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. 2023 Mar 7;24(1):102.
doi: 10.1186/s12864-023-09192-w.

Comparison of the Illumina NextSeq 2000 and GeneMind Genolab M sequencing platforms for spatial transcriptomics

Iamshchikov Pavel #  1   2 , Larionova Irina #  3   4 , Gerashchenko Tatiana  1   5 , Piankov Denis  6 , Koshkin Philipp  6 , Korostelev Sergei  6 , Denisov Evgeny  7   8
Affiliations

Comparison of the Illumina NextSeq 2000 and GeneMind Genolab M sequencing platforms for spatial transcriptomics

Iamshchikov Pavel et al. BMC Genomics. .

Abstract

Background: The Illumina sequencing systems demonstrate high efficiency and power and remain the most popular platforms. Platforms with similar throughput and quality profiles but lower costs are under intensive development. In this study, we compared two platforms Illumina NextSeq 2000 and GeneMind Genolab M for 10x Genomics Visium spatial transcriptomics.

Results: The performed comparison demonstrates that GeneMind Genolab M sequencing platform produces highly consistent with Illumina NextSeq 2000 sequencing results. Both platforms have similar performance in terms of sequencing quality and detection of UMI, spatial barcode, and probe sequence. Raw read mapping and following read counting produced highly comparable results that is confirmed by quality control metrics and strong correlation between expression profiles in the same tissue spots. Downstream analysis including dimension reduction and clustering demonstrated similar results, and differential gene expression analysis predominantly detected the same genes for both platforms.

Conclusions: GeneMind Genolab M instrument is similar to Illumina sequencing efficacy and is suitable for 10x Genomics Visium spatial transcriptomics.

Keywords: 10x Genomics Visium; GeneMind Genolab M; Illumina NextSeq 2000; Sequencing; Spatial transcriptomics.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Characterization of the read counts, UMIs, and detected genes obtained from two sequencing platforms. A Correlation between the number of transcripts (UMIs) and the number of detected genes in the tissue spots. B The genes and UMIs distribution in the tissue spots in two sequencing platforms. C GC-content profiles of raw reads in two sequencing platforms. D The unique and common genes between two sequencing platforms. E Distribution of the Pearson correlation coefficients of the common SCT transformed gene counts
Fig. 2
Fig. 2
Downstream non-linear dimension reduction, clusterization, and differential expression of the filtered and normalized data. A The UMAP dimension reduction and clusterization results of the sequencing counts from two sequencing platforms without batch effect correction. B Spatial distribution of the obtained clusters. C-E Venn diagrams of the common and the platform-unique differentially expressed genes (DEGs) of the slide clusters
Fig. 3
Fig. 3
Cluster DEG characterization by the log twofold-change (LFC) and the FDR. A LFC distribution of the common and unique DEGs in the clusters. B FDR distribution of the common and unique DEGs in the clusters. Count is a number of DEGs
See this image and copyright information in PMC

References

    1. Karlsson M, Zhang C, Méar L, Zhong W, Digre A, Katona B, et al. A single–cell type transcriptomics map of human tissues. Sci Adv. 2021;7:eabh2169. doi: 10.1126/sciadv.abh2169. - DOI - PMC - PubMed
    1. Regev A, Teichmann SA, Lander ES, Amit I, Benoist C, Birney E, et al. The human cell atlas. Elife. 2017;6:e27041. doi: 10.7554/eLife.27041. - DOI - PMC - PubMed
    1. Luecken MD, Theis FJ. Current best practices in single-cell RNA-seq analysis: a tutorial. Mol Syst Biol. 2019;15:e8746. doi: 10.15252/msb.20188746. - DOI - PMC - PubMed
    1. Tang X, Huang Y, Lei J, Luo H, Zhu X. The single-cell sequencing: New developments and medical applications. Cell Biosci. 2019;9:1–9. doi: 10.1186/s13578-019-0314-y. - DOI - PMC - PubMed
    1. Adil A, Kumar V, Jan AT, Asger M. Single-cell transcriptomics: current methods and challenges in data acquisition and analysis. Front Neurosci. 2021;15:398. doi: 10.3389/fnins.2021.591122. - DOI - PMC - PubMed