Biology subjects

Thomson, M.

Publications and source records attributed to Thomson, M..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

Unexplored regions of the protein sequence-structure map revealed at scale by a library of foldtuned language models

Amino-acid sequence space is combinatorially vast, with well-folded proteins distributed sparsely and connected by vanishingly few permissible mutational paths. Novel-in-sequence versions of structures observed in nature promise to sample features such as new binding motifs and active site geometries but are rendered inaccessible to evolution or direct search by the extent of sequence perturbations required. Here we introduce a novel algorithm - termed "foldtuning" - that leverages principles of adversarial learning to drive protein language models (PLMs) to erase detectable homology to natural sequences while preserving a target structure, systematically traversing protein-space without being limited by evolutionary barriers. We build foldtuned PLMs for >700 targets including membrane-bound receptors, redox enzymes, and signaling domains. Foldtuned proteins are diverse and far-from-natural in sequence, filling out structurally-equivalent families defined by fundamental biophysical constraints invisible to traditional sequence-based bioinformatics methods. Experimental characterization demonstrates that foldtuned proteins express stably in vitro and function in vivo. By revealing sequence-structure information at scale beyond evolution, foldtuning promises to accelerate the reconstitution and realization of novel-to-nature systems for synthetic biology problems from therapeutics to catalysis.

bioengineering↗

bioRxiv2023

Spatial transcriptomics defines injury-specific microenvironments in the adult mouse kidney and novel cellular interactions in regeneration and disease

Kidney injury disrupts the intricate renal architecture and triggers limited regeneration, and injury-invoked inflammation and fibrosis. Deciphering molecular pathways and cellular interactions driving these processes is challenging due to the complex renal architecture. Here, we applied single cell spatial transcriptomics to examine ischemia-reperfusion injury in the mouse kidney. Spatial transcriptomics revealed injury-specific and spatially-dependent gene expression patterns in distinct cellular microenvironments within the kidney and predicted Clcf1-Crfl1 in a molecular interplay between persistently injured proximal tubule cells and neighboring fibroblasts. Immune cell types play a critical role in organ repair. Spatial analysis revealed cellular microenvironments resembling early tertiary lymphoid structures and identified associated molecular pathways. Collectively, this study supports a focus on molecular interactions in cellular microenvironments to enhance understanding of injury, repair and disease. One-Sentence Summary: Spatial transcriptomics predicted a molecular interplay amongst neighboring cell-types in the injured mammalian kidney Main Text:

molecular biology↗

bioRxiv2023

Generating counterfactual explanations of tumor spatial proteomes to discover effective strategies for enhancing immune infiltration

Cancer progression can be slowed down or halted via the activation of either endogenous or engineered T cells and their infiltration of the tumour microenvironment. Here we describe a deep-learning model that uses large-scale spatial proteomic profiles of tumours to generate minimal tumour perturbations that boost T-cell infiltration. The model integrates a counterfactual optimization strategy for the generation of the perturbations with the prediction of T-cell infiltration as a self-supervised machine-learning problem. We applied the model to 368 samples of metastatic melanoma and colorectal cancer assayed using 40-plex imaging mass cytometry, and discovered cohort-dependent combinatorial perturbations (CXCL9, CXCL10, CCL22 and CCL18 for melanoma, and CXCR4, PD-1, PD-L1 and CYR61 for colorectal cancer) that support T-cell infiltration across patient cohorts, as confirmed via in vitro experiments. Leveraging counterfactual-based predictions of spatial omics data may aid the design of cancer therapeutics.

cancer biology↗

bioRxiv2023

Automated construction of cognitive maps with predictive coding

Humans construct internal cognitive maps of their environment directly from sensory inputs without access to a system of explicit coordinates or distance measurements. While machine learning algorithms like SLAM utilize specialized inference procedures to identify visual features and construct spatial maps from visual and odometry data, the general nature of cognitive maps in the brain suggests a unified mapping algorithmic strategy that can generalize to auditory, tactile, and linguistic inputs. Here, we demonstrate that predictive coding provides a natural and versatile neural network algorithm for constructing spatial maps using sensory data. We introduce a framework in which an agent navigates a virtual environment while engaging in visual predictive coding using a self-attention-equipped convolutional neural network. While learning a next image prediction task, the agent automatically constructs an internal representation of the environment that quantitatively reflects spatial distances. The internal map enables the agent to pinpoint its location relative to landmarks using only visual information.The predictive coding network generates a vectorized encoding of the environment that supports vector navigation where individual latent space units delineate localized, overlapping neighborhoods in the environment. Broadly, our work introduces predictive coding as a unified algorithmic framework for constructing cognitive maps that can naturally extend to the mapping of auditory, sensorimotor, and linguistic inputs.

neuroscience↗

bioRxiv2023

Unraveling cell differentiation mechanisms through topological exploration of single-cell developmental trajectories

Understanding the circuits that control cell differentiation is a fundamental problem in developmental biology. Single-cell RNA sequencing has emerged as a powerful tool for investigating this problem. However, the reconstruction of developmental trajectories is based on the assumption that cell states traverse a tree-like structure, which may bias our understanding of critical developmental mechanisms. To address this limitation, we developed a framework, TopGen, that enables identifying topological signatures of functional biological circuits as persistent homology groups in transcriptome space. First, we show that TopGen can identify genetic drivers of topological structures in simulated datasets. We then applied our approach to more than ten single-cell developmental atlases and found that topological transcriptome spaces are predominantly path-connected and only sometimes simply connected. Finally, we applied TopGen to analyze gene expression patterns in topological loops representing stem-like, transdifferentiation, and convergent cell circuits, found in C. elegans, H. vulgaris, and N. vectensis, respectively. Our results show that some essential differentiation mechanisms use non-trivial topological motifs, and that these motifs can be conserved in a cell-type-specific manner. Thus, our approach to studying the topological properties of developmental transcriptome atlases opens new possibilities for understanding cell development and differentiation.

bioinformatics↗

bioRxiv2023

Configurational entropy is an intrinsic driver of tissue structural heterogeneity

Tissues comprise ordered arrangements of cells that can be surprisingly disordered in their details. How the properties of single cells and their microenvironment contribute to the balance between order and disorder at the tissue-scale remains poorly understood. Here, we address this question using the self-organization of human mammary organoids as a model. We find that organoids behave like a dynamic structural ensemble at the steady state. We apply a maximum entropy formalism to derive the ensemble distribution from three measurable parameters - the degeneracy of structural states, interfacial energy, and tissue activity (the energy associated with positional fluctuations). We link these parameters with the molecular and microenvironmental factors that control them to precisely engineer the ensemble across multiple conditions. Our analysis reveals that the entropy associated with structural degeneracy sets a theoretical limit to tissue order and provides new insight for tissue engineering, development, and our understanding of disease progression.

bioengineering↗

bioRxiv2023

Patterning and folding of intestinal villi by active mesenchymal dewetting

Tissue folding generates structural motifs critical to organ function. In the intestine, bending of a flat epithelium into a periodic pattern of folds gives rise to villi, the numerous finger-like protrusions that are essential for nutrient absorption. However, the molecular and mechanical mechanisms driving the initiation and morphogenesis of villi remain a matter of debate. Here, we identify an active mechanical mechanism that simultaneously patterns and folds intestinal villi. We find that PDGFRA+ subepithelial mesenchymal cells generate myosin II-dependent forces sufficient to produce patterned curvature in neighboring tissue interfaces. At the cell-level, this occurs through a process dependent upon matrix metalloproteinase-mediated tissue fluidization and altered cell-ECM adhesion. By combining computational models with in vivo experiments, we reveal these cellular features manifest at the tissue-level as differences in interfacial tensions that promote mesenchymal aggregation and interface bending through a process analogous to the active de-wetting of a thin liquid film.

developmental biology↗

bioRxiv2023

D-SPIN constructs gene regulatory network models from multiplexed scRNA-seq data revealing organizing principles of cellular perturbation response

Gene regulatory networks within cells modulate the expression of the genome in response to signals and changing environmental conditions. Reconstructions of gene regulatory networks can reveal the information processing and control principles used by cells to maintain homeostasis and execute cell-state transitions. Here, we introduce a computational framework, D-SPIN, that generates quantitative models of gene regulatory networks from single-cell mRNA-seq datasets collected across thousands of distinct perturbation conditions. D-SPIN constructs probabilistic models of regulatory interactions between genes or gene-expression programs to fit the cell state distributions under different perturbations. Using large Perturb-seq and drug-response datasets, we demonstrate that D-SPIN models reveal key regulators of cell fate decisions and the coordination of distant cellular pathways in response to gene knockdown perturbations. D-SPIN also dissects gene-level drug response mechanisms in heterogeneous cell populations, elucidating how combinations of immunomodulatory drugs acting on distinct regulators induce novel cell states through additive recruitment of gene expression programs. The D-SPIN model further predicts cell state distributions under drug dosage combinations beyond the training data. D-SPIN provides a computational framework for constructing interpretable models of gene regulatory networks to reveal principles of cellular information processing and physiological control.

bioinformatics↗

bioRxiv2022

Control of spatio-temporal patterning via cell density in a multicellular synthetic gene circuit

A major goal in synthetic development is to design and construct gene regulatory circuits that control the patterning and morphogenesis of synthetic multicellular structures. In natural development, an interplay between mechanical and chemical communication shapes the dynamics of gene regulatory circuits that underlie patterning and morphogenesis. However, for synthetic gene circuits, how the non-genetic properties of the growth environment impact circuit behavior remains poorly understood. Here, we describe an occurrence of mechano-chemical coupling in synthetic contact-dependent synNotch patterning circuits demonstrating that cell density modulates the transduction of signal between a sender and receiver cell. By exploiting density-dependent signaling, we construct multicellular signal propagation circuits with synNotch and control the patterning outcome both temporally and spatially via cell density gradients established in vitro via plating or small-molecule mediated modulation of proliferation. Our work demonstrates that synthetic gene circuits can be critically impacted by their context, providing an alternate means for programming multi-cellular circuit patterning outcomes.

synthetic biology↗

bioRxiv2022

A prebiotic diet modulates microglial states and motor deficits in α-synuclein overexpressing mice

Parkinsons disease (PD) is a movement disorder characterized by neuroinflammation, -synuclein pathology, and neurodegeneration. Most cases of PD are non-hereditary, suggesting a strong role for environmental factors, and it has been speculated that disease may originate in peripheral tissues such as the gastrointestinal (GI) tract before affecting the brain. The gut microbiome is altered in PD and may impact motor and GI symptoms as indicated by animal studies, though mechanisms of gut-brain interactions remain incompletely defined. Intestinal bacteria ferment dietary fibers into short-chain fatty acids, with fecal levels of these molecules differing between PD and healthy controls and in mouse models. Among other effects, dietary microbial metabolites can modulate activation of microglia, brain-resident immune cells implicated in PD. We therefore investigated whether a fiber-rich diet influences microglial function in -synuclein overexpressing (ASO) mice, a preclinical model with PD-like symptoms and pathology. Feeding a prebiotic high-fiber diet attenuates motor deficits and reduces -synuclein aggregation in the substantia nigra of mice. Concomitantly, the gut microbiome of ASO mice adopts a profile correlated with health upon prebiotic treatment, which also reduces microglial activation. Single-cell RNA-seq analysis of microglia from the substantia nigra and striatum uncovers increased pro-inflammatory signaling and reduced homeostatic responses in ASO mice compared to wild-type counterparts on standard diets. However, prebiotic feeding reverses pathogenic microglial states in ASO mice and promotes expansion of protective disease-associated macrophage (DAM) subsets of microglia. Notably, depletion of microglia using a CSF1R inhibitor eliminates the beneficial effects of prebiotics by restoring motor deficits to ASO mice despite feeding a prebiotic diet. These studies uncover a novel microglia-dependent interaction between diet and motor symptoms in mice, findings that may have implications for neuroinflammation and PD.

physiology↗

bioRxiv2022

A universal stress protein acts as a metabolic rheostat controlling carbon flux in mycobacteria

Universal stress proteins (USPs) are ubiquitous amongst prokaryotes and have empirically determined roles in adaptation to stress, while their specific biochemical function in cellular processes is opaque. The most frequently encountered USP is a small ~15kDa protein comprised of a single domain (PF00582). In Mycobacterium tuberculosis Rv1636 is the sole single domain protein amongst its 10 USPs. We were unable to delete rv1636 from M. tuberculosis, supporting previous data that it is essential for growth. We deleted its orthologue, MS3811, from M. smegmatis, and in exponential phase competition experiments we observed that the advantage switched between wildtype and the mutant depending on the growth environment. In rich medium the wildtype had the competitive advantage, but in a defined medium with a single carbon source the competitive advantage switched, with the mutant rapidly taking over the culture. We hypothesised the USP is regulating metabolic flux, with its deletion leading to increased flux and more rapid turn-over of central-carbon metabolites. We tested this by performing 13C-stable isotope tracing, using [U-13C6] glucose as sole carbon source for growth of the parental and deletion strains. The findings were unequivocal; deletion of the USP led to an increase in label incorporation in central carbon metabolites and a profound change in the isotopologue distribution. Furthermore, in vivo protein-crosslinking provided evidence that Rv1636 interacts directly with key central metabolic enzymes, including the glycolytic enzymes pyruvate kinase, pyruvate dehydrogenase, pyruvate synthase. We propose the mycobacterial single domain USP acts as a metabolic rheostat to regulate central carbon metabolism. ImportanceThere are more than 62,000 universal stress proteins (USPs) found mostly in prokaryotes, but very few examples where the role of a USP is unequivocally linked to a specific biological or biochemical function. For the first time we were able to assign a role in metabolic regulation to a bacterial single domain universal stress protein. We demonstrate a mycobacterial USP regulates flux through central catabolic pathways, and our evidence, based on 13C-stable isotope tracing, indicates that the USP acts as a brake on carbon flux, and we assign to it a role as a metabolic rheostat.

microbiology↗

bioRxiv2022

Enhanced recovery of single-cell RNA-sequencing reads for missing gene expression data

Droplet-based 3 single-cell RNA-sequencing (scRNA-seq) methods have proved transformational in characterizing cellular diversity and generating valuable hypotheses throughout biology1,2. Here we outline a common problem with 3 scRNA-seq datasets where genes that have been documented to be expressed with other methods, are either completely missing or are dramatically under-represented thereby compromising the discovery of cell types, states, and genetic mechanisms. We show that this problem stems from three main sources of sequencing read loss: (1) reads mapping immediately 3 to known gene boundaries due to poor 3 UTR annotation; (2) intronic reads stemming from unannotated exons or pre-mRNA; (3) discarded reads due to gene overlaps3. Each of these issues impacts the detection of thousands of genes even in well-characterized mouse and human genomes rendering downstream analysis either partially or fully blind to their expression. We outline a simple three-step solution to recover the missing gene expression data that entails compiling a hybrid pre-mRNA reference to retrieve intronic reads4, resolving gene collision derived read loss through removal of readthrough and premature start transcripts, and redefining 3 gene boundaries to capture false intergenic reads. We demonstrate with mouse brain and human peripheral blood datasets that this approach dramatically increases the amount of sequencing data included in downstream analysis revealing 20 - 50% more genes per cell and incorporates 15-20% more sequencing reads than with standard solutions5. These improvements reveal previously missing biologically relevant cell types, states, and marker genes in the mouse brain and human blood profiling data. Finally, we provide scRNA-seq optimized transcriptomic references for human and mouse data as well as simple algorithmic implementation of these solutions that can be deployed to both thoroughly as well as poorly annotated genomes. Our results demonstrate that optimizing the sequencing read mapping step can significantly improve the analysis resolution as well as biological insight from scRNA-seq. Moreover, this approach warrants a fresh look at preceding analyses of this popular and scalable cellular profiling technology.

genomics↗

bioRxiv2021

Motor processivity and speed determine structure and dynamics of microtubule-motor assemblies

1Active matter systems can generate highly ordered structures, avoiding equilibrium through the consumption of energy by individual constituents. How the microscopic parameters that characterize the active agents are translated to the observed mesoscopic properties of the assembly has remained an open question. These active systems are prevalent in living matter; for example, in cells, the cytoskeleton is organized into structures such as the mitotic spindle through the coordinated activity of many motor proteins walking along microtubules. Here, we investigate how the microscopic motor-microtubule interactions affect the coherent structures formed in a reconstituted motor-microtubule system. This question is of deeper evolutionary significance as we suspect motor and microtubule type contribute to the shape and size of resulting structures. We explore key parameters experimentally and theoretically, using a variety of motors with different speeds, proces-sivities, and directionalities. We demonstrate that aster size depends on the motor used to create the aster, and develop a model for the distribution of motors and microtubules in steady-state asters that depends on parameters related to motor speed and processivity. Further, we show that network contraction rates scale linearly with the single-motor speed in quasi one-dimensional contraction experiments. In all, this theoretical and experimental work helps elucidate how microscopic motor properties are translated to the much larger scale of collective motor-microtubule assemblies.

biophysics↗

bioRxiv2021

Signaling receptor localization maximizes cellular information acquisition in spatially-structured, natural environments

Cells in natural environments like tissue or soil sense and respond to extracellular ligands with intricately structured and non-monotonic spatial distributions, sculpted by processes such as fluid flow and substrate adhesion. In this work, we show that spatial sensing and navigation can be optimized by adapting the spatial organization of signaling pathways to the spatial structure of the environment. We develop an information-theoretic framework for computing the optimal spatial organization of a sensing system for a given signaling environment. We find that receptor localization maximizes information acquisition in simulated natural contexts, including tissue and soil. Receptor localization extends naturally to produce a dynamic protocol for continuously redistributing signaling receptors, which when implemented using simple feedback, boosts cell navigation efficiency by 30-fold. Broadly, our work shows how cells can maximize the fidelity of information transfer by adapting the spatial organization of signaling molecules to the spatial structure of the environment.

systems biology↗

bioRxiv2021

Deep parallel characterization of AAV tropism and AAV-mediated transcriptional changes via single-cell RNA sequencing

Engineered variants of recombinant adeno-associated viruses (rAAVs) are being developed rapidly to meet the need for gene-therapy delivery vehicles with particular cell-type and tissue tropisms. While high-throughput AAV engineering and selection methods have generated numerous variants, subsequent tropism and response characterization have remained low throughput and lack resolution across the many relevant cell and tissue types. To fully leverage the output of these large screening paradigms across multiple targets, we have developed an experimental and computational single-cell RNA sequencing (scRNA-seq) pipeline for in vivo characterization of barcoded rAAV pools at unprecedented resolution. Using our platform, we have corroborated previously reported viral tropisms and discovered unidentified AAV capsid targeting biases. As expected, we observed that the tropism profile of AAV.CAP-B10 in mice was shifted toward neurons and away from astrocytes when compared with AAV-PHP.eB. Our transcriptomic analysis revealed that this neuronal bias is mainly due to increased targeting efficiency for glutamatergic neurons, which we confirmed by RNA fluorescence in situ hybridization. We further uncovered cell subtype tropisms of AAV variants in vascular and glial cells, such as low transduction of pericytes and Myoc+ astrocytes. Additionally, we have observed cell-type-specific responses to systemic AAV-PHP.eB administration, such as upregulation of genes involved in p53 signaling in endothelial cells three days post-injection, which return to control levels by day twenty-five. Such ability to parallelize the characterization of AAV tropism and simultaneously measure the transcriptional response of transduction will facilitate the advancement of safe and precise gene delivery vehicles.

bioengineering↗

bioRxiv2021

Active feature selection discovers minimal gene-sets for classifying cell-types and disease states in single-cell mRNA-seq data

Sequencing costs currently prohibit the application of single-cell mRNA-seq to many biological and clinical analyses. Targeted single-cell mRNA-sequencing reduces sequencing costs by profiling reduced gene sets that capture biological information with a minimal number of genes. Here, we introduce an active learning method (ActiveSVM) that identifies minimal but highly-informative gene sets that enable the identification of cell-types, physiological states, and genetic perturbations in single-cell data using a small number of genes. Our active feature selection procedure generates minimal gene sets from single-cell data through an iterative cell-type classification task where misclassified cells are examined at each round of analysis to identify maximally informative genes through an active support vector machine (ActiveSVM) classifier. By focusing computational resources on misclassified cells, ActiveSVM scales to analyze data sets with over a million single cells. We demonstrate that ActiveSVM feature selection identifies gene sets that enable 90% cell-type classification accuracy across a variety of data sets including cell atlas and disease characterization data sets. The method generalizes to reveal genes that respond to genetic perturbations and to identify region specific gene expression patterns in spatial transcriptomics data. The discovery of small but highly informative gene sets should enable substantial reductions in the number of measurements necessary for application of single-cell mRNA-seq to clinical tests, therapeutic discovery, and genetic screens.

bioinformatics↗

bioRxiv2020

A telomere to telomere assembly of Oscheius tipulae and the evolution of rhabditid nematode chromosomes

Eukaryotic chromosomes have phylogenetic persistence. In many taxa, the number of chromosomes is related to the number of centromeres. However, in some groups, such as rhabditid nematodes, centromeric function is distributed across multiple sites on each chromosome. These holocentric chromosomes might, a priori, be expected to be permissive of large-scale chromosomal rearrangement, as chromosomal fragments could still partition correctly and fusions would not generate lethal conflict between multiple centromeres. Here, we explore the phylogenetic stability of nematode chromosomes using a new telomere-to-telomere assembly of the rhabditine nematode Oscheius tipulae generated from nanopore long reads. The 60 Mb O. tipulae genome is resolved into six chromosomal molecules. We find evidence of specific chromatin diminution at all telomeres. Comparing this chromosomal O. tipulae assembly with chromosomal assemblies of diverse rhabditid nematodes we identify seven ancestral chromosomal elements (Nigon elements), and present a model for the evolution of nematode chromosomes through rearrangement and fusion of these elements. We identify frequent fusion events involving NigonX, the element associated with the rhabditid X chromosome, and thus sex-chromosome associated gene sets differ markedly between species. Despite the karyotypic stability, gene order within chromosomes defined by Nigon elements is not conserved. Our model for nematode chromosome evolution provides a platform for investigation of the tensions between local genome rearrangement and karyotypic evolution in generating extant genome architectures.

genomics↗

bioRxiv2020

Modulation of the cAMP levels with a conserved actinobacteria phosphodiesterase enzyme reduces antimicrobial tolerance in mycobacteria

Antimicrobial tolerance (AMT) is the gateway to the development of antimicrobial resistance (AMR) and is therefore a major issue that needs to be addressed. The second messenger cyclic-AMP (cAMP), which is conserved across all taxa, is involved in propagating signals from environmental stimuli and converting these into a response. In bacteria, such as M. tuberculosis, P. aeruginosa, V. cholerae and B. pertussis, cAMP has been implicated in virulence, metabolic regulation and gene expression. However, cAMP signalling in mycobacteria is particularly complex due to the redundancy of adenylate cyclases, which are enzymes that catalyse the formation of cAMP from ATP, and the poor activity of the only known phosphodiesterase (PDE) enzyme, which degrades cAMP into 5- AMP. Based on these two features, the modulation of this system with the aim of investigating cAMP signalling and its involvement in AMT in mycobacteria id difficult. To address this pressing need, we identified a new cAMP-degrading phosphodiesterase enzyme (Rv1339) and used it to significantly decrease the intrabacterial levels of cAMP in mycobacteria. This analysis revealed that this enzyme increased the antimicrobial susceptibility of M. smegmatis mc2155. Using a combination of metabolomics, RNA-sequencing, antimicrobial susceptibility assays and bioenergetics analysis, we were able to characterize the molecular mechanism underlying this increased susceptibility. This work represents an important milestone showing that the targeting of cAMP signalling is a promising new avenue for antimicrobial development and expands our understanding of cAMP signalling in mycobacteria.

microbiology↗