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Spatial mapping of bacteria and transcriptomes

Time: Thu 2022-04-28 10.00

Location: Air&Fire, Tomtebodavägen 23A, SciLifeLab, Solna

Video link:

Subject area: Biotechnology

Doctoral student: Britta Lötstedt , Genteknologi, Science for Life Laboratory, SciLifeLab

Opponent: Docent Petri Auvinen, University of Helsinki

Supervisor: Professor Joakim Lundeberg, Science for Life Laboratory, SciLifeLab, Genteknologi; Universitetslektor Anders F. Andersson, Science for Life Laboratory, SciLifeLab, Genteknologi


Novel insights into biological functions and mechanisms, cell networks and evolutionary relationships are gained through development of sequencing technologies and sequencing based applications. Massively parallel sequencing has enabled analysis of big data at gene and protein expression levels, but has also characterized bacterial communities. Additionally, different technological advancements enabled us to track those expression changes in single cells, to reveal insights into rare cell populations, or with added spatial resolution, to explore highly complex environments such as tissues. This thesis gives an overview of different technical, biological and computational methods used in genomics today with a specific focus on spatial techniques for detailed tissue characterization. This is followed by a chapter summarizing recent scientific contributions made by the author that have been included as part of this thesis. In Paper I, 16S sequencing was used to study the diversity and composition of bacterial communities with specific focus on the aerodigestive microbiome in children who had undergone a lung transplant. Potential connections between the microbiome and irregular gastric muscle movements were also examined. Patients with a lung transplant had significantly lower microbial diversity in the gastric and oropharyngeal sites as compared to controls, however, lung transplant recipients showed similar bacterial compositions, independent of motility status. Samples in the lung transplant patient group were in general dominated by Staphylococcaceae but Streptococcus, Prevotella and Veillonella were common in the gastric and oropharyngeal samples. Next, an automated method for simultaneous spatial analysis of both gene and antibodybased protein expression in tissue sections, named SM-Omics, was developed in Paper II. SM-Omics enabled simultaneous detection of proteins, by using either immunofluorescence or DNAbarcoded antibodies, and analysis of the spatial transcriptome in the same tissue section. SM-Omics was applied to the mouse brain and spleen and obtained correlated spatial patterns between respective gene and antibody measurements. The method allowed processing of up to 64 in situ spatial reactions or up to 96 sequencing-ready libraries, of high complexity, in a ~2 days process. The spatial host-microbiome sequencing method, presented in Paper III, was used to concurrently study the spatial environment created between bacteria and host cells within a tissue section. Using spatial host-microbiome sequencing, colonic sections from three different mouse models were examined by simultaneous in situ capture of both mRNA and 16S sequences, followed by sequencing and taxonomic assignment of bacterial 16S sequences using a deep learning model. ~17,000 genes and 39 bacteria genera across 16 different morphological regions were quantitatively assessed in the mouse colon. We reported specific genera in the interfold and lumen regions of the colon, as well as spatially variable genes across 100 tissue sections. To better understand genotype-relevant changes impacted by bacterial presence, we defined cell-type specific interactions described with sets of activated pathways. Finally, consecutive tissue sections of multiple synovial biopsies from patients suffering from rheumatoid arthritis were processed using the Spatial Transcriptomics method and sequenced in Paper IV. The alignment and transformation of the consecutive tissue sections enabled spatial profiling in 3D of genes and cell types within the biopsies. Spatially variable gene expression patterns revealed clusters radially distributed around organized structures of infiltrating leukocytes (TLOs). In patients with developed TLOs, these structures contained proinflammatory B cells, while the surrounding areas were high in fibroblasts.