Biology subjects

Ferguson, A. L.

Publications and source records attributed to Ferguson, A. L..

12 recordsLinked to original sources

bioRxiv2026

Combining xenium in situ spatial transcriptomics and imaging mass cytometry on a single tissue section

Spatial imaging technologies provide an expansive view of tissue microenvironments through high-plex profiling of protein and molecular targets in situ. Imaging mass cytometry (IMC; Standard BioTools) is a trusted method for defining immune phenotypes based on up to 40 protein targets, whilst Xenium in situ spatial transcriptomics (Xenium; 10x Genomics) is an emerging platform that can measure up to 5000 mRNA markers simultaneously. Although these platforms can reveal valuable insights on their own, there is an increasing need to analyse samples using a multi-omics approach to further our understanding of complex biological processes. To address this, we have assessed a novel dual-platform workflow that combines Xenium and IMC on a single formalin-fixed paraffin-embedded tissue section to enable the spatial profiling of both mRNA and protein targets at single-cell resolution. The feasibility of the workflow was determined by comparing the staining quality of IMC performed after Xenium to that of IMC performed alone on an adjacent tissue section, confirming that Xenium has little to no negative impact on subsequent IMC protein staining. Although the location of transcripts picked up by Xenium correlated with the corresponding proteins picked up by IMC at a global scale, discrepancies between the two technologies were apparent at the single-cell level. This is to be expected, as biologically transcript expression does not always correlate with protein, and both platforms have their own technical limitations. However, when we analyse T cells identified by both technologies, as opposed to T cells identified by Xenium or IMC alone, it produces the most biologically meaningful results at both the transcript and protein level for specific T cell markers. These results highlight how integration of the two platforms, identifying the presence of both RNA and protein, can foster a more comprehensive view of cellular landscapes and provide a greater depth of functional capabilities and cellular interactions.

immunology↗

bioRxiv2025

Cytoplasmic Abundant Heat-Soluble Proteins from Tardigrades Protect Synthetic Cells Under Stress

Cytoplasmic abundant heat-soluble (CAHS) proteins, a stress-responsive intrinsically disordered protein from tardigrades, have been discovered to form gel-like networks providing structural support during dehydration, thus enabling anhydrobiosis. However, the mechanism by which CAHS proteins protect the dehydrating cellular membrane remains enigmatic. Using giant unilamellar vesicles (GUVs) as a model membrane system, we show that encapsulated CAHS12 undergoes a reversible structural transformation that reinforces membrane integrity and preserves encapsulated components, mimicking natural anhydrobiosis. CAHS12-containing GUVs demonstrated stability for weeks and mechanical robustness under dehydration, elevated temperature, and osmotic stresses. Molecular simulations suggest that CAHS12 forms a filamentous network within the vesicle lumen that mitigates membrane collapse and preserves compartmental architecture. Synthetic cells with cell-free transcription-translation capabilities withstand desiccation and recover biochemical activities, akin to the tun state of the tardigrade. This discovery opens up synthetic cell applications in bioengineering, cold-chain-independent biomanufacturing, and adaptive biointerfaces.

synthetic biology↗

bioRxiv2025

Prediction of αIIbβ3 integrin structures along its minimum free energy activation pathway

The adhesion protein integrin is a transmembrane heterodimer that plays a pivotal role in cellular processes such as cell signaling and cell migration. To execute its function, integrin undergoes extensive conformational changes from a bent-closed to an extended-open state. Resolving the structures across these changes remains a challenge with both experimental and computational methods, but is crucial for understanding the activation mechanism of integrin. We address this challenge for the platelet integrin IIb {beta}3 by employing finite temperature string method with structures of the images along the initial guess path generated by a multiscale data-driven framework. The full-length all-atom structures along the resulting minimum free energy path between the inactive bent-closed and active extended-open states of IIb {beta}3 integrin are consistent with a variety of experimentally resolved structures. Changes in these predicted structures along the path show that the extension and separation of the and {beta} subunits from the bent-closed to the extended-open state require correlated movements between the subdomain pairs in IIb {beta}3. These results provide new insights into integrin activation mechanism and the predicted structures have potential applications in guiding the design of integrin targeting therapeutics. SIGNIFICANCEIntegrins are receptor proteins that mediate a number of critical cellular processes, but it remains challenging to resolve the molecular details of integrin activation by both experiments and simulations. We address this challenge by employing a computational method to study rare events and resolve the all-atom transient structures of a platelet integrin, IIb {beta}3. The resulting structures are in good agreement with experimentally resolved partial structures and additionally reveal correlated movements between the IIb {beta}3 integrin subdomain pairs during its transition from the inactive to the active conformational state. The structures predicted in this work have applications as therapeutic targets to address diseases linked to IIb {beta}3 integrin dysfunction, such as bleeding and thrombotic disorders.

biophysics↗

bioRxiv2024

Natural Language Prompts Guide the Design of Novel Functional Protein Sequences

The advent of natural language interaction with machines has ushered in new innovations in text-guided generation of images, audio, video, and more. In this arena, we introduce Biological Multi-Modal Model (BioM3), as a novel framework for designing functional proteins via natural language prompts. This framework integrates natural language with protein design through a three-stage process: aligning protein and text representations in a joint embedding space learned using contrastive learning, refinement of the text embeddings, and conditional generation of protein sequences via a discrete autoregressive diffusion model. BioM3 synthe-sizes protein sequences with detailed descriptions of the protein structure, lineage, and function from text annotations to enable the conditional generation of novel sequences with desired attributes through natural language prompts. We present in silico validation of the model predictions for subcellular localization prediction, reaction classification, remote homology detection, scaffold in-painting, and structural plausibility, and in vivo and in vitro experimental tests of natural language prompt-designed synthetic analogs of Src-homology 3 (SH3) domain proteins that mediate signaling in the Sho1 osmotic stress response pathway in bakers yeast. BioM3 possesses state-of-the-art performance in zero-shot prediction and homology detection tasks, and generates proteins with native-like tertiary folds and wild-type levels of experimentally assayed function.

synthetic biology↗

bioRxiv2024

Data-Driven Equation-Free Dynamics Applied to Many-Protein Complexes: The Microtubule Tip Relaxation

Microtubules (MTs) constitute the largest components of the eukaryotic cytoskeleton and play crucial roles in various cellular processes, including mitosis and intracellular transport. The property allowing MTs to cater to such diverse roles is attributed to dynamic instability, which is coupled to the hydrolysis of GTP (guanosine-5-triphosphate) to GDP (guanosine-5-diphosphate) within the {beta}-tubulin monomers. Understanding the equilibrium dynamics and the structural features of both GDP- and GTP-complexed MT tips, especially at an all-atom level, remains challenging for both experimental and computational methods because of their dynamic nature and the prohibitive computational demands of simulating large, many-protein systems. This study employs the "equation-free" multiscale computational method to accelerate the relaxation of all-atom simulations of MT tips toward their putative equilibrium conformation. Using large MT lattice systems (14 protofilaments x 8 heterodimers) comprising [~]21-38 million atoms, we applied this multiscale approach to leapfrog through time and nearly double the computational efficiency in realizing relaxed all-atom conformations of GDP- and GTP-complexed MT tips. Commencing from an initial 4 s unbiased all-atom simulation, we interleave coarse projective "equation-free" jumps with short bursts of all-atom molecular dynamics simulation to realize an additional effective simulation time of 1.875 s. Our 5.875 s of effective simulation trajectories for each system expose the subtle yet essential differences in the structures of MT tips as a function of whether {beta}-tubulin monomer is complexed with GDP or GTP, as well as the lateral interactions within the MT tip, offering a refined understanding of features underlying MT dynamic instability. The approach presents a robust and generalizable framework for future explorations of large biomolecular systems at atomic resolution. SIGNIFICANCEThe dynamic instability of microtubules (MTs) is essential for a plethora of biological functions, from cell division to intracellular transport. Despite their importance, current computational models often struggle to handle the large-scale, long-term dynamics of all-atom simulations. This computational study employs the "equation-free" method to accelerate relaxation of the all-atom structures of MT tips in both GDP and GTP states which nearly halves the computational demands of simulating very large biomolecular systems. Our findings expose subtle but crucial structural differences between GDP-bound and GTP-bound MTs, particularly in the number of protofilament clusters, and is consistent with a relatively stronger lateral interactions in GTP-bound MT tips.

biophysics↗

bioRxiv2024

Spatial mapping reveals unique cellular interactions and enhanced tertiary lymphoid structures in responders to anti-PD-1 therapy in mucosal head and neck cancers.

Survival in recurrent/metastatic head and neck mucosal squamous cell carcinoma (HNmSCC) remains poor. Anti-programmed death (PD)-1 therapies have demonstrated improved survival with lower toxicity when compared to standard chemotherapy. However, response to anti-PD-1 therapy remains modest, at 13-17%. We evaluated the tumor microenvironment (TME) using Imaging Mass Cytometry (IMC) on 27 tumor specimens from 24 advanced HNmSCC patients prior to receiving anti-PD-1 based treatment. We show significantly increased central memory T cells and B cells in responders (n=8) when compared to non-responders (n=16). Spatial mapping identified interactions between phenotypically distinct malignant squamous cells with CD8+ T cells, CD4+ Tregs and endothelial cells in responders, and avoidance of these cells in non-responders. Importantly, regional analysis shows responders have more abundant tertiary lymphoid structures (TLS), with TLS proportion >20% also associated with longer progression free survival. Together these findings define the immune landscape associated with response to anti-PD-1 treatment in HNmSCCs.

cancer biology↗

bioRxiv2023

Molecular insight into how the position of an abasic site and its sequence environment influence DNA duplex stability and dynamics

Local perturbations to DNA base-pairing stability from lesions and chemical modifications can alter the stability and dynamics of an entire oligonucleotide. End effects may cause the position of a disruption within a short duplex to influence duplex stability and structural dynamics, yet this aspect of nucleic acid modifications is often overlooked. We investigate how the position of an abasic site (AP site) impacts the stability and dynamics of short DNA duplexes. Using a combination of steady-state and time-resolved spectroscopy and molecular dynamics simulations, we unravel an interplay between AP-site position and nucleobase sequence that controls energetic and dynamic disruption to the duplex. The duplex is disrupted into two segments by an entropic barrier for base pairing on each side of the AP site. The barrier induces fraying of the short segment when an AP site is near the termini. Shifting the AP site inward promotes a transition from short-segment fraying to fully encompassing the barrier into the thermodynamics of hybridization, leading to further destabilization the duplex. Nucleobase sequence determines the length scale for this transition by tuning the barrier height and base-pair stability of the short segment, and certain sequences enable out-of-register base pairing to minimize the barrier height.

biophysics↗

bioRxiv2023

Data-driven discovery of innate immunomodulators via machine learning-guided high throughput screening

The innate immune response is vital for the success of prophylactic vaccines and immunotherapies. Control of signaling in innate immune pathways can improve prophylactic vaccines by inhibiting unfavorable systemic inflammation and immunotherapies by enhancing immune stimulation. In this work, we developed a machine learning-enabled active learning pipeline to guide in vitro experimental screening and discovery of small molecule immunomodulators that improve immune responses by altering the signaling activity of innate immune responses stimulated by traditional pattern recognition receptor agonists. Molecules were tested by in vitro high throughput screening (HTS) where we measured modulation of the nuclear factor{kappa} -light-chain-enhancer of activated B-cells (NF-{kappa}B) and the interferon regulatory factors (IRF) pathways. These data were used to train data-driven predictive models linking molecular structure to modulation of the NF-{kappa}B and IRF responses using deep representational learning, Gaussian process regression, and Bayesian optimization. By interleaving successive rounds of model training and in vitro HTS, we performed an active learning-guided traversal of a 139,998 molecule library. After sampling only[~] 2% of the library, we discovered viable molecules with unprecedented immunomodulatory capacity, including those capable of suppressing NF-{kappa}B activity by up to 15-fold, elevating NF-{kappa}B activity by up to 5-fold, and elevating IRF activity by up to 6-fold. We extracted chemical design rules identifying particular chemical fragments as principal drivers of specific immunomodulation behaviors. We validated the immunomodulatory effect of a subset of our top candidates by measuring cytokine release profiles. Of these, one molecule induced a 3-fold enhancement in IFN-{beta} production when delivered with a cyclic di-nucleotide stimulator of interferon genes (STING) agonist. In sum, our machine learning-enabled screening approach presents an efficient immunomodulator discovery pipeline that has furnished a library of novel small molecules with a strong capacity to enhance or suppress innate immune signaling pathways to shape and improve prophylactic vaccination and immunotherapies.

immunology↗

bioRxiv2023

Direct monitoring of the thermodynamics and kinetics of DNA and RNA dinucleotide dehybridization from gaps and overhangs

Hybridization of short nucleic acid segments (<4 nucleotides) to single-strand templates occurs as a critical intermediate in processes such as non-enzymatic nucleic acid replication and toehold-mediated strand displacement. These templates often contain adjacent duplex segments that stabilize base pairing with single-strand gaps or overhangs, but the thermodynamics and kinetics of hybridization in such contexts are poorly understood due to experimental challenges of probing weak binding and rapid structural dynamics. Here we develop an approach to directly measure the thermodynamics and kinetics of DNA and RNA dinucleotide dehybridization using steady-state and temperature-jump infrared spectroscopy. Our results suggest that dinucleotide binding is stabilized through coaxial stacking interactions with the adjacent duplex segments as well as from potential non-canonical base pairing configurations and structural dynamics of gap and overhang templates revealed using molecular dynamics simulations. We measure timescales for dissociation ranging from 0.2 to 40 {micro}s depending on the template and temperature. Dinucleotide hybridization and dehybridization involves a significant free energy barrier with characteristics resembling that of canonical oligonucleotides. Together, our work provides an initial step for predicting the stability and kinetics of hybridization between short nucleic acid segments and various templates.

biophysics↗

bioRxiv2023

ProT-VAE: Protein Transformer Variational AutoEncoder for Functional Protein Design

The data-driven design of protein sequences with desired function is challenged by the absence of good theoretical models for the sequence-function mapping and the vast size of protein sequence space. Deep generative models have demonstrated success in learning the sequence to function relationship over natural training data and sampling from this distribution to design synthetic sequences with engineered functionality. We introduce a deep generative model termed the Protein Transformer Variational AutoEncoder (ProT-VAE) that furnishes an accurate, generative, fast, and transferable model of the sequence-function relationship for data-driven protein engineering by blending the merits of variational autoencoders to learn interpretable, low-dimensional latent embeddings and fully generative decoding for conditional sequence design with the expressive, alignment-free featurization offered by transformers. The model sandwiches a lightweight, task-specific variational autoencoder between generic, pre-trained transformer encoder and decoder stacks to admit alignment-free training in an unsupervised or semi-supervised fashion, and interpretable low-dimensional latent spaces that facilitate understanding, optimization, and generative design of functional synthetic sequences. We implement the model using NVIDIAs BioNeMo framework and validate its performance in retrospective functional prediction and prospective design of novel protein sequences subjected to experimental synthesis and testing. The ProT-VAE latent space exposes ancestral and functional relationships that enable conditional generation of novel sequences with high functionality and substantial sequence diversity. We anticipate that the model can offer an extensible and generic platform for machine learning-guided directed evolution campaigns for the data-driven design of novel synthetic proteins with "super-natural" function.

synthetic biology↗

bioRxiv2022

Deep learning-enabled design of synthetic orthologs of a signaling protein

Evolution-based deep generative models represent an exciting direction in understanding and designing proteins. An open question is whether such models can represent the constraints underlying specialized functions that are necessary for organismal fitness in specific biological contexts. Here, we examine the ability of three different models to produce synthetic versions of SH3 domains that can support function in a yeast stress signaling pathway. Using a select-seq assay, we show that one form of a variational autoencoder (VAE) recapitulates the functional characteristics of natural SH3 domains and classifies fungal SH3 homologs hierarchically by function and phylogeny. Locality in the latent space of the model predicts and extends the function of natural orthologs and exposes amino acid constraints distributed near and far from the SH3 ligand-binding site. The ability of deep generative models to specify orthologous function in vivo opens new avenues for probing and engineering protein function in specific cellular environments.

molecular biology↗

bioRxiv2022

High-dimensional and spatial analysis reveals immune landscape dependent progression in cutaneous squamous cell carcinoma

PurposeThe tumour immune microenvironment impacts the biological behaviour of the tumour but its effect on clinical outcomes in head and neck cutaneous squamous cell carcinomas (HNcSCC) is largely unknown. Experimental DesignWe compared the immune milieu of high-risk HNcSCC that never progressed to metastasis with those that metastasised using multi-parameter imaging mass cytometry. The cohort included both immunosuppressed patients (IS) and patients with an absence of clinical immune-suppression (ACIS). Spatial analyses were used to identify cellular interactions that were associated with tumour behaviour. ResultsNon-progressing primary HNcSCC were characterised by higher CD8+ and CD4+ T cell responses, including numerically increased Regulatory T cells. By contrast, primary lesions from HNcSCC patients who progressed were largely devoid of T cells with lower numbers of innate immune cells and increased expression of checkpoint receptors and in the metastatic lesions were characterised by an accumulation of B cells. Spatial analysis reveals multiple cellular interactions associated with non-progressing primary tumours that were distinct in primary tumours of disease progressing patients. Cellular regional analysis of the tumour microenvironment also shows squamous cell-enriched tumour regions associated with primary non-progressing tumours. ConclusionsEffective responses from both CD8+ and CD4+ T cells in the tumour microenvironment are essential for immune control of primary HNcSCC. Our findings indicate that the early events that shape the immune responses in primary tumours dictate progression and disease outcomes in HNcSCC. Translational RelevanceThe ability to predict metastatic tumour progression at the time of initial diagnosis of primary HNcSCC could tailor personalised medical care including disease surveillance strategies and identifying patients who will benefit most from adjuvant therapy. One Sentence SummaryThe immune landscape of high-risk cutaneous squamous cell carcinoma differs in tumours that never progress compared to those that progress to metastasis.

pathology↗