Single-cell Research
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Spatial Isoform Transcriptomics (SiT): spatial landscape of gene expression isoforms in tissue sections
Current high throughput scRNA-seq technologies, including spatial transcriptomics, rely on 3’ short-read sequencing to identify… read more
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Spatial Transcriptomics: 3 visualization methods to interact with your in situ imaging data.
The introduction of multiplexing in cellular imaging enabled the identification and spatial localization of thousands… read more
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Spatial Transcriptomics: 5 tools to visualize your scRNA-seq data
Adding to the existing complexity of single-cell resolution datasets, the spatial dimension also presents… read more
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Spatially Resolved Single-Cell Data in 2021: where are we?
The terms “spatial” or “spatially-resolved data” have been increasingly thrown around in the field of… read more
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Using Spatial Transcriptomics to map an entire adult mouse brain
Source: Ortiz et al., Molecular atlas of the adult mouse brain, Science Advances 26 Jun… read more
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Taking the leap into single-cell bioinformatics
When I began dabbling into single-cell RNA sequencing (scRNA-seq), the field was just emerging… read more
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Collaborating for optimal scRNA-seq bioinformatic analysis
Dr. Marc Beyer agreed to meet us to discuss his journey and the best collaborations… read more
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Key Numbers in Single Cell Bioinformatics
Sources: Angerer at al., Single cells make big data: New challenges and opportunities in… read more
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scRNA-seq: artifacts from sample preparation
Independently of the ScRNA-seq methodology you plan to use, there are genetic artifacts bound to… read more
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Cell sample size vs sequencing depth: find your compromise.
With newer technologies enabling the screening of an ever-higher number of cells at a cheaper… read more