Biology subjects

Croft, M.

Publications and source records attributed to Croft, M..

3 recordsLinked to original sources

bioRxiv2026

AI4Life Open Calls and Public Challenges: why, how, and what we have learned.

Within AI4Life, we ran three Open Calls and three Public Challenges (2023-2025), supporting 22 bioimage analysis projects from 151 applications and engaging 225 challenge participants, with the aim of applying FAIR deep learning in the life sciences. Our experience offers a view of the current state of bioimage analysis, the landscape of available tools, as well as the existing gaps between method developers, tool producers and potential users. It highlights that even after careful selection for AI-ready projects, most still require substantial effort to apply deep learning, and that the field still relies heavily on established, well-rounded methods to solve common problems. We come to the conclusion that for scientific AI in biology, the rate-limiting step is not methods and models but data, annotations, and shared infrastructure underneath them.

bioinformatics↗

bioRxiv2025

MicroSplit: Semantic Unmixing of Fluorescent Microscopy Data

Fluorescence microscopy, a key driver for progress in the life sciences, faces limitations due to the microscopes optics, fluorophore chemistry, and photon exposure limits, necessitating trade-offs in imaging speed, resolution, and depth. Here, we introduce Micro[S]plit, a computational multiplexing technique based on deep learning that allows multiple cellular structures to be imaged in a single fluorescent channel and then unmix them by computational means, allowing faster imaging and reduced photon exposure. We show that Micro[S]plit efficiently separates up to four superimposed noisy structures into distinct denoised fluorescent image channels. Furthermore, using Variational Splitting Encoder-Decoder (VSE) networks, our approach can sample diverse predictions from a trained posterior of solutions. The diversity of these samples scales with the uncertainty in a given input, allowing us to estimate the true prediction errors by computing the variability between posterior samples. We demonstrate the robustness of Micro[S]plit networks, which are trained for each splitting task at hand, across various datasets and noise levels and show its utility to image more, to image faster, and to improve downstream analysis. We provide Micro[S]plit along with all associated training and evaluation datasets as open resources, enabling life scientists to immediately benefit from the potential of computational multiplexing and thus help accelerate the rate of their scientific discovery process.

bioinformatics↗

bioRxiv2018

Crystal structure of m4-1BB/4-1BBL complex reveals an unusual dimeric ligand that undergoes structural changes upon receptor binding.

The interaction between the 4-1BB and its ligand 4-1BBL provides co-stimulatory signals for T cell activation and proliferation, but differences in the mouse and human molecules might result in differential engagement of this pathway. Here, we report the crystal structure of mouse 4-1BBL and of the mouse 4-1BB/4-1BBL complex, together provide insights into the molecular recognition of the cognate receptor by m4-1BBL. In contrast to all human or mouse TNF ligands that form non-covalent mostly trimeric assemblies, the m4-1BBL structure formed a novel disulfide linked dimeric assembly. The structure showed that certain differences in the amino acid composition along the intramolecular interface, together with two specific residues (Cys 246 and Ser 256) that are exclusively present in m4-1BBL, are responsible for unique dimerization. Unexpectedly, upon binding to m4-1BB, m4-1BBL undergoes structural changes within each protomer, in addition the individual m4-1BBL protomers rotate with respect to each other, leading to a different dimerization interface with more inter-subunit interactions. In the m4-1BB/4-1BBL complex, each receptor monomer binds exclusively to a single ligand subunit with contributions of cysteine-rich domain (CRD) 1, CRD2 and CRD3. Furthermore, structure-guided mutagenesis of the binding interface revealed that novel binding interactions with the GH loop, rather than the DE loop, are energetically critical and define the species based receptor selectivity for m4-1BBL. A comparison with the human 4-1BB/4-1BBL complex highlighted several differences between the ligand and receptor binding interfaces and provide an explanation for the absence of inter species cross-reactivity between human and mouse 4-1BB and 4-1BBL molecules.

immunology↗