Spatial transcriptomics maps gene expression across tissues, posing the challenge of determining the spatial arrangement of different cell types. However, spatial transcriptomics spots contain multiple cells. Therefore, the observed signal comes from mixtures of cells of different types. Here, we propose an innovative probabilistic model, Celloscope, that utilizes established prior knowledge on marker genes for cell type deconvolution from spatial transcriptomics data. Celloscope outperforms other methods on simulated data, successfully indicates known brain structures and spatially distinguishes between inhibitory and excitatory neuron types based in mouse brain tissue, and dissects large heterogeneity of immune infiltrate composition in prostate gland tissue.

Antimicrobial peptides emerge as compounds that can alleviate the global health hazard of antimicrobial resistance, prompting a need for novel computational approaches to peptide generation. Here, we propose HydrAMP, a conditional variational autoencoder that learns lower-dimensional, continuous representation of peptides and captures their antimicrobial properties. The model disentangles the learnt representation of a peptide from its antimicrobial conditions and leverages parameter-controlled creativity. HydrAMP is the first model that is directly optimized for diverse tasks, including unconstrained and analogue generation and outperforms other approaches in these tasks. An additional preselection procedure based on ranking of generated peptides and molecular dynamics simulations increases experimental validation rate. Wet-lab experiments on five bacterial strains confirm high activity of nine peptides generated as analogues of clinically relevant prototypes, as well as six analogues of an inactive peptide. HydrAMP enables generation of diverse and potent peptides, making a step towards resolving the antimicrobial resistance crisis.

Antimicrobial peptides (AMP) emerge as compounds that can alleviate the global health hazard of antimicrobial resistance. Since the repertoire of experimentally verified AMPs is limited, there is a need for novel computational approaches to peptide generation. For such approaches, exploring the amino-acid peptide representation space is infeasible due to its sparsity and combinatorial complexity. Thus, we propose HydrAMP, a conditional variational autoencoder that learns a lower-dimensional and continuous space of peptides' representations and captures their antimicrobial properties. HydrAMP outperforms other approaches in generating peptides, either de novo, or by analogue discovery, and leverages parameter-controlled creativity. The model disentangles the latent representation of a peptide from its antimicrobial conditions, allowing for targeted generation. Wet-lab experiments and molecular dynamics simulation confirm the increased activity of a Pexiganan-based analogue produced by HydrAMP. HydrAMP proposes new promising AMP candidates, enabling progress towards a new generation of antibiotics.

Stress resilience is the ability of neuronal networks to maintain function despite exposure to stress. In this study, we investigated whether stress resilience is an actively developed dynamic process in adult mice. To assess the resilient and anhedonic behavioral phenotypes developed after induction the chronic unpredictable stress, we quantitatively characterized the structural and functional plasticity of excitatory synapses in the hippocampus using a combination of proteomic, electrophysiological, and imaging methods. Our results indicate that stress resilience is a dynamic and multifactorial process manifested by structural, functional, and molecular changes in synapses. We reveal that chronic stress influences palmitoylation, the profiles of which differ between resilient and anhedonic animals. We also observed that stress resilience is associated with structural compensatory plasticity of the postsynaptic parts of synapses.One Sentence SummaryCompensatory remodeling of dendritic spines at the structural and molecular levels underlies stress resilience.