Lars E. Borm

This paper identifies and categorizes technical biases in imaging spatial transcriptomics and proposes a unified framework to model these biases.

The paper compares enhancer codes of cell types between mammalian neocortex and avian pallium using deep learning, finding higher divergence in excitatory neurons than other cell types.

Nova-ST is a cost-effective, high-resolution spatial transcriptomics platform that outperforms existing methods in sensitivity for mouse brain sections.

This study creates a comprehensive cell atlas of the first-trimester human brain using single-cell RNA sequencing and spatial transcriptomics, identifying over 600 distinct cell states and their spatial organization.

This paper introduces EEL FISH, a method for spatial transcriptome profiling that improves throughput while maintaining single-cell resolution, applied to mouse and human tissues.

This paper introduces SSAM, a cell segmentation-free method for cell-type identification and tissue domain detection in in situ transcriptomics data, showing it detects known and discovers new cell types in mouse brain tissues.

The paper introduces an imaging-free spatial transcriptomics technique called STRP-seq, validated on murine and lizard brains, revealing regional molecular anatomy.

Unspliced versus spliced mRNA in standard scRNA-seq gives RNA velocity, a per-cell vector predicting future state over hours, validated in neural crest and used to trace mouse hippocampal lineages.

The paper maps cell types in the mouse nervous system using single-cell RNA sequencing and identifies distinct astrocyte and oligodendrocyte subtypes with specific molecular and spatial characteristics.

The paper develops a cyclic smFISH method to map cell-specific markers in the somatosensory cortex, revealing its spatial organization using markers from single-cell transcriptomics.