Opportunities and challenges of single-cell and spatially resolved genomics methods for neuroscience discovery.

Over the past decade, single-cell genomics technologies have allowed scalable profiling of cell-type-specific features, which has substantially increased our ability to study cellular diversity and transcriptional programs in heterogeneous tissues. Here, we summarize the most promising and robust technologies in these areas, discuss their strengths and limitations and discuss key computational approaches for analysis of these complex datasets.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 Yet our understanding of mechanisms of gene regulation or the rules that govern interactions between cell types is still limited.
• 2 The advent of new computational pipelines and technologies, such as single-cell epigenomics and spatially resolved transcriptomics, has created opportunities to explore two new axes of biological variation: cell-intrinsic regulation of cell states and expression programs and interactions between cells.
• 3 Here, we summarize the most promising and robust technologies in these areas, discuss their strengths and limitations and discuss key computational approaches for analysis of these complex datasets.

Understanding this could lead to better treatments, improved diagnostics, or a deeper grasp of how the human body works — benefiting patient care globally.

This summary is based on publicly available metadata and abstract. For the full research paper, visit the original source: