Redefining clinical practice through spatial profiling: a revolution in tissue analysis

In an era where transcriptomics-based subtyping, phenotyping and mechanistic understanding is increasingly being driven by state-of-the-art spatially resolved transcriptomic (ST) technologies, it is imperative that researchers, journals, and funders do all they can to ensure that as a community we are interpreting these exciting data as accurately as possible with awareness of their limitations. In this short report, we highlight one potential bias in ST data that could undermine accurateinterpretationof transcriptional signatures, providing the field with an opportunity to identify and avoid this issue prior to release of new mechanistic findings. This issue is particularly relevant for platforms that produce some of the most granular and high-resolution spatial information at single cell (and sub-cellular) resolution, with the compromise of a reduced transcriptome panel of genes (Figure 1A).

show abstract

Imaging spatial transcriptomics reveals molecular patterns of vulnerability to pathology in a transgenic α-synucleinopathy model

Horan-Portelance,

Iba,

Acri

et al. 2024

Preprint

View full text Add to dashboard Cite

One of the unifying pathological hallmarks of Parkinson’s disease (PD) and dementia with Lewy bodies (DLB) is the presence of misfolded, aggregated, and often phosphorylated forms of the protein α-synuclein in neurons. α-Synuclein pathology appears in select populations of neurons throughout various cortical and subcortical regions, and little is currently known about why some neurons develop pathology while others are spared. Here, we utilized subcellular-resolution imaging-based spatial transcriptomics (IST) in a transgenic mouse model that overexpresses wild-type human α-synuclein (α-syn-tg) to evaluate patterns of selective neuronal vulnerability to α-synuclein pathology. By performing post-IST immunofluorescence for α-synuclein phosphorylated at Ser129 (pSyn), we identified cell types in the cortex and hippocampus that were vulnerable or resistant to developing pSyn pathology. Next, we investigated the transcriptional underpinnings of the observed selective vulnerability using a set of custom probes to detect genes involved in α-synuclein processing and toxicity. We identified expression of the kinase:substrate pairPlk2, which phosphorylates α-synuclein at Ser129, and humanSNCA(hSNCA), as underlying the selective vulnerability to pSyn pathology. Finally, we performed differential gene expression analysis, comparing non-transgenic cells to pSyn-and pSyn+α-syn-tg cells to reveal gene expression changes downstream ofhSNCAoverexpression and pSyn pathology, which included pSyn-dependent alterations in mitochondrial and endolysosomal genes. This study provides a comprehensive use case of IST, yielding new biological insights into the formation of α-synuclein pathology and its downstream effects in a PD/DLB mouse model.

show abstract

Redefining clinical practice through spatial profiling: a revolution in tissue analysis

Cited by 4 publications

References 53 publications

Artificial intelligence in surgical pathology – Where do we stand, where do we go?

Artificial intelligence in surgical pathology – Where do we stand, where do we go?

Library size can undermine accurate molecular and phenotypic subtyping in spatial transcriptomics data

Imaging spatial transcriptomics reveals molecular patterns of vulnerability to pathology in a transgenic α-synucleinopathy model

Contact Info

Product

Resources

About