The approach to omic data analysis combines multiple types of omic datasets in the hope of understanding multiple layers of a biological system.

On-Demand Webinar with Aditya Pratapa, Senior Data Scientist & Amélie Viratham at Akoya Biosciences

Advancements in mass spectrometry and single-molecule sequencing should accelerate protein analysis, assisting in approaches for cancer research and accessible single-cell measurements. 

Here, we speak to Fan Zhang, Assistant Professor at the University of Colorado, about the applications of computational biology in studying disease behaviour.

Unlocking the potential for FFPE samples in advancing single-cell sequencing for tumour microenvironment research.

Machine learning methods can be hugely important in aiding the interpretation and integration of data generated through single-cell omics analysis, assisting in disease detection and modelling.

A new AI model - scGPT - improves single cell RNA sequencing by predicting gene expression in individual cells, although it may be some time before it enters general use.

On-Demand Webinar featuring Didem Arikaya, Sr. Product Manager, Software at 10x Genomics

What are the current opportunities and challenges facing single-cell analysis today? We ask key opinion leaders from Janssen Pharmaceuticals, SciLifeLab, and the Karolinska Institutet.

Single-cell metabolomics is a rapidly growing area of study within the field of molecular imaging. By applying metabolomic imaging techniques at a single-cell resolution, it can be used to investigate cellular progression in diseases such as NASH and liver cirrhosis.

Good quality samples are integral to single-cell data analysis, but maintaining sample integrity presents a logistical hurdle for bioinformatics teams worldwide. Read on to learn about how senior healthcare researchers circumvent the issues associated with sample degradation.

The expression of the protein coding gene CXCR6 was observed to be significantly lower in patients with severe cases of COVID-19, suggesting a genetic correlation in severe cases.

In this Commentary article, we explore the opportunity afforded by Sparsely Connected Autoencoders to transform data in a controlled way to enable the comprehension of hidden biological information in single-cell data. Autoencoders provide researchers with a simplified means of inspecting functional relationships among regulatory elements as transcription factors.

This Discussion Group Report investigates some of the various applications of single cell analysis, and explores the differences in its utility relative to other analysis techniques such as spatial transcriptomics.

Our April 2022 Omics Series discussion group focused on the potential and future of single-cell proteomics.

Single-cell proteomics has developed quickly, despite being a relatively new field. NanoPOTS and FACS are being used to improve proteome coverage and minimise sample size.

The emergence of single-cell sequencing has opened a new field in analytical biological approaches particularly allowing to uncover cell-to-cell heterogeneity of 3D cellular models and preventing incomplete conclusions drawn from bulk cell populations

Due to the heterogeneity seen in both eukaryotic and prokaryotic cell populations, single cell analysis opens up many new possibilities.