Plant function is the result of coordinated interactions between different cell types and their specific functions. Thus, to fully understand and identify the most critical cellular processes in plant tissues, changes need to be captured at the cell type or even single cell level. Advances in cell type-specific transcriptomics represent an essential first step in this direction and help to reveal the fundamental cellular activities involved in plant development and stress adaptation. High-throughput single-cell RNA-seq (scRNA-seq) based on the droplet method has made it possible to capture cells at various times during plant development. Thus, by mapping plant single-cell transcripts, not only the major cell types in plant tissues can be identified, but also rare cell types in plant tissues.

Fig.1. Analysis workflow for cell-type identification in single-cell transcriptomic data. (Rich-Griffin C et al., 2020)

What We Offer

One of the main applications of single-cell sequencing is the discovery and characterization of novel and/or rare cell types from complex tissues in health and disease. Based on the scRNA-seq platform, Lifeasible offers single-cell solutions for identifying rare plant cell types, helping clients to comprehensively characterize rare plant cell types and cell states, discover novel cell types and reveal how cell types are spatially and developmentally interrelated.

Our lab has a variety of computational methods for analyzing scRNA-seq data, including tools for cell-centric analyses such as unsupervised clustering for cell type identification, developmental trajectory analysis, or identification of rare cell types. We offer three methods to identify cell types assigned to clusters:

(1) We offer the Cell Identification Index (ICI) method to identify cell types. Different cell types are identified by calculating an ICI score for each cell to indicate the relative contribution of each known tissue type to cell identity and comparing gene expression within the cell cluster to existing markers.

(2) We provide unsupervised cell clustering to identify potentially novel cell type markers and validate these markers by comparing them to expression in existing plant expression profiles and then fusing them to transcriptional reporter genes in vivo.

(3) Our technical team is focused on developing cutting-edge computational methods to identify rare cell types in plant scRNA-seq datasets, using initial coarse clustering as input and identifying rare cell subtypes based on subpopulation-specific sets of related genes. Our computational approach offers superior performance in terms of specificity and selectivity in rare cell types and aberrant gene identification.

We will carefully rely on single or few genes to annotate rare cell types, and both cell number and sequencing depth significantly contribute to classification. Identifying rare cell types will facilitate deeper insight into the critical functions they exercise during plant development and differentiation.

Lifeasible has successfully applied scRNA-seq to Arabidopsis roots to identify rare cell types from the data, identifying clusters corresponding to mid-column sheath cells, silique sieves, and different epidermal subpopulations. Our plant scRNA-seq analysis can be used as a springboard to study the organization of gene networks in rare cell types, helping to further your understanding of fundamental aspects of plant life. If you are interested in our services or have some questions, please feel free to contact us or make an online inquiry.

Reference

1. Rich-Griffin C, Stechemesser A, Finch J, et al. Single-cell transcriptomics: a high-resolution avenue for plant functional genomics[J]. Trends in plant science, 2020, 25(2): 186-197.

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