Latest App Notes & Case Studies

This application note introduces an automated solution for flexible and sensitive multichannel analysis of neurite outgrowth for high-throughput assays.

Delve into this case study to explore how a research lab at King’s College London used hiPSC-derived neurons to shed light on synaptic biology and potential treatments for psychiatric conditions.

This app note delves into the role of TDP-43 protein aggregates in the disease's progression. Charles River Laboratories and bit.bio present a breakthrough in ALS research with genetically matched ioGlutamatergic Neurons, providing a crucial in vitro model.

This app note highlights a highly sensitive method developed for the detection of GalSPH and GluSPH in CSF.

Studying DNA methylation at correlated gene regions can provide researchers with insights into disease-associated epigenetic variation. Hence, cost-effective sequencing is essential for our advancement and understanding of epigenetic epidemiology.

There is currently no effective cure or treatment to slow down or stop the progression of Huntington's disease (HD). One reason for this is the lack of accurate and easy-to-use HD models.

Single cell analysis in a spatially resolved context is the gateway to deciphering scientific questions in various application areas. Such analysis requires the highest available spatial precision combined with highly sensitive detection.

Highly ordered protein aggregates, termed amyloid fibrils, are associated with a broad range of diseases, many of which are neurodegenerative for example Alzheimer’s and Parkinson’s. The transition from soluble, functional protein to insoluble amyloid fibril occurs via a complex process involving the initial generation of highly dynamic early stage aggregates or prefibrillar species.

The specificity of fluorescence microscopy allows researchers to accurately observe and analyze biological processes and structures quickly and easily, even when using thick or large samples. However, out-of-focus fluorescence increases background, reduces contrast and makes accurate image segmentation more challenging.

Spatially resolved gene expression can provide a powerful complement to traditional histopathology methods, enabling a greater understanding of cellular heterogeneity and organization within the central nervous system.