Biology subjects

Le Quy, K.

Publications and source records attributed to Le Quy, K..

5 recordsLinked to original sources

bioRxiv2024

Systematic benchmarking of mass spectrometry-based antibody sequencing reveals methodological biases

The circulating antibody repertoire is crucial for immune protection, holding significant immunological and biotechnological value. While bottom-up mass spectrometry (MS) is the most widely used proteomics technique for profiling the sequence diversity of circulating antibodies (Ab-seq), it has not been thoroughly benchmarked. We quantified the replicability and robustness of Ab-seq using six monoclonal antibodies with known protein sequences in 70 different combinations of concentration and oligoclonality, both with and without polyclonal serum IgG background. Each combination underwent four protease treatments and was analyzed across four experimental and three technical replicates, totaling 3,360 LC-MS/MS runs. We quantified the dependence of MS-based Ab-seq identification on antibody sequence, concentration, protease, background signal diversity, and bioinformatics setups. Integrating the data from experimental replicates, proteases, and bioinformatics tools enhanced antibody identification. De novo peptide sequencing showed similar performance to database-dependent methods for higher antibody concentrations, but de novo antibody reconstruction remains challenging. Our work provides a foundational resource for the field of MS-based antibody profiling.

immunology↗

bioRxiv2024

Baselining the Buzz. Trastuzumab-HER2 Affinity, and Beyond!

Strong antibody-antigen binding is the primary consideration when developing an efficacious therapeutic antibody. In recent years, much work has been devoted to applying complex machine learning models to this cause, yet simple baselines are often lacking. Here, we show that the widely used sequence alignment method, BLOSUM, can yield diverse, binder-enriched libraries from a single starting antibody. Using Trastuzumab-HER2 as a model system, we experimentally validated 720 novel designs generated with five different computational methods using surface plasmon resonance. The BLOSUM substitution matrix outperformed all four deep learning design approaches tested, achieving an estimated minimum binder enrichment of 12.5% and producing nine sub-nanomolar binders. These results underscore the importance of comparing against simple baselines and set a benchmark to guide future computational antibody library design. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=141 SRC="FIGDIR/small/586756v2_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@1597ee9org.highwire.dtl.DTLVardef@9af4b6org.highwire.dtl.DTLVardef@1380e61org.highwire.dtl.DTLVardef@1380d29_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2023

Benchmarking and integrating human B-cell receptor genomic and antibody proteomic profiling

Immunoglobulins (Ig), which exist either as B-cell receptors (BCR) on the surface of B cells or as antibodies when secreted, play a key role in the recognition and response to antigenic threats. The capability to jointly characterize the BCR and antibody repertoire is crucial in understanding human adaptive immunity. From peripheral blood, bulk BCR sequencing (bulkBCR-seq) currently provides the highest sampling depth, single-cell BCR sequencing (scBCR-seq) allows for paired chain characterization, and antibody peptide sequencing by tandem mass spectrometry (Ab-seq) provides information on the composition of secreted antibodies in the serum. Although still rare, studies combining these three technologies would comprehensively capture the humoral immune response. Yet, it has not been benchmarked to what extent the datasets generated by these three technologies overlap and complement each other. To address this question, we isolated peripheral blood B cells from healthy donors and sequenced BCRs at bulk and single-cell level, in addition to utilizing publicly available sequencing data. Integrated analysis was performed on these datasets, resolved by replicates and across individuals. Simultaneously, serum antibodies were isolated, digested with multiple proteases, and analyzed with Ab-seq. Systems immunology analysis showed high concordance in repertoire features between bulk and scBCR-seq within individuals, especially when replicates were utilized. In addition, Ab-seq identified clonotype-specific peptides using both bulk and scBCR-seq library references, demonstrating the feasibility of combining scBCR-seq and Ab-seq for reconstructing paired-chain Ig sequences from the serum antibody repertoire. Collectively, our work serves as a proof-of-principle for combining bulk sequencing, single-cell sequencing, and mass spectrometry as complementary methods towards capturing humoral immunity in its entirety.

immunology↗

bioRxiv2023

Cartography of the developability landscapes of native and human-engineered antibodies

Designing effective monoclonal antibody (mAb) therapeutics faces a multi-parameter optimization challenge known as "developability", which reflects an antibodys ability to progress through development stages based on its physicochemical properties. While natural antibodies may provide valuable guidance for mAb selection, we lack a comprehensive understanding of natural developability parameter (DP) plasticity (redundancy, predictability, sensitivity) and how the DP landscapes of human-engineered and natural antibodies relate to one another. These gaps hinder fundamental developability profile cartography. To chart natural and engineered DP landscapes, we computed 40 sequence- and 46 structure-based DPs of over two million native and human-engineered single-chain antibody sequences. We found lower redundancy among structure-based compared to sequence-based DPs. Sequence DP sensitivity to single amino acid substitutions varied by antibody region and DP, and structure DP values varied across the conformational ensemble of antibody structures. Sequence DPs were more predictable than structure-based ones across different machine-learning tasks and embeddings, indicating a constrained sequence-based design space. Human-engineered antibodies were localized within the developability and sequence landscapes of natural antibodies, suggesting that human-engineered antibodies explore mere subspaces of the natural one. Our work quantifies the plasticity of antibody developability, providing a fundamental resource for multi-parameter therapeutic mAb design.

bioinformatics↗

bioRxiv2022

Using TCR and BCR sequencing to unravel the role of T and B cells in abdominal aortic aneurysm

BackgroundAbdominal aortic aneurysm (AAA) is a life-threatening cardiovascular disease, and the pathogenesis is still poorly understood. Recent evidence suggests that AAA displays characteristics of an autoimmune disease and it gained increasing prominence that specific antigen-driven T cells in the aortic tissue may contribute to the initial immune response. Single-cell RNA T- and B cell receptor (TCR and BCR) sequencing is a powerful tool to investigate TCR and BCR clonality and thus to further test this hypothesis. However, difficulties such as very limited numbers of isolated cells must be considered during implementation and data analysis making biological interpretation of the data challenging. Here, we perform a representative analysis of scRNA TCR and BCR sequencing data of experimental murine AAA and show a reliable and streamlined bioinformatic processing pipeline highlighting opportunities and limitations of this approach. MethodsWe performed single-cell RNA TCR and BCR sequencing of isolated lymphocytes from the infrarenal aortic segment of male C57BL/6J mice 3, 7, 14, and 28 days after AAA induction via elastase perfusion of the aorta. Sham operated mice at day 3 and 28 as well as non-operated mice served as controls. ResultsComparison of complementarity-determining region (CDR3) length distribution of 179 B cells and 796 T cells revealed no differences between AAA and control nor between the disease stages. We found no clonal expansion of B cells in AAA. For T cells, we identified multiple clones in 11 of 16 AAA samples and in 1 of 8 control samples. Comparison of the immune receptor repertoires indicated that only few clones were shared between the individual AAA samples. The most frequently used V-genes in the TCR beta chain in AAA were TRBV3, TRBV19, and TRBV12-2+TRBV13-2. ConclusionIn summary, we found no clonal expansion of TCRs or BCRs in elastase-induced AAA in mice. Our findings imply that a more precise characterization of TCR and BCR distribution requires a more extensive amount of T and B cells to prevent undersampling and to enable detection of potential rare clones. Using this current scSeq-based approach we did not identify clonal enrichment of T or B cells in experimental AAA.

immunology↗