Biology subjects

Khatri, I.

Publications and source records attributed to Khatri, I..

7 recordsLinked to original sources

bioRxiv2026

A Cross-Study Multi-Organ Cell Atlas ofMacaca fascicularis Informed by Human Foundation Model Annotation: A Resource for Translational Target Assessment

Non-human primates (NHPs), particularly Macaca fascicularis (cynomolgus macaque), represent an essential model for preclinical assessment of biologics due to their high genetic and physiological similarity to humans. However, mounting regulatory pressure to reduce NHP use and the lack of a unified, well-annotated single-cell atlas currently limits both target qualification and mechanistic interpretation of toxicity in this species. To address this gap, we assembled and harmonized the largest single-cell transcriptomic atlas of M. fascicularis to date, integrating 30 publicly available studies spanning 57 anatomical regions, 43 organs and 14 physiological systems. We implemented a scalable framework for cross-species cell type annotation by embedding both cynomolgus monkeys and human (Tabula Sapiens V2) datasets into a shared reference space using Universal Cell Embeddings (UCE), enabling consistent harmonization of cell identities. In total, 27 organs were annotated using human reference labels, while the remaining sets retained author-provided annotations or labels transferred from other cynomolgus studies with available annotations. The resulting atlas comprises over 2.5 million cells and demonstrates concordance in cell-type-specific expression patterns between cynomolgus and humans, including tissue-specific markers and targets relevant for biologics development. Through translational use cases, we illustrate how this resource can be applied to assess target expression in tissues affected by concordant human-NHP toxicities, investigate ocular adverse events associated with antibody-drug conjugates (ADCs), and identify species-specific features of immune cell subtypes with known safety implications. By enabling scalable, high-resolution, cross-species comparisons of gene expression across organs, tissues, and cell states, this atlas supports improved target qualification, more mechanistic interpretation of toxicities, and evidence-based decisions on the relevance and design of NHP studies. Collectively, this work provides a unified cross-species single-cell resource for cynomolgus monkey and a modular computational framework that advances new approach methodologies and contributes to the refinement and reduction of NHP use in preclinical research.

bioinformatics↗

bioRxiv2025

Integrative immune subtyping of HNSCC reveals clinically relevant phenotypes and treatment-associated transitions

Head and neck squamous cell carcinoma (HNSCC) exhibits profound heterogeneity in clinical presentation, treatment response, and immune landscape. While prior classification systems have identified molecular and immune subtypes in this disease, their applicability to real-world clinical settings remains restricted to small, homogeneous cohorts and limited by lack of multimodal data integration and interpretation. We performed integrated multi-omics analysis including transcriptomic, genomic (copy number, single-nucleotide variants) on 1,149 tumors from 1,102 HNSCC patients across treatment settings. Using the Similarity Network Fusion (SNF) algorithm, we defined immune subtype clusters (ISCs) based on the full immune gene landscape. These clusters were characterized using mutational, transcriptional, and immune cell enrichment analyses, and mapped to hypoxia and traditional subtypes. Associations with clinical outcomes, including progression-free survival, were evaluated across first-line and post-metastatic treatment settings. Four distinct immune subtype clusters (ISC1-ISC4) were identified: ISC1: immune-cold and EMT-enriched; ISC2: immune activated; ISC3: mixed immune-regulatory and stromal-enriched phenotype; and ISC4: immunosuppressed. Distinct treatment response patterns were observed across subtypes in subjects treated with checkpoint inhibitors, chemotherapy, and combination regimens. 44 Patients with matched pre/post treatment tumors revealed treatment-associated transitions between immune subtypes: checkpoint inhibitor treatment enriched for immune activation, while chemotherapy treatment enriched for immunosuppressive signaling pathways. This study provides a clinically relevant immune subtyping framework for HNSCC based on real-world, multi-omics data. These subtypes reflect dynamic tumor-immune states and associated with treatment response and survival, supporting their use in guiding immune-based therapy in HNSCC.

immunology↗

bioRxiv2023

VIBE: An R-package for advanced RNA-seq data exploration, disease stratification and therapeutic targeting

BackgroundDevelopment of therapies e.g. antibody-based treatments, rely on several factors, including the specificity of target expression and characterization of downstream signaling pathways. While existing tools for analyzing and visualizing RNA-seq data offer evaluation of individual gene-level expression, they lack a comprehensive assessment of pathway-guided analysis, relevant for single- and dual-targeting therapeutics. Here, we introduce VIBE (VIsualization of Bulk RNA Expression data), an R package which provides a thorough exploration of both individual and combined gene expression, supplemented by pathway-guided analyses. VIBEs versatility proves pivotal for disease stratification and therapeutic targeting in cancer, immune, metabolic, and other disorders. ResultsVIBE offers a wide array of functions that streamline the visualization and analysis of transcriptomics data for single- and dual-targeting therapies such as antibodies. Its intuitive interface allows users to evaluate the expression of target genes and their associated pathways across various indications, aiding in target and disease prioritization. Metadata, such as specific treatment or number of prior lines of therapy, can be easily incorporated to refine the identification of patient cohorts hypothesized to derive benefit from a given drug. Through real-world scenario representations using simulated data, we demonstrate how VIBE can be used to assist in indication selection for several user cases. VIBE integrates statistics in all graphics, enabling data-informed decision-making. Its enhanced user experience features include boxplot sorting and group genes either individually or averaged based on pathways, ensuring custom visuals for insightful decisions. For a deeper dive into its extensive functionalities, please review the vignettes on the GitHub repository (https://github.com/genmab/VIBE). ConclusionsVIBE facilitates detailed visualization of individual and cohort-level summaries such as concordant or discordant expression of two genes or pathways. Such analyses can help to prioritize disease indications that are amenable to treatment strategies like bispecific antibody therapies or pathway-guided monoclonal antibody therapies. By using this tool, researchers can enhance the indication selection and potentially accelerate the development of novel targeted therapies with the end goal of precision, personalization, and ensuring treatments align perfectly with individual patient needs across a spectrum of medical domains.

bioinformatics↗

bioRxiv2023

Determining epitope specificity of T-cell receptors with transformers

MotivationT-cell receptors (TCR) on T cells recognize and bind to epitopes presented by the major histocompatibility complex (MHC) in case of an infection or cancer. However, the high diversity of TCRs, as well as their unique and complex binding mechanisms underlying epitope recognition, make it difficult to predict the binding between TCR and epitope. Here, we present the utility of transformers, a deep learning strategy that incorporates an attention mechanism that learns the informative features, and show that these models pretrained on a large set of protein sequences outperform current strategies. MethodWe compared three pre-trained auto-encoder transformer models (ProtBERT, ProtAlbert, ProtElectra) and one pre-trained auto-regressive transformer model (ProtXLNet) to predict the binding specificity of TCRs to 25 epitopes from the VDJdb database (human and murine). Two additional modifications were performed to incorporate gene usage of the TCRs in the four transformer models. ResultsOf all 12 transformer implementations (4 models with 3 different modifications), a modified version of the ProtXLNet model could predict TCR-epitope pairs with the highest accuracy (weighted F1 score 0.55 simultaneously considering all 25 epitopes). The modification included additional features representing the gene names for the TCRs. We also showed that the basic implementation of transformers outperformed the previously available methods, i.e. TCRGP, TCRDist and DeepTCR, developed for the same biological problem, especially for the hard-to-classify labels. ConclusionWe show that the proficiency of transformers in attention learning can indeed be made operational in a complex biological setting like TCR binding prediction. Further ingenuity in utilizing the full potential of transformers, either through attention head visualization or introducing additional features, can further extend T-cell research avenues. AvailabilityData and code are available on https://github.com/InduKhatri/tcrformer

bioinformatics↗

bioRxiv2021

Longitudinal dynamics of human B-cell response at single-cell level in response to Tdap vaccination

Adaptation of the immune system to mount an adequate immune response against pathogens is a crucial function of the adaptive immune system. To better characterize a successful vaccination response, we performed longitudinal (days 0, 5, 7, 10, and 14 after Boostrix vaccination) analysis of the single cell transcriptome as well as the B-cell receptor (BCR) repertoire (scBCR-rep) in plasma cells of an immunized donor and compared it with baseline B cell characteristics as well as flow cytometry findings. Based on the flow cytometry knowledge and literature findings, we discriminated individual B cell subsets in the transcriptomics data and traced over-time maturation of plasmablasts/plasma cells (PB/PCs) and identified the pathways associated with the plasma cell maturation. We observed that the repertoire in PB/PCs differed from the baseline B cell repertoire e.g. regarding expansion of unique clones in post-vaccination visits, high usage of IGHG1 in expanded clones, increased class switching events post-vaccination represented by clonotypes spanning multiple IGHC classes and positive selection of CDR3 sequences over time. Importantly, the Variable gene family-based clustering of BCRs represented a similar measure as the gene-based clustering, however, certainly improved the clustering of BCRs, as BCRs from duplicated Variable gene families could be clustered together. Finally, we developed a query tool to dissect the immune response to the components of Boostrix vaccine. Using this tool, we could identify the BCRs related to anti-tetanus and anti-pertussis toxoid. Collectively, we developed a workflow which allows description of key features of an ongoing immune response, such as activation of PB/PCs, Ig class switching, somatic hypermutation, and clonal expansion, all of which are hallmarks of antigen exposure.

immunology↗

bioRxiv2020

pmTR database: population matched (PM) germline allelic variants of T-cell receptor (TR) loci

T-cell receptor (TR) germline alleles are arranged, organized and made available to the research community by the IMGT database. This state-of-the-art database, however, does not provide information regarding population specificity and allelic frequencies of the genes all four human TR loci (TRA, TRB, TRG and TRD). The specificity of allelic variants to different human populations can, however, be a rich source of information when studying the genetic basis of population-specific immune responses in vaccination and disease. To make TR germline alleles available for such population-specific studies, we meticulously identified true germline alleles enriched with complete TR allele sequences and their frequencies across 26 different human populations, profiled by "1,000 Genomes data". We identified 205 TRAV, 249 TRBV, 16 TRGV and 5 TRDV germline alleles supported by at least four haplotypes (= minimum of two individuals). The diversity of germline allelic variants in the TR loci is highest in Africans followed by Non-African populations. A majority of the Non-African alleles are specific to the Asian populations, suggesting a diverse profile of TR germline alleles in different human populations. Interestingly, the alleles known in the IMGT database are frequent and common across all the superpopulations. We believe that this new set of genuine germline TR sequences represents a valuable new resource which we have made available through the new population-matched TR (pmTR) database, accessible via https://pmtrig.lumc.nl/.

immunology↗

bioRxiv2020

Population matched (PM) germline allelic variants of immunoglobulin (IG) loci: New pmIG database to better understand IG repertoire and selection processes in disease and vaccination

At the population level, immunoglobulin (IG) loci harbor inter-individual allelic variants in the many different germline IG variable (V), Diversity (D) and Joining (J) genes of the IG heavy (IGH), IG kappa (IGK) and IG lambda (IGL) loci, which together form the genetic basis of the highly diverse antigen-specific B-cell receptors. These inter-individual allelic variants can be shared between or be specific to human populations. The current IG databases IMGT, VBASE2 and IgPdb hold information about germline alleles, most of which are partial sequences, obtained from a mixture of human (B-cell) samples, many with sequence errors and/or acquired (non-germline) IG variations, induced by somatic hypermutation (SHM) during antigen-specific B-cell responses. We systematically identified true germline alleles (without SHM) from 26 different human populations around the world, profiled by the "1000 Genomes data". Our resource is uniquely enriched with complete IG allele sequences and their frequencies across human populations. We identified 409 IGHV, 179 IGKV, and 199 IGLV germline alleles supported by at least seven haplotypes (= minimum of four individuals), after removal of potential false-positives, based on using other genomic databases, i.e. ENSEMBL, TopMed, ExAC, ProjectMine. Remarkably, the positions of the identified variant nucleotides of the different alleles are not at random (as observed in case of SHM), but show striking patterns, restricted to limited nucleotide positions, the same as found in other IG data bases, suggesting over-time evolutionary selection processes. The identification of these specific patterns provides extra evidence that the identified variant nucleotides are not sequencing errors, but genuine allelic variants. The diversity of germline allelic variants in IGH and IGL loci is the highest in Africans, while the IGK locus is most diverse in Europeans. We also report on the presence of recombination signal sequences (RSS) in V pseudogenes, explaining their usage in V(D)J rearrangements. We propose that this new set of genuine germline IG sequences can serve as a new population-matched IG (pmIG) database for better understanding B-cell repertoire and B-cell receptor selection processes in disease and vaccination within and between different human populations. The database in format of fasta is available via GitHub (https://github.com/InduKhatri/pmIG). Contribution to the Field StatementWe present a catalogue of immunoglobulin (IG) germline-alleles of unprecedented completeness and accuracy from 26 different human populations belonging to five different large ethnicities (Source: 1000 Genomes). We identified the population distribution of several known germline alleles and identified multiple new alleles, especially in African populations, indicative of high allelic diversity of IG genes in Africa. Strikingly, the identified variant nucleotides of the different alleles are not at random, but show striking patterns, restricted to limited nucleotide positions, the same as found in other IG databases, suggesting over-time evolutionary selection processes. Furthermore, we identified recombination signal sequences in pseudogenes (previously not known). We provide an overview of IG germline alleles shared with and between known databases and also point to potential sources of non-germline variation and incompleteness of the existing IG databases. More importantly, we believe that this information can serve as a novel population-matched IG (pmIG) database, highly valuable for the research community in supporting the dissection and understanding of differences in effectiveness of antibody-based immune responses in infectious diseases, other (immune) diseases and vaccination within and between human populations. Such knowledge might be used in developing population-specific vaccination strategies e.g. for currently ongoing SARS-CoV2 pandemic.

immunology↗