Biology subjects

Lennon, A.

Publications and source records attributed to Lennon, A..

4 recordsLinked to original sources

bioRxiv2026

Arrayed hydrogels pair whole-cell imaging with single-cell mass spectrometry proteomics

Multimodal single-cell analysis aims to elucidate cellular-level phenotype within heterogeneous cell populations. Advancements in single-cell proteomics (SCP) seek to deepen quantitative depth, coverage, and reproducibility for a robust view of functional cell state. However, broadly accessible multimodal approaches are poised to benefit from sample-preparation advancements upstream of the mass spectrometer. Here, we introduce ProteoParcel, a multimodal SCP platform for indexing upstream widefield single-cell images to downstream label-free, bottom-up proteomics. Parcels are spatially arrayed planar polyacrylamide gels patterned with microwells. Each parcel, containing one microwell and an abutting gel region, is designed to integrate the single-cell imaging and SCP analysis modes. First, for whole-cell imaging, each microwell isolates an intact, individual breast cancer cell (MCF-7). After imaging, cells are subjected to in-microwell chemical cell lysis, electro-injection of whole-cell lysate from the microwell into the abutting gel region, in-gel chemical fixation, and finally in-gel tryptic digestion prior to peptide extraction for SCP. Location-indexed parcels are independently releasable to confer single-cell resolution to downstream mass spectrometry. To ensure SCP-suitable proteome solubilization and trypsin/Lys-C digestion, we optimize cell lysis, electrophoresis, and gel pre-equilibration conditions within the gel. Using ProteoParcel, we identify over 1,400 protein species from single, imaged MCF-7 cells. Scrutiny of the gel preparation conditions confirms that hydrogel-lysate interactions introduce predictable, physicochemically interpretable (cell membrane, hydrophobicity) detection biases, while broad functional-class composition and subcellular compartment coverage are preserved when benchmarked to in-solution digestion. ProteoParcel makes facile same-cell multimodal SCP and live-cell imaging.

bioengineering↗

bioRxiv2026

AI-Guided CRISPR Screen Accelerates Discovery of New Drug Targets

Psoriasis affects over 125 million people worldwide, yet the mechanistic understanding of keratinocyte-driven inflammation remains incomplete, limiting therapeutic innovation beyond costly systemic biologics that are prone to side effects. Here, we performed the first genome-wide CRISPR knockout screen in primary human adult epidermal keratinocytes to systematically identify regulators of IL-17 receptor A (IL17RA), a central node in psoriatic inflammation. To prioritize therapeutically tractable targets from over 19,000 screened genes, we integrated a large language model - VirtualCRISPR - trained on functional genomics data, identifying arachidonate 5-lipoxygenase (ALOX5) and oxytocin receptor (OXTR) as high-confidence novel hits with minimal prior association with psoriasis. Multi-omics validation revealed that ALOX5 and OXTR regulate IL17RA expression through distinct signaling pathways - ALOX5 through lipid mediators that stabilize the receptor at the cell surface, and OXTR through calcium signaling that reprograms cellular metabolism. Topical delivery of their inhibitors Zileuton (ALOX5) and Cligosiban (OXTR) exhibited therapeutic efficacy comparable to systemic anti-IL17RA antibody in the imiquimod-induced psoriasis model, suppressing pathogenic Th17/Tc17 responses, polarizing macrophages toward anti-inflammatory phenotypes, and normalizing epidermal hyperproliferation. Proteomic profiling in human 3D organotypic skin and murine models confirmed on-target pharmacology and revealed convergent suppression of neutrophil-keratinocyte inflammatory circuits. The use of VirtualCRISPR significantly shortened the timescale from screen to the identification of druggable hits with robust validation, and this work establishes a blueprint for integrating AI-driven target prioritization with functional genomics to accelerate therapeutic discovery.

genomics↗

bioRxiv2026

Codon Deoptimization of Multispecific Biologics Reduces Mispairing During Transient Mammalian Protein Expression

Codon optimization is utilized in biologics design to maximize protein expression. Selecting the host organisms most frequently used codons for each amino acid can significantly enhance recombinant protein expression yields. However, non-optimal codons in mRNA can be critical for functional protein production through inducing pauses in or attenuating protein translation. In our study, we have investigated the effect of deoptimizing serine codons in biologics by shifting them from the five most frequently used codons to the least (TCG). Rare serine codons were strategically inserted into the coding sequences of the constant regions in a trispecific antibody (Protein 1), a bispecific antibody (Protein 2), and multiple non-proprietary bispecific antibodies. We observed that inserting 1-2 rare serine codons within an open reading frame led to expression changes that reduced the formation of mispaired 2x light chain and half-molecule species. Protein purity was drastically increased by incorporating two deoptimized serine codons into a single chain. Notably, we observed a negative correlation between total protein expression yield and final product purity. Taken together, our work demonstrates that incorporation of deoptimized serine codons into a single chain can significantly influence multispecific biologic pairing and enhance final product purity. Our findings align with existing literature showing that rare codon usage modulates translation kinetics and protein folding. Future investigation is warranted to enable a priori identification of the rate-limiting chain in multispecific biologics, thereby guiding strategic codon deoptimization prior to expression.

bioengineering↗

bioRxiv2019

Multi-objective optimisation of material properties and strut geometry for poly(L-lactic acid) coronary stents using response surface methodology

Coronary stents for treating atherosclerosis are traditionally manufactured from metallic alloys. However, metal stents permanently reside in the body and may trigger undesirable immunological responses. Bioresorbable polymer stents can provide a temporary scaffold that resorbs once the artery heals but are mechanically inferior, requiring thicker struts for equivalent radial support, which may increase thrombosis risk. This study addresses the challenge of designing mechanically effective but sufficiently thin poly(L-lactic acid) stents through a computational approach that optimises material properties and stent geometry. Forty parametric stent designs were generated: cross-sectional area (post-dilation), foreshortening, stent-to-artery ratio and radial collapse pressure were evaluated computationally using finite element analysis. Response surface methodology was used to identify performance trade-offs by formulating relationships between design parameters and response variables. Multi-objective optimisation was used to identify suitable stent designs from approximated Pareto fronts and an optimal design is proposed that offers comparable performance to designs in clinical practice. In summary, a computational framework has been developed that has potential application in the design of high stiffness, thin strut polymeric stents that contend with the performance of their metallic counterparts.

bioengineering↗