ArticleJournal of translational medicine2023
Size matters: the impact of nucleus size on results from spatial transcriptomics.
Article in Journal of translational medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- Emerging role of N6-methyladenosine (m6A) epitranscriptomic changes in adult anxiety after adolescent alcohol exposure.Neuropharmacology · 2026Article
- Spatial transcriptomics of developing human lungs defines cellular phenotypes associated with age, lineage and location.Scientific reports · 2026Article
- Astrocyte diversity and subtypes: aligning transcriptomics with multimodal perspectives.EMBO reports · 2025Review
- STMiner: Gene-centric spatial transcriptomics for deciphering tumor tissues.Cell genomics · 2025Article
- STimage-1K4M: A histopathology image-gene expression dataset for spatial transcriptomics.Advances in neural information processing systems · 2024Article
- Disparities in spatially variable gene calling highlight the need for benchmarking spatial transcriptomics methods.Genome biology · 2023Article
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Authors and funding
16 authors at 3 institutions in 4 countries.
Funding
Abstract
backgroundVisium Spatial Gene Expression (ST) is a method combining histological spatial information with transcriptomics profiles directly from tissue sections. The use of spatial information has made it possible to discover new modes of gene expression regulations. However, in the ST experiment, the nucleus size of cells may exceed the thickness of a tissue slice. This may, in turn, negatively affect comprehensive capturing the transcriptomics profile in a single slice, especially for tissues having large differences in the size of nuclei.
methodsHere, we defined the effect of Consecutive Slices Data Integration (CSDI) on unveiling accurate spot clustering and deconvolution of spatial transcriptomic spots in human postmortem brains. By considering the histological information as reference, we assessed the improvement of unsupervised clustering and single nuclei RNA-seq and ST data integration before and after CSDI.
resultsApart from the escalated number of defined clusters representing neuronal layers, the pattern of clusters in consecutive sections was concordant only after CSDI. Besides, the assigned cell labels to spots matches the histological pattern of tissue sections after CSDI.
conclusionCSDI can be applied to investigate consecutive sections studied with ST in the human cerebral cortex, avoiding misinterpretation of spot clustering and annotation, increasing accuracy of cell recognition as well as improvement in uncovering the layers of grey matter in the human brain.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.