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Methodology development promotes science advancement. Application of new scientific discoveries to biological processes, organisms, or systems can help to sustainably produce products for healing, feeding, or fueling. This page showcases our recent publications that report methodological or biotechnological advances relevant to biological, biomedical, and agricultural sciences.
Ultrasound localisation microscopy enables deep tissue microvascular imaging. Here, authors introduce LOCA-ULM, a deep learning pipeline enhancing localisation accuracy in high microbubble concentrations. LOCA-ULM reveals dense cerebrovascular networks and enhances the sensitivity of functional ULM.
While tricarboxylic acid cycle (TCA cycle) is required for heterotrophic microbes, it reduces carbon yield of industrial products due to the release of excess CO2. Here, the authors construct an E. coli strain without a functional TCA cycle and demonstrate its feasibility as a chassis strain for production of four separate compounds.
The unification of decision-making, communication, and memory would enable the programming of intelligent biotic systems. Here, the authors achieve this goal by engineering E. coli chassis cells with an array of inducible recombinases that mediate diverse genetic programs.
Extracellular microinterfaces provide cells with migration tracks in vivo. Here, the authors introduce these microtracks into bicontinuous hydrogels to elicit rapid cell migration in 3-dimensional contexts.
RNA splicing serves as a critical layer of gene expression regulation. Here, authors introduce SCASL for investigating the heterogeneity of RNA splicing landscapes at single-cell resolution, offering a novel scheme for classifying cell identities with physiological relevance.
2D visualisation of single-cell data is highly impacted by the hyperparameter setting of the 2D embedding method, such as t-SNE and UMAP. Here, authors develop a statistical method scDEED to detect dubious cell embeddings and optimise the hyperparameter setting for trustworthy visualisation.
Selecting omic biomarkers using both their effect size and their differential status significance (i.e., selecting the “volcano-plot outer spray”) has long been equally biologically relevant and statistically troublesome. However, recent proposals are paving the way to resolving this dilemma.
Fungi have the potential to produce sustainable foods for a growing population, but current products are based on a small number of strains with inherent limitations. Here, the authors develop genetic tools for an edible fungus and engineer its nutritional value and sensory appeal for alternative meat applications.
Machine learning applied to large compendia of transcriptomic data has enabled the decomposition of bacterial transcriptomes to identify independently modulated sets of genes. Here the authors present iModulon-based engineering for precise identification of genes for cross-species function transfer to streamline synthetic biology for strain development and biomanufacturing.
Proteomics at the organelle contact site remains challenging due to the spatial and temporal dynamics of proteins. Here, the authors developed OrthoID, a mutually orthogonal dual enzymatic proteomics approach to explore the proteome at the contact site of the endoplasmic reticulum and mitochondria.
Vaccines combat global influenza threats, relying on timely selection of optimal seed viruses. Here, authors introduce MAIVeSS, a machine learning assisted framework to streamline vaccine seed virus selection using genomic sequence, expediting seasonal flu vaccine production and supply.
Batch effects hinder multi-sample single-cell data analyses. Here, authors present STACAS, a scalable single-cell RNA-seq data integration tool that uses prior cell type knowledge to preserve biological variability, demonstrating robustness to noisy input cell type labels.
Typical single-cell RNAseq pipelines will subcluster homogeneous cells. Here, authors present a computational algorithm for accurately identifying cell-type marker genes in single-cell data analysis with a low false discovery rate.
No consensus exists on the computationally tractable use of dynamic models for strain design. To tackle this, the authors report a framework, nonlinear-dynamic-model-assisted rational metabolic engineering design, for efficiently designing robust, artificially engineered cellular organisms.
Synthetic microbial communities are suitable for mixed substrates fermentation and long metabolic pathway engineering. Here, the authors combine fermentation experiments with mathematical modeling to reveal the effect of compositional and temporal changes on division of labor in cellulosic ethanol production using two yeast strains.
Achieving genetic circuits on single DNA molecules could have varied applications. Here, authors observed proteins emerging from single DNA molecules through coupled transcription-translation complexes, and show that nascent proteins lingered on DNA, regulating cascaded reactions on the same DNA and allowing the design of a pulsatile genetic circuit.
Utilising geometric information and reducing computational costs are key challenges in the molecular modelling field. Here, authors propose ViSNet, which efficiently extracts geometric features, accurately predicts molecular properties, and drives simulations with interpretability.
Subcellular in situ spatial transcriptomics offers the promise to address biological problems that were previously inaccessible but requires accurate cell segmentation to uncover insights. Here, authors present BIDCell, a biologically informed, deep learning-based cell segmentation framework.
Identifying rare cell populations is key to understanding cancer progression and response to therapy. Here, authors introduce MarsGT, an end-to-end deep learning model for rare cell population identification from scMulti-omics data.
Derivation of human primordial germ cell-like cells (hPGCLCs) is critical for reproductive medicine. Here, authors report the induction of hPGCLCs in a bioengineered human pluripotent stem cell culture that mimics peri-implantation human development.
Somatic cloning of rhesus monkey has not been successful until now. Here, authors report epigenetic abnormalities in SCNT embryos and placentas and develop a trophoblast replacement method that enables them to successful clone of a healthy male rhesus monkey.
Reproducibility is essential for the progress of research, yet achieving it remains elusive even in computational fields. Here, authors develop the rworkflows suite, making robust CI/CD workflows easy and freely accessible to all R package developers.
Copy number variants (CNV) are shown to contribute to the etiology of various genetic disorders. Here, authors present ECOLE, a deep learning-based somatic and germline CNV caller for WES data. Utilising a variant of the transformer architecture, the model is trained to call CNVs per exon.
Batch integration is a critical yet challenging step in many single-cell RNA-seq analysis workflows. Here, authors present JOINTLY, a hybrid linear and non-linear NMF-based algorithm, providing interpretable and robust cell clustering against over-integration.
Deciphering the roles of gene regulation in cell fate decisions is crucial. Here, authors present CEFCON, a network-based framework that reveals cell-lineage-specific gene regulatory networks and identifies driver regulators controlling cell fate decisions from single-cell transcriptomics data.
Spatial transcriptomics (ST) enables gene expression characterisation within tissue sections, but comparing across sections and technologies remains challenging. Here, authors develop STalign to spatially align ST data and demonstrate applications including aligning to common coordinate frameworks.
Cultured meat technology promises to alleviate protein shortages, but still faces many challenges. Here, the authors achieve serum-free myogenic differentiation of porcine pre-gastrulation epiblast stem cells and generate meat-like tissue via edible plant-based scaffolds without any animal compounds.
The functional heterogeneity of autophagy in endothelial cells during angiogenesis remains incompletely understood. Here, the authors apply a 3D angiogenesis-on-a-chip coupled with single-cell RNA sequencing to find distinct autophagy functions in two different endothelial cell populations during angiogenic sprouting.
PSC-brain organoids are typically formed by static medium switches. Here, authors show that a temporal morphogen gradient during neural induction allows the formation of well-specified cortical organoids with a self-organized single neuroepithelium.
Here, Urzi et al. pioneered a 2D self-organizing neuromuscular junction (soNMJ) model from human pluripotent stem cells, with implications for neuromuscular disease modeling and drug screening approaches.
Dose-response curves are ubiquitous in pharmacology and biology, yet potency and effect size are often estimated even when there is no response. Here, authors present a statistical framework to assess curve significance and demonstrate how this aids drug mode of action analysis in large public datasets.
A pan-betacoronavirus vaccine will likely require the elicitation of antibodies against spike regions conserved across diverse coronaviruses. Here, authors computationally engineer and experimentally validate immunogens to elicit antibodies against two such spike regions.
Despite promising advantages, bioresorbable electronics face practical limitations due to unpredictable device lifetimes. Here, the authors introduce an on-demand bioresorbable neurostimulator powered by biosafe ultrasound to treat peripheral nerve injury and neuropathies.
Geometrical complexities of blood vessels alter biophysical behaviors of circulating tumor cells, influencing cancer metastasis. Here, the authors develop a 3D bioprinted in vitro brain blood vessel-on-a-chip to investigate continuities between vascular geometry and metastatic cancer development.
One carbon compounds such as CO2, methanol and formate are cost-effective and environmentally friendly microbial feedstocks for biomanufacturing. Here, the authors report the oxygen tolerant reductive glycine pathway in Komagataella phaffii can co-assimilate CO2, methanol and formate.
Formic acid (FA) is a promising CO2-equivalent feedstock for onecarbon biorefinery, but microbial host that can efficiently utilize FA is unavailable. Here, the authors engineer a non-native closed loop in Vibrio natriegens and demonstrate its application in promoting FA utilization.
The SARS-CoV-2 pandemic highlighted our need for methods that allow rapid viral surveillance. Here, authors report a wireless, battery-free and wearable self-diagnosis platform that can continuously capture viral particles, diagnose infection status and evaluate symptom severity via breath and blow.
Accurate property prediction relies on effective molecular representation. Here, the authors introduce KPGT, a knowledge-guided self-supervised framework that improves molecular representation, leading to superior predictions of molecular properties and advancing AI-driven drug discovery.
Identifying spatially variable genes (SVGs) is essential for linking molecular cell functions with tissue phenotypes. Here, authors introduce a non-parametric model that detects SVGs from two or three-dimensional spatial transcriptomics data by comparing gene expression patterns at granularities.
Spatial omics technologies reveal the organisation of cells in various biological systems. Here, authors propose SLAT, a graph-based algorithm for aligning heterogenous data across technologies, modalities and timepoints, enabling spatiotemporal reconstruction of complex developmental processes.
Endo-lysosomal escape is a highly inefficient process. Here the authors present a lipid-based nanoscale molecular machine that achieves efficient cytosolic transport of biologics by destabilizing endo-lysosomal compartments through nanomechanical action upon light irradiation.
Five-prime single-cell RNA-seq, especially the read 1, has precise capture of transcription start sites (TSS), but such information is often overlooked. Here, authors present a computational method suite, CamoTSS, to precisely identify TSS and quantify its expression, enabling effective detection of alternative TSS usage in different biological processes.
Unlocking the blood proteome requires exquisite sensitivity and multiplexing to detect low and high abundance proteins simultaneously. Here the authors describe a 200-plex immunoassay with attomolar sensitivity to detect important low abundance proteins in inflammatory diseases and COVID-19.
A wide variety of tissues exhibit nested hierarchical organisation of cells in gene expression and activities. Here, authors present NeST, a method for spatial transcriptomics to identify such structures and uncover their functions via ligand-receptor communication, in both two and three dimensions.
Many diseases are driven by the insufficient expression of critical genes, but few technologies are capable of rescuing these endogenous protein levels. Here, Cao et al. present an RNA-based technology that boosts protein production from endogenous mRNAs by upregulating their translation.
l-Lactate is increasingly recognized as a key metabolite and signalling molecule in mammals, but the methods to investigate it in vivo have been limited. Here, authors report a pair of improved biosensors—one green and one red—for visualizing l-lactate both inside and outside of cells.
Designing promoters with desired properties is crucial in synthetic biology. Here, authors introduce DeepSEED, an AI-aided flanking sequence optimisation framework which combines expert knowledge with deep learning techniques to efficiently design promoters in both eukaryotic and prokaryotic cells.