| 1. |
- Partel, Gabriele, 1988-, et al.
(author)
-
Automated identification of the mouse brain’s spatial compartments from in situ sequencing data
- 2020
-
In: BMC Biology. - : Springer Nature. - 1741-7007. ; 18:1
-
Journal article (peer-reviewed)abstract
- Background Neuroanatomical compartments of the mouse brain are identified and outlined mainly based on manual annotations of samples using features related to tissue and cellular morphology, taking advantage of publicly available reference atlases. However, this task is challenging since sliced tissue sections are rarely perfectly parallel or angled with respect to sections in the reference atlas and organs from different individuals may vary in size and shape. With the advent of in situ sequencing technologies, it is now possible to profile the gene expression of targeted genes inside preserved tissue samples and thus spatially map biological processes across anatomical compartments. This also opens up for new approaches to identifying tissue compartments.Results Here, we show how in situ sequencing data combined with dimensionality reduction and clustering can be used to identify spatial compartments that correspond to known anatomical compartments of the brain. We also visualize gradients in gene expression and sharp as well as smooth transitions between different compartments. We apply our method on mouse brain sections and show that computationally defined anatomical compartments are highly reproducible across individuals and have the potential to replace manual annotation based on cell and tissue morphology. Conclusion Mapping the brain based on molecular information means that we can create detailed atlases independent of angle at sectioning or variations between individuals.
|
|
| 2. |
- Partel, Gabriele, et al.
(author)
-
Identification of spatial compartments in tissue from in situ sequencing data
- 2024
-
Other publication (other academic/artistic)abstract
- Spatial organization of tissue characterizes biological function, and spatially resolved gene expression has the power to reveal variations of features with high resolution. Here, we propose a novel graph-based in situ sequencing decoding approach that improves recall, enabling precise spatial gene expression analysis. We apply our method on in situ sequencing data from mouse brain sections, identify spatial compartments that correspond with known brain regions, and relate them with tissue morphology.
|
|
