FITdb) is a freely accessible resource that harmonizes functional genomics datasets for the study of immune cell biology and provides a comprehensive, user-friendly platform for accelerating the discovery of immune regulatory programs.
Abstract
Genetic screens in immune cells enable the systematic interrogation of gene function at scale, uncovering key regulators of cell functions such as tumor cell killing and persistence. However, existing datasets typically focus on specific biological questions, employ targeted gene panels, are generated under diverse experimental conditions, and are not readily accessible, which together limit their integration and future usability. To address this, we developed the Functional Immunogenomics and Transcriptomics Database (FITdb), a freely accessible resource that harmonizes functional genomics datasets for the study of immune cell biology. FITdb currently integrates 43 independent functional genetics screens, including 32 pooled and 11 single-cell screens, spanning 20, 696 mouse genes and 22, 293 human genes across 199 immune cell types and conditions. All datasets are uniformly re-analyzed to enable cross-study comparisons. FITdb provides intuitive, gene-centric visualizations, detailed exploration of individual screens, and access to sgRNA-level data. Additionally, built-in tools such as “Compare MyGeneSet” and “Compare MyScreen” identify statistically significant overlaps between user-defined gene lists and functional gene sets in FITdb, and enable direct comparison of user-generated screening data with existing datasets, respectively. Together, FITdb provides a comprehensive, user-friendly platform for accelerating the discovery of immune regulatory programs. The database is freely available at https://fitdb.lji.org. Graphical Abstract
Single-cell RNA-seq (scRNA-seq) analyses rely on accurate gene annotations, a challenge for many species with less completely curated genomes. Rhesus macaque (RM), a widely used model for human biomedical research, is one such case where missing gene annotations have hindered the study of immune responses. We aim to develop a computational framework to identify and reintegrate missing gene features, improving immune response characterization in RM.
In a preliminary analysis, we computationally searched in a RM peripheral blood mononuclear cell (PBMC) scRNA-seq dataset from a kidney allograft study for unannotated but transcriptionally active regions (uTARs). We then performed cell clustering twice, once on annotated-gene expression and again on uTAR expression, and assessed uTAR expression for cell-type specificity and association with immune-related pathways.
We identified >5,500 uTARs, indicating that numerous features–e.g., long non-coding RNAs or alternative transcripts of existing genes–are missing from current RM annotations. uTARs exhibit cell-type-specific expression and, when used to group cells, separate major cell types, paralleling cell clustering using annotated rhesus genes. These findings illustrate substantial gaps in the RM genome annotation and highlight the biological relevance of these missing genes or transcripts.
uTARs likely harbor many previously unannotated genes or transcripts that are involved in immune regulation in RM. Ongoing work will denoise the signals in scRNA-seq data and prioritize a subset of uTARs as candidate transcriptional regulators. We will also infer regulatory relationships between candidate regulators and downstream targets. Our immediate goal is to identify drivers of transplant rejection. However, this framework is broadly applicable to scRNA-seq datasets across species and experimental contexts.
NIAID U19 AI131471
Technological Innovations in Immunology (TECH)
Ethan Smith, Matthew Tunbridge, T. Tollison et al.· Journal of Immunology· 0 citations
Perturb-seq enables pooled genetic screens with rich single-cell profiling readouts, but genome-scale profiling remains costly and may not be associated with other established functional characteristics. Moreover, as screens grow in size and complexity, interpreting the resulting data comprehensively is challenging and slow. Here, we introduce Perturb-seq with Marker Enrichment (Perturb-ME), which combines genome-scale CRISPR screening, phenotype-based enrichment and multimodal single-cell profiling. Applied to MHC-I cell surface protein expression in melanoma, Perturb-ME profiled HLA-low and HLA-high cells with matched RNA, surface-protein and guide measurements. A regulatory model with 221 impactful regulators affecting 1,998 responsive genes recovered seven coherent co-functional regulatory modules governing nine gene programs, including the canonical IFNγ-MHC-I axis regulating an antigen-presentation and interferon-response program. Agentic interpretation of the entire model with an AI co-scientist linked additional modules to trafficking, proteostasis and chromatin regulation. Perturb-ME, along with agentic interpretation, provide a scalable framework for comprehensive functional discovery from phenotype-enriched genetic screens.
Hanchen Wang, Jiacheng Gu, Chris J. Frangieh et al.· bioRxiv· 0 citations
It is demonstrated that integrated Perturb-seq experiments spanning diverse contexts enable hypotheses about gene function specific to tissue types or cancer subtypes – suggesting large-scale, genome-wide datasets would offer invaluable insight into the highly context-dependent nature of cancer biology.
Samuel Maffa, Isabella Boyle, Lie Ward et al.· bioRxiv· 0 citations
Complex traits and diseases arise from the interplay between genetic variation and cellular heterogeneity, making it essential to understand how genetic risk manifests at the cellular level. However, connecting genome-wide association studies (GWAS) to specific cell populations remains challenging due to cellular complexity and the prevalence of noncoding variants. Here, we present DPCGS, a computational framework that systematically integrates GWAS summary statistics with single-cell RNA-sequencing (scRNA-seq) data to identify trait-associated cell subpopulations, genes, and regulatory programs. Unlike existing approaches that primarily evaluate pathway enrichment or cell-type-level associations, DPCGS quantifies the enrichment of genetically prioritized genes within individual cells through a statistically calibrated gene-set scoring strategy, enabling high-resolution mapping of genetic risk to cellular states. Benchmarking across simulated and diverse human single-cell datasets demonstrates that DPCGS achieves superior accuracy, sensitivity, and robustness compared with existing methods, including scDRS and scPagwas. Applying DPCGS to Alzheimer’s disease and asthma reveals disease-associated cellular populations and uncovers potential molecular drivers, including CD74, FOS, and AP-1 family regulatory programs, providing insights into disease-specific immune and cellular mechanisms. By bridging genetic discoveries from GWAS with functional interpretation at single-cell resolution, DPCGS establishes a generalizable framework for dissecting the cellular architecture of complex traits and diseases. This approach enables systematic discovery of disease-relevant cell subpopulations, regulatory networks, and potential therapeutic targets, offering broad applications in human genetics, single-cell biology, and precision medicine.
Chonghui Liu, Bo Yuan, Baihan Shen et al.· bioRxiv· 0 citations
Understanding how immune cells develop and function requires insight into the epigenomic mechanisms that regulate gene expression. While many genomic studies focus on transcriptional outputs, changes in the chromatin landscape play a central role in shaping lineage commitment. The mammalian immune system is composed of highly diverse and dynamic cell types, but detailed epigenomic studies have been severely limited by technical challenges in profiling rare cell populations.
We developed and validated a low-input, automated CUT&RUN workflow that incorporates standardized sample preparation to ensure reliable generation of data at the consortium scale. This method minimizes sample handling and applies internal controls to monitor assay performance during experimental and sequencing stages.
Extensive optimization of assay conditions and antibody reagents enabled robust mapping of histone post-translational modifications (PTMs) from as few as 10,000 cells per reaction. Applying this approach, we profiled >170 immune subpopulations collected from 11 ImmGen consortium labs over two years.
These innovations establish a scalable, high-resolution platform for profiling chromatin landscapes from minimal cell inputs. Our automated CUT&RUN pipeline enables standardized, reproducible analysis across diverse immune cell types and can distinguish technical issues from true biological insights. Together, these advances lay the foundation for a companion study presenting the first comprehensive epigenomic atlas of immune lineages and provide a framework for studying chromatin regulation in rare or limited samples across the life sciences.
NIH R44 AI167215
Technological Innovations in Immunology (TECH)
Aaron J. Alcala, M. Marunde, C. L. Windham et al.· Journal of Immunology· 0 citations
Background WD Repeat Domain 76 (WDR76) plays a potential role in cellular regulation; however, its comprehensive landscape across human malignancies and its specific biological function in hepatocellular carcinoma (HCC) remain largely unexplored. Methods We conducted a systematic pan-cancer analysis utilizing multi-omics data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Cancer Cell Line Encyclopedia (CCLE) atabases to evaluate WDR76 expression, subcellular localization, and its correlation with clinicopathologic features, genomic instability, and immune infiltration. Diagnostic and prognostic values were assessed via Receiver operating characteristic (ROC) and Kaplan-Meier analyses. Furthermore, the functional role of WDR76 in HCC was validated in vitro using Hep-3B and Huh7 cell lines through siRNA-mediated knockdown, followed by CCK-8, wound-healing, and transwell assays. Results WDR76 was significantly upregulated in the majority of tumor types, including LIHC, LUAD, and COAD, while exhibiting nuclear localization. Elevated WDR76 expression correlated with advanced tumor staging, metastasis, and poor clinical outcomes across multiple cohorts, particularly in ACC, KIRP, and LIHC. ROC analysis highlighted its exceptional diagnostic precision in cancers such as GBM and LIHC. Immunologically, WDR76 expression was intricately linked to immune cell infiltration, immune checkpoint markers, and genomic instability parameters, suggesting a role in shaping the tumor microenvironment. Drug sensitivity profiling revealed that high WDR76 levels correlate with resistance to specific chemotherapeutic agents. Experimentally, silencing WDR76 in HCC cells significantly suppressed cell proliferation, migration, and invasion capabilities. Conclusion Our study establishes WDR76 as a robust pan-cancer prognostic biomarker and a potential immunotherapeutic target. Specifically, we provide experimental evidence that WDR76 functions as an oncogenic driver in liver cancer, promoting malignant phenotypes and offering a novel avenue for targeted therapeutic intervention.
Yi-Fan Wang, Rong Zhou, Shen-Long Guo et al.· International Journal of Imm...· 0 citations
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