Biology subjects

Hillsley, A.

Publications and source records attributed to Hillsley, A..

7 recordsLinked to original sources

bioRxiv2026

Tabular Foundation Models Are Competitive Cellular Perturbation Predictors Across Biological Scales

Predicting how cells respond to genetic and chemical perturbations is a central challenge in drug discovery and functional genomics. A growing ecosystem of specialized single-cell foundation models has been developed to address this problem, yet their practical advantage over domain-agnostic approaches remains unclear. Here we evaluate the power of Tabular Foundation Models such as TabICL and TabPFN, general-purpose pre-trained regression models, against domain-specific architectures including PRESAGE, scGPT, scLAMBDA, STACK and Prophet across four complementary evaluation settings: cell-level in-context cross-cell-type prediction, pseudobulk perturbation prediction on five Perturb-seq datasets of cell-lines, a genome-wide CRISPR screen in primary human CD4+ T cells, and embryo-level cell-type composition prediction in a zebrafish developmental perturbation atlas. In the cell-level cross-cell type perturbation prediction, Tabular Foundation Models perform on par or better than specialized models. On pseudobulk perturbation prediction, Tabular Foundation Models consistently out-perform specialized baselines across multiple evaluation metrics and datasets. On whole-embryo cell-type composition prediction, Tabular Foundation Models are competitive with specialized baselines. These results demonstrate that general-purpose tabular in-context learning provides a strong and scalable alternative to bespoke biological architectures for perturbation response modeling across cell systems and scales.

bioinformatics↗

bioRxiv2026

A multimodal perturbation atlas defines the phenotypic resolution of cellular morphology.

Modeling cellular behavior requires measurements that capture how cells evolve across time, environments, and interventions. Microscopy is uniquely suited to this goal: it is non-destructive and can be applied to living cells in their native context. Yet its phenotypic resolving power remains incompletely characterized relative to molecular assays. Here, we present a multimodal perturbation atlas of 1,000 pooled CRISPR knockouts in A549 cells, profiled by fluorescence microscopy (42 live, 13 fixed markers), label-free quantitative phase imaging of the same live cells (at single timepoints), and single-cell RNA sequencing (scRNA-seq). We develop deep learning frameworks to interpret the rich cell-biological signatures in these ~65M single-cell profiles. At matched reagent cost, phase imaging exceeds the phenotypic resolution of both fluorescence imaging and scRNA-seq, and more reliably recovers higher-order pathway organization. These results establish intrinsic morphology as a high-precision readout of cellular state, and lay a foundation for live-cell profiling of phenotypic trajectories.

systems biology↗

bioRxiv2024

Spatial single-cell Organellomics reveals nutrient dependent hepatocyteheterogeneity and predicts pathophysiological status in vivo

Cell state diversity drives tissue adaptability, repair, and disease resilience, but fully capturing this cellular complexity remains a challenge. Most current approaches rely on transcriptional profiling and often overlook functional insights embedded in organelle structure, key indicators of cellular metabolism and stress. Here, we introduce spatial Organellomics (sOrganellomics), an imaging workflow integrating automated segmentation with machine learning to classify and spatially map cell states based on multi-organelle features. Across metabolically specialized organs, these features distinguish organ-specific cellular identities. In the liver, sOrganellomics reveals a previously unrecognized hepatocyte organization: different hepatocyte states form intermixed communities rather than classical homogeneous graded zones, a pattern disrupted by nutritional stress. Intravital imaging further links fasting-associated architectural remodeling to altered mitochondrial membrane potential and bioenergetic heterogeneity in vivo. This approach maps metabolic transitions and early disease progression, establishing organelle architecture as a scalable readout of functional cell state diversity in tissues.

cell biology↗

bioRxiv2024

A Bayesian Solution to Count the Number of Molecules within a Diffraction Limited Spot

We address the problem of inferring the number of independently blinking fluorescent light emitters, when only their combined intensity contributions can be observed at each timepoint. This problem occurs regularly in light microscopy of objects that are smaller than the diffraction limit, where one wishes to count the number of fluorescently labelled subunits. Our proposed solution directly models the photo-physics of the system, as well as the blinking kinetics of the fluorescent emitters as a fully differentiable hidden Markov model. Given a trace of intensity over time, our model jointly estimates the parameters of the intensity distribution per emitter, their blinking rates, as well as a posterior distribution of the total number of fluorescent emitters. We show that our model is consistently more accurate and increases the range of countable subunits by a factor of two compared to current state-of-the-art methods, which count based on autocorrelation and blinking frequency. Furthermore, we demonstrate that our model can be used to investigate the effect of blinking kinetics on counting ability, and therefore can inform experimental conditions that will maximize counting accuracy.

biophysics↗

bioRxiv2022

A Strategy to Quantify Myofibroblast Activation on a Continuous Spectrum

Myofibroblasts are a highly secretory and contractile phenotype most commonly identified by the de novo expression and assembly of alpha-smooth muscle actin stress fibers. Traditionally, this activation process has been thought of as a binary process, with cells being labeled as "activated" or "quiescent (non-activated)". More recently, this view has been expanded to consider activation on a continuous spectrum. However, there is no established method to quantify a cells position on this spectrum, and as a result, the binary labeling system is still widely used. While transcriptomic analyses provide a continuous measure of myofibroblast markers, a faster and more facile screening method is needed. To this end, we utilized optical microscopy and machine learning methods to quantify myofibroblast activation on a spectrum. We first measured size and shape features of over 1,000 individual cardiac fibroblasts and found that these features provide enough information to predict activation state, on the binary scale, with 94% accuracy as compared to manual classification. We next performed dimensionality reduction techniques on these features to create a continuous scale of activation. Importantly, this new classification system captures a range of fibroblast activation states, but still possesses inherent bias due to choice of morphological features. Thus, we next used self-supervised machine learning to create a second continuous labeling system free from biases associated with the manually measured features. Lastly, we compared our findings for mechanically activated cardiac fibroblasts to a distribution of cell phenotypes generated from transcriptomic data using single-cell RNA sequencing. Altogether, these results demonstrate a continuous spectrum of activation from fibroblast to myofibroblast and provide a strategy to quantify a cells position on that spectrum.

bioengineering↗

bioRxiv2020

Quantification of iPSC-derived vascular networks in novel phototunable angiogenic hydrogels

Vascularization of engineered scaffolds remains a critical obstacle hindering the translation of tissue engineering from the bench to the clinic. Previously, we demonstrated the robust micro-vascularization of collagen hydrogels with induced pluripotent stem cell (iPSC)-derived endothelial progenitors; however, physically cross-linked collagen hydrogels compact rapidly and exhibit limited strength. To address these challenges, we synthesized an interpenetrating polymer network (IPN) hydrogel comprised of collagen and norbornene-modified hyaluronic acid (NorHA). This dual-network hydrogel combines the natural cues presented by collagens binding sites and extracellular matrix (ECM)-mimicking fibrous architecture with the in situ modularity and chemical cross-linking of NorHA. We modulated the stiffness and degradability of this novel IPN hydrogel by varying the concentration and sequence, respectively, of the NorHA peptide cross-linker. Rheological characterization of the photo-mediated gelation process revealed that the stiffness of the IPN hydrogel increased with cross-linker concentration and was decoupled from the bulk NorHA content. Conversely, the swelling of the IPN hydrogel decreased linearly with increasing cross-linker concentration. Collagen microarchitecture remained relatively unchanged across cross-linking conditions, although the mere addition of NorHA delayed collagen fibrillogenesis. Upon iPSC-derived endothelial progenitor encapsulation, robust, lumenized microvascular networks developed in IPN hydrogels over two weeks. Subsequent computational analysis showed that an initial rise in stiffness increased the number of branch points and vessels, but vascular growth was suppressed in high stiffness IPN hydrogels. These results suggest that an IPN hydrogel consisting of collagen and NorHA is highly tunable, compaction resistant, and capable of stimulating angiogenesis. STATEMENT OF SIGNIFICANCEWe have synthesized the first tunable collagen and norbornene functionalized hyaluronic acid (NorHA) interpenetrating polymer network hydrogel. This unique biomaterial allows for control over hydrogel stiffness, independent of the total polymer concentration, by varying the concentration of a peptide cross-linker and was specifically designed to produce a biomimetic vasculogenic microenvironment. Using the system, we performed a detailed study of the vasculogenesis of induced pluripotent stem cell-derived (iPSC) endothelial progenitors, a poorly studied cell source with considerable therapeutic potential. Our results show that vascular growth can be tuned by altering the stiffness and degradability of the scaffolds independently. Finally, we improved upon our open-source computational pipeline programmed in ImageJ and MATLAB to further quantify vascular topologies in three dimensions. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=68 SRC="FIGDIR/small/259630v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@1d3ee04org.highwire.dtl.DTLVardef@af739aorg.highwire.dtl.DTLVardef@cec470org.highwire.dtl.DTLVardef@c2ae18_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2019

Genetic Control of Radical Crosslinking in a Semi-Synthetic Hydrogel

Enhancing materials with the qualities of living systems, including sensing, computation, and adaptation, is an important challenge in designing next-generation technologies. Living materials seek to address this challenge by incorporating live cells as actuating components that control material function. For abiotic materials, this requires new methods that couple genetic and metabolic processes to material properties. Toward this goal, we demonstrate that extracellular electron transfer (EET) from Shewanella oneidensis can be leveraged to control radical crosslinking of a methacrylate-functionalized hyaluronic acid hydrogel. Crosslinking rates and hydrogel mechanics, specifically storage modulus, were dependent on a variety of chemical and biological factors, including S. oneidensis genotype. Bacteria remained viable and metabolically active in the crosslinked network for a least one week, while cell tracking revealed that EET genes also encode control over hydrogel microstructure. Moreover, construction of an inducible gene circuit allowed transcriptional control of storage modulus and crosslinking rate via the tailored expression of a key electron transfer protein, MtrC. Finally, we quantitatively modeled dependence of hydrogel stiffness on steady-state gene expression, and generalized this result by demonstrating the strong relationship between relative gene expression and material properties. This general mechanism for radical crosslinking provides a foundation for programming the form and function of synthetic materials through genetic control over extracellular electron transfer.\n\nSignificance StatementNext-generation materials will require coupling the advantages of engineered and natural systems to solve complex challenges in energy, health, and the environment. Living cells, such as bacteria, naturally possess many of the qualities essential to addressing these challenges, including sensing, computation, and actuation, using their genetic and metabolic machinery. In addition, bacteria are attractive for incorporation into materials due to their durability, ease-of-use, and programmability. Here, we develop a platform for controlling hydrogel properties (e.g., stiffness, crosslinking rate) using extracellular electron transfer from the bacterium Shewanella oneidensis. In our system, metabolic electron flux from S. oneidensis to a metal catalyst generates radical species that crosslink an acrylate-based macromer to form the gel. This synthetic reaction is under direct control of bacterial genetics and metabolism, which we demonstrate through inducible circuits and quantitative modeling of gene expression and resultant hydrogel properties. Developing methods that capitalize on the programmability of biological systems to control synthetic material properties will enable hybrid material designs with unprecedented functions.

synthetic biology↗