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Results for "cell morphology"

Teaching AI to predict what cells will look like before running any experiments

MBZUAI · Invalid Date

MBZUAI researchers have developed MorphDiff, a diffusion model that predicts cell morphology from gene expression data. MorphDiff uses the transcriptome to generate realistic post-perturbation images, either from scratch or by transforming a control image. The model combines a Morphology Variational Autoencoder (MVAE) with a Latent Diffusion Model, enabling both gene-to-image generation and image-to-image transformation. Why it matters: This could significantly accelerate drug discovery and biological research by allowing scientists to preview cellular changes before conducting experiments.

Lab grown stem cells used to study embryogenesis

KAUST · Sep 13

Researchers at KAUST and Peking University Third Hospital have created a novel blastoid model for studying early human development using extended pluripotent stem cells (EPSCs). The blastoid is a 3D cell model mimicking the blastocyst phase, avoiding ethical concerns associated with using human embryos. The team showed that blastoids can be cultured to mimic post-implantation development, offering insights into early cell lineages. Why it matters: This innovation provides a way to study human embryogenesis without the ethical constraints of using actual embryos, potentially advancing our understanding of miscarriage and birth defects.

A secret language of cells? New cell computations uncovered

KAUST · Jul 25

KAUST and EPFL Blue Brain Project researchers propose a new theory about a 'secret language' used by cells for internal communication regarding the external world. Using a computational model, they suggest that metabolic pathways can code details about neuromodulators that stimulate energy consumption. The model focuses on astrocytes and their cooperation with neurons in fueling the brain. Why it matters: This suggests a new avenue for understanding information processing in the brain and how cells contribute to the energy efficiency of brains compared to computers.