Biology subjects

Sarah A Teichmann

Publications and source records attributed to Sarah A Teichmann.

4 recordsLinked to original sources

bioRxiv2016

Cotranslational assembly imposes evolutionary constraints on homomeric proteins

There is increasing evidence that some proteins fold during translation, i.e. cotranslationally, which implies that partial protein function, including interactions with other molecules, could potentially be unleashed early on during translation. Although little is known about cotranslational assembly mechanisms, for homomeric protein complexes, translation by the ribosome, folding and assembly, should be well-coordinated to avoid misassembly in the context of polysomes. We analysed 3D structures of homomers and identified a statistically significant trend conserved across evolution that supports this hypothesis: namely that homomeric contacts tend to be localized towards the C-terminus rather than N-terminus of homomeric polypeptide chains. To probe this in more detail, we expressed a GFP-based library of 611 homomeric E. coli genes, and analyzing their folding and assembly in vivo. Consistent with our hypothesis, interface residues tend to be located near the N-terminus in cotranslationally aggregating homomers. In order to dissect the mechanisms of folding and assembly under controlled conditions, we engineered a protein library with three variable components: (i) the position and type homomerization domain, (ii) the reporter domain and (iii) the linker length that connects the two. By analyzing the misassembly rates of these engineered constructs in vivo, in vitro and in silico, we confirmed our hypothesis that C-terminal homomerization is favorable to N-terminal homomerization. More generally, these results provide a set of spatiotemporal constraints within polypeptide chains that favor efficient assembly, with implications for protein evolution and design.

Cell Biology↗

bioRxiv2016

Temporal mixture modelling of single-cell RNA-seq data resolves a CD4+ T cell fate bifurcation

Differentiation of naive CD4+ T cells into functionally distinct T helper subsets is crucial for the orchestration of immune responses. Due to multiple levels of heterogeneity and multiple overlapping transcriptional programs in differentiating T cell populations, this process has remained a challenge for systematic dissection in vivo. By using single-cell RNA transcriptomics and computational modelling of temporal mixtures, we reconstructed the developmental trajectories of Th1 and Tfh cell populations during Plasmodium infection in mice at single-cell resolution. These cell fates emerged from a common, highly proliferative and metabolically active precursor. Moreover, by tracking clonality from T cell receptor sequences, we infer that ancestors derived from the same naive CD4+ T cell can concurrently populate both Th1 and Tfh subsets. We further found that precursor T cells were coached towards a Th1 but not a Tfh fate by monocytes/macrophages. The integrated genomic and computational approach we describe is applicable for analysis of any cellular system characterized by differentiation towards multiple fates.\n\nOne Sentence SummaryUsing single-cell RNA sequencing and a novel unsupervised computational approach, we resolve the developmental trajectories of two CD4+ T cell fates in vivo, and show that uncommitted T cells are externally influenced towards one fate by inflammatory monocytes.

Systems Biology↗

bioRxiv2016

Single-cell transcriptome analysis of fish immune cells provides insight into the evolution of vertebrate immunity

The immune system of vertebrate species consists of many different cell types that have distinct functional roles and are subject to different evolutionary pressures. Here, we first analysed gene conservation of all major immune cell types in human and mouse. Our results revealed higher gene turnover and faster evolution of trans-membrane proteins in NK cells compared to other immune cell populations, and especially T cells, but similar conservation of nuclear and cytoplasmic protein coding genes. To validate these findings in a distant vertebrate species, we used single-cell RNA-Sequencing of lck:GFP cells in zebrafish to obtain the first transcriptome of specific immune cell types in a non-mammalian species. Unsupervised clustering and single-cell TCR locus reconstruction identified three cell populations, T-cells, a novel type of NK-like cells and a smaller population of myeloid-like cells. Differential expression analysis uncovered new immune cell specific genes, including novel immunoglobulin-like receptors, and neofunctionalization of recently duplicated paralogs. Evolutionary analyses confirmed a higher gene turnover and lower conservation of trans-membrane proteins in NK cells compared to T cells in fish species, suggesting that this is a general property of immune cell types across all vertebrates.

Immunology↗

bioRxiv2016

Power Analysis of Single Cell RNA‐Sequencing Experiments

High-throughput single cell RNA sequencing (scRNA-seq) has become an established and powerful method to investigate transcriptomic cell-to-cell variation, and has revealed new cell types, and new insights into developmental process and stochasticity in gene expression. There are now several published scRNA-seq protocols, which all sequence transcriptomes from a minute amount of starting material. Therefore, a key question is how these methods compare in terms of sensitivity of detection of mRNA molecules, and accuracy of quantification of gene expression. Here, we assessed the sensitivity and accuracy of many published data sets based on standardized spike-ins with a uniform raw data processing pipeline. We developed a flexible and fast UMI counting tool (https://github.com/vals/umis) which is compatible with all UMI based protocols. This allowed us to relate these parameters to sequencing depth, and discuss the trade offs between the different methods. To confirm our results, we performed experiments on cells from the same population using three different protocols. We also investigated the effect of RNA degradation on spike-in molecules, and the average efficiency of scRNA-seq on spike-in molecules versus endogenous RNAs.

Genomics↗